Reviewerly/exHarmony huggingface dataset:
Application of Digital Technology in Professional Training

Considering the requirements of the Government Program “Digital Economy of the Russian Federation,” organizations of all spheres of activity and management levels are gradually introducing information technology, digital platforms, and applications into their accounting, analytics, and planning. Sustainable development of the digital economy is impossible without training specialists with appropriate digital competencies. It is necessary to review the methodological techniques and technical means used in teaching disciplines and modules and adapt training courses to digital technology. The strategic goal of developing the agro-industrial complex during the digitalization of the Russian economy is the balanced development of highly efficient agricultural production and social infrastructure based on the implementation of digital innovations and the formation of a management complex with the scientific developments in the agricultural sector. The full-scale introduction of digital solutions into production is impossible without the appropriate professional training of specialists in educational institutions. For several years, digital platforms and data transmission networks have been used in business. Nevertheless, these advanced technologies are implemented in the agricultural sector at an extremely slow pace and even slower in education. The paper describes an example of the practical application of specialized business process management systems of the Russian company ELMA (Izhevsk, the Udmurtian Republic, Russia) in teaching the discipline “Organization of business activities” to economists and managers for the agro-industrial complex (Russian State Agrarian University—Moscow Timiryazev Agricultural Academy, Moscow, Russia). The mechanism for implementing a digital platform in the management of business processes and in the subject matter of the academic discipline is described in detail. The platform provides a high-quality, fast, and accessible process of corporate interaction and administration of organizational processes, allowing to bypass the routine bureaucracy in the coordination of decisions, operational planning, product design, and development of marketing strategy. Graduates of a higher education institution should know not only the legal framework and theoretical issues of the organization of activities but also be able to use modern information technology in their work and have the necessary digital competencies.
On the Influence of Feature Selection and Regression Models on the Decoding Accuracy of Seen and Imagery Objects Using Hierarchical Visual Features

With the development in brain decoding and encoding, the seen and imagery object identification tasks achieve increasingly higher accuracy by using more intricate features. However, the experiment always requires a huge amount of data to process which will consume a lot of time. With available seen and imagery BOLD signals, we leverage the Generic Brain Decoding model to fit and then predict visual feature vectors extracted from neural networks. Given the hierarchy of AlexNet and that of the human visual cortex, the accuracies of the couplings between low layers of AlexNet and human visual cortex and those between high layers outperform other coupling cases. Using a different number of feature dimensions during feature selection induces a significant difference in the decoding performance at the high layers. Moreover, the shallow neural network achieves higher accuracies at the high layers. The results demonstrate that when the number of available data samples is not sufficient, reducing the input feature dimensions can still ensure a comparable decoding accuracy.
A Power Consumption Model of IT Equipment in Data Centers

Information technology (IT) equipment is considered to be one of the major power consumption components in data centers. It is mainly composed of the server, the network component and the power distribution component, among which the most important component is server. Based on the problems that the existing modeling methods have low precision and mainly used for the same type servers, a cubic polynomial IT equipment model (CPIT) is proposed. Three evaluation indicators are used to compare the precision and error of different server models based on real data, and results show the superiority of the server modeling. Finally, the proposed CPIT and the existing models are compared and analyzed in a real data center, which validates the accuracy of the CPIT and it also shows the rationality to represent data center IT equipment power consumption.
Adaptive Sampling Saliency Detection Based on Content Selective Mapping

In order to improve the efficiency of local contrast-based saliency detection algorithm and obtain accurate saliency detection maps, this paper proposes an adaptive sampling saliency detection method based on selective mapping of image content. We transform the Gaussian differential feature spectrum into the spatial domain to produce a density mapping scatter plot that resembles the distribution of human eye gaze points. According to the generated density scatter plot, the randomly generated initial sampling windows are moved to the most salient object position by Mean-shift to achieve adaptive sampling. It simulates eye movement switching. Moreover, we extract image edges by super-pixel segmentation as a reference to get more background prior features, which can further complementary fuse with the foreground information from the sampling feature map by PCA to generate the saliency map with adequate details. Experiments show that the proposed adaptive sampling mechanism is effective and outperforms existing region detection approaches, while our method with good performance is verified on four publicly available datasets.
Anomaly Analysis in Machine Sensors

