Reviewerly/exHarmony huggingface dataset:
Collaborative Information System Toward a Robotic Process Automation Solution

Nowadays information systems need to be open and communicative across their environment. However, the processes that hold the enterprise applications often have a heterogeneity and a technological complexity which makes the task of re-engineering difficult and costly. In this paper, we adopted a robotic process automation approach for deploying a software automated interoperability framework according to an event-driven architecture. The robot is an agent able to analyze a graphical interface of any application in order to retrieve meaningful information and produce an adequate action. Once the action reaches the interoperability framework, the event will be interpreted according to the capability of the involved robots. The study is useful in fraud detection as well as risk assessment.
Neighborhood kinship preserving hashing for supervised learning

Most existing hashing methods rarely utilize the label information to learn the hashing function. However the label information of the training data is very important for classification. In this paper, we develop a new neighbor kinship preserving hashing based on a learned robust distance metric, which can pull the intra-class neighborhood samples as close as possible and push the inter-class neighborhood samples as far as possible, such that the discriminant information of the training data is incorporated into the learning framework. Furthermore, the discriminant information is inherited into the hamming space by the proposed neighbor kinship preserving hashing which can obtain highly similar binary representation for kinship neighbor pairs and highly different binary representation for non-kinship neighbor pairs. Moreover, the proposed priori intervention iterative optimization algorithm can better apply the learned discriminant information for classification and matching. Experimental results clearly demonstrate that our method achieves leading performance compared with the state-of-the-art supervised hashing learning methods.
SparseMAAC: Sparse Attention for Multi-agent Reinforcement Learning

In multi-agent scenario, each agent needs to aware other agents’ information as well as the environment to improve the performance of reinforcement learning methods. However, as the increasing of the agent number, this procedure becomes significantly complicated and ambitious in order to prominently improve efficiency. We introduce the sparse attention mechanism into multi-agent reinforcement learning framework and propose a novel Multi-Agent Sparse Attention Actor Critic (SparseMAAC) algorithm. Our algorithm framework enables the ability to efficiently select and focus on those critical impact agents in early training stages, while eliminates data noise simultaneously. The experimental results show that the proposed SparseMAAC algorithm not only exceeds those baseline algorithms in the reward performance, but also is superior to them significantly in the convergence speed.
Feasible Personal Identification by Eye Blinking Using Wearable Device

Personal identification remains extremely important for the enhancement of digital lives. Biometrics is a powerful tool for identification, however, special tools, most of which are large and expensive are required. Due to the rapid advances in wearable and/or smart/intelligent sensor technologies, capturing human movements has become easier and is an attractive approach to identification. This paper experimentally reveals the feasibility of using glass type wearable sensors for personal identification based on eye blinking. Several preliminary experiments are performed and the results clarify that the proposed method has potential for human identification.
Awareness and Usage of Emerald Insight Database as Determinant of Research Output for Researcher Scholar of Aligarh Muslim University, India

Purpose: This paper focuses on the usage of Emerald Insight database by the university researchers of all branches. This study intends to determine the researchers’ usage pattern and their level of satisfaction toward the Emerald Insight database.Methodology: A total of 260 well-structured questionnaires were randomly distributed to the researchers of distinguish faculties and out of these 187 questionnaires were received. Later, each response was examined to check whether the respondent had filled questionnaires appropriately or not. Only six questionnaires were found to be discarded due to incomplete information provided by the respondents. Finally, a total of 181 questionnaires were included in the study constituting 69.61% response rate. Afterward, these responses were coded and various statistics such as Central Tendency, Cumulative Frequency, Percentage, etc. were applied upon the data using SPSS. According to the nature of data graphs, charts, and tables were drawn using MS Excel. The usage data of Emerald Insight for last six years was also clubbed into the study which was arranged by the Maulana Azad (MA) Library on a request.Findings: Demographically, a total of 181 respondents were participated in the study of which 170 respondents were working on PhD and only 11 respondents were working on PDF (Post Doctorate Fellowship) program. All respondents were found aware of Emerald Insight database and none was found unaware; however some respondents (16.57%) were averagely aware, a very few respondents (10.49%) were scarcely aware, and remaining (72.89%) were fully aware. The availability of full text, multidisciplinary nature, and friendly interface were major reasons that compelled researchers to use Emerald Insight database. More or less, both searching techniques were used by respondents. In advance search category, phrase search (55.24%) appeared as most common method followed by author search (24.30%) and Boolean operators (12.15%). Their order of preference was as the newest one (46.96%) than relevancy (45.86%) and lastly, the oldest one (7.18%). Besides, the researchers were also found highly satisfied in all aspects of features provided by the Emerald Insight database from searching techniques to its recency and adequacy of literature. Furthermore, the majority of researchers (84.53%) were found fully satisfied, whereas only 2.20% respondents were fully dissatisfied. Their satisfaction was supported by the usage date received by the MA Library which had revealed a gradual increment in the number of items downloaded per year from 2012 to 2017. The usage just doubled from 4546 items downloaded in 2012 to 9541 items downloaded in 2017.Originality: Indeed, this study is original and guarantees that none of such study has been conducted earlier. After going through the profound literature review, no similar study is found. Hence, this study, of course, is new and original.
A Knowledge-Based Augmented Reality Tool for Managing Design Variations

