Reviewerly/exHarmony huggingface dataset:
FuRA: Fully Random Access Light Field Image Compression

Light fields are typically represented by multi-view images, and enable post-capture actions such as refocusing and perspective shift. To compress a light field image, its view images are typically converted into a pseudo video sequence (PVS) and the generated PVS is compressed using a video codec. However, when using the inter-coding tool of a video codec to exploit the redundancy among view images, the possibility to randomly access any view image is lost. On the other hand, when video codecs independently encode view images using the intra-coding tool, random access to view images is enabled, however, at the expense of a significant drop in the compression efficiency. To address this trade-off, we propose to use neural representations to represent 4D light fields. For each light field, a multi-layer perceptron (MLP) is trained to map the light field four dimensions to the color space, thus enabling random access even to pixels. To achieve higher compression efficiency, neural network compression techniques are deployed. The proposed method outperforms the compression efficiency of HEVC inter-coding, while providing random access to view images and even pixel values.
Mobile Health With Head-Worn Devices: Challenges and Opportunities

Monitoring human behavior and health status using mobile devices, a.k.a. Mobile Health, has gained increasing attention from both academia and industry in recent years. It allows imperceptible health tracking from the users and remote health management from the healthcare service providers. Head-worn devices, such as earbuds, glasses, and brain–computer interfaces, exhibit great potential for mobile health due to their advantageous wearing position, the human head, which is motion-resilient and full of human biosignals. Although initial attempts have been conducted for different healthcare applications with head-worn devices, this fast-growing area is still underexplored and retains great promises. With this work, we investigate the most pressing challenges to fully exploit the potential of head-worn devices for mobile health, from the perspective of sensing, computing, and system design. Our exploration reveals key guidelines and lessons to inform future efforts in this space.
Human Suspicious Activity Detection Using Ensemble Machine Learning Techniques

Public safety is a concern, especially with our growing population. An increasing population also calls for effective crowd management and it is not possible to manually monitor a huge crowd. A lot of approaches to this problem are being analyzed, and with this paper, we have implemented the problem with ensemble learning techniques which will detect suspicious activities. An automated alert system is also set up to detect, record and report suspicious activities to the concerned authorities. Therefore, the anomalous activity detection system will provide a basic surveillance system with an alarm which will help for safety of the public along with lesser costs and more security.
Dynamic Algorithm for Path Planning using A-Star with Distance Constraint

Path planning is one of the most well-researched topics in regard of autonomous agents. The task is to determine an optimal path from the source to the destination by avoiding obstacles at the same time. The optimality of the path is evaluated using performance indicators that reward the shortest route that is planned between the source and the destination. The A-star algorithm is popularly used for effective path planning. The current objective of this paper is to augment the A-star algorithm with the constraint of distance or cost that can be found in real-time scenarios where the battery charge of the mobile robot gets depleted over time before the destination is reached. The modified A-star algorithm tackles this problem by allowing the mobile robot to reach a not yet visited checkpoint that is closest to the destination and within the distance limit. This checkpoint is considered as a charging point, and a new path is planned from this checkpoint to the destination after resetting the cost function of the A-star algorithm.
Density Approximation Error Assessment and Compensation in Point-Mass Filter

This paper deals with the state estimation of non-linear stochastic dynamic systems with an emphasis on a probability density function approximation used by point-mass filters. Approximation error of the standard point-mass density is analysed and quantified, and a novel point-mass density approximation with inherent approximation error minimisation is developed. The properties of the proposed point-mass are theoretically analysed and numerically illustrated.
Object Detection for Connected and Autonomous Vehicles using CNN with Attention Mechanism

This paper addresses object detection and scene perception for connected and autonomous vehicles. Road object detection at high accuracy and fast inference speed is a challenging task for safe autonomous driving as false positives arising from false localization can lead to fatal outcomes. The paper proposes a convolutional neural network (CNN) to recognize images to enhance intelligent adaptive behavior in autonomous vehicles by correctly classifying, detecting, and segmenting spatially distributed objects in the driving environment. By focusing on specific regions of an image, the most significant region of the image is learned by appending a CNN with probabilistic attention mechanism aided with transformers. The proposed approach is analyzed for detection efficiency and accuracy to distinguish different objects to make appropriate driving decisions. The proposed method is validated on the publicly available Berkeley deep drive (BDD) dataset and shows an accuracy comparable to other state-of-the-art deep learning algorithms to make driving decisions based on real-time assessment of the temporal states encountered while navigating the driving environment. The proposed model performance is evaluated using mean average precision (mAP) and speed-accuracy trade-off.
Snap! 7 - Microworlds, Scenes, and Extensions!

