Reviewerly/exHarmony huggingface dataset:
Information System for Asset Management

Abstract The purpose of this study is to design an asset management information system in the form of desktop-based application design so that it can be used as a reference in implementing it into the application. With the application, it can facilitate BMN in making reporting related to assets control and management. Universities cannot be separated from the existence of assets owned, by seeing so many assets and development opportunities, there are often mistakes in planning, procurement, and inventory of data relating to assets. Sekolah Tinggi Pariwisata National Hotel Institute (NHI) Bandung is a College that organizes professional education programs in the field of tourism. In implementing its business processes, STP involves supporting assets owned by the State managed by the State Property section (BMN). In managing assets, which include the process of registration, request, acceptance, and maintenance of BMN goods still use the written recording process in the paper, as well as the process of making reports still using Microsoft Excel applications taken from the data written on the paper. From these problems, BMN requires a computerized information system design. System analysis methods uses Object Oriented Analysis and Design (OOAD), and Unified Modelling Language (UML).
A Dataset for Evaluating Query Suggestion Algorithms in Information Retrieval

This paper presents a dataset that can be used for evaluating query suggestion algorithms in textual information retrieval. The dataset is public and offered free of charge to the information retrieval research community. The data was gathered in an experiment that lasted more than 2 months and to which participated a number of 119 users, mainly faculty students. The dataset contains web browsing history and query history (submitted to the Google search engine) from all these users. The data is indexed in a database and downloadable in a database dump format. The dataset is very useful for evaluating general query suggestion algorithms by themselves (in a standalone manner) or against Google's MPC query suggestion algorithm. At the same time, the dataset supports building and testing personalized query suggestion algorithms that consider the user context/profile when computing query suggestions.
IR saliency detection via a GCF-SB visual attention framework

Infrared (IR) saliency detection with high detection accuracy is a challenging task due to the complex background and low contrast of IR images. In this paper, an IR saliency detection method via a new visual attention framework is proposed, which comprises two phases. In the first phase, a Gray & Contrast Features (GCF) model is established, in which the IR image is processed in two feature channels, a gray feature channel and a contrast feature channel. And then a primary feature map can be obtained by fusing the gray and contrast features from these two channels, which is the basis of the second phase. In the second phase, a Similarity-based Bayes (SB) model is established, in which two prior probabilities and two likelihood functions are calculated according to the previously obtained primary feature map. Finally, the saliency map is calculated with the obtained prior probabilities and likelihood functions by Bayes formula. Experimental results indicate that the proposed method can effectively reduce noise and enhance contrast of IR images with complex background and low contrast, and obtain a higher detection accuracy and robustness than seven state-of-the-art methods.
An Analysis of Evolutionary Algorithms for Multiobjective Optimization of Structure and Learning of Fuzzy Cognitive Maps Based on Multidimensional Medical Data

The paper concerns the use of evolutionary algorithms to solve the problem of multiobjective optimization and learning of fuzzy cognitive maps (FCMs) on the basis of multidimensional medical data related to diabetes. The aim of this research study is an automatic construction of a collection of FCM models based on various criteria depending on the structure of the model and forecasting capabilities. The simulation analysis was performed with the use of the developed multiobjective Individually Directional Evolutionary Algorithm. Experiments show that the collection of fuzzy cognitive maps, in which each element is built on the basis of particular patient data, allows us to receive higher forecasting accuracy compared to the standard approach. Moreover, by appropriate aggregation of these collections we can also obtain satisfactory accuracy of forecasts for the new patient.
Deep Learning for People Counting Model

Modeling of automatic people detection and counting in a real-time video is an important feature in a smart surveillance system for safety and security management, marketing research, etc. Face recognition is one of the methods which is used for people detection. In this paper, a real-time automated model is designed using deep learning algorithm such as convolutional neural network which is computationally efficient. The face detected using the proposed algorithm exploits the challenges such as variations in size and shape of the head region to achieve robust detection of a human, even under partial occlusion, dynamically changing background, and varying illumination condition. Here, we have used WIDER face dataset and FDDB dataset to show the results of the proposed method.
RC-CNN: Representation-Consistent Convolutional Neural Networks for Achieving Transformation Invariance

