Reviewerly/exHarmony huggingface dataset:
Design of Automatic Door Lock Control System Library Nurul Jadid University Based On Arduino Uno R3 and QR-Code

The Nurul Jadid University Library (UNUJA) currently requires valid visitor data, because the number of visitors is an indicator of library performance in terms of disciplining staff / employees. For this reason, the UNUJA library requires an automatic library door lock control system using Arduino Uno and QR-Code. In designing tools, Arduino Uno and QR-Code are integrated with the library database application. Based on testing and analysis, it can be obtained that the success rate of testing tools in reading QR-code is 100 percent. The conclusion is that the assembly of the library door lock control system based on Arduino Uno and integrated QR-Code with the database has been able to read digital input and output.
Numerical Modeling of Dual Pump Amplifiers in Standard Silicon-on-Insulator Platform for Random Number Generation

The impulse response of a dual pump parametric process in silicon is numerically studied under different scenarios. Strong signal amplification can be seen (up to 30 mW) despite thermal detuning without active temperature stabilization.
Vulnerability Assessment for Machine Learning Based Network Anomaly Detection System

In this paper, we assess the vulnerability of network anomaly detection systems that use machine learning methods. Although the performance of these network anomaly detection systems is high in comparison to that of existing methods without machine learning methods, the use of machine learning methods for detecting vulnerabilities is a growing concern among researchers of image processing. If the vulnerabilities of machine learning used in the network anomaly detection method are exploited by attackers, large security threats are likely to emerge in the near future. Therefore, in this paper we clarify how vulnerability detection of machine learning network anomaly detection methods affects their performance.
A Siamese Network-based Approach For Matching Various Sizes Of Excavated Wooden Fragments

This paper presents an approach for matching various sizes of excavated wooden fragments based on siamese neural networks. We propose a siamese network composed of global average pooling and a fine-tuned Resnet encoder (GA-S-net). We also propose an elaborated siamese network by replacing the global average pooling with a spatial pyramid pooling layer and add a new dense absolute difference layer (SP-S-net). Samples of 37,760 fragments were prepared from 268 complete wooden tablets excavated from the Heijo-Kyo Palace ruins used during the Nara period in Japan. Both of the networks answer whether two fragments are from the same tablet or not. The result of both networks for the testing set is similar to AUC (Area under the curve) of ROC (Receiver Operating Characteristic) curve being around 90%. In AUC of large fragments, however, SP-S-net is better than GA-S-net with 97.1% versus 93.8%. These networks are rather new for dealing with various sizes of inputs for the matching problem.
MLEDet: Vehicle Detection in UAV Images

Unmanned Aerial Vehicles (UAV) have become popular in many applications. In this paper, we focus on the vehicle detection in UAV images. Compared with generic object detection, the vehicle detection in UAV images is more challenging, which contain a large amount of small objects and complex backgrounds. We propose E-Dnet by enhancing feature maps to obtain the semantic information of small objects. Multi-level feature maps are utilized to adapt various sizes of vehicles. Moreover, there is an inequality between confidence score and intersection-over-union. Then, IoU-Guided Loss is proposed to handle the inequality. In addition, we construct a vehicle detection dataset named VUVD, which is based on UAV images collected from various road conditions in the city. In particular, our method achieves start-of-the-art performance on our VUVD dataset and VisDrone2018-vehicle dataset.
Pre-training phenotyping classifiers

Recent transformer-based pre-trained language models have become a de facto standard for many text classification tasks. Nevertheless, their utility in the clinical domain, where classification is often performed at encounter or patient level, is still uncertain due to the limitation on the maximum length of input. In this work, we introduce a self-supervised method for pre-training that relies on a masked token objective and is free from the limitation on the maximum input length. We compare the proposed method with supervised pre-training that uses billing codes as a source of supervision. We evaluate the proposed method on one publicly-available and three in-house datasets using the standard evaluation metrics such as the area under the ROC curve and F1 score. We find that, surprisingly, even though self-supervised pre-training performs slightly worse than supervised, it still preserves most of the gains from pre-training.
Students’ Attitude Toward Learning and Practicing English in a VR Environment

