FLSTRA: Federated Learning in Stratosphere

Abstract: We propose a federated learning (FL) in stratosphere (FLSTRA) system,where a high altitude platformstation (HAPS) facilitates a large number of terrestrial clients to collaboratively learn a global model without sharing the training data. FLSTRA overcomes the challenges faced by FL in terrestrial networks, such as slow convergence and high communication delay due to limited client participation and multi-hop communications. HAPS leverages its altitude and size to allow the participation of more clients with line-of-sight (LOS) links and the placement of a powerful server. However, handling many clients at once introduces computing and transmission delays. Thus, we aim to obtain a delay-accuracy trade-off for FLSTRA. Specifically, we first develop a joint client selection and resource allocation algorithm for uplink and downlink to minimize the FL delay subject to the energy and quality-of-service (QoS) constraints. Second, we propose a communication and computation resource-aware (CCRA-FL) algorithmto achieve the target FL accuracy while deriving an upper bound for its convergence rate. The formulated problemis non-convex; thus, we propose an iterative algorithmto solve it. Simulation results demonstrate the effectiveness of the proposed FLSTRA system, compared to terrestrial benchmarks, in terms of FL delay and accuracy.


Deep image prior inpainting of ancient frescoes in the Mediterranean Alpine arc

Abstract: Abstract The unprecedented success of image reconstruction approaches based on deep neural networks has revolutionised both the processing and the analysis paradigms in several applied disciplines. In the field of digital humanities, the task of digital reconstruction of ancient frescoes is particularly challenging due to the scarce amount of available training data caused by ageing, wear, tear and retouching over time. To overcome these difficulties, we consider the Deep Image Prior (DIP) inpainting approach which computes appropriate reconstructions by relying on the progressive updating of an untrained convolutional neural network so as to match the reliable piece of information in the image at hand while promoting regularisation elsewhere. In comparison with state-of-the-art approaches (based on variational/PDEs and patch-based methods), DIP-based inpainting reduces artefacts and better adapts to contextual/non-local information, thus providing a valuable and effective tool for art historians. As a case study, we apply such approach to reconstruct missing image contents in a dataset of highly damaged digital images of medieval paintings located into several chapels in the Mediterranean Alpine Arc and provide a detailed description on how visible and invisible (e.g., infrared) information can be integrated for identifying and reconstructing damaged image regions.


NRP: A Multi-source, Heterogeneous, Automatic Data Collection System for Infants in Neonatal Intensive Care Units

Abstract: The incidence of premature births continues to rise each year and premature babies require specialized care in neonatal intensive care units (NICUs). The intermittent monitoring of these babies, however, poses challenges for clinicians in terms of visualizing and detecting meaningful diagnostic trends. In order to address this issue, the adoption of continuous, multi-parameter, electronic medical record-keeping methods is promising in terms of leveraging advanced analytics techniques and potentially improving health outcomes. In this article, we present the design and implementation of the Neonates Recording Platform ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP ), a hardware-software tool to be deployed at the bedside in a real NICU environment. The <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP enables data from various sources to be collected, labelled, processed and stored. We conducted tests involving the acquisition of neonates' physiological parameters, synchronized with video recordings, in addition to real-time analysis of body pose, with the capture of up to 33 reference points, and audio files from both the infant and the environment. In <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP , the collected data is organized hierarchically in a portable format and is automatically cleaned and validated, thereby ensuring its usability for healthcare professionals and data scientists. Additionally, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP enables medical staff to configure trials and add customized text or tagging events. A significant contribution of the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP platform is the integration of a unique computer vision algorithm called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CardMed , which extracts physiological information (such as heart rate, breath rate, and blood oxygenation) directly from any monitoring device. The development of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP involved collaboration with medical staff and data scientists, and evaluation took place at the NICU of the Puerta del Mar University Hospital in Cádiz, Spain.


Metadata and Image Features Co-Aware Semi-Supervised Vertical Federated Learning With Attention Mechanism

Abstract: Recently, vertical federated learning (VFL) has gained more attention due to its ability to combine various valuable features for better model performance without violating privacy. However, the development of VFL still faces three challenges: 1) insufficient aligned samples, 2) monotone data modality, and 3) simple concatenation-based collaborative training strategy. In this paper, we construct a metadata and image features co-aware VFL model for smart healthcare, from which users can obtain convenient and timely online auxiliary diagnostic services. Furthermore, we propose a metadata and image features co-aware semi-supervised VFL scheme with attention mechanism, which aims to jointly train the VFL models by utilizing both metadata and image data from the entire sample space. Specifically, a generative-adversarial method is designed to train the generator to estimate the missing features, so as to expand the training set and improve the accuracy of model. Moreover, using semi-supervised clustering to forecast pseudo labels for the missing part of the active party, we further boost the performance of the VFL model by increasing the proportion of labeled samples. Meanwhile, inspired by the attention mechanism, we establish an entirely novel VFL training mode that emphasizes the underlying effect of metadata features on image features, which makes metadata features highlight crucial areas in the image features to improve the training effect. Finally, the experimental results confirm that our proposed scheme achieves higher accuracy compared with other related works in the VFL scenario.


Distance- and Velocity-Based Simultaneous Obstacle Avoidance and Target Tracking for Multiple Wheeled Mobile Robots

Abstract: This paper proposes the distance-and velocity-based simultaneous obstacle avoidance and target tracking (DV-SOATT) method for the trajectory tracking problem of multiple wheeled mobile robots (MWMRs) operating in a shared workspace based on the relative positions and velocities of the wheeled mobile robots (WMRs) and their encountered obstacles. Compared to the previous arts considered only their relative positions, the DV-SOATT method that adds an auxiliary velocity vector lessens needless activation of the collision avoidance maneuvers, where the DV-SOATT introduces radial bounds for forecasting a collision. We provide two decision criteria for the addition of the auxiliary velocity term and compare the DV-SOATT method with the original method proposed by Li et al. (2021). The problem of the WMRs pause from the path conflict is addressed. Bound constraints on MWMRs’ velocities are considered to restrict the movement speed of the robot so as to ensure smoothness. The control law is built on Lagrange multipliers on basis of constructing a quadratic programming problem. Slack variables are discarded. Bound constraints on optimization variables are included in the piecewise-linear projection function. The stability of the control law, together with the efficiency of the DV-SOATT method, is discussed based on the Lyapunov function. The efficiency is tested on multiple omnidirectional Mecanum-wheeled mobile robots and validated through physical experiments and simulation.


A Novel Real-Time Obstacle Avoidance Method in Guidance Layer for AUVs' Path Following

Abstract: This paper addresses the real-time obstacle avoidance problem in 2D/3D path following missions for autonomous underwater vehicles (AUVs). To efficiently solve this problem, a novel universal obstacle avoidance framework based on the guidance layer is proposed for under-actuated AUVs to follow arbitrary referenced path. Based on this, a real-time avoidance line-of-sight guidance law is designed to avoid multi-static or slow time-varying obstacles that appear on the desired route and are pretreated by the oriented bounding box method. The strategy of single-obstacle avoidance, multi-obstacle avoidance, and vertical-obstacle avoidance is designed according to the geometrical features of unknown obstacles. Following the command of guidance law, kinematic and dynamic controllers are designed based on integral terminal sliding mode control to enhance robustness under environmental disturbances and parameter perturbation whether in path following or obstacle avoidance missions. Finally, Lyapunov theory proves stability of control system while numerical simulations demonstrate effectiveness and robustness of proposed control scheme.


T-Distributed Stochastic Neighbor Embedding for Co-Representation Learning

Abstract: Co-clustering is the simultaneous clustering of the samples and attributes of a data matrix that provides deeper insight into data than traditional clustering. However, there is a lack of representation learning algorithms that serve this mechanism of co-clustering, and the current representation learning algorithms are limited to the sample perspective and lack the use of information in the attribute perspective. To solve this problem, in this article, ctSNE , a co-representation learning model based on t-distributed stochastic neighbor embedding, is proposed for unsupervised co-clustering, where ctSNE makes the dataset representation outputted more discriminative of row and column clusters (i.e. co-discrimination). On the basis of t-distributed stochastic neighbor embedding retaining the sample data distribution and local data structure, the philosophy of collaboration is introduced (i.e., row and column hidden relationship information) so that the ctSNE model is equipped with co-representation learning capability, which can effectively improve the performance of co-clustering. To prove the effectiveness of the ctSNE model, several classic co-clustering algorithms are used to check the co-representation performance of ctSNE, and a novel internal index based on an internal clustering index, known as total inertia, is proposed to demonstrate the effect of co-clustering. The numerous experimental results show that ctSNE has tremendous co-representation capability and can significantly improve the performance of co-clustering algorithms.


Cooperative Multiagent Reinforcement Learning With Partial Observations

Abstract: In this paper, we propose a distributed zeroth-order policy optimization method for Multi-Agent Reinforcement Learning (MARL). Existing MARL algorithms often assume that every agent can observe the states and actions of all the other agents in the network. This can be impractical in large-scale problems, where sharing the state and action information with multi-hop neighbors may incur significant communication overhead. The advantage of the proposed zeroth-order policy optimization method is that it allows the agents to compute the local policy gradients needed to update their local policy functions using local estimates of the global accumulated rewards that depend on partial state and action information only and can be obtained using consensus. Specifically, to calculate the local policy gradients, we develop a new distributed zeroth-order policy gradient estimator that relies on one-point residual-feedback which, compared to existing zeroth-order estimators that also rely on one-point feedback, significantly reduces the variance of the policy gradient estimates improving, in this way, the learning performance. We show that the proposed distributed zeroth-order policy optimization method with constant stepsize converges to the neighborhood of a policy that is a stationary point of the global objective function. The size of this neighborhood depends on the agents' learning rates, the exploration parameters, and the number of consensus steps used to calculate the local estimates of the global accumulated rewards. Moreover, we provide numerical experiments that demonstrate that our new zeroth-order policy gradient estimator is more sample-efficient compared to other existing one-point estimators.


