Reviewerly/exHarmony huggingface dataset:
Blockchain technology through the lens of disruptive innovation theory

In recent years Blockchain technology as a decentralized database of transactions has received massive attention in academia and the professional world and it's called one of the next disruptive technologies. However, almost all these studies have been applying the concept of disruption as a loose word in explaining Blockchain technology without any reference to disruptive technology theory in business and innovation research. Thus, this ambiguity necessitates a look into the juncture of Blockchain technology and disruptive innovation theory. This paper first seeks to shed some light on the fundamental concepts, features, and discussions of disruptive innovation theory and then summarizes and critiques the research in this area. Furthermore, the characteristic features of Blockchain technology and its applications in academic research are briefly discussed. Finally, by integrating ideas and looking at Blockchain technology from the disruptive innovation theory perspective in supply chain as a case industry, the author makes contribution by outlining a set of comparisons that passes the Blockchain technology from the lens of disruptive innovation theory to show whether there are features in this technology and its initial applications that signal potential disruptiveness in the supply chain.
Quantum Optimization in Large Resource Management Systems

Classical computing methods do not enable to withstand the cumbersome computational operations of complex applications and a bunch of untouched problems that still exist until today. The proposed quantum method tackles the computational complexity problem by exploiting the parallel processing techniques of quantum computing. This paper aims to take advantage of quantum computing power in resource distribution management for a large scale of resources. To achieve this, we use quantum extreme value searching algorithm as a minimum searching algorithm. Moreover, to prove the efficiency of the quantum strategy, we compare the quantum method and the randomized one.
Geolocation for the Improvement of Spatial and Naturalist Intelligence in Primary Education

Geolocation provides a new learning model by combining physical and digital content, creating an enriched and interactive universe. This mobile technology offers new opportunities for the promotion of learning inside and outside the classroom, linked to multiple intelligences, in particular, to the spatial and naturalistic intelligences. Taking these premises into account, an analysis of several geolocation applications (N=20) is carried out in order to determine their potential to develop spatial and naturalistic intelligences. To this end, case study methodology is adopted, and an analysis instrument is proposed consisting of 15 indicators grouped into three dimensions: 1) geolocation, 2) spatial intelligence (spatial orientation and spatial representation), and 3) naturalistic intelligence (physical geography and environment). Although the geolocation applications analyzed boost spatial intelligence through the different options they incorporate, the same cannot be said of the naturalist one. It is considered relevant that they include contents oriented to environmental awareness.
Deep Progressive Convolutional Neural Network for Blind Super-Resolution With Multiple Degradations

Blind super-resolution (SR) of blurry and noisy low-resolution (LR) images is still a challenging problem in single image super-resolution (SISR). The performance of most existing convolutional neural network (CNN)-based models is inevitably degraded when LR images are corrupted by both blur and noise. For those blind SR methods based on kernel estimation, accurate estimation is barely attained under complex degradations and this gives rise to poor-quality results. To address these problems, we propose a deep progressive network under a probabilistic framework and a novel up-sampling method for blind super-resolution with multiple degradations, which effectively utilizes image priors across scales. Experimental results show that the proposed method achieves promising performance on images with multiple degradations.
Implementing Bio-Inspired Algorithm for Pathfinding in Flood Disaster Prevention Game

Flooding is one of the most frequent disasters. There are many risks due to flood incidents. Floods can cause a lot of damage and casualties. Flood prevention measures are needed to reduce the impact of flooding. Throwing the garbage in its place, making tree planting movements and making Bio-pore are some examples that can be done in maintaining the surrounding environment. Our game proposes an alternative tool that can be used to educate and motivate people to take appropriate action in protecting the environment to avoid flooding. This paper discusses the design and implementation of game creation of environmental maintenance. The Flood Disaster Prevention Game developed with Unity. This paper proposes the firefly algorithm as an algorithm in a flood rescue simulation game. The proposed firefly algorithm will be used to pathfinding. The fire-fly algorithm is simulated with MATLAB R2016a. The simulation result shows the best position that can be reached is (0.00006, -0,00045) with the final value is 12. This position is assumed as the rescue location of flood victims.
On the Bounds of Function Approximations

