Reviewerly/exHarmony huggingface dataset:
Multi-input MLP and LSTM-Based Neural Network Model for SQL Injection Detection

One of the most dangerous attacks on web applications is the SQL injection attack. These attacks are mainly transmitted by clients on web applications that create SQL queries using low-level strings. An SQL injection can make our database susceptible to unknown users. Though a lot of SQL Injection detection techniques and methodologies have been used to prevent web applications from such injection attacks, the evolution in different types of injections by the attackers made it impossible to detect all types of injections. The use of new technologies and methodologies by the attackers or the intruders in different types of injections made the SQL attacks stronger and undetectable easily. Hence, a lot of machine learning detection techniques and methodologies and their combinations are used by the injection or attack detectors to detect such undetectable attacks and try to secure web applications from such SQL injection attacks completely. From the literature review, we concluded that the LSTM and MLP classifiers have the best performance among all the machine learning classifiers. In this study, an LSTM and MLP ensemble multi-input neural network model is proposed. The results show that the proposed model is the best model for detecting SQL injection attacks.
Packing large segmented volumes for visual analysis

Volumetric data subject to visual analysis can occupy hundreds of gigabytes, which excludes the possibility of their visualization with an acceptable frame rate on conventional computers. If the data is segmented, its volume can be significantly reduced by tringulating the surfaces of the segments. This solution works well for opaque segments. This paper proposes an algorithm for packing segmented volumes, which allows the user to dynamically control the transparency of the segments, which facilitates the visual analysis of volumetric data. The algorithm assumes that the direction of observation is fixed. Therefore, if you want to analyze the volume from several angles, packing must be performed for each angle independently.
A Robust Approach for Arabic Document Images Segmentation and Indexation

Libraries contain huge quantities of printed Arabic historical documents. These documents usually contain both text and graphics. In order to digitize them in an automatic way, it is essential to be able to first separate the text from the graphics and then to use OCR for text recognition. This separation of text and graphics is a crucial step that conditions the performance of document recognition and indexing systems. It involves the text area identification and automatic separation from graphical components in a document image. This work presents a fast and efficient method based on hybrid modeling using graphs and structural analysis for textual separation of non-textual components in document images. We propose to use the RLSA smoothing algorithm for segmentation and the DTW (Dynamic Time Warping) algorithm will be used to match word features. Simulation results show the efficiency of the proposed approach.
Research and development of methods for automating the design of scientific papers according to specified requirements

This article discusses the concept of using the Markdown markup language instead of the WYSIWYG approach to create an automated system for the design of scientific texts. The paper demonstrates how the markup language, Markdown and the Pandoc converter, can significantly reduce the time for editing and making edits to scientific papers due to the possibility of automatic linking, bibliography generation and collaboration. The architecture of the system of automated registration of scientific documents is also considered, which allows you to eliminate the problem of registration and transfer it to the shoulders of a program compiling DOCX, PDF or other necessary text format from markup according to pre-written style templates. In general, the use of markup languages is a more flexible and convenient approach for writing scientific papers, such as academic assignments, term papers, articles, and can be widely used in educational institutions, both by students and teaching staff.
IoT forensic analysis: A family of experiments with Amazon Echo devices

Criminal Investigations involving digital devices often focus on the analysis of mobile phones, tablets, and computers associated with the suspect or the victim. The connection of IoT devices to criminal investigations may not always be considered or understood by professionals handling the crime scene. The language, experience, and understanding needed to articulate the appropriate causes required to include IoT devices in search warrant affidavits are not always known or available to case agents overseeing the evidence-gathering portion of the investigation. For this reason, we introduce a novel methodology that shows how to locate and identify IoT device owner account information, device specifications and configurations, and the location of user activity on the device. We use this methodology to carry out a family of eight experiments on IoT devices that can assist law enforcement professionals in the construction of search warrant affidavits with information that will help satisfy the legal requirement to show evidence of a crime is likely contained on IoT devices. In this paper, we focus on Amazon Echo Show IoT devices and the legal justification for seizing and examining the devices, methods of extraction, and location of user-related artifacts on IoT device hardware. Overall, the implications of our study offer law enforcement professionals specific, practical instructions on how to deal with Amazon IoT devices involved in a crime scene. The analysis of data related to these devices is presented through practical demonstrations of these devices in action.
Resource Allocation of a Modular ANN Implementation Using LabView and FPGA Board

