Automated Mapping Methodology and Case Study of Healthcare User Activities and Spaces from a Sustainable Perspective

Abstract: Operational inefficiencies in healthcare buildings and sustainability issues have become increasingly prominent. The root cause of this phenomenon is the irregular and incomplete structured data related to hospital user activities and spaces that are difficult to resolve using traditional manual methods. In order to solve this problem, an automated mapping approach is presented for hospital user activity and space. Using the Revit API technology and the C# language on the Visual Studio platform, a program for automated mapping is developed. It achieves the automatic mapping of hospital user activities and spaces, along with automatic calculation of utilization rates. An illustrative case study is conducted, focusing on the activity data and building space utilization of healthcare users at Shanghai Renji Hospital. The results indicate that (1) This method can realize the automatic combination of hospital user activities and hospital spaces, and hospital administrators can optimize the utilization rate of hospital spaces quickly and accurately. (2) This approach is highly adaptable, especially to changes in hospital user activities and hospital spatial data. (3) This method promotes the sustainable development of healthcare buildings and has great practical application value.


The findings of the marketing analysis academic library’s services

Abstract: The author accomplished the marketing analysis of academic library services as the case study of Cherepovets State University Library, a flagship university of Vologda Region. She characterizes the status of library services, strengths and weaknesses, opportunities and threats, challenges and expectations of modern library users. The novelty of the article lies in the use of marketing analysis tools and the results obtained. The practical value of library services for users is revealed. With four criteria, namely demographic, geographic, psychographic and behavioral, the target user of university library services is characterized in detail. The author applies the research methods of analysis, comparison, observation, Google survey, in-depth interview, and empathy mapping. Four levels of university library services aree analyzed based on the “service pyramid”. The quantitative findings of marketing research are provided. The author demonstrates that despite most students prefer electronic communications, they do not ignore the hybrid, online and offline, mode of interaction with the library. She also shows that modern academic library users are emotionally neutral towards the library services. Recommendations are offered for the further development of academic library services.


Grants for the libraries: Where to look

Abstract: The authors discuss the challenges and ways of grant support of librarianship, and review the most popular Russian sponsor foundations. The grants make the commonly available way to get investment support that enables noncommercial organizations to accomplish their socially and profes- sionally valuable projects. To win a grant, the applicant has to identify the “right” sponsor among different categories, their specialization and recommendations on application and documents to be submitted for the contest. The authors characterize Russian charity foundations and classify them by the founder type, e. g. governmental, private, corporate, and agencies. The foundations provide grants along the lines corresponding to projects and programs of non-profit organizations within the information and library sphere. The authors describe the key thematic vectors for grants awarding; indicate the websites, and provide brief recommendations on how to prepare grant applications.


Lossless data hiding technique reducing cover data size for compressed videos

Abstract: In recent years, multimedia techniques have rapidly advanced. Video discs with massive storage capacities exemplify widely used multimedia storage mediums. However, online multimedia services face strict storage limits, and compression rates impact streaming quality. This poses a dilemma—data hiding risks exceeding storage limits and affecting the quality of video streams. To address this, we propose a lossless data hiding technique focusing on cover data size. The technique embeds new data into compressed cover video data, decreasing data size post-embedding. Extraction requires no key. In addition, the processing time is short enough that the technique can be used in real-time applications. We verified the technique through experiments with MPEG-2 videos. Video data size decreased after hiding data, and quality degradation remained acceptable. Results confirm the technique's efficacy.


KDP-Net: An Efficient Semantic Segmentation Network for Emergency Landing of Unmanned Aerial Vehicles

Abstract: As the application of UAVs becomes more and more widespread, accidents such as accidental injuries to personnel, property damage, and loss and destruction of UAVs due to accidental UAV crashes also occur in daily use scenarios. To reduce the occurrence of such accidents, UAVs need to have the ability to autonomously choose a safe area to land in an accidental situation, and the key lies in realizing on-board real-time semantic segmentation processing. In this paper, we propose an efficient semantic segmentation method called KDP-Net for characteristics such as large feature scale changes and high real-time processing requirements during the emergency landing process. The proposed KDP module can effectively improve the accuracy and performance of the semantic segmentation backbone network; the proposed Bilateral Segmentation Network improves the extraction accuracy and processing speed of important feature categories in the training phase; and the proposed edge extraction module improves the classification accuracy of fine features. The experimental results on the UDD6 and SDD show that the processing speed of this method reaches 85.25 fps and 108.11 fps while the mIoU reaches 76.9% and 67.14%, respectively. The processing speed reaches 53.72 fps and 38.79 fps when measured on Jetson Orin, which can meet the requirements of airborne real-time segmentation for emergency landing.


Twitter’s pulse on hydrogen energy in 280 characters: a data perspective

Abstract: Abstract Uncovering the public discourse on hydrogen energy is essential for understanding public behaviour and the evolving nature of conversations over time and across different regions. This paper presents a comprehensive analysis of a large multilingual dataset pertaining to hydrogen energy collected from Twitter spanning a decade (2013–2022) using selected keywords. The analysis aims to explore various aspects, including the temporal and spatial dimensions of the discourse, factors influencing Twitter engagement, user engagement patterns, and the interpretation of conversations through hashtags and ngrams. By delving into these aspects, this study offers valuable insights into the dynamics of public discourse surrounding hydrogen energy and the perceptions of social media users.


More-than-human and more-than-digital collecting among young children in Norway

Abstract: This study aims to advance our understanding of young children’s contemporary collecting. Collecting is a prevalent practice among young children. However, extant research typically highlights children of school age and emphasizes functions and motives of collecting, but rarely considers the relevant affective dimensions. Furthermore, young children today collect on digital interfaces through gaming, and their play tends to unsettle digital–analog binaries. Still, no explicit attention has been paid to young children’s collecting in these broader contemporary playscapes. In this study, I plug into ethnographic accounts of young children’s collecting in Norway, sociomaterial affect theorizing of early childhood play and literacy, and the key concept of ‘answering the world.’ The children in this case are found to enact relational sensibilities to their surroundings and collect in ways that leave the unfolding of the activity up to chance. They put less stress on the acquisition of a set collection than on moving with and feeling the collecting. The world answered refers to broad playscapes composed of spaces of tangible, fleshy, organic forest floors, and shiny, blocky, vividly colored Super Mario Worlds. The relational sensibilities enacted are found to be entangled with the tension and discord of young children’s material–discursive conditions.


Simulating the operation of a quantum computer in a dissipative environment

Abstract: The operations of current quantum computers are still significantly affected by decoherence caused by interaction with the environment. In this work, we employ the non-perturbative hierarchical equations of motion (HEOM) method to simulate the operation of model quantum computers and reveal the effects of dissipation on the entangled quantum states and on the performance of well-known quantum algorithms. Multi-qubit entangled states in Shor’s factorizing algorithm are first generated and propagated using the HEOM. It is found that the failure of factorization is accompanied by a loss of fidelity and mutual information. An important challenge in using the HEOM to simulate quantum computers in a dissipative environment is how to efficiently treat systems with many qubits. We propose a two-dimensional tensor network scheme for this problem and demonstrate its capability by simulating a one-dimensional random circuit model with 21 qubits.


Learner experience and motivational beliefs for a VR Lab in advanced undergraduate biology

Abstract: Recently, interest in understanding the prospects of virtual environments, specifically virtual reality laboratories (VR Lab) – those that involve a first-person practica experience via a desktop computer or head-mounted display – in science education has increased. Seemingly ingrained in the process of implementing a VR Lab is a supposition of interest, intrinsic motivation, and confidence on the part of participants. These attributes influence the participant's ability to start and complete the task and support sensemaking and meaningful learning. Thus, this study used expectancy-value theory with the intent of learning experience design to assess the relationship between task value beliefs and those for usability of a VR Lab. Beliefs were also compared to those for physical laboratories, where only a higher cost for the VR Lab was found. Increased cost beliefs suggest that participants perceived the VR Lab as requiring more from them, a negative consequence, which would decrease their potential for seeing it as a viable learning alternative. This finding suggests that a student's level of content knowledge may influence thier VR Lab experience. Cost belief was significantly related to utility value, giving credence to the theoretical model where cost is a value component. The VR Lab was determined to be marginally usable, but usability was not related to any motivational beliefs.


TSE-GAN: strain elastography using generative adversarial network for thyroid disease diagnosis

Abstract: Over the past 35 years, studies conducted worldwide have revealed a threefold increase in the incidence of thyroid cancer. Strain elastography is a new imaging technique to identify benign and malignant thyroid nodules due to its sensitivity to tissue stiffness. However, there are certain limitations of this technique, particularly in terms of standardization of the compression process, evaluation of results and several assumptions used in commercial strain elastography modes for the purpose of simplifying imaging analysis. In this work, we propose a novel conditional generative adversarial network (TSE-GAN) for automatically generating thyroid strain elastograms, which adopts a global-to-local architecture to improve the ability of extracting multi-scale features and develops an adaptive deformable U-net structure in the sub-generator to apply effective deformation. Furthermore, we introduce a Lab-based loss function to induce the networks to generate realistic thyroid elastograms that conform to the probability distribution of the target domain. Qualitative and quantitative assessments are conducted on a clinical dataset provided by Shanghai Sixth People’s Hospital. Experimental results demonstrate that thyroid elastograms generated by the proposed TSE-GAN outperform state-of-the-art image translation methods in meeting the needs of clinical diagnostic applications and providing practical value.