Snap! version 7 introduces three big new features: microworlds, scenes, and extensions. Microworlds allow curriculum designers to hide blocks for novice users, allowing them to focus only on the blocks they need. This is similar to Parson's Problems, in which students rearrange commands to solve a challenge. The new version makes it easy for curriculum developers to design these microworlds; we think this can be truly transformative for younger learners. Scenes provide the ability for a single project to have different resources (sprites, sounds, costumes) that would normally have been cluttered in the same "namespace", but now can be nicely organized into different scenes. The last addition may be the most exciting - extensions allow the many Snap! forks now to be brought back in under the primary Snap! umbrella. To make that work, we have exposed low-level JavaScript functionality, and extensions can make use of those functions to adjust the look and feel of Snap! in many ways, provide different tabs or default blocks, and more. Finally, we would like to present some new ways that our teachers say they are using Hyperblocks (vector operations), introduced last year with Snap! 6. Overall, this new release represents more than 18 months of hard work by a dedicated team, and we would be delighted to share these new features with our SIGCSE family.
Evaluation and Recognition of Handwritten Chinese Characters Based on Similarities

To accurately recognize ordinary handwritten Chinese characters, it is necessary to recognize the normative level of these characters. This study proposes methods to quantitatively evaluate and recognize these characters based on their similarities. Three different types of similarities, including correlation coefficient, pixel coincidence degree, and cosine similarity, are calculated between handwritten and printed Song typeface Chinese characters. Eight features are derived from the similarities and used to verify the evaluation performance and an artificial neural network is used to recognize the character content. The results demonstrate that our proposed methods deliver satisfactory evaluation effectiveness and recognition accuracy (up to 98%~100%). This indicates that it is possible to improve the accuracy in recognition of ordinary handwritten Chinese characters by evaluating the normative level of these characters and standardizing writing actions in advance. Our study can offer some enlightenment for developing methods for the identification of handwritten Chinese characters used in transaction processing activities.
Introduction to Machine Learning

Machine Learning (ML) [1-4] is a field of science that deals with learning patterns from data features and statistics, without explicit rule-based programming. ML provides an important capability for computers to learn from data examples and experience. A few simple machine learning examples include image classification [5, 6], spam email filtering [7], and stock price prediction [8]. For image classification such as dog vs. cat, an ML model is trained on thousands of images of dogs and cats until it can learn to distinguish the two. Similarly, for spam email filtering, an ML model can be trained with a lot of benign and spam emails to filter future spam messages.
Small Target Foreign Object Detection Based on Improved YOLO Network

We propose an improved YOLOv4 small target detection method for small target foreign object (FOD) recognition that requires as few detection accuracy, speed, and model volume parameters as possible so that it can be easily ported to other mobile devices such as video surveillance or UAVs in the future. Firstly, the backbone feature extraction network of YOLOv4 is replaced by the MobileNetV2 model, which aims to reduce the number of model parameters. Secondly, the three enhanced feature extraction layers of the original YOLOv4 are increased to four, and a new shallow-scale enhanced feature extraction layer is added, which enhances the characterization capability of the model without increasing the model complexity, making the improved YOLOv3 network structure better for small target detection.
Image Based Recommender System using Transfer Learning

Recommender systems have become extremely com-mon nowadays. These are used in various fields such as e-commerce, video streaming platforms, social media etc. Based on a person's previous history and taste, a person is recommended items. These recommender systems are software tools and techniques that are most commonly used by the companies to target customers. Traditional recommender systems use user feedback such as ratings, likes/dislikes etc to provide recommendations through various techniques such as Content Based Filtering, Collaborative Filtering etc. However there are many instances when a user has interacted with item but has not rated the same item. Also in many cases the rating data is mostly sparse i.e. either the rating is 0 or the rating has not been provided by the user, so the images of items that the users interacted with can be used to provide recommendations. Many e-commerce sites use this method for recommending items to users. In this paper we will be discussing an image recommendation system that can be used to recommend items only using images. It is implemented using transfer learning and cosine similarity, the system attempts to find a result nearest to the image interacted.
A BP-based Key Management Protocol for Data Sharing on Cloud Storage with Access Control

