Reviewerly/exHarmony huggingface dataset:
Interactive Learning Media for Hearing-Impaired Children using Indonesian Sign Language (SIBI) — Simple Sentence Arrangement

Learning media for hearing-impaired children have been developed, but very few used Indonesian sign language in it. The lack of technology-based learning media for children with hearing impairment contrasts with current information and communication technology advancements. Hearing-impaired children learning systems tend to use conventional methods in which learning media in the form of original objects, artificial objects, and pictures/flashcards are used to show objects that will be recognized. Though the technological approach to learning systems will make learning hearing-impaired children more interesting than conventional learning. Here we are developing interactive learning media for hearing-impaired kindergarten students with a focus on constructing simple sentences consisting of subject, predicate, and object according to the kindergarten curriculum. Answering the problem of RAM and processor capacity in previous studies, in this study we developed a learning media with an online system. All data and videos are stored in the cloud so as not to burden the user's device capacity. From the results test and discussion, it can be concluded that the application of this learning media can run perfectly as indicated by the results of the functional test system and the detection of markers entirely correct. Learning media with a cloud storage system is more excellent than offline storage systems because they do not overload the device's storage capacity. From the test results of the marker detection distance, we recommend that the best distance between the flashcard and the camera is 13 to 27 cm.
WITHDRAWN: APPLICATION OF IMAGE MODAL ANALYSIS IN ENGLISH PICTURE AND TEXT RECOGNITION

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Hameem Shanavas, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal’s standard workflow.
Variability management

Combining microservices and software product line engineering (SPLE) is a challenge in variability management. This paper proposes a solution to that challenge by re-engineering existing webshop systems into a product line application. We first perform feature identification to analyze the features of subject systems. We introduce a mechanism that models the variability and designs a software product line architecture based on existing features. We use a UML diagram with the UML-DOP profile to abstract microservice variability in SPLE. Then, a transformation into a product line application is conducted to generate running applications based on selected features. We utilize a preliminary framework of microservice variability based on delta-oriented programming.
A Practical Monochrome Video Colorization Framework for Broadcast Program Production

Techniques of using convolutional neural networks (CNNs) to colorize monochrome still images have been widely researched. However, the results of automatic colorization are often different from the user's intentions and historical fact. A lot of color correction work still needs to be done in order to produce a colorized video. This is a major problem in situations such as broadcasting production where footage must be appropriately colorized in accordance with historical fact. In this article, we propose a practical video colorization framework that can easily reflect the user's intentions. The proposed framework uses a combination of two CNNs-a user-guided still-image-colorization CNN and a color-propagation CNN-that allows the correction work to be performed efficiently. The user-guided still-image-colorization CNN produces key frames by colorizing several monochrome frames from the target video on the basis of user-specified colors and color-boundary information. The color-propagation CNN automatically colorizes the entire video on the basis of the key frames, while suppressing discontinuous changes in color between frames. A quantitative evaluation showed that it is possible to produce color video reflecting the user's intention with less effort than with earlier methods.
Detecting Wireless Steganography With Wavelet Analysis

In wireless communications, information is transmitted through a modulated waveform according to the associated modulation technique. However, there has been growing research to transmit information covertly by modulating waveforms further while pretending to be a normal transmission. Modulation-based wireless steganography techniques cause malicious information leakage without being aware of it by nearby devices. In this letter, we propose a detection scheme based on wavelet analysis with various well-known wavelets (e.g., Haar, Morlet, Shannon) to distinguish effectively between the primary channel and a channel created from the modulation-based technique. Practical experiments show that the proposed scheme can detect the modulation-based techniques at least with 93.25% accuracy under most circumstances.
A Measurement Study on the Advertisements Displayed to Web Users Coming from the Regular Web and from Tor

Online advertising is an effective way for businesses to find new customers and expand their reach to a great variety of audiences.Due to the large number of participants interacting in the process, advertising networks act as brokers between website owners and businesses facilitating the display of advertisements.Unfortunately, this system is abused by cybercriminals to perform illegal activities such as malvertising.In this paper, we perform a measurement of malvertising from the user point of view.Our goal is to collect advertisements from a regular Internet connection and using The Onion Router in an attempt to understand whether using different technologies to access the Web could influence the probability of infection.We compare the data from our experiments to find differences in the malvertising activity observed.We show that the level of maliciousness is similar between the two types of accesses.Nevertheless, there are significant differences related to the malicious landing pages delivered in each type of access.Our results provide the research community with insights into how ad traffic is treated depending on the way users access web content.
Gaze Estimation Based on Deep Learning Method