In Industries, cutting blades is considered the main role in manufacturing the products. The cutting gathering is also a significant part of the machine to meet the high accessibility target. Along these lines, the edge should be set-up and kept up with appropriately. In Industries, the repairing of cutting blades is a major disadvantage. During, the time of heavy workload of machines, failure of blades may easily happen. Those incidents may happen due to damage to machine parts, blade stroking, and reducing the quality of blades. This leads to low costs, productivity will be increased and it is more safety. In this paper, our main aim is to find the machine anomalies. In this, we used a few algorithms to find anomalies. The approaches are One-class SVM, K-Means, and Autoencoder. We proposed an approach to find machine degradation and anomalies.
An Efficient Cluster by Cluster Head Selection Approach in Big Data

Clustering is essential for exploring data, making predictions, and overcoming data anomalies. Reiterative approaches are used to organize clusters in a dataset that have intrinsic, identical properties. Considering the exponential growth of data in the real world, very huge datasets with little or no prior information can be clustered to reveal fascinating patterns. This paper presents Log Centroid-based K-Means (LC-KMeans) clustering techniques in order to optimize the complexity of large data in the Reducer phase and this proposed LCK Means will help to select an efficient Cluster Head using another proposed algorithm Minkowski Distance-based Sea Lion Optimization (MD-SLnO). We compared the number of clusters, the time required to cluster, and the time required to pick a cluster head to existing algorithms.
A Wearable Electrooculogram System with Parallel Motion Artifact Sensing and Reduction

Electrooculogram signal is a well-known physiological metric. Electrooculogram measurements suffer from motion artifact and environmental vibrations. Such artifact are random in nature, may have large dynamic range, and may saturate the overall measurement system output. In this manuscript, we present a single channel, wireless, flexible EOG monitoring system which has capability to reduce motion artifact. The system uses dry non-contact electrodes which makes it mountable with minimal assistance required. The entire EOG system is implemented on a four-layer flexible polyimide substrate, with the EOG acquisition unit on the top layer, noncontact measurement electrodes printed on the bottom layer, circuit ground on the second layer, and active shielding on the third layer. The system uses parallel non-contact electrode pair for EOG signal detection and motion artifact reduction. The battery operated system utilizes only 56 mW of power while using a BLE 5.0 transceiver for wireless EOG data transmission. The system is designed for an effective EOG signal bandwidth of 1 Hz to 40 Hz with an effective signal gain above 35 dB over the signal bandwidth. The capability of the system for motion artifact reduction and EOG detection are experimentally validated. With only 8.75 gram weight the system does not cause any discomfort to the wearer during EOG recording.
CEG4N: Counter-Example Guided Neural Network Quantization Refinement

Neural networks are essential components of learning-based software systems. However, deploying neural networks in low-resource domains is challenging because of their high computing, memory, and power requirements. For this reason, neural networks are often quantized before deployment, but existing quantization techniques tend to degrade network accuracy. We propose Counter-Example Guided Neural Network Quantization Refinement (CEG4N). This technique combines search-based quantization and equivalence verification: the former minimizes the computational requirements, while the latter guarantees that the network's output does not change after quantization. We evaluate CEG4N on a diverse set of benchmarks, including large and small networks. Our technique successfully quantizes the networks in our evaluation while producing models with up to 72% better accuracy than state-of-the-art techniques.
WPNet: Wide Pyramid Network for Recognition of HER2 Expression Levels in Breast Cancer Evaluation

Among the research methods for HER2 automatic evaluation in recent years, most of the methods using deep learning framework have both segmentation and classification functions. Although these methods provide pathologists with reference lesions, they increase the dependence on dataset and the computational cost. Therefore, we propose a Wide Pyramid Network (WPNet) based on deep learning to solve this problem. Our designed WPNet is different from other neural network models, which is mainly extended in the width of the network, and uses the wide pyramid structure to extract the features of different scales on the image for training. Since HER2 score is determined according to the degree of cell membrane staining and the proportion of cells with different degrees of staining, the WPNet model capable of multi-scale feature extraction can facilitate the determination of HER2 score. Compared with other models, for HER2 score classification based on a small sample set, the proposed model not only accelerates the convergence speed during training, reduces the calculation cost but also improves the classification effect.
Can Git Repository Visualization Support Educators in Assessing Group Projects?

