Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Reversible Data Hiding for Securing Medical Images

Medical images carry a lot of important information for making a medical diagnosis. Since the medical images need to be communicated frequently to allow timely and accurate diagnosis, it has become a target for malicious attacks. Hence, medical images are protected through encryption algorithms. Recently, reversible data hiding on the encrypted images (RDHEI) schemes are employed to embed private information into the medical images. This allows effective and secure communication, wherein the privately embedded information (e.g., medical records and personal information) is very useful to the medical diagnosis. However, existing RDHEI schemes still suffer from low embedding capacity, which limits their applicability. Besides, such solution still lacks a good mechanism to ensure its integrity and traceability. To resolve these issues, a novel approach based on image block-wise encryption and histogram shifting is proposed to provide more embedding capacity in the encrypted images. The embedding rate is over 0.8 bpp for typical medical images. On top of that, a blockchain-based system for RDHEI is proposed to resolve the traceability. The private information is stored on the blockchain together with the hash value of the original medical image. This allows traceability of all the medical images communicated over the proposed blockchain network.
Medical Image Encryption Based on 2D Zigzag Confusion and Dynamic Diffusion

For the security of medical image, a new algorithm of medical image encryption is designed. The novel algorithm is based on a chaotic system composed of the two-dimensional Sine Logistic modulation map (2D-SLMM) and the two-dimensional Hénon-Sine map (2D-HSM). The main encryption procedure includes zigzag scan scramble, pixel grey value transformation, and dynamic diffusion. On the pixel grey value transformation stage, a password feedback is added. This makes the relationship between password and key more complicated. The proposed scheme is lossless for medical image encryption and decryption. It avoids the problems of low-dimensional chaotic map such as narrow interval and few parameters, as well as the problem of the special texture and contour of medical images. The key space of the novel algorithm is big enough, and the encryption and decryption processing are sensitive to the key. Simulation and experiments validate the effectiveness and efficiency of the novel algorithm. Security analysis proves the algorithm is resistant to common attacks.
A fast and accurate circle detection algorithm based on random sampling

Circle detection in digital images is an important problem in computer vision, pattern recognition, and artificial intelligence. However, the common circle detection strategies, including random sample consensus, randomized Hough transform, and randomized circle detection, have a very low sampling efficiency and thus a slow detection speed, owing to aimless random sampling. This paper proposes a fast and accurate randomized circle detection algorithm, with the aim to improve the speed and accuracy of circle detection based on random sampling. The proposed algorithm mainly focuses on four aspects: calculating circle parameters, determining candidate circles, searching for true circle, and improving detection accuracy. To verify the effectiveness of our algorithm, contrastive experiments were conducted on lots of synthetic and real images. The results show that our algorithm achieved much higher detection speed and accuracy than random sample consensus, randomized Hough transform, and randomized circle detection, and realized similar robustness as the three contrastive strategies. The research ideas can also be applied to ellipse detection.
The Systematic Review of K-Means Clustering Algorithm

Recently, the world is experiencing generating the huge amount of data in different domains. Data mining and Data analytics and are the practices used for analyzing data and extracting hidden knowledge. One of the major data mining methods which is used to analysis of data is data clustering. Data clustering ease the extract information from each cluster separately. There are many algorithms used to perform the clustering. One of the most famous algorithms which is used for clustering for more than half a century is k-means. By many optimization and enhancement, K-means still considers as the most popular clustering algorithm which is still being used in various domains. This research attempts to conduct a Systematic literature Review (SLR) to collect, classify, and analyze the primary studies about the different version of k-means clustering algorithm. This SLR gives a means of finding, appraising, and interpreting existing researches pertinent to the topic. By narrowing down the crucial sections of debate, we are hoping to establish a foundation for upcoming researches.
SafeMobility: An IoT- based System for safer mobility using machine learning in the age of COVID-19

In the face of the COVID-19 pandemic and the absence of a vaccine or an effective treatment against the virus, the available studies show that today, the most effective measure for prevention continues to be social distancing. In this sense, in this article, we focus implementing an IoT-based System for safer mobility in the age of COVID-19 using machine learning called SafeMobility. This system has been designed to monitor in real-time the social distancing between people and control the capacity in common interior spaces via a multilayer architecture that integrates IoT, fog, and cloud solutions. To control the capacity safely, we have detected the location of people using machine learning models. We have trained and evaluated these models from a data set containing the RSSI signals of the different surrounding WiFi networks obtained via a portable IoT device. Besides, this portable device integrated with a high precision laser sensor has also been used to detect the distance between people, thus avoiding potential infections. Also, we have exploited the advantages of fog computing to perform data processing and analysis in a fog node using the machine learning model that presented the highest accuracy in the evaluation. In case of non-compliance with the allowed social distance or the established peak capacity, alert messages are sent via a lightweight and optimal protocol in using IoT applications. A web application hosted on a cloud server receives the information from the fog node in real-time and dynamically displays the congestion sites in the environment. Our experiments demonstrate the effectiveness of the system to determine the position of the people with an accuracy of 91%.
Eat: Enhanced ASR-TTS for Self-Supervised Speech Recognition

