Reviewerly/exHarmony huggingface dataset:
A comparative analysis of image restoration techniques

Image restoration is the process of extracting clean images, i.e., beneficial information from the noisy and blurred input images. Image processing may be carried out for various image restoration purposes, such as astronomical imaging, medical imaging, digital media restoration, law enforcement, modular spectroscopy, etc. But the image is often corrupted by atmospheric noises, camera sensors, instrumentation noises, etc. This effect will decrease the accuracy of the image and harm the details it holds. A restoration process can restore a degraded image to its actual state. The image gets distorted due to camera motion of relative objects, sudden atmospheric turbulence, and the camera's poor focus. This chapter provides the workflow, methodologies, and comparative performance analysis of image restoration techniques. Some of the methods discussed in this chapter are filtering techniques, model selection criterion, hybrid kernel padding algorithm, fast Fourier transform, iterative denoising, and backward projection and boosting techniques. The performance of these methods is evaluated on several benchmark metrics, namely signal-to-noise ratio (SNR), mean square error (MSE) and peak signal-to-noise ratio (PSNR). After the detailed analysis, it is observed that among all the above evaluation parameters hybrid sparsity learning technique performs better than the other state-of-the-art techniques.
Literature For Children: The Role Of Parents In Choosing Good Fairy Tales And Reading Material For Children

Literature is very important for our life. Literature can be enjoyed by all people, including children. There are many kinds of literature for children, namely fairy tales, folklore, children's songs, folklore, legends, and others. Literature for children is made like children's views. Imagination that emerges from children is summarized and researched into children's literature. Children's literature is made for education and as a means for children to grow to know literature. However, children need to get guidance from parents or adults in side by side with children's literature. Not all children's literature can be accepted by children. In some examples of children's literature there are things that are inappropriate for children to accept, such as fairy tales with a romantic genre. Therefore, the role of parents is important to help children get to know children's literature. Children cannot but coexist with literature. Literature for children helps children in their development. Literature for children exists so that children can express themselves as freely and as broadly as possible. Children must also accept literature and pass it on to the next generation. Without realizing it, humans live side by side with literature. Therefore, the role of parents and adults is very important in introducing literature to children.
Influence of Socio-Cultural Environmental Factors on Business Performance

There are things, events, or situations that occur that affect the way a business operates, either in a positive or negative way. These things, situations or events that occur affect the organizational performance in either positive or negative ways are called driving forces or environmental factors. This study thereby aims at studying the impact of socio-cultural environmental factors on business performance using International Breweries Plc., Ilesha, Osun State, Nigeria as a case study. Sample of 40 respondents derived from the company were put for study from various sectors of the company using stratified random sampling. The analysis was done with the aid of a descriptive statistical tool and categorical regression analysis for inference with the aid of IBM Statistical Package for Social Statistics (SPSS) version 23. It was found that socio-cultural environmental factors have a positive impact on business performance. It is therefore recommended that effort should be made to improve on advertisement based on the studied environmental factors identified.
Efficacy of information extraction from bar, line, circular, bubble and radar graphs

With the emergence of enormous amounts of data, numerous ways to visualize such data have been used. Bar, circular, line, radar and bubble graphs that are ubiquitous were investigated for their effectiveness. Fourteen participants performed four types of evaluations: between categories (cities), within categories (transport modes within a city), all categories, and a direct reading within a category from a graph. The representations were presented in random order and participants were asked to respond to sixteen questions to the best of their ability after visually scanning the related graph. There were two trials on two separate days for each participant. Eye movements were recorded using an eye tracker. Bar and line graphs show superiority over circular and radial graphs in effectiveness, efficiency, and perceived ease of use primarily due to eye saccades. The radar graph had the worst performance. "Vibration-type" fill pattern could be improved by adding colors and symbolic fills. Design guidelines are proposed for the effective representation of data so that the presentation and communication of information are effective.
A Trust Inference Method Employing Combinatorial Strategies

