Reviewerly/exHarmony huggingface dataset:
Multi-level Competitive Swarm Optimizer for Large Scale Optimization

In this paper, a new multi-level competitive swarm optimizer (MLCSO) is proposed for large scale optimization. As a variant of particle swarm optimization (PSO), MLCSO first divides the particles of original swarm into two groups randomly and then compares the particles according to their fitness values. The loser with worse fitness value will be put into the first level. The winner with better fitness becomes a new little swarm. New little swarm continues to be divided and compared until the new swarm has only one particle. This process forms a multi-level mechanism. The loser will be updated by the winner. It not only shows a great balance between exploration and exploitation but also enhances the diversity. 20 different kinds of test functions are selected for the experiments. Despite MLCSO algorithm is simple, the experimental results on high-dimension by comparing it with five state-of-the-art algorithms demonstrated its effectiveness.
Cloud-Based Simulation

In order to accommodate the development and application of simulation systemsSimulation systems engineering in network environments, modeling and simulation technology embraced increasingly web-basedWeb-based to cloud-basedCloud based simulation solutions. This chapter describes the development from the early application of web services, the use of simulation grids, towards modeling and simulation as a service. A current architecture for cloud simulation platforms is presented and key technologies for its implementation are identified. The chapter deals with big dataBig data challenges as well as digital twinsDigital twin and provides some applications of cloudCloud based simulation-based simulation. It closes with the conclusion that the trend of simulation technology will be cloud-based and intelligent and motivates an intelligent cloud in support of simulation.
Mineral composition and paragenesis of altered and mineralized zones in the Gadir low sulfidation epithermal deposit (Lesser Caucasus, Azerbaijan)

Mineralogy, gold mineralization and metal contents of the Gadir deposit have been investigated during current research in order to determine the geological conditions, temporal and spatial relationship with certain mineral assemblages and associations. The mineralogy of orebodies is mainly composed of pyrite, chalcopyrite, sphalerite, galena, petsite, native gold, electrum and subordinate molybdenite. Gold is hosted by pyrite and chalcopyrite minerals in fracture-filling textures and forms a thin dispersion condition. The native gold was observed in chalcopyrite, which is probably related to the second stage of ore deposition. The Gadir deposit can be classified to Au-Ag-Cu-Zn±Pb stockwork-type mineralization which is characteristic of low sulfidation epithermal deposit.
An Efficient Image Haze Removal Algorithm based on New Accurate Depth and Light Estimation Algorithm

Single image Dehazing has become a challenging task for a variety of image processing and computer applications. Many attempts have been devised to recover faded colors and improve image contrast. Such methods, however, do not achieve maximum restoration, as images are often subject to color distortion. This paper proposes an efficient single image Dehazing algorithm that offers satisfactory scene radiance restoration. The proposed method stands on the estimation of two key indices; image blur and atmospheric light that can be employed in the Image Formation Model (IFM) to recover scene radiance of the hazy image. More clearly, we propose an efficient depth estimation method using image blur. Most existing algorithms implement atmospheric light as a constant which often leads to inaccurate estimations, we propose a new algorithm “A-Estimate” based on blur and energy to estimate the atmospheric light accurately, an adaptive transmission map also has been proposed. Experimental results on real and synthesized hazy images demonstrate an improved performance in the proposed method when compared to existing state-of-the-art methods.
k-Core: Hardware Accelerator for k-Mer Generation and Counting used in Computational Genomics

In computational genomics, the term k-mer typically refers to all the possible subsequences of length k from a single read obtained through DNA sequencing. In genome assembly, generating frequency of k-mers takes the highest compute time. k-mer counting is considered as one of the important analyses and the first step in sequencing experiments. Here, we present an FPGA based fast k-mer generator and counter, k-core to generate unique k-mers and count their frequency of occurrence. The IP core is parameterizable in terms of the line length (l) and k-mer size (k) and is implemented on XCVU095 FPGA. A speed up of about 6.6×, 5.46× and 8.5×, are achieved compared to [1], [2], [3] CPU implementations respectively and about 5.26× with respect to [1]'s GPU version.
Handwritten digit recognition based on improved VGG16 network

This paper presents a new strategy of handwritten digit recognition based on the improved VGG16 model that aims at the lack of texture features, the large differences between handwritings, and the difficulty of extracting effective information. In order to maximize the efficiency of the improved VGG16 model, an automatic drop-based learning rate scheduling is proposed to improve the SGD algorithm in the learning procedure. And automatic adaptation technique of the SGD optimizer's learning rate parameter according to the changes of accuracy rate in the previous training iterations is utilized for the training of the model, which not only apparently improves the learning results, but also speeds up the training convergence. The improved VGG16 model was evaluated on the extended MNIST dataset, achieving a high recognition accuracy rate of 99.97%. Experimental results demonstrate that the improved VGG16 model has obviously higher recognition accuracy than traditional classifiers, has stronger feature extraction ability and can meet the requirements of handwritten digits classification and recognition.
IP Generator Tool for Efficient Hardware Acceleration of Self-organizing Maps

