Reviewerly/exHarmony huggingface dataset:
GEMM-Based Quantized Neural Network FPGA Accelerator Design

In this study, we will explore Neural Network based FPGA acceleration based on accelerating General Matrix Multiplication (GEMM). GEMM acceleration allows regularized and modular implementation of accelerator design, as well as providing the benefits of scalability. GEMM based designs also offer a degree of functional flexibility which is a key benefit to understand the highly dynamic architectural developments in Deep Learning algorithms. We quantify the theoretical performance model and tradeoffs of a GEMM accelerator along with exploration of the design space. Moreover, we propose a design for an accelerator exploiting 8-bit quantization to increase bandwidth while preserving model accuracy, exploiting FPGAs for model parallelization and data re-use for high performance and low latency neural network inference. Lastly, we test and evaluate our design on the MNIST dataset. The proposed method is useful to optimize the hardware area in Deep Learning systems without sacrificing performance.
What Makes You Look Like You: Learning an Inherent Feature Representation for Person Re-Identification

In this work, we address person re-identification (ReID) by learning an inherent feature representation (inherent code) that is unique to each individual. This task is difficult because the appearance of a person may vary dramatically due to diverse factors, such as illuminations, viewpoints, and human pose changes. To tackle this issue, we propose new learning objectives to learn the inherent code for each person based on deep learning. Specifically, the proposed deep-net model is trained by jointly optimizing the multiple objectives that pulls the instances of the same person closer while pushing the instances belonging to different persons far from each other. Owing to such complementary designs, the deep-net model yields a robust code for each individual and hence better solve person ReID. Promising experimental results demonstrate the robustness and effectiveness of our proposed method.
Active Sampling-based Data-driven Reachability Verification for Proportional Navigation Guidance Law

Reachability verification for the capture region of missile guidance law is conducted. It is challenging to analytically obtain the precise capture region especially when the guaranteed interception is needed against moving evader. Therefore, the capture region of a guidance law is usually shown through numerical simulations, which requires heavy computation. In this paper, an active sampling-based reachability verification is performed based on numerical simulation. Proportional navigation guidance is considered as a guidance law, and the capture region is obtained while the computational burden is alleviated relatively. The prediction error of the active sampling-based approach is compared with that of the conventional random sampling-based approach.
Review of NoSQL Data Stores: Using a reactive three-tier application for software developers to achieve a high availability application design architecture

Every software developer needs to understand that the application architecture is essential. Despite this, many software developers follow the design of software architects and believe that software architects are always right. Changes in the design and application requirements have changed in the last years. However, with the rise of the World Wide Web (WWW), software developers had to store more data on servers. Relational databases cannot scale the data fast enough on web servers. Applications are deployed on every mobile device to cloud devices hosted on servers, and because of this change, users expect faster response times. There is minimal empirical evidence to help developers choose which data store to use. In addition, more NoSQL data stores are added to the ecosystem. In this paper, we used a systematic review to collect evidence on the best performing data store for a highly available application design architecture. This review allowed us to study experiments performed by researchers on NoSQL data stores namely wide-column, key-value, graph and document stores and which programming language driver to use for implementing such research. Our empirical findings suggest to develop a conceptual framework to design a reactive three-tier application for software developers to achieve a high availability application design architecture.
Stochastic and evolutionary optimization algorithms

Stochastic and evolutionary optimization methods are increasingly used to solve challenging optimization problems. These methods are typically inspired by some phenomena from nature and they are robust. These methods are capable of finding the global optimum of multimodal functions and they have flexibility with ease of operation. These algorithms do not require any gradient information and are suitable to solve discrete optimization problems. These methods are extensively used in the analysis, design, and operation of systems that are highly nonlinear, high dimensional and noisy or for solving problems that are not easily dealt by classical deterministic methods of optimization. This chapter mainly focuses on evolutionary and stochastic optimization algorithms such as Genetic algorithm (GA), Simulated annealing (SA), Differential evolution (DE), Ant colony optimization (ACO), Tabu search (TS), Particle Swarm Optimization (PSO), Artificial Bee colony (ABC) algorithm and Cuckoo search (CS) Algorithm. All these methods are discussed with their algorithmic schemes and implementation procedures.
Implementation of a Fuzzy Controller for an Autonomous Mobile Robot in the PIC18F4550 Microcontroller

