Reviewerly/exHarmony huggingface dataset:
Ethical Hacking: A Necessity

In today’s era, our data is as valuable as money and sometimes more. This is the reason the fear of information being compromised by a cybercriminal worries everyone including the government, private industry and the everyday computer user. Cyber Crime is estimated to cost $6 trillion by 2021. Because of the increasing number of attacks, people need ethical hackers. The task of an ethical hacker is to find vulnerabilities and weakness in the system or network that can be exploited to gain access to the data and then provide a solution to prevent this from happening. This paper will describe what is the need of ethical hacking, why it is so crucial regarding data security, and how it is performed. Keywords: cybercriminals, ethical hacker, vulnerabilities, exploited, technology. Cite this Article: Sagar Sharma, Ashish Rana, Rohit Prajapati. Ethical Hacking: A Necessity. Journal of Network Security. 2020; 8(2): 11–15p.
IMPLEMENTATION OF MOMENT INVARIANT IN RECOGNIZING OF ELECTRICAL METER NUMBERS

Implementation of computer vision can be done in the introduction of images or pictures of characters of numbers or letters. Based on this, then the computer vision can be used in the introduction of numbers on the electric meter or commonly called kWh meter. The underlying thing for the electric meter to be the object of research is to look at the situation, where the electric meter recorder keeps the record using the camera. Furthermore, the value shown on the electric meter will be inputted manually. Manual input requires a relatively long time because the amount of electricity meter input value is not small data. One method that can be used in recognizing the shape of the image in computer vision is the invariant moment. The results of this study indicate that the quality of the image gives effect, both in terms of the extraction of features and the accuracy of the recognition of the figure on the image of the electric meter. In addition to this, the threshold value of the euclidian distance method should also be used to limit the recognition process.
Manual estratégico para el uso del vídeo sobre estudios sociales en Educación General Básica.

Abstract In current educational practice the use of technology has intensified and one of theelements is video in the classroom. For most educators this resource is no stranger, butwithout an adequate methodology and if basic pedagogical and technical aspects are notmastered, it could prove unsuccessful in achieving educational purposes. At the San Jose deQuito Technical College in 2019, a study was conducted aimed at finding the methodologicalstrategy that scientifically supported provides guidelines on the proper use of video in theSocial Studies course. In this study, information was collected from teachers on how to usethe video in the classroom, for this, interviews were conducted with teachers in charge ofthis subject. A representative group of students was also asked about their perception of theuse of this resource, for which purpose the survey technique was used. The fact of providingthe possibility of improving the strategies for using video in the classroom will undoubtedlycontribute to achieving better results in the Social Studies topics. Multimedia, that is, theimage, the sound, the narrative and other elements that make it up, provide an ideal way foradolescents to “digest” aspects that may be abstract, without counting the motivationalburden that this resource has, hence It is very important to know how to use this resource inthe most appropriate way.Keywords: video, manual, multimedia, education, strategy
Computing Boundary Cycle of a Pseudo-Triangle Polygon from Its Visibility Graph

Visibility graph of a simple polygon is a graph with the same vertex set in which there is an edge between a pair of vertices if and only if the segment through them lies completely inside the polygon. Each pair of adjacent vertices on the boundary of the polygon are assumed to be visible. Therefore, the visibility graph of each polygon always contains its boundary edges. This implies that we have always a Hamiltonian cycle in a visibility graph which determines the order of vertices on the boundary of the corresponding polygon. In this paper, we propose a polynomial time algorithm for determining such a Hamiltonian cycle for a pseudo-triangle polygon from its visibility graph.
Pre-service Elementary School Teachers’ Reflection Before, During and After Their Social Studies Teaching Demonstration: Focusing on the Development of Instructional Design Competency

