Reviewerly/exHarmony huggingface dataset:
Chapter 10: <i>Approximations and asymptotic formulas</i>

Countless approximations for estimating effective moduli have been introduced; some are semi-empirical, some are based on ad-hoc assumptions, and some have a reasonable theoretical basis. Here we will only review those well-known approximations that have a reasonable theoretical foundation, and which have withstood the test of time; see also the reviews of Van Beek (1967), Landauer (1978), Willis (1981), Markov (2000), and Buryachenko (2001). In addition, we discuss various asymptotic formulas that are applicable in high-contrast media.
How Does the Virtual Reality work on Science Education: An Exploratory Study of Emotional Effects on Learning

As an undergraduate student, STEM learning, especially in subjects such as electrical and electronic engineering, are very difficult. It was not until the junior year when the professor introduced virtual reality to the classroom lecturing for the course Electromagnetic Fields and Waves as an experimental part of a study on engineering education and it was discovered that more innovative possibilities for learning in engineering and science with novel approaches. Existing research has demonstrated that the use of VR in teaching and learning can enhance the motivation and assessment performance of students. Nonetheless, the mechanisms of VR's impact on learning are not yet clear. In particular, whether the impact of VR on science education comes partly from the sensorial novelty and whether this effect is sustainable. Due to student concentration problem, this study aims to explore the sustainability of using VR in engineering and science education, and seeks to fill the research gap regarding the impact of VR technologies on the learning experience in the field of science and engineering. This study, consisting of three sessions VR science learning interventions, was conducted by using a mixed-methods approach to obtain quantitative and qualitative data. Firstly, quantitative data for this study was obtained from questionnaires and interviews were conducted after the questionnaires were collected. A semi-structured interview method was used to understand students' experiences of the use of VR in teaching and learning. This study found that motivation and engagement play a key mediating role in the application of VR to learning. Individuals' interest and motivation in learning is significantly increased by novelty in the initial stages of the VR learning experience, and interestingly the novelty effect does not necessarily decline in vain in subsequent stages of learning as students become familiar with the novelty.
DLMP-Net: A Dynamic Yet Lightweight Multi-pyramid Network for Crowd Density Estimation

The current deep neural networks used for crowd density estimation face two main problems. First, due to different surveillance distance from the camera, densely populated regions are characterized by dramatic scale change, thus using vanilla convolution kernels for feature extraction will inevitably miss discriminative information and reduce the accuracy of crowd density estimation results. Second, popular networks for crowd density estimation still depend on complex encoders with a large number of parameters, and adopt fixed convolutional kernels to extract image features at different spatial positions, resulting in spatial-invariance and computation-heavy. To remedy the above problems, in this paper, we propose a Dynamic yet Lightweight Multi-Pyramid Network (DLMP-Net) for crowd density estimation. The proposed DLMP-Net mainly makes two contributions. First, we design a shuffle-pyramid feature extraction and fusion module (SPFFM), which employs multi-dilated convolution to extract and fuse various scale features. In addition, we add group and channel shuffle operation to reduce the model complexity and improve the efficiency of feature fusion. Second, we introduce a Dynamic Bottleneck Block (DBB), which predicts exclusive kernels pixel by pixel and channel by channel dynamically conditioned on an input, boosting the model performance while decreasing the number of parameters. Experiments are conducted on five datasets: ShanghaiTech dataset, UCF_CC_50 dataset, UCF_QRNF dataset, GCC dataset and NWPU dataset and the ablation studies are performed on ShanghaiTech dataset. The final results show that the proposed DLMP-Net can effectively overcome the problems mentioned above and provides high crowd counting accuracy with smaller model size than state-of-the-art networks.
MOST-Net: A Memory Oriented Style Transfer Network for Face Sketch Synthesis

Face sketch synthesis has been widely used in multimedia entertainment and law enforcement. Despite the recent developments in deep neural networks, accurate and realistic face sketch synthesis is still a challenging task due to the diversity and complexity of human faces. Current image-to-image translation-based face sketch synthesis frequently encounters over-fitting problems when it comes to small-scale datasets. To tackle this problem, we present an end-to-end Memory Oriented Style Transfer Network (MOST-Net) for face sketch synthesis which can produce high-fidelity sketches with limited data. Specifically, an external self-supervised dynamic memory module is introduced to capture the domain alignment knowledge in the long term. In this way, our proposed model could obtain the domain-transfer ability by establishing the durable relationship between faces and corresponding sketches on the feature level. Furthermore, we design a novel Memory Refinement Loss (MR Loss) for feature alignment in the memory module, which enhances the accuracy of memory slots in an unsupervised manner. Extensive experiments on the CUFS and the CUFSF datasets show that our MOST-Net achieves state-of-the-art performance, especially in terms of the Structural Similarity Index(SSIM).
Object Detection for Autonomous Driving: High-Dynamic Range vs. Low-Dynamic Range Images

