Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment

Abstract: Deep learning models are trained with certain assumptions about the data during the development stage and then used for prediction in the deployment stage. It is important to reason about the trustworthiness of the model's predictions with unseen data during deployment. Existing methods for specifying and verifying traditional software are insufficient for this task, as they cannot handle the complexity of DNN model architecture and expected outcomes. In this work, we propose a novel technique that uses rules derived from neural network computations to infer data preconditions for a DNN model to determine the trustworthiness of its predictions. Our approach, DeepInfer involves introducing a novel abstraction for a trained DNN model that enables weakest precondition reasoning using Dijkstra's Predicate Transformer Semantics. By deriving rules over the inductive type of neural network abstract representation, we can overcome the matrix dimensionality issues that arise from the backward non-linear computation from the output layer to the input layer. We utilize the weakest precondition computation using rules of each kind of activation function to compute layer-wise precondition from the given postcondition on the final output of a deep neural network. We extensively evaluated DeepInfer on 29 real-world DNN models using four different datasets collected from five different sources and demonstrated the utility, effectiveness, and performance improvement over closely related work. DeepInfer efficiently detects correct and incorrect predictions of high-accuracy models with high recall (0.98) and high F-1 score (0.84) and has significantly improved over the prior technique, SelfChecker. The average runtime overhead of DeepInfer is low, 0.22 sec for all the unseen datasets. We also compared runtime overhead using the same hardware settings and found that DeepInfer is 3.27 times faster than SelfChecker, the state-of-the-art in this area.


On the Helpfulness of Answering Developer Questions on Discord with Similar Conversations and Posts from the Past

Abstract: A big part of software developers' time is spent finding answers to their coding-task-related questions. To answer their questions, developers usually perform web searches, ask questions on Q&A websites, or, more recently, in chat communities. Yet, many of these questions have frequently already been answered in previous chat conversations or other online communities. Automatically identifying and then suggesting these previous answers to the askers could, thus, save time and effort. In an empirical analysis, we first explored the frequency of repeating questions on the Discord chat platform and assessed our approach to identify them automatically. The approach was then evaluated with real-world developers in a field experiment, through which we received 142 ratings on the helpfulness of the suggestions we provided to help answer 277 questions that developers posted in four Discord communities. We further collected qualitative feedback through 53 surveys and 10 follow-up interviews. We found that the suggestions were considered helpful in 40% of the cases, that suggesting Stack Overflow posts is more often considered helpful than past Discord conversations, and that developers have difficulties describing their problems as search queries and, thus, prefer describing them as natural language questions in online communities.


LibvDiff: Library Version Difference Guided OSS Version Identification in Binaries

Abstract: Open-source software (OSS) has been extensively employed to expedite software development, inevitably exposing downstream software to the peril of potential vulnerabilities. Precisely identifying the version of OSS not only facilitates the detection of vulnerabilities associated with it but also enables timely alerts upon the release of 1-day vulnerabilities. However, current methods for identifying OSS versions rely heavily on version strings or constant features, which may not be present in compiled OSS binaries or may not be representative when only function code changes are made. As a result, these methods are often imprecise in identifying the version of OSS binaries being used.


Towards Reliable AI: Adequacy Metrics for Ensuring the Quality of System-level Testing of Autonomous Vehicles

Abstract: AI-powered systems have gained widespread popularity in various domains, including Autonomous Vehicles (AVs). However, ensuring their reliability and safety is challenging due to their complex nature. Conventional test adequacy metrics, designed to evaluate the effectiveness of traditional software testing, are often insufficient or impractical for these systems. White-box metrics, which are specifically designed for these systems, leverage neuron coverage information. These coverage metrics necessitate access to the underlying AI model and training data, which may not always be available. Furthermore, the existing adequacy metrics exhibit weak correlations with the ability to detect faults in the generated test suite, creating a gap that we aim to bridge in this study.


Characterizing Software Maintenance Meetings: Information Shared, Discussion Outcomes, and Information Captured

Abstract: A type of meeting that has been understudied in the software engineering literature to date is what we term the software maintenance meeting: a regularly scheduled team meeting in which emergent issues are addressed that are usually out of scope of the daily stand-up but not necessarily challenging enough to warrant an entirely separate meeting. These meetings tend to discuss a wide variety of topics and are crucial in keeping software development projects going, but little is known about these meetings and how they proceed. In this paper, we report on a single exploratory case study in which we analyzed ten consecutive maintenance meetings from a major healthcare software provider. We analyzed what kind of information is brought into the discussions held in these meetings and how, what outcomes arose from the discussions, and what information was captured for downstream use. Our findings are varied, giving rise to both practical considerations for those conducting these kinds of meetings and new research directions toward further understanding and supporting them.


VeRe: Verification Guided Synthesis for Repairing Deep Neural Networks

Abstract: Neural network repair aims to fix the 'bugs'1 of neural networks by modifying the model's architecture or parameters. However, due to the data-driven nature of neural networks, it is difficult to explain the relationship between the internal neurons and erroneous behaviors, making further repair challenging. While several work exists to identify responsible neurons based on gradient or causality analysis, their effectiveness heavily rely on the quality of available 'bugged' data and multiple heuristics in layer or neuron selection. In this work, we address the issue utilizing the power of formal verification (in particular for neural networks). Specifically, we propose VeRe, a verification-guided neural network repair framework that performs fault localization based on linear relaxation to symbolically calculate the repair significance of neurons and furthermore optimize the parameters of problematic neurons to repair erroneous behaviors. We evaluated VeRe on various repair tasks, and our experimental results show that VeRe can efficiently and effectively repair all neural networks without degrading the model's performance. For the task of removing backdoors, VeRe successfully reduces attack success rate from 98.47% to 0.38% on average, while causing an average performance drop of 0.9%. For the task of repairing safety properties, VeRe successfully repairs all the 36 tasks and achieves 99.87% generalization on average.


A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges

Abstract: The software engineering community recently has witnessed widespread deployment of AI programming assistants, such as GitHub Copilot. However, in practice, developers do not accept AI programming assistants' initial suggestions at a high frequency. This leaves a number of open questions related to the usability of these tools. To understand developers' practices while using these tools and the important usability challenges they face, we administered a survey to a large population of developers and received responses from a diverse set of 410 developers. Through a mix of qualitative and quantitative analyses, we found that developers are most motivated to use AI programming assistants because they help developers reduce key-strokes, finish programming tasks quickly, and recall syntax, but resonate less with using them to help brainstorm potential solutions. We also found the most important reasons why developers do not use these tools are because these tools do not output code that addresses certain functional or non-functional requirements and because developers have trouble controlling the tool to generate the desired output. Our findings have implications for both creators and users of AI programming assistants, such as designing minimal cognitive effort interactions with these tools to reduce distractions for users while they are programming.


Dynamic and typological explanations of planning in complex problem-solving

Abstract: Planning is an important component of complex problem-solving (CPS) skills, exerting a profound impact on student learning and development. Previous studies have primarily employed variable-centered methods to explore the influence of planning on CPS skills, thereby neglecting the dynamic role and mechanisms of planning as a metacognitive strategy in CPS processes. Through the use of sequence analysis of process data drawn from the finite state automata measurement framework within the PISA 2012 assessment, our study underscores the importance of planning in the inception of goal-directed behaviors, while also revealing typological differences in the use of planning strategy. By examining the role, mechanisms, and typologies of planning strategy, this study offers considerable pedagogical insights for enhancing CPS instruction. The study demonstrates that planning is instrumental in initiating goal-directed behaviors and enhencing interactions quality in CPS process. It sheds light on distinct typological differences in the use of planning strategies. Moreover, the use of planning is closely intertwined with motivation, cognitive load and personality traits. The findings advocate for instruction that is tailored to individual students' planning typologies, incorporating tailored scaffolding to foster CPS skills. Further research into personalized planning interventions has the potential to translate these insights into enhanced practical applications.


Inference serving with end-to-end latency SLOs over dynamic edge networks

Abstract: Abstract While high accuracy is of paramount importance for deep learning (DL) inference, serving inference requests on time is equally critical but has not been carefully studied especially when the request has to be served over a dynamic wireless network at the edge. In this paper, we propose Jellyfish—a novel edge DL inference serving system that achieves soft guarantees for end-to-end inference latency service-level objectives (SLO). Jellyfish handles the network variability by utilizing both data and deep neural network (DNN) adaptation to conduct tradeoffs between accuracy and latency. Jellyfish features a new design that enables collective adaptation policies where the decisions for data and DNN adaptations are aligned and coordinated among multiple users with varying network conditions. We propose efficient algorithms to continuously map users and adapt DNNs at runtime, so that we fulfill latency SLOs while maximizing the overall inference accuracy. We further investigate dynamic DNNs, i.e., DNNs that encompass multiple architecture variants, and demonstrate their potential benefit through preliminary experiments. Our experiments based on a prototype implementation and real-world WiFi and LTE network traces show that Jellyfish can meet latency SLOs at around the 99th percentile while maintaining high accuracy.


