Reviewerly/exHarmony huggingface dataset:
Adaptive Bayesian variable clustering via structural learning of breast cancer data

The clustering of proteins is of interest in cancer cell biology. This article proposes a hierarchical Bayesian model for protein (variable) clustering hinging on correlation structure. Starting from a multivariate normal likelihood, we enforce the clustering through prior modeling using angle-based unconstrained reparameterization of correlations and assume a truncated Poisson distribution (to penalize a large number of clusters) as prior on the number of clusters. The posterior distributions of the parameters are not in explicit form and we use a reversible jump Markov chain Monte Carlo based technique is used to simulate the parameters from the posteriors. The end products of the proposed method are estimated cluster configuration of the proteins (variables) along with the number of clusters. The Bayesian method is flexible enough to cluster the proteins as well as estimate the number of clusters. The performance of the proposed method has been substantiated with extensive simulation studies and one protein expression data with a hereditary disposition in breast cancer where the proteins are coming from different pathways.
Multi-task Deep-Learning Vehicle Detection and Tracking based on Aerial Views from UAV

For vehicle management systems, vehicle detection, tracking, and recognition which provides statistical information on the number of vehicles and their characters are essential task, however, not only the vehicles themselves but also its characteristic such as types, and colors,.. is important for management. Besides, not only fixed traffic cameras, autonomous UAVs with cameras also can bring more flexibility and extend the management areas from an aerial view. In this paper, we propose and implement a real-time multi-task deep-learning for four-wheel vehicle detection, classification, and tracking system on UAVs. A dataset for detection and multi-task classification including multiple colors and types is built. To archive real-time the operation, we used the ByteTrack algorithm with YOLOv5, which is more efficient for mobile and embedded vision applications. Experimental results achieved high accuracy, more than 90% in multi-task classifying, and 56.21 HOTA tracking evaluation metrics on our created test set. The processing time is fast enough for real-time operation, 14 FPS for the detector, and 64 FPS for classification based on an embedded computer, Jetson Nano.
Design of Retransmission Mechanism for Decentralized Inference with Graph Neural Networks

Graph neural network (GNN) is widely applied in various fields, especially for graph data. Moreover, it is an effective technique for decentralized inference tasks, where information exchange among neighbors relies on wireless communications. However, wireless channel impairments and noise decrease the accuracy of prediction. To remedy imperfect wireless transmission and enhance the prediction robustness, we propose a novel retransmission mechanism with adaptive modulation that could select the appropriate modulation order adaptively for each retransmission. Compared to the traditional method that determines the modulation order based on bit error ratio (BER), we bring in a new indicator called robust prediction to select an appropriate modulation order for each transmission. Under the requirement of prediction robustness, the error-tolerance of GNNs is exploited and a higher modulation order can be used in the proposed mechanism compared with the traditional method, thus reducing the communication overhead and improving the data rate. Meanwhile, we combine the signals of different retransmission with the soft-bit maximum ratio combine (SBMRC) technique. Simulation results verify the effectiveness of the proposed retransmission mechanism.
Planning the optimal trajectory for a dual-arm robot system using a genetic algorithm considering the controller

Optimal trajectory planning is a problem that needs to be solved in robotic applications. The trajectory is optimally designed in terms of travel time at the same time must satisfy different constraints such as limited torque, range of movement of joints in the workspace, and velocity of joints. This paper presents the optimal trajectory planning of the object in the dual-arm system cooperative movement of the object using a genetic algorithm, the genetic algorithm determines the minimum time motion of the object and then builds the optimal trajectory. Being different from previous research, the reference trajectory is assumed to be the same as the real one. Therefore, the torque at the joints is calculated by using the inverse kinematic and dynamic of the dual-arm robot system. The paper proposes to add a controller when planning optimal trajectory, the torque at the robot joints is calculated from the output of the controller. This design ensures similarity between the design trajectory and the actual implementation. Finally, simulation on Matlab-Simulink with different types of orbits has proved the feasibility of the proposed solution.
Analysis of HOTS Instrument for Prospective Physics Teacher Using Generalized Partial Credit Model

This study aims to analyze item parameters' characteristics and estimate prospective physics teachers' ability on the Higher Order Thinking Skills (HOTS) Instrument using the Generalized Partial Credit Model (GPCM). The research subjects were 251 prospective physics teacher students in two universities concerned with producing prospective teacher graduates. Two-Tier Multiple Choice (TTMC) forms with a polytomous score of four categories make up the test instrument. Data analysis includes two stages: testing assumptions and the suitability of the Politomus IRT model. The results showed that the most suitable polytomous scoring IRT model was GPCM2PL. The item parameter analysis for the HOTS Instrument test shows the discriminating power parameter (a) value for all items included in the good category, namely the interval 0.00 to 2.00. The difficulty level analysis (b) also shows the percentage of 100% of items included in the medium category because the b value of all items is in the interval (-2) to (2).
Fine-grained Image Recognition Method using Discriminative Region-based Data Augmentation

