Reviewerly/exHarmony huggingface dataset:
Variance Suppression: Balanced Training Process in Deep Learning

Stochastic gradient descent updates parameters with summation gradient computed from a random data batch. This summation will lead to unbalanced training process if the data we obtained is unbalanced. To address this issue, this paper takes the error variance and error mean both into consideration. The adaptively adjusting approach of two terms trading off is also given in our algorithm. Due to this algorithm can suppress error variance, we named it Variance Suppression Gradient Descent (VSSGD). Experimental results have demonstrated that VSSGD can accelerate the training process, effectively prevent overfitting, improve the networks learning capacity from small samples.
Well-M$^3$N: A Maximum-Margin Approach to Unsupervised Structured Prediction

Unsupervised structured prediction is of fundamental importance for the clustering and classification of unannotated structured data. To date, its most common approach still relies on the use of structural probabilistic models and the expectation maximization (EM) algorithm. Conversely, structural maximum-margin approaches, despite their extensive success in supervised and semi-supervised classification, have not raised equivalent attention in the unsupervised case. For this reason, in this paper, we propose a novel approach that extends the maximum-margin Markov networks (M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> N) to an unsupervised training framework. The main contributions of our extension are new formulations for the feature map and loss function of M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> N that decouple the labels from the measurements and support multiple ground-truth training. Experiments on two challenging segmentation datasets have achieved competitive accuracy and generalization compared to other unsupervised algorithms such as k-means, EM and unsupervised structural SVM, and comparable performance to a contemporary deep learning-based approach.
Blockchain and Bitcoin

Bitcoin and blockchain are two new and innovative technologies that may be confusing. This purpose of this paper is to differentiate these two new technologies and explain their functionalities. The concept of Bitcoin “mining” will be addressed, as well as the impact it has had on the hardware market. Finally, the benefits and concerns of implementing blockchain and Bitcoin will be provided. Despite the concerns, both blockchain and Bitcoin provide a plethora of possible new technological advanced, both in the terms of digital currencies as well as other avenues.
Designing the Ecology and Pervasive Information Architecture

In this chapter we describe how to design for the foundational layer of the human needs pyramid—the ecology. In the setting of UX design, the ecology is the entire set of surrounding parts of the world, including with which a user, product, or system interacts. The ecology is about usage context in the broadest sense. Designing the ecology forces us to think about the overall system and how it addresses broader work activities. In order for users to be productive in the work practice, they need to first understand the broader ecology and be able to participate in it, but designing for ecological needs is one of the most overlooked aspects in UX practice. UX design for the ecology, almost always depends on pervasive information architecture, which provides ever-present information availability across devices, users, and other parts of a broad ecology. This allows users to interact with the same information, perhaps in different forms and accessed and displayed in different ways, on different devices at different times and in different places. We conclude the chapter with an extended example about an ecology for a smart shopping application.
Hybridization of Biogeography-Based Optimization and Gravitational Search Algorithm for Efficient Face Recognition

This chapter aims to apply a novel hybridized evolutionary algorithm to the application of face recognition. Biogeography-based optimization (BBO) has some element of randomness to it that apart from improving the feasibility of a solution could reduce it as well. In order to overcome this drawback, this chapter proposes a hybridization of BBO with gravitational search algorithm (GSA), another nature-inspired algorithm, by incorporating certain knowledge into BBO instead of the randomness. The migration procedure of BBO that migrates SIVs between solutions is done between solutions only if the migration would lead to the betterment of a solution. BBO-GSA algorithm is applied to face recognition with the LFW (labelled faces in the wild) and ORL datasets in order to test its efficiency. Experimental results show that the proposed BBO-GSA algorithm outperforms or is on par with some of the nature-inspired techniques that have been applied to face recognition so far by achieving a recognition rate of 80% with the LFW dataset and 99.75% with the ORL dataset.
Usage Research Data Modeling

In this chapter, we focus on the data modeling subactivity within the Understand Needs UX design lifecycle activity. We discuss the construction of simple but expressive representations of usage research data to inform UX design. Each model provides a different perspective into the overall picture of work practice. Models use abstraction to boil data down to the essence and turn it into actionable items for design. Models offer an immersive mental framework of design parameters. User models include work roles, user classes, social models, and user personas. Usage models include the flow model, and task-structure and task-sequence models. Work environment models include the artifact model, physical model, and social model. Each model contains a representation of user needs and work barriers which must be addressed by design solutions.
Cyber Crime and Challenges of Securing Nigeria's Cyber-Space Against Criminal Attacks

