Reviewerly/exHarmony huggingface dataset:
High-reflective surfaces shape measurement technology based on adaptive fringe projection

Fringe projection profilometry (FPP) is limited by the pixel depth of the camera and cannot correctly reconstruct complex objects with highly reflective surfaces. In this paper, we proposed an improved adaptive fringe technique to avoid image saturation. This method only needs to project a uniform gray level image and a set of horizontal and vertical phase-shifting fringe patterns to generate adaptive fringes. The gray level of saturated regions can be fitted by the uniform image and the coordinates of the areas are obtained by calculating the horizontal and vertical fringes. To avoid the process of recursion, we introduce the concept of the grayscale correction function. Furthermore, a modified mapping formula is adopted and refined, which enhances the mapping effect and reconstruction accuracy. Compared with the original adaptive fringe projection (AFP), the modified method further decreases the number of projected fringe patterns. We analyzed the error between the fitted value and the standard value to evaluate the performance of the method. Experimental results demonstrated that the proposed method maintains the accuracy of reconstruction while improving time efficiency compared to other high dynamic range (HDR) measurement methods.
An Internet of Things (IoT)-Based Optimization to Enhance Security in Healthcare Applications

The Internet of Things (IoT) is a network that connects a large number of items. Each thing uses sensors to create and gather data from its surroundings and then sends to other objects or a central database through a channel. Keeping and transforming this created data is one of the most difficult tasks in IoT today, and it is one of the top worries of all enterprises that deploy IoT technology. Sensing equipment together with communication, storing, and display devices get benefit from technological advancements in the healthcare sector. Moreover, medical parameters and postoperative days require close observation. Therefore, the most cutting-edge approach to healthcare communication is adopted, which makes use of the Internet of Things (IoT). We can use the Internet of Things to speed changes in the healthcare environment, such as enhancing patient involvement and outcomes and shifting healthcare from reactive to proactive accessibility. Nonetheless, the growth of IoT exposes healthcare practitioners and their patients to new vulnerabilities, risks, and security concerns. However, there is currently a scarcity of research on how to improve IoT security in healthcare. Existing studies tend to concentrate only on the installation of IoT peripherals in a healthcare setting and to include a secure application solution. Because healthcare data and information are extremely sensitive, it is critical to have a secure health IoT application in place. As the IoT gets more widely used in healthcare, there will inevitably be more instances of sensitive patient information being made public. This paper proposes an optimized hashing algorithm with digital certificates to enhance the security. Initially, the health data are collected and preprocessed using normalization. The data are then stored in the IoT device. Here, the digital certificates are used for authentication purpose. The proposed discrete decision tree hashing algorithm (DDTHA) with ant colony optimization (ACO) hashes the unsigned digital certificates. The blowfish algorithm is used for encryption, and the signed digital certificate is obtained which is used for authentication purpose. The performance of the proposed system is evaluated and compared with conventional methodologies to prove the efficiency of the system.
Reference-free low-light image enhancement by associating hierarchical wavelet representations

For computer vision and image content understanding, the low-light image becomes an obvious challenge as it suffers from poor contrast and illumination. Hence, low-light image enhancement(LLIE) technology has attracted considerable attention. However, most of the existing LLIE methods based on deep learning technology rely on referenced images to guide supervised training. It is very difficult to capture the referenced images in a real-world scene. To remedy this, we propose a reference-free low-light enhancement framework by estimating pixel-wise curves with wavelet decomposition and association. Typical LLIE methods apply illumination adjustment on the RGB image roughly. In contrast to that, we re-examine current LLIE pipelines and propose a fine-grained image enhancement framework by abstracting and associating frequency prior. Firstly, we decompose the image to the frequency domain by wavelet transform. Then, we realize frequency-away message passing with hierarchical wavelet decomposition. Finally, reference-free loss functions are applied with the consistent association between wavelet frequencies. These losses are used to guide a fine-grained reference-free LLIE paradigm. We verify our method through comprehensive experiments, and prove that our model is superior to other relevant methods under the quantitative index and visual perception.
Community smells—The sources of social debt: A systematic literature review

