Reviewerly/exHarmony huggingface dataset:
Target settings for modeling career guidance in the education system of the region

Introduction. The article is devoted to the problem of insufficient theoretical justification of modeling career guidance in the education system of the region, primarily to identifying foundations of goal-setting. The aim of the study is to identify the features of the leading targets in modeling career guidance in the education system of the region, and to consider the possibility of their simultaneous use in the modeling of career guidance in the education system of the region. Materials and Methods. The study follows paradigmatic, socio-pedagogical and learner-centered approaches. The authors summarize and integrate educational, psychological and sociological theoretical ideas on the problem of this research. Methods of theoretical analysis, comparison, generalization, analysis of infographic representations of models of career guidance are used in order to solve the research problem. Results. Literature analysis enabled the authors to identify the leading constructs used as the basis of goal-setting in the models of career guidance in the educational system of the region (choosing a career, professional self-determination, career growth, professionalism), and to clarify their content characteristics. The construct ‘choosing a career’ focuses on a personal specific result; ‘professional self-determination’ deals with the process of personal and professional development through making meaningful decisions, involving a possibility of a career choice. ‘Career growth’ concentrates on the process of movement to social understanding of profession success. ‘Professionalism’ implies self-transformation in the interests of the profession not only in terms of technological readiness, but also personal compliance and moral attitudes. The authors proposed a way of coordinating the main targets of modeling career guidance in the education system of the region: a) the ideal goal (strategic goal) is defined through the formation of professionalism (at the personal level); orientation to career planning and career growth (in broad connotations) can be included in the structure of professionalism; b) the goal as the expected result of career guidance practices (tactical goal) is defined through professional self-determination. Conclusions. The authors conclude that the appropriate selection of target foundations contribute to clarifying qualitative characteristics of career guidance models in the education system of the region. Based on selecting leading constructs within the target setting, a typology of regional models and evaluation of their qualitative effectiveness can be made. The variants of combining leading constructs allow to define priorities, which determine the main direction and specifies organizational and methodological content of the model.
Proving hypersafety compositionally

Hypersafety properties of arity $n$ are program properties that relate $n$ traces of a program (or, more generally, traces of $n$ programs). Classic examples include determinism, idempotence, and associativity. A number of relational program logics have been introduced to target this class of properties. Their aim is to construct simpler proofs by capitalizing on structural similarities between the $n$ related programs. We propose an unexplored, complementary proof principle that establishes hyper-triples (i.e. hypersafety judgments) as a unifying compositional building block for proofs, and we use it to develop a Logic for Hyper-triple Composition (LHC), which supports forms of proof compositionality that were not achievable in previous logics. We prove LHC sound and apply it to a number of challenging examples.
A study of inline assembly in solidity smart contracts

The Solidity programming language is the most widely used language for smart contract development. Improving smart contracts’ correctness, security, and performance has been the driving force for research in vulnerability detection, program analysis, and compiler techniques for Solidity. Similar to system-level languages such as C, Solidity enables the embedding of low-level code in programs, in the form of inline assembly code. Developers use inline assembly for low-level optimizations, extending the Solidity language through libraries, and using blockchain-specific opcodes only available through inline assembly. Nevertheless, inline assembly fragments are not well understood by an average developer and can introduce security threats as well as affect the optimizations that can be applied to programs by the compiler; it also significantly limits the effectiveness of source code static analyzers that operate on the Solidity level. A better understanding of how inline assembly is used in practice could in turn increase the performance, security, and support for inline assembly in Solidity. This paper presents a large-scale quantitative study of the use of inline assembly in 6.8M smart contracts deployed on the Ethereum blockchain. We find that 23% of the analyzed smart contracts contain inline assembly code, and that the use of inline assembly has become more widespread over time. We further performed a manual qualitative analysis for identifying usage patterns of inline assembly in Solidity smart contracts. Our findings are intended to help practitioners understand when they should use inline assembly and guide developers of Solidity tools in prioritizing which parts of inline assembly to implement first. Finally, the insights of this study could be used to enhance the Solidity language, improve the Solidity compiler, and to open up new research directions by driving future researchers to build appropriate methods and techniques for replacing inline assembly in Solidity programs when there is no real necessity to use it.
Edge-Focus Thermal Image Super-Resolution using Generative Adversarial Network

