Reviewerly/exHarmony huggingface dataset:
Stroke Sequence Identification in Handwritten Urdu Alphabets Using Convolutional Neural Networks

There is a lack of attention to early childhood handwriting in developing countries like Pakistan, where a single teacher teaches 50 to 100 students at a time. Children need a lot of attention from the teacher during their early stages (4-8 years), especially when they start writing the alphabet to assure that they adopted a correct stroke sequence. It is almost impossible for a teacher to pay attention to all his/her students to observe their stroke sequences. Due to a lack of teacher attention, the students may write the alphabet with correct or incorrect stroke sequences. If a student follows an incorrect stroke sequence, it badly affects the writing speed and the beauty of his/her cursive handwriting. As the research on this topic is concerned, so far, it has been conducted mainly in Chinese and Japanese languages to find out the correct stroke sequence. Arabic has received far fewer studies and Urdu is the most neglected till now. In this paper, we have proposed Urdu Handwriting E-Tutor (UHET). UHET is a Computer Vision based method that continuously monitors the handwriting activity of a child and successfully points out whether the stroke sequence (while writing an Urdu alphabet) is correct or not. To conduct this study, we created a new dataset that consists of images and videos of five Urdu alphabets. UHET exploits Convolutional Neural Network to train the model for predicting the alphabet (written by the child) and its stroke sequence. Results show that our UHET performs well achieving 80% accuracy on the average on the given data set.
Emotional States Detection Model from Handwriting by using Machine Learning

Handwriting analysis is a multi-level approach for detecting emotion. Every person’s handwriting is different, representing how we think, feel, and act at that time. A person’s emotion is expressed in his handwriting, whether happy or sad, excited, angry, or depressed. We transmit our feelings to paper during the writing process and the way we write symbolizes those emotions. Emotion detection is an important area of study in human-computer interaction. Researchers have done a substantial amount of work to detect emotion from visual and auditory data but recognizing emotions from handwritten data is still a new and active study topic. Mostly, studies on emotion recognition focus on only 3 basic emotions: anxiety, stress, and depression. The main objective of this research is to detect the writer’s emotions from his or her handwriting, so to determine the writer’s mental state and identify those who are emotionally disturbed or sad and require mental help to overcome negative feelings and analyze the changes in handwriting patterns in different emotional states.
Impact Of Code Smells On Software Fault Prediction At Class Level And Method Level

The main aim of software fault prediction is the identification of such classes and methods where faults are expecting at an early stage using some properties of the project. Early-stage prediction of software faults supports software quality assurance activities. Evaluation of code smells for anticipating software faults is basic to ensure its importance in the field of software quality. In this paper, we investigate the impact of code smells on software fault prediction at the class level and method level. Previous studies show the impact of code smells on fault prediction. However, using code smells for class level faults prediction and method level fault prediction need more concern. We use defects4j repository for the creation of datasets used in building software fault prediction model-based. We use pseudo labeling for class level prediction and bagging for method level prediction. We extract code smells from different classes and methods and then used these extracted code smells for fault prediction. We compare our prediction results with actual results and see if our prediction is correct in order to do validation.
Contextual Word Embedding based Clustering for Extractive Summarization

Currently, the amount of content on the internet is expanding tremendously. One reason for the abundance of information is that numerous online resources cover similar themes, posing challenges and opportunities for natural language processing (NLP). People find it challenging to summarize thousands of documents on the same topic manually. Consequently, it is desirable to have multiple documents automatically summed up. This work proposed a contextual word embedding-based clustering technique for extractive summarization. At first, documents are split into sentences, and then each word in all sentences is given an embedding based on its context using the FastText embedding method. The averaged word embeddings are then used to create sentence embeddings/vectors. The Fuzzy C-Mean clustering algorithm is then applied to the collection of sentence embeddings to form clusters of semantically similar sentences. Based on the text features, the sentences inside each cluster are ranked. The final extracted summary comprises representative sentences taken from the highest-ranked sentences within each cluster. The effectiveness of the suggested methodology is tested in the context of the ROUGE evaluation metric and Document Understanding Conference (DUC) 2002 data set. Experimental results demonstrated that the presented technique outperformed the benchmark summarization techniques in terms of ROUGE measures.
A Systematic Literature Review on Block-chain Attacks

