Reviewerly/exHarmony huggingface dataset:
Automating document classification with distant supervision to increase the efficiency of systematic reviews: A case study on identifying studies with HIV impacts on female sex workers

There remains a limited understanding of the HIV prevention and treatment needs among female sex workers in many parts of the world. Systematic reviews of existing literature can help fill this gap; however, well-done systematic reviews are time-demanding and labor-intensive. Here, we propose an automatic document classification approach to a systematic review to significantly reduce the effort in reviewing documents and optimizing empiric decision making. We first describe a manual document classification procedure that is used to curate a pertinent training dataset and then propose three classifiers: a keyword-guided method, a cluster analysis-based method, and a random forest approach that utilizes a large set of feature tokens. This approach is used to identify documents studying female sex workers that contain content relevant to either HIV or experienced violence. We compare the performance of the three classifiers by cross-validation in terms of area under the curve of the receiver operating characteristic and precision and recall plot, and found random forest approach reduces the amount of manual reading for our example by 80%; in sensitivity analysis, we found that even trained with only 10% of data, the classifier can still avoid reading 75% of future documents (68% of total) while retaining 80% of relevant documents. In sum, the automated procedure of document classification presented here could improve both the precision and efficiency of systematic reviews and facilitate live reviews, where reviews are updated regularly. We expect to obtain a reasonable classifier by taking 20% of retrieved documents as training samples. The proposed classifier could also be used for more meaningfully assembling literature in other research areas and for rapid documents screening with a tight schedule, such as COVID-related work during the crisis.
ASTENS-BWA: Searching partial syntactic similar regions between source code fragments via AST-based encoded sequence alignment

Code search is a common activity in software development, and code-to-code search can benefit in a wide range of use-case scenarios. Code-to-code search uses a code fragment as the query for searching similar code fragments from large corpora. The results of a search can be applied to some software engineering tasks, such as search-based code recommendation, data-driven program repairing, and software plagiarism detection. To be put into daily use, the code-to-code search needs to find similar code fragments accurately and efficiently in a large dataset. Some search engines can locate exactly similar code, but are not able to search syntactical clones. Therefore, we propose ASTENS-BWA, a novel approach for searching syntactic similar code regions between code fragments via a tree-based sequence alignment. Source code has been transformed into a tree-based sequence that contains the structure information, and a sequence alignment algorithm has been applied to find similar regions. We evaluate ASTENS-BWA on three different tasks, the results demonstrate that our approach can find syntactical similar regions for programming code and retrieve similar code fragments fast and with high accuracy. As a code clone detection tool, ASTENS-BWA can report clone pairs in a high recall, but it needs manually check to reduce the false alarms. ASTENS-BWA is scalable and can report cloned code fragments in seconds for a code corpus of million lines of code.
Applications of Orthogonal Polynomials to Solving the Schrödinger Equation

The article reveals possibilities of using Hermite polynomials and related Hermite-Weber functions to solve a wide range of problems in mathematical physics. The obtained properties of the considered functions allow for constructing solutions for problems of wave dynamics. The solution of the Schrödinger integral equation constructed on their basis is highly accurate since, in this case, waves of matter can freely pass along the entire real axis, and there is no need for “matching” solutions on the finite interval.
Channel prior based Retinex model for underwater image enhancement

Since light undergoes absorption and scattering when it travels in water, images taken under water fall prey to color distortion, fuzziness/haziness, underexposure, and color cast. To alleviate these issues, this paper proposes a novel Retinex-based enhancing approach aimed at enhancing low-quality under-water images. Firstly, the input image undergoes pre-processing, where a color correction technique is employed to address the problem of color distortion. This is followed by conversion of the input image from the Red, Green and Blue color space to the Lab color space. Then, by means of the multi scale Retinex, the illumination component of the image is procured. Image dehazing algorithms, i.e., bright channel prior and underwater dark channel prior algorithms are applied individually on the procured illumination component. The image gets enhanced and is then converted back to the Red, Green and Blue color space from the Lab color space. The final enhanced image is obtained by performing histogram equalization on the enhanced Red, Green and Blue image. This is intended to make the output color intensity more realistic. The efficacy of the algorithms are gauged by means of some image quality metrics. Compared to pre-existing techniques, the method proposed manages to effectively enhance images while keeping detail loss to a minimum.
Axiological Aspect of Professional Training of Future Pedagogues

