Reviewerly/exHarmony huggingface dataset:
Enhanced Word Embeddings for Anorexia Nervosa Detection on Social Media

Anorexia Nervosa (AN) is a serious mental disorder that has been proved to be traceable on social media through the analysis of users’ written posts. Here we present an approach to generate word embeddings enhanced for a classification task dedicated to the detection of Reddit users with AN. Our method extends Word2vec’s objective function in order to put closer domain-specific and semantically related words. The approach is evaluated through the calculation of an average similarity measure, and via the usage of the embeddings generated as features for the AN screening task. The results show that our method outperforms the usage of fine-tuned pre-learned word embeddings, related methods dedicated to generate domain adapted embeddings, as well as representations learned on the training set using Word2vec. This method can potentially be applied and evaluated on similar tasks that can be formalized as document categorization problems. Regarding our use case, we believe that this approach can contribute to the development of proper automated detection tools to alert and assist clinicians.
Design and Implementation of Interactive English Translation System in Internet of Things Auxiliary Information Processing

Information technology has penetrated into all aspects of human life. Nowadays, with the rapid development of science and technology, information technology has gradually become the cornerstone of the development of other technologies. The Internet of Things is an important part of the new generation of information technology. Language is the medium of communication between people. Driven by economic globalization and the development of the Internet, information is growing rapidly, and there are more and more exchanges and exchanges between countries. The emergence of high-efficiency and high-economic machine translation solves these difficulties, and the interactive English translation system is the current research hotspot, which is intended to improve the output translation quality of the English translation system. The main work of this paper is to analyze the existing interactive machine translation technology, especially the interactive machine translation based on a phrase model, using the Internet of Things as a knowledge source. According to the characteristics of segment analysis and human-computer interaction mechanism, from the network, a wealth of information are available from open sources. In this paper, on the basis of the segment analysis, the human-machine cooperation translation strategy of human-machine cooperation with complementary human-machine advantages was discussed, and the system designed was verified. It is proved that the system has high performance in improving the accuracy and recall rate of machine English translation. Compared with the existing English translation system, the accuracy has improved by more than 20% in the case of fewer iterations, and in the case of 90 iterations, the accuracy can improve by 100%.
CHAGSKODE Algorithm for Solving Real World Constrained Optimization Problems

Real-world optimization problems are often gov- erned by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Opti- mization Problems (COPs) fueled by advances in computational power. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose a novel hybrid Evolutionary Algorithm based on the Differential Evolution algorithm and Adaptive Parameter Gaining Sharing Knowledge-based algo- rithm to solve global real-world constrained parameter space. The proposed CHAGSKODE algorithm leverages the power of multiple adaptation strategies concerning the control parameters, search mechanisms, as well as uses knowledge sharing between junior and senior phases. We test our method on the benchmark functions taken from the CEC2020 special session &amp;amp; competition on real-world constrained optimization. Experimental results indicate that CHAGSKODE is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.
Modeling DTA by Combining Multiple-Instance Learning with a Private-Public Mechanism

The prediction of drug-target interactions plays a fundamental role in facilitating drug discovery, where the goal is to find prospective drug candidates. With the increase in the number of drug-protein interactions, machine learning techniques, especially deep learning methods, have become applicable for drug-target interaction discovery because they significantly reduce the required experimental workload. In this paper, we present a spontaneous formulation of the drug-target interaction prediction problem as an instance of multi-instance learning. We address the problem in three stages, first organizing given drug and target sequences into instances via a private-public mechanism, then identifying the predicted scores of all instances in the same bag, and finally combining all the predicted scores as the output prediction. A comprehensive evaluation demonstrates that the proposed method outperforms other state-of-the-art methods on three benchmark datasets.
The Construction of English Smart Classroom Teaching Mode Based on Deep Learning

Deep learning has an increasingly far-reaching impact on classroom teaching and is an important trend driving the application of educational technology in schools. In the traditional lecture-style teaching process, students are mostly in a passive listening and memorizing state. Simple memorization and repeated training have a certain hindering effect on promoting the transfer and application of the learner's knowledge, and it is not suitable to exert the learner's subjectivity. This research uses the current rapidly developing wireless communication technology to transmit data to the English teaching management platform, so as to realize the information exchange between the English classroom monitoring terminal and the English teaching management platform. The monitoring terminal of the system is mainly responsible for data collection of information, such as student campus card information, the location of the monitoring terminal, and terminal equipment parameters, and uses wireless communication information technology to upload it to the English teaching management platform according to the communication protocol agreed between the terminal and the platform, and the English teaching management platform can issue instructions to the monitoring terminal, so as to realize the control and management functions of the monitoring terminal. The design of the English teaching management platform of this system is a Web system designed based on B/S architecture. The interface of the system is diverse, and English teachers can easily view the monitoring data on the platform through this system. The main function of the English teaching management platform is to receive the data collected from the monitoring terminal, parse the information, and display the processing results to the English teacher in the form of a page. In order to facilitate the storage of data, MySQL database is used for the background data storage of the English teaching management platform. Through the design of the hardware terminal module and the development of the English teaching management platform, a deep learning-based English smart classroom management prototype system is realized.
Empirical Analysis of Vulnerabilities in Blockchain-based Smart Contracts

