Reviewerly/exHarmony huggingface dataset:
Saksham: Resource Aware Block Rearrangement Algorithm for Load Balancing in Hadoop

Big Data Analytics demands huge processing power. Distributed computing seems to be a viable solution with the usage of commodity computers. But, processing big data optimally, on distributed systems poses the number of challenges, such as seamlessly integrating different resources of distributed systems. Hadoop provides this ease and hence is one of the most commonly used tools for the processing of big data. But, it is observed that the performance of Hadoop is compromised, when it comes to the heterogeneous environment. This paper proposes Saksham: The block rearrangement algorithm which optimizes the processing time by efficient file system management in the heterogeneous environment. Our proposed scheme successfully optimizes the big data processing over the homogeneous and heterogeneous environment. To achieve better performance for big data processing, we target two important aspects of heterogeneous distributed computing: file system management and process management. First, file system management basically controls the block placement and allows us to rearrange the blocks on specified nodes, considering the node's storage and processing capability. Second, we use the concept of node labeling and scheduling to achieve better process management. The results prove that the proposed approach has optimized job execution time along with reduced latency and data-skew substantially.
Causal Discovery of Gene Regulation with Incomplete Data

Summary Causal discovery algorithms aim to identify causal relations from observational data and have become a popular tool for analysing genetic regulatory systems. In this work, we applied causal discovery to obtain novel insights into the genetic regulation underlying head-and-neck squamous cell carcinoma. Some methodological challenges needed to be resolved first. The available data contained missing values, but most approaches to causal discovery require complete data. Hence, we propose a new procedure combining constraint-based causal discovery with multiple imputation. This is based on using Rubin's rules for pooling tests of conditional independence. A second challenge was that causal discovery relies on strong assumptions and can be rather unstable. To assess the robustness of our results, we supplemented our investigation with sensitivity analyses, including a non-parametric bootstrap to quantify the variability of the estimated causal structures. We applied these methods to investigate how the high mobility group AT-Hook 2 (HMGA2) gene is incorporated in the protein 53 signalling pathway playing an important role in head-and-neck squamous cell carcinoma. Our results were quite stable and found direct associations between HMGA2 and other relevant proteins, but they did not provide clear support for the claim that HMGA2 itself is a key regulator gene.
Capacitance Requirement for Rated Current and Rated Voltage Operation of SEIG Using Whale Optimization Algorithm

In today's world non-conventional energy resources are being considered as powerful resources in the field of power generation. To optimize the resources required to generate the power is always be a challenging task. Many meta-heuristic algorithms have been applied for solving the complex optimization problems. Resource optimization is also a n-p hard problem. Whale optimization (WO) is newly developed meta-heuristic performed efficiently for solving complex engineering problems. In this paper WO is used as an optimization algorithm in order to optimize the value of excitation capacitance for rated voltage and rated current operation of self-excited induction generator. The simulation has been carried out on a 5.5 kw rating induction generator and the same has been used for the experimental validation. The results as obtained shows that WO outperformed as compared to other meta-heuristic
A scalable approach to T2-MRI colon segmentation

The study of the colonic volume is a procedure with strong relevance to gastroenterologists. Depending on the clinical protocols, the volume analysis has to be performed on MRI of the unprepared colon without contrast administration. In such circumstances, existing measurement procedures are cumbersome and time-consuming for the specialists. The algorithm presented in this paper permits a quasi-automatic segmentation of the unprepared colon on T2-weighted MRI scans. The segmentation algorithm is organized as a three-stage pipeline. In the first stage, a custom tubularity filter is run to detect colon candidate areas. The specialists provide a list of points along the colon trajectory, which are combined with tubularity information to calculate an estimation of the colon medial path. In the second stage, we delimit the region of interest by applying custom segmentation algorithms to detect colon neighboring regions and the fat capsule containing abdominal organs. Finally, within the reduced search space, segmentation is performed via 3D graph-cuts in a three-stage multigrid approach. Our algorithm was tested on MRI abdominal scans, including different acquisition resolutions, and its results were compared to the colon ground truth segmentations provided by the specialists. The experiments proved the accuracy, efficiency, and usability of the algorithm, while the variability of the scan resolutions contributed to demonstrate the computational scalability of the multigrid architecture. The system is fully applicable to the colon measurement clinical routine, being a substantial step towards a fully automated segmentation.
Foveation Pipeline for 360° Video-Based Telemedicine

