Reviewerly/exHarmony huggingface dataset:
Public Perception Based Recommendation System for Cryptocurrency

Cryptocurrency is one of the emerging online currency of the modern era. Big companies are investing in this technology. However, some established companies still hesitate to use it. According to them, it is a volatile trend which will fade up eventually. There is no such authority which will provide them feasibility information. So, investors can be helped by providing them feasibility information about locations for cryptocurrency investment around the world. This paper aims to provide the aforementioned information to the investors. The proposed methodology is based on Topic modeling along with public opinion mining about cryptocurrencies, blockchain network, bitcoin, litecoin, and ethereum. The crawled data for other cryptocurrencies are much insufficient, so that are excluded from the study. In the proposed methodology, the top locations where cryptocurrency is widely used are identified, then in that particular locations' users concerns along with their sentiment analyses are investigated. Top locations are identified such as Australia, Denmark, Netherlands, and the USA etc. Almost 83.7% tweets of Sweden show positive sentiment for cryptocurrency investment which ranks as the highest having friendly environment for cryptocurrency investment. Similarly, the UK shows the least positive perception of cryptocurrency and blockchain technology usage. Some of the noteworthy terms found are legitimacy, authorization rules, volatility, profit, investment, and fluctuations. Which describe the users' concerns/ interests' about cryptocurrency. Investors can focus on all these areas during business. These subtopics can help business experts to evolve their businesses' and to make them more sustainable on the basis of public perception.
Unbounded Sparse Census Transform Using Genetic Algorithm

The Census Transform (CT) is a well proven method for stereo vision that provides robust matching, with respect to object boundaries, outliers and radiometric distortion, at a low computational cost. Recent CT methods propose patterns for pixel comparison and sparsity, to increase matching accuracy and reduce resource requirements. However, these methods are bounded with respect to symmetry and/or edge length. In this paper, a Genetic algorithm (GA) is applied to find a new and powerful CT method. The proposed method, Genetic Algorithm Census Transform (GACT), is compared with the established CT methods, showing better results for benchmarking datasets. Additional experiments have been performed to study the search space and the correlation between training and evaluation data.
A unique message encryption technique based on enhanced blowfish algorithm

Online transaction especially sending of messages has become easier and simpler to be manipulated. Nonetheless, security is the important aspect to secure its content and raise difficulty to unauthorized attempt. This study aims to secure both plaintext and file message content through encryption technique which is based on enhanced Blowfish algorithm. An enhanced Blowfish algorithm is developed to improve its performance by reducing the number of rounds and by increasing the block length with fixed length during encryption and decryption with added transformation method on selected rounds. The study used Visual Studio application running at x64 operating system.
Face recognition using linear sparse approximation with multi-modal feature fusion

In the past few years, face recognition (FR) based on sparse representation is providing very good classification accuracy which calls for further research to exploit its capabilities to the maximum. In this paper, a novel FR method is developed as linear sparse approximation with multi-modal feature fusion (SMF classifier). SMF classifier works in three steps; multi-modal feature extraction, weight calculation for linear sparse representation and contribution calculation for final classification. In multi-modal feature extraction, a novel feature vector is developed by combining a local texture feature and a global color feature for better classification accuracy. To highlight the competency of proposed method, experiments are conducted for SMF classifier and simple sparse classifier for all possible training sets. Results are compared with simple sparse classifier in terms of mean classification accuracy. An analysis of percentage improvement in classification accuracy is performed to showcase the reliability of proposed method.
Reliable Medical Image Communication in Healthcare IoT

Images have been widely used in the medical field for various diagnostic purposes. In the field of healthcare IoT, secure communication of a medical image concerned with an individual is a crucial task. Embedding patients' personal information as an invisible watermark in their medical images helps to authenticate the ownership identification process. Reliable communication of medical image can be thereby ensured concerning authentication and integrity. Images in DICOM format with a pixel resolution of 8-bit depth are used for medical diagnostics. This chapter deals about the development of a lightweight algorithm to insert patients' identities as an invisible watermark in random edge pixels of DICOM images. This chapter describes the implementation of the proposed lightweight watermarking algorithm on a RISC microcontroller suitable for healthcare IoT applications. Imperceptibility level of the watermarked medical image was analyzed besides its lightweight performance validation on the constrained IoT platform.
A New Topological Method for Examining Historical Inscriptions

