Reviewerly/exHarmony huggingface dataset:
Hardware-accelerated Fast Lossless Compression Based on LZ4 Algorithm

Data compression can efficiently utilize the storage capacity. Especially for mobile devices, they have limited resources such as a CPU (Central Processing Unit) operation clock and a power source, so hardware designed compression method is required. Most adaptive dictionary-based compression methods have their root in Lempel-Ziv algorithm. LZ4 is one of the fastest compression algorithms. In this paper, we propose an advanced algorithm and hardware architecture with improved compression ratio and speed. In order to achieve a higher compression ratio, the proposed algorithm has a variable length format while LZ4 has a fixed length format. Experimental results show that the proposed architecture can achieve compression throughput up to 3.84Gbps and compression ratio up to 4. In this way, our hardware-based architecture improves memory performance and battery life of mobile devices with low power consumption.
Semantically Explainable Fuzzy Classifier

In machine learning, there are many high-performance classifiers. However, because of lack of transparency, they are not able to explain the data in a human-friendly form. In this paper, Cumulative Fuzzy Class Membership Criterion (CFCMC), a recently proposed fuzzy modeling classifier, is modified and utilized for a novel approach of information extraction from the labeled data. This approach is able to explain the classifiability of the data in the form of semantics. Extracted semantics give information about the structure of the data and the similarities between classes. To get a relevant image of its classification performance, it is compared to three well-known and frequently used classifiers, which are considered as black boxes, namely, SVM, MLP, and kNN, and to a similar transparent approach, MF ARTMAP. To validate extracted semantics, they are compared to visualization of classified data and to confusion matrices generated during the evaluation of the created CFCMC models. The experimental result shows that CFCMC is not necessarily the best classifier, although, in most cases, it is not too far from the best performing methods. However, the semantical explanation potentially allows the classifier to be applied as a support for human decision processes in real-world problems.
Character Network Embedding-based Plot Structure Discovery in Narrative Multimedia

This study aims to discover plot structures of narrative multimedia (i.e., creative works that contain stories and are distributed through multimedia). Although a few studies have been conducted to extract subplots from narrative works based on occurrences of characters, the subplots do not always have distinctive compositions of characters. Therefore, we propose a method for discriminating the subplots based on overall social relationships among characters. To understand ways how a story is interwoven with subplots, it is important to describe how story lines in each subplot are developed and how subplots are split and rearranged. As a first step, we propose a measurement for estimating rapidity of story development based on changes in the social relationships among characters. Subsequently, we suggest a model for representing plot structures based on transitions among subplots, the narrative significance of each scene, and the main characters of each subplot. We anticipate that the plot structures can be applied for summarizing narrative multimedia regarding the story.
Radar probabilistic data association filter/DGPS fusion for target selection and relative range determination for a ground vehicle convoy

As navigation systems are being developed, it is apparent that cost-effective accurate and precise positioning is an imperative for both military and civilian ground vehicle guidance. As this need increases, navigation systems incorporating multiple sensors have been developed and relied upon in many navigation situations. In this work, radar and global positioning system (GPS) measurements are utilized in a multi-sensor fusion scheme allowing for a robust ranging solution utilizing the accuracy of a differential GPS (DGPS) solution and higher update rate of the radar solution in a Kalman filter. A probabilistic data association filter (PDAF) is utilized to determine a weighted mean of the radar channels' solutions that fall within a validation region set in the algorithm. In this work, radar was used to track vehicles and not surrounding landmarks. This paper intends to evaluate the accuracy and availability of a GPS/radar fusion algorithm in vehicle convoying scenarios.
DOPSIE: Deep-Order Proximity and Structural Information Embedding

Graph-embedding algorithms map a graph into a vector space with the aim of preserving its structure and its intrinsic properties. Unfortunately, many of them are not able to encode the neighborhood information of the nodes well, especially from a topological prospective. To address this limitation, we propose a novel graph-embedding method called Deep-Order Proximity and Structural Information Embedding (DOPSIE). It provides topology and depth information at the same time through the analysis of the graph structure. Topological information is provided through clustering coefficients (CCs), which is connected to other structural properties, such as transitivity, density, characteristic path length, and efficiency, useful for representation in the vector space. The combination of individual node properties and neighborhood information constitutes an optimal network representation. Our experimental results show that DOPSIE outperforms state-of-the-art embedding methodologies in different classification problems.
Collaborative Multi-Auxiliary Information Variational Autoencoder for Recommender Systems

