Reviewerly/exHarmony huggingface dataset:
Computational Intelligence for Process-optimization Software

This work describes a general algorithm for a cooperative hyper-heuristics that enables the optimization of systems of nonlinear algebraic equations with algebraic constraints. The hyper-heuristics comprises the following agents: Genetic Algorithms, Simulated Annealing and Particle Swarm Optimization. Information exchanges take place effectively among them since the immediate incorporation of solution candidates speeds up the search. Algorithmic performance is illustrated with general test models, most of them corresponding to process systems that have currently been employed in PSE. When running in parallel, numerical results demonstrate that the collaborative hybrid structure with embedded intelligent learning contributes to improve results in terms of effectiveness and accuracy. The combination of several heuristic optimization approaches into a hyper-heuristics provides enhanced benefits over traditional strategies since this method helps to find proper comprehensive solutions, also contributing to achieve and accelerate convergence.
Atomic Grammar: A Learning Source of ESL Error Identification and Correction

Error analysis is deemed a fundamental component in language teaching which helps language learners to identify, understand and eventually correct language errors on their own. Atomic Grammar is created as a source of learning English language grammar based on error identification. The website which is accessible via personal computers and smartphones stores a compilation of the common errors made by ESL learners in their compositions followed by detailed explanation which include reasons and rules that formulate the correct use of the parts of speech that have been corrected. Atomic Grammar offers benefits to both language learners and teachers at all levels of education.
Research Institutes’ websites of indian council of social science research: A webometric study

The paper presented a webometric study of the websites of a total of 29 research institutes that were under ICSSR (Indian Council of Social Science Research). The study attempted to measure the number of root domain authority, web pages, total links, total internal links, total external links, and just discovered links of the websites. The selected websites were ranked based on their web impact factor. For the study, data were collected from websites of research institutes using Moz's link explorer tool (http://moz>link-explorer). After analyzing the data, it was visualized that, out of 29 institutes, highest 10 (34.49%) institute websites had‘.org’ extension. The website of the Centre for Policy Research (CPR) secured the highest position in domain authority with 52(6.07%). Centre for Policy Research (CPR) website also got the highest total links with 10236553 and the Institute of Development Studies (IDS) website was at the lowest place in total links with 430.
Correction to: On the feasibility of automated prediction of bug and non-bug issues

The original version of this article unfortunately contained mistakes. Figures 8, 9 and 10 were incorrectly captured. Somehow, the plots in Fig. 8 were replaced with those from Fig. 9 and the original Fig. 8 was lost.
Qualitative Meta Integration of the Real Experience of Bank Employee Burnout Based on Computer System

Abstract Bank employee burnout is a state of exhaustion in the physical, mental, emotional, emotional and behavioral aspects of bank employees caused by excessive working hours, excessive workload, excessive work intensity and ignoring their individual needs during the work process. Since bank employees belong to the piece-rate work system, not the hourly work system, they are different from the elementary and middle school bank employees who can arrange their own time outside of work. Therefore, the definition of bank employee burnout refers to the conflict between work and family. The symptoms of emotional exhaustion, depersonalization and reduced personal accomplishment caused by conflicts between audit and banking business, work pressure and banking system problems, show a state of low work input and low work efficiency. Through computer technology, it is possible to conduct a comprehensive analysis of its experience, thereby completing scientific integration and making it more reasonable.
Price volatility of Bitcoins in India - An empirical analysis of Bitcoin prices

Cryptography is a type of computer technology that is used for ensuring security, hiding information, and more. Bitcoins is the most recognized cryptocurrency. It is a person to person virtual currency which is used for online transactions. In this paper, an attempt has been made to analyses the price volatility of bitcoins. Further, a detailed content analysis has been presented in this paper. This study considers a study period of 8 and half years ranging from 19th June 2010 to 31st December 2018. Bitcoins prices were considered as variable to study the volatility of Bitcoins prices. Unit Root analysis is employed to check the Stationarity of the data series of bitcoins prices. ARCH Test, Volatility Clustering, ARCH, GARCH, TARCH and EGARCH have been used to analyze the price volatility. It was found that good news has more effect on the volatility of Bitcoins price returns than the bad news.
Computational Analysis of Robustness in Neural Network Classifiers

