Reviewerly/exHarmony huggingface dataset:
Face-GCN: A Graph Convolutional Network for 3D Dynamic Face Recognition

Face recognition has significantly advanced over the past years. However, most of the proposed approaches rely on static RGB frames and on neutral facial expressions. This has two disadvantages. First, important facial shape cues are ignored. Second, facial deformations due to expressions can have an impact in the performance of such a method. In this paper, we propose a novel framework for dynamic 3D face recognition based on facial keypoints. Each dynamic sequence of facial expressions is represented as a spatio-temporal graph, which is constructed using 3D facial landmarks. Each graph node contains local shape and texture features that are extracted from its neighborhood. For the classification of face videos, a Spatio-temporal Graph Convolutional Network (ST-GCN) is used. Finally, we evaluate our approach on a challenging dynamic 3D facial expression dataset.
Impact of Social Media Technology in The Development of Igbo Langauge Among University Students: A Study of Imo State University Students

The paper studied the potential of using social media in the teaching and learning of Igbo language among linguistics students of Imo State University, Owerri. Igbo language is native to Igbo race of Nigeria. Descriptive method, involving questionnaires, were distributed to 140 Igbo linguistics students in the university. Data collected were analysed using SPSS package into percentages and means scores. Results showed students are aware of the existence of social media. Students strongly agreed that social media can be used in the teaching and learning Igbo. But factors, such as cost and poor internet connectivity, unavailability of Igbo modules, and lack of computer sets with Igbo vowels are limiting factors. Therefore, the university can provide free internet services for students. Igbo Modules can be prepared uploaded to the internet. Computer sets and keyboards with Igbo vowels can be manufactured.
A Lightweight Method for Vehicle Classification Based on Improved Binarized Convolutional Neural Network

Vehicle classification is an important part of intelligent transportation. Owing to the development of deep learning, better vehicle classification can be achieved compared to traditional methods. Contemporary deep network models have huge computational scales and require a large number of parameters. Binarized convolutional neural networks (CNNs) can effectively reduce model computational size and the number of parameters. Most contemporary lightweight networks are binarized directly on a full-precision model, leading to shortcomings such as gradient mismatch or serious accuracy degradation. To addresses the inherent defects of binarization networks, herein, we adjust and improve residual blocks and propose a new pooling method, which is called absolute value maximum pooling (Abs-MaxPooling). The information entropy after weight binary quantization is used to propose a weight distribution binary quantization method. A binarized CNN-based vehicle classification model is constructed, and the weights and activation values of the model are quantified to 1 bit, which saves data storage space and improves classification accuracy. The proposed binarized model performs well on the BIT-Vehicle dataset and outperforms some full-precision models.
Gradient dynamics in reinforcement learning

Despite the success achieved by the analysis of supervised learning algorithms in the framework of statistical mechanics, reinforcement learning has remained largely untouched by physicists. Here we move towards closing the gap by analyzing the dynamics of the policy gradient algorithm. For a convex problem, namely the k-armed bandit, we show that the learning dynamics obeys a drift-diffusion motion described by a Langevin equation, the coefficients of which can be tuned by the learning rate. We explore the striking similarity between our Langevin equation and the Kimura equation, describing genotypes evolution. Furthermore, we propose a mapping between a nonconvex reinforcement learning setting describing multiple joints of a robotic arm and a disordered system, namely a p-spin glass. This mapping enables us to show how the learning rate acts as an effective temperature and thus is capable of smoothing rough landscapes, corroborating what is displayed by the drift-diffusive description and paving the way for physics-inspired algorithmic optimization based on annealing procedures in disordered systems.
Real Time Sign Language Recognition System for Hearing and Speech Impaired People

Abstract: Sign Language is globally used by more than 70 million impaired people to communicate and is characterized by fast, highly articulate motion of hand gesture which is difficult for verbal speakers to understand. This limitation combined with the lack of knowledge about sign language by verbal speakers creates a separation where both parties are unable to effectively communicate, to overcome this limitation we propose a new method for sign language recognition using OpenCV (A python library) which is used for pre-processing images and extracting different skin toned hands from the background. In this method hand gesture are used to make signs which are detected by YOLOv5 algorithm for object detection which is the fastest algorithm till date while Convolutional-Neural-Networks (CNN) are used for training gesture and to classify the images, and further we proposed a system which translates speech into sign language so that the words of the verbal speaker can be transmitted to the deaf/mute. This automated system first detects speech using the JavaScript Web-Speech API and converts it into text because the recognized text is processed using the Natural Language Toolkit and aligns token text with the sign language library (sign language videos) videos according to well-known text and finally shows a compiled output which is displayed through avatar animation for a deaf / dumb person. The proposed system has various advantages like Portability, User-friendly Interface and Voice Module. The software is also very cost-effective which only needs a laptop camera or webcam and hand gesture, system accuracy is compared to high-quality methods and is found to be the best. Keywords: Sign language, Hand gesture, OpenCV, Python library, Image processing, YOLOv5, Object detection, Pose estimation, Machine learning
Web APIs: Features, Issues, and Expectations – A Large-Scale Empirical Study of Web APIs From Two Publicly Accessible Registries Using Stack Overflow and a User Survey