In view of the wide scope of challenges concerning Industry 4.0, a variety of enabling digital industrial technologies can support the digitization of the manufacturing sector. Among them, Augmented Reality represents one of the most promising innovation accelerators that will support human workers and bring Smart Factories to a higher level of efficiency. To this end, the paper presents an Augmented Reality tool that provides support at the workplace to easily detect and collect design changes by augmenting virtual 3D models, as defined in the project plan, on the actual design. The proposed tool runs on a consumer smartphone and adopts hybrid tracking techniques to allow workers to formalize and make more efficient the knowledge management of the design changes within the overall design process.
Querying relational event graphs using colored range searching data structures

We present a general approach for analyzing structural parameters of a relational event graph within arbitrary query time intervals using colored range query data structures. Relational event graphs generally represent social network datasets, where each graph edge carries a timestamp. We provide data structures based on colored range searching to efficiently compute several graph parameters (e.g., density, neighborhood overlap, h-index).
System analysis of monitoring ecological information using Fuzzy ART neural network

The article considers the peculiarities of clustering of monitoring ecological information using a generalized model of ART family network. It formulates the approach to obtaining a comprehensive assessment of a region's ecological situation (by the example of Voronezh oblast) using Fuzzy ART network. The suggested method helps to identify the structural links between the monitoring indicators of the ecological system being investigated, which allows creating a basis for a logical and consistent approach to the decision-making problem. The clustering results and the analysis of the obtained cluster prototypes make it possible to select a grounded mathematical model of a comprehensive assessment of the region's ecological situation. Moreover, the constructed ecological situation analysis model and the developed software allow experts to keep track of trends in the region's ecological situation.
Amalgam: Hardware Hacking for Web Developers with Style (Sheets)

Web programming technologies such as HTML, JavaScript, and CSS have become a popular choice for user interface design due to their capabilities: flexible interface, first-class networking, and available libraries. In parallel, driven by the standards set by the mobile companies, embedded devices manufacturers now want to replicate these capabilities. As a result, embedded devices that use web technologies for their graphical interface have started to emerge. However, the programming effort required to integrate web technologies with embedded software hinders its adaption. In this paper, we introduce Amalgam, a system that facilitates the development of embedded devices that use web programming technologies. Amalgam does this by translating the physical interface of embedded hardware components found (e.g., a push button) directly into the HTML and CSS syntax. Our system reduces the programming effort required to develop new embedded devices that use web technologies, as well as adds new interesting capabilities to the design of these. We show Amalgam’s capabilities by exploring three embedded devices built using web programming technologies. Also, we demonstrate how Amalgam reduces programming effort by comparing two traditional approaches of building one of these devices against Amalgam. Results show our system reduces the lines of code required to integrate hardware elements into an embedded device application to a line of code per hardware component added to the device.
A Hardware Accelerator Based on Quantized Weights for Deep Neural Networks