Modified Playfair for Text File Encryption and Meticulous Decryption with Arbitrary Fillers by Septenary Quadrate Pattern

Abstract: Abstract Cryptography secures data and serves to ensure the confidentiality of records. Playfair is a cryptographic symmetrical algorithm that encrypts statistics based on key costs. This secret is shared with an authorized person to retrieve data. In the conventional pattern, there is an area complexity and deficiency in letters, numbers, and special characters. This hassle has been overcome in previous studies by editing pattern dimensions. The fillers used throughout the enciphering were not eliminated during the retrieval process, which resulted in the indiscrimination of the retrieved statistics. The proposed method uses a separate quadrate pattern that strengthens the Playfair cipher and guarantees that the fillers are eliminated to ensure the authentic retrieval of records. The fillers indiscriminate and strengthen the set of rules in opposition to brute force and avalanche impact. The proposed algorithm was evaluated with a minimal change in the key, and was found to have an avalanche effect between 65% and 93.7%. The encrypted document is further encoded using the Lempel–Ziv–Markov chain algorithm (LZMA) to provide compressed second-level secured text with a compression ratio of 0.75 for a file size of 100 KB. The pattern was designed to subsidize the integrated characters found on the keyboard.


Being on the inside: a research methodology for data collection within the inner circle of the domain of video game translation/localization in Thailand

Abstract: Purpose The Thai video game domain has witnessed substantial growth in recent years. However, many games enjoyed by Thai players are in foreign languages, with only a handful of titles translated/localized into the Thai locale. Some Thai video game enthusiasts have taken on the role of unofficial translators/localizers, contributing to a localization domain that accommodates both official and unofficial translation/localization efforts. This general review paper aims to outline the author's experiences in collecting data within the domain of video game translation/localization in Thailand. Design/methodology/approach Using a descriptive approach, this general review paper employs the netnography method. It sheds light on the complexities of video game translation/localization in Thailand and incorporates semi-structured interviews with a snowball sampling technique for the selection of participants and in-game data collection methods. Findings The netnography method has proved instrumental in navigating the intricacies of this evolving landscape. Adopting the netnography method for data collection in this research contributes to establishing more robust connections with the research sites. “Inside” professionals and individuals play a significant role in data gathering by recommending additional sources of information for the research. Originality/value While netnography is conventionally applied in the market and consumer research, this paper demonstrates its efficacy in unraveling the dynamics of video game translation/localization in Thailand.


Impact factor analysis affecting the operation of asset lifecycle by using asset information modeling and blockchain technology

Abstract: Purpose Currently, owners find it difficult to manage their assets throughout their project life cycle. The fact that asset information models (AIMs) are mandatory as deliverables for building information modeling-driven projects makes it a key requirement for the client to understand in detail those factors affecting asset operation. Hence, because the Kingdom of Saudi Arabia is the most significant market in the Middle East, this study aims to investigate those factors where blockchain and AIMs could impact the asset management (AM) life cycle. Design/methodology/approach Researchers used a hypothesis-based approach over a systematic literature review and a workshop (descriptive statistics) to understand the current challenges in AM. Later, a second workshop was run to understand the impact factor analysis affecting the operation of the asset life cycle by using asset information modeling and blockchain technology over a multiquantitative method. Findings Results found that factors affecting the operation of assets could be the improvement of trust and stakeholder’s influences; the availability of handover process products’ accurate data; manufacturers providing detailed product models; increasing the speed of preparing holistic and integrated AM systems; improving collaboration between stakeholders; and returning clients’ investments faster. Originality/value Understanding the factors affecting AM life cycle based on the utilization of AIMs and blockchain then allows investors and their team members to work in a secure and collaborative environment that helps them to pre-identify certain risks and improve decision-making in a more effective way, as is required by ISO55000.


Enhanced Security in Public Key Cryptography: A Novel Approach Combining Gaussian Graceful Labeling and NTRU Public Key Cryptosystem

Abstract: This research explores an encryption system that combines the Nth-degree Truncated Polynomial Ring Unit (NTRU) public key cryptosystem with Gaussian Graceful Labeling. This process assigns distinct labels to a graph's vertices, resulting in successive Gaussian integers. The NTRU method offers enhanced security and efficient key exchange. The communication encryption process depends on integers P, a, and b, with P being the largest prime number in the vertex labeling. The original receivers are the vertex labeling with the largest prime number coefficient, while all other receivers receive messages from the sender. A polynomial algebraic mixing system and a clustering principle based on the abecedarian probability proposition are used in NTRU encryption and decryption. The choice of relatively prime integers p and q in NTRU plays a role in the construction of polynomial rings used for encryption and decryption, with specific choices and properties designed to ensure scheme security.


How body postures affect gaze control in scene viewing under specific task conditions

Abstract: Gaze movements during visual exploration of natural scenes are typically investigated with the static picture viewing paradigm in the laboratory. While this paradigm is attractive for its highly controlled conditions, limitations in the generalizability of the resulting findings to more natural viewing behavior have been raised frequently. Here, we address the combined influences of body posture and viewing task on gaze behavior with the static picture viewing paradigm under free viewing as a baseline condition. We recorded gaze data using mobile eye tracking during postural manipulations in scene viewing. Specifically, in Experiment 1, we compared gaze behavior during head-supported sitting and quiet standing under two task conditions. We found that task affects temporal and spatial gaze parameters, while posture produces no effects on temporal and small effects on spatial parameters. In Experiment 2, we further investigated body posture by introducing four conditions (sitting with chin rest, head-free sitting, quiet standing, standing on an unstable platform). Again, we found no effects on temporal and small effects on spatial gaze parameters. In our experiments, gaze behavior is largely unaffected by body posture, while task conditions readily produce effects. We conclude that results from static picture viewing may allow predictions of gaze statistics under more natural viewing conditions, however, viewing tasks should be chosen carefully because of their potential effects on gaze characteristics.


Semantic search as extractive paraphrase span detection

Abstract: Abstract In this paper, we approach the problem of semantic search by introducing a task of paraphrase span detection, i.e. given a segment of text as a query phrase, the task is to identify its paraphrase in a given document, the same modelling setup as typically used in extractive question answering. While current work in paraphrasing has almost uniquely focused on sentence-level approaches, the novel span detection approach gives a possibility to retrieve a segment of arbitrary length. On the Turku Paraphrase Corpus of 100,000 manually extracted Finnish paraphrase pairs including their original document context, we find that by achieving an exact match of 88.73 our paraphrase span detection approach outperforms widely adopted sentence-level retrieval baselines (lexical similarity as well as BERT and SBERT sentence embeddings) by more than 20pp in terms of exact match, and 11pp in terms of token-level F-score. This demonstrates a strong advantage of modelling the paraphrase retrieval in terms of span extraction rather than commonly used sentence similarity, the sentence-level approaches being clearly suboptimal for applications where the retrieval targets are not guaranteed to be full sentences. Even when limiting the evaluation to sentence-level retrieval targets only, the span detection model still outperforms the sentence-level baselines by more than 4 pp in terms of exact match, and almost 6pp F-score. Additionally, we introduce a method for creating artificial paraphrase data through back-translation, suitable for languages where manually annotated paraphrase resources for training the span detection model are not available.


Differentially private Riemannian optimization

Abstract: Abstract In this paper, we study the differentially private empirical risk minimization problem where the parameter is constrained to a Riemannian manifold. We introduce a framework for performing differentially private Riemannian optimization by adding noise to the Riemannian gradient on the tangent space. The noise follows a Gaussian distribution intrinsically defined with respect to the Riemannian metric on the tangent space. We adapt the Gaussian mechanism from the Euclidean space to the tangent space compatible to such generalized Gaussian distribution. This approach presents a novel analysis as compared to directly adding noise on the manifold. We further prove privacy guarantees of the proposed differentially private Riemannian (stochastic) gradient descent using an extension of the moments accountant technique. Overall, we provide utility guarantees under geodesic (strongly) convex, general nonconvex objectives as well as under the Riemannian Polyak-Łojasiewicz condition. Empirical results illustrate the versatility and efficacy of the proposed framework in several applications.


Research on multi-image encryption method based on image scaling and ghost imaging

Abstract: Abstract Optical information processing technology itself is characterized by high speed and parallelism, while the wavelength of light is short and the loading information capacity is large. Therefore, optical encryption is of great significance in the field of image encryption. For multi-image encryption, a multi-image encryption system (ISK-MGI) based on image scaling and ghost imaging is proposed in this paper. In the encryption process, image scaling is used to embed the image information to be encrypted into the artifact image to achieve the first step of artifact encryption; then the ghost imaging encryption is used to encrypt any of the artifact images and the intensity sequence obtained from the barrel detector is used as the cipher text; the integration property of Fourier transform is used to obtain the modulation patterns of other artifact images as the key. The feasibility, security and robustness of the scheme are verified by experiments and simulations. The scheme not only solves the image information crosstalk problem of multi-image encryption, but also has high security. Each image in this method has a primary public key and a secondary private key, which solves the potential risk of sharing public keys and provides a new idea for optical information encryption.


Semantics of Belief Change Operators for Intelligent Agents

Abstract: Abstract This paper summarises several contributions to the theory of belief change by the authors’ dissertation thesis. First, a relational characterization of belief revision for Tarskian logics is considered, encompassing first-order predicate logic, description logic, modal logics and many monotonic logics with model-theoretic semantics. Those logics where total preorders are the standard semantics for revision are characterized. The second contribution considered is a theory of belief revision that builds upon the idea that agents are limited in what the outcome of a revision is. Furthermore, advancements in principles for iterated belief contraction given in the thesis are outlined.


