Reviewerly/exHarmony huggingface dataset:
Deep Learning Toward Preventing Web Attacks

Cyberattacks are one of the most pressing issues of our time. The impact of cyberthreats can damage various sectors such as business, health care, and governments, so one of the best solutions to deal with these cyberattacks and reduce cybersecurity threats is using Deep Learning. In this paper, we have created an in-depth study model to detect SQL Injection Attacks and Cross-Site Script attacks. We focused on XSS on the Stored-XSS attack type because SQL and Stored-XSS have similar site management methods. The advantage of combining deep learning with cybersecurity in our system is to detect and prevent short-term attacks without human interaction, so our system can reduce and prevent web attacks. This post-training model achieved a more accurate result more than 99&#x0025; after maintaining the learning level, and 99&#x0025; of our test data is determined by this model if this input is normal or dangerous.
Scientific-Research Potential of the National Librarianship and Book Industry

2021 was declared the Year of Science and Technology by the Decree of the President of the Russian Federation No. 812 of December 25, 2020. The main purpose of its holding is the development of science and technology in our country. Support and improvement of the effectiveness of scientific research are directly related to libraries — scientific-research institutions. Within the framework of the Year of Science and Technology, the Ministry of Culture of the Russian Federation resumed the All-Russian competition of scientific works in the area of library science, bibliography and book studies, which took place in 1978—2014. The competition has established itself as one of the most effective methods of evaluating and encouraging the results of research work in librarianship. In 2021, it was organized and conducted by the National Library of Russia and the Russian State Library. On November 18, 2021, within the framework of the Annual Meeting of the Heads of federal and central regional libraries of Russia, the solemn awarding of the winners took place. In 2021, the jury of the competition considered 105 works (42 — in library science, 17 — in bibliography, 22 — in book studies, 24 works related to other categories). 150 authors from 41 subjects of the Russian Federation presented their works. The article presents in tabular form the winners in six nominations (“Best scientific work in the field of library science”; “Best scientific work in the field of bibliography science”; “Best scientific work in the field of book studies”; “Best scientific multimedia project”; “Best scientific work of regional libraries”; “Best scientific work of young specialist under the age of 35”). The article also presents the winners in additional nominations established by national libraries. The competition has shown significant scientific-research potential; and the materials presented are of great importance not only for the development of libraries and the branch as a whole, but also largely serve to solve social and humanitarian problems set out in the “Fundamentals of State Cultural Policy”, established by Decree of the President of the Russian Federation No. 808 of December 24, 2014. It is advisable to consider the holding of the All-Russian competition of scientific papers in library science, bibliography and book studies in conjunction with other major scientific events in the library field that took place in 2021: publication of the collective monograph “Scientific research in libraries: topics, organization, presentation of results” and the International scientific and Practical Conference “Library science in the XXI century: content, organization, digitalization and scientometrics”. These events contributed to the support and development of science in librarianship in the framework of implementation of the “Strategy for the Development of Librarianship in the Russian Federation for the period up to 2030”.
SOCIAL CONTROL THEORY AND CYBERCRIME

The paper pays attention to the potential role that the social control theory has in the improvement and development of previous Information-Communication Technology (ICT) Policy. Online activities are daily routine of people all around the world. Emerging information and communication technologies create numerous challenges and issues how to regulate its use. ICT policy interests are closely related to concern of social order in cyberspace. The development of ICT possibilities at the same time follows development of new cybercrime forms. The ICT policy needs to explore new strategies and theoretical integration that transcend the existing criminological frame. The sociological theory of social control to a significant degree may predict and explain crime and how people define and respond to deviant behavior. How social control theory may order individual behavior in cyberspace? May it help prevention of cybercrime and maintain more safer and conscientious use of the internet? The following article provides an analysis of the possibilities of social control theory in maintaining the order in cyber society. Throughout the article, the social control theory is discussed by content analysis scientific method. Main purpose of the paper is to encourage concerning use of social control mechanisms as one of the effective tools for regulation in cyberspace and prevention of cybercrime.
Improvement in Object Detection Using Multi-Scale RoI Pooling and Feature Pyramid Network

The feature pyramid network (FPN) enhances the localization accuracy and detection performance of small objects using multiple scales of the features. FPN adopts lateral connections and a top-down pathway to make low-level features semantically more meaningful. However, it uses only single-scale features to pool regions of interest (RoIs) when detecting objects. In this study, we showed that single-scale RoI pooling may not be the best solution for accurate localization and proposed multi-scale RoI pooling to improve the minor drawbacks of the FPN. The proposed method pools RoIs from three feature levels and concatenates the pooled features to detect objects. Thus, the FPN with multi-scale RoI pooling, called FPN+, detects objects by taking into account all information scattered across three feature levels. FPN+ improved the FPN by 2.81 and 1.1 points in COCO-style average precision (AP) when tested on PASCAL VOC 2007 test and COCO 2017 validation datasets, respectively.
Statistical Analysis Based Feature Selection Enhanced RF-PUF With &gt;99.8% Accuracy on Unmodified Commodity Transmitters for IoT Physical Security

