Reviewerly/exHarmony huggingface dataset:
An Overview of a State of the Art on Developing Soft Computing-Based Language Education and Research Systems: A Survey of Engineering English Students in Uzbekistan

This manuscript presents a state of –the –art overview on developing soft computing-based intelligent systems relevant to language education and research to inspire technology-savvy teacher-researchers to create their interactive tools. In this regard, two important fields (Natural Language Processing and Data Analysis) of computer science have been considered as the main directions of that linguistic computation because of their amazing software applications like automatic writing tutors, text summarizers, and so-called corpus-based apps. The paper also outlines some ethical considerations of system usage so that system designers should devise relevant design features in the future thereby teachers overcoming some classroom challenges. Unlike previous methods of studies on computer-assisted language teaching and research, our work devises a multidisciplinary method to reach the state-of-art. Through this method, we summarize the relevant literature source from two perspectives, linguistic and computational ones. The rationale of this method by the survey of engineering English students confirms that fuzzy logic-based Natural Language Processing and Data Analysis approaches can be employed effectively in language education and research. Finally, based on the multidisciplinary literature review method, the manuscript produces an overview of state-of-the-art performance as the outcome.
Improving operational efficiency through blockchain: evidence from a field experiment in cross-border trade

Based on the practical needs of cross-border trade between Yunnan Province, China, and Southeast Asian countries, this paper studies the impact of blockchain technology usage on operational performance. Owing to immutability and traceability, the implementation of a blockchain-based platform solves several existing problems of cross-border trade. We conducted a field experiment to verify the effect of blockchain technology on the operational efficiency of cross-border trade companies. The results show that operational, agency, and time costs significantly decrease. Blockchain usage reduces the intermediary agencies of cross-border trade and settlement payment for traders, also improves the timeliness of business processing and the efficiency of cross-departmental business collaboration. This study provides new insights on the interface of blockchain technology and supply-chain operation management.
Hybrid Anomaly Detection via Multihead Dynamic Graph Attention Networks for Multivariate Time Series

In the real world, a large number of multivariate time series data are generated by Internet of Things systems, which are composed of many connected sensing devices. Therefore, it is impractical to consider only a single univariate time series for decision-making. High-dimensional time series decrease the performance of traditional anomaly detection methods. Moreover, many previously developed methods capture temporal correlations instead of spatial correlations. Therefore, it is necessary to learn the temporal and spatial correlations between different time series and timestamps. In this paper, to achieve improved anomaly detection performance for multivariate time series, we propose a novel architecture based on a graph attention network (GAT) with multihead dynamic attention (MDA). This framework simultaneously learns the dependencies between sensors in both the temporal and spatial dimensions. To tackle the overfitting problem in autoencoder (AE)-based methods, we propose a hybrid approach that combines a novel generative adversarial network (GAN) architecture as a reconstruction model with a multilayer perceptron (MLP) as a prediction-based model to detect anomalies together. The detection framework proposed in this paper is called the HAD-multihead dynamic GAT (MDGAT). Extensive experiments on different public benchmarks demonstrate the superior performance of HAD-MDGAT over state-of-the-art methods.
An ant colony optimization algorithm with evolutionary experience-guided pheromone updating strategies for multi-objective optimization

Since the multi-objective ant colony optimization algorithm consumes a massive cost of time and computation resources, improving its convergence performance is essential. This paper proposes a historical experience-guided pheromone updating approach to improve the efficiency and enhance the optimization quality, which uses a learning automata to choose suitable pheromone updating methods adaptively according to the searching history. The learning automata performs different kinds of pheromone updating schemes, including a novel intragroup evolutionary information-guided strategy and two widely accepted strategies. A comparative experiment tests the proposed algorithm on multi-objective benchmark traveling salesman problems with a high-dimensional search space, as well as the primer design problem, which is practical in the biology area. The experimental results indicate that the proposed algorithm has a competitive performance on both the benchmark problems and the practical problem.
RSNet: Relation Separation Network for Few-Shot Similar Class Recognition

