Reviewerly/exHarmony huggingface dataset:
Location detection of key areas in medical images based on Haar-like fusion contour feature learning

BACKGROUND Key area location is an important content of medical image processing and an important detail of auxiliary medical diagnosis. OBJECTIVE In this paper, a prior knowledge fusion method based on Haar-like feature and contour feature is proposed to locate and detect key areas in medical images. METHOD For the image to be processed, six Haar-like features and five contour features are extracted respectively. The improvement of Haar-like feature extraction template better adapts to the complexity of regional structure of medical images. The design of the contour feature extraction process fully reflects the consideration of feature invariance. The two features, together with prior knowledge, are fed into their respective decision makers and final fusers as the basis for determining and locating key regions. RESULTS The experimental results show that the proposed method has excellent performance in locating key regions of medical images on MRI. When the capacity of the database increases from 10 to 200, the accuracy of locating the key areas of the image to be processed still reaches more than 90%. CONCLUSION The proposed method realizes the accurate location of the key areas of medical images, which is of great significance for the auxiliary medical diagnosis.
Machine Learning-Based Sentiment Analysis of Twitter Data

In these recent days, evaluation and review marks a most important role in achieving the name for a brand or an item and its administration. The basic idea behind this assessment and audit is to make the brands to be familiar with customers which empower the variety of utilization. Twitter data is used for testing the above scenario due its ample size and scattered behavior. This paper performs the hypothesis investigation about the individual's feelings depending on the collected data. The motive behind the proposed work is to assess many individual's positive view and negative view based on the content audit using the surveys from the twitter. The twitter data is estimated by applying different weighting schemes to improve the classifier accuracy.
An enhanced artificial bee colony algorithm for numerical function optimization

Abstract In response to the problems of slow convergence, low convergence accuracy and easy to fall in local optimum of the standard artificial bee colony (ABC) algorithm, this paper proposed the following two improvement measures: Firstly, on the basis of using the current optimal solution, the gradient modification strategy was used to reduce the leading role of the current optimal solution, so that the nectar resources could be fully searched to ensure the diversity of the population; Secondly, the crossover and mutation operation in genetic algorithm was used to replace the random search of scout bees, and the prior information was effectively utilized, which not only improved the search efficiency but also ensured the diversity of population to a certain extent. The numerical experiment results show that the proposed algorithm has good convergence accuracy and robustness, and also presents an excellent performance in multi-dimensional optimization.
Performance Enhancement in Big Data handling

In the contemporary world, with the emergence of information technologies, Big data is one of the rising IT trends. An overwhelming amount of data and information is generated every day throughout the world. Storing and processing this huge volume of data is named by a ubiquitous term: Big Data Management. The traditional database architectures are not designed to face the challenges associated with huge data. The Apache Hadoop is the well-known software library framework that allows distributed processing of large data sets across clusters of low-end computers using simple programming models. Hadoop is designed to scale up from single servers to thousands of machines, each offering local computation and storage. When HDFS takes in data, it breaks the raw data into separate blocks and distributes them to different nodes(DataNode) in a cluster for parallel processing. But there is no scope to determine, which block is stored in which DataNode. Thus if there is a need to run a query on a specific item/keyword/key, the Hadoop runs the query or analytics program to all its cluster, which somehow increase the overall cost. To overcome these issues, We propose a scheme where we store the data in such a way that the system responses faster to each query and overall processing time decreases. Our motivation is to deliver an efficient and reliable storage and retrieval system in the health-care industry.
Special Issue on Advances in Deep Learning

Nowadays, deep learning is the fastest growing research field in machine learning and has a tremendous impact on a plethora of daily life applications, ranging from security and surveillance to autonomous driving, automatic indexing and retrieval of media content, text analysis, speech recognition, automatic translation, and many others [...]
A Novel Entitlement-based Blockchain-enabled Security Architecture for IoT

There has been a massive growth of Internet of Things (IoT) applications recently for both personal and organization use cases. But an IoT network is vulnerable to many privacy and security concerns. While some researchers have tried to define different access control models for IoT and tried to use blockchain in some cases, the solutions presented to date lacks a flexible and robust access control model that can be used for complex organization and cross-organization resource access scenarios and lacks the scalability and performance needed for IoT. This paper introduces a novel entitlement-based access control model that provides an efficient and secure way to delegate resource access rights to any entity. The solution uses service-oriented approach and a combination of public blockchain (with smart contracts) and local off-chain data for entitlements management and control. To analyze the proposed architecture, a large-scale cross-organization IoT scenario is taken into consideration and some qualitative evaluations are presented while implementation with Ethereum blockchain and smart contracts are currently in progress.
A conceptual model for the use of artificial intelligence for credit card fraud detection in banks

