Reviewerly/exHarmony huggingface dataset:
Blended-Learning: The Responses from Non-English Students in the Indonesian Tertiary Context

The process of language teaching and learning has undergone major changes due to the developments of technology. The use of technology in education field has paved the way for higher education institution to innovatively shape their modern media in a language teaching and learning. Subsequently, the implementation of blended-learning has aroused at the Universitas Teknokrat Indonesia for approximately one and a half year ago to maximize the use of technology. Most lecturers in all study programs have increasingly utilized the social network sites such as Instagram, Facebook, WhatsApp, etc. for the successfulness of blended-learning. This present study aims at exploring the students’ responses on how blended-learning might be used to develop their language learning and discovering their attitudes towards the implementation of blended-learning as an interactional teaching and learning tool in English for Business course. Employing a qualitative in form of a case study, eighty-two undergraduate students from study program of Informatics Engineering were observed, interviewed, and distributed questionnaires. The data were performed to collect the students’ responses and students’ attitudes toward the implementation of blended-learning in the process of their language learning. The findings were found out that most students from Informatics Engineering major showed their positive responses and positive attitudes using blended learning for the language teaching and learning. They also gained some educational benefits for their English language development. Thus, this blended learning brings us to the new trend for language teaching and learning media in order to motivate the students in enhancing their language acquisition.
An Efficient Dense Stereo Matching Method for Planetary Rover

Stereo matching is one of the most important and challenging subjects in the field of planetary rover with a stereo vision system. The generated disparity map can make rover to avoid the obstacle and explore the planetary surface automatically. In this paper, we propose an efficient dense stereo matching method to generate disparity maps for a planetary rover, which relies on 3-D plane fitting, adaptive penalties, and coarse-to-fine disparity constraint. In order to achieve efficient stereo matching at the coarsest level of the pyramid, we present a 3-D plane fitting to reduce the disparity search range and propose adaptive penalties in the more-global matching method for obtaining an accurate disparity map. At the finer level, our method then estimates the disparity search range based on coarse-to-fine disparity constraint and utilizes adaptive penalties to obtain an accurate disparity map. The extensive experiments with stereo images of Chang’e-3 rover demonstrate that our approach can generate disparity maps efficiently and accurately compared with the most state-of-the-art semi-global matching methods, especially at low texture regions, depth discontinuities, and occlusion regions.
Efficient and Switchable CNN for Crowd Counting Based on Embedded Terminal

Crowd counting plays an important role in urban management and public security. Recently, deep learning has shown a great advantage in making the quality of crowd counting more accurate. However, how to apply deep learning models to embedded terminals is still a challenging issue. The main contradiction of the problem lies in high demand for computing resources for deep learning and strict limitation of power consumption from embedded devices. In order to achieve the crowd counting by edge computing (in embedded terminals), we propose a tiny model based on convolutional neural networks. The model can be switched into other two forms to adapt tradeoff between the accuracy and efficiency. Especially, different forms share main parameters so as to save storage and avoid retraining. What is more, this model supports variant sizes of input images, which benefits the applications to a variety of embedded devices. The experiments on two different embedded terminals have shown the effectiveness, efficiency, and flexibility of our proposed model.
High-Resolution and Low-Resolution Video Person Re-Identification: A Benchmark

Person re-identification has recently attracted increasing interest in the computer vision and safety-critical applications. In practice, due to poor quality of cameras or long distance away from person, the captured pedestrian videos usually suffer from low resolution, which will result in the loss of useful information contained in videos and make person re-identification between low-resolution (LR) and high-resolution (HR) videos (PRLHV) be a challenging task. However, the problem of PRLHV has not been well studied. In this paper, we propose a semi-coupled mapping based set-to-set distance learning (SMDL) approach for PRLHV. Specifically, by regarding each video as a set of features extracted from several walking cycles, we learn a discriminative set-to-set distance metric to enhance the separability between videos from different persons. To decrease the influence of low resolution on the distance learning, we design a clustering-based semi-coupled mapping term for our approach, which can reduce the variation between features of low-resolution and high-resolution videos by a semi-coupled mapping matrix. Since there exists no low-resolution video pedestrian re-identification dataset under real-world scenario up to now, we contribute a benchmark dataset for PRLHV, named high-resolution and low-resolution video person re-identification dataset (HLVID). Although this dataset is challenging and difficult for person re-identification, it is a useful attempt for further studies on low-resolution video-based pedestrian re-identification under the real-world scene. The extensive experiments on the newly collected video dataset demonstrate that our approach performs better than the state-of-the-art person re-identification methods in the PRLHV task.
Large-Scale Person Re-Identification Based on Deep Hash Learning

