Reviewerly/exHarmony huggingface dataset:
AI-Based Deep Learning Chatbot for Career and Personal Mentorship

Mentoring is an inseparable part of any student’s life. As the level of education increases, every student requires support and guidance regarding career choice, program and major selection, as well as individual and personal counseling. The proposed system has been made to meet this facility, where a student can express themselves anonymously, and without any criticism and partiality. The chat-bot will be used to interact with the student regarding their professional and personal life both, which will solve their issues and doubts based on the questions they have asked. The student will input their query through text, and the bot will generate an output best suited in the student’s interest. The proposed system created can provide the students with an alternative to hectic and physical mentoring sessions.
Handling Class Imbalance Problem using Oversampling Techniques for Breast Cancer Prediction

As a primary cause of death, breast cancer requires the highest care. Using machine learning models, recent next-generation sequencing methods capable of recording gene expression data have been utilized successfully to diagnose breast cancer. But the class imbalance issue affected the diagnostic outcomes of classification models. This issue is the source of the diagnostic errors. The diagnostic results have an impact on the lives of patients. Using oversampling techniques, this issue is resolved by balancing the samples of minority and majority classes. This paper examines the performance of all eight classification models with and without oversampling techniques using different metrics like precision, recall, and accuracy. The gene expression dataset, which contains samples from four molecular subtypes of breast cancer and one class of normal (non-cancer) samples, is used. It is publicly accessible on the TCGA-BRCA portal. Also, this study explores five oversampling strategies (Random, SMOTE, Borderline-SMOTE, SVMSMOTE, and SMOTENC) and their impact on model performance as a result of how they handle data imbalance problems.
Prediction of Solar Power Generation and Maintenance Activities for 1MW Power Plant

Solar energy is going to play a crucial role in a green and clean environment by reducing pollution and global warming. Solar power generation forecasting is very important for the energy market and smart grids. The semi-structured data set was acquired from our university’s 1-Megawatt solar power facility. The data set was having twelve structured and one unstructured column. Manual entries concerning maintenance tasks and weather data are included in the unstructured column. With the help of weather parameters and maintenance operations, the unstructured column is utilized to generate new features. Random forest regressor, XG-boost, Naive Bayesian, univariate LSTM, and moving average models are applied to predict solar power generation using this data set. XG-boost and random forest regression models have less RMSE and MAE as compared to Naive Bayesian and univariate LSTM. The tree-based approaches work well with time-series data which has exogenous variables. The maintenance activities are also predicted in this research work. It is very difficult to predict the module cleaning in advance.
Decentralized Framework for Collection and Secure Storage of Google Street View Data: Case Study

Image data plays a pivotal role in the current data-driven era, particularly in applications such as computer vision, object recognition, and facial identification. Google Maps <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> stands out as a widely used platform that heavily relies on street view images. To fulfill the pressing need for an effective and distributed mechanism for image data collection, we present a framework that utilizes smart contract technology and open-source robots to gather street-view image sequences. The proposed framework also includes a protocol for maintaining these sequences using a private blockchain capable of retaining different versions of street views while ensuring the integrity of collected data. With this framework, Google Maps <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> data can be securely collected, stored, and published on a private blockchain. By conducting tests with actual robots, we demonstrate the feasibility of the framework and its capability to seamlessly upload privately maintained blockchain image sequences to Google Maps <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> using the Google Street View <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Publish API.
A Retinex Prior to Multi-Scale Fusion for Single Image Dehazing

This Image-Dehazing paper proposes to combine the Multi-Scale Fusion technique with the Retinex Algorithm. The paper proposes to extract reflectance matrices and incorporate them into the multi-scale fusion algorithm. The technique proposed aims to reduce the halo effect observed in image-dehazing applications and related works for heavily hazy images. Moreover, an improvement in the quality of the output using the proposed novel algorithm is observed. Quantitative, as well as a visual display of results, using the DENSE HAZE dataset, give an accurate interpretation of the effectiveness of the proposed work. The best value of Structural Similarity Index (SSIM) obtained is 0.9128 which shows a 62% increase in image quality as compared to average SSIM values of previously known methods. The Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) show improvement by 78% (TT Playroom) and 95% (Castle) respectively. To allow analysis with regards to pixel compression that may have resulted during the process, two No Reference Image Quality Metrics have been also computed.
Analysis of Key Based Cryptographic Algorithms and its Applications