The purpose of this research work is to identify and state the main components of the axiological aspect of the professional training of future workers in the pedagogical industry, which is able to impart integrity to the entire process of their training. The leading approach is a combination of quantitative and qualitative methodological approaches to the study of issues related to the axiological aspect of the professional training of future workers in the pedagogical industry, in order to determine the main components of the factors affecting the quality and effectiveness of this training and further professional activities of teachers. The main results were: a statement of the necessity to develop and take a set of measures to clarify the existing value criteria of the education system to future teachers at the stage of their university preparation, as well as the acceptance of the fact of the need for the constant practice of future teachers in instilling in students respect for existing values in the context modern education system.
A live mindset in Live Virtual Constructive simulations: a spin-up for future LVC air combat training

Combining Live, Virtual, and Constructive (LVC) aircraft in the same training scenario holds promise for developing and enhancing fighter pilot training. The simulator study reported here builds on joint pilot-researcher co-design work of beyond visual range LVC training (LVC-T) scenarios to provide training value to pilots in both Live and Virtual aircraft. One fourship of pilots simulated Live entities by acting under peacetime restrictions, while other pilots acted as during regular Virtual training. The objective was to investigate pilots’ reflections on the implications of LVC-T and on the methodology used to provide hands-on experience of a plausible LVC-T scenario. The purpose is to inform the design and use of future LVC in air combat training from the perspective of training value. Results indicate that pilots are positive toward the LVC scenario design, especially the dynamics that a large-scale scenario brings to training of decision making. They indicate a high degree of presence, the need for specific regulations to enforce flight safety, and that restrictions put on the simulated Live entities had implications for the other pilots. In addition to regular Live (L) and simulator (V + C) training, LVC-T may enhance pilots’ repertoires and decision-making patterns.
Intelligent Flight-Data-Recorders; a Step Toward a New Generation of Learning Aircraft

To understand how aerial accidents occur, the installation of flight data recorders (FDR) and cockpit voice recorders (CVR) have become mandatory by law. However, such devices play a passive role and are used once an accident has occurred. Recent advances in machine learning techniques and their application in solving engineering problems are the keys to creating a more active role for both FDR as well as CVR. Here, we investigate a new approach to bringing intelligence to an FDR (called I-FDR). Through a continuous data-mining process, an I-FDR could bring better situational awareness to the flight crew. An I-FDR, similar to the FDR, records all pertinent flying parameters. In addition, proper data-mining and machine learning techniques bring more insight into the current state of the flight as well as its immediate future. Once the aircraft completes its mission, all recorded flight data is transferred to a data bank for learning purposes on future flights of a similar aircraft type. This study compares three unsupervised machine learning algorithms to examine their effectiveness in guiding flight crew in hazardous situations. Methods include DBSCAN (Density-based spatial) clustering, isolation forest, and LSTM (Long Short-Term Memory). This study uses simulated takeoff data sets and a runway excursion accident as the test case to determine which method is more accurate. The assessment shows that LSTM is superior in detecting anomalous data.
A Novel Method to Estimate Measurement Error in AI-Assisted Measurements

Adopting Artificial Intelligence (AI) systems in measurement instruments and systems entails a necessity to predict the error contributed by the AI model to the measured value, especially on out-of-sample data. However, reporting aggregated error estimates, such as model accuracy or Root Mean Square Error (RMSE) as is customary in AI, cannot quantify the error of the AI model for a single measurement instance, which is what we need in measurement. In this paper, we propose a novel method to estimate the AI model’s error for a single measurement. Our goal is to predict the error and use it to correct the predicted measurand’s quantity. To do so, in the first step we use an existing dataset to train an AI model that predicts measurement values, which is the usual approach for designing AI-assisted measurement systems. Our contribution is in the second step, where we create a secondary dataset that consists of the errors between the ground truth and the predicted values, and we use this dataset to train a secondary AI model that predicts the errors. We then adjust the predicted measurement values with the predicted error values. Our performance evaluations on the well-known California Housing dataset shows that our approach lowers the measurement predictions’ Mean Absolute Percentage Error from 21% to 16%, resulting in more accurate measurements.
Improved DWT Algorithm for Filtering of MRI Images for an Efficient Diagnosis

