Reviewerly/exHarmony huggingface dataset:
DIGITAL EVIDENCE AS A SHAHADA IN PAKISTANI LAWS AND ITS APPLICATION IN THE COURTS

Because of globalization, world is moving fast and keeping pace with the Information technology is difficult for the countries, however, this makes more difficult for developing countries to handle this situation. As for the law-makers it cannot be expected to predict for the future, therefore, legislation designed for a specific objective may fail when a new situation arises. The same situation is faced by the legal fraternity, executive, legislature and judiciary alike in Pakistan, while dealing with digital evidence when the entire previous instrument on the law of evidence does not cover many aspects of the digital evidence. This article analyze, in the light of Shari’ah that how digital evidence is seen by the Courts in Pakistan. Digital evidence is brought through expert witness, thus the role of expert witness is also examined. At the end, assessment of digital evidence by the judges is discussed and lastly online courts and recording of evidence through video conferencing is deliberated.
An analysis of the bitcoin blockchain cryptography

Bitcoin is a virtual currency that was first introduced in 2009. The Bitcoin mining process relies on the hashing function SHA-256. There are strong indications that the Bitcoin mining process was exploited in 2010, only a year after its initial release [9]. This raises significant questions over the security of cryptocurrencies, and if they are to be used in the future, it is imperative that their integrity remains unchallenged. Further research on this topic is therefore warranted. In this paper additional frequency analyses were conducted on the blockchain in an attempt to narrow down and identify related anomalies. In order to identify how this might have been done in practice, the SHA-256 algorithm was analysed in the context of Bitcoin mining. Analyses show that the timestamp could potentially be manipulated for an easier mining process due to the Bitcoin network's lax timestamp range. In addition, invariant cycles can be found in the logical functions used in SHA-256 which could potentially allow a backdoor into the cipher without leaving an obvious trace.
Improved Neural Network and the Pontryagin's minimum Principle for Solve Fuzzy Optimal Control Problems

In this paper, a novel and practical approach are proposed to solve the fuzzy optimal control (FOC) using an improved multi-layer perceptron (IMLP) network along with the Pontryagin minimum principle (PMP). Here, it is worthwhile to mention that in the fuzzy Hamilton function, instead of functions of control and trajectory and the Lagrange multipliers, the approximate solutions are replaced based on the IMLP neural network, which is a Three-layer type.
DEVELOPING READING MATERIALS BY USING EDMODO FOR GRADE TENTH AT SMA NEGERI 11 MEDAN

The objective of this study was to develop reading materials by using Edmodo for grade tenth at SMAN 11 Medan. This study was conducted by using Research and Development design through six stages; gathering data and information, need analysis, designing materials and  media, validating, revising (final product). The subject of the study was grade tenth of  SMAN 11 Medan. The data were gathered by reviewing documents, conducting interview  to English teacher and distributting questionnaire to X IPA 7 consists of 36 students to get the students’ need. The interview and questionnaire result prove that the students need the high order thinking reading materials and media. The developed reading materials were validated by two experts which are the average scores (4.55 and 4.05) show that the reading materials are valid and suitable to use for grade tenth students of SMAN 11 Medan. The result of product distributed to the students through edmodo.
UNDERGRADUATE EDUCATION OF STUDENTS OF THE BUSINESS CAREERS OF THE STATE TECHNICAL UNIVERSITY OF QUEVEDO

Studying the satisfaction of students with respect to the educational experience lived at the time of COVID-19 will facilitate making decisions to optimize the educational service. The quality assessment criteria in virtual university education in an institution (1UADLA) were described. The methodology was exploratory-descriptive, the sample made up of 159 subjects, a questionnaire was applied in Google Form, analyzed with descriptive statistics and a discussion forum on the educational platform, interpreted with Content Analysis. The questionnaire showed student satisfaction when evaluating the dimensions Instructional Design, Teacher's Role, Technology and Organization. The data obtained from the forum revealed little satisfaction due to connectivity problems, absence of competencies for virtual education, multiplicity of distractors, lower quality of learning.
A New Framework to Protect Privacy of Location from Malicious Applications