An important problem in autonomous driving is to perceive objects even under challenging illumination conditions. Despite this problem, existing solutions use low-dynamic range (LDR) images for object detection for autonomous driving. In this paper, we provide a novel analysis on whether high-dynamic range (HDR) images can provide better performance for object detection for autonomous driving. To this end, we choose a seminal deep object detector and systematically evaluate its performance when trained with (i) LDR images, (ii) HDR images, and (iii) tone-mapped LDR images for scenes with different illuminations. We show that a detector with HDR images pre-processed with normalization and gamma correction can only marginally perform better than a detector with LDR or tone-mapped LDR images. Our analysis of this unexpected finding reveals that a detector with HDR images requires significantly more samples as the space of HDR images is significantly larger than that of LDR images.
Robust Watermarking for Neural Network Models Using Residual Network

The training process of a neural network model requires plenty of costs, and so the intellectual property of neural network models should be protected. To this end, we propose a robust watermarking scheme for neural network models in this paper. In our scheme, an independent network is specially designed to help embedding watermarks into a given host network, and also be used for watermark extraction. The independent network is designed based on the residual structure which is sensitive to the parameter changes of the host network and conducive to finding suitable embedding locations. In addition, some residual blocks are randomly discarded during watermark embedding, which can increase the robustness against popular model attacks. Experimental results show that our scheme achieves satisfactory watermark verification performance without decreasing the original performance of the host network, even if the host network has been maliciously tampered.
Probing Semantic Grounding in Language Models of Code with Representational Similarity Analysis

Representational Similarity Analysis is a method from cognitive neuroscience, which helps in comparing representations from two different sources of data. In this paper, we propose using Representational Similarity Analysis to probe the semantic grounding in language models of code. We probe representations from the CodeBERT model for semantic grounding by using the data from the IBM CodeNet dataset. Through our experiments, we show that current pre-training methods do not induce semantic grounding in language models of code, and instead focus on optimizing form-based patterns. We also show that even a little amount of fine-tuning on semantically relevant tasks increases the semantic grounding in CodeBERT significantly. Our ablations with the input modality to the CodeBERT model show that using bimodal inputs (code and natural language) over unimodal inputs (only code) gives better semantic grounding and sample efficiency during semantic fine-tuning. Finally, our experiments with semantic perturbations in code reveal that CodeBERT is able to robustly distinguish between semantically correct and incorrect code.
Blockchain-Oriented Services Computing in Action: Insights from a User Study

Blockchain architectures promise disruptive innovation but factually they pose many architectural restrictions to classical service-based applications and show considerable design, implementation, and operations overhead. Furthermore, the relation between such overheads and user benefits is not clear yet. To shed light on the aforementioned relations, a service-based blockchain architecture was designed and deployed as part of a field study in real-life experimentation. An observational approach was then performed to elaborate on the technology-acceptance of the service-based blockchain architecture in question. Evidence shows that the resulting architecture is, in principle, not different than other less complex equivalents; furthermore, the architectural limitations posed by the blockchain-oriented design demand a significant additional effort to be put onto even the simplest of functionalities. We conclude that further research shall be invested in clarifying further the design principles we learned as part of this study as well as any trade-offs posed by blockchain-oriented service design and operation.
Performance Evaluation of Video Analytics Workloads on Emerging Processing-In-Memory Architectures

Deep learning based artificial intelligence algorithms are widely deployed in the video analytics pipelines as they drastically reduce the need for manual analysis and achieve human-like accuracy. However, they have high compute memory/storage requirements due to ever increasing model architecture size and large volumes of data. Processing-in-memory architectures are gaining prominence for efficient execution of deep learning workloads as they reduce the data movement bottlenecks by moving compute closer to the data. In this work, we present the system level analysis of processing-in-memory architectures across the memory hierarchy for the execution of deep learning algorithms in the video analytics workloads using the proposed SysPIM methodology. We compare processing-in-memory architectures at cache memory, main memory, and non-volatile memory in terms of their execution latency, energy consumption, and overall data movement for representative video analytics workloads.
Coarse to Fine: Image Restoration Boosted by Multi-Scale Low-Rank Tensor Completion