Cloud computing is an emerging technology, which provides a variety of services including computational platform, computational power, storage and applications. Many enterprises are gaining great benefits from cloud storage services by sharing their corporate data with other users. However, the massive use of cloud computing and the lack of effective security mechanisms lead to many challenges in cloud computing. There are significant security and privacy concerns regarding data sharing as data might be stolen, eavesdropped or destroyed. In addition, data needs to be kept confidential not only from users sharing the cloud, but also from the cloud provider itself. In this paper, we propose a key management scheme for data sharing on cloud computing. Our proposed scheme allows data owners to store their files on remote servers using the cloud storage, and to share those files with other users in a secure manner. The proposed mechanism is based on bilinear pairing and provides access control by providing data owners the power to set permissions and rules over the data. Therefore, it guarantees that only authorized users can access the right data. Moreover, the proposed scheme ensures the security and privacy of data stored on cloud. Finally, informal security analysis of the proposed scheme is presented and analyzed, which demonstrates that the scheme is secure and can resist a variety of attacks.
Object Tracking Based on the Fusion of Roadside LiDAR and Camera Data

Tracking road users with high resolution is important for connected vehicles. Due to the complicated environments, tracking objects with a single sensor could not meet the requirements of high-resolution trajectories due to occlusions. How to acquire accurate and complete trajectories based on multi-source data is a major challenge for researchers and engineers. This paper developed a novel tracking method based on the fusion of roadside LiDAR and camera. According to the relationship between the number of points and distance, the adaptive weight coefficient related to 3D trajectory information was determined. The performance of the proposed method was evaluated at five selected sites. The proposed tracking method had high performance in terms of speed calculation, tracking range, the rate of object loss, and the repairing rate of disconnected trajectories. The proposed method can benefit many transportation areas, such as traffic volume counting, vehicle speed tracking, and traffic safety analysis.
Fuzzy Strategy Grey Wolf Optimizer for Complex Multimodal Optimization Problems

Traditional grey wolf optimizers (GWOs) have difficulty balancing convergence and diversity when used for multimodal optimization problems (MMOPs), resulting in low-quality solutions and slow convergence. To address these drawbacks of GWOs, a fuzzy strategy grey wolf optimizer (FSGWO) is proposed in this paper. Binary joint normal distribution is used as a fuzzy method to realize the adaptive adjustment of the control parameters of the FSGWO. Next, the fuzzy mutation operator and the fuzzy crossover operator are designed to generate new individuals based on the fuzzy control parameters. Moreover, a noninferior selection strategy is employed to update the grey wolf population, which makes the entire population available for estimating the location of the optimal solution. Finally, the FSGWO is verified on 30 test functions of IEEE CEC2014 and five engineering application problems. Comparing FSGWO with state-of-the-art competitive algorithms, the results show that FSGWO is superior. Specifically, for the 50D test functions of CEC2014, the average calculation accuracy of FSGWO is 33.63%, 46.45%, 62.94%, 64.99%, and 59.82% higher than those of the equilibrium optimizer algorithm, modified particle swarm optimization, original GWO, hybrid particle swarm optimization and GWO, and selective opposition-based GWO, respectively. For the 30D and 50D test functions of CEC2014, the results of the Wilcoxon signed-rank test show that FSGWO is better than the competitive algorithms.
Review of Optimal Convolutional Neural Network Accelerator Platforms for Mobile Devices

In recent years, convolutional neural networks (CNNs) have achieved remarkable performance enhancement, and researchers have endeavored to use CNN applications on power-constrained mobile devices. Accordingly, low-power and high-performance CNN accelerators for mobile devices are receiving significant attention. This paper presents the overall process of designing optimal CNN accelerator platforms for mobile devices based on algorithm, architecture, and memory system co-design while introducing various existing studies related to specific research fields.
Supporting Speech Therapies at (Smart) Home Through Voice Assistance

Nowadays, homes are much more than just a place to live. Pervasive technology is slowly changing people’s lives as well as their way of living, supporting them in daily activities. This wind of change is also touching homes which are becoming more and more “smart”, automating at a glance users’ routine. Voice assistant devices are able to connect and control many actuators that are spread all over the house network so that the user can easily control them with the voice. In this article we want to propose a new way to exploit the Smart Home technology, exploring a new dimension to assist people in the home environment. In particular we investigate how this technology can support speech therapy and return the therapy results to the physicians. We define a cycle that can be easily implemented in an existing smart environment, to maximise the therapy results and adapt it to the changing therapy requirements. In particular, we focus our attention on how common voice assistants (such as Amazon Alexa and Google Home) can, with their interaction and control capabilities, support speech therapy, ensuring it is correctly completed at the given time.
A Performance Evaluation of Tandem Queueing Systems by Machine Learning

This paper presents a performance evaluation of tandem queueing systems by machine learning. Recently, tandem queueing systems have been increasing in variety of service formats. Hence, it is important for service design to evaluate the performance of tandem queueing systems. However, the exact solutions of tandem queueing systems have not been fully developed. Alternatively, we can apply the discrete simulation to evaluate the performance. However, it takes much time. In this paper, we propose the performance evaluation method by machine learning to solve the problems.
Edge-Aware Graph Matching Network for Part-Based Semantic Segmentation