With the increasing adoption of services-oriented computing and cloud computing technologies, web APIs have become the fundamental building blocks for constructing software applications. Web APIs are developed and published on the internet. The functionality of web APIs can be used to facilitate the development of software applications. There are numerous studies on retrieving and recommending candidate web APIs based on user requirements from a large set of web APIs. However, there are very limited studies on the features of web APIs that make them more likely to be used and the issues of using web APIs in practice. Moreover, users’ expectations on the development and management of web APIs are rarely investigated. In this paper, we conduct a large-scale empirical study of 20,047 web APIs published at two popular and publicly accessible web API registries: ProgrammableWeb and APIs.guru. We first extract the questions posted in Stack Overflow (SO) that are relevant to the web APIs. We then manually analyze 1,885 randomly sampled SO questions and identify 24 web API issue types (e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">authorization error</i> ) that are encountered by users. Afterwards, we conduct a user survey to investigate the features of web APIs that users often consider when shortlisting a web API for testing before they adopt it, validate the identified types of web API issues, and understand users’ expectations on the development and management of web APIs. From the 191 received responses, we extract 14 important features for users to decide whether to use a web API (e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">well-organized documentation</i> ). We also gain a better understanding of web API issue types and summarize 11 categories of user expectations on web APIs (e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">documentation</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SDK/library</i> ). As the result of our study, we provide guidelines for web API developers and registry managers to improve web APIs and promote the use of web APIs.
Arbitrary-Shape Scene Text Detection via Visual-Relational Rectification and Contour Approximation

One trend in the latest bottom-up approaches for arbitrary-shape scene text detection is to determine the links between text segments using Graph Convolutional Networks (GCNs). However, the performance of these bottom-up methods is still inferior to that of state-of-the-art top-down methods even with the help of GCNs. We argue that a cause of this is that bottom-up methods fail to make proper use of visual-relational features, which results in accumulated false detection, as well as the error-prone route-finding used for grouping text segments. In this paper, we improve classic bottom-up text detection frameworks by fusing the visual-relational features of text with two effective false positive/negative suppression (FPNS) mechanisms and developing a new shape-approximation strategy. First, dense overlapping text segments depicting the “characterness” and “streamline” properties of text are constructed and used in weakly supervised node classification to filter the falsely detected text segments. Then, relational features and visual features of text segments are fused with a novel Location-Aware Transfer (LAT) module and Fuse Decoding (FD) module to jointly rectify the detected text segments. Finally, a novel multiple-text-map-aware contour-approximation strategy is developed based on the rectified text segments, instead of the error-prone route-finding process, to generate the final contour of the detected text. Experiments conducted on five benchmark datasets demonstrate that our method outperforms the state-of-the-art performance when embedded in a classic text detection framework, which revitalizes the strengths of bottom-up methods.
Higher education in the mirror of challenges and modern alternatives

The growing role of education in society has actually begun since the division of labor and continues to this day. The highest GDP growth rates in the second half of the twentieth century were due to the development and impact of higher education. Nevertheless, there are growing concerns in the expert community about the controversial development of education in general and the higher education segment in particular; saying that the modern higher education institution has almost lost its influence on the formation of not only society but itself, and the higher education institution itself is transformed into obedient object of external regulations. This feature contains not so much advantages as disadvantages, as «harmonious interaction» with the megatrends of today is achieved through the loss of precious capital of critical and systematic thinking, as well as intellectual responsibility and civic position. This factor forms a grand conceptual and praxeological problem, the interpretation of which naturally claims the status of scientific novelty. The phenomenon of the university is analyzed through the prism of its historical dynamics and role. Particular attention is paid to the qualitative characteristics and virtues due to which the university has acquired a respectable status, hoping to maintain these positions in the future. The leadership reputation of a modern university is reflected: what is the basis for the leadership of the university, what is necessary and sufficient for the leadership status quo, which features of leadership are meaningful, and which are purely formal, stereotypical and inadequate. The university is understood as a model for building human capital: whether and to what extent modern universities meet these requirements and what stands in the way of optimizing the current status quo.
Data Mining of Digital Footprint during Assessment Grading for Intelligent Decision Making during Learning Process

