Reviewerly/exHarmony huggingface dataset:
Research on Detection and Fault Diagnosis Technology of Electric Vehicle Charging Facilities

Abstract This paper uses waveform analysis method and data flow analysis method to study fault diagnosis and elimination. Based on the experimental results, the data flow analysis method and waveform analysis method of the vehicle were analyzed in depth. Apply fault diagnosis to find a fast fault diagnosis scheme to determine the hidden dangers of charging pile failure. The advantages and disadvantages of various fault diagnosis methods in maintenance practice are obtained.
Voice Command Fingerprinting with Locality Sensitive Hashes

Smart home speakers are deployed in millions of homes around the world. These speakers enable users to interact with other IoT devices in the household and provide voice assistance such as telling the weather and reminding appointments. Although smart home speakers facilitate many aspects of our life, security and privacy concerns should be analyzed and addressed. In this paper, we show that an attacker sniffing the network traffic of smart speakers can infer voice commands and compromise the privacy of users. Specifically, we propose a method that utilizes the network traffic of the speakers to fingerprint the voice commands of users without a need for extracting traffic features with machine learning algorithms. We evaluated the proposed method on traffic traces of 100 different voice commands on smart home speakers. Our approach correctly infers 42% of voice commands while machine learning models infer 22% to 34%. We also evaluated the effectiveness of the padding method recommended for preventing voice command fingerprinting and observed that the accuracy of proposed fingerprinting method drops down to 15% and accuracy of machine learning methods ranges from 6% to 15% with traffic padding.
Development of Landslide Victim Detection System using Thermal Imaging and Histogram of Oriented Gradients on E-PUCK2 Robot

Thermal imaging currently have been widely used in many aspects; one of them is in search and rescue application. The system utilizes thermal imaging as a detection system to differentiate between living human and post-landslide environment. This paper proposes the use of a histogram of oriented gradient (HOG) features from the images taken by the thermal imaging camera to be used to classify a landslide victim (target) from its surrounding environment. The model is built by three different algorithms, i.e., support vector machine (SVM), K-nearest neighbor, and random forest. Those algorithms were used to classify the target from its surrounding based on the image taken by FLIR Camera. Based on the simulation results, the model built with 5×5 pixels of HOG feature and SVM with linear kernel achieved 81.82% accuracy and has the lowest computation time among other algorithms, i.e., 0.526 seconds per identified image. This best model is then implemented and tested on an EPUCK2 swarm robot as a search robot in the designed landslide search and rescue scenario. Experimental testing results show that the developed system can successfully classify seven targets out of 12 different experiments with 72.43% accuracy.
Software Engineering: The First Line of Defense for Cybersecurity

Cybersecurity has become an area of critical concern due to an ever-growing number of security breaches. Some of these breaches compromise personal information, exposing individuals and firms to potential identity theft, fraud and other maladies. Other attacks seek to gain control of systems for use in attacking as part of bot nets and other indirect attack techniques. Yet other attacks target cyber-physical systems whose compromise could potentially lead to the injury or death of individuals relying on or nearby the equipment. This paper considers the role of software engineering in preventing cyberattacks and discusses the types of software engineering failures that translate into vulnerabilities that can be attacked. Prospective solutions and areas of needed future research are discussed.
Online planning low-cost paths for unmanned surface vehicles based on the artificial vector field and environmental heuristics

The study is concerned with the problem of online planning low-cost cooperative paths; those are energy-efficient, easy-to-execute, and low collision probability for unmanned surface vehicles (USVs) based on the artificial vector field and environmental heuristics. First, we propose an artificial vector field method by following the global optimally path and the current to maximize the known environmental information. Then, to improve the optimal rapidly exploring random tree (RRT*) based planner by the environment heuristics, a Gaussian sampling scheme is adopted to seek for the likely samples that locate near obstacles. Meanwhile, a multisampling strategy is proposed to choose low-cost path tree extensions locally. The vector field guidance, the Gaussian sampling scheme, and the multisampling strategy are used to improve the efficiency of RRT* to obtain a low-cost path for the virtual leader of USVs. To promote the accuracy of collision detection during the execution process of RRT*, an ellipse function-based bounding box for USVs is proposed with the consideration of the current. Finally, an information consensus scheme is employed to quickly calculate cooperative paths for a fleet of USVs guided by the virtual leader. Simulation results show that our online cooperative path planning method is performed well in the practical marine environment.
Material Characterization While Drilling on the Moon: Comparing the Atmospheric and Cryogenic Drilling Data

