Reviewerly/exHarmony huggingface dataset:
A Method for Identifying eHealth Applications Using Side-Channel Information

eHealth applications become popular with the increasing incidence of cancer and postoperative rehabilitation, that require continuous remote monitoring of patients. Given the huge diversity of eHealth applications, their proper and non- invasive identification assist in attaining important requirements as low latency and reliability. But, their identification is not trivial once they have similar characteristics to common applications. Also, the time taken to identify an eHealth application is crucial, however usually it is not addressed as relevant. This paper presents MOTIF, a method for identifying eHealth applications from side-channel information extracted from network traffic. It is non-invasive and does not inspect packet payload, employing machine learning algorithms for the particularities of healthcare scenarios. Results show MOTIF feasibility and point out an accuracy higher than 90% in less than 30 seconds.
End‐user modeling of quality for web components

Abstract With years of frantic development, when release fast and release often was the mandatory rule for web technologies and services, the open source paradigm and online distribution repositories have imposed de facto standards for quality assessment in fast‐paced innovation processes. Nowadays, however, in pursuit of productivity, security, and user satisfaction, the industry is beginning, through the introduction of new standards such as ECMAScript 6 or web components, to consider software engineering mandates for web technologies. This article reports a quality model aligned with international standard ISO/IEC 25010, covering web components technology, which ultimately aims to improve adoption by the software engineering industry, traditionally wary of agile Internet practices, the open source paradigm, and public repositories. Our research also presents an experimentation platform on which end users have validated the quality properties, highlighting the implicit connection with the perceived quality. The key result of our research convinces us that user ratings are suitable as a testing mechanism for product quality and quality‐in‐use metrics in order to define an absolute scale of comparison for web component quality.
Thermal entanglement phase transition in coupled harmonic oscillators with arbitrary time-dependent frequencies

We derive explicitly the thermal state of the two-coupled harmonic oscillator system when the spring and coupling constants are arbitrarily time-dependent. In particular, we focus on the case of sudden change of frequencies. In this case we compute purity function, Rényi and von Neumann entropies, and mutual information analytically and examine their temperature dependence. We also discuss on the thermal entanglement phase transition by making use of the negativity-like quantity. Our calculation shows that the critical temperature $$T_c$$ increases with increasing the difference between the initial and final frequencies. In this way we can protect the entanglement against the external temperature by introducing large difference of initial and final frequencies.
Compromised Tweet Detection Using Siamese Networks and fastText Representations

The aim of this work is to detect compromised users of tweets based on their writing styles. In this paper, we use Siamese Networks to learn a representation of user tweets that allows us to classify them based on a limited amount of ground truth data. We propose the employment of this classification model to identify compromised user accounts of tweets.
Sensory Integration of a Mobile Robot Using the Embedded System Odroid-XU4 and ROS

This article describes the development of a sensory integration architecture of a robotic vehicle using the Odroid-XU4 device and the Robot Operating System (ROS). The ODROID and ROS help us to integrate the hardware components of the robot, develop new tools, as the web interface and Arduino integration, and use different ROS packages for the construction, functioning and navigation of the robot. Also, a 3D model of the robot for simulation was designed and the integration of different sensors allowing to execute basic mobile robot navigation behaviors, such as, obstacle avoidance, remote control and SLAM.
Daily Direct Normal Irradiance Forecasting by Support Vector Regression Case Study: in Ghardaia-Algeria

Concentrated solar thermal plants (CST) generate electricity from the direct normal irradiance (DNI) component of solar irradiance. Accuracy forecasting of DNI can reduce the uncertainty of solar power plant output caused by solar irradiance intermittency, in the objective to increase CST plant profitability. In this study, the support vector regression (SVR) methodology was adopted to forecast the DNI based upon some meteorological and radiometric data such as, measured mean daily values of Temperature (T), Humidity (H), Global Horizontal Irradiation (GHI), sunshine duration (SS) and the calculated Fractal Dimension (FD) which is tested for the first time here. The capability of the SVRs-Radial Basis Function (RBF) constructed with different combinations of the parameters mentioned above are investigated. For this purpose, long-term measured data (one year) for the city of Ghardaia situated in sunny part of Algeria was utilized. The sunshine hours (SS) have been widely endorsed as the most effective parameters in forecasting of the DNI in the horizon of 122 days ahead by an error NRMSE =14.7% and R2=0.87. A slight improvement in the accuracy is performed using other parameters as inputs to get NRMSE =12.41% and R2=0.90.
A Note on Complexities by Means of Quantum Compound Systems