Soft Computing has been gaining popularity in real world applications in many fields, an example of the area that has a wide variety of applications of these techniques is the Robotics area. In this work, we introduce the design of a hardware system for an autonomous mobile robot and the development of a Fuzzy Logic Controller for the control of the motion of a robot to follow a trajectory. We consider the error in the distance to the path as the unique input to the fuzzy controller and as the outputs, the linear velocity of each of the two wheels of the robot. We also show the development of the firmware in the PIC18F4550 microcontroller as the implementation of the Fuzzy Logic Controller.
Development of information security system mathematical models by the solutions of the multigrade Diophantine equation systems

In this paper there are theorems that demonstrate the validity of using Diophantine equations parametric solutions properties for information security system mathematical models. For this the proof of a generalization of the known Frolov's theorem has been done and represented. Also we present new theorem as a mathematical model of information security system containing Diophantine problems.
A Hybridized Data Clustering for Breast Cancer Prognosis and Risk Exposure Using Fuzzy C-means and Cohort Intelligence

Breast cancer is the most prevailing type of cancer responsible for a large number of deaths every year. However, at the same time, this is largely a curable type of cancer if identified at initial stages. With major advances in research in the areas of image processing, data mining and clustering and machine learning, a more precise prognosis and prediction of breast cancer are possible at earlier stages. A fuzzy clustering model is a popular model used across various researches in image processing to predict the malignancy of breast tumor. The partitional clustering method finds its strength in its fuzzy partitioning such that a data point may belong to different classes with varying degrees of membership (ranging between 0 and 1), which is less rigid as compared to an older and still popular k-means clustering algorithm. The current article attempts to hybridize the fuzzy C-means with the cohort intelligence (CI) algorithm to optimize cluster formation. CI is a robust optimization metaheuristic belonging to the class of socio-inspired optimizers (Kumar M, Kulkarni A Socio-cultural inspired metaheuristics, pp 1–28, Springer International Publishing, 2019 [22]), motivated from self-adapting behavior of candidates in a cohort or a group. CI is typically characterized by its simple algorithmic nature, robust structure and a faster convergence rate, hence gaining popularity. This novel hybridized data clustering algorithm fuzzy-CI imitates the soft clustering and communal learning attitude of clusters and candidates. The hybridized method of fuzzy-CI is validated by testing it on the Breast Cancer Wisconsin (Diagnostic) Dataset. The results validate that the hybridized version exhibits better cluster formation in comparison with the non-hybridized version.
Toward a Definition of Complex Cognitive Skill

Over the past several decades of information technology growth, work has become increasingly cognitive. It is also becoming more complex, thanks in part to growing networks of information, capability, and resources that link us and our work into large, distributed teams and organizations. In this paper, we assert that skills required for complex cognitive work are acquired in ways that qualitatively differ from the ways used for training routine procedural and rule-based skill. We conducted a literature review, peer workshop, and candidate-definition evaluation to derive a definition of complex cognitive skill (CCS). The CCS definition meets a number of evaluative criteria that include addressing complex cognitive work characteristics described in the relevant literature. The definition is expected to help researchers develop a more focused and coherent CCS research base and to help training practitioners justify the types of methods and resources required for supporting CCS acquisition. Future work will include assessing the definition for clarity, unambiguity, and, more generally, usefulness and usability.
A Point Symmetry Distance Based K-Means Algorithm for Distributed Clustering in Peer to Peer Networks

In this paper, a distributed K-Means algorithm is proposed based on the point symmetry distance measure which is termed as “Point symmetrical based distributed K-Means (PSDK-Means)” algorithm. Conventional distributed K-Means (DK-Means) clustering is able to detect only spherical shape clusters and it is not suitable for identifying the convex and concave (arbitrary shaped) clusters. The proposed method is implemented to detect spherical, convex and non-convex shape clusters which are distributed over the network at different peers. In the proposed method, cluster centers are shared using the diffusion based cooperation to achieve global clustering of the network. The cluster assignment is carried out using minimum point symmetry distance instead of Euclidean distance. Effectiveness of the proposed PSDK-Means algorithm has been validated on four synthetic and two real life datasets where it is observed to outperform conventional DK-Means algorithm.
Digital Forensics Investigation Reduction Model (DIFReM) Framework for Windows 10 OS

