Reviewerly/exHarmony huggingface dataset:
A new efficient tour construction heuristic for the Traveling Salesman Problem

The creation of the initial population is an essential part of the population based evolutionary algorithms. An appropriate initial population could lead to much faster convergence speed; in contrast, an inappropriate initial population could even cause getting stuck in a local optimum. In this paper, we will propose a new efficient heuristic method to create initial individuals for the Traveling Salesman Problem (TSP), which we will call Circle Group Heuristic (CGH). The results show that CGH creates better tours compared with other well-known heuristic tour construction methods.
Cybercrime victims: victim policy through a vulnerability lens

Many initiatives in England and Wales (E&W) have sought to ‘put the victim first’ within the criminal justice process (Hall, 2018). However, who is able to claim ‘the victim’ label and how decisions are made to identify and respond to victimisation are central questions to a critical victimology (Walklake, 2007). Such questions remain under-explored in the context of cybercrime, particularly with respect to victims of computer crimes and online fraud (Correia 2019). This paper explores the extent to which understandings of ‘the victim’ and ‘vulnerability’ in E&W, form an adequate basis to ‘put the victim first’ in responding to cybercrime victimisation. Firstly, it reviews how these concepts are deployed within the Victims’ Code and Strategy (MOJ, 2018; MOJ, 2015) and demonstrates how current victim policy is ill suited to providing an adequate response to victims of cybercrime. Secondly, it introduces Fineman’s (2008; 2017) vulnerability theory as a lens to better understand cybercrime vulnerability. Finally, with reference to three scenarios based on real cases reported in Wales, it shows how a broader vulnerability framework provides a lens through which to identify areas for intervention, aimed at increasing victim resilience are addressing victims’ immediate needs, in E&W and beyond.
3D Polygon Mesh Encryption Based on 3D Lorenz Chaotic Map

&lt;p class="0abstract"&gt;The multimedia application developments in recent years lead to the widespread of 3D model applications. It becomes more popular in various fields as well as exchanging it over the internet. The security of the 3D models is a very important issue now a day, so the scheme for encrypting the 3D model will be proposed in this work. In this proposed scheme, the 3D polygon mesh model will be protected through the encrypting process based on a 3D Lorenz Chaotic map where the vertices value of the 3D polygon mesh model will be modified using 3D keys generated by 3D Lorenz Chaotic Map, which has excellent property and provides a good diffusion. The proposed scheme was implemented on various 3D models, which have a different number of vertices and faces. The experimental results show that the proposed scheme has good encryption results, which were noted through completely deforming and changing the whole shape of the 3D models. The Hausdorff Distance (HD) and histogram metrics are adopted to calculate the matching degree between the original and extracted model. The results show that the original and extracted model are identical through the values of HD, where they are approximately zero, and the histogram visually is identical. &lt;/p&gt;
INTRINSIC OR EXTRINSIC DISCIPLINE: CASE STUDY OF PUBLIC SECONDARY SCHOOLS IN PUNJAB, PAKISTAN

Discipline is a major component in school for the promotion of quality education and responsible citizen for society. The major purpose of the study was to explored intrinsic or extrinsic discipline in schools at secondary level; to compare the results by locality, gender and grade wise. The province of Punjab was divided into three (north, central, southern) zones. Sample was selected from 2304 schools by equal ratio from each zone. Self-constructed instrument on five point likert scale having 50 items was used to collect data from schools. Instrument sent to eight research experts for validation, then the instrument considered final for data collection. Descriptive and inferential statistic used for data analysis. The study concluded that discipline practiced at public secondary schools was intrinsic in nature and the condition of discipline in schools was satisfactory. Significant difference observed in boys and girls students’ opinions; female schools were more disciplined as compared to male. The results reveal significant difference in rural and urban students’ opinions; rural schools were more disciplined as compared to urban. There was also significant difference in the opinions of 9th and 10th grade respondents, 10th grade was more disciplined as compared to 9th grade
Optimization of COD Removal from Pharmaceutical Wastewater by Electrocoagulation process using Response Surface Methodology (RSM)

In this research, the feasibility of using an electrocoagulation method as a process for treating of hospital wastewater generated from Al-Dewaniya hospital located at Al-Dewaniya city/Iraq was confirmed. Batch experimental runs were conducted using an electrochemical reactor with stainless steel and aluminum as cathode and anode, respectively. The impact of operating variables like current density, pH, and addition of NaCl on the removal of chemical oxygen demand (COD) was investigated. For this purpose, Box-Behnken (BB) design based on the Response Surface Methodology (RSM) was adopted to design and analyze the results. The results revealed that the current density has the major impact on the efficiency of COD removal followed by addition of NaCl while pH has the lower effect on the COD removal under the studied range of pH. The optimized operating parameters were a current density of 25 A/cm2, pH of 8.6,and NaCl addition of 2.06 g/l in which COD removal efficiency of 99.45% was achieved with a specific energy consumption of 26.079kWh/kgCOD.
Musical Grid Interfaces: Past, Present, and Future Directions