ArZiGo: A recommendation system for scientific articles

Abstract: The large number of scientific publications around the world is increasing at a rate of approximately 4%–5% per year. This fact has resulted in the need for tools that deal with relevant and high-quality publications. To address this necessity, search and reference management tools that include some recommendation algorithms have been developed. However, many of these solutions are proprietary tools and the full potential of recommender systems is rarely exploited. There are some solutions which provide recommendations for specific domains, by using ad-hoc resources. Furthermore, some other systems do not consider any personalization strategy to generate the recommendations. This paper presents ArZiGo, a web-based full prototype system for the search, management, and recommendation of scientific articles, which feeds on the Semantic Scholar Open Research Corpus, a corpus that is growing continually with more than 190M papers from all fields of science so far. ArZiGo combines different recommendation approaches within a hybrid system, in a configurable way, to recommend those papers that best suit the preferences of the users. A group of 30 human experts has participated in the evaluation of 500 recommendations in 10 research areas, 7 of which belong to the area of Computer Science and 3 to the area of Medicine, obtaining quite satisfactory results. Besides the appropriateness of the articles recommended, the execution time of the implemented algorithms has also been analyzed.


Analysing persuasion strategies in online advertising for third-hand cars in Tanzania: translanguaging in the virtual linguistic landscape

Abstract: The main argument of this paper is that the Virtual Linguistic Landscape (VLL) of advertising allows the utilisation of persuasion strategies that transcend the traditional separation of named languages to produce the maximum effect on potential customers. The paper challenges the segregational view of language, which assumes that communication involves selecting linguistic resources from pre-determined sets of possibilities in a separate linguistic system from other communicative possibilities (Pennycook, A. (2017). Translanguaging and semiotic assemblages. International Journal of Multilingualism, 14(3), 269–282, p. 8). It used the Snipping Tool to collect 79 online car adverts from X, Facebook, Instagram, Kupatana, Jiji Tanzania, and Jamii Forums. Thematic and socio-semiotic analyses indicate that advertisers for used cars integrate icons, indices, and symbols in the VLL to communicate messages effectively. They also utilise repetitions, typography, nonce borrowings, nicknames, colloquialisms, endorsements, and testimonials to influence their audiences' attitudes and behaviours. The paper also shows that communication in the VLL is shaped by the relations between users, semiotic possibilities, and referents in making meaning, lending support to translanguaging.


Prioritization and exchange chains in privacy-preserving kidney exchange

Abstract: The Kidney Exchange Problem (KEP) aims at finding an optimal set of exchanges among pairs of patients and their medically incompatible living kidney donors as well as altruistic donors who are not associated with any particular patient but want to donate a kidney to any person in need. Existing platforms that offer the finding of such exchanges for patient-donor pairs and altruistic donors are organized in a centralized fashion and operated by a single platform operator. This makes them susceptible to manipulation and corruption. Recent research has targeted these security issues by proposing decentralized Secure Multi-Party Computation (SMPC) protocols for solving the KEP. However, these protocols fail to meet two important requirements for kidney exchange in practice. First, they do not allow for altruistic donors. While such donors are not legally allowed in all countries, they have been shown to have a positive effect on the number of transplants that can be found. Second, the existing SMPC protocols do not support prioritization, which is used in existing platforms to give priority to certain exchanges or patient-donor pairs, e.g., to patients who are hard to match due to their medical characteristics. In this paper, we introduce a generic gate for implementing prioritization in kidney exchange. We extend two existing SMPC protocols for solving the KEP such that they allow for altruistic donors and prioritization and present one novel SMPC protocol for solving the KEP with altruistic donors and prioritization based on dynamic programming. We prove the security of all protocols and analyze their complexity. We implement all protocols and evaluate their performance for the setting where altruistic donors are legally allowed and for the setting where they are not. Thereby, we determine the performance impact of the inclusion of altruistic donors and obtain those approaches that perform best for each setting.


A Framework for Reconstructing Super-Resolution Magnetic Resonance Images from Sparse Raw Data Using Multilevel Generative Methods

Abstract: Super-resolution magnetic resonance (MR) scans give anatomical data for quantitative analysis and treatment. The use of convolutional neural networks (CNNs) in image processing and deep learning research have led to super-resolution reconstruction methods based on deep learning. The study offers a G-guided generative multilevel network for training 3D neural networks with poorly sampled MR input data. The author suggest using super-resolution reconstruction (SRR) and modified sparse sampling to address these issues. Image-based Wasserstein GANs retain k-space data sparsity. Wasserstein Generative Adversarial Networks (WGANs) store and represent picture space knowledge. The method obtains null-valued k-space data and repairs fill gaps in the dataset to preserve data integrity. The proposed reconstruction method processes raw data samples and is able to perform subspace synchronization, deblurring, denoising, motion estimation, and super-resolution image production. The suggested algorithm uses different preprocessing methods to deblur and denoise datasets. Preliminary trials contextualize and speed up assessments. Results indicate that reconstructed pictures have better high-frequency features than sophisticated multi-frame techniques. This is supported by rising PSNR, MAE, and IEM measurements. A k-space correction block improves GAN network refinement learning in the suggested method. This block improves the network’s ability to avoid unnecessary data, speeding reconstruction. A k-space correction module can limit the generator’s output to critical lines, allowing the reconstruction of only missing lines. This improves convergence and speeds rebuilding. This study shows that this strategy reduces aliasing artifacts better than contemporaneous and noniterative methods.


A Survey on Comprehensive Exploration of Modern Perception System

Abstract: This review paper offers an in-depth analysis of recent advancements in image-based intelligent transportation systems. It provides insights into various approaches employed for detecting false images, license plate recognition, distributing emergency messages, as well as detecting and classifying vehicle accidents and road accidents. The research critically evaluates the pros and cons of deep learning techniques, including position-based routing, enhanced YOLOv8, optimal K-means with a convolutional neural network, and gated hierarchical multi-task learning. The investigation of an Internet of Things-based vehicle accident detection and classification system highlights the benefits of sensor fusion for precise accident detection; nonetheless, the evaluation raises questions regarding the scalability and reliability of smartphone sensors. The utility of a deep learning system for road accident identification utilizing artificially created multi-perspective movies is emphasized, along with warnings about possible limits in capturing real-world circumstances


Chinese Cyberbullying Detection Using XLNet and Deep Bi-LSTM Hybrid Model

Abstract: The popularization of the internet and the widespread use of smartphones have led to a rapid growth in the number of social media users. While information technology has brought convenience to people, it has also given rise to cyberbullying, which has a serious negative impact. The identity of online users is hidden, and due to the lack of supervision and the imperfections of relevant laws and policies, cyberbullying occurs from time to time, bringing serious mental harm and psychological trauma to the victims. The pre-trained language model BERT (Bidirectional Encoder Representations from Transformers) has achieved good results in the field of natural language processing, which can be used for cyberbullying detection. In this research, we construct a variety of traditional machine learning, deep learning and Chinese pre-trained language models as a baseline, and propose a hybrid model based on a variant of BERT: XLNet, and deep Bi-LSTM for Chinese cyberbullying detection. In addition, real cyber bullying remarks are collected to expand the Chinese offensive language dataset COLDATASET. The performance of the proposed model outperforms all baseline models on this dataset, improving 4.29% compared to SVM—the best performing method in traditional machine learning, 1.49% compared to GRU—the best performing method in deep learning, and 1.13% compared to BERT.


High utility itemsets mining based on hybrid harris hawk optimization and beluga whale optimization algorithms

Abstract: The commonly used high utility itemsets mining method for massive data is the intelligent optimization algorithm. In this paper, the WHO (Whale-Hawk Optimization) algorithm is proposed by integrating the harris hawk optimization (HHO) algorithm with the beluga whale optimization (BWO) algorithm. Additionally, a whale initialization strategy based on good point set is proposed. This strategy helps to guide the search in the initial phase and increase the diversity of the population, which in turn improve the convergence speed and algorithm performance. By applying this improved algorithm to the field of high utility itemsets mining, it provides new solutions to optimization problems and data mining problems. To evaluate the performance of the proposed WHO, a large number of experiments are conducted on six datasets, chess, connect, mushroom, accidents, foodmart, and retail, in terms of convergence, recall rates, and runtime. The experimental results show that the convergence of the proposed WHO is optimal in five datasets and has the shortest runtime in all datasets. Compared to PSO, AF, BA, and GA, the average recall rate in the six datasets increased by 32.13%, 49.95%, 12.15%, and 16.24%, respectively.