In this paper, authors present an IP generator for FPGA-based hardware acceleration of Kohonen’s Self-Organizing Maps (SOM). The IP generator is realized in MATLAB and offers the user the possibility to design an efficient FPGA hardware accelerator with several settings such as the number of features and the number of neurons. The optimization is achieved by applying some approximations to the original SOM algorithm, these modifications do not affect the functionality of the map. The generated IP cores can be used both for training and inference and the software can check the clustering performances.
Meeting Summarization, A Challenge for Deep Learning

Text summarization is one of the challenges of Natural Language Processing. Given the volume of texts produced daily on the Internet, managers can no longer have an exhaustive reading of current events, or progress reports from their employees, etc. They urgently need tools to automatically produce a summary of this flow of information. As a first approach, extractive summarization tools have been produced and there are now commercial tools available. However, this family of systems is not well suited to certain types of texts such as written transcriptions of dialogues or meetings. In that case, abstractive summarization tools are needed. Research in that field is very old but has been particularly stimulated since the mid-2010s by the recent successes of deep learning. This paper presents a short survey of deep learning approaches to abstractive text summarization and then highlights the various challenges that will have to be solved in the coming years to deal with meeting summaries in order to be able to provide a text summarization tool that generates good quality summaries.
Perancangan Aplikasi Augmented Reality Untuk Pengenalan Ruangan Menggunakan Marker 3D Objects Tracking

Android-based mobile application about room recognition is an application that is used to provide information to users about the location of the rooms on the STIKI Malang Campus. This application is a mobile application based on Augmented Reality (AR) technology that is built on the Unity platform, using Blender in its design. In the introduction of room location, this application utilizes existing cameras on Smartphones that have been integrated with Unity applications, with the aim of bringing up objects for the part that the camera has highlighted in a certain area.
Data-efficient Neural Text Compression with Interactive Learning

Avinesh P.V.S, Christian M. Meyer. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Urgent and Emergency Care: An Academic Application System Case Study

During the 1st Semester of 2018, at the Brazilian Aeronautics Institute of Technology (Instituto Tecnologico de Aeronautica – ITA), a successful Collaborative Interdisciplinary Problem-Based Learning (Co-IPBL) experience took place. At that time, more than 20 undergrad and graduate students from 3 different courses within just 17 academic weeks had the opportunity of conceptualizing, modeling, developing, and testing a Computer System based upon Big Data, Blockchain Hyperledger, Micro-services, and other emerging technologies for government and private organizations. This Co-IPBL was performed with the participation of a medical technical team from the Hospital of Clinics at the Faculty of Medicine of the University of Sao Paulo focusing in Urgency and Emergency Care. This research work was stored in a Google site and implemented as a Proof of Concept (PoC), by using emerging technologies such as the Blockchain Hyperledger. It represents one example of how to address the old problem of teaching, learning, designing, and implementing complex intelligent systems to solve health care problems, by collaboratively working with medical technical teams facing real problems of Urgency and Emergency Health Care.
A Survey of Multilingual Neural Machine Translation

We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in recent years. MNMT has been useful in improving translation quality as a result of translation knowledge transfer (transfer learning). MNMT is more promising and interesting than its statistical machine translation counterpart, because end-to-end modeling and distributed representations open new avenues for research on machine translation. Many approaches have been proposed to exploit multilingual parallel corpora for improving translation quality. However, the lack of a comprehensive survey makes it difficult to determine which approaches are promising and, hence, deserve further exploration. In this article, we present an in-depth survey of existing literature on MNMT. We first categorize various approaches based on their central use-case and then further categorize them based on resource scenarios, underlying modeling principles, core-issues, and challenges. Wherever possible, we address the strengths and weaknesses of several techniques by comparing them with each other. We also discuss the future directions for MNMT. This article is aimed towards both beginners and experts in NMT. We hope this article will serve as a starting point as well as a source of new ideas for researchers and engineers interested in MNMT.
Improving Deep Reinforcement Learning via Transfer

