Reviewerly/exHarmony huggingface dataset:
A Novel Teaching Method for the Advanced Engineering Electromagnetics Course Based on a Three-in-one Mode

Advanced engineering electromagnetics (AEE) is characterized by its many mathematical formulas and conceptual abstractions, making students particularly difficult to learn, which also leads students to be not interested in this course. How to help students to understand the content of AEE and increase students' interest in learning is a problem faced by many universities. This paper proposes a "three-in-one" teaching program that combines Harkness table, flipping classrooms and simulations. In this way, the interaction between teachers and students, students and students can be increased, the quality of classroom teaching can be improved, and students' interest in learning can be improved.
The User Interface and The Implementation of Mobile apps for Donors in Natural Disaster in East Java, Indonesia

Natural disasters occur quite often in Indonesia. Relief can be in the forms of logistics or funds. Indonesian Red Cross is one of the institutions most trusted in distributing donations. Unfortunately, there is still no easy facilitation for donors in transferring funds. An effective system is needed to ease donors in transferring funds using an online application, to guarantee that the funds are not misused, to inform the kind of disasters requiring financial aids or to report the utilization of the aids. Not many researches have been conducted in this subject. Those related to the Human Computer Interaction in Health Care were focused on patients. The ones in the field of e-commerce were focused on customers. Meanwhile this research on the case of natural disaster was focusing on donors, which was not quite ordinary. The researches in the case of natural disasters were mostly focusing on the problem of distribution and supply of logistics or searching the nearest locations using GIS. This research aims to explore the user expectations of the mobile apps on the process of donating applying the principles of HCI such as conveniences, effectiveness, security, completeness of information, feedback, and visibility. It also includes the expectations on features for donors and volunteers in using secured payment gateway linked to the bank and on the history of the donation including the channel and their utilization. The result of this research shows that the expected users wish this application could provide quality of information, continuity, easy of acces and high security. The results of Developing a mobile application with effective interaction between the application and the users is included in this paper.
The Architecture of Speech-to-Speech Translator for Mobile Conversation

With competencies and the results of the engineering of natural language processing technology owned by BPPT since 1987, BPPT develops an English-Bahasa Indonesia speech-to-speech translation system (S2ST). In this paper, we propose an architecture of speech-to-speech translation system for Android-based mobile conversation using separate mobile devices for each language. This architecture applies three leading technologies, namely: WebSocket, REST, and JSON. The system utilizes a two-way communication protocol between two users and a simple voice activation detector that can detect a boundary of user's utterance.
Kaskus Smilies Application as an Alternative Instructional Media in BIPA (Indonesian Language For Foreign Speakers) Teaching

Abstract Through the utilization of appropriate learning resources, learning process will be effectively and efficiently. The type of learning resource that can be used by teachers is instructional media. One of the instructional media in the present paradigm is using an emoticon characters in BIPA (Indonesian Language for Foreign Speakers) teaching where teachers can use emoticon that was adjusted with the principles of selection and function of instructional media to support their teaching. Kaskus Smilies that have become “visual languages” among the Kaskus network users can be used as an alternative instructional media in BIPA teaching that will be very helpfull if it is adapted with teaching media. By applying it in BIPA teaching, foreign speakers who want to study or explore Indonesian language are increasingly facilitated and also enable them to learn better and able to improve their Indonesian language skills in accordance with the goals to be achieved.
Understanding Usage of Memes over Social Medias through Semantics: A Survey

This article investigates the context in which people use memes and their intentions behind using them. Memes, as content or concept that are derived from culture or inside jokes and take the form of an image, video, or gif, users of social media often use their memes in various types of situations. By gathering opinions from memes users on their memes usage and interactions, the results tackle semantics (usage and relationships, situations and circumstances) in the context of the memes. The result shows that people tend to 1) use memes with close friends in the social media channels; 2) vary use of memes depending on the size and relationship of audience of their posts; 3) incorporate them as a lubrication of their conversations to make it more relatable, fun, and light-hearted; 4) utilize memes to express their intended emotions more effectively. Understanding how people use memes can be useful for designing spaces and tools that can best facilitate this new form of conversation in CMC (Computer Mediated Communication) platforms.
Security Evaluation of the Enhanced IDEA Algorithm