Convolutional neural networks (CNNs) are powerful and have achieved state-of-the-art performance in many visual recognition tasks. Despite their impressive performance, CNNs are still unable to remain invariant while some spatial transformations are applied on images. Herein, we propose representation-consistent neural networks to solve this problem. By introducing consistent losses between the representations in different layers of transformed images, the recognition performance of transformed images is significantly improved. This model not only learns to map from the transformed images to the pre-defined labels but each layer also learns to generate invariant representations when the input images are transformed. All the characteristics of transformation invariance are embedded in the model, which means that no extra parameters or computations are introduced in the well-trained model. Comparative experiments demonstrate the superiority of our model when learning invariance to rotation, translation, and scaling on large-scale image recognition and retrieval tasks.
Discriminative, Competitive, and Collaborative Representation-Based Classification with l2-Norm Regularizations

Recently, collaborative representation-based classification (CRC) and its many variations have been widely applied for various classification tasks in pattern recognition. To further enhance the pattern discrimination of CRC, in this article we propose a novel extension of CRC, entitled discriminative, competitive, and collaborative representation-based classification (DCCRC). In the proposed DCCRC, the class discrimination information is fully utilized for promoting the true class of each testing sample to dominantly represent the testing sample during collaborative representation. The class discrimination information is well considered in the newly designed discriminative <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>-norm regularization that can decrease the ability of representation from the interclasses of each testing sample. Simultaneously, a competitive <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>-norm regularization is introduced to the DCCRC model with the class discrimination information with the aim of enhancing the competitive ability of representation from the true class of each testing sample. The effectiveness of the proposed DCCRC is explored by extensive experiments on the several public face databases and some real numerical UCI data sets. The experimental results demonstrate that the proposed DCCRC achieves the superior performance over the state-of-the-art representation-based classification methods.
A video-based tracking system for football player analysis using Efficient Convolution Operators

Computer vision has been applied in sports analysis under the demand of the media as well as a training activity. This paper presents work on a system for tracking multiple football players in video streams. The challenges of the task are: the players are relatively small in the video with chaos movements; the processing time is efficient to ensure the analyzed data is reported during the match while the accuracy is required to be sufficient; the hardware of the system needs to be high mobility. To overwhelm those, we apply Efficient Convolution Operators (ECO) as a core tracking method to track the targets on two synchronized laptops, then the data is merged in a post-processing stage. Besides, user interactive functions are also provided to assist the operators to correct failed tracks. The tracking method is qualitatively evaluated on videos from professional football matches with two resolution settings. The number of user interactions to correct the failed tracks and the time processing are chosen as criteria for the evaluation. The results show that ECO tracking outperforms several well-known tracking methods with less than 1 tracking loss in 2 minutes on average with processing rate of 12-17 fps. In conclusion, the proposed system is a promising tool for football player tracking and statistical analysis in practice.
Analysis of a cloud-based mobile device management solution on android phones: technological and organizational aspects

Mobile Device Management (MDM) in companies becomes more and more important due to security reasons. Since a mobile device—as the term expresses—is mobile, it cannot be controlled by a company and local administrators like stationary equipment. Most of today’s mobiles are operated by an Android system. Current MDM-solutions, as well as their implementation and impact on change management, will be discussed. This paper will not provide any recommendations for or against any MDM-system. Instead, the goal is to provide a general How-To, to illustrate what an MDM-system is capable of, especially when implemented as a cloud-based solution and how to make the best use of it for your company. As an example, one commercial and two open-source systems will be discussed.
A Gradient-Based Algorithm to Deceive Deep Neural Networks

Deep neural networks have achieved high performance in a variety of image recognition tasks. However, it is reported that the performance on image recognition of these networks is unstable to slight perturbations of images. To verify this weakness, we propose DeceiveDeep, a gradient-based algorithm for deceiving deep neural networks in this paper. There exists a lot of gradient-based attack methods, such as the L-BFGS, FGSM, and Deepfool. Specifically, based on an original method, L-BFGS, we exploit the Euclid norm of the gradient to update the space vector in an image to generate a deceivable image for fooling deep neural networks. We construct three types of deep neural network models and one convolutional neural network for testing the proposed algorithm. Based on the MNIST dataset and the Fashion-MNIST dataset, we evaluate the effectiveness of DeceiveDeep in terms of accuracy on training and testing data, and CNN model, respectively. The experimental results show that, comparing with L-BFGS, DeceiveDeep dramatically decreases the accuracy of the deep models on image recognition.
Modulation recognition using an FPGA-based convolutional neural network