Within machine learning, the subfield of Neural Architecture Search (NAS) has recently garnered research attention due to its ability to improve upon human-designed models. However, the computational requirements for finding an exact solution to this problem are often intractable, and the design of the search space still requires manual intervention. In this paper we attempt to establish a formalized framework from which we can better understand the computational bounds of NAS in relation to its search space. For this, we first reformulate the function approximation problem in terms of sequences of functions, and we call it the Function Approximation (FA) problem; then we show that it is computationally infeasible to devise a procedure that solves FA for all functions to zero error, regardless of the search space. We show also that such error will be minimal if a specific class of functions is present in the search space. Subsequently, we show that machine learning as a mathematical problem is a solution strategy for FA, albeit not an effective one, and further describe a stronger version of this approach: the Approximate Architectural Search Problem (a-ASP), which is the mathematical equivalent of NAS. We leverage the framework from this paper and results from the literature to describe the conditions under which a-ASP can potentially solve FA as well as an exhaustive search, but in polynomial time.
Utilizing Weak Supervision to Infer Complex Objects and Situations in Autonomous Driving Data

While the detection and classification of simple objects encountered during autonomous driving sessions has been widely researched, the detection of complex objects and situations based on the combinations of objects in a scene remains relatively overlooked. This is especially difficult due to the cost of gathering labels for each complex scenario of interest before training a specialized model. To address this bottleneck of training data, we explore the applicability of weak supervision, or relying on higher level, noisier forms of supervision to label training data. Specifically, we use data programming, a paradigm that can learn the accuracy and dependency structure of these sources without using any ground truth labels and assign training labels accordingly. We focus on an example task of cyclist detection by comparing weak supervision, which relies on a set of user-defined rules over the outputs of detectors that identify people and bikes separately, to CyDet [1], which detects the cyclist as a complete object. We find that the weak supervision method can achieve a performance of 96.8 F1 points, 4.6 F1 higher than CyDet, without relying on any ground truth labels on the newly released Specialized Cyclist Dataset. We then discuss how heuristics can detect complex objects such as cyclists and by extension, situations, based on the output of existing object detection algorithms.
Texture Representation Using Local Binary Encoding Across Scales, Frequency Bands and Image Domains

Most of the local binary pattern (LBP) variants improve LBP by simply concatenating multi-scale LBP histograms or by encoding complementary components in one single image domain, thereby ignoring the correlation information between different scales and image domains. In this paper, we propose a novel LBP-based texture representation by exploring local binary encoding across scales, frequency bands and image domains. Specifically, given a texture image, the multi-scale low- and high-frequency images are obtained by Gaussian filtering and image subtraction. Meanwhile, the multi-scale gradient images are computed based on Gaussian derivative filtering. Then, the LBP code maps are extracted from the low-frequency, high-frequency and gradient images. Finally, the joint LBP encoding across scales, frequency bands and image domains is explored to construct histogram features for texture representation. Experimental results for texture classification demonstrate the superiority of our method over the state-of-the-art LBP variants under both noise-free and noisy conditions.
Flight Parameter Model Based Route Planning Method of UAV Using Stepped-Adaptive Improved Particle Swarm Optimization

The objective of route planning is to find a path with minimum cost on the basis of satisfying the constraints of the aircraft and the planning constraints due to obstacles. Aiming at improving the accuracy of the environmental model of route planning, this paper introduces the concept of threat function to make every location in space have specific threat value. Multiple methods of path planning are used to compare the advantages and disadvantages of point representation method and flight parameter representation method. Based on the point representation method and the flight parameter representation method, this paper simulates and compares the path planning of particle swarm optimization (PSO) algorithm and improved PSO algorithm respectively.
Spin-Torque-Based Quantum Fourier Transform

Quantum computing (QC) provides an efficient platform to solve complex problems such as number factoring and searching. The quantum Fourier transform (QFT) is an integral part of quantum algorithms for integer number factoring, phase estimation, discrete algorithms, interchange of position and momentum states, quantum key distribution protocol, multiparty quantum telecommunication, and quantum arithmetic. The theoretical and experimental implementations of QFT on various platforms have been proposed by researchers. Spin-torque-based qubit(s) manipulation is one of the encouraging solid-state device technologies. However, to date, QFT is not realized by spin-torque-based QC architecture. In this article, the spin-torque-based architecture has been modeled with the help of optimized decomposition of quantum circuits for the QFT. Moreover, an optimal-depth Clifford + T gates set-based quantum circuit is utilized to implement the QFT. The performance analysis in terms of fidelity (>99%), magnitude, and phase difference of respective density matrices for different forms of three-qubit QFT provides a novel way of its physical realization to address the complex problems.
Leverage Temporal Convolutional Network for the Representation Learning of URLs

