Reviewerly/exHarmony huggingface dataset:
DarkSLAM: GAN-assisted Visual SLAM for Reliable Operation in Low-light Conditions

Existing visual SLAM approaches are sensitive to illumination, with their precision drastically falling in dark conditions due to feature extractor limitations. The algorithms currently used to overcome this issue are not able to provide reliable results due to poor performance and noisiness, and the localization quality in dark conditions is still insufficient for practical use. In this paper, we present a novel SLAM method capable of working in low light using Generative Adversarial Network (GAN) preprocessing module to enhance the light conditions on input images, thus improving the localization robustness. The proposed algorithm was evaluated on a custom indoor dataset consisting of 14 sequences with varying illumination levels and ground truth data collected using a motion capture system. According to the experimental results, the reliability of the proposed approach remains high even in extremely low light conditions, providing 25.1% tracking time on darkest sequences, whereas existing approaches achieve tracking only 0.6% of the sequence time.
Sharing and Generating Privacy-Preserving Spatio-Temporal Data Using Real-World Knowledge

Privacy-preserving spatio-temporal data sharing is vital in many machine learning and analysis tasks, such as managing disease spread or tailoring public services to a population's travel patterns. Current methods for data release are insufficiently accurate to provide meaningful utility, and they carry a high risk of deanonymization or membership inference attacks. These limitations and public concern over privacy and data protection has limited the extent to which data is shared. This work presents approaches generating and publishing spatio-temporal data, such as geographic locations and trajectories, with differential privacy. In the first solution, differentially private spatial data is generated using kernel density estimation and a road network-aware approach. In the second solution, a local differentially private mechanism is developed by perturbing hierarchically-structured, overlapping n-grams of trajectory data. Both of the solutions incorporate publicly available information, such as the road network or categories of places of interests, to enhance the utility of the output data without negatively affecting privacy or efficiency. Experiments with real-world data demonstrate that the private data can perform as well as the non-private data in a range of practical data science tasks.
A Novel Time-Stamp-Based Audio Encryption Scheme Using Sudoku Puzzle

An innovative alternative method in the field of security that can enforce safety and security of information has been proposed here by performing audio encryption. Cryptography, a way to secure communications, allows only the sender and the intended recipient of a message to access its contents. Cryptography is derived from the Greek word “Kryptos,” meaning hidden, which is associated with content hiding by encryption. In this paper, we develop a novel time-stamp-based audio encryption method using the mind-cracking number place challenger Sudoku puzzle of higher dimension, i.e., 16 × 16 (popularly known as Super Sudoku) to add a new era in the field of security. In this work, we have developed a Super Sudoku cipher by applying time-stamp in it. Immediately after that, the extracted audio frames of the input audio files are XORed with our developed innovative Cipher to generate encrypted audio frames. Then these encoded audio frames are converted to an encrypted audio file to transmit with safety and security. At the receiver end, by applying the same cipher key, we can extract our original audio file.
Religion and foreign direct investment

This study examines the effect of religion on foreign direct investment (FDI). Using a large sample of directional FDI flows and religious data between 1985 and 2019, we calculate the religious distance between home and host countries and find that FDI flows are smaller for country pairs with greater religious distance. This finding remains intact after a host of variables affecting FDI are controlled. Moreover, the negative effect of religious differences is less pronounced if the host country has higher religious diversity or both countries have a bilateral investment treaty (BIT) in force. Finally, we construct a country-level measure for religiosity and find an asymmetric effect of religiosity on FDI flows. Overall, our study suggests that both religious differences and the level of religiosity play important roles in explaining international FDI flows. (JEL F21, F41, Z12) • We examine the effect of religion on foreign direct investment (FDI). • We use a large sample of directional FDI flows between 1985 and 2019. • We calculate the religious distance between home and host countries. • We find that FDI flows are smaller for country pairs with greater religious distance. • This negative effect is less pronounced if the host country has higher religious diversity.
“Go[ing] Hard...as a Woman of Color”: A Case Study Examining Identity Work within a Performative Dance and Computing Learning Environment