Credit cards play a role in economic growth because they allow for a cashless society which in turn reduces government expenditure on the manufacturing and distribution of monetary notes. A cashless society would allow governments to save billions of money that can be ploughed back into the economy for other purposes. However, mediums of achieving a cashless society such as credit cards are under attack from fraudsters. Recent studies show that more and more money is being fraudulently withdrawn from accounts. This paper aims to evaluate the credit card fraud detection methods used by banks and the difficulties in implementing the said methods. The study suggests the use of artificial intelligence, geolocation and data mining in credit card fraud detection methods to mitigate the weaknesses that current credit card fraud detection methods have. The use of artificial intelligence, data mining and geolocation would enable credit card fraud detection methods to analyse and identify trends in customer spending to identify fraudulent transactions. A model is introduced to help mitigate the weaknesses. An indepth literature review was undertaken and secondary research was used throughout the study as the main source of information.
Multiple countermeasures on unrolled architecture PRINCE against power analysis and its evaluation

Abstract Lightweight block ciphers, which can be implemented with low cost, are suitable for improving the safety of small devices. PRINCE is a typical lightweight block cipher and suitable for unrolled architecture implementation. In addition, PRINCE can be achieved low latency and embedded in a small area. However, it has been reported that PRINCE with unrolled architecture implementation is vulnerable to power analysis. Regarding countermeasure against power analysis, the threshold implementation and rotating S‐boxes masking are popular countermeasures, these require large implementation overhead though. Therefore, this study proposes a low‐overhead power analysis countermeasure method for PRINCE with unrolled architecture. Moreover, evaluation experiments using a field‐programmable gate array verify the effectiveness of the proposed method. In experiments, the proposed method improved tamper resistance and reduced implementation overhead in comparison with the conventional method.
Neural network feature learning based on image self-encoding

With the rapid development of information technology and the arrival of the era of big data, people’s access to information is increasingly relying on information such as images. Today, image data are showing an increasing trend in the form of an index. How to use deep learning models to extract valuable information from massive data is very important. In the face of such a situation, people cannot accurately and timely find out the information they need. Therefore, the research on image retrieval technology is very important. Image retrieval is an important technology in the field of computer vision image processing. It realizes fast and accurate query of similar images in image database. The excellent feature representation not only can represent the category information of the image but also capture the relevant semantic information of the image. If the neural network feature learning expression is combined with the image retrieval field, it will definitely improve the application of image retrieval technology. To solve the above problems, this article studies the problems encountered in deep learning neural network feature learning based on image self-encoding and discusses its feature expression in the field of image retrieval. By adding the spatial relationship information obtained by image self-encoding in the neural network training process, the feature expression ability of the selected neural network is improved, and the neural network feature learning based on image coding is successfully applied to the popular field of image retrieval.
Historical roots of Judit Bar-Ilan’s research: a cited-references analysis using CRExplorer

Abstract Judit Bar-Ilan (JB) was an influential researcher in information science and scientometrics. She published more than 100 papers about different topics. We used the CRExplorer (see www.crexplorer.net ) to investigate the historical roots of JB’s research. In this program, the N_TOP10 indicator is available. We applied this indicator to identify those publications which have been very frequently cited by JB during several citing years. These might be the publications by which JB was mostly influenced in her research. Our results show that the identified publications are seminal works in information science and scientometrics as well as methodologically oriented publications dealing with text or content analyses as well as influence or distance measures.
A Network Covert Timing Channel Detection Method Based on Chaos Theory and Threshold Secret Sharing

Network covert timing channel(NCTC) is a process of transmitting hidden information by means of inter-packet delay (IPD) of legitimate network traffic. Their ability to evade traditional security policies makes NCTCs a grave security concern. However, a robust method that can be used to detect a large number of NCTCs is missing. In this paper, a NCTC detection method based on chaos theory and threshold secret sharing is proposed. Our method uses chaos theory to reconstruct a high-dimensional phase space from one-dimensional time series and extract the unique and stable channel traits. Then, a channel identifier is constructed using the secret reconstruction strategy from threshold secret sharing to realize the mapping of the channel features to channel identifiers. Experimental results show that the approach can detect varieties of NCTCs with a guaranteed true positive rate and greatly improve the versatility and robustness.
Optimizing Ontology Materialization with Equivalent Role and Inverse Role Rewriting