Location-based applications have become very popular in recent years, especially during the ongoing COVID-19 pandemic. The pandemic has intensified the trend towards applications for electronic purchases and delivery services, to provide customers the facility of buying from anywhere and anytime, with great ease. However, there are some issues with these kind of purchases and delivery services. In particular, the data collected by these applications has the potential of having their security and privacy breached. Thus, trust of the service provider cannot be taken for granted. In fact, the service provider may prove to be a source of real danger to the privacy of users, especially if the service provider itself is a malicious party. Thus, it is difficult for the user to determine which applications can be trusted, and who to share their home or business location with. To solve this problem, in this research, we propose a new central platform that enables users to gather all of their (main) locations and control access to these locations in one place. Then the user can share them with other applications, such as link with hash-code included in a quick response (QR) barcode without the need to save their data in any application database. Moreover, the QR-code will be deactivated once it is used by the service provider, after the order is delivered. Therefore, the service provider will not be able to exploit the data stored in it, which means greater protection for the user's privacy. The system will offer other features for the user and the service provider as well. We have also implemented this system in a real manner to confirm its ability to improve the level of service in electronic purchase applications, in addition to improving the level of privacy of users' data.
River: Machine learning for streaming data in Python

River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performance metrics and evaluators for different stream learning problems. It is the result from the merger of the two most popular packages for stream learning in Python: Creme and scikit-multiflow. River introduces a revamped architecture based on the lessons learnt from the seminal packages. River's ambition is to be the go-to library for doing machine learning on streaming data. Additionally, this open source package brings under the same umbrella a large community of practitioners and researchers. The source code is available at this https URL.
Traffic Sign Classification Using Deep Learning

The electronic Traffic symbols are a basic aspect of our everyday lives. It contains fundamental information that guarantees the security of the apparent multitude of individuals around us. Lacking like valuable symbols, like possibly be present confronted through further mishaps, as operators accept simple grievance on how speedy they may well steadily move, educated regarding street facility, jagged rotate, academy intersections further on. Young age, approximately 13 lakh personnel pass on streets each time. This quantity could be loads better without our road symbols. In general, independent transports must equally maintain lane passing and thusly recognize and figure out traffic symbols. A deep learning design that can recognize traffic signs with near 98% exactness on the test set. These self-governing vehicles need to adhere to the traffic decides and for that, they need to comprehend the message passed on through traffic signs.
String choice: Why do harpists still prefer gut?

There are three materials commonly used for non-metallic musical strings: nylon, fluorocarbon and natural gut. Classical guitarists have, almost universally, switched from gut to nylon or fluorocarbon, but many harpists still prefer gut. A design chart for string choice will be presented, and versions of it will be shown for the three materials. These will be used to demonstrate that harpists make a choice of gauges and tensions that contrasts dramatically with the practice of guitarists. This leads them to be interested in a different corner of the design chart, where gut indeed has an advantage over nylon in terms of the internal damping behaviour of bass strings. It will then be suggested that this advantage could be eliminated by the use of fluorocarbon strings, with appropriate gauges, in the lower register of plain strings (without over-winding).
EDUCATIONAL-METHODICAL COMPLEX AS A MODERN AND INNOVATIVE TOOL

The article defines the concept of an educational-methodical complex (TMC). The characteristics of its components and structure of construction are given. The shortcomings in the construction of the teaching materials are revealed. The pedagogical requirements for the methods of constructing the teaching materials are formulated. The structural components of the teaching materials and their dialectical interrelation are analyzed.
Tremor Waveform Denoising and Automatic Location with Neural Network Interpretation

&amp;lt;p&amp;gt;Active faults release tectonic stress imposed by plate motion through a spectrum of slip modes, from slow, aseismic slip, to dynamic, seismic events. Slow earthquakes are often associated with tectonic tremor, non-impulsive signals that can easily be buried in seismic noise and go undetected.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We present a new methodology aimed at improving the detection and location of tremors hidden within seismic noise. After detecting tremors with a classic convolutional neural network, we rely on neural network attribution to extract core tremor signatures. By identifying and extracting tremor characteristics, in particular in the frequency domain, the attribution analysis allows us to uncover structure in the data and denoise input waveforms. In particular, we show that these cleaned signals correspond to a waveform traveling in the Earth's crust and mantle at wavespeeds consistent with local estimates. We then use these cleaned waveforms to locate tremors with standard array-based techniques.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We apply this method to the Cascadia subduction zone. We analyze a slow slip event that occurred in 2018 below the southern end of the Vancouver Island, Canada, where we identify tremor patches consistent with existing catalogs. Having validated our new methodology in a well-studied area, we further apply it to various tectonic contexts and discuss the implications of tremor occurrences in the scope of exploring the interplay between seismic and aseismic slip.&amp;lt;/p&amp;gt;
The Impact Of Hrm Practices On Job Satisfaction In Government Agencies

HRM practices and employee satisfaction has been a leading area of study in government sector all over the world. The influence of HRM practices on the satisfaction of employees has been in debates by scholars in the past. Accordingly, the aim of this study is to examine the impact of HRM practices on job satisfaction in Federal Authority for Identity and Citizenship (ICA) in UAE. To achieve this aim, quantitative methods was used and survey on selected sample consists of 377 respondents working in ICA. The result of this study revealed that HRM practice has a significant effect on job satisfaction. This result lead to a final conclusion that HRM practices and job satisfaction are fundamental variables to the work of government organizations. Therefore, to achieve success in public workplace, it is essential to focus on the factors that influence HRM practices such as planning, recruitment of new staff, and compensations.
Fingerprint Based Voting System Using Arduino