The sustainable presence of humans on the Moon relies heavily on using the water-ice available on the Moon. However, the large variability in our understanding of the form, quantity, vertical & spatial distribution, and composition of water-ice has been a roadblock in designing the technologies to identify, excavate, and process the water-ice. This uncertainty can be reduced significantly by acquiring ground-truth data. Upcoming missions such as VIPER and Luna-27 contain a drill that will help reduce this uncertainty. The goal of this NASA ESI funded project is to develop pattern-recognition algorithms to characterize lunar subsurface based on drilling data. This work focuses on developing a part of the algorithm for low-porosity water-bearing lunar regolith. Three low-porosity analogs were created to acquire high-frequency drilling data. This data was used to train a complex pattern-recognition algorithm which was then tested on water-bearing lunar regolith simulant with varying water-content to estimate the UCS of the sample, identify layer boundaries, detect auger choking, and identify the form of the water-bearing lunar regolith.
The Current Situations of Mobile Assisted Language Learning

The pervasive use of mobile devices has profoundly influenced people’s life style and leaning style. Currently, it is pretty popular to use smart phones, IPADs, or PCs to have language learning. Mobile assisted language learning (MALL) makes it possible for language learning to occur at any time and place, and it can also compensate the interactive, cooperative and authentic learning environment which is often lack in tradition foreign language learning classroom. The paper mainly discussed the current situation of MALL and its application in the foreign language learning and teaching by summarizing the definition, characteristics, merits and flaws of MALL. It concluded that foreign language teachers should fully consider the functions and advantages of different mobile devices and design appropriate tasks according to learners’ cognitive level and foreign language proficiency in order to simulate learners’ interest in MALL and also encourage learners to develop their own learning strategies in MALL so as to promote their independent and autonomous learning ability.
Tibetan Text Classification Algorithm Based on Syllables

Tibetan text classification is one of the core technologies in the field of Tibetan information processing. With the rapid development of the Internet, a large amount of Tibetan Internet text data will be generated every day. Text classification technology can quickly and accurately obtain the required information to solve the problem of out-of-order in text. Tibetan syllables are the basic components of Tibetan text, and each syllable in Tibetan is divided by syllable nodes. This paper proposes a Tibetan syllable as a text representation feature, and uses deep neural network models such as CNN, BiL STM and RCNN to classify Tibetan text. Experiments show that this method has achieved prefect results in different depth neural network classification models.
Herbal leaf authentication using convolutional neural network on Raspberry Pi 3

Currently, there are many methods for classifying herbal plants based on leaf authentication. Basically, the method of leaf authentication is a visual comparison of images taken by a camera with a reference visual image. This paper aims to identify herbal leaves using the artificial intelligence method i.e. the convolutional neural network (CNN) applied to the Raspberry Pi. The advantage of CNN is that it does not need feature extraction because it contains an automatic feature extraction process. In this paper, there are 10 types of leaves from different herbal plants which are divided into two-third training data and one-third testing data. The results of the identification system using CNN will be validated with other data that are not included in training and testing. Furthermore, it will be tested with different types of leaves that are outside ten types of leaves in the experiment. The accuracy using this CNN method is above 90%. These results indicate that the CNN method is more accurate in herbal leaf authentication.
Bassa-Scalable Blockchain Architecture for Smart Cities

Smart cities will depend on reliable Internet of things to effectively provide residential and commercial services. Smart city applications generate vast amount of data and will need the infrastructure to support high transaction throughput. The data associated with smart cities can include sensitive information and there is a need to guarantee the security and privacy through a blockchain-based decentralized infrastructure. Current blockchain environments face several challenges, such as, lack of high transaction throughput, high scalability, real-time transaction processing, and back-end stress operations, etc. In this paper, we propose “Bassa”, a blockchain platform that will meet the aforementioned challenges. Bassa employs a Apache Kafka based consensus and `validate-execute-group” blockchain architecture to realize real-time transaction. Bassa's smart contract platform realizes concurrent transaction by leveraging actor-based concurrency.
GrapeL: Combining Graph Pattern Matching and Complex Event Processing

Incremental Graph Pattern Matching (IGPM) offers an elegant approach to find patterns in graph-based models, reporting newly added and recently removed pattern matches. However, analyzing these matches w.r.t. temporal and causal dependencies can in general only be done by extending not just the IGPM engine but also the underlying model, which often is impractical and sometimes even impossible. Therefore, we transform the stream of pattern matches to a stream of events and employ Complex Event Processing (CEP) to detect such dependencies and derive more complex events from them. For this purpose, we introduce GrapeL as a textual language to specify and generate integrated solutions using both IGPM and CEP to benefit from the synergy of both approaches, which we present in the context of a flight and booking scenario. Finally, we show that our solution can compete with an optimized hand-crafted version without GrapeL and CEP while offering a specification that yields a less tedious and error-prone design process.
Measurement-Device-Independent Two-Party Cryptography with Error Estimation