SSANet: normal-mode interference spectrum extraction via SSA algorithm-unrolled neural network

Abstract: In ocean acoustic fields, extracting the normal-mode interference spectrum (NMIS) from the received sound intensity spectrum (SIS) plays an important role in waveguide-invariant estimation and underwater source ranging. However, the received SIS often has a low signal-to-noise ratio (SNR) owing to ocean ambient noise and the limitations of the received equipment. This can lead to significant performance degradation for the traditional methods of extracting NMIS at low SNR conditions. To address this issue, a new deep neural network model called SSANet is proposed to obtain NMIS based on unrolling the traditional singular spectrum analysis (SSA) algorithm. First, the steps of embedding and singular value decomposition (SVD) in SSA is achieved by the convolutional network. Second, the grouping step of the SSA is simulated using the matrix multiply weight layer, ReLU layer, point multiply weight layer and matrix multiply weight layer. Third, the diagonal averaging step was implemented using a fully connected network. Simulation results in canonical ocean waveguide environments demonstrate that SSANet outperforms other traditional methods such as Fourier transform (FT), multiple signal classification (MUSIC), and SSA in terms of root mean square error, mean absolute error, and extraction performance.


Novel iterative cubature Kalman filters under maximum correntropy criterion for the robust state estimation

Abstract: Abstract The cubature Kalman filter (CKF) based on maximum correntropy criterion (MCC) is robust under non‐Gaussian noises, but it may encounter numerical problems when there are large outliers. First, based on the MCC and weighted least square (WLS) methods, a new cost function is introduced to calculate the state and covariance updates, which can better solve numerical problems caused by large outliers. Then, a nonlinear measurement function is used, and the measurement information is updated through the latest iterative values, which can obtain more accurate results under various non‐Gaussian noises. In addition, the Gauss–Newton, Levenberg–Marquardt, and Quasi–Newton methods are used, and three iterative algorithms named GN‐IMCC‐CKF, LM‐IMCC‐CKF, and QN‐IMCC‐CKF are respectively derived for the updating steps. Compared with the existing algorithms, the simulation results of two classical models show that the proposed algorithms have better numerical stability and estimation accuracy under non‐Gaussian systems.


The Bitcoin price and Bitcoin price uncertainty: Evidence of Bitcoin price volatility

Abstract: Abstract This study examines the Bitcoin price by taking into account global factors, including the Chicago Board Options Exchange's Market Volatility Index (VIX), the US dollar index, the gold price, the oil price, and Bitcoin price volatility. The analysis is conducted using the structural vector autoregression (SVAR) model. The variance decomposition findings revealed that the influence of the VIX on the Bitcoin price was initially restricted, but progressively intensified over time. Among the indicators, Bitcoin price volatility had the highest explanatory share in both daily and weekly data analysis. The impulse response functions demonstrated a statistically significant inverse relationship between the VIX and the Bitcoin price. Furthermore, the analysis revealed that the Bitcoin price was mostly impacted by its own volatility. This implies that investing in Bitcoin requires a certain level of risk‐taking.


Measuring the similarity between shapes of buildings using graph edit distance

Abstract: Building data play a crucial role in geographic information science, and building shape similarity can be used for spatial inquiry, cartographic generalization, map updating, data quality assessment, and other spatial data mining applications. However, conventional shape representation and measurement methods use geometric and statistical measures and may not effectively capture the subtle differences among building shapes. Geospatial artificial intelligence methods often overlook nuanced differences in building contours, posing a challenge when precise fine-grained measurements are required. Furthermore, they have low interpretability. This study proposes a graph edit distance-based method for measuring the similarity between building shapes. It uses geometrics characteristics to construct a graph, matches and associates similar regional features between two building contours, and defines edit costs based on geometric and positional differences that align with the Gestalt principle of visual similarity. It converts the optimal edit path problem to the maximum weighted cluster solution problem and utilizes a heuristic approach to achieve the exact optimal solution within a moderately suitable timeframe. The experimental results demonstrated that the proposed method identifies detailed differences between building contours, providing accurate similarity measurement with high interpretability.


Prediction of solar irradiance with machine learning methods using satellite data

Abstract: Solar energy is used in many domestic and industrial areas. To make maximum use of solar energy, it is important to know the irradiance value per unit area. Irradiance measurements are carried out at many stations. In cases where the irradiance value is not measured, empirical models are generally used. With the widespread use of machine learning methods in prediction problems in many fields, it has gained increasing popularity recently. This study aims to predict the solar irradiance of all cities in the Mediterranean region in Turkiye using statistical method and popular machine learning methods. The performances of these models are compared. Ensemble Learning methods (Gradient Boosting, Extreme Gradient Boosting) which are among the popular machine learning methods, and Artificial Neural Networks were used. The data used belong to 8 cities in the Mediterranean Region, Türkiye. Declination angle, wind speed, ambient temperature, relative humidity, and cloudiness index were chosen as input variables. When the results of the models established for each city are examined, it is seen that the models established with machine learning methods are more successful than statistical methods. The best results were obtained from models established with the Extreme Gradient Boosting method (R2 = 0.9993, MAPE = 0.0119).


EPolar‐UNet: An edge‐attending polar UNet for automatic medical image segmentation with small datasets

Abstract: Medical image segmentation is one of the most key steps in computer-aided clinical diagnosis, geometric characterization, measurement, image registration, and so forth. Convolutional neural networks especially UNet and its variants have been successfully used in many medical image segmentation tasks. However, the results are limited by the deficiency in extracting high resolution edge information because of the design of the skip connections in UNet and the need for large available datasets.In this paper, we proposed an edge-attending polar UNet (EPolar-UNet), which was trained on the polar coordinate system instead of classic Cartesian coordinate system with an edge-attending construction in skip connection path.EPolar-UNet extracted the location information from an eight-stacked hourglass network as the pole for polar transformation and extracted the boundary cues from an edge-attending UNet, which consisted of a deconvolution layer and a subtraction operation.We evaluated the performance of EPolar-UNet across three imaging modalities for different segmentation tasks: CVC-ClinicDB dataset for polyp, ISIC-2018 dataset for skin lesion, and our private ultrasound dataset for liver tumor segmentation. Our proposed model outperformed state-of-the-art models on all three datasets and needed only 30%-60% of training data compared with the benchmark UNet model to achieve similar performances for medical image segmentation tasks.We proposed an end-to-end EPolar-UNet for automatic medical image segmentation and showed good performance on small datasets, which was critical in the field of medical image segmentation.


Deep Cryogenic Temperature CMOS Circuit and System Design for Quantum Computing Applications

Abstract: Quantum computing is a fascinating and rapidly evolving field of technology that promises to revolutionize many areas of science, engineering, and society. The fundamental unit of quantum computing is the quantum bit that can exist in two or more states concurrently, as opposed to a classical bit that can only be either 0 or 1. Any subatomic element, including atoms, electrons, and photons, can be used to implement qubits. The chosen sub-atomic elements should have quantum mechanical properties. Most commonly, photons have been used to implement qubits. Qubits can be manipulated and read by applying external fields or pulses, such as lasers, magnets, or microwaves. Quantum computers are currently suffering from various complications such as size, operating temperature, coherence problems, entanglement, etc. The realization of quantum computing, a novel paradigm that uses quantum mechanical phenomena to do computations that are not possible with classical computers, is made possible, most crucially, by the need for a quantum processor and a quantum SOC. As a result, Cryo-CMOS technology can make it possible to integrate a Quantum system on a chip. Cryo-CMOS devices are electronic circuits that operate at cryogenic temperatures, usually below 77 K (−196 °C).


Exploring the application of dual‐energy <scp>CT</scp> to discriminate sediment facies in a varved sequence

Abstract: Abstract Dual‐energy X‐ray computed tomography consists of imaging objects using two incident X‐ray beams of different energy to distinguish the different compounds within a sample based on their density (electron density, ρ e ) and elemental composition (effective atomic number, Z eff ). The stoichiometric calibration for dual‐energy X‐ray computed tomography was already successfully implemented to identify single and homogeneous minerals easily and non‐destructively. It is here applied for the first time to a more complex and heterogeneous sample, a varved sediment core with three distinct facies. The output of dual‐energy X‐ray computed tomography was compared against elemental geochemistry obtained at the same resolution using a micro‐XRF core scanner. The three individual facies can be successfully differentiated using dual‐energy X‐ray computed tomography because their range of ρ e and Z eff values allow their discrimination. Correlations with elemental geochemistry are also discussed but are less conclusive, probably because of variations in grain size and porosity, and because these high resolution analyses were not performed at the exact same location. The paper not only eventually discusses the limitations when using dual‐energy X‐ray computed tomography on sediments but also demonstrates its potential to quantitatively study sediment cores in a non‐destructive way.


