Reviewerly/exHarmony huggingface dataset:
Trust Concerns Regarding Health-Related Smartphone Apps in Collecting Personally Identifiable Information Throughout COVID-19-like Zoonosis

Coronavirus disease 2019 or COVID-19 is a zoonosis, which means a disease that contaminates from the animals to the humans. Since it is very highly epizootic, it has forced the public health experts to implement smartphone-based applications to trace its swift transmission trajectory as well as the affected individuals. For this, the individuals’ personally identifiable information is utilized. Nonetheless, these information may hamper privacy and cyber security, especially the trust concerns, if not handled properly. If the issues are not resolved at this very moment, the consequences will induce the mass level population to use the health-related applications in their smartphones inadequately. Therefore, a catastrophe will be imminent for another COVID-19-like zoonosis to come. So, to mitigate, an extensive study was required to address this severe issue, namely, trust concern. This paper has studied the needed by discussing the recently designed and developed health-related applications region by region across the world. Moreover, it has analyzed the benefits and drawbacks. The trust defiance is recognized and inspected from the perspective of an end-user. Some recommendations are advised in the later part of this paper to leverage and collaborate the awareness campaign between the Government, the App Developers and the common individuals.
A LoRa-based Remote Gesture Monitoring System Using Deep Learning

Abstract To solve the problems of high power consumption, low transmission distance and low recognition accuracy of the gesture monitoring system of traditional wearable devices, this paper designs a remote gesture monitoring system based on LoRa. In terms of data transmission, LoRa Internet of Things technology is used, which has the characteristics of low power consumption, high speed and long-distance transmission, and can meet the needs of multi-user long-term use. The identification module is built on the remote server and can be used directly without configuration. Based on the multi-sensor data, this paper also designs a deep learning model to complete the task of human gesture recognition, which can recognize 7 kinds of gesture data and the effect meets the expectations.
A Potential Solutions-Based Parallelized GA for Application Graph Mapping in Reconfigurable Hardware

High-performance computing systems including Reconfigurable Hardware (RH) such as Field Programmable Gate Array (FPGA) proved a significant impact on the speed of application execution with useful reconfiguration and parallelism attributes. To make one application executable on RH, it is required to perform some heavy computational compilation preprocessing phases. In this paper, we aim to reduce compilation overhead in the NP-hard problem of the mapping phase by utilizing a novel Parallelized Genetic Algorithm (PGA) which is based on potential solutions in the search space. In the search space of possible solutions, we analytically separate weak and potential solutions to guide the GA for reaching the optimal solution faster. Moreover, this separation has been carried out independently to add parallelism into our GA and also, to switch between search spaces for keeping the generalization of GA exploration. Comparison results showed that our approach could make a considerable gap at the starting points of solution searching and therefore, found the optimal solution in a more reasonable time.
Remove the HFM noise by using Wavelet Neural Network

Abstract During the HTEM signal processing we found that some kind of short-term noise till remained after the conventional denoising procedure. It is called high frequency motion-induced noise according to the research on its induced mechanism and time-frequency features. In this paper, we propose an approach to suppress the high frequency motion-induced noise based on wavelet neural network. The method uses high frequency motion-induced noise in dominance as a sample and then trains the wavelet neural network by it. After processing the field data, it is considered that the method proposed in this paper can effectively suppress the high frequency motion noise and the processing results build a good data foundation for subsequent processing.
Sequential Transfer Learning for Event Detection and Key Sentence Extraction

Sequential transfer learning (STL) techniques aim to improve learning in the target task by leveraging knowledge from a related domain using pre-trained representations. Approaches based on these techniques have achieved state-of-the-art results on a wide range of natural language processing (NLP) tasks. In the context of event detection and key sentence extraction, we propose to explore STL-based techniques using the last generation of pre-trained language representations, namely, ALBERT, BERT, DistilBERT, ELECTRA, OpenAI GPT2, RoBERTa and, XLNet. Experiments are conducted as a part of our contribution to the CLEF 2019 ProtestNews Track, which aims to classify and identify protest events in English-language news from India and China. Averaged results show that a STL-based method with OpenAI GPT2 outperforms prevailing methods in this domain by achieving better performance across event detection and key sentence extraction tasks. In addition, OpenAI GPT2 also obtains the best results on the majority of datasets tested in comparison to the best system presented during the CLEF 2019 ProtestNews challenge.
Research on the evaluation system of online education service quality: Evidence from college students