While achieving the state-of-the-art performance in complex sequential tasks, deep reinforcement learning (deep RL) remains extremely data inefficient. Many approaches have been studied to improve the data efficiency of deep RL algorithms. This dissertation focuses on leveraging various transfer learning techniques to tackle this problem. We first show that positive transfer can be achieved cross-domains via direct weight transfer if the two agents share a certain amount of similarities. Then we look into how could the similarity between cross-domain tasks be quantified, such that we only transfer useful information from one task to another while blocking information that might have a negative effect. The third direction we studied is the human-agent transfer mechanism, which we integrate human knowledge via supervised pre-training on a set of demonstration data collected from a human then transfer to an agent. Lastly, several future directions are proposed for the remainder of this dissertation.
Geodesic via Asymmetric Heat Diffusion Based on Finsler Metric

Current image segmentation involves strongly non-uniform, anisotropic and asymmetric measures of path length, which challenges available algorithms. In order to meet these challenges, this paper applies the Finsler metric to the geodesic method based on heat diffusion. This metric is non-Riemannian, anisotropic and asymmetric, which helps the heat to flow more on the features of interest. Experiments demonstrate the feasibility of the proposed method. The experimental results show that our algorithm is of strong robustness and effectiveness. The proposed method can be applied to contour detection and tubular structure segmentation in images, such as vessel segmentation in medical images and road extraction in satellite images and so on.
Networking Through Scholarly Communication: Case IRNet Project

The most effective indicators of the quality of higher education are the openness and transparency of all the results of the university’s activities, including the results of the educational and scientific work of each teacher and community of researchers. It is suggested that international cooperation promotes the development of both scientific direction and increase in the rating indicators of higher education institutions. The article describes the effect of networking through scholarly communication on the professional development of the modern teacher of the university on the basis of the wide use of ICT tools. The model of scholarly content and communication landscape is presented using the example of the IRNET project. The modern tools for evaluating the effectiveness of scholarly communication of researchers who carry out the tasks of a research project are analyzed. The analysis of network resources of project participants, tools, and forms of scholarly communication and their influence on the effectiveness of joint network activity is given. The analysis of efficiency of application of tools of scholarly communication in the process of the realization of project tasks and professional development of participants is given (according to the survey results).The results show a positive dynamics in the growth of the ranking indicators for teachers, participants of international projects, and as a result an increase in quality, openness, and globalization of the educational process.
Quo Vadis Altertumswissenschaft? The Command of Foreign Languages and the Future of Classical Studies

This paper discusses the question of whether the “internationalization” of science means that students of Classics in the broadest sense, who have long been multilingual, must now in the twenty-first century are obligated to write in English, even if not all of us can adequately express our thoughts in the language of Shakespeare. In order to be noticed at all in the Anglo-American world, scientific publications increasingly have to appear in English, regardless of the language skills of the authors. Native English speakers, on the other hand, no longer consider it necessary to read in other languages and they thus run the risk of reinventing the wheel. In addition, criticism is levelled at national research policies in European countries that carelessly abandon their own languages in favor of the dominant tongue (not always used with complete accuracy). This paper argues in favor of multilingualism.
Convolutional auto-encoder for vehicle detection in aerial imagery (Conference Presentation)

Much research has been done in implementing deep learning architectures in detection and recognition tasks. Current work in auto-encoders and generative adversarial networks suggest the ability to recreate scenes based on previously trained data. It can be assumed that with the ability to recreate information is the ability to differentiate information. We propose a convolutional auto-encoder for both recreating information of the scene and for detection of vehicles from within the scene. In essence, the auto-encoder creates a low-dimensional representation of the data projected in a latent space, which can also be used for classification. The convolutional neural network is based on the concept of receptive fields created by the network, which are part of the detection process. The proposed architecture includes a discriminator network connected in the latent space, which is trained for the detection of vehicles. Through work in multi-task learning, it is advantageous to learn multiple representations of the data from different tasks to help improve task performance. To test and evaluated the network, we use standard aerial vehicle data sets, like Vehicle Detection in Aerial Imagery (VEDAI) and Columbus Large Image Format (CLIF). We observe that the neural network is able to create features representative of the data and is able to classify the imagery into vehicle and non-vehicle regions.
Hybrid parliamentary optimization and big bang-big crunch algorithm for globaloptimization

Researchers have developed different metaheuristic algorithms to solve various optimization problems. The efficiency of a metaheuristic algorithm depends on the balance between exploration and exploitation. This paper presents the hybrid parliamentary optimization and big bang-big crunch (HPO-BBBC) algorithm, which is a combination of the parliamentary optimization algorithm (POA) and the big bang-big crunch (BB-BC) optimization algorithm. The intragroup competition phase of the POA is a process that searches for potential points in the search space, thereby providing an exploration mechanism. By contrast, the BB-BC algorithm has an effective exploitation mechanism. In the proposed method, steps of the BB-BC algorithm are added to the intragroup competition phase of the POA in order to improve the exploitation capabilities of the POA. Thus, the proposed method achieves a good balance between exploration and exploitation. The performance of the HPO-BBBC algorithm was tested using well-known mathematical test functions and compared with that of the POA, the BB-BC algorithm, and some other metaheuristics, namely the genetic algorithm, multiverse optimizer, crow search algorithm, dragonfly algorithm, and moth-flame optimization algorithm. The HPO-BBBC algorithm was found to achieve better optimization performance and a higher convergence speed than the above-mentioned algorithms on most benchmark problems.
A Parallel Bit-Plane Operation Based Chaotic Image Encryption Scheme