Collaborative filtering is widely used in recommendation systems. Hybrid approach has been proposed since the collaborative-based method is susceptible to problems such as sparsity and cold start. Recently, the related methods have pointed out that it is very effective to alleviate the above problem by inferring the stochastic distribution of the latent variables for item's auxiliary information. Usually, item boasts more than one kind of auxiliary information. How do we infer the stochastic distribution of the latent variables with multiple auxiliary information? In this paper, we proposed a collaborative multi-auxiliary information autoencoder that can simultaneously consider multiple types of auxiliary information correspondingly. On the one hand, we can successfully accomplish the above issues via the improvement of variational autoencoder. On the other hand, we demonstrated the effectiveness of our method through experiments on real datasets.
Towards an adaptive gaming framework for TeleRehabilitation

This paper introduces a framework for building adaptive serious games in the context of telerehabilitation. This framework takes advantage of 3D motion tracking and virtual reality devices, to present a personalized and adaptive gaming system, which allows patients to perform exercises as suggested by their physiotherapy experts, while being fully engaged in a gamified 3D scene. Virtual characters are dynamically displayed within scenes to guide users through the different set of required gestures. Virtual assistants have the ability to learn and adapt to the best level of difficulty in real-time based on the user performance. An intelligent alerting and automatic correction components are embedded in our engine, so that pre-calculated gesture patterns are correlated and matched with patients' gestures. Consequently, the system estimates the perceived difficulty of gestures by the patient, so that automatic adjustments are executed to ensure a highly engaging and adaptive serious game experience. A pilot study has been conducted to prove the usability and effectiveness of our gamified physiotherapy solution.
Dynamic digest based authentication for client–server systems using biometric verification

The client authentication is a significant process in client–server systems. Such a process is highly secure when a client may be authenticated according to a set of unique verifiable data, i.e., biometric traits. However, biometric based systems with the low-cost, dense biometric sensors, and power of fast processing need a method of automatic client recognition for the robust client authentication. Such a method faces three challenges: (1) the effective recognition of the biometric patterns inputted to the system, (2) the provision of security to prevent the vulnerability of the system, and (3) the preparation of personal privacy. Many remote biometric authentication schemes have been developed to establish secure mutual communication between a client as a device node and server over an untrusted channel. By employing a secure remote biometric based authentication protocol, a client that acts in a node and a server that contains resources can authenticate each other in a secure and trustable manner. In our previous work, we proposed a digest based authentication method that preserves privacy of client’s biometric templates and authenticates the client securely by generating non-deterministic semi-digest. By reviewing and cryptanalyzing this method, in the current paper, we focus on the improvement of the method for providing the invulnerability against user anonymity and server masquerading attacks. We show that our improved scheme is secure against the attacks and prove its functionality features.
Multispectral mapping of evaporite minerals using ASTER data: A methodological comparison from central Turkey

Evaporites are regarded as industrial raw materials with high economic value and are represented by chemical sedimentary rocks deposited in terrestrial and marine environments. Evaporite deposits rich in calcium sulphate, such as gypsum and anhydrite, are typically formed under arid climate conditions. These rocks are very important not only in paleo-environmental and paleo-climatic studies but also for oil and natural gas exploration as they may form trap where such hydrocarbons are accumulated. Some of the central Anatolian Tertiary basins such as Çankırı-Çorum, Sivas, Ulukışla, Tuz Gölü, Haymana and Beypazarı basins host outcrops of various terrestrial and marine evaporite deposits. In this study, gypsum minerals in the Aksaray and Niğde provinces (central Turkey), was mapped with ASTER imagery using band rationing, decorrelation stretching (DECOR), feature-oriented principal components (FOPCA) and sulphate index methods. Geochemical and spectroradiometer measurements were taken on samples collected during the field studies. Accuracy assessments using the maximum likelihood technique yielded highly accurate results (average 86%) and the best results were obtained from FOPCA and DECOR methods. Results were also checked with the field studies.
Computing Qur’ans: A Suggestion for a Digital Humanities Approach to the Question of Interrelations between English Qur’an Translations

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Deep Learning Application in Mechatronics Systems’ Fault Diagnosis, a Case Study of the Demand-Controlled Ventilation and Heating System