Text Document Clustering Using Modified Particle Swarm Optimization with k-means Model

Abstract: In the present digital era, vast amounts of data are generated by millions of Internet users in the form of unstructured text documents. The clustering and organizing of text documents play a crucial role in the applications of data analysis and market research. In this research manuscript, a new modified version of metaheuristic-based optimization technique is proposed with k-means for clustering the text documents. In the initial phase, the input data are acquired from the three-benchmark databases such as Reuters-21578, 20-Newsgroup and British Broadcasting Corporation (BBC)-sport. Further, the data denoising is accomplished by using the common techniques: stemming, lemmatization, tokenization, and stop word removal. In addition to this, the denoised data are transformed into feature vectors by utilizing Term Frequency (TF)-Inverse Document Frequency (IDF) technique. The computed feature vectors are given to the Modified Particle Swarm Optimization (MPSO) with k-means to group the closely related text documents by minimizing the similarity in different clusters. The experimental examination showed that the proposed MPSO with k-means model achieved accuracy of 0.85, 0.85 and 0.86 on the Reuters-21578, 20-Newsgroup and BBC-sport databases, which are superior to the comparative models.


Composite Recommendation of Artworks in E-Commerce Based on User Keyword-Driven Correlation Graph Search

Abstract: With the ever-increasing diversification of people's interests and preferences, artwork has become one of the most popular commodities or investment goods in E-commerce, and it increasingly attracts the attention of the public.Currently, many real-world or virtual artworks can be found in E-commerce, and finding a means to recommend them to appropriate users has become a significant task to alleviate the heavy burden on artwork selection decisions by users.Existing research mainly studies the problem of single-artwork recommendation while neglecting the more practical but more complex composite recommendation of artworks in E-commerce, which considerably influences the quality of experience of potential users, especially when they need to select a set of artworks instead of a single artwork.Inspired by this limitation, we put forward a novel composite recommendation approach to artworks by a user keyword-driven correlation graph search named ART com-rec .Through ART com-rec , the recommender system can output a set of artworks (e.g., an artwork composite solution) in E-commerce by considering the keywords typed by a user to indicate his or her personalized preferences.Finally, we validate the feasibility of the ART com-rec approach by a set of simulated experiments on a real-world PW dataset.


Efficient Barrett Modular Multiplication Based on Toom-Cook Multiplication

Abstract: The Toom-Cook multiplier (TCM) is the most suitable method for multiplying large integers with key widths typically employed in ECC or RSA due to its computational efficiency. In this brief, we improve the accuracy of hardware TCM algorithm, and propose a TCM-based Barrett modular multiplication algorithm. We provide detailed mathematical proofs for the determinantion of parameters, error ranges, and input-output bounds, which serve to validate the robustness and reliability of our proposed algorithm. Further, the hardware architecture and the ASIC implementation results under 40nm, 65nm, 90nm process are presented. Compared with existing works, the proposed modular multiplier exhibits superior performance in terms of Area-Time-Product.


Cerise: Program Verification on a Capability Machine in the Presence of Untrusted Code

Abstract: A capability machine is a type of CPU allowing fine-grained privilege separation using capabilities , machine words that represent certain kinds of authority. We present a mathematical model and accompanying proof methods that can be used for formal verification of functional correctness of programs running on a capability machine, even when they invoke and are invoked by unknown (and possibly malicious) code. We use a program logic called Cerise for reasoning about known code, and an associated logical relation, for reasoning about unknown code. The logical relation formally captures the capability safety guarantees provided by the capability machine. The Cerise program logic, logical relation, and all the examples considered in the paper have been mechanized using the Iris program logic framework in the Coq proof assistant. The methodology we present underlies recent work of the authors on formal reasoning about capability machines [Georges et al. 2021 ; Skorstengaard et al. 2019a ; Van Strydonck et al. 2022 ], but was left somewhat implicit in those publications. In this paper we present a pedagogical introduction to the methodology, in a simpler setting (no exotic capabilities), and starting from minimal examples. We work our way up to new results about a heap-based calling convention and implementations of sophisticated object-capability patterns of the kind previously studied for high-level languages with object-capabilities, demonstrating that the methodology scales to such reasoning.


A novel fixed-time error-monitoring neural network for solving dynamic quaternion-valued Sylvester equations

Abstract: This paper addresses the dynamic quaternion-valued Sylvester equation (DQSE) using the quaternion real representation and the neural network method. To transform the Sylvester equation in the quaternion field into an equivalent equation in the real field, three different real representation modes for the quaternion are adopted by considering the non-commutativity of quaternion multiplication. Based on the equivalent Sylvester equation in the real field, a novel recurrent neural network model with an integral design formula is proposed to solve the DQSE. The proposed model, referred to as the fixed-time error-monitoring neural network (FTEMNN), achieves fixed-time convergence through the action of a state-of-the-art nonlinear activation function. The fixed-time convergence of the FTEMNN model is theoretically analyzed. Two examples are presented to verify the performance of the FTEMNN model with a specific focus on fixed-time convergence. Furthermore, the chattering phenomenon of the FTEMNN model is discussed, and a saturation function scheme is designed. Finally, the practical value of the FTEMNN model is demonstrated through its application to image fusion denoising.


Sketch-based semantic retrieval of medical images

Abstract: The volume of medical images stored in hospitals is rapidly increasing; however, the utilization of these accumulated medical images remains limited. Existing content-based medical image retrieval (CBMIR) systems typically require example images, leading to practical limitations, such as the lack of customizable, fine-grained image retrieval, the inability to search without example images, and difficulty in retrieving rare cases. In this paper, we introduce a sketch-based medical image retrieval (SBMIR) system that enables users to find images of interest without the need for example images. The key concept is feature decomposition of medical images, which allows the entire feature of a medical image to be decomposed into and reconstructed from normal and abnormal features. Building on this concept, our SBMIR system provides an easy-to-use two-step graphical user interface: users first select a template image to specify a normal feature and then draw a semantic sketch of the disease on the template image to represent an abnormal feature. The system integrates both types of input to construct a query vector and retrieves reference images. For evaluation, ten healthcare professionals participated in a user test using two datasets. Consequently, our SBMIR system enabled users to overcome previous challenges, including image retrieval based on fine-grained image characteristics, image retrieval without example images, and image retrieval for rare cases. Our SBMIR system provides on-demand, customizable medical image retrieval, thereby expanding the utility of medical image databases.


Adaptive Shape Servoing of Elastic Rods Using Parameterized Regression Features and Auto-Tuning Motion Controls

Abstract: The robotic manipulation of deformable linear objects has shown great potential in a wide range of real-world applications. However, it presents many challenges due to the objects' non-linear properties and high-dimensional geometric configuration. In this paper, we propose an efficient shape servoing framework to manipulate elastic objects through real-time visual feedback automatically. The proposed parameterized regression features are used to construct a compact (low-dimensional) feature vector (Bézier and NURBS) that quantifies the object's shape, thus enabling the establishment of an explicit shape servo-loop. To automatically manipulate the object into a desired configuration, our adaptive controller can iteratively estimate the sensorimotor model that relates the robot's motion and shape changes. This valuable capability enables the effective deformation of objects with unknown mechanical models. An auto-tuning algorithm is introduced to adjust the controller's gain and, thus, modulate the shaping motions based on optimal performance criteria. To validate the proposed framework, a detailed experimental study with vision-guided robot manipulators is presented.


Adapting COVID-19 Contact Tracing Protocols to Accommodate Resource Constraints, Philadelphia, Pennsylvania, USA, 2021

Abstract: Because of constrained personnel time, the Philadelphia Department of Public Health (Philadelphia, PA, USA) adjusted its COVID-19 contact tracing protocol in summer 2021 by prioritizing recent cases and limiting staff time per case. This action reduced required staff hours to prevent each case from 21-30 to 8-11 hours, while maintaining program effectiveness.


Quantum Key Distribution Based on the Quantum Eraser

Abstract: Quantum information and quantum foundations are becoming popular topics for advanced undergraduate courses. Many of the fundamental concepts and applications in these two fields, such as delayed choice experiments and quantum encryption, are comprehensible to undergraduates with basic knowledge of quantum mechanics. In this paper, we show that the quantum eraser, usually used to study the duality between wave and particle properties, can also serve as a generic platform for quantum key distribution. We present a pedagogical example of an algorithm to securely share random keys using the quantum eraser platform and propose its implementation with quantum circuits.