Although deep learning methods have drastically improved the performance on visual recognition tasks in which large inter-class variances exist, similar-class recognition continues to pose significant challenges, mainly due to the close resemblance between similar classes. The challenge is further compounded in the case of few-shot learning because only a very small amount of training data is available; accordingly, a certain performance degradation has been observed when some few-shot methods are applied for classification tasks. To address the aforementioned issue, we propose a novel Relation Separation Network (RSNet) in this paper, aiming to boost few-shot learning by improving similar-class recognition performance. We assume that image features consist of common and private features, where the common features capture the basic attributes shared among similar classes and their private counterparts capture the unique attributes of each class. Our RSNet learns to decouple the common and private features of an image. As a result, the feature representation of an image is composed of two weakly associated but easily aligned components, and better classification performance is achieved by giving more attention to subtle features. Experimental results on the publicly available datasets miniImageNet, CUB, and CIFAR-FS show that the proposed model outperforms existing state-of-the-art methods. Specifically, compared to PT+MAP, RSNet improves the accuracy of classification on the CUB dataset by approximately 5% and that of similar-class classification by more than 10%.
A Task Category Space for User-Centric Comparative Multimedia Search Evaluations

In the last decade, user-centric video search competitions have facilitated the evolution of interactive video search systems. So far, these competitions focused on a small number of search task categories, with few attempts to change task category configurations. Based on our extensive experience with interactive video search contests, we have analyzed the spectrum of possible task categories and propose a list of individual axes that define a large space of possible task categories. Using this concept of category space, new user-centric video search competitions can be designed to benchmark video search systems from different perspectives. We further analyse the three task categories considered so far at the Video Browser Showdown and discuss possible (but sometimes challenging) shifts within the task category space.
Face Recognition Method Based on Siamese Networks Under Non-Restricted Conditions

Face recognition is an important part of computer vision, and has a vital role to play in public safety. Because of the problems of low accuracy and low efficiency in the case of uneven image quality and occlusion due to sudden changes in light in face recognition, we design a Siamese Neural Network based on Local Binary Pattern (also called LBP) and Frequency Feature Perception. The network is based on the Siamese neural network and adopts the Uniform LBP algorithm and Frequency Feature perception to achieve face recognition under non-restricted conditions. the LBP algorithm can eliminate the effect of lighting on the image and provide vector-level input to the network model; the frequency feature perception divides the image features into low-frequency features and high-frequency features, and compresses the low-frequency features in the Siamese neural network to increase the network’s recognition efficiency while exchanging information with high-frequency features to retain their feature data while eliminating target noise data. This maintains the recognition rate of the network and improves the computational speed of the network. Simulation experiments are conducted on the standard face datasets CASIA-WebFace, Yale-B, and LFW, and compared with other network models. The experimental results show that the proposed SN-LF network structure in this paper can improve the recognition accuracy of the algorithm and obtain a better recognition accuracy.
Advancing Static Code Analysis With Language-Agnostic Component Identification

Static code analysis of software systems has proven beneficial for a broad range of domains, including security assessments, coding practice, error detection, and others.However, as modern systems have grown in complexity and heterogeneity over the past few decades, advances in development frameworks have dominated.Rather than involving low-level language constructs, these frameworks typically focus on software components, including data entities, controllers, and endpoints.As a result, current code analysis approaches have become unsuitable for analyzing these modern systems due to their focus on low-level constructs in a single language.Thus, code analysis has become a far more complicated endeavor thanks to the plethora of languages, frameworks, and design approaches in modern software development.This paper presents a novel approach to solving the problem of being tied to a single language and its low-level constructs.The system's source code is transformed into an intermediate representation called a languageagnostic abstract-syntax tree.This system representation is then assessed by generalized component parsers that extract relevant high-level information, such as components, from low-level structures.The design of the approach is presented here in detail, along with its evaluation in a case study involving two large, heterogeneous, cloud-native system benchmarks (Java and C++ microservices).The study demonstrates a unified identification approach to determine system data entities and endpoints.Utilizing higher-level constructs, such as components, can advance the current practice of system analysis to better face broader problems introduced by modern system development practices.
Opinion Mining Using Topic Modeling: A Case Study of Firoozeh Dumas’s Funny in Farsi in Goodreads

The rapid growth of the Internet and the social networking services (SNS) has allowed readers from across the globe to share their thoughts and feelings about literary works through social media platforms like Twitter, Goodreads, and Facebook, to name only a few. This study aims to perform text mining techniques—opinion mining using topic modeling—to examine the range of topics that are explored within reader reviews in social media. In pursuit of this goal, the study focuses on 844 Goodreads reviews of the Iranian diasporic writer Firoozeh Dumas's Funny in Farsi to analyze the variety of topics that are covered in readers' responses. As topic modelling techniques, LDA and LSA are utilized to detect, compare and evaluate the major topics within the dataset. The optimum number of topics for both LDA and LSI is 3 with a coherence score of 0.50451438 and 0.4573542, respectively. Based on the overall performance, LDA is considered the best topic modelling method for this study.
Computational metrics and parameters of an injection-locked large area semiconductor laser for neural network computing [Invited]