Cyber crimes including computer virus/malwares, spam, illegal sales, and phishing websites are proliferated aggressively via the disguised Uniform Resource Locators (URL). Although numerous studies were conducted for the URL classification task, the traditional URL classification solutions retreated due to the hand-crafted feature engineering and the boom of newly generated URLs. In this paper, we study the representation learning of URLs, and explore the URL classification using deep learning. Specifically, we propose URL2vec to extract both the structural and lexical features of URLs, and apply temporal convolutional network (TCN) for the URL classification task. The experimental results show that URL2vec outperforms both word2vec and character-level embedding for URL representation, and TCN achieves the best performance than baselines with the precision up to 95.97%.
Load Balancing in Cloud Environment using Self-Governing Agent

Cloud computing is the revolutionizing technology that initiates a new paradigm shift towards an enabling technology for providing ubiquitous and on-demand access with rapid resource provisioning feature. In the present scenarios, the cloud environment is overloaded with increasing tenant requests. The large data centers of the cloud environment are emerging as the highly responsible complex structures for satisfying multiple tenant requirements and assigning job requests to appropriate servers and thereby uncompromising client resource demands without overloading the server. This constraint is overcome by the tremendous need for an autonomous load balancing algorithm in the cloud environment. The prime objective of load balancing is to reassign the total load of the overall system to individual nodes or sub-nodes to achieve effective resource utilization and thereby improve the overall response time of the job, amidst balancing overloaded nodes. The crux of this work makes use of a Self-Governing Agent Based Load Balancing Algorithm (SGA_LB), where an autonomous migration agent provides dynamic load balancing by efficiently balancing the workload. Highly dynamic and robust load balancing considers the present behavior of the system to balance the load among the nodes. The load characteristics of the nodes is considered based on the CPU load, memory used and network load. The overall performance of the proposed load balancing algorithm is measured considering some factors like reliability, throughput and fault tolerance, which thereby efficiently balances the overall load.
Framework for Cloud Service Selection and Ranking

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Design and Implementation of an Educational Game Considering Issues for Visual Impaired People Inclusion

In recent years there has been an increase in researches focusing on the effectiveness of using videogames as educational digital resources that can contribute to the learning process at different levels, which has also subsidised the increase the development of educational games. However, these games are mostly visual and not accessible to people with some type of visual impairment. This article aims to describe the design, implementation and evaluation of an accessible version of the educational game Em Busca do Santo Grau, based on EduGameAccess - a set of recommendations that integrates educational, playability and accessibility aspects for people with visual impairments.
Polynomial Equivalence of the Problems “Predicate Formulas Isomorphism and Graph Isomorphism”

The problem of isomorphism checking of two elementary conjunctions of predicate formulas is considered in this work. Such a problem appears while solving some Artificial Intelligence problems, admitting formalization by means of predicate calculus language. The exact definition of the concept of isomorphism of such formulas is given in this paper. However, isomorphic elementary conjunctions of predicate formulas are formulas that, with some substitution of variables instead of their arguments, coincide with the accuracy of the order of writing literals. Problems are described that, when solved, mean the necessity of testing formulas for isomorphism arises. Polynomial equivalence of this problem with the Graph Isomorphism (GI) problem is proved.
An Improved K-medoids Algorithm Based on Binary Sequences Similarity Measures

Nowadays a massive amount of data is generated as the development of technology and services has accelerated. Therefore, the demand for data clustering in order to gain knowledge has increased in many sectors such as medical sciences, risk assessment and product sales. Moreover, binary data has been widely used in various applications including market basket data and text documents. While applying classic widely used k-means method is inappropriate to cluster binary data, this work proposes an improvement of K-medoids algorithm using binary similarity measures instead of Euclidean distance which is generally deployed in clustering algorithms. We have considered two challenging applications, namely; text clustering and binary images categorization to show the merits of the proposed framework.
You have my sword; and my bow; and my axe

