Reviewerly/exHarmony huggingface dataset:
On acceleration of the k-ary GCD Algorithm

Abstract In our paper we analyze a method of acceleration of the k -ary Algorithm of finding the greatest common divisor for long natural numbers. The k -ary GCD algorithm was invented in 1990 by J. Sorenson. We show that a small modification of Sorenson’s Algorithm gives a sufficient acceleration of its work. This is useful for applications using the GCD operation like calculations in finite fields, generation of Cryptography keys etc.
Towards a Robot Architecture for Situated Lifelong Object Learning

The ability to acquire knowledge incrementally and after deployment is of utmost importance for robots operating in the real world. Moreover, robots that have to operate alongside people need to be able to interact in a way that is intuitive for the users, e.g., by understanding and producing natural language. In this paper we present a first prototype of a robot architecture developed for situated lifelong object learning. The system is able to communicate with its users through natural language and perform object learning and recognition on the spot through situated interactions. In this first stage, we evaluate the system in terms of recognition accuracy which gives an indirect measure of the quality of the collected data with the proposed pipeline. Our results show that the robot can use this data for both learning and recognition with acceptable incremental performance. We also discuss limitations and steps that are necessary in order to improve performance as well as to shed some light on system usability.
Annotation with adpositional argumentation

This paper explains Adpositional Argumentation (AdArg), a new method for annotating arguments expressed in natural language. In describing this method, it provides the guidelines for designing a Gold Standard Corpus (GSC) of argumentative discourse in terms of so-called argumentative adpositional trees (arg-adtrees). The theoretical starting points of AdArg draw on the combination of the linguistic representation framework of Constructive Adpositional Grammars (CxAdGrams) with the argument categorisation framework of the Periodic Table of Arguments (PTA). After an explanation of these two frameworks, it is shown how AdArg can be used for annotating arguments expressed in natural language. This is done by providing the arg-adtrees of four concrete examples of arguments, which substantiate the four basic argument forms distinguished in the PTA. The present exposition of the fundamental tenets of AdArg enables the building of a GSC of argumentative discourse, that means an annotated corpus of texts and discussions of undisputable high-quality according to argumentation theory experts. Such a GSC should be conveniently annotated in terms of arg-adtrees, which is a time-consuming process, as it needs highly skilled annotators and human supervision. However, its role is crucial for developing instruments for computer-assisted argumentation analysis and eventual application based on machine learning natural language processing algorithms.
Decision Templates based Ensemble Classifiers for Automobile Insurance Fraud Detection

In this paper, we have removed the class imbalance problem using SMOTE and used Decision Templates ensemble technique for efficiently detecting the auto insurance fraud. We employed three supervised classifiers namely, Support Vector Machine, Multilayer Perceptron and K-nearest Neighbors for classification purpose. The final classification results are obtained by aggregating the results obtained from the classifiers using Decision Templates technique. Our model has been experimentally evaluated with a real -world automobile insurance dataset.
The Improved Artificial Neural Network Based on Cosine Similarity in Facial Emotion Recognition

In this study, we present the improved artificial neural network based on cosine similarity in facial emotion recognition. We apply a shifting window that employs a neural network for two concurrent processes consisting of face detection and emotional recognition. To prevent the slow and futile computations, non-face areas need to be filtered from neurons on each network layer, thus we propose the improved artificial neural network based on cosine similarity. Cosine similarity is employed to bypass the process of non-face areas in the neural network. The accuracy of the proposed method reaches 0.84, while the accuracy of the original neural network method reaches 0.74. It can be concluded that our methods work accurately. It can be concluded that our method works accurately. The proposed method is superior to the state-of-the-art algorithms.
Detection of Microcalcifications in Digital Mammography Images, using Deep Learning Techniques, based on Peruvian Casuistry

Breast cancer is one of the most critical and aggressive pathologies suffered in the majority of women in the world, women in Peru are not free to suffer from this pathology, this paper presents a technique for detection of the malignant and benign microcalcifications, using digital mammography images, for the training and validation stage the use of a database containing images corresponding to microcalcifications classified as benign and malignant was used, these images of the database were created From mammographic images containing microcalcifications, these images correspond to Peruvian patients, the Python programming language was used with the TensorFlow and Keras library, with the use of them a deep learnig network was designed with which results were obtained that give a high probability of being used in the clinical environment, these results are the order of 0.94. This article presents as a proposed methodology the design of the database of the images used for the training of the deep learning network as well as the structure of the network.
A policy driven, human oriented information security model: a case study in UAE banking sector