With the evolution of technology, blockchain a swiftly impending phenomenon i.e., "decentralized computing” is observed. The emergence of Smart Contracts (SC) has resulted in advancements in the application of blockchain technology. The Ethereum network’s computing capabilities and functionalities are founded on the basis of SC. A smart contract is a self-executing agreement between buyer and seller with the terms of the settlement between them, written directly as lines of code, existing across a distributed decentralized blockchain network. It is a decentralized software that runs on a blockchain autonomously, consistently, and publicly. Conversely, due to the complex semantics of fundamental domain-specific languages and their testability, constructing reliable and secure SC can be extremely difficult. SC might contain some vulnerabilities. Security vulnerabilities can originate from financial tribulations; there are a number of notorious events that specify blockchain SC could comprise numerous code-security vulnerabilities. Security and privacy of blockchain-based SC are very important, we must first identify their vulnerabilities before implementing them widely. Therefore, the purpose of this paper is to conduct a comprehensive experimental evaluation of two current security testing tools: Remix solidity static analysis plugin and Solium which are used for static analysis of SC. We have conducted an empirical analysis of SC for finding tangible and factual evidence, controlled by the scientific approach. The methodology’s first step is to gather all of the Ethereum SC and store them in a repository. The next step is to use the Remix solidity static analysis plugin and Solium to perform vulnerability assessments. The last step is to analyze the result of both tools and evaluate them on the basis of accuracy and effectiveness. The goal of this empirical analysis is to evaluate the two FOSS tools: Remix solidity static analysis plugin and Solium on the basis of accuracy and effectiveness. Some research questions were considered to reach the stated goal: What automated tools and frameworks are proposed in supporting the state-of-the-art empirical approach to SC vulnerability detection? How accurate are security analysis tools? And which tool has more accuracy rate? How effectively security analysis tools are detecting vulnerabilities in SC? And which is the most effective security analysis tool? We investigated the effectiveness and accuracy of security code analysis tools on Ethereum by testing them on a random sample of vulnerable contracts. The results indicate that the tools have significant discrepancies when it comes to certain security characteristics. In terms of effectiveness and accuracy, the Remix plugin outperformed and beat the other tool.
Object detection and multi‐object tracking based on optimized deep convolutional neural network and unscented Kalman filtering

Summary The tracking of multiple objects has gained immense interest in several applications that ranges from video surveillance and robotics. The goal of tracking multiple objects is to discover correspondences and perform matching among various objects in frames. However, occlusion is a major issue in several classical techniques, which fail to track multiple moving objects. Here, the extraction of frames is done to obtain information about an action. Thereafter, the segmentation of objects is done using the entropy weighting K‐means (EWKM) algorithm to generate optimum segments from the frame. After that, the detection of the object is done using a deep convolutional neural network (DCNN) to obtain objects from segments. Proposed Jaya‐political optimizer (Jaya‐PO) is used to train DCNN, which is devised by collaborating political optimizer (PO) and Jaya optimization algorithm. After detecting the object, the tracking of multiple objects is done using the proposed Henry gas solubility optimized unscented Kalman filtering (HGSO‐based UKF), wherein the UKF is modified with HGSO. The proposed Jaya‐PO‐based DCNN and HGSO‐based UKF outperformed other methods with high accuracy of 92%, high multiple object tracking precision (MOTP) of 87.6%, high sensitivity of 91.7%, and high specificity of 92%.
Identification of Encrypted Traffic Using Advanced Mathematical Modeling and Computational Intelligence