Data security is an important issue, and cryptographic algorithms are essential parts of security. IDEA is a symmetric block cipher, and it uses the mixing of three different algebraic operations. In this study, an enhanced IDEA algorithm is presented, and it is based on the middle square method and circular shifting techniques. The enhancement will cover the number of rounds, bit adjustment, shifting, during the encryption and decryption phase. Findings reveal that the proposed method could generate a ciphertext with only 28.83 ms. As to the avalanche effect, it achieved 50.35%, which passed the criteria of encryption. Also, the sequence of the output is random. For its frequency (monobit) test, the result shows 0.789, frequency test within a block has 0.997462, and a run test of 0.897437. The result showed that the enhanced IDEA passed the different tests. To further assess the algorithm, other techniques to evaluate the performance of the enhanced IDEA are considered, and modification is also encouraged to strengthen the security.
FPGA Implementation of I<sup>2</sup>C Based Networking System for Secure Data Transmission

In this paper, the main focus was to implement the I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C (Inter Integrated Circuit) bus on Altera DE2-115 development board. I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C bus design is modeled using VHDL and synthesised using Quartus II. In this work, an attempt to simulate the I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C write and read operation using ModelSim is made. Therefore implement I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C bus on Field Programmable Gate Arrays(FPGA)gives greater straightforwardness since it requires just two-wires and less number of association pins. FPGA provides multiple IO interfaces as compared other hard core. The sole reason to use I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C instead of other protocol is because of its two wire data transfer and security. This work also presents the number of Logic Elements, Logic registers, Pins, Memory Bits, Embedded Multiplier, and PLLs used in the design.
SAR ATR by Decision Fusion of Multiple Random Convolution Features

As the appearance of the target is sensitive to its aspect angle in one Synthetic Aperture Radar (SAR) image, the classification accuracy for target recognition task can be significantly improved through pose estimation when there are only limited training samples. A pose estimation algorithm is first proposed in this paper based on second moments of region of interest, which is enhanced based on an improved multi-scale fractal feature and detected by adaptive threshold segmentation. To compensate for pose estimation errors, two sets of random convolution features are extracted and concatenated using randomly generated kernels with a predefined set of sizes from the original and rotated images, respectively. Extreme learning machine (ELM) is used as base classifier, and an ensemble of ELMs is then trained based on feature resampling. Decision fusion rule based on the confidence of each base classifier is presented to combine these two classification results. MSTAR dataset is finally used to verify the effectiveness of the proposed algorithm in two standard and extended operating conditions, respectively.
Mobile robot tele-operation support using gaze intention estimator with saliency map and outlier test

Narrow angle of view of the camera mounted on the mobile remote-controlled robots makes the operation difficult. In order to solve this problem, a panning support system utilizing the followings is presented in this paper: estimation of operator's attention movement and estimation of gaze point candidate. In this system, the gaze movement is detected using a Kullback-Leibler outlier test, and the gaze points are estimated utilizing a saliency map. This paper introduces the configuration of this support system and the outlier test method using Kullback-Leibler, and the generation of the saliency maps. The primely measurement / analysis using a mobile robot operation experiment in virtual space was performed to confirm the presented algorithms and methods.
Pseudorandom Number Generators with Predeterminated Period and Pre-period

One of the characteristics of pseudorandom number generator is the period of the sequence generated by it. Usually it is required that the period value should be as maximum as possible for a given software or hardware-software configuration. However, in some cases, a departure from this rule is necessary. The paper proposes the variant of constructing a non-linear pseudorandom number generator with a predetermined period and pre-period. The application areas of these generator types are considered. The results of the statistical properties evaluation of the generated sequences are presented.
A Joint Transform Correlator Encryption System Based On Complex Amplitude Modulation for Grayscale Images

Abstract Aiming at the problem that the joint transform correlator (JTC) encryption system is difficult to perform experiments on encrypted grayscale images, a JTC encryption system based on complex amplitude modulation for grayscale images system is proposed. In this system, the complex amplitude modulation is implemented by means of single pixel hologram coding, so that the encryption of the grayscale image can be completed by using a single spatial light modulator with pure phase in the JTC encryption experiment.
Adaptive N-Gram Model Based Differentially Private Trajectory Synthesis Method

With the development of data science and deep learning, people tend to share their data to boost the precision of the trained model. However, the data to be shared may contain a very large number of private information, which may harm user privacy. Therefore, in this paper, based on n-gram model, a differentially private trajectory data publishing framework is proposed. To mitigate the impact of noise on the utility, we novelly propose a sparse vector technique based tree pruning method to generate an adaptive n-gram model which can adapt to height-unlimited tree. We also combine n-gram model and start-end distribution to solve the intrinsic problem of existing methods based on Markov model. Finally, we use empirical experiments to verify that our method could synthesize more similar trajectories to the original dataset than the state-of-the-art method.
Optimal Energy aware Dynamic Virtual Machine consolidation in Cloud Data Centers