Thermal imaging has played an important role in a wide range of areas of life. However, thermal cameras often produce low-resolution images, which limits the ability to observe objects in thermal imaging applications. Modern thermal cameras often include a built-in high-resolution visible camera to supplement the thermal image information. This paper proposes a method to increase the resolution of thermal images using edge features of corresponding high-resolution visible images. The Canny edge detection and thin-line downscaling algorithms are used to generate edge maps from high-resolution visible images to contribute to the super-resolution network. The proposed super-resolution model is designed based on generative adversarial network architecture for $\times$ 2, $\times$ 3, and $\times$ 4 magnification. The KAIST dataset is used to train and test the model. Peak signal-to-noise ratio (PSNR) and structure similarity index (SSIM) are used to evaluate the quality of super-resolution images. After the training process, to prove the effectiveness of edge features, we compare the quality of the super-resolution images generated from the proposed method with other methods. The comparison results show that the proposed method has the highest performance in terms of PSNR and SSIM.
Developing a User-Centered Educational Game to Prevent Cyberbullying Among Elementary School Students

Technologies are becoming more accessible to both adults and young people. Although technology can form many opportunities, it exposes children to many threats, including cyberbullying. This research aims to develop an interactive game prototype to foster an understanding of cyberbullying in elementary school students. This research uses the User-Centered Design method with a qualitative approach in designing interaction designs based on user needs. Then, the prototype is piloted to users to evaluate user experiences and learning outcomes. Based on the evaluation results, the game prototype provides a positive user experience regarding satisfaction, learnability, effectiveness, immersion, motivation, and emotion. Participants were able to elaborate on positive ways of interacting through digital platforms. However, they had difficulties in responding appropriately to negative behavior and content on digital platforms. The findings of this research and suggestions for future improvements are presented.
Video2IMU: Realistic IMU features and signals from videos

Human Activity Recognition (HAR) from wearable sensor data identifies movements or activities in unconstrained environments. HAR is a challenging problem as it presents great variability across subjects. Obtaining large amounts of labelled data is not straightforward, since wearable sensor signals are not easy to label upon simple human inspection. In our work, we propose the use of neural networks for the generation of realistic signals and features using human activity monocular videos. We show how these generated features and signals can be utilized, instead of their real counterparts, to train HAR models that can recognize activities using signals obtained with wearable sensors. To prove the validity of our methods, we perform experiments on an activity recognition dataset created for the improvement of industrial work safety. We show that our model is able to realistically generate virtual sensor signals and features usable to train a HAR classifier with comparable performance as the one trained using real sensor data. Our results enable the use of available, labeled video data for training HAR models to classify signals from wearable sensors.
Augmented Reality and Gamification technics for visit enhancement in archaeological parks

The use of innovative technological approaches in the field of cultural heritage is now becoming essential to preserve and enhance tangible and intangible cultural heritage. Special attention can be paid to enhancing the visitor experience using gamification approaches by exploiting new technologies. Augmented reality is one of the most significant tools that enable a special interaction between the user and the environment. The possibility of enriching the real environment with three-dimensional models opens up the possibility of narrating the real place with innovative storytelling not only of the environment itself but also and above all, telling and conveying all that is the intangible heritage that a place inevitably brings with it. To take advantage of this opportunity, the study focused on defining a methodology to apply advanced augmented reality and storytelling techniques combined with gamification techniques to enhance the visitor experience in a medium to large-scale archaeological sites. The methodology was then applied to a case study on the archaeological park of Pompeii, which saw the creation of an application prototype that demonstrated the validity of the approach.
Direct and Indirect Relationship of Employee Engagement, Corporate Image, Employee Loyalty, and Business Performance among Private Business Organizations