Overcoming Diverse Undesired Effects in Recommender Systems: A Deontological Approach

Abstract: In today’s digital landscape, recommender systems have gained ubiquity as a means of directing users towards personalized products, services, and content. However, despite their widespread adoption and a long track of research, these systems are not immune to shortcomings. A significant challenge faced by recommender systems is the presence of biases, which produces various undesirable effects, prominently the popularity bias. This bias hampers the diversity of recommended items, thus restricting users’ exposure to less popular or niche content. Furthermore, this issue is compounded when multiple stakeholders are considered, requiring the balance of multiple, potentially conflicting objectives. In this paper, we present a new approach to address a wide range of undesired consequences in recommender systems that involve various stakeholders. Instead of adopting a consequentialist perspective that aims to mitigate the repercussions of a recommendation policy, we propose a deontological approach centered around a minimal set of ethical principles. More precisely, we introduce two distinct principles aimed at avoiding overconfidence in predictions and accurately modeling the genuine interests of users. The proposed approach circumvents the need for defining a multi-objective system, which has been identified as one of the main limitations when developing complex recommenders. Through extensive experimentation, we show the efficacy of our approach in mitigating the adverse impact of the recommender from both user and item perspectives, ultimately enhancing various beyond accuracy metrics. This study underscores the significance of responsible and equitable recommendations and proposes a strategy that can be easily deployed in real-world scenarios.


Generative machine learning for detector response modeling with a conditional normalizing flow

Abstract: Abstract In this paper, we explore the potential of generative machine learning models as an alternative to the computationally expensive Monte Carlo (MC) simulations commonly used by the Large Hadron Collider (LHC) experiments. Our objective is to develop a generative model capable of efficiently simulating detector responses for specific particle observables, focusing on the correlations between detector responses of different particles in the same event and accommodating asymmetric detector responses. We present a conditional normalizing flow model (𝒞𝒩ℱ) based on a chain of Masked Autoregressive Flows, which effectively incorporates conditional variables and models high-dimensional density distributions. We assess the performance of the 𝒞𝒩ℱ model using a simulated sample of Higgs boson decaying to diphoton events at the LHC. We create reconstruction-level observables using a smearing technique. We show that conditional normalizing flows can accurately model complex detector responses and their correlation. This method can potentially reduce the computational burden associated with generating large numbers of simulated events while ensuring that the generated events meet the requirements for data analyses. We make our code available at https://github.com/allixu/normalizing_flow_for_detector_response .


Distributed computing with the cloud

Abstract: Abstract We investigate the effect of omnipresent cloud storage on distributed computing. To this end, we specify a network model with links of prescribed bandwidth that connect standard processing nodes, and, in addition, passive storage nodes. Each passive node represents a cloud storage system, such as Dropbox, Google Drive etc. We study a few tasks in this model, assuming a single cloud node connected to all other nodes, which are connected to each other arbitrarily. We give implementations for basic tasks of collaboratively writing to and reading from the cloud, and for more advanced applications such as matrix multiplication and federated learning. Our results show that utilizing node-cloud links as well as node-node links can considerably speed up computations, compared to the case where processors communicate either only through the cloud or only through the network links. We first show how to optimally read and write large files to and from the cloud in general graphs using flow techniques. We use these primitives to derive algorithms for combining , where every processor node has an input value and the task is to compute a combined value under some given associative operator. In the special but common case of “fat links,” where we assume that links between processors are bidirectional and have high bandwidth, we provide near-optimal algorithms for any commutative combining operator (such as vector addition). For the task of matrix multiplication (or other non-commutative combining operators), where the inputs are ordered, we present tight results in the simple “wheel” network, where procesing nodes are arranged in a ring, and are all connected to a single cloud node.


Full-function Pavlov associative learning photonic neural networks based on SOA and DFB-SA

Abstract: Pavlovian associative learning, a form of classical conditioning, has significantly impacted the development of psychology and neuroscience. However, the realization of a prototypical photonic neural network (PNN) for full-function Pavlov associative learning, encompassing both photonic synapses and photonic neurons, has not been achieved to date. In this study, we propose and experimentally demonstrate the first InP-based full-function Pavlov associative learning PNN. The PNN utilizes semiconductor optical amplifiers (SOAs) as photonic synapses and the distributed feedback laser with a saturable absorber (DFB-SA) as the photonic spiking neuron. The connection weights between neurons in the PNN can be dynamically changed based on the fast, time-varying weighting properties of the SOA. The optical output of the SOA can be directly coupled into the DFB-SA laser for nonlinear computation without additional photoelectric conversion. The results indicate that the PNN can successfully perform brain-like computing functions such as associative learning, forgetting, and pattern recall. Furthermore, we analyze the performance of PNN in terms of speed, energy consumption, bandwidth, and cascadability. A computational model of the PNN is derived based on the distributed time-domain coupled traveling wave equations. The numerical results agree well with the experimental findings. The proposed full-function Pavlovian associative learning PNN is expected to play an important role in the development of the field of photonic brain-like neuromorphic computing.


A deep clustering-based state-space model for improved disease risk prediction in personalized healthcare

Abstract: Abstract Decision support systems are being developed to assist clinicians in complex decision-making processes by leveraging information from clinical knowledge and electronic health records (EHRs). One typical application is disease risk prediction, which can be challenging due to the complexity of modelling longitudinal EHR data, including unstructured medical notes. To address this challenge, we propose a deep state-space model (DSSM) that simulates the patient’s state transition process and formally integrates latent states with risk observations. A typical DSSM consists of three parts: a prior module that generates the distribution of the current latent state based on previous states; a posterior module that approximates the latent states using up-to-date medical notes; and a likelihood module that predicts disease risks using latent states. To efficiently and effectively encode raw medical notes, our posterior module uses an attentive encoder to better extract information from unstructured high-dimensional medical notes. Additionally, we couple a predictive clustering algorithm into our DSSM to learn clinically useful representations of patients’ latent states. The latent states are clustered into multiple groups, and the weighted average of the cluster centres is used for prediction. We demonstrate the effectiveness of our deep clustering-based state-space model using two real-world EHR datasets, showing that it not only generates better risk prediction results than other baseline methods but also clusters similar patient health states into groups.


A Novel Dual-Channel Temporal Convolutional Network for Photovoltaic Power Forecasting

Abstract: A large proportion of photovoltaic (PV) power generation is connected to the power grid, and its volatility and stochasticity have significant impacts on the power system. Accurate PV power forecasting is of great significance in optimizing the safe operation of the power grid and power market transactions. In this paper, a novel dual-channel PV power forecasting method based on a temporal convolutional network (TCN) is proposed. The method deeply integrates the PV station feature data with the model computing mechanism through the dual-channel model architecture; utilizes the combination of multihead attention (MHA) and TCN to extract the multidimensional spatio-temporal features between other meteorological variables and the PV power; and utilizes a single TCN to fully extract the temporal constraints of the power sequence elements. The weighted fusion of the dual-channel feature data ultimately yields the ideal forecasting results. The experimental data in this study are from a 26.52 kW PV power plant in central Australia. The experiments were carried out over seven different input window widths, and the two models that currently show superior performance within the field of PV power forecasting: the convolutional neural network (CNN), and the convolutional neural network combined with a long and short-term memory network (CNN_LSTM), are used as the baseline models. The experimental results show that the proposed model and the baseline models both obtained the best forecasting performance over a 1-day input window width, while the proposed model exhibited superior forecasting performance compared to the baseline model. It also shows that designing model architectures that deeply integrate the data input method with the model mechanism has research potential in the field of PV power forecasting.


A theory of L<i>n</i> grammars

Abstract: Preview this article: A theory of Ln grammars, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/lab.23060.gur-1.gif


Cultivating Ensemble Diversity through Targeted Injection of Synthetic Data: Path Loss Prediction Examples

Abstract: Machine Learning (ML)-based models are steadily gaining popularity. Their performance is determined from the amount and the quality of data used at their inputs, as well as from the competence and proper tuning of the ML algorithm used. However, collecting high-quality real data is time-consuming and expensive. Synthetic Data Generation (SDG) is therefore employed in order to augment the limited real data. Moreover, Ensemble Learning (EL) provides the framework to optimally combine a set of standalone ML algorithms (base learners), capitalizing on their individual strengths. Base learner diversity is essential to build a strong ensemble. The proposed method of Targeted Injection of Synthetic Data (TIoSD) combines the EL and SDG concepts in order to further diversify the base learners’ predictions, thus giving rise to an even stronger ensemble model. We have applied TIoSD in two different Path Loss (PL) datasets, using two well-established SDG methods (namely SMOGN and CTGAN). While the conventional ensemble model reached a Minimum Absolute Error (MAE) value of 3.25 dB, the TIoSD-triggered ensemble provided a MAE value of 3.16 dB. It is therefore concluded that targeted synthetic data injection, due to its diversity-triggering characteristics, enhances the ensemble’s performance. Moreover, the ratio between synthetic and real data has been investigated. The results showed that a proportion of 0.1 is optimal.


Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

Abstract: In the cooperative multi-sensor multi-vehicle (MSMV) localization domain, the data incest problem yields inconsistent data fusion results, thereby reducing the accuracy of vehicle localization. In order to address this problem, we propose the interval split covariance intersection filter (ISCIF). At first, the proposed ISCIF method is applied to the absolute positioning step. Then, we combine the interval constraint propagation (ICP) method and the proposed ISCIF method to realize relative positioning. Additionally, in order to enhance the robustness of the MSMV localization system, a Kullback–Leibler divergence (KLD)-based fault detection and exclusion (FDE) method is implemented in our system. Three simulations were carried out: Simulation scenarios 1 and 2 aimed to assess the accuracy of the proposed ISCIF with various capabilities of absolute vehicle positioning, while simulation scenario 3 was designed to evaluate the localization performance when faults were present. The simulation results of scenarios 1 and 2 demonstrated that our proposed vehicle localization method reduced the root mean square error (RMSE) by 8.9% and 15.5%, respectively, compared to the conventional split covariance intersection filter (SCIF) method. The simulation results of scenario 3 indicated that the implemented FDE method could effectively reduce the RMSE of vehicles (by about 55%) when faults were present in the system.