Medical scan images allow the Expert to identify the abnormal regions present in the image at its earlier stage. The detection of irregularity in the images becomes a difficult task; if the noise captures under any noisy effect. Such a difficulty is overwhelmed by image filtering after the scanning process. The methodology first converts the RGB to a grayscale image. After that, image filtering has done by the modified DWT (Discrete Wavelet Transform) filtering approach. To evaluate the performance of the proposed algorithm, it utilizes variegated performance metrics includes PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity Index Measure), NK (Normalized Cross-Correlation), SC (Structural Content), MD (Maximum Difference), and NAE (Normalized Absolute Error). It has computed between the denoised image and the input image. The experimentation has to be computed on four different medical images, which incorporates brain tumor, skin lesion, chest X-Ray, and lung cancer. The average attained for PSNR, SSIM, NK, SC, and NAE were better for overall set of images. These results exploit that the new approach can potentially improve the filtering performance.
Optimization of the CF<sup>2</sup> mobile robot motion planning approach and adaptation for moving targets

This paper presents the PSO-CF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -mt motion planning approach that we propose for two wheeled mobile robots for tracking moving targets in known dynamic environments. The Particle Swarm Optimization Canonical Force Field (PSO-CF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) is a mobile robot motion planning approach that we have previously proposed for static and dynamic environments[9]. PSO-CF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -mt is an improved version of PSO-CF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to become capable of tracking a moving target. The basic concept of PSO-CF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -mt is to generate a continually changing parameterized Force Field for the robot based on the characteristics of all objects presents in the environment. The simulation results prove clearly the ability of PSO-CF2-mt to follow and reach a moving target by choosing the shortest and the most secure paths whatever the complexity of the environment and the form of the target's trajectory. A comparative study with APF (Artificial Potential Field) proves the quality of our proposed approach.
Infographics as a Way of Information Visualisation in Professionally Oriented Texts When Teaching a Foreign Language to Future Law Enforcement Specialists

The research aims to identify the advantages of using visualisation tools in the process of teaching a foreign language to future law enforcement specialists. The paper provides insight into the concept of “information visualisation” and presents its role in the educational process when teaching a foreign language to future law enforcement specialists. The work identifies the particularities of professionally oriented texts in a foreign language used in teaching a foreign language to future law enforcement specialists; formulates recommendations on the use of infographics as a way of information visualisation in professionally oriented texts when teaching a foreign language to future law enforcement specialists. Scientific novelty lies in developing a methodology for working with professionally oriented texts on law enforcement using infographics. The research findings indicate an increase in the quality of foreign language education among students with the help of visualisation tools.
A Shilling Group Detection Framework Based on Deep Learning Techniques

Research studies have shown that online recommender systems are under the threat of group shilling attacks, in which attackers attempt to distort the recommendation results of particular items by cooperatively injecting fake profiles. Existing detection methods usually divide candidate groups at first and then use the hand-crafted features to recognize shilling groups. However, the detection performance of existing methods depends highly on the quality of candidate groups obtained. Moreover, extracting features manually is time-consuming. To overcome these limitations, we propose a shilling group detection framework based on the sparse autoencoder and modified GraphSAGE model. First, we use the sparse autoencoder to obtain rating features of users from the rating dataset. Second, we analyse user collusive degrees to calculate user transition probabilities. Third, we build a user relation graph and utilize the modified GraphSAGE model to perform user classification. Finally, shilling groups are gathered according to the neighbour relations. Extensive experiment demonstrates that the proposed framework performs better on different datasets than baseline methods.
On the Need for Collaborative Intelligence in Cybersecurity

The success of artificial intelligence (and particularly data-driven machine learning) in classifying and making predictions from large bodies of data has led to an expectation that autonomous AI systems can be deployed in cybersecurity applications. In this position paper we outline some of the problems facing machine learning in cybersecurity and argue for a collaborative approach where humans contribute insight and understanding, whilst machines are used to gather, filter and process data into a convenient and understandable form. In turn this requires a convenient representation for exchanging information between machine and human, and we argue that graded concepts are suitable, allowing summarisation at multiple levels of discernibility (granularity). We conclude with some suggestions for developing a hierarchical and graded representation.
Secure Virtual Machine Image Storage Process into a Trusted Zone-based Cloud Storage