This study is very crucial for private business organizations to achieve business performance by formulating short and long-term strategies and plans to achieve the set business performance.The topic being studied is important for private organizations because it will allow organizations to improve their performance and business sustainability in the future.The purpose of this study is to measure the direct and indirect relationship between employee engagement, employee loyalty, corporate image, and business performance.This study uses a quantitative approach by using primary data for data analysis.A total of 329 clean data were used in data analysis using the Structural Equation Modeling (SEM) technique.This study uses Smartpls3 software to analyze multivariate data and the proposed hypotheses.Furthermore, model measurement and structural model assessment procedures were also carried out by using Smartpls3.An assessment of construct reliability and validity was done to confirm the existence of convergent validity and reliability.Then, discriminant validity is assessed by calculating cross-loadings and Hetrotrait-Monotrait (HTMT) ratios.Next, the inner model was assessed and from the results of hypotheses testing, it was found that employee engagement does not have a direct relationship with business performance, while employee loyalty and corporate image have a direct relationship with business performance.On the other hand, employee loyalty and corporate image have a significant mediating effect on the relationship between employee engagement and business performance.Findings from the analysis show that employee loyalty and corporate image are important mediators in the relationship between employee engagement and business performance.For future studies, it is suggested that other variables such as leadership style.Organizational commitment and peer influence are to be included in the model so that it will
Intel HERACLES

In spite strong advances in confidential computing technologies to protect data, the status is that data is encrypted while temporarily not in use and unencrypted during computation. The inability to keep data encrypted during computation can inhibit the ability to fully share and extract the maximum value out of data, e.g., via statistical and machine learning methods with the additional risk of third-party data leakage. Fully Homomorphic Encryption (FHE) enables users to delegate computation to the cloud by enabling the cloud to process users' encrypted inputs without decryption and return encrypted output to the intended recipients. The adoption of FHE by the industry has been slow. First, processing encrypted data incurs a huge performance tax even for simple operations - ciphertexts operations can be several orders of magnitude slower with respect to cleartext operations on existing hardware. Second, there is lack of automation tools for translating data and applications to enable FHE. Third, there is lack of standards and best practices for FHE secure deployment in combination with other confidential computing techniques. The DARPA DPRIVE program [1] is the first publicly visible program aiming to build a platform to accelerate FHE and a path to their commercialization, for their use in healthcare, communication (5G to XG), and cloud computing. The program represents a steppingstone toward FHE adoption. Under the DARPA DPRIVE program, Intel® is designing a new type of computer architecture to reduce the performance tax currently associated with FHE. Intel® collaborates with Microsoft® Azure Global in realization of the project [2]. The design includes flexible arithmetic circuits for algebraic lattices with unprecedented vector parallelism and data transfer capacity between vector slots, to increase ciphertext processing speed, coupled with near-memory computation, to reduce data movement. The software stack leverages the Microsoft® SEAL library [3] augmented the BGV mechanism including bootstrapping [4], and automatic translation tools to explore trade-offs in algorithmic optimization and data encoding to fit the performance requirements [5]. When fully realized, the HERACLES platform will deliver a massive improvement in executing FHE workloads over existing CPU-driven systems, potentially reducing ciphertext processing time by five orders of magnitude. Beyond the new hardware and software stack, Intel® work with international standard bodies to develop standards for and best practices for FHE secure deployment [6] and invests in academic research to advance both theory and application [7].
Towards a Unified View of Unsupervised Non-Local Methods for Image Denoising: The NL-Ridge Approach

We propose a unified view of unsupervised non-local methods for image denoising that linearly combine noisy image patches. The best methods, established in different modeling and estimation frameworks, are two-step algorithms. Leveraging Stein’s unbiased risk estimate (SURE) for the first step and the “internal adaptation”, a concept borrowed from deep learning theory, for the second one, we show that our NL-Ridge approach enables to reconcile several patch aggregation methods for image denoising. In the second step, our closed-form aggregation weights are computed through multivariate Ridge regressions. Experiments on artificially noisy images demonstrate that NL-Ridge may outperform well established state-of-the-art unsupervised denoisers such as BM3D and NL-Bayes, as well as recent unsupervised deep learning methods, while being simpler conceptually.
Enhancing Underwater Image Using Degradation Adaptive Adversarial Network

Underwater imaging severely suffers from color distortion and poor visibility due to light refraction and absorption and suspended particles in the water. Existing learning-based underwater image restoration methods highly rely on paired training datasets. However, distorted/non-distorted image pairs in realistic underwater scenes are unavailable and synthetic data-based trained models greatly degrade the performance in real-world applications. In this paper, an unpaired underwater image restoration method is proposed using a cycle adversarial generative network (CycleGAN) with an integrated distortion adaptive (DA) module. CycleGAN is used to perform image-to-image translation and the DA module can learn various degradation information from the unpaired training samples with flexible adaption. Experimental results demonstrate the superiority of our proposed method against existing state-of-the-art algorithms in restoring underwater images in terms of color correction and visual quality improvement.
Want more unikernels?

