Reviewerly/exHarmony huggingface dataset:
Gesture Recognition Based on Video Sequence with B3D Convolutional Neural Network

Gesture recognition has attracted great attention owing to its wide applications. However, until now, the current gesture recognition methods still have some drawbacks like the background handicap the improvement of recognition accuracy. in order to solve the issue, this paper presents a method based on a threedimensional convolution neural network and bi-directional LSTM networks, Which is named as B3D network. In this paper, Faster R-CNN is used to capture the information of hand position. The method of B3D Network is to capture the spatiotemporal feature informmation for a video representation and analyze temporal feature sequences. The experiment is conducted on DEVISIGN_D dataset and the lab self-built SGR dataset, Compared with other traditional methods such as Alex, C3D+LSTM and so on, Experiments results show that the object location strategy and bi-directional LSTM improved recognition accuracy, achieves 88.7% accuracy of SGR-Dataset, and 86.1% accuracy on DEVISIGN_D dataset, The recognition accuracy of our method both outperform traditional method.
Performance of prior and weighting bias correction methods for rare event logistic regression under the influence of sampling bias

The problem of classifying events to binary classes has been popularly addressed by Logistic Regression Analysis. However, there may be situations where the most interested class of event is rare such as an infectious disease, earthquake, financial crisis etc. The model of such events tends to focus on the majority class, resulting in the underestimation of probabilities for the rare class. Additionally, the model may incorporate sampling bias if the rare class of the sample is not representative of its population. It is therefore important to investigate whether such rareness is genuine or caused by an improperly drawn sample. We conducted a simulation study by creating three populations with different rarity levels and drawing samples from each of those which are either compatible or incompatible with the actual rare classes of the population. Then, the effect of sampling bias is discussed under the two correction methods of bias due to rareness as suggested by King and Zeng.
Radial Distortion Correction of Grid Images Based on Support Vector Machines for Regression Using Particle Swarm Optimization Algorithm

Distortion correction is a key technology in the filed of machine vision. A radial distortion correction method based on support vector machines for regression is proposed and studied thoroughly. Firstly, a grid image with radial distortion is segmented. Secondly, the segmentation image is refined to get a skeleton image, and intersection points of the grid are extracted. Thirdly, the actual distortion locations of these intersection points and their corresponding ideal distortionless locations are combined to construct a training set and test set of support vector machine, and a particle swarm optimization algorithm is used to determine the optimal parameters of support vector machine. Finally, a support vector machine for regression is utilized to correct the radial distortion of the grid image. Some intuitive and persuasive simulation experiments are carried out by using the proposed distortion correction method. Experimental results demonstrate that the proposed method can correct the radial distortion of the grid image. The proposed distortion correction method can be used in the fields of image measurement, three-dimensional reconstruction, collaborative augmented reality and teleoperation robots.
Pose-robust Feature Learning for Pose-Invariant Face Recognition

Pose variation is the important factor that affects the performance of face recognition. Almost all of the methods need to calculate the deflection angle by manual or deep learning to handle pose-invariant face recognition (PIFR). We propose a new network including a stem CNN and a mapping module to automatically project different poses into the deep features under the shared latent subspace. The stem CNN and the mapping module could be trained together in an end-to-end manner. And by using the mapping module, the calculation of deflection angle is not necessary. According to the experiments on two databases, the superiority of the proposed network over the nine state-of-the-arts is demonstrated on face identification task.
Unsupervisedly Learned Representations – Should the Quest Be Over?

After four decades of research there still exists a Classification accuracy gap of about 20% between our best Unsupervisedly Learned Representations methods and the accuracy rates achieved by intelligent animals. It thus may well be that we are looking in the wrong direction. A possible solution to this puzzle is presented. We demonstrate that Reinforcement Learning can learn representations which achieve the same accuracy as that of animals. Our main modest contribution lies in the observations that: a. when applied to a real world environment Reinforcement Learning does not require labels, and thus may be legitimately considered as Unsupervised Learning, and b. in contrast, when Reinforcement Learning is applied in a simulated environment it does inherently require labels and should thus be generally be considered as Supervised Learning. The corollary of these observations is that further search for Unsupervised Learning competitive paradigms which may be trained in simulated environments may be futile.
Proposal of a Light-Field Descriptor Considering Light-Ray Direction