The paper describes the problems of generation and modeling of learning assessments, their grading, and analyzing the process of their completion by learners using data mining methods. The authors propose to enhance the digital footprint of learner’s interactions with e-assessment systems with the information allowing determining demonstrated level competence development and the causes of learners’ mistakes and hypo. The learner’s level of competence development can be evaluated using expert estimates and the Certainty Factor. Generating assessments and determining the causes of learner’s mistakes can be done using ontological models of subject domains, built on the comprehension level of Bloom’s taxonomy. The paper explores the properties of such models. Examples of applying the proposed approaches to intelligent decision making during the learning process of different subject domains are shown. This leads to formulating the requirements for subject domain models for intelligent tutoring systems
Detection of traffic congestion based on twitter using convolutional neural network model

&lt;span lang="EN-US"&gt;Microblogging is a form of communication between users to socialize by describing the state of events in real-time. Twitter is a platform for microblogging. Indonesia is one of the countries with the largest Twitter users, people can share information about traffic jams. This research aims to detect traffic jams by extracting tweets in the form of vectors and then inserting them into the Convolution neural network (CNN) model and getting the best model from CNN+Word2Vec, CNN+FastText, and support vector machine (SVM). Data retrieval was conducted using the Rapidminer application. Then, the context of the tweets was checked so that there were 2777 data consisting of 1426 congestion road data and 1351 smooth road data. The data was taken from certain coordinate points in around Jakarta, Indonesia. Then, preprocessing and changes to vector form were carried out using the Word2Vec and FastText methods, then inserted into the CNN model. The results of CNN+Word2Vec and CNN+FastText were compared to the SVM method. The evaluation was done manually using the actual traffic conditions. The highest result obtained using test data by the CNN+FastText method are 86.33% while CNN+Word2Vec is 85.79% and SVM is 67.62%.&lt;/span&gt;
Game innovation: a case study using the Kizzugemu visual novel game with Tyranobuilder software in elementary school

This research aims to find out the innovation of the Kizzugemu visual novel game with TyranoBuilder software in elementary school Javanese language learning. The lack of game media innovation in elementary school Javanese language learning is the background of this research. The research method is descriptive and qualitative, with data mining techniques; observation, indepth interviews, and documentation that present the results of descriptions of the innovation, features, application, and impact of using the game in elementary Javanese language learning. The research subjects were the class teacher and 24 fifth-grade students of Madrasah Ibtidaiyah Ma’arif Bulurejo Magelang, Indonesia. The results showed the game’s innovation in multimedia-based game updates, applications, videos, sounds, and texts with the meaning of children’s games in Javanese, Javanese alphabet, Indonesian, and English. Game features are in the form of reading text in each scene and visualized in the material of the Javanese language. The implementation of the game is carried out through five stages which impact teacher awareness, learning media innovation, improved learning outcomes, activeness, understanding of Javanese, and support from parents and schools. Future research needs to explore visual novel game innovation through the latest software.
Error controlled actor-critic

The approximation inaccuracy of the value function in reinforcement learning (RL) algorithms unavoidably leads to an overestimation phenomenon, which has negative effects on the convergence of the algorithms. To limit the negative effects of the approximation error, we propose error controlled actor-critic (ECAC) which ensures the approximation error is limited within the value function. We present an investigation of how approximation inaccuracy can impair the optimization process of actor-critic approaches. In addition, we derive an upper bound for the approximation error of the Q function approximator and discover that the error can be reduced by limiting the KL- divergence between every two consecutive policies during policy training. Experiments on a variety of continuous control tasks demonstrate that the proposed actor-critic approach decreases approximation error and outperforms previous model-free RL algorithms by a significant margin.
Silicon photonic secure communication using artificial neural network