As companies continue to invest in information security, human weaknesses continue to remain a root cause of data breaches in organisations. Several security models have been proposed in the literature but largely remain ineffective at addressing this human vulnerability. In this paper, a policy-driven, human-oriented information security model is proposed. By adopting an information security policy, organizations set strong foundations on which sound security practices can be disseminated and enforced within the organisation. Instead of viewing human as the source of problem, it is a model that put human as the primary source of effectiveness to implement security policy. In this model, staffs in an organization will collectively secure an organisation from attacks. From existing literature and interviews conducted with selected banks in UAE, three primary factors, namely information security policy awareness, security training, and computer & security technology proficiency have been identified and incorporated into the new security model.
Kidney and Tumor Segmentation using U-Net Deep Learning Model

Medical Image Segmentation is a challenging field in the area of Computer Vision. In this paper U-Net deep learning model was used for semantic segmentation. The reason for shortlisting U-Net was its suitability on small data set and also it was originally designed for Biomedical Image segmentation process. Visual representations of the predicted results have shown promising results using U-Net. Experimental results were computed on two different cases. Case No 1, includes testing the method on images for which labelled information was available and considering only those slices where the presence of kidney was detected. Case No 2, involves testing the method on those images whoâ€™s labelled information was not available and applying the method on all the CT slices with respect to a patient. Experimental results was based on a metric called IOU (Intersection over Union) score which is one of the most commonly used metric in semantic segmentation.
Investigating the Efficiency of Parallel Algorithms for Stochastic Optimization

The paper presents the results of investigating the efficiency indicators of two parallel algorithms for stochastic optimization-the genetic algorithm and the particle-swarm algorithm. The paper discusses experimental relationships for the efficiency indicators of the parallel algorithms: runtime, acceleration, error, and solution stability, depending on the type and dimension of the target function, the number of agents used, and processor cores. The comparative estimates for the algorithms are given.
Hopfield Network Based Approximation Engine for NP Complete Problems

NP Complete problems belong to a computational class of problems that have no known polynomial time solutions. Many popular and practically useful problems in the field of optimization and graph theory which have real life application are known to be NP Complete and solving them exactly is intractable. The existing problem with these is that there is no known efficient way to locate a solution in the first place, the most notable characteristic of NP-complete problems is that no fast solution to them is known. However approximate solutions can be obtained in polynomial time. Hopfield networks are one of the ways to obtain approximate solution to the problems in polynomial time. Exploiting the reducibility property and the capability of Hopfield Networks to provide approximate solutions in polynomial time we propose a Hopfield Network based approximation engine to solve these NP complete problems.
Tracing Ecologies of Code Literacy and Constraint in Emojis as Multimodal Public Pedagogy

This article explores the role of Bitzer's constraint through Brooke's ecologies of code, culture, and practice as a method to analyze emoji as a form of multimodal public rhetoric. In particular, this article suggests that understanding digital forms such as emoji, which are controlled by the Unicode consortium, requires understanding the essential role that code literacy can play in shaping public writing practices in social media through two case studies: one on the multicultural skin tone emoji iOS update of 2015 and another on public emoji proposals as a way to illustrate how everyday rhetors can use networked writing intervene at the level of code.
Determining Principal Component Cardinality Through the Principle of Minimum Description Length

PCA (Principal Component Analysis) and its variants are ubiquitous techniques for matrix dimension reduction and reduced-dimension latent-factor extraction. One significant challenge in using PCA, is the choice of the number of principal components. The information-theoretic MDL (Minimum Description Length) principle gives objective compression-based criteria for model selection, but it is difficult to analytically apply its modern definition - NML (Normalized Maximum Likelihood) - to the problem of PCA. This work shows a general reduction of NML problems to lower-dimension problems. Applying this reduction, it bounds the NML of PCA, by terms of the NML of linear regression, which are known.
Spark Performance Optimization Analysis in Memory Tuning On GC Overhead for Big Data Analytics

Apache spark is one of the high speed "in-memory computing" that run over the JVM. Due to increasing data in volume, it needs performance optimization mechanism that requires management of JVM heap space. To Manage JVM heap space it needs management of garbage collector pause time that affects application performance. There are different parameters to pass to spark to control JVM heap space and GC time overhead to increase application performance. Passing appropriate heap size with appropriate types of GC as a parameter is one of performance optimization which is known as Spark Garbage collection tuning. To reduce GC overhead, an experiment was done by adjusting certain parameters for loading and dataframe creation and data retrieval process. The result shows 3.23% improvement in Latency and 1.62% improvement in Throughput as compared to default parameter configuration in garbage collection tuning approach.
Long-term Prediction of Motion Trajectories Using Path Homology Clusters

