Reviewerly/exHarmony huggingface dataset:
Association Rules-Based Gray Relational Approach for E-Commerce Recommender System

Recommender systems cannot provide healthy results in case of similar products that cannot be identified in e-commerce sites. Insufficient information about users or items is one of the most crucial problems, especially with adding new users or products. The inability to perform relational analysis in the system is due to insufficient data. In this case, the system cannot recommend or bring the non-related items to the users. This chapter suggests the gray relational approach to identify more healthy recommendation lists when there are few relational items. The data was obtained from an e-commerce company and apriori algorithm was applied to the dataset that a randomly chosen user purchased. Gray relational analysis was applied for the most suitable recommendation by using support, confidence, number of likes, adding favorite, deleting from basket, and return information of the products in the dataset. In addition, the most appropriate product sequencing of the recommendation list was realized by gray relational degrees.
Knowledge Discovery From Evolving Data Streams

Data coming from different sources is referred to as data streams. Data stream mining is an online learning technique where each data point must be processed as the data arrives and discarded as the processing is completed. Progress of technologies has resulted in the monitoring these data streams in real time. Data streams has created many new challenges to the researchers in real time. The main features of this type of data are they are fast flowing, large amounts of data which are continuous and growing in nature, and characteristics of data might change in course of time which is termed as concept drift. This chapter addresses the problems in mining data streams with concept drift. Due to which, isolating the correct literature would be a grueling task for researchers and practitioners. This chapter tries to provide a solution as it would be an amalgamation of all techniques used for data stream mining with concept drift.
Establishment and Application of Evaluation Index System of Soil Salinization in Plain Reservoir: An Example for Shifosi Reservoir

Plain reservoir surrounding soil salinization assessment method for the small scope of application, low evaluation precision of the common problems proposed for underground water level elevation, soil water salinity, evaporation and precipitation ratio were used as evaluation factors of the soil salinization assessment index system. Shifosi Reservoir, as an example, in the field water test obtained evaluation parameters based on using AHP fuzzy comprehensive evaluation method of the reservoir after impoundment and the degree of soil salinization was evaluated. The results show that the most serious salinization area is in the left bank of Shifosi Reservoir, after the impoundment of Shifosi reservoir; and closer to the main salinization is more serious, and away from the main dam area the salinization degree is gradually reduced. The right bank of the terrain is relatively low in soil salinization degree due to a serious problem in most parts; less parts presented mild salinization. According to the result of the evaluation, the measures of prevention and control of the salinization of precipitation and water supply were determined, and the rational development and utilization of water resources were realized. This study provides a new idea for the evaluation of soil salinization and provides a scientific basis for the control and prevention of environmental geological hazards in the reservoir area.
Introducing Cuts Into a Top-Down Process for Checking Tree Inclusion

By the ordered tree inclusion we will check whether a pattern tree P can be included in a target tree T, where the order of siblings in both Pand T matters. This problem has many applications in practice, such as retrieval of documents, data mining, and RNA structure matching. In this paper, we propose an efficient algorithm for this problem. Its time complexity is bounded by O(|T| · min{h <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</sub> ; |leaves(P)|}), with O(|T| + |P|) space being used, where h <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</sub> (h <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sub> ) represents the height of P (resp., T) and leaves (P) stands for the set of the leaves of P. Up to now the best algorithm for this problem needs Θ(|T| · |leaves(P)|) time and O(|P| + |T|) space. Extensive experiments have been done, which show that the new algorithm can perform much better than the existing ones in practice.
Two-stage iterative Procrustes match algorithm and its application for VQ-based verification

In the past decades, Vector Quantization (VQ) model has been very popular across different pattern recognition areas, especially for feature-based tasks. However, the classification or regression performance of VQ-based systems always confronts the feature mismatch problem, which will heavily affect the performance of them. In this paper, we propose a two-stage iterative Procrustes algorithm (TIPM) to address the feature mismatch problem for VQ-based applications. At the first stage, the algorithm will remove mismatched feature vector pairs for a pair of input feature sets. Then, the second stage will collect those correct matched feature pairs that were discarded during the first stage. To evaluate the effectiveness of the proposed TIPM algorithm, speaker verification is used as the case study in this paper. The experiments were conducted on the TIMIT database and the results show that TIPM can improve VQ-based speaker verification performance clean condition and all noisy conditions.
Agent-based Turing Complete Transactions integrating feedback within a Blockchain System