The growing menace of cyber-related crimes in Nigeria is giving the government and other stakeholders in the information and communication technology sector a cause to worry. Apart from taking a toll on the nation's economic sphere, it has also affected the image of the country negatively especially when viewed against the backdrop of the recent ranking of Nigeria as third in global internet crimes behind United Kingdom and the United States. This scenario, no doubt, requires urgent attention. This chapter, therefore, proffer solutions and recommend ways to make the country's cyberspace free from incessant criminal attacks.
Development of Secured Log Management System Over Blockchain Technology

A log is a record of events that happens within an organization containing systems and networks. These logs are very important for any organization, because a log file will able to record all user activities. Due to this, log files play a vital role and contain sensitive information, and therefore security should be a high priority. It is very important to the proper functioning of any organization, to securely maintain log records over an extended period of time. So, management and maintenance of logs is a very difficult task. However, deploying such a system for high security and privacy of log records may be overhead for an organization and require additional costs. Many techniques have been designed for security of log records. The alternative solution for maintaining log records is using Blockchain technology. A blockchain will provide security of the log files. Log files over a Blockchain environment leads to challenges with a decentralized storage of log files. This article proposes a secured log management over Blockchain and the use of cryptographic algorithms for dealing the issues to access a data storage. This proposed technology may be one complete solution to the secure log management problem.
Low complexity decoding of Reed–Solomon codes over magnetic recording channels

A low complexity soft-decision decoding technique is presented for Reed–Solomon codes, over magnetic recording channels (MRC), by considering symbol and bit reliability, jointly. A symbol detection scheme for burst error scenarios has been put forward for the iterative error-and-erasure decoding algorithm (IEED), which is based on multiple trials, and is a combination of the generalised minimum distance and Chase-2 decoding schemes. In order to decrease complexity, the decoding test patterns are used in a certain order, while the stopping criteria is also discussed. Simulations show that the proposed algorithms can achieve almost the same performance as that of IEED for the additive white Gaussian noise channel and slightly better performance in MRC, but with significantly less complexity.
Towards multi-modality invariance: A study in visual representation

The ability of learning representation is widely required by multi-modal systems. However, the ways of achieving invariance are quite different among different modalities. This chapter is motivated by a typical multi-modal content analysis problem. From the vision perspective, we present a comprehensive study in extracting invariant compact visual features, i.e., visual features that resist the changes in position, viewpoints, geometry, etc., serving as a basic of understanding video signals and assisting other modalities such as audio signals. In particular, we emphasize the importance of invariant visual representation by presenting several examples. After that, we show two examples on how to achieve reversal invariance in the Bag-of-Visual-Words (BoVWs) model and the Convolutional Neural Networks (CNNs). Experiments verify that reversal-invariant representation enjoys significant advantages in image classification. In the end, we give some conclusive remarks and suggest future work on multi-modal content analysis.
Dialogue With Interfaces

This chapter grapples with the hegemony of the visual and its pervasiveness in current urban installations. It discusses how technology and the visual are fetishized instead of used in their dialogical potential to engage people in socio-spatial transformation. This chapter presents the trajectory of the Graphics Laboratory for Architectural Experience at Universidade Federal de Minas Gerais, Brazil (LAGEAR) in its theoretical and practical development. This chapter then discusses LAGEAR's main drives, which are the playful interaction, the distinction between interface, and interaction and dialogue, in order to create interactive interfaces that actually engage people in socio-spatial transformation. It presents examples of the authors' works, drawing from visually based to bodily engaging and socio-political installations. Discussion concerns the problematization that leads to the need of engagement rather than the bodily engagement. Emphasis was put on working with the socio-spatial context and proposing interfaces that take into account the process in its openness and indeterminacy instead of prescribing a product (even if an interface-product).
Using two-phase evolutionary computation approach for nonlinear constrained signal detectors allocation problems

