Reviewerly/exHarmony huggingface dataset:
Generalizing Rate Control Strategies for Realtime Video Streaming via Learning from Deep Learning

The leading learning-based rate control method, i.e., QARC, achieves state-of-the-art performances but fails to interpret the fundamental principles, and thus lacks the abilities to further improve itself efficiently. In this paper, we propose EQARC (Explainable QARC) via reconstructing QARC's modules, aiming to demystify how QARC works. In details, we first utilize a novel hybrid attention-based CNN+GRU model to re-characterize the original quality prediction network and reasonably replace the QARC's 1D-CNN layers with 2D-CNN layers. Using trace-driven experiment, we demonstrate the superiority of EQARC over existing state-of-the-art approaches. Next, we collect several useful information from each interpretable modules and learn the insight of EQARC. Following this step, we further propose AQARC (Advanced QARC), which is the light-weighted version of QARC. Experimental results show that AQARC achieves the same performances as the QARC with an overhead reduction of 90%. In short, through learning from deep learning, we generalize a rate control method which can both reach high performance and reduce computation cost.
The Management of Physical Evidence and Chain of Custody (CoC) in Digital Forensic Laboratory Storage

Computer crime has 2 types of evidence, namely: physical evidence and digital evidence. Storage on physical evidence requires a special space that can hold physical evidence. However, a system that can store and manage physical evidence is needed.The current problem is the absence of a concept of storing physical evidence and its documentation (Chain of Custody). Management of Physical Evidence is proposed as a solution to solve the problem. This concept is in the form of a Physical Evidence Management System and Chain of Custody by taking the analogy of a Data Inventory. Problems with Physical Evidence Management require a Management System for Physical Evidence that is suitable for use in the UII Digital Forensics Laboratory. This research has successfully implemented the concept of Data Inventory. It is expected that with the concept of Physical Evidence Management the control of physical evidence and all activities related to it can be maintained and documented properly.
Proof of Bid as Alternative to Proof of Work

Proof of Work (PoW) protocol for cryptocurrency uses an excessive amount of electricity to secure the network. Many PoW coins do not have sufficient hashing power to secure itself. There are many alternatives to PoW, such as Proof of Stake (PoS), merge-mining etcetera, which uses much less electricity. However, these alternatives have some drawbacks either in terms of security, complexity, and scalability. In this paper, an alternative to Proof of Work (PoW) called “Proof of BID” (PoB) protocol introduced. PoB makes use of existing bitcoin PoW to secure all transactions, thus consuming virtually no electricity. PoB also addresses most of the drawbacks faced by PoW alternatives. We have disclosed a systematic method on how to effectively re-used bitcoin PoW to secure a blockchain with the same level of bitcoin security. A few designs issue to improve the blockchain scalability is given. We have explored various attack scenarios and suggested some remedies.
Representative Feature Matching Network for Image Retrieval

Recent convolutional neural network (CNNs) have shown promising performance on image retrieval due to the powerful feature extraction capability. However, the potential relations of feature maps are not effectively exploited in the before CNNs, resulting in inaccurate feature representations. To address this issue, we excavate feature channel-wise realtions by a matching strategy to adaptively highlight informative features. In this paper, we propose a novel representative feature matching network (RFMN) for image hashing retrieval. Specifically, we propose a novel representative feature matching block (RFMB) that can match feature maps with their representative one. So, the significance of each feature map can be exploited according to the matching similarity. In addition, we also present an innovative pooling layer based on the representative feature matching to build relations of pooled features with unpooled features, so as to highlight the pooled features retained more valuable information. Extensive experiments show that our approach can promote the average results of conventional residual network more than 2.6% on Cifar-10 and 1.4% on NUS-WIDE dataset, meanwhile achieve the state-of-the-art performance.
KEEFEKTIFAN MODEL PEMBELAJARAN KOOPERATIF TIPE PICTURE AND PICTURE DAN THINK-PAIR-SHARE DALAM PEMBELAJARAN MENULIS DESKRIPSI

&lt;p&gt;The current study aimed at investigating (1) a significant difference of cooperative models of picture and picture, think-pair-share, and conventional model, (2) the most significant effectiveness of cooperative models of picture and picture, think-pair-share, and conventional model in learning process of descriptive writing at grade X of the students of SMAK Frateran Ndao Ende. This study belonged to quasi-experimental study with randomized control-group pretest-posttest design. 90 students with 30 students in each class were employed as samples using total sampling technique with three intact classes. The data were a non-test method in the form of a descriptive writing assignment. The data were analyzed using one-way ANOVA continued by Scheffe test. The upshots of the study denote that (1) there is a significant difference of cooperative models of picture and picture, think-pair-share, and conventional model in the learning process of descriptive writing with F=140.578, and a significant value of p&amp;lt;0.05, (2) cooperative model of think-pair-share is the most effective type in the learning process of descriptive writing.&lt;/p&gt;
Effect of macroeconomic, demographic and governance factors on income inequality of selected sub-Saharan Africa Countries