Pan-tilt-zoom (PTZ) and omnidirectional cameras serve as a video-mediated communication interface for telemedicine. Most cases use either PTZ or omnidirectional cameras exclusively; even when used together, images from the two are shown separately on 2D displays. Conventional foveated imaging techniques may offer a solution for exploiting the benefits of both cameras, i.e., the high resolution of the PTZ camera and the wide field-of-view of the omnidirectional camera, but displaying the unified image on a 2D display would reduce the benefit of “omni-” directionality. In this paper, we introduce a foveated imaging pipeline designed to support virtual reality head-mounted displays (HMDs). The pipeline consists of two parallel processes: one for estimating parameters for the integration of the two images and another for rendering images in real time. A control mechanism for placing the foveal region (i.e., high-resolution area) in the scene and zooming is also proposed. Our evaluations showed that the proposed pipeline achieved, on average, 17 frames per second when rendering the foveated view on an HMD, and showed angular resolution improvement on the foveal region compared with the omnidirectional camera view. However, the improvement was less significant when the zoom level was 8× and more. We discuss possible improvement points and future research directions.
Semisupervised Consistent Projection Metric Learning for Person Reidentification

Person reidentification is a hot topic in the computer vision field. Many efforts have been paid on modeling a discriminative distance metric. However, existing metric-learning-based methods are a lack of generalization. In this article, the poor generalization of the metric model is argued as the biased estimation problem that the independent identical distribution hypothesis is not valid. The verification experimental result shows that there is a sharp difference between the training and test samples in the metric subspace. A semisupervised consistent projection metric-learning method is proposed to ease the biased estimation problem by learning a consistent constrained metric subspace in which the identified pairs are forced to follow the distribution of the positive training pairs. First, a semisupervised method is proposed to generate potential matching pairs from the k -nearest neighbors of test samples. The potential matching pairs are used to estimate the distances' distribution center of the positive test pairs. Second, the metric subspace is improved by forcing this estimation to be close to the center of the positive training pairs. Finally, extensive experiments are conducted on five datasets and the results demonstrate that the proposed method reaches the best performance, especially on the rank-1 identification rate.
The influence of Taiwan’s stock market on Bitcoin’s price under Taiwan’s monetary policy threshold

This study uses a smooth transition autoregressive model with exogenous variables (STARX) to investigate whether there is a nonlinear relationship between Bitcoin and Taiwan’s stock market taking into account Taiwan’s monetary policy threshold during 2 February 2012 to 31 August 2019. The statistical results show there is a threshold effect and confirm a nonlinear relationship between Taiwan’s stock market and Bitcoin, with variations over time and across Bitcoin and Taiwan’s stock market. Specifically, we find that Bitcoin responds asymmetrically to Taiwan’s stock market according to the threshold value. Furthermore, the return on the closing price of TAIEX with a lag of two periods under Taiwan’s monetary policy threshold has a nonlinear impact on the return on the closing price of Bitcoin.
Modular Blockchain Implementation in Intensive Medicine

Blockchain has its focus around sharing, distribution and encryption. Particularly, the newer blockchain implementations revolve around the implementation of smart-contracts, second-layer systems and permissioned blockchains. This type of potential has generated a lot of attention towards blockchain, thus making it a great candidate technology within healthcare. In this paper, the role of permissioned and permissionless blockchain and its possible implementations will be discussed and compared as well as the process in which a blockchain network agrees whether a transaction is valid or not, maintaining consistency in ledger synchronization, or in other words, Consensus Algorithms. Several should be considered as viable and many can be used by both permissioned and permissionless blockchain frameworks. As chosen as part of this implementation Practical Byzantine Fault Tolerance (Pbft) is presented and described. This paper presents a solution, as part of the Intelligence Decision Support Systems for Intensive Medicine (ICDS4IM) project, which objective is to increase veracity and value to data from vital sensors and monitors by assuring its immutability and oversee; and also privacy and accountability for inadequate data management.
Absolute Maximum Nonlinear Functions on Finite Nonabelian Groups