Fast simulation of planar Clifford circuits

Abstract: A general quantum circuit can be simulated classically in exponential time. If it has a planar layout, then a tensor-network contraction algorithm due to Markov and Shi has a runtime exponential in the square root of its size, or more generally exponential in the treewidth of the underlying graph. Separately, Gottesman and Knill showed that if all gates are restricted to be Clifford, then there is a polynomial time simulation. We combine these two ideas and show that treewidth and planarity can be exploited to improve Clifford circuit simulation. Our main result is a classical algorithm with runtime scaling asymptotically as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>n</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi>&#x03C9;</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>&#x003C;</mml:mo></mml:math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>n</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>1.19</mml:mn></mml:mrow></mml:msup></mml:math> which samples from the output distribution obtained by measuring all <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> qubits of a planar graph state in given Pauli bases. Here <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>&#x03C9;</mml:mi></mml:math> is the matrix multiplication exponent. We also provide a classical algorithm with the same asymptotic runtime which samples from the output distribution of any constant-depth Clifford circuit in a planar geometry. Our work improves known classical algorithms with cubic runtime.A key ingredient is a mapping which, given a tree decomposition of some graph <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>G</mml:mi></mml:math>, produces a Clifford circuit with a structure that mirrors the tree decomposition and which emulates measurement of the corresponding graph state. We provide a classical simulation of this circuit with the runtime stated above for planar graphs and otherwise <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi><mml:msup><mml:mi>t</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi>&#x03C9;</mml:mi><mml:mo>&#x2212;</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:math> where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>t</mml:mi></mml:math> is the width of the tree decomposition. Our algorithm incorporates two subroutines which may be of independent interest. The first is a matrix-multiplication-time version of the Gottesman-Knill simulation of multi-qubit measurement on stabilizer states. The second is a new classical algorithm for solving symmetric linear systems over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msub></mml:math> in a planar geometry, extending previous works which only applied to non-singular linear systems in the analogous setting.


Comparative Performance of Hay Densification Machines

Abstract: Densification and baling of hay material is required to reduce its volume for easy transport. Three types of densification machines viz. i) stationary baler; ii) movable baler; and iii) field baler were evaluated for densification of different hay materials. The output from stationary, movable and field baler were 0.045, 0.21 and 0.86 t.h-1, respectively. Densification with stationary baler needed all the hay material to be carried to the densification yard, thus increasing man power requirement and costs while movable baler could be moved to the place of availability of raw material. Field baler could collect and bale the harvested hay material spread on field. Field baler also operated under stationary condition with manual feeding of hay material heaped at one place. The cost of operation with stationary, movable and field baler were Rs.14400, 4700 and 1500 Rs.t-1 of bales formed, respectively.


Numerical Composition of Differential Privacy

Abstract: We give a fast algorithm to optimally compose privacy guarantees of differentially private (DP) algorithms to arbitrary accuracy. Our method is based on the notion of \emph{privacy loss random variables} to quantify the privacy loss of DP algorithms.The running time and memory needed for our algorithm to approximate the privacy curve of a DP algorithm composed with itself $k$ times is $\tilde{O}(\sqrt{k})$. This improves over the best prior method by Koskela et al. (2021) which requires $\tilde{\Omega}(k^{1.5})$ running time. We demonstrate the utility of our algorithm by accurately computing the privacy loss of DP-SGD algorithm of Abadi et al. (2016) and showing that our algorithm speeds up the privacy computations by a few orders of magnitude compared to prior work, while maintaining similar accuracy.


Grounded language acquisition through the eyes and ears of a single child

Abstract: Starting around 6 to 9 months of age, children begin acquiring their first words, linking spoken words to their visual counterparts. How much of this knowledge is learnable from sensory input with relatively generic learning mechanisms, and how much requires stronger inductive biases? Using longitudinal head-mounted camera recordings from one child aged 6 to 25 months, we trained a relatively generic neural network on 61 hours of correlated visual-linguistic data streams, learning feature-based representations and cross-modal associations. Our model acquires many word-referent mappings present in the child’s everyday experience, enables zero-shot generalization to new visual referents, and aligns its visual and linguistic conceptual systems. These results show how critical aspects of grounded word meaning are learnable through joint representation and associative learning from one child’s input.


Who Wants to Shop With You: Joint Product–Participant Recommendation for Group-Buying Service

Abstract: Recent years have witnessed the great success of group buying (GB) in social e-commerce, opening up a new way of online shopping. In this business model, a user can launch a GB as an initiator to share her interested product with social friends. The GB is clinched once enough friends join in as participants to copurchase the shared product. As such, a successful GB depends on not only whether the initiator can find her interested product but also whether the friends are willing to join in as participants. Most existing recommenders are incompetent in such complex scenario, as they merely seek to help users find their preferred products and cannot help identify potential participants to join in a GB. To this end, we propose a novel joint product-participant recommendation (J2PRec) framework, which recommends both candidate products and participants for maximizing the success rate of a GB. Specifically, J2PRec first designs a relational graph embedding module, which effectively encodes the various relations in GB for learning enhanced user and product embeddings. It then jointly learns the product and participant recommendation tasks under a probabilistic framework to maximize the GB likelihood, i.e., boost the success rate of a GB. Extensive experiments on three real-world datasets demonstrate the superiority of J2PRec for GB recommendation.


Proposal for sequential Stern–Gerlach experiment with programmable quantum processors

Abstract: The historical significance of the Stern–Gerlach (SG) experiment lies in its provision of the initial evidence for space quantization. Over time, its sequential form has evolved into an elegant paradigm that effectively illustrates the fundamental principles of quantum theory. To date, the practical implementation of the sequential SG experiment has not been fully achieved. In this study, we demonstrate the capability of programmable quantum processors to simulate the sequential SG experiment. The specific parametric shallow quantum circuits, which are suitable for the limitations of current noisy quantum hardware, are given to replicate the functionality of SG devices with the ability to perform measurements in different directions. Surprisingly, it has been demonstrated that Wigner’s SG interferometer can be readily implemented in our sequential quantum circuit. With the utilization of the identical circuits, it is also feasible to implement Wheeler’s delayed-choice experiment. We propose the utilization of cross-shaped programmable quantum processors to showcase sequential experiments, and the simulation results demonstrate a strong alignment with theoretical predictions. With the rapid advancement of cloud-based quantum computing, such as BAQIS Quafu, it is our belief that the proposed solution is well-suited for deployment on the cloud, allowing for public accessibility. Our findings not only expand the potential applications of quantum computers, but also contribute to a deeper comprehension of the fundamental principles underlying quantum theory.


Stochastic Circuits for Computing Weighted Ratio With Applications to Multiclass Bayesian Inference Machine

Abstract: Bayesian inference is one method of statistical inference in machine learning. It predicts the probability that a given test belongs to a certain class and is widely used in various applications such as medical diagnosis, spam classification and fraud detection. The conventional binary architecture of computing the posterior probability is inefficient in practical implementation, which is involved in multiplication, addition and division operations. Recently, it has been shown that simple Muller C-elements, the asynchronous logic units, can perform stochastic Bayesian inference motivated by its truth table when the data is encoded as the bit-stream. The Bayesian inference machine is therefore implemented with low hardware cost. However, such an architecture is employed to compute the posterior probability of two classes only. This brief presents two stochastic circuit designs for computing the weighted ratio with multiple weights for generalized multi-class Bayesian machines. The first design is mainly based on the JK flip flop and multiplexers. The second approach is to construct the finite state machine (FSM) by manipulating the correlation between the input bit-streams. The FSM-based design requires fewer random number sources (RNSs) as compared to the JK flip flop-based implementation. These facts lead to a reduction of hardware area and energy. Simulation results show that the accuracy of the proposed JK flip flop-based and FSM-based designs is almost the same in the tested data sets. As compared to the traditional binary design, the circuit area of the proposed stochastic design is improved by 96% at least in the cases of three and four classes. The consumed energy per operation is reduced by 58.1% at least in the cases of three and four classes.


Deep sequential pattern mining for readability enhancement of Indonesian summarization

Abstract: In text summarization research, readability is a great issue that must be addressed. Our hypothesis is readability can be accomplished by using text representations that keep the meaning of text documents intact. Therefore, this study aims to combine sequential pattern mining (SPM) in producing a sequence of a word as text representation with unsupervised deep learning to produce an Indonesian text summary called DeepSPM. This research uses PrefixSpan as an SPM algorithm and deep belief network (DBN) as an unsupervised deep learning method. This research uses 18,774 Indonesian news text from IndoSum. The readability aspect is evaluated by recall-oriented understudy for gisting evaluation (ROUGE) as a co-selection-based analysis; Dwiyanto Djoko Pranowo metrics, Gunning fog index (GFI), and Flesch-Kincaid grade level (FKGL) as content-based analysis; and human readability evaluation with two experts. The experiment result shows that DeepSPM yields better than DBN, with the F-measure value of ROUGE-1 enhanced to 0.462, ROUGE-2 is 0.37, and ROUGE-L is 0.41. The significance of ROUGE results also be tested using T-Test. The content-based analysis and human readability evaluation findings are conformable with the findings of co-selection-based analysis that generated summaries are only partially readable or have a medium level of readability aspect.


Partitioning intensity inhomogeneity colour images via Saliency-based active contour

Abstract: Partitioning or segmenting intensity inhomogeneity colour images is a challenging problem in computer vision and image shape analysis. Given an input image, the active contour model (ACM) which is formulated in variational framework is regularly used to partition objects in the image. A selective type of variational ACM approach is better than a global approach for segmenting specific target objects, which is useful for applications such as tumor segmentation or tissue classification in medical imaging. However, the existing selective ACMs yield unsatisfactory outcomes when performing the segmentation for colour (vector-valued) with intensity variations. Therefore, our new approach incorporates both local image fitting and saliency maps into a new variational selective ACM to tackle the problem. The euler-lagrange (EL) equations were presented to solve the proposed model. Thirty combinations of synthetic and medical images were tested. The visual observation and quantitative results show that the proposed model outshines the other existing models by average, with the accuracy of 2.23% more than the compared model and the Dice and Jaccard coefficients which were around 12.78% and 19.53% higher, respectively, than the compared model.