Based on the traditional evaluation method of service quality and network service quality, this paper develops an evaluation system of online education service quality, which is verified by factor analysis and regression analysis. It is concluded that college students pay more attention to tangible, empathic, and resource-effective services when using online education. This is helpful for improvement of the quality of online education service and more efficient future teaching.
A Web-Based Eye Tracking Data Visualization Tool

Visualizing eye tracking data can provide insights in many research fields. However, visualizing such data efficiently and cost-effectively is challenging without well-designed tools. Easily accessible web-based approaches equipped with intuitive and interactive visualizations offer to be a promising solution. Many of such tools already exist, however, they mostly use one specific visualization technique. In this paper, we describe a web application which uses a combination of different visualization methods for eye tracking data. The visualization techniques are interactively linked to provide several perspectives on the eye tracking data. We conclude the paper by discussing challenges, limitations, and future work.
Computer Teaching in Colleges and Universities—On the Orientation of Talent Training Objectives in Colleges and Universities

Abstract Universities should be based on the needs of society and industry, industrial progress and scientific and technological innovation, actively cultivate pioneering and creative computer talents, clarify their own talent training objectives and positioning, strengthen and accelerate the training of high-level technology application-oriented computer talents. Based on this, this paper first analyzes the basis of the orientation of university personnel training objectives, then studies the current problems and current situation of university personnel training, as well as the actual demand of computer industry for talent ability. Finally, this paper puts forward the strategy of improving university computer teaching based on the talent training goal and orientation.
Diffie-Hellman Algorithm for Securing Medical Record Data Encryption keys

Data security is an issue that must be a concern in the current era of information technology. Medical data in the form of medical digital images is also important data to protect its security. This is to avoid being used by irresponsible parties. This data security can apply data encryption. The application of this encryption is by developing a medical digital image security that uses a combination of the advanced encryption standard (AES) algorithm in the data encryption process and the Diffie-Hellman algorithm in the key exchange process to maintain the security and confidentiality of medical images. The AES algorithm is used in the encryption and decryption process, while the Diffie-Hellman algorithm is used to secure the key of the AES algorithm. The results of this research showed that the combination of AES and Diffie-Hellman algorithms succeeded in securing medical images. Diffie-Hellman algorithm will generate different keys which are then used as AES keys. In this research, the Diffie-Hellman algorithm will produce a key which will be combined with the encryption key from the AES algorithm. In addition, the Diffie-Hellman algorithm is used for security in the exchange process and key generation.
P‐2.2: Anomaly Detection Based on Generative Adversarial Network in the Manufacturing Process of LCD/OLED Display Panels

In the manufacturing process of LCD/OLED, defects on display panels need to be localized and classified according to certain criterion. Recent triumph of deep learning model in defects detection on LCD/OLED panels greatly reduce the miss and mistake rate of defects while depends tightly on the industrial training data. These image data, acquired from the industrial display pipelines, show great imbalance with the positive sample way surpassing negative defective ones. Despite data imbalance, the diversity in the negative samples make the data preparation trick and certainly impossible to exhaust all kinds of negative samples for training. Based on the above observation, it would be enormously beneficial if the anomaly detection on display panels can be purely based on the overwhelming positive samples with enough variations. This insight serves as our motivation for GAN‐based anomaly detection on LCD/OLED display panels. In this paper, we propose the utilization of one specific anomaly GAN to the real time anomaly detection of display panels online and also automatic data labeling offline. The result shows its efficiency in detecting all kinds of prominent anomalies.
A Bioeducational Approach to Virtual Learning Environments

Knowledge carries some general characteristics related to the socio-environmental, cultural, and bio-physiological contexts. These three coordinates help us to understand under which condition knowledge is achieved/gained and they do it. Along the same line, the real or virtual learning contexts being essential and unique, the possibilities offered by the VLE which give the opportunity of programming environmental challenges, complexity, and support for subjects open up a series of educational perspectives that support individual differences even when they reproduce social platforms as virtual worlds. Programming that through adequate representations of environments, situations, problems, and specific actions are able to work on more complex neuronal patterns usually activated in the presence of real objects, especially in light of the current structures present in formal contexts of education.
An RLS-Based Instantaneous Velocity Estimator for Extended Radar Tracking

Radar sensors have become an important part of the perception sensor suite due to their long range and their ability to work in adverse weather conditions. However, several shortcomings such as large amounts of noise and extreme sparsity of the point cloud result in them not being used to their full potential. In this paper, we present a novel Recursive Least Squares (RLS) based approach to estimate the instantaneous velocity of dynamic objects in real-time that is capable of handling large amounts of noise in the input data stream. We also present an end-to-end pipeline to track extended objects in real-time that uses the computed velocity estimates for data association and track initialisation. The approaches are evaluated using several real-world inspired driving scenarios that test the limits of these algorithms. It is also experimentally proven that our approaches run in real-time with frame execution time not exceeding 30 ms even in dense traffic scenarios, thus allowing for their direct implementation on autonomous vehicles.
Machine-Checking Unforgeability Proofs for Signature Schemes with Tight Reductions to the Computational Diffie-Hellman Problem

