Reviewerly/exHarmony huggingface dataset:
Input/Output Check Bugs Taxonomy: Injection Errors in Spotlight

In this work, we present an orthogonal classification of input/output check bugs, allowing precise structured descriptions of related software vulnerabilities. We utilize the Bugs Framework (BF) approach to define two language-independent classes that cover all possible kinds of data check bugs. We also identify all types of injection errors, as they are always directly caused by input/output data validation bugs. In BF each class is a taxonomic category of a weakness type defined by sets of operations, cause→consequence relations, and attributes. A BF description of a bug or a weakness is an instance of a taxonomic BF class with one operation, one cause, one consequence, and their attributes. Any vulnerability then can be described as a chain of such instances and their consequence-cause transitions. With our newly developed Data Validation Bugs and Data Verification Bugs classes, we confirm that BF is a classification system that extends the Common Weakness Enumeration (CWE). It allows clear communication about software bugs and weaknesses, providing a structured way to precisely describe real-world vulnerabilities.
A Comprehensive Assessment of Privacy Preserving Data Mining Techniques

The process of knowledge extraction from large datasets is the primary task in Data Mining. Data warehouse is one of the central repositories of data. When data is available only at a single location, a number of serious security problems may crop up. So, various data mining techniques are applied to the data located in data warehouse to extract useful patterns from the data and protect privacy of sensitive data. In recent times, attacks on people have increasingly become common. So, people are reluctant to share their data for loss of privacy. In such scenarios, privacy preserving data mining techniques play a major role in ensuring security of sensitive data. Sometimes, non-sensitive data may also convey sensitive information to attackers. This paper reviews various privacy preserving data mining techniques in literature specifying their details. A table providing a comparison of such techniques is also presented here.
Developing soft skills in high school students using digital tools

Introduction. The article presents an overview of the research on developing soft skills in high school students, research findings on the assessment of universal competencies in high school students and the search for approaches to the development of universal competencies in high school students by means of using digital tools. The purpose of the study is to evaluate the level of soft skills among high school students and to justify the implementation of practical procedures within the framework of a digital learning environment. Materials and Methods. In order to assess the level of soft skills (e.g. emotional intelligence and self-monitoring) in Russian ninth graders (n=400) the authors applied the following general research methods: analysis, synthesis, and reviewing research literature. Empirical methods including M.Snyder’s Self-monitoring scale and Thomas–Kilmann’s inventory were used to reveal the types of behaviors for handling conflicts. The authors developed practical procedures aimed at enhancing self-control, communication and cooperation skills, and emotional intelligence, using Lift to Future and Learnis.ru educational platforms. The research methodology was based on the competence approach (A. M. Aronov, A. V. Barannikov, A. G. Burmus, I. A. Zimnaya, A. V. Khutorskoy) and modern approaches related to the problem of digitalization of education (Ya. A. Vahramenko, A. P. Ershov, I. V. Robert, S. A. Tanganyan). Results. The study revealed the main deficiencies in soft skills among the surveyed 9th graders. The authors identified invariant (lack of self-reflection, lack of motivation for self-development, unwillingness to change) and variable difficulties that impede the development of soft skills in ninth graders and are associated with individual personality traits: lack of self-confidence, excessive ambitions and desires, emotional immaturity, and increased fatigue. The study revealed a low level of emotional intelligence in 50 % of the respondents and a low level of cooperation skills in 75 % of the respondents. The authors presented a range of practical procedures based on using digital tools, which contributed to the development of soft skills indicators in high school students. The study suggests that measures aimed at enhancing soft skills should include both group and individual activities and take into account invariant and variable negative factors. The authors enrich the term ‘digital literacy’ with new content. Conclusions. Finally, the authors emphasize that digital tools contributed to maintaining motivation and increasing digital literacy in all the participants of the experiment.
Dynamic S-Box and PWLCM-Based Robust Watermarking Scheme

Abstract Due to the increased number of cyberattacks, numerous researchers are motivated towards the design of such schemes that can hide digital information in a signal. Watermarking is one of the promising technologies that can protect digital information. However, traditional watermarking schemes are either slow or less secure. In this paper, a dynamic S - Box based efficient watermarking scheme is presented. The original image was extracted at the receiver’s end without any loss of sensitive information. Firstly, the Secure Hash Algorithm is applied to the original image for the generation of the initial condition. Piece Wise Linear Chaotic Map is then used to generate 16 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> 16 dynamic Substitution Box ( S - Box ). As an additional security feature, the watermark is substituted through dynamic S - Box . Hence, it is hard for the eavesdroppers to attack the proposed scheme due to the dynamic nature of S - Box . Lastly, lifting wavelet transform is applied to the host image and the High Low and High High blocks of host image are replaced with least significant bits and most significant bits of the substituted watermark, respectively. Robustness, efficiency and security of the proposed scheme is verified using Structure Similarity Index, Structure Dissimilarity Index, Structure Content, Mutual Information, energy, entropy, correlation tests and classical attacks analysis.
An Efficient Stacked Deep Transfer Learning Model for Automated Diagnosis of Lyme Disease

