Reviewerly/exHarmony huggingface dataset:
Feature Selection Based on the Discriminative Significance for Sparse Binary-Valued and Imbalanced Dataset

Identifying the significant, or dominant, features is important to reveal the cause-and-effect relations in many pattern recognition applications, such as medical diagnosis, gene analysis, cyber security, finance and insurance fraud detection, etc. Samples that are sparsely populated and binary-valued in highly imbalanced datasets pose a challenge to the identification of these features. This paper explores an approach based on the confusion matrix measurement of the feature values with respect to their potential classification outcomes. The approach is able to compute the Discriminative Significances of the features and rank the features unbiasedly with respect to the imbalance ratios of the datasets. Experiment results on real-world and experimental datasets show that the approach made consistent evaluations of the features and identified the most significant ones accordingly on the sparse and binary-valued samples of the class-imbalanced datasets.
Customizable tubular model for n-furcating blood vessels and its application to 3D reconstruction of the cerebrovascular system

Understanding the 3D cerebral vascular network is one of the pressing issues impacting the diagnostics of various systemic disorders and is helpful in clinical therapeutic strategies. Unfortunately, the existing software in the radiological workstation does not meet the expectations of radiologists who require a computerized system for detailed, quantitative analysis of the human cerebrovascular system in 3D and a standardized geometric description of its components. In this study, we show a method that uses 3D image data from magnetic resonance imaging with contrast to create a geometrical reconstruction of the vessels and a parametric description of the reconstructed segments of the vessels. First, the method isolates the vascular system using controlled morphological growing and performs skeleton extraction and optimization. Then, around the optimized skeleton branches, it creates tubular objects optimized for quality and accuracy of matching with the originally isolated vascular data. Finally, it optimizes the joints on n-furcating vessel segments. As a result, the algorithm gives a complete description of shape, position in space, position relative to other segments, and other anatomical structures of each cerebrovascular system segment. Our method is highly customizable and in principle allows reconstructing vascular structures from any 2D or 3D data. The algorithm solves shortcomings of currently available methods including failures to reconstruct the vessel mesh in the proximity of junctions and is free of mesh collisions in high curvature vessels. It also introduces a number of optimizations in the vessel skeletonization leading to a more smooth and more accurate model of the vessel network. We have tested the method on 20 datasets from the public magnetic resonance angiography image database and show that the method allows for repeatable and robust segmentation of the vessel network and allows to compute vascular lateralization indices.
Approaches to Develop Digital Competences to Create a Portrait of the Graduate of the Future

The purpose of this article is to analyze pedagogical technologies that are used to develop digital competencies and digital literacy in primary, middle, and high schools. Analysis of the problem field of research shows that increasing modernization of the industry associated with the processes of digitalization and automatization of technological lines to significant changes in the labor market, as well as adjustments in the portraits of graduates of both schools and higher education institutions. Transformation to an information and digital society causes other ways of presenting information as well as an emphasis on the development of relevant universal competences: critical and analytical types of thinking; ability to work and systematize large volumes of information; leadership skills, as well as the basics of time-management for organizing and planning independent work. The novelty of the work lies in the description of the real experience of modernization of the educational process in the private educational organization "Funscool", evaluation of the advantages and disadvantages of applied technologies compared to similar examples in secondary education in Russia or Europe, USA. At the same time, the analysis was carried out both for real-time and distance learning formats. Research methodology and methods: in writing the article methods of induction and deduction, methods of comparative analysis and synthesis of information were used, the analysis of various forms of questionnaire survey was carried out.
Spatial and temporal massive memory in humans

It is well known that humans have a massive memory for pictures and scenes.1,2,3,4 They show an ability to encode thousands of images with only a few seconds of exposure to each. In addition to this massive memory for "what" observers have seen, three experiments reported here show that observers have a "spatial massive memory" (SMM) for "where" stimuli have been seen and a "temporal massive memory" (TMM) for "when" stimuli have been seen. The positions in time and space for at least dozens of items can be reported with good, if not perfect accuracy. Previous work has suggested that there might be good memory for stimulus location,5,6 but there do not seem to have been concerted efforts to measure the extent of this memory. Moreover, in our method, observers are recalling where items were located and not merely recognizing the correct location. This is interesting because massive memory is sometimes thought to be limited to recognition tasks based on sense of familiarity.
Towards dynamic reconfiguration of composite services via failure estimation of general and domain quality of services