Existing low-rank tensor completion (LRTC) approaches aim at restoring a partially observed tensor by imposing a global low-rank constraint on the underlying completed tensor. However, such a global rank assumption suffers the trade-off between restoring the originally details-lacking parts and neglecting the potentially complex objects, making the completion performance unsatisfactory on both sides. To address this problem, we propose a novel and practical strategy for image restoration that restores the partially observed tensor in a coarse-to-fine (C2F) manner, which gets rid of such trade-off by searching proper local ranks for both low- and high-rank parts. Extensive experiments are conducted to demonstrate the superiority of the proposed C2F scheme. The codes are available at: https://github.com/RuiLin0212/C2FLRTC.
SBFSelector

Tracking changes in code using revision history shared by collaborative teams during software evolution improves traceability. Existing techniques provides incomplete and inaccurate revision history due to lack in detection of renaming and shifting at file, class, and method granularities simultaneously. This research analyzes and prioritizes the metrics responsible for detecting such changes and update the revision history. This improves the traceability by tracking complete and accurate revision history that further improves the processes related to mining software repositories. It proposes SBFSelector algorithm that uses Jaccard Similarity and cosine similarity based on the prioritized metrics to identify these changes. Result shows that 73% metrics belongs to size and complexity that holds more significance over remaining categories. Random forest is best classifier for tracking changes with 0.99 true positive rate and 0.01 false positive rate. It improves traceability by increasing the Kappa statistic and true positive rate as compared to Understand tool.
Word Clouds in the Wild

Word clouds are frequently used to analyze and communicate text data in many domains. In order to help guide research on improving the legibility of word clouds, we have conducted a survey of their usage in Digital Humanities academia and journalism. Using a modified grounded theory approach, we sought to identify the most common purposes for which word clouds were employed and the most common visual encodings they contained. Our findings indicate that font size, color, and word placement dominate as the primary data-encoding channels, as we hypothesized. Perhaps more surprisingly, we found that asking viewers to perform analytical tasks with word clouds was relatively common, especially in DH sources. This suggests that research into the interactions of these visual encoding channels (particularly in regards to legibility) is warranted.
A Case Study of Artificial Intelligence Applied to Safe Area Assessment

This paper mainly studies the safe area assessment, using the jetbot (artificial intelligence robot) to do experiments, when the jetbot walks in the safe area, it walks freely. When the jetbot walks to the boundary, it will turn to a safe area. The safe area assessment can be applied to the working field of the robotic arm. When a worker enters the working range of the robotic arm, the robotic arm system must identify and evaluate the safety of the worker to achieve the safety of the working field.
Secure Image Watermarking in Cloud Computing with Distributed Paillier Cryptosystem

As individuals and companies become more aware of copyright, image watermarking technology is being used more and more in reality. Due to the limitation of local storage and computing power and privacy protection concerns, privacy-preserving watermarking techniques based on cloud computing are gradually gaining more and more attention. We propose an encrypted image watermarking scheme based on discrete cosine transform (DCT) under cloud computing. Using homomorphic encryption (HE), we achieve secure embedding of watermarks on images in the homomorphic encryption domain. The cloud server accomplishes the watermark embedding algorithm. During the embedding process, the cloud server does not need to decrypt the encrypted image, thus ensuring the security of the original image data. We theoretically analyze the security and computational complexity of the whole scheme. Our experimental results show the effectiveness and reliability of our watermarking scheme.
A Secure Aggregation Scheme for Model Update in Federated Learning

Federated learning is a novel machine learning framework that effectively satisfies the requirements of multiple organizations for data usage and model training while meeting privacy protection, data security, and government regulations. However, recent research has shown that attackers can infer users’ private information from their shared model parameters. To address the issue, in this paper, we propose the smart contract assisted secure aggregation scheme (SCSA). Firstly, we present a triple layers architecture based on blockchain for secure aggregation, which can adapt to application scenarios where a large amount of devices are involved in model training. Then, with the help of smart contracts, our scheme can efficiently distribute security masks to users in a decentralized form to ensure the security of parameters, and combine with secret sharing to design a double fault tolerance mechanism to effectively improve the robustness of the system. Finally, the theoretical analysis and simulation experiments prove that our scheme has high security and robustness while maintaining efficiency.
Mining Annotation Usage Rules: A Case Study with MicroProfile