In order for robots to share their workspace with people, they need to reason about human motion efficiently. In this work we leverage large datasets of paths in order to infer local models that are able to perform long-term predictions of human motion. Further, since our method is based on simple dynamics, it is conceptually simple to understand and allows one to interpret the predictions produced, as well as to extract a cost function that can be used for planning. The main difference between our method and similar systems, is that we employ a map of the space and translate the motion of groups of paths into vector fields on that map. We test our method on synthetic data and show its performance on the Edinburgh forum pedestrian long-term tracking dataset [1] where we were able to outperform a Gaussian Mixture Model tasked with extracting dynamics from the paths.
Multi-level Stereo Attention Model for Center Channel Extraction

In recent years, the spatial audio reproduction of digital media has become popular. Despite the demand for such spatial audio content, very little content is produced with multi-channel audio. Moreover, it is difficult to provide interactive services to users owing to the lack of object-based content. In this paper, we propose a center channel extraction method based on a multi-level convolutional neural network structure to generate object-based content. In addition, we present a novel stereo attention model which considers each channel's characteristics. By applying the proposed method to stereo audio content, we achieve better extraction performance than existing commercial application.
An optimization framework for simulation and kinematic control of Constrained Collaborative Mobile Agents (CCMA) system

We present a concept of constrained collaborative mobile agents (CCMA) system, which consists of multiple wheeled mobile agents constrained by a passive kinematic chain. This mobile robotic system is modular in nature, the passive kinematic chain can be easily replaced with different designs and morphologies for different functions and task adaptability. Depending solely on the actuation of the mobile agents, this mobile robotic system can manipulate or position an end-effector. However, the complexity of the system due to presence of several mobile agents, passivity of the kinematic chain and the nature of the constrained collaborative manipulation requires development of an optimization framework. We therefore present an optimization framework for forward simulation and kinematic control of this system. With this optimization framework, the number of deployed mobile agents, actuation schemes, the design and morphology of the passive kinematic chain can be easily changed, which reinforces the modularity and collaborative aspects of the mobile robotic system. We present results, in simulation, for spatial 4-DOF to 6-DOF CCMA system examples. Finally, we present experimental quantitative results for two different fabricated 4-DOF prototypes, which demonstrate different actuation schemes, control and collaborative manipulation of an end-effector.
A Distributed Architecture for Modular and Dynamic Augmented Reality Processes

To fulfill the promises of Augmented Reality (AR) in the manufacturing and maintenance industry, the development of AR application has to become faster, simpler and cheaper. Since typical AR applications make use of basic functionality like reading markers and displaying simple overlays, a clever combination of such basic building blocks would ease the development of complex AR applications. In this paper, we present a distributed architecture for creating and managing basic AR services, e.g. for information management, user interaction, and documentation. This way, complex AR processes can be orchestrated from those services for being used in different application domains.
Competitive Multi-agent Deep Reinforcement Learning with Counterfactual Thinking

Counterfactual thinking describes a psychological phenomenon that people re-infer the possible results with different solutions about things that have already happened. It helps people to gain more experience from mistakes and thus to perform better in similar future tasks. This paper investigates the counterfactual thinking for agents to find optimal decision-making strategies in multi-agent reinforcement learning environments. In particular, we propose a multi-agent deep reinforcement learning model with a structure which mimics the human-psychological counterfactual thinking process to improve the competitive abilities for agents. To this end, our model generates several possible actions (intent actions) with a parallel policy structure and estimates the rewards and regrets for these intent actions based on its current understanding of the environment. Our model incorporates a scenario-based framework to link the estimated regrets with its inner policies. During the iterations, our model updates the parallel policies and the corresponding scenario-based regrets for agents simultaneously. To verify the effectiveness of our proposed model, we conduct extensive experiments. Experimental results show that counterfactual thinking can actually benefit the agents to obtain more accumulative rewards from the environments with fair information by comparing to their opponents.
Find A Loo: an app for sanitation governance