This paper examines grid interfaces which are currently used in many musical devices and instruments.This type of interface concept has been rooted in the NIME community since the early 2000s.We provide an overview of research projects and commercial products and conducted an expert interview as well as an online survey.In summary this work shares: (1) an overview on grid controller research, (2) a set of three usability issues deduced by a multi method approach, and (3) an evaluation of user perceptions regarding persistent usability issues and common reasons for the use of grid interfaces.
Mobile Learning as Teaching Aid and Learning Media for Special Teacher of Deaf Students

This study aims to develop learning media android based which can be used by teachers of special schools to teach a special program for deaf children. This research uses a qualitative approach. Data collected by conducting interviews with the teachers about the obstacles, desires and media needs of the teacher in teaching auditory perception and communication programs. While the data for developing the media was conducted through focus group discussion. Data results of the focus group discussion were analyzed in the reduction and concluded to be the basis of the development of the media. Based on the results, it can be concluded that mobile learning is the alternative media to learn and helps teachers in teaching auditory perception and communication programs to deaf children.
Challenge Accepted – Paratope States in Solution Improve Structure Prediction and Docking

The rise of antibodies as biotherapeutic proteins has attracted the interest in characterizing and understanding the antibody binding interface. Structure-based antibody design and accurate predictions of antibody-antigen interactions remain major challenges in computational biology. By using molecular dynamics simulations in combination with enhanced sampling techniques, we show that a single static X-ray structure is not sufficient to identify determinants of antibody recognition and to characterize functionally relevant conformational rearrangements. Thus, we suggest that antibodies should be described as ensembles of paratope states in solution, which are defined by a combination of correlated loop movements, which favor distinct interface orientations and elbow angles. In this study, we focus our attention on characterizing paratope states in solution of antibodies which undergo substantial conformational changes upon antigen binding. Because of their conformational changes, they have been classified as “difficult” cases for antibody-antigen docking in the Critical Assessment of PRediction of Interactions (CAPRI) blind docking challenge. Here, we present transition kinetics and thermodynamics of these conformational rearrangements of the paratope and show that kinetically relevant states can be used to improve antibody-antigen docking. We use the Rosetta SnugDock docking algorithm to show that by even using the unbound antibody X-ray structure as starting structures for molecular dynamics simulations, a binding competent conformation substantially different to the unbound antibody X-ray structure, can be retained. We also observe that the kinetically dominant antibody conformations are chosen by the bound antigen conformation with the highest probability. Thus, we show that kinetically relevant antibody conformations obtained by a combination of molecular dynamics simulations and Markov-state models can improve antibody-antigen docking and structure prediction.
Automatic Fall Detection Using Long Short-Term Memory Network

Falls, especially in the elderly, are one of the main factors of hospitalization. Time-consuming intervention can be fatal or cause irreversible damages to the victims. On the other hand, there is currently a significant amount of smart clothing equipped with various sensors, particularly gyroscopes and accelerometers, which can be used to detect accidents. The creation of a tool that automatically detects eventual falls allows helping the victims as soon as possible. This works focuses in the automatic fall detection from sensors signals using long short-term memory networks. To train and test this approach, the Sisfall dataset is used, which considers the simulation of 23 adults and 15 older people. These simulations are based on everyday activities and the falls that may result from their execution. The results indicate that the procedure provides an accuracy score of 97.1% on the test set.
Learning To Compose 6-DOF Omnidirectional Videos Using Multi-Sphere Images

Omnidirectional video is an essential component of Virtual Reality. Although various methods have been proposed to generate content that can be viewed with six degrees of freedom (6-DoF), existing systems usually involve complex depth estimation, image inpainting or stitching pre-processing. In this paper, we propose a system that uses a 3D ConvNet to generate a multi-sphere images (MSI) representation that can be experienced in 6-DoF VR. The system utilizes conventional omnidirectional VR camera footage directly without the need for a depth map or segmentation mask, thereby significantly simplifying the overall complexity of the 6-DoF omnidirectional video composition. By using a newly designed weighted sphere sweep volume (WSSV) fusing technique, our approach is compatible with most panoramic VR camera setups. A ground truth generation approach for high-quality artifact-free 6-DoF contents is proposed and can be used by the research and development community for 6-DoF content generation.
Are Senior High School Students Interested in Learning Vocabulary in ELT Context Using Instagram Platform?