Performing arts computing environments have received little attention in the educational sphere; yet, they offer opportunities for learners to validate their efforts, ideas, and skills through showcasing their work in a public-facing performance. In this work, we explore an out-of-school dance and computing educational program run by the organization, STEM From Dance. The organizational mission is to create an equitable learning experience for young women of color to engage with computing while exposing them to STEM careers. Through an analysis of eleven interviews with youth participants, instructors, and the executive director, we examine how the social, cultural, and political dimensions of the learning environment facilitate identity work in computing and dance. Our findings point to three primary activities used by the organization to promote equity: (1) providing psychological safety through a supportive community environment, (2) meaningfully engaging with learners’ social and cultural context through creative work with constructionist artifacts, and (3) actively promoting identity work as women of color in computing and STEM through both artifact work and community events. Applying the constructs of identity and psychological safety we explore the tensions and synergies of designing for equity in this performing arts and computing learning environment. We demonstrate how the seemingly contradictory elements of a high-stakes performance within a novice learning environment provides unique opportunities for supporting young women of color in computing, making them non-negotiable in the organization’s efforts to promote equity and inclusion. Our work illustrates how attending closely to the sociocultural dimensions in a constructionist learning environment provides lenses for navigating equity, identity work, and support for inclusive computing.
Simulating Realistic Rain, Snow, and Fog Variations For Comprehensive Performance Characterization of LiDAR Perception

For robust object detection on LiDAR data, neural networks have to be trained on diverse datasets that contain many different environmental influences like rain, snow, or fog. To this date, few datasets, with those features, are available while there exist many datasets recorded under perfect weather conditions. Repurposing those datasets by simulating adverse environmental conditions on top of them and training networks with the resulting enhanced datasets, is intended to lead to more robust neural networks. In the following we propose models to realistically simulate the effects of rain, snow, and fog on LiDAR datasets based on physical and empirical fundamentals. Then we parameterize our simulation to best fit real LiDAR data that was captured in those environments, in order to achieve a highly accurate simulation. Finally, the impact of adverse weather on neural network detection performance is demonstrated.
Full-Reference Image Quality Assessment Based on an Optimal Linear Combination of Quality Measures Selected by Simulated Annealing

Digital images can be distorted or contaminated by noise in various steps of image acquisition, transmission, and storage. Thus, the research of such algorithms, which can evaluate the perceptual quality of digital images consistent with human quality judgement, is a hot topic in the literature. In this study, an image quality assessment (IQA) method is introduced that predicts the perceptual quality of a digital image by optimally combining several IQA metrics. To be more specific, an optimization problem is defined first using the weighted sum of a few IQA metrics. Subsequently, the optimal values of the weights are determined by minimizing the root mean square error between the predicted and ground-truth scores using the simulated annealing algorithm. The resulted optimization-based IQA metrics were assessed and compared to other state-of-the-art methods on four large, widely applied benchmark IQA databases. The numerical results empirically corroborate that the proposed approach is able to surpass other competing IQA methods.
Framework for Video Steganography Using Integer Wavelet Transform and JPEG Compression

In today’s world of computers everyone is communicating their personal information through the web. So, the security of personal information is the main concern from the research point of view. Steganography can be used for the security purpose of personal information. Storing and forwarding of embedded personal information specifically in public places is gaining more attention day by day. In this research work, the Integer Wavelet Transform technique along with JPEG (Joint Photograph Expert Group) compression is proposed to overcome some of the issues associated with steganography techniques. Video cover files and JPEG compression improve concealing capacity because of their intrinsic properties. Integer Wavelet Transform is used to improve the imperceptibility and robustness of the proposed technique. The Imperceptibility of the proposed work is analyzed through evaluation parameters such as PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), SSIM (Structure Similarity Metric), and CC (Correlation Coefficient). Robustness is validated through some image processing attacks. Complexity is calculated in terms of concealing and retrieval time along with the amount of secret information hidden.
Why VR Games Sickness? An Empirical Study of Capturing and Analyzing VR Games Head Movement Dataset

Virtual reality (VR) technology is gaining popularity in a variety of fields, including education, games, movies, medicine, and engineering, to name a few. 360$^\circ$∘ VR video could provide an immersive experience and attract more researchers’ and developers’ attention. Some literature focused on the head movement when users watched 360$^\circ$∘ videos and released head tracking datasets. With the popularity of VR games, how the game contexts influence players’ head movement and the effect of head movement on VR sickness is a topic worth studying. In this article, we collected a head movement dataset of 30 participants while playing five different VR games (Aircar, Beat Saber, Moss, Arizona Sunshine, and SUPERHOT), and the participants filled the Simulator Sickness Questionnaire (SSQ) after playing VR games. Finally, we analyzed the SSQ scores and the impact of VR games on VR sickness. To the best of our knowledge, this is the first available head movement trajectory dataset based on playing several types of VR games.
Multifeature Fusion Keyword Extraction Algorithm Based on TextRank