Biometrics are one of the non-transferrable characters by which a human be identified uniquely. After the expansion of technology these have been used in different sectors for authentication by which we can increase security. This paper describes about a system which can be used for voting process which has fingerprint verification as a step of authentication by which we can eradicate double voting by an individual. System centrally depends on Arduino board where whole data of fingerprints and recorded votes will be stored and vote count and results are stored in firebase using ESP8266-01 module.We have proposed such system to use in organizations, educational institutions, co-operative banks where the person who secured maximum number of votes elected as head of the organization holds the office of public interest
Motivating High Performance Serverless Workloads.

The historical motivation for serverless comes from internet-of-things, smartphone client server, and the objective of simplifying programming (no provisioning) and scale-down (pay-for-use). These applications are generally low-performance best-effort. However, the serverless model enables flexible software architectures suitable for a wide range of applications that demand high-performance and guaranteed performance. We have studied three such applications - scientific data streaming, virtual/augmented reality, and document annotation. We describe how each can be cast in a serverless software architecture and how the application performance requirements translate into high performance requirements (invocation rate, low and predictable latency) for the underlying serverless system implementation. These applications can require invocations rates as high as millions per second (40 MHz) and latency deadlines below a microsecond (300 ns), and furthermore require performance predictability. All of these capabilities are far in excess of today's commercial serverless offerings and represent interesting research challenges.
Automate The Mapping Phase Of Drug Side Effects Using NLP Techniques

Drug safety is an important science to detect threats related to medication consumption. Drug monitoring is often referred to as drug defence. For example, medicine's side effects can be caused by interactions, excessive doses and violence. In addition, information must be collected and mapped to predefined terms to find models in which the unintended effects are induced. This mapping is today manually conducted by experts, which can be very time consuming and challenging. In this paper the aim is to automate the mapping phase of side effects using techniques for machine learning.The model was created using data of pre-existing mappings of literal side effect expressions. The last design used the pre-trained BERT language model and the latest findings were obtained inside the NLP. The final model was correct at 80.21 percent in the evaluation of the test range. Some wordings were found to be very difficult to define for our model, mainly because of uncertainty or lack of literal knowledge. Since a threshold was introduced that left the most difficult to identify wordings for manual mapping, which is very important to make mapping correctly. However, this method could still be improved because suggested terms were created from the model, which could be used as support for the manual mapping specialist.
Bank Management System using Blockchain

Only authorized and legitimate users have access to their database banking data thanks to our remote authentication system.The user will make a cash withdrawal from his account.Users can move money to various accounts by listing the account number.To withdraw money, users must first enter the amount they wish to withdraw and then decide which account they wish to withdraw from (savings account, current account) from all bank accounts of individual users.To make purchases, the user's bank account must have sufficient funds.The blockchain has made remarkable progress in bitcoin, and its decentralization concept has gotten a lot of attention from budgetary foundations, financial markets, and academics.Decentralization is the most important aspect of the blockchain, but it penalizes proficiency, while mining results in high coin exchange costs; in some projects, such as retail, high efficiency, and minimal effort are needed.The consensus algorithm is at the heart of the blockchain.They have power over how a blockchain runs.However, there are still numerous barriers to blockchain innovation, such as adaptability and security problems, that must be addressed.We provide a comprehensive overview of blockchain innovation and blockchain architectures in this article.We also correlate certain average agreement equations used in different blockchain.The proposed framework manages blockchain-based transaction management banking records and automatically recovers data from third-party attacks.It also alerts users to data inconsistency problems throughout transactions.
1<sup>st</sup> Paris Van Java International Seminar on Computer, Science, Engineering and Technology (PVJ ISComSET) 2020