Many camera and photo applications, for example image mosaicing, Simultaneous Localization And Mapping (SLAM), and so on, use image keypoints, descriptors, and their matching. Recently, obtaining light-field has become easier thanks to the commercialization of light-field cameras. Light-field is a record of not only light ray intensity but also directions, and multi-view images can be acquired with it. In this paper, a light-field descriptor with light ray directions using light-field is proposed. A light-field descriptor is defined as a set of feature vectors that change according to the direction of the light ray. In addition, a method to match these descriptors is proposed. As the method focuses on the direction of the light ray, it allows more accurate matching than a conventional method when the camera pose changes against the target. To confirm the effectiveness, the proposed method is evaluated by using light-field image recorded in a real scene. As a result, the proposed method is shown to improve the accuraccy over a conventional method. In particular, the closer the distance between the target and the camera, the better accuracy of the proposed method could be achieved.
The Power of Free Speech without Adequate Tools – Political Rhetoric in Croatia

This essay focuses on the power of rhetoric in a political discourse analyzing the rhetorical skills of a contemporary Croatian politicians. Croatia as a post-communist country paid very little attention to rhetorical education, and as a consequence people who enter political arena are not competent public speakers and often showed a lack of persuasive power. This essay analyses part of the rhetorical ability of a politicians including strategies for building ethos and argumentation skills. Based on a real-life examples, it can be concluded that both politicians as well their audience (i.e. voters) need to be better rhetorically educated which will help in resolving disputes and using persuasion for the benefits of the whole society.
Mining and Creating a Software Repositories Dataset

Mining software repositories to extract meaningful information from them has become an important topic in software engineering. This paper presents our study to mine a very large dataset consisting of over three million software repositories across many version control systems and create derived data for future studies. Through this study, we propose a method for detecting forks and duplicates in repositories. We also preliminarily investigate the possible correlations between forking patterns, software health and risks, and success indicators.
Gait Analysis Using Video for Disabled People in Marginalized Communities

The goal of this project is to remotely analyze the gait of people walking with crutches. To that objective, the use of video analysis based on the open-source software OpenPose is compared with the data collected from a sensor mounted on a human subject. The results show that the average value of acceleration between the video analysis and the sensor differs by 0.05%. All steps are clearly identified and synchronized. As a consequence, it is possible to validate non-contact acceleration data from video analysis with an inexpensive setup described in this paper. The results show a promise that this non-contact method can be used to assess the gait of disabled people with assistive devices in remote locations.
Facial Emotion Based Review Accumulation System

Multiple cases of fake reviews and false ratings concerning any place on Google Maps or any other advertising platform have been recorded over the past few years. In this paper, we develop a Visitor Rating System by employing a Raspberry Pi camera integrated with Raspberry Pi. The system mainly depends on the AWS cloud services which are utilized for storing and analyzing the collected ratings. For recording authentic ratings of actual visitors, the system uses Facial Emotion Recognition model to generate the rating data. The experimental results state that the system can recognize the emotions with 75% accuracy having more than 90% probability of recognition for any particular emotion. The proposed system which overcomes the limitation of false ratings is estimated to cost around 7,000 INR.
Graph Node Strength Histogram Publication Method with Node Differential Privacy

Abstract The node differential privacy (node-DP) can be used to protect the private information of nodes and edges in the graph. In this paper, we propose an algorithm of publishing node strength histogram under node-DP, which could improve the accuracy of publishing the node strength histogram. In this algorithm, we use the Sequence Edge removal to reduce the sensitivity of query function and restrict the weight of edges to make the distribution of node strength denser. Furthermore, we use the histogram grouping algorithm Hierarchical Cluster Grouping to group the buckets to merge buckets with close values into one group. The experiments show that our algorithm maintains higher data utility than those traditional histogram publishing algorithms under the same privacy budget.
A Study of the Creative Design Education In View of Convergence Design

