Reviewerly/exHarmony huggingface dataset:
Team Recommendation Using Order-Based Fuzzy Integral and NSGA-II in StarCraft

As one of the well-known real-time strategy games, StarCraft has become an important benchmark for game artificial intelligence research.Previous works in StarCraft mostly focused on strategic planning and tactical reasoning.One of the key issues in strategic planning, namely unit selection, is to build up an army, so called team in this article, with appropriate units which can gain massive destroy power against enemy's army.It is still a challenge to determine a team that has a good chance of defeating a specified army and there is no any formal algorithm solving it directly at present.Considering that the number of each unit will change during a game, if the player encounters the same enemy for multiple times, we need to select multiple winning troops, which will give the player a number of choices so as to increase the winning chance.In this article, we formulate the team recommendation as a multi-objective optimization problem and propose a novel team recommendation algorithm to solve the problem.We add a normalization of the team size to the order-based fuzzy integral and the normalized order-based fuzzy integral can better estimate the relative combat power of a team.We use genetic algorithm (GA) to learn the fuzzy measure in the fuzzy integral from the StarCraft replay data and adopt Non-Dominated Sorting Genetic Algorithm (NSGA-II) combined with the fuzzy integral for team recommendation.Finally we use a simulator called SparCraft to examine the new algorithm.The experimental results show that our proposed algorithm can recommend winning teams with a high accuracy for ordinary units in StarCraft, and the sizes of recommended teams are mostly not larger than the size of the enemy's team.
DBSCAN Algorithm for Document Clustering

Abstract Document clustering is a problem of automatically grouping similar document into categories based on some similarity metrics. Almost all available data, usually on the web, are unclassified so we need powerful clustering algorithms that work with these types of data. All common search engines return a list of pages relevant to the user query. This list needs to be generated fast and as correct as possible. For this type of problems, because the web pages are unclassified, we need powerful clustering algorithms. In this paper we present a clustering algorithm called DBSCAN – Density-Based Spatial Clustering of Applications with Noise – and its limitations on documents (or web pages) clustering. Documents are represented using the “bag-of-words” representation (word occurrence frequency). For this type o representation usually a lot of algorithms fail. In this paper we use Information Gain as feature selection method and evaluate the DBSCAN algorithm by its capacity to integrate in the clusters all the samples from the dataset.
Data behind mobile behavioural biometrics – a survey

Behavioural biometrics is becoming more and more popular. It is hard to find a sensor that is embedded in a mobile/wearable device, which cannot be exploited to extract behavioural biometric data. In this study, the authors give the reader an overview of mobile device behavioural biometric data and how this data is used in experiments, especially examining papers that introduce new datasets. They will not examine performance accomplished by the algorithms used since a system's performance is enormously affected by the data used, its amount and quality. Altogether, 40 papers are examined, assessing how often they are cited, have databases published, what modality data are collected, and how the data is used. They offer a roadmap that should be taken into account when designing behavioural data collection and using collected data. They further look at the General Data Protection Regulation, and its significance to the scientific research in the field of biometrics. It is possible to conclude that there is a need for publicly available datasets with comprehensive experimental protocols, similarly established in facial recognition.
India invests heavily into quantum computing

India has announced $1.12bn over five years towards a national programme for quantum technology and applications. The measures were presented by India’s finance minister Nirmala Sitharaman to parliament on 1 February as part of the country’s 2020–2021 budget.
Short time prediction of cloud server round-trip time using a hybrid neuro-fuzzy network

The paper presents a cloud server roundtrip time prediction approach for cloud datacenters using neuro-fuzzy network with eight probability distribution functions (Normal, Rayleigh, Weibull, Gamma, Birnbaum-Saunders, Extreme Value, and Generalized Pareto) used for fuzzification and defuzzification. We predict the Round-Trip Time (RTT), i.e., the time for a network packet to travel from a client to a server and back. The proposed approach can achieve significant reduction in the short-time RTT prediction error, achieving an accuracy of 79.36%. The approach could be useful for increasing the efficiency of client-cloud systems, for example, when taking effective decisions for computational offloading, and contribute to the development of smart cloud computing.
Adaptive merging of large datasets of a 3D measuring endoscope in an industrial environment