Social debt describes the accumulation of unforeseen project costs (or potential costs) from sub-optimal software development processes. Community smells are sociotechnical anti-patterns and one source of social debt. Because community smells impact software teams, development processes, outcomes, and organizations, we to understand their impact on software engineering. To provide an overview of community smells in social debt, based on published literature, and describe future research. We conducted a systematic literature review (SLR) to identify properties, understand origins and evolution, and describe the emergence of community smells. This SLR explains the impact of community smells on teamwork and team performance. We include 25 studies. Social debt describes the impacts of poor socio-technical decisions on work environments, people, software products, and society. For each of the 30 community smells identified as sources of social debt, we provide a detailed description, management approaches, organizational strategies, and mitigation effectiveness. We identify five groups of management approaches: organizational strategies, frameworks, models, tools, and guidelines. We describe 11 common properties of community smells. We develop the Community Smell Stages Framework to concisely describe the origin and evolution of community smells. We then describe the causes and effects for each community smell. We identify and describe 8 types of causes and 11 types of effects related to the community smells. Finally, we provide 8 comprehensive Sankey diagrams that offer insights into threats the community smells pose to teamwork factors and team performance. Community smells explain the influence work conditions have on software developers. The literature is scarce and focuses on a small number of community smells. Thus, the community smells still need more research. This review helps by organizing the state of the art about community smells. Our contributions provide motivations for future research and provide educational material for software engineering professionals.
Norm Learning with Reward Models from Instructive and Evaluative Feedback

People are increasingly interacting with artificial agents in social settings, and as these agents become more sophisticated, people will have to teach them social norms. Two prominent teaching methods include instructing the learner how to act, and giving evaluative feedback on the learner’s actions. Our empirical findings indicate that people naturally adopt both methods when teaching norms to a simulated robot, and they use the methods selectively as a function of the robot’s perceived expertise and learning progress. In our algorithmic work, we conceptualize a set of context-specific norms as a reward function and integrate learning from the two teaching methods under a single likelihood-based algorithm, which estimates a reward function that induces policies maximally likely to satisfy the teacher’s intended norms. We compare robot learning under various teacher models and demonstrate that a robot responsive to both teaching methods can learn to reach its goal and minimize norm violations in a navigation task for a grid world. We improve the robot’s learning speed and performance by enabling teachers to give feedback at an abstract level (which rooms are acceptable to navigate) rather than at a low level (how to navigate any particular room).
Explainable Reinforcement Learning in Human-Robot Teams: The Impact of Decision-Tree Explanations on Transparency

Understanding the decisions of AI-driven systems and the rationale behind such decisions is key to the success of the human-robot team. However, the complexity and the "black-box" nature of many AI algorithms create a barrier for establishing such understanding within their human counterparts. Reinforcement Learning (RL), a machine-learning algorithm based on the simple idea of action-reward mappings, has a rich quantitative representation and a complex iterative reasoning process that present a significant obstacle to human understanding of, for example, how value functions are constructed, how the algorithms update the value functions, and how such updates impact the action/policy chosen by the robot. In this paper, we discuss our work to address this challenge by developing a decision-tree based explainable model for RL to make a robot’s decision-making process more transparent. Set in a human-robot virtual teaming testbed, we conducted a study to assess the impact of the explanations, generated using decision trees, on building transparency, calibrating trust, and improving the overall human-robot team’s performance. We discuss the design of the explainable model and the positive impact of the explanations on outcome measures.
A Transformer-based System for Action Spotting in Soccer Videos

Action Spotting in the broadcast soccer game is important to understand salient actions and video summary applications. In this paper, we propose an efficient transformer-based system for action spotting in soccer videos. We first use the multi-scale vision transformer to extract features from the videos. Then we adopt a sliding window strategy to further utilize temporal features and enhanced temporal understanding. Finally, the features are input to NetVLAD++ model to obtain the final results. Our model can learn a hierarchy of robust representations and perform well in the Action Spotting Task of SoccerNet Challenge 2022. Our method achieves excellent results and outperforms the baseline and previous published works.
Image-based polarization detection and material recognition

We demonstrate a single-shot and image-based polarization detection system for material recognition. The Stokes parameters are measured under a single-shot measurement using 4 electrically tunable liquid crystal wave plates, 4 polarizers, and 4 camera modules. The optical principle is introduced and the experiments are performed. We also use a metallic plate and a glass substrate to demonstrate the material recognition. The impact of this study is to provide a practical way in image-based polarization detection in Advanced Driver Assistance Systems for material recognition which could help in driving safety.
A Robust Framework for Governing Blockchain-Based Distributed Ledgers During COVID-19 for Academic Establishments

