Reviewerly/exHarmony huggingface dataset:
A Grounded Theory Approach for Modelling the Knowledge Construction Process in Exploratory Search

Searching on the Web is not only an issue of finding answers to a specific question. In fact, searching is closely related to learning and users intend to increase their knowledge about new topics. To better understand users' knowledge construction process in exploratory search, we conducted a qualitative user study with 25 participants from a variety of domains. We collected data from audio and video recordings of think-aloud sessions and semi-structured interviews. The preliminary findings reveal a number of core processes, such as knowledge nodes, knowledge communities and knowledge networks, each of them composes several activities. Based on this findings, a conceptual model of knowledge construction process in exploratory search is presented.
Integrating heuristiclab with compilers and interpreters for non-functional code optimization

Modern compilers and interpreters provide code optimizations during compile and run time, simplifying the development process for the developer and resulting in optimized software. These optimizations are often based on formal proof, or alternatively stochastic optimizations have recovery paths as backup. The Genetic Compiler Optimization Environment (GCE) uses a novel approach, which utilizes genetic improvement to optimize the run-time performance of code with stochastic machine learning techniques.
Handling Class Imbalance Problem in Heterogeneous Cross-Project Defect Prediction

Software Defect Prediction (SDP) is one of the key tasks in the testing phase of Software Development Life Cycle (SDLC) that discovers modules that are more susceptible to defects and therefore requires significant testing to identify these flaws early in order to cut up the extra cost for software development. Much research has been performed on Cross-Project Defect Prediction (CPDP), which seeks to predict defects in the target application that lacks historical defect prediction information or has restricted defect information to construct an efficient generalized model for forecasting defects in a software project. The proposed research work focuses on defect forecast using a heterogeneous metric set so that there are no common metrics between the source and the target applications. This paper also discusses the Class Imbalance Problem (CIP) that occurs in a dataset because of the disproportionate number of favorable and unfavorable cases. If trained using imbalance dataset, a classifier will offer biased outcomes. We used Adaptive Boost (AdaBoost) method to manage CIP in Heterogeneous Cross-Project Defect Prediction (HCPDP), and after managing CIP, experimental findings demonstrate significant improvements.
Construction of University Scientific Data Management Frame

This article explores the related concepts and theoretical basis of scientific data management in universities, and builds a scientific data management frame in universities based on the theoretical basis of data science and information life cycle management. Starting from the links of scientific data collection, organization, storage, sharing and utilization, confidentiality and security, etc, the implementation strategies of each link were discussed, hoping to provide references for universities to develop scientific data management services.
Microscopic Image Noise Reduction Using Mathematical Morphology

In image processing to enhance theShetty, Mangala region of an image, mathematical morphological (MM) operations are taken an importantBalasubramani, R. role. Mainly application of basic morphological techniques are useful in improving the quality of an image. In the collection and delivery process, the image will be polluted by salt-and-pepper noise, which would lead directly to image quality reduction throughout subsequent processes of image analysis. Thus obtaining the actual image from the image that is distorted by noise is therefore of great importance [1]. This paper deals with an approach using morphological functions to reduce the salt-and-pepper noise from scanning electron microscopic(SEM) images of bacteria cell. The noise removal has a wide effect in getting the accurate segmentation and classification of bacteria cells thereby cell identification accuracy increases to identify the bacteria cells within a short period of time automatically, the noise has to remove from the SEM image of bacteria. Various quality assessment operations are used to measure the quality of enhanced images. The results of the experiment indicate that without blurring edges, this experiment can reduce noise effectively from the input image. The validation outcomes of denoised images with a higher peak signal-to-noise ratio (PSNR) and mean squared error (MSE) show their reliable application potential.
KONTRIBUSI PERAMALAN PENERIMAAN MAHASISWA BARU POLITEKNIK : SEBAGAI ALAT ESTIMASI PENDAPATAN

Purpose of this research was to determine the forecasting results of new student admissions and the estimated amount of income from a recurrent academic fees. The data was secondary data from the list of enthusiasts and the capacity of new students. This research was quantitative descriptive and using Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Deviation (MAD) as a calculation of the level of error accuracy, and single exponential smoothing method as forecasting of new student admissions. Forecasting results obtained DIII Accounting enthusiasts and capacity totaling 2951 and 181 students for 2020 while 2186 students and 191 students for 2021. For the DIV Management accounting produces 4184 and 238 students interested, 238 students for 2020, whereas in 2021 produced 5106 enthusiasts and 226 students for capacity of new students. the total estimated revenues in 2020 and 2021 have a significant amount of interest from new students and the interest of new students through a recurrent academic fees. It was concluded that forecasting can also be used to calculate the estimated income in an institution or even a company so that this paper will contribute to the field of information and finance that can help in decision making.
An Adaptive Weighted Bagging Ensemble Learning Model for Zombie Enterprise Identification