Mechatronics systems include a vast range of interdisciplinary area of electrical and mechanical systems e.g. heating, ventilation, and air-conditioning systems in building automation systems that are responsible to provide occupants a comfortable and productive environment. The demand-controlled ventilation system as an advanced control approach in smart buildings is used for the main goal of energy saving. But, these kinds of systems because of their numerous components such as sensors and actuators are very prone to the faults. Arise of the faults, if they are not detected and diagnosed early, can lead to the system's performance degradation or extra maintenance cost and effort. Nowadays, introducing a suitable generic technique for fault detection and diagnosis is an utmost challenge. The contribution of this paper is to present a novel fault detection and diagnosis framework based on deep learning method for a case study of mechatronics systems, the demand-controlled ventilation and heating system. This paper presents all the steps including data acquisition, data preprocessing, network model design, model optimization, and network model evaluation. Ten types of faults in different classes as well as the healthy data are used to train and evaluate the performance of the designed network model. The results describe a high accuracy (97.4% in confusion matrix) via the designed deep neural network. Also, this study describes the methodology of selecting the optimum parameters of the training process by analyzing the effect of each parameter on the training accuracy.
Enumeration of Boolean Mapping with Given Cryptographic Properties for Personal Data Protection in Blockchain Data Storage

Currently, blockchain data storage systems are considered promising for storing important information, including personal data. However, the requirements of modern legislation in various countries pose challenges to such systems, including the need to delete data at the request of users, which contradicts the very concept of the blockchain. One way to solve this problem is to use encryption. The paper presents the most powerful asymptotic estimates of the cardinality of sets of correlation-immune and (n,m,k)-resilient Boolean mappings that are used in the construction of stream encryption systems. Also, a recurrence relation is proved, allowing to calculate the number of (n,m,k)-resilient mappings for small values n, m and k.
Algorithmic Solution for Effective Selection of Elicitation Techniques

The process of software development involves many significant knowledge-intensive areas. To execute a software project successfully, Requirement elicitation (RE) plays a critical role and need to be focused. RE is a process to establish information about the end user's requirements clearly. Normally, the requirement engineer and organization policies affect the selection of the usual requirement elicitation technique. Furthermore, there is no concrete rule or direction available on an effective selection of elicitation techniques for a new or existing software project. In this paper, we proposed an algorithm for effective selection of elicitation technique, as the first step; we developed a matrix of different parameters and populated the matrix with the list of consolidated parameters along with possible values, suggested by various practitioners and researchers, for different elicitation techniques. Parameters are broadly categorized into three categories Project, Stakeholders and Requirement Engineer. As a second step, we designed an algorithm based on the mapping matrix which takes situational values as input and suggests a suitable and effective elicitation technique. For validation, we executed an algorithm for various examples and found better results. We can add new parameters and categories to the matrix for selection of elicitation technique without any inconvenience.
Multimodal Multiclass Boosting and its Application to Cross-modal Retrieval

Although Boosting approach has been proved to be a very successful ensemble learning technology, the conventional ones are limited to two classes or single modality. In this paper, to deal with multiclass setting and heterogeneous modalities, we propose a multimodal multiclass Boosting framework called MMBoost, in which the intra-modal semantic information and inter-modal semantic correlation can be captured at the same time. By utilizing the multiclass exponential and logistic loss functions, we further acquire two new versions of MMBoost, namely MMBoost_exp and MMBoost_log. The empirical risk, which simultaneously considers the intra-modal and inter-modal losses, is designed and then minimized by gradient descent in the multidimensional functional spaces. More concretely, the optimization problem is solved in turn for each modality. The posterior probability of semantic category can be naturally attained by applying sigmoid function to the multiclass margin. A series of experiments on the Wiki and NUS-WIDE datasets demonstrate that the performance of our proposed method significantly outperforms those of existing Boosting approaches for cross-modal retrieval.
Overview Feature Selection using Fish Swarm Algorithm

Feature selection is a process of representing wanted features based on the requirement needed by selecting the best subset of a dataset without changing the originality of the dataset. The aim of feature selection is to obtain most optimal feature subset to represent the data and for that purpose feature selection offered a few methods. This paper gives an easy understanding of the feature selection concept and the available methods in feature selection. As nowadays metaheuristics is catching attention researchers in many fields and feature selection is one of them, this paper intentionally brief feature selection using metaheuristics that implement Fish Swarm Algorithm (FSA) in the feature selection process. FSA classified as one of the Swarm Intelligence (SI) techniques have several advantages mainly to solve optimization problems. A number of previous works are reviewed. Based on the reviewed and the outcome results that has been tested using high dimensional, real-valued benchmark data sets, FSA reflect good performance among others SI.
Design of adversarial targets: fooling deep ATR systems