Materialization, as a basic approach to ontology-mediated query answering, extends an ontology by exploiting the semantic knowledge expressed in the ontology to improve query answering over data. However, those existing materialization approaches are often inefficient, especially when ontologies consist of complex roles. In this paper, we propose a new rewriting algorithm for equivalent roles and inverse roles to both improve materialization efficiency and reduce memory consumption. Moreover, we develop a role scoring algorithm to optimize role rewriting. The preliminary experiments show that our approach can significantly reduce materialization times and memory consumption.
Examination of Bitcoin Exchange Through Agent-Based Modeling: Focusing on the Perceived Fundamental of Bitcoin

Bitcoin prices have fluctuated greatly, and news media have warned investors about a possible price bubble, arguing that the fluctuation arises mainly from people's blind pursuit of short-term trends. Despite these increasing concerns, however, only a few studies have addressed them. This article examines the problem using agent-based modeling. In our model, agents are designed to interact with one another in two ways: Price and social interactions. In their price interactions, agents adopt one of three strategies (fundamentalist, momentum trading, and contrarian trading) in investing at each time and adopt a strategy to maximize their expected benefits. In their social interactions, uninvolved agents become involved at various times based on their network properties (word-of-mouth effect). To examine the distinctive properties of Bitcoin from a comparative perspective, two representative currencies (Euro and Turkish lira) and two financial assets (Nasdaq and Nasdaq leverage index) are used in the agent-based model. The results show that the fraction of fundamentalists and price volatility have mutual Granger causal relationships overall. Also, no significant differences are found in the parameters of social interaction. These results are contrary to commonly held beliefs that Bitcoin prices are merely a result of blind pursuit and herding behavior.
Students’ Independent Cognitive Activity and Its Formation at Universities

Nowadays students should have such qualities as efficiency, independence, commitment, discipline and the abilities to self-identify and self-realize in a situation of uncertainty. To implement these requirements, it is necessary to change approaches to the organization of the process of education and enhance the formation of independent study skills in order to improve the quality of education, develop the creative abilities of students, their desire for continuous acquisition of new knowledge and readiness for further development or self-education. The main strategy of learning should create conditions that generate intellectual initiative and activate students’ thinking processes in autonomous learning. This article discusses implementation of the course New information technologies (project-based learning)–designed to develop the skills of independent cognitive activity in university students. The study was conducted in Smolensk Sport Academy of Physical Education and Tourism (SSA, Smolensk) and North-Eastern Federal University (NEFU, Yakutsk). The pedagogical experiences of the practical application of project-based learning foreign languages (NEFU), information technology (SSA) and organization of independent studies with information sources has been studied. The authors of the article analyzed psychological, pedagogical, linguistic and methodological literature on the studied issue. The article presents the results of the analysis of interviewing and testing of students as well as the results of pilot training. The developed course showed its effectiveness not only in increasing the level of formation of students’ independent cognitive activity but also in motivating students to study, and adjusting communication skills and creativity.
A Systematic Literature Review for Software Portability Measurement

Software developers agree that software portability is a desirable attribute for their software quality. Software portability is mostly acquired by ad-hoc techniques when trying to port existing products. There is a lack of unified measuring approach of software portability in most computing platforms. This paper presents preliminary results of a systematic literature review, conducted to collect evidence on measuring software portability. The evidence was gathered from selected studies and based on a set of meaningful and focused questions. 49 studies of these were selected for data extraction performed against the research questions. We provide an overview of used\proposed measurement metrics of software portability. Our results suggested that there are scattered efforts to understand measurement of software portability, and no census has been achieved.
Classification of Tank Images Using Convolutional Neural Network

Recent years, deep learning has made huge progress in various fields including image classification. Battlefield target recognition is the foundation of battlefield situation research and tank is one of the most important targets of land battlefield. Tracked tank and wheeled tank which also known as wheeled vehicles are two kinds of tank, but there are a few researches on classification of tank. In this paper, a convolutional neural network (CNN) is utilized to classify tank images. The contribution of this work are as follows: First of all, we collected tank images and tag them. Secondly, we used CNN to classify wheeled tank and tracked tank, and the experiments show that the test accuracy achieves 0.93 with accuracy of tracked tank is 0.9268 and wheeled tank is 0.9368, reaching a good performance.
Clutter Mitigation in Range Enhanced Radar Images Using Sparsity Based Denoising Autoencoders

