Reviewerly/exHarmony huggingface dataset:
Quantum state engineering using weak measurements

State preparation via postselected weak measurements in a three-wave mixing process is studied. We assume the signal input mode is prepared in a vacuum state, coherent state, or squeezed vacuum state, while the idler input is prepared in a weak coherent state and passes the medium characterized by the second-order nonlinear susceptibility. It is shown that when the single photon is detected at one of the output channels of the idler beam's path, the signal output channel is prepared in a single-photon Fock state, single-photon-added coherent state, or single-photon-added squeezed vacuum state with very high fidelity, depending upon the input signal states and related controllable parameters. The properties of squeezing, signal amplification, second-order correlation, and the Wigner functions of the weak-measurement-based output states are also investigated. Our scheme promises to provide an alternate effective method for producing useful nonclassical states in quantum information processing.
Image Classification of Autism Spectrum Disorder Children Using NaÃ¯ve Bayes Method With Hog Feature Extraction

Autism Spectrum Disorder (ASD) is a developmental disorder that affects a person's ability to communicate and interact socially. Every year, the number of people diagnosed with Autism Spectrum Disorder rises, necessitating early detection in order to limit the number of people affected and provide proper treatment. As a result, a system was developed in this study to detect Autism Spectrum Disorder in facial photos utilizing versions of the Nave Bayes approach and HoG feature extraction. HoG feature extraction is a local intensity gradient distribution or edge direction perpendicular to the gradient direction without influencing the geometric and photometric transformations, and Nave Bayes is a method that classifies images based on probability. The experimental results of three types of naive Bayes, Bernoulli naive Bayes is the most reliable than Multinomial naive Bayes and Gaussian Naive Bayes. Accuracy, Precision, Recall, and the highest F1-Score using this method, with each value of 89.72%; 90.54%; 89.72%; and 89.9%. The next best performing Gaussian Naive Bayes, the most laborious results were obtained using Naive Bayes multinomials, which had Accuracy, Precision, Recall, and F1-Score of 65.91% each; 68.09%; 65.91%, and 64.19%.
Voice and Text Encryption using Chaotic Circuits

An ever-increasing usage of internet from simple data transmission to highly sensitive data storage has given rise to the need to establish techniques for securing such transactions. Cryptography has been widely used in recent decades to provide protection for the Internet data, although mainly used in the diplomatic and military societies. These regularities have made randomness in data processing a key element for security. The generation of random numbers is a subject which continues to interest the researchers since several decades. Chaos theory is one such approach to demonstrate the randomness of the data in Cryptosystems. Chaotic circuits have been used in this paper to produce different multiple keys to be used for both text and voice encryption. The significant difference in the initial constraints of chaotic circuits is seen to produce entirely different keys and cipher text.
Low Complexity Correlation Power Analysis by Combining Power Trace Biasing and Correlation Distribution Techniques

Side channel attack (SCA) is a class of crypt-analytic attacks for security evaluation of cryptographic and embedded microprocessor implementations.Among several SCA approaches, the correlation power analysis (CPA) is an efficient way to recover the secret key of the specific cryptographic algorithms running on the target devices such as embedded microprocessors.However, the evaluation process is time-consuming since a large number of traces are required to overcome the impact of noise.Hence, this paper proposes new methods to reduce the computation time by using Point of Interest (POI) extractor with the power trace biasing technique and the correlation distribution for the low complexity correlation power analysis (CPA).The theoretical explanations are provided and the experiments on different platforms such as ASCAD and RISC-V processor based databases are conducted to justify the proposed techniques.Especially, our experiments are performed with different protected schemes such as masking, hiding and combined hiding-masking techniques.The experimental results indicate that our proposed methods provide reliable results in comparison with the standard CPA.By using only a half of the power traces for taking the POIs, our first proposal not only decreases the execution time approximately by half but also enhances the success rate of the attack.Moreover, the second method based on power trace biasing technique is proposed in order to achieve better results and reduce the number of traces needed for selecting the POIs.With only 28.9% of given power traces needed, our second proposed technique reduces the execution time to only 2.6 times of the standard CPA.
PUBG ban and issues of online child safety during COVID-19 lockdown in India: A critical review from the Indian information technology act perspectives