Application of facial expression recognition technology based on feature fusion in teaching

Abstract: With the development of artificial intelligence technology, the digital transformation of student-oriented education becomes particularly important. How to promote real-time interaction between teachers and students in the classroom is an urgent issue which is needed to pay attention to. Based on the facial expression features of students in a classroom, this paper analyzes the changes in angles between facial expression feature points using Dlib. Additionally, this paper proposes a novel algorithm for extracting variable scale template edge trend features. The algorithm adaptively processes the template based on the edge trend features of expression feature points, and use the proposed template slope normalization algorithm to achieve multi-scale template edge trend extraction. Then, DNN are used to recognize different listening expressions. The experimental results show that the proposed algorithm has faster recognition speed and better robustness when applied to classroom expression recognition. By identifying students’ class status to remind teachers to adjust their class progress, the goal of improving classroom learning effectiveness is achieved


The ringed residual u-net with non-natural regions feature for image splicing forgery detection and localization

Abstract: Recently, with the emergence of many image editing tools (photoshop, Topaz studio, etc.), the authenticity of images has been severely challenged. However, the performance of some existing traditional feature extraction methods and detection methods based on convolutional neural network (CNN) is poor, and the information provided by the features extracted from the network is limited and single. In this paper, an end-to-end ringed residual U-Net is proposed to detect image splicing forgery by blending features of non-natural regions. Some regions with significant differences from the image background are defined as non-natural regions(such as the irregular border at the splicing of images). In this paper, a feature enhancement module for non-natural regions is constructed, which the image through the pooling of four different scales, and these features are then combined with the original image and input to the backbone network for processing, aiming to highlight regions of the image that differ significantly from the background. Therefore, after adding the feature enhancement module for non-natural regions to the end-to-end ring residual U-Net, more attention will be paid to the tampering regions in the feature extraction stage, image manipulation detection and localization will also become more accurate. Compared with some mainstream methods, this method achieves better performance on the three standard datasets(CASIA2.0, NIST2016, COLUMBIA). In addition, it has excellent robustness under JPEG compression attack and noise corruption attack.


Avoiding Illegal Land Registry using Blockchain

Abstract: The Land Registry system is an essential department for any government system, who maintains the crucial information about the land like ownership, transaction history and many more. There are various escape clauses in existing framework that raise the possibilities of misdirection and questions. To resolve this issue, we have accompanied an answer of carrying out blockchain in the land vault framework. A safe stage for land library framework utilizing Blockchain has been anticipated. Blockchain here is utilized as partner electronic record of computerized records and exchanges that are scrambled utilizing cryptography[1]. This framework targets concocting a model for secure and consistent land enlistment framework upheld blockchain innovation, which can make simple to dispose of the provisos in the ongoing area vault framework. Our framework gives one-of-a-kind character to each land, enlisted and checked for the land proprietor which can't be altered or repeated in this way, this gives secure exchanging of land. The land proprietor will be furnished with the ID when the land is enrolled in our framework and will be moved to the next client once he sells his territory. So, nobody can imitate as other client and there will no opportunities for deceitful or trick


A Survey of Health Care Chatbot for Patient Support

Abstract: This literature survey delves into the evolving realm of healthcare chatbots, analyzing a variety of research papers on AI chatbot systems. The papers cover diverse objectives, methodologies, and applications within healthcare, including microservice architectures for chronic patient support, disease prediction, herbal remedies, and mental health support. Common threads across the papers include the adoption of natural language processing, machine learning, and AI markup languages. Ethical considerations, such as data privacy and consent, are identified as crucial aspects. Some papers focus on specific medical domains, while others propose comprehensive frameworks integrating IoT and health knowledge graphs. The survey highlights challenges like unified semantic approaches for patient data and underscores the ongoing need for research to address gaps, enhance chatbot intelligence, and ensure ethical deployment in real-world healthcare scenarios


Relevancy transformation operators in the form of polynomial functions

Abstract: Relevancy transformation operators (RET operators) have been widely used in fuzzy systems modelling and the construction of weighted aggregation functions. Several construction methods of RET operators based on different aggregation functions such as t-norm, t-conorm and copula, have been proposed. In this paper, the attention is paid to the expression of RET operators, which is an important feature from an application the point of view. Polynomial RET operators are introduced as those RET operators in the form of polynomial functions of two variables. A complete characterisation of polynomial RET operators of degree less than 4 are presented.


Information extraction from automotive reports for ontology population

Abstract: In this paper, we showcase our research on the use of ontologies and information extraction for the purpose of modeling damages incurred on car bodies. With the increasing use of technology in the automotive industry, it is important to have a standardized and efficient way of documenting and analyzing car damage reports. Most existing reports are unstructured, and there is a lack of standardization in describing the damage. To address this issue, we have developed a domain ontology for car damage modeling (OCD),11 industryportal.enit.fr/ontologies/OCD,22 github.com/OntologyCarDamage/OCD and proposed an end-to-end system to extract information from French automotive reports. The information extraction process involves using named entity recognition (NER) and relationship extraction (RE) techniques to identify and extract relevant information from the reports. Then, the extracted information is used to populate the OCD ontology, allowing a structured and standardized representation of the damage information. The proposed system was tested on a real dataset of automotive reports and showed promising results.


A Literature Survey on SpeakSmart: AI – Enhanced language learning guide

Abstract: This extensive review of the literature examines the pervasive impact of artificial intelligence (AI) on language learning and teaching, covering innovative uses like real-time language practice, personalized language learning, and automated testing methods in the teaching of English as a foreign language (EFL). The survey covers a wide range of topics, such as the use of Natural Language Processing (NLP) to extract Business Process Models in Business Process Management (BPM), the deployment of English-language chat-bots operating on the WeChat platform that are based on transfer learning, and the incorporation of NLP tools and virtual reality in the e-learning platform Exills. Additionally, it delves into cutting-edge technologies such as chat-bots for language learning, low-resource spoken language learning applications with AI support, and the broad application of AI in EFL teaching, tackling issues and offering encouraging results. The poll also addresses the use of AI in language learning tools, focusing on transparency and ethical issues while utilizing chat-bots, machine translation, speech recognition, and AI-generated content. The study highlights the importance of exposure to different cultures, community interaction, and multilingual voice recognition in creating a comprehensive language learning environment. It also highlights the limitations and enormous possibilities of integrating AI into language instruction.


Identification of Counterfeit/Fake Products using Blockchain

Abstract: Due to a lack of transparency, supply chain management regularly experienced problems such service redundancy, insufficient departmental coordination, and a lack of standardization .These days, product counterfeiting is a highly common occurrence, and it is nearly impossible to identify a counterfeit goods by its appearance alone. For reputable businesses, counterfeiters pose serious problems, yet the full impact of counterfeit goods on brands is too often unknown. Several strategies have been developed in the past to deal with the issue of product counterfeiting.The most widely used techniques include the use of QR code-based systems, artificial intelligence, and RFID tags. However, there were a few drawbacks to each of them artificial intelligence employs CNN and machine learning, which require a lot of processing power a QR code can be duplicated from a real product and placed on a false one, and so on. The goal of this research is to enhance the identification of counterfeit goods by tracing their supply chain's past. Blockchain technology, which guarantees the identification and traceability of genuine products across the supply chain, makes this possible. With a blockchain-based system, several parties can access everything simultaneously and it becomes decentralized. One of its key benefits is that the recorded data is very safe and protected from any vulnerabilities because it is hard to alter without the agreement of all parties involved. This study describes a system for detecting counterfeit goods that uses blockchain technology.


Integrating Artificial Intelligence into Cybersecurity Curriculum: New Perspectives

Abstract: Abstract As societies rely increasingly on computers for critical functions, the importance of cybersecurity becomes ever more paramount. Even in recent months there have been attacks that halted oil production, disrupted online learning at the height of COVID, and put medical records at risk at prominent hospitals. This constant threat of privacy leaks and infrastructure disruption has led to an increase in the adoption of artificial intelligence (AI) techniques, mainly machine learning (ML), in state-of-the-art cybersecurity approaches. Oftentimes, these techniques are borrowed from other disciplines without context and devoid of the depth of understanding as to why such techniques are best suited to solve the problem at hand. This is largely due to the fact that in many ways cybersecurity curricula have failed to keep up with advances in cybersecurity research and integrating AI and ML into cybersecurity curricula is extremely difficult. To address this gap, we propose a new methodology to integrate AI and ML techniques into cybersecurity education curricula. Our methodology consists of four components: i) Analysis of Literature which aims to understand the prevalence of AI and ML in cybersecurity research, ii) Analysis of Cybersecurity Curriculum that intends to determine the materials already present in the curriculum and the possible intersection points in the curricula for the new AI material, iii) Design of Adaptable Modules that aims to design highly adaptable modules that can be directly used by cybersecurity educators where new AI material can naturally supplement/substitute for concepts or material already present in the cybersecurity curriculum, and iv) Curriculum Level Evaluation that aims to evaluate the effectiveness of the proposed methodology from both student and instructor perspectives. In this paper, we focus on the first component of our methodology - Analysis of Literature and systematically analyze over 5000 papers that were published in the top cybersecurity conferences during the last five years. Our results clearly indicate that more than 78% of the cybersecurity papers mention AI terminology. To determine the prevalence of the use of AI, we randomly selected 300 papers and performed a thorough analysis. Our results show that more than 19% of the papers implement ML techniques. These findings suggest that AI and ML techniques should be considered for future integration into cybersecurity curriculum to better align with advancements in the field.