The role of Louvain-coloring clustering in the detection of fraud transactions

Abstract: Clustering is a technique in data mining capable of grouping very large amounts of data to gain new knowledge based on unsupervised learning. Clustering is capable of grouping various types of data and fields. The process that requires this technique is in the business sector, especially banking. In the transaction business process in banking, fraud is often encountered in transactions. This raises interest in clustering data fraud in transactions. An algorithm is needed in the cluster, namely Louvain’s algorithm. Louvain’s algorithm is capable of clustering in large numbers, which represent them in a graph. So, the Louvain algorithm is optimized with colored graphs to facilitate research continuity in labeling. In this study, 33,491 non-fraud data were grouped, and 241 fraud transaction data were carried out. However, Louvain’s algorithm shows that clustering increases the amount of data fraud of 90% by accurate.


The preliminary study of carbon x-change rakyat using blockchain application

Abstract: Today’s air pollution is detrimental to the environment, particularly in Indonesia. Carbon dioxide (CO2) and nitrogen oxide (NOx) are present in the atmosphere due to air pollution. Many individuals employ reforestation to lessen the influence of CO2 and NOx gases on the atmosphere. However, in the digitalized era, lowering carbon emissions may also be accomplished through a carbon credit exchange. Thus, in this study we investigate the performance of the carbon x-change rakyat (CXR) based on blockchain platform utilizing the stress test approach. We provided four scenarios with 10,000 to 100,000 transactions evaluated on the CXR blockchain system i.e., transfer, insert, remove, and update. The outcome demonstrates CXR’s effectiveness with 100% success and 0% failure rate based on testing and statistical computations calculation. The mean absolute error (MAE), variance accounted for (VAF), and percent error (PE) are obtained with values ranging from 0.38% to 4.67%. In this study, the transaction per-second (TPS) is used to calculate include error request (IER) and exclude error request (EER) values around 312 to 746 milliseconds (ms). In addition, the TPS of CXR based on blockchain platform is a capability to create and trace database carbon certificate ownership (nonfinancial activity). It means CXR based on the blockchain platform has a fast response to process carbon certificate ownership for transactions across local and international countries in the world.


Enhancing cryptographic protection, authentication, and authorization in cellular networks: a comprehensive research study

Abstract: This research article provides an extensive analysis of novel methods of cryptographic protection as well as advancements in authentication and authorization techniques within cellular networks. The aim is to explore recent literature and identify effective authentication and authorization methods, including high-speed data encryption. The significance of this study lies in the growing need for enhanced data security in scientific research. Therefore, the focus is on identifying suitable authentication and authorization schemes, including blockchain-based approaches for distributed mobile cloud computing. The research methodology includes observation, comparison, and abstraction, allowing for a comprehensive examination of advanced encryption schemes and algorithms. Topics covered in this article include multi-factor authentication, continuous authentication, identity-based cryptography for vehicle-to-vehicle (V2V) communication, secure blockchain-based authentication for fog computing, internet of things (IoT) device mutual authentication, authentication for wireless sensor networks based on blockchain, new secure authentication schemes for standard wireless telecommunications networks, and the security aspects of 4G and 5G cellular networks. Additionally, in the paper a differentiated authentication mechanism for heterogeneous 6G networks blockchain-based is discussed. The findings presented in this article hold practical value for organizations involved in scientific research and information security, particularly in encryption and protection of sensitive data.


An introduction to double stain normalization technique for brain tumour histopathological images

Abstract: Stain normalization is an image pre-processing method extensively used to standardize multiple variances of staining intensity in histopathology image analysis. Staining variations may occur for several reasons, such as unstandardized protocols while preparing the specimens, using dyes from different manufacturers, and varying parameters set while capturing the digital images. In this study, a double stain normalization technique based on immunohistochemical staining is developed to improve the performance of the conventional Reinhard’s algorithm. The proposed approach began with preparing a target image by applying the contrast-limited adaptive histogram equalization (CLAHE) technique to the targeted cells. Later, the colour distribution of the input image will be matched to the colour distribution of the target image through the linear transformation process. In this study, the power-law transformation was applied to address the over-enhancement and contrast degradation issues in the conventional method. Five quality metrics comprised of entropy, tenengrad criterion (TEN), mean square error (MSE), structural similarity index (SSIM) and correlation coefficient were used to measure the performance of the proposed system. The experimental results demonstrate that the proposed method outperformed all conventional techniques. The proposed method achieved the highest average values of 5.59, 3854.11 and 94.65 for entropy, TEN, and MSE analyses.


The $U(1)_{L_\mu-L_\tau}$ breaking phase transition, muon $g-2$, dark matter, collider and gravitational wave

Abstract: Abstract Combining the dark matter and muon <?CDATA $ g-2 $?> anomaly, we study the <?CDATA $ U(1)_{L_\mu-L_\tau} $?> breaking phase transition, gravitational wave spectra, and direct detection at the LHC in an extra <?CDATA $ U(1)_{L_\mu-L_\tau} $?> gauge symmetry extension of the standard model. The new fields include vector-like leptons ( <?CDATA $ E_1,\; E_2,\; N $?> ), the <?CDATA $ U(1)_{L_\mu-L_\tau} $?> breaking scalar S , and the gauge boson <?CDATA $ Z' $?> , as well as the dark matter candidate <?CDATA $ X_I $?> and its heavy partner <?CDATA $ X_R $?> . A joint explanation of the dark matter relic density and muon <?CDATA $ g-2 $?> anomaly excludes the region where both <?CDATA $\min(m_{E_1},m_{E_2},m_N,m_{X_R})$?> and <?CDATA $\min(m_{Z'},m_S)$?> are much larger than <?CDATA $ m_{X_I} $?> . In the parameter space accommodating the DM relic density and muon <?CDATA $ g-2 $?> anomaly, the model can achieve a first-order <?CDATA $ U(1)_{L_\mu-L_\tau} $?> breaking phase transition, whose strength is sensitive to the parameters of the Higgs potential. The corresponding gravitational wave spectra can reach the sensitivity of U-DECIGO. In addition, the direct searches at the LHC impose stringent bounds on the mass spectra of the vector-like leptons and dark matter.


A versatile dataset for intrinsic plagiarism detection, text reuse analysis, and author clustering in Urdu

Abstract: Plagiarism detection (PD) is a process of identifying instances where someone has presented another person's work or ideas as their own. Plagiarism detection is categorized into two types (i) Intrinsic plagiarism detection primarily concerns the assessment of authorship consistency within a single document, aiming to identify instances where portions of the text may have been copied or paraphrased from elsewhere within the same document. Author clustering, closely related to intrinsic plagiarism detection, involves grouping documents based on their stylistic and linguistic characteristics to identify common authors or sources within a given dataset. On the other hand, (ii) extrinsic plagiarism detection delves into the comparative analysis of a suspicious document against a set of external source documents, seeking instances of shared phrases, sentences, or paragraphs between them, which is often referred to as text reuse or verbatim copying. Detection of plagiarism from documents is a long-established task in the area of NLP with remarkable contributions in multiple applications. A lot of research has already been conducted in the English and other foreign languages but Urdu language needs a lot of attention especially in intrinsic plagiarism detection domain. The major reason is that Urdu is a low resource language and unfortunately there is no high-quality benchmark corpus available for intrinsic plagiarism detection in Urdu language. This study presents a high-quality benchmark Corpus comprising 10,872 documents. The corpus is structured into two granularity levels: sentence level and paragraph level. This dataset serves multifaceted purposes, facilitating intrinsic plagiarism detection, verbatim text reuse identification, and author clustering in the Urdu language. Also, it holds significance for natural language processing researchers and practitioners as it facilitates the development of specialized plagiarism detection models tailored to the Urdu language. These models can play a vital role in education and publishing by improving the accuracy of plagiarism detection, effectively addressing a gap and enhancing the overall ability to identify copied content in Urdu writing.


Boosting semi-supervised learning with Contrastive Complementary Labeling

Abstract: Semi-supervised learning (SSL) approaches have achieved great success in leveraging a large amount of unlabeled data to learn deep models. Among them, one popular approach is pseudo-labeling which generates pseudo labels only for those unlabeled data with high-confidence predictions. As for the low-confidence ones, existing methods often simply discard them because these unreliable pseudo labels may mislead the model. Unlike existing methods, we highlight that these low-confidence data can be still beneficial to the training process. Specifically, although we cannot determine which class a low-confidence sample belongs to, we can assume that this sample should be very unlikely to belong to those classes with the lowest probabilities (often called complementary classes/labels). Inspired by this, we propose a novel Contrastive Complementary Labeling (CCL) method that constructs a large number of reliable negative pairs based on the complementary labels and adopts contrastive learning to make use of all the unlabeled data. Extensive experiments demonstrate that CCL significantly improves the performance on top of existing advanced methods and is particularly effective under the label-scarce settings. For example, CCL yields an improvement of 2.43% over FixMatch on CIFAR-10 only with 40 labeled data.