The article presents a new method developed to increase the efficiency of the identification algorithm for historical inscriptions of unknown origin. The authors extracted topological properties of the symbols containing different script relics, and analyzed them by using statistical tools. The considered topological properties are circular loop, oblique lines, vertical section, and crossing, among others. The article describes the use of the number of a three-circle grapheme intersection point vectors to identify unknown symbols. The number of intersections of the three circles and the examined symbol is stored in a feature vector. By supplementing the feature vectors with the circle vectors, the authors succeeded in improving the efficiency of the algorithm designed to decipher hard to read historical inscriptions.
A Multisensory Design Probe

Technostress is an emerging and significant psychological phenomenon associated with the use of technology. As humans increasingly encounter computational technology on a daily basis, there is a need to manage the anxieties and tensions that can result from these interactions. Using the lens of critical design, we created a design probe to explore this concept of technology induced stress. The probe builds on the topic of slow technology and embraces multisensory experiences as a tool for individuals to reflect on their relationship with technology.
Research on Different Heuristics for Minimax Algorithm Insight from Connect-4 Game

Minimax algorithm and machine learning technologies have been studied for decades to reach an ideal optimization in game areas such as chess and backgammon.In these fields, several generations try to optimize the code for pruning and effectiveness of evaluation function.Thus, there are well-armed algorithms to deal with various sophisticated situations in gaming occasion.However, as a traditional zero-sum game, Connect-4 receives less attention compared with the other members of its zero-sum family using traditional minimax algorithm.In recent years, new generation of heuristics is created to address this problem based on research conclusions, expertise and gaming experiences.However, this paper mainly introduced a self-developed heuristics supported by well-demonstrated result from researches and our own experiences which fighting against the available version of Connect-4 system online.While most previous works focused on winning algorithms and knowledge based approaches, we complement these works with analysis of heuristics.We have conducted three experiments on the relationship among functionality, depth of searching and number of features and doing contrastive test with sample online.Different from the sample based on summarized experience and generalized features, our heuristics have a basic concentration on detailed connection between pieces on board.By analysing the winning percentages when our version fights against the online sample with different searching depths, we find that our heuristics with minimax algorithm is perfect on the early stages of the zero-sum game playing.Because some nodes in the game tree have no influence on the final decision of minimax algorithm, we use alpha-beta pruning to decrease the number of meaningless node which greatly increases the minimax efficiency.During the contrastive experiment with the online sample, this paper also verifies basic characters of the minimax algorithm including depths and quantity of features.According to *These authors contributed equally to this work and should
Conversation Quest in MEGA World (Multiplayer Educational Game for All)

The research team has developed a web-based massively multiplayer educational game platform named MEGA World. It allows teachers to create their own virtual worlds and to design a series of learning and assessment activities (i.e., quests and quest chain in a commercial game) for their students. The platform is also a multilingual platform that supports any languages and makes teachers capable of accessing external resources (e.g., multimedia, materials, online meetings, etc.). Students can learn and reach the predefined learning goals by taking and solving the quests while playing. Seven types of quests have been provided for teachers, including greeting, item collection and delivery, sorting, treasure hunting and digging, calculation, fill-in-the-blank, and short answer quest types. The research team is further design and develop conversation quest activities for Foreign English Learners (EFLs) to practice their speaking skills. While people believe that learners’ communicating skills can be improved by using a language to interact with native speakers, in many cases to have a native speaker practicing with the learners is not an available or an affordable option for them. This paper mainly focuses on how the teachers can build a place that provide simulated conversation scenarios within a no-pressure environment via the creation of conversation quests and the use of MEGA World.
MIP-VR: An omnidirectional navigation and jumping method for VR shooting game using IMU

We suggest an omnidirectional navigation and jumping method using walking-in-place (WIP) interface. This method supports forward, backward, sideways walking and jumping include vertical jump. The shooting game is one of the contents that can best take advantage of VR. Since the WIP interface is a hands-free navigation method, it works well with VR shooting game. However, we need to extend WIP because this interface is not possible for sideways walking and jumping that is possible with existing keyboards or joysticks. We propose a forward and sideways walking method suitable for VR shooting game. Our method is inspired by human behavior and using a state diagram to support backward walking, running and vertical jump. We call it motion-in-place (MIP). Our method uses inertial measurement unit (IMU) only. A pilot test of 10 subjects showed that our method is less tiring and useful than a forward-only WIP interface. Our method can be used for most HMDs with built-in IMU and can be suitable for VR shooting game.
A Comprehensive Study of the Parameters in the Creation and Comparison of Feature Vectors in Distributional Semantic Models

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Multi-View Classification via Adaptive Discriminant Analysis