The advent of the digital age, globalization and automation has made life easier for people and businesses. However, the ubiquitous use of digital devices and the Internet also heightens the risk and incidents of cybercrimes. Under these circumstances, Digital Forensics has become a critical countermeasure. The ISO/IEC 27001 (Information security standards published jointly by the International Organization for Standardization – ISO and the International Electrotechnical Commission-IEC) provides guidance on identifying, gathering/collecting/acquiring, handling and protecting/preserving Digital Forensic evidence for use in court. The most challenging and important part of Digital Forensic Investigation (DFI) is data examination. Knowing the data created by the Operating System (OS) or user beforehand would ease the process. Unfortunately, most of the time, such details are not available to facilitate investigation. The examination phase is the most challenging for an investigator; in Microsoft Windows OS (Operating System). Investigators have to go through terabytes of system data, most of which are OS and application files irrelevant to the investigation from a suspect’s computer. To address the problem highlighted above, this research proposes a data reduction model (DIFReM) and a tool which will not only help the investigator in identifying modified system files but also has the ability to detect files inserted into system directories and also be able to verify integrity using hashing. In the end, this research will provide the investigator with a more effective and efficient digital forensics tools.
A Preliminary Investigation on a Graph Model of Differential Evolution Algorithm

Graph based analysis of Evolutionary Algorithms (EAs), though having received little attention, is a propitious method of analysis for the understanding of EA behavior. However, apart from mere proposals in literature, the graph model(s) of EAs have not been aptly demonstrated of their full potential. This paper presents a critical analysis of an existing prominent graph model of EAs. The comprehensive analysis involved modelling as graphs, the interactions within Differential Evolution (DE) algorithms (DE/rand/1/bin and DE/best/1/bin by way of example), while solving CEC2005 benchmark functions. Subsequently, the graph-theoretic measures- degree centrality, clustering coefficient, betweenness centrality and closeness centrality- were deployed to investigate the potential of graph model in providing insights about EA dynamics. However, the model falls short in providing useful insights about the DE runs. The observed insights were qualitative in nature and not competitive compared to empirical analysis. This indicates the need for considerable research attention in designing robust graph models.
LSTM-Based Neural Network Model for Semantic Search

To improve web search quality and serve a better search experience for users, it is important to capture semantic information from user query which contains user’s intention in web search. Long Short-Term Memory (LSTM), a significant network in deep learning has made tremendous achievements in capturing semantic information and predicting the semantic relatedness of two sentences. In this study, considering the similarity between predicting the relatedness of sentence pair task and semantic search, we provide a novel channel to process semantic search task: see semantic search as an atypical predicting the relatedness of sentence pair task. Furthermore, we propose an LSTM-Based Neural Network Model which is suitable for predicting the semantic relatedness between user query and potential documents. The proposed LSTM-Based Neural Network Model is trained by Home Depot dataset. Results show that our model outperforms than other models.
Knowledge Management Model for Nursing Services of Hospital

The purpose of this study is to produce a model that can be used as a reference by nursing managers in monitoring each action taken by nurses, updating Standard Operating Procedures (SOPs), and being able to assist the head of the room to share action expe riences between nursing staff. Nurses as nursing services implementers have different knowledge in carrying out nursing services, even though the nursing services provided are in accordance with existing procedures. This can occur due to several factors such as differences in a work period, experience habits, and knowledge from various seminar or training that have been followed by each nurse. The difference in knowledge will greatly affect the quality of nursing services to patients in hospitals, so that good and correct handling through nursing knowledge management will be very helpful in terms of managing the resources they have. The model used for knowledge conversion uses the Nonaka and Takeuchi models which consist of processes of socialization, externalization, combination, and internalization (SECI). The method used to monitor the actions taken by nurses on patients is the text mining method, in which the stemming process used the Nazief and Adriani Algorithm and in the analysis process using the Jaro -Winkler algorithm to measure the level of similarity of a nurse's experience with Standard Operating Procedure (SOP). The results of this study are to recommend a model of knowledge management at hospital nursing services so that it can build learning organizations gained from sharing knowledge and experiences between nursing staff. The model is obtained by adopting the model presented by Nonaka and Takeuchi which is combined with the knowledge taxonomy of hospital nursing services.
Method of Creating the 3D Face Model of Character Based on Textures Maps Module

The 3D character development process is long, heavy and requires a high level of professional skills. For today, traditional approaches require from the developer a lot of time, resources and high-professional skills for making a 3D character model. To facilitate this work, we propose the approach with using the textures maps module. This module includes a set of prepared and it has divided into segments textures of different types (diffuse, specular, displacement, bump, SSS). It enables to simplify the texture stage and requires less professional skills, since the developer no longer needs to create a new texture. In addition, the module enables to skip the stages of sculpting and retopology, and get various visual outcomes for same 3D model geometry. The result of texturing affects directly the quality of both shader and visualization. The implementation of the proposed approach is described, and case-study results are presented.
Design of the Information System for Kindergarten Learning Evaluation used Kanban Methodology