Artificial neural networks have become a staple computing technique in many fields. Yet, they present fundamental differences with classical computing hardware in the way they process information. Photonic implementations of neural network architectures potentially offer fundamental advantages over their electronic counterparts in terms of speed, processing parallelism, scalability and energy efficiency. Scalable and high performance photonic neural networks (PNNs) have been demonstrated, yet they remain scarce. In this work, we study the performance of such a scalable, fully parallel and autonomous PNN based on large area vertical-cavity surface-emitting lasers (LA-VCSEL). We show how the performance varies with different physical parameters, namely, injection wavelength, injection power, and bias current. Furthermore, we link these physical parameters to the general computational measures of consistency and dimensionality. We present a general method of gauging dimensionality in high dimensional nonlinear systems subject to noise, which could be applied to many systems in the context of neuromorphic computing. Our work will inform future implementations of spatially multiplexed VCSEL PNNs.
Noncontact Electromagnetic Wireless Recognition for Prosthesis Based on Intelligent Metasurface

With the development of artificial intelligence and Internet of Things, hand gesture recognition techniques have attracted great attention owing to their excellent applications in developing human-machine interaction (HMI). Here, the authors propose a non-contact hand gesture recognition method based on intelligent metasurface. Owing to the advantage of dynamically controlling the electromagnetic (EM) focusing in the wavefront engineering, a transmissive programmable metasurface is presented to illuminate the forearm with more focusing spots and obtain comprehensive echo data, which can be processed under the machine learning technology to reach the non-contact gesture recognition with high accuracy. Compared with the traditional passive antennas, unique variations of echo coefficients resulted from near fields perturbed by finger and wrist agonist muscles can be aquired through the programmable metasurface by switching the positions of EM focusing. The authors realize the gesture recognition using support vector machine algorithm based on five individual focusing spots data and all-five-spot data. The influences of the focusing spots on the gesture recognition are analyzed through linear discriminant analysis algorithm and Fisher score. Experimental verifications prove that the proposed metasurface-based non-contact wireless design can realize the classification of hand gesture recognition with higher accuracy than traditional passive antennas, and give an HMI solution.
Designing PairBuddy—A Conversational Agent for Pair Programming

From automated customer support to virtual assistants, conversational agents have transformed everyday interactions, yet despite phenomenal progress, no agent exists for programming tasks. To understand the design space of such an agent, we prototyped PairBuddy—an interactive pair programming partner—based on research from conversational agents, software engineering, education, human-robot interactions, psychology, and artificial intelligence. We iterated PairBuddy’s design using a series of Wizard-of-Oz studies. Our pilot study of six programmers showed promising results and provided insights toward PairBuddy’s interface design. Our second study of 14 programmers was positively praised across all skill levels. PairBuddy’s active application of soft skills—adaptability, motivation, and social presence—as a navigator increased participants’ confidence and trust, while its technical skills—code contributions, just-in-time feedback, and creativity support—as a driver helped participants realize their own solutions. PairBuddy takes the first step towards an Alexa-like programming partner.
Motion-induced error reduction for phase-shifting profilometry with phase probability equalization

Phase-shifting algorithms have been widely used for three-dimensional (3D) shape measurement. However, when measuring moving objects, the object motion may distort the linear phase and lead to the phase error. This paper presents an effective phase probability equalization (PPE) method for motion-induced error reduction, which directly equalizes the distorted phase to estimate the linear phase. In general, adequate sampling pixels are needed to compute the accurate phase probability (PP). This paper further presents an improved shifted-phase probability equalization (SPE) method, which combines the original phase and its π-shifted phase to double the sampling pixels. Both simulations and experiments have been conducted to measure objects moving in z-direction, and their results validate that the proposed PPE and SPE methods could effectively compensate the motion-induced error.
TinyFedTL: Federated Transfer Learning on Ubiquitous Tiny IoT Devices