A breakthrough in Machine Learning (ML) based technologies has been seen during last years. To overcome energy, latency and security issues, new concepts are developed to bring the ML codes, namely the Artificial Neural Network (ANN), closer to the systems (Edge-Computing devices, AI-dedicated hardware, smart embedded systems, and derivatives). In this paper, we present a modular implementation approach of the Feed-Forward ANN using LabView VIs and compiled on a Field Programmable Gate Array (FPGA) board embedded in the NI Elvis III station, and we explore the number of resources required to run each part of the ANN modules (Look-Up-Table (LUT) slices, RAM blocks, Register slices, and DSP slices). The modular programming of the ANN allows getting detailed percentages of the resource allocation and helps identifying the most resource-hungry module of the ANN. The experimental results of the ANN implementation on the FPGA show that the LUT represents the highest percentage of the FPGA resource used by the different ANN modules (Input generation, desired output, Loss Function, Update Function, Feed-Forward). The second most used resource is the Register Slices, followed by the DSPs then the block of RAMs. The experiment also shows that the Feed-Forward module of the ANN is the most resource-hungry due to the computation and normalization of the ANN mathematical functions.
Optimal Color Image Watermarking Based on DWT-SVD Using an Arithmetic Optimization Algorithm

Digital watermarking is now recognized as an effective method to ensure authorized access, facilitate content verification, and prevent unauthorized copying, which helps to increase the security of image sharing, transfer, and indexing. In this paper, we propose an optimal watermarking algorithm for color images based on discrete wavelet transform (DWT), singular value decomposition (SVD), and arithmetic optimization algorithm (AOA). The host image is initially divided into subbands via the proposed watermarking process, and each subband singular value is then altered by a scaling factor (α) to achieve the highest robustness possible. Finding the optimal value of α is a challenging problem. To determine this optimal value automatically, we use the AOA algorithm, which allows us to achieve high robustness without reducing transparency. The simulation results clearly show the effectiveness of our proposed watermarking algorithm in terms of image quality and confidentiality.
Stocks, Memes or Miles? A New Crypto Taxonomy &amp;amp; Regulatory Paradigm

Crypto is at a crossroads. After trillions in value destruction, a cascade of bankruptcies – including FTX’s implosion – and millions of defrauded believers, many are wondering whether the sector has a future. Regulators, meanwhile, aren’t taking chances, “carpet bombing” crypto with legal actions. The stakes could not be higher. Yet, consensus regarding first-order questions – including, what is crypto? – could not be lower. Part of the problem, this Article posits, is that we have largely been thinking about crypto incorrectly. Rather than a single asset type, crypto represents a highly heterogenous, 10,000-instrument universe, encompassing distinctive features and economic characteristics. One size cannot possibly fit all. This Article’s essential contribution to the literature is a novel unifying framework to taxonomize the crypto sector based on asset-specific economic substance and legal attributes. The taxonomical framework is a powerful tool for understanding market structure, illuminating abusive industry practices and identifying areas for regulatory action – findings with critical implications for policymakers, regulators and jurists. In particular, the Article shines a light on crypto’s murkiest corner: so-called “utility tokens,” which it finds most economically analogous to loyalty programs, such as airline miles. However, the Article uncovers a day-and-night mismatch between regulatory guidance and market practice around these instruments, which it terms the “Pre-Utility Token Problem.” Pre-Utility Tokens are legally problematic instruments (i) labeled as “utility tokens,” (ii) marketed like stocks, but (iii) offering minimal legal rights – most akin to digital trinkets. Unfortunately, such “assets” are quite common, creating multi-layered classification incongruencies that harm all stakeholders – exploiting investors, ensuring poor governance and facilitating accounting shenanigans.Fortunately, the most acutely problematic aspects of the status quo can be alleviated. First, regulators must enforce asset-level delineations for crypto while emphasizing long-neglected consumer protection. Second, the accounting mismatches for company-created tokens must be remedied to ensure financial stability and appropriate incentives. Finally, calls for relaxing crypto regulatory standards should be met with skepticism, given proponents’ unpersuasive arguments and significant risks to the public.
An Overview of the Impact of Partial Occlusions on Automatic Facial Expression Recognition

Automatic facial expression recognition is useful in various areas, such as health, road safety and marketing, where feedback on user state is significant. Even with very good results in controlled settings (frontal face, no occlusion, good illumination), facial expression recognition is still challenging today under unconstrained environments (natural environment). Occlusions are particularly difficult to handle because they hide parts of the information on the images. Various solutions have been proposed to address this issue. These solutions can be generally categorized into two categories: those that focus on visible regions of the face and those that try to reconstruct the hidden part. Up-to-date solutions are mainly based on texture or geometry, and few are based on movement. In this paper, we show the impact of occlusions on facial expression recognition, the methods proposed to address them, and the protocols used in the literature to evaluate these methods.
Image Captioning: An Understanding Study