This research tried to look effect of macroeconomic, demographic and governance factors on income inequality in 34 sub-Saharan Africa countries by using unbalance panel data spanning from 2010 to 2017. Two step System GMM estimation was conducted for the econometrics analysis of Dynamic Panel Data model. The finding revealed income inequality has dynamic nature. Also Kuznets’ hypothesis is worked in these countries: where economic growth is found to have significant increasing effect on income inequality in short run while in the long run its’ effect is significant and decreasing. Population growth is the other variable which is found to have increasing effect on income inequality. Differently, FDI and low perception of corruption decrease income inequality. Therefore, much focus will have to be given to achieve sustainable development objective, promoting FDI and controlling of corruption. Also managing the population growth is important.
Competences for the Future: A Comparative Analysis of Agile Certifications

Worldwide project management organizations are constantly looking for the right set of skills for professionals in an agile world, training people to acquire them and offering different kinds of certifications.A person who wants to be certified in agile practice has a difficult choice when looking for a right certificate.This paper aims to provide a comparative analysis of the most eminent agile certifications in the global market in order to help people finding the most appropriate certification for them.
Government Governance, Fiscal Transparency, and Government Accounting Information Sharing: Realization Mechanism and Path

Fiscal transparency is an important prerequisite for improving government governance, and the sharing of government accounting information is the basic guarantee for fiscal transparency.Aiming at the problems existing in the public disclosure of government accounting information in China, this paper puts forward the sharing mechanism and path of government accounting information under the network environment.We should establish an accounting information platform, and build a government accounting information sharing service system of vertical transfer, horizontal sharing, business integration, and openness and transparency, so as to realize the "single out and share resources" of government accounting information.Therefore, information users such as the public can easily obtain accounting information, and the government departments can realize the integration of accounting information and related businesses.It's helpful to improve fiscal transparency, and give full play to the role of government accounting in government governance.
KOMPARASI METODE KOMBINASI SELEKSI FITUR DAN MACHINE LEARNING K-NEAREST NEIGHBOR PADA DATASET LABEL HOURS SOFTWARE EFFORT ESTIMATION

The methods for Software Effort Estimation are divided into two, these methods are grouped into Non Machine Learning (non-ML) and Machine Learning (ML) methods [1]. The k-NN method has the disadvantage of being unable to tolerate irrelevant features and greatly affect the accuracy of k-NN. The k-NN method is also difficult to deal with missing data problems and feature categorization problems such as features that are not relevant, weight features that are not optimal, and the same features [2]. Whereas the dataset of Software Effort Estimation still has some serious challenges such as the characteristics of the data set, which are irrelevant features and the level of influence of each feature in the estimated data of the software effort [3]. This study compared the k-NN individual method with the combination of feature selection method with k-NN to find out which method was the best. The results showed that the Forward Selection (FS) method and Median Weighted Information Gain with k-Nearest Neighbor can overcome the problem of irrelevant features so as to increase accuracy in the RMSE Software Effort Estimation dataset, which is smaller in the Albrecht dataset of 5,953 using the Median method -WIG k-NN, the Miyazaki dataset is 55,421 and Kemerer is 123,081 using the FS k-NN method. The combination of kNN with Feature Selection is proven to be able to improve the estimation results better than kNN individuals. With the FS k-NN method being the best by winning in 2 datasets Miyazaki and Kemerer.
Exploring a touch-based flight control panel for pilots using tangible design principles

Touch technologies tend to replace the existing pilot-system interfaces in airliner cockpits. The use of touch screens offers many advantages for pilots and manufacturers. However it also presents major potential risks for air safety. In this paper, we explore the design space of future touch-based flight control panels for aircraft pilots. We attempt to design gestures that are more physical and robust in unstable conditions and require less visual focus, based on directional gestures and layouts that leverage spatial and proprioceptive skills. We observed the use of the control panel during a real flight in turbulent conditions. This let us explore the limits of touch-based interaction techniques in degraded contexts of use and to explore how tangible properties found in tangible and embodied interaction could help design these gestures. This also let us better understand the blurred frontier between touch-based and tangible interaction, and to reflect on interaction design principles in degraded contexts through the iterative building of an explicit design space.
Book Review: Bayesian Statistics the Fun Way: Understanding Statistics and Probability With Star Wars, Lego, and Rubber Ducks