It has been shown that joint probability distributions of quantum systems generally do not exist, and the key to solving this concern is the compound state invented by Ohya. The Ohya compound state constructed by the Schatten decomposition (i.e., one-dimensional orthogonal projection) of the input state shows the correlation between the states of the input and output systems. In 1983, Ohya formulated the quantum mutual entropy by applying this compound state. Since this mutual entropy satisfies the fundamental inequality, one may say that it represents the amount of information correctly transmitted from the input system through the channel to the output system, and it may play an important role in discussing the efficiency of information transfer in quantum systems. Since the Ohya compound state is separable state, it is important that we must look more carefully into the entangled compound state. This paper is intended as an investigation of the construction of the entangled compound state, and the hybrid entangled compound state is introduced. The purpose of this paper is to consider the validity of the compound states constructing the quantum mutual entropy type complexity. It seems reasonable to suppose that the quantum mutual entropy type complexity defined by using the entangled compound state is not useful to discuss the efficiency of information transmission from the initial system to the final system.
Developing a Mobile Multimedia-Based Learning Resource on Living of Komodo Dragons

The study aimed developing a mobile multimedia-based learning resource model on living komodo dragons. The model is a supplementary resource for teaching materials of learning process and lecturer's guide in a university. The research conducted using research and development method from January 2018 to January 2019. Outcome of this study was a set of information about living of Komodo dragons that can be accessed using smartphone, complemented with the set of teaching tools; i.e. teaching materials, student worksheets, and lecturer's guide. Results of the testing in development phase indicated that the product was proper to be implemented in learning after some revision had been conducted, so the product could be used as a learning model using mobile device.
Adapting Style and Content for Attended Text Sequence Recognition

In this paper, we address the problem of learning to perform sequential OCR on photos of street name signs in a language for which no labeled data exists. Our approach leverages easily-generated synthetic data and existing labeled data in other languages to achieve reasonable performance on these unlabeled images, through a combination of a novel domain adaptation technique based on gradient reversal and a multi-task learning scheme. In order to accomplish this, we introduce and release two new datasets - Hebrew Street Name Signs (HSNS) and Synthetic Hebrew Street Name Signs (SynHSNS) - while also making use of the existing French Street Name Signs (FSNS) dataset. We demonstrate that by using a synthetic dataset of Hebrew characters and a labeled dataset of French street name signs in natural images, it is possible to achieve a significant improvement on real Hebrew street name sign transcription, where the synthetic Hebrew data and real French data each overlap with different features of the images we wish to transcribe.
One-Class Classification with Deep Adversarial Learning

One-class classification (OCC) seeks to build a machine-learning model when the negative class is either absent, poorly sampled, or not well defined. In this paper, we present a deep adversarial learning based architecture for one-class classification. Our architecture is composed of two deep neural networks, a generator and a discriminator, that are competing while collaborating with each other since it is inspired by the success of Generative Adversarial Networks (GANs). The generator network contains a Deconvolutional Neural Networks (aka. decoder), which is trained using a zero-centered Gaussian noise as the feature representation of the pseudo-negative class to learn a good representation as well as the boundary for the classifiable distortion of the target (or positive) class with the assistance from the target class. The outputs produced by the generator network are aggregated with the real positive class data samples, which are then used to train the discriminator network, whose goal is to understand the underlying concept in the positive class, and then classify the negative testing samples. The proposed architecture applies to a variety of OCC problems such as novelty detection, anomaly detection, and mobile user authentication. The experiments on MNIST and Caltech-256 images demonstrate that our architecture achieves superior results over the recent state-of-the-art approaches.
GPS-TonoHime: An Android Application for Explaining Ancient Tomb Information

This paper presents an application for explaining ancient tomb information on Android terminals. Curators have described orally to students who visit Shibayama ancient tomb cluster that is nationally designated historic site. However, it is difficult to understand in depth only on the basis of oral explanation. Therefore, by using GPS data and azimuth data, our system shows photos of the relics and the excavation situations on the just position in the current scene at which each photo was taken. A system user can choose either a photograph mode or a description mode while listening to voice of an explanation. Many people were interested in Tonozuka and Himezuka ancient tomb owing to our system.
RETRACTED: Research on the Performance of Robot Multiprocessor Control System Based on BS Structure Digital Media

