Reviewerly/exHarmony huggingface dataset:
An Efficient Mining Approach for Handling Web Access Sequences

The World Wide Web (WWW) becomes an important source for collecting, storing, and sharing the information. Based on the users query the traditional web page search approximately retrieves the related link and some of the search engines are Alta, Vista, Google, etc. The process of web mining defines to determine the unknown and useful information from web data. Web mining contains the two approaches such as data-based approach and process-based approach. Now a day the data-based approach is the widely used approach. It is used to extract the knowledge from web data in the form of hyper link, and web log data. In this study, the modern technique is presented for mining web access utility-based tree construction under Modified Genetic Algorithm (MGA). MGA tree are newly created to deploy the tree construction. In the web access sequences tree construction for the most part relies upon internal and external utility values. The performance of the proposed technique provides an efficient Web access sequences for both static and incremental data. Furthermore, this research work is helpful for both forward references and backward references of web access sequences.
A PSO-Inspired Multi-Robot Map Exploration Algorithm Using Frontier-Based Strategy

Map exploration is a fundamental problem in mobile robots. This paper presents a distributed algorithm that coordinates a team of autonomous mobile robots to explore an unknown environment. The proposed strategy is based on frontiers which are the regions on the boundary between open and unexplored space. With this strategy, robots are guided to move constantly to the nearest frontier to reduce the size of unknown region. Based on the Particle Swarm Optimization (PSO) model incorporated in the algorithm, robots are navigated towards remote frontier after exploring the local area. The exploration completes when there is no frontier cell in the environment. The experiments implemented on both simulated and real robot scenarios show that the proposed algorithm is capable of completing the exploration task. Compared to the conventional method of randomly selecting frontier, the proposed algorithm proves its efficiency by the decreased 60% exploration time at least. Additional experimental results show the decreased coverage time when the number of robots increases, which further suggests the validity, efficiency and scalability.
Advanced Recommender Systems

This chapter presents a brief and systematic overview of four major advanced recommender systems: group recommender systems, context-aware recommender systems, multi-criteria recommender systems, and cross-domain recommender systems. These advanced recommendations are characterized and compared in a unifying model as extensions of basic recommender systems. Future research topics and directions in the area of advanced personalized recommendations are discussed. Advanced recommender technologies will continue to advance.
Distributed Training for High Resolution Images: A Domain and Spatial Decomposition Approach

In this work we developed two Pytorch libraries using the PyTorch RPC interface for distributed deep learning approaches on high resolution images. The spatial decomposition library allows for distributed training on very large images, which otherwise wouldn’t be possible on a single GPU. The domain parallelism library allows for distributed training across multiple domain unlabeled data, by leveraging the domain separation architecture. Both of those libraries where tested on the Summit supercomputer at Oak Ridge National Laboratory at a moderate scale.
Privacy-Preserving MAX/MIN Query Processing for WSN -as-a -Service

WSN-as-a-Service (WaaS) is a novel application model of wireless sensor networks (WSNs). Owners of WSNs provide data queries as services, while users pay for needed services as they use such services. The adoption of WaaS improves the usage of WSNs and reduces the cost of network deployment and maintenance. It is challenging to protect data from curious users while, at the same time, providing MAX/MIN query services. In this paper, we propose a privacy-preserving MAX/MIN query processing method for WaaS. To the best of our knowledge, this work is the first to discuss a privacy-preserving data query method in the WaaS environment. To implement privacy-preserving MAX/MIN queries, we propose a novel query protocol by adopting the idea of secure multiparty computation. The protocol consists of two cooperative query processing algorithms that are deployed in the aggregate sensor and normal sensors. During query processing, multiple rounds of secure interactions between sensors are performed. In each round, one bit of the query result is determined through cooperation of sensors, while the data of sensors participating in query processing remain private. Curious users cannot obtain any private data from the network even if a few compromised sensors collude with them. The analysis and evaluations indicate that the proposed protocol computes query results reliably, avoids the energy hole problem and is efficient in terms of communication cost.
Genetic Algorithms

Genetic algorithm, a very simple but very powerful stochastic global optimizer, has been applied to many fields in search and optimization. This capture study aims to provide overall information for a genetic algorithms user to choose the most appropriate scheme for his or her specific application problem for his or her specific application problem. In this section, we mainly address three well-known genetic algorithms, namely, artificial bee colony, particle swarm optimization, and differential evolution. The evolution mechanism, current research status, and applications of different genetic algorithm have been investigated in detail for the users to choose the most appropriate strategy.
DocEng'2020 Competition on Extractive Text Summarization