Composite services (CSs) are large-grained services composed of simple ones to satisfy users' complex requirements. A major issue concerning a CS is that the general quality of service (GQoS) and the domain quality of service (DQoS) of its component services often fail due to environmental changes. However, existing research fails to consider how to actively perceive GQoS failure and DQoS failure of component services, and cannot perform dynamic reconfiguration of composite services proactively. To tackle this problem, we propose an approach for dynamic reconfiguration of CSs by automatically detecting both GQoS and DQoS failures. Our approach includes three stages: at the first stage, we estimate the failure judgement basis of GQoS and DQoS for each component service based on a preferences-aware global GQoS decomposition method and a statistical method. In the second stage, during the execution of a CS, we proactively estimate whether the component services to be executed will encounter GQoS or DQoS failure based on a novel GQoS and DQoS joint prediction method. At the third stage, when the component service to be executed encounters GQoS or DQoS failure, we reconfigure the CS by replacing the failed component service with a best-matched similar service, while we also apply the GQoS and DQoS joint prediction method to predict GQoS and DQoS of the matched service, so as to ensure that the matched service can satisfy the local GQoS and DQoS constraints of the failed component services. Moreover, to improve the efficiency of replacement services selection, we cluster the large scale services based on a multi-attributes aware services clustering method in advance. Extensive experiments have been carried out to demonstrate the effectiveness and performance superiority of our approach.
Bounds and algorithms for generalized superimposed codes

We introduce a class of generalized superimposed codes that include several cases already studied in the literature. We give bounds on their size and algorithms for their construction.
EXERT: EXhaustive IntEgRiTy Analysis for Information Flow Security

Hardware information flow analysis detects security vulnerabilities resulting from microarchitectural design flaws, design-for-test/debug (DfT/D) backdoors, and hardware Trojans. Though information flow violations can be manifested through a multitude of possible ways, prior research has only focused on detecting the existence of such vulnerabilities and no approach has been proposed to exhaustively activate all vulnerable points and reduce false positives. In this paper, we propose EXERT, a novel analysis framework that combines ATPG, SAT, and FSM analysis to detect information flow violations and perform exhaustive analysis that reports the complete set of violating input patterns for vulnerable control points. The FSM analysis, in particular, can be performed offline and helps resolve scalability limitations in prior approaches while remaining exhaustive. As proof-of-concept, EXERT is evaluated on multiple Trojan benchmarks from Trust-Hub. It detects rare Trojan triggers (activation probability ≈ 1.4243e-70), generates all activation patterns within minutes, and shows a 15 x to 110 x faster run time compared with Cadence Jasper Security Path Verification (SPV). EXERT is also applied to a larger RISC-V benchmark to identify instruction sequences that result in privilege escalation.
Utilizing Blockchain and Distributed Storage to Enhance Security and Privacy in the IoT Ecosystem

Blockchain and Internet of Things (IoT) have become one of the most researched topics in recent years. The application areas of both these fields are quite broad and one can complement the other in bringing in the best possible outcome. Keeping that in mind, this paper proposed how Blockchain can be utilized to provide a more secure and improved IoT ecosystem where the components of the ecosystem will be more protected and less susceptible to unwanted intrusions. We also considered using InterPlanetary File System (IPFS) and Hyperledger Fabric as a means of IoT data storage, which can ensure better protection compared to centralized cloud storage. We have also utilized Ethereum Smart Contracts (SC) for Proof of Concept deployment of our model. The experimental outcome as well as the cost and security analysis of our deployed Smart Contracts suggest that it is more beneficial to use Blockchain and distributed storage systems to safeguard the IoT ecosystem and its data.
Feeling Artificial Intelligence for AI-Enabled Autonomous Systems

The need for more advanced Unmanned Systems (US) is supported by the development trends of the world society. AI plays an important role in supporting the required level of autonomy. AI-enabled US developers focus on Feeling AI (FAI). The considered approaches of cognitive sciences and AI do not fully support such US criterion as autonomy. This article proposes FAI based on the conceptual model of L. Zadeh “Computing With Words” (CWW) to support US autonomy. Proposed FAI architecture bridges the gap between two paradigms “sensor data” and “word”, which is the main challenge for deployment the CWW in US. Blueprint FAI architecture, which satisfies the requirements of autonomy, implemented on the basis of cognitive models and is presented as a set of homogeneous knowledge granules, is considered. Architecture includes four knowledge bases and supports nine engines, including Perception, Short Term memory, Control, Goal-Driving, Planning, Attention, Emotion and Need Engines. Homogeneity is achieved due to the unification of the knowledge granular in the form of external and internal meanings of the word. Formal representation models and processing algorithms based on fuzzy systems are given. A numerical example is considered.
Financial Convergence Test with Fourier Panel KPSS Stationarity Test: Findings from Fragile Five Countries