본 연구는 초등 예비교사들의 사회과 수업실연 전-중-후의 반성 과정을 탐색하는 것이 목적이다. 이를 위해 초등사회교육 방법론 강의 과정을 저널쓰기 &#8211; 수업구상하기 &#8211; 수업실연하기 &#8211; 반성하기의 단계로 설계하였다. 그리고 강의 과정에서 나타난 예비교사들의 저널쓰기, 사전·사후협의 기록, 공동사회과 수업지도안 작성, 예비교사 동료들의 평가 및 사회과 수업실연에서 초등학생들의 사회과 학습결과 기록, 자기반성 쓰기를 바탕으로 자료를 수집하였다. 연구결과는 다음과 같다. 첫째, 예비교사들은 자신의 사회과와 관련된 삶을 되돌아보고, 자신이 실연할 수업 모습을 상상하면서 사회과 수업을 설계하고, 동료들과 협의를 통해 교사로서의 사회과에 대한 학습과 초등학생의 사회과 학습 증진을 지향하는 가르침의 분석요소를 나타내었다. 둘째, 예비교사들은 실제 삶과 연결된 사회과 수업과 실제 교육현장의 사회과 수업에 대해 고민하는 사회과 수업설계 역량 형성과정을 경험하였다. 셋째, 예비교사들의 사회과 수업설계 역량 형성과정은 가르침에 대한 3가지 접근방식의 균형을 찾으려는 노력을 통해 이루어질 수 있다는 것을 확인하였다.The purpose of this study is to explore the course of pre-service elementary school teachers’ before, during and after reflection on their social studies teaching demonstration. The results of the study are as follows. First, pre-service elementary school teachers design social studies instruction by reflecting on the life associated with their social studies and imagining how they would demonstration, and through consultations with colleagues, they represented an element of analysis of teaching aimed at improving social studies learning as teachers and the social studies learning of pre-service elementary school teachers. Second, the pre-service elementary school teachers experienced social studies instructional design competency building process in which they thought about the social studies instructions connected to real life and the social studies instructions at the actual educational field. Third, it was confirmed that the pre-service elementary school teachers’ design competence of social studies instructions development process can be achieved through efforts to find a balance between the three approaches to teaching.
Chordless Cycle Packing Is Fixed-Parameter Tractable

A chordless cycle or hole in a graph G is an induced cycle of length at least 4. In the Hole Packing problem, a graph G and an integer k is given, and the task is to find (if exists) a set of k pairwise vertex-disjoint chordless cycles. Our main result is showing that Hole Packing is fixed-parameter tractable (FPT), that is, can be solved in time f(k)n^O(1) for some function f depending only on k.
Design and implementation of a cloud encryption transmission scheme supporting integrity verification

In the cloud storage environment, the integrity of private data is one of the most concerned issues for users, which has become the focus of cloud storage research. For this kind of problem, the existing schemes usually sacrifice the communication efficiency of users for higher security, which often causes a lot of computing overhead. Therefore, the purpose of this paper is to achieve the coexistence of safety and efficiency, and adopts the ideas of Chinese commercial encryption algorithms SM2 and SM3, proposes a cloud encryption transmission scheme that supports integrity verification, and gives a security analysis under the assumption of discrete logarithm problem on elliptic curve and Diffie-Hellman problem. Finally, the actual test and comparative experiment results show that our scheme can realise the cloud data transmission encryption and cloud storage integrity verification functions at the same time without affecting the performance of the cloud server. Moreover, it can effectively resist all kinds of common attacks, reduce the storage and computing burden of cloud users, and has certain guiding significance for the research of user privacy protection in the cloud environment.
CO-DESIGN VISIONS OF PUBLIC MAKERSPACES IN CHINA 

This paper discusses the design and outputs of co-design workshops used to facilitate the co-creation of a shared vision for public makerspaces in China and the design of such space.The workshops were held with Chinese and non-Chinese participants to identify similarities and differences in terms of approaches, outputs and key considerations.Initial results suggested that these workshops were effective at supporting value co-creation, as they excel at engaging participants and enable them to collaborate as equal partners.It was observed that Chinese participants were rather pragmatic and conscious of sustainable growth.
Research on Application of Big Data Combined with Probability Statistics in Training Applied Talents