In June 2020 India opted for banning 59 Chinese Apps. Contrary to the apprehension of banning gaming apps including PUBG, the government did not do so when the Chinese apps were banned. But soon PUBG also got banned in India. Several stakeholders had raised their concerns in this regard as PUBG may affect the mental health of children who are the major users of this app. But on the other hand, it had been observed that PUBG had remained the biggest respite for children who were confined to homes due to COVID- 19. S.69A of the Information Technology Act 2000 (amended in 2008) had been the main tool by the government to block access to Chinese apps. PUBG developers had however tried to outsmart S.69A of the Information Technology Act, 2000 (amended in 2008), which had been used primarily to block the website and by using Artificial Intelligence for preventing children below 18 from being engaged for a continuous period, being exposed to nudity, etc. This article aims to analyse the tussle between the scope and inherent meaning of S.69A and 79 of the Indian Information technology Act, 2000 (amended in 2008) and the ever-developing due diligence of the game developers with special concern for child safety and mental health of the children, which may make the later a winner or a loser in the legal battleground.
Cryptography: A Brief Review

Abstract: This paper reviews the concept of cryptography and some of the cryptographic algorithms. It discusses the importance of cryptography and how it is useful in the data security world. The history of this concept goes way back and has found uses in the times of wars. It has become way more advanced and complicated than it used to be but is still not perfect. Keywords: Cryptography, Asymmetric Cryptosystem, Cipher text, Encryption Algorithm, Diffie-Hellman, DES, 3DES
AMR-CNN: Abstract Meaning Representation with Convolution Neural Network for Toxic Content Detection

Recognizing the offensive, abusive, and profanity of multimedia content on the web has been a challenge to keep the web environment for user’s freedom of speech. As profanity filtering function has been developed and applied in text, audio, and video context in platforms such as social media, entertainment, and education, the number of methods to trick the web-based application also has been increased and became a new issue to be solved. Compared to commonly developed toxic content detection systems that use lexicon and keyword-based detection, this work tries to embrace a different approach by the meaning of the sentence. Meaning representation is a way to grasp the meaning of linguistic input. This work proposed a data-driven approach utilizing Abstract meaning Representation to extract the meaning of the online text content into a convolutional neural network to detect level profanity. This work implements the proposed model in two kinds of datasets from the Offensive Language Identification Dataset and other datasets from the Offensive Hate dataset merged with the Twitter Sentiment Analysis dataset. The results indicate that the proposed model performs effectively, and can achieve a satisfactory accuracy in recognizing the level of online text content toxicity.
Verification Assisted Gas Reduction for Smart Contracts

Smart contracts are computerized transaction protocols built on top of blockchain networks. Users are charged with fees, a.k.a. gas in Ethereum, when they create, deploy or execute smart contracts. Since smart contracts may contain vulnerabilities which may result in huge financial loss, developers and smart contract compilers often insert codes for security checks. The trouble is that those codes consume gas every time they are executed. Many of the inserted codes are however redundant. In this work, we present sOptimize, a tool that optimizes smart contract gas consumption automatically without compromising functionality or security. sOptimize works on smart contract bytecode, statically identifies 3 kinds of code patterns, and further removes them through verification-assisted techniques. The resulting code is guaranteed to be equivalent to the original one and can be directly deployed on blockchain. We evaluate sOptimize on a collection of 1,152 real-world smart contracts and show that it optimizes 43&#x0025; of them, and the reduction on gas consumption is about 2.0&#x0025; while in deployment and 1.2&#x0025; in transactions, the amount can be as high as 954,201 gas units per contract.
Teacher and Students’ Talk in Speaking Classroom Interaction

Classroom interactions are typical class situations where verbal contact is dominant, which means that the teacher has a significant impact on the students speaking ability. Classroom interaction and speaking ability have related to created interaction on teaching classroom process. This study purpose at analyzing the realization of classroom interaction especially teacher talk and students talk occurred during teaching speaking class. The data were taken from SMA Negeri 3 Seluma in the form of observation, interview, and documentation. The data then were analyzed by the framework adapted from Flanders Interaction Analysis Categories (FIAC). The findings showed that the classroom interaction most dominant is the teacher while students during speaking class. It also described those teacher roles during classroom interaction as director, manager, and facilitator. The study recommends the teacher support students to actively speak when learning the classroom process. Finally, this study suggests a further study to find various frameworks in analyzing classroom interaction discourse which covers different abilities.
Using Google Trends to analyze top EdTech-trends 2020 in Russia and worldwide