The DocEng'2020 Competition on Extractive Text Summarization assessed the performance of six new methods and fourteen classical algorithms for extractive text sumarization. The systems were evaluated using the CNN-Corpus, the largest test set available today for single document extractive summarization using two different strategies and the ROUGE and the direct match measures.
Intelligent Early Warning Method for Financial Sharing Center Expense Reimbursement Audit

The widespread application of big data and artificial intelligence technology is having a huge impact on the business activities of group companies. Based on the research of financial sharing model, internal audit, audit early warning, and blockchain technology, this article builds a data bridge based on the data of the financial sharing center to achieve off-site audits, comprehensive audits, and prior affairs. audit. This paper constructs expense reimbursement audit data center, and realizes real-time data transmission between the financial sharing center and expense reimbursement audit early warning system through blockchain technology, including expense reimbursement audit data collection, cleaning and desensitization, and storage. Based on the expense reimbursement audit knowledge base, build an expense reimbursement audit expert system. This article uses MECE analysis method to determine the key points of the expense reimbursement audit, and realizes the follow-up feedback of the doubtful points in the expense reimbursement audit. It takes nearly a week for an original payment to be booked, but it has been shortened to about 1 day. Through the research on the audit early warning of the financial sharing center expense reimbursement, it is helpful to improve the utilization efficiency of the financial sharing center's data, and timely early warning of risks.
A Study of Cyber Crime and Perpetration of Cyber Crime in India

The world is facing a new era of criminal activities in cyber space, which are being committed across the world, irrespective of geographical boundaries. These cybercrime acts may be financially driven acts, related to computer content, or against the confidentiality, integrity, and accessibility of computer systems. The relative risk and threat differs between governments and businesses. The level of criminal organization represents a defining feature of the human association element behind criminal conduct. India accounts for close to $8bn of the total $110bn cost of global cyber crime. The Information Technology (IT) Act, 2000, specifies the acts that are punishable. Cyber crime has also affected the social media. A crime prevention plan with clear priorities and targets needs to be established, and government should include permanent guidelines in its programmes and structure for controlling crime and ensuring that clear responsibilities and goals exist within government for the organization of crime prevention. This chapter seeks to find out the motives and suspects of cyber crime perpetration and suggests measures for crime prevention.
DECISION MAKING PROCESS FOR SERVING RESTAURANTS USING INTUITIONISTIC FUZZY SET THEORY VIA CONTROLLED SETS

The Intuitionistic fuzzy set theory gives quite successful results in decision-making processes when compared to other set theories. For this reason, it finds application areas in many areas of daily life such as political science, robotic systems, economic research, medical studies. The success of the IFS concept in decision-making processes in these areas has also been proven. In this study, it is aimed to create an IFS model that can make suggestions to support the end user in the process of choosing a product. Hamming measure will be used to achieve this goal. As per the definition of this measure, the degree of non-membership of the data is as important as the degree of membership. These values also determine the degree of intuition. However, the easier it is to estimate the yield value of a property of an object, the more difficult it is to estimate the non-fulfillment value. For this reason, in this study, the data will be intuitiveized by the controlled set theory and the relationships with the results will be determined. In all these processes The Microsoft SQL Server data structure for coding was used and algorithms were created according to this coding. A healthy evaluation of the data is as important as the value of the data's feature of not having that feature. This situation is the most important factor determining the value of intuition.
Decentralized Finance

We describe the value of decentralized decision-making in the context of the assessment of systemic risks in the financial ecosystem and draw parallels to the lessons learned from the polycentric provision of local public goods. Greater resilience of the ecosystem requires methods of decentralized recruitment and quorum sensing, features that induce rapid optimization without centralized regulation in ecological systems. We describe the potential for recruitment and quorum sensing in two financial applications: securitization and thematic investing. Finally, we consider the potential and pitfalls presented by the use of blockchain and distributed ledgers to specific applications in sustainable finance, including prediction markets, distributed corporate governance, and trade finance.
Audiovisual Materialism

This article looks at the emergence of physical material in a growing number of contemporary sound-based art practices. From academic symposia to music festivals and media art exhibitions, the material presence of physical artefacts is notable in a variety of sound-based disciplines and scenarios. Considering the significance of the visual aspect of these works, this article proposes a reassessment of what audiovisual entails. I argue that our understanding of audiovisual status needs to be expanded beyond the scope of screen-based applications and move into the physical realm of objects and material. Further to this, I outline how the dominant discussions around materiality in sound-based art do not speak sufficiently to the physical materiality manifested in a growing wave within the field. Using an example of my own creative work, I will then suggest audiovisual materialism as an alternative lens through which such practices can be better examined, understood and built upon.
Collaborative and Clustering Based Strategy in Big Data