Highly nonlinear functions (perfect nonlinear, maximum nonlinear, etc.) between finite fields have been studied in numerous papers. These concepts have been generalized first to finite abelian groups and then to finite nonabelian groups. In 2011, Poinsot and Pott introduced a new class of highly nonlinear functions between finite nonabelian groups, which are closely related to maximum nonlinear functions. They found such a function by a computer search, and proposed to find more such functions by theoretical constructions. Since then there is no progress in this topic. In this paper we continue the research in the direction proposed by Poinsot and Pott. Our purpose is to study the properties and constructions of this new class of highly nonlinear functions, called the absolute maximum nonlinear functions in this paper. In particular, for arbitrary finite (abelian or nonabelian) groups K and N with an absolute maximum nonlinear function f : K → N, we will prove that N has to be abelian, and f cannot be perfect nonlinear if K is not abelian. Then we investigate the numerical constraints on the groups that have absolute maximum nonlinear functions. These constraints will eliminate the existence of absolute maximum nonlinear functions for many finite nonabelian groups. More importantly, using these constraints we will develop a method to construct absolute maximum nonlinear functions for some groups. In particular, we will determine all absolute maximum nonlinear functions on A4 (the alternating group of degree 4). Furthermore, a general method to construct new absolute maximum nonlinear functions from the old ones will be given.
Mining the Local Dependency Itemset in a Products Network

Many studies have been conducted on market basket analysis such as association rules and dependent patterns. These studies mainly focus on mining all significant patterns or patterns directly associated with a given item in a dataset. The problem that has not been addressed is how to mine patterns associated with a given item from the local view. This problem becomes very meaningful when the market basket dataset is huge. To address this problem, in this study, first, a new idea called “local dependency itemset” is put forward, which refers to patterns associated with the given item. Second, a framework of mining the local dependency itemset is presented. The framework has two steps, which are executed iteratively. One is expanding the local dependency itemset that initially consists of only the given item; the other is updating the local products network. Third, this framework is implemented by three different dependence indicators and a typical local community detection algorithm. The experimental results confirm that the local dependency itemset is meaningful.
Empirical study of the relationship between design patterns and code smells

Software systems are often developed in such a way that good practices in the object-oriented paradigm are not met, causing the occurrence of specific disharmonies which are sometimes called code smells. Design patterns catalogue best practices for developing object-oriented software systems. Although code smells and design patterns are widely divergent, there might be a co-occurrence relation between them. The objective of this paper is to empirically evaluate if the presence of design patterns is related to the presence of code smells at different granularity levels. We performed an empirical study using 20 design patterns and 13 code smells in ten small-size to medium-size, open source Java-based systems. We applied statistical analysis and association rules. Results confirm that classes participating in design patterns have less smell-proneness and smell frequency than classes not participating in design patterns. We also noticed that every design pattern category act in the same way in terms of smell-proneness in the subject systems. However, we observed, based on the association rules learning and the proposed validation technique, that some patterns may be associated with certain smells in some cases. For instance, Command patterns can co-occur with God Class, Blob and External Duplication smell.
PSNet: Parametric Sigmoid Norm Based CNN for Face Recognition

The Convolutional Neural Networks (CNN) have become very popular recently due to its outstanding performance in various computer vision applications. It is also used over widely studied face recognition problem. However, the existing layers of CNN are unable to cope with the problem of hard examples which generally produce lower class scores. Thus, the existing methods become biased towards the easy examples. In this paper, we resolve this problem by incorporating a Parametric Sigmoid Norm (PSN) layer just before the final fully-connected layer. We propose a PSNet CNN model by using the PSN layer. The PSN layer facilitates high gradient flow for harder examples as compared to easy examples. Thus, it forces the network to learn the visual features of difficult examples. We conduct the face recognition experiments to test the performance of PSN layer. The suitability of the PSN layer with different loss functions is also experimented. The widely used YouTube Faces (YTF) and Labeled Faces in the Wild (LFW) datasets are used in the experiments. The experimental results confirm the relevance of the proposed PSN layer.
Machine learning identifies the dynamics and influencing factors in an auditory category learning experiment

Abstract Human learning is one of the main topics in psychology and cognitive neuroscience. The analysis of experimental data, e.g. from category learning experiments, is a major challenge due to confounding factors related to perceptual processing, feedback value, response selection, as well as inter-individual differences in learning progress due to differing strategies or skills. We use machine learning to investigate (Q1) how participants of an auditory category-learning experiment evolve towards learning, (Q2) how participant performance saturates and (Q3) how early we can differentiate whether a participant has learned the categories or not. We found that a Gaussian Mixture Model describes well the evolution of participant performance and serves as basis for identifying influencing factors of task configuration (Q1). We found early saturation trends (Q2) and that CatBoost, an advanced classification algorithm, can separate between participants who learned the categories and those who did not, well before the end of the learning session, without much degradation of separation quality (Q3). Our results show that machine learning can model participant dynamics, identify influencing factors of task design and performance trends. This will help to improve computational models of auditory category learning and define suitable time points for interventions into learning, e.g. by tutorial systems.
Human Robot Interaction Using Sensor Based Hand Gestures For Assisting Disable People