In order to improve the training effect of applied talents, quantitative evaluation and big data analysis are used to evaluate the training of applied talents. A quantitative analysis model of applied talents training based on big data and probability statistics is proposed. The statistical mathematical analysis model of applied talents training is constructed, and the significance of applied talents training is analyzed by using T statistical test analysis method, and the benefit distribution model of applied talents training is established. The cumulative average analysis method is used to evaluate the bilateral reliability of applied talents training, and the big data mining and feature extraction methods are used to analyze the characteristics of applied talents training. The big data robust mining model for the cultivation of applied talents is constructed, and the descriptive statistical analysis method of single variable is taken, the statistical analysis of probability theory and big data analysis method are used to realize the evaluation of benefit index for the cultivation of applied talents. The results of empirical analysis show that the model has good accuracy and high level of confidence in the quantitative evaluation of applied talents training.
BIVARIATE CORRELATIVE OPPOSITIONAL BASED ARTIFICIAL FISH SWARM RESOURCE OPTIMIZED TASK SCHEDULING IN CLOUD

Cloud computing is an Internet-based approach provisioning of various computing services, to the users. In cloud, task scheduling is a significant process to allocate the workload for the different servers. Different evolutionary algorithms have been designed to solve the task scheduling issues in the cloud. But, the makespan and resource utilization performance were not improved during task scheduling by using population-based algorithms. In order to improve the task scheduling efficiency with minimum makespan and resource utilization, Bivariate Correlative Oppositional based Multiobjective Artificial Fish Swarm Resource Optimized Task Scheduling (BCO-MAFSROTS) technique is introduced. The main objective of the BCO-MAFSROTS technique is to reduce the workload across the cloud server by distributing the number of user-requested tasks to the optimal virtual machines. Initially, the number of user tasks are taken from the database. Then, incoming user tasks is given to the cloud sever. In the cloud server, bivariatecorrelation-based tasks prioritization method is performed for prioritized task as a high priority and low priority. Based on the prioritization, user tasks are distributed to the optimal virtual machines with the help of optimization algorithm. In the proposed BCO-MAFSROTS technique, oppositional based multiobjective artificial fish swarm optimization algorithm is utilized to perform task scheduling according to identifying optimal virtual machines in the cloud. The fitness is determined for each virtual machines based on multiple objective functions such as CPU time, bandwidth, memory and energy. From the fitness function estimation cloud server finds the optimal virtual machines for processing user-request task. Next, the experimental evaluation is carried out on factors such as task scheduling efficiency, false-positive rate, makespan and memory consumption with respect to a number of user tasks. The results discussion proves that the presented BCO-MAFSROTS technique improves the task scheduling efficiency and minimizes false-positive rate, makespan as well as memory consumption as compared to state-of-the-art methods.
A Mixed Technique to Detect Automated Spam Senders in a Network

Twitter, a popular micro-blogging service, is traditionally used to share messages and updates with a maximum of 280 characters. It is very open in nature and has a large user base which is often exploited by spammers to commit cybercrimes, like phishing, cyber bullying, harassment and spreading rumors. The proposed approach is to discriminate users depending on their activities with their corresponding followers where the user can bypass the features related to his/her activities, but avoiding those which are based on the followers is challenging. In this paper, a technique to detect automated spammers by combining metadata based features, content based features and interaction based features is proposed. Nine different features are identified for learning the dataset that includes both the legitimate users and spammers. The distinction between the feature categories is analyzed; interaction-based and content-based features are decided to be more effective for the detection of spammers, while metadata-based features are less effective.
LoRa Devices Identification Based on Differential Constellation Trace Figure

With the development of the Internet of Things, the application of LoRa devices is more and more extensive. However, the security access issues of LoRa terminals has not been paid enough attention yet. In this paper, a novel device identification method is proposed according to the modulation principle and frame format of LoRa signals, which is based on the implementation of differential constellation figure on the physical layer. Unique RF fingerprint features can be extracted from each LoRa signal frame or even the preamble part through this method, thus realizing device identification. The method principle is theoretically analyzed and then experiments are conducted with four LoRa devices using the method of pattern classification, which turns out to be a high accuracy rate device identification for every signal frame of these devices, verifying the effectiveness of the identification method.
An Analysis of Empathy-related Activities in Social Studies Textbooks of Middle School