While Application Programming Interfaces (APIs) allow easier reuse of existing functionality, developers might make mistakes in using these APIs (a.k.a. API misuses). If an API usage specification exists, then automatically detecting such misuses becomes feasible. Since manually encoding specifications is a tedious process, there has been a lot of research regarding pattern-based specification mining. However, while annotations are widely used in Java enterprise microservices frameworks, most of these pattern-based rule discovery techniques have not considered annotation-based API usage rules. In this industrial case study of MicroProfile, an open-source Java microservices framework developed by IBM and others, we investigate whether the idea of pattern-based discovery of rules can be applied to annotation-based API usages. We find that our pattern-based approach mines 23 candidate rules, among which 4 are fully valid specifications and 8 are partially valid specifications. Overall, our technique mines 12 valid rules, 10 of which are not even documented in the official MicroProfile documentation. To evaluate the usefulness of the mined rules, we scan MicroProfile client projects for violations. We find 100 violations of 5 rules in 16 projects. Our results suggest that the mined rules can be useful in detecting and preventing annotation-based API misuses.
Application of generalized space time autoregressive (GSTAR) model to predict positive case number of COVID-19

Corona Virus Disease 2019 (COVID-19) is a new virus that can be contagious and its worst effects can lead to death. COVID-19 first appeared in Wuhan, China until it finally spread throughout the country, one of which is Indonesia. The spread of COVID-19 cases in Indonesia itself is quite rapid until finally the World Health Organization (WHO) designates COVID-19 cases as pandemics. Based on current conditions, this paper discuss about predict positive case data of COVID-19 at five locations in East Java (Malang City, Batu City, Pasuruan Regency, Malang Regency, Pasuruan City) using a space-time model namely Generalized Space-Time Autoregressive (GSTAR). Considering that COVID-19 is very easy to spread not only depending on the time but also the proximity between locations, the GSTAR method is good enough to be used to predict the assumption of parameters between heterogeneous locations. The estimation used is OLS with the location weight of cross-correlation normalization. The results of this study obtained the GSTAR(21)-OLS model is the best model to predict the number of positive cases of COVID-19 in.five locations in East Java by weighting the normalization of cross-correlation based on the smallest RMSE value in data out sample. Forecast results for the next 10 days of positive cases of COVID-19 in.all five locations show not very significant changes.
DaDe: A Study of Chunking Algorithm and Hashing Function in Block Level Chunk based Data Deduplication

As the amount of storage employment grows vaster and vaster, people havebeen strived to observe the efficient technique to protect storage space. Data deduplication (DaDe) grows intact in storage administration as it can bring down the storage space powerfully when there are occurred duplicated data. This paper presents data deduplication methodology that can be used in a few methods for optimized storage system. Data deduplication is one of the best strategies which removes duplicate data, shortenbandwidth and also reduces the disk usage. This paper ventures to synopsis deduplication with the interspersed use of file chunking algorithm and hash function, storage optimization concepts. Additionally, an Asymmetric Extremum content defined chunking algorithm (AE) and Message Digest algorithm (MD5) are includedin depth [1].
Lightweight and Effective Convolutional Neural Networks for Vehicle Viewpoint Estimation From Monocular Images

Vehicle viewpoint estimation from monocular images is a crucial component for autonomous driving vehicles and for fleet management applications.In this paper, we make several contributions to advance the state-of-the-art on this problem.We show the effectiveness of applying a smoothing filter to the output neurons of a Convolutional Neural Network (CNN) when estimating vehicle viewpoint.We point out the overlooked fact that, under the same viewpoint, the appearance of a vehicle is strongly influenced by its position in the image plane, which renders viewpoint estimation from appearance an ill-posed problem.We show how, by inserting in the model a CoordConv layer to provide the coordinates of the vehicle, we are able to solve such ambiguity and greatly increase performance.Finally, we introduce a new data augmentation technique that improves viewpoint estimation on vehicles that are closer to the camera or partially occluded.All these improvements let a lightweight CNN reach optimal results while keeping inference time low.An extensive evaluation on a viewpoint estimation benchmark (Pascal3D+) and on actual vehicle camera data (nuScenes) shows that our method significantly outperforms the state-of-the-art in vehicle viewpoint estimation, both in terms of accuracy and memory footprint.
Feature Extraction for Image Processing and Computer Vision—A Comparative Approach

Digital image processing is a technique to process image digitally. This means, image is represented using pixels and all the algorithms are implemented using that value. A digital image consists of a finite variety of parts remarkably named as image parts, pixels. Pixel value determines the type of image, i.e. whether it is grey scale or RGB. Computer vision is a part of deep learning in which processing is done on images. The application of image processing includes robotics, object detection, weather forecasting, etc. In this paper, the main goal is to focus on different feature extraction techniques applied by computer vision and digital image processing. Image features are important input for any image processing tasks. Features include blobs, corner, edges, etc.