The paper describes the implementation of systolic array-based hardware accelerator for multilayer perceptrons (MLP) on FPGA. Full precision hardware implementation of neural network increases resource utilization. Therefore, it is difficult to fit large neural networks on FPGA. Moreover, these implementations have high power consumption. Neural networks are implemented with numerous multiply and accumulate (MAC) units. The multipliers in these MAC units are expensive in terms of power. Algorithms have been proposed which quantize the weights and eliminate the need of multipliers in a neural network without compromising much on classification accuracy. The algorithms replace MAC units with simple accumulators. Quantized weights minimize the weight storage requirements. Quantizing inputs and constraining activations along with weights simplify the adder as well as further reduce the resource utilization. A systolic array-based architecture of neural network has been implemented on FPGA. The architecture has been modified according to Binary Connect and Ternary Connect algorithms which quantize the weights into two and three levels, respectively. The final variant of the architecture has been designed and implemented with quantized inputs, Ternary connect algorithm and activations constrained to +1 and −1. All the implementations have been verified with MNIST data set. Classification accuracy of hardware implementations has been found comparable with its software counterparts. The designed hardware accelerator has achieved reduction in flip-flop utilization by 7.5 times compared to the basic hardware implementation of neural network with high precision weights, inputs and normal MAC units. The power consumption also has got reduced by half and the delay of critical path decreased by three times. Thus, larger neural networks can be implemented on FPGA that can run at high frequencies with less power.
Web Page Structured Content Detection Using Supervised Machine Learning

In this paper we present a comparative study using several supervised machine learning techniques, including homogeneous and heterogeneous ensembles, to solve the problem of classifying content and noise in web pages. We specifically tackle the problem of detecting content in semi-structured data (e.g., e-commerce search results) under two different settings: a controlled environment with only structured content documents and; an open environment where the web page being processed may or may not have structured content. The features are automatically obtained from a preexisting and publicly available extraction technique that processes web pages as a sequence of tag paths, thus the features are extracted from these sequences instead of the DOM tree. Besides comparing the performance between different models we have also conducted extensive feature selection/combination experiments. We have achieved an average F-score of about 93% in a controlled setting and 91% in an open setting.
The Formal Understanding Models

This paper aims to introduce three types of understanding models from the perspective of human cognitive systems and machine information processing. The method steps are as follows: 1. Obtain a complete all equal formal understanding model (A) by constructing a twin Turing machine between numbers and numbers. 2. Obtain an approximately equal intelligent understanding model (B) by constructing a twin Turing machine between numbers and symbols. 3. Obtain a similar socialized understanding model (C) by constructing a twin Turing machine between numbers and characters, which is characterized by: the model A to B and then C gradually converge. As a result, it was found that the machine formal information processing and the human content information processing are opposite in convergence. It is clear that the combination of the three formalized understanding models and the bilingual model of interpretative translation is the key to formal understanding, intelligent understanding and social understanding. Based on them, ambiguity, misunderstanding and understanding are all well understood. The significance is that it proves that the three types of understanding models and the two sets of convergence modes can effectively determine the formal understanding process. Furthermore, it is clear that the ways of human and computer are combined completely which is better than pure humans or simple machines. That can be applied to cognitive systems and information processing perfectly. And its application is in the combination of human-machine-specific personalized ability training and standardized knowledge learning and management, especially based on the targeted reuse of subject knowledge centers.
Victim detection platform in IoT paradigm

Summary The advent of Internet of Things (IoT) paradigm is having a great ripple effect in the field of disaster management as well as many other areas. Victim detection has been studied as rescuer‐ or victim‐oriented approach, which utilises the IoT advanced technologies, but they have weaknesses according to disaster types. In this paper, we propose a Victim Detection Platform (VDP) architecture combining both approaches using various advanced technologies such as IoT, drone and edge/cloud computing to offer higher quality services, which ultimately result in swifter, better responses and more lives saved. First, we have reviewed related literature regarding three crucial issues (ie, multi‐modal evaluation for reliable data, edge‐based real‐time response, and privacy‐preserved Big Data analysis) to satisfy the service. In order to achieve these important aspects, the VDP architecture is described along with roles/relations of technologies and concepts leveraged to and data sources considered in the platform. To explore the realisation possibility of the proposed approach in real disasters, a validation scenario considering the critical issues and details in a victim detection task is illustrated, and then appropriate techniques, which will be utilised in the proposed VDP, and its justifications are discussed. Finally, we debate how the techniques are harmonised in the VDP and what the challenges are.
Systolic Array Implementation of Mix Column and Inverse Mix Column of AES

This paper construes systolic array based architecture for the implementation of mix column and inverse mix column layers of AES block cipher algorithm. Mix column layer oriented processing units (PEs) that relies on Galois field operations drives the platform for the systolic arrays. Usage of systolic approach for mix column provides better through- put implementations. Inference of high throughput for systolic architecture were obtained on comparing FOM computed for the systolic architecture implementation against conventional hardware implementations.
A sustainable process and toolbox for geographical linked data generation and publication: a case study with BTN100

