Reviewerly/exHarmony huggingface dataset:
An Improved Resource Allocation Process Using Modified Max-Min Algorithm for Dynamic Cloud

Cloud computing is an emerging resource sharing platform, which provides the resources on-demand. It follows the pay-per-use pattern. The Cloud Service provider offers a variety of services such as Platform, Infrastructure, and software. They should assure that each user receives a high-quality service. But cloud usage has increased drastically day by day. To keep the SLA (Service Level Agreement) in a cloud system, heavy load should be distributed across the system. Here, we propose a novel technique of effective load balancing for resource scheduling technique that boosts profitability and stability. Max-Min algorithm is fine-tuned based on Profit and priority of the tasks named as Modified Max-Min algorithm. It is designed and adopted in cloud environment. Simulation result are observed, that shows the given approach improves the stability of load balancing and assures maximum profit by optimizing the response time and waiting time of the cloud users. This increases the cloud’s overall efficiency which results more profitability.
Confidence-selective moving trace approximation using ellipse and line models for hand gesture recognition

Hand gestures play an important role in human-computer interaction (HCI) system. Areas of applications such as sign language recognition, virtual environment, video games and even surgery require people to interact with devices directly with hands. In this paper, we propose a vision-based system to deal with dynamic hand gesture recognition. We focus on 6 types of gestures: 4 directions for swipe and 2 orientations for rotation. The hand gesture can be identified based on the moving trace of the hand in different video frames. However, different from existing algorithms, the proposed method applies the techniques of ellipse and line approximation to well identify the moving trace and reduce the effect of noise. Moreover, the confidence of each frame is determined by the velocity and the continuity of the motion vector. The frame with less confidence will not be adopted in hand gesture approximation. We also develop a face-based skin region detection to adaptively choose a proper color model. The proposed system is hardware-efficient and can achieve a good accuracy.
An examination of weight illusion by adding visual effects on the arm

In this study, we investigate the weight illusion produced by changing the impression of an arm by displaying visual effects on an avatar's arm superimposed on its own arm. We have confirmed that visual changes using virtual arms produce an illusion of weight in comparisons between different weight conditions. It was pointed out that differences in the impression of the weight of the arm itself due to differences in the appearance of the virtual arms may affect the perception of weight. In this paper, we examine the effect of changes in the impression of arm strength on the perception of weight by changing the impression of the arm by visual effects that do not affect the impression of the arm's weight.
Theft detection by patterns of walking behavior using motion-based artificial intelligence

Because safety is important in everyday life, according to the National Retail Federation, organized retail theft accounts for an estimated 30 billion dollars each year in the U.S. Therefore, this paper proposes the detection system of theft walking patterns using motion-based Artificial Intelligence from a stationary camera. This method uses object detection by the Yolov5 model for localization of the objects, which is less affect to a background problem to detect the moving objects from the RGB video. After that, the coordinates of bounding boxes from Yolov5 are used to calculate the centroid of the box; then, it is used to make the motion of walking pattern behaviour. Finally, the sequences of the motion patterns are used with a bi-directional long-short term memory network, which is used for the time-independent pattern using 10-fold cross validation at training size 50% of total dataset. To evaluate the performance of the method, the datasets consist of normal walking people, which have 30 videos, and theft walking people, which have 30 videos. The accuracy of the system is approximately 97%.
Semi-supervised 3D holistic human mesh reconstruction from a single image

3D holistic human mesh reconstruction has gained attention in the computer vision field. However, the absence of an in-the-wild training dataset with 3D annotations, makes this field more challenging. There is a large-scale synthetic dataset called AGORA to address this problem. Since this dataset is still synthetic, there is still a gap to deal with the real scenario. In this paper, we introduce a semi-supervised approach to bridge the gap between synthetic and real data. Our experiment shows that our approach could improve in terms of the quantitative and qualitative results.
Deformable offset gating network with variational auto-encoder for compression artifacts reduction

JPEG is a widely used image compression standard that shows a reasonable image quality for a wide range of compression rates. However, when compressed with a low compression quality factor to increase the compression rate, it brings a large loss in the frequency domain, which turns into visible artifacts in the image domain. Accordingly, removing artifacts in JPEG-compressed images has been an essential image restoration task. While most previous methods use the information on compression quality factors available in the header of the JPEG file, we note this approach is not practical in the real-world scenario because many compressed images’ metadata are not exist. To deal with this issue, we propose a new method based on a Deformable Offset Gating Network (DOGNet) and a Variational Autoencoder (VAE). We train the overall network in an end-to-end manner, where the role of the VAE is to guide the offset of the deformable convolution to flexibly deal with images compressed with diverse and unknown quality factors. Extensive experiments validate that our method achieves better or comparable results to the state-of-the-art methods in JPEG Artifact Removal.
Novel CNN approach for video prediction based on FitVid