Neural networks, especially deep architectures, have proven excellent tools in solving various tasks, including classification. However, they are susceptible to adversarial inputs, which are similar to original ones, but yield incorrect classifications, often with high confidence. This reveals the lack of robustness in these models. In this paper, we try to shed light on this problem by analyzing the behavior of two types of trained neural networks: fully connected and convolutional, using MNIST, Fashion MNIST, SVHN and CIFAR10 datasets. All networks use a logistic activation function whose steepness we manipulate to study its effect on network robustness. We also generated adversarial examples with FGSM method and by perturbing those pixels that fool the network most effectively. Our experiments reveal a trade-off between accuracy and robustness of the networks, where models with a logistic function approaching a threshold function (very steep slope) appear to be more robust against adversarial inputs.
An Online Pyramidal Embedding Technique for High Dimensional Big Data Visualization

Visualizing multidimensional Big Data is defying: high dimensionalities hinder or even preclude visual inspections. A means of tackling this issue is to use DR (Dimensionality Reduction) techniques, producing low-dimensional representations of high-dimensional data. Popular DR algorithms (e.g., Principal Component Analysis, t-Distributed Stochastic Neighbor Embedding), albeit helpful, are computationally expensive. Most have \(\mathcal {O}(n^2)\) or \(\mathcal {O}(n^3)\) ATC (Asymptotic Time Complexity) and/or calculate pairwise distances of the entire data set, exceeding available memory and rendering Big Data DR time-consuming or impracticable. These issues impede the employment of DR for online learning appliances, where recurrent, cumulative model updates are habitual. The stochastic factor of some approaches similarly obstructs any meaningful inspection on how knowledge is spatially disposed. The recently introduced PCS (Polygonal Coordinate System)—an incremental, geometric-based technique with linear ATC—is compelling; however, its restriction to 2-D embeddings amounts to significant information loss. We propose the Big Data ready, incremental PES (Pyramidal Embedding System), which builds on PCS virtues by additionally generating 3-D embeddings through its pyramid-like interspace, mitigating quality degradation. Visual inspections, as well as pairwise distance based statistical analyses, validate the PES ability to retain structural arrangements when embedding high- and low-dimensional data while retaining flexibility in resources consumption.
Why samples are rejected and how to reduce the risk

Repeat sampling puts patients at risk due to delayed diagnosis and treatment and is costly too – so what can nurses do to enable trouble-free processing?
A locally weighted KNN algorithm based on eigenvector of SVM

K-Nearest Neighbours (KNN) is one of the fundamental classification methods in machine learning. The performance of KNN method is restricted by the number of neighbours k. It is obvious that the outliers appear when dealing with small data samples. In this paper, we propose a hybrid framework of the feature weighted support vector machine as well as locally weighted k-nearest neighbour (SLKNN) to overcome this problem. In our method, we first use support vector machine to calculate the eigenvector of feature of data, then apply this eigenvector into distance metric as the weight of the feature. Finally, the distance metric is used in locally weighted k-nearest neighbour. The experiments on UCI data sets show that the proposed SLKNN performs better than some KNN-based methods.
Hybrid Approach Towards Removal of Streak Artifacts and Region Identification in Brain Radiology

A streak artifact is an anomaly seen in the CT image of the brain. It is like a dark bands or streaks appearing in an image that is not present in the original region. The appearance of these artifacts leads to misinterpretation of medical diagnosis. Under sampling, photon starvation, motion and beam hardening are some of the reasons for occurrence of streak artifacts. These type of artifacts commonly occur in the posterior fossa of the brain. This paper mainly focuses on region shape and texture identification and removal of streak artifacts exists in the brain image. Initially morphological operators and fuzzy k -means algorithms are used to identify the different regions in the CT brain image. Each region is treated as a single image and features are extracted for each image. The feature descriptors are constructed using region properties and statistical features. The extracted features are compared with threshold brain image to identify the dark bands of streak artifacts.
Webs of Trust: Choosing Who to Trust on the Internet

How to decide whether to engage in transactions with strangers? Whether we’re offering a ride, renting a room or apartment, buying or selling items, or even lending money, we need a degree of trust that the others will behave as they should. Systems like Airbnb, Uber, Blablacar, eBay and others handle this by creating systems where people initially start as untrusted, and they gain reputation over time by behaving well. Unfortunately, these systems are proprietary and siloed, meaning that all information about transactions becomes property of the company managing the systems, and that there are two types of barriers to entry: first, whenever new users enter a new system they will need to restart from scratch as untrusted, without the possibility of exploiting the reputation they gained elsewhere; second, new applications have a similar cold-start problem: young systems, where nobody has reputation yet, are difficult to kickstart.We propose a solution based on a web of trust: a decentralized repository of data about past interactions between users, without any trusted third party. We think this approach can solve the aforementioned issue, establishing a notion of trust that can be used across applications while protecting user privacy. Several problems require consideration, such as scalability and robustness, as well as the trade-off between privacy and accountability.In this paper, we provide an overview of issues and solutions available in the literature, and we discuss the directions to take to pursue this project.
Clustering by Finding Average Density