Zombie enterprise portrait and classification is one of the urgent problems in the current society, which has important practical significance and research value. This paper presents an adaptive weighted Bagging integrated learning method, which integrates 5 regular models and 8 pattern recognition models. The weight of the base classifier in integrated learning can be adjusted adaptively according to the training process to reduce the subjectivity and limitation of the regular model as much as possible. The accuracy, precision and recall rate of the model are all up to 1.0 in the experiment. At the same time, the strategy of data cleaning and missing item completion for problem domain is proposed. The research methods and results proposed in this paper have certain reference significance for the study of zombie enterprise portrait and classification and its related fields.
Tullock Contest: A Model of Proof-of-Work Mining in Cryptocurrencies

This paper presents an N-player Tullock contest where players purchases shares in a lottery at a fixed common cost. Once all players have purchased their shares, a common value reward is raffled off. The model has some similarities to all-pay auctions, however the assignment mechanism differs. This difference ensures that, unlike all-pay auctions, the N-player Tullock contest has an unique pure strategy Nash equilibrium. This article presents also a new kind of Tullock contest that integrates the main mechanism behind proof-of-work mining of cryptocurrencies such as Bitcoin. The application of the model is expanded to analyze some topics of proof-of-work protocols, such as concentration of hashing power, double-spending attacks and environmental externalities.
Temporal Summarization of Scholarly Paper Collections by Semantic Change Estimation: Case Study of CORD-19 Dataset

The new pandemic disease caused by COVID-19 virus is the crucial event over the world in the beginning of 2020. Studies on corona viruses have been however carried since several decades ago, with recent research papers published on weekly basis. We demonstrate a simple approach to explore CORD-19 dataset to provide a high level overview of important semantic changes that occurred over time. Our method aims to support better understanding of large domain-specific collections of scholarly publications that span long time periods and could be regarded as complementary to frequency-based analysis.
A Generic Framework for Evolution of Deep Neural Networks Using Genetic Algorithms

Shukla, Deepraj Singh, UpasnaThis paper presents an approach for the evolution of the deep neural networks (DNN) using genetic algorithms. The deep artificial neural networks are used generally for classification tasks. Depending upon the problem at hand, the designers decide on how many layers, how many number of nodes in each layers, what activation functions to be used at layers, etc. The term genetic algorithms is taken from the biological world and used in the evolution process. Here, we utilized a basic genetic algorithm concept to automatically build the optimum deep neural network suiting for the given classification task. The evolved networks are evaluated on the accuracy of classification needed, and genetic algorithm will find the best-suited DNN architecture automatically.
Efficient machine learning through evolving combined deep neural networks

The usage of Artificial Neural Networks (ANNs) with a fixed topology is becoming more popular in daily life. However, there are problems where it is difficult to build an ANN manually. Therefore, genetic algorithms like NeuroEvolution of Augmented Topologies (NEAT) have been developed to find topologies and weights. The downside of NEAT is that it often generates inefficient large ANNs for different problems.
Debugging strongly-compartmentalized distributed systems

Splitting software into distributed compartments is an important software security technique that limits the effect of vulnerabilities. Unfortunately the resulting systems are difficult to analyze or debug interactively when compared to the original. Not only are compartments distributed and executed in parallel, but they may also be strongly isolated by being sandboxed or run in minimal environments that lack debugging facilities.This paper is the first to study practical debugging techniques for strongly-isolated distributed compartments. We adapt ideas from other remote or distributed debugging settings to this domain, and implement and describe two radically different approaches to this problem. We evaluate these approaches both qualitatively and quantitatively, and using both toy examples and real-world open-source software. Our main finding is that out of the two approaches, using GDB remote stubs presents a good balance of performance, flexibility and usability, and we characterize this more precisely in our evaluation.
Blind Tone-Mapped Image Quality Assessment Based on Regional Sparse Response and Aesthetics