Information security is the process of safeguarding data. It safeguards its availability, privacy, and integrity. The capacity to secure data from attacks, as well as efficiency and speed are the two key features that separate one cryptography algorithm from another. Security is the most difficult issue in the present world, and the various security dangers in data security must be avoided in order to provide consumers with more privacy while still permitting high information availability and Integrity. Data encryption employing various data encryption techniques will increase the security of data transmission. This paper primarily focuses on a comparative analysis of symmetric algorithms (AES, DES, Ceaser Cipher, Stream Cipher) and asymmetric algorithms (Diffie Hellman, RSA), and also file and image encryption using symmetric algorithms.
Progressive Web Apps to Support (Critical) Systems in Low or No Connectivity Areas

Web applications are popular in our world today and every organization or individual either build or access at least one each day. It’s important for every application user to continue accessing contents of a web application irrespective of the network connectivity and user’s location. Previous researchers have tried to solve this problem by using the browser’s cache to make the web application work offline. Although this approach has worked, it lacks the aspect of permitting a user to send information to the server when offline. This work proposed an architecture to solve this by sending essential data through an SMS platform. A Minimum Viable Product (MVP) that fits into the proposed architecture was implemented leveraging on service worker to detect an offline application and send the data through SMS. The information is retrieved by a webhook and sent to the webserver. The result of this research validates that the proposed architecture can be used to send data when a web application is offline. The research also recommends areas where further work can be explored to improve on this solution.
A Multithreaded Algorithm for the Computation of Sample Entropy

Many popular entropy definitions for signals, including approximate and sample entropy, are based on the idea of embedding the time series into an m-dimensional space, aiming to detect complex, deeper and more informative relationships among samples. However, for both approximate and sample entropy, the high computational cost is a severe limitation. Especially when large amounts of data are processed, or when parameter tuning is employed premising a large number of executions, the necessity of fast computation algorithms becomes urgent. In the past, our research team proposed fast algorithms for sample, approximate and bubble entropy. In the general case, the bucket-assisted algorithm was the one presenting the lowest execution times. In this paper, we exploit the opportunities given by the multithreading technology to further reduce the computation time. Without special requirements in hardware, since today even our cost-effective home computers support multithreading, the computation of entropy definitions can be significantly accelerated. The aim of this paper is threefold: (a) to extend the bucket-assisted algorithm for multithreaded processors, (b) to present updated execution times for the bucket-assisted algorithm since the achievements in hardware and compiler technology affect both execution times and gain, and (c) to provide a Python library which wraps fast C implementations capable of running in parallel on multithreaded processors.
AI-Enabled Consensus Algorithm in Human-Centric Collaborative Computing for Internet of Vehicle

With the enhanced interoperability of information among vehicles, the demand for collaborative sharing among vehicles increases. Based on blockchain, the classical consensus algorithms in collaborative IoV (Internet of Vehicle), such as PoW (Proof of Work), PoS (Proof of Stake), and DPoS (Delegated Proof of Stake), only consider the node features, which is hard to adapt to the immediacy and flexibility of vehicles. On the other hand, classical consensus algorithms often require mass computing, which undoubtedly increases the communication overhead, resulting in the inability to achieve collaborative IoV under asymmetric networks. Therefore, proposing a low failure rate consensus algorithm that takes into account running time and energy consumption becomes a major challenge in IoV applications. This paper proposes an AI-enabled consensus algorithm with vehicle features, combining vehicle-based metrics and neural networks. First, we introduce vehicle-based metrics such as vehicle online time, performance, and behavior. Then, we propose an integral model and a hierarchical classification method, which combine with a BP neural network to obtain the optimal solution for interconnection. Among them, we also use Informer to predict the future online duration of vehicles, which effectively solves the situation that the primary node vehicle drops off in collaborative IoV. Finally, the experimentations show that the vehicle-based metrics eliminate the problem of the primary node vehicle being offline, which realizes the collaborative IoV considering vehicle features. Meanwhile, it reduces the vehicle network system delay and energy consumption.
Realtime Vehicle Tracking Method Based on YOLOv5 + DeepSORT

In actual traffic scenarios, the environment is complex and constantly changing, with many vehicles that have substantial similarities, posing significant challenges to vehicle tracking research based on deep learning. To address these challenges, this article investigates the application of the DeepSORT (simple online and realtime tracking with a deep association metric) multitarget tracking algorithm in vehicle tracking. Due to the strong dependence of the DeepSORT algorithm on target detection, a YOLOv5s_DSC vehicle detection algorithm based on the YOLOv5s algorithm is proposed, which provides accurate and fast vehicle detection data to the DeepSORT algorithm. Compared to YOLOv5s, YOLOv5s_DSC has no more than a 1% difference in optimal mAP0.5 (mean average precision), precision rate, and recall rate, while reducing the number of parameters by 23.5%, the amount of computation by 32.3%, the size of the weight file by 20%, and increasing the average processing speed of each image by 18.8%. After integrating the DeepSORT algorithm, the processing speed of YOLOv5s_DSC + DeepSORT reaches up to 25 FPS, and the system exhibits better robustness to occlusion.
MAL-Net: Multiscale Attention Link Network for accurate eye center detection