InterCap: Joint Markerless 3D Tracking of Humans and Objects in Interaction from Multi-view RGB-D Images

Abstract: Abstract Humans constantly interact with objects to accomplish tasks. To understand such interactions, computers need to reconstruct these in 3D from images of whole bodies manipulating objects, e.g., for grasping, moving and using the latter. This involves key challenges, such as occlusion between the body and objects, motion blur, depth ambiguities, and the low image resolution of hands and graspable object parts. To make the problem tractable, the community has followed a divide-and-conquer approach, focusing either only on interacting hands, ignoring the body, or on interacting bodies, ignoring the hands. However, these are only parts of the problem. On the contrary, recent work focuses on the whole problem. The GRAB dataset addresses whole-body interaction with dexterous hands but captures motion via markers and lacks video, while the BEHAVE dataset captures video of body-object interaction but lacks hand detail. We address the limitations of prior work with InterCap, a novel method that reconstructs interacting whole-bodies and objects from multi-view RGB-D data, using the parametric whole-body SMPL-X model and known object meshes. To tackle the above challenges, InterCap uses two key observations: (i) Contact between the body and object can be used to improve the pose estimation of both. (ii) Consumer-level Azure Kinect cameras let us set up a simple and flexible multi-view RGB-D system for reducing occlusions, with spatially calibrated and temporally synchronized cameras. With our InterCap method we capture the InterCap dataset, which contains 10 subjects (5 males and 5 females) interacting with 10 daily objects of various sizes and affordances, including contact with the hands or feet. To this end, we introduce a new data-driven hand motion prior, as well as explore simple ways for automatic contact detection based on 2D and 3D cues. In total, InterCap has 223 RGB-D videos, resulting in 67,357 multi-view frames, each containing 6 RGB-D images, paired with pseudo ground-truth 3D body and object meshes. Our InterCap method and dataset fill an important gap in the literature and support many research directions. Data and code are available at https://intercap.is.tue.mpg.de .


Abstract: The rise of graph neural networks has greatly contributed to the development of recommendation systems, and self-supervised learning has emerged as one of the most important approaches to address sparse interaction data. However, existing methods mostly focus on the recommendation’s accuracy while neglecting the role of recommended item diversity in enhancing user interest and merchant benefits. The reason for this phenomenon is mainly due to the bias of popular items, which makes the long-tail items (account for a large proportion) be neglected. How to mitigate the bias caused by item popularity has become one of the hot topics in current research. To address the above problems, we propose a Popularity-Debiased Graph Self-Supervised for Recommendation (PDGS). Specifically, we apply a penalty constraint on item popularity during the data enhancement process on the user–item interaction graph to eliminate the inherent popularity bias. We generate item similarity graphs with the popularity bias removed to construct a self-supervised learning task under multiple views, and we design model optimization strategies from the perspectives of popular items and long-tail items to generate recommendation lists. We conduct a large number of comparison experiments, as well as ablation experiments, on three public datasets to verify the effectiveness and the superiority of the model in balancing recommendation accuracy and diversity.


Quantum algorithms for state preparation and data classification based on stabilizer codes

Abstract: Quantum error correction (QEC) is a way to protect quantum information against noise. It consists of encoding input information into entangled quantum states known as the code space. Furthermore, to classify if the encoded information is corrupted or intact, a step known as syndrome detection is performed. For stabilizer codes, this step consists of measuring a set of stabilizer operators. In this paper, inspired by the QEC approach, and specifically stabilizer codes, we propose a prototype quantum circuit model for classification of classical data. The core quantum circuit can be considered as a quantum perceptron where the classification is based on syndrome detection. In this proposal, a quantum perceptron is realized by one stabilizer as part of a stabilizer code, while a quantum neural network (QNN) layer is realized by a stabilizer code which consists of many stabilizers. The concatenation of stabilizer codes results in complex QNNs. The QNN is trained by performing measurements and optimization of a set of parametrized stabilizers. We demonstrate the concept numerically. In this paper we also consider the first challenge to most applications of quantum computers, including data classification, which is to load data into the memory of the quantum computer. This loading amounts to representing the data as a quantum state, i.e., quantum state preparation. An exact amplitude encoding algorithm requires a circuit of exponential depth. We introduce an alternative recursive algorithm which approximates amplitude encoding with only a polynomial number of elementary gates. We name it the recursive approximate-scheme algorithm.


Agreement and Information Structure in Spanish PRO[PL] with-DP

Abstract: This paper aims to revisit a construction found in some Spanish varieties which refers to a set constituted by a singular referent and an annex introduced by the item con ‘with’: PRO(noun)[PL] with-DP. This construction triggers plural agreement and can be doubled by a plural pronoun, indicating that the annex is included in the set to which verbal agreement and the plural pronoun refer. For example, Nosotros con Juan viajamos ayer (literally, ‘We with Juan travelled.1PL yesterday’) means ‘Juan and I travelled yesterday’. We explore the Spanish PRO[PL] with-DP, taking into account its discursive properties together with the syntactic requirements involved in the agreement patterns. In fact, although the two individuals denoted by this construction are involved as equal participants in the event, they have a different discursive status: one of them introduces new information, while the other refers to the immediate communicative situation. If some notions regarding information structure can be coded by binary features such as [+/−anaphor] and [+/−contrast], it is possible to find plurality triggered by the opposite combination of features within the same syntactic object. PRO[PL] with-DP is a possibility that the lexicons of some languages offer.


HaCk: Hand Gesture Classification Using a Convolutional Neural Network and Generative Adversarial Network-Based Data Generation Model

Abstract: According to global data on visual impairment from the World Health Organization in 2010, an estimated 285 million individuals, including 39 million who are blind, face visual impairments. These individuals use non-contact methods such as voice commands and hand gestures to interact with user interfaces. Recognizing the significance of hand gesture recognition for this vulnerable population and aiming to improve user usability, this study employs a Generative Adversarial Network (GAN) coupled with Convolutional Neural Network (CNN) techniques to generate a diverse set of hand gestures. Recognizing hand gestures using HaCk typically involves a two-step approach. First, the GAN is trained to generate synthetic hand gesture images, and then a separate CNN is employed to classify gestures in real-world data. The evaluation of HaCk is demonstrated through a comparative analysis using Leave-One-Out Cross-Validation (LOO CV) and Holdout Cross-Validation (Holdout CV) tests. These tests are crucial for assessing the model’s generalization, robustness, and suitability for practical applications. The experimental results reveal that the performance of HaCk surpasses that of other compared ML/DL models, including CNN, FTCNN, CDCGAN, GestureGAN, GGAN, MHG-CAN, and ASL models. Specifically, the improvement percentages for the LOO CV Test are 17.03%, 20.27%, 15.76%, 13.76%, 10.16%, 5.90%, and 15.90%, respectively. Similarly, for the Holdout CV Test, HaCk outperforms HU, ZM, GB, GB-ZM, GB-HU, CDCGAN, GestureGAN, GGAN, MHG-CAN, and ASL models, with improvement percentages of 56.87%, 15.91%, 13.97%, 24.81%, 23.52%, 17.72%, 15.72%, 12.12%, 7.94%, and 17.94%, respectively.


Merging of Neural Networks

Abstract: Abstract We propose a simple scheme for merging two neural networks trained with different starting initialization into a single one with the same size as the original ones. We do this by carefully selecting channels from each input network. Our procedure might be used as a finalization step after one tries multiple starting seeds to avoid an unlucky one. We also show that training two networks and merging them leads to better performance than training a single network for an extended period of time.


Improving Learners’ Self-Efficacy in Performing Design Reviews with Virtual Reality

Abstract: Research on the use of innovative technology, such as virtual reality (VR), in Architecture, Engineering, and Construction (AEC) education, has been growing in the past twenty years. However, such research still requires robust investigation, as few studies have adopted educational psychology theories and rigorous methods. Furthermore, most of the research has focused on the cognitive effects of VR and additional research is also needed to investigate the affective effects, such as motivation, engagement, and self-efficacy. This study aims to evaluate the effects of technology decision-making between immersive virtual reality (IVR) and non-immersive virtual reality (nIVR) setups for a learning activity on AEC learners. For this, three hypotheses are formulated and tested on 165 UK students. Based on the results, both the designed IVR and nIVR learning activities had significant positive effects on learners’ self-efficacy and user experience with no significant difference between IVR and nIVR delivery. With this research, the authors contribute to the growing literature on VR implementation in AEC classrooms by showcasing a study founded on educational psychology theory and by using a rigorous research methodology. Furthermore, this study illustrates the effects that IVR and nIVR have on students’ affective learning and opens the possibility of new research in the field.


An Enhanced Extreme Learning Machine Based on Square-Root Lasso Method

Abstract: Abstract Extreme learning machine (ELM) is one of the most notable machine learning algorithms with many advantages, especially its training speed. However, ELM has some drawbacks such as instability, poor generalizability and overfitting in the case of multicollinearity in the linear model. This paper introduces square-root lasso ELM (SQRTL-ELM) as a novel regularized ELM algorithm to deal with these drawbacks of ELM. A modified version of the alternating minimization algorithm is used to obtain the estimates of the proposed method. Various techniques are presented to determine the tuning parameter of SQRTL-ELM. The method is compared with the basic ELM, RIDGE-ELM, LASSO-ELM and ENET-ELM on six benchmark data sets. Performance evaluation results show that the SQRTL-ELM exhibits satisfactory performance in terms of testing root mean squared error in benchmark data sets for the sake of slightly extra computation time. The superiority level of the method depends on the tuning parameter selection technique. As a result, the proposed method can be considered a powerful alternative to avoid performance loss in regression problems .