In the past years numerous software visualization tools have been introduced to support the analysis of software systems and their evolution as captured in the versioning systems. Usually the target audience of such tools comprises software engineering professionals. In this paper we argue that such tools are also beneficial for educators who need to evaluate the quality of software systems developed by students. However, since the needs of educators are different than those of the software engineering professionals, we discuss several educator needs first. We report several usage examples that we believe are useful for educators when using repository visualization tools. We illustrate them with examples from several student projects from different courses in two universities. We conclude with a series of considerations that should be heeded by both educators and future tool-builders.
Architectural Degradation and Technical Debt Dashboards

Background. Companies frequently try to improve the quality of their software by resolving technical issues that are perceived to have an impact on software quality. Technical information is any information that may be gathered from the source code or the software development process, for instance: code or documentation guidelines, and the use of specific patterns. If these issues are not fixed they may generate technical debt. Goal. The goal of the Ph.D., which started on January 2022, is to understand which are the causes of Architectural Technical Debt in a real case study and to develop a Dashboard to notify developers as earlier as possible. Methods and expected contribution. We first plan to investigate the actual production code process and then, and then to work with teams to find the best practices and strategies to easily manage Architectural Technical Debt.
Adaptive Open Set Recognition with Multi-modal Joint Metric Learning

Open set recognition (OSR) aims to simultaneously identify known classes and reject unknown classes. However, existing researches on open set recognition are usually based on single-modal data. Single-modal perception is susceptible to external interference, which may cause incorrect recognition. The multi-modal perception can be employed to improve the OSR performance thanks to the complementarity between different modalities. So we propose a new multi-modal open set recognition (MMOSR) method in this paper. The MMOSR network is constructed with joint metric learning in logit space. By doing this, it can avoid the feature representation gap between different modalities, and effectively estimate the decision boundaries. Moreover, the entropy-based adaptive weight fusion method is developed to combine the multi-modal perception information. The weights of different modalities are automatically determined according to the entropy in the logit space. A bigger entropy will lead to a smaller weight of the corresponding modality. This can effectively prevent the influence of disturbance. Scaling the fusion logits by the single-modal relative reachability further enhances the unknown detection ability. Experiments show that our method can achieve more robust open set recognition performance with multi-modal input compared with other methods.
Improvement of Information Technology for Person Identification for Usage in Energy Smart Systems

This paper is devoted to the research of information technology of person identification with an aim of its improvement for further usage in energy smart systems. As far as face recognition technologies usually exploit high-dimensional information it makes the processes of face recognition and identification computationally intensive. The purpose of this research is to overcome this problem by facing the challenges of low-quality image recognition. Therefore, there were studied the following issues of low-quality image recognition: area of face region, image resolution variety and image compression variety due to the file format. The experimental research was performed in three stages with the use of the Database of Faces, FERET and SCface database.
Optimization of CT Image Quality Assessment Metric Based on Genetic Algorithm

Image Quality Assessment (IQA) metrics play an important role in helping measure computed tomography (CT) image reconstruction algorithms and trade-off dose reduction and imaging quality. However, some studies have shown that the results of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), which are commonly used IQA metrics are often inconsistent with the subjective opinions of radiologists when applied to medical images, and it is unknown whether other IQA algorithms which outperform PSNR and SSIM on natural images are equally good on CT images. In this paper, we explore the performance of nine IQA metrics on CT images by comparing their running time and their correlation with mean opinion score (MOS). Moreover, inspired by the two-step framework of traditional IQA metrics, we propose a combination IQA mdoel based on genetic algorithm to optimize the existing metrics from comparative features and pooling strategies. The obtained results show that most traditional FR-IQA metrics suffer from unsatisfactory accuracy or high computational cost, while the proposed method achieves superior performance in both aspects.
Modeling and Analysis of Chronic Disease Processes Under Intermittent Observation

A wealth of information is available from clinic-based registries of individuals with chronic diseases but modeling and analysis of disease processes can be challenging because of the ways individuals are enrolled in the registry and because they are only seen at intermittent clinic visits post-enrolment. Such features are common in observational cohort studies in general, but even in randomized trials, intermittent observation of individuals is common, resulting in incomplete information about transitions among disease states and about time-dependent covariates. In this chapter we describe independence conditions needed for valid likelihood-based inference about multistate disease processes under intermittent observation schemes. In addition, we describe how joint models for disease and observation processes can be used to address disease-related clinic visits. We also describe how joint models can be used to deal with internal time-dependent markers when marker values are observed only at clinic visits, and investigate the limiting values of regression coefficients of marker effects when the common approach of carrying forward the most recently recorded value is used. In addition to failure time processes, we consider more general multistate disease processes, and partially specified models for state occupancy probabilities. An application to a study of cancer metastatic to bone is considered, involving a serum marker of bone activity and its association with risk of skeletal complications.
Abnormal motor detection based on vibration signal

