Reviewerly/exHarmony huggingface dataset:
Unscented Tobit Kalman filtering for switched nonlinear systems with censored measurement

This paper presents a new unscented Tobit Kalman filtering (UTKF) algorithm for switched nonlinear systems with unknown modes and censored measurements. The stochastic switching is considered where the mode information cannot be accessed directly. To reflect the censoring phenomenon, a series of decision variables are introduced and the Type-II Tobit Model is used to characterize the censored measurements where measurements can be transmitted when the decision variables are greater than prescribed thresholds. The aim of this paper is to design a filtering algorithm such that unknown modes, parameters and state are estimated simultaneously over a given finite horizon. By employing the Variational Bayesian (VB) algorithm, several marginal distributions are generated to approximate the joint distribution with the unknown variables. Subsequently, by resorting to the UTKF and Bayesian inference, a new filtering algorithm is developed under the unknown modes and censored measurements. Finally, a simulation result is employed to illustrate the proposed algorithm.
Short Stick Exercise Tracking System for Elderly Rehabilitation using IMU Sensor

Stick exercises, which have been attracting attention for improving the health of the elderly, are usually performed in nursing homes under the guidance of nursing staff. However, in the current pandemic in which the elderly are advised to refrain from going out unnecessarily, it is desirable for each individual to be able to perform the stick exercises alone. In this study, we aim to develop a stick exercise support system that can automatically record the number of times an elderly person performs each type of stick exercise and provide feedback to improve the movement for each exercise. As a first step toward the realization of this stick exercise support system, we investigated a method for recognizing exercise movements using inertial measurement unit (IMU) sensors. In the evaluation experiment, 21 subjects performed 3 sets (10 times per set) of eight basic stick exercises. The exercise movements were classified based on the linear acceleration and quaternion data obtained from the IMU. As a result, 90% of F-measure was achieved when using Light GBM as the learning algorithm.
Evaluating the Throughput of Video Transcoding in Cloud Services

In this paper, we propose a method to evaluate the throughput of video transcoding in cloud services. This method can quickly estimate the maximum number of transcoding video concurrence on the current transcoding unit.
Applying Practical Parallel Grammar Compression to Large-scale Data

Re-pair is a grammar-based compression algorithm. In the poster session of the Data Compression Conference 2021, we propose the basic concepts of Parallel Re-pair, a parallel variant of Re-pair that achieves shorter compression time with multi-core CPUs. However, our experimental results show it achieves only 1.6 to 2.4 times faster with 32 processors. In this poster session, we propose practical implementation of Parallel Re-pair with Intel Threading Building Blocks.
Complex Network-Based Image Classification Method

Complex networks are topologically invariant. Using complex network in image recognition and classification can greatly reduce the influence of image rotation, translation and scaling on recognition accuracy. Complex networks are modeled by mathematical graph theory and consist of numerous interconnected nodes. The topology of a complex network consists of “connections” and “disconnections” between nodes. The existence of connected edges between vertices depends on the weight of the connected edges. Based on this, we process the traditional image recognition dataset and construct the complex network model of the image. Then the neural network is used to train and classify the processed images. The results show that the accuracy of image classification based on complex network is higher than that of traditional methods.
Non-Classicality Versus Non-Gaussianity of Photon Subtracted Displaced Fock State

In this paper, a quantitative analysis of the non-classical and non-Gaussian features of the photon subtracted displaced Fock state is performed, using a set of measures like linear entropy potential, skew information based measure, Wigner logarithmic negativity and relative entropy of non-Gaussianity. It is observed that photon subtraction (Fock parameter) significantly increases the amount of non-classicalty and non-Gaussianity for small (large) values of the displacement parameter, which decreases both the quantum features monotonically. In this respect, the role of Fock parameter is found to be stronger compared to the photon subtraction. Finally, the dynamics of Wigner function under the effect of photon loss channel is used to show that only highly efficient detectors are able to detect Wigner negativity.
Digital Imaging and Formats

Expensive digital or phone cameras do not guarantee a perfect picture and a good presenter must learn to use these to produce high-quality pictures for medical presentations. Image files are classified as raster and vector files on the basis of their resolution and the quantum of detail they contain. Raster images are dependent on resolution because each pixel is contributing to the whole; more pixels mean higher resolution and higher quality of the image. Conversely, fewer pixels mean when enlarged the image will breakup or pixilate. Repeated editing of raster images lowers their quality. A vector image is a flexible and scalable digital graphic based on mathematically calculated paths and are unaffected by resolutions and maintain image quality even after multiple handling. Two types of digital colour system are discussed. Since all the presentations are in digital format, every picture projected is an image file. There are a variety of formats, and each has special qualities and shortcomings. One needs to choose horses for courses. Useful practical information on where, when, and how to use a JPEG, HEIF, BMP, TIFF, GIF, PNG, RAW, or DICOM file is provided.
Language Use and Susceptibility in Online Conversation