In order to reduce the complexity of the network model and improve the accuracy of image recognition, a fine-grained image recognition method using discriminative region-based data augmentation is proposed. The method obtains the discriminative regions of the image through the attention mechanism, and then performs diversity data augmentation based on the discriminative regions, including region crop, region drop and region mix, and then uses the generated augmented samples to train the network model, the backbone network of the model is ResNet50. The proposed method is tested on 4 commonly used fine-grained image datasets CUB-200-2011, Stanford Cars, FGVC Aircraft and Stanford Dogs, and achieves high accuracy. The experimental results show that the proposed method can improve the localization ability and feature extraction ability of the model for discriminative regions, and it is more lightweight and easier to implement.
Modeling and Analysis of Discrete Facial Expressions with Dense Optical Flow-derived Features

Facial expressions (FEs) are one of the most preeminent means of conveying one’s emotions and are pivotal to nonverbal communication. Potential applications in a wide range of areas in computer vision have lent a strong impetus to research in the domain of automatic facial expression recognition. This work discusses the effectiveness of two optical flow-based features for modeling the FEs associated with prototypic emotions based on the pattern of nonrigid deformable motion of facial components occurring during their portrayal. The discernible motion patterns are categorized into distinct discrete classes with the descriptive features indicating the global spatial distribution of deformation derived from the dense optical flow field associated with emotional and neutral face images. Results obtained with evaluation on images and video clips taken from Extended Cohn-Kanade, Japanese Female Facial Expressions, and Dynamic Karolinska Directed Emotional Faces datasets with multi-class support vector machine and [Formula: see text]-nearest neighbor classifiers are competent with the state-of-the-art techniques and concordant with empirical psychological studies in emotion science.
Sketch face recognition based on domain adaptation scaled entropy meta-network

In recent years, sketch face recognition has a wide application in law enforcement agencies and criminals. Deep learning plays a crucial role in the recent developments of face recognition, however, it is challenging to employ deep learning methods for sketch face recognition due to insufficient face photo–sketch data. Moreover, compared to photos, sketches lack detailed texture, and there exists a domain gap between photos and sketches, hence, traditional homogeneous face recognition methods perform poorly in sketch face recognition. In this paper, a novel deep learning method termed Domain Adaptation Scaled Entropy Meta-Network (DASEMN) is proposed to tackle sketch face recognition tasks. Specifically, a meta-learning training strategy is designed to tackle the few-shot problem and improve the generalization ability of the network. Then, a generalized entropy loss termed scaled mean entropy loss is proposed to guide the network to extract discriminate features. Finally, a domain adaptation module is introduced in the training set to reduce the domain gap between the sketch domain and the photo domain. Experiments on UoM-SGFS and CUFSF sketch face databases show that the proposed method is superior to other sketch face recognition methods.
Digital Public Archaeology with the ArchaeoTrail App

Archaeological sites offer valuable insights into the past. Without a good presentation, however, it is difficult to communicate this knowledge to the general public. The newly developed ArchaeoTrail App does just this and offers the opportunity for visitors to discover the archaeological sites on their own.
Optimal planning of a quantum key distribution system

This work presents a novel formulation for the optimal planning of a quantum key distribution system in a multi-objective optimization context. Furthermore, a mathematical formulation of the problem is proposed followed by simulations with 5, 12, and 14 nodes using the National Science Foundation Network (NSFNET) as a test network to illustrate the resolution of the problem. The following objective functions were considered: Total cost (f1); number of links required (f2); total distance (f3); maximum distance (f4); load balancing (f5); and variance (f6). After presenting experimental results, the paper is concluded by outlining research proposals that would improve this work.
Data Visualization for High-Throughput Experimentation

Data visualization is an easily-overlooked part of high-throughput experimentation (HTE): part chemistry, part psychology, but crucial for sharing the experimental results. Despite the importance of data visualization, knowledge in this area is often built through word of mouth and hands-on experience, rather than formal guidelines. To begin to remedy this situation, this chapter describes best practices and recommendations.
Robust adaptive multi‐target tracking with unknown heavy‐tailed noise