Video prediction is a task in computer vision that predicts future frames from the past few frames of video. In video prediction, a simple CNN-based approach called SimVP has marked remarkable performance without using RNN or vison transformer (ViT). In this paper, we propose a model structure to improve performance of video prediction based on FitVid. FitVid is a regression-based method of predicting future videos using not only video but also motion. We focus on video prediction only conditioned on videos. To this goal, we introduce network modules used in SimVP to FitVid. Experimental results show that the proposed structure shows better prediction accuracy compared to SimVP.
A study on radiometric compensation for moving non-rigid surfaces using motion prediction

In this research, we aim to realize the improvement of the accuracy of radiometric compensation for moving non-rigid bodies. By predicting the movement of objects using deep learning, the influence of delays in measurement and projection depending on cameras and projectors on correction accuracy is reduced.
A novel feature map compression method based on feature transformation for VCM

In this paper, we propose a novel feature map compression method for Video Coding for Machines (VCM). The proposed method performs a principal component analysis (PCA)-based transform on feature pyramid network (FPN) feature maps using predefined basis and mean vectors. In addition, the proposed method reduces redundancy between different resolution levels within FPN feature maps based on redundancy between FPN layers. The fixed predefined basis and mean are employed through PCA with a set of training data set. For any input videos, transform coefficients are obtained by performing transform with the fixed basis and compressed using Versatile Video Coding (VVC). Experimental results show that the proposed method achieves 89.22% and 86.57% BD-rate gain compared to the VCM feature anchor in instance segmentation, and object detection, respectively.
Complex Number and Linear Algebra

The full description of quantum mechanics needs a lot of advanced mathematics, which includes calculus and partial differential equations. However, the essence of quantum mechanics can be described with just complex number and some basics of linear algebra on top of high-school mathematics.
Quantum Computation

The concept of quantum computation was first suggested in the early 1980s. In 1980, the American physicist Benioff (Paul A. Benioff, 1930–) proposed an implementation of classical computer with quantum systems. In the same year, the Russian mathematician Manin (Yuri Ivanovitch Manin, 1937–)
Quantum Communication

In the information age, our daily lives are becoming increasingly digitalized, and they are stored in magnetic memories, processed by computers, and transmitted via optical fibers and electromagnetic waves. Yet our world is quantum in nature. Therefore, a natural question is how to store, process and transmit the information encoded in qubits. These questions are addressed by quantum information theory. Quantum computation, as introduced in the previous chapter, explores how to process quantum information. Quantum communication is another important branch of quantum information science, where quantum entanglement has been successfully exploited to achieve quantum teleportation for transferring quantum information.
Analysis of differences in writing and motivation based on topic selection in middle school student’s expository writing

This study empirically compared differences between the expository writing performance and writing motivation of secondary student writers according to whether they selected a topic. In total, 196 middle school students from Seoul participated in this study, which analyzed the associations of the student’s topic-presented and topic-selective expository writing in two sessions with their motivation. This study found the following. First, a paired sample t-test showed that secondary students had higher writing fluency(number of letters, words, sentences) and took longer when performing topic-selective writing than topic-presented writing. Second, the paired sample t-test, showed that secondary students achieved better writing quality(only content and expression were significant). Third, paired sample t-test, found that secondary students had higher motivation(just in writing attitude and writing efficacy were significant) for topic-selective writing. Fourth, one-way ANOVA of writing motivation according to the group by writing level found that the sensitivity to the type of topic presentation increased in the order upper &gt; lower &gt; middle group levels.
A Study of Educational Methods for Determining the Topic of Academic Writing for Foreign Students: Through the Expansion of the KWL Model

This study aimed to propose an educational method for determining the topic of academic writing for foreign students using the KWL(Know-Want-Learn) model. To this end, an expanded model called KWLC(Know-Want-Learn-Compare) was presented by analyzing the subject-determining teaching method and previous relevant studies. The proposed model was applied to three classes of college writing in the spring and fall semesters of 2022, and the results were analyzed through case studies. The results were as follows. First, the KWLC model made reading and writing easier through critical thinking. Second, the contents written in stage C played a decisive role in deriving the topic. Third, through stage C, thinking developed in the direction of paying attention not only to the “gap in knowledge” and “gap in thinking” but also to the “gap in objects,” enabling the reader to develop an extended theme beyond the subject of the given text. Fourth, KWLC was helpful in deriving specific and creative themes. It is expected that the KWLC instructional model of this study can be actively used to help the student realize the subject from their own perspective through critical and integrative thinking.
Towards Collaborative Conversational User Interfaces for Team Training