Nowadays, the Internet is the medium through which peoples are sharing digital images. However, security is an issue in sharing of those digital images. So the main objective is to provide security in digital images. This paper proposes a bit-plane operation based encryption scheme which to provide security in digital images. In this encryption scheme, first, the bits in bit-planes are confused by using 4D hyper-chaotic map and then diffused by using PWLCM (Piecewise Linear Chaotic Map) system. Both the confusion and diffusion operations are performed in parallel and independent to get better security in images. The main advantage of this scheme is the use of separate chaotic sequences and separate key images to perform parallel and independent bit-level operation. This type of parallel and independent operation confuses pixels, reduces in attacking the cryptosystem by attackers, and thus increases the level of security. Moreover, the use of 4-D hyper-chaotic map increases the key space of the algorithm and thus more resists the algorithm against brute-force attack. Apart from that, a “Secure Hash Algorithm SHA-256” is used in this scheme to generate the secret keys which to provide resistivity against “Known-Plaintext Attack (KPA)” and “Chosen-Plaintext Attack (CPA)”. The proposed method has better performance in terms of encryption efficiency, security, and resistivity for most of the common attacks.
Pelabelan L(2,1) pada Graf Sierpinski S(n,k)

Labeling L (2; 1) on a graph G is the function f of the set of vertices V (G) to the set of all non-negative numbers so that │f (u) - f (w) │ ≥ 2 if d (u; w) = 1 and │f (u) - f (w)│ ≥ 1 if d (u; w) = 2. The labeling number L (2; 1) of a graph G is the smallest k number so G has labeling L (2; 1) with max {f (v): v Є V (G) g} = k. The Sierpinski Graph is a form of expansion graph specifically from a complete graph. This study shows labeling on Sierpinski graph using Chang-Kuo algorithm and obtained the values L (2; 1) {S (n; 2)} = 4 and the value of L (2; 1) {S (n; 3)} = 6 for n ≥ 2, with L (2; 1) {G} is the smallest maximum number labeling L (2; 1) from a graph G. [LABELING L(2,1) IN SIERPINSKI S(N,K)](J. Sains Indon., 42(1): 22-24, 2018) Keywords: Graph Labeling L(2,1), Sierpinski Graph
Testing Deep Neural Network based Image Classifiers.

Image classification is an important task in today's world with manyapplications from socio-technical to safety-critical domains. The recent adventof Deep Neural Network (DNN) is the key behind such a wide-spread success.However, such wide adoption comes with the concerns about the reliability ofthese systems, as several erroneous behaviors have already been reported inmany sensitive and critical circumstances. Thus, it has become crucial torigorously test the image classifiers to ensure high reliability. Many reportederroneous cases in popular neural image classifiers appear because the modelsoften confuse one class with another, or show biases towards some classes overothers. These errors usually violate some group properties. Most existing DNNtesting and verification techniques focus on per image violations and thus failto detect such group-level confusions or biases. In this paper, we design,implement and evaluate DeepInspect, a white box testing tool, for automaticallydetecting confusion and bias of DNN-driven image classification applications.We evaluate DeepInspect using popular DNN-based image classifiers and detecthundreds of classification mistakes. Some of these cases are able to exposepotential biases of the network towards certain populations. DeepInspectfurther reports many classification errors in state-of-the-art robust models.
Intervention or Collaboration?: Rethinking Information and Communication Technologies for Development

Over the past decades information system developers and knowledge engineers in ICT projects in wealthy regions of the world have come to realize that technical work can only be successful when situated in a broader organizational context. However, for low-resource environments (or example rural Africa), where contextual embedding is even more demanding given the complexity of these environments, practical, context-oriented methodologies how to do information systems engineering are still lacking. This book gives a basic but thorough insight how to develop information systems and services for people in low resource environments, from a socio-technical, information systems engineering perspective, presenting field-validated methods that cover the complete lifecycle of information systems engineering, with emphasis on context analysis, needs assessment, use case and requirements analysis and (business) sustainability analysis. Since technical development does not go without critical reflection, this book also investigates which (tacit) assumptions affect the way technologies are implemented in poor, low-resource environments. Linking collaborative sociotechnical development with theories of complexity and social networks of innovation, this book offers a reflective and critical approach to information and communication technologies for development.