High dynamic range (HDR) images give a strong disposition to capture all parts of natural scene information due to their wider brightness range than traditional low dynamic range (LDR) images. However, to visualize HDR images on common LDR displays, tone mapping operations (TMOs) are extra required, which inevitably lead to visual quality degradation, especially in the bright and dark regions. To evaluate the performance of different TMOs accurately, this paper proposes a blind tone-mapped image quality assessment method based on regional sparse response and aesthetics (RSRA-BTMI) by considering the influences of detail information and color on the human visual system. Specifically, for the detail loss in a tone-mapped image (TMI), multi-dictionaries are first designed for different brightness regions and whole TMI. Then regional sparse atoms aggregated by local entropy and global reconstruction residuals are presented to characterize the regional and global detail distortion in TMI, respectively. Besides, a few efficient aesthetic features are extracted to measure the color unnaturalness of TMI. Finally, all extracted features are linked with relevant subjective scores to conduct quality regression via random forest. Experimental results on the ESPL-LIVE HDR database demonstrate that the proposed RSRA-BTMI method is superior to the existing state-of-the-art blind TMI quality assessment methods.
Dynamic Fusion: Attentional Language Model for Neural Machine Translation

Neural Machine Translation (NMT) can be used to generate fluent output. As such, language models have been investigated for incorporation with NMT. In prior investigations, two models have been used: a translation model and a language model. The translation model’s predictions are weighted by the language model with a hand-crafted ratio in advance. However, these approaches fail to adopt the language model weighting with regard to the translation history. In another line of approach, language model prediction is incorporated into the translation model by jointly considering source and target information. However, this line of approach is limited because it largely ignores the adequacy of the translation output. Accordingly, this work employs two mechanisms, the translation model and the language model, with an attentive architecture to the language model as an auxiliary element of the translation model. Compared with previous work in English–Japanese machine translation using a language model, the experimental results obtained with the proposed Dynamic Fusion mechanism improve BLEU and Rank-based Intuitive Bilingual Evaluation Scores (RIBES) scores. Additionally, in the analyses of the attention and predictivity of the language model, the Dynamic Fusion mechanism allows predictive language modeling that conforms to the appropriate grammatical structure.
Leveraging Book Indexes for Automatic Extraction of Concepts in MOOCs

Concepts are basic elements in any learning module and are thus very useful for modeling, summarizing, and previewing the content of any module. Automatic extraction of the major concepts from online education materials enables many useful applications. In this paper, we propose to leverage textbooks and their back-of-the-book indexes as training data to train a supervised machine learning algorithm for automatic extraction of concepts from text data in the education domain. We evaluate this idea by training neural networks on three textbooks and applying the trained neural networks to extract concepts from the lecture transcripts of two MOOCs. Our results suggest great promise for further exploration of this direction.
Kcollections: A Fast and Efficient Library for K-mers

K-mers form the backbone of many bioinformatic algorithms. They are, however, difficult to store and use efficiently because the number of k-mers increases exponentially as k increases. Many algorithms exist for compressed storage of kmers but suffer from slow insert times or are probabilistic resulting in false-positive k-mers. Furthermore, k-mer libraries usually specialize in associating specific values with k-mers such as a color in colored de Bruijn Graphs or k-mer count. We present kcollections <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , a compressed and parallel data structure designed for k-mers generated from whole, assembled genomes. Kcollections is available for C++ and provides set-and maplike structures as well as a k-mer counting data structure all of which utilize parallel operations designed using a MapReduce paradigm. Additionally, we provide basic Python bindings for rapid prototyping. Kcollections makes developing bioinformatic algorithms simpler by abstracting away the tedious task of storing k-mers.
Overview of High Performance Computing Power Building for the Big Data of Marine Forecasting

This thesis introduced the construction and development process of high-performance computing of marine forecasting from scratch, from small to large, from weak to strong by National Marine Environmental Forecasting Center of the Ministry of Natural Resources of the People's Republic of China in the past half century; expounded the “two milestones”: zero breakthrough of high-performance computing of marine forecasting and computing power exceeding 1 billion times per second for the first time from the early 1970s to the late 1990s in high-performance computing of ocean forecasting, and the achievements of ocean forecasting on the journey of globalization, refinement and high resolution with the help of high-performance computing after entering the 21st century; studied, designed and showed a better architecture blueprint for the next generation of the high-performance computer system for marine forecasting; pointed out that there were gaps between the high-performance computing of marine forecasting and the international advanced one, domestic advanced one and professional requirements, and put forward two suggestions to enhance the computing capability of existing equipment through technological innovation and scientific research innovation from the perspective of professional technology; and finally held that there was still a long way to go to build China's high-performance computing power for marine forecasting in comparison with the medium and long-term objectives and tasks of the modernization of marine forecasting.
Digital Image Retrieval Based on Selective Conceptual Based Features for Important Documents