In multi-target tracking, non-Gaussian heavy-tailed process noise (PN) and measurement noise (MN) are introduced by unknown manoeuvring and noise-corrupted measurements. This study proposes a Gaussian approximation approach based on multivariate Student-t distribution, which is designed to characterise non-Gaussian heavy-tailed MN covariance and PN covariance. The variational Bayesian approach is applied to a generalised labelled multi-Bernoulli (GLMB) with an augmented state, and a robust adaptive generalised labelled multi-Bernoulli (RAGLMB) framework is derived to recursively propagate the joint posterior density of noise covariance and target state. The simulation results indicate that the proposed RAGLMB filter is robust to targets affected by non-Gaussian heavy-tailed PN and MN.
Multi-instance embedding learning with deconfounded instance-level prediction

Confounded information is an objective fact when using multi-instance learning (MIL) to classify bags of instances, which may be inherited by MIL embedding methods and lead to questionable bag label prediction. To respond to this problem, we propose the multi-instance embedding learning with deconfounded instance-level prediction algorithm. Unlike traditional embedding-based strategies, we design a deconfounded optimization goal to maximize the distinction between instances in positive and negative bags. In addition, we present and use bag-level embedding with feature distillation to reduce the MIL classification task to a single-instance learning problem. Under the theoretical analysis, the embedding cohesiveness and feature magnitude metrics are developed to explain the benefits of the proposed deconfounded technique in MIL settings. Extensive experiments on thirty-four data sets demonstrate that our proposed method has the best overall performance over other state-of-the-art MIL methods. This strategy, in particular, has a substantial advantage on web data sets. Source codes are available at https://github.com/InkiInki/MEDI .
Printing Utopia: The Domain of the 3D Printer in the Making of Commons-Based Futures

The 3D printer is a “projection screen” for the eco-social maker movement. It signifies a desire for networked collaboration, ecological and social participation, political empowerment, and socioeconomic transformation. Yet the 3D printer is not producing anything that fulfils such a comprehensive and disruptive potential. While it has become a profound agent for a commons-based future that aims to solve the global challenges of modernity, it is a tool, rather than an agent of the maker movement. This article explores the utopian potential of the 3D printer within the discourse of commons-based future-making. Along with a wide range of academic and popular literature, the sociotechnical motives of a commons-based imaginary are analyzed and discussed in their historical construction and social order. Indeed, the 3D printer revitalizes longstanding desires for social transformation, giving them fresh impetus. Because of its interpretative flexibility, the 3D printer has become a “weak desire machine” that allows members of the maker movement to express their utopian desires. On the one hand, the 3D printer helps make utopian desires tangible and negotiable. On the other hand, the 3D printer tends to promote a techno-positivist approach that oversimplifies social change, losing sight of alternatives and ambiguities.
Handling Cold-Start Problem in Restaurant Recommender System Using Ontology

In today’s data-driven world, users are often overwhelmed by the vast amount of data available on the internet. Recommender systems provide users an efficient way to sort through and filter massive amounts of data, by considering their preferences or past history. It has become a ubiquitous tool utilized by several e-commerce companies to direct customers toward products or services they are more likely to purchase or use. It benefits the customers as well, as it saves them the time and effort to sift through endless amounts of information. Since recommender systems require data to give recommendations, many of them face the cold start problem, where the recommender systems do not have adequate information to provide relevant recommendations. Ontology-based recommender systems are popular among researcher as they do not rely on past historical data. In this paper, we create an ontology-based recommendation system to provide restaurant recommendations based on user preferences such as location, cuisine, etc. The user will be able to input the desired preferences and the appropriate recommendation, i.e., the restaurant name, will be fetched. The hierarchical relationships between the different concepts are defined by the ontology which we have created. The performance of the proposed system will be evaluated by standard metrics.
Design of Information System for Educational Organization

The official website of educational organization currently must comply with the requirements established by a number of federal regulations. The identified shortcomings of the software offered by the market, including the high cost of implementation and maintenance, the complexity of integration with existing software products and information systems in the organization, led to the need to create its own modular information system of the university. The article presents the structure of the designed information system and the stack-technology for its implementation. The application of free software allowed to reduce the costs of development, implementation and operation of the proposed system. The approach to the design of the information system described by the authors is shown on the example of the “Educational programs” module, which made it possible to systematize information flows for the collection, storage, analysis of educational and methodological documentation and the formation of reporting forms. Automation of the information flows of the university has simplified the work of accounting for the scientific, educational and methodological activities of the teaching staff and the educational activities of students. The designed information system meets the requirements for the official website of the educational organization.
A transformer fine-tuning strategy for text dialect identification