TinyML refers to the intersection of machine learning (ML), mathematical optimization, and tiny IoT embedded systems. In the current era of ubiquitous connectivity and pervasive data, TinyML has emerged as an effective method to continuously analyze real-world data without the resource overhead of traditional ML hardware. However, as valuable data around us is subjected to strict privacy and security guarantees, there is a distinct lack of ML solutions that operate under both constrained computing environments and privacy concerns.In light of these challenges, we present TinyFedTL<sup>1</sup>, the first open-sourced implementation of federated learning (FL) at the most resource-constrained level in IoT containing microcontroller unit (MCU) and small CPU based devices. Using transfer learning (TL) as a representative task, we demonstrate that privacy-centric FL on devices with a tiny memory footprint (less than 1MB) is not only possible but also effective. Researchers and engineers can use TinyFedTL to open up data across various fields that can be used to gain insights for improving quality of life or user experience without sacrificing privacy.
A hybrid feature selection scheme for high-dimensional data

There is a growing interest in developing feature subset selection schemes for high-dimensional datasets by filter, wrapper, embedded, and hybrid manners. In this paper, we propose a new hybrid (filter-wrapper) feature selection approach. At first, in the filter step, we rank input features according to their relevance with the class label. Afterwards, we apply different clustering methods for the classification of the selected features. We perform an inner and outer cluster ranking based on the primary feature ranking in the next step. Then, different search strategies are performed on the best cluster of features in the wrapper phase. Moreover, we add some of them to the feature set based on the classifiers (nearest neighbor, decision tree, support vector machine, and random forests) feedback. Then, the algorithm goes to the next cluster, and this process is continued till all clusters are met. Finally, we compare the results of the proposed method to the state-of-the-art schemes. Comparison results imply the superiority of the proposed method to the counterparts on eight high-dimensional datasets in terms of accuracy and computational complexity.
PathAL: An Active Learning Framework for Histopathology Image Analysis

Deep neural networks, in particular convolutional networks, have rapidly become a popular choice for analyzing histopathology images. However, training these models relies heavily on a large number of samples manually annotated by experts, which is cumbersome and expensive. In addition, it is difficult to obtain a perfect set of labels due to the variability between expert annotations. This paper presents a novel active learning (AL) framework for histopathology image analysis, named PathAL. To reduce the required number of expert annotations, PathAL selects two groups of unlabeled data in each training iteration: one "informative" sample that requires additional expert annotation, and one "confident predictive" sample that is automatically added to the training set using the model's pseudo-labels. To reduce the impact of the noisy-labeled samples in the training set, PathAL systematically identifies noisy samples and excludes them to improve the generalization of the model. Our model advances the existing AL method for medical image analysis in two ways. First, we present a selection strategy to improve classification performance with fewer manual annotations. Unlike traditional methods focusing only on finding the most uncertain samples with low prediction confidence, we discover a large number of high confidence samples from the unlabeled set and automatically add them for training with assigned pseudo-labels. Second, we design a method to distinguish between noisy samples and hard samples using a heuristic approach. We exclude the noisy samples while preserving the hard samples to improve model performance. Extensive experiments demonstrate that our proposed PathAL framework achieves promising results on a prostate cancer Gleason grading task, obtaining similar performance with 40% fewer annotations compared to the fully supervised learning scenario. An ablation study is provided to analyze the effectiveness of each component in PathAL, and a pathologist reader study is conducted to validate our proposed algorithm.
A Hybrid Recommendation System of Upcoming Movies Using Sentiment Analysis of YouTube Trailer Reviews

Movies are one of the integral components of our everyday entertainment. In today’s world, people prefer to watch movies on their personal devices. Many movies are available on all popular Over the Top (OTT) platforms. Multiple new movies are released onto these platforms every day. The recommendation system is beneficial for guiding the user to a choice from among the overloaded contents. Most of the research on these recommendation systems has been conducted based on existing movies. We need a recommendation system for forthcoming movies in order to help viewers make a personalized decision regarding which upcoming new movies to watch. In this article, we have proposed a framework combining sentiment analysis and a hybrid recommendation system for recommending movies that are not yet released, but the trailer has been released. In the first module, we extracted comments about the movie trailer from the official YouTube channel for Netflix, computed the overall sentiment, and predicted the rating of the upcoming movies. Next, in the second module, our proposed hybrid recommendation system produced a list of preferred upcoming movies for individual users. In the third module, we finally were able to offer recommendations regarding potentially popular forthcoming movies to the user, according to their personal preferences. This method fuses the predicted rating and preferred list of upcoming movies from modules one and two. This study used publicly available data from The Movie Database (TMDb). We also created a dataset of new movies by randomly selecting a list of one hundred movies released between 2020 and 2021 on Netflix. Our experimental results established that the predicted rating of unreleased movies had the lowest error. Additionally, we showed that the proposed hybrid recommendation system recommends movies according to the user’s preferences and potentially promising forthcoming movies.