Reviewerly/exHarmony huggingface dataset:
Implementing Parallel Computing to Enhance the Performance of K-mean Algorithm

Clustering is a common tool in data science, and it is used in many areas, such as statistics and bioinformatics. Clustering has a number of algorithms. The most well-known is k-means, which is popular due to its simplicity and efficiency. Similarly, as a programming model, the Message Passing interface (MPI4py) improves performance by reducing the execution time and increasing the speedup. By applying the K-means clustering algorithm to the MPI4py library, the performance of the algorithm in a parallel setting improved. The efficiency of clustering data using K-means is examined in this study in terms of overhead expenditure and execution between running the K-means algorithm sequentially and running the K-means algorithms using a Message Passing Interface (MPI) parallel architecture.
Learning temporal action models from multiple plans: A constraint satisfaction approach

Learning, as a discovery task from past observations, is interesting in engineering contexts for identifying structures and improving accuracy. Learning in planning scenarios aims at recognizing past behavior to predict action models to improve decisions. This is appealing because practical scenarios are usually complex, sometimes difficult to be described formally, which require expert knowledge and engineering that becomes impractical in real-world applications. We introduce a Constraint Satisfaction formulation for learning PDDL2.1 temporal action models in planning. Given a collection of observations on multiple plans and a set of empty operators, we automatically create a learning task that identifies which conditions+effects are necessary, together with their temporal annotation, and induces durations and costs. Our formulation encapsulates planning (causal links, threats and effect interferences) and mutex (to avoid contradictions) constraints to be fully satisfied from the observed plans. The formulation is simple, but it proves very effective and easily adaptable to different levels of expressiveness. We evaluate such effectiveness in different IPC domains and compare the quality of the learning vs. other state-of-the-art learning approaches.
Deblending based on the frequency-structure weighted threshold: The case of the slope-constrained <i>f</i>-<i>k</i> transform

In the process of separating blended data, conventional methods based on sparse inversion assume that the primary source is coherent and the secondary source is randomized. The L1-norm, the commonly used regularization term, uses a global threshold (GT) to process the sparse spectrum in the transform domain; however, when the threshold is relatively high, more high-frequency information from the primary source will be lost. For this reason, we have analyzed the generation principle of blended data based on the convolution theory and then conclude that the blended data are only randomly distributed in the spatial domain. Taking the slope-constrained frequency-wavenumber ( f- k) transform as an example, we adopt a frequency-dependent threshold, which reduces the high-frequency loss during the deblending process. Then, we use a structure-weighted threshold (SWT) in which the energy from the primary source is concentrated along the wavenumber direction. The combination of frequency and SWTs effectively improves the deblending performance. Model and field data find that our frequency-structure weighted threshold has better frequency preservation than the GT. The weighted threshold can better retain the high-frequency information of the primary source, and the similarity between other frequency-band data and the unblended data has been improved.
GOFS: Geo-distributed Scheduling in OpenFaaS

OpenFaaS is a popular open-source serverless platform in the academic and industrial world. Based on Ku-bernetes, OpenFaaS includes a simple scheduling policy that spreads functions on cluster computing resources. As such, it is not well-suited for managing latency-sensitive applications in a geo-distributed environment, where network latencies are nonnegligible and negatively affect the application response time. To overcome this issue, in this paper we present GOFS (Geo-distributed Scheduling in OpenFaaS), which extends OpenFaaS with network-aware scheduling capabilities. GOFS addresses the serverless application scheduling in a geo-distributed environment by either solving a suitable integer linear programming problem or using a greedy network-aware heuristic. However, its modular architecture facilitates the integration of other custom scheduling policies. A wide set of prototype-based results shows the advantages of the proposed network-aware solutions over other benchmark scheduling policies.
Practical Approach to Monitor Runtime Engine Statistics for Apache Spark and Docker Swarm using Graphite, Grafana, and CollectD

Monitoring runtime engine statistics to identifying and manage bottlenecks and resource availability is a current and heavily debated topic in academics as well as in the industry. Due to the application variety and the complexity of distributed computer clusters, this problem is still important, and many proofs of concept emerge to challenge this issue. This paper aims to summarize the system’s specification, component design, implementation, and evaluation of chosen infrastructure to develop a monitoring engine for exposing JVM and network-related statistics of the mentioned system using Docker Swarm, Apache Spark, Graphite, and Grafana.
Semi-Supervised Self Training to Assess the Credibility of Tweets