We present an innovative method for quantum two-party cryptography. Our protocol introduces joint measurement and error estimation to improve the security of two-party cryptographic protocols. Our protocol removes the assumption of the attacker's limited power and catches the attacking actions through highly estimated bit error rate. Our protocol is formally proved to be secure against both eavesdroppers and dishonest communication parties. We also utilize our designed protocol to construct two specific two-party cryptographic applications: Quantum bit commitment and quantum password identification.
Building a scaffold for transdisciplinary design processes: helping art-science residencies explore the design space of new technologies

Design practices have become more hybrid and interaction design does not happen only in corporate or academic settings, nor is it always done by design professionals. Design activities are ever more carried out by people with diverse backgrounds while the lines of traditional design domains are being blurred. A multitude of perspectives has the potential to support creativity, however it can also make the collaboration more difficult. This case study presents a design-led research process of developing a set of guidelines that enable to better organize and facilitate art and science collaboration projects exploring the design space for new technologies.
Analyzing the Trade-off between Selection and Navigation in VR

Navigation and selection are critical in very large virtual environments, such as a model of a whole city. In practice, many VR applications require both of these modalities to work together. We compare different combinations of two navigation and two selection methods in VR on selection tasks involving distant targets in a user study. The aim of our work is to discover the trade-off between navigation and selection techniques and to identify which combination leads to better interaction performance in large virtual environments. The results showed that users could complete the task faster with the fly/drive method and traveled less, compared to the teleportation method. Additionally, raycasting exhibited a better performance in terms of time and (less) distance traveled, however, it significantly increased the error rate for the selection of targets.
Design of Intelligent Watering Robot

This paper proposes to design an intelligent watering robot designed to reduce human resources, reduce the labor intensity of manual spraying, improve the efficiency of watering bonsai, and accomplish the automatic intelligent function of watering bonsai. By selecting stm32f4 based on ARM Cortex-M4 core as the control center of mobile robot, linear CCD image acquisition sensor TSL1401 as line patrol module, microcontroller MSP430, YL-69 soil moisture detection sensor as soil moisture detection module, NRF24L01 wireless transceiver chip as a wireless communication module, it can automatically walk along a predetermined path and perform watering operations on bonsai that needs to be watered. In addition, a D-H coordinate system was established for the RPP spray manipulator mounted on the robot mobile platform. On this basis, the Lagrangian approach was employed in deriving the dynamic model of a three-DOF manipulator, and the dynamic simulation analysis is carried out to analyze the force/torque change curve of the manipulator, which can provide a theoretical basis for the dynamic design and dynamic control of the manipulator.
Paying Per-Label Attention for Multi-label Extraction from Radiology Reports

Training medical image analysis models requires large amounts of expertly annotated data which is time-consuming and expensive to obtain. Images are often accompanied by free-text radiology reports which are a rich source of information. In this paper, we tackle the automated extraction of structured labels from head CT reports for imaging of suspected stroke patients, using deep learning. Firstly, we propose a set of 31 labels which correspond to radiographic findings (e.g. hyperdensity) and clinical impressions (e.g. haemorrhage) related to neurological abnormalities. Secondly, inspired by previous work, we extend existing state-of-the-art neural network models with a label-dependent attention mechanism. Using this mechanism and simple synthetic data augmentation, we are able to robustly extract many labels with a single model, classified according to the radiologist's reporting (positive, uncertain, negative). This approach can be used in further research to effectively extract many labels from medical text.
Multi-template supervised descent method for face alignment

Supervised Descent Method (SDM) is a highly efficient and accurate approach for facial landmark locating and face alignment. In the training phase, it learns a sequence of descent directions to minimize the difference between the estimated shape and the ground truth in feature space. Then in the testing phase, it utilizes these descent directions to predict shape increment iteratively. However, when the facial expression or direction changes too much, the general SDM cannot obtain good performance due to the large variations between the initial shape and the target shape. In this paper, we propose a multi-template SDM (MtSDM) which can maintain high accuracy on training data and meanwhile improve the accuracy on testing data. Instead of only one model is constructed in the training phase, multiple different models are constructed by repeatedly inputting the images which have large variations on expressions or head poses. And in the testing phase, the distances between some specific landmarks are calculated to select an optimal model to update the point location. The experimental results show that our proposed method can improve the performance of traditional SDM and performs better than several existing state-of-the-art methods.
Level of Presence in Max Where Virtual Reality