In recent years, due to wide potential applications like human–computer interactions, eye center detection has received growing research interest. Although numerous approaches have been proposed, achieving high accuracy in the wild remains challenging because of the variations in appearance, shape, and illumination. In this paper, we formulate the eye center detection problem as an approximate segmentation task. We propose a novel method to detect pupil positions from a face image using a deep neural network with synthetic data. In particular, we introduce a deep neural network named Multiscale-Attention-Link Network (MAL-Net), where we design a Link Attention Module (LAM) and a novel Multiscale Link Structure (MLS) for accurate and robust eye center detection. Besides, a weighted loss is proposed to make the deep model pay more attention to eye centers during training. Furthermore, to address the problem of insufficient training data with enough variations in eye shape and illumination, we propose a GAN-based method named shape-GAN to generate synthetic eye images with various shapes for training. The proposed MAL-Net is evaluated on widely-used benchmarks such as TFV, GI4E, and BioID. The results demonstrate that our proposed method outperforms state-of-the-art methods for eye center detection.
Light can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Spot Light

With the development of machine learning models throughout industries, there has been a corresponding increase in research demonstrating its vulnerability to adversarial examples (AE). Realizing physically robust AE that survive real-world environmental conditions faces the challenges such as varied viewing distances or angles. Laser beam-based methods claim to overcome the obviousness, semi-permanence, and unchangeable drawbacks of adversarial patches. However, laser beam-based AE could not be captured by the camera under daylight, which makes the application scenarios limited. In this research, we introduce Adversarial Spot Light (AdvSL), a novel approach that enables adversaries to build physically robust real-world AE by utilizing spotlight flashlights. Since the spotlight flashlights may be switched on and off as required, AdvSL allows adversaries to perform more flexible attacks than adversarial patches. Especially, AdvSL is feasible in a variety of ambient light conditions. As a first step, we modeled a spot light with a set of parameters that can be physically controlled by the adversary. To determine the optimal parameters for the light, a heuristic optimization approach is adopted. Further, we use the k-random-restart technique to prevent the approach from being stuck in a local optimum. To demonstrate the effectiveness of the proposed approach, we conduct experiments under different physical conditions, including indoor and outdoor tests. In the digital test, AdvSL causes misclassifications on state-of-the-art neural networks with up to 93.7% attack success rate. In the outdoor test, AdvSL causes misclassifications on the traffic sign classification model with up to 84% attack success rate. In the physical setting, experiments show that the AdvSL is robust in non-bright settings and is feasible in bright settings. Finally, we discuss the defense of AdvSL and evaluate an adaptive defender using adversarial learning, which is able to reduce the attack success rate from 92.2% to 54.8% in the digital.
Issue 113C: A Note from the Editor in Chief

The use of social media has become so popular that people share photos every day on them. Automatic face recognition and tagging of people’s photos have caused privacy preservation issues and some methods have been proposed for hiding the identity of presented people in these images. Blurring and blacking the face area, adding physical adversarial patches to the face, and adding adversarial masks are some proposed methods for this purpose. However, these methods particularly suffer from dissimilarity of the input and output images and inadequate performance in identity concealment from automatic face recognition (AFR) systems. In this paper, we propose the Generative Mask-guided Face Image Manipulation (GMFIM) model based on Generative Adversarial Networks (GANs) to apply imperceptible edits to the input face image to preserve the identity of the person in the image. Our model consists of a face mask module, a GAN-based optimization module, and a merge module. Different criteria are considered in the objective function of the optimization step to produce high-quality images that are as similar as possible to the input image while they cannot be recognized by AFR systems. The results of the experiments on different datasets show that our model provides promising results in terms of the quality of the generated images and the identity concealment performance.
A way to build sound-based quantum computers

A few languages are unlike any of the other tongues in use today. They hold clues to the rules of language and even our experience of reality, finds Andrea Valentino
Towards a better understanding of the mechanics of refactoring detection tools