This study is to investigate the nonlinearly constrained various signal detector location allocation problems in which the types of detectors and the corresponding numbers and locations can be determined at the same time so as to minimize the maximum detecting failure rate in a specified area. In other words, the objective of the detector location allocation problem is to minimize the maximum failure rate by determining the best possible conjunction of three types of decision variables, i.e., the type of detector, the numbers of each detector type and where to build up each of them with a limited resources. So, the quality of reliability of event detecting can be assured and consistent. By the way, the signal intensity usually disintegrates proportionally to some power of the distance from the detector. That makes the longer distance far away the detector, the bigger failure rate in detection of the event. The signal detector allocation problem is described as a mixed-integer nonlinear programming model, usually using math programming or heuristic optimization methods for finding the optimal solution or near optimal solution. While using the both methods, the difficulties encountered are the amount of decision variables and the difficulty of not violating the constraints. In this study, a two-phase evolutionary computation approach based on the immune algorithm and particle swarm optimization has been developed for overcoming the difficulties and finding the optimal solutions for the detector allocation problems effectively. Finally, the performance of the proposed methodology has been evaluated with the commercial optimization software. Numerical results illustrate that our approach is with well performance for the constrained detector allocation problems considered in this paper. As reported, solutions acquired by using our approach are as well as or better than those found by using LINGO®.
Fine‐tuning restricted Boltzmann machines using quaternions and its application for spam detection

Restricted Boltzmann Machines (RBMs) have been used in a number of applications, but only a few works have addressed them in the context of information security. However, such models have their performance severely affected by some hyperparameters that are usually hand-tuned. In this work, the authors consider learning features in an unsupervised fashion by means of RBMs fine-tuned by hypercomplex-based metaheuristic techniques in the context of malicious content detection. Experiments are conducted over three public datasets and six metaheuristic techniques, which are used to fine-tune RBM hyperparameters such that RBM extracts features that best represent malicious content present in spam e-mail messages, and generates a dataset to be used as input to classification through the Optimum Path Forest supervised algorithm. Experimental results demonstrate that a small number of features generated through RBM can achieve a competitive accuracy in relation to the original dataset, however, with lower computational cost. Furthermore, this study presents the power of quaternions for RBMs parameter optimisation, comparing it against the well-known Harmonic Search, as well as its variants Improved Harmonic Search and Parameter Setting-Free Harmonic Search. It was concluded that RBM-based learning techniques are suitable for features extraction in the context of this work.
Promoting library and information services in a digital age in Zimbabwe

Purpose This study aims to demonstrate how librarians in Zimbabwe are promoting library and information services in a digital environment. Design/methodology/approach An online questionnaire administered using SurveyMonkey was used to gather data from the participants who comprised 14 academic, one public, three school and three special librarians. Findings The study confirms that there is competition for the attention of the citizens who have social media, television, social life, work, family life, education, and so on before them. Despite the challenges being faced, librarians in the country are prepared to promote their services and are therefore using a number of strategies to promote the use of library and information services. Originality/value Promoting library and information services is important considering the waning reading culture in Zimbabwe. The paper recommends possible ways of promoting reading culture in the digital age through the use of library and information services.
A Transductive Transfer Learning Approach Based on Manifold Learning

Traditional machine learning approaches usually assume that the training data and the test data follow the same distribution. When the training data and the test data are not drawn from the same distribution, most traditional machine learning approaches cannot work well. Transfer learning approaches allow the distributions of the training data and the test data to be different. Transfer learning approaches mainly focus on how to reduce the difference between the training data and the test data, but mostly ignore the potential semantic information. This paper proposes a transductive transfer learning approach based on manifold learning (TTL-ML). TTL-ML designs a new feature representation space, where the semantic information hidden in the dataset structure is preserved, and the distribution difference between the source domain and the target domain is reduced. Experimental results on the common transfer learning datasets demonstrate that the proposed approach outperforms the several state-of-the-art approaches on image classification.
The number of effective moves for Chinese chess

The accurate measurement for opening positions in a game is an important issue in computer games. Evaluation functions used in game-playing programs are designed for the middlegame, and may not suitable for the opening phase. This paper proposes a method, named the number of effective moves, which is composed by a set of opening heuristics. It can be seen as an evaluation function for the opening. In addition, we implement a GUI tool based on an opening book to facilitate adjustments. The tool helps to discover new opening lines in practice and to assist move selection in the opening.
High correlation-based banknote gradient assessment of ensemble classifier

This research presents a client and server-based mobile application for recognition and authentication of banknotes; the system extracted the shape context (SC), Scale Invariant Feature Transform (SIFT), gradient location and orientation histogram (GLOH), and Histogram of Gradient (HOG). It then reduces the feature set using Principal Component Analysis (PCA), Bag of Words and proposed two-dimension reduction approach based on low variance and high correlation filter. The classification was done using a 2-fold Weighted Majority Average (WMA) Ensemble technique with MPLNN and MCSVM as base classifiers. The application was built using Unity 3D; it was tested on Naira, USD, CAD and Euro banknotes and the experimental results proved that the implemented feature vector and the proposed feature reduction and classification technique presented the best results and with promising recognition accuracy, detection rate, and processing time.
From Videos to URLs: A Multi-Browser Guide to Extract User’s Behavior with Optical Character Recognition