In our daily lives, social media generates a large amount of data, and the value of this information is becoming increasingly obvious today. However, identifying the credibility of information is a complex issue, and manual assessment to evaluate the credibility of information is very time consuming. Therefore, semi-supervised learning using a small amount of labeled data, combined with a large amount of unlabeled data can be a practical approach for evaluating the credibility of information in the data generated in social media. In this experiment, a typical self-training algorithm in semi-supervised learning is used for evaluating the credibility of Tweeter data. The algorithm first trains the classifier with the labeled data using a supervised learning algorithm, and then predicts the category of the unlabeled data with the classifier obtained from the training. We implemented an improved self-training algorithm by using the repeated labeling strategy. We conducted experiments on disaster related Twitter data and predicted the credibility of disaster related Twitter data. Logistic regression and random forest classifiers were used in our experiments. Our experiments show the improved self-training algorithm produced better accuracy in the classification.
First-Break Picking Classification Models Using Recurrent Neural Network

Abstract Manual first-break picking from a large volume of seismic data is extremely tedious and costly. Deployment of machine learning models makes the process fast and cost effective. However, these machine learning models require high representative and effective features for accurate automatic picking. Therefore, First- Break (FB) picking classification model that uses effective minimum number of features and promises performance efficiency is proposed. The variants of Recurrent Neural Networks (RNNs) such as Long ShortTerm Memory (LSTM) and Gated Recurrent Unit (GRU) can retain contextual information from long previous time steps. We deploy this advantage for FB picking as seismic traces are amplitude values of vibration along the time-axis. We use behavioral fluctuation of amplitude as input features for LSTM and GRU. The models are trained on noisy data and tested for generalization on original traces not seen during the training and validation process. In order to analyze the real-time suitability, the performance is benchmarked using accuracy, F1-measure and three other established metrics. We have trained two RNN models and two deep Neural Network models for FB classification using only amplitude values as features. Both LSTM and GRU have the accuracy and F1-measure with a score of 94.20%. With the same features, Convolutional Neural Network (CNN) has an accuracy of 93.58% and F1-score of 93.63%. Again, Deep Neural Network (DNN) model has scores of 92.83% and 92.59% as accuracy and F1-measure, respectively. From the pexperiment results, we see significant superior performance of LSTM and GRU to CNN and DNN when used the same features. For robustness of LSTM and GRU models, the performance is compared with DNN model that is trained using nine features derived from seismic traces and observed that the performance superiority of RNN models. Therefore, it is safe to conclude that RNN models (LSTM and GRU) are capable of classifying the FB events efficiently even by using a minimum number of features that are not computationally expensive. The novelty of our work is the capability of automatic FB classification with the RNN models that incorporate contextual behavioral information without the need for sophisticated feature extraction or engineering techniques that in turn can help in reducing the cost and fostering classification model robust and faster.
ASSIE: Application of Squirrel Search Algorithm for Information Extraction Problem

Currently, within the recent era of huge data, a large volume of unstructured data is being produced in various sorts of audio, video, images, text, and animation. The effective use of those unstructured big data could be a laborious and tedious task. Because of this information overload problem, we face lots of difficulties to extract vital information. For this problem, there's an answer like Information Extraction (IE). IE systems help to extract useful information and structured data from this huge sort of unstructured or semi-structured machine-readable documents and other electronically represented sources. In this study, to extract useful information, a biological-inspired optimization algorithm Squirrel Search Algorithm (SSA) is implemented. The model is validated over traditional benchmark Document Understanding Conferences (DUC) dataset. The result of proposed model is compare with respect to few online line summarizers.
Practical Multi-Authority Ciphertext Policy Attribute-Based Encryption from R-LWE

Attribute-Based Encryption (ABE) has been caught a wide range of attention owing to its advantage of achieving both fine-grained access control and protection for data. In recent years, the constructions of ABE have developed to lattice-based schemes, while most of them exist bottlenecks in efficiency and security as a result of constructing on the Learning With Errors (LWE) assumption and relying on a single central authority. In this paper, we propose a decentralized multi-authority Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme from the Ring Learning With Errors (R-LWE) assumption, which supports flexible access policies and is built on a new trapdoor with efficient algorithms. Moreover, in our system, management and distribution for the private keys are accomplished by multiple authorities utilizing the technique of Shamir threshold secret sharing, which makes it more efficient and applicable to the distributed storing environments. We then prove that the scheme is selective secure under chosen plaintext attacks. The experimental results manifest that the proposed scheme can be carried out in a comparatively short amount of time, which confirms its practicality.
Designing a User Interface for Improving the Usability of a Statistical Disclosure Control Tool