This system relates generally to blockchain implementations and is suited for, but not limited to, use with the Bitcoin blockchain. It can be used for the implementation of automated processes such as device/system control, process control, distributed computing and storage and others. The system provides a solution which uses a blockchain to control a process executing on a computing resource. In a preferred implementation, the computing re-source, running simultaneously and in parallel to the blockchain, manages a loop-based operation. The computing resource continuously monitors the state of the blockchain as well as any other off-blockchain input data or source. The execution of the loop is influenced by the state of the block-chain. Each iteration of the loop that is executed by the computing resource is recorded in a transaction that is written to the blockchain. It is stored as a hash within the transaction's metadata. If the computing resource finds a transaction which contains a hash relating to the loop it accesses the relevant portion of code. The loop contains a conditional statement which enables the computing resource to decide which action to take. The condition may be dependent upon the state of the blockchain or any other data source. The action can be any type of action, on or off the blockchain. Thus, the combi-nation of the computing resource and blockchain provide a solution which is (at least partially) Turing-complete.
Appraisal of Cloud Multi-Tenant Database Infrastructure Usability in Public-Safety and Emergency Delivery Services

In this era of multiplicity of security challenges, information technology (IT) techniques and infrastructures could enhance public safety services. Cloud computing models can serve hundreds of thousands of customers using sharable resource (database) to reduce costs and offer performance. With these models, organizations no longer have to buy, build and manage costly computing resources to provide for security via public safety networking. IaaS and SaaS models made IT more affordable than Traditional IT. Data activities (processing, storage, management) and collaboration over ‘encapsulated’ Internet environment is enabled for optimal performance. This study critically appraises multi-tenant database as IT infrastructure and/or software for public safety networking where robust, fail-safe mission critical communication deliveries are expedient. Emergency situations and daily routine of public safety agencies require more broadband services for life-saving information to be securely/reliably transmitted between control centers and field responders using separate databases. Through virtualization, database is separated from infrastructure but is also conceptualized as infrastructure for users/customers to benefit as tenants and multi-tenants (MTs) in data centres. Using decision tree and Bayesian classifier models, database is classified into public utility (PU) or emergency need (EN) servicing two-defined customer segments: low-workload and high-workload tenants. A confusion matrix detailed the service requirement instances’ analysis of MT infrastructure for performance while the research concludes by iterating MT database excellence for collaborative work. Cloud concept, IaaS and SaaS as power station and core modules leverage MT database as infrastructure and software to increase usability and provide trade-off between storage-space (cost) and performance. It helps overcome challenges of continuous changing and higher-frequency data-refreshing needs of both emergent and autonomous computing.
Exploring Multiple Dynamic Social Networks in Computer-Mediated Communications

This chapter discusses concepts and tools for the exploration and visualization of computer-mediated communication (CMC), especially communication involving multiple users and taking place asynchronously. The work presented here is based on experimentally validated social networks (SN) extraction methods and consists of a diverse number of techniques for conveying the data to a business analyst. The chapter explores a large number of contexts ranging from direct social network graphs to more complex geographical, hierarchical, and conversation-centric approaches. User validation studies were conducted for the most representative techniques, centered both on extracting and on conveying of CMC data. The chapter examines methods for automatically extracting social networks, which is determining who is communicating with whom across different CMC channels. Beyond the network, the chapter focuses on the end-user discovery of topics and on integrating those with geographical, hierarchical, and user data. User-centric, interactive visualizations are presented from a functional perspective.
Reader Comment Digest through Latent Event Facets and News Specificity

When a significant event occurs, many news articles from different newsagents often report it. Moreover, these newsagents also provide platforms for their readers to write comments expressing their views or understanding. Through digesting these reader comments, we can gain insights into the reactions, suggestions, personal experiences, or public opinions with respect to the emerging event. However, these reader comments from different sources are often rapidly accumulated resulting in an enormous volume. It becomes difficult to manually analyze these comments. In this paper, we propose a framework that can digest reader comments automatically through latent event facets and news specificity. An event facet refers to the aspect of the event concerned by many readers. Specifically, some of the reader comments, despite coming from different sources, discuss a certain facet of the event. Such facets provide an effective means for organizing news comments in a global manner. On the other hand, some comments discuss the specific topic of the corresponding news article. These specific topics demonstrate the specific focus of readers on the piece of news locally. Such reader comment digest in different granularities facilitates readers deeper understanding of these enormous comments. To achieve the above desirable goal of digesting reader comments, we propose an unsupervised model called EFNS which is capable of capturing the intricate fine-grained associations among events, news, and comments. We also develop a multiplicative-update method to infer the parameters and prove the convergence of our algorithm. Our framework can also visualize reader comments according to the relationship with latent event facets and the degree of news specificity. Experimental results show that our proposed EFNS model can provide an effective way to digest news reader comments and outperform the state-of-the-art method.
To Minimize Fault Report and Bug Fixing Time using an Efficient Integration of Instance and Aspect Preferment Algorithm