Inline classification of polymer films using Machine learning methods

Abstract: Improving the sortability of plastic packaging film waste (PPFW) is crucial for increasing the recycling rate in Austria as they account for 150,000 t of the annually produced 300,000 t of plastic packaging waste. Currently PPFW is thermally recovered, as it is impossible to separate the mechanically recyclable monomaterial films from the non mechanically-recyclable multimaterial films. In this study, machine learning models capable of classifying inline into monolayer and multilayer films of PPFW according to their spectral fingerprint taken in transflection were created. Feature selection methods, like PCA and MRMR F-Tests, identified the most relevant spectral ranges for classification, that show the least redundancy and highest relevance. This effective subset of features decreases the required complexity of the model while reducing prediction time without compromising accuracy. The resulting models achieved a prediction accuracy of 85 % on unseen specimens with minimal prediction latency, effectively showing the inline applicability of these models in sorting aggregates.


Bayesian probabilistic modeling in robosoccer environment for robot path planning

Abstract: The main goal of a route planning approach is to find a trajectory that safely transports the robot from one site to the next. Furthermore, it should provide an energy-efficient path so the computer can calculate it rapidly. This study develops a path-planning system for robots to approach the ball without collision. The Bayesian optimization algorithm (BOA) is used to identify the shortest path between the robot and the ball. BOA employs a probabilistic model to seek the optimum of an uncertain objective function efficiently. The performance of the BOA-based path planning system is compared to other optimization algorithms such as genetic algorithm, ant colony optimization, and firefly algorithm. BOA’s acquisition functions such as expected improvement, probability of improvement (PI), and upper confidence bound, are investigated. The exact locations of the robots and the ball are fed into optimization problems to discover the optimum path. The results reveal that the BOA system outperforms other systems in terms of computational time for planning the optimum path in dynamic situations and BOA-PI is the fastest algorithm.


Efficient VVC Encoding Using Hierarchical Parallelization: A Comprehensive Analysis

Abstract: This paper presents and analyzes different parallelization strategies in VVenC, an open and optimized software encoder implementation of the Versatile Video Coding (VVC) standard. VVC has been developed to address the increasing demand for higher compression of digital video data, and it reduces the bitrate by around 50% for the same perceived quality compared to its predecessor, the High-Efficiency Video Coding (HEVC) standard. However, this increase in compression efficiency comes with an increase in computational complexity, particularly on the encoder side. VVenC integrates algorithmic optimizations for each coding tool in VVC and defines a set of five presets from faster to slower that provide Pareto-optimal tradeoffs between runtime and efficiency. Multithreading is employed to further reduce runtime while preserving most of the compression efficiency of each preset. With a hierarchical combination of pre-processing, picture-level and in-picture parallelization, VVenC achieves a 4× speedup for four threads. Further speedup using a higher number of threads depends on the video resolution and used encoder preset. For 16 threads, it ranges from 6 to 9 for high definition to 10-12 for ultra-high-definition video. Compared to previous work, the usage of temporal prediction for the adaptive loop filter reduces the associated coding efficiency loss from 0.4% to almost zero. A better scaling for higher numbers of threads can be achieved at the cost of a higher coding efficiency loss. In the presented framework, increased speedup by smaller block Coding Tree Unit (CTU) sizes, a combination of wavefront parallel processing and various tiles picture partitioning configurations is examined. Furthermore, results on a 20-core ARM-based Apple M1 computer indicate a better scaling for multithreading compared to ×86-based architectures. The analysis is complemented by profiling, which exhibits the overhead by idle threads and identifies the mode estimation as the main bottleneck of the presented framework.


Evaluation on model-driven knowledge graph and platform for grid operation and maintenance

Abstract: With the continuous expansion of the power grid, the forms of faults are becoming increasingly complex, with a wide range of impacts and long maintenance cycles, posing increasingly severe challenges for power grid operators. In the current power system (PS), due to system limitations, there is a large amount of data, and the current computing system is limited by hardware and computing power, making it difficult to satisfy the requirements of real-time computing, comprehensive analysis, and expansion. This paper analyzed the technical basis of the knowledge graph (KG) of power grid operation and maintenance (O&M), and clarified the key role of model driving in the current large-scale smart grid (SG), which can effectively improve the efficiency and reliability of data processing in the SG. This paper also analyzed the model-driven PG O&M KG platform, and analyzed the role of particle swarm optimization (PSO) algorithm in PG operation and fault maintenance. After applying the PSO algorithm in this article, in the experimental results section, the fault rate of Transformer 5 was 2.1% lower than that of Transformer 4. The knowledge mapping model of grid O&M and the particle swarm algorithm in this paper can significantly reduce the failure rate of the grid, which has wide extension value.


Target-Free Stereo Camera-GNSS/IMU Self-Calibration Based on Iterative Refinement

Abstract: Camera and GNSS/IMU are widely used in autonomous vehicles and robots due to their light weight and low cost, in these cases, an accurate extrinsic calibration is essential for sensor fusion. Traditionally, specific vehicle movements or scenarios with known fiducial markers are required for the sophisticated calibration process, however, the former is difficult to eliminate the impact of drift made by the techniques used to estimate the motion of each sensor, and the latter is poorly automated and needs specialist human expertise. To tackle above problems, this paper proposes a target-free stereo camera-GNSS/IMU self-calibration method based on iterative refinement. The initial calibration parameters are calculated with relative pose constraints derived from visual odometry. To eliminate the impact of visual odometry drift, parameters are further refined by iteratively using paired relative pose constraints and absolute pose constraints, which are derived from scan-global map matching. Quantitative calibration results on simulated data show the robustness and accuracy of this approach with RMSEs of 10 −3 deg and 10 −3 m for rotation and translation, respectively. Further experiments in real world confirm the superior of our method in natural environments.


Internal and External Double-Cycle High-Order Kalman Filter Design

Abstract: Accurate state estimation depends on the design of a high-precision filter, while the design of a high-precision filter relies on the use of high-order nonlinear information of the model. For a class of nonlinear dynamic systems whose state and measurement model are composed of several nonlinear functions respectively, and each nonlinear function consists of the product of several weak nonlinear multipliers functions, a series of high-order Kalman filter bank constructed with the internal and external double-cycle is designed. In this proposed filter bank, each of the additive nonlinear functions is defined as the overall hidden variable, and the weak nonlinear multipliers in the additive nonlinear functions are defined as the basic hidden variables. Linear dynamic modeling of hidden variables is carried out, an internal cycle high-order Kalman filter bank is designed by implementing the step-by-step linearization for each basic hidden variable, an external cycle high-order Kalman filter bank is designed by making use of all the sequentially estimated overall hidden variables. Original state variables are estimated based on internal and external double-cycle Kalman filter bank. Compared with some existing methods, the effectiveness of the proposed method is verified by comparative experiments.


Graph-based Text Classification by Contrastive Learning with Text-level Graph Augmentation

Abstract: Text Classification (TC) is a fundamental task in the information retrieval community. Nowadays, the mainstay TC methods are built on the deep neural networks, which can learn much more discriminative text features than the traditional shallow learning methods. Among existing deep TC methods, the ones based on Graph Neural Network (GNN) have attracted more attention due to the superior performance. Technically, the GNN-based TC methods mainly transform the full training dataset to a graph of texts; however, they often neglect the dependency between words, so as to miss potential semantic information of texts, which may be significant to exactly represent them. To solve the aforementioned problem, we generate graphs of words instead, so as to capture the dependency information of words. Specifically, each text is translated into a graph of words, where neighboring words are linked. We learn the node features of words by a GNN-like procedure and then aggregate them as the graph feature to represent the current text. To further improve the text representations, we suggest a contrastive learning regularization term. Specifically, we generate two augmented text graphs for each original text graph, we constrain the representations of the two augmented graphs from the same text close and the ones from different texts far away. We propose various techniques to generate the augmented graphs. Upon those ideas, we develop a novel deep TC model, namely Text-level Graph Networks with Contrastive Learning (TGN cl ). We conduct a number of experiments to evaluate the proposed TGN cl model. The empirical results demonstrate that TGN cl can outperform the existing state-of-the-art TC models.


Bayesian Updating on Time Intervals at Different Magnitude Thresholds in a Marked Point Process and Its Application to Synthetic Seismic Activity

Abstract: We present a Bayesian updating method on the inter-event time intervals at different magnitude thresholds in a marked point process toward probabilistic forecasting of an upcoming large event using temporal information on smaller events. Bayes’ theorem that yields the one-to-one relationship between intervals at lower and upper magnitude thresholds is presented. This theorem is extended to Bayesian updating for an uncorrelated marked point process that yields the relationship between multiple consecutive lower intervals and one upper interval. We derive the inverse probability density function and the condition under which it has a peak. Further, we derive its approximation function that consists of the kernel part that includes the product of conditional probabilities and a correction term. Bayesian updating is applied numerically to the time series of a seismic activity model. We estimate the time of an upcoming large event using the maximum point of the kernel part and evaluate its accuracy by the relative error with the actual occurrence time. We further evaluate the forecasting effectiveness by the continuity of updates with acceptable accuracy before the large event. Statistical results indicate that forecasting is relatively effective immediately or long after the last major event in which stationarity dominates in time series.