As wireless communications continues to gain in importance and spectrum traffic becomes more and more dense, the challenge of fast modulation classification is also increasing. Although modulation classification has been a well-studied problem, achieving high accuracy from a small number of samples is difficult. Moreover, most methods are not designed for line-speed operation over multiple channels or bands. In this paper, a deep learning model using a constellation diagram approach and its FPGA implementation are applied to solve this problem.
Massive Similar Function Searching for Cross-Architecture Binaries

Code reuse is widespread in software development for various CPU architectures. It can cause many problems, such as software plagiarism and known vulnerabilities. To tackle these problems, we propose CrossDiff, a cross-architecture binary function searching system. We aim to find similar functions in binaries belonging to different architectures such as x86, ARM, MIPS, and PowerPC. The system has four phases. The pre-processing phase lifts the binaries into LLVM IR codes and optimizes the codes to reduce the impacts of different architectures and other factors. Secondly, it extracts Bytehash, Simhash, and other function semantic features. Then it uses Bytehash to find equal function matches, Simhash to find similar function candidates and semantic features for precise comparison. Finally, it optimizes the result leveraging the structural information in the disassembly code. Results show that CrossDiff is efficient and scalable with high accuracy.
Prediction of Refactoring-Prone Classes Using Ensemble Learning

A considerable amount of software engineers' efforts go into maintaining code repositories, which involves identifying code whose structure can be improved. This often involves the identification of classes whose code requires refactoring. The early detection of refactoring-prone classes has the potential to reduce the costs and efforts that go into maintaining source code repositories. The purpose of this research is to develop prediction models using source code metrics for detecting patterns in object oriented source code, which are indicators of classes that are likely to be refactored in future iterations. In this study, four different sets of source code metrics have been considered as an input for refactoring prediction to evaluate the impact of these source code metrics on model performance. The impact of these source code metrics are evaluated using eleven different classification technique, and two different ensemble classes on seven different open source projects. Ensemble learning techniques have been shown to incorporate the diversity of patterns learnt by different classifiers, resulting in an augmented classifier that is more robust than any individual classifier. Our work also creates distinction between various sets of features for the task of predicting refactoring-prone classes.
Landslide Detection Method using Laser Beam

Events of landslides in the tropics often occur, and the main causes of these events are often caused by the high intensity of rainfall. Before an avalanche occurs, it will usually be preceded by land movements that can occur slowly (creeping) or fast, depending on the condition of the type of soil that exists in that location. Some ground motion detectors include sliding stakes and inclinometers. This tool is useful as a measure of the amount of movement, especially on sloping land. The purpose of this study is to make a movement detection device that can be an alarm or early warning for residents who live in locations that are prone to movement. The benefit for science is the development of monitoring methods using laser light that have not been done so far. The results of this study will be useful for the development of methods for measuring land shifts. The signal is sent using an internet network, so that it can be monitored continuously. The significance of this research for science is the development of a method of applying sensors using laser light which has not been done so far. Recording is done by using a web-based data logger by sending visual measurement data to the data center. The accuracy of the laser beam that spreads when it is received on the target board, is done using the center cluster method.
Performance Testing in Mobile Application

Context: The technological evolution of wireless networks and the technological advancement of mobile devices make them ever more present in daily life, becoming almost indispensable solutions. With the popularization of mobile devices, developers need to be committed to building applications that can be reliable, robust, secure and that ensure adequate performance for their end users. A good practice to ensure the performance of mobile applications is through a performance testing approach. Although the literature can be used by specialists and non-specialists for decision-making and selection approaches for performance testing, it is limited in the sense of providing an overview. Goal: Our main objective is to contribute to the performance testing body of knowledge. Method: A protocol was formulated and executed according to the guidelines for performing systematic literature mappings in Software Engineering. Results: This study identifies, through a systematic mapping, the tools, strategies, approaches, methods and processes of performance testing in mobile applications. Providing answers and filling a research gap identified in the literature. Conclusions: It is worth highlighting the results on rating performance metrics and problems reported on performance testing for mobile applications. Therefore, this systematic literature map is a valuable contribution to making decisions about performance testing strategies for mobile applications.
Attention-Based Image Captioning Using DenseNet Features

We present an attention-based image captioning method using DenseNet features. Conventional image captioning methods depend on visual information of the whole scene to generate image captions. Such a mechanism often fails to get the information of salient objects and cannot generate semantically correct captions. We consider an attention mechanism that can focus on relevant parts of the image to generate fine-grained description of that image. We use image features from DenseNet. We conduct our experiments on the MSCOCO dataset. Our proposed method achieved 53.6, 39.8, and 29.5 on BLEU-2, 3, and 4 metrics, respectively, which are superior to the state-of-the-art methods.
Enhanced Text Stemmer with Noisy Text Normalization for Malay Texts