Hybrid Uncertainty Calibration for Multimodal Sentiment Analysis

Abstract: In open environments, multimodal sentiment analysis (MSA) often suffers from low-quality data and can be disrupted by noise, inherent defects, and outliers. In some cases, unreasonable multimodal fusion methods can perform worse than unimodal methods. Another challenge of MSA is effectively enabling the model to provide accurate prediction when it is confident and to indicate high uncertainty when its prediction is likely to be inaccurate. In this paper, we propose an uncertain-aware late fusion based on hybrid uncertainty calibration (ULF-HUC). Firstly, we conduct in-depth research on the issue of sentiment polarity distribution in MSA datasets, establishing a foundation for an uncertain-aware late fusion method, which facilitates organic fusion of modalities. Then, we propose a hybrid uncertainty calibration method based on evidential deep learning (EDL) that balances accuracy and uncertainty, supporting the reduction of uncertainty in each modality of the model. Finally, we add two common types of noise to validate the effectiveness of our proposed method. We evaluate our model on three publicly available MSA datasets (MVSA-Single, MVSA-Multiple, and MVSA-Single-Small). Our method outperforms state-of-the-art approaches in terms of accuracy, weighted F1 score, and expected uncertainty calibration error (UCE) metrics, proving the effectiveness of the proposed method.


Digital Twin of Worker of Nuclear Facility at the Stage of Pre-Shift Control

Abstract: Introduction. Digital twin. Digital twin in medicine using the example of Philips. Digital twin as an object of medical information space. Digital twin as an abstraction. Digital twin of worker of nuclear facility at the stage of pre-shift control. Conclusion.


Leveraging Artificial Intelligence and Provenance Blockchain Framework to Mitigate Risks in Cloud Manufacturing in Industry 4.0

Abstract: Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to the Internet opens cloud manufacturing to the massive attack risk of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because the physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks so that the blockchain peers can detect high deviations and take actions in a timely manner. The research defined the constraints related to physical boundaries and weightlifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This study followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, boost risk management, strengthen cybersecurity, and advance sustainability efforts.


Score-Based Black-Box Adversarial Attack on Time Series Using Simulated Annealing Classification and Post-Processing Based Defense

Abstract: While deep neural networks (DNNs) have been widely and successfully used for time series classification (TSC) over the past decade, their vulnerability to adversarial attacks has received little attention. Most existing attack methods focus on white-box setups, which are unrealistic as attackers typically only have access to the model’s probability outputs. Defensive methods also have limitations, relying primarily on adversarial retraining which degrades classification accuracy and requires excessive training time. On top of that, we propose two new approaches in this paper: (1) A simulated annealing-based random search attack that finds adversarial examples without gradient estimation, searching only on the l∞-norm hypersphere of allowable perturbations. (2) A post-processing defense technique that periodically reverses the trend of corresponding loss values while maintaining the overall trend, using only the classifier’s confidence scores as input. Experiments applying these methods to InceptionNet models trained on the UCR dataset benchmarks demonstrate the effectiveness of the attack, achieving up to 100% success rates. The defense method provided protection against up to 91.24% of attacks while preserving prediction quality. Overall, this work addresses important gaps in adversarial TSC by introducing novel black-box attack and lightweight defense techniques.


A Regional Brightness Control Method for a Beam Projector to Avoid Human Glare

Abstract: In this study, we proposed a system to reduce the speaker’s suffering from the strong light of a beam projector by applying regional brightness control over the screen. Since the original image and the projected one on the screen are quite different in area, brightness, and color, the proposed system first transforms them so that they have the same area and similar color tone. Then, to accurately determine the difference between those images, we have introduced a SSIM map, which is a perception-based method of measuring image similarity. Accordingly, an image segmentation model is used to determine the speaker’s silhouette from the SSIM map. We applied a couple of well-trained segmentation models, such as Selfie and DeepLab-v3, provided with MediaPipe. The experimental results showed the operability of the proposed system and that it determines most of a lecturer’s body area on the screen. To closely evaluate the system’s effectiveness, we have measured error rates consisting of false-positive and false-negative errors in the confusion matrix. With the measured results, the error rates appeared so insignificant and stable that the proposed system provides a practical effect for the speakers, especially in the case of applying DeepLab-v3. With the results, it is implied that an accurate segmentation model can considerably elevate the effectiveness of the system.


IG-Based Method for Voiceprint Universal Adversarial Perturbation Generation

Abstract: In this paper, we propose an Iterative Greedy-Universal Adversarial Perturbations (IGUAP) approach based on an iterative greedy algorithm to create universal adversarial perturbations for acoustic prints. A thorough, objective account of the IG-UAP method is provided, outlining its framework and approach. The method leverages a greedy iteration approach to formulate an optimization problem for solving acoustic universal adversarial perturbations, with a new objective function designed to ensure that the attack has higher accuracy in terms of minimizing the perceptibility of adversarial perturbations and increasing the accuracy of successful attacks. The perturbation generation process is described in detail, and the resulting acoustic universal adversarial perturbation is evaluated in both target-attack and no-target-attack scenarios. Experimental analysis and testing were carried out using comparable techniques and dissimilar target models. The findings reveal that the acoustic generality adversarial perturbation produced by the IG-UAP method can obtain effective attack results even when the audio training data sample size is minimal, i.e., one for each category. Moreover, the human ear finds it difficult to detect the loss of original data information and the addition of adversarial perturbation (for the case of a target attack, the ASR values range from 82.4% to 90.2% for the small sample data set). The success rates for untargeted and targeted attacks average 85.8% and 84.9%, respectively.


Data literacy in the new EU DigComp 2.2 framework how DigComp defines competences on artificial intelligence, internet of things and data

Abstract: Purpose The purpose of this paper is to analyse data literacy in the new Digital Competence Framework for Citizens (DigComp 2.2). Mid-2022 the Joint Research Centre of the European Commission published a new version of the DigComp (EC, 2022). This new version focusses more on the datafication of society and emerging technologies, such as artificial intelligence. This paper analyses how DigComp 2.2 defines data literacy and how the framework looks at this from a societal lens. Design/methodology/approach This study critically examines DigComp 2.2, using the data literacy competence model developed by the Knowledge Centre for Digital and Media Literacy Flanders-Belgium. The examples of knowledge, skills and attitudes focussing on data literacy ( n = 84) are coded and mapped onto the data literacy competence model, which differentiates between using data and understanding data. Findings Data literacy is well-covered in the framework, but there is a stronger emphasis on understanding data rather than using data, for example, collecting data is only coded once. Thematically, DigComp 2.2 primarily focusses on security and privacy (31 codes), with less attention given to the societal impact of data, such as environmental impact or data fairness. Originality/value Given the datafication of society, data literacy has become increasingly important. DigComp is widely used across different disciplines and now integrates data literacy as a required competence for citizens. It is, thus, relevant to analyse its views on data literacy and emerging technologies, as it will have a strong impact on education in Europe.


Visualizing high-dimensional loss landscapes with Hessian directions

Abstract: Abstract Analyzing the geometric properties of high-dimensional loss functions, such as local curvature and the existence of other optima around a certain point in loss space, can help provide a better understanding of the interplay between neural-network structure, implementation attributes, and learning performance. In this paper, we combine concepts from high-dimensional probability and differential geometry to study how curvature properties in lower-dimensional loss representations depend on those in the original loss space. We show that saddle points in the original space are rarely correctly identified as such in the expected lower-dimensional representations if random projections are used. The principal curvature in the expected lower-dimensional representation is proportional to the mean curvature in the original loss space. Hence, the mean curvature in the original loss space determines if saddle points appear, on average, as either minima, maxima, or almost flat regions. We use the connection between expected curvature in random projections and mean curvature in the original space (i.e. the normalized Hessian trace) to compute Hutchinson-type trace estimates without calculating Hessian-vector products as in the original Hutchinson method. Because random projections are not suitable for correctly identifying saddle information, we propose to study projections along the dominant Hessian directions that are associated with the largest and smallest principal curvatures. We connect our findings to the ongoing debate on loss landscape flatness and generalizability. Finally, for different common image classifiers and a function approximator, we show and compare random and Hessian projections of loss landscapes with up to approximately <?CDATA $7\times 10^6$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>7</mml:mn> <mml:mo>×</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>6</mml:mn> </mml:msup> </mml:math> parameters.


Implications of metaverse, virtual reality, and extended reality for development and learning in organizations

Abstract: Purpose This study investigates the implications and significance of immersive technologies (viz. metaverse, virtual reality, and extended reality) in organizational learning and development. Design/methodology/approach Employing a strategy map (using Biblioshiny) and network analysis (using VOSViewer), the study analyzed a total of 189 Scopus-indexed documents, spanning the period 1997 to 2023. Findings The findings revealed four thematic clusters, knowledge management, training, e-learning, and technology. Each cluster provided insights into the various facets of development and learning in respect to these technologies. The strategy map identified key themes, revealing core ideas, emerging trends, and foundational concepts within the domain. Practical implications Organizations can utilize this study to incorporate metaverse, virtual reality, and extended reality in organizational development, optimizing training and boosting employee experience and productivity. Originality/value While most literature delves into the technical side of immersive technologies, our study uncovers their impact on development and learning in organizations, addressing a critical research gap and offering a guiding framework.