Optical chaotic secure communication based on silicon photonic microcavities has the advantages of high integration capability and natural compatibility with complementary metal oxide semiconductor (CMOS) technology. To overcome the difficulties of physical device fabrication and parameter matching, we propose a hybrid scheme that combines silicon photonic microcavity (SPM) chaos and artificial neural networks (ANNs) to achieve chaotic secure communication, which could significantly enhance the feasibility of silicon photonic secure communication. We trained three typical ANNs: multilayer perceptron (MLP), long short-term memory network (LSTM), and co`nvolutional neural network (CNN). Compared with the traditional Runge-Kutta-4 function, the prediction accuracy of three networks is greatly improved. All the networks provide an R 2 of 99.9 %, with root-mean-square errors (RMSEs) and mean-absolute errors (MAEs) below 10 −4 . Furthermore, by loading non-return-to-zero (NRZ) information and using CNN to synchronise SPM chaos, 5 Mb/s, 10 Mb/s, and 15 Mb/s NRZ encryption and decryption are all realised. This can provide synchronisation coefficients of 99.8 % and clear decrypted eye diagrams. Additionally, return-to-zero (RZ), non-return-to-zero-inverted (NRZI) information are further demonstrated and a FPGA-based CNN design is given, proving the generality of CNN decryption scheme. These results could provide a useful foundation for advancing physical and secure communication with silicon photonics chips and ANNs.
Short‐term photovoltaic power prediction based on MDCM‐GA‐LSTM model

In recent years, photo-voltaic (PV) system has become one of the most potential renewable energy power generation technologies because of its many advantages. Considering the influence of the randomness of PV system on the operation and dispatching of power system, the necessity of a comprehensive forecasting model is increased rapidly. This paper proposes a short-term PV power forecasting method based on MDCM-GA-LSTM model to solve the problem of low accuracy of traditional and single forecasting model. First, the meteorological data are pre-processed based on wavelet denoising algorithm to improve the data quality. Second, a meteorological model is established based on genetic algorithm (GA) to optimize long short-term memory (LSTM) neural network. Finally, the network is trained with the modified data of the meteorological model as the input and the actual power as the output, and the power forecasting model is established. In this study, the forecasting accuracy is evaluated based on mean absolute error (MAE) and root mean square error (RMSE). The experimental results demonstrated that the MDCM-GA-LSTM model outperforms the conventional and single model by a difference of about 98.43% of the MAE and 98.97% of the RMSE error.
Optimization of Industrial Cluster Management in Urban Development Zones Driven by Big Data

This paper analyzes the optimization of industrial cluster management in urban development zones driven by Big Data. This paper introduces model calculation to improve management optimization, analyzes the industrial clusters of urban development zones driven by Big Data through the model, and verifies the driving role of industrial cluster management optimization of urban development zones driven by Big Data with the help of empirical analysis. According to the experimental data, the total assets of target task decomposition increased from 20 to 200, the net assets increased from 13 to 110, and the debt ratio decreased year by year from 2017 to 2020. This paper analyzes the problems existing in the management of Liling Economic Development Zone and investigates the reasons, which are mainly caused by the following reasons: the inaccurate positioning of management functions has scattered the energy of park management and weakened the initiative and enthusiasm of work; affected by the changes of national policies, the work efficiency is reduced and the economic cost is increased; resources and energy have a great impact and are facing transformation and upgrading; the degree of marketization of the park is not high, which is difficult to adapt to the development situation. At the same time, by summarizing the advantages of Big Data, the research on the optimization of industrial cluster management in urban development zones driven by Big Data is realized. Driven by Big Data, the industrial cluster management of urban development zones has strong efficiency in economic promotion. We should actively promote this management optimization method, which can promote the improvement of economic construction level.
Interpreting Universal Adversarial Example Attacks on Image Classification Models

Mitigating adversarial deep learning attacks remains challenging, partly because of the ease and low cost in carrying out such attacks. Therefore, in this paper, we focus on the understanding of universal adversarial example attack on image classification models. Specifically, we seek to understand the difference(s) between adversarial examples in two adversarial datasets (DAmageNet and PGD dataset) and clean examples in ImageNet learned by the classification model, and whether we can use such findings to resist adversarial example attacks. We also seek to determine if we can retrain a discriminator to discriminate whether the input image is an adversarial example, using adversarial training. We then design a number of experiments (e.g., class activation map (CAM) analysis, feature map analysis, feature maps/filters changing, adversarial training, and binary classification model) to help us determine whether the universal adversarial dataset can be successfully used to attack the classification model. This, in turn, contributes to a better understanding of adversarial defenses over pretrained classification model from an interpretation perspective. To the best of our knowledge, this work is one of the earliest works to systematically investigate the interpretation of universal adversarial example attack on image classification models, both visually and quantitatively.