Reviewerly/exHarmony huggingface dataset:
Image Error Concealment Method by Hiding a Copy of the Same Image in it in Spatial and Wavelet Domain before Transmission

Image is two dimensional representation of object like person, plant or thing. Image processing is a technique or method to enhance the quality of image. During transmission of image due to noise and other factors like interference etc some packets of data may be lost. To recover this loss many methods were proposed image error concealment is one of them, in this method packets lost are recovered from the received packets at receiver by using image processing techniques. In the proposed method a copy of the image is hidden as a embedded information in the Least Significant bit and low pass wavelet coefficient of the image before transmission and this embedded information is used to recover lost packets at the receiver. Compare to the existing methods peak signal to noise value of proposed method is high.
How Are Czech Individuals Willing to Protect Themselves: A Comparison of Cyber and Physical Realms

Endpoint users are usually viewed as the highest-risk element in the field of cybersecurity. At the same time, they need to be secured not just from the individual-level prism, but also from the state's perspective to counter threats like botnets that harvest weakly secured endpoints and forge an army of so-called zombies out of them that are often used for attacks on critical infrastructure or other systems vital to a state. We can already see steps like the Israeli hotline for cybersecurity incidents of Estonian educational efforts in this area aimed at citizens. Little effort is although made to understand recipients of such measures. Our study partly fills this gap and investigates how endpoint users (citizens) are willing to secure themselves against cyber threats using the survey method. To make results more valid, a unique comparison with comparable physical threats through their impacts was made. The results show statistically significant differences between comparable cyber-physical pairs indicating that a large portion of the sample was not able to assess the threat environment appropriately and that state's intervention with fitting countermeasures is needed. The resultant matrix containing frequencies of answers denotes what portion of respondents are willing to invest a certain amount of time and money into countering given threats providing a possible identification of weak points where state investments are needed the most.
Sentiment deep learning algorithm for multi-criteria recommendation

Multi-criteria recommender systems have emerged as an important trend in studying recommendation systems. They outperform and have been proved to be more precise than single-criterion systems. On the other hand, deep learning (DL) techniques have shown promising results in various fields. Recently, several studies on single-criterion recommender systems explored DL and sentiment analysis. We propose in this article a novel sentiment DL based algorithm for multi-criteria recommendation using autoencoders and sentiment information. Two sentiment analysis models have been employed, LSTM and LSTM with Word2Vec. Experiments on the TripAdvisor multi-criteria dataset demonstrated the contribution of sentiment information and that our approach outperformed other exiting methods.
CHALLENGES IN WRITING ACADEMIC RESEARCH: AN EXPLORATION OF MASTER’S STUDENTS’ EXPERIENCES

This study attempted to investigate the students’ difficulties in research writing involving 39 master’s students majoring in English Language Education at one of the universities in Malang. The data on the thesis writing difficulties were gathered through a close-ended questionnaire designed based on the theory proposed by Harris (2020), consisting of three aspects: research skills in the introduction, research method, and finding and discussion parts. The data was analyzed quantitatively by measuring the mean, mode, and percentage from each scale. The results revealed that many master’s students encountered difficulties in most three aspects. First, one-third of 39 students perceived difficulty in the introduction part, such as selecting a research topic and formulating research problems. Second, the students also affirmed that they found challenging in the research method part, such as the dilemma of selecting an appropriate method, determining the sample, and designing instruments in which the results were slightly different from the students who were in the level of disagreement. However, finding appropriate instruments, determining the questionnaire scale, and collecting data were less problematic. Third, the study highlighted that the students found it difficult to process data, sort the data the results, and conclude the results by elaborating on research implications. This study concluded that master’s students still faced difficulties although they have learned research at undergraduate and master’s levels. It was recommended that further studies should intensively investigate students’ difficulties and factors in research in linguistics.
DETERMINING THE DEGREE OF PLAYER ENGAGEMENT IN A COMPUTER GAME WITH ELEMENTS OF A SOCIAL CAMPAIGN USING COGNITIVE NEUROSCIENCE TECHNIQUES

Due to the popularity of video games in various applications, including both commercial and social marketing, there is a need to assess their content in terms of player satisfaction, already at the production stage. For this purpose, the indices used in EEG tests can be used. In this publication, a formula has been created based on the player's commitment to determining which elements in the game should be improved and for which graphic emblems connected with social campaigns were more memorable and whether this was related to commitment. The survey was conducted using a 2D platform game created in Unity based on observations of 28 recipients. To evaluate the elements occurring in the game at which we obtain a higher memory for graphic characters, a corresponding pattern was created based on player involvement. The optimal Index for moving and static objects and the Index for destruction were then selected based on the feedback. Referring to the issue of graphic emblems depicting social campaigns should be placed in a place where other activities such as fighting will not be distracted, everyone will be able to reach the level where the recently placed advertisement is. This study present the developed method to determine the degree of player's engagement in particular elements in the game using the EEG and to explore the relationship between the visibility of social advertising and engagement in a 2D platform game where the player has to collect three keys and defeat the ultimate opponent.
Optimal Motion Planning Considering Task Redundancy in Trajectory Tracking Applications for Industrial Robot