Abstract This study aims to produce an information system design that helps educators to evaluate learning in each semester systematically and easily accessible. Learning evaluation aims to determine the growth and development of students and see the success of the learning plan. 2013 curriculum is a curriculum that is currently being used by early childhood education. Therefore, it is necessary to design a kindergarten learning evaluation system to then create an information system for learning evaluation for kindergartens that helps teachers know the abilities of their students. The design of this system used descriptive research methods and information system design development methods that use the Kanban methodology, where the Kanban method helps design of the information systems for kindergarten learning evaluation for with the aim of harmonizing development in accordance with the wishes of users, educators who are early childhood education teachers directly in system development. The programmer to then be made by the programmer and used by kindergarten teachers later will use the results of this information system design later. The use of the Kanban method in this study makes it easy for programmers to be able to design their information systems.
Ontology-Based Model of User Activity Data for Cyber-Physical Systems

The chapter focuses on the issue of modeling of user interaction with the graphical interface of cyber-physical systems. The primary subject of the presented study is users activity data, that is the user’s actions with system graphical interface and their characteristics. This data is collected during the process of software testing or experimental operation. An overview of existing ontologies of domain «Information Systems Graphic Interface» is given. We propose an open model of user activity data in the form of ontology based on the OWL 2 DL language. The main classes, properties, and axioms of this model are covered in the report. This model differs from the other existing ontologies in that it is focused on user activity data rather than the interface and its elements. In addition, it is based on description logic SHOIQ (D), which makes it possible to draw logical conclusions in the process of analysis by experts of the system usability. We also present the structure of software developed for user activity data collection which allows filling the ontology with specific data on the user experience with the interface. As an example, we describe possible tasks where the model and collected data can be used.
A comparative study of the optimal and interpolation methods for restoration a stationary continuous signal from discrete values

Abstract In this paper, we consider the task of restoration of a continuous signal by its discrete values. We compare the optimal and interpolation restoration techniques for the continuous-discrete observation model. The expression for the root-mean-square error of the restoration of a stationary continuous signal from its discrete values is derived. For the optimal restoration case, the signal is reconstructed by the means of an optimal restoration filter. For the interpolation restoration, the signal is reconstructed with the use of linear interpolation. The results of a comparison of the root-mean-square errors of the optimal restoration and interpolation procedures with the same parameters of the input signal, additive noise, and dynamic distortion are presented.
MeshDapp – Blockchain-Enabled Sustainable Business Models for Networks

The digital world demands a network infrastructure to supply connectivity to any participant anywhere. Sustainable networks require balanced value flows. Value is connectivity delivered at a material and service cost to compensate, involving diverse participants, ranging from consumers to providers, such as last mile access, regional transport, Internet carriers, or content providers. We focus on the case of wireless mesh networks that deliver connectivity through access points and a mesh network that routes traffic to Internet gateways, provisioned by several device owners and service operators [1–3]. The presented work is motivated by the need for balance and automation among services delivered, costs and incentives for participation in these decentralised networks. This balance is key for achieving extensible network infrastructures that can deliver widespread availability of Internet connectivity with minimal barriers of entry.
Decomposition Based Multi-objective Workflow Scheduling for Cloud Environments

Workflow is a group of tasks that are processed in a particular order to complete an application. Also, it is a popular paradigm used to model complex applications. Executing complex applications in a distributed system such as cloud computing implicates optimization of several conflicting objectives such as monetary cost, energy consumption, total execution time of the application (makespan), etc. Regardless of this trend, most of the workflow scheduling approaches focused on single or bi-objectives optimization problem. In this paper, we considered the problem of workflow scheduling in a cloud environment as a multi-objective optimization problem, and hence proposed a multi-objective workflow-scheduling algorithm based on decomposition (WSABD). The proposed algorithm is capable of finding optimal solutions with a single run. Our evaluation results show that, by a single run, the proposed approach manages to obtain the Pareto Front solutions which are at least as good as schedules produced by running a single-objective scheduling algorithm with contraint for multiple times.
Framework For UAV Mobile Object Tracking based on UE4SIM

Nowadays, Unmanned Aerial Vehicles (UAVs) gain popularity and become more and more deployed for many applications. Tracking moving object is among the widespread UAVs related applications that attract researchers. Accordingly, in this paper, we propose an autonomous object tracking framework for (UAVs) called MOSSE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CA-UAV</sub> . Our proposed framework integrates the MOSSE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CA</sub> tracker and adapted it for UAV target tracking. Simulation results demonstrate the robustness of our framework.