A 3D measuring endoscope with a small measuring head and parallel arrangement of the fibers can be guided into forming plants and carry out precise measurements of geometries which are unreachable for most three-dimensional measuring systems. The data obtained can be used to quantify the wear of highly stressed structures and thus provide information for maintenance. Due to the compact sensor design and the required accuracy, optics with small working distance and a small measuring volume are used. In addition to in situ single measurements of highly stressed structures, over a hundred individual measurements are conceivable in order to convert large and complex geometries into point clouds. Besides the robust and accurate registration of all measurements, merging is one of the main causes of inaccurate measurement results. Conventional merging algorithms merge all points within a voxel into a single point. Due to the large overlap areas required for registration, points of diverse quality are averaged. In order to perform an improved adaptive merging, it is necessary to define metrics that robustly identify only the good points in the overlapping areas. On the one hand, the 2D camera sensor data can be used to estimate signal-based the quality of each point measured. Furthermore, the 3D features from the camera and projector calibration can evaluate the calibration of a triangulated point. Finally, the uniformity of the point cloud can also be used as a metric. Multiple measurements on features of a calibrated microcontour standard were used to determine which metrics provide the best possible merging.
Low-Latency Lightweight Streaming Speech Recognition with 8-Bit Quantized Simple Gated Convolutional Neural Networks

Automatic speech recognition (ASR) is very important for mobile devices. However, deep neural network-based ASR demands a large number of computations, while the memory bandwidth and battery capacity of mobile devices are limited. Server-based implementations are mostly employed, but this increases latency or privacy concerns. Efficient on-device ASR is the solution for these issues. In this paper, we propose a low-latency on-device speech recognition system with a simple gated convolutional network (SGCN). The SGCN shows a competitive recognition accuracy even with 1M parameters. In addition, SGCN is advantageous for parallelization which enables efficient cache utilization. 8-bit quantization is applied to reduce the memory size and computation time. The proposed system features online recognition fulfilling the 0.4s latency limit and operates with the real-time factor of 0.2 using only a single 900MHz CPU core. The system occupying 1.2MB memory footprint shows 19.75% word error rate (WER) with greedy decoding.
Optimal Bandwidth Allocation for Web Crawler Systems

Web crawler is an important tool to obtain the information from the Internet in time. In a typical web crawler system with the limited bandwidth, there are many websites required to be crawled with different time constraints. Existing works about web crawler systems have not considered the bandwidth allocation in such a complex environment and hence the time constrains may not be properly satisfied. In this study, we investigate the bandwidth allocation approaches for such a web crawler system. The approaches are designed for both the scenarios that the sequences of crawling websites can not be changed and the scenarios that can. For the former scenario, we propose approaches to control bandwidth for web crawlers to minimize the maximum complete time or minimize the sum of complete times of all web crawlers. For the latter scenario, we propose a greedy algorithm based on crawling priorities, and a optimal algorithm based on mixed integer programming. Extensive simulations are conducted for validating the proposed approaches, and the result show that our approaches achieve desirable performance.
Validating of Digital Forensic Images Using SHA-256

Volumes of digital images are produced per time, whiles these digital images may be for friendly and or social activities they can be manipulated for illegal purposes. Sensitive digital images have been a core part of security correspondences between sensitive institutions. Such sensitive digital images security has been a prime concern. The advancement in technology has made it easy for unauthorized persons to manipulate and illegally use sensitive digital images that are been acquired, processed, compressed, stored, broadcasted, and reproduced. This has necessitated the authentication of digital images. In this paper we propose SHA - 256 to validate digital forensic images.
A Memetic Artificial Bee Colony Algorithm for High Dimensional Problems

This work proposed a memetic version of Artificial Bee Colony algorithm, or called LSABC, which employed a “shrinking” local search strategy. By gradually shrinking the local search space along with the optimization process, the proposed LSABC algorithm randomly explores a large space in the early run time. This helps to avoid premature convergence. Then in the later evolution process, the LSABC finely exploits a small region around the current best solution to achieve a more accurate output value. The optimization behavior of the LSABC algorithm was studied and analyzed in the work. Compared with the classic ABC and several other state-of-the-art optimization algorithms, the LSABC shows a better performance in terms of convergence rate and quality of results for high-dimensional problems.
Transfer Learning Based Kernel Fuzzy Clustering

Transfer Learning is utilized to the traditional clustering methods to enhance the clustering effect in the target domain with insufficient data. These methods can obtain good performance in handling the linear data. But for the `non-linear' data, they cannot perform effectively. To solve this issue, this paper proposes a novel transfer learning based kernel fuzzy clustering algorithm. In this method, the kernel clustering is first performed to the source data to achieve the important knowledge, i.e. cluster prototypes. Then these cluster prototypes are transferred to guide the data clustering in the target domain. Experiments conducted on the `non-linear' data prove the superiority and efficiency of the proposed method.
Reliability Maximization in Uncertain Graphs