Tracking users’ activities on the World Wide Web (WWW) allows researchers to analyze each user’s internet behavior as time passes and for the amount of time spent on a particular domain. This analysis can be used in research design, as researchers may access to their participant’s behaviors while browsing the web. Web search behavior has been a subject of interest because of its real-world applications in marketing, digital advertisement, and identifying potential threats online. In this paper, we present an image-processing based method to extract domains which are visited by a participant over multiple browsers during a lab session. This method could provide another way to collect users’ activities during an online session given that the session recorder collected the data. The method can also be used to collect the textual content of web-pages that an individual visits for later analysis.
Supervised Dimensionality Reduction on Grassmannian for Image Set Recognition

Modeling videos and image sets by linear subspaces has achieved great success in various visual recognition tasks. However, subspaces constructed from visual data are always notoriously embedded in a high-dimensional ambient space, which limits the applicability of existing techniques. This letter explores the possibility of proposing a geometry-aware framework for constructing lower-dimensional subspaces with maximum discriminative power from high-dimensional subspaces in the supervised scenario. In particular, we make use of Riemannian geometry and optimization techniques on matrix manifolds to learn an orthogonal projection, which shows that the learning process can be formulated as an unconstrained optimization problem on a Grassmann manifold. With this natural geometry, any metric on the Grassmann manifold can theoretically be used in our model. Experimental evaluations on several data sets show that our approach results in significantly higher accuracy than other state-of-the-art algorithms.
Time evolution of two-mode squeezed vacuum in a single-mode diffusion channel

Based on the infinite sum solution of the master equation corresponding to the diffusion channel we derive the law of time evolution of a two-mode squeezed vacuum state in a single-mode (say a -mode) diffusion channel. We find that the squeezing effect decreases, while two different chaotic field modes emerge. After t time period of the diffusion, the a -mode photon number is larger than b -mode photon number by tκ, where κ is the diffusion coefficient, thus by adjusting κ, photon number can controlled. Therefore, the diffusion process can be considered a quantum controlling scheme through photon addition.
Emotion Recognition through Intentional Context

Recently there is a growing need for affect-awareness in computer games. In-game emotion recognition, however, requires applying costly feature extraction methods and/or labor-demanding annotation of large datasets. To make emotion recognition cost-efficient, this study proposes (A) a social relations-directing, “emotion-sensitive” dialogue act model consisting of social acts, and (B) an approach for emotion recognition, utilizing the association strength ratio of emotion types and the proposed acts. In the study, five Japanese in-game dialogues were tagged with labels of emotion types and of social acts. Emotion type-social act co-occurrence was analyzed, and the corresponding association strength ratios were computed. In a validation experiment, the proposed approach was tested, enhancing a baseline emotion classifier's recognition accuracy by 8%.
Solving 7,7,5-game and 8,8,5-game

An mnk-game is a kind of k-in-a-row game played on an m×n chess board, where two players alternatively mark empty squares with their own colors and the first one to get k-consecutive marks wins. This paper solves 7,7,5-game and 8,8,5-game as draws. In the proof, we propose some methods based on pai ring strategies and potential weights, and then use a program implementing these methods to solve 7,7,5-game in about one minute and 8,8,5-game in about three days.
A characterization of those categories whose internal logic is Hilbert's ε-calculus

We characterize categories whose internal logic is Hilbert's ε -calculus as those categories which have a proper factorization system satisfying the axiom of choice and in which every non-initial object is injective. We provide an example of such a category where the law of excluded middle is not valid.
A Mutation Operator-Based Scenario for Evaluating Software Clone Detection Tools and Techniques

Over the past few years, several software clone detection tools and techniques have been introduced by numerous researchers. The software clone detection techniques and tools are based on their numerous attributes and sub-attributes which make them difficult to complete a comparative study. Therefore, the authors propose a mutation operator-based editing taxonomy for generating different software clone types. In addition, a hypothetical scenario is developed using mutation operator-based editing taxonomy and this hypothetical scenario is used to evaluate various software clone detection techniques and tools. Further, the existing evaluation criterion is extended by the hypothetical scenario which is clearly represented by the analysis of results.