Deep Convolutional Neural Networks (DCNN) have proven to be an exceptional tool for object recognition in various computer vision applications. However, recent findings have shown that such state of the art models can be easily deceived by inserting slight imperceptible perturbations to key pixels in the input image. In this paper, we focus on deceiving Automatic Target Recognition(ATR) classiers. These classiers are built to recognize specified targets in a scene and also simultaneously identify their class types. In our work, we explore the vulnerabilities of DCNN-based target classifiers. We demonstrate significant progress in developing infrared adversarial target by adding small perturbations to the input image such that the image perturbation cannot be easily detected. The algorithm is built to adapt to both targeted and non-targeted adversarial attacks. Our findings reveal promising results that reflect serious implications of adversarial attacks.
Uncertainty-Aware Visualization for Analyzing Heterogeneous Wildfire Detections

There is growing interest in using data science techniques to characterize and predict natural disasters and extreme weather events. Such techniques merge noisy data gathered in the real world, from sources such as satellite detections, with algorithms that strongly depend on the noise, resolution, and uncertainty in these data. In this study, we present a visualization approach for interpolating multiresolution, uncertain satellite detections of wildfires into intuitive visual representations. We use extrinsic, intrinsic, coincident, and adjacent uncertainty representations as appropriate for understanding the information at each stage. To demonstrate our approach, we use our framework to tune two different algorithms for characterizing satellite detections of wildfires.
Software Architecture Decision-Making Practices and Recommendations

The role of Software Architect plays a vital role in entire software development process and becomes highly responsible when the software goes into deployment as business operations and automations are based on the software system. Since business operations are fully dependent on the software system, software architectural decision-making process becomes one of the major activities of a successful software projects. In this paper, we have depicted a series of empirical ways based on real life experiences so that software architects able to make accurate decision before going into software development. Regardless of architecture decision-making process, this paper may help software engineers, project managers, business owners and stakeholders in developing enterprise solutions.
How ethical hacking can protect organisations from a greater threat

As digital technologies are becoming embedded in all aspects of life, cyber attacks can come from many directions. A significant proportion of these attacks pose serious risks to critical data, infrastructure and processes within all manner of organisations, both large and small.
Legislative Document Content Extraction Based on Semantic Web Technologies

This paper describes the system architecture for generating the History of the Law developed for the Chilean National Library of Congress (BCN). The production system uses Semantic Web technologies, Akoma-Ntoso, and tools that automate the marking of plain text to XML, enriching and linking documents. These documents semantically annotated allow to develop specialized political and legislative services, and to extract knowledge for a Legal Knowledge Base for public use. We show the strategies used for the implementation of the automatic markup tools, as well as describe the knowledge graph generated from semantic documents. Finally, we show the contrast between the time of document processing using semantic technologies versus manual tasks, and the lessons learnt in this process, installing a base for the replication of a technological model that allows the generation of useful services for diverse contexts.
Estimation of Solar Potential for Thailand Using Adaptive Neurofuzzy Inference System Models

This research paper proposes a new method of global solar radiation prediction for Thailand using adaptive neurofuzzy inference system (ANFIS) models. Contrary to mathematical-based modeling approaches, the proposed models are able to estimate the monthly mean of daily global solar radiation at the ground level without using the earth's atmospheric layer model. The proposed technique alternately utilizes the 9-year long recorded spatiotemporal data of solar irradiance from meteorological ground stations in the modeling process. With a limited number of ground stations, it covered six regions of Thailand, ANFIS modeling; testing and restructuring have been performed repetitively; and finally, the best-fit models with minimum mean absolute percentage errors (MAPEs) corresponding to six regions of Thailand are obtained. Moreover, the ANFIS models have been tested comparatively to the measured data and the multilayer feed forward artificial neural network (ANN) models, which has a good agreement to real data for the proposed models, can be met with the average accuracy of 7.07% MAPE. By applying this model as a tool to estimate solar potential, the local government or the business sector can provide basic information, which is useful for solar energy system planning and project development.