Due to the diverse and mobile nature of the deployment environment, smart commodity devices are vulnerable to various spoofing attacks which can allow a rogue device to get access to a large network. The vulnerability of the traditional digital signature-based authentication system lies in the fact that it uses only a key/pin, ignoring the device fingerprint. To circumvent the inherent weakness of the traditional system, various physical signature-based RF fingerprinting methods have been proposed in literature and RF-PUF is a promising choice among them. RF-PUF utilizes the inherent nonidealities of the traditional RF communication system as features at the receiver to uniquely identify a transmitter. It is resilient to key-hacking methods due to the absence of secret key requirements and does not require any additional circuitry on the transmitter end (no additional power, area, and computational burden). However, the concept of RF-PUF was proposed using MATLAB-generated data, which cannot ensure the presence of device entropy mapped to the system-level nonidealities. Hence, an experimental validation using commercial devices is necessary to prove its efficacy. In this work, for the first time, we analyze the effectiveness of RF-PUF on commodity devices, purchased off-the-shelf, without any modifications whatsoever. We have collected data from 30 Xbee S2C modules used as transmitters and released as a public dataset. A new feature has been engineered through PCA and statistical property analysis. With a new and robust feature set, it has been shown that 95% accuracy can be achieved using only ∼1.8 ms of test data fed into a neural network of 10 neurons in 1 layer, reaching <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mo>&gt;</mml:mo></mml:math> 99.8% accuracy with a network of higher model capacity, for the first time in literature without any assisting digital preamble. The design space has been explored in detail and the effect of the wireless channel has been investigated. The performance of some popular machine learning algorithms has been tested and compared with the neural network approach. A thorough investigation of various PUF properties has been done. With extensive testing of 41238000 cases, the detection probability for RF-PUF for our data is found to be 0.9987, which, for the first time, experimentally establishes RF-PUF as a strong authentication method. Finally, the potential attack models and the robustness of RF-PUF against them have been discussed.
Exploring Continuous Integrate-and-Fire for Adaptive Simultaneous Speech Translation

Simultaneous speech translation (SimulST) is a challenging task aiming to translate streaming speech before the complete input is observed. A SimulST system generally includes two components: the pre-decision that aggregates the speech information and the policy that decides to read or write. While recent works had proposed various strategies to improve the pre-decision, they mainly adopt the fixed wait-k policy, leaving the adaptive policies rarely explored. This paper proposes to model the adaptive policy by adapting the Continuous Integrate-and-Fire (CIF). Compared with monotonic multihead attention (MMA), our method has the advantage of simpler computation, superior quality at low latency, and better generalization to long utterances. We conduct experiments on the MuST-C V2 dataset and show the effectiveness of our approach.
Stock price prediction using improved extreme learning machine methods during the Covid-19 pandemic and selection of appropriate prediction method

Purpose Coronavirus disease (Covid-19) has created uncertainty in all countries around the world, resulting in enormous human suffering and global recession. Because the economic impact of this pandemic is still unknown, it would be intriguing to study the incorporation of the Covid-19 period into stock price prediction. The goal of this study is to use an improved extreme learning machine (ELM), whose parameters are optimized by four meta-heuristics: harmony search (HS), social spider algorithm (SSA), artificial bee colony algorithm (ABCA) and particle swarm optimization (PSO) for stock price prediction. Design/methodology/approach In this study, the activation functions and hidden layer neurons of the ELM were optimized using four different meta-heuristics. The proposed method is tested in five sectors. Analysis of variance (ANOVA) and Duncan's multiple range test were used to compare the prediction methods. First, ANOVA was applied to the test data for verification and validation of the proposed methods. Duncan's multiple range test was used to identify a suitable method based on the ANOVA results. Findings The main finding of this study is that the hybrid methodology can improve the prediction accuracy during the pre and post Covid-19 period for stock price prediction. The mean absolute percent error value of each method showed that the prediction errors of the proposed methods were all under 0.13106 in the worst case, which appears to be a remarkable outcome for such a difficult prediction task. Originality/value The novelty of this study is the use of four hybrid ELM methods to evaluate the automotive, technology, food, construction and energy sectors during the pre and post Covid-19 period. Additionally, an appropriate method was determined for each sector.
Bound on optimal local discrimination of multipartite quantum states