This paper presents an anomaly detection method of motor which combined time-frequency characteristics and fractal dimension. The energy of the original signal is characterized by the time-domain characteristics, and the multiple frequency characteristics describe the components of the frequency-domain signal which is after the Fourier transform of the time-domain signal. Fractal represents the similarity of whole and local existence, and completely describes the characteristic information in the process of motor operation; Then, the genetic algorithm is used to select the features, and the optimal feature subset in the anomaly diagnosis model is selected and sent to the classifier to detect the equipment anomaly. In this paper, the experimental data are used for simulation, support vector machine (SVM) and support vector data description (SVDD) classifiers are used for experiments, and the accuracy is 98.92% and 97.04% respectively. The algorithm in this paper is comprehensive feature extraction, by eliminating redundant features through genetic algorithm, motor anomaly detection can be realized efficiently and accurately, which is more suitable for the application and implementation of detection equipment.
Novel Innovations that Failed to Improve Weak PRNGs

Weak Pseudo-random Number Generators (PRNGs) can be improved with additional operations and techniques. However, some techniques added to the RNGs in an attempt to improve the quality and randomness of the generator fail to produce the desired results. Literature surveys have produced very few ideas and information related to the constituent steps involved in the individual algorithms used in constructing the PRNGs. In preference to studying and understanding the individual elements of the algorithms, designers have preferred to create new PRNGs and have missed the opportunity to increase the basic understanding of the field. If the building blocks of PRNGs are well understood, along with their interactions, the design of high quality PRNGs is possible. Understanding what works also requires understanding what does not work. Those techniques can then be avoided. This paper is an exploration of some novel techniques for improving the quality of weak PRNGs that failed to live up to the promise of improving PRNGs.
Reverse-Engineering and Exploiting the Frontend Bus of Intel Processor

The frontend of modern Intel processors will decode instructions into <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> ops and stream them to the backend by the frontend bus, which is shared between two logical cores to maximize utilization without sharing mechanism fully disclosed. Taking Haswell as an example, we reverse the bus from Decoded ICache to Instruction Decode Queue and the bus from Instruction Decode Queue to backend. We find that they are dynamically shared between two logical cores, which makes it possible for observable timing differences in one another through different instructions. Based on these differences, we propose the Synthetical bus covert channel for LSD-enabled architectures like Haswell and the DI bus covert channel for LSD-disabled architectures like Cometlake. We test our covert channels in physical machines and virtual machines. The bandwidth of Synthetical bus covert channel achieves 870 Kbps with 95.69% accuracy in physical machines and 145 Kbps with 92.83% accuracy in virtual machines. The bandwidth of DI bus covert channel reaches 1450 Kbps with 97.2% accuracy in physical machines and 70.33 Kbps with 92.3% accuracy in virtual machines. We further demonstrate a new Spectre variant. Finally, we propose two possible mitigations against our covert channels due to the limitations of existing protection strategies.
In situ training with silicon photonics neural networks

Deep learning hardware accelerators based on analog photonic networks are trained on standard digital electronics. We discuss on-chip training of neural networks enabled by a silicon photonic architecture for parallel, efficient, and fast data operations.
Detection of injections in API requests using recurrent neural networks and transformers

Application programming interfaces (APIs) are playing a vital role in every online business. The objective of this study is to analyse the incoming requests to a target API and flag any malicious activity. This paper proposes a solution based on sequence models and transformers for the identification of whether an API request has SQL injections, code injections, XSS attacks, operating system (OS) command injections, and other types of malicious injections or not. In this paper, we observe that transformers outperform B-RNNs in detecting malicious activity which is present in API requests. We also propose a novel heuristic procedure that minimises the number of false positives. We observe that the RoBERTa transformer outperforms and gives an accuracy of 100% on our dataset. We observe that the heuristic procedure works well in reducing the number of false positives when a large number of false positives exist in the predictions of the models.