EEG Opto-Processor: Epileptic Seizure Detection Using Diffractive Photonic Computing Units

Abstract: Electroencephalography (EEG) analysis extracts critical information from brain signals, enabling brain disease diagnosis and providing fundamental support for brain–computer interfaces. However, performing an artificial intelligence analysis of EEG signals with high energy efficiency poses significant challenges for electronic processors on edge computing devices, especially with large neural network models. Herein, we propose an EEG opto-processor based on diffractive photonic computing units (DPUs) to process extracranial and intracranial EEG signals effectively and to detect epileptic seizures. The signals of the EEG channels within a second-time window are optically encoded as inputs to the constructed diffractive neural networks for classification, which monitors the brain state to identify symptoms of an epileptic seizure. We developed both free-space and integrated DPUs as edge computing systems and demonstrated their applications for real-time epileptic seizure detection using benchmark datasets, that is, the Children’s Hospital Boston (CHB)–Massachusetts Institute of Technology (MIT) extracranial and Epilepsy-iEEG-Multicenter intracranial EEG datasets, with excellent computing performance results. Along with the channel selection mechanism, both numerical evaluations and experimental results validated the sufficiently high classification accuracies of the proposed opto-processors for supervising clinical diagnosis. Our study opens a new research direction for utilizing photonic computing techniques to process large-scale EEG signals and promote broader applications.


Particle guided metaheuristic algorithm for global optimization and feature selection problems

Abstract: Optimization problems can be seen in numerous fields of practical studies. One area making waves in the application of optimization methods is data mining in machine learning. An important preprocessing technique of data mining where irrelevant variables are discarded from the datasets and holding onto variables with important information is referred to as feature selection (FS). FS is critical to tackling the ‘curse of dimensionality’ by reducing the number of features, minimizing computational expensiveness and maximizing the accuracy of the machine learning models. Swarm Intelligence (SI)-based meta-heuristic algorithms (MAs) have been widely employed to solve several optimization problems like FS. However, common drawbacks identified with these algorithms include getting trapped in local optima, especially in situations where the search space is large (high dimensional space). This study proposes a new hybrid SI-based MA called Particle Swarm-guided Bald Eagle Search (PS-BES). The algorithm utilizes the speed of Particle Swarm to guide Bald Eagles in their search to ensure a smooth transition of the algorithm from exploration to exploitation. Additionally, we introduce the Attack-Retreat-Surrender technique, a new local-optima escape technique to enhance the balance between diversification and intensification of PS-BES. To establish the outstanding performance of the proposed algorithm, PS-BES is comprehensively analyzed utilizing 26 Benchmark functions. Further, the practicality of PS-BES is highlighted by its binary version for feature selection and evaluated using 27 classification datasets from the UCI repository. The results prove the overall superiority of PS-BES and bPS-BES as opposed the 10 state-of-the-art algorithms employed in the study.


A multi-scale information fusion-based multiple correlations for unsupervised attribute selection

Abstract: With the continuous evolution of artificial intelligence and sensor technology, there is a growing accumulation of unlabeled data. Uncovering valuable insights from this data has become a crucial task, propelling the ongoing development of unsupervised knowledge discovery techniques. Unsupervised attribute selection plays a pivotal role in data preprocessing for knowledge discovery, aiming to extract relevant attributes from the dataset without relying on prior labeling or category information. This article introduces an unsupervised attribute selection method that integrates multi-scale information while considering multiple correlations. Initially, a multi-scale fuzzy similarity relation information system (MsFSRIS) is established by integrating multi-scale information with fuzzy relations. This system unveils fuzzy similarity relations stemming from both numerical and categorical attributes, while also integrating information across multiple scales. Within this MsFSRIS, information measures of fuzzy multi-scale are defined, encompassing multiple correlations related to multi-scale fuzzy relevance, multi-scale fuzzy redundancy, multi-scale fuzzy complementarity, and multi-scale fuzzy interaction during the attribute selection process. Furthermore, we propose an unsupervised attribute selection algorithm (UAS_MsIFMC) that fuses information from multiple scales and considers multiple interactions among selected, unselected, and candidate attributes, enhancing the identification of valuable attributes. Finally, the experimental results demonstrate the superiority of the proposed UAS_MsIFMC algorithm, presenting a promising solution for unsupervised attribute selection methods.


The inadvertently revealing statistic: A systemic gap in statistical training?

Abstract: Abstract While concerns around data privacy are well-known, there's a lack of awareness and training when it comes to the confidentiality risk of published statistics, argue Ben Derrick, Elizabeth Green, Felix Ritchie, Jim Smith, Paul White


Development of central bank digital currencies: a bibliometric analysis

Abstract: Purpose We aim to conduct a bibliometric analysis that explores and maps quantitative data of the emerging field of central bank digital currencies in science and its implications in practice. We seek to clarify the underlying research structures and streams of the new phenomena, and our motivation is the rising number of pilots between governments seeking to implement different types of central bank digital currency. Design/methodology/approach We designed the unique set of keywords to explore ongoing projects on central bank digital currencies and the evolution of scientific thought on the topic. We conducted a descriptive analysis and an evaluating bibliometric analysis on the timeline from 2018 to April 18, 2023 and investigated 76 articles in the Web of Science database and 152 articles in the Scopus database using VOSviewer. Findings We highlight three main directions of discourse on central bank digital currencies in economics using authors keyword analysis, that are: (1) cash, (2) monetary policy and (3) financial stability. We conducted a map-based text analysis of the abstracts and identified the following main streams of discussion in the field: (1) policy-related research on financial systems, (2) a comprehensive review of the design and features of central bank digital currencies and (3) research on the impact of central bank digital currencies on the banking system. Originality/value The unique set of keywords allows us to continue the discourse on central bank digital currencies including implications of ongoing governmental projects on the topic and provide directions for future research. We brought the focus on the impact of central bank digital currencies on the banking sector and the new possible order for cash, deposits and payments.


Exploring Usage of AWS Lambda in Data Processing

Abstract: Cloud computing is changing game all around the industry. It’s delivering computing resources, such as storage, processing power without investing in and managing physical infrastructure, offering a more flexible and cost-effective approach to meet computational demands. Serverless computing is a cloud computing execution model where cloud providers automatically manage the infrastructure needed to run applications and scale. This paper examines use of AWS Lambda in Data processing, the leading serverless computing service of Amazon Web Services (AWS). Our study will explore the architecture, integration of AWS Lambda with other AWS services, real-world use cases, proposing best practices for Data processing. This research will provide a complete understanding of AWS Lambda and findings here are a valuable resource for people looking to unleash the benefits of AWS Lambda in a scalable and high-performance cloud-native application architecture.


Linguistic annotation of cuneiform texts using treebanks and deep learning

Abstract: Abstract We describe an efficient pipeline for morpho-syntactically annotating an ancient language corpus which takes advantage of bootstrapping techniques. This pipeline is designed for ancient language scholars looking to jump-start their own treebank projects, which can in turn serve further pedagogical research projects in the target language. We situate our work in the field of similar ancient language treebank projects, arguing that our approach shows that individual humanities scholars can leverage current machine-learning tools to produce their own richly annotated corpora. We illustrate this pipeline by producing a new Akkadian-language treebank based on two volumes from the online editions of the State Archives of Assyria project hosted on Oracc, as well as a spaCy language model named AkkParser trained on that treebank. Both of these are made publicly available for annotating other Akkadian corpora. In addition, we discuss linguistic issues particular to the Neo-Assyrian letter corpus and data-encoding complications of cuneiform texts in Oracc. The strategies, language models, and processing scripts we developed to handle both linguistic and data-encoding issues in this project will be of special interest to scholars seeking to develop their own cuneiform treebanks.


Mathematical diversity of parts for a continuous distribution

Abstract: Abstract The current paper is part of a series exploring how to link diversity measures (e.g., Gini-Simpson index, Shannon entropy, Hill numbers) to a distribution’s original shape and to compare parts of a distribution, in terms of diversity, with the whole. This linkage is crucial to understanding the exact relationship between the density of an original probability distribution, denoted by p ( x ), and the diversity D in non-uniform distributions, both within parts of a distribution and the whole. Empirically, our results are an important advance since we can compare various parts of a distribution, noting that systems found in contemporary data often have unequal distributions that possess multiple diversity types and have unknown and changing frequencies at different scales (e.g. income, economic complexity ratings, rankings, etc.). To date, we have proven our results for discrete distributions. Our focus here is continuous distributions. In both instances, we do so by linking case-based entropy, a diversity approach we developed, to a probability distribution’s shape for continuous distributions. This allows us to demonstrate that the original probability distribution g 1 , the case-based entropy curve g 2 , and the slope of diversity g 3 ( c ( a , x ) versus the <?CDATA ${c}_{(a,x)}* \mathrm{ln}{A}_{(a,x)}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>c</mml:mi> </mml:mrow> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>a</mml:mi> <mml:mo>,</mml:mo> <mml:mi>x</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:msub> <mml:mo>*</mml:mo> <mml:mi>ln</mml:mi> <mml:msub> <mml:mrow> <mml:mi>A</mml:mi> </mml:mrow> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>a</mml:mi> <mml:mo>,</mml:mo> <mml:mi>x</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:msub> </mml:math> curve) are one-to-one (or injective). Put simply, a change in the probability distribution, g 1 , leads to variations in the curves for g 2 and g 3 . Consequently, any alteration in the permutation of the initial probability distribution, which results in a different form, will distinctly define the graphs g 2 and g 3 . By demonstrating the injective property of our method for continuous distributions, we introduce a unique technique to gauge the level of uniformity as indicated by D / c . Furthermore, we present a distinct method to calculate D / c for different forms of the original continuous distribution, enabling comparison of various distributions and their components.


Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data

Abstract: Creating large-scale and well-annotated datasets to train AI algorithms is crucial for automated tumor detection and localization. However, with limited resources, it is challenging to determine the best type of annotations when annotating massive amounts of unlabeled data. To address this issue, we focus on polyps in colonoscopy videos and pancreatic tumors in abdominal CT scans; Both applications require significant effort and time for pixel-wise annotation due to the high dimensional nature of the data, involving either temporary or spatial dimensions. In this paper, we develop a new annotation strategy, termed Drag&Drop, which simplifies the annotation process to drag and drop. This annotation strategy is more efficient, particularly for temporal and volumetric imaging, than other types of weak annotations, such as per-pixel, bounding boxes, scribbles, ellipses and points. Furthermore, to exploit our Drag&Drop annotations, we develop a novel weakly supervised learning method based on the watershed algorithm. Experimental results show that our method achieves better detection and localization performance than alternative weak annotations and, more importantly, achieves similar performance to that trained on detailed per-pixel annotations. Interestingly, we find that, with limited resources, allocating weak annotations from a diverse patient population can foster models more robust to unseen images than allocating per-pixel annotations for a small set of images. In summary, this research proposes an efficient annotation strategy for tumor detection and localization that is less accurate than per-pixel annotations but useful for creating large-scale datasets for screening tumors in various medical modalities. Project Page: https://github.com/johnson111788/Drag-Drop


Twitter Sentiment Analysis in Tourism with Polynomial Naïve Bayes Classifier

Abstract: Lombok has become a favorited tourist destination in the world. Therefore, tourism is a mainstay sector in regional development in West Nusa Tenggara. The contribution of the tourism sector shows an increasing trend. Tourist expenditures are distributed to various sectors. The tourism sector has a positive impact on the regional economy. The local government has prepared to improve the quality and quantity of tourism in Lombok. The results of local government efforts need to be analyzed so that future policies are on target. Analysis can be done on the satisfaction of tourists who travel to Lombok. It would be very difficult to get satisfaction data from all tourists through questionnaires. But on the other hand, tourist satisfaction is usually posted on their social networks. One of the social media that is widely used by tourists is Twitter. Their tweets contain not only expressions of natural beauty but also criticism, suggestions, and complaints during their visit. In addition, the tweet data on twitter is open access. This study tries to analyze the sentiment on Twitter which contains tweets of tourists who have visited Lombok. Sentiment analysis is performed using the Polynomial Naive Bayes Classifier. Sentiment results are classified into positive and negative sentiments. The results of this sentiment are expected to help related agencies or other tourism actors to improve the quality and quantity of regional tourism. The results showed that the positive sentiment on the security factor were 50.65%, the cost 75.32%, accommodation 62.33% and the cleanness factor 77.92%.


Effect of Connected Courses on Student Retention in the First year Curriculum

Abstract: This paper describes results of an initiative to place freshmen residents of an engineering and technology residential community into a common course in their first semester.The motivation of this initiative is to enhance retention of first-year freshmen by facilitating study group formation.Freshman engineering students at the investigator's university are provided access to multiple retention-focused initiatives including residential hall-based mathematics tutoring, supplemental instruction in mathematics and physics sections, and industry mentoring.The school began an initiative to intentionally enroll freshmen in the engineering-focused residential hall in a common course in the first year of engineering curricula.Students in the focused interest community were pre-registered for selected courses, based upon their interest in engineering and their academic preparedness.During the registration period, students could opt out of the pre-registered course.The purpose of this effort is to enhance student success by fostering social connections of students outside of class, facilitating study group formation.This paper evaluates the performance of students in first year connected courses and lessons learned from the connected course cohorts.First-year retention of students in connected classes was higher in each cohort in comparison to students without connected classes.


Improving Access to Engineering Education: Unlocking Text and Table Data in Images and Videos

Abstract: Accessibility of media, including visual media, is a significant concern in creating engineering education that is inclusive and accessible; without access, students who are blind or have low vision are unable to learn from today's engineering materials.This project presents a new accessibility tool, implemented as a user-friendly browser extension to extract and create accessible structured text -with a focus on tables of information -when embedded within web-based images and video media.The app can extract information to be used in a speech-to-text output of a screen reader; or text-to-braille device; pasted into a programming editing environment (e.g., Matlab, Jupyter notebook, Microsoft Code, or text editor); and further, used as the initial source input for manual or automated construction of audio descriptions to accompany the original media for future dissemination.In addition to improving access to engineering education, this project is a productivity tool for students seeking more access to textual data presented in image form.For example, it serves as a tool for all engineers and student engineers who seek to extract and re-use tabular information embedded inside an image or video that otherwise would require manual entry.The system uses the React.jsframework and Tesseract Optical Character Recognition (OCR) engine.The tool preserves privacy because it runs entirely inside the browser: no image data leaves the client.It can extract information from any web page on any website supported by the Chrome browser.Users can screenshot images and videos, extract text and numbers in scientific notation, determine the tabular structure, and meaningfully extract text from tables in tabular form (separated into rows and columns).We describe its design: the user interface features that allow its use by people with low-vision and access specialists.Upon initiation, the user selects a media HTML element to process; the user can choose to extract text from this frame or change the focus to another object on the page.The extraction area is adjustable by keyboard or pointer/touch interaction.Finally, the user chooses to either screen-capture the area and download the image, extract text from the area, or extract tabular information.To make the tool intuitive, interaction is structured as a wizard where users are guided along the stepwise process but can go back to previous steps.We provide examples of the best and worst output of our accessibility tool when applied to engineering education content and evaluate its accuracy and performance to extract tabular information from image samples from engineering disciplines.


System Design, Evaluation and Applications of Domain Term Extraction from Engineering Videos

Abstract: Understanding the meanings of domain-specific terms is essential to academic success in college-level STEM courses.However, it can be challenging for students to obtain correct spellings and precise definitions of domain-specific terms from lecture videos, given the limited lecture time, rarity of the terms, and possibly confusing pronunciations.To provide accurate speech-to-text transcription, and enable students to search for domain-specific terms and obtain term definitions in real-time, we designed, implemented, and evaluated the PhraseHinter tool, a text analytics pipeline that efficiently extracts domain-specific terms from engineering educational videos.The tool is lightweight and adaptable to online instruction platforms.In our approach, a series of key scenes are initially extracted from a lecture video using a novel scene detection algorithm.The algorithm employs a support vector machine to classify image differences based on pixel, face, and text similarity information [2].A domain corpus is built by using the optical character recognition (OCR) technique to extract text from the scenes.A sequence of text-cleaning algorithms is applied to the domain corpus to filter out invalid characters, punctuation, and stop words.Frequent phrases are identified using standard text mining algorithms including PrefixSpan [15].Using the TF-IDF metric [16], we compare the cleaned corpus to the background corpus to determine domain-specific terms and phrases.The proposed PhraseHinter tool has been successfully integrated into ClassTranscribe [4,11,3,19,2,10], a web-based video lecture platform, for multiple purposes: 1) Improve the Microsoft Azure Speech-to-Text accuracy by preparing a list of domainspecific terms with high confidence of occurrence in the audio, 2) Provide the input for the glossary tool, another text analytics service in ClassTranscribe that automatically generates the explanation for the domain-specific terms, and, currently in progress, 3) Provide search capability in order to locate the moments in the video when a domainspecific term is visually presented.In this paper, we evaluate the performance and accuracy of the PhraseHinter system based on a representative corpus of videos from different engineering disciplines with domain-specific terms and phrases correctly pre-identified.We share the evaluation dataset to the education community for further research.In addition, we present the source code and provide guidance for instructors who would like to adopt the tool.


Criticality in FitzHugh-Nagumo oscillator ensembles: Design, robustness, and spatial invariance

Abstract: Abstract Reservoir computing is an efficient and flexible framework for decision-making, control, and signal processing. It uses a network of interacting components varying from abstract nonlinear dynamical systems to physical substrates. Despite recent progress, the hardware implementation with inherent parameter variability and uncertainties, such as those mimicking the properties of living organisms’ nervous systems, remains an active research area. To address these challenges, we propose a constructive approach using a network of FitzHugh-Nagumo oscillators, exhibiting criticality across a broad range of resistive coupling strengths and robustness without specific parameter tuning. Additionally, the network’s activity demonstrates spatial invariance, offering freedom in choosing readout nodes. We introduce an alternative characterization of criticality by analyzing power dissipation, and demonstrate that criticality supports the robustness of the classification accuracy with respect to the readout shrinkage. Our results indicate criticality as a valuable property for classification problems, and provides design concepts for bio-inspired computational paradigms.


U.S. Copyright Office's Questions about Generative AI

Abstract: Generating more questions than answers.