We consider the unambiguous discrimination of multipartite quantum states and provide an upper bound for the maximum success probability of optimal local discrimination. We also provide a necessary and sufficient condition to realize the upper bound. We further establish a necessary and sufficient condition for this upper bound to be saturated. Finally, we illustrate our results using examples in multidimensional multipartite quantum systems.
Efficient Image Deblurring Application Using Combination of Blind Deconvolution Method and Blur Parameters Estimation Method

Image deblurring can be formulated as the inverse process of image blurring. The degraded image can be improved and reconstructed to its original form using image deblurring. Image reconstruction in elementary mode begins with estimation of degradation parameters followed with application of some classical restoration approach. This paper introduces a new approach for image restoration. Here, removal of motion blurs is done through blind deconvolution technique. Two motion parameters angle (θ) and length have been considered for estimation. Hough transform method is used for estimation of blur parameters. PSNR and MSE are employed to exploit important image characteristics, such as denoising, error, randomness, etc. Proposed algorithm is tested for accuracy. Experiments are carried out to study important image features, which play a major role during image restoration.
A Deep Learning Model to Identify Twins and Look Alike Identification Using Convolutional Neural Network (CNN) and to Compare the Accuracy with SVM Approach

Aim: The main aim of this work is to identify twins and look alike using Innovative convolutional neural networks (CNN) and comparing them with SVM. Materials and Methods: Two groups such as Support Vector Machine and convolutional Neural Network were considered. Each algorithm took N=2 samples from the dataset collected and performed two iterations on each algorithm to identify twin faces. Result: The accuracy of identical twin-face detection for biometrics was calculated. It seems Innovative CNN got a significantly better accuracy of (94.01%) when compared with SVM, which has had an accuracy of (83.98%) and having the significance value of (p=0.029). Conclusion: In the work, it could have recognized identical twin faces and differentiate them with different facial features for easy detection to biometrics where Innovative CNN got better accuracy than SVM.
A Comparative Analysis on Offline Signature Recognition System Using Deep Convolutional Neural Network

The aim of signature recognition is to recognize name of author of the signature. It is a difficult work because scanned images are used for signature recognition. Identifying signatory is behavioral biometrics and has lot of potential of research in this area. There is a scope of accuracy improvement because of high variations of signature of same user. This paper investigates how can best feature extracted for signature recognition. In this research, pre-trained CNNs were used to fetch features and then fetched features were entered into machine learning algorithm, such as Random Forest, KNN, NN, and Decision Tree. This research is a comparative study on performance of model for particular datasets.
Genetic improvement in the shackleton framework for optimizing LLVM pass sequences

Genetic improvement is a search technique that aims to improve a given acceptable solution to a problem. In this paper, we present the novel use of genetic improvement to find problem-specific optimized LLVM pass sequences. We develop a pass-level patch representation in the linear genetic programming framework, Shackleton, to evolve the modifications to be applied to the default optimization pass sequences. Our GI-evolved solution has a mean of 3.7% runtime improvement compared to the -O3 optimization level in the default code generation options which optimizes on runtime. The proposed GI method provides an automatic way to find a problem-specific optimization sequence that improves upon a general solution without any expert domain knowledge. In this paper, we discuss the advantages and limitations of the GI feature in the Shackleton Framework and present our results.
Sarcasm Detection: A Contemporary Research Affirmation of Recent Literature

Sarcasm identification is a confined research area in NLP, a specific case of opinion mining where the focal point in the process is identification of sarcasm, instead of sentiment extraction. Sarcasm is a specific type of opinion which is expressed as a negative feeling in form of anger, frustration, or derision veiled by the intense positive words in the text. Detection of sarcasm, which is an elusive problem for machines, has gained wide popularity in the research community in recent years. Accurate identification and analysis of sarcasm improves the performance of sentiment identification models. This manuscript details various sarcasm detection approaches, models and features used, issues, challenges, and further research scope. The various machine learning and deep learning models used to identify sarcasm are detailed in this script.
Prediction of Temperature for Next Three Days Using Decision Tree Algorithm by Comparing Sliding Window Algorithm for Better Accuracy

The aim of this study is to predict the temperature for the next three days using a machine learning algorithm and the objective of this research is to improve the accuracy of temperature prediction. Materials and methods: The study used 36 samples with two groups of algorithms with the g-power value of 80%. To predict the temperature, the Sliding Window Algorithm (SLA) machine learning algorithm has found 89% of accuracy and this study will find better accuracy for temperature prediction with the Decision Tree (DT) algorithm. Results: This research study found 92% of accuracy for temperature prediction using a DT algorithm. Conclusion: This study concludes that the DT algorithm on temperature prediction is significantly better than the SLA.
Analysis on Content Based Image Retrieval Using Image Enhancement and Deep Learning Convolutional Neural Networks

