Reviewerly/exHarmony huggingface dataset:
Online information service for training in Industry 4.0 technologies

Abstract A new element of the information service in the field of technical training of specialists for Industry 4.0 is an online platform that implements distance forms of e-learning for specialists. An online learning service is an interactive transdisciplinary infrastructure that accumulates knowledge in demand by the intellectual industry. The information potential of the online learning service allows students to create curricula based on an arbitrary sequence of studying theoretical disciplines. The designer of professional courses ensures the structuring of content and its presentation to the listener in informational forms that cause maximum involvement of students in the disciplines studied. The online training information service contains methods and scenarios based on generally recognized world practices and adapting the trajectories of individual training of specialists to modern industry requirements. A scheme of an online information service for training technical specialists for Industry 4.0 based on network technologies of interaction between the listener and educational content is proposed. It is shown that online training services form an informational competitive environment that integrates the resources of private business, state educational institutions, scientific, industrial and academic communities.
Time-Domain Quantum Maxwell Solver

We present finite-difference time-domain schemes to numerically solve quantum Maxwell's equations for arbitrary inhomogeneous media based on the macroscopic theory on quantum electrodynamics. In the Heisenberg picture, quantum Maxwell dynamical operators are expanded by a set of ladder operators defined in the coordinate space while non-orthogonal basis corresponding to a new long-range propagators different from the classical Green's function. The proposed time-domain quantum Maxwell solver is useful to study the local nature of quantum information propagation such as virtual photons or near single photon sources. We provide numerical examples associated with two photon interference occurring in a quantum beam splitter, called Hong-Ou-Mandel effect. Moreover, we emulate the recent interesting experiment on quantum interference across an astronomical distance.
QoE-VS: A Cross-Layer QoE-Aware Video Streaming Platform Using Software-Defined Radio

The prevalence of wireless imaging devices in various Internet of Things (IoT) applications is demanding for efficient wireless video communication solutions. In particular, the need to deliver real-time videos to human users with satisfactory Quality of Experience (QoE) is driven by many human-centered applications. A software-defined radio (SDR) trans-receiver has the flexibility and the re-configurable ability of software-based programming, and it has the potential to handle the dynamic needs of wireless real-time video streaming. This paper presents QoE-VS: a cross-layer QoE-aware video streaming platform based on SDR that could provide videos with satisfactory perceptual quality to human users. The platform is built upon GNU radio, Universal Software Radio Peripheral (USRP) B210 hardware, and GStreamer, a comprehensive library for video processing. A QoE measurement component is integrated in the platform for monitoring the QoE of video streaming in real-time. It also provides the capability of jointly adjusting physical layer parameters and video application settings to maximize the QoE in changing wireless conditions. The capabilities of QoE-VS have been validated through different video streaming use cases.
Evaluation of Generators' Market Power By Combination Empowerment and Trapezoidal Cloud Model

The uncertainty and fuzziness of the index data are ignored in the market power evaluation of power generators at present. In order to solve this problem, this paper proposes a comprehensive evaluation method based on combination empowerment and trapezoidal cloud model. We set up an evaluation index system of market power of generators. The weight of each index is obtained by combination weighting method. The membership degree of each index to each grade is calculated by trapezoidal cloud model. Then combination empowerment and trapezoidal cloud model are integrated to realize a comprehensive evaluation of the market power of the generator according to the principle of maximum membership degree. The case study verified feasibility and effectiveness of this method.
The Implementation of Probabilistic Neural Network Algorithm for Classification of Family Hope Program in Pekanbaru City

Abstract The poverty rate in Indonesia continues to increase, especially in Pekanbaru city. It was recorded in 2017 the poverty in Pekanbaru City reached 176.908 inhabitants. The poverty can be seen from the Education Factor, Economic Factor, Health Factor and Infrastructure Factor. This aim of this study is to classify beneficiary of poverty from education and health factors using the PNN Algorithm. The criteria used for the classification of education classes and health classes include elementary, junior high, high school, toddlers and pregnant women. The data used in this study were from the Harapan family program in Pekanbaru City. In clustering training data and test data, K-Means Algorithm was used. The results of the clustering are 3,543 test data and 1,520 testing data with DBI value of 0.194 and the result of calculation of Probabilistic Neural Network algorithm with accuracy value is 99.07%. In testing the algorithm using the confucion matrix method, the recall value is 99.30% and the precision value is 97.81%. The high level of health and education is an important factor in the development and progress of an area.
Method of Generating a Blacklist for Mobile Devices by Searching Malicious Websites