Block-chain is contemplate as a highly protected, distributed, and immutable source of transaction that is being applied in enormous sectors to intensify the privacy and security. It contains information of crypto currency Bitcoin and transactions, which has gained much attention in many industries. Besides the mentioned tremendous feature of blockchain technology, it can be vulnerable to attacks. attack, Double Spending attack, Denial of Service attack and many more. This paper presents a systematic literature review on different attacks executed on block-chain as well as their proposed detection, prevention, and avoidance methods that reveal their strengths and limitation. Although that there is still no way to detect many attacks like 51 percent attack until it is fully deployed and all the proposed security measure previously proposed are failed to provide real protection against cyber-attacks on block-chain.
Sequence-driven Neural Network models for NER Tagging in Roman Urdu

Modern Natural Language Processing research has taken a flight as it moves to address the issues of mapping contextual sequence labeling for low-resource languages. Named-Entity Recognition is one such labeling application; where text is considered contextually and labeled with the named entities. NER for Roman Urdu aims to achieve tasks such as Information Extraction, Machine Translation, and even big data operations on live digital content. There has been limited research on such NLP applications in Roman Urdu, however, work on Urdu and other languages of the family encourage active research. This paper holds comparisons using a few deep learning-based models that learn the importance of word classification by mapping to a specific context based on placement. Our model is trained on a hand-annotated corpus covering several domains. After a detailed comparison and evaluation, Bi-LSTM yields an exceptional F1-score of 82.7%. Our work demonstrates the possibility of long-range contextual understanding for processing morphologically rich low-resource languages.
Let's Prevent Spectre Attacks in the Docker Containers Too

The Spectre attacks in modern processors have been inherently conveyed in the major Docker clients. The speculative execution mechanism in a processor can be maliciously used to access unauthorized content of other users, where the processor is the same for all the tenants. Instructions and code that completed execution and remained in the micro-architecture as cache could be accessed by the attacker through cache-side channel attacks. In this paper, we propose an automated solution to detect susceptible code snippets in the binary program and implement a patch to avoid further attacks. The proposed methodology extracts control flow, address analysis and taint analysis to detect the conditional branches that maliciously access memory speculatively. We have used the Kocher tests, which are a set of susceptible code patterns to generate malicious snippets. In a nutshell, the proposed system implements fences around suspicious conditional branches that stop speculative execution in the processor. Moreover, our evaluation also considers runtime overhead, analysis time, and effectiveness.
Determining the Level of Detail of Software Requirements

Requirements Engineering is an important step in a software engineering projects. It starts with requirement gathering and documentation of user requirements. If a requirement is documented at the appropriate level of detail, it will not only help improve the quality of the requirements consequently increasing the chances of a successful project, but will also help in the reuse of the same requirements for similar types of projects and problems. This research proposes the use of Parts of Speech (POS) to determine the level of detail of software requirements. These POS based features are used for classification and each requirement is classified as either of the three i.e. Low-level (a statement contains enough information to implement the intended functionality), Intermediate-level (a statement contains multiple tasks and further details will be required in order to implement the particular requirement), High-level (a statement contains many intended functions with incomplete details). These POS frequencies are extracted from natural language text of requirements and are given as input to three classifiers namely Logistic Regression, Multinomial Naïve Bayes (MNB), and Support Vector Machines (SVM). The paper evaluates the performance of each classifier with POSF based features and the traditional Term Frequency Inverse Document Frequency (TFIDF) based features. A 6.8% increase in accuracy for MNB has been observed when POSF based features are used. These results are based on the publicly available requirements that have an inconsistent level of detail. Availability of requirements with consistent level of detail can further improve the results. We used Structural Depth Metric to measure the consistency in level of detail.
Primary Exploration on the Difference of the Automatic Picked P-Wave Onset between Three-Component Seismic Records

The determination of P-wave onset is important in built of seismic ground motion database for the construction of ground motion prediction equation, seismic site effect model, response spectra for the earthquake-resistant design and et al. Various P-wave onset automatic picking algorithm have developed for vertical seismic records. For horizontal seismic records, of which the P-wave onset are simply set the same as the vertical records. In this paper, the performance of one recently proposed P-wave onset automatic picking algorithm for horizontal and vertical seismic records has been tested using 3000 ground motion records of 51 earthquake events recorded by KiK-net and K-net of Japan Station Motion Observatory Networks in the Kanto Basin from 2000 to 2020. The results shown that the picked P-wave onset for North-South (NS) and East-West (EW) component are quite similar with the mean absolute difference equals 0.0918s. The mean absolute difference of picked P-wave onset between NS (EW) and UD component equals 0.9989s (0.9070s). Moreover, according to the picked P-wave onset, the P-wave arrival of NS and EW components are lagged behind that of UD component. Thus, the tested P-wave onset automatic picking algorithm, developed on basis of vertical component records cannot be directly applied for horizontal component records. Finally, the discussion of the variation of picked P-wave onset on the late computation of signal-to-noise ratio are included.
Baseline Correction Analysis of Luding Seismic Data