Unikernels are on the rise in the cloud. These lightweight virtual machines (VMs) specialized to a single application offer the same level of isolation as full-blown VMs, while providing performance superior to standard Linux-based VMs or even to containers. However, their inherent specialization renders memory deduplication ineffective, causing unikernels, in practice, to consume more memory than their small memory footprint would suggest. This makes them less advantageous when thousands of SaaS and/or FaaS unikernels instances have to run on the same server.
Effects of educational technology on reading achievement for Chinese K-12 English second language learners: A meta-analysis

No systematic published research has reviewed the impact of educational technology on English reading outcomes targeting the Chinese-speaking population. Therefore, this review intended to examine the impact of educational technology and its alternative types on reading achievement for Chinese English second language learners (ESLs) to understand how to best use technology applications to facilitate reading instruction. A total of 35 qualified studies were included in our analysis covering a sample size of 7,989 Chinese K-12 participants. Consistent with previous meta-analyses, our findings indicated a modest positive impact of educational technology on reading outcomes compared with the traditional teaching method (d = +0.37). For the five types of intervention identified in this review, we found that the comprehensive model had the largest impact (d = +0.60), followed by social media tools (d = +0.46), integrated online-learning system (d = +0.31), and multimedia-transmission model (d = +0.27). However, supplementary activities did not generate educationally meaningful effects on reading outcomes for Chinese ESLs (d = +0.05). The impacts of different moderators, implications, and limitations were also discussed. We argue for further integrating technology with the existing curriculum and pedagogy. The study adds to the second language (L2) reading literature corpus.
Emulator of Gene Mutation: Interfering True Random Number Generators Using Resistive-Gate Non-Volatile-Memories

We present the interfering True-random-number-generators (TRNG) composed of 2 parallel resistive-gate non-volatile memory cells. Both cells store closer but different resistance states. 2 cells output current flows separately, which are received concurrently by a current-reference sense amplifier (SA). The SA compares difference of received flows and then continues outputting the same bits if difference of current-flows remains stable. However, if randomness happens, levels of current flows from the cells are interlaced; the SA will output a flipped bit, which can be in analogy to gene mutation. So, we emulate mutation by the interfering TRNGs and propose an algorithm to implement emulator of mutation. The TRNGs are also qualified by cryptographic tests, including the Hamming distance, Hamming weight, auto-correlation, and tests of National Institute of Standards and Technologies.
Adaptive Multi-layer Contrastive Graph Neural Networks

Inspired by recent success of graph contrastive learning methods, we propose a self-supervised learning framework for Graph Neural Networks (GNNs) named Adaptive Multi-layer Contrastive Graph Neural Networks (AMC-GNN). Specifically, AMC-GNN generates different graph views through data augmentation and compares the output embeddings at different layers of graph neural network encoders to obtain feature representations for downstream tasks. Meanwhile, AMC-GNN learns the importance weights of embeddings at different layers adaptively through the attention mechanism, and an auxiliary encoder is adopted to train graph contrastive encoders better. The accuracy is improved by maximizing the representation’s consistency of positive pairs in the intermediate layers and the final embedding space. Experiments on node classification and link prediction demonstrate that the AMC-GNN framework outperforms state-of-the-art contrastive learning methods and even sometimes outperforms supervised methods.
DeepCrash: deep metric learning for crash bucketing based on stack trace

Some software projects collect vast crash reports from testing and end users, then organize them in groups to efficiently fix bugs. This task is crash report bucketing. In particular, a high precision and fast speed crash similarity measurement approach is the critical constraint for large-scale crash bucketing. In this paper, we propose a deep learning-based crash bucketing method which maps stack trace to feature vectors and groups these feature vectors into buckets. First, we develop a frame tokenization method for stack trace, called frame2vec, to extract frame representations based on frame segmentation. Second, we propose a deep metric model to map the sequential stack trace representations into feature vectors whose similarity can represent the similarity of crashes. Third, a clustering algorithm is used to rapidly group similar feature vectors into same buckets to get the final result. Additionally, we evaluate our approach with the other seven competing methods on both private and public data sets. The results reveal that our method can speed up clustering and maintain high competitive precision.