BOOK REVIEW article Front. Psychol., 15 January 2020Sec. Quantitative Psychology and Measurement Volume 10 - 2019 | https://doi.org/10.3389/fpsyg.2019.03021
Outlier Detection for Improving Data Robust by ODAD Clustering Technique

The paper presents the concept of outliers and its detection by applying an altogether a new approach.Outliers are the odd man out data points falling under the domain of data mining.Data Mining is the evolving heading, now a days because of its ability to deal large amount of data.This paper identifies the outliers in the dataset through an algorithm named outlier detection based on angle and distance based (ODAD) which is based on clustering techniques (which is combination of the angle based and distance based approaches).It encompasses basic five steps: density calculation, cluster identification, angle calculation, Euclidean distance calculation and finally outlier identification and detection.It first calculates the density between the data points to identify the "clusters".Further, to circle out the outliers, the distance-based method of clustering is applied collectively with angle based method to calculate the distance between the data points.The algorithm assigns the rank value to top most outlier data points.Data points having highest rank values considered as outliers.ODAD is implemented in both R and MSSQL.
Investigation and Analysis on the Satisfaction of Master&amp;apos;s Education Quality in Chongqing

Through large-scale data collection and analysis, in-depth investigation and research, combined with the vertical and horizontal comparison of Postgraduates inside and outside the city, this study will finally show the overall advantages and gaps of graduate education in the form of data.Guided by the macro strategic development of Chongqing's economic and social construction, aiming at the current gap, the corresponding policy education is promoted from the three levels of the state, Chongqing and each training unit, so as to provide intellectual support for comprehensively improving the quality of graduate education in Chongqing.Through consulting relevant documents, define the key concepts such as graduate quality, graduate education quality, graduate training quality and graduate education evaluation; analyze the overall goal of graduate education; understand the current situation of graduate education quality evaluation in relevant provinces and cities; comprehensively collect the current situation of graduate education quality in our city, summarize and sort out the quality quantitative indicators; and influence the quality of graduate education The evaluation factors of quantity are summarized.This paper investigates the basic situation of master's education in Chongqing, investigates and analyses the satisfaction degree of master's training quality of 16 master's training units in Chongqing by means of questionnaires, analyses the relative indexes of higher and lower satisfaction degree of master's training quality in Chongqing, and considers how to further improve the satisfaction degree of master's training quality in Chongqing.
The Design for Electric Marketing Service Data Middle-Platform

In order to comprehensively improve the electric power marketing business data integration level, enhance the data value mining and innovation driving ability, and promote the business-oriented level of data, it is an effective way to build the electric power marketing service data middle-platform. Referring to the middle-platform design concept and engineering practices of e-business companies, combined with the characteristics of the electric power marketing, this paper analyzed the design principles of the data middle-platform, designed the overall structure of the marketing data middle-platform consisting of the gathering data center, the public data center, the data extraction center, the service ability center and the supporting system, and studied the key technology framework of the data middle-platform. In the end, taking the electric service applying timing limit analysis as an example, the application scene of the data middle-platform is introduced.
Quantum Entanglement Distribution Coexisting with Classical Fiber Communication

We investigate the performance of quantum entanglement distribution coexisting with classical fiber communication, and experimentally demonstrate the energy-time entanglement distribution with bidirectional 10 Gbps data communication on 40 km optical fiber.
Detection Mechanism for Malicious Messages on KSU Student Social Network

The internet has a considerable effect on social relations and connections among people.Social networking platforms have been an enormous medium for establishing relations and connections among different people all over the world.People, organizations and companies use these platforms to communicate and interact with their communities and audience.These platforms have made it easy for people to share information, create content, and communicate and connect with others online; however, online interaction and communication among people have resulted in the creation of many problems.Malicious contents can easily be shared and populated to reach a wider audience than by using the traditional sharing methods.Detection mechanism is a growing area of research that can detect any inappropriateness of data that is more sensitive to malicious behavior.The detection mechanism needs to be involved in the analysis of the abusing messages posted on the Twitter account of King Saud University (KSU).Text mining is one approach that can be used to detect such malicious or abusing messages.Text mining techniques provide the means to perform data classification where messages can be classified into malicious and non-malicious messages.In addition, Sentiment Analysis is used to identify user tendencies, trends, and opinions by classifying a text into positive, negative and neutral.In this paper, we aim to provide a literature review to investigate the current techniques.The study also addresses the detection of malicious messages which identifies the behavior of malicious and abusive messages.Based on the extensive review of the current techniques, our focus is on the analysis of Arabic and English tweets on KSU's Twitter account.First, data was collected from Twitter.This was followed by the preprocessing phase.Then, a corpus was produced applying a machine learning based approach by using Naive Bayes and Random Forest Classifier algorithms.Subsequently, the study focused on comparing the accuracy and performance of the Naive Bayes classifier with Random Forest Classifier algorithms in analyzing Arabic and English texts.In order to ensure reaching accurate results, Arabic and English tweets were analyzed.
Effects of the Dictogloss Method in Teaching Listening Skills to Students