Abstract Semantic segmentation of parts of objects is a marginally explored and challenging task in which multiple instances of objects and multiple parts within those objects must be recognized in an image. We introduce a novel approach (GMENet) for this task combining object-level context conditioning, part-level spatial relationships, and shape contour information. The first target is achieved by introducing a class-conditioning module that enforces class-level semantics when learning the part-level ones. Thus, intermediate-level features carry object-level prior to the decoding stage. To tackle part-level ambiguity and spatial relationships among parts we exploit an adjacency graph-based module that aims at matching the spatial relationships between parts in the ground truth and predicted maps. Last, we introduce an additional module to further leverage edges localization. Besides testing our framework on the already used Pascal-Part-58 and Pascal-Person-Part benchmarks, we further introduce two novel benchmarks for large-scale part parsing, i.e., a more challenging version of Pascal-Part with 108 classes and the ADE20K-Part benchmark with 544 parts. GMENet achieves state-of-the-art results in all the considered tasks and furthermore allows to improve object-level segmentation accuracy.
An Integrated Clustering and BERT Framework for Improved Topic Modeling

Abstract Topic modelling is a machine learning technique that is extensively used in Natural Language Processing (NLP) applications to infer topics within unstructured textual data. Latent Dirichlet Allocation (LDA) is one of the most used topic modeling techniques that can automatically detect topics from a huge collection of text documents. However, the LDA-based topic models alone do not always provide promising results. Clustering is one of the effective unsupervised machine learning algorithms that are extensively used in applications including extracting information from unstructured textual data and topic modeling. A hybrid model of Bidirectional Encoder Representations from Transformers (BERT) and Latent Dirichlet Allocation (LDA) in topic modeling with clustering have been studied in detail. As the clustering algorithms are computationally complex, the complexity increases with the higher number of features, the PCA, t-SNE &amp; UMAP-based dimensionality reduction methods are also performed. Finally, a unified clustering-based framework using BERT and LDA is proposed as part of this study for mining a set of meaningful topics from the massive text corpora. The experiments are conducted to demonstrate the effectiveness of the cluster-informed topic modeling framework using BERT and LDA by simulating user input on benchmark datasets. The experimental results show that clustering would help infer more coherent topics and hence this unified clustering and BERT-LDA based approach can be effectively utilized for building topic modeling applications.
Boosting Feature-Aware Network for Salient Object Detection

Deep convolutional neural networks have demonstrated competitive performance in salient object detection. To capture more precise saliency maps, recent approaches mainly concentrate on aggregating deep features from convolutional networks and introducing edge supervision for a guarantee of compact targets. Though significant progress has been accomplished, the problems of low-contrast by targets against backgrounds and the inconsistency of object size are still challenging to tackle. To relieve these troubles, we propose a boosting feature-aware network (BFANet) following a dual-stream architecture, including an object sub-network for salient objects detection and a boundary guidance sub-network supervised by enhanced labels for edge detection. Specifically, for the interesting yet low-confidence areas, we mold a boosting feature-aware module (BFAM) hammering at suppressing the background while highlighting the weak features. In addition, considering the different responses of channels to output, we present a weighted aggregation block (WAB) to strengthen the significant channel features and recalibrate channel-wise feature responses. Extensive experiments on five benchmark datasets demonstrate that our proposed model outperforms most other state-of-the-art methods.
Deep Learning driven automated person detection and tracking model on surveillance videos

Recently, person detection and tracking in a video scene of a surveillance system were grabbing higher interest because of its extensive range of applications in gender classification, abnormal event recognition, person identification, human gait characterization, individual counting in a dense crowd, and fall detection for older people, etc. Several methodologies were implied for person detection for surveillance applications. This paper presents a new DL driven automated person detection and tracking (DLD-APDT) model on surveillance videos. The major goal of the proposed DLD-APDT model is to identify people and track them in the videos. To accomplish this, the proposed DLD-APDT model initially performs frame conversion process where the input video is converted into a set of frames. In addition, the proposed DLD-APDT model employs EfficientDet model as object detector, i.e., persons and track them. EfficientDet is a recently developed object detector that makes use of different optimization and backbone tweaks, like bi-directional feature pyramid network (BiFPN) and a compound scaling approach. Moreover, root means square propagation (RMSProp) optimizer is applied to optimally choose the hyperparameters related to the EfficientNet model which helps in accomplishing enhanced performance. The experimental validation of the DLD-APDT model takes place using two training datasets namely PascalVOC and PenFudan datasets. The simulation results reported the enhanced detection and tracking performance of the DLD-APDT technique over recent approaches.