The existing COVID-19 global epidemic has altered our lifestyles in different manners. It has created the need to always understand our position, which we have been seen in interaction with, as well as other specifics such as our body temp and to prevent the spread of such a viral infection. The above knowledge is especially important for students, as their yield to college tends to increase their occupational exposure. To solve this issue, several control systems must always be incorporated. Humans presently use thermoelectric detectors to search a person's temperature changes, card machines in bus routes to verify educators’ existence, and web platforms in which educators can register the attendance of the student for every class. Humans can use blockchain technology to connect and interact and develop an efficient, translucent COVID-19 tracking system. Throughout this paper, we start debating a fresh distributed ledgers traceability system that guarantees accountability among both students, schools, and the authorities to avoid virus spread and aid in interaction tracing. Utilizing blockchain technology and classroom Identity card, we support the implementation of the ancient nearby.
Characterization of the in-focus droplets in shadowgraphy systems via deep learning-based image processing method

It is important to accurately identify and measure in-focus droplets from shadowgraph droplet images that typically contain a large number of defocused droplets for the research of multiphase flow. However, conventional in-focus droplet identification methods are time-consuming and laborious due to the noise and background illumination in experimental data. In this paper, a deep learning-based method called focus-droplet generative adversarial network (FocGAN) is developed to automatically detect and characterize the focused droplets in shadow images. A generative adversarial network framework is adopted by our model to output binarized images containing only in-focus droplets, and inception blocks are used in the generator to enhance the extraction of multi-scale features. To emulate the real shadow images, an algorithm based on the Gauss blur method is developed to generate paired datasets to train the networks. The detailed architecture and performance of the model were investigated and evaluated by both the synthetic data and spray experimental data. The results show that the present learning-based method is far superior to the traditional adaptive threshold method in terms of effective extraction rate and accuracy. The comprehensive performance of FocGAN, including detection accuracy and robustness to noise, is higher than that of the model based on a convolutional neural network. Moreover, the identification results of spray images with different droplet number densities clearly exhibit the feasibility of FocGAN in real experiments. This work indicates that the proposed learning-based approach is promising to be widely applied as an efficient and universal tool for processing particle shadowgraph images.
A machine and deep learning analysis among SonarQube rules, product, and process metrics for fault prediction

Abstract Background Developers spend more time fixing bugs refactoring the code to increase the maintainability than developing new features. Researchers investigated the code quality impact on fault-proneness, focusing on code smells and code metrics. Objective We aim at advancing fault-inducing commit prediction using different variables, such as SonarQube rules, product, process metrics, and adopting different techniques. Method We designed and conducted an empirical study among 29 Java projects analyzed with SonarQube and SZZ algorithm to identify fault-inducing and fault-fixing commits, computing different product and process metrics. Moreover, we investigated fault-proneness using different Machine and Deep Learning models. Results We analyzed 58,125 commits containing 33,865 faults and infected by more than 174 SonarQube rules violated 1.8M times, on which 48 software product and process metrics were calculated. Results clearly identified a set of features that provided a highly accurate fault prediction (more than 95% AUC). Regarding the performance of the classifiers, Deep Learning provided a higher accuracy compared with Machine Learning models. Conclusion Future works might investigate whether other static analysis tools, such as FindBugs or Checkstyle, can provide similar or different results. Moreover, researchers might consider the adoption of time series analysis and anomaly detection techniques.
Supervised Code-Mixed Data Sentiment Analysis

Sentiment Analysis of data extracted from social media is a very important and necessary task gaining popularity for many years applicable on various fields like opinion mining. Analyzing code-mixed English data is a typical task due to the complication of dataset. Machine Learning procedures has paved the way to handle these datasets with utmost ease and efficiency. In this research paper Sentiment Analysis on code-mixed tweets of English by means of different Machine Learning Classification techniques is implemented after pre-processing the gathered data using Count Vectorizer and Tf-Idf Vectorizer. The classification of a tweet or tweets into subjectivity, polarity and its analysis is our main objective. Supervised learning models like SVM, Decision Tree, Logistic Regression and K-nearest neighbor are instigated for analyzing the tweets and the result based on evaluation metrics like F1-score, and accuracy is used to find the most appropriate and efficient technique.
Adversarial machine learning in IoT from an insider point of view