Game-theoretic private blockchain design in edge computing networks

Abstract: Considering the privacy challenges of secure storage and controlled flow, there is an urgent need to realize a decentralized ecosystem of private blockchain for cyberspace. A collaboration dilemma arises when the participants are self-interested and lack feedback of complete information. Traditional blockchains have similar faults, such as trustlessness, single-factor consensus, and heavily distributed ledger, preventing them from adapting to the heterogeneous and resource-constrained Internet of Things. In this paper, we develop the game-theoretic design of a two-sided rating with complete information feedback to stimulate collaborations for private blockchain. The design consists of an evolution strategy of the decision-making network and a computing power network for continuously verifiable proofs. We formulate the optimum rating and resource scheduling problems as two-stage iterative games between participants and leaders. We theoretically prove that the Stackelberg equilibrium exists and the group evolution is stable. Then, we propose a multi-stage evolution consensus with feedback on a block-accounting workload for metadata survival. To continuously validate a block, the metadata of the optimum rating, privacy, and proofs are extracted to store on a lightweight blockchain. Moreover, to increase resource utilization, surplus computing power is scheduled flexibly to enhance security by degrees. Finally, the evaluation results show the validity and efficiency of our model, thereby solving the collaboration dilemma in the private blockchain.


Separate and conquer heuristic allows robust mining of contrast sets in classification, regression, and survival data

Abstract: Identifying differences between groups is one of the most important knowledge discovery problems. The procedure, also known as contrast sets mining, is applied in a wide range of areas like medicine, industry, or economics. In the paper we present RuleKit-CS, an algorithm for contrast set mining based on separate and conquer – a well established heuristic for decision rule induction. Multiple passes accompanied with an attribute penalization scheme provide contrast sets describing same examples with different attributes, distinguishing presented approach from the standard separate and conquer. The algorithm was also generalized for regression and survival data allowing identification of contrast sets whose label attribute/survival prognosis is consistent with the label/prognosis for the predefined contrast groups. This feature, not provided by the existing approaches, further extends the usability of RuleKit-CS. Experiments on over 130 data sets from various areas and detailed analysis of selected cases confirmed RuleKit-CS to be a useful tool for discovering differences between defined groups. In particular, the presented method identified contrast sets with higher support and precision than popular competitors like STUCCO, CSM-SD, or pysubgroup. The algorithm was implemented as a part of the RuleKit suite.


PhDnet: A novel physic-aware dehazing network for remote sensing images

Abstract: Remote sensing haze removal is a popular computational imaging technique that directly obtains clear remote sensing data from hazy remote sensing images. Apart from prior-based methods, deep-learning-based methods have performed well in the past years for their powerful non-linear mapping from hazy to haze-free domains. Most dehazing networks are established on atmospheric scattering models. However, these models cannot be easily embedded into deep learning to guide feature extraction. In this study, we introduce a haze extraction model that combines residual learning with the atmospheric scattering model and build a novel physic-aware dehazing network based on this model to achieve effective haze removal for remote sensing images with physical interpretability. By combining multi-scale-gating convolution with a haze extraction unit, PhDnet meets the need for remote sensing image haze removal. We verify the advantage of PhDnet by applying this model on multiple synthetic hazy datasets and real hazy images. We also implement comprehensive studies about the effectiveness of its haze extraction unit and other components. The source code and pre-trained models are available at https://github.com/colacomo/PhDnet.


Complete genuine multipartite entanglement monotone

Abstract: A complete characterization and quantification of entanglement, particularly the multipartite entanglement, remains an unfinished long-term goal in quantum information theory. As long as the multipartite system is concerned, the relation between the entanglement contained in different partitions or different subsystems need to take into account. The complete multipartite entanglement measure and the complete monogamy relation is a framework that just deals with such a issue. In this paper, we put forward conditions to justify whether the multipartite entanglement monotone (MEM) and genuine multipartite entanglement monotone (GMEM) are complete, completely monogamous, and tightly complete monogamous according to the feature of the reduced function. Especially, with the assumption that the maximal reduced function is nonincreasing on average under LOCC, we proposed a class of complete MEMs and a class of complete GMEMs via the maximal reduced function for the first time. By comparison, it is shown that, for the tripartite case, this class of GMEMs is better than the one defined from the minimal bipartite entanglement in literature under the framework of complete MEM and complete monogamy relation. In addition, the relation between monogamy, complete monogamy, and the tightly complete monogamy are revealed in light of different kinds of MEMs and GMEMs.


A Formulation of Structural Design Optimization Problems for Quantum Annealing

Abstract: We present a novel formulation of structural design optimization problems specifically tailored to be solved by qa. Structural design optimization aims to find the best, i.e., material-efficient yet high-performance, configuration of a structure. To this end, computational optimization strategies can be employed, where a recently evolving strategy based on quantum mechanical effects is qa. This approach requires the optimization problem to be present, e.g., as a qubo model. Thus, we develop a novel formulation of the optimization problem. The latter typically involves an analysis model for the component. Here, we use energy minimization principles that govern the behavior of structures under applied loads. This allows us to state the optimization problem as one overall minimization problem. Next, we map this to a qubo problem that can be immediately solved by qa. We validate the proposed approach using a size optimization problem of a compound rod under self-weight loading. To this end, we develop strategies to account for the limitations of currently available hardware. Remarkably, for small-scale problems, our approach showcases functionality on today’s hardware such that this study can lay the groundwork for continued exploration of qa’s impact on engineering design optimization problems.


Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation

Abstract: This study focused on seismic event detection in a volcano using machine learning by leveraging the advantages of software/hardware co-design for a system on a chip (SoC) based on field-programmable gate array (FPGA) devices. A case study was conducted on the Copahue Volcano, an active stratovolcano located on the border between Argentina and Chile. Volcanic seismic event processing and detection were integrated into a PYNQ-based implementation by using a low-end SoC-FPGA device. We also provide insights into integrating an SoC-FPGA into the acquisition node, which can be valuable in scenarios where stations are deployed solely for data collection and holds the potential for the development of an early alert system.


STAVOS: A Medaka Larval Cardiac Video Segmentation Method Based on Deep Learning

Abstract: Medaka (Oryzias latipes), as a crucial model organism in biomedical research, holds significant importance in fields such as cardiovascular diseases. Currently, the analysis of the medaka ventricle relies primarily on visual observation under a microscope, involving labor-intensive manual operations and visual assessments that are cumbersome and inefficient for biologists. Despite attempts by some scholars to employ machine learning methods, limited datasets and challenges posed by the blurred edges of the medaka ventricle have constrained research to relatively simple tasks such as ventricle localization and heart rate statistics, lacking precise segmentation of the medaka ventricle edges. To address these issues, we initially constructed a video object segmentation dataset comprising over 7000 microscopic images of medaka ventricles. Subsequently, we proposed a semi-supervised video object segmentation model named STAVOS, incorporating a spatial-temporal attention mechanism. Additionally, we developed an automated system capable of calculating various parameters and visualizing results for a medaka ventricle using the provided video. The experimental results demonstrate that STAVOS has successfully achieved precise segmentation of medaka ventricle contours. In comparison to the conventional U-Net model, where a mean accuracy improvement of 0.392 was achieved, our model demonstrates significant progress. Furthermore, when compared to the state-of-the-art Tackling Background Distraction (TBD) model, there is an additional enhancement of 0.038.


Positivity-preserving non-Markovian master equation for open quantum system dynamics: Stochastic Schrödinger equation approach

Abstract: Positivity preservation is naturally guaranteed in exact non-Markovian master equations for open quantum system dynamics. However, in many approximated non-Markovian master equations, the positivity of the reduced density matrix is not guaranteed. In this paper we provide a general class of time-local, perturbative, and positivity-preserving non-Markovian master equations generated from stochastic Schr\"odinger equations, particularly quantum-state-diffusion equations. Our method has an expanded range of applicability for accommodating a variety of non-Markovian environments. We show the positivity-preserving master equation for a three-level system coupled to a dissipative bosonic environment as a particular example to exemplify our general approach. We illustrate the numerical simulations with an analysis explaining why the previous approximated non-Markovian master equations cannot guarantee positivity. Our work provides a consistent master equation for studying the non-Markovian dynamics in ultrafast quantum processes and strong-coupling systems.


Power of noisy quantum states and the advantage of resource dilution

Abstract: Entanglement distillation allows to convert noisy quantum states into singlets, which can, in turn, be used for various quantum technological tasks, such as quantum teleportation and quantum key distribution. Entanglement dilution is the inverse process: singlets are converted into quantum states with less entanglement. While the usefulness of distillation is apparent, practical applications of entanglement dilution are less obvious. Here, we show that entanglement dilution can increase the resilience of shared quantum states to local noise. The increased resilience is observed even if diluting singlets into states with arbitrarily little entanglement. We extend our analysis to other quantum resource theories, such as quantum coherence, quantum thermodynamics, and purity. For these resource theories, we demonstrate that diluting pure quantum states into noisy ones can be advantageous for protecting the system from noise. Our results demonstrate the usefulness of quantum resource dilution, and provide a rare example for an advantage of noisy quantum states over pure states in quantum information processing.