Financial development is an important component of economic development. In particular, it is very important for developing countries to converge to developed countries in terms of financial development level. In this study, the convergence of the financial development indicators of the Fragile Five Countries to the average of the indicators of the four selected countries (USA, England, Australia and Japan) in the top ten in terms of financial development level for the period 1980-2020 is tested. In addition, stochastic convergence based on relative financial indicator figures, that is, convergence to the mean, is also investigated. The data used in the study were accessed from the World Bank official database. Stationarity test which is introduced to the literature by Nazlıoğlu and Karul (2017) based on the Fourier stationarity test developed first by Becker et al. (2006). This test allows for gradual structural changes and cross-section dependence and cross-section heterogeneity. As in all stationarity tests, the basic hypothesis of this test is that the series is stationary, while the alternative hypothesis claims that it contains a unit root. This test gives results for both the individual and the panel as a whole. As a result of the tests, it is seen that the financial development indicators of the Fragile Five Countries do not converge to the selected country group and its average.
One-step unsupervised clustering based on information theoretic metric and adaptive neighbor manifold regularization

Graph-based clustering is a basic subject in the field of machine learning, but most of them still have the following deficiencies. First, the extra discretization procedures leads to instability of the algorithm. In addition, the traditional method of constructing similarity graphs is based on the pairwise distance, so it is extremely sensitive to the original data, and also lacks specific physical meaning from the perspective of probabilistic prediction. Final, the traditional metrics based on Euclidean distance is difficult to tackle non-Gaussian noise. In order to eliminate these limitations, a one-step unsupervised clustering based on information theoretic metric and adaptive neighbor manifold regularization method (ITMNMR) is proposed. (1) The clustering results are directly obtained according to the constructed similarity graph, avoiding extra discretization procedures; (2) A maximum entropy regularization term is introduced into the probabilistic model to avoid trivial similarity distributions. Furthermore, we introduce a Laplacian rank constraint and ℓ0-norm to construct adaptive neighbors with sparsity and strength segmentation capabilities; (3) To overcome the impression of noise, reconstruction based on correntropy is introduced to solve the non-Gaussian noise, and graph regularization is performed based on clean data. Furthermore, a half-quadratic optimization method is used to transform the problem into a quadratic form to facilitate subsequent solutions. Finally, our empirical study shows encouraging results of ITMNMR in comparison to classical algorithms and the state-of-the-art algorithms on 9 datasets. The robustness of the proposed method is also demonstrated from three experiments of adding Laplacian noise, salt&pepper noise, and block occlusion.
Low-energy Pipelined Hardware Design for Approximate Medium Filter

Image and video processing algorithms are currently crucial for many applications. Hardware implementation of these algorithms provides higher speed for large computation applications. Removing noise is often a typical pre-processing step to enhance the results of later analysis and processing. Median filter is a typical nonlinear filter that is very commonly used for impulse noise elimination in digital image processing. This article suggests a low-energy median filter hardware design for battery-based hardware applications. An approximate solution with high accuracy is investigated to speed up the filtering operation, reduce the area, and consume less power/energy. Pipelining and parallelism are used to optimize the speed and power of this technique. Non-pipelined, two different pipelined structures, and two parallel architectures versions are designed. The design versions are implemented first with a Virtex-5 LX110T FPGA and then using the UMC 130nm standard cell ASIC technology. The selection and the even-odd sorting-based median filters are also implemented for an equitable comparison with the standard median filtering techniques. The suggested non-pipelined median filter design enhances the throughput 35% more than the highest investigated state of the art. The pipelining enhances the throughput to more than twice its value. Additionally, the parallel architecture decreases the area and the consumed power by around 40%. The simulation results reveal that one of the suggested designs significantly decreases the area, with the same speed as the fastest design in the literature, without noticeably degrading the accuracy, and a significant decrease in energy consumption by about 60%.
Image-Based Detection of Modifications in Assembled PCBs with Deep Convolutional Autoencoders