With the rapid progress and significant successes in various applications, machine learning has been considered a crucial component in the Internet of Things ecosystem. However, machine learning models have recently been vulnerable to carefully crafted perturbations, so-called adversarial attacks. A capable insider adversary can subvert the machine learning model at either the training or testing phase, causing them to behave differently. The vulnerability of machine learning to adversarial attacks becomes one of the significant risks. Therefore, there is a need to secure machine learning models enabling the safe adoption in malicious insider cases. This paper reviews and organizes the body of knowledge in adversarial attacks and defense presented in IoT literature from an insider adversary point of view. We proposed a taxonomy of adversarial methods against machine learning models that an insider can exploit. Under the taxonomy, we discuss how these methods can be applied in real-life IoT applications. Finally, we explore defensive methods against adversarial attacks. We believe this can draw a comprehensive overview of the scattered research works to raise awareness of the existing insider threats landscape and encourages others to safeguard machine learning models against insider threats in the IoT ecosystem.
Greybox XAI: A Neural-Symbolic learning framework to produce interpretable predictions for image classification

Although Deep Neural Networks (DNNs) have great generalization and prediction capabilities, their functioning does not allow a detailed explanation of their behavior. Opaque deep learning models are increasingly used to make important predictions in critical environments, and the danger is that they make and use predictions that cannot be justified or legitimized. Several eXplainable Artificial Intelligence (XAI) methods that separate explanations from machine learning models have emerged, but have shortcomings in faithfulness to the model actual functioning and robustness. As a result, there is a widespread agreement on the importance of endowing Deep Learning models with explanatory capabilities so that they can themselves provide an answer to why a particular prediction was made. First, we address the problem of the lack of universal criteria for XAI by formalizing what an explanation is. We also introduced a set of axioms and definitions to clarify XAI from a mathematical perspective. Finally, we present the Greybox XAI, a framework that composes a DNN and a transparent model thanks to the use of a symbolic Knowledge Base (KB). We extract a KB from the dataset and use it to train a transparent model (i.e., a logistic regression). An encoder–decoder architecture is trained on RGB images to produce an output similar to the KB used by the transparent model. Once the two models are trained independently, they are used compositionally to form an explainable predictive model. We show how this new architecture is accurate and explainable in several datasets.
Small-Scale Robust Digital Recognition of Meters Under Unstable and Complex Conditions

Digital recognition of meters aims to identify numbers in complex environments. Existing methods of digital recognition of meters are dependent on deep networks supported by high-quality large-scale data and features extraction, whereas low-quality small-scale digital datasets are not effective in recognition. Moreover, occlusion of digital images obstructs the extraction of fixed features. Multi-classifier under Feature Engineering (MC-FE) is proposed to solve the above problems. To be specific, MC-FE builds a feature library containing a variety of mainstream features. It directly selects the optimal combination of distinguishing features applicable to the current dataset, rather than using the fixed features. In addition, 10 regression machines integrated by support vector machines are adopted. The respective regression machine determines the probability of every number from 0 to 9. The positioning of the meter is the premise of accurate identification. A multi-layer kernel regression positioning (ML-KRP) is designed to increase the accuracy of meter identification. The results of the experiments on several digital recognition datasets reveal that MC- FE and ML-KRP outperform the state-of-the-arts in digital recognition under this small-scale setting.
A novel machine learning‐based framework for detecting fake Instagram profiles

Summary Recently, there has been a massive rise in the popularity of Instagram, which connects individuals globally and allows videos and images to be uploaded and exchanged, and communicated over social media. Instagram is also an online playground of deceit. The use of filters, lighting, and cunning angles transforms the mundane into something spectacular. Automated spam accounts and fake profiles use this to their malicious advantage for executing attacks targeting high‐profile executives. Creating fake Instagram identities is easy to reproduce the idea of being accepted by many fans on social media. Fake accounts are used in the marketing of fake services and products. This research focused on designing and training a unique neural network model and proposed a new algorithm for detecting automated spam and fake Instagram account profiles. The precision and accuracy of the proposed method were achieved at 93% and 91%, respectively.
Algebraic Function Induced Banach Space Valued Ordinary and Fractional Neural Network Approximations

Here we research the univariate quantitative approximation, ordinary and fractional, of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its Banach space valued high order derivative or fractional derivatives. Our operators are defined by using a density function generated by an algebraic sigmoid function. The approximations are pointwise and of the uniform norm. The related Banach space valued feed-forward neural networks are with one hidden layer. It relies as [14].