Research on the method of building power transformer fault tree based on fault cases

Abstract: This paper proposes a method for establishing a power transformer fault tree based on fault cases. The method includes obtaining multiple fault cases of power transformers, dismantling multiple fault cases according to preset dismantling rules, classifying the faults of power transformers according to multiple fault cases, and establishing the basic framework of the fault tree. According to the results of dismantling, we are able to determine the logical relationship between the fault cause and the fault phenomenon of the power transformer. Based on the basic framework of the fault tree and the determined logical relationship, we build a fault tree for power transformers based on fault cases. This method will effectively improve the role of fault cases in the establishment of power transformer fault trees, realize fault identification and diagnosis of power transformers with high efficiency and high practicability, improve the efficiency of transformer fault analysis and disposal, and save power outage time caused by transformer faults.


Fast Hybrid Search for Automatic Model Compression

Abstract: Neural network pruning has been widely studied for model compression and acceleration, to facilitate model deployment in resource-limited scenarios. Conventional methods either require domain knowledge to manually design the pruned model architecture and pruning algorithm, or AutoML-based methods to search the pruned model architecture but still prune all layers with a single pruning algorithm. However, many pruning algorithms have been proposed and they all differ regarding the importance they attribute to the criterion of filters. Therefore, we propose a hybrid search method, searching for the pruned model architecture and the pruning algorithm at the same time, which automatically finds the pruning ratio and pruning algorithm for each convolution layer. Moreover, to be more efficient, we divide the search process into two phases. Firstly, we search in a huge space with adaptive batch normalization, which is a fast but relatively inaccurate model evaluation method; secondly, we search based on the previous results and evaluate models by fine-tuning, which is more accurate. Therefore, our proposed hybrid search method is efficient, and achieves a clear improvement in performance compared to current state-of-the-art methods, including AMC, MetaPruning, and ABCPruner. For example, when pruning MobileNet, we achieve a 59.8% test accuracy on ImageNet with only 49 M FLOPs, which is 2.6% higher than MetaPruning.


Hidden Tensor Structures

Abstract: Any single system whose space of states is given by a separable Hilbert space is automatically equipped with infinitely many hidden tensor-like structures. This includes all quantum mechanical systems as well as classical field theories and classical signal analysis. Accordingly, systems as simple as a single one-dimensional harmonic oscillator, an infinite potential well, or a classical finite-amplitude signal of finite duration can be decomposed into an arbitrary number of subsystems. The resulting structure is rich enough to enable quantum computation, violation of Bell’s inequalities, and formulation of universal quantum gates. Less standard quantum applications involve a distinction between position and hidden position. The hidden position can be accompanied by a hidden spin, even if the particle is spinless. Hidden degrees of freedom are, in many respects, analogous to modular variables. Moreover, it is shown that these hidden structures are at the roots of some well-known theoretical constructions, such as the Brandt–Greenberg multi-boson representation of creation–annihilation operators, intensively investigated in the context of higher-order or fractional-order squeezing. In the context of classical signal analysis, the discussed structures explain why it is possible to emulate a quantum computer by classical analog circuit devices.


Abstract: Abstract Pure Circular Motion with Non-Angular Variables in One-Dimensional Motion Physics Problems In standard algebra and calculus-based college physics textbooks, chapter two normally introduces one-dimensional motion for a particle with the following equations v = dx/dt and a = dv/dt. The kinematic equations with constant acceleration are then derived for motion along the x-axis. Then a second section is introduced for motion along the y-axis for freely falling objects in which the constant acceleration is ay = g = 9.81 m/s2. This pedagogical paper suggests that a third section be included for a particle travelling in pure circular motion. With the introduction of one new formula for Radial Acceleration, the standard kinematic equations can be used. The standard kinematic equations will be defined with tangential velocity and acceleration. This paper proposes to model particles travelling along the circumference of a circle with radius R relating displacement x as multiples of n Straight-Line segments of length C = 2πR along a horizontal axis with markings of 0C, 1C, 2C, 3C etc. Once the formula for radial acceleration is derived, the following axes will also be defined. The positive x and positive Tangential Velocity axes. For these problems we simplify things and will consider only counterclockwise motion. This direction will be considered positive. In addition, a moving particle will not come to rest and reverse direction. The positive x-axis can then be related to the positive circumference axis through the equation x = nC in which n is the number of circumference lengths travelled by the particle or the number of times the particle travelled around its circular path. Lastly, the tangential acceleration axis which will have both positive and negative acceleration values and perpendicular to that axis, the positive radial acceleration axis will be introduced. The positive radial acceleration axis will point to the center of the circle. Both uniform and non-uniform circular motion will be discussed. The difference in these pure circular motion problems versus one-dimensional motion along the x or y axes is that the resultant acceleration vector will have two components. In addition, an angle will need to be calculated between these two acceleration components. There will be no formulas that contain standard rotational variables such as ω and α. Also, a new notation for the introduction of time will be introduced. For example, with an initial velocity of 1 m/s and a given tangential acceleration how long will it take to reach a final velocity of 4m/s? The notation will be as follows (1m/s, 0s) and (4m/s,?s) This alternative method eliminates the need for plotting variables in a two-dimensional graph with time on the horizontal axis. This third introduced section of pure simple circular motion expressed as straight-line motion with the particle travelling along the positive n-axis can greatly help students visualize the more difficult general curvilinear motion seen in their sophomore year dynamics course. Except for the derivation of radial acceleration this paper will contain the full written section needed to be included in a standard Physics textbook with the appropriate number of fully worked example problems.


Algorithm for generating secure communication protocol test sequences under hyperparameter optimization of correlation vector machines

Abstract: Secure communication protocols are an important means to ensure the security of information transmission, and test sequence generation algorithms are used to evaluate and verify secure communication protocols. To improve the performance and reliability of secure communication protocols, a correlation vector machine hyperparameter optimization algorithm for generating secure communication protocol test sequences is proposed. We build an FSM model for secure communication protocols, analyze the optimization process of vector machine hyperparameters related to the FSM model, and generate a test sequence for secure communication protocols based on this. The experimental results show that the proposed algorithm performs well in generating secure communication protocol test sequences and can effectively improve the efficiency of generating secure communication protocol test sequences.


A Node Selection Strategy in Space-Air-Ground Information Networks: A Double Deep Q-Network Based on the Federated Learning Training Method

Abstract: The Space-Air-Ground Information Network (SAGIN) provides extensive coverage, enabling global connectivity across a diverse array of sensors, devices, and objects. These devices generate large amounts of data that require advanced analytics and decision making using artificial intelligence techniques. However, traditional deep learning approaches encounter drawbacks, primarily, the requirement to transmit substantial volumes of raw data to central servers, which raises concerns about user privacy breaches during transmission. Federated learning (FL) has emerged as a viable solution to these challenges, addressing both data volume and privacy issues effectively. Nonetheless, the deployment of FL faces its own set of obstacles, notably the excessive delay and energy consumption caused by the vast number of devices and fluctuating channel conditions. In this paper, by considering the heterogeneity of devices and the instability of the network state, the delay and energy consumption models of each round of federated training are established. Subsequently, we introduce a strategic node selection approach aimed at minimizing training costs. Building upon this, we propose an innovative, empirically driven Double Deep Q Network (DDQN)-based algorithm called low-cost node selection in federated learning (LCNSFL). The LCNSFL algorithm can assist edge servers in selecting the optimal set of devices to participate in federated training before the start of each round, based on the collected system state information. This paper culminates with a simulation-based comparison, showcasing the superior performance of LCNSFL against existing algorithms, thus underscoring its efficacy in practical applications.


Improved Glass Composition Analysis and Identification of Cultural Heritage with Limited Data Using Data Augmentation and CatBoost

Abstract: Glass artifacts play a significant role in cultural heritage, offering valuable insights into ancient craftsmanship and cultural exchange. However, accurately analyzing and identifying ancient glass objects presents challenges due to limited data. This study aims to enhance the analysis and identification of glass compositions in cultural heritage by employing data augmentation techniques and the CatBoost prediction model. Firstly, data augmentation techniques are applied to expand the limited dataset, increasing sample quantity and diversity to improve the model’s generalization capability. The TOPSIS method is employed to comprehensively evaluate different augmentation factors and select the most suitable ones. Subsequently, the CatBoost prediction model is utilized, and the model parameters are optimized using a random search method to further enhance predictive performance. Experimental research on ancient glass artifacts validates the effectiveness and feasibility of the proposed methods. The final model demonstrates high predictive performance and a good fit on the training set, cross-validation set, and test set. For example, when predicting the sodium oxide content before weathering in glass artifacts, the average R-squared(R2) reaches 0.998, and the Mean Squared Error(MSE) is 0.003. These results signify the accurate prediction of glass artifact compositions and the model’s stable predictive capabilities across different datasets. Utilizing the predicted chemical composition, the identification of glass artifacts achieves a classification accuracy of 100%, indicating the excellence of the model. In conclusion, this study presents an improved approach for analyzing and identifying glass compositions by overcoming the limitations posed by limited data through data augmentation and the CatBoost model. These advancements provide valuable tools and methods for preserving and researching cultural heritage, contributing to the progress of ancient civilization studies and technological development.