Keyword extraction is the predecessor of many tasks, and its results directly affect search, recommendation, classification, and other tasks.In this study, we take Chinese text as the research object and propose a multi-feature fusion keyword extraction algorithm combined with BERT semantics and K-Truss graph(BSKT).The BSKT algorithm is based on the TextRank algorithm, which combines BERT semantic features, K-Truss features, and other features.First, the BSKT algorithm obtains the word vectors from the BERT pretraining model to calculate the semantic difference, which is used to optimize the iterative process of the TextRank word graph.Then, the BSKT algorithm obtains its K-Truss graph by decomposing the TextRank word graph and obtains the truss level feature of the word.Finally, by combining the word IDF and truss level features, the BSKT algorithm scores the words to extract keywords.Experimental results show that the BSKT algorithm achieves better performance than the latest keyword extraction algorithm SCTR in the task of extracting 1-10 keywords.Furthermore, the increment in F1 increased by 11.2% when the BSKT algorithm was used to extract three keywords from the Sensor dataset.
The Economic Limits of Bitcoin and Anonymous, Decentralized Trust on the Blockchain

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Quasi-reflection based multi-strategy cuckoo search for parameter estimation of photovoltaic solar modules

To efficiently model, evaluate and track the maximum power point on Photovoltaic (PV) systems, it is of great significance to estimate appropriate model parameters. In this study, a Quasi-Reflection based Multi-Strategy Cuckoo Search algorithm (QRMSCS) is proposed for accurate parameter estimation in different PV systems. In QRMSCS, the Quasi-Reflection operation is introduced as the initialization operation to create a promising search area and improve the convergence speed. Besides, the previous Lévy flight is replaced by a three-period search strategy consisting of multi-dimension learning, Gaussian distribution, and single-dimension learning to ensure search ability and balance the exploration and exploitation. Meanwhile, the adaptive switch parameter Pa is also applied instead of the constant one to improve convergence speed. The optimization of QRMSCS is firstly validated on the benchmark test briefly. Then, the performance of QRMSCS in parameter estimation problems is evaluated in detail through standard PV models and two datasets at different temperature and irradiance. Extensive experimental results prove that QRMSCS is the most robust and superior algorithm compared with other included algorithms. Consequently, QRMSCS is a promising algorithm that can be utilized in future practical applications.
Unsupervised Multiview Distributed Hashing for Large-Scale Retrieval

Multi-view hashing (MvH) learns compact hash code by efficiently integrating multi-view data, and has achieved promising performance in large-scale retrieval task. In real-world applications, multi-view data is often stored or collected in different locations, and learning hash code in such case is more challenging yet less studied. In addition, unsupervised MvHs hardly achieve impressive retrieval performance due to absence of supervision. To fulfill this gap, this paper introduces a novel unsupervised multi-view distributed hashing (UMvDisH) to learn hash code from multi-view data, which is distributed in different nodes of a network. UMvDisH jointly performs latent factor model and spectral clustering to generate latent hash code and pseudo label respectively in each node. The consistency between hash code and pseudo label improves discrimination of hash code. The proposed distributed learning problem is divided into a set of decentralized subproblems by imposing local consistency among neighbor nodes. As such, the subproblems can be solved in parallel, and training time can be reduced. The communication cost is low due to no exchange of training data. Experimental results on four benchmark image datasets including a very large-scale image dataset show that UMvDisH achieves comparable retrieval performance and trains faster than state-of-the-art unsupervised MvHs in the distributed setting.
SRNet: Sparse representation-based network for image denoising