Image captioning consists of describing the image content with words and sentences. This task can be seen as the junction of Natural Language Processing (NLP) and computer vision. Describing the visual content of images remains a difficult task because it involves both image and text processing algorithms. To better understand this new research area, the main objective of this paper is to present an image captioning comprehensive study. In this work, the most used techniques, datasets, and evaluation metrics will be presented and discussed. At the end, a comparative study between an attention model and a transformer model for image captioning will be presented.
Multiple visual relationship forecasting and arrangement in videos

Visual relationships in videos are often causally connected and time-dependent. The complicated temporal dependency between visual relationships is overlooked in visual relationship modeling. To explore the realistic interactions and model the causality between visual relationships in videos, we propose a new task named Multiple Visual Relation Forecasting and Arrangement (MVRFA). Given a series of frames focusing on a <subject-object> pair, MVRFA aims to forecast the future visual relationships and their arrangement which reflects their temporal interrelationship. To evaluate the MVRFA task, we build a video dataset consisting of 1195 videos with 2666 visual relationships and corresponding arrangements annotated by 5 temporal relational classes. In addition, we present a Multi-task Relationship-centric Transformer model as a baseline, which applies graph convolution to aggregate subject-relationship-object information and uses the relationship multi-query transformer to predict the visual relationships with their arrangement. Experiments on the proposed dataset demonstrate that the proposed baseline achieves superior performance and can better handle the MVRFA task than related models.
Quality Assurance and Quality Practices in Teaching and Learning Data Sciences and Artificial Intelligence

The development of data science and artificial intelligence (DS/AI) will change the skills expected by society and the form of teaching (DS/AI) courses in higher education. This article proposes a Quality Assurance (QA) system adapted to teaching and learning DS/AI by integrating a framework of quality for learning and teaching DS/AI based on standards and references to measure learning outcomes. This system is designed to assure continuous improvement in teaching and learning DS/AI.
Label Propagation Based on Bipartite Graph

Label propagation (LP) is a popular graph-based semi-supervised learning framework. Its effectiveness is limited by the distribution of prior labels. If there are no objects with prior labels in parts of classes, label propagation has very poor performance. To address this issue, we propose a label propagation based on bipartite graph (LPBBG) algorithm. This approach try to learn a bipartite graph as exemplar constraints that reflect the relations between objects and exemplars to guide the learning process instead of label constraints in the traditional label propagation. In this paper, we provide a method for producing high-quality exemplars from two channels to represent the known classes (where some objects have prior labels) and the missing classes (where all the objects have no prior labels). Given generated exemplars, exemplar constraints can be learned using relationships in the known classes to evaluate that in the missing classes. Our experimental results show that the LPBBG algorithm outperforms existing LP methods in overcoming the label missing problem in some classes.
A Generalized Parallel Prefix Sums Algorithm for Arbitrary Size Arrays

The prefix sums algorithm is a fundamental parallel programming building block used to solve significant problems in engineering, mathematical software, and big data analytics. In this paper, we present a generalization of the work-efficient prefix sums algorithm introduced by Blelloch, which in its original form is particularly well-performing on highly parallel architectures. However, the algorithm works only with arrays whose size is a power of 2. While various solutions have been developed to alleviate this limitation, we propose a canonical extension of the classical algorithm, which preserves its original form and maintains the performance characteristics of the work-efficient algorithm.
Adoption Strategy for Cloud Computing in Research Institutions: A Structured Literature Review

Currently, Kenyan research institutions Information Technology operations use external storage, within or without institutional network environments.This study presents a structured literature review of a cloud computing adoption strategy for research institutions in Kenya.The reviews objectives are: 1) to establish the characteristics/peculiarities and IT environments of research institutions in Kenya, 2) current cloud computing technologies, adoption approaches and drivers in the adoption of cloud computing.The findings are that there is need for an adoption strategy for Cloud computing in the Kenyan research Institutions that will use a DOI and TOE combined approach while addressing technological, organizational and environmental factors.The findings are used to propose a cloud computing adoption strategy for the Kenyan Research Institutions in future research.
MLP Tabanlı DNN Modeli Kullanılarak Akıllı Alanlar İçin Yürüyüş Analizinden Kişi Tanıma