The emerging trend of Cloud Computing inducts virtualization as the quintessence concept to offer to end users a large scale Infrastructure-as-a-Service (IaaS) paradigm. Virtualization leverages virtual environment instantiated from user namely Virtual Machine Images (VMI) where user has the ability to provision and manage its own operating system. Therefore, user VMI dynamic data privacy depends upon the supplied security policy by Cloud Service Provider (CSP) and cloud storage architecture which remains the most prominent cloud delivered services. This paper covers thoroughly a secure storage process of user VMI in a cloud zone-based storage based on Proof-of-Retrievability (PoR). The proposed security architecture ensures the elementary security triad CIA (Confidentiality, Integritty and availability). VMI data confidentiality comprises generation of encryption key pair based on Goppa Code and McEliece cryptosystem, thus, integrity and stored VMI dynamic data correctness have been ensured using public auditing privacy-preserving in cloud zone-based storage. a know practical Error-Correcting-Codes (ECC) in heuristic researches
Delta-DAGMM: A Free Rider Attack Detection Model in Horizontal Federated Learning

Federated learning is a machine learning framework proposed in recent years. In horizontal federated learning, multiple participants cooperate to train and obtain a common final model. Participants only need to transmit the local updated model instead of local datasets. Some participants do not use effective local data sets, but provide disguised model parameters to participate in federal training and obtain common training models. This attack is called Free-rider attack. To the best of our knowledge, researches have proposed some Free-rider attack strategies with theoretical support, but there are few researches on Free-rider attack detection. However, the model disguised by some attackers using special attack strategies is similar to the real model in terms of convergence and weight, so it is difficult to detect the model provided by attacker as abnormal data. Based on DAGMM, a high-dimensional abnormal data detection model, this paper optimizes the sample processing and compression model, and proposes an improved detection algorithm, called Delta-DAGMM. Two types of large datasets are used for experiments. The experimental results show that Delta-DAGMM has higher precision and F1 score than DAGMM. On average, the Delta-DAGMM algorithm achieves a precision of 98.42% and an F1 score of 98.36%.
A Public Infrastructure for a Trusted Wireless World

The novelty of this work lies in examining how 5G, blockchain-based public key infrastructure (PKI), near field communication (NFC), and zero trust architecture securely provide not only a trusted digital identity for telework but also a trusted digital identity for secure online voting. The paper goes on to discuss how blockchain-based PKI, NFC, and the cloud provide a roadmap for how industry and governments can update existing frameworks to obtain a trusted digital identity in cyberspace that would provide secure telework and online voting capabilities.
Hybridized Yolov4 for Detecting and Counting People in Congested Crowds

Crowd monitoring has been gaining attention among researchers for a wide range of applications. Such applications may include abnormal behavior detection for security reasons, monitoring of crowds for reporting, and people counting for structural and facility planning purposes. Traditional detecting models fail to detect people and count them in the crowded scenes. Deep learning networks have proven costly requiring energy and memory for computations beyond means in resource-constrained devices. Thus, we propose the YOLOv4 detection algorithm fused with Pruning technique during training and convolutional attention module. The proposed pruning strategy makes the model compact and light and thus reduces memory for the processing needs. Adding a Convolutional Block Attention Module to the YOLOv4 network improves detection accuracy by increasing the weights of valuable features while lowering the weights of invalid data. The Improved YOLOv4 then is not only used to locate everyone in the crowd, rather it counts them. The JHU-Crowd dataset of crowd images is used to train YOLOv4. The findings of this hybrid network are authenticated against the experimental results by the original YOLOv4. The hybrid YOLOv4 mAP is boosted by 21% and inference time is reduced by 11% and the loss reduced to 0.1. The proposed pruning mechanism reduces the number of computations and memory during training, which makes it better a better candidate for Real-Time monitoring.
PRNU Estimation based on Weighted Averaging for Source Smartphone Video Identification

Photo response non-uniformity (PRNU) noise is a sensor pattern noise characterizing imperfections in the imaging device. The PRNU is a unique noise for each sensor device, and it has been generally utilized in the literature for source camera identification and image authentication. In video forensics, the traditional approach estimates the PRNU by averaging a set of residual signals obtained from multiple video frames. However, due to lossy compression and other non-unique content-dependent noise components that interfere with the video data, constant averaging does not take into account the intensity of these undesirable noise components which are content-dependent. Different from the traditional approach, we propose a video PRNU estimation method based on weighted averaging. The noise residual is first extracted for each single video. Then, the estimated noise residuals are fed into a weighted averaging method to optimize PRNU estimation. Experimental results on two video datasets captured by various smartphone devices have shown a significant gain obtained with the proposed approach over the conventional state-of-the-art one.