Task redundancy which is defined as that a robotic manipulator has more degrees of freedom than those strictly required to execute a given task exists in most industrial applications. This paper focuses on how to automatically generate a collision-free motion trajectory for an industrial robot with task redundant applications in a complex environment. The task redundant space is divided into grid for trajectory tracking motion planning. The expanding process map the joint space to the task redundant space for neighborhood searching. The cost evaluation not only calculates the distance in task redundant space but also considers the cost defined in joint space including both singularity and joint limits. The simulation experiments verify that our proposed algorithm not only could realize a collision-free motion and handle the singularity and joint limits, but also be able to track complex end effector trajectory within an obstacle-intensive environment. The results show that the proposed algorithm could be used in all kinds of task redundant trajectory applications in industry.
Multiple Subgoals-guided Hierarchical Learning in Robot Navigation

Solving obstacle-clustered robotic navigation tasks via model-free reinforcement learning (RL) is challenging due to the extended decision horizon and sparse rewards. Previous work has demonstrated efficient learning with single subgoal-conditioned hierarchical approaches. The subgoal is the action from the high-level policy and it operates on the low-level module, which could invoke sub-optimal policy when the selected subgoal is suboptimal. This work introduces multiple subgoals-guided navigation (MSGN) which consists of a high-level multiple subgoals Planner and a low-level goal-conditioned RL Controller. By passing multiple subgoals to the low-level agent, MSGN could alleviate the suboptimal subgoal problem by transferring the subgoal selection process to the RL agent. At the same time, multiple subgoals could help the goal-conditioned RL agent better explore and understand the environment and task. We tested our method on the Safety Gym suite. The results verified that MSGN could achieve a higher success rate and lower collision cost compared to baselines.
Decomposed Soft Compression for Remote Sensing Image

Modern imaging modalities from airborne or space platforms generate a large amount of remote sensing images, which places a burden on storage and transmission. These remote sensing images contain lots of ground targets which cannot tolerate fidelity loss in many fields. In this paper, a novel lossless compression method named Decomposed Soft Compression is proposed which takes advantage of rich details in images. The proposed method exploits image redundancy through integer wavelet transform. Then the decomposed images are encoded with shape-based encoder. Transformation on image layers can increase the sparsity and improve the compression ratio. Ex-periments on large-scale remote sensing image datasets show that the proposed method achieves up to 17.1 % improvement in compression ratio compared with JPEG 2000.
Multi-Robot Real-time Game Strategy Learning based on Deep Reinforcement Learning

With the booming development of artificial in-telligence, the autonomous decision-making of robots on the battlefield has become a future trend. In the field of en-tertainment games, some institutions have already attained outstanding results in multi-agent decision-making tasks by using reinforcement learning algorithms. But in the field of robot confrontation game, the robot confrontation strategies are still based on the simple logic of artificial design in most cases, which hinders the further improvement of robot strategies. In this paper, a reinforcement-learning-based multi-robot control method is proposed to improve the strategy performance. We enhanced the reinforcement learning algorithm PPO with the self-play method by easy-to-implement tricks for multi-agent training and improve training efficiency. In simulated experiments based on the rules of the RoboMaster University AI Challenge, we show our method has a much higher winning percentage than traditional approaches like behavior tree.
Applying Monocular Depth Estimation in RoboCup Soccer

We showcase a pipeline to train, evaluate, and deploy deep learning architectures for monocular depth estimation in the RoboCup Soccer Humanoid domain. In contrast to previous approaches, we apply the methods on embedded systems in highly dynamic but heavily constrained environments. The results indicate that our monocular depth estimation pipeline is usable in the RoboCup environment.
Detection of attacks based on compromise marks

In this article, we built on the concepts of cyber threat intelligence and were introduced to threat hunting. In exploring threat hunting, we discussed various models used to frustrate the adversary and interact with other analysts, operators, and infrastructure teams; data profiling exercises to understand what data you are being presented with (and maybe what data you're missing); and how the data pattern of life can be observed and managed.
An Optimal-Parent-Crossover based Differential Evolution