Deep Learning Method for Heliostat Instance Segmentation

Abstract:
Heliostat instance segmentation (HST-IS) is a crucial component of the heliostat tracking system at Heliogen’s Lancaster test facility. The system estimates the mirror normal of each heliostat by performing a nonlinear optimization-based fitting strategy using approximations of the non-shaded, non-blocked sunlit pixels on each heliostat, and the tracking system uses these estimates to improve performance.
 HST-IS is fundamentally challenging due to variability in lighting conditions and heliostat size relative to the capturing camera. Deep learning-based convolutional neural networks (CNN) have emerged in recent years by demonstrating noteworthy precision in tasks such as object recognition, detection, and segmentation. CNN-based methods offer a robust augmentation to HST-IS methods as they capture a context-less hierarchy of image features.
 In this study, we developed deep learning models to automatically segment heliostat instances from elevated images taken from the field. We study various image parameters and architectural customizations to optimize for scalability, robustness, and accuracy in our predictions. We perform robust evaluations of our best model to quantify gaps between model development and real-world deployment and provide evidence for utility in the field.


The Role of Artificial Intelligence Autonomy in Higher Education: A Uses and Gratification Perspective

Abstract: With the rapid development of artificial intelligence (AI) technology, AI educators have become a reality. The advancement and increasing applications of AI technology in higher education not only provide more efficient tools for teachers in long-term and focused teaching, but also provide new active and independent spaces for sustainable self-motivated learning for college students. It is of great importance that the effects of AI educator design are understood to ensure the sustainable development and deployment of AI-driven courses at universities. This paper investigates the influences of AI educators’ autonomy design on students’ usage intentions by delving into how the artificial autonomy of AI educators satisfies students’ needs. Drawing on the uses and gratification (U&G) framework, we theoretically elaborate on how AI educator autonomy (i.e., sensing autonomy, thought autonomy, and action autonomy) influences students’ intentions to use an AI educator through the mediating effects of U&G benefits (i.e., information-seeking gratification, social interaction gratification, and entertainment gratification). By conducting an online survey (N = 673) on college students, we found that the sensing autonomy of AI educators is positively associated with usage intention due to the mediating effects of social interaction and entertainment gratifications; the thought autonomy of AI educators is positively related to usage intention, mediated by information-seeking and social interaction gratifications, and the action autonomy of AI educators is positively linked with usage intention through the paths of information-seeking and entertainment gratifications. Our findings provide both theoretical contributions and practical implications.


Improved Nuclear Reaction Heuristic Intelligence Algorithm for Online Learning in Self-Monitoring Strategy Convergence

Abstract: INTRODCTION: By analyzing the problem of self-monitoring in English online learning and constructing a strategy-integrated evaluation method, we can not only enrich the theoretical research results of self-monitoring in online learning, but also improve the independent learning ability and self-monitoring ability of students in English online learning. OBJECTIVES: To address the problem of poor optimization performance of current fusion optimization methods.METHODS:This paper proposes an online learning self-monitoring strategy fusion method based on improved nuclear reaction heuristic intelligent algorithm. First, the problems and enhancement strategies of online learning self-monitoring are analyzed; then, the online learning self-monitoring strategy fusion model is constructed by improving the nuclear reaction heuristic intelligent algorithm; finally, the proposed method is verified to be effective and feasible through the analysis of simulation experiments. RESLUTS: The results show that the fusion method of learning self-monitoring strategies on the line at the 20th iteration number starts to converge to optimization with less than 0.1s optimization time, and the error of the statistical score value before and after weight optimization is controlled within 0.05. CONCLUSION:Addressing the Optimization of Convergence of Self-Monitoring Strategies for English Online Learning.


Design of New Media Event Warning Method Based on K-means and Seasonal Optimization Algorithm

Abstract: INTRODUCTION: Timely and effective early warning of new media events not only provides academic value to the study of new media events, but also can play a positive role in promoting the resolution of public opinion. OBJECTIVES: Aiming at the current research on early warning of new media events, there are problems such as the theoretical research is not in-depth and the early warning model is not comprehensive. METHOD: In this paper, K-means and seasonal optimization algorithm are used to construct new media event early warning method. Firstly, by analyzing the construction process of new media event early warning system, extracting text feature vector and carrying out text feature dimensionality reduction; then, combining with the random forest algorithm, the new media event early warning method based on intelligent optimization algorithm optimizing K-means clustering algorithm is proposed; finally, the validity and superiority of the proposed method is verified through the analysis of simulation experiments. RESULTS: The method developed in this paper improves the accuracy, time performance of new media event warning techniques. CONCLUSION: Addresses the lack of comprehensiveness of current approaches to early warning of new media events.


An efficient quantum algorithm for ensemble classification using bagging

Abstract: Abstract Ensemble methods aggregate predictions from multiple models, typically demonstrating improved accuracy and reduced variance compared to individual classifiers. However, they often come with significant memory usage and computational time requirements. A novel quantum algorithm that leverages quantum superposition, entanglement, and interference to construct an ensemble of classification models using bagging as an aggregation strategy is introduced. Through the generation of numerous quantum trajectories in superposition, the authors achieve B transformations of the training set with only operations, allowing an exponential enlargement of the ensemble size while linearly increasing the depth of the corresponding circuit. Moreover, when assessing the algorithm's overall cost, the authors demonstrate that the training of a single weak classifier contributes additively to the overall time complexity, as opposed to the multiplicative impact commonly observed in classical ensemble methods. To illustrate the efficacy of the authors’ approach, experiments on reduced real‐world datasets utilising the IBM qiskit environment to demonstrate the functionality and performance of the proposed algorithm are introduced.


Conjecturing-Based Discovery of Patterns in Data

Abstract: We propose the use of a conjecturing machine that suggests feature relationships in the form of bounds involving nonlinear terms for numerical features and Boolean expressions for categorical features. The proposed Conjecturing framework recovers known nonlinear and Boolean relationships among features from data. In both settings, true underlying relationships are revealed. We then compare the method to a previously proposed framework for symbolic regression on the ability to recover equations that are satisfied among features in a data set. The framework is then applied to patient-level data regarding COVID-19 outcomes to suggest possible risk factors that are confirmed in the medical literature. Discovering patterns in data is a first step toward establishing causal relationships, which can be the basis for effective decision making. Data Ethics & Reproducibility Note: Code and data to reproduce results are available here: https://github.com/jpbrooks/conjecturing . COVID-19 synthetic patient data were obtained as part of the Veterans Health Administration (VHA) Innovation Ecosystem and precisionFDA COVID-19 Risk Factor Modeling Challenge and are used here with permission from the Food and Drug Administration (FDA). The e-companion is available at https://doi.org/10.1287/ijds.2021.0043 . History: Olivia Sheng served as the senior editor for this article.


Federated Offline Reinforcement Learning

Abstract: Evidence-based or data-driven dynamic treatment regimes are essential for personalized medicine, which can benefit from offline reinforcement learning (RL). Although massive healthcare data are available across medical institutions, they are prohibited from sharing due to privacy constraints. Besides, heterogeneity exists in different sites. As a result, federated offline RL algorithms are necessary and promising to deal with the problems. In this paper, we propose a multi-site Markov decision process model which allows for both homogeneous and heterogeneous effects across sites. The proposed model makes the analysis of the site-level features possible. We design the first federated policy optimization algorithm for offline RL with sample complexity. The proposed algorithm is communication-efficient, which requires only a single round of communication interaction by exchanging summary statistics. We give a theoretical guarantee for the proposed algorithm, where the suboptimality for the learned policies is comparable to the rate as if data is not distributed. Extensive simulations demonstrate the effectiveness of the proposed algorithm. The method is applied to a sepsis dataset in multiple sites to illustrate its use in clinical settings.


The Mediating Role of Parasocial Relationship in Customer Services Chatbots Among Millennials and Gen Z Population

Abstract: In the fast-evolving landscape of human–computer interaction (HCI), customer service chatbots have emerged as a new way for engaging with users. However, a critical challenge lies in ensuring continued usage of these chatbot's services. This study argues that by tapping into the emotional dimension of chatbot–user interactions, Parasocial Relationships (PSRs) can influence customers' continuance intention (CI) to use chatbot services. The findings from PLS-SEM shed light on the significance of Information Quality and System Quality in fostering PSRs. The study further identifies PSR as a significant mediator between Service Quality and Continuous intention to use. This research primarily examines Millennials and Gen Z. Future studies may explore consumers of different age groups. The implications of this research are crucial for advancing the field of chatbot–human interaction. Organizations can benefit from these insights by prioritizing personalized and empathetic service experiences to nurture stronger PSRs and promote user satisfaction, trust, and intention to continue using chatbot services.


Automatic Construction of Interval-Valued Fuzzy Hindi WordNet using Lexico-Syntactic Patterns and Word Embeddings

Abstract: A computational lexicon is the backbone of any language processing system. It helps computers to understand the language complexity as a human does by inculcating words and their semantic associations. Manually constructed famous Hindi WordNet (HWN) consists of various classical semantic relations (crisp relations). To handle uncertainty and represent Hindi WordNet more semantically, Type- 1 fuzzy graphs are applied to relations of Hindi WordNet. But uncertainty in the crisp membership degree is not considered in Type 1 fuzzy set (T1FS). Also collecting billions (5,55,69,51,753 relations in HWN) of membership values from experts (humans) is not feasible. This paper applied the concept of Interval-Valued Fuzzy graphs and proposed Interval- Valued Fuzzy Hindi WordNet (IVFHWN). IVFHWN automatically identifies Interval- Valued Fuzzy relations between words and their degree of membership using word embeddings and lexico-syntactic patterns. The experimental results for the word sense disambiguation problem show better outcomes when IVFHWN is being used in place of Type 1 Fuzzy Hindi WordNet and classical Hindi WordNet.