Many mature methods of gaze estimation are available in various scenarios. Relying on additional hardware or platforms with professional equipment to tackle intensive computation tasks is a prominent problem of traditional methods, which usually involves high costs and is relatively tedious. Besides, the implementation of traditional gaze estimation method is typically complex. Traditional gaze estimation approaches require systematic prior knowledge or expertise for practical operations, and the gaze is estimated through the representation of pupil and iris, so high-quality images shot in special environments are required. This paper proposes a data-driven method for gaze estimation. It can be applied to various mobile platforms with deep learning methods instead of additional hardware devices or systematic prior knowledge. When collecting gaze data set, the paper designs a set of automatic and fast data collection mechanism on the mobile platform. Beyond that, the paper proposes an annotation method on collected gaze dataset that improves the predicted accuracy. The results demonstrate that the deep learning method performs well and can satisfy the task need of different applications.
Augmented Reality Performance in Detecting Hardware Components Using Marker Based Tracking Method

Computers have been widely used for various activities. The computer is composed of several components, including hardware components. Each piece of hardware has different specifications from the others. So, components that are installed depending on the specifications. Currently, users still don't know the components they use. Introducing hardware components to users can be done using Augmented Reality (AR) technology. The proposed system is developed using research and development (R&D) methods. The system was built using Unity3D and Vuforia SDK. This paper aims to analyze the performance of AR applications in detecting hardware components based on the light intensity, distance, and the angle of detection of a smartphone camera. The result performances show that the system works well in the distance parameters of ± 15 cm and ± 25 cm; while angles parameters in 5 °, 60 °, or 90 °. The proposed system was also working well when the components were partially covered by about 20% and 50%. Furthermore, the proposed system still works well when light intensity decreases by 100 Lux. The result shows that AR is potentially used for the user to know the components.
A Dynamic Pricing and Leasing Module for 5G Networks

In this paper, a novel framework for enabling dynamic Policy and Charging Rules in modern 5G multi-tenancy environments is designed and presented. More specifically, adaptive pricing schemes are utilized by MNOs for instantiating tailored slices, when the resources of a Mobile Network Operator (MNO) are insufficient to serve the connected users. A scheme where an MNO seeks and leases additional resources from an Infrastructure Provider (InP) is examined in this paper. Their negotiation is modeled as a Service Level Agreement (SLA) and the dynamic charging rules are implemented as an extension of the 5G Policy Charging Function (PCF). The analytical structure of the proposed architecture as well as all the methods and signals developed for MNO and InP collaboration, are described in the context of this work. Finally, extensive testbed experimentation proves proper functioning of the proposed framework, under both UDP and TCP additional traffic demands.
Predicting Quality and Popularity of a Movie From Plot Summary and Character Description Using Contextualized Word Embeddings

Narrative is an essential factor that makes games more enjoyable. However, predicting the story quality has been challenging for decades. In this paper, we propose to use contextual word embedding models such as BERT and ELMo, for predicting a story's success in terms of quality and popularity by using the story text only. Since deep learning models generally require extensive data, we conducted experiments to test the efficacy of our proposed model by leveraging the movie plot summaries. We present the results of the evaluations and conclude with discussions.
Design and implementation of a visual cryptography application

This paper describes the design and implementation of a visual cryptography application. The initial information is processed in the form of an image, following that, a visual encryption algorithm will be applied to this image. It will generate between 2 and 4 sheets that will represent the results of encryption. The decryption is performed by overlapping them in the correct position and visual recognizing by the human eye of the information. Each encryption will generate a set of unique results and the repetition of the encryption will generate another set. This is due, as we explained in this paper, to random choices used in decision points.
Semisupervised Learning for Seismic Monitoring Applications