Differential Evolutionary (DE) is an important part of evolutionary algorithms (EAs) and it is eﬃcient in solving complex optimization problems and has a straight- forward algorithmic structure. In order to further exploit the advantages of DE, we propose a new variant of DE called optimal-parent-crossover based differential evolution (OPCDE). Optimal-parent individuals participate in the crossover in a simple way, effectively increasing the convergence speed of the original algorithm without additional parameters. On the basis of the 30 optimization problems of the IEEE Congress on Evolutionary Computation (CEC) 2017 bench- mark, experimental results show that OPCDE significantly outperforms the previous algorithm and is competitive with one of the state-of-the-art DE variants, as well as outperforming two other state-of-the-art algorithms of two important branches of the meta-heuristics, GSA and PSO. At the same time, OPCDE is able to converge quickly when solving unimodal problems and always maintains a high degree of diversity when solving simple multimodal, hybrid and composition optimization problems.
E-Learning Assistive System for Deaf and Mute Students

E-learning has become a popular digital platform among both students and teachers. When using an e-learning system, deaf-mute students can get significant benefits. It allows students to better grasp their studies by providing additional details. The major problem that the deaf and mute community encounters in the e-learning environment is that they are no longer attempting to enroll in normal institutions, which do not provide many facilities for them due to a lack of resources, a lack of learning facilities, and some social disturbances. To achieve that problem this system will translate the lecturer’s voice into text, map words with pre-created sign language animations, generate subtitles for lecture videos, clearly identify the face position of the lecturer, detect difficult words, track the hand gestures, and practice sign language so that it will increase learning resources, facilities, usability and help teachers to execute their teaching process through this platform. Therefore, normal institutions can use this system as their learning management system. It will approach deaf and mute students to enroll in normal institutions and do their studies as typical students.
SUPPLYDECK – A Blockchain-Based Pharmaceutical Supply Chain Management

With the rising competition among pharmaceutical companies, the current drug supply chain market has become more competitive with high-quality product segments. The rapid growth of internet pharmacies has made it more difficult to standardize drug safety throughout in complex distributed supply chain networks. There is a high chance of introducing counterfeit drugs which are almost the same as the original pharmaceuticals. Due to the lack of transparency the possibility of tampering with drugs in the current manual and web-based pharmaceutical systems is extremely high. Throughout the past recent years, these counterfeit drugs were recognized as the one of major worldwide problems. This situation is worse in developing countries. The unavailability of verifying the legitimacy of drug suppliers and poor mechanisms to trace the drugs were identified as critical points that need to be resolved. SUPPLYDECK comes as a solution for the critical scenario mentioned. This solution addresses the above problems using blockchain technology, as a distributed digital ledger that ensures transparency, traceability, and security. This can show promise for solving some global supply chain management problems using smart contracts and user authentication along with IoT technology and machine learning concepts that are critically examined with this potential application.
Qualitative Analysis of Automated Visual Tracking of Objects Through Head Pose Estimation

An automated approach for object tracking and gaze estimation via head pose estimation is crucial, to facilitate a range of applications in the domain of -human-computer interfacing, this includes the analysis of head movement with respect to a stimulus in assessing one’s level of attention. While varied approaches for gaze estimation and object tracking exist, their suitability within such applications have not been justified. In order to address this gap, this paper conducts a quantitative comparison of existing models for gaze estimation including Mediapipe and standalone models of Openface and custom head pose estimation with MTCNN face detection; and object detection including models from CSRT object tracker, YOLO object detector, and a custom object detector. The accuracy of the aforementioned models were compared against the annotations of the EYEDIAP dataset, to evaluate their accuracy both relative and non-relative to each other. The analysis revealed that the custom object detector and the Openface models are relatively more accurate than the others when comparing the number of annotations, absolute mean error, and the relationship between x displacement-yaw, and y displacement-pitch, and thereby can be used in combination for gaze tracking tasks.
“Stonelia” – Prehistoric Stone Tool Identification Android App for Archaeological Researchers

Prehistoric stone tools can be considered one of the oldest artifacts created by ancient humans. Lithic archeology’s study of stone tools provides important information about early humans’ technologies, agility, and mental and innovative abilities. A vital issue in lithic archeology is the identification and analysis of stone tools found at the excavation sites. Archeologists need to observe and analyze a stone tool under different aspects for a long time to verify whether it is a stone tool or a geofact, the techniques used to create it, and identify its rough relative date and functional value. This can be challenging for amateur scholars studying archeology since it requires a lot of experience and time to identify by a glance. As a solution, ‘Stonelia,’ a mobile-based android application, can be introduced to identify and analyze stone tools. The images captured through the mobile app are preprocessed using image processing. Using Convolutional Neural Network models identifies the stone artifact from a geofact, the mineral type, the rough relative date, techniques used to create the stone artifact, and its functional value. This mobile application provides prompt identification and analysis of stone artifacts within a short time and with higher accuracy.
“SmartGo” – Intelligent Traffic Controlling System with Violators Detection