This paper proposed a hybrid approach for the identification of encrypted traffic based on advanced mathematical modeling and computational intelligence. Network traffic identification is the premise and foundation of improving network management, service quality, and application security. It is also the focus of network behavior analysis, network planning and construction, network anomaly detection, and network traffic model research. With the increase in user and service requirements, many applications use encryption algorithms to encrypt traffic during data transmission. As a result, traditional traffic classification methods classify encrypted traffic on the network, which brings great difficulties and challenges to network monitoring and data mining. In our article, a nonlinear modified DBN method is proposed and applied to encrypted traffic identification. Firstly, based on Deep Belief Networks (DBN), this paper introduces the proposed Eodified Elliott (ME)-DBN model, analyzes the function image, and presents the ME-DBN learning algorithm. Secondly, this article designs an encrypted traffic recognition model based on the ME-DBN model. Feature extraction is carried out by training the ME-DBN model, and finally, classification and recognition are carried out by the classifier. The experimental results on the ISCX VPN-non-VPN database show that the MEDBN method proposed in this article can enhance the classification and recognition rate and has better robustness to encrypt traffic recognition from different software.
Security and Privacy in Distributed Health Care Environments

There is an increasing demand for distributed health care systems. Nevertheless, distributed health care environments do not come without risks. At the same time that distributed health care systems are growing, so are the cybersecurity threats targeting them. Additionally, the demand for compliance to new regulations increases as these distributed health care systems hold sensitive patient data. The use of data-driven technologies presents a promising opportunity for significant advances in the field toward improved health care for patients and the general public.[1] [2] Several recent studies have highlighted the importance and the necessity of developing a data-driven approach where patient data are collected, analyzed, and leveraged for medical research purposes with the help of different types of artificial intelligence. To address the privacy-related challenges, novel methods, such as protection of personal health information, ensuring compliance, guaranteeing FAIR information processing, and building of trust, are required. In this issue, new paradigms and prominent applications are presented for secure, trustworthy, and privacy-preserving data sharing and knowledge representation to address the emerging needs.
Combining SMOTE and OVA with Deep Learning and Ensemble Classifiers for Multiclass Imbalanced

The classification of real-world problems always consists of imbalanced and multiclass datasets.A dataset having unbalanced and multiple classes will have an impact on the pattern of the classification model and the classification accuracy, which will be decreased.Hence, oversampling method keeps the class of dataset balanced and avoids the overfitting problem.The purposes of the study were to handle multiclass imbalanced datasets and to improve the effectiveness of the classification model.This study proposed a hybrid method by combining the Synthetic Minority Oversampling Technique (SMOTE) and One-Versus-All (OVA) with deep learning and ensemble classifiers; stacking and random forest algorithms for multiclass imbalanced data handling.Datasets consisting of different numbers of classes and imbalances are gained from the UCI Machine Learning Repository.The research outputs illustrated that the presented method acquired the best accuracy value at 98.51% when the deep learning classifier was used to evaluate model classification performance in the new-thyroid dataset.The proposed method using the stacking algorithm received a higher accuracy rate than other methods in the car, pageblocks, and Ecoli datasets.In addition, the outputs gained the highest performance of classification at 98.47% in the dermatology dataset where the random forest is used as a classifier.
Obstacle Avoidance and Path Planning of a Wheeled Mobile Robot Using Hybrid Algorithm

 The proposed hybrid algorithm outperforms the PSO, IPSO, and GWO algorithms. The proposed method outperforms class PSO and GWO algorithms in determining the shortest and collision-free path for a mobile robot under the same environmental restrictions. The performance made the hybrid algorithm more effective in finding the best potential solution.In the mobile robot workplace, the path planning problem is crucial.Robotic systems employ intelligence algorithms to plan the robot's path from one point to another.This paper proposes the fastest and optimal path planning of the wheeled mobile robot with collision avoidance to find the optimal route during wheeled mobile robot navigation from the start point to the target point.It is done using a modern meta-heuristic hybrid algorithm called IPSOGWO by combining Improved Particle Swarm Optimization (IPSO) with Grey Wolf Optimizer (GWO).The principal idea is based on boosting the ability to exploit in PSO with the exploration ability in GWO to the better-automated alignment between local and global search capabilities towards a targeted, optimized solution.The proposed hybrid algorithm tackles two objectives: the protection of the path and the length of the path.During, Simulation tests of the route planning by the hybrid algorithm are compared with individual results PSO, IPSO, and GWO concepts about the minimum length of the path, execution time, and the minimum number of iterations required to achieve the best route.This work's effective proposed navigation algorithm was evaluated in a MATLAB environment.The simulation results indicated that the developed algorithm reduced the average path length and the average computation time, less than PSO by (1%, 1.7%), less than GWO by (1%, 1.9%), and less than IPSO by (0.05%, 0.4%), respectively.Furthermore, the superiority of the proposed algorithm was proved through comparisons with other famous path planning algorithms with different static environments.
The Effect Of Gallery Walk Strategy On Students' Ability In Writing Procedure Text