Tabletop role-playing Games (RPG), such as Dungeons & Dragons (D&D) use dice in order to control the outcome actions by characters when the Game Master needs to introduce randomness. While dice are fundamental to such games, the examination of dice as objects of design has not been explored. This study examines fifty-nine participants (thirty familiar with the D20 set system) and asks them to examine two 7-die sets commonly used in D&D, the first set being a common set of polyhedrons, and the other set designed to replicate the objects used by a Wizard. It examines the fairness perceptions of the participants and finds that players who have experience with the polyhedral set in the past are more likely to accept the fairness of Wizard dice, and that all players are more likely to accept the fairness of the Wizard set after a play session.
Design of Fuzzy PID Controller with Reference Model for Ball and Beam Mechanism

One of a popular laboratory equipment used to study nonlinear dynamics and open-loop unstable system is ball and beam mechanism. The unstable behavior of ball and beam can be observed where the ball will keeps moving on a beam when the beam is tilted. Many controllers had been designed to achieve stability for this mechanism including a PID controller. Lately, the fuzzy incorporation in the system to facilitate PID tuning is basically simplifying the design problems especially in PID tuning. This research on the other hand, will propose a modified structure to a general fuzzy PID so that specific design requirements can be realized with ease. A reference model was introduced at the set point which will bring information about the desired output specifications. Hence, different specifications can be expected from a different reference model. This paper presents the results obtained from few first-order model references.
Tilt Angle Optimization of Photovoltaic Panels

The solar PV power sector in Spain has been developing at a spectacular rate in recent years. The energy cost and the dependence on fossil fuels can be reduced by improving the efficiency of photovoltaic energy production. The performance of a solar radiation conversion system is affected by tilt angle with the horizontal plane. Thus, a photovoltaic array needs to be tilted at the correct angle to maximize its performance. In this paper, we investigate how to determine the optimum tilt angle. This procedure is illustrated for the city of Barcelona (Spain) located at latitude 41o 22' 56” North and longitude 2o 6'56” East. The optimal tilt angle varies according the seasons: In Winter (December, January, February) is 56.4, in Spring (March, April, May) is 29.11° in Summer (Jun, July, August) is 13.76° and in Autumn (September, October, November) is 48.14°. Finally, the annual optimum tilt angle for our latitude is 36.87°, with this optimal slope angle, maximum monthly and annual solar radiation is calculated. So, by means of the proposed approach, the energy generation is increased in 10.54% thanks to we are able to benefit from the maximum solar radiation.
Contour detection based on gaussian filter

Generally, computer vision shows two problems: image segmentation and contour detection. Contour is nothing but used to locate object boundary. Therefore, many times contour detection is obtained after edge detection of an image. Gray scale image have various contours those are depending on edge detection. Edge detection gives only image points at which intensity difference in pixels is considerable. And contour gives the boundary of an object and importantly, it gives proper curves present in the desired area while edge detection provides only edges and pixel points. Hence, contour detection is performed after edge detection process. So it is required to detect contours from an image for better segmentation. In this paper, Gaussian filter is used for detection of contour and segmentation of an image.
Exploring Pattern Mining for Solving the Ontology Matching Problem

This paper deals with the ontology matching problem, and proposes a pattern mining approach that exploits the different correlation and dependencies between the different properties to select the most appropriate features for the matching process. The proposed method first discovers the frequent patterns from the ontology database, and then find out the most relevant features using the patterns derived. To demonstrate the usefulness of the suggested method, several experiments have been carried out on the DBpedia ontology databases. The results show that our proposal outperforms the state-of-the-art ontology matching approaches in terms of both execution time and quality of the matching process.
Towards a generalized player model through the PEAS framework

This paper presents steps towards a generalized player model built around an external personalization and design framework. The external framework, the Player, Environment, Agents, System (PEAS) framework, resulted from a broad scope review of the personalization, player modeling, and game design literature. Leveraging this framework allows us to define a mapping from existing player and personality models to a uniform representation of player preferences over game components. We present our pipeline for developing a generalized player model from these existing models, how to translate and blend them, and finally how to use the blended model for recommending and personalizing games. We follow up the presentation of our pipeline with an extended example to highlight how two existing player modeling approaches can be combined into a singular model, and how that blended model can be used.