Prior study indicates that persuasion attempts in small and private online communications can be very influential. Yet, most existing online persuasion studies focus on large-scale and open online discussions. In this study, we investigated whether and how one’s language use indicates their susceptibility to persuasion in one-to-one synchronous online text-based chats. We analyzed 815 one-to-one online discussions by 321 pairs. Our results show that discussions in which one or both participants change their views tend to have more positive emotions, more affective processes, and more impersonal pronouns. Additionally, individuals who did not change their minds tend to focus more on problem solving whereas those who changed their minds focus more on the relationship building. Our findings imply the potential of using surface level linguistic features in predicting the persuasion outcome in a one-to-one online discussion, shedding light on the development of persuasive dialogue system which is on the rise.
A Novel Clustering Anomaly Detection of PCA Based Time Series Features with CNC Machines Data

With the 4th industrial revolution, the manufacturing technology of computer numerical control (CNC) has been one of the irreplaceable important technologies in the manufacturing industry. If the factory’s machinery stops working for some reason, such as overheating or wear and tear, the damage to the factory is very high. Therefore, in order to prevent this in advance, research on a method for detecting anomalies using data collected from a machine is currently being actively conducted. This paper presents a method for detecting anomalies using only the data received from the machine without using additionally installed sensors and visually expressing them. Anomaly detection was successfully performed on both the actually collected dataset and the open dataset for validation using the clustering method after principal component analysis using the tendency.
Quasi-Area-Scan Chromatic Confocal Microscopy for Full-Field Surface Profilometry with Sub-Micrometer Accuracy

In various manufacturing processes like semiconductor fabrication, highly accurate and efficient surface profilometry on microstructures is critically demanded. This article presents a quasi-area-scan chromatic confocal microscopy for full-field surface profilometry with high precision and accuracy for in-line automated optical inspection (AOI). Unlike conventional confocal microscopy, the proposed opto-mechatronic confocal measuring system does not require mechanical scanning of the test object. Moreover, it is bi-telecentric to satisfy full-field measurement without optical aberration. Optical line-scan illumination and imaging modules are integrated with an electrically driven galvanometer to perform line scanning. Most importantly, the proposed system is superior to traditional chromatic confocal scanning in that positioning uncertainties can be avoided, and high-speed detection with a large field of view and high accuracy can be achieved. Pre-calibrated step-height gauges were experimentally measured to verify the precision and accuracy of the developed probe. Results showed measurement bias kept below 0.239 μm with a standard deviation of 0.126 μm. When the Galvanometer reaches its design specifications, the measuring speed of the system can exceed 8000 lines/s.
Blockchain and Tourism: Transformation of Three Research Propositions to the Policy Paradigm

This paper extends Önder and Treiblmaier (Ann Tourism Res 72:180–182, 2018’s [1]) propositions regarding blockchain’s integration with tourism to valorize the policy angle. This is because policy across countries on blockchain is different. This difference is because of varied levels and stages of acceptance due to differential trust in a distribute ledger.
AAT: An Efficient Adaptive Adversarial Training Algorithm

Adversarial training is one of the most promising methods to improve the model's robustness, while the expensive training cost keeps a huge problem for this method. Recent researchers have made great effort to improve its performance by reducing the inner adversarial sample construction cost. Their works have alleviated this problem to some extent while the overall performance is still expensive and not interpretable. In this work, we propose AAT (Adaptive Adversarial Training) algorithm utilizing the inherent relationship between the model's robustness and the effects of the adversarial samples to accelerate the overall performance. Our method offers more interpretable robustness improvement while achieving higher efficiency than the state-of-the-art works on standard datasets. We have reduced more than 56% training time than traditional adversarial training on CIFAR10.
An adaptive clustering algorithm by neighbourhood search for large-scale data