Due to the increased level of digitalization, data exchange and collection has grown to a greater extent. This bulk data needs categorization else data collected will be meaningless. The exchange of multimedia data has also increased due to the availability of the Internet. Images collected need a proper categorization that can help in fetching the required data. Government documents need to be uniquely identified from the set of plenty of data to help in different kinds of proceedings. It could help to fetch images only of the required government document. Due to the increased size of data as well as an increased quantity of data, different document image identification techniques are time-consuming. The proposed model quickly identifies specific government documents from a chunk of images with ease. Various filtering categories are applied to ease the process of categorization. To speed up the process of categorization, selective efficient features are shortlisted which contributes majorly toward substantiation of a government document.
Optimisation of the implementations in fixed and floating point of tracking algorithm on DSP of C6000 of TI’s familly

In this article, the implementation of tracker named interacting multiple models (Interacting Multiple Models, `IMM') on two types of card digital signal processor (Digital Signal Processor, `DSP'), the floating-point TMS320C6713 and TMS320C6416 fixed-point will be presented in detail. A comparative study of the performances of DSP, to implement the same scenarios and prosecution for the same algorithm will be presented in detail. With the aim of achieving a realtime application, the optimization techniques developed by Texas Instrument (TI) will be used to reduce the execution time. New optimization solutions are also proposed.
Evolutionary algorithms in biomedical data mining

tutorial Evolutionary algorithms in biomedical data mining: challenges, solutions, and frontiers Share on Authors: Ryan J. Urbanowicz University of Pennsylvania University of PennsylvaniaView Profile , Moshe Sipper University of Pennsylvania and Ben-Gurion University, Beer Sheva, Israel University of Pennsylvania and Ben-Gurion University, Beer Sheva, IsraelView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 1224–1253https://doi.org/10.1145/3377929.3389862Online:08 July 2020Publication History 1citation66DownloadsMetricsTotal Citations1Total Downloads66Last 12 Months25Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
TEXT SENTIMENT ANALYSIS BASED ON CNNS AND SVM

The traditional text sentiment analysis method is mainly based on machine learning. However, its dependence on emotion dictionary construction and artificial design and extraction features makes the generalization ability limited. In contrast, depth models have more powerful expressive power, and can learn complex mapping functions from data to affective semantics better. In this paper, a Convolution Neural Networks (CNNs) model combined with SVM text sentiment analysis is proposed. The experimental results show that the proposed method improves the accuracy of text sentiment classification effectively compared with traditional CNN, and confirms the effectiveness of sentiment analysis based on CNNs and SVM
Parallelizing Maximal Clique Enumeration on Modern Manycore Processors

Many fundamental graph mining problems, such as maximal clique enumeration and subgraph isomorphism, can be solved using combinatorial algorithms that are naturally expressed in a recursive form. However, recursive graph mining algorithms suffer from a high algorithmic complexity and long execution times. Moreover, because the recursive nature of these algorithms causes unpredictable execution and memory access patterns, parallelizing them on modern computer architectures poses challenges. In this work, we describe an efficient manycore CPU implementation of maximal clique enumeration (MCE), a basic building block of several social and biological network mining algorithms. First, we improve the single-thread performance of MCE by accelerating its computation-intensive kernels through cache-conscious data structures and vector instructions. Then, we develop a multi-core solution and eliminate its scalability bottlenecks by minimizing the scheduling and the memory-management overheads. On highly-parallel modern CPUs, we demonstrate an up to 19-fold performance improvement compared to a state-of-the-art multi-core implementation of MCE.
A Quick Survey of Security and Privacy Issues in Cloud and a Proposed Data-Centric Security Model for Data Security

Cloud is being utilized by the majority of Internet users. Businesses storing their critical information in the cloud have increased over the ages due to simplistic and attractive features the cloud possesses. In spite of cloud utilization, there are concerns raised by users regarding cloud security. Hackers are using this opportunity to steal data stored in the cloud. In this regard, researchers have proposed techniques to provide security and privacy to the cloud. Some researchers focused on securing the cloud server, while others concentrated on securing the data transmitted to the cloud. In this research, a comparative analysis is done on papers that were published between 2013 and 2019. Based on the challenges identified in those techniques, a well-secured scheme is proposed to provide extensive security to data before it is transmitted to the cloud. The proposed technique is Data-Centric Security (DCS), and it utilizes Attribute-Based Encryption (ABE) as its framework. This scheme is at the implementation stage, and we believe it will come to address the security and privacy flaws observed.