Background: Data mining is an emerged and promising technology, and it utilized in the software engineering development process. It does not only enhance the accurateness, it also improves the reliability of the software. A software development application error is a fault or bug in a computer program. It produces wrong or unexpected outcomes. In traditional software development, faults manually triaged through a specialist developer, i.e., a human being triaged. Methods: Manual fault triage takes long time and produce low accuracy for the huge amount of faults. To resolve above issues, An Efficient Integration of Instance and Aspect Preferment Algorithm (EIIAPA) is proposed to decrease the scale of fault report data concurrently and to enhance the accurateness of data. Results &amp; Conclusion: The proposed technique helps to validate &amp; verify software application in effective way. Reduction of data on fault triage aims to construct a high-superiority set of fault data in the small-scale system through eliminating the fault report. To applying an algorithm, fault data set and attributes are extracted from every fault data set and train a predictive model based on the historical dataset. Based on Experimental evaluations, proposed methodology reduces 0.06 ET (Execution Time), and improves 0.5 P (Precision), 0.75 R (Recall), 0.39 F-M (F-Measure) and 5.07% (accuracy) compared than existing methodologies.
Adaptive target tracking with a mixed team of static and mobile guards: deployment and activation strategies

This work explores a variation of the art gallery problem in which a team of static and mobile guards track a mobile intruder with unknown maximum speed. We consider the special case when the mobile guards are restricted to move along the diagonals of a polygonal environment. First, we present an algorithm to identify candidate vertices in a polygon at which either static guards can be placed or they can serve as an endpoint of the segment on which mobile guards move. Next, we present a technique to partition the environment based on the triangulation of the environment, and allocate guards to each partition to track the intruder. The allocation strategy leads to a classification of the mobile guards based on their task and coordination requirements. Finally, we present a strategy to activate/deactivate static guards based on the speed of the intruder. Simulation results are presented to validate the efficacy of the proposed techniques.
Hybrid temporal situation calculus

We extend Reiter's temporal situation calculus by introducing continuous change due to passage of time in addition to discrete change due to actions. We define regression for hybrid action theories and show that hybrid action theories can capture hybrid automata.
Tunable Neuron With PWL Approximation Based on the Minimum Operator

In this brief, hardware implementation of a novel tunable neuron is proposed based on choosing the minimum operator to generate the piecewise linear approximation of the neuron's transfer function. The proposed structure is compatible with analog and mixed-signal neural networks. An accurate model of the sigmoid function is developed by using minimum operator. Moreover, if required, the activation function of the neuron can be adjusted during the training to provide various maximal slope sigmoid, hard-limit, and linear functions. The shape and the slope of the transfer function can be controlled by a 2-bit digital to analog converter. This allows the neuron to have different transfer functions based on the network requirements and during the training.
An Efficient Match Search Approach Using Two-Dimensional Hash Function in Hardware-Based Dictionary Compression

The hardware-based dictionary compression is widely adopted for high speed requirement of real-time data processing. Hash function helps to manage large dictionary to improve compression ratio but is prone to collisions, so some phrases in match search result are not true matches. This paper presents a novel match search approach called dual chaining hash refining, which can improve the efficiency of match search. From the experimental results, our method showed obvious advantage in compression speed compared with other approach that utilizes single hash function described in the previous publications.
Integrating selection-based aspect sentiment and preference knowledge for social recommender systems