Most trust inference algorithms based on <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>Q</mi> </math> -learning and learning automata often suffer from the problem of insufficient storage space, as well as some algorithms using the shortest trust path do not fully utilize the trust information. To solve the above problems, a trust inference algorithm (DuelingDQNTrust) based on deep reinforcement learning is proposed. It uses Dueling DQN to compute <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>Q</mi> </math> values to reduce the required storage space and does not limit the traversal depth. Moreover, the way to find out the trusted neighbors of a user is crucial because it affects the reliability of the trust path. So three combination strategies are proposed to find users’ trusted neighbors more accurately. One strategy is to define a priority metric by combining node importance and user similarity to find the top <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>k</mi> </math> neighbor users of the user as the trusted neighbor set. Another two strategies consider node importance and user similarity in a different order of priority, which first uses a metric to find the top <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>k</mi> </math> neighbor users of the user, and then further filters these <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>k</mi> </math> users on another metric to determine the trusted neighbor set of the user. The evaluation of the algorithm on the FilmTrust dataset indicates that the algorithm outperforms several existing trust inference algorithms.
Compiling Discrete Probabilistic Programs for Vectorized Exact Inference

Probabilistic programming languages (PPLs) are essential for reasoning under uncertainty. Even though many real-world probabilistic programs involve discrete distributions, the state-of-the-art PPLs are suboptimal for a large class of tasks dealing with such distributions. In this paper, we propose BayesTensor, a tensor-based probabilistic programming framework. By generating tensor algebra code from probabilistic programs, BayesTensor takes advantage of the highly-tuned vectorized implementations of tensor processing frameworks. Our experiments show that BayesTensor outperforms the state-of-the-art frameworks in a variety of discrete probabilistic programs, inference over Bayesian Networks, and real-world probabilistic programs employed in data processing systems.
The registration of visible and thermal images through multi-objective optimization

Multimodal imaging with visible and thermal sensors attracts much attention due to its robustness under challenging illumination conditions. Due to spectral differences, the visible and thermal images are typically misaligned, where image registration is necessary before high-level vision tasks such as information fusion, multimodal object detection, etc. Contemporary registration methods measure either an angular distance or a linear distance as an objective function to solve affine parameters between visible and thermal images. It is noticeable that both distances have unique advantages in image alignment, which suggests that integrating these measurements may enhance the registration quality. Thus, this paper proposes a new algorithm, namely multi-objective optimization-based image registration (MOIR), to align visible and thermal images. The MOIR comprises a normalized gradient measurement (NGM) that quantifies and normalizes differences of image gradient in both angular and linear aspects, and a regularized stochastic gradient descent (RSGD) that smoothly solves affine parameters. Both quantitative and qualitative results validate the effectiveness and robustness of MOIR for the registration of visible and thermal images in both controlled and real-world scenarios. Some animated examples can be found in https://github.com/jb2849/MOIR.
Combining Classical and Neural Approaches for Image Segmentation

In addition to detecting the 2D bounding boxes of objects in the image, instance segmentation determines on a pixel level, which pixel corresponds to the object. Although recent deep learning based instance segmentation methods are currently state of the art in accuracy, they are relatively slow, use massive computational resources, or are flat-out incapable of running in real-time on cheaper hardware. Classical algorithms can perform background segmentation on images made by infrastructure mounted cameras using far less computational power, however are less robust to changes in scene lighting. They are sensitive to shadows, and cannot perform pixel-level classification of detected objects. In this work, we compare the state of the art of deep learning based methods to classical solutions, and we present a hybrid CNN and classical algorithm based segmentation method, that is capable of running up to 71 frames per second on a standard PC. The method uses Yolov7 for object detection and performs segmentation based on background subtraction only within detected bounding boxes of objects. The background model is created by a classical background segmentation algorithm. The method works on an infrastructure mounted camera, and is robust versus changes in lighting, and even performs well on infrared images.
Map Merging for Multi-Robotic Applications

With the development of robotics, multi-robot applications are increasingly coming to the foreground, where groups of robots, generally vehicles, are used to perform different tasks. There is also a growing need to integrate UAVs into existing transportation systems. One of the fundamental problems to be addressed is the mapping of a given environment by individual robots and their sharing with each other. The most efficient way of sharing is to combine these local maps into one global one. For this purpose, the paper deals with the problem of merging metric maps and a custom system for their merging is proposed. The proposed system has been verified on simulation experiments and their results have been summarized.
Extending Minecraft for Teaching Bitcoin Cryptocurrency

This paper describes the author’s additions to Minecraft and Bitcoin. The topic of the paper is an implementation of modification into the popular game Minecraft, which demonstrates basic user concepts of cryptocurrency Bitcoin in a simple and free of charge manner.
Co-occurrence Networks for Word Sense Induction