Problem and goal. The top Educational Technology (EdTech) trends 2020 in the world and in Russia particularly are examined. The analysis involves the research of Gartner - the worlds leading advisory company that is specialized in information technology market, the Organization for Economic Co-operation and Development, The Open University - a public research university, LD Global Sentiment Survey and HolonIQ - a market intelligence platform. Methodology. The data from these reports was aggregated, grouped and analyzed by using Google Trends. The geographical areas embraced Russia and the whole world. The data was sorted by choosing Jobs and Education section and 2020 year as a time period. Results. Four dominant areas were identified: 1) industries; 2) formats; 3) tools; 4) analytics. Only the first two categories were involved in the analysis. The section formats embraced massive open online courses, hybrid education, blended learning, personalization, flipped classroom, inclusive education and simulation-based education. The section tools included mobile technologies, social networks, mixed and augmented reality (AR/VR), virtual classrooms, artificial intelligence (AI) and learning management system (LMS). Each section element was illustrated by the EdTech case. Conclusion. The most popular search query in the formats group among Russian users and the global audience in general was the implementation of inclusive education during the COVID-19 pandemic. Concerning the tools category, all users were interested in the features and types of LMS platforms for organizing online classes in 2020.
Machine Learning-Based Time-Series Data Analysis in Edge-Cloud-Assisted Oil Industrial IoT System

With the rapid development of the Industrial Internet of Things (IIoT) and edge computing techniques, in situ intelligent sensors are continuously generating increasing and vast amounts of time-series data. In many industrial applications, particularly highly distributed systems positioned in remote areas, repeated transmission of large amounts of raw data onto the remote server is not possible. This poses a significant challenge to the timely processing of these data in IIoT. Analyzing and processing all the raw data remotely in the cloud server is impractical and has very low efficiency owing to network latency and the limited cloud computing resources. Failure of detecting abnormal data may result in major production safety problems. Therefore, businesses are moving machine learning capabilities to the edge to enable real-time actions in the field. In this study, we present a machine-learning-based edge-cloud framework to solve this problem. First, robust random cut forest and isolation forest algorithms are employed at the edge gateway to the collected data for the detection of anomalously changing data. Subsequently, these preprocessed time-series data are transmitted to cloud services for data trend prediction and missing data completion using the long short-term memory recurrent neural network method feed in conjunction with the original sequence of historical data combined with the first-order forward difference data. The experimental results show that the machine-learning-based edge-cloud-assisted oil production IIoT system can improve substantially the efficiency and accuracy of time-series data analyses.
Mobile Application for Data Acquisition in Integrated Forest and Land Fires Patrols

Hotspot is one of the indicators of forest and land fires which is usually checked in the field through an integrated patrol program by a team formed by the Ministry of Environment and Forestry (KLHK). The findings from these patrol activities are usually reported through texts posted in the chat group media using the WhatsApp application. This method is, however, considered ineffective in presenting information queries from the patrol data. Therefore, this research aims to develop a mobile application to record data retrieved during forest and land fire patrols in the Sumatra region. The prototyping system development method was applied and this includes analyzing the needs of the user, designing and modeling the prototype, coding the system, testing the system, and system usage. Moreover, the Ionic 3 framework was employed in the system development through the application of the API from the back-end module. The application was integrated into the Google Maps Application Programming Interface (API) and the system was tested using the Blackbox method to show the features in the system function effectively.
An Automatic Post Editing With Efficient and Simple Data Generation Method

Automatic post-editing (APE) research considers methods for correcting translation results inferred by machine translation systems. The training of APE models, generally require triplets including a source sentence ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$src$ </tex-math></inline-formula> ), machine translation sentence ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$mt$ </tex-math></inline-formula> ), and post-edited sentence ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$pe$ </tex-math></inline-formula> ). As considerable expert-level human labor is required in creating <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$pe$ </tex-math></inline-formula> , APE researches have encountered difficulty in constructing suitable dataset for most of language pairs. This has led to the absence of APE data for most of language pairs, such as Korean-English, and imposed limitation to the sustainable researches of APE. Motivated by this problem, we propose a method that can generate APE triplets using only a parallel corpus without human labor. Our proposal comprises three noise generation techniques, including random, part of speech tagging (POS) based, and semantic level noises, and the effectiveness of these methods are verified by the results of quantitative and qualitative experiments on Korean-English APE tasks. As a result of our experiments, we find that POS based noise encourages the best APE performance. The proposed method is influential in that it can obviate expert human labor which was generally required in APE data construction, and enable the sustainable APE researches for the most language pairs where human-edited APE triplets are unavailable.
Role of Transformational Leadership Style of Accounting Professionals towards Subordinates Performance, OCB, and Innovation: Mediating Effect of Integrity and Thriving