In general, the current text stemmers for Malay texts were not developed for text stemming against social media texts. Therefore, there is a need to develop an enhanced text stemmer that is able to map morphological variants based on the characteristics of non-standard derived word patterns on social media platforms. It deals with noncompliance word patterns (also called noisy texts or micro text) such as misspelled word and texting language which are often being used as informal conversation. This paper proposes an enhanced text stemmer to perform text stemming against social media texts. The investigation focuses on different patterns of non-standard, non-derived words (mechanics, non-standard word formation, code-switching, and slang words) and also non-standard derived words. The experimental results show that the performance of the proposed text stemmer depends on how much "noise" is in social media texts.
Improving Action Recognition with Valued Patches Exploiting

Recent human action recognition methods mainly model a two-stream or multi-stream deep neural network, with which human spatiotemporal features can be exploited effectively. However, due to the ignoring of interactive scenes, most of these methods cannot achieve impressive performance. In this paper, we propose a novel multi-stream fusion framework based on discriminative scene patches and motion patches. Unlike existing two-stream or multi-stream methods, our work improves the accuracy by 1) Attaching more attention to the exploiting of discriminative scene patches and motion patches. 2) Proposing a novel 2D+3D multi-stream feature aggregation mechanism: 2D features from RGB images and 3D features of valued patches are combined to improve the representation of spatiotemporal features. Our framework is evaluated on three widely used video action benchmarks, where it outperforms other state-of-the-art recognition approaches by a significant margin: the accuracy up to 85.7% at JHMDB, 87.7% at HMDB51, and 98.6% at UCF101, respectively.
A Framework for the Data Integration of Earthquake Events

Recently, data integration has attracted increasing interest from different research domains. In most situations, data integration systems are provided for users to access homogeneous data from a set of private databases. However, open and public databases provide access through the internet. To make better use of data, we need to integrate the data from different data sources. This situation is also true in regard to earthquake event data. Thus, to solve such problems, in this paper, we provided a framework that integrates the earthquake-related information published on different websites. The most important aspect is how to determine which disaster events are the same. The main work in this paper consists of identifying the data entities from different data sources. The application is based on the integration of online data on earthquake events from different websites. By integrating the data from different data sources, we can spend less time sorting the data, and we can more quickly complete the next steps faster, such as data analysis and the development of disaster relief strategies.
Less Is More: Deep Learning Using Subjective Annotations For Sentiment Analysis From Social Media

Acquiring reliable training annotations for the huge amounts of data collected in large-scale applications is often infeasible, especially for inherently subjective tasks, such as sentiment analysis. In these cases, the data are usually annotated using semi-automated methods. Even when crowd-sourcing is used, ensuring the quality of the acquired annotations can be challenging. Therefore, a number of important questions arise when annotating such datasets: Does using more data always increase the accuracy of a model regardless the quality of the annotations? Is there any way of selecting which data samples we should use when the annotations are unreliable? Is there any point at which using unreliable annotations actually harms the performance of deep models instead of helping? In this work we provide an extensive study on training deep sentiment analysis models with unreliably annotated data, as well as propose a simple, yet effective semi-supervised learning method to overcome the aforementioned limitations.
Decentralized Blockchain-Based Android App Store with P2P File System

In the Android app store, developers must provide a fee of at least 15% of revenue, and there is no reliable evaluation system for apps, so it can lead to occur the damage with installation of malicious file. Blockchain technology which supports decentralized infrastructure can support transactions between developers and buyers without a central organization. Through the Blockchain technology, the fee-free app trading environment can be created. Ethereum’s smart contract provides permissions through token issuance and can manages the buyers. Therefore, we implement DAS (Decentralized Android app Store) which provides app trading environment without decentralized organization using smart contract. In addition, IPFS is used for APK file sharing to reduce the amount of gas consumed according to the data size. Since only the hash value of the file is registered, not the whole data of the file, it is possible to consume a little constant average gas costs in the process of file registering and purchasing. We implemented as DApp in Ethereum platform. After purchasing the app, it is possible to induce the appraisal of the app reliability through the evaluation permission assigned to the purchaser, and measuring reliability through the purchase rate and the appraisal rate.