Abstract The impressive performance that deep neural networks demonstrate on a range of seismic monitoring tasks depends largely on the availability of event catalogs that have been manually curated over many years or decades. However, the quality, duration, and availability of seismic event catalogs vary significantly across the range of monitoring operations, regions, and objectives. Semisupervised learning (SSL) enables learning from both labeled and unlabeled data and provides a framework to leverage the abundance of unreviewed seismic data for training deep neural networks on a variety of target tasks. We apply two SSL algorithms (mean-teacher and virtual adversarial training) as well as a novel hybrid technique (exponential average adversarial training) to seismic event classification to examine how unlabeled data with SSL can enhance model performance. In general, we find that SSL can perform as well as supervised learning with fewer labels. We also observe in some scenarios that almost half of the benefits of SSL are the result of the meaningful regularization enforced through SSL techniques and may not be attributable to unlabeled data directly. Lastly, the benefits from unlabeled data scale with the difficulty of the predictive task when we evaluate the use of unlabeled data to characterize sources in new geographic regions. In geographic areas where supervised model performance is low, SSL significantly increases the accuracy of source-type classification using unlabeled data.
Emotional Response Increments Induced by Equivalent Enhancement of Different Valence Films

The arrangements of emotional intensity in film which related to the plot and rhythm are important to the audience's feeling of the film. The arousal degree brought by different emotional intensity is the core concern of the film creators. Whether the equal enhancement of positive and negative emotion stimulus will bring equal emotion arousal to audiences has not been determined in current researches. Previous studies usually just simple compare positive and negative films, but the emotional intensity of positive and negative films is already different, so it is difficult to compare them accurately. In this paper, participants were asked to watch one of the formats of Virtual Reality (VR) and 2D (Monoscopic) emotional films, and to compare the arousal increments of participants under the condition of equivalent enhancement of positive and negative valence films. The experiment results show that the same incremental negative films bring more incremental responses than the positive ones. Combined with subjective scales, skin conductivity response and pulse rate can better reflect the physiological indicators of emotional arousal. In addition, in both positive and negative films, skin conductance level, skin conductance response, blood volume pulse and subjective evaluation showed significant differences between VR and 2D, and the valence judgment of negative films was not affected by the display modes.
Development of a Low-Cost Embedded Flight Controller for Quadcopter

Quadcopter has been interesting researchers due to its wide application, such as surveillance, mapping, delivery drone, etc... Developing a low-cost embedded flight controller unit (EFCU) for making the quadcopter autonomous is still challenging problem. In this work, an EFCU is developed based on ARM STM32F103C8T6 microcontroller, the MPU6050 IMU sensor and the HC-SR04 ultrasonic sensor. An PID control strategy is implemented for automatically take-off, landing, and stabilizing control of the quadcopter. The EFCU and PID controller is developed in this work. It is implemented and tested experimentally by providing error on roll and pitch position in 0.71° and 0.68° respectively.
Power Performance Analysis of Solar Tracking System in UTP

In the recent years, Renewable energy has been a lot in talks, the most booming renewable energy currently is the Solar energy. This is because solar energy is a simple, cheap and easy method to generate electricity. In Malaysia, if you have installed solar panel the government will provide a certain compensation, as well as buy excessive solar power which is used to supply power to homes or buildings around it. This study presents the design of prototype and analysis of performance and effectiveness of a single axis and a fixed axis solar tracker with an active closed loop system utilizing Arduino as a microcontroller. Furthermore, the ultimate aim of this study is to test the performance and effectiveness of the two types of tracking system, developing and testing the prototype for the two systems. This study can be divided into two phases, which is the software and hardware development. In hardware development for the single axis tracker, two light dependent resistor (LDR) is being used in order to detect light rays from the sunlight. The highest light intensity which is detected by the LDR will determine the position of the sun. Hence, upon determining the position of sunlight, the LDR will send information on the highest light intensity produced to the linear actuator in order to direct the solar photovoltaic panel to maintain perpendicular to the light intensity which will maximize the output produced. Based on conventional research, the single axis solar tracking system is shown to have the higher effectiveness compared to the fixed axis solar tracker.
Post-Quantum Constant-Round Group Key Exchange from Static Assumptions

Abstract We revisit a generic compiler from a two-party key exchange (KE) protocol to a group KE (GKE) one by Just and Vaudenay. We then give two families of GKE protocols from static assumptions, which are obtained from the general compiler. The first family of the GKE protocols is a constant-round GKE by using secure key derivation functions (KDFs). As special cases, we have such GKE from static Ring-LWE (R-LWE), where “static” means that the parameter size in the R-LWE does not depend on the number of group members, n , and also from the standard SI-DDH and CSI-DDH assumptions. The second family consists of two-round GKE protocols from isogenies, which are proven secure from new isogeny assumptions, the first (resp. second) of which is based on the SIDH (resp. CSIDH) two-party KE. The underlying new static assumptions are based on indistinguishability between a product value of supersingular invariants and a random value.
Joulin: Blockchain-based P2P Energy Trading Using Smart Contracts