Online medical consultation can significantly improve the efficiency of primary health care. Recently, many online medical question–answer services have been developed that connect the patients with relevant medical consultants based on their questions. Considering the linguistic variety in their question, social background identification of patients can improve the referral system by selecting a medical consultant with a similar social origin for efficient communication. This paper has proposed a novel fine-tuning strategy for the pre-trained transformers to identify the social origin of text authors. When fused with the existing adapter model, the proposed methods achieve an overall accuracy of 53.96% for the Arabic dialect identification task on the Nuanced Arabic Dialect Identification (NADI) dataset. The overall accuracy is 0.54% higher than the previous best for the same dataset, which establishes the utility of custom fine-tuning strategies for pre-trained transformer models.
Digital Learning Tools and Devices for the Implementation of an Electronic Educational Resource

The combination of digital tools and services considered in this article, as a means for the implementation of an electronic educational resource, provides the educational process with a variety of unique methods that affect the motivation and cognitive interest of students. We presented a classification of digital services with an indication of a brief characteristic of the direct purpose of the tool. The material of the article is based on the experience of teachers of Mathematics and Informatics Department of Naberezhnye Chelny State Pedagogical University (Russian Federation). The example of the Zoom service, designed for video conferencing, is used to examine the technology of organizing a training lesson for students on a specific topic of a geometry lesson. On the basis of the digital tool LearningApps and other similar services there are demonstrated methods of organizing didactic material within the framework of a training session. The practical part of the article gives some recommendations for working in virtual environments when developing interactive tasks. Recommendations for the organization of e-learning and application of distance learning technologies are focused on a user who implements an e-learning course in accordance with the academic subject. The use of various digital services and tools allows us to minimize the labor costs of educational organizations and provides a sufficient range of new opportunities to increase the level of information competence of a teacher.
The Effect of Visual Information Contained in Digital Training Courses on the Renewal of Working Memory

Digital technologies in education are gradually replacing traditional teaching methods. Working memory is actively used in digital learning, which leads to cognitive overload and reduced performance. Scientists are looking for technologies that speed up the renewal of working memory. The purpose of this study was to compare the updating of visual information in working memory depending on its affective dimension and familiarity. Familiar affective images were expected to be updated more efficiently than unfamiliar neutral ones. To assess the effectiveness of updating working memory, an affective version of the “n-back” task was used, where positive, negative and neutral images acted as stimuli. The study involved 111 people. The experiment was conducted in a remote format on the computers of the subjects without control by the experimenter. Significant differences were found in the proportion of false alarms, depending on the familiarity of the information, and close to significant differences in the reaction rate. The obtained results did not prove the significance of the influence of the affective dimension factor, as well as the interaction of the affective dimension and familiarity factors on the renewal of working memory.
Uncertainty principles and applications of quaternion windowed linear canonical transform

Quaternion signal processing is frequently used in color image processing. The quaternion windowed linear canonical transform (QWLCT), a generalization of the windowed linear canonical transform (WLCT), has a wide range of application domains, including signal processing and optics. In this paper, we study QWLCT-based characterization range, reproducing kernel, one-one map, Donoh stark inequality, and Pitt’s inequality. Some useful uncertainty principles (UP) like Heisenberg UP in the mixed norm, Lieb UP, and local UP are discussed. Moreover, some applications associated with QWLCT in linear time-varying (TV) systems are explained in detail.
Digital Simulators and Training Devices for Solving Geometric Problems

Advances in digital technologies give the opportunity to create a variety of simulators that imitate real objects in a virtual environment, and thereby help to reduce the labor and time spent on solving professional problems. This also applies to the modern educational system, where there has emerged a trend to introduce digital educational and methodological complexes, educational simulators, training devices and virtual laboratories. Their application has a significant impact on all aspects of educational strategy, methods of solving pedagogical problems. The article describes an experiment on the implementation of an authentic digital simulator for solving geometric problems and a simulator of pedagogical activity in the process of training future mathematics teachers. We present here a constructive model of the proposed digital simulator, with its plot composition based on the formation of a strategy for finding solutions to geometric problems being described, and reveal the technology of creating a simulator. The experiment was carried out at Naberezhnye Chelny State Pedagogical University (Russia). Its aim was to monitor the formed competencies of mathematics teachers, as well as to study if student trainees are prepared to learn a strategy for solving mathematical problems. We present here an assessment of the implementation of the developed simulator on the basis of statistical processing of the experimental results using the apparatus of statistical analysis.