In many researches of seismic engineering, low-frequency seismic waves have important effects on bridges, dams, high-rise buildings, etc. However, the existence of various noises leads to baseline drift, which limits the availability of low-frequency signals. In order to obtain high-quality strong motion acceleration records, it is necessary to analyze the impact of various noises on the digital strong motion instrument, and conduct baseline correction analysis on the records to make them conform to the correction criteria. Based on the analysis of the Luding earthquake of magnitude 6.8 on September 5, 2022, and the baseline correction of its strong motion acceleration records, high-quality data including low-frequency seismic waves are finally obtained, and the relationship between its PGD and permanent displacement is explored.
Intelligent Monitoring of Flood Disasters Based on Medium-Sized Sponge Facilities

With the development of artificial intelligence, water conservancy informatization and disaster monitoring and early warning system has become a common development trend, looking at the water problems of urban development, combined with the current development of sponge cities, this paper proposes the intelligent monitoring concept of flood disasters based on medium-sized sponge facilities. The city itself is systematic, and the city's water problems, such as the decline in groundwater level, the shortage of freshwater resources, the prominent problem of internal waterlogging, the difficulty of water pollution to be eradicated, the ineffective protection of the water environment, the fragile fracture of the water ecology, the climate problem caused by the change of hydrological cycle, etc., have a correlation and complex cause relationship. From the perspective of solving problems, whether it is a “water-sensitive city”, low-impact development, or a sponge city, or a “near-natural system”, they are emphasizing the way to transform nature, and to a certain extent, they ignore the purpose and effect of transforming nature and solving urban water problems. Therefore, in the current urban planning and construction process, the emphasis is placed on “small sponge” facilities and medium-sized sponge facilities are ignored. The current water cycle is not only driven by natural elements, but also affected by human transformation activities, medium-sized sponge facilities can play a role in replenishing groundwater, intercepting peak flow, regulating water resources, optimizing wetland ecosystems, and at the same time, under the means of intelligent monitoring, flood disasters can be effectively prevented and avoided.
Evaluation and Analysis of Water Resources Scheduling in The Yellow River Basin Based on Improved Fuzzy C-Means

This paper established a comprehensive evaluation index system of economic development-ecological environment-water resource scheduling in 2019 by combining the status of water resources development and scheduling, economic development and ecological environment in the Yellow River Basin. The existing water resource scheduling and coordinated development in the Yellow River Basin was evaluated through Fuzzy C-means (FCM) based on genetic simulated algorithm. The application example indicated that the improved FCM could avoid falling into the local optimal, and be used for grading the clustering results, as well as getting the classification of different coordination degrees in the Yellow River Basin, then evaluate the current water resources scheduling situation of each district.
Occupations and Jobs in Cybersecurity and Related Education

New technologies and their implementation into various spheres of human life are followed by a number of changes. Some of these changes are related to the emergence of new occupations and jobs. The paper aims to present the changes in occupations and jobs, as well as in work positions caused by modern socio-economic and technological conditions. These changes inevitably create new requirements for the education and the training in the specialties related to these new occupations and jobs. The focus is on the cybersecurity occupations, the related education and its quality.
METHODS OF ANALYTICS OF BIG DATA OF POPULAR ELECTRONIC NEWSPAPERS ON FACEBOOK

Due to the popularity of social networks, all famous brands use them to promote and support their product.For example, well-known foreign newspapers such as: Washington Post, New York Times, Time, Reuters, Forbes duplicate information about news on the Facebook social network for greater readership.Using methods of automated data collection from web pages, a list of posts is formed based on which data analysis is performed.Statistical results of frequency, popularity of certain articles, audience reach and people's reaction to posts are obtained from a large volume of data.In the Java programming language and using additional Selenium, JavaFX libraries, all processes for data normalization is developed and data visualization is used.In addition, the dependence of the post coverage of newspaper editions on the number of posts published during the day is investigated in Facebook social network.The work also examines the most popular posts and their topics.The relationship between keywords and real events is analyzed.
Implementation of a secure wireless communication system using true random number generator for internet of things