Lyme disease is one of the most common vector-borne infections. It typically causes cardiac illnesses, neurologic illnesses, musculoskeletal disorders, and dermatologic conditions. However, most of the time, it is poorly diagnosed due to many similarities with other diseases such as drug rash. Given the potentially serious consequences of unnecessary antimicrobial treatments, it is essential to understand frequent and uncommon diagnoses that explain symptoms in this population. Recently, deep learning models have been used for the diagnosis of various rash-related diseases. However, these models suffer from overfitting and color variation problems. To overcome these problems, an efficient stacked deep transfer learning model is proposed that can efficiently distinguish between patients infected with Lyme (+) or infected with other infections. 2nd order edge-based color constancy is used as a preprocessing approach to reduce the impact of multisource light from images acquired under different setups. The AlexNet pretrained learning model is used for building the Lyme disease diagnosis model. To prevent overfitting, data augmentation techniques are also used to augment the dataset. In addition, 5-fold cross-validation is also used. Comparative analysis indicates that the proposed model outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and area under the curve.
ARticulate

Smart space technologies have entered the mainstream home market. Most users currently interact with smart homes that they (or an acquaintance) have set up and know well. However, as these technologies spread to commercial or public environments, users will need to frequently interact with unfamiliar smart spaces where they are unaware of the available capabilities and the system maintainer will not be present to help. Users will need to quickly and independently 1) discover what is and is not possible, and 2) make use of available functionality. Widespread adoption of smart space systems will not be possible until this discoverability issue is solved. We design and evaluate ARticulate, an interface that allows users to have successful smart space interactions with an intelligent assistant while learning transferable information about the overall set of devices in an unfamiliar space. Our method of using Snapchat-like contextual photo messages enhanced by two technologies---augmented reality and autocomplete---allows users to determine available functionality and achieve their goals in one attempt with a smart space they have never seen before, something no existing interface supports. The ability to easily operate unfamiliar smart spaces improves the usability of existing systems and removes a significant obstacle to the vision of ubiquitous computing.
Risks of 5-100 Project: Perceptions of Academic Staff of Different Ages

The implementation of the 5-100 Project in Russian universities, aimed at improving their competitiveness through adaptation to world standards and inclusion in the international educational environment has produced some quantitative and qualitative results. Despite the undoubted achievements of the universities participating in the Project, the mechanisms of its implementation have revealed some problematic areas that in the end can primarily affect the young generations of scholars. Therefore, assessing the perception of the 5-100 Project by scholars of different ages is important. The purpose of this article is to analyze, through the lens of a generational approach, the risks associated with the two aspects of universities’ activities. First, the principles of funding under the 5-100 Project and second, the approaches applied for assessment of academic performance. The analysis is based on the results of semi-structured interviews with scientific and pedagogical staff at five universities that participated in the 5-100 Project (108 respondents). We analyzed the answers of respondents in three age groups: young scientists, middle-aged and older scholars. The study showed that there are areas where representatives of all generations are unanimous and areas where the young academics have perceptions different from other generations. There is unanimity in the attitudes to principles of financing practiced in the universities. The younger generation turned to be the most sensitive to insufficient spending on research equipment. In terms of performance evaluation indicators, the younger generation demonstrated the greatest adaptability to the system of formal quantitative assessment, while noting its dysfunctional effects. They have also revealed that it is easier for them to change jobs than for other generations of scholars.
Getting started with TikTok for library marketing

Many libraries are turning to rapidly-growing social media app TikTok to marketing their services, but joining can be intimidating and a lot of work. Nicholls Library has used the app to promote their services on campus, gaining recognition among students, but not without clear goals and expectations. In this column, the social media chair at Nicholls Library discusses strategies for getting started with TikTok.
Take the red pill for H3 and see how deep the rabbit hole goes

With the introduction of HTTP/3 (H3) and QUIC at its core, there is an expectation of significant improvements in Web-based secure object delivery. As HTTP is a central protocol to the current adaptive streaming methods in all major streaming services, an important question is what H3 will bring to the table for such services. To answer this question, we present the new features of H3 and QUIC, and compare them to those of H/1.1/2 and TCP. We also share the latest research findings in this domain.
Neighborhood Learning-Based Cuckoo Search Algorithm for Global Optimization