Network reliability measures the probability that a target node is reachable from a source node in an uncertain graph, i.e., a graph where every edge is associated with a probability of existence. In this paper, we investigate the novel and fundamental problem of adding a small number of edges in the uncertain network for maximizing the reliability between a given pair of nodes. We study the <inline-formula><tex-math notation="LaTeX">$\mathbf {NP}$</tex-math></inline-formula> -hardness and the approximation hardness of our problem, and design effective, scalable solutions. Furthermore, we consider extended versions of our problem (e.g., multiple source and target nodes can be provided as input) to support and demonstrate a wider family of queries and applications, including sensor network reliability maximization and social influence maximization. Experimental results validate the effectiveness and efficiency of the proposed algorithms.
Distribution Tableaux, Distribution Models

The concept of distribution is a concept within traditional logic that has been fundamental for the syntactic development of Sommers and Englebretsen’s term functor logic, a logic that recovers the term syntax of traditional logic. The issue here, however, is that the semantic counterpart of distribution for this logic is still in the making. Consequently, given this disparity between syntax and semantics, in this contribution we adapt some ideas of term functor logic tableaux to develop models of distribution, thus providing some alternative formal semantics to help close this breach.
SPARC: SODA, an interactive software for curating SPARC datasets

In this era of data-intensive research, data sharing and reuse are becoming increasingly important for rapid discovery and innovation. While sharing can be as easy as a few clicks thanks to numerous data sharing platforms, data reuse is still an issue as standards for data curation and annotation are not rigorously or consistently followed by all researchers. As a result, it is often difficult, if not impossible, for researchers to understand and work with data they were not involved with during collection or analysis. To address this concern, the NIH SPARC program has imposed strict guidelines for curating and sharing data resulting from SPARC-funded research. Complying with the guidelines requires additional time investment from the researchers (as with any data curation and submission standards), and the data curation process could thus become overwhelming as more and more data is generated. The objective of this project was to develop computer software to improve the curation workflow for SPARC researchers. To achieve that, we are developing a software named SODA (Software for Organizing Data Automatically) to assist SPARC investigators in curating and annotating their datasets. Distributed as an open-source desktop application (Windows, MAC, Linux), the goal of SODA is to bridge a long-standing and overlooked gap between comprehensive data standards and their convenient application by researchers. SODA provides an interactive interface that, without requiring any coding knowledge, walks SPARC investigators step-by-step through the data organization and sharing requirements, while automating repetitive, complex and/or time-consuming tasks (Fig 1). An introduction to the software and the algorithms implemented will be given. Quantitative results from ongoing performance evaluation (time to curate a SPARC dataset with and without SODA) by beta testers, all of whom receive funding from the SPARC program, will be presented as well. Overall, the beta testers for early versions have acknowledged that SODA has the potential to improve their curation workflow. Data curation is becoming a critical issue, even beyond the SPARC consortium, and SODA could impact the broader research community by providing an exemplar and foundational tool for convenient and time-efficient curation of data, which could be adopted by other projects. Potential applications, beyond the SPARC program, will be thus discussed as well. Support or Funding Information SPARC OT2OD025308-01S2 Figure 1Open in figure viewerPowerPoint Overview of the major features included in SODA during the on-going development phase
Smart Contract Locator (SCL) and Smart Contract Description Language (SCDL)

Today’s blockchain technologies focus mostly on isolated, proprietary technologies, yet there are application scenarios that ask for interoperability, e.g., among blockchains themselves or with external applications. This paper proposes the Smart Contract Locator (SCL) for the unambiguous identification of smart contracts over the Internet and across blockchains, and the Smart Contract Description Language (SCDL) for the abstract description of the external interface of smart contracts. The paper derives a unified metamodel for blockchain smart contract description and equips it with a concrete, JSON-based description language for smart contract search and discovery. The goal of the proposal is to foster smart contract reuse both inside blockchains and through the integration of smart contracts inside enterprise applications. The idea is inspired by the Service-Oriented Architecture (SOA) and aims to provide a high-level, cross-blockchain interoperability layer.
Trusting Security When Sharing Knowledge?