Digital signatures based on the Discrete Logarithm (DL) problem often suffer from long signature sizes, and reductions made loose by the use of Pointcheval and Stern's Forking Lemma. At EUROCRYPT 2003, Goh and Jarecki provided the first forking-free proof of unforgeability for a DL-based signature scheme-rooting its security in the hardness of the Computational Diffie-Hellman problem in the random oracle model. In this paper, we present and discuss the first machine-checked proofs for DL-based signature schemes reducing tightly to CDH, produced using EasyCrypt. We craft our proofs around a shim which reduces the local proof effort, and helps us identify patterns that can be easily adapted to similar tightly-secure DL-based schemes.
Learning Numerics Words Through Online Games Learning

Abstract Mandarin numeric words are an important part in learning Mandarin. Mandarin numeric words reflect the types of objects that they follow. Chinese literature students study nouns for four years because sentence construction cannot be separated from nouns. Unfortunately, not all students consider nouns important so that up to the junior year, there are still some errors in using nouns. This research uses questionnaire distribution to 38 sophomore students of Chinese literature who have studied Mandarin for 2 years. Through this research, as many as 89.47% of students say mastery of Mandarin numeric words are important. As many as 81.57% of students feel learning Mandarin numeric words is difficult due to the numbers or words in Mandarin numeric words. As many as 71.05% of students think that the teaching methods of using Mandarin numeric words by lecturers are good. As many as 55.26% of respondents answer that they often doing exercises 量词 since this could help them in learning Mandarin numeric words. From the results of this study, in order to master Mandarin numeric words, students are expected to be given task of summarizing Mandarin numeric words in each lesson and the lecturer also provides the exercises. Exercises kata bantu bilangan bisa dilakukan dengan mendesign online games learning karena pembelajaran online sudah menjadi bagian dalam kehidupan mahasiswa dan pembelajaran melalui games akan memudahkan mahasiswa untuk belajar.
A Holistic Review of Building Energy Efficiency and Reduction Based on Big Data

The construction industry is recognized as a major cause of environmental pollution, and it is important to quantify and evaluate building energy. As interest in big data has increased over the past 20 years, research using big data is active. However, the links and contents of much literature have not been summarized, and systematic literature studies are insufficient. The objective of this study was a holistic review of building energy efficiency/reduction based on big data. This review study used a holistic analysis approach method framework. As a result of the analysis, China, the Republic of Korea, and the USA had the most published papers, and the simulation and optimization area occupied the highest percentage with 33.33%. Most of the researched literature was papers after 2015, and it was analyzed because many countries introduced environmental policies after the 2015 UN Conference on Climate Change. This study can be helpful in understanding the current research progress to understand the latest trends and to set the direction for further research related to big data.
Max orientation coverage: efficient path planning to avoid collisions in the CNC milling of 3D objects

Most path planning algorithms for covering a complex 3D object ignore physical limitations or constraints on a robot's motion. Adhering to such constraints for a given path can slow down the time to cover the path because the motion may need to be adjusted. This work considers a scenario in computer numerical control (CNC) milling applications, where the robot is a cutting tool that needs to cover the surface of a complex 3D object under the following constraint: for every point on the generated path, the robot must be assigned an accessible orientation to avoid collisions between it and other parts of the object. Our proposed approach, which we call max orientation coverage, employs a two-step optimization scheme. It can improve path efficiency with respect to both the length of the path and the cost of dealing with the collision-avoiding constraints. We evaluate our approach through extensive simulation studies on four CAD benchmarks against a state-of-the-art baseline. We show that our proposed approach can improve the efficiency of the path by 29.7% on average compared with the baseline and the improvement goes up to 46.5% for certain complex objects.
A Quantitative Analysis on Different Carbonate Indicators Based on Spaceborne Data in a Controlled Karst Area

New sensors aboard recently launched satellites have induced the development of several measures aimed to indicate the presence of many materials over the Earth. Karsts are places rich in carbonate rocks and present large economic and environmental importance. This paper aimed at assessing the performance and consistency of different carbonate estimators derived from orbital images acquired over a controlled karst area. Experiments were assisted by a multi-scaled reference data built through a high spatial resolution Unmanned Aerial Vehicle (UAV) image acquired over the selected area. Results show a considerable unconformity among selected measures and better performance presented by indices exploiting measures along visible and infrared spectral regions.
Image enhancement using convolutional neural network to identify similar patterns