Mujiarto 1* , As’ari Djohar 2 , Sundarapandian Vaidyanathan 3 , Mustafa Bin Mamat 4 ,C-H Lien 5 , Md. Mamun Habib 6 , Erwin Sulaema 7 , Suswandari 8 , Muhtadi 9 , Sarjito 9 , Mumu Komaro 2 , Sigit Hermawan 10 , Prantasi Harmi Tjahjanti 10 , Irwan Alnarus Kautsar 10 , Jumadil Saputra 11 , Aceng Sambas 1 , Budi Hendrawan 1 1 Universitas Muhammadiyah Tasikmalaya, Tasikmalaya Indonesia 2 Universitas Pendidikan Indonesia, Bandung, Indonesia 3 Research and Development Centre, Vel Tech University, Avadi, Chennai, India 4 Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Kuala Terengganu, Malaysia 5 Department of Marine Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan 6 Independent University, Dhaka, Bangladesh 7 International Islamic University Malaysia, Kuala Lumpur, Malaysia 8 Universitas Muhammadiyah Prof. HAMKA, Jakarta, Indonesia 9 Universitas Muhammadiyah Surakarta, Surakarta, Indonesia 10 Universitas Muhammadiyah Sidoarjo, Sidoarjo, Indonesia 11 Universiti Malaysia Terengganu, Kuala Terengganu, Malaysia * mujiarto@umtas.ac.id Preface I want to say thank you to he Keynote speakers in The 1st Paris Van Java International Seminar on Computer, Science, Engineering and Technology (PVJ-ISComSET 2020) conjuction with Business Innovation and Engineering Conference (BIEC 2020), for your willingness to become the speakers in this conference. And also to all the participants of The 1st PVJ-ISComSET 2020. The 1st Paris Van Java International Seminar on Computer, Science, Engineering and Technology (PVJ-ISComSET), provides an excellent international forum for sharing knowledges and results in theory, methodology, and applications of Computer, Science, Engineering and Technology in theoretical and practical aspects. The conference aims to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development. It was carried out online through the Zoom application with a capacity of 1000 people. The Zoom application is also equipped with a Zoom breakout feature to support the implementation of contribution and streaming sessions on YouTube. List of figures are available in this pdf.
Training Optimization for Gate-Model Quantum Neural Networks

Gate-based quantum computations represent an essential to realize near-term quantum computer architectures. A gate-model quantum neural network (QNN) is a QNN implemented on a gate-model quantum computer, realized via a set of unitaries with associated gate parameters. Here, we define a training optimization procedure for gate-model QNNs. By deriving the environmental attributes of the gate-model quantum network, we prove the constraint-based learning models. We show that the optimal learning procedures are different if side information is available in different directions, and if side information is accessible about the previous running sequences of the gate-model QNN. The results are particularly convenient for gate-model quantum computer implementations.
PRODUCT REVIEW SENTIMENT ANALYSIS USING RECURRENT NEURAL NETWORK

Determining the sentiment polarity of product reviews has become a landmark work in natural language processing (NLP) and data science. This is perhaps easing the way of understanding and it is also easy to get good results with very simple methods (e.g. positive – negative words counting).In this paper deep architecture based sentiment analysis product reviews are done by Convolution Neural Network (CNN) and Long Short Term Memory (LSTM). The publically available online item reviews from Amazon reviews web portal are used for analysis. Experiments with neural network methods show promising outcomes.
Modern Technologies for Electronic Forensics

With the rapid development of cyberspace technology, electronic forensics technology has become one of the important research directions in the field of cyberspace security defense. This article focuses on the application technology of electronic forensics, and the forensics technology is divided into three categories according to the technology and method of forensics: computer forensics, mobile forensics, and network forensics. The introduction of computer forensics technology in this article is mainly based on Windows and Mac systems, including browser forensics, mail forensics and memory forensics in Windows, as well as log files, diagnostic reports, crash logs, and plist files in Mac. Mobile forensics is mainly divided into manual extraction, logical forensics, physical forensics, chip disassembly, and microcode reading, based on Android and iOS. Finally, this article briefly explains two methods of network forensics: server forensics and router forensics
Implementation of digital constant fraction timing based on field programmable gate array

Abstract Compared with analog circuits, FPGA(Field Programmable Gate Array) had the advantages of redevelopment and low cost. Compared with MCU, FPGA used hardware description language to construct the circuit, which had the advantages of simple structure and fast parallel operation speed. In this paper, the simulation of digital Constant Fraction Timing can be realized based on FPGA, and the feasibility and accuracy of the design can be proved by using the simulation waveform of Modelsim and the waveform can be captured by signaltap. Modelsim can verify the correctness of the timing between modules, and signaltap can verify the actual running effect of the program on the board.
PPLFS: A High Performance Consensus Method for Improving Throughput and Scalability in Blockchain Network

Blockchain has decentralized and immutable data structures, similar to traditional transaction systems. In the proposed PPLFS (Parallel Proof of Luck and Fair Share Model) framework, a scheme is designed to allow more than one miner to use the block of transactions, eventually increasing the throughput in terms of transactions per second.  In the meantime, more than one leader could increase the availability and avoid network roadblock. This work leverages fair share technique and parallel mining technique to transform the blockchain into a multi-users operating system and multiple leaders could be permissible to promote their computing resources. This also reduces the transaction confirmation time for the stakeholders.