Singularity-Free Flight using Rotor-Distributed Aerial Manipulator

Abstract: Aerial manipulators have been developed in recent years. The most popular type is the multirotor equipped with arms. By employing these rotor-concentrated manipulators (RCMs), various manipulations have been conducted in high places. Compared with RCMs, rotor-distributed manipulators (RDMs) can generate relatively larger wrenches at the end effector. However, certain types of singular configurations exist during flight. In this letter, we propose design and control methods for the RDM based on previous works [1]-[3]. Additionally, we propose a planning method for optimizing the angles of the thrust vectoring apparatus to overcome singularities and maximize controllability in arbitrary poses. Finally, we verify the feasibility of the proposed methods through experiments. By using a quad-rotor, we achieve stable flight in challenging poses, such as horizontal or vertical line configurations.


Accurate Gaussian-Process-Based Distance Fields With Applications to Echolocation and Mapping

Abstract: This paper introduces a novel method to estimate distance fields from noisy point clouds using Gaussian Process (GP) regression. Distance fields, or distance functions, gained popularity for applications like point cloud registration, odometry, SLAM, path planning, shape reconstruction, etc. A distance field provides a continuous representation of the scene defined as the shortest distance from any query point and the closest surface. The key concept of the proposed method is the transformation of a GP-inferred latent scalar field into an accurate distance field by using a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reverting</i> function related to the kernel inverse. The latent field can be interpreted as a smooth occupancy map. This paper provides the theoretical derivation of the proposed method as well as a novel uncertainty proxy for the distance estimates. The improved performance compared with existing distance fields is demonstrated with simulated experiments. The level of accuracy of the proposed approach enables novel applications that rely on precise distance estimation: this work presents echolocation and mapping frameworks for ultrasonic-guided wave sensing in metallic structures. These methods leverage the proposed distance field with a physics-based measurement model accounting for the propagation of the ultrasonic waves in the material. Real-world experiments are conducted to demonstrate the soundness of these frameworks.


Flexible Calibration Method for a Quad-Directional Stereo Vision Sensor Based on Unconstraint 3-D Target

Abstract: Multi-directional stereo vision sensors have been paid increasing attention in the fields of environment perception and 3D reconstruction attributed to their advantage of wide field of view (FOV). Vision sensor setup requires the parameters to be calibrated accurately, which remains a challenging problem because of the discontinuity in the camera field. This paper proposes a flexible calibration method using an unconstrained 3D target for a quad-directional stereo vision sensor (QSVS) composed of a single camera and a dual mirror pyramid. We build the measurement models of the local virtual binocular structures and the global virtual multi-camera based on the optical properties of the catadioptric elements to reduce the calibration steps of the mirror poses. On this basis, this paper resolves the following three problems. First, the unique intrinsic parameters of the virtual multi-camera are obtained through the intrinsic calibration of the actual camera. Second, based on the proposed sorting algorithm of the feature points, we can determine the correspondence between the spatial feature points and the mirrored 2D points in the global calibration image. Third, we can determine the global structure parameters of the QSVS from a single calibration image based on the designed unconstrained 3D target with 6-DOF. And we optimize the calibration parameters by flexibly adjusting the target pose. Simulation and physical experiments are conducted to demonstrate the performance of the proposed method. This method also provides a new way to calibrate the miniaturized multi-camera vision systems and omnidirectional vision sensors without an overlapping FOV.


Schema Extraction in NoSQL Databases: A Systematic Literature Review

Abstract: Introduction: Nowadays, NoSQL databases have taken on an increasingly important role in the storage of massive data within companies. Due to a common property called schema-less, NoSQL databases offer great flexibility, particularly for the storage of data in different formats. However, despite their success in data storage, schema-less databases are a major obstacle in areas requiring precise knowledge of this schema, especially in the field of data integration. Method: This study presents a Systematic Literature Review (SLR) to explore, evaluate, and discuss relevant existing research and endeavors using novel schema extraction approaches. Furthermore, we conducted this study using a well-defined methodology to examine and study the problem of schema extraction from NoSQL databases. Results: Our research results highlight and emphasize the scheme extraction approaches and provide knowledge to researchers and practitioners by proposing schema extraction approaches and their limitations, which contributes to inventing new, more efficient approaches. Conclusion: In our future work, inspired by the recent advances in quantum computing and the emergence of post-quantum cryptography (PQC), we aim to propose a schema extraction approach that blends cutting-edge technologies with a strong focus on database security.


Toward Accurate and Real-Time Binocular Vision: A Lightweight Stereo Matching Network for RGB Stereo Images

Abstract: Existing stereo matching networks with high accuracy generally demand high computational overhead, making them expensive to deploy and unsuitable for many real-time applications, especially on source-constrained hardware platforms. Some recent works have tried to build lightweight stereo matching networks, but their accuracy and inference speed is far from satisfactory when deployed on source-constrained GPUs. This paper aims to build a lightweight stereo matching network with high accuracy. First, we claim that a reasonable disparity upsampling strategy is crucial for lightweight stereo matching to achieve high accuracy, whose importance is underestimated in existing works. Accordingly, an efficient and effective disparity upsampling strategy named Spatial Adaptive Disparity Shuffle (SADS) is proposed to meet the upsampling requirements of disparities within diverse regions. Second, a novel Channel-Disparity-Mixed Attention (CDMA) mechanism is proposed to regularize the high-resolution 4D compact cost volume. The attention values in CDMA are adaptive across channels, pixels and disparity candidates, making CDMA suitable for regularizing the compact cost volume. Based on the proposed methods, a lightweight stereo matching network (SADSNet) is designed that achieves real-time performance with high accuracy on source-limited hardware platforms (e.g., the NVIDIA Jetson TX2). In addition, SADSNet can be easily scaled up or down to suit different application scenarios according to available computational power. Extensive experimental results on multiple datasets show its clear superiority over SOTA lightweight stereo models and even many large accuracy-oriented models.


Quasi-Real-Time Inversion of Large Earthquake Rupture Process Based on Earthquake Early Warning System in China: Applications to 2017 MW 6.5 Jiuzhaigou and 2022 MW 6.7 Menyuan Earthquakes

Abstract: The study of seismic ruptures is crucial for understanding major earthquake events. Historically, research on large earthquake rupture processes has required a long time to be published. However, recent advancements in earthquake early warning (EEW) systems have allowed for more rapid analyses of earthquake rupture inversions. The widespread and dense distribution of EEW stations provides in China an opportunity to study and invert rupture processes in near-real time. Two notable earthquakes were studied using this technology: the 2017 Jiuzhaigou MW 6.5 earthquake in Sichuan Province, China, and the 2022 Menyuan MW 6.7 earthquake in Qinghai Province, China. In both instances, numerous strong-motion sensors captured the seismic events and transmitted waveform data to data analysis centers in real time. Following automated site selection and preparation procedures, the rupture processes of these earthquakes were analyzed and released within 30[Formula: see text]min of the event origin. This rapid response time demonstrates that the seismic rupture process determined through inversion using existing EEW systems can serve as a guide for emergency rescue work after earthquakes.


MCRPL: A Pretrain, Prompt, and Fine-tune Paradigm for Non-overlapping Many-to-one Cross-domain Recommendation

Abstract: Cross-domain Recommendation is the task that tends to improve the recommendations in the sparse target domain by leveraging the information from other rich domains. Existing methods of cross-domain recommendation mainly focus on overlapping scenarios by assuming users are totally or partially overlapped, which are taken as bridges to connect different domains. However, this assumption does not always hold, since it is illegal to leak users’ identity information to other domains. Conducting Non-overlapping MCR (NMCR) is challenging, since (1) the absence of overlapping information prevents us from directly aligning different domains, and this situation may get worse in the MCR scenario, and (2) the distribution between source and target domains makes it difficult for us to learn common information across domains. To overcome the above challenges, we focus on NMCR and devise MCRPL as our solution. To address Challenge 1, we first learn shared domain-agnostic and domain-dependent prompts and pre-train them in the pre-training stage. To address Challenge 2, we further update the domain-dependent prompts with other parameters kept fixed to transfer the domain knowledge to the target domain. We conduct experiments on five real-world domains, and the results show the advance of our MCRPL method compared with several recent SOTA baselines. Moreover, our source codes have been publicly released. 1


Wizard of Props: Mixed Reality Prototyping with Physical Props to Design Responsive Environments

Abstract: Driven by the vision of future responsive environments, where everyday surroundings can perceive human behaviors and respond through intelligent robotic actuation, we propose Wizard of Props (WoP): a human-centered design workflow for creating expressive, implicit, and meaningful interactions. This collaborative experience prototyping approach integrates full-scale physical props with Mixed Reality (MR) to support ideation, prototyping, and rapid testing of responsive environments. We present two design explorations that showcase our investigations of diverse design solutions based on varying technology resources, contextual considerations, and target audiences. Design Exploration One focuses on mixed environment building, where we observe fluid prototyping methods. In Design Exploration Two, we explore how novice designers approach WoP, and illustrate their design ideas and behaviors. Our findings reveal that WoP complements conventional design methods, enabling intuitive body-storming, supporting flexible prototyping fidelity, and fostering expressive environment-human interactions through in-situ improvisational performance.