Cloud computing has become successful in provisioning of different types hosted services through internet. In order to provide Quality of Service(QoS), all the cloud service providers were looking to ensure availability of resources all the time. This will have direct impact on power consumption of physical hosts in data centers and CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions. VM consolidation is a one of the most useful solution to reduce energy consumption in cloud computing . However truculent VM consolidation leads increase in SLA violations for which cloud service provider need to bear some penalty as per the agreement with the clients. So there is a great need of efficient algorithms which can show a better trade-off between energy consumption and SLA violations. In this paper, we propose Optimal Energy aware Dynamic Virtual Machine consolidation by using new VM Selection Policy (MCSSD), which is Maximum Correlation of Sum of Squares of ordinary least squares regression. the experimental results shows that better trade-off between energy consumption and SLA violations.
An Integrated Two-Stage Medical Pre-Checkup and Subsequent Validation Key Agreement Authentication Mechanism

In the global village era, several competitions require pre-checkups for the participants who are qualified to participate that must be passed before the competition, so the accuracy of the checkup data must be confirmed and must not be leaked or tampered with. This is a new challenge to the accuracy of medical checkups data in the information and communication era. How to protect the rights of participants and the non-repudiation of participants are the main issues of this study. We have designed a two-phase user identity embedding and authentication scheme for pre-checkups and subsequent validations. A participant’s private key is added to the physical examination data, and the identity of the examinations data is confirmed by the contestant before the competitions. Our work integrates lightweight Exclusive-OR (XOR) operations, fuzzy extractor biometric personal passwords, and a fixed-length hash operation accords with post-quantum operations to solve the problem of two-stage medical pre-checkup and subsequent validation key agreement authentication. The random oracle authentication mechanism proves the security of the protocols, and the security analysis proves that the protocols can resist the vulnerability attacks.
Blue-Noise Sampling of Signals on Graphs

In this work we introduce the concept of blue noise sampling, traditionally used in imaging applications, for bandlimited signals on graphs. We show how the spectral and vertex domain characterization of these patterns is connected with results about the quality of the sampling sets already existing in the literature. We provide numerical evidence that shows that these patterns are also competitive with respect to the state of the art sampling techniques in terms of the reconstruction error.
The Research of VR and AR Technology on Cultural Communication

The aim of this study is to emulate real scene that help user get immersive experience. In the paper, the tasks of the VR panorama fused construction include image acquisition, image registration, image smoothing and image fusion. Specifically, the image information can be acquired according to the projection model. To render 3D realistic scene, the key steps are that using linear fusion algorithm, the gradually fading out algorithm and the image registration algorithm based on feature point recognition. Based on the positioning of promoting Maritime Silk Route Culture, we choose 18 unique scenic spots in Quanzhou known as an oriental seaport and the start of Marine Silk Road, and make an overall design that connects the separate parts to the virtual scene roaming system. By integrating the virtuality and reality, the civilization of maritime silk route is better integrated into modern technology.
The Effects of Sampling Methods on Machine Learning Models for Predicting Long-term Length of Stay

The ability to predict the patients with long-term length of stay (LOS) can aid a hospital's admission management, maintain effective resource utilization and provide a high quality of inpatient care. Hospital discharge data from the Rhode Island Department of Health from the time period between 2010 to 2013 reveals that inpatients with long-term stays, i.e. two weeks or more, costs about six times more than those with short stays while only accounting for 4.7% of the inpatients. With the imbalance in the distribution of long-stay patients and short-stay patients, predicting long-term LOS patients becomes an imbalanced classification problem. Sampling methods—balancing the data before fitting it to a traditional classification model—offer a simple approach to the problem. In this work, the authors propose a new resampling method called RUBIES which provides superior predictive ability when compared to other commonly used sampling techniques.
Enhanced Detection Reliability for Human Tracking Based Video Analytics