The purpose of this study was to investigate the effect of the dictogloss method in teaching listening to students' listening skills. This research was designed as a quasi-experimental. This study aims to investigate whether a material or treatment yields a different outcome for participants. There are two classes in this study, namely the experimental class (which is a sample to be treated with the dictogloss method) and the control class (using a method other than dictogloss). The results of this study are the results of data analysis showing that the results of learning skills in listening to the experimental group obtained the average value for the initial test of 60.33 and 71 for the final test score. While the control group using conventional methods has a mean value for the initial test of 60.1 and 70 for the final test. T test results also indicate that the significance value is 0,000. The significance value states smaller 0.05, it can be stated that Ha is accepted and Ho is rejected, which means that there is an influence of the dictogloss method in improving listening skills in students
Resource Separation in Dynamic Logic of Propositional Assignments

We extend dynamic logic of propositional assignments by adding an operator of parallel composition that is inspired by separation logics. We provide an axiomatisation via reduction axioms, thereby establishing decidability. We also prove that the complexity of both the model checking and the satisfiability problem stay in PSPACE.
Thematic Geovisualization of the Data Profile of Kaligesing, Purworejo, Central Java

The scientific field has a variety of purposes, one of which is the presentation of data and information which can be used by other parties to support their decision making. Moreover, the information is presented spatially. This research aims to map the data profile of Kaligesing district to establish the region’s potential through thematic geovisualization of its data profile, such as slopes, land use, livelihoods and population. The primary data were obtained from visual interpretation of remote sensing images to extract land use information, and DEM processing to extract slope information. Secondary data were provided by the Kaligesing district government. In order to build tiered spatial modelling, each thematic map was classified and weighted according to its contribution to the potential of the region. Based on this modelling, each village was given a compilation of weights, which were used as a basis for regional potential analysis. From the results of the thematic mapping, Kaligesing has three villages that have the potential for development in the agricultural, trade and service sectors, supported by the potential of human resources, and the abundant non-residential land available.
The Ethics of the Digital: Crowds and Popular Justice in Bangladesh

Bloggers are not really street activists, " Anik, 1 a Bangladeshi online activist told me.We were chatting weeks after a massive crowd of protesters had taken over a major junction in the capital city in early 2013.A group of bloggers who had long been demanding justice for war crimes sensed a conspiracy in the workings of the International Crimes Tribunal (ICT).Set up in 2010 by the government of Bangladesh under the leadership of the Awami League to try the collaborators of the war of independence of 1971, the ICT had become the target of a spontaneous outburst.The bloggers occupied Shahbag, a busy crossroads in the middle of Dhaka and had rallied a crowd of thousands in a matter of days.The platform that quickly became known as the Shahbag Movement questioned the seeming lenience in one of the tribunal's verdicts.Because 2013 was an election year, the protesters suspected strategic negotiations between the government and Jamaate-Islami, the largest Islamist opposition party.A number of Jamaat-e-Islami's high-ranking members were under trial, and it was believed that the verdicts could very well be used in exchange for political concessions.In other words, long-held suspicions of foul play in the judicial process, the electoral system, the party in power, and the state in general had stoked the anger of the relatively young organizers of the protests.
ROI Estimation in a Scrum Project: A case study

Financial metrics are necessary to inform decisions about the beginning or continuity of a software development project to justify investments. This research discuses initial ROI (Return on Investment) estimates in a software project using Scrum and how to analyze variations in the initial calculations to make return on investment decisions during partial deliveries of the product. The case study included a survey, a review of documentation, two focus group sessions, and an exercise involving application of the proposed technique. Twenty-four professionals participated, of which 4 were Scrum trainers (17%), 4 were officials of the company where the estimation technique was applied (17%), and 16 were project managers of domestic and foreign software development companies (66%), all of whom had experience in project management. This study provides elements to be considered in future research on ROI calculation in projects using Scrum, and can be used as a guide to estimate and review financial metrics during the execution of an actual project.