본 연구의 목적은 중학교 사회 교과서에 나타난 공감 관련 활동을 분석하는 것이다. 이를 위해 구체적인 연구문제를 3가지 설정하였다. 공감 관련 활동이 어떤 내용과 관련하여 나타나고 있고, 공감의 유형으로 볼때 개인적 공감과 사회적 공감이 어떤 비중으로 나타나는지, 그리고 사회적 공감을 다루는 활동은 사회적 공감의 단계 중 어느 단계에 해당하는지 현행 8종 교과서를 대상으로 분석해보았다. 그 결과, 4개의 단원에 총 25개의 공감 관련 활동이 제시되어 있었는데, 내용별로 볼 때 모든 교과서가 공감 관련 활동을 제시하는 내용(역할갈등)도 있었으나 대부분은 몇몇 교과서에서만 다루는 등 교과서별 편차가 크게 나타났다. 공감의 유형별로 볼 때, 25개 중 20개가 개인적 공감을 다루는 사례이고 5개만 사회적 공감으로 나타났다. 사회적 공감을 다루는 5개의 사례를 대상으로 사회적 공감의 단계(노출, 설명, 경험)를 확인해보았을 때 다수의 사례는 3단계 즉 경험 단계에 해당하는 활동을 제시하고 있었다. 공감의 교육적 필요성이 커지고 있는 상황에서 앞으로 사회과 교과서에서 보다 적극적으로 공감 관련 활동을 다룰 필요가 있으며, 타 문화나 타 집단에 대한 이해, 특히 사회적 갈등이나 특정 집단의 문제, 불평등의 문제를 다루고 해결방법을 모색하는 과정에서 공감 관련 활동을 포함시켜 다룰 수 있도록 노력할 필요가 있다.The purpose of this study is to analyze activities related to empathy that appear in middle school social studies textbooks. For this, analysis was conducted on eight types of textbooks to figure out which contents are covered in the empathy-related activities, how much portion of them personal empathy and social empathy have respectively , and which stage of social empathy encompasses the social empathy-related activity of the textbooks. Results showed that a total of twenty five empathyrelated activities were presented in four units and although there were contents which can address empathy-related activities in all the textbook, the activities were covered only in some textbooks. Observations of empathy types show that twenty out of twenty five activities are cases that deal with personal empathy and only five were those of social empathy. Also, most of the five cases correspond to level 3 ‘experience’in social empathy stage. As educational needs for empathy is growing, empathyrelated activities should be dealt with more actively in social studies textbooks and efforts should be made to deal with empathy-related activities in processes that deal with an understanding of other cultures and groups and in particular, resolution of social conflict and issues of particular groups.
QoE in video streaming over ad hoc networks: comparison and analysis of AODV and OLSR routing protocols

Video streaming services are easily among of the most solicited services on the internet. Indeed, they are single-handedly accountable for up to 85% of overall internet traffic. In this vein, new venues of marketing strategies are constantly witnessing the emergence of ever-higher quality standards emphasising on end-user appraisal. And so, in addition to the quality of service (QoS) which is built upon network-oriented metrics, it behooves us to pay greater heed to the quality of experience (QoE); whence ensuring decent overall performance re-emerges as a challenge to be reckoned with. In fact, the extant routing protocols tend to be network-oriented and are, as such, chiefly dependent upon objective quality parameters. This article purports to experiment on video transmission through an ad hoc network, based on the two emblematic routing protocols (i.e., AODV and OLSR), so as to pinpoint the one most in-step with the afore-mentioned subjective quality (QoE) trends.
Applying machine learning to relevance evidence fusion at indexing time