Adaptive cluster sampling (ACS) is a sampling method relies on the neighbourhood search on a grid structure. It has an adaptive selection process of units and recursively added units reveal the batched individuals easily and quickly. In this paper, we propose a new clustering method called spatial adaptive clustering (SAC) based on the idea of ACS design. The SAC algorithm forms clusters based on neighbourhood search using grid structures and is able to detect noise points. The performance of the proposed algorithm is evaluated through comparison with the results from well-known density-based clustering approaches in the literature using real and artificial data sets. Computational results indicate that the proposed algorithm is effective in terms of external validation measures for clustering of arbitrary shaped data with noise. Additionally, the SAC algorithm is tested on artificial data sets of varying sizes for the runtime criterion. The results reveal that it also performs superbly for the objective of reducing the runtime.
Self-Supervised Attention Learning for Depth and Ego-motion Estimation

We address the problem of depth and ego-motion estimation from image sequences. Recent advances in the domain propose to train a deep learning model for both tasks using image reconstruction in a self-supervised manner. We revise the assumptions and the limitations of the current approaches and propose two improvements to boost the performance of the depth and ego-motion estimation. We first use Lie group properties to enforce the geometric consistency between images in the sequence and their reconstructions. We then propose a mechanism to pay an attention to image regions where the image reconstruction get corrupted. We show how to integrate the attention mechanism in the form of attention gates in the pipeline and use attention coefficients as a mask. We evaluate the new architecture on the KITTI datasets and compare it to the previous techniques. We show that our approach improves the state-of-the-art results for ego-motion estimation and achieve comparable results for depth estimation.
A Siamese Based One Shot Learning Network with a Watermark Enhancement Technique for Historical Watermark Recognition

Historical watermark identification is of paramount importance to archivists and historians for undertaking studies on historical documents. The identification problem is usually treated as an image processing task since most archives and documents are preserved in pictorial format. However, because of the large variety of watermarks, scarcity of clean samples for new watermarks, crowded and noisy samples, diverse forms of representations, both modest distinctions between classes and substantial intra-class variations, these watermarks are often difficult to recognize. This paper proposes a one-shot classification pipeline for historical watermark identification using only one example of each class. The proposed pipeline is designed based on the Siamese network architecture where an EfficientNetB0 backbone is employed as the feature extractor. Due to the use of Siamese based proposed architecture, a contrasting loss function can be formulated to update the network parameters, which enables the network to learn to distinguish between similar and dissimilar pairs of watermark images more efficiently. A thresholding-based image pre-processing technique is also proposed for enhancing the watermarks before feeding them to the feature extractor. Experimental results on a publicly available large-scale historical watermark dataset reveal that the proposed method can lead to better performance than some one-shot classification methods reported in the literature.
Towards a Competitive 3-Player Mahjong AI using Deep Reinforcement Learning

Mahjong is a multi-player imperfect-information game with challenging features for AI research. Sanma, being a 3-player variant of Japanese Riichi Mahjong, possesses unique characteristics and a more aggressive playing style than the 4-player game. It is thus challenging and of research interest in its own right, but has not been explored. We present Meowjong, the first ever AI for Sanma using deep reinforcement learning (RL). We define a 2-dimensional data structure for encoding the observable information in a game. We pre-train 5 convolutional neural networks (CNNs) for Sanma’s 5 actions—discard, Pon, Kan, Kita and Riichi, and enhance the major (discard) action’s model via self-play reinforcement learning. Meowjong demonstrates potential for becoming the state-of-the-art in Sanma, by achieving test accuracies comparable with AIs for 4-player Mahjong through supervised learning, and gaining a significant further enhancement from reinforcement learning.
Blockchain-Based Solution for Trusted Charity Donations

AbstractNowadays, many people show interest to do social services. It is common that people running charity asks for a help from public people. People who help the charity does not have a way to check proper utilization of the help. Even though many people organizations and industries are extending their help for charities, the people staying in charities are not getting even the basic needs. Recently, we can observe decrease in number of people helping charities due to lack of transparency in the working of charities. Because of lack of transparency in donations, the donors are not able to track the proper utilization of their donated funds. Blockchain technology gives solution to all the above-mentioned problems. Blockchain is a platform which has inherent characteristics like transparency, immutability and security. In blockchain, all transactions will be stored in an immutable ledger and can be accessed transparently by each user. It helps charity organizations to work transparently which builds trust among donors, and it helps donors to monitor their fund utilization with transparent donation route.KeywordsBlockchainCharityImmutabilityTransparency
Sentiment Analysis in Sundanese Using Pre-trained Multilingual Language Models