This paper describes the design and implementation of an internet of thing (IoT)-based application that uses a true random number generator (TRNG) with an all digital phase locked loop (ADPLL) for secure wireless communication. Field programmable gate array (FPGA) boards were used on the transmitter and receiver sides and were interfaced with Esp8266 chips to wirelessly send and receive encrypted sensor data. The MQ-2 gas sensor and tracking sensor were connected to the FPGA board on the transmitter side, where data from the sensors was encrypted using the exclusive-OR (XOR) function and the TRNG architecture. The system can be controlled by users through a web browser served by the ThingSpeak cloud. The Artix-7 FPGA device is used to implement the proposed wireless communication system, for which design and synthesis were done using the Xilinx Vivado 2015.2 tool. The proposed system uses a low amount of power and is suitable for a standalone, highly secure TRNG-based IoT application. The National Institute of Standard and Testing (NIST SP 800-22) test showed that ADPLL with finite impulse response (FIR) filter-based TRNGs are better for encrypting IoT devices for secure wireless communication.
Supply chain strategy during the COVID-19 terms: sentiment analysis and knowledge discovery through text mining

The coronavirus pandemic has affected not only health but also the economy. The use of big data in finding information can be used to gain profits that logistics companies can utilize to survive during the pandemic. This study conducted text-mining research on service consultant sites in the logistics sector. This study aims to present frequency diagrams, analyze sentiment using the National Research Council (NRC) lexicon, present bigrams, and seek knowledge about strategies to minimize shipping costs and maintain inventories of manufactured goods. The words "supply", "chain", and "COVID-19" are words that are used frequently throughout the article. The results of this study showed that the words that often appear from word excavation are the words "supply", "chain", "logistics", "kpis," and "inventory". Then emotion trust becomes an emotional word that often appears in articles. The words "Supply" and "pandemic" are the words that seem the most positive and negative words, respectively. The words "COVID-19", "safety stock", and "inventory management" are words that often appear together. The result of discovery knowledge is that logistics consultants offer emotions of trust and provide many insights on minimizing shipping costs and maintaining inventory during a pandemic.
RDP-GAN: A Rényi-Differential Privacy Based Generative Adversarial Network

Generative adversarial networks (GANs) have attracted increasing attention recently owing to their impressive abilities to generate realistic samples with high privacy protection. Without directly interacting with training examples, the generative model can be used to estimate the underlying distribution of an original dataset while the discriminator can examine model quality of the generated samples by comparing the label values with training examples. In considering privacy issues in GANS, existing works focus on perturbing the parameters and analyzing the corresponding privacy protection capability, and the parameters are not directly exchanged between the generator and discriminator in GANs. Thus, in this work, we propose a Rényi-differentially private-GAN (RDP-GAN), which achieves differential privacy (DP) in a GAN by carefully adding random Gaussian noise to the value of the exchanged loss function during training. Moreover, we derive analytical results characterizing the total privacy loss under the subsampling method and cumulative iterations, which show its effectiveness for the privacy budget allocation. In addition, in order to mitigate the negative impact of injecting noises, we enhance the proposed algorithm by adding an adaptive noise tuning step, which will change the amount of added noise according to the testing accuracy. Through extensive experimental results, we verify that the proposed algorithm can achieve a better privacy level while producing high-quality samples compared with a benchmark DP-GAN scheme based on noise perturbation on training gradients.
Computer crime motives: Do we have it right?

Abstract Academic, legal and practitioner responses to cyber threats have been predominantly reactive, punitive, and deterrence‐based, with limited attention given to the motives underlying computer criminals' behaviors. This paper reasons that new and better theoretical perspectives are needed to explain computer criminals' motives. Following a review of the computer crime behavioral literature, a summary review of core philosophies and theories used to explain generalized crime and criminal motives is provided. A framework is proposed suggesting that criminological theories have evolved along two categorical dimensions: determinism‐indeterminism, and individualism‐collectivism. The paper then reasons that future computer crime research will benefit by considering indeterminist‐collectivist (constructivist) theories. Two such theories, social construction of technology, and actor‐network theory, are proposed in the discussion section, along with some cybercrime examples. The paper invites a deeper consideration of the origins and motivations of computer‐based criminality as a means of building stronger theory and ultimately advancing more proactive and effective solutions.