The production of high quality ranking results is the main goal of web search engines. An important aspect of modern search engines is the use of a large number of distinct sources of relevance evidence to build the learning to rank (L2R) model. Collectively, they determine whether the document is relevant to a query or not. The ranking of query results is computed by fusing all sources of evidence into a single document score, for each document in the final ranking. In the past few decades, most of the works on evidence fusion has been done with the implementation of L2R methods.L2R methods use examples of queries and their respective results to train supervised learning models that determine the relative position of the documents in the result list. Once trained, the model can be used during query processing to determine the final ranking. This approach, however, inadvertently adds computational costs to query processing, which may lead to a drop in time performance. To mitigate this problem, an alternative approach was proposed in literature — Learn to Precompute Evidence Fusion (LePrEF), based on supervised learning techniques with GP (Genetic Programming). LePrEF proposes to implement the bulk of the evidence fusion during indexing time, generating a single inverted index containing unified entries representing all sources of evidence. These unified entries are called Unified Term Impacts (UTIs). Each unified term impact replaces several features with a single value in the document index, thereby reducing the effort to compute the document scores at query processing time because the system fetches and processes fewer values. The adoption of UTI values produces competitive ranking results. However, the lack of features available only at query time might lead to accuracy loss.In this dissertation we study and propose a modified LambdaMART, named UTI-LambdaMART, a gradient boosting algorithm to generate unified term impacts (UTI) values at indexing time. We also propose and evaluate a hybrid model that uses UTI values with query-dependent features. We demonstrate that our hybrid methods can deliver high-quality results on par with those of the existing state-of-the-art neural ranking models. The experimental results show that our best hybrid model, HLambdaMART, achieves an NDCG@10 value of 0.495 using only 36 features at query processing time when applied to the MQ2007 collection, while the best baseline achieves 0.490 using a larger set of features at query processing time. The use of our hybrid framework reduces the time to run LambdaMART to about 35% of the time to run it without using our proposals. In addition, we study and propose a simple method to obtain significant gains in UTI-index compression with virtually no loss in the quality of search results. Our approach was able to achieve 79% compression rate of the index, while keeping the quality of results on par with methods that do not use compression. We also conduct experiments that demonstrate the use of the UTI-LambdaMART as a base ranker.
Structural Patch Decomposition Fusion for Single Image Dehazing

In this paper, we present a new image dehazing method via structural patch decomposition image fusion, which does not rely on the accuracies of global atmospheric light and transmission. Instead of estimating the exact global atmospheric and the transmission separately as most previous methods, our method directly constructs initial dehazing images with different exposure through the histogram analysis and structural patch decomposition image fusion filter to improve the visual dehazing effect. Experimental results show that this method outperforms state-of-the-art haze removal methods in terms of both efficiency and the dehazing visual effect.
Nexus Between Financial Sector And Sustainable Development In Nigeria: A Principal Component Analysis

The financial sector has been recognized in the literature as a potential that could drive sustainable development in the developing nations and Nigeria is no exception. This study examined the link between financial sector and sustainable development in Nigeria. Specifically, study seeks to investigate the effects of the banking, stock market, and the insurance segments on sustainable development in Nigeria. Based on data availability, a time series data from 1986-2015 were obtained. The data for sustainable development-economic development, social development, and environmental quality were aggregated using the principal component analysis (PCA). The aggregated index for sustainable development and the financial sector data were subjected to ARDL-Bounds estimations. Findings reveal that the banking and the stock market subsectors positively and significantly propel sustainable development in Nigeria, while the insurance subsector drives sustainable development in Nigeria only in the short run. Study therefore; recommend policies strengthening these main segments of the financial sector in order to drive sustainable development in Nigeria. This study contributed to knowledge being a forerunner to holistically combine and study the three main pillars of sustainable development in Nigeria. Study also stands out by connecting the financial sector to sustainable development in Nigeria.
HLSGD Hierarchical Local SGD With Stale Gradients Featuring.

While distributed training significantly speeds up the training process of the deep neural network (DNN), the utilization of the cluster is relatively low due to the time-consuming data synchronizing between workers. To alleviate this problem, a novel Hierarchical Parallel SGD (HPSGD) strategy is proposed based on the observation that the data synchronization phase can be paralleled with the local training phase (i.e., Feed-forward and back-propagation). Furthermore, an improved model updating method is unitized to remedy the introduced stale gradients problem, which commits updates to the replica (i.e., a temporary model that has the same parameters as the global model) and then merges the average changes to the global model. Extensive experiments are conducted to demonstrate that the proposed HPSGD approach substantially boosts the distributed DNN training, reduces the disturbance of the stale gradients and achieves better accuracy in given fixed wall-time.