Person re-identification in the image processing domain has been a challenging research topic due to the influence of pedestrian posture, background, lighting, and other factors. In this paper, the method of harsh learning is applied in person re-identification, and we propose a person re-identification method based on deep hash learning. By improving the conventional method, the method proposed in this paper uses an easy-to-optimize shallow convolutional neural network to learn the inherent implicit relationship of the image and then extracts the deep features of the image. Then, a hash layer with three-step calculation is incorporated in the fully connected layer of the network. The hash function is learned and mapped into a hash code through the connection between the network layers. The generation of the hash code satisfies the requirements that minimize the error of the sum of quantization loss and Softmax regression cross-entropy loss, which achieve the end-to-end generation of hash code in the network. After obtaining the hash code through the network, the distance between the pedestrian image hash code to be retrieved and the pedestrian image hash code library is calculated to implement the person re-identification. Experiments conducted on multiple standard datasets show that our deep hashing network achieves the comparable performances and outperforms other hashing methods with large margins on Rank-1 and mAP value identification rates in pedestrian re-identification. Besides, our method is predominant in the efficiency of training and retrieval in contrast to other pedestrian re-identification algorithms.
An RF-ID based solution of Assistive Technology for User Activity Monitoring

The population ageing is increasing the incidence of serious health issues and solutions are needed to reduce the costs of health and social care. Among the challenges falls, nutrition and hydration are gaining ground. From the falls point of view, exercise can improve older peoples balance but, to provide a measure of the amount of exercise performed, low-cost wearable devices, estimating the users mobility and the activity rates, needs to be used. Poor nutrition and hydration also contribute to increasing the probability to incur in a mobility decline with consequent augmented fatigue and dependency. Technology plays an important part in increasing awareness or providing constant reminders to eat and drink regularly. These solutions allow monitoring deviations from normal patterns of behaviour, which might indicate nutrition or dehydration scarcity thus enabling the possibility to provide reminders to users and caregivers. To fill this need, in this paper, an RF-ID based system meant to monitor user's habits, such as the use of food/beverage/drugs, uses of equipment, exploitation of the indoor environment and the activity rate, is presented. The solution proposed aims to asses the RF-ID measurement system developed as a function of the reading range and the reader power output. Moreover, the system has been further assessed by computing Specificity, S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> and Sensitivity, S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> , thus allowing to find the minimal inter-object distance that guarantees exclusive discrimination of the object. Main advantages are related to the low invasivity and flexibility and robustness to a non-static position of tools/appliances to be monitored.
An Improved Electromagnetic Field Optimization for the Global Optimization Problems

Electromagnetic field optimization (EFO) is a relatively new physics-inspired population-based metaheuristic algorithm, which simulates the behavior of electromagnets with different polarities and takes advantage of a nature-inspired ratio, known as the golden ratio. In EFO, the population consists of electromagnetic particles made of electromagnets corresponding to variables of an optimization problem and is divided into three fields: positive, negative, and neutral. In each iteration, a new electromagnetic particle is generated based on the attraction-repulsion forces among these electromagnetic fields, where the repulsion force helps particle to avoid the local optimal point, and the attraction force leads to find global optimal. This paper introduces an improved version of the EFO called improved electromagnetic field optimization (iEFO). Distinct from the EFO, the iEFO has two novel modifications: new solution generation function for the electromagnets and adaptive control of algorithmic parameters. In addition to these major improvements, the boundary control and randomization procedures for the newly generated electromagnets are modified. In the computational studies, the performance of the proposed iEFO is tested against original EFO, existing physics-inspired algorithms, and state-of-the-art meta-heuristic algorithms as artificial bee colony algorithm, particle swarm optimization, and differential evolution. Obtained results are verified with statistical testing, and results reveal that proposed iEFO outperforms the EFO and other considered competitor algorithms by providing better results.
TASKWORK: A Cloud-aware Runtime System for Elastic Task-parallel HPC Applications