The futurist, Daniel H. Pink, characterized the 21st century as an era of "convergence" and "perpetual". In his book, "A Whole New Mind", he proposed that we are transforming from a philosophy that emphasizes logic, order and computer efficiency to the age of innovation, empathy, and inter-disciplinary integration [1]. The integration of traditional business models, technologies, and related processes will become the driving force for next industrial revolution. Toffler &amp; Alvin also argued in his book "The Third Wave" that with the continuous development of the knowledge and information, the partitioned societal structure will become meaningless [2]. Therefore, in the age of perceptivity that emphasizes on the integration, if the country or organization wants to take the lead, the education of integration and perceptivity in the education system will become indispensable. Abraham Maslow said that education is a process of acquiring knowledge, a means that the national and economic development depends on, and a planned change in human behavior. Therefore, developing creative future integrated designers whose minds and talents are consist with the converging markets, and who are sensitive to customers’ needs in a timely manner is an important issue in the field of education today. This study attempts to investigate the current status of convergence design and its education. Based on this study, some new suggestions will be proposed on the necessity of convergence design and convergence design education.
Big Data Adaptive Encryption Technology for Remote Network Storage

The traditional network storage big data encryption method had the problem of taking a long time to encrypt. The adaptive encryption technology of big data storage with remote network in the context of big data was proposed. The adaptive encryption principle to shorten the encryption program, selected based on the principles of the selected encryption designed adaptive encryption operation structure, through the calculation of chaos in the structure to realize remote network adaptive storing big data encryption. Through the calculation of the chaotic encryption process of big data in the model, the encryption of adaptive storage big data with remote network was realized. Simulation test was carried out in the simulated environment. The structure showed that the design remote network storage big data adaptive encryption technology taked 14% less time than the traditional encryption technology, the adaptive encryption method of big data showed good performance with the short time consumed of 2186s, and had high operating efficiency.
A Two-step Registration Approach: Application in MRI-based Strain Calculation of the Left Ventricle

Registration and finding anatomical correspondent landmarks amongst the volumetric images in both inter and intra-subject studies are prevalent challenges, especially in the field of cardiac image analysis. On the other hand, among other functional criteria, the strain is promising to identify differences in the early stages of cardiac diseases. This study aims to develop a transformation model on the existing FFD non-rigid registration algorithm using combining the shape-based and the intensity-based approaches. A novel two-step multi-resolution non-rigid FFD-based registration algorithm was proposed to measure the radial strain of the left ventricle during the cardiac cycle using cine-MRI images. The endocardial wall shape information was introduced to improve the registration accuracy by combining the original intensity-based registration with the shape-based registration approach. The proposed algorithm was evaluated on ten sequences of cine-MR images: First, the proposed algorithm was validated by two identified 3D-landmarks and then applied to a healthy subject and an LVH patient to calculate the left ventricle regional radial strain during a cardiac cycle. Regarding two anatomical landmarks, their estimated displacements from our proposed two-step registration algorithm showed a better match with the reference values compared to the classical registration method. Moreover, a comparison of normal and abnormal radial segmental strain values calculated by our proposed algorithm showed a clear difference in their functional properties. The promising results showed that the proposed registration algorithm outperformed the conventional one in terms of the accuracies of the point-tracking.
KIP Recipient Decision Making For Students Affected by Covid_19 Pendemi Using Fuzzy MADM Method

The impact of the Covid-19 pandemic on largescale social restrictions forced the economic activities of the Indonesian population to cease, which had an impact on the educational funding process.This makes some people expect a helping hand from the government to cover some of the shortcomings in daily spending, one of them through the Indonesia Smart Card.The provision of aid funds in these conditions must make the process of channeling aid on target.With this problem, it is important to develop a Decision Making System to help the KIP acceptance selection process for students who are indeed eligible to get it.The purpose of this study is that the provision of KIP can be right on target for students who really need it according to specified criteria.For decision making, three stages are used with the method used, the first stage the C-45 method is used for decision making whether students graduate or not, the second stage is the Fuzzy MADM method used for decision making of students who get KIP, and the third stage ranks according to specified total quota.Initial selection uses the C-45 method with variable GPA (V1), distance from home to campus (V2), length of study (V3), work (V4), family (V5), and tuition payment bills (V6).The calculation yields 5 rules which are used to determine normal (graduated) or DO (not) students.Students who pass the initial selection are processed using the MADM fuzzy method for the decision-making process that is truly feasible for KIP.Furthermore, the number that passes is ranked and the amount is taken based on the quota in accordance with the results of the ranking.Of the 456 student data received based on 1024 registrants after ranking, 300 students were drawn based on KIP target recipients.The results of testing the accuracy of 300 KIP recipients who obtained data actually had 284 rights.In order to obtain an accuracy of 98% of students who are eligible for KIP.
Handling BigInteger Computations in the Implementation of the RSA Model