Currently, in the world, millions of people are suffering from paralysis. They have difficulties with walking. Therefore, we have introduced a new type of robot that can help these peoples to walk. By using this robot and (hand gloves or wheelchair handle) gesture-based controller, these patients can improve their quality of leaving. The proposed robot has two parts, one is the gesture controller and, another is the Robot Wheelchair (RW). One can interact with the robotic-base wheelchair-using sensor-based hand gesture. With this human-robot interaction, a patient can quite easily control the robot and can move freely with this system we can reduce the effort to control the RW, which is very difficult for paralyzed or old persons. Our system can work with about 94% accuracy with very minimal delay.
Real Time Hand Gesture Recognition by Skin Color Detection for American Sign Language

This paper demonstrates a system that can translate American Sign Language into text from real time video with computer vision based approach. This system has two parts, Skin Color Detection (SCD) and Hand Gesture Recognition (HGR). Skin pixels are detected from the image by applying some heuristic rules and using an Artificial Neural Network (ANN). The ANN is trained by H, S, V component values from HSV color space along with five texture features. Automated threshold value for SCD is achieved by plotting Q component value from YIQ color space vs the result from ANN for each pixel and applying K-means Clustering on it. XOR operation is used to find out a standstill hand image from the output images of SCD system. For HGR another ANN is used. Features are extracted using two algorithms. They are fingertip finding algorithm (a combination of convex hull and K curvature) and pixel segmentation.
A New Lossless DNA Compression Algorithm Based on A Single-Block Encoding Scheme

With the emergent evolution in DNA sequencing technology, a massive amount of genomic data is produced every day, mainly DNA sequences, craving for more storage and bandwidth. Unfortunately, managing, analyzing and specifically storing these large amounts of data become a major scientific challenge for bioinformatics. Therefore, to overcome these challenges, compression has become necessary. In this paper, we describe a new reference-free DNA compressor abbreviated as DNAC-SBE. DNAC-SBE is a lossless hybrid compressor that consists of three phases. First, starting from the largest base (Bi), the positions of each Bi are replaced with ones and the positions of other bases that have smaller frequencies than Bi are replaced with zeros. Second, to encode the generated streams, we propose a new single-block encoding scheme (SEB) based on the exploitation of the position of neighboring bits within the block using two different techniques. Finally, the proposed algorithm dynamically assigns the shorter length code to each block. Results show that DNAC-SBE outperforms state-of-the-art compressors and proves its efficiency in terms of special conditions imposed on compressed data, storage space and data transfer rate regardless of the file format or the size of the data.
Feature-based Sketch-Photo Matching for Face Recognition

Matching of sketch and photo is very important in police verification and intelligence as it is used to track the criminals or some person. Therefore, it should be accurate so that mismatching of the sketch with photo can be avoided. For this, feature based matching method is introduced through this paper. Feature based matching includes a feature vector which contains features of the face image (either it is a sketch or a photo) in terms of histogram of oriented gradients (HoG) features and gray level co-occurrence matrix (GLCM) features. Computing the features, first, increases the chances of correct matches. It can be depicted from the results as well that, comparing with other state-of-art approaches, proposed method is more accurate in matching the sketches with photos of the same person.
A community‐based hierarchical user authentication scheme for Industry 4.0

Summary The vision of Industry 4.0 is characterized by the amalgamation of cyber‐physical systems and industrial Internet of Things. Such a complex ecosystem urges for the requirement of novel security protocol and mechanisms for access control so as to allow the smart devices to authorize external entities and granting them access rights without depending on centralized authentication entities. The work proposed in this article aims to utilize a community‐based hierarchical approach to define the procedure for obtaining access rights in the Industry 4.0 ecosystem. The proposed scheme considers a hierarchy of authorizing devices that work in collaboration for providing access control of the smart end devices to the users. The adoption of hierarchical structure ensures that the access rights are eventually given to only those users that have passed multiple levels of successful authorization. The proposed scheme also combats any infringement of users identity since the authorizing entities involved in the proposed system work in close collaboration for user authentication. The proposed user authentication scheme has been validated using burrows‐abadi‐needham (BAN)‐logic and is proved to be secure against a variety of security attacks.