Refactoring is a crucial practice used by many developers, available in popular IDEs, like Eclipse. Moreover, refactoring detection tools, such as RefDiff and RefactoringMiner, help improve the comprehension of refactoring application changes. In this article, we better understand to what extent refactoring detection tools (RefDiff and RefactoringMiner) identify refactoring operations that developers apply in practice. We survey with 53 developers of popular Java projects on GitHub. We asked them to identify six refactoring transformations applied to small programs. There is no unanimity in all questions of our survey. Refactoring detection tools do not detect many refactoring operations expected by developers. In 4 out of 6 questions, most developers prefer the Eclipse refactoring mechanics. The results highlight the importance of diving deep into the refactoring mechanics and defining a baseline. Empirical studies focused on mining refactoring operations may be limited by an incomplete or unrepresentative sample of such operations, thus posing a challenge for researchers in this field.
Machine learning based prediction of phase ordering dynamics

Machine learning has proven exceptionally competent in numerous applications of studying dynamical systems. In this article, we demonstrate the effectiveness of reservoir computing, a famous machine learning architecture, in learning a high-dimensional spatiotemporal pattern. We employ an echo-state network to predict the phase ordering dynamics of 2D binary systems-Ising magnet and binary alloys. Importantly, we emphasize that a single reservoir can be competent enough to process the information from a large number of state variables involved in the specific task at minimal computational training cost. Two significant equations of phase ordering kinetics, the time-dependent Ginzburg-Landau and Cahn-Hilliard-Cook equations, are used to depict the result of numerical simulations. Consideration of systems with both conserved and non-conserved order parameters portrays the scalability of our employed scheme.
Remaking Everyday Objects for Physical Computing

We are surrounded by objects that have been designed and made for a wide range of purposes. Alongside the development of specialized electronic devices, we can look to these objects as a functional resource for tangible computing. By deconstructing such everyday objects and uncovering their structures, they become a material that can be remade into new physical interactive systems.
Robust quantum circuit for clique problem with intermediate qudits

Clique problem has a wide range of applications due to its pattern matching ability. There are various formulations of clique problem like k-clique problem, maximum clique problem, etc. The k-Clique problem determines whether an arbitrary network has a clique or not whereas maximum clique problem finds the largest clique in a graph. It is already exhibited in the literature that the k-clique or maximum clique problem (NP-problem) can be solved asymptotically faster by using quantum algorithms compared to conventional computing. Quantum computing with higher dimensions is gaining popularity due to its large storage capacity and computation power. In this article, we have shown an improved quantum circuit implementation for the k-clique problem and maximum clique problem (MCP) with the help of higher-dimensional intermediate temporary qudits for the first time to the best of our knowledge. The cost of the state-of-the-art quantum circuit for the k-clique problem is colossal due to a huge number of n-qubit Toffoli gates. We have exhibited an improved cost and depth over the circuit by applying a generalized n-qubit Toffoli gate decomposition with intermediate ququarts (4-dimensional qudits).
GAMA: Geometric analysis based motion-aware architecture for moving object segmentation

Moving object segmentation in real-world scenes is of critical significance for many computer vision applications. However, there are many challenges in moving object segmentation. It is difficult to distinguish objects with motion degeneracy. Besides, complex scenes and noisy 2D optical flows also effect the result of moving object segmentation. In this paper, to address difficulties caused by motion degeneracy, we analyze the classic motion degeneracy from a new geometric perspective. To identify objects with motion degeneracy, we propose a reprojection cost and an optical flow contrast cost which are fed into the network to enrich motion features. Furthermore, a novel geometric constraint called bidirectional motion constraint is proposed to detect moving objects with weak motion features. In order to tackle more complex scenes, we also introduce a motion-aware architecture to predict instance masks of moving objects. Extensive experiments are conducted on the KITTI dataset, the JNU-UISEE dataset and the KittiMoSeg dataset, and our proposed method achieves excellent performance.
Analysis on the inherent noise tolerance of feedforward network and one noise-resilient structure

In the past few decades, feedforward neural networks have gained much attraction in their hardware implementations. However, when we realize a neural network in analog circuits, the circuit-based model is sensitive to hardware nonidealities. The nonidealities, such as random offset voltage drifts and thermal noise, may lead to variation in hidden neurons and further affect neural behaviors. This paper considers that time-varying noise exists at the input of hidden neurons, with zero-mean Gaussian distribution. First, we derive lower and upper bounds on the mean square error loss to estimate the inherent noise tolerance of a noise-free trained feedforward network. Then, the lower bound is extended for any non-Gaussian noise cases based on the Gaussian mixture model concept. The upper bound is generalized for any non-zero-mean noise case. As the noise could degrade the neural performance, a new network architecture is designed to suppress the noise effect. This noise-resilient design does not require any training process. We also discuss its limitation and give a closed-form expression to describe the noise tolerance when the limitation is exceeded.