There is a proliferation in the amount of data generated and its volume, which is going to persevere for many coming years. Big data clustering is the exercise of taking a set of objects and dividing them into groups in such a way that the objects in the same groups are more similar to each other according to a certain set of parameters than to those in other groups. These groups are known as clusters. Cluster analysis is one of the main tasks in the field of data mining and is a commonly used technique for statistical analysis of data. While big data collaborative filtering defined as a technique that filters the information sought by the user and patterns by collaborating multiple data sets such as viewpoints, multiple agents and pre-existing data about the users' behavior stored in matrices. Collaborative filtering is especially required when a huge data set is present.
Designing Interactive Cross-Cultural Mobile-Assisted Language Learning

Using communication technology for learning is a path to new patterns of thinking. This chapter examines the affordances provided by using smartphone technology in English for cross-cultural understanding. University students from Japan and Taiwan used the popular app LINE collaboratively for five weeks, culminating in jointly-written essays. Data collection used a survey, open-ended questions, and analysis of the essays. The students saw English as an important international language in which they need competency, strongly favored use of technology to assist language learning, appreciated the study's cross-cultural experience, and found the dynamics of the group to be interesting and motivating. The authors provide four best practices for using LINE and similar applications for English learning: that they be thought of as communication tools, that students need strong support concerning required tasks, that students must understand how tasks using technology benefit them, and that true online communities may include multiple platforms.
VFAT: A Personalized HAR Scheme Through Exploiting Virtual Feature Adaptation Based on Transfer Learning

Recently, on one hand, human activity recognition (HAR) has witnessed great application on portable smart devices (e.g., smart phones and wearables, etc.) as they are widely used around the world. On the other hand, HAR methods based on deep learning have attracted much attention, for they possess excellent performance due to their strength on extracting virtual features automatically and hierarchically. However, to establish a personalized deep learning based HAR scheme based on smart devices, insufficient records from target users and heavy computation cost on training from scratch are two challenges. Considering that, in transfer learning, the knowledge learnt in the source domain could be appropriately transferred to help accomplish tasks in the target domain, this paper proposes a personalized HAR scheme through exploiting virtual feature adaptation based on transfer learning (i.e., VFAT) to achieve high recognition accuracy with low computation time. VFAT is composed of pre-training phase on sufficient labeled records in source-domain, and adaption phase on target-domain that uses the few labeled records available. Specifically, VFAT scheme pre-trains the LSTM-based feature extraction component in the pre-training phase and then introduces domain loss in the adaptation phase to minimize the similarity between target-domain virtual features and source-domain activity patterns (i.e., virtual features averaged by activity labels). The HAR scheme applied to the MotionSense dataset and results demonstrate the effectiveness of our proposed VFAT scheme. Moreover, we also investigate the impact of domain division on the performance of transfer learning based HAR.
Tableau-based translation from first-order logic to modal logic

We define a procedure for translating a given first-order formula to an equivalent modal formula, if one exists, by using tableau-based bisimulation invariance test. A previously developed tableau procedure tests bisimulation invariance of a given first-order formula, and therefore tests whether that formula is equivalent to the standard translation of some modal formula. Using a closed tableau as the starting point, we show how an equivalent modal formula can be effectively obtained.
CapsNets algorithm

Abstract Capsule networks (CapsNets) is a commonly used neural network structure, especially in computer vision research., which may have a significant impact on deep learning. Convolutional neural networks (CNNs) have reached superhuman level in various computer classification tasks such as classification, parsing, object detection, semantic segmentation, and instance segmentation. Traditional deep learning tends to deteriorate when the relevance of detection objects changes. For this kind of situation, this paper improves a new network, namely the capsule network(CapsNets), the network can achieve a higher detection rate in the hostile environment.
Underwater Image Quality Evaluation based on Human Visual System

With increasing attention to underwater observation and utilization of marine resources, people's demand for underwater vision continues to increase. The processing of underwater images has received more and more attention. Underwater image quality evaluation plays a crucial role in underwater image processing technology and is also an important standard for underwater image enhancement. In this paper, we proposed a new no-reference underwater image quality evaluation method, which selects three indexes related to the human visual system: chroma, definition, and contrast. The chroma is calculated by saturation and the ratio of red channels to green and blue channels; the definition is calculated through the maximum gradient value and the detail content of the image; the contrast is calculated by the standard deviation of the image pixel value. The new underwater image evaluation index uses a linear combination of three attribute values. Experimental results show that this method can effectively evaluate the quality of underwater images based on the perception of human eyes.
Transforming Web data into knowledge: Implications for management