Self-supervised ASR-TTS models suffer in out-of-domain data conditions. Here we propose an enhanced ASR-TTS (EAT) model that incorporates two main features: 1) The ASR→TTS direction is equipped with a language model reward to penalize the ASR hypotheses before forwarding it to TTS. 2) In the TTS→ASR direction, a hyper-parameter is introduced to scale the attention context from synthesized speech before sending it to ASR to handle out-of-domain data. Training strategies and the effectiveness of the EAT model are explored under out-of-domain data conditions. The results show that EAT reduces the performance gap between supervised and self-supervised training significantly by absolute 2.6% and 2.7% on Librispeech and BABEL respectively.
A Cloud Secure Storage Mechanism Based on Data Dispersion and Encryption

Cloud storage service has shown its great power and wide popularity which provides fundamental support for rapid development of cloud computing.However, due to management negligence and malicious attack, there still lie enormous security incidents that lead to quantities of sensitive data leakage at cloud storage layer.From the perspective of protecting cloud data confidentiality, this paper proposed a Cloud Secure Storage Mechanism named CSSM.To avoid data breach at the storage layer, CSSM integrated data dispersion and distributed storage to realize encrypted, chucked and distributed storage.In addition, CSSM adopted a hierarchical management approach and combined user password with secret sharing to prevent cryptographic materials leakage.The experimental results indicate that proposed mechanism is not only suitable for ensuring the data security at storage layer from leakage, but also can store huge amount of cloud data effectively without imposing too much time overhead.For example, when users upload/download 5G sized file with CSSM, it only takes 646seconds/269seconds, which is acceptable for users.
Optimal control for quantum metrology via Pontryagin's principle

Quantum metrology comprises a set of techniques and protocols that utilize quantum features for parameter estimation which can in principle outperform any procedure based on classical physics. We formulate the quantum metrology in terms of an optimal control problem and apply Pontryagin's Maximum Principle to determine the optimal protocol that maximizes the quantum Fisher information for a given evolution time. As the quantum Fisher information involves a derivative with respect to the parameter which one wants to estimate, we devise an augmented dynamical system that explicitly includes gradients of the quantum Fisher information. The necessary conditions derived from Pontryagin's Maximum Principle are used to quantify the quality of the numerical solution. The proposed formalism is generalized to problems with control constraints, and can also be used to maximize the classical Fisher information for a chosen measurement.
Correlating clearness index with cloud cover and other meteorological parameters for forecasting the global solar radiation over Morocco

In this study, new correlations between clearness index and cloud cover without and/or with other meteorological parameters were proposed for estimating the monthly average daily global solar radiation (MADGSR) on a horizontal surface. By using a long-term of continuously observed meteorological data at fourteen cities over Morocco, seventeen new correlation models were established, evaluated and compared with the other seven cloud-based models selected from the literature. The statistical analysis was done based on the coefficient of determination (R²), mean bias error (MBE), mean square error (MSE), root mean square error (RMSE), standard deviation (σ) and performance score (φ). Results revealed that the new proposed model, which combines the clearness index with the cloud cover and all other meteorological parameters, has a good prediction level of the MADGSR for the considered Moroccan cities. The accuracy of the newly developed model may recommend it to be employed in the future design and assessment of the performance of different solar energy applications in Morocco and in other locations with similar climatic conditions.
Public-key encryption with bidirectional keyword search and its application to encrypted emails

A traditional Public-key Encryption scheme with Keyword Search (PEKS) allows multiple senders to encrypt keywords under the public key of a receiver such that the receiver can search on these encrypted keywords using his/her searching secret key. In encrypted email systems, an email user not only needs to search on encrypted emails received from other users, but also needs to search on encrypted emails sent to other users. Motivated by this, the paper proposes a cryptographic method to allow these two types of user (i.e., senders and receivers) to search on encrypted keywords, which is called Public-key Encryption with Bidirectional Keyword Search (PEBKS). We give formal definitions of a PEBKS scheme and its indistinguishable security model to capture the scenario that no adversary can efficiently distinguish two ciphertexts of keywords from each other, even if the adversary can adaptively obtain search trapdoors of many keywords. Specifically, we propose a concrete PEBKS scheme, whose security relies on a standard hard problem, i.e., bilinear Diffie–Hellman problem, in the random oracle model. Finally, we simulate the proposed PEBKS scheme to assess its practicability and convinces that its feasibility to be applied to encrypted email systems.
Design of Sign Language Recognition Using E-CNN

Most people do not understand sign language, so they need a bridge for the community to be able to communicate with deaf people. Technology that continues to develop and continues to strive to help humans, can be a solution that can be used to