Vocabulary is one important aspect of learning a foreign language. With a limited vocabulary, anyone will also have a limited understanding. Learning vocabulary therefore is perceived important. However, EFL learners often have a limited acces to learn vocabualary. With the growing of technological uses in language teaching, this study aims at unveil the level of students’ interest in learning vocabulary through Instagram. The quantitative approach therefore is used in this study. The study involves 49 senior high school students of SMAN 1 Palopo. The findings indicate that that the average score of students’ interest in learning vocabulary through Instagram is 67,58. The highest percentage of the result is 44.41% that indicates that the students of SMAN 1 Palopo are interested in learning vocabulary through Instagram.
Blockchain Collaborative Network Development Framework

Blockchain technologies have been recognised as having the potential to transform and disrupt a wide range of industries, but the vast majority of developments are still at proof-of-concept and exploratory stages. Recent research efforts have provided guidance in relation to how blockchain solutions might be architected, and flowcharts to help practitioners decide if a blockchain is appropriate. There does not exist, however, a framework to guide practitioners forward once the decision has been made to implement a blockchain application network. Following the time honoured Information Systems tradition of adapting theories and models from other disciplines, this study investigated the research field of Collaborative Networks. The ARCON modeling framework was used as the structural basis to conceptually develop the “Blockchain Collaborative Network Development Framework”. This framework can help both researchers and practitioners consider the wide range of potential issues involved when investigating or implementing blockchain based collaborative networks.
Data Preparation for Deep Learning

A data format describes how to organize and save data in files, and can be in the form of numbers, characters, or binary numbers. Storage in a character format typically offers a high level of transparency but consumes more storage space, whereas that in a binary format—a compressed format—offers a low level of transparency but consumes less storage space.
Distinguishing phases via non-Markovian dynamics of entanglement in topological quantum codes under parallel magnetic field

We investigate the static and the dynamical behavior of localizable entanglement and its lower bounds on nontrivial loops of topological quantum codes with parallel magnetic field. Exploiting the connection between the stabilizer states and graph states in the absence of the parallel field and external noise, we identify a specific measurement basis, referred to as the canonical measurement basis, that optimizes localizable entanglement when measurement is restricted to single-qubit Pauli measurements only, thereby providing a lower bound. In situations where computing even the lower bound is difficult, we propose an approximation of the lower bound that can be computed for larger systems according to the computational resource in hand. Additionally, we compute a lower bound of the localizable entanglement that can be computed by determining the expectation value of an appropriately designed witness operator. We investigate the behavior of these lower bounds in the vicinity of the topological to nontopological quantum phase transition of the system, and perform a finite-size scaling analysis. We also investigate the dynamical features of these lower bounds when the system is subjected to Markovian or non-Markovian single-qubit dephasing noise. We find that in the case of the non-Markovian dephasing noise, at large time, the canonical measurement-based lower bound oscillates with a larger amplitude when the initial state of the system undergoing dephasing dynamics is chosen from the nontopological phase, compared to the same for an initial state from the topological phase. On the other hand, repetitive collapses followed by revivals to high value with time are observed for the proposed witness-based lower bound in the nontopological phase, which is absent in the topological phase. These features can be utilized to distinguish the topological phase of the system from the nontopological phase in the presence of dephasing noise.
STRESS RESISTANCE AND COPY STRATEGIES OF STUDENTS ’BEHAVIOR DURING THE EDUCATIONAL PROCESS IN HIGHER EDUCATION INSTITUTIONS

The article considers the main approaches to the problem of stress resistance of students in higher education institutions of Ukraine. It is determined that a number of scientists consider stress resistance as a property of the individual that determines its effective activity, and others as cognitively conditioned mechanisms for overcoming stress. It is noted that in psychological science there are other views on the stress resistance of the individual, but when studying the stress resistance of students in the educational process of higher education, it is advisable to use the above approaches. Studies suggest that most students have low levels of psychological stress, mental stress and medium, high levels of resilience. The main coping strategies of students’ behavior in stressful situations are “confrontational coping”, “avoidance”, “self-control” and “problem solving planning”.
Prediction of Photovoltaic Power Generation Based on LSTM Considering Daylight and Solar Radiation Data