As mobile devices have become more popular, mal-ware and attacks directed at them have significantly increased. One of the methods to attack mobile devices is redirecting a user to unwanted websites by unwanted page transition. One of the countermeasures against such attacks is to generate a blacklist of URLs and hostnames, which can prevent access to malicious websites. To generate a blacklist, first, malicious websites are collected in the web space. Then, URLs and hostnames of the malicious websites are added to the blacklist. However, URLs of the malicious websites are typically changed frequently; thus, it is necessary to keep track of the malicious websites and update the blacklist in a timely manner. In this study, we proposed a method to generate blacklists for mobile devices by searching malicious websites. The method collects many HTML files from the web space using a crawler and searches for HTML files that are highly likely to be malicious using keywords extracted from the known malicious websites to discover the new ones. Thus, new malicious websites can be added to the blacklist in a timely manner. Using the proposed method, we discovered malicious websites that were not detected by Google Safe Browsing. Moreover, the blacklist generated using the method had a high detection rate for certain malicious websites. This paper reports the design process and the results of the evaluation of the new method.
Explaining End-to-End ECG Automated Diagnosis Using Contextual Features

We propose a new method to generate explanations for end-to-end classification models. The explanations consist of meaningful features to the user, namely contextual features. We instantiate our approach in the scenario of automated electrocardiogram (ECG) diagnosis and analyze the explanations generated in terms of interpretability and robustness. The proposed method uses a noise-insertion strategy to quantify the impact of intervals and segments of the ECG signals on the automated classification outcome. These intervals and segments and their impact on the diagnosis are common place to cardiologists, and their usage in explanations enables a better understanding of the outcomes and also the identification of sources of mistakes. The proposed method is particularly effective and useful for modern deep learning models that take raw data as input. We demonstrate our method by explaining diagnoses generated by a deep convolutional neural network.
Using Inclusion / Exclusion to find Bent and Balanced Monomial Rotation Symmetric Functions

Abstract There are many cryptographic applications of Boolean functions. Recently, research has been done on monomial rotation symmetric (MRS) functions which have useful cryptographic properties. In this paper we use the inclusion/exclusion principle to construct formulas for the weights of two subclasses of MRS functions: degree d short MRS functions and d -functions. From these results we classify bent and balanced functions of these forms.
Lessons From Joint Improvisation Workshops for Musicians and Robotics Engineers

We report on a series of workshops with musicians and robotics engineers aimed to study how human and machine improvisation can be explored through interdisciplinary design research. In the first workshop, we posed two leading questions to participants. First, what can AI and robotics learn by how improvisers think about time, space, actions, and decisions? Second, how can improvisation and musical instruments be enhanced by AI and robotics? The workshop included sessions led by the musicians, which provided an overview of the theory and practice of musical improvisation. In other sessions, AI and robotics researchers introduced AI principles to the musicians. Two smaller follow-up workshops comprised of only engineering and information science students provided an opportunity to elaborate on the principles covered in the first workshop. The workshops revealed parallels and discrepancies in the conceptualization of improvisation between musicians and engineers. These thematic differences could inform considerations for future designers of improvising robots.
A Survey for Recommender System for Groups

Recommendation system has been seen to be very concentrated on individual recommendations but few of the new techniques are now concentrated on groups. The aim of this paper is to provide an overview of the existing state of the art techniques for collecting ratings, strategies used in aggregating these strategies and the practical application for group recommendations. This study explored five databases which include IEEE, Science Direct, Springer, ACM and Google Scholar, from which 300 publications were screened. Irrelevant, duplicate and ambiguous papers were removed. At the end, 26 papers were used for depth analysis. This study provides a systematic review of the available evidence based literature concerning recommender systems for groups.
A scalable and low latency probe-based scheduler for data analytics frameworks

Today’s data analytics frameworks divide jobs into many parallel tasks such that each task operates on a small partition of data in order to execute jobs with low latency. Such frameworks often rely on probe-based distributed schedulers to tackle the challenge of reducing the associated overhead. Unfortunately, the existing solutions do not perform efficiently under workload fluctuations and heterogeneous job durations. This is due to a problem called Head-of-Line blocking, i.e., short tasks are enqueued at workers behind longer tasks. To overcome this problem, we propose Peacock (Khelghatdoust and Gramoli, 0000) [25] a new fully distributed probe-based scheduling method. Unlike the existing methods, Peacock introduces a novel probe rotation technique. Workers form a ring overlay network and rotate probes using elastic queues of workers. It is augmented by a novel starvation-free probe reordering algorithm executed by workers. We evaluate Peacock against two existing state-of-the-art probe based solutions through a trace driven simulation of up to 20,000 workers and a distributed experiment of 100 workers in Apache Spark under Google, Cloudera, and Yahoo! traces. The performance results indicate that Peacock outperforms the state-of-the-art in all cluster sizes and loads. Our distributed experiments confirm our simulation results.
Min-Time TS: task resource mapping algorithm in cloud computing