Complex propagation channels in indoor environments introduce considerable clutter and distortions to frontal radar images. Autoencoders, when provided with corrupted and corresponding clean images during training, can learn how to mitigate clutter in these images during testing. The main advantage is that an analytical framework of the channel is not required. On the other hand, the algorithm relies on the availability of a large database of correctly labelled training data - the target scenarios in cluttered environments must be exactly replicated in free space. We propose to augment the conventional denoising autoencoder with sparsity based multiple layer representations to handle the labelling mismatch errors. We hypothesize that the deeper representations with sparsity constraints will enable extraction of more fundamental features of the images. Our radar data consists of measurements made with a wideband RF imaging sensor called the Walabot. Our algorithm improves upon the conventional autoencoder in terms of performance with respect to signal to clutter and signal to noise ratios, labelling mismatch errors and computational time during testing.
Dimension Reduction for Efficient Pattern Recognition in High Spatial Resolution Data Using Quantum Algorithms

With the promising advancement in quantum computing technology in the last decade, there is a strong motivation to find suitable applications for quantum algorithms and quantum computers. Domains such as High Energy Physics (HEP) have an enormous readout count of high-resolution data. Performing pattern recognition on this readout is computationally challenging and time-consuming because of the multi-dimensionality of the data. In this paper, we propose a methodology that employs quantum algorithms such as Quantum Wavelet Transform and Grover's search algorithm for timeefficient pattern recognition in data sets that are characterized by high spatial resolution and high dimensionality. The motivation behind using quantum algorithms is the potential speedup relative to classical methods, when performed by a quantum computer. In our proposed methodology, Quantum Wavelet Transform is performed on the high spatial resolution data to reduce its dimensionality while quantum Grover's search algorithm is employed to search for target patterns in the reduced data set. Performing the search operation on data with reduced spatial resolution, minimizes processing overheads and computation times. Moreover, use of quantum techniques yield faster results, compared to classical dimension reduction and search methods. We demonstrate the feasibility of the proposed methodology by emulating the quantum algorithms on classical hardware based on field programmable gate arrays (FPGAs). A high performance reconfigurable computer (HPRC) was used for the experimental evaluation. The obtained results are favorable towards our proposed approach.
Filtration of images with constant brightness areas corrupted by additive noise

In this paper the algorithm of filtration of digital images that consisted of objects that described as areas with constant grey levels and corrupted by additive noise with uncorrelated samples is investigated. The developed algorithm uses the technique of two-stage processing allowing performing of digital images processing that decreases a computational complexity and in the same time preserve in some manner two-dimensional processing of an image. Results of simulation obtained while processing synthesized image. The comparison of the results of filtration made for different noise level. Comparison with known and widespread algorithms of image filtration is carried out using MATLAB.
Norms in Social Simulation: Balancing Between Realism and Scalability

Agent-based modelling (ABM) has been used to study the dynamics of complex systems, including human societies. However, the design of such models often fails to capture one of the key features of human behavior: norms. While norms and normative behavior are extensively studied in artificial intelligence (AI), especially in the context of multi-agent systems (MAS), their approaches are often very complex and formalized, going against the prevailing discourse of ABM, which advocates keeping the models as simple as possible and pruning any unnecessary complexity. Nevertheless, norms are relevant and integral to many social contexts, and capturing their effect and dynamics often requires agents that, while not as complex as those developed for AI, are capable of sophisticated cognition. We present a normative architecture that attempts to capture the ways norms affect cognition and behavior, while at the same time being lightweight enough to be suitable for ABM use in simulations.
Multiview learning with variational mixtures of Gaussian processes

Gaussian processes (GPs) are powerful Bayesian nonparametric tools widely used in probabilistic modeling, and the mixture of GPs (MGPs) were introduced afterwards to make data modeling more flexible. However, MGPs are not directly applicable to multiview learning. In order to improve the modeling ability of MGPs, in this paper, we propose a new framework of multiview learning for the MGPs and instantiate it for classification. We make the divergence between views as small as possible while ensuring that the posterior probability of each view is as large as possible. Specifically, we regularize the posterior distribution of latent variables with the consistency of posterior distributions of the latent functions between different views. Since it is intractable to solve the model analytically, the variational inference and optimization algorithms of the classification model are also presented in this paper. Experimental results on multiple real-world datasets have shown that the proposed method has outperformed the original MGP model and several state-of-the-art multiview learning methods, which indicate the effectiveness of the proposed multiview learning framework for MGPs.