In many real applications, an object is usually represented with multiple views, providing compatible and complementary information to each other. Therefore, it is highly desirable to recognize the object from distinct and even heterogeneous views. In this paper, we propose a novel method, the named multi-view locality adaptively discriminant analysis (MvLADA), for multi-view classification. The MvLADA integrates subspace learning and weighted matrix learning into a uniform framework, where the weighted matrix is adaptively attained and shared by all views. Compared with the most existing LDA-based multi-view methods, the MvLADA adaptively assigns different weights to each sample, which enhances MvLADA's flexibility in practical applications. Moreover, the learned weighted matrix shared by all views exploits the point's neighbor relationship automatically without requiring a k NN procedure. Besides, the MvLADA is a parameter-free method without imposing any additional parameters. We validate the proposed MvLADA on three real-world datasets, indicating a better performance than the state-of-the-art multi-view algorithms.
Study on the use of different quality measures within a multi-objective evolutionary algorithm approach for emerging pattern mining in big data environments

Emerging pattern mining is a data mining task that extracts rules describing discriminative relationships amongst variables. These rules should be understandable for the experts. Comprehensibility of a rule is traditionally determined by several objectives, which can be calculated by different measures. In this way, multi-objective evolutionary algorithms are suitable for this task. Currently, the growing amount of data makes traditional data mining tasks unable to process them in a reasonable time. These huge amounts of data make even more interesting the extraction of rules that can easily describe the underlying phenomena of this big data. So far there is only one algorithm for emerging pattern mining developed based on multi-objective evolutionary algorithms for big data, the BD-EFEP algorithm. The influence of the selection of different quality measures as objectives in the search process is analysed in this paper. The results show that the use of the combination based on Jaccard index and false positive rate is the one with the best trade-off for descriptive induction of emerging patterns. It is recommended the use of this combination of quality measure as optimisation objectives in future multi-objective evolutionary algorithm developments for emerging pattern mining focused in big data.
Another Look on Bucketing Attack to Defeat White-Box Implementations

White-box cryptography was first introduced by Chow et al. in 2002 as a software technique for implementing cryptographic algorithms in a secure way that protects secret keys in a compromised environment. Ever since, Chow et al.'s design has been subject to mainly two categories of attacks published by the cryptographic community. The first category encompasses the so-called differential and algebraic cryptanalysis. Basically, these attacks counteract the obfuscation process by inverting the applied encoding functions after which the used secret key can easily be recovered. The second category comprises the software counterpart of the well-known physical attacks often applied to thwart hardware cryptographic implementations on embedded devices. In this paper, we turn a cryptanalysis technique, called statistical bucketing attack, into a computational analysis one allowing an efficient key recovery from software execution traces. Moreover, we extend this cryptanalysis technique, originally designed to break DES white-box implementations, to target AES white-box implementations. To illustrate the effectiveness of our proposal, we apply our attack on several publicly available white-box implementations with different level of protections. Based on the obtained results, we argue that our attack is not only an alternative but also a more efficient technique compared to the existing computational attacks, especially when some side-channel countermeasures are involved as a protection.
Factors underlying frequently cited journal articles: A retrospective commentary

Citation counts can be used as an indicator of research quality. The question addressed herein was can we identify any variables that are associated with or could even be used to foretell whether a manuscript will become well cited or be a high impact journal article? To answer this question, a review was conducted of papers that cited a published article that, according to Google Scholar, had been cited 23 times. Ten of these articles, written in English, were located using available library resources (e.g. library subscriptions, interlibrary loan). Upon reviewing these articles and noting how the targeted article had been used within each, three categories emerged that likely exerted some influence on whether the paper was cited. These categories were labeled paper attributes, journal attributes, and author attributes. These attributes are discussed and suggestions are provided for increasing citation counts.
PPEM: Privacy‐preserving EM learning for mixture models