Density Peak Clustering (DPC) algorithm can get better clustering results of data sets with lower dimensions. However, for the high-dimensional data sets, there are many nodes in the clustering center area; their densities are relatively high and close to each other, so that it is hard to identify the centering node accurately. It is found that the accuracy of the traditional DPC algorithm decreases terribly with the increasing of data set dimensions. In order to deal with the indistinguishable density problem, we propose a new clustering method, called Clustering by Finding Average Density (CFAD), which can enhance the clustering effect of high-dimensional data sets. Experiments show that the proposed algorithm outperforms DPC for both the artificial and the real data sets.
Designing and using rubric for assessment writing competency of high school student through demonstrative texts

Teaching and assessment toward competency-approached are the most crucial changes in the 2018 Language Arts and Literature Curriculum. Demonstrative text is a basic form of informative text, which is important and popular in high school curiculum. This paper suggests how to design and use the rubric as an assessment tool for learning to grade high school student’s writing competency through demonstrative text in order to meet the requirements of the Curriculum.
BOLD signal changes associated with orientation learning and transfer at different brain areas

Perceptual learning research often targets learning specificities as means to understand the underlying mechanisms. However, learning specificities diminish with double training (Xiao_et_al_2008), and learning of basic visual features like orientation is not much location specific (Zhang_et_al_2010). Here we used fMRI to investigate the brain mechanisms underlying orientation learning and transfer. Twenty-three participants practiced orientation discrimination at the lower-left or -right quadrant at 135o or 45o. Five sessions of training improved orientation discrimination by 43.2±2.5% for the trained orientation at the trained quadrant, and 32.5±3.4% for the orthogonal orientation at the untrained quadrant, with 75% learning transfer. Subjects participated fMRI experiments before and after training. After training, BOLD responses in V1-V3 increased only for the trained stimulus. But BOLD responses in frontoparietal areas decreased for both trained and untrained stimulus, especially more for the untrained stimulus. Importantly, the amount of learning was negatively correlated with response changes to the trained stimulus in V1 and frontoparietal cortex, while the amount of learning transfer were positively correlated with a transfer index (trained-untrained) of BOLD response in V1, IFJ, IPS and PFC. Moreover, the task-based functional connectivity between frontoparietal cortex (IFJ/IPS/PFC) and V3 became weaker for the trained stimulus, but the connectivity between frontoparietal cortex and V2 became stronger for the untrained stimulus. The negative correlation between learning and BOLD signal change, and the decrease of functional connectivity between frontoparietal areas and early visual cortex for the trained stimulus, suggest that orientation learning may lead to a more efficient decision making in fronto-parietal areas. But the positive correlations between the transfer index of BOLD changes and perceptual learning transfer, and the increased functional connectivity between frontoparietal areas and early visual cortex for the untrained stimulus, suggest that learning transfer may require more engagement of frontoparietal areas and their top-down modulation of early visual activities.
Optimization of Routes of a Robot Using Bioinspired Algorithms

We propose in this paper the use fuzzy logic to adjust parameters in thein the particle swarm optimization and ant colony optimization.This paper describe the comparison of the results obtained in the particle swarm optimization (PSO) and the ant colony optimization (ACO) of the resolution of the traveling salesman person (TSP), the adjustment (Xu, 2019 Chinese control and decision conference (CCDC), Nanchang, China, pp 3760–3763 []) is performed to improve the behavior of both methods. The particle swarm method and the ant colony methods have parameters, which need to dynamically adjust to improve the performance of both algorithms.
Uranium Transport in the Sub-Surface Environment Koongarra - A Case Study

The weathered zone clays and iron minerals in the vicinity of the Koongarra ore deposit form, at least in part, by alteration of chlorite, the major rock-forming mineral of the quartz-chlorite schist. Activity ratios in both solid and liquid phases, and the interrelationship between the two have been investigated extensively in the Alligator Rivers Analogue Project and are discussed briefly below. The TAO extractable fraction is nominally equated with the fraction of solid readily accessible to groundwaters though it is recognized that it gives only a crude estimate of this fraction with a rather indistinct cut-off between amorphous, crystalline phases. The approaches used by these authors have been critically reviewed and have provided a valuable basis for the preliminary modeling of the partitioning of uranium onto solid substrates from the Koongarra region. In these studies, a simulated groundwater with a composition similar to natural Koongarra groundwaters, containing artificial uranium isotope, was contacted with solid phases that contained natural uranium.
Statistical Testing on ASR Performance via Blockwise Bootstrap