This paper presents a new variant of cuckoo search (CS) algorithm named neighborhood learning-based CS (NLCS) to address global optimization problems. Specifically, in this modified version, each individual learns from the personal best solution rather than the best solution found so far in the entire population to discourage premature convergence. To further enhance the performance of CS on complex multimode problems, each individual is allowed to learn from different learning exemplars on different dimensions. Moreover, the exemplar individual is chosen from a predefined neighborhood to further maintain the population diversity. This scheme enables each individual to interact with the historical experience of its own or its neighbors, which is controlled by using a learning probability. Extensive comparative experiments are conducted on 39 benchmark functions and two application problems of neural network training. Comparison results indicate that the proposed NLCS algorithm exhibits competitive convergence performance.
Hierarchical Echo State Network With Sparse Learning: A Method for Multidimensional Chaotic Time Series Prediction

Echo state network (ESN), a type of special recurrent neural network with a large-scale randomly fixed hidden layer (called a reservoir) and an adaptable linear output layer, has been widely employed in the field of time series analysis and modeling. However, when tackling the problem of multidimensional chaotic time series prediction, due to the randomly generated rules for input and reservoir weights, not only the representation of valuable variables is enriched but also redundant and irrelevant information is accumulated inevitably. To remove the redundant components, reduce the approximate collinearity among echo-state information, and improve the generalization and stability, a new method called hierarchical ESN with sparse learning (HESN-SL) is proposed. The HESN-SL mines and captures the latent evolution patterns hidden from the dynamic system by means of layer-by-layer processing in stacked reservoirs, and leverage monotone accelerated proximal gradient algorithm to train a sparse output layer with variable selection capability. Meanwhile, we further prove that the HESN-SL satisfies the echo state property, which guarantees the stability and convergence of the proposed model when applied to time series prediction. Experimental results on two synthetic chaotic systems and a real-world meteorological dataset illustrate the proposed HESN-SL outperforms both original ESN and existing hierarchical ESN-based models for multidimensional chaotic time series prediction.
Underwater occlusion object recognition with fusion of significant environmental features

Complicated underwater environments, such as occlusion by foreign objects and dim light, cause serious loss of underwater object features. This certainly increases the challenge of accurate detection of underwater objects. To tackle the above problems, we proposed an underwater occlusion object recognition algorithm combined with significant environmental features. First, the salient features of the underwater object were extracted by the salient feature extraction network. Second, salient environmental features associated with the object were extracted by the environmental feature attention mechanism. Finally, the contrast graph structure of the object’s salient features and related environmental features was constructed. The model proposed is compared with the existing object recognition model in the simulation of the three datasets: the underwater image enhancement benchmark dataset, video diver dataset, and underwater image dataset. The average recognition accuracy of the proposed model is 80.07%, which is improved by 2.38%. Experiments show that the object recognition algorithm proposed is effective and superior to existing algorithms.
Self-Supervised Robust Scene Flow Estimation via the Alignment of Probability Density Functions

In this paper, we present a new self-supervised scene flow estimation approach for a pair of consecutive point clouds. The key idea of our approach is to represent discrete point clouds as continuous probability density functions using Gaussian mixture models. Scene flow estimation is therefore converted into the problem of recovering motion from the alignment of probability density functions, which we achieve using a closed-form expression of the classic Cauchy-Schwarz divergence. Unlike existing nearest-neighbor-based approaches that use hard pairwise correspondences, our proposed approach establishes soft and implicit point correspondences between point clouds and generates more robust and accurate scene flow in the presence of missing correspondences and outliers. Comprehensive experiments show that our method makes noticeable gains over the Chamfer Distance and the Earth Mover’s Distance in real-world environments and achieves state-of-the-art performance among self-supervised learning methods on FlyingThings3D and KITTI, even outperforming some supervised methods with ground truth annotations.
ESAR, An Expert Shoplifting Activity Recognition System

Abstract Shoplifting is a troubling and pervasive aspect of consumers, causing great losses to retailers. It is the theft of goods from the stores/shops, usually by hiding the store item either in the pocket or in carrier bag and leaving without any payment. Revenue loss is the most direct financial effect of shoplifting. Therefore, this article introduces an Expert Shoplifting Activity Recognition (ESAR) system to reduce shoplifting incidents in stores/shops. The system being proposed seamlessly examines each frame in video footage and alerts security personnel when shoplifting occurs. It uses dual-stream convolutional neural network to extract appearance and salient motion features in the video sequences. Here, optical flow and gradient components are used to extract salient motion features related to shoplifting movement in the video sequence. Long Short Term Memory (LSTM) based deep learner is modeled to learn the extracted features in the time domain for distinguishing person actions (i.e., normal and shoplifting). Analyzing the model behavior for diverse modeling environments is an added contribution of this paper. A synthesized shoplifting dataset is used here for experimentations. The experimental outcomes show that the proposed approach attains better consequences up to 90.26% detection accuracy compared to the other prevalent approaches.
Unexpected Vection Exacerbates Cybersickness During HMD-Based Virtual Reality