Spectral Total-variation Processing of Shapes—Theory and Applications

Abstract: We present a comprehensive analysis of total variation (TV) on non-Euclidean domains and its eigenfunctions. We specifically address parameterized surfaces, a natural representation of the shapes used in 3D graphics. Our work sheds new light on the celebrated Beltrami and Anisotropic TV flows and explains experimental findings from recent years on shape spectral TV [Fumero et al. 2020 ] and adaptive anisotropic spectral TV [Biton and Gilboa 2022 ]. A new notion of convexity on surfaces is derived by characterizing structures that are stable throughout the TV flow, performed on surfaces. We establish and numerically demonstrate quantitative relationships between TV, area, eigenvalue, and eigenfunctions of the TV operator on surfaces. Moreover, we expand the shape spectral TV toolkit to include zero-homogeneous flows, leading to efficient and versatile shape processing methods. These methods are exemplified through applications in smoothing, enhancement, and exaggeration filters. We introduce a novel method that, for the first time, addresses the shape deformation task using TV. This deformation technique is characterized by the concentration of deformation along geometrical bottlenecks, shown to coincide with the discontinuities of eigenfunctions. Overall, our findings elucidate recent experimental observations in spectral TV, provide a diverse framework for shape filtering, and present the first TV-based approach to shape deformation.


CoBjeason: Reasoning Covered Object in Image by Multi-Agent Collaboration Based on Informed Knowledge Graph

Abstract: Object detection is a widely studied problem in existing works. However, in this paper, we turn to a more challenging problem of “ Covered Object Reasoning ”, aimed at reasoning the category label of target object in the given image particularly when it has been totally covered (or invisible ). To resolve this problem, we propose CoBjeason to seize the opportunity when visual reasoning meets the knowledge graph, where “ empirical cognition ” on common visual contexts have been incorporated as knowledge graph to conduct reinforced multi-hop reasoning via two collaborative agents. Such two agents, for one thing, stand at the covered object (or unknown entity ) to observe the surrounding visual cues in the given image and gradually select entities and relations from the global gallery-level knowledge graph which contains entity-pairs frequently occurring across the entire image-collection, so as to infer the main structure of image-level knowledge graph forward expanded from the unknown entity . In turn, for another, based on the reasoned image-level knowledge graph, the semantic context among entities will be aggregated backward into unknown entity to select an appropriate entity from the global gallery-level knowledge graph as the reasoning result. Moreover, such two agents will collaborate with each other, securing that the above Forward & Backward Reasoning will step towards the same destination of the higher performance on covered object reasoning. To our best knowledge, this is the first work on Covered Object Reasoning with Knowledge Graphs and reinforced Multi-Agent collaboration. Particularly, our study on Covered Object Reasoning and the proposed model CoBjeason could offer novel insights into more basic Computer Vision (CV) tasks, such as Semantic Segmentation with better understanding on the current scene when some objects are blurred or covered, Visual Question Answering with enhancement on the inference in more complicated visual context when some objects are covered or invisible, and Image Caption Generation with the augmentation on the richness of visual context for images containing partially visible objects. The improvement on the above basic CV tasks can further refine more complicated ones involved with nuanced visual interpretation like Autonomous Driving, where the recognition and reasoning on partially visible or covered object are critical. According to the experimental results, our proposed CoBjeason can achieve the best overall ranking performance on covered object reasoning compared with other models, meanwhile enjoying the advantage of lower “ exploration cost ”, with the insensitivity against the long-tail covered objects and the acceptable time complexity.


Enhancing diagnostic capabilities in Tonga: a 12-month analysis of telepathology implementation

Abstract: Telepathology is suggested as a solution for delivering essential surgical pathology diagnostic support to remote or under-resourced communities. Although the technologies involved are increasingly robust and cost-effective, successful implementation relies on more than just technology.1 We critically review a year-long experience with telepathology, evaluating its effectiveness as a diagnostic tool and as a teaching method in our pathology laboratory.


Prerequisite-Enhanced Category-Aware Graph Neural Networks for Course Recommendation

Abstract: The rapid development of Massive Open Online Courses (MOOCs) platforms has created an urgent need for an efficient personalized course recommender system that can assist learners of all backgrounds and levels of knowledge in selecting appropriate courses. Currently, most existing methods utilize a sequential recommendation paradigm that captures the user’s learning interests from their learning history, typically through recurrent or graph neural networks. However, fewer studies have explored how to incorporate principles of human learning at both the course and category levels to enhance course recommendations. In this article, we aim at addressing this gap by introducing a novel model, named Prerequisite-Enhanced Catory-Aware Graph Neural Network (PCGNN), for course recommendation. Specifically, we first construct a course prerequisite graph that reflects the human learning principles and further pre-train the course prerequisite relationships as the base embeddings for courses and categories. Then, to capture the user’s complex learning patterns, we build an item graph and a category graph from the user’s historical learning records, respectively: (1) the item graph reflects the course-level local learning transition patterns and (2) the category graph provides insight into the user’s long-term learning interest. Correspondingly, we propose a user interest encoder that employs a gated graph neural network to learn the course-level user interest embedding and design a category transition pattern encoder that utilizes GRU to yield the category-level user interest embedding. Finally, the two fine-grained user interest embeddings are fused to achieve precise course prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of PCGNN compared with other state-of-the-art methods.


An Analysis of the Ingredients for Learning Interpretable Symbolic Regression Models with Human-in-the-loop and Genetic Programming

Abstract: Interpretability is a critical aspect to ensure a fair and responsible use of machine learning (ML) in high-stakes applications. Genetic programming (GP) has been used to obtain interpretable ML models because it operates at the level of functional building blocks: if these building blocks are interpretable, there is a chance that their composition (i.e., the entire ML model) is also interpretable. However, the degree to which a model is interpretable depends on the observer. Motivated by this, we study a recently-introduced human-in-the-loop system that allows the user to steer GP’s generation process to their preferences, which shall be online-learned by an artificial neural network (ANN). We focus on the generation of ML models as analytical functions (i.e., symbolic regression) as this is a key problem in interpretable ML, and propose a two-fold contribution. First, we devise more general representations for the ML models for the ANN to learn upon, to enable the application of the system to a wider range of problems. Second, we delve into a deeper analysis of the system’s components. To this end, we propose an incremental experimental evaluation, aimed at (1) studying the effectiveness by which an ANN can capture the perceived interpretability for simulated users, (2) investigating how the GP’s outcome is affected across different simulated user feedback profiles, and (3) determining whether humans participants would prefer models that were generated with or without their involvement. Our results pose clarity on pros and cons of using a human-in-the-loop approach to discover interpretable ML models with GP.


Q&A Aftab Khan, a career in geophysics

Abstract: Abstract Sue Bowler talks to Aftab Khan, who recently celebrated his 90th birthday, about his career in geophysics, from the origins of the modern subject, through the exploration of the African Rift Valley and the steps to a nuclear test ban treaty


Fault prediction of unmanned aerial vehicles based on entropy weight fusion and temporal graph convolutional network with non-Euclidian structure

Abstract: Abstract Predicting weak and hidden faults in unmanned aerial vehicles (UAVs) is challenging due to their variable operation conditions and complex mechanisms. Conventional neural network models process the multisensory data in the form of Euclidean structure, the intrinsic connections among the individual data points are easy to be disregarded. Additionally, multisensory data are always directly fed into the model without adequately considering the importance or contribution of each sensor. Hence, an UAV fault prediction method is proposed by combining entropy weight fusion with a temporal graph convolutional network (GCN) to address the above problems. Firstly, the importance of multisensory data of UAVs are evaluated by each entropy value, and the multisensory data fusion is further realized by multiplying corresponding signal and entropy weight. Secondly, the multisensory data combined with fusion data are transferred together into graph-structure by adjacent matrix based on the node connection between different sensor data. Finally, the graph-structure data with non-Euclidian distance properties are input into temporal GCN to both capture the spatial and temporal relationship of the data, achieving better fault prediction results of UAVs. It is demonstrated that the proposed method is both applicable and superior in characterizing and predicting fault time series information of UAVs through parameter analysis and comparison studies with various existing algorithms.


METHODOLOGY FOR IDENTIFYING CURRICULAR WEAKNESSES FROM ENADE COURSE REPORT: performance of a Physics degree in ENADE 2021

Abstract: It is understood that the National Student Performance Exam (ENADE) makes it possible to analyze curricular weaknesses capable of supporting the improvement of curricular structures.A methodology is proposed to identify these weaknesses per observation unit (OU), based on the ENADE course report.The method is exemplified, focusing on 27 objective questions (64% of the grade) from the specific component (CE) of ENADE 2021 for a degree in Physics, in which the trainees showed themselves committed to the Exam (74% stayed more than 3 hours).Four items were disregarded, due to a discrimination rate of less than 20%, consolidating the analysis into 26 distractors that attracted at least 20% of respondents.Three items are exemplified that consolidate weaknesses linked to basic mechanics and other items that materialized difficulties in Practice in Physics Teaching and Inclusion are highlighted.It is understood that this systematic analysis can contribute as a subsidy in studies of curricular changes in Undergraduate courses.


MEASUREMENT OF EMPLOYEE INFORMATION SECURITY AWARENESS: CASE STUDY AT FINANCIAL INSTITUTION

Abstract: The lack of awareness regarding information security among employees in financial institutions can have detrimental impacts on both customers and the institution itself, both financially and in terms of trust. Therefore, this research aims to assess the information security awareness at PT XYZ, a financial institution, in order to identify the existing level of awareness, which will be used to provide recommendations. The method applied in this research is quantitative, using a questionnaire as a tool for data collection and distribution with a voluntary sampling technique among PT XYZ employees concerning their awareness of information security. The assessment of information security awareness covers 8 specific aspects, with 7 of them drawing sources from HAIS-Q and another 1 referring to the KAMI Index, using the Analytic Hierarchy Process (AHP) for weighting each area. The total number of respondents participating in this research is 52. The research results affirm that PT XYZ employees have a positive awareness of information security, indicating that there are no urgent actions needed at present. However, there are specific areas with potential for improvement, hence recommendations are provided to enhance and sustain information security awareness among employees.