Due to the rapid progress of artificial intelligence, computer science, sensor technology, and other related disciplines, multiprocessor control systems can effectively improve the computing power of controllers and meet robot control performance. However, the amount of information storage and data is very large, and it is difficult to achieve centralized storage of resources. Therefore, it is necessary to use the continuous advancement of video digitization technology and storage technology to effectively solve the problems of multimedia storage, long-distance transmission, and sharing of courseware. Based on this, this paper studies the robotic multiprocessor control system of digital media based on BS structure, which can effectively reduce the bandwidth pressure and conflicts of the interconnected network and memory, and overcome the bottleneck caused by the performance of the multiprocessor system. The actual operation proves that the robot can improve the computing power of the controller and meet the needs of the robot's control performance.
Reverse Variational Autoencoder for Visual Attribute Manipulation and Anomaly Detection

In this paper, we introduce the `Reverse Variational Autoencoder" (Reverse-VAE) which is a generative network. On the one hand, visual attributes can be manipulated and combined while generating images. On the other hand, anomalies, meaning deviations from the data space used for training, can be detected. During training the generator network maps samples from stochastic latent vectors to the data space. Meanwhile the encoder network takes these generated images to reconstruct the latent vector. The generator and discriminator are trained adversarially. The discriminator is trained to distinguish between real and generated data. Overall, our model tries to match the joint latent/data-space distribution of the generator and the latent/data-space joint distribution of the encoder by minimizing their Kullback-Leibler divergence. Desired visual attributes of CelebA images are successfully manipulated. The performance of anomaly detection is competitive with state-of-the-art on MNIST.
Digital Image Encryption based on Transformation and Henon Chaotic Substitution

This paper intends a fast and efficient cryptosystem based on confusion and Henon chaotic diffusion. The algorithm encrypts an image in two stages. In the first stage, the original images is transformed using Pseudo Hadamard transformation (PHT) along with substitution image. The key length used in the algorithm is 128 bits. These secrete key digits are used as initial conditions for Henon chaotic generator. A substitution box (S-box) is constructed using random sequences generated by chaotic generator. The cipher image from confusion stage is then subjected for diffusion using S-box. Based on the results obtained from differential and analytical tests, the proposed algorithm can withstand such security attacks compared to existing systems.
Product appearance detection based on visual keywords matching

The product appearance detection device based on the USB (Universal Serial Bus) camera is studied. The USB camera is used as the front device of the image acquisition and the host computer is a PC for the detection system. Aiming at low resolution of USB camera, the super-resolution image reconstruction method based on sparse representation is used to overcome the low resolution for the low-cost imaging sensor and improve the image resolution. A method of product appearance detection based on visual keywords is proposed. Visual keywords are used to describe characteristics of defects. Visual keyword extraction is performed on sample images and test images, and visual keywords are used for matching. Identify defective products by setting different thresholds. Experiments show that the proposed system has better detection results and offers good robustness to the angle and illumination of the image from the product appearance. In this paper, the theory of visual keywords is applied to product appearance detection for the first time, which provides a reference for further research.
A Hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) Approach for Professional Bloggers Classification

Despite their small numbers, some users of the online social networks demonstrate the ability to influence others. Bloggers are one of such kind of users that through their ideas and opinions on different topics, influence other users. Their identification may be beneficial for several purposes, such as online marketing for products. Much effort has been expanded towards finding the impact of such bloggers within the blogging community. We have expanded on their work by identifying influential bloggers using labeled data. We have improved upon the accuracy of the classification of professional and nonprofessional bloggers. We have made use of Adaptive Neuro-Fuzzy Inference System (ANFIS), and the Fuzzy Inference System (FIS) models. Their performance has been gauged and compared with the existing techniques and approaches, such as an Artificial Neural Network (ANN), Alternating Decision Tree (ADTree) algorithm, and Classification Based on Associations (CBA) algorithm. Adaptive techniques (ANFIS and ANN) are found better than the aforementioned rule-based classifiers. The FIS model outperformed the CBA algorithm, but showed similar performance to the ADTree algorithm. Our proposed ANFIS model showed improved results in terms of performance measures with 93% accuracy for blogger classification.
E-Learning as Connector among Education Institution in the 4th Industrial Revolution

Abstract This paper presents the possibility of e-learning as connector among education institutions in the fourth industrial revolution. Since industry started emerging in this world, the industrial journey has reached level four that is currently well known as the fourth industrial revolution. Each stage of revolution has its own impact on education. The fourth industrial revolution requires educational sector synchronize the educational process in order for students or graduates to smoothly interact to the revolutionized changes. E-learning is one among educational entities that is relevant to the fourth industrial revolution in the area of education. With the connectedness as the main characteristic of the fourth industrial revolution, it gives a wide range of opportunities among education institutions particularly for higher education institutions to work, collaborate, and help each other within the platform of e-learning. Besides that, e-learning also enable the developed higher education to help primary and secondary schools at low cost and energy, particularly for professional development of teacher.
A Proposed Model for Bengali Named Entity Recognition Using Maximum Entropy Markov Model Incorporated with Rich Linguistic Feature Set