Monitoring the Workflow of Earthquake by Using Machine Learning

Abstract: This study explores the innovative application of machine learning techniques in monitoring and analyzing the workflow associated with earthquakes. The primary objective is to enhance the accuracy and efficiency of earthquake detection, prediction, and response mechanisms the development of machine learning technology enables more robust real-time earthquake monitoring through automated implementations. However, the application of machine learning to earthquake location problems faces challenges in regions with limited available training data. This paper proposes a machine learning-based approach to monitor seismic workflows efficiently. We apply various Machine Learning algorithms, including deep learning neural networks and decision trees, to process and interpret vast amounts of seismic data. These algorithms were trained on historical earthquake records to identify patterns and anomalies that precede seismic events. Our approach not only focused on the detection of seismic activities but also on the prediction of potential aftershock locations and magnitudes. Earthquake monitoring and response systems play a crucial role in mitigating potential damages and ensuring rapid response to seismic events. Traditional methods rely on sensor networks and manual analysis, often leading to delays in detection and response. The system leverages real-time data from seismic sensors, incorporating advanced machine learning algorithms for data analysis and pattern recognition. By employing deep learning models, such as convolution neural networks (CNNs) and recurrent neural networks (RNNs), the system can detect and classify seismic activities accurately. Additionally, anomaly detection techniques enable the identification of unusual seismic patterns that might indicate impending earthquakes or aftershocks. The system integrates geographical information systems (GIS) to map the seismic activities spatially, aiding in the visualization and understanding of seismic patterns across regions. The machine learning models are trained on historical seismic data to enhance their predictive capabilities and adaptability to varying seismic behaviors. This proposed system offers a real-time monitoring solution that can assist in early earthquake detection, accurate event classification, and timely response coordination. Its ability to continuously learn from incoming data ensures adaptability to changing seismic behaviors, thereby improving overall efficiency and reliability in earthquake monitoring workflows.


Cloud Based Data Centre for Managing Logistics in the Disaster

Abstract: Considering the many man-made and natural disasters that have occurred in recent years, such as the September 2001 earthquake and the tsunami in Japan, the importance of problem solving processes cannot be ignored. The total damage caused by the disaster in Japan alone is estimated at more than $300 billion. Transportation and communications play an important role in disaster response and management to minimize loss of life, economic costs and impacts. Our research includes the development of emergency response systems for disasters of all sizes, focused on transportation systems designed to use and communicate information and technology. In this article, we discuss the use of technology for disaster management using smart transportation, including mobile, mobile and cloud. The system is intelligent because it can collect information from many places and locations, including the situation and make decisions regarding the use of the communication process, making the vehicle more efficient. Hybrid vehicle communications based on vehicle-to-vehicle and vehicle-to-infrastructure protocols are being exploited opportunistically. The effectiveness of our system has been demonstrated by simulating the impact of disasters on the real urban environment and comparing the situation with our existing disaster management systems. We declare that the adoption of our plan has led to the improvement and stabilization of traffic and evacuation.


Earthquake Early Warning Systems by Using Cloud and IoT

Abstract: Earthquake early warning systems play a crucial role in minimizing the impact of seismic events on human life and infrastructure. This research paper explores the integration of Cloud Computing and Internet of Things (IoT) technologies to enhance the effectiveness of earthquake early warning systems. The proposed system aims to provide real-time data collection, analysis, and dissemination of alerts through a distributed network, leveraging the scalability and flexibility of cloud-based platforms. This paper discusses the architecture, components, and benefits of the proposed system, along with a case study to illustrate its potential in earthquake-prone regions.


Chapter 9: Audiovisual sample 9.1 -9.4

Abstract:
Audiovisual sample 9.1:

Recorded by Luting Hsia in Longgang Grand Sports Field during the Rice Noodles Festival, April 29, 2018. Used with permission by the Yunnanese Dage Association.

Audiovisual sample 9.2:

Videotaped by Luting Hsia in Longgang Grand Sports Field during the Rice Noodles Festival, April 20, 2019. Used with permission by the Yunnanese Dage Association.

Audiovisual sample 9.3:

Videotaped by the author in Yunnanese Cultural Park during the Rice Noodles Festival, 14 April 2017. With thanks to the Yunnanese Dage Association.

Audiovisual sample 9.4:
 Videotaped by Luting Hsia in Longgang Grand Sports Field during the Rice Noodles Festival, April 13, 2019. Thanks to the Yunnanese Dage Association.


Chapter 9: Audiovisual sample 9.1 -9.4


FAST AND SMART 3D MODELLING: AN ALGORITHMIC TOOL BASED ON CHURCH TYPOLOGY

Abstract: Abstract. Increasingly advanced technological development and the broad possibilities introduced by computer graphics and parametric-semantic modelling force us to reflect on the concept of smart models, especially in relation to the purposes for which the models themselves are created, directing research towards in-depth studies linked to the type of information that the model is intended to convey. This research shows the results obtained in the development and experimentation of a generative modelling algorithm dedicated to the rapid and semi-automatic modelling of churches. The work stems from the need to elaborate a useful tool to prepare, in a short time, an intelligent database - graphic and informative - rapidly visualisable, dedicated to the management of churches in post-earthquake emergency conditions.The main objective is to make efficient the processes of data management and visualisation based on the seismic damage assessment sheets [D.P.C.M. 23 February 2006 (G.U. 7.3.2006, no. 55)] through procedures capable of expanding the information patrimony and, at the same time, optimising documentation and intervention times, costs and resource management (Chevrier, et al., 2009). The procedure for realising the parametric model is based on the concept of shape grammar. It allows different types of churches to be generated from the modification of basic shapes prepared according to the concept of a macro-element. It makes it possible to generate different types of churches from the modification of basic shapes prepared according to the macro-element concept (Lanzara, et al., 2021). The algorithm was tested by applying it to several case studies to evaluate its effectiveness and future implementations.


Students' Thriving and Well-Being in Online Learning Environments in Vocational Education and Training

Abstract: Context: Recent research on students' experiences with the quality of online learning during the COVID-19 pandemic has increased the challenge to the development of online learning. During the pandemic, anxiety, depression, and fatigue occurred in online studies, which also weakened students' well-being. In this quantitative study, we examined how students thrive in synchronous and asynchronous online implementations. The goal of the research was to support students' well-being in online studies; therefore, the study uses the PERMA well-being theory. Approach: Finnish vocational education and training students (N = 363) participated in the study and answered the questions on positive emotions, engagement, relationships, meaning, and accomplishment from both asynchronous and synchronous online delivery perspectives using a questionnaire. Findings: This study highlighted the importance of the teacher's online learning facilitation skills and task design skills, and the emotions generated in an online course. Based on the results, the respondents were divided into three student profiles: (1) Students thriving in online courses in general; (2) Students thriving, especially in asynchronous online courses; and (3) Students not thriving in online courses in general. Conclusions: According to the findings, students need different options for completing online courses. The role of the teacher in creating a positive and supportive atmosphere, fostering relationships, and creating clear work-related and versatile tasks is of great importance in the generation of emotions, whether it is an asynchronous or synchronous online implementation.


Denoising GNSS Velocities for Earthquake Ground Motions with Deep Learning

Abstract: Distributed measurements of earthquake ground shaking are critical to understanding seismic processes and hazards. High rate, continuous GNSS reference stations are alternative sources of these ground motion observations that complement the dynamic range of traditional inertial-based seismic sensors. GNSS time differenced carrier phase velocity processing (TDCP) has shown to have increased sensitivity to coseismic dynamics when compared to precise point positioning (PPP) while also offering distinct computational advantages. Furthermore, data-driven machine learning (ML) models have proven beneficial for detection and phase picking of seismic waveforms embedded in high-dimensionality noise environments. In this study, we address this challenging detection problem by modifying existing domain-similar deep learning strategies to take advantage of an established labeled TDCP 5Hz earthquake catalog. We present the results of training a U-net convolutional network architecture used for image processing on denoising temporal windows from a catalog of synthetic 5Hz TDCP waveforms. Unlike traditional fixed bandwidth frequency filtering, the neural network learns a sparse representation mask of time-frequency domain features to separate complex, time-variant noise signatures from a range of signal inputs. These signal inputs from the existing catalog consist of over 2000 observed strong motion velocity waveforms, each augmented with 7 unique noise time series. We use time-frequency domain features from 30 second windows to train a mask on noise-free target waveforms. We report on results of testing denoising unseen synthetic noisy GNSS TDCP time series. We then validate this strategy against both real TDCP waveforms and co-located lower-noise inertial instrumentation. We find the trained neural network mask filters background noise across a range of time- and spatially-dependent GNSS noise environments. This deep-learned mask permits a spectrum of strong-motion seismic signal signatures to pass through for rapid event characterization and destructive ground motion now-casting. This denoising has the potential to expand the operational role of GNSS in seismic hazard monitoring by increasing signal-to-noise (SNR) of computationally-lightweight TDCP waveforms.