This paper proposes a document-based sentiment analysis in Twitter for mixed language of Indonesian and Sundanese, 2nd most active speakers of regional language in Indonesia. The existing pre-train language model is finetuned using our new-built Sundanese sentiment training data. We compared the usage of multilingual pre-train language models (XLM-R and XLM-Tweet) with monolingual pre-train language model for Sundanese (SundaBERT). The data used in this study were taken from the social media Twitter, with as many as 7,771 tweets that have been annotated with neutral, negative and positive. The data is divided into 60% training data, 20% validation data and 20% test data. The experimental results showed that the highest performance of 87% F1-score was achieved by using XLM-Tweet pre-train language model. Furthermore, we conducted experiments on the available Sundanese sentiment data set (translated from Indonesian sentiment data set) and obtained F1-score of 82%.
Mobile Robots for COVID-19 Pandemic: A State of the Art

This paper aims to show how technology and automation have supported the fight against the covid-19 pandemic and how have contributed to assistance and personal interaction, the state of the art and some of the models that are being used in common applications that have a risk of contact or transmission are presented. Finally, the use of technology in the health area in El Salvador is discussed.
An image correction method based on corner point detection

To better achieve image correction effect, an image correction method based on corner point detection is proposed. In image preprocessing, firstly, image equalization is achieved based on the contrast limited adaptive histgram equalization to avoid the problems caused by illumination and suppress noise while maintaining details, and then adaptive threshold segmentation is performed using the OTSU to obtain the binarized image. In the corner point detection stage, the contours of the binarized image are extracted firstly and the closed contours are filled to avoid the independent contours from affecting the accuracy of region growth, then the center point of the image is used as the seed pixel for region growth, and finally the four corner points are calculated based on the linear contours of the growth region and Hough theory, where the accurate region growth result can avoid the influence of the background on the corner point detection. In the correction stage, the perspective matrix is calculated by the four corner points, and the image is corrected by the perspective transformation. The experiments show that the proposed method can accurately find the corner points of document images and achieve efficient correction.
Personal Information Space High School Student-Innovator

In the Federal State Educational Standard of Secondary General Education, the «Portrait of a school graduate» presents such a personal characteristic of a student as «motivated for creativity and innovative activity».&#x0D; In the Federal State Educational Standard of Secondary General Education in Computer Science, «the ability to organize personal information space using various means of digital technologies» is named as mandatory personality qualities of students. At the same time, the target orientation of the formation of a personal information space is not specified.&#x0D; There are didactic grounds to consider it expedient to orient students to the formation of a specific personal information space focused on a specific type of activity, in particular, innovation. &#x0D; Nowadays, during the difficult historical development of Russia in the conditions of increasing sanctions pressure of unfriendly states and the need for Russian citizens to master «new competencies» for effective participation in import substitution processes in all types of production activities, in order to ensure technological sovereignty, it is necessary to put the mastery of citizens, including schoolchildren, flexible skills of innovation orientation in the process of innovative universal education.&#x0D; It is advisable to ensure that high school students develop psychological and functional readiness to participate in import substitution processes in their future professional activities as a manifestation of active patriotism using the skills of innovation acquired in general education and, in particular, in a specialized school. It would be right for teachers to focus on the formation of the personality of a high school student-innovator using the technologies of informatization of education.
A Pinch Operated Input Interface of Japanese Syllabary Table for an Optical See-through HMD

This paper proposes an input interface of Japanese for an optical see-through HMD in mixed reality environment, which enables a user input Japanese easily with hands-free and view the real/virtual world relatively widely and clearly the world a little shielded by a small output screen caused by the input. The proposed system adopts pinch gesture, which is popular to HMD users, and the Japanese syllabary table, which is always studied by Japanese learner. The system is implemented on Microsoft Hololens2 and introduces the hand-tracking technology in Microsoft Hololens2 to the recognition of the pinch gesture. Selecting one of 50 Kana from the Japanese syllabary table of the 10-line and 5 column Japanese fundamental character set, a user selects one of the 10 lines with his/her dominant hand, and one of the 5 columns with his/her other hand. A user test is conducted by 10 Japanese adults in comparison with the system keyboard input on Hololens2. The evaluation shows that the system achieves some important functions such as hands-free, low screen occupancy, and scalability to other language, while the precision of the recognition of the input is to be improved.
News Popularity Prediction with Machine Learning

Online news is quite popular as it offers valuable information quickly at no cost. However, it can be challenging to write news articles that can get immediate attention from the viewers. Journalists and writers adopt different approaches to writing similar content. However, only a handful of stories receive much attention and are further shared. The probability of an article being circulated widely online depends on various factors. Hence in this work, we predict the popularity of news articles. We formulate the problem as a regression problem and use nine different regression techniques to provide an optimal recommender system. Additionally, we assess the effectiveness of different feature reduction techniques and apply robust feature scaling. Our analysis shows that Gradient Boosting outperforms the other techniques.