In smart fields, security measures are taken to protect people against threats that may arise by using technology and to provide crisis management, and the functions of measuring area security and ensuring its effectiveness are carried out. As an element of this measurement, it is thought that person recognition may be the most important factor in the future. It is seen that deep learning-based algorithms, which can provide fast and high-accuracy results with many data, will be an integral part of this sector in the future as they are today. However, when the literature is examined, it is understood that the number of research in which Deep learning algorithms are used in order to increase the success of the studies in this direction and the system practicality is insufficient. For this reason, in this study, deep learning was used to recognize people by using the walking data of 15 people obtained thanks to wearable sensors. Since the increase in the diversity of the data will positively affect the learning of the created model, data augmentation has been made and these data have been classified in the MLP-based DNN model. The results were statistically analyzed and showed that this model exhibited excellent performance in person recognition from walking data. In addition, the ACC rate was found to be 100%, and it proved that the method used to increase the data also produced successful results in walking data. It is thought that the success of the study can provide important perspective support to new studies for smart fields in the literature.
New Arabic Word Embeddings to Improve NLP Applications

The choice of a good word representation is often essential to carry out automatic processing tasks of written or spoken natural language. The “bag-of-words” representation is the most commonly used, where documents are represented by the frequency vectors of the words that compose them. However, this representation has the drawback of capturing very little information about the structure of the documents and their relationship to each other. For this reason, approaches based on n-grams have been introduced. More recently, methods based on artificial neural networks such as Word2Vec, GloVe and BERT have become popular. These methods are able to represent a word in the form of real-valued vectors with controlled size. This vector corresponds to the position of the word in a multi-dimensional space. This type of technique is called Word embedding. This paper proposes an original method based on AL-khalil Morpho Sys to generate morphological word embeddings. The results show that the information carried by the new vector allows to obtain relevant results for a concrete application such as Part of speech tagging.
Social Distancing Detector Framework Using Deep Learning and Computer Vision Principles

In this post-pandemic era, it is the responsibility of the people to follow all COVID related protocols and ensure that the society is a safe place to live in, with mutations still possible. Hence there is a pressing need to monitor and assess whether people in an environment are following Social Distancing or not. Social Distancing is one of the best methods to prevent the spread of the disease. This paper proposes to employ a blend of Deep Learning and Computer Vision techniques to detect whether people in an environment are following Social Distancing norms or not. A web application that accepts video capture from different sources of input has been developed. This detector also gives importance to real-time input sources like Web Camera and External Cameras like IP Cameras. The main goal is to detect people in the given environment, draw bounding boxes and check if they are socially distant or not. The YOLO v3 algorithm is used for detecting pedestrians/people in a given environment. The pairwise distances between each individual are computed and a safety decision check was applied to determine if a person is a violator or not. Further, the count of violators is maintained and an alarm is raised whenever the count exceeds the threshold indicating large violations in that space. The detector is finally tested with different inputs such as—live stream video, and pre-recorded videos.
Blockchain-Based Higher Educational Data Management: Enhanced Architecture for Data Security

Education is one of the key sectors that suffered significantly in this pandemic. With 35 million students enrolled in its higher educational institutions, India has been grappling with various technological challenges, such as effective data management - the issue of generating, handling, and securing millions of Gigabytes of information. The Education Information System needs a technological overhaul to face unprecedented times. The paper delivers a brief understanding of Big data and blockchain technology, followed by an observation of the Indian higher educational sector, the challenges, and if Blockchain could be a veritable solution for addressing these challenges. The ramifications of such a change and the scope for future research on Information systems are deliberate. This theoretical study investigates the impact blockchain technology could have on the higher education sector. The slow adoption of blockchain technology in education mirrors some serious problems, such as the lack of concrete incentives for blockchain mining. This paradox of weighing Blockchain's benefits with the issues in adopting the technology is critically discussed.
Multi-cost fusion stereo matching algorithm based on guided filter aggregation

Aiming at the low matching accuracy of existing local stereo matching algorithms in weak texture areas, a local stereo matching algorithm based on multi-matching cost fusion and guided filtering cost aggregation with adaptive parameters is proposed. First, use the gradient direction to improve the gradient cost, and calculate the matching cost by combining the gradient cost with the Census transform and color cost. Secondly, the cost is aggregated by the guided filtering of adaptive parameters; Finally, the final disparity map is obtained through disparity calculation and multi-step disparity refinement. The improved algorithm is tested on 15 training sets on the Middlebury3 platform, and the average false matching rates of bad4.0 in all areas and non-occluded areas are 19.9% and 13.2%, respectively, which is improved compared with AD-Census and other algorithms.