Virtual Reality (VR) technology has been engaged in educational content for students of all ages. A VR headset completes a new world with user-friendly interface, gesture controls, customized educational resources, and hands-on teacher controls. Learners are able to apply and practice their target language. Thus, the purpose of this study was to investigate learners’ attitude and usefulness toward the implementation of VR technique in L2 learning. VR technique assists target language learning was designed according to the curricular objectives and the subject matter of a private electrical and technical university. Mixed method was applied and 117 freshmen participated in the study. A list of questionnaires of VR technique assists target language learning was applied for the first phase of data collection. Furthermore, a semi-structure interview and a field note techniques were carried out to be the main method for the detailed qualitative data. 15 participants were randomly involved in reflecting the experience of learning English in a VR environment. The results indicated that learning the target language in a VR environment was a practical intervention for developing independent motivator. L2 students have better attitudes toward their learning experiences. Meanwhile, the perceived satisfaction and usefulness positively affect learners’ behavioral intention of VR usage and target language learning.
Prediction of Syncope based on Physiological Data Analysis using Decision Tree Algorithm

In this paper, we propose a method to predict the chance of Syncope or falling due to temporary loss of consciousness, based on an analysis of physiological data. We consider an authentic data set containing the measures of electroencephalogram, blood pressure, heart rate and blood circulation during different human activities, including fall. We experiment with this data set by employing supervised learning techniques, in order to predict Syncope efficiently. The proposed model gives an accuracy of 82.514% in Syncope prediction.
Path-Following Navigation Network Using Sparse Visual Memory

Following a demonstration path without observing exact location of an agent is a challenging navigation problem. Especially, considering the probabilistic transition function of the agent makes the problem hard to solve with an exact action decision, so learning-based approaches have been used to solve this task. For example, a previous method by Kumar and Gupta et al., robust path following network (RPF), is a neural-network-based method using visual memories of the demonstration. Although the RPF shows good performances on the path-following task, it does not consider the efficiency of the visual memory since it requires the entire visual memory of the demonstration. In this paper, we propose a path-following network using sparse memory of the demonstration path that can deal with various sparsity of the visual memory. For each time step, the proposed network makes soft attention on the sparse memory to control the agent. We test the proposed model on the Habitat simulator using MatterPort3D dataset with various sparsity of memory. The experimental results show that the proposed method achieves 81.9% of success rate and 73.7% of SPL on a model with 0.8 memory sparsity, and also the results of the models with other memory sparsity achieve reasonable performances compare to the baseline methods.
Collaborative Filtering Recommendation Based on Statistical Implicative Analysis

In recent studies on recommender models, association rules have been applied in many studies to improve the effectiveness of recommender models. However, these studies also reveal some drawbacks, such as the models take a considerable amount of time to generate association rules for large datasets; generation algorithms can ignore rules with the significant implication that affect the quality of recommender models. This study proposes collaborative filtering recommender models (CF models) based on association rules following an asymmetric approach of the statistical implicative analysis method to enhance the precision of recommender models. Through experiments on standard datasets and quality comparison with other CF models, we conclude that the proposed models based on the asymmetric relationship achieve better accuracy on the experimental datasets.
BiLSTM-LAN based Medical Named Entity Recognition

With the wide application of information technique in the medical field, various medical information systems have produced massive medical data. The problem of mining knowledge from medical data has been extensively studied. For the medical named entity recognition problem, Bidirectional Long-Short-Term Memory model with Conditional Random Field (biLSTM-CRF) is commonly used. However, CRF cannot capture long term output label relations because of the Markov assumptions. To solve this problem, we propose to use a bidirectional Long-Short-Term Memory model with Label Attention Network (biLSTM-LAN) for medical named entity recognition. The experimental results show that using LAN to replace CRF can improve the recognition accuracy when their number of parameters are similar.
Study on the Training Effectiveness of Deep Learning with Synthesized Underwater Sonar Image Using Pix2Pix and FCN

This research is a study to check whether the synthesized experimental data in the laboratory without experimentation is useful as experimental data. In the paper, synthesized data is generated base on the pix2pix algorithm. The training data used in the FCN checks the performance of the FCN by adjusting the ratio between the synthetic data and the experimental data obtained through the experiment. Various cases are used to confirm that synthesized data can be replaced with data obtained through actual experimentation.
Source camera identification in LINE social network via CCD fingerprint