As a decentralized immutable ledger where several trustless peers can reach consensus with each other without the need of any trusted third party, blockchain technology fits perfectly with the peer-to-peer (P2P) energy trading paradigm. In this paper, we propose, design and analyze a marketplace for energy trading based on smart contracts on the blockchain. The proposed system named Joulin serves as a competitive and efficient marketplace where peers can both produce, buy and sell energy depending on their needs. As a proof-of-concept, we developed the prototype of the Joulin system using Ethereum blockchain. Our results, in terms of usability, flexibility and resiliency, demonstrate the potential to achieve an easily extendable and reliable system with low transaction costs. Low Ethereum gas costs and quick response times demonstrate usability. Our smart contracts have also been tested with security tools to ensure that they are not vulnerable to outside manipulations.
Deep Residual Neural Networks for Image in Audio Steganography (Workshop Paper)

Steganography is the art of hiding a secret message inside a publicly visible carrier message. Ideally, it is done without modifying the carrier, and with minimal loss of information in the secret message. Recently, various deep learning based approaches to steganography have been applied to different message types. We propose a deep learning based technique to hide a source RGB image message inside finite length speech segments without perceptual loss. To achieve this, we train three neural networks; an encoding network to hide the message in the carrier, a decoding network to reconstruct the message from the carrier and an additional image enhancer network to further improve the reconstructed message. We also discuss future improvements to the algorithm proposed.
Airborne hyperspectral characterisation of hydrothermal alteration in a regolith-dominated terrain, southern Gawler Ranges, South Australia

Key alteration minerals associated with epithermal and porphyry Cu–Au mineralisation have been successfully identified using HyMap airborne hyperspectral imagery in a regolith-dominated terrain in the southern Gawler Ranges, South Australia. Alteration assemblages were mapped using Spectral Feature Fitting, a spectral matching algorithm, identifying the spatial distribution of localised advanced argillic and broader argillic alteration. X-ray diffraction (XRD) analysis was undertaken to independently identify the mineralogy of 57 surface soil and rock samples collected from the study area. This analysis confirmed the presence of key alteration minerals including alunite, pyrophyllite and dickite. The integration of spectral and XRD analytical techniques allowed interpretation of mineralogical patterns across the landscape. This study demonstrates that it is possible to identify surface alteration related to potential mineralisation using airborne hyperspectral imagery and semi-quantitative XRD even in a weathered regolith-dominated terrain.KEY POINTSAdvanced argillic and argillic alteration identified through hyperspectral image analysis in a regolith-dominated terrainX-ray diffraction used to validate the presence of advanced argillic alteration minerals alunite, pyrophyllite and dickite in the landscapeRelationship of advanced argillic and argillic alteration interpreted in two key landscape features in the context of a porphyry mineral systemDemonstrates potential for detection of alteration signatures with airborne hyperspectral imagery in regolith-dominated terrains elsewhere
Real-Time Sign Language Recognition

We propose an automatic system to recognize sign language using principal component analysis (PCA) and one-vs.-all support vector machines (SVM) classification. The algorithm was trained and tested using a total of 500 images of the five vowels. The method includes color information, to detect skin regions, hand segmentation, using morphological operations and filters, feature extraction in hand regions using PCA, and classification using SVM. A graphical user interface was implemented for real-time recognition. For this first approach, the system was optimized for working with the five vowels showing results of a testing accuracy above 80% and an execution time of 59 milliseconds per frame.
Explainable AI for Inspecting Adversarial Attacks on Deep Neural Networks

Deep Neural Networks (DNN) are state of the art algorithms for image classification. Although significant achievements and perspectives, deep neural networks and accompanying learning algorithms have some important challenges to tackle. However, it appears that it is relatively easy to attack and fool with well-designed input samples called adversarial examples. Adversarial perturbations are unnoticeable for humans. Such attacks are a severe threat to the development of these systems in critical applications, such as medical or military systems. Hence, it is necessary to develop methods of counteracting these attacks. These methods are called defense strategies and aim at increasing the neural model's robustness against adversarial attacks. In this paper, we reviewed the recent findings in adversarial attacks and defense strategies. We also analyzed the effects of attacks and defense strategies applied, using the local and global analyzing methods from the family of explainable artificial intelligence.