Billions are faced with poor sanitation in developing countries, forcing United Nations' to miss sanitation targets over the years. This is mainly due to the poor condition of sanitation in the global-south comprising mostly developing countries. International and national organizations are mainly focused on building sanitation infrastructure to target the Sustainable Development Goal 6.2 (i.e., eradicating open defecation by 2030). But the service delivery in terms of monitoring & maintenance along with other governance issues is often overlooked in most cases. How can we arrange sustainable monitoring of a global sanitation community? In this paper, we discuss how information and communications technology (ICT) may be used to address sanitation governance. We outline the necessary governance criteria found in our field-observations and then explore ICT options to address these issues. We conceptualized and created 'Find A Loo' – a mobile application designed to be a 'good-governance' enabler. We tested this application with the help of System Usability Scale (SUS) and Potential Capability Scale (PCS), where it appeared as 'Good' with an 'A Grade' score, based on evaluators' satisfaction on usability and capability. Overall the evaluation affirms that the application is not only useful and ready to use, but also capable of helping in addressing governance issues in sanitation. This work will be of interest to policy & decision-makers, researchers, service providers, NGOs and people working in the sanitation space. Further, such a system lends itself to analytics that helps optimize the performance of sanitation infrastructure.
Service Application Knowledge Graph and Dependency System

Service architecture adoption is widespread and brings many benefits, such as agile development and immutable infrastructure. However, it's hard to govern and understand the vast service ecosystem as each application evolved independently and differently (e.g., features and development methods) from each team. In this paper, we present an approach to model and process application ecosystem as a knowledge graph. The application knowledge graph can help with architectural visibility, operational efficiency, and developer productivity.
SIBI (Sistem Isyarat Bahasa Indonesia) translation using Convolutional Neural Network (CNN)

Abstract Deaf people are one of those with disabilities who cannot speak and hear. Deaf people using sign language for communication who use sign language with hand gestures, body and convey facial expressions using sign language. One important component in sign language is the alphabetical finger or the manual alphabet needed to complete communication. The alphabet of the finger is done by spelling words on spoken language, by spelling letter by letter using a finger. This method is used to spell the name or mention the word. However, not everyone understands sign language, so tools needed to bridge communication between people who are deaf with normal people. One solution that will be offered is to use computer technology as a tool to recognize sign language. The technology is an automatic language translator system that process input images using by using the Convolutional Neural Network (CNN). On this research consist of 3 class such us A, assalamualaikum and hallo which get the accuracy respectively 100% for each class.
BlockVOTE : An Architecture of a Blockchain-based Electronic Voting System

Electronic voting systems provide many advantages over traditional ballot based voting systems mainly over the accuracy and speed of the tallying process of the voting. However, electronic voting systems suffer from many technical and security issues which have limited its deployment in voting scenarios such as company voting and political elections. Centralized electronic voting systems are, by nature not secure, and there are many avenues of cyber-attacks that could tamper the voting result. Electronic voting system should be highly secured, tampered-proof guaranteed, and the voting should be trusted worthy. In this study, we propose BlockVOTE, a Blockchain-based electronic voting system. Our proposal uses Blockchain to ensure that the voting process can be kept secure and trustable through the consensus handling mechanism of the Blockchain. The architecture design and implementation suggestion are provided in this study. The implementation of the proposal was developed and tested via experimentation. The experiment result and the discussion on the possibility of adopting our proposal in an actual election is provided at the end of this study.
Maneuvering Assistance of Teleoperation Robot Based on Identification of Gaze Movement

Narrow angle of view and the time delay of command make operation of a teleoperation robots difficult. In order to solve this problem, a teleoperation system utilizing the followings is presented in this paper: measurement of operator's gaze movement, estimation of gaze point, and the saliency of monitor image proposes. In this system, operator's future gaze target beyond several seconds is estimated using the gaze motion model and an estimated gaze region from probability of saliency map. This paper introduces the configuration of the support system, modeling of gaze movement, generation of the probability of saliency map, and measurement/analysis in robot maneuver preliminary experiment.
A Survey of Low Resource Neural Machine Translation

Machine translation is one of the important research directions in the field of artificial intelligence and natural language. It has important scientific research value and practical value. Neural machine translation (NMT) is a new machine translation model which uses neural network to realize the transformation from source language to target language. In recent years, neural machine translation method based on sequence to sequence has major breakthrough in machine translation task, in most of the language of the translation effect beyond the effects of statistical machine translation, but for the translation under the condition of scarce resources, neural machine translation has yet to achieve the desired effect, this paper mainly introduces several effective methods and models under low resource conditions, as well as their problems and challenges, and the existing problems and challenges. Then look forward to the future research direction and development trend.