Word sense induction (WSI) is the unsupervised and knowledge-free task of clustering occurrences of homonymous or ambiguous words by their meanings. This problem has been relevant since the second half of the 20th century [1] and arises in various natural language processing areas, such as machine translation, sentiment analysis, chatbots, etc. In this work, we applied graph neural networks to the WSI-problem using a co-occurrence network and evaluated it on the RUSSE’2018 task [2]. Proposed approach demonstrates satisfactory results with low consumption of computational resources.
HMD in General Public for Training: Case Study of Standardized Dementia-Friendly Initiative in Japan

Use of Head-Mounted Display (HMD) and Virtual Reality has become popular and the consumer market is growing rapidly with some help from the trend of metaverse. However, practical use of HMD is still limited to either personal use for entertainment, experimental use within industrial training or professional training, or for research purpose within academic practice. Therefore, this paper introduced the HMD to a practical training program, a standardized dementia friendly initiative which trains the general public to become a “Dementia Supporters” in Japan. The training program was performed by a formal lecturer for “Dementia Supporters”, and supporting staffs who are not familiar with VR technology. The HMD was used to show a short movie of a first-person view in 360 degree, associated with a normal dramatic movie about a story of dementia. Of the 157 participants who applied to participate the training program, 62 participants in the intervention group experienced the HMD, were evaluated using the User Experience Questionnaire and the Simulator Sickness Questionnaire. The evaluation results clarified that the training program using HMDs has positive effect in the index of appearance and practicality, low evaluation in the novelty, and there were no sickness observed within the participants. These results may imply the use of HMD to the general public is practical with carefully designed VR contents and training program.
Quantum sensing: target detection with coherence

We propose a novel approach for target detection based on quantum coherence. We exploit a time-bin quantum interferometer to create sequences of coherent optical pulses that are sent towards a target. The photons scattered off the target are then analyzed, and the observed coherence is used as a parameter to distinguish signal photons from noise photons. We confirm experimentally that coherence-based detection achieves a better performance than conventional intensity-based detection. We believe our method will open up new application avenues of coherence to quantum sensing, imaging, and communication.
Disentanglement-Based Multi-Vehicle Detection and Tracking for Gate-Free Parking Lot Management

Multiple object tracking (MOT) techniques can help to build gate-free parking lot management systems purely under vision-based surveillance. However, conventional MOT methods tend to suffer long-term occlusion and cause ID switch problems, making applying them directly in crowded and complex parking lot scenes challenging. Hence, we present a novel disentanglement-based architecture for multi-object detection and tracking to relieve the ID switch issues. First, a background image is disentangled from the original input frame; then, MOT is applied separately to the background and original frames. Next, we design a fusion strategy that can solve the ID switch problem by keeping track of the occluded vehicles while considering complex interactions among vehicles. In addition, we provide a dataset with annotations in severe occlusions parking lot scenes that suits the application. The experiment results show our superiority over the state-of-the-art trackers quantitatively and qualitatively.
A Fragmented Target Recognition System Based on Zero-Shot Learning

In recent years, target recognition and detection methods based on deep learning have shown great application prospects in many fields, such as smart house, driverless technology, product detection and military equipment, etc. However, in some extreme application scenarios, such as the emergency rescue, the target is inevitably fragmented due to the impact of explosion and many other factors, which leads the lack of effective feature information in the target image and affects the accuracy of target recognition and classification. In order to solve this problem, this paper proposes a new method to recognize fragmented targets based on zero-shot learning. This method solves the problem of target recognition under the condition of zero samples by introducing some high-level attributes. For verifying the effectiveness of this method, this paper takes five kinds of ingredients after cutting in daily life: cucumber, potato, tomato, eggplant, and bamboo as an example to illustrate and verify the whole process of the feature extraction, attribute recognition and the target classification in this method. The experiment results show that the highest recognition accuracy for the ingredients after processing in this fragmented recognition system is 76%. In addition, this paper also develops and verifies the real-time recognition of this system on the embedded platform PYNQ.
Efficient Multi-band MLP Based Intra-prediction for Lenslet Image Compression

This paper presents a novel method for lenslet image compression. In the plenoptic representation of lenslet imagery, pixels from adjacent blocks show significantly reduced correlation as compared to what has been observed in natural images. The proposed method first leverages boundary matching to search for the reference blocks of better correlating pixels for intra-prediction. The prediction unit is then decomposed by 2D-DCT into a multi-band representation. It later uses multiple MLP (multi-layer perception) networks for the prediction to explore statistical redundancy and thus reduce the residue for compression.
Action Context Estimation Method Based on AoA of Bluetooth 5.1