With the capability of employing virtually unlimited compute resources, the cloud evolved into an attractive execution environment for applications from the High Performance Computing (HPC) domain.By means of elastic scaling, compute resources can be provisioned and decommissioned at runtime.This gives rise to a new concept in HPC: Elasticity of parallel computations.However, it is still an open research question to which extent HPC applications can benefit from elastic scaling and how to leverage elasticity of parallel computations.In this paper, we discuss how to address these challenges for HPC applications with dynamic task parallelism and present TASKWORK, a cloud-aware runtime system based on our findings.TASKWORK enables the implementation of elastic HPC applications by means of higher-level development frameworks and solves corresponding coordination problems based on Apache ZooKeeper.For evaluation purposes, we discuss a development framework for parallel branch-and-bound based on TASKWORK, show how to implement an elastic HPC application, and report on measurements with respect to parallel efficiency and elastic scaling.
A comparison of Neural Network and SVM on the multi-label classification of Quran verses topic in English translation

Indeed the Quran is the main guideline for Muslims. The enticing thing in the Quran is one verse of the Quran can be classified into more than one topic of discussion in the Quran, for example one verse discusses about prayer and faith or arkanul Islam and religions, thus this called multilabel case. In this study, we compared the multi-label Quran verse topic classification techniques using Na¨ıve bayes, ANN and SVM. Based on the experiment conducted, combination Na¨ıve Bayes algorithm with Mutual Information or Information Gain gives the best performance when it applied to construct the model with Hamming Loss of 0.0938.
Visual Saliency Detection Using a Rule-Based Aggregation Approach

In this paper, we propose an approach for salient pixel detection using a rule-based system. In our proposal, rules are automatically learned by combining four saliency models. The learned rules are utilized for the detection of pixels of the salient object in a visual scene. The proposed methodology consists of two main stages. Firstly, in the training stage, the knowledge extracted from outputs of four state-of-the-art saliency models is used to induce an ensemble of rough-set-based rules. Secondly, the induced rules are utilized by our system to determine, in a binary manner, the pixels corresponding to the salient object within a scene. Being independent of any threshold value, such a method eliminates any midway uncertainty and exempts us from performing a post-processing step as is required in most approaches to saliency detection. The experimental results on three datasets show that our method obtains stable and better results than state-of-the-art models. Moreover, it can be used as a pre-processing stage in computer vision-based applications in diverse areas such as robotics, image segmentation, marketing, and image compression.
E-Commerce Merchant Classification using Website Information

With the rapid growth of the e-commerce landscape, classifying e-commerce merchants has become an important task as it is an integral part of various processes in e-commerce. One of the examples is merchant on boarding, where the category of an e-commerce merchant has proven to be a good indicator of the risk of the merchant. However, since most of e-commerce businesses do not have brick-and-mortar stores from which we can assess it directly, the only source of information regarding the merchant itself is its website. Thus, we can view this problem as a web classification problem, where we classify e-commerce websites into a category. In this research, we aim to build an end-to-end classification system for e-commerce websites. There are a few challenges such as the number of pages to be processed, imbalanced dataset, and the language of e-commerce websites that can be mixed language. We built a website classification system and experimented with case study of Indonesian and English e-commerce webs, that are classified into 37 different categories. Our best result achieved an F-score of 0.83.
Exploring New Statistical Frontiers at the Intersection of Survey Science and Big Data: Convergence at “BigSurv18”

Held in October 2018, The Meets Survey Science conference, also known as BigSurv18, provided a first-of-its-kind opportunity for survey researchers, statisticians, computer scientists, and data scientists to convene under the same roof. At this conference, scientists from multiple disciplines were able to exchange ideas about their work might influence and enhance the work of others. This was a landmark event, especially for survey researchers and statisticians, whose industry has been buffeted of late by falling response rates and rising costs at the same time as a proliferation of new tools and techniques, coupled with increasing availability of data, has resulted in Big Data approaches to describing and modelling human behavior.
A revocable storage CP-ABE scheme with constant ciphertext length in cloud storage

The ciphertext policy attribute-based encryption (CP-ABE) is widely used in cloud storage. It not only provides a secure data sharing scheme but also has the characteristics of fine-grained access control. However, most CP-ABE schemes have problems such as the ciphertext length increases with the complexity of the access policy, the encryption scheme is complex, the computational efficiency is low, and the fine-grained revocation cannot be performed. In view of the above problems, this pa-per proposes an efficient CP-ABE scheme with fine-grained revocable storage and constant ciphertext length. The scheme combines proxy re-encryption with CP-ABE technology, adopts the flexible access strategy AND-gates on multi-valued attributes with wildcards (AND$_{m}^{*}$), and realizes revocable storage and fixed-length ciphertext. At the same time, in order to reduce the amount of user decryption calcu-lation, the complex operation in the decryption process is outsourced to the third-party server and the decryption result is verified to ensure the correctness of the information. Finally, the security of the scheme is proved under the decisional bilinear Diffie-Hellman (DBDH) assumption. In addition, the performance analysis shows that the scheme is efficient and feasible in cloud storage.
Open Source Vulnerability Notification