Data sets often contain personal data that are not needed for analysis. Statistical Disclosure Control (SDC) is a personal data minimizing technology to reduce personal data in a data set while maintaining the utility of the data set at an acceptable level. SDC tools usage lags due to the complexity encountered by users. The aim of the research in this paper is to reduce the complexity of SDC tools via improving their ease of use. For this study information system adoption literature is reviewed, some usability challenges are identified, and a User Interface (UI) prototype is developed. From the literature, some design principles are derived related to minimal memory, feature self-descriptiveness, user guidance, navigability, minimal action, and UI familiarity. Based on these principles, a prototype is developed and evaluated. The findings suggest that the designed UI overcomes the identified UI-related issues of an SDC tool called ARX but show no significance in reducing the complexity associated with the SDC-related complexity. The findings suggest that while reducing the complexity of the UI might be a good approach to address the problem of adopting SDC technology within organizations, there is a need for complementary approaches to increase the usability and adoption of an SDC tool.
Enhancing Lexical Translation Consistency for Document-Level Neural Machine Translation

Document-level neural machine translation (DocNMT) has yielded attractive improvements. In this article, we systematically analyze the discourse phenomena in Chinese-to-English translation, and focus on the most obvious ones, namely lexical translation consistency. To alleviate the lexical inconsistency, we propose an effective approach that is aware of the words which need to be translated consistently and constrains the model to produce more consistent translations. Specifically, we first introduce a global context extractor to extract the document context and consistency context, respectively. Then, the two types of global context are integrated into a encoder enhancer and a decoder enhancer to improve the lexical translation consistency. We create a test set to evaluate the lexical consistency automatically. Experiments demonstrate that our approach can significantly alleviate the lexical translation inconsistency. In addition, our approach can also substantially improve the translation quality compared to sentence-level Transformer.
A Novel Metric based Detection of Temporary Field Code Smell and its Empirical Analysis

Code smell causes side effects in the source code and impact the code quality. It is beneficial to recognize code smells to improve software quality. Despite 22 classical code smells as characterized by Martin Fowler, all classical code smells have not been considered for the identification and refactoring. Temporary field code smell is one such code smell that has not been given an appropriate level of attention so far regarding its detection as well as refactoring. In this paper, we have proposed a novel metric-based method and developed a tool to detect temporary field code smell. The proposed method works on three novel metrics in addition to existing metric TCC (tight class cohesion) and three new rules (R1, R2, and R3) to detect temporary field code smell. Detection rules were tested on ten open-source GitHub Java projects used in the literature. Results demonstrate that projects under the study that had non-cohesive classes have shown the presence of temporary field code smell ranging from 54% to 100%. Findings have additionally demonstrated that for the undertaken projects, there exists a strong positive correlation between the number of classes exhibiting temporary field smell and number of non-cohesive classes present in a project.
Image Matting: A Comprehensive Survey on Techniques, Comparative Analysis, Applications and Future Scope

In the era of rapid growth of technologies, image matting plays a key role in image and video editing along with image composition. In many significant real-world applications such as film production, it has been widely used for visual effects, virtual zoom, image translation, image editing and video editing. With recent advancements in digital cameras, both professionals and consumers have become increasingly involved in matting techniques to facilitate image editing activities. Image matting plays an important role to estimate alpha matte in the unknown region to distinguish foreground from the background region of an image using an input image and the corresponding trimap of an image which represents a foreground and unknown region. Numerous image matting techniques have been proposed recently to extract high-quality matte from image and video sequences. This paper illustrates a systematic overview of the current image and video matting techniques mostly emphasis on the current and advanced algorithms proposed recently. In general, image matting techniques have been categorized according to their underlying approaches, namely, sampling-based, propagation-based, combination of sampling and propagation-based and deep learning-based algorithms. The traditional image matting algorithms depend primarily on color information to predict alpha matte such as sampling-based, propagation-based or combination of sampling and propagation-based algorithms. However, these techniques mostly use low-level features and suffer from high-level background which tends to produce unwanted artifacts when color is same or semi-transparent in the foreground object. Image matting techniques based on deep learning have recently introduced to address the shortcomings of traditional algorithms. Rather than simply depending on the color information, it uses deep learning mechanism to estimate the alpha matte using an input image and the trimap of an image. A comprehensive survey on recent image matting algorithms and in-depth comparative analysis of these algorithms has been thoroughly discussed in this paper.
Fast Sparse Deep Neural Network Inference with Flexible SpMM Optimization Space Exploration