Summary Privacy is becoming increasingly important in collaborative data analysis, especially those involving personal or sensitive information commonly arising from health and commercial settings. The aim of privacy preserving statistical algorithms is to allow inference to be drawn on the joint data without disclosing private data held by each party. This paper presents a privacy‐preserving expectation–maximization (PPEM) algorithm for carrying out maximum likelihood estimation of the parameters of mixture models. We address the scenario of horizontally partitioned data distributed among three or more parties. The PPEM algorithm is a two‐cycle iterative distributed algorithm for fitting mixture models under privacy‐preserving requirements. A distinct advantage of PPEM is that it does not require a trusted third party for cooperative learning, unlike most existing schemes that implement a master/slave hierarchy. By adopting a ring topology and adding random noises to messages before encryption, PPEM helps prevent information leakage in the case of corrupted parties. Furthermore, in contrast to existing works, which typically assume a Honest‐but‐Curious adversary, we consider the much stronger case of a Malicious adversary. For illustration, PPEM is applied to two of the most popular mixture models, namely, the normal mixture model (NMM) and t ‐mixture model ( t MM), and their effectiveness is analyzed through a security analysis. A real data example is also presented to evaluate the computational complexity and accuracy of PPEM relative to its non‐privacy‐preserving version.
Evaluating a thesaurus for discovery of ecological data

The increasing availability of data has driven a need for improved capabilities to discover data. Use of keywords drawn from a thesaurus is one way to enable browse-based discovery and to enhance searching. To assess the effect of using a thesaurus on the discoverability of ecological data, use of 81,415 keywords derived from 6132 data packages drawn from 28 ecological research projects in the U.S. Long-term Ecological Research Network were examined. The vast majority (95%) of data packages included at least one keyword drawn from the thesaurus, thus enabling their discovery using a hierarchical browse interface. For searching, keywords derived from the thesaurus would reveal 17 times more data packages than ad hoc keywords not in the thesaurus. Additionally, searches using keywords derived from the thesaurus returned data from a median of four different projects, whereas ad hoc search terms would typically yield data from only a single project. Of the search terms that yielded more than five data packages across two or more projects, 78% were found in the thesaurus. Use of keywords drawn from the thesaurus increased when compared to their use prior to establishment of the thesaurus, indicating that terms from the thesaurus are being actively added to metadata. A questionnaire assessed the process by which keywords were selected and indicated that information management personnel played an important role in assigning keywords drawn from the thesaurus. These results support the idea that adoption of a thesaurus can be an effective way to enhance the discoverability of ecological data, and that keywording practices play an important role in supporting that enhancement.
Enslaved to the Trapped Data

Systematic reviews are a comprehensive and parameterised form of literature review, found in most disciplines, that involve exhaustive analyses and rigorous interpretation of prior literature. Performing systematic reviews, however, can involve repetitive and laborious work in order to reach reliable standards. Strict guidelines and availability of published reviews make the task amenable to computerised assistance and automation using text mining, information extraction, and machine learning techniques. However, it is unclear which aspects of this Work Task are best suited for such support. This paper describes a three-month ethnographic study and CognitiveWork Analysis of the systematic reviews performed by a medical research group. Our findings show that the IR aspects of systematic reviews involve many tasks at two separate levels: 1) taxonomic organisation of documents and sub-document elements in relation to topic queries and domain-specific resources, and 2) extraction methods for structured summaries from the classified resources. This provides the basis for future work designing search tools with localised optimization and subtask automation to support specific phases of the process.
Revisiting VM performance and optimization challenges for big data

The concept of virtualization in cloud computing aims to maximize resource utilization and minimize cost by deploying multiple Virtual Machines (VMs) on a single physical server sharing resources such as CPU, Cache, I/O, and Memory. The sharing of these resources can cause severe performance degradation, thus requiring VM migration techniques for performance enhancement. The introduction of big data has made the performance enhancement more challenging due to extensive volume, velocity, variety, variability, and veracity of data. The existing VM performance and optimization challenges need to be optimized for big data use cases. This chapter presents big data triggered VM performance challenges focusing big data applications and storage migration in cloud computing. State of the art VM migration techniques are evaluated against challenges posed by big data to outline possible solutions and research challenges.
Generating Stereoscopic Images With Convergence Control Ability From a Light Field Image Pair

With the advances in commercial light field cameras, light field image processing has attracted considerable attention from researchers. In this paper, we propose a novel method for generating stereoscopic images from a light field image pair with flexible control over the convergence of the virtual stereo cameras. We have developed a light field image-capturing prototype that consists of two horizontally arranged light field cameras (i.e., with their optical axes being parallel to each other). When using our proposed device for image/video capture, stereo photographers can concentrate on how to capture the desired visual experience without being frequently disturbed by having to manipulate the stereo camera parameters, i.e., the convergence angle of a stereo camera. During postprocessing, our method estimates accurate disparity maps for the light field image pair and then generates the target stereoscopic images that satisfy the desired stereo camera convergence requirements by adopting a novel view synthesis method for light field images. We have conducted extensive experiments to demonstrate the effectiveness of our proposed method.