Public Library Websites in the Reaching Across Illinois Library System: A Webometric Examination

Abstract: A public library’s website is often the first introduction that a user has to the library. Users come to the website looking for information about the library’s location, hours, how to get a library card, library programs, search the library catalog, or other services. As Poll writes, “Libraries have started to offer a new virtual ‘entrance’ to their services: the library website” (2007, p. 1). As such, a library’s website must provide users with the information they are seeking simply and clearly. People expect websites to be simple to use and to be able to find the information they are seeking quickly; simply put “if it’s convenient, they will use it; if not, they won’t” (Nielsen & Loranger, 2006, Preface, xxi). Ideally, a public library’s website will fulfill these needs for their community. The library website should provide information to patrons in a way that the majority of its users will understand. Providing access to information is a key component of the mission of public libraries and the library website is often the first stop for patrons looking to access library resources. This study is a web analysis of Chicago area public libraries in RAILS (Reaching Across Illinois Library System) using checklists based on the work of Chow, Commander, and Bridges (2014), Powers (2011) and Vargas Ochoa (2020).


College Virtual Tour using Virtual Reality

Abstract: Abstract: Virtual reality has become one of the highly interesting and popular fields. Also in recent times, the concept of the metaverse and its idea of a virtual world is being spread rapidly. As the technological connotation of the term VR has been changing and encompasses various VR systems with different capabilities, it has also paved a way for virtual tourism. The current pandemic situation made people around the world stay inside their houses, which has led to the growth of virtual tourism. Yet the usage of VR is not as wide as its reach. The main reason for VR’s less usage is its affordability, not everyone could afford a VR headset. The objective of this project is to create an immersive virtual tour of the college that can be used by even an individual without any proper gear. Virtual tours are a way for people to experience and explore a place that exists in reality as a physical space via the internet. The old techniques of the virtual tour are either of a pre-recorded video form or just some collections of 360-degree images. To improve the experience, we are creating a 3D virtual environment that the user can access without any restrictions. The user can download the file and can run the program on their desired platform


The Effects of Augmented Reality on Very Young Learners’ Motivation and Learning of the Alphabet and Vocabulary

Abstract: This study aspires to contribute some initial results to the growing area of research regarding technology potential in the field of early foreign language literacy. An experiment was conducted to examine very young learners’ alphabet and vocabulary learning and retention in an early foreign language (FL) learning context when implementing augmented reality (AR) applications, while very young learners’ motivation was also assessed. A pilot intervention was implemented in a state school in northern Greece. The participants (n = 26) were primary school first-graders (5.5–6 years old) and were assigned into two groups, experimental (13) and control (13). To examine the effects of the intervention, this current study employed two instruments: (a) a pre-test–post-test model to assess young learners’ alphabet and vocabulary learning during three phases and (b) a questionnaire to assess their motivation during the learning process. The findings of this study reveal that both groups displayed significant improvements in FL alphabet and vocabulary learning; however, there are statistical differences in favor of the experimental group regarding long-term alphabet and vocabulary learning and retention. Furthermore, qualitative results regarding children’s perceptions of the technology used indicate that AR was highly appealing and motivating to participating students.


Orthogonal Neural Network: An Analytical Model for Deep Learning

Abstract: In the current deep learning model, the computation between each feature and parameter is defined in the real number field. This, together with the nonlinearity of the deep learning model, makes it difficult to analyze the relationship between the values of the computational process and the original features from computation in the real number field. We extend the operational rules of the deep learning model in space and propose the orthogonal neural network (ONN) model, in which the features are set orthogonally to each other in space by “modulating” each input feature of the deep learning model to different orthogonal bases. Because the modulated numerical features are orthogonal to each other, they can be separated from the computations of the ONN model. By “demodulating” the model during and after the calculation, we can obtain a numerical relationship between the results and the original features, which can further provide theoretical and computational support for our analysis of the model. Finally, we compute the weights for each input feature as an interpretable deep learning approach, and describe how the model focuses attention on each feature based on the application of the orthogonal neural network model on two typical models: convolutional neural networks and graph neural networks.


Cyclic Homomorphic Encryption Aggregation (CHEA)—A Novel Approach to Data Aggregation in the Smart Grid

Abstract: The transactive energy market is an emerging development in energy economics built on advanced metering infrastructure. Data generated in this context is often required for market operations, while also being privacy sensitive. This dual concern has necessitated the development of various methods of obfuscation in order to maintain privacy while still facilitating operations. While data aggregation is a common approach in this context, many of the existing aggregation methods rely on additional network components or lack flexibility. In this paper, we introduce Cyclic Homomorphic Encryption Aggregation (CHEA), a secure aggregation protocol that eliminates the need for additional network components or complicated key distribution schemes, while providing additional capabilities compared to similar protocols. We validate our scheme with formal security analysis as well as a software simulation of a transactive energy network running the scheme. Results indicate that CHEA performs well in comparison to similar works, with minimal communication overheads. Additionally, CHEA retains all standard security properties held by other aggregation schemes, while improving flexibility and reducing infrastructural requirements. Our scheme operates on similar assumptions as other works, but current smart metering hardware lags in terms of processing power, making the scheme infeasible on the current generation of hardware. However, these capabilities should quickly advance to an accommodating state. With this in mind, and given the results, we believe CHEA is a strong candidate for aggregating transactive energy data.


Analytical Listening and Aesthetic Experience in Music Criticism

Abstract: In this article, I discuss the methodological and contextual aspects of writing music criticism, drawing cues from applied musicology and autoethnography. The challenge for any music critic is the question of the relationship between objective and subjective approaches. I analyze the relationship between analytical listening and aesthetic experience, using the examples of two music reviews of Ivo Pogorelić’s piano recitals that I wrote. The interpretations of this pianist are suitable for the analysis precisely because he is commonly seen as an unconventional, even controversial pianist, and his interpretations of romantic music are often regarded as examples of anti-academicism and even deconstruction of pianistic canons accumulated during the 20th century. Against that term, I will talk about liberation, which is perhaps a more suitable label for Pogorelić’s modernist approach to performance.


The Impact of ChatGPT on Tourists' Trust and Travel Planning Intention: International Researches and Current Situation in Vietnam

Abstract: This research investigates the impact of ChatGPT on tourists' trust and travel planning intention, with a focus on international perspectives and the current situation in Vietnam. The study comprises three main sections: Introduction, where the significance of ChatGPT in tourism is highlighted; Literature Review, encompassing an exploration of ChatGPT, its application in tourism, and the influence of trust and intention in utilizing ChatGPT for travel planning; and Current Situation, providing insights into the existing scenario. By examining factors affecting trust and intention in ChatGPT-based travel planning, this research contributes valuable insights for the tourism industry. The study concludes with a synthesis of findings and potential implications for future research and industry practices.


Test Deployed

Abstract: The CNTs also could enhance the resistance to photo degradation by UV irradiation during cyclical ageing tests


Resilient Strategies for Internet-Based Education: Investigating Engineering Students in the Canary Islands in the Aftermath of COVID-19

Abstract: The COVID-19 pandemic has brought about notable changes in the education sector, specifically the shift towards online learning. This study examined the experiences of 124 engineering students in the Canary Islands, an EU ultra-peripheral region, as they adapted to online education during the pandemic. A comprehensive survey assessed students' experiences in five key dimensions, including satisfaction with traditional face-to-face learning, perceptions of the engineering department's transition to online learning, module-specific adaptations, personal adaptation strategies, and the adaptation of teaching staff. The study's methodology involved statistical analyses using Microsoft Excel v16.0 and SPSS 27 tools to identify patterns and draw conclusions. The findings indicate a nuanced landscape. Students demonstrated strong technological literacy and readiness for online learning. However, they expressed concerns about educators' digital proficiency and perceived a decline in educational quality. These results emphasize the critical need for sustainable, adaptable, and inclusive educational strategies, particularly in regions like the Canary Islands that face unique challenges. The implications of the study have broader relevance to digital education. It is essential to note the need for educators to receive comprehensive training in digital tools and methodologies to improve the quality of online learning.


Social Media and Higher Education: A Linguistic Analysis of Identity Construction of Pakistani Universities

Abstract: This study rigorously explores the linguistic strategies employed by Pakistani universities for identity construction on social media platforms, focusing primarily on X (formerly known as Twitter). Utilizing a robust methodology of corpus-assisted discourse analysis, we scrutinized a substantial dataset comprising 2861 tweets. These were collected from the official X accounts of two leading Pakistani universities, National University of Sciences and Technology (NUST) and Lahore University of Management Sciences (LUMS), during the academic year 2021/2022. Our in-depth analysis uncovered distinctive linguistic patterns, particularly in the use of pronouns and specific multi-word key terms that each university employs to construct its identity. Intriguingly, the linguistic choices made by these institutions were found to significantly shape their digital identities, reflecting both individuality and broader cultural themes. The study augments existing research in the field by offering novel and context-specific insights into the dynamics of identity construction via social media. It underscores the pivotal role language plays in the organizational branding landscape. Furthermore, the findings from this research serve as a valuable resource for informing and refining communication strategies, not only for educational institutions but also for organizations at large that aim to construct and effectively manage their digital identities on social media platforms.


Special Issue on Transportation Big Data and Its Applications

Abstract: Large-scale traffic sensors are strategically deployed across various infrastructures and modes of transportation (e [...]