"Content-Based" means that an image contents search analyzes instead of meta data, including keywords, tags or image descriptions. The word contents could apply in this sense to colours, structures, textures or any details extracted from the picture itself. CBIR is desirable as searches relying purely on metadata depend on the quality and the completeness of annotations. CBIR method for the recovery of images from huge, unshaped image databases is commonly used. The CBIR method is used. Therefore, users are not satisfied with standard knowledge collection methods. In addition, there are more images available to users, as well as the advent of web creation and transmission networks. Consequently, there is a permanent and important output of digital images in many regions. Hence the rapid access to these enormous picture collections and the identical image retrieval from this broad image collection presents major challenges and demands efficient techniques. The efficiency of a content-based image retrieval system depends on the characteristic representation and similarity calculation. We therefore have a simple but powerful, profound, CNN-based, and feature-extraction and classification-based imaging system. Some promising results have been obtained from a range of empirical studies on a variety of CB IR tasks through the image database. Content-based image recovery systems (CBIR) allow you to find images identical to a query image among a picture dataset. The best-known CBIR system is Google's search by image feature.
In-situ real-time imaging of subsurface damage evolution in carbon fiber composites with shearography

Carbon fiber composites are complex material systems comprising components of different length scales. It is important to understand the interactions of these components and their influence on the overall mechanical properties of the composites for development of new or improved composite materials. However, it is challenging to experimentally image the damage evolution in composites with existing tools. In this paper, we apply shearography technique for real-time in-situ imaging of subsurface damage evolution in carbon fiber composites during open-hole tensile (OHT) tests. Due to its high sensitivity to out-of-plane displacement derivative, shearography can record the initiation and growth of subsurface defects in real-time. Comparison with digital image correlation shows that shearography can provide better defect imaging results. The shearography technique provides a convenient and high-sensitive solution to visualize damage evolution in carbon fiber composites during mechanical tests.
High Temporal Resolution-Based Temporal Iterative Tracking for High Framerate and Ultra-Low Delay Dynamic Tracking System

High frame rate and ultra-low delay vision system, which can finish reading and processing of 1000fps sequence within 1ms/frame, draws increasing attention in the field of robotics that requires immediate feedback from image process core. Meanwhile, tracking task plays an important role in many computer vision applications. Among various tracking algorithms, Lucas Kanade (LK)-based template tracking, which tracks targets with high accuracy over the sub-pixel level, is one of the keys for robotic applications, such as factory automation (FA). However, the substantial spatial iterative processing and complex computation in the LK algorithm, make it difficult to achieve a high frame rate and ultra-low delay tracking with limited resources. Aiming at an LK-based template tracking system that reads and processes 1000fps sequences within 1ms/frame with small resource costs, this paper proposes: 1) High temporal resolution-based temporal iterative tracking, which maps the spatial iterations into the temporal domain, efficiently reduces resource cost and delay caused by spatial iterative processing. 2) Label scanner-based multi-stream spatial processing, which maps the local spatial processing into the labeled input pixel stream and aggregates them with a label scanner, makes the local spatial processing in the LK algorithm possible be implemented with a small resource cost. Algorithm evaluation shows that the proposed temporal iterative tracking performs dynamic tracking, which tracks object with coarse accuracy when it's moving fast and achieves higher accuracy when it slows down. Hardware evaluation shows that the proposed label scanner-based multi-stream architecture makes the system implemented on FPGA (zcu102) with resource cost less than 20%, and the designed tracking system supports to read and process 1000fps sequence within 1ms/frame.
Academic libraries versus the doom scroll: Engaging with at-home users on social media during COVID-19

The authors collected data from a sample size of academic library social media accounts from around the U.S., starting with the onset of the COVID-19 pandemic until Fall semester 2020, by using measurable attributes for engagement. They report on the findings of that data by highlighting examples of accounts that have successfully taken advantage of these frequented platforms to effectively engage with their patrons by publicizing essential, up-to-date information to active seekers. The authors also discuss best practices for how to use social media in a way that addresses the unique needs of each university.
Learning to Think Like a Patron: Improving User Experience, E-Resources Management, and Departmental Outcomes Beyond COVID-19

Two years after the onset of the COVID-19 health pandemic, electronic resources librarians are assessing how the work in their libraries has changed and determining if certain modifications made to services and workflow processes are, in fact, transformational. The authors detail how service changes and telework during the pandemic affected e-resources workflows and interdepartmental collaboration in two academic libraries, and how these experiences will have an effect in improving their organizational cultures and the patron experience moving forward.