In this paper, we introduce a one-class learning approach for detecting modifications in assembled printed circuit boards (PCBs) based on photographs taken without tight control over perspective and illumination conditions. Anomaly detection and segmentation are essential for several applications, where collecting anomalous samples for supervised training is infeasible. Given the uncontrolled environment and the huge number of possible modifications, we address the problem as a case of anomaly detection, proposing an approach that is directed towards the characteristics of that scenario, while being well suited for other similar applications. We propose a loss function that can be used to train a deep convolutional autoencoder based only on images of the unmodified board-which allows overcoming the challenge of producing a representative set of samples containing anomalies for supervised learning. We also propose a function that explores higher-level features for comparing the input image and the reconstruction produced by the autoencoder, allowing the segmentation of structures and components that differ between them. Experiments performed on a dataset built to represent real-world situations (which we made publicly available) show that our approach outperforms other state-of-the-art approaches for anomaly segmentation in the considered scenario, while producing comparable results on a more general object anomaly detection task.
UAV Photogrammetry Application for Determining the Influence of Shading on Solar Photovoltaic Array Energy Efficiency

The field of solar photovoltaic (PV) plants has seen significant growth in recent years, with an increasing number of installations being developed worldwide. However, despite advancements in technology and design, the impact of shading on the performance of PV plants remains an area of concern. Accurate 3D models produced using unmanned aerial vehicle (UAV) photogrammetry can provide aid to evaluate shading from nearby surroundings and to determine the potential of a site for electricity production via solar PV plants. The main objective of this paper is to address the problem of shadows significantly reducing energy yield in solar PV plants by proposing a methodology that aims at assessing the shading effects on PV systems and determining the optimal configuration for a PV module array using an accurate digital environment 3D model built using UAV photogrammetry. A high-level-of-detail 3D model allows us to evaluate possible obstacles for PV module array construction and accurately recreate the proximities that can cast shadows. The methodology was applied to grid-connected PV systems in Kaunas, Lithuania. The results of the case study show that electricity production in PV modules is highest at a 15° tilt angle when the distance between PV rows is 1.25 m. The proposed methodology gives an 11% difference in PV yield due to shading compared with other tools that do not include shading. This study also highlights that at least 30% financing support is necessary for solar PV plants to be economically attractive, resulting in a payback of 9 years and an internal rate of return of 8%. Additionally, this study can help optimize the design and layout of PV systems, making them more efficient and cost-effective.
NeuralMinimizer: A Novel Method for Global Optimization

The problem of finding the global minimum of multidimensional functions is often applied to a wide range of problems. An innovative method of finding the global minimum of multidimensional functions is presented here. This method first generates an approximation of the objective function using only a few real samples from it. These samples construct the approach using a machine learning model. Next, the required sampling is performed by the approximation function. Furthermore, the approach is improved on each sample by using found local minima as samples for the training set of the machine learning model. In addition, as a termination criterion, the proposed technique uses a widely used criterion from the relevant literature which in fact evaluates it after each execution of the local minimization. The proposed technique was applied to a number of well-known problems from the relevant literature, and the comparative results with respect to modern global minimization techniques are shown to be extremely promising.
Prediction via Similarity: Biomedical Big Data and the Case of Cancer Models

Abstract In recent years, the biomedical field has witnessed the emergence of novel tools and modelling techniques driven by the rise of the so-called Big Data. In this paper, we address the issue of predictability in biomedical Big Data models of cancer patients, with the aim of determining the extent to which computationally driven predictions can be implemented by medical doctors in their clinical practice. We show that for a specific class of approaches, called k-Nearest Neighbour algorithms, the ability to draw predictive inferences relies on a geometrical, or topological, notion of similarity encoded in a well-defined metric, which determines how close the characteristics of distinct patients are on average. We then discuss the conditions under which the relevant models can yield reliable and trustworthy predictive outcomes.
A Mathematical Model for Harvesting in a Stage-Structured Cannibalistic System

ToNankinga, Loy Carlsson, Linus increase the production of proteins in East Africa, aquaculture gained increased attention recently. In this paper, we study the interactions of a consumer-resource system with harvesting, in which African Catfish (Glarias gariepinus) consume a food resource. The cannibalistic behavior of African Catfish is captured by using a four stage-structured system. The dynamics of food resource and African Catfish result in a system of ordinary differential equations called a stage-structured fish population model. Existence and stability of steady states are analyzed quantitatively. We have investigated eight different harvesting scenarios which account for yield of the fish stock. Results from the simulations revealed that harvesting large juveniles and small adults under equal harvesting rates gives the highest maximum sustainable yield compared to other harvesting scenarios. In contrast to non-cannibalistic models, we find an increase of the proportion of the adult individuals under harvesting.