This paper presents a method to predict the photovoltaic power generation using daylight and solar radiation data. Keras based long short-term memory(LSTM) model, a deep learning library, is used to predict the photovoltaic power generation and compared with a simple machine learning model. Based on the annual power generation, the weather parameters are selected with the highest correlation such as sunshine time and solar radiation. The prediction of Keras based LSTM model is superior to the prediction of the photovoltaic power generation using the simple machine learning model. This is because the probabilistic characteristics of actual variables are considered forecasting with actual weather parameters in the prediction of photovoltaic power generation.
The Development of Learning Media of Islamic Education Based on Flipbook in Covid-19 Pandemic at Elementary School

An Education in the 4.0 era requires teachers and students to be literate in technological developments. Conventional methods that are not relevant to technological advances must be updated. There are many teachers who have not mastered information technology in the learning process. Boredom to do learning activities that emphasize student textbooks and worksheets, ultimately have an impact on students’ enthusiasm for learning that that are less than optimal. In order to achieve the maximum learning process, one of the alternatives offered is to use learning media with the help of the Flipbook application. This study aims to determine the validity of flipbook-based learning media and to measure the practicality of flipbook-assisted learning media in overcoming boredom. This research is a research and development that uses the ADDIE model (analyzing, design, development, implementation, evaluation). Researcher Collected data by means of observations, interviews, and distributing questionnaires, then analyzed descriptively quantitatively and qualitatively. The research subjects were 30 students of grade V SDN Sumput Sidoarjo. Based on the validation test from material experts, the average value is 84% ​​which means very valid, while the media expert test is 80% with the valid category. The practicality test of the development product obtained an average score of 80% in the attractive category. The results of the study showed that the development product is in the valid and attractive category. It can be concluded that the flipbook-based PAI learning media is practical and interesting to use as learning media.&#x0D; Keywords: Learning Media, PAI, Flipbook
A Stochastic Model for Electric Scooter Systems

Electric scooters are becoming immensely popular across the world as a means of reliable transportation around many cities. As these e-scooters rely on batteries, it is important to understand how many of these e-scooters have enough battery life to transport riders and when these e-scooters might require a battery replacement. To this end, we develop the first stochastic model to capture the battery life dynamics of e-scooters of a large scooter network. In our model, we assume that e-scooter batteries are removable and replaced by agents called swappers. Thus, to gain some insight about the large scale dynamics of the system, we prove a mean field limit theorem and a functional central limit theorem for the fraction of e-scooters that lie in a particular interval of battery life. Exploiting the mean field limit and the functional central limit theorems, we develop an algorithm for determining the number of swappers that are needed to guarantee levels of probabilistic performance of the system. Finally, we show through a stochastic simulation and real data that our stochastic model captures the relevant dynamics.
Blockchain Solutions for International Logistics Networks along the New Silk Road between Europe and Asia

The primary research that underpins this paper seeks to explore the applications of blockchain technology on a specific international corridor and to draw policy implications for decision makers. To analyze the bottlenecks of operating on the New Silk Road and to identify opportunities for applying the blockchain technology on this corridor, a survey was conducted among main train operators and experts working on this route. These responses provide insight into the issues related to the adoption of blockchain technology from front-line actors. The top three challenges are lack of capacities, congestion at transshipment terminals, and slow border crossing. Through the application of blockchain technology, the operators are presented with opportunities for improved accuracy in the processing of data and information, higher reliability of information flows through failure-free transfer of information, and improved traceability of supply chains through irrevocable input of status information. Currently, 50% of the respondents have started to implement blockchain applications or have an actual interest to apply blockchain solutions. For a wider implementation of blockchain solutions, business models need to be developed allowing private and permissioned access that is accepted and open for parties involved. Policy makers should facilitate these digital innovations through flexible and harmonized legal regulations on an international level.
Analysis of Machine Learning Techniques for Spam Detection

This paper discusses the spam detection and different machine learning models to detect these spam messages. The present work also discusses the testing and evaluation of spam messages. Spam messages have always been very dangerous for computers and networks. They have a very bad effect on the computer security. With the emergence of social media platforms, many people are dependent on emails to communicate, and, with this, there is always a need to detect and prevent spam mails before it enters a user’s inbox. The paper also presents the analyses of different machine learning techniques to detect spam messages. Finally, the paper describes the algorithm which is best to detect spam messages. Spam messages are basically redundant messages which are sent in a large number at once. They can be seen in many forms like free services, cheap SMS plans, lottery, etc. Growing spam messages in your mail can make your inbox filled with ridiculous mails, slow down your Internet speed, and retrieve your private information like credit card details, and has many more drawbacks. Therefore, it is important to prevent it in the best way possible.