This chapter tackles knowledge sharing by focusing on security and trust issues. Although trust is recognized as important in security issues, few studies on information systems (ISs) deal with both trust and security. Knowledge sharing relies on sense-giving and sense-reading processes which require, encourage, and even create trust within individuals. We argue that individuals are processors of information and interpret information to create their own tacit knowledge. Recent security reports from organizations have presented that the majority of ISs security threats involve employees within the organizations. Individuals, as well as computers, maybe attacked through social engineering techniques in order to gain their trust. Despite this evidence, most of the work has focused on the control of outsider security threats rather than of insider security threats, particularly when humans are perpetrators. We propose to study insider threats through a trust factor during the knowledge sharing process. Knowledge sharers may induce insider threats for security due to trust-related attitudes and behaviours. The proposition is twofold with interviews and self-report questionnaires to collect information about the trust, and ontologies to categorize such information. The proposition is then discussed, notably in terms of problems and answers leading to study trust in security when sharing knowledge.
Consumer perception towards corporate social responsibility practices: a study of the Malaysian banking sector

Organisations can gain enormous benefits when they are perceived as being socially responsible to their stakeholders. One of the important stakeholder groups that appear to be particularly susceptible to the banks' Corporate Social Responsibility (CSR) initiatives is their consumers. Thus, this research aims to investigate how banks' customers perceived CSR practices in the banking industry in Malaysia. Administering through a Google-form survey instrument, supplemented with a created link via Facebook and WhatsApp, a total of 240 responses were collected, resulting in a response rate of 80%. Data collected were analysed using statistical tools such as descriptive statistics, correlations and multiple regressions mainly on the predictive power of the constructs in this study. The notable results indicated 'consumer awareness' tended to be positively correlated to CSR practice and it was also one of the most important constructs contributing to the prediction of CSR with the exception of 'knowledge of consumers' and 'purchasing decision'. This suggests that bank managers need to take seriously 'consumer awareness' into the next level of analysis, in which social media has an essential role to play in disseminating important information relating to CSR practices and implementation, including the use of mobile learning as an educative tool.
δ-calculus: A New Approach to Quantifying Location Privacy☆

With the rapid development of mobile wireless Internet and high-precision localization devices, location-based services (LBS) bring more convenience for people over recent years. In LBS, if the original location data are ... | Find, read and cite all the research you need on Tech Science Press
SoRecGAT: Leveraging Graph Attention Mechanism for Top-N Social Recommendation

Social recommendation systems typically combine extra information like a social network with the user-item interaction network in order to alleviate data sparsity issues. This also helps in making more accurate and personalized recommendations. However, most of the existing systems work under the assumption that all socially connected users have equal influence on each other in a social network, which is not true in practice. Further, estimating the quantum of influence that exists among entities in a user-item interaction network is essential when only implicit ratings are available. This has been ignored even in many recent state-of-the-art models such as SAMN (Social Attentional Memory Network) and DeepSoR (Deep neural network model on Social Relations). Many a time, capturing a complex relationship between the entities (users/items) is essential to boost the performance of a recommendation system. We address these limitations by proposing a novel neural network model, SoRecGAT, which employs multi-head and multi-layer graph attention mechanism. The attention mechanism helps the model learn the influence of entities on each other more accurately. The proposed model also takes care of heterogeneity among the entities seamlessly. SoRecGAT is a general approach and we also validate its suitability when information in the form of a network of co-purchased items is available. Empirical results on eight real-world datasets demonstrate that the proposed model outperforms state-of-the-art models.
Safety Framework Based on Blockchain for Intelligent Manufacturing Cyber Physical System

With the development of intelligent manufacturing CPS, the traditional security architecture has been unable to meet its common requirements for functional security and information security. In this paper, based on the decentralization, security and trustworthiness of blockchain, a block-based intelligent manufacturing CPS safety protection framework is proposed, and the concrete structure of the framework at the basic level and integration level is introduced. Finally, the safety of the framework is demonstrated from five dimensions for the information security features and functional security features. This research provides new research ideas for building a safer and more robust intelligent manufacturing CPS.
Understanding and Using Common Similarity Measures for Text Analysis

This lesson introduces three common measures for determining how similar texts are to one another: city block distance, Euclidean distance, and cosine distance. You will learn the general principles behind similarity, the different advantages of these measures, and how to calculate each of them using the SciPy Python library.
Design and Control of a Passive Solar Tracking System Using a Sky Imager

Solar energy is considered as one of the most important sources of renewable energy due to the possibility of being converted into electrical power, which could result in a feasible approach to global energy problems. Concentrated Solar Power (CSP) and High Concentration Photovoltaics (HCPV) systems present an alternative by the use of cost-efficient concentrating optics. However, these systems require a high-precision solar tracking system, allowing to stay perpendicular to the sun rays in order to achieve its correct functioning. In this work, a methodology for the detection and monitoring of the apparent movement of the sun through a passive image acquisition system is presented. The setup operates by processing sky dome images to find the solar-center. The development cost of this device is relatively low, it does not require articulated elements, can detect the presence of clouds, and could be compatible with multiple CSP systems. Preliminary results of a two-axis solar tracking by locating position of the sun in the sky with the proposed system are depicted.