Digital forensics has developed vigorously, aod the demaod for traceability of digital imagiog equipmeo t is iocreasiog day by day. Receotly, a sigoificaot brea kthrough is achieved by using the Photo-Response Non-Uniformity (PRNU) ooise of images to trace the imagiog device. However, digital images are often taken with mobile phoones aod theo traosmitted usiog social media (such as LINE software io Taiwan ) in real cases. During the transmission process, most of the images are resized and compressed. To explore the impact of this issue on image traceability technology, related experiments are desigoed to evaluate in this study. 15 different Apple mobile phooes were used to iodividually capture digital images to create the data sets. After being transmitted through LINE software, they were downloaded. The correlation evaluation method is based on the modified correlation energy peak (Modified Signed Peak Correlation Energy, MSPCE) statistics to evaluate aod analyze the correlation between the PRNU factors of the disputed images and those io the original data sets. Experimental results show that the proposed method could effectively trace the source of the imaging device usiog the distorted images which are resized and compressed during the traosmission in LINE.
Process data based Anomaly detection in distributed energy generation using Neural Networks

The increasing share of renewable energies in the total energy supply includes a growing number of small, decentralized energy generation which also provides control energy. These decentralized stations are usually combined to a virtual power plant which takes over the monitoring and control of the individual participants via an Internet connection. This high degree of automation and the large number of frequently changing subscribers creates new challenges in terms of detecting anomalies. Quickly adaptable, variable and reliable methods of anomaly detection are required. This paper compares two approaches using Neural Networks (NN) with respect to their ability to detect anomalous behavior in real process data of a combined heat and power plant. In order to include process dynamics, one approach includes specifically engineered features, while the other approach uses Long-Short-Term-Memory (LSTM). Both approaches are able to detect rudimentary anomalies. For more demanding anomalies, the respective strengths and weaknesses of the two approaches become apparent.
Modification of DSDV and Secure Routing using Blockchain Technology

Wireless networking have increased considerably, and the network deployed on ad hoc basis called MANET is the need for todays' communication activities. Also, the security issues of a MANET and its routing are equally important, otherwise, the benefits and easy usability would be seriously hampered. This paper proposes a modification of a well-known proactive routing protocol called DSDV and provides security protection using blockchain technology. In our protocol, instead of allowing all nodes to prepare their routing tables as in DSDV, a miner-node, who creates blocks and maintains blockchain, generates a common routing table containing all-pair shortest paths using Floyd-Warshall algorithm. This routing table is then floated to all nodes publicly for their uses. A group of trusted nodes are used for miners, however, one such node acts as a miner at a time for managing of power consumption. The security and performance analysis of our protocol are provided, and it found that a MANET with modified DSDV and blockchain is well protected, however, some extra computation cost for running the Floyd-Warshall algorithm is required.
A Critical Assessment of Canadian Earthquake Monitoring and Alerting Practice versus the Initial Challenges of the 2020 COVID-19 Experience

Abstract We describe the regular pre-COVID mode of operations for the Canadian National Seismograph Network and the associated monitoring, alerting, and analysis for earthquakes in Canada; we describe how the current operational posture evolved and discuss the ways in which the posture was and was not suitable to respond to the challenges and constraints of the COVID-19 situation in Canada. We find that many of the design and operation decisions that have been taken over the last several decades for earthquake monitoring in Canada, collectively driven largely by considerations of resilience and cost-effectiveness and further refined after the experience of the H1N1 pandemic, resulted in a system that continued to function effectively under lockdown conditions. There were many earthquakes in Canada that required seismologist response during the lockdown, all of which were handled remotely without issue. Specific challenges and lessons learned from the first few months of the pandemic are noted.
An Improvement of Apriori Mining Algorithm using Linked List Based Hash Table

Today, the huge amount of data was using in organizations around the world. This huge amount of data needs to process so that we can acquire useful information. Consequently, a number of industry enterprises discovered great information from shopper purchases found in any respect times. In data mining, the most important algorithms for find frequent item sets from large database is Apriori algorithm and discover the knowledge using the association rule. Apriori algorithm was wasted times for scanning the whole database and searching the frequent item sets and inefficient of memory requirement when large numbers of transactions are in consideration. The improved Apriori algorithm is adding and calculating third threshold may increase the overhead. So, in the aims of proposed research, Improved Apriori algorithm with LinkedList and hash tabled is used to mine frequent item sets from the transaction large amount of database. This method includes database is scanning with Improved Apriori algorithm and frequent 1-item sets counts with using the hash table. Then, in the linked list saved the next frequent item sets and scanning the database. The hash table used to produce the frequent 2-item sets Therefore, the database scans the only two times and necessary less processing time and memory space.
Deep Learning-based Moiré Pattern Removal from a Single Image: A Survey and Comparative Study