An image may be disturbed by impulse noise during transmission or acquisition. To effectively restore the disturbed image is important for the applications of image processing. This study aims at enhancing the disturbed images by using the convolutional neural network (CNN) to identify similar patterns for the restoration of noisy pixels. In the training phase, each noisy pixel is analysed and compared with the noise-free image to find the closest neighbouring pixels. The pixels in a local window form a micro-pattern. All the captured micro-patterns, whose centre pixel is noisy, become a dataset for the training of a position CNN. The closest neighbouring pixel of a noisy image to the centre one of the noise-free image at the same position of each micro-pattern is selected to be the target. In the enhancement phase, a noisy micro-pattern, where the centre pixel is noisy, is input into the trained position CNN. The top N pixels are recognised and averaged to replace the grey level of the centre pixel. An enhanced pixel is obtained. The experimental results show that the position CNN can well recognise the similar neighbouring pixels and effectively enhance the noisy pixels in an image disturbed by salt-and-pepper noise.
Rebuilding Trust in Active Learning with Actionable Metrics

Active Learning (AL) is an active domain of research, but is seldom used in the industry despite the pressing needs. This is in part due to a misalignment of objectives, while research strives at getting the best results on selected datasets, the industry wants guarantees that Active Learning will perform consistently and at least better than random labeling. The very one-off nature of Active Learning makes it crucial to understand how strategy selection can be carried out and what drives poor performance (lack of exploration, selection of samples that are too hard to classify, …). To help rebuild trust of industrial practitioners in Active Learning, we present various actionable metrics. Through extensive experiments on reference datasets such as CIFAR100, Fashion-MNIST, and 20Newsgroups, we show that those metrics brings interpretability to AL strategies that can be leveraged by the practitioner.
SGM-MDE: Semi-global optimization for classification-based monocular depth estimation

Depth estimation plays a crucial role in robotic applications that require environment perception. With the introduction of convolutional neural networks, monocular depth estimation (MDE) methods have become viable alternatives to LiDAR and stereo reconstruction-based solutions. Such methods require less equipment, fewer resources and do not need additional sensor alignment requirements. However, due to the ill-posed formulation of MDE, such algorithms can only rely on learning mechanisms, which makes them less reliable and less robust. In this work we propose a novel method to cope with the lack of geometric constraints inherent to monocular depth computation. Towards this goal, we initially mathematically transform the feature vectors from the last layer inside a MDE CNN such that a 3D stereo-like cost volume is generated. We then adapt the semi-global stereo optimization to the aforementioned volume, global consistency of the map being ensured. Furthermore, we enhance the results by adding a sub-pixel stereo post-processing be means of interpolation functions, a larger range of depth values being obtained. Our method can be applied to any classification-based MDE, experiments showing an increase in accuracy with an additional time cost of only 8 ms on a regular GPU, making the technique usable for real-time applications.
Localization Uncertainty-driven Adaptive Framework for Controlling Ground Vehicle Robots

Modern localization techniques allow ground vehicle robots to determine their position with centimeter-level accuracy under nominal conditions, enabling them to utilize fixed maps to navigate their environments. However, when localization measurements become unavailable, the position accuracy will drop and uncertainty will increase. While research and development on localization estimation seeks to reduce the severity of these outages, the question of what actions a robot should take under high localization uncertainty is still unresolved, and can vary on a platform-by-platform and mission-by-mission basis. In this paper, we exploit localization uncertainty measures to adapt system control parameters in real time. Offline, we optimize non-linear activation functions whose control parameters and relevant weights are trained and learned using Evolutionary Algorithm (EA). Subsequently, in real time, we apply the optimized adaptation functions to the controller look-ahead distance and intermediate linear and angular velocity commands, which we identify as the most sensitive to localization error. Evolutionary runs are conducted in which a simulated target vehicle is tasked with following a randomly generated path while minimizing cross-track error, with time varying localization uncertainty added. These runs produce situation-dependent weights for parameters to the adaptation functions, which are transferred to the physical platform, a 1:5-scale autonomous vehicle. In simulation, our system was able to reduce cross-track error, which in certain cases exceeds 250 centimeters on non-adapted systems, to below 15 centimeters on average using EA-derived weights and parameters applied to our proposed adaptation system. Evaluation on the physical platform demonstrates that without the adaptation module in place, the platform is unable to successfully follow the path; with the adaptation module, the platform automatically adjusts its velocity and look-ahead distance to compensate for localization uncertainty.