Abstract Internet-established cloud environment provides enormous amount of ubiquitous, distributed resources such as processors, memory, and software. In recent years, task scheduling and resource allocation has become a significant issue in cloud computing. Task scheduling plays a crucial role in cloud system performance. For achieving elegant performance, arises a need of efficient scheduling technique. Numerous algorithms developed for scheduling. In this paper, we have proposed an efficient approach which schedules the tasks to the available cloud resources by forming a task pair. Min-Time TS algorithm forms a finite number of task sets; each set consists a pair of tasks one from Group G1 and another from Group G2 and are scheduled with minimum layover time.
See Fine Color from the Rough Black-and-White

Image super-resolution and colorization are two important research fields in computer vision. In previous studies, they have been considered separately as two unrelated tasks. However, for the task of restoring gray video to high-definition color video, when the network learns to abstract features from low-resolution images and maps them to high-resolution images, the abstract understanding of images by the network is also useful for colorization task. Treating them as two unrelated tasks have to construct two different models, which needs more time and resources. In this paper, we propose a framework to combine the tasks of image super-resolution and colorization together. We design a new network model to directly map low-resolution gray images into high-resolution color images. Moreover, this model can obtain motion information of objects in the video by predicting surrounding frames with the current frame. Thus, video super-resolution and colorization can be realized. To support studying super-resolution and colorization together, we build a video dataset containing three scenes. As far as we know, this is the first dataset for such kinds of tasks combination.
WITHDRAWN: A novel generative adversarial block truncation coding schemes for high rated image compression on E-learning resource environment

Image compression is a useful technique that is inevitable to store any images in compressed pattern. The compressed image is reconstructed using image retrieval process for any applications. This technique ensures storage efficiency in various content management applications. Particularly, E-Learning resource environment deals with more space complexity. The E-Learning storage space complexity can be reduced with the help of image compression techniques. There are different types of image compression techniques invented for building compressed resources. Block Truncation Coding (BTC) is a type of lossy compression technique for reducing grey scale quantities using blocking and quantizing phases. This is an effective technique for compressing the images. In extend, Absolute Moment BTC (AMBTC) technique is proposed for minimizing the reconstruction error rate of standard BTC model. The image blocks generation and compression are the main phases of BTC model. This can be applied for both colour images and grey scale images. However, the conventional BTC procedures lacks for edge reconstructions and noise reductions in the output images. This problem creates BTC with lack of inaccuracy in complex image compression platforms such as E-Learning image sources. In the scope, the need for more active and generative image compression procedures is necessary to improve BTC principles. This work proposes highly complex and novel Generative Adversarial BTC (GA-BTC) compression models with multiple variances. GABTC is developed with multi-layered Deep Neural Network (DNN) structures with GA neural models. The integration of both GA models and BTC principles improve the quality of block constructions and reconstructions significantly. This work evaluates the model complexity and efficiency using various E-Learning images (Colour and Grey Scale) with different compression quality measurements.
Dual-Axis Solar Tracker Without Microcontroller

Solar energy is the most abundant renewable energy source on the Earth. It is one of the best replacement of fossil fuel which is considered as a secure, lower-risk renewable energy source. Solar energy comes from the sun and can be captured by various technologies primarily Solar Panel. Solar tracking system is mainly of two types: Single-axis solar tracking system and Dual-axis solar tracking system. Although many paper has been published on sun tracking solar panel based on microcontroller, in this paper we are trying to make a dual-axis solar panel without using microcontroller. Previously published papers have shown that dual axis solar panel is 31.4% more power efficient than stationary or single axis solar panel (Amadi and Gutiérrez in Eur J Electr Comput Eng 3:1–2, 2019). Our paper is based on a dual-axis solar panel which tracks the sun using four reference panels. To make the system cheaper we are using two servomotors which will move the panel according to the sun’s position in the horizontal and vertical axis using the voltage differences of the four reference panels. The main aim is to maximize the power received from the solar panel using a dual axis solar tracking system so that the energy obtained from the solar panel can be efficiently used to run the household appliances. Use of theoretical concepts has been put into use.
On sufficient statistics in quantum cryptography: quantum key distribution with mismatching detector efficiency