Moiré pattern in a single image is common in image visual quality degradation induced by frequency aliasing between cameras and monitors when taking a screen-shot photo. It is mainly resulted from the interference between the pixel grids of the camera sensor and the device screen. However, removal of the Moiré patterns is challenging based on the complex frequency distribution and imbalanced magnitude in color channels. Only a few studies in the literature focused on the solution of Moiré pattern removal from a single image. Traditional studies usually treated the problem as an image denoising problem and applied some filtering or signal decomposition operations based on some image priors (e.g., sparsity). Relying on the rapid development of the deep learning techniques, some deep learning-based approaches of Moiré pattern removal have been presented recently. This paper presents a brief survey and comparative study for the recent deep learning-based research works on Moiré pattern removal and discusses possible further research directions.
Multi-Granular BERT: An Interpretable Model Applicable to Internet-of-Thing devices

With the development of the Energy Internet (EI), its applications have gradually spread from industrial uses to smart homes. Specifically, home Internet of Things(IoT) devices have become popular in the field of smart homes. In this paper, we propose an interpretable model that can be applied on the IoT devices. When Chinese characters are grouped into words, the meaning may vary. Inspired by the observation, we convert character-level Bi-directional Transformer (BERT) to word-level, which we call it multi-granular BERT (MLGB). It constructs the n-gram representation of different lengths within a model. It also learns the self-attention between n-grams during both pre-training and task-specific fine-tuning to learn both the word representation and word-word self-attention at the same time. As a diagnostic task, we evaluate our model on two Chinese text pair classification tasks and observe the model’s behavior. The MLGB retains the BERT’s accuracy on the tasks while demonstrates more interpretable word-level self-attention. Multi-granularity may also have served as a regularization of attention that alleviates the non-identifiability issue of self-attention.
Impossible Individuals as Necessarily Empty Individual Concepts

We talk about ‘impossible objects’ in many areas, ranging from empirical and non-empirical theories to the realm of fiction, myth and folklore: a mathematical pendulum, a perfect market, the set of all sets that are not members of themselves, Kafka’s Gregor Samsa, Pegasus, The Puss in the Boots, and so forth. This paper proposes a hyperintensional account of a special case of impossible objects, so-called ‘impossible individuals’. Our (broadly Fregean) proposal is to identify ‘impossible individuals’ with necessarily empty individual concepts. The main goal of the paper is to develop a method that enables us to discover inconsistencies in specifications of individual concepts and thus prove that such concepts could not possibly be matched by an extension (an individual). Furthermore, this approach allows for a fine-grained individuation of impossible individuals. Fine-graining will not be explored in the present paper, but its very possibility adds to the overall plausibility of the present account.
An Efficient Solution for People Tracking and Profiling from Video Streams Using Low-Power Compute

Balancing between performance and speed is vital for real-time applications. Given some of the latest edge devices, such as Raspberry Pi 4, Intel Neural Compute Stick 2, or Nvidia Jetson series, edge processing can become a valid choice for deploying computer vision algorithms in real-time scenarios. Object detection and tracking are two common problems that can be solved using such algorithms, which can be deployed with reasonable performance and speed on edge devices. In this paper, we show that the YOLO architecture can be successfully used for object detection and DeepSORT for object tracking on edge devices. The objects of interest in our scenario are persons, thus indicating face detection and tracking as another problem that is solved in the scope of the paper. Using Raspberry Pi 4 and Intel Neural Compute Stick 2, object detection and tracking models can be run on edge devices, though at around half the performance and more than 10 times slower than on a GPU server.
Analysis of Community Satisfaction Level Against the Ministry of Health’s Infection Emerging Websites Using Webqual 4.0

Abstract As one form of communication and information media, web sites have a very big role in representing a government institution to interact with the public. A website is designed in such a way as to meet certain service quality standards set by the developer. However, good service quality must also consider perceptions from the point of view of users in this case the wider community. The study purpose is to determine the level of community satisfaction with the quality of the website as an indicator of success by the government in conveying information to the public. The method used in this study is to distribute questionnaires with a Webqual approach which is of three categories, specifically Usability Dimension, Information Quality Dimension, and Service Interaction Dimension. The data were obtained then analyzed using the Structural Equation Model (SEM) Technique with the SmartPLS 3 software. Based on a survey of 104 respondents, it was found that in general users were satisfied with the services on the website.