A common question being raised in automatic speech recognition (ASR) evaluations is how reliable is an observed word error rate (WER) improvement comparing two ASR systems, where statistical hypothesis testing and confidence interval (CI) can be utilized to tell whether this improvement is real or only due to random chance. The bootstrap resampling method has been popular for such significance analysis which is intuitive and easy to use. However, this method fails in dealing with dependent data, which is prevalent in speech world - for example, ASR performance on utterances from the same speaker could be correlated. In this paper we present blockwise bootstrap approach - by dividing evaluation utterances into nonoverlapping blocks, this method resamples these blocks instead of original data. We show that the resulting variance estimator of absolute WER difference between two ASR systems is consistent under mild conditions. We also demonstrate the validity of blockwise bootstrap method on both synthetic and real-world speech data.
Competitor Mining from Web Encyclopedia: A Graph Embedding Approach

Mining competitors from the web has been a valuable and emerging topic in big data and business analytics. While normal web pages may include incredible information like fake news, in this paper, we aim to extract competitors from web encyclopedia like Wikipedia and DBpedia, which provide more credible information. We notice that the entities in web encyclopedia can form graph structures. Motivated by this observation, we propose to extract competitors by employing a graph embedding approach. We first present a general framework for mining competitors from web encyclopedia. Then, we propose to mine competitors based on the similarity among graph nodes and further present a similarity computation method combing graph-node similarity and textual relevance. We implement the graph-embedding-based algorithm and compare the proposed method with four existing algorithms on the real data sets crawled from Wikipedia and DBpedia. The results in terms of precision, recall, and F1-measure suggest the effectiveness of our proposal.
LightSeq: A High Performance Inference Library for Sequence Processing and Generation.

LightSeq is a high performance inference library for sequence processing andgeneration implemented in CUDA. To our best knowledge, this is the firstopen-source inference library which fully supports highly efficient computationof modern NLP models such as BERT, GPT, Transformer, etc. This library isefficient, functional and convenient. A demo usage can be found here:https://github.com/bytedance/lightseq/blob/master/example.
An Investigation of the Reading Habits among Pakistani University Students

Reading is an essential skill, which further paves the way for excellence in life both academically and non-academically. Reading habit does not necessarily include the reading of academic texts, but it also includes reading for pleasure, reading for information, reading for knowledge etc. It can also be a valuable essence to constructively progress the academic achievements of the students. The present study investigated the reading habits of Pakistani university students. The sample of the study was 600 university pupils from various disciplines of 6 universities. The quantitative survey showed that students were not inclined towards reading habits other than their course books.
A Taxonomy on Continuous Integration and Deployment Tools and Frameworks

Software development has become a critical activity in modern companies. Competitive advantage and customer satisfaction depend strongly on the efficient delivery of new software capabilities. This requires the application of agile methodologies and the use of tools and frameworks which allow a fast and reliable integration and evolution of software systems. Currently, there exist several tools which are used for different purposes all along the software development process. Therefore, it is important for researchers on software engineering and developers to have a wide overview and understanding of the capabilities and limitations of current technology. This paper presents an insightful study of the state of the art on continuous integration and deployment tools. It depicts a taxonomy of current approaches based on their usability during the software development process. Furthermore, it discusses the current challenges and propose a set of study opportunities which could lead further research in this field.
Introduction to Burp Suite

Application Security has evolved to a large extent in the last decade or so. A decade ago, finding SQL Injections in applications was easier than it is today. The applications were more prone to vulnerabilities as there were fewer defenses and less awareness among the developers. However, the situation has drastically changed today. Developers are much more aware and conscious about security, and security controls are placed throughout the Software Development Life Cycle (SDLC), making the end application comparatively secure.
QoE and Immersive Media

In the past 20 years the "Quality of Experience" (QoE) has increasingly become a necessary aspect to be taken into account by the multimedia industry. In fact, the QoE has allowed to extend the concept of image quality by considering other aspects to evaluate "the delight or annoyance of a customer's experiences with a service" [1]. The concept of QoE is extremely important since it has a big impact on several fields, ranging from the acquisition to the rendering of multimedia contents.