Human tracking based video analytics has become a popular approach to develop smart surveillance systems, with particular interest in anomaly detection and healthcare systems. Handling inaccurate detections plays a key element in the tracking pipeline. To achieve this, modern human tracking methods have often relied on setting up a threshold on confidence scores, but missing in-depth analysis between these scores and true human detections. This may render an undesirable selection performance. For this purpose, we firstly analyze the misalignment between the given confidence scores and true human detections in the MOT16 Challenge benchmark [1]. Then we propose a global-to-local enhanced confidence rescoring strategy by exploiting the classification power of a mask region-convolutional neural network (Mask R-CNN) [2], in order to mitigate the misalignment issue. Moreover, we devise an improved pruning algorithm namely Soft-aggregated non-maximal suppression (Soft-ANMS) to further enhance the detection reliability. The proposed method can be applied as a generic early processing step to any online tracking methods for robust measurement selection. Experimental results on both tasks of measurement selection and human tracking confirm the effectiveness of the proposed method.
Improving Recommender Systems Accuracy in Social Networks Using Popularity

With the rapid advancement of World Wide Web, people can share their knowledge and information via online tools such as sharing systems and ecommerce applications. Many approaches have been proposed to process and organize information. Recommender systems are good successful examples of such tools in providing personalized suggestions. The main purpose of a recommender system is to identify and introduce desired items of a user among many other options (e.g. music, movies, books, news and etc). The goal of our proposed method is to provide a recommender system based on information diffusion and popularity in social networks. By adding popularity, similarity and users' trusts a more efficient system is proposed. This approach makes an improvement in tackling the issues and defects of the previous methods such as prediction accuracy and coverage. The evaluation of the simulated proposed method on MovieLens and Epinions datasets shows that it provides more accurate recommendations in comparison to other approaches.
The effect of augmented reality based mobile software on students’ academic achievement

This study aims to determine the effect of augmented reality-based mobile software on students’ academic achievement in the technical drawing lesson. Quantitative method was used in the study. In order to determine the effect of augmented reality-based mobile software developed on the students’ aca demic achievement, a static group pre-test-posttest experimental design was used. The study group consists of 84 engineering students. Thirty-seven of the students participating in the study are female and 47 are male. Academic achievement test was used in the study. T-test was used in data analysis. In the results of study it was concluded that the use of augmented reality technology in technical drawing lessons increased students’ academic achievement.
Chinese Sentence Decomposition Based on Hierarchical Word Order

At present, the field of parsing is occupied by statistical-based methods, and a large number of parsing methods used in English are applied to Chinese. Because of the difference between Chinese and English, it exposes a lot of shortcomings. In dependency parsing, there are many verbs in Chinese sentences, but dependency parsing only uses one core verb, which is not in line with the characteristics of Chinese. Based on the knowledge of Chinese grammar, statistics, and the hierarchical analysis theory of Chinese dichotomy of Zhu Dexi and Lu Jianming, this paper proposes a hierarchical word order-based decomposition of Chinese simple sentences. This method makes up for the deficiency of dependency parsing, and deeply combines the theory of Chinese grammar to decompose Chinese sentences hierarchically in order to extract sentences provides a better basis. Experiments were carried out on the People’s Daily corpus labeled manually by Peking University from January to June 1998. The correct decomposition rate reached 46.692%.
Improving Energy Consumption in Iterative Problems Using Machine Learning

To reach the new milestone in High Performance Computing, energy and power constraints have to be considered. Optimal workload distributions are necessary in heterogeneous architectures to avoid inefficient usage of computational resources. Static load balancing techniques are not able to provide optimal workload distributions for problems of irregular nature. On the other hand, dynamic load balancing algorithms are coerced by energy metrics that are usually slow and difficult to obtain. We present a methodology based on Machine Learning to perform dynamic load balancing in iterative problems. Machine Learning models are trained using data acquired during previous executions. We compare this new approach to two dynamic workload balancing techniques already proven in the literature. Inference times for the Machine Learning models are fast enough to be applied in this environment. These new predictive models further improve the workload distribution, reducing the energy consumption of iterative problems.
Interacting with Real Objects in Virtual Worlds

In a virtual reality (VR) experience, the manner in which a user interacts with the virtual environment and computer generated objects in the scene greatly effects the feeling of immersion. Traditionally, VR systems use controllers as a means of facilitating interaction, with a sequence of button presses corresponding to a particular action. However, controllers do not accurately model the intuitive way to interact with a real-world object and offer limited tactile feedback. Alternatively, a physical object can be tracked and used to regulate the behaviour of a virtual object. In this chapter, we review a range of approaches which use the tracked behaviour of a physical object to control elements of the virtual environment. These virtual props have the potential to be used as a more immersive alternative to the traditional controllers. We discuss how motion capture systems and external sensors can be used to track rigid and non-rigid objects, in order to drive the motion of computer generated 3D models. We then consider two neural network based tracking solutions and explain how these can be used for transporting real objects into virtual environments.