We describe the process and tools that we have used to generate and publish the BTN100 Linked Dataset, based on the original data from the Spanish Topographic Base (1:100.000 scale) from the Spanish Instituto Geográfico Nacional. We have taken into account the limitations and lessons learned from our initial experience on the generation and publication of Linked Data from a range of geographical sources in Spain, in 2010, and we have now refined the process in order to facilitate: declarative mappings for the transformations from existing open data (shapefiles), automation of transformations whenever there are changes in the original data sources, version control, and alignment with INSPIRE URIs. As a result of this transformation and publication process we have also updated the reference ontology for geographical features and aligned with general ontologies such as GeoSPARQL.
Subspace Quantization on the Grassmannian

We extend the K-means and LBG algorithms to the framework of the Grassmann manifold to perform subspace quantization. For K-means it is possible to move a subspace in the direction of another using Grassmannian geodesics. For LBG the centroid computation is now done using a flag mean algorithm for averaging points on the Grassmannian. The resulting unsupervised algorithms are applied to the MNIST digit data set and the AVIRIS Indian Pines hyperspectral data set.
OCR System for Automating Answer Script Marks using Auto Resonance Network

Optical Character Recognition is one of the challenging areas in pattern recognition systems. It has gained a lot of importance in the fields of image processing and computer vision. In this paper, the handwritten digits on an evaluated answer booklet are taken for recognition by the neural network. Image acquisition, connected component analysis and contour detection methods are performed prior to the recognition process. The segmented handwritten digits are recognized through neural networks based on Auto Resonance Network as described in this paper. Auto Resonance Network can classify real valued multi-dimensional inputs and can have an adjustable threshold for each node in the network. The Modified National Institute of Standards and Technology database is used for training the network. An accuracy of 91% is achieved by using a single layer Auto Resonance Network.
The eXeLearning for social arithmetics through scientific approach

This study aims to develop mobile learning applications in learning mathematics on social arithmetic materials. This research is development research with the 4D model that is defined, design, develop, and disseminate. The subjects of this study were seventh-grade students of Muhammadiyah Tepus Junior High School with ten students chosen randomly. The object in this study is the student's response to developing eXe Learning media in the learning of social arithmetic. The research instrument is a student response questionnaire. Based on the results of the study, development of the mobile learning mathematics application with the criteria of usefulness, ease of use, ease of learning, and satisfaction include in the excellent category. From the research conducted can be obtained the calculation results of the standard overall criteria of the questionnaire by 90. These results indicate average with a good category so that development the Mobile Learning application on social arithmetic material for students based on the 2013 curriculum through the scientific approach can be used in the learning process independently at school or outside a school.
Distributed training of deep neural networks with spark: The MareNostrum experience

Deployment of a distributed deep learning technology stack on a large parallel system is a very complex process, involving the integration and configuration of several layers of both, general-purpose and custom software. The details of such kind of deployments are rarely described in the literature. This paper presents the experiences observed during the deployment of a technology stack to enable deep learning workloads on MareNostrum, a petascale supercomputer. The components of a layered architecture, based on the usage of Apache Spark, are described and the performance and scalability of the resulting system is evaluated. This is followed by a discussion about the impact of different configurations including parallelism, storage and networking alternatives, and other aspects related to the execution of deep learning workloads on a traditional HPC setup. The derived conclusions should be useful to guide similarly complex deployments in the future.
Look and Feel What and How Recurrent Self-Organizing Maps Learn

This paper introduces representations and measurements for revealing the inner self-organization that occurs in a 1D recurrent self-organizing map. Experiments show the incredible richness and robustness of an extremely simple architecture when it extracts hidden states of the HMM that feeds it with ambiguous and noisy inputs.
High-Performance Soft Decision Algorithm for Bursty Channel Decoding of Reed–Solomon Codes

A novel burst-error correcting algorithm-based ordered-statistics decoding (BC-OSD) is proposed for the Reed-Solomon (RS) decoder. It combines the characteristics of the bursty channel and takes an advantage of the channel information to determine the information set. The multisegment burst error can be decoded, which makes significant improvement on the decoding performance. Moreover, we propose a reduced complexity version of the Gaussian elimination, which can save more than 70% of storage resources that is optimized for efficient hardware implementation. The proposed algorithm can give a coding gain reaching up to 0.95, 0.82, and 0.75 dB compared with reformulated inversionless burst-error correcting algorithm (RiBC), hard-decision decoding (HDD), and BCHDD-LCC algorithm, respectively, when SER is 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> .