Much of one's online behavior, including browsing, shopping, posting, is recorded in databases on companies' computers on a daily basis. Those data sets are referred to as Web data. The patterns which are the indicators of one's interests, habits, preferences or behaviors are stored within those data. More useful than an individual indicator is when a company records data on all its users and when it gains an insight into their habits and tendencies. Detecting and interpreting such patterns can help managers to make informed decisions and serve their customers better. Utilizing data mining with respect to Web data is said to turn them into Web knowledge. The research study conducted in this paper demonstrates how data mining methods and models can be applied to the Web-based forms of data, on the one hand, and what the implications of uncovering patterns in Web content, the structure and their usage are for management.
Impact of Georegistration Accuracy on Wide Area Motion Imagery Object Detection and Tracking

Advances in sensor technologies and embedded low-power processing provide new opportunities for using Wide Area Motion Imagery (WAMI) across a spectrum of mapping and monitoring applications covering large geospatial areas for extended time periods. While significant developments have been made in video analytics for ground or low-altitude aerial videos, methods for WAMI have been limited due to lack of benchmarking datasets, data format complexities, lack of labeled training videos, and high data processing requirements. This paper aims to help advance the broader use of WAMI by evaluating the georegistration accuracy and its impact on downstream video analytics using two benchmark datasets (CLIF 2007, ABQ 2013). In addition to the current intensified interest in using deep learning for aerial object recognition and tracking, this paper motivates the need for further development of more robust and fast georegistration algorithms for multi-camera WAMI systems.
Earthquake Seismology

Earthquakes have a strong impact on society, producing casualties and extended damage to buildings and infrastructures. Understanding how the earthquake machine works, from its generation, to propagation and arrest, is thus fundamental for Earth Science. Here we present the state-of-the-art knowledge about earthquakes; we move from recent techniques to infer earthquake physical parameters from observations to actual findings about the nature and origin of seismic events. The improvement in earthquake knowledge over recent years is grounded on the increasing capacity of observation systems. These systems are based on surface and borehole sensors, resulting in dense arrays, and they can deliver multi-scale, high-resolution data that allow details of large earthquakes and source properties of microseismicity to be determined. In this respect, we illustrate here the models that explain how a fault slips during large events, how fast a seismic rupture progresses and what are the mechanisms for its arrest. We also examine, by simulation approaches, how geological structures control earthquake ruptures. Though being the main interest of seismology, large events are rare; to complement the view on earthquake faulting seismologists investigate microseismicity based on the evidence of earthquake self-similarity over many orders of magnitude. We discuss here how source properties of small earthquakes contribute to an understanding of the role of fluids, the geometry and mechanics of fault systems, and the preparatory phase of larger magnitude events. We conclude the paper by presenting how these methods are tuned for real-time applications, such as earthquake early warning.
Adversarial Attacks on Faster R-CNN: Design and Ablation Study

Most adversarial attack methods focus on image classifiers rather than the other more common models deployed in security-critical applications. This work focuses on the design of the attack on the object detection model, Faster Region-based Convolutional Neural Network (Faster R-CNN). To this end, we first formulate adversarial attacks against Faster R-CNN as a multi-objective optimisation problem. Second, we apply Projected Gradient Descent (PGD) to solve the defined optimisation problem. In addition, we conduct an ablation study on the loss terms of the adversarial attacks’ objective function. Finally, the proposed attack is evaluated on the object detection task MSCOCO2017. Our experimental results show that attacking the Fast R-CNN submodule or attacking both Fast R-CNN and backbone network submodules leads to the best attack results at our widely selected evaluation metrics, and our best attack can reduce the detection accuracy mAP@[.5, .95] by 17.4%.
Nigeria: Selected Issues

SECTOR AND AML/CFT 1Accountability, governance, and transparency in the state-owned enterprise (SOE) sector facilitate the efficient use of public resources and help to monitor fiscal risks.Reducing leakages in the petroleum sector is especially macroeconomically critical, given Nigeria's current fiscal and external dependence on oil revenue.Against this background, this paper provides an overview of developments, recent reforms, and challenges, and outlines policy recommendations for stronger governance and corruption prevention, detection, and resolution, including through anti-money laundering and combating the financing of terrorism measures that are useful beyond the petroleum sector.