The use of third-party libraries to manage software complexity can expose open source software projects to vulnerabilities. However, project owners do not currently have a standard way to enable private disclosure of potential security vulnerabilities. This neglect may be caused in part by having no template to follow for disclosing such vulnerabilities. We analyzed 600 GitHub projects to determine how many projects contained a vulnerable dependency and whether the projects had a process in place to privately communicate security issues. We found that 385 out of 600 open source Java projects contained at least one vulnerable dependency, and only 13 of those 385 projects had a security vulnerability reporting process. That is, 96.6% of the projects with a vulnerability did not have a security notification process in place to allow for private disclosure. In determining whether the projects even had contact information publicly available, we found that 19.8% had no contact information publicly available, let alone a security vulnerability reporting process. We suggest two methods to allow for community members to privately disclose potential security vulnerabilities.
Rapid Vulnerability Mitigation with Security Workarounds

The existence of pre-patch windows allows adversaries to exploit vulnerabilities before they are patched.Prior work has proposed to harden programs with security workarounds to enable users to mitigate vulnerabilities before a patch is available.However, it requires access to the source code of the programs.This paper introduces RVM, an approach to automatically hardening binary code with security workarounds.RVM statically analyzes binary code of programs to identify error-handling code in the programs, in order to synthesize security workarounds.We designed and implemented a prototype of RVM for Windows and Linux binaries.We evaluate the coverage and performance of RVM on binaries of popular Windows and Linux applications containing real-world vulnerabilities.
Periodic Path Changes in RIPE Atlas

Large-scale data sets of the Internet measurements are commonly used by researchers and operators for investigating Internet performance or for tackling network issues. Looking at sequences of traceroutes in such data sets, it is common to observe paths that change over time. We are interested in verifying if there are periodic phenomena affecting such path changes and, if yes, in determining if they depend on artifacts of the used data set or on topology changes of the network. For this purpose, we devise a novel algorithm for detecting periodicities in sequences of traceroutes. Then, we exploit the algorithm for analyzing the traceroutes produced by the RIPE Atlas, a popular public measurement platform. We study and report the features of the found periodicities and some of their causes. We found that: 1) a surprisingly large percentage of the traceroutes exhibit a periodic behavior; 2) a large number of periodicities depend on the RIPE Atlas platform itself; and 3) a smaller amount is related to the MPLS and load balancing.
Distortion Function for Emoji Image Steganography

Nowadays, emoji image is widely used in social networks. To achieve covert communication in emoji images, this paper proposes a distortion function for emoji images steganography. The profile of image content, the intra- and inter-frame correlation are taken into account in the proposed distortion function to fit the unique properties of emoji image. The three parts are combined together to measure the risks of detection due to the modification on the cover data. With the popular syndrome trellis coding (STC), the distortion of stego emoji image is minimized using the proposed distortion function. As a result, less detectable artifacts could be found in the stego images. Experimental results show that the proposed distortion function performs much higher undetectability than current state-of-the-art distortion function HILL which is designed for natural image.
CVSS-based Estimation and Prioritization for Security Risks

During software development, it is of essential importance to consider security threats. The number of reported incidents and the harm for organizations due to such incidents highly increased during the last few years. The efforts for treating threats need to be spent in an effective manner. A prioritization can be derived from the risk level of a threat, which is defined as the likelihood of occurence and the consequence for an asset. In this paper, we propose a risk estimation and evaluation method for information security based on the Common Vulnerability Scoring System (CVSS). Our method can be applied during requirements engineering. The application in one of the earliest stages of a software development lifecycle enables security engineers to focus on the most servere risks right from the beginning. As initial input, we make use of a pattern-based description of relevant threats to the software. When estimating the risk level of those threats, we consider three perspectives: (1) software providers, (2) data owner, and (3) third parties for which a potential harm may exist, too. Our method combines attributes of the pattern and the different perspectives to estimate and prioritize risks. The pattern-based description allows a semi-automatic application of our method, which ends with a ranking of risks according to their priority as final outcome.
Cyborg-Computer Interaction: Designing New Senses

(2019). Cyborg-Computer Interaction: Designing New Senses. The Design Journal: Vol. 22, Running with Scissors: 13th International Conference of the European Academy of Design, Dundee, 10-12th April, 2019, edited by Louise Valentine, Joanna Bletcher and Leon Cruickshank, pp. 1215-1225.