Conversational interfaces are quickly becoming a prominent type of intelligent user interface. However, much of the current work has focused on dyadic interaction between a single user and a single conversational agent. The aim of my PhD thesis is to examine how conversational interfaces can be adapted to support humans engaged in a team task. My work so far has identified the key types of functionality such conversational interfaces should entail and via a series of a Wizard of Oz studies I have validated these findings, and obtained the data needed to train a conversational model to support human-artificial agent teaming. My aim at the Doctoral Consortium would be seek guidance from and form research collaborations with my peers on a general approach to designing, training and evaluating collaborative conversational agents for human-machine teaming.
Customer interest estimation method in real store using re-identification and 3D posture estimation models

This paper presents a novel Transformer-based customer interest estimation method using videos from a security camera in a real store. Expectations for the application of Artificial Intelligence (AI) technology in various industrial fields have increased. In this study, we focus on the retail industry field rather than on the manufacturing industry field where AI technology has already been widely introduced. In the retail industry, understanding customer interest in products is one of the significant issues, and Point-of-Sales (POS) data have been used for the analysis of customer data. However, the information of customers before the purchase cannot be obtained from the POS data, which was a limitation due to the characteristics of the data. To provide a solution to the problem, we propose a new customer interest estimation method using visual information obtained from a security camera. The proposed method consists of three phases: Re-identification phase, 3D posture estimation phase, and interest estimation phase. The advantage of our architecture is that the Reidentification phase enables individual identification of multiple persons, and the 3D posture estimation phase obtains highly expressive posture information. Then interest estimation phase is used to enable individual interest estimation. Finally, we achieve a high-level customer interest estimation performance using the data obtained from a real store.
Grasping method and voice coil motor design considering 3D directional presentation of virtual force sensation

This study proposes a motor design and presentation method for presenting virtual force sensation in the three-dimensional direction. Various grasping methods and device performances need to be considered in order to present virtual force perception in three-dimensional directions. However, commercially available motors do not allow changing parameters such as the surface magnetic flux density of the magnets inside and the number of turns of the coils, and the shape of the motor affects the design of the device. Therefore, we created motors with the same amplitude and shape for each device. By grasping that motor with the side of the fingertip, the directional presentation of virtual force sensation in the vertical direction was possible, showing its potential for 3D directional presentation.
An examination on operation methods of AR multi-viewpoint viewing system for Ohayashi

Japan is rich in cultural properties of great historical and artistic value, of which the most important are protected as nationally designated cultural properties. Intangible cultural properties are techniques that have been handed down from generation to generation, such as theater, music, and craft techniques. In Japan, where the population is aging rapidly, digital archiving is essential for the transmission of intangible cultural assets. In this study, we focused on the musical accompaniment of the Kanuma Imamiya Shrine Festival (Kanuma Autumn Festival), which is designated as a UNESCO Intangible Cultural Heritage and a nationally designated Important Intangible Folk Cultural Property. Although the Kanuma Autumn Festival had to be canceled last year due to the emergency declaration requested by the spread of coronavirus infection, the festival is still being carried on. In this study, we have developed multi-viewpoint viewing system for ohayashi content with the cooperation of the Kamifukatsu Music Preservation Society and evaluated the system through experiments with participants. As a result, we obtained positive feedback from the participants, while those who had experience with musical performances pointed out points that needed to be improved. The controller was used as a method of interaction with the contents, but other methods have not yet been compared. Therefore, we aim to support the inheritance of tradition and examine the most user-friendly interaction method in terms of operability.
Investigation of the effectiveness by redirected walking with tilt presentation to the sole

Walking through real space and moving through virtual space is one of the methods to improve the immersive experience of VR content. However, the space available for movement in a virtual environment is limited by the size of the real space. As a result, the experience of VR content may be compromised. One solution to this problem is Redirected Walking (RDW). RDW is a method that allows walking on a large virtual space rather than a limited real space. In this method, the virtual space is rotated to create the illusion of walking direction. Previous research has shown that the presentation of tactile sensation and traction increases the degree of illusion. In this study we propose a Redirected Walking technique combined with tilt presentation to the sole of the foot. An experiment was conducted with four subjects to investigate the effect on RDW of the presence or absence of tilt presentation to the sole. The results of the experiment showed the possibility of shifting the range over which the illusion is induced by the presentation of the tilt to the sole.
A study of Japanese sign language recognition using human skeleton data