Presence is the sense of being inside a virtual environment, that can be experienced also in desktop virtual realities not only in full-immersive VRs. MaxWhere is a desktop virtual reality that is used in education and for collaborative work. This VR can be considered as a 3D browser because in the space there are several smartboards where any web-content, document, or image can be displayed. This study compares the sense of presence in MaxWhere virtual reality to other desktop virtual realities with the Igroup Presence Questionnaire. This VR is in focus of numerous scientific research, thus this comparison of presence level could be a great starting point for them. The analysis showed that the level of presence in MaxWhere virtual reality corresponds to the average presence level in other desktop virtual realities. The analysis of the presence profile has shown that the general sense of presence and the spatial presence are relatively high in this VR. The experienced realism scale is the lowest because this VR is designed to foster working and learning inside the virtual space and not for having a high fidelity.
Cloud Computing Task Scheduling Based on Improved Differential Evolution Algorithm

In recent years, the introduction of intelligent optimization algorithm into cloud computing task scheduling to deal with the problem of massive task scheduling is a research hotspot. This paper proposes three improved differential evolution cloud computing task scheduling algorithms, and the application of the improved differential evolution algorithm in cloud computing task scheduling problem is mainly studied. The maximum task completion time is optimized by improving parameters F, CR, and variation strategies. Through two sets of simulation experiments, it is proved that three improved differential evolutionary cloud task scheduling algorithms have less task completion time than the traditional differential evolution algorithm, and the bigger the number of tasks, the more obvious the performance optimization of the algorithm.
Bayesian Deghosting Algorithm for Multiple Target Tracking

This paper deals with bistatic track association in classical Frequency Modulation (FM) based Multi Static Primary Surveillance Radar (MSPSR). We formulate deghosting procedure as Bayesian inference of association matrix between bistatic tracks and targets as well as target positions. To do that, we formulate prior probability distribution for the association matrix and develop custom Monte Carlo Markov Chain (MCMC) sampler, which is necessary to solve such a hybrid inference problem. Using simulated data, we compare the performance of the proposed algorithm with two others and show its superior performance in such a setup. At the end of the paper, we also outline further research of the algorithm.
Performance Analysis Graph-Based Keyphrase Extraction in Indonesia Scientific Paper

The increasing number of research papers from Indonesia and the higher the need to manage large amounts of documents, including memory capacity and the availability of time to manage research documents, keyphrase can provide a picture representing a text document. Thus, the management of documents in large quantities will become easier to prepare and find a collection of documents. For this reason, proper and correct keyphrase extraction can assist in managing paper documents. This study tried to compare three extraction methods using unsupervised graph-based techniques, including TextRank, TopicRank, and PositionRank. We compare the extraction results with the keyphrase made by the authors of the paper to see the three method's performance. From the results of our research, we found that the method TextRank Providing the highest performance compared to other methods using the ROUGE test with average Recall, Precision, and F1-Score are 0.558, 0.71, and 0.225. However, the extracted phrases are longer compared to others. The performance of TopicRank, although it does not provide higher results with TextRank, the extracted keyphrase is more compact than TextRank and PositionRank methods.
Automatic Discovery of Motion Patterns that Improve Learning Rate in Communication-Limited Multi-Robot Systems

Learning in robotic systems is largely constrained by the quality of the training data available to a robot learner. Robots may have to make multiple, repeated expensive excursions to gather this data or have humans in the loop to perform demonstrations to ensure reliable performance. The cost can be much higher when a robot embedded within a multi-robot system must learn from the complex aggregate of the many robots that surround it and may react to the learner’s motions. In our previous work [1], [2], we considered the problem of Remote Teammate Localization (ReTLo), where a single robot in a team uses passive observations of a nearby neighbor to accurately infer the position of robots outside of its sensory range even when robot-to-robot communication is not allowed in the system. We demonstrated a communication-free approach to show that the rearmost robot can use motion information of a single robot within its sensory range to predict the positions of all robots in the convoy. Here, we expand on that work with Selective Random Sampling (SRS), a framework that improves the ReTLo learning process by enabling the learner to actively deviate from its trajectory in ways that are likely to lead to better training samples and consequently gain accurate localization ability with fewer observations. By adding diversity to the learner’s motion, SRS simultaneously improves the learner’s predictions of all other teammates and thus can achieve similar performance as prior methods with less data.