Developing systems that can handle very large integers is challenging, especially if we are trapped in using some of the well-known programming languages such as VB, C++, Delphi, or Java. Their numeric data types have defined finite ranges. Innovative methods are required to enable the handling of large integer over and above the ranges supported by available primitive data types. Implementation of the RSA model is a typical case where very large integers are used. This paper investigates incorporation of very large integers into the implementation of the RSA model. We precisely describe parts of the RSA algorithm where such innovations are sought and how the concept of the BigInteger class fits in. Our illustrations assume programming in Java. While the BigInteger class is available for use in mathematical procedures involving very large integral sums beyond the limits of the appropriate basic data type, developers either do not or rarely use the class due to lack of functional examples of where such libraries were used. This paper provides valuable insights into the potential advent of new data types. These insights provide basic insights into the potential need for new developments in programming languages, especially the need to invent even larger types of numerical data types in programming languages. In our views, this work in progress offers foundational views towards implementation of better siblings of the RSA model.
Extended Single Shoot Multibox Detector for Traffic Signs Detection and Recognition in Real-time

Real-time traffic signs detection and recognition is an essential task in autonomous driving and technology-assisted driving. In the past decades, a significant improvement in traffic sign detection and recognition has been gained due to deep learning methods. However, there is still considerable room for improvement. The traffic sign appearance variations limit the performance of the current state of the arts from perfection. Previous results have shown that the detection performance degrades for low-resolution and far scale traffic signs, resulting in high localization and recognition error and degrades CNN's object representation. Since feature maps of top layers of CNN illustrate low-level patterns that cause learning and discrimination of feature patterns challenging for the downstream recognizer. Significantly when a small-scale object passes through the layers likely to vanishes in the middle of the framework. To compensate for the loss of low-level details and localization, we proposed an Extended Single Shoot MultiBox Detector(ESSD), which integrates high resolution and easy-to-compute handcrafted feature channels with the low resolution computational expensive CNN feature channels. We apply DeepMultiBox on the hand-engineered channels (i.e., HOG+LUV and Fishers Discriminant Analysis HOG(FDA HOG) and CNN feature maps in parallel to keep a full set of object representation. We also apply color channels and shape information in a novel way to refine the localization. Experimental results on the German TSR(Trafic signs recognition) benchmark dataset shows the efficiency of the proposed approach, which is 99% mAP@0.5 IoU (intersection over union) and processes up to 60+ FPS(frames per second) in real-time and achieves recognition accuracy competitive to the state of the arts.
Quantum Computing and Programming

The reason behind developing quantum computing is not only related to achieving higher computing capacity but also to be able to properly simulate and numerically study quantum systems. It is still not clear what all the computational tasks for quantum networking are and which of them need to be or are preferably solved with a quantum computer. The goal of this chapter is to provide an understanding of these key quantum ideas that are standard must-do topics in quantum computation and which might find their applications in quantum networks.
Text-Type Variation in Vietnamese: Corpus Mining for Linguistic Features in Narrative and Non-Narrative Genres

In this study, we exploit two Vietnamese corpora: a narrative corpus and a non-narrative corpus. For each of these corpora, there are 24 million words collected from documents of the two genres with publication dates from 2000 to 2020. All of these words are annotated with word boundaries and parts of speech. To examine the use of linguistic features in different genres, we implement statistical analysis for word frequency, parts of speech, linguistic features, and the correlation among these features. The results show that the frequencies of the pronoun “I” and of exclamation words in narrative texts are significantly higher than those in non-narrative texts. Moreover, while adjectives are not correlated with any other features in the narrative genre, they are most likely to co-occur with third-person pronouns in the non-narrative genre.
Towards a user-centric IAM entitlement shop - Learnings from the e-commerce

Nowadays, the use of identity and access management systems is the norm in large organizations. Therefore, great efforts are made in science and practice to ensure that these systems will cope with their tasks in the future. However, from a scientific point of view, an important part of the systems has been neglected so far: the user who uses the systems for ordering software or authorizations daily. Therefore, this paper first examines through a survey the challenges these users face and then presents opportunities on how these challenges can be solved. For this, concepts from e-commerce are used, as there are various mechanisms which are already studied both in science and practice.