Sign language is a visual language that uses hand signs, arm movements and facial expressions to convey information. However, learning sign language is challenging task, due to variety of movements that should be executed precisely to express the correct information. Many studies have been made to recognize and translate the sign languages. Traditional methods face problems when used in a real-world situation due to the background and lighting variations. Recently, the result obtained from research using multi-modal data, including human skeleton data to Sign Language Recognition task (SLR), have archived a remarkable success.<sup>1</sup> This paper aims to make a model that uses only skeleton data to recognize sign language using Graph Convolutional Network (GCN) and make a Japanese Sign Language dataset to help future SLR research.
Reducing the number of masks to accelerate the neural network visualization of RISE

RISE is one of the methods used for visualizing the basis of neural network decisions in image recognition. RISE creates a heat map showing the importance of various parts of an image by observing the response of the network while partially obscuring the input image with a random mask. However, this method requires many mask images to obtain stationarity, resulting in a huge amount of computation time. In this study, we use a non-random patch mask that passes through only one limited region in addition to an improved random mask to reduce the number of masks needed, thereby speeding up the RISE process.
A source-free unsupervised domain adaptation method based on feature consistency

A typical unsupervised domain adaptation (UDA) task assumes that labeled source data is available for model adaptation. However, this assumption is often infeasible due to confidentiality issues or memory constraints on mobile devices. To address these problems, we propose a simple yet effective source-free UDA method. Motivated by self-training methods, the proposed method constrains multiple views of the same image to have similar features and prediction outputs. The feature generator is encouraged to learn consistent visual features that are away from the decision boundaries of the head classifier. On popular benchmarks, our approach demonstrates comparable or even superior performance to vanilla UDA methods without using source images.
Evaluation of an old-fashioned style picture transformation system using generative adversarial networks and instance segmentation

In recent years, tourism services and workshops that allow visitors to go back in time and relive history have become popular, and AR systems that recognize landmarks and superimpose old information for the viewer have been developed AR. In this study, we apply an old-fashioned style transformation to the entire image using GAN and segmentation techniques to photos taken on the spot. Then, we propose a method to merge the current and old scenes while maintaining continuity by mapping current items to old items on a domain basis. Specifically, we created a system that makes users feel as if they have been transported back in time to the Edo period by utilizing Ukiyo-e content using CycleGAN and Instance Segmentation on real images, and evaluated viewer impressions. As a result, it was confirmed that the system generally allowed visitors to feel the atmosphere of the Edo period, although the three-dimensionality and coloring of the playhouse and teahouse remained unnatural. In the future, we plan to improve the quality of the system in order to offer it to actual tourists.
A study on selecting the optimal number of features for clustering without label information

Most studies on high-dimensional data preprocessing for data mining and pattern recognition, attention have focused on unsupervised feature selection, which aims to reduce data redundancy and select the most representative features from massive unlabelled data. Previous work on convex nonnegative matrix factorization with adaptive graph constraint has indicated that selecting a few essential features helps replace the original features for clustering. However, the problem exists in how to decide the quantity of these essential features without prior label information. This study proposes a customized evaluation criterion to evaluate each essential feature set. A significant advantages of this method is that the optimal number of features can be determined without knowing their true labels. Experiments were verified by the consistency between the results of our method without label information and that of the comparison method, which required label information.
Seq2Code: Transformer-Based Encoder-Decoder Model for Python Source Code Generation

Deep learning for Natural Language Processing has advanced at breakneck speed. Deep learning techniques have recently demonstrated outstanding results in machine translation, question answering, and computer vision applications. Transformer-based architectures, such as BERT, and GPT produced results that are state of the art across all the tasks in NLP. BERT uses an encoder transformer, and GPT uses a decoder transformer architecture for different kinds of NLP tasks. This paper introduces Seq2Code, a transformer-based model, that generates source code in Python for the corresponding problem statement in natural language. We generate source code corresponding to the input statement using an encoder-decoder-based transformer model with multi-head attention. We have collected more than 10,000 pairs of problem statements and code snippets. Special characters play a more significant role in code snippets. We have trained embeddings for all the special characters. The model performance is evaluated using perplexity. The Seq2Code proposal outperformed the existing models.