Visually induced illusions of self-motion (vection) are thought to cause cybersickness during head-mounted display based virtual reality (HMD VR). However, the empirical support for this widespread belief is rather mixed. Our exploratory study examined the possibility that only unexpected experiences of vection provoke cybersickness. Fifteen males and 15 females played an HMD VR game (Mission: ISS) for up to 14 min with: 1) their experiences of vection and cybersickness assessed every 2 minutes; and 2) the game being terminated whenever they reported feeling sick. Of the 30 participants tested, 17 reported feeling sick and 13 remained well. Sick and well participants did not differ in terms of the strength of their vection experiences. However, the sick participants were significantly more likely to report unexpected/uncontrolled vection. When these data were subjected to machine learning analysis, unexpected vection was found to be the most important predictor of cybersickness. These preliminary findings suggest that vection can be used to safely enhance experiences in HMD VR–as long as developers ensure that any simulated self-motions are expected and perceived to be under the user’s control.
Qualitative and Quantitative Characterization of Municipal Waste in Uncontrolled Dumpsites and Landfills Using Integrated Remote Sensing, Geological and Geophysical Data: A Case Study

The conducted research offers an environmental assessment of municipal waste (MW) using remote sensing (RS), geological, and geophysical datasets. As a test site, the present study aims to characterize one of the largest uncontrolled dumpsites in Egypt. RS data analysis indicates that high temperature values are concentrated at the MW and landfill site allocations as a result of the decomposition process, leading to fire risks. Moreover, the geological and structural data draw attention to the structural-controlled old topography role on MW distributions. Consequently, the dumpsite MW has larger surface quantities near the downthrows of the mapped faults. For MW characterization, geophysical data are acquired to produce2D/3D resistivity models. Because MW has the ability to become soft clay, the municipal organic waste and landfill leachate resistivities are lower than that of municipal solid waste and sandy soils. The geophysical inversion results indicate that the maximum thickness of MW calibrating with the drilled borehole data is 60 m. Furthermore, the estimated MW dumpsite volume is about 42.32 MCM. Accordingly, the MW can be characterized in an accurate qualitative and quantitative manner. Our findings, therefore, help the efforts of uncontrolled dumpsite development and thus contribute to sustainability plans.
Group WiM: A Group Navigation Technique for Collaborative Virtual Reality Environments

In this work, we present a group World-in-Miniature (WiM) navigation technique that allows a guide to navigate a team in collaborative virtual reality (VR) environments. We evaluated the usability, discomfort, and user performance of the proposed technique compared to state-of-the-art group teleportation in a user study <tex>${\left(n\,=\,21\right)}$</tex>. The results show that the proposed technique induces less discomfort for the guide and has slight usability advantages. Additionally, the group WiM technique seems superior in regards to task completion time for obstructed target destination. However, it performs similarly to the group teleportation technique in direct line of sight cases. The group WiM technique provides potential benefits for effective group navigation in complex virtual environments and harder-to-reach target locations.
Image Super-Resolution via Dual-Level Recurrent Residual Networks

Recently, the feedforward architecture of a super-resolution network based on deep learning was proposed to learn the representation of a low-resolution (LR) input and the non-linear mapping from these inputs to a high-resolution (HR) output, but this method cannot completely solve the interdependence between LR and HR images. In this paper, we retain the feedforward architecture and introduce residuals to a dual-level; therefore, we propose the dual-level recurrent residual network (DLRRN) to generate an HR image with rich details and satisfactory vision. Compared with feedforward networks that operate at a fixed spatial resolution, the dual-level recurrent residual block (DLRRB) in DLRRN utilizes both LR and HR space information. The circular signals in DLRRB enhance spatial details by the mutual guidance between two directions (LR to HR and HR to LR). Specifically, the LR information of the current layer is generated by the HR and LR information of the previous layer. Then, the HR information of the previous layer and LR information of the current layer jointly generate the HR information of the current layer, and so on. The proposed DLRRN has a strong ability for early reconstruction and can gradually restore the final high-resolution image. An extensive quantitative and qualitative evaluation of the benchmark dataset was carried out, and the experimental results proved that our network achieved good results in terms of network parameters, visual effects and objective performance metrics.