Abstract Accounting for the different quantum efficiency of detectors is fundamentally important for ensuring the cryptographic security of practical systems of quantum cryptography. A method for accounting for different quantum efficiency of detectors is proposed. The method is, in fact, analytical and applicable to any quantum key distribution protocols.
Experimental Comparison between Genetic Algorithm and Ant Colony Optimization on Traveling Salesman Problem

This paper is based on bio-inspired optimization algorithms. Optimization is the process of selecting the best element by following some rules and criteria from some set of available alternatives. In this paper, we have solved Traveling Salesman Problem (TSP) using Swarm Intelligence algorithms and we have compared them. First we have implemented the basic Genetic Algorithm (GA) on TSP. Then we have implemented Ant Colony Optimization (ACO) Algorithm on TSP. In optimization problem, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) Algorithm have been known as good meta-heuristic techniques. GA is designed by adopting the natural law of evolution, while ACO is inspired by the foraging behavior of ant species. Balancing the exploitation-exploration tradeoff is required in ACO. In contrast with the GA implementation, ACO was much easier to control.
A Formal Model for a Linear Time Correctness Condition of Proof Nets of Multiplicative Linear Logic

In a previous paper, we have reported a new linear time correctness condition for proof nets of Multiplicative Linear Logic without units, where we gave a description of the algorithm in an informal way. In this paper, we give a formal model for the algorithm. Our formal model is based on a finite state transition system with queues as well as union-find trees as data structures. The model has been obtained by trial and error based on a concrete implementation of the algorithm. In addition, the algorithm has a subtle mechanism in order to avoid deadlock. We give an invariant property of the state transition system and it guarantees the deadlock-freedom.
Identification of duplicate bug reports in software bug repositories: a systematic review, challenges, and future scope

Handling duplicate bug reports in a software bug repository is a very tedious and time-consuming task. Duplicate bug identification can lead to delay in development and deployment of software. It is also a major problem to be addressed in development of open-source software. Duplicate bug report identification is one of the popular research topics in mining of software repositories in the last decade. Timely identification of duplicate bug reports is an essential requirement of faster software development. Most of the software bug repositories are managed online so retrieving bug reports and finding duplicates are a challenging task. Researchers have proposed numerous solutions to handle the problem of identification of duplicate bug reports in a software bug repository in recent times. Machine learning techniques are utilized efficiently in the majority of the presented works. The key information of a software bug report is of textual data type. The important textual attributes are summary and description. Summary, which is also known as title of a software bug, represents the summarized information of a software bug, whereas description represents the detailed information of software bug. The steps by which the bug can be reproduced are also mentioned in the description of a software bug. By considering and analyzing both these attributes only, one can identify whether the bug report is duplicate to earlier reported bugs or not? In some of the exceptional works only one attribute, that is, summary (or title) is considered for detection of duplicates in the bug repositories. As both of the key attributes summary and description are textual in nature, text mining and natural language processing play a major role in identification of duplicate bug reports. This chapter focuses on presenting a systematic literature review of existing techniques of identification and management of duplicate bug reports in a software bug repository. Finding of existing machine learning and other advanced techniques for duplicate bug report identification is summarized in this chapter. Challenges and future scope in the direction of duplicate bug reports identification in software bug repositories are also discussed.
Data Flow Implementation of Erosion and Dilation

This chapter describes a data flow implementation of the image processing algorithms Erosion and Dilation. Erosion and Dilation are basic image processing algorithms which are used to reduce or increase the size of objects in images, respectively, and which are used in a wide number of image processing applications. The chapter first describes the control flow versions of the algorithms in detail. Subsequently, the translation of these algorithms to the Data Flow paradigm is examined, and the details of the data flow implementation as well as possible optimizations are discussed.
An Intelligent Algorithm for Mapping of Applications on Parallel Reconfigurable Systems

An increasing demand for computational power in daily used applications has led to implementation of different computing platforms. Among them, reconfigurable platforms which are based on FPGAs, have been widely used in recent years. While they are flexible, the communication cost between different computational tasks implemented in different locations of FPGA is one of the main challenges when it comes to improving the performance. In this paper, mapping of application tasks into processing platforms is studied. We presented a new method based on neural networks. Firstly, using Node2vec embedding algorithm, dimensions reduction, and rotation and scale, an initial mapping of task interaction graph nodes into processing structure FPGAs was obtained. Secondly, the dilation and maximum capacity utilization optimization were done using the stochastic gradient descent (SGD) method and loss functions. After performing experiments and comparing the proposed method with a similar method, results showed that the proposed method was superior in performance in terms of dilation (11.28%) and showed a poorer performance in terms of maximum capacity utilization (4.30%).