Purpose Recommender system approaches such as collaborative and content-based filtering rely on user ratings and product descriptions to recommend products. More recently, recommender system research has focussed on exploiting knowledge from user-generated content such as product reviews to enhance recommendation performance. The purpose of this paper is to show that the performance of a recommender system can be enhanced by integrating explicit knowledge extracted from product reviews with implicit knowledge extracted from analysis of consumer’s purchase behaviour. Design/methodology/approach The authors introduce a sentiment and preference-guided strategy for product recommendation by integrating not only explicit, user-generated and sentiment-rich content but also implicit knowledge gleaned from users’ product purchase preferences. Integration of both of these knowledge sources helps to model sentiment over a set of product aspects. The authors show how established dimensionality reduction and feature weighting approaches from text classification can be adopted to weight and select an optimal subset of aspects for recommendation tasks. The authors compare the proposed approach against several baseline methods as well as the state-of-the-art better method, which recommends products that are superior to a query product. Findings Evaluation results from seven different product categories show that aspect weighting and selection significantly improves state-of-the-art recommendation approaches. Research limitations/implications The proposed approach recommends products by analysing user sentiment on product aspects. Therefore, the proposed approach can be used to develop recommender systems that can explain to users why a product is recommended. This is achieved by presenting an analysis of sentiment distribution over individual aspects that describe a given product. Originality/value This paper describes a novel approach to integrate consumer purchase behaviour analysis and aspect-level sentiment analysis to enhance recommendation. In particular, the authors introduce the idea of aspect weighting and selection to help users identify better products. Furthermore, the authors demonstrate the practical benefits of this approach on a variety of product categories and compare the approach with the current state-of-the-art approaches.
Coupling one-shot learning and online discriminative learning for robust object tracking

Visual tracking is a very challenging task in computer vision. In this paper, we present a general-purpose framework for robust tracking. We propose to couple one-shot learning and online discriminative learning together to address the fundamental stability-plasticity issue for tracking. A one-shot learner through offline training on large-scale datasets is used as a stable detector which does not suffer model drift while an online discriminative learner is adopted as the tracker which is adaptive to significant appearance changes. Based on the directive framework, we design a baseline tracking model to verify its effectiveness. In practice, a deep Siamese network trained offline plays as the one-shot learner which can re-detect the target in case of tracking drift and failure. A correlation classifier which incorporates a translation model and a scale model plays as the online learner. Through the coupling of the offline and online learning, the simple baseline tracker achieves a good balance between stability and adaptivity without time-consuming optimization. Experimental results on the large-scale benchmark dataset demonstrate the effectiveness of the proposed framework within which the designed baseline tracker outperforms many state-of-the-art methods both in precision and robustness.
Big Data and Digital Analytics

Data becomes big data when then the size of data exceeds the ability of our IT systems in terms of 3Vs (volume, velocity, and variety). When the data sets are large and complex, it becomes a great difficult task for handling such voluminous data. This chapter will provide a detailed knowledge of the major concepts and components of big data and also the transformation of big data in to business operations. Collection and storage of big data will not help out in creation of business values. Values and importance are created once when the action starts on data by performing an analysis. Hence, this chapter provides a view on various kinds of analysis that can be done with big data and also the differences between traditional analytics and big data analytics. The transformation of digital data into business values could be in terms of reports, research analyses, recommendations, predictions, and optimizations. In addition to the concept of big data, this chapter discuss about the basic concepts of digital analytics, methods, and techniques for digital analysis.
Predicate Specialization for Definitional Higher-Order Logic Programs

Higher-order logic programming is an interesting extension of traditional logic programming that allows predicates to appear as arguments and variables to be used where predicates typically occur. Higher-order characteristics are indeed desirable but on the other hand they are also usually more expensive to support. In this paper we propose a program specialization technique based on partial evaluation that can be applied to a modest but useful class of higher-order logic programs and can transform them into first-order programs without introducing additional data structures. The resulting first-order programs can be executed by conventional logic programming interpreters and benefit from other optimizations that might be available. We provide an implementation and experimental results that suggest the efficiency of the transformation.
Mining Data Streams

In recent years, advancement in technologies has made it possible for most of the present-day organizations to store and record large streams of data. Such data sets which continuously and rapidly grow over time are referred to as data streams. Mining of such data streams is a unique opportunity and also a challenging task. Data stream mining is a process of gaining knowledge from continuous and rapid records of data. Due to increased streaming information, data stream mining has attracted the research community in the recent past. There is voluminous of literature which has been published in this domain over the past few years. Due to this, isolating the correct literature would be a grueling task for researchers and practitioners. While addressing a real-world problem, it would be more difficult to find relevant information as it would be hidden in data streams. This chapter tries to provide solution as it would be an amalgamation of all techniques used for data stream mining.