This study aims to examine the role of the transformational leadership (TL) style of accounting professionals towards subordinates’ performance, organizational citizenship behaviour (OCB), and innovation with the mediating role of integrity and thriving at the workplace in Karachi. A clustered sample of 471 accounting professionals has participated in the research based on the nonprobability convenience sampling method. A five-point Likert scale questionnaire was developed as a data collection instrument while the survey method was used to collect responses from the sample population. PLS-SEM has been employed in the study for data analysis using Smart PLS. The results demonstrate that integrity has a positive effect on performance, OCB has a negative but insignificant effect on performance whereas TL has a positive effect on integrity, integrity at the workplace (IWB), and thriving at the workplace. As far as mediation effects are concerned, it was found that TL has a positive effect on IWB with the mediation of thriving at the workplace, has a positive but insignificant effect on OCB with the mediation of thriving at the workplace, has a positive effect on performance with the mediation of integrity, has a positive but insignificant effect on performance with the sequential mediation of thriving at workplace and OCB, and has a positive effect on performance with the mediation of thriving at the workplace. Furthermore, thriving at the workplace has a positive effect on IWB and performance, and has a positive but insignificant effect on OCB as well as has a positive but insignificant relationship with the mediation of OCB and TL. The study findings propose that the managers should focus on developing a conducive network to help the organization strive. Moreover, the focus should be driven towards employees’ learning to share their views with fellow auditors whereas the controllers should discuss the intended primary purposes and nature of organizational financial targets.
A Novel Feature Extraction Descriptor for Face Recognition

This paper presents a new feature extraction technique for face recognition. The new model, called multi-descriptor, is based on the well-known method of local binary patterns. It involves many different neighborhoods of the central pixel. Its unique advantage is that this descriptor allows the use of different neighborhood sizes instead of only one point. This structure ensures reasonable effectiveness and also provides the possibility to obtain a different distribution of features. Based on the new descriptor, a face recognition model using the pairwise feature descriptor based on the proposed descriptor was developed in this work, and local binary patterns were created to investigate the similarity and dissimilarity between the two models. For both models, the training was done using the support vector machine method on different face databases to overcome face recognition problems such as camera distance, expression, large head size, and illumination variations. The proposed technique achieved perfect accuracy on almost all tested databases including the Extended Yale B and Grimace database.
Sistem Pakar Diagnosa Penyakit Kulit Kucing Menggunakan Metode Naive Bayes Berbasis Web

This study aims to implement the Naive Bayes method in a skin disease diagnosis system in cats. The Naive Bayes method in this application system uses disease probability calculations, calculates the probability of symptoms, calculates the posterior probability of disease, and calculates the maximum value of each disease. This method is used to analyze the results of the diagnosis of skin diseases in cats based on the symptoms of skin diseases. Data analysis on this application system is based on the results of tests carried out 50 times to produce disease diagnosis results from the user and calculated by the system to produce the same diagnostic results as calculations performed manually. With this facility, cat owners can provide first aid to help their cat before being taken to the clinic and further examined by a veterinarian.
Institutionalizing Technologies in South African Universities towards the Fourth Industrial Revolution

This study investigated the essentials for institutionalizing technologies for teaching and learning across the three types of universities in South Africa. This was with a view to determining the skills required for teaching and learning in the 4IR era. In order to obtain the data needed for the study, cross-sectional survey design was employed and online interview was conducted on Microsoft Teams and Zoom with the participants to elicit first-hand information. The population consisted of all the Executive Deans/Deans of Faculties in twenty-six (26) South African Universities. A total of twenty-two (22) Universities were purposefully selected to allow fair representation to make the findings generalizable. Seventy (70) Executive Deans/Deans participated in the online data collection via Microsoft Teams and Zoom. Content analysis and descriptive statistics were employed for analyzing data. Findings revealed that the universities in the Republic deployed fifty-seven different technologies to facilitate their teaching and learning activities, and teaching platforms had been supplemented with new technologies such as WhatsApp, Zoom, and MS Teams. Although resistance had been experienced in the past, the teaching staff is competent to moderately competent in the use of existing technologies for teaching because most faculties had provided training in an on-going basis. Improvements in the standard and quality of teaching and learning were observed with the aid of emerging technologies. The study concludes that while Universities are settled to adopt blended learning as the strategy to implement the convergence between human and machines in the era of the fourth industrial revolution, there is a need to have a national policy in place that deals with funding (special grant) to plug the gap on the digital divide.