Road traffic is mostly regulated by Traffic Light Control Systems (TLCS) in Sri Lanka. Conventional traffic signals have many problems, including unproductive time management at road intersections. Drivers and pedestrians would prefer to use the TLCS without any difficulties. The study aims to implement a system on a real-time basis using video monitoring and image processing technology. The system will facilitate vehicles to pass the traffic lights within a minimum waiting time and utilize time according to the number of disabled persons. Emergency vehicles will be released soon by detection using real-time data processing. This system includes the highest vehicle count detection accuracy level, nearly 92%. and the red-light violators’ detection system with an accuracy of 88.6%. SmartGo also detects emergency vehicles and smooths the way of passing through the traffic lights, including a YOLOv5 model with an accuracy of 82.3%. Subsequently, a fine-tuning YOLOv5 is then used to detect vehicles and pedestrians. According to experimental data, the suggested method provides a detection accuracy of 90%. SmartGo is a comprehensive system different from the existing traffic light control system in Sri Lanka which provides flexible and efficient service to both drivers and pedestrians.
Success Factors of Requirement Elicitation in the Field of Software Engineering

Requirement elicitation (RE) is a cognitively challenging and time-consuming task in software development due to the numerous challenges associated with it including conflicting requirements, unspoken, or assumed requirements, difficulty meeting with relevant stakeholders, stakeholder resistance to change, and not enough time set aside for meetings with all stakeholders. The prime causes of software implementation failure have been identified as inadequacies in the treatment of requirements. Without collecting the quality requirement, cannot achieve the goal of a quality software product. Through identifying the success factors affecting requirement elicitation, the paths to the quality requirements can be identified. The success factors identify through this research are experience, business analyst skills, stakeholder relationship, organizational elicitation process. This study aims to identify the factors affecting requirement elicitation based on the data collected from business analysts and similar positions in the software industry through a survey, interviews, and analyzed data to provide the initial validation for the identified factors. Through the analysis, we identified the main factors affecting successful requirement elicitation with a perfect significance value of less than 0.05 for all factors.
Oxygen: A Distributed Health Care Framework for Patient Health Record Management and Pharmaceutical Diagnosis

With the COVID-19 pandemic, the world is confronting various healthcare issues, and healthcare automation is more crucial than ever. The pandemic has revealed the limitations of existing digital healthcare systems to manage public health emergencies. There is no registered population for many healthcare institutions in Sri Lanka, as a result, there is a communication gap. Electronic Health Record systems (EHRs) are becoming popular to share patient details but accessing scattered data across several EHRs while safeguarding patient privacy remains a challenge. Most of these medical records are in printed format and manually entering those into EHR systems is time-consuming and error prone. Not only that pharmaceutical error is a critical healthcare problem, but it is even riskier to visit doctors for pharmaceutical diagnosis during a pandemic. This research introduces a Blockchain-based patient health record system, an Optical Character Recognition (OCR) and Natural Language Processing (NLP) based Medical Document Scanner, a Drug Identifier based on Image Processing and a Medical Chatbot powered by NLP as four novel approaches to address these issues. Altogether with the results, this research aims at introducing a solution for the limitations in healthcare while providing a distributed healthcare framework for the healthcare community worldwide.
Triplet Loss based Siamese Networks for Automatic Short Answer Grading

Grading student work is critical for assessing their understanding and providing necessary feedback. However, answer grading can become monotonous for teachers. On the standard ASAG data set, our system shows substantial improvements in classification disparity of correct and incorrect answers from a reference answer compared to baseline methods. Our supervised model (1) utilizes recent advances in semantic word embeddings and (2) implements ideas from one-shot learning methods, which are proven to work with minimal. We present experimental results from a model based on different approaches and demonstrates decent performance on standard benchmark dataset.
Software Complexity Automation Tool for Industrial Practices with Qualitative and Quantitative Aspects

With the evolution of software development, the complexity of a system must be handled to increase its stability in real-world usage. Software complexity is involving with the degree of the user’s difficulty in comprehending its logic. Numerous software complexity metrics have been introduced to quantitatively measure software complexity based on different quantifiable aspects. However, the success of the current software complexity metrics is limited due to the lack of aspects and the incapability of addressing user understandability. Therefore, an automated tool for introducing software complexity with respect to the possible quantitative and qualitative aspects has been proposed.