Named Entity Recognition (NER) is a subpart of Information Extraction task that helps to find named entities and their class in a document. This is one of the fundamental tasks for many natural language processing tasks. NER categorizes the named entities in some predefined class like person, name, organization, date, number etc. In this paper, we have proposed a model to recognize the named entities from Bengali language. Three major approaches are followed to recognize named entities. They are rule-based approach, machine learning based approach and hybrid approach. Most of the existing Bengali named entity recognition model followed machine learning approaches. Existing work done on Conditional Random Forest (CRF), Support Vector Machine (SVM) and Hidden Markov Model (HMM) etc. As. Bengali is a complex language enriched with complex linguistic feature set., in this paper we have adopted a Maximum Entropy Markov Model (MEMM) based machine learning approach that can deal with complex sequences better than other approaches to classify the named entities from Bengali language. Also, we have described a rich linguistic feature set for training our model.
Research on Bipartite Network Embedding with Auxiliary Information

Learning a low-dimensional vector representation for each node in a network is called network embedding. These learned embeddings have shown promising results on network mining tasks such as link prediction, recommendation, and node classification. Most of the published work preserve network structure only; however, there also exist rich auxiliary information (i.e., attributes, text) along with network structure in a real-world scenario. One can consider that information along with network structure to measure and enhance the capacity of representation learning methods. Many real-world applications such as user/movies (recommendation system), traders/stocks (financial system) can be demonstrated as a bipartite network (a particular class of network). In our work, we present a method named ABiNE, short for Attributed Bipartite Network Embedding, to learn the latent representations of nodes for attributed bipartite networks. We investigate and develop to incorporate both structural information, in the context of explicit, implicit relations, and attributes proximity under the framework of bipartite network embedding (BiNE). We evaluate our method by conducting experiments on a real-world dataset, i.e., MovieLens. The results have shown the improvement in link prediction, and recommendation (personalized ranking) tasks as compared to other attributed or plain network embedding methods.
Performance of Deep Learning LDPC Coded Communications in Large Scale MIMO Channels

In this paper, we investigate the performance of a large-scale multiple-input multiple-output (LS-MIMO) receiver, which deploys a deep neural network and a low-density parity-check (LDPC) code for detecting and decoding disturbed signals. The structure of the low-complexity receiver is also proposed. The proposed receiver was tested with different LS-MIMO configurations to reveal the performance and complexity tradeoff. Besides, our investigation shows that the performance gap of the proposed receiver and the conventional one decreases as the number of transmitting and receive antennas increase. In particular, our experiment results show that the proposed low-complexity receiver has performance loss of about 1.8 dB and 1.5 dB in 10 × 10 and 32 × 32 LS-MIMO configurations, respectively.
Ultraviolet Imaging Detection for the Discharge of Polluted Insulators Based on Iterative Threshold Segmentation

Insulators in operating state will produce partial discharge under the influence of pollution. Ultraviolet (UV) imaging technology is a new method for detecting insulator discharge. For the partial discharge of polluted insulators, this paper proposes an UV imaging detection method based on the iterative threshold segmentation method. To preprocess the image, the mathematical morphology algorithm is used to remove the discrete noises and the insulator discharge spot is accurately identified. By combining the quantized information of the spot area, the level of the polluted insulator is then detected and the UV images of the insulator are obtained by artificial experiments. The detection examples are provided to verify the feasibility and quality of the method.
Teaching Speaking Through Techno Driven Task (Zig Zag Task)

Abstract English lecturers play a vital role in making their students to communicate well in English. They should be able with new and innovative teaching skill and strategis in the classroom to enhance students skill significantly. This research is motivated by the importance of having good English speaking skills, especially for English teachers. The traditional learning methods has been less successful in improving students’ speaking skill. Therefore, one of the solutions offered is to use the Techno Driven Task (Zig Zag Task) in teaching speaking skill for students. This study aims to determine the speaking skills of English students at Pasir Pengaraian University by using computer-aided Techno Driven Task (Zig Zag Task). The research designs is descriptive quantitative research. The participants of this research was the fourth semester students of English Study Program in University of Pasir Pengaraian. The number of samples of this research was 19 students. In collecting the data, the researcher took from students’ presentation when they were using zig zag task. The results showed that the level of students’ speaking skills was at a reasonable to very good level and only a few students had poor skills.