Semi-device-independent quantum random number generator with a broadband squeezed state of light

Abstract: Abstract Random numbers are a basic ingredient of simulation algorithms and cryptography, and play a significant part in computer simulation and information processing. One prominent feature of a squeezed light is its lower fluctuation and more randomness in a pair of orthogonal oriented quadratures, thus it prompts a significant application in not only quantum information and quantum precision measurement but also an excellent entropy source for true random number generation. Here we report a generation of a high-efficiency semi-device-independent quantum random number based on a broadband squeezed light, where a reliable randomness source is unnecessary and a noisy local oscillator is allowed for homodyne detection. The equivalent generation of private random bits is at a rate of 580.7 Mbps. In addition, the use of squeezed light at 1.3 μm enables the transmission of entropy sources and local oscillators at the metropolitan scale, thus expanding the potential applications of quantum random number generators based on non-classical state of light.


Patenting artificial intelligence: the High Court’s decision in <i>Emotional Perception</i>

Abstract: Abstract In Emotional Perception, the EWHC held that an artificial neural network implementing a media recommendation system did not engage the ‘computer program’ exclusion under the Patents Act 1977. Following the ruling, the UK Intellectual Property Office suspended its existing rules on AI patents and issued fresh guidance on the issue.


Training children's cognitive functions using eye-tracking technology

Abstract: The active development of eye-tracking, that is, recording of eye movements, gives opportunities for the creation of new approaches to the improving of cognitive functions and learning. Eye-tracking based methods can be useful not only for children with impaired motor and speech functions, but also for children with attention disorders, since gaze control is closely related to attention. Feedback from the gaze accuracy can improve the effectiveness of attention training techniques. In this paper, we describe studies of the effectiveness of the use of correctional techniques based on eye-tracking in infants (healthy group and groups with various risks for cognitive impairment) and in children with developmental disorders (autism, Rett syndrome, attention deficit hyperactivity disorder). In almost all of the studies reviewed, positive effects of training were observed, i. e. both the productivity of the tasks performance and sustained attention were improved. A number of studies have revealed the transfer of the training effect to untrained functions, which is an important indicator of the effectiveness of correction techniques. All this indicates the perspective of development in this direction. However, much work is to be done, such as selecting and creating the most effective tasks, determining the optimal frequency and duration of training for different clinical groups.


Path tracking control for autonomous vehicles using hybrid fault-tolerant approach

Abstract: Signal delay, execution deviation and reference model mismatch are common faults in autonomous vehicles, resulting in reduced path tracking control performance. To improve the path tracking performance of autonomous vehicles under fault conditions, this paper proposes a hybrid fault-tolerant (HFT) path tracking control approach, in which passive fault-tolerant path tracking control and fault observer-based hybrid active fault-tolerant path tracking control are introduced. The probable usual errors in path tracking are first examined and analyzed, together with the vehicle kinematic model and a typical autonomous driving system. Then, a slide-mode-based passive fault-tolerant path tracking controller is designed and integrated with a fault observer to create a hybrid active fault-tolerant controller. Finally, simulations and experiments are conducted, and the results demonstrate that our proposal is effective. After convergence of the fault observations, the lateral tracking error is less than 0.2m and the root mean square of the tracking error is 44% smaller than the LQR approach.


A Robust Sequential Recommendation Model Based on Multiple Feedback Behavior Denoising and Trusted Neighbors

Abstract: Abstract At present, most of the personalized sequential recommendations utilize users’ implicit positive feedback (such as clicks) to predict user behavior, ignoring the impact of implicit negative feedback and explicit feedback on the accuracy of recommendation results prediction. In this paper, we propose a robust sequence recommendation model based on multi feedback behavior denoising and trusted neighbors, which utilizes multiple feedback behavior data for feature denoising and considers trusted nearest neighbor information to improve model performance. Firstly, by learning the feature representations and interactions of various types of feedback, explicit feedback is used to map and purify implicit feedback with the same and different attributes, resulting in unbiased user performance. Then, we design a filter attention network to identify highly trusted neighbor information. Finally, we integrate pure user interest representations and trusted nearest neighbor representations to improve the accuracy and robustness of the model. The experimental results on two publicly available datasets show that the proposed sequential recommendation model can achieve superior results to baseline methods in both AUC and RelaImpr.


State-of-the-Art Review of the Corpus Linguistics Field From the Beginning Until the Development of ChatGPT

Abstract: The present paper highlights the recent state of and development in the corpus linguistics (CL) field. Although several reviews have been conducted on CL, these reviews have focused on specific areas, such as education, or did not provide an overall clear overview of the future implications of the field (Baker et al., 2008; Biber & Reppen, 2020; Biber et al., 1998; G. N. Leech, 1991; Mcenery et al., 2019; McEnery & Hardie, 2012). The author begins this paper with providing an overview that can guide new researchers in this field as well as postgraduates who require a general historical and thematic map of CL. The general overview discusses the publications of scholars who have participated in this field as well as the central tools that have been applied in CL. For specific details regarding the development of the field, the author analysed 217 articles from the 3 highest-impact factor journals according to the Web of Science over the last four years (2019–2022). The findings reveal a rapid development of the field in terms of practical and methodological perspectives, specifically regarding the investigations of language uses in different contexts. Thus, this paper indicates a significantly strong correlation between CL and technological development, such as natural language processing (NLP), and how this approach could fill the research gap of utilising CL in other areas of linguistics.


Student Performance in Machine Translation Training: Assessment and Exploration

Abstract: The present research aims to conduct a process-oriented analysis to measure whether a group of graduate students enrolled in a translation course made steady progress in their performance of identifying machine translation (MT) errors and post-editing MT drafts of company web texts and news texts. A mixed methods approach consisting of quantitative and qualitative analyses was used. The findings show that there was a steady decline in the average number of MT errors that students could not spot or correctly identify in their three assignments. However, there was no significant improvement in student MTPE performance, with only a slight decrease in errors in the final MTPE assignment, which still remained worse than the first one. Finally, student responses in their reflection essays indicated that their reception of MT and MTPE had shifted from negative denial to positive acceptance. Overall, the findings of the present study reveal the need to extend the period of MTPE training for students. Incorporating MT training into the translation course has proven to be worthwhile for students, as it helps to dispel students’ previous misconception about MT and MTPE.


Penggunaan Media Video & Gambar Dalam Pembelajaraan IPAS Pada Siswa Kelas 4 SDN 1 Wonoboyo

Abstract:
This research aims to explore the use of video and image media in learning Natural Sciences (IPAs) for grade 4 students at SDN 1 Wonoboyo. Visual media has become an important component in modern education, and this research investigates how the use of this media can influence students' understanding and motivation in learning sciences.
 The research results show that the use of video and image media in science learning positively influences students' understanding of the concepts being taught. Students also show higher interest and motivation to learn when visual media is used. Even though it requires more intensive preparation for teachers, the use of visual media is considered effective in explaining complex science concepts.
 This research provides insight into the importance of using visual media in improving the quality of science learning at the elementary level and suggests that teachers and schools continue to develop their skills in integrating this media in the curriculum. Video and image media have great potential in motivating students and facilitating better understanding of science material, so they can be effective tools in improving education at the elementary level.


Advancements in Machine Learning: A Comprehensive Exploration of Methods, Applications, and Future Perspectives

Abstract: Machine learning, a specialized subset of artificial intelligence, imparts the ability to machines to learn, while artificial intelligence (AI) encompasses the broader field dedicated to emulating human capabilities. Within AI, machine learning employs computational techniques to instruct computers on learning from their historical experiences. Unlike models based on predetermined equations, machine learning algorithms derive insights directly from data, progressively improving their performance as the volume of learning examples grows. This paper presents a comprehensive overview of the domain, exploring diverse machine learning methodologies such as supervised, unsupervised, and reinforcement learning, along with an examination of various programming languages employed in machine learning applications. Keywords: Machine learning, Artificial intelligence, Computational techniques, Historical experiences, Learning examples, Supervised learning, Unsupervised learning, Reinforcement learning, Programming languages, Machine learning applications


The IBdata Web System for Biological Collections: Design Focused on Usability

Abstract: The software design process must put users at the core of the process to enable them to meet their specific objectives effectively, efficiently, and successfully. Thus, a software design for a computing system to consult biological collections guided by the concept of usability will result in an effective and efficient biodiversity informatics tool. Here, we introduce IBdata, a web system to consult biological collections, developed using a design approach based on the architecture of three layers: database, business rules, and user interface. The user interface design was guided by the concept of usability focused on four core concepts: simplicity, adaptability, guide the user through the journey, and feedback. The IBdata web system that we developed is composed of three modules (query, capture and editing, and administration), permitting it to query a database with about 1.7 million specimen records. Biodiversity data query systems must be effective and efficient and should meet the user’s expectations. Software design methodologies play a central role in achieving these goals, and, in this context, interface design techniques that put the user at the core of development are valuable, as in the development of the IBdata web system.