Deep neural networks (DNN) have been widely used in many fields. With the ever-increasing model size, the DNN scalability suffers. Sparse deep neural networks (SpDNN) are promising to resolve this problem, but the sparse data makes it difficult to execute efficiently on GPUs due to load imbalance and irregular memory accesses. The recent MIT/IEEE/Amazon GraphChallenge has shown several big advances to fit sparse DNNs into GPUs, but we observe that none of these earlier efforts can be an absolute winner for all dataset cases due to their limited optimization space considerations. In this paper, we identify some new opportunities in optimizing the execution of SpDNN via a comprehensive analysis of previous works. Based on this new large design space of SpDNN, we present sparsity-aware SpMM algorithms that can systematically explore a performance-optimal solution of SpDNN execution on GPU and further generate optimized SpMM kernel implementations. Compared to the 2020 HPEC Sparse DNN Challenge champions, our approach achieves up to 55.6 TeraEdges per second inference throughput with the speedups of up to 13.74_ [1] and 22.29_ [2] on a single NVIDIA V100 GPU. We also show that our approach under 4 GPUs can be superior to the 2020 Challenge Champion using 768 GPUs in many cases. The source codes are available at https://github.com/CGCL-codes/Graphchallenge21.
Mental Health Question and Answering System Based on Bert Model and Knowledge Graph Technology

With the development and progress of society, people are facing increasing pressure. The emergence of this phenomenon has led to a rapid increase in the incidence of mental illness. In order to deal with this phenomenon, this paper proposes a system of question and answering on the basic knowledge of mental health (MHQ&A) by using deep learning retrieval technology and knowledge graph technology. The system MHQ&A is designed mainly for the general public, to answer the basic knowledge of mental health, especially the field of depression. First of all, the basic and the professional question and answer data about mental health were respectively obtained by the reptilian bot from the "IASK" website knowledge and the "Dr. Dingxiang" website. Then, the questions and answers obtained through the crawler are made into a Question and Answering Knowledge Graph of Basic Health Knowledge in the mental health field, which is combined with semantic data of antidepressants and the semantic data of depression papers. Finally, a set of template matching rules is designed to determine the type of problem of users. If the questions are about the professional knowledge of medicine or thesis, the reasoning template will be used to reason and search the answer in the "Question and Answering Knowledge Graph of Basic Health Knowledge in the Mental Health Field". If the questions are about other basic knowledge in the field of mental health, the BERT model is used to vectorize the questions of users, and the matching questions and corresponding answers in the MHQ&A are found through cosine similarity calculation. Through the test of system accuracy, it is proved that the system can effectively combine deep learning technology and knowledge.
Global Information Network and Conflicts of Interest (Parties, Interests and Conflicts)

Specialists working in the global information space, as organizers and participants in information processes, become witnesses to the problems that arise in this space. The user has difficulty making the right decisions, especially in the event of a conflict of interest in the information space. In some cases, a &#x201C;conflict of interest&#x201D; leaves the consumer with a choice. Such cases are more common on social networks. The consumer of information sometimes participates in this information space as a distributor of information. In some cases, the user becomes a disseminator of disinformation because the reliability of the information is &#x201C;in doubt&#x201D;. In some cases, disinformation is more interesting because of its attractiveness. We also see an increase in the number of &#x201C;products of disinformation&#x201D; in the global information space. The article discusses a project aimed at resolving conflicts of interest in the global information space. Conflict of interest is a widespread problem in the global information space. Between &#x201C;information monopolists&#x201D; and the state, between sellers and consumers, between sources of information and consumers of information, etc. As a result of systematic discussions, it can be assumed that new approaches to the problem of conflicts of interest may appear that can contribute to &#x201C;accelerating progress.&#x201D; In this regard, the idea that libraries are a reliable source of information, especially on global networks, is supported and encouraged. The purpose of the article is to analyze the features of a project aimed at solving the problem of conflict of interest, based on modeling within an integrated system and finding optimal solutions in the context of various corporate interests. This will exclude the manipulation of conflicts in the global network. The abundance and dynamics of information growth, the problem of the reliability of information products and attempts to manipulate the growing information products show that the problem of information reliability will remain relevant in the next 10&#x2013;20 years. We believe that at present the project&#x0027;s orientation towards &#x201C;library and information&#x201D; sources in the field of information reliability may be the best choice in this direction, and this may partially solve the problem of data reliability.