Image denoising is a foundational but important task in image processing. Various traditional methods have been proposed to remove noise from noisy images. Sparse representation (SR) is a representative image denoising method. Although with good denoising effects, sparse representation suffers from performance bottlenecks and large time consumption. Recently, deep learning has demonstrated excellent ability in image denoising. Therefore, we consider combining the sparse representation with deep learning to make this traditional model more effective and efficient. The sparse representation-based network (SRNet) is proposed by embedding the convolutional neural network (CNN) into the sparse representation framework, in which all the parameters are learned by training. The iterative optimization process of sparse coding is unrolled into a network with several similar phases. In each phase, two subnetworks are designed with the multiscale residual block (MSR-block) to model the updating of sparse coefficient and image, respectively. The experimental results show that compared with the traditional sparse representation denoising methods, the proposed method can significantly reduce time consumption and improve denoising performance, especially in terms of reconstructing textures. The PyTorch code and models of the proposed SRNet can be released at https://github.com/JiechaoSheng/SRNet .
Financial Fraud Detection and Prediction in Listed Companies Using SMOTE and Machine Learning Algorithms

This paper proposes a new method that can identify and predict financial fraud among listed companies based on machine learning. We collected 18,060 transactions and 363 indicators of finance, including 362 financial variables and a class variable. Then, we eliminated 9 indicators which were not related to financial fraud and processed the missing values. After that, we extracted 13 indicators from 353 indicators which have a big impact on financial fraud based on multiple feature selection models and the frequency of occurrence of features in all algorithms. Then, we established five single classification models and three ensemble models for the prediction of financial fraud records of listed companies, including LR, RF, XGBOOST, SVM, and DT and ensemble models with a voting classifier. Finally, we chose the optimal single model from five machine learning algorithms and the best ensemble model among all hybrid models. In choosing the model parameter, optimal parameters were selected by using the grid search method and comparing several evaluation metrics of models. The results determined the accuracy of the optimal single model to be in a range from 97% to 99%, and that of the ensemble models as higher than 99%. This shows that the optimal ensemble model performs well and can efficiently predict and detect fraudulent activity of companies. Thus, a hybrid model which combines a logistic regression model with an XGBOOST model is the best among all models. In the future, it will not only be able to predict fraudulent behavior in company management but also reduce the burden of doing so.
From Artifact Removal to Super-Resolution

Deep-learning-based super-resolution methods have been extensively studied and achieved significant performance with deep convolutional neural networks. However, the results still suffer from the ringing effect, especially in satellite image super-resolution tasks, due to the loss of image details in the satellite degradation process. In this paper, we build a novel satellite super-resolution framework by decomposing a high-resolution image into three components, i.e., low-resolution, artifact, and high-frequency information. Specifically, we propose an artifact removal network with a self-adaption difference convolution (SDC) to fully exploit the structure prior in the low-resolution image and predict the artifact map. Considering that the artifact map and the high-frequency map share a similar pattern, we introduce the supervised structure correction block (SSC) that establishes a bridge between the high-frequency generation process and the artifact removal process. Experimental results on satellite images demonstrate that the proposed method owns an improved tradeoff between the performance and the computational cost compared to existing state-of-the-art satellite and natural super-resolution methods. The source code is available at https://github.com/jiaming-wang/ARSRN.
Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

One of the fast-growing disease affecting women's health seriously is breast cancer.It is highly essential to identify and detect breast cancer in the earlier stage.This paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer accurately.Deep learning algorithms are fully automatic in learning, extracting, and classifying the features and are highly suitable for any image, from natural to medical images.Existing methods focused on using various conventional and machine learning methods for processing natural and medical images.It is inadequate for the image where the coarse structure matters most.Most of the input images are downscaled, where it is impossible to fetch all the hidden details to reach accuracy in classification.Whereas deep learning algorithms are high efficiency, fully automatic, have more learning capability using more hidden layers, fetch as much as possible hidden information from the input images, and provide an accurate prediction.Hence this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram images.The performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.
Digital Certifications in Moroccan Universities: Concepts, Challenges, and Solutions

Nowadays, many Moroccan universities are working on digitizing their certifications. Digital certificates (e.g., official academic transcript, registration certificate, diploma) are secure by digital signatures provided by Barid eSign. To have a certificate, the student needs to send a request. After successful verification and validation by the administrator, the document desired will be signed by the university’s heads and sent to the student’s account. All signed documents are stored on a central server for authenticity verification. Herein, we overview the Moroccan universities’ system of digital certifications. We then discuss its benefits and challenges. Finally, we propose a decentralized digital certifications model based on blockchain technology.