The purposes of researcher was about figuring out if there was an effect in improving the students' ability on writing procedure text by using the Gallery Walk. The research problem was whether there is effect in Gallery Walk on students’ ability in learning writing procedure text. The aim of the research was to find out the effectiveness of the Gallery Walk in the students' writing ability on procedure text. In this research, the researcher used the pre-experimental design method in one group pre-test and post-test design. There were two variables research namely the independent variable (Gallery Walk Strategy) and dependent variable (students' ability in writing procedure text). The researcher collected the data on the written test (pre-test and post-test). Each written test was a type of questions in the multiple choice form. Each test consisted of 30 items. These data would be calculated into the total and mean score. It would be analyzed the validity, reliability, homogenity, normality and T-test by using SPSS. Based on the findings and discussion of the study, the research showed that the use of gallery walk had an effect in students' ability on procedure text. It could be seen by the mean of pre-test which was Mean Score 63,73 and post-test was 85,43. The value of to (t-count) = 0,716 and (t-table) = 0,266. It meant that the value of t-count was higher than t-table. It could be stated that the gallery walk was effective in students' writing ability on procedure text.
G2P-DDM: Generating Sign Pose Sequence from Gloss Sequence with Discrete Diffusion Model

The Sign Language Production (SLP) project aims to automatically translate spoken languages into sign sequences. Our approach focuses on the transformation of sign gloss sequences into their corresponding sign pose sequences (G2P). In this paper, we present a novel solution for this task by converting the continuous pose space generation problem into a discrete sequence generation problem. We introduce the Pose-VQVAE framework, which combines Variational Autoencoders (VAEs) with vector quantization to produce a discrete latent representation for continuous pose sequences. Additionally, we propose the G2P-DDM model, a discrete denoising diffusion architecture for length-varied discrete sequence data, to model the latent prior. To further enhance the quality of pose sequence generation in the discrete space, we present the CodeUnet model to leverage spatial-temporal information. Lastly, we develop a heuristic sequential clustering method to predict variable lengths of pose sequences for corresponding gloss sequences. Our results show that our model outperforms state-of-the-art G2P models on the public SLP evaluation benchmark. For more generated results, please visit our project page: \textcolor{blue}{\url{https://slpdiffusier.github.io/g2p-ddm}}
Developing Serious Video Games for Data Communication Courses

The development of educational games for data communication courses aims to help students understand data communication subject matter. The research method used in the development of educational games for the Data Communication course is Research and Development (R&amp;D) with the DDD-E development model. The instruments used in the development of educational games are documentation and questionnaires. The research subjects consisted of 2 media experts, 2 material experts, 2 linguists and 30 potential users. The test results show that the educational games for the data communication course get good categories from media experts, material experts, and linguists, while the responses of potential users get very good categories.
Teacher Educators Perspectives on The Use of Augmented Reality for Foreign Language

The purpose of this research is to share reflections on Augmented Reality (AR) technology as an upgrade tool for foreign language development. Exploring the short but considerable literature research, this post discusses AR technology for the rise in the philosophy of upgrading, upgrading teacher nursery in position, teachers, pupils, customs, infrastructure, and sustainability, using the framework of activities outlines the suitability of the use of upgrading technology in development programs. For analysis, AR technology has essential benefits for language development; however, it is not suitable for all language types. Not only that, but this information also offers solid recommendations for activities that are enhanced with AR in 4 skills as well as language-specific applications. This information has some relevance for instructors, teachers, researchers, and creators of Augmented reality content.
The Development of Android-Based Mobile Learning on Materials for Preparation and Proclamation of Indonesian Independence for Class VIII Blind Students at MTs Yaketunis Yogyakarta

This research aims to find out the feasibility of mobile learning product. This research refers to the development model by Alessi &amp; Trollip. The procedure of mobile learning development consists of (1) planning, (2) designing, and (3) developing that was equipped with component attributes including standards, ongoing evaluation, and project management. The product testing consists of alpha test assessed by media experts, material experts, and instructional experts, while, the beta test involvs impairment students. The data collection techniques used questionnaire. The instrument of data collections used questionnaire. The data analysis techniques used descriptive analysis techniques. The research results showed the feasibility of the product was considered as highly-feasible based on the assessment of media experts, material experts, instructional experts, and the first students’ responses.