Context-aware systems are becoming the fundamental technology for various services focusing on users' actions and situations. Fall detection systems are a new interest in preventing accidents involving falls for elderly persons. According to the spread of wearable devices, some smart watches implemented the fall detection function. However, typical wearable devices require frequent charging. Therefore, typical elderly persons require non-wearable systems not to charge a device frequently. This paper proposes an action context estimation method based on the Angle of Arrival (AoA) of Bluetooth 5.1. AoA is one of the direction-finding functions added in Bluetooth 5.1. It estimates an angle of arrival signals from a BLE beacon by evaluating the phase difference between each antenna element. Bluetooth also has low power consumption, so that it can operate with a single-button battery for long periods. As the context estimation, the proposed system uses machine learning to estimate four actions: “Falling,” “Walking,” “Lying,” and “Standing.” The proposed method uses two types of data: time-series data or statistical values calculated from the time-series data as the feature values. The time-series data includes sampled data series of azimuth and elevation angles obtained by the AoA technique. The statistics are the average and variance of the entire time series of azimuth and elevation angle data. We have experimented with collecting data sets for each of the four actions to evaluate the accuracy of multiple machine-learning models. The results showed that overall accuracy was higher for statistical data than for time-series data. The most accurate machine learning model was Random forest, which showed a classification accuracy of 98.2% with statistical data.
Speaker Verification Systems with Robustness to Various Noises Using Sparse Processing

In recent years, speaker verification systems have been used in various places. Among them, we focused on a speaker verification system with robustness to various noises. Currently, speaker verification is achieved using Deep Neural Networks (DNN), but the accuracy is greatly degraded with background noise and conversational voice. Therefore, we propose SparseSincNet, which applies sparse processing to SincNet, a highly accurate speaker verification model, and build a speaker verification system that can be used in various noisy environments. As a result, the accuracy of the proposed method is 13.8% higher than that of SincNet when white noise with a signal-to-noise ratio of 20 dB is used. Moreover, the Sparse algorithm has the potential for faster inference speeds, which could lead to smaller authentication systems.
Edge-Assisted Object Detection Using Eye Tracking Function of Mixed Reality Devices

Although object detection methods have significant potential to extend the boundaries of mixed reality (MR), they are often difficult to execute on current MR devices that have limited computing resources. In this paper, we propose an edge-assisted object detection method that uses the eye-tracking function of MR devices to determine whether an object detection task should be offloaded to an edge server for processing and thus reduce the MR device processing load. We implement and evaluate the proposed method using a Microsoft HoloLens 2 to confirm its effectiveness.
Top-K Confidence Map Aggregation for Robust Semantic Segmentation Against Unexpected Degradation

Convolutional Neural Networks (CNNs) have become the mainstream for computer vision tasks like semantic segmentation. The CNNs have substantially improved the performance of semantic segmentation. The implicit assumption for those CNN approaches is that the input image is clean without any image degradation. In an actual situation, however, the input image contains many unexpected image degradations such as noise, blur, or compression distortion. The performance of the conventional CNN approach is severely degraded by those unexpected image degradations. This paper presents a simple framework that makes the semantic segmentation algorithms robust against image degradation without re-training the original semantic segmentation CNN. Our key observation is that the down-sampled image is less sensitive to various types of degradation than the original-sized image. Therefore, we adopt a multi-scale test time augmentation (TTA). In the multi-scale TTA, the input image is firstly down-sampled with multiple scales. Then, inferred confidence maps are aggregated to obtain the final inference result. We also propose a top-K confidence map aggregation to further performance improvement. Experimental results demonstrate that our framework is more robust than existing methods for images with degradations and a real compressed video with H.264.
A Note on Improvement of Multi Object Tracking by Frame Interpolation for Intersection Traffic

For video-based vehicle counting, methods utilizing object detection based on deep learning and DeepSORT for multi-object tracking have been proposed. Tracking accuracy degradation has been observed in DeepSORT for turning vehicles at traffic intersections. According to recent work, tracking accuracy is expected to be improved by increasing the frame rate of the input video. Therefore, this paper verifies the accuracy of a vehicle counting system for intersection traffic by changing the input video frame rate. Also, in order to discuss about efficient frame interpolation, this paper verified the accuracy of MOT in frame interpolated video. Specifically, the vehicle counting system is applied to low frame-rate video and video with simple frame interpolation based on averaged images.
Low Resource Vs High Resource solutions for Federated learning sentiment analysis

In this paper we demonstrate the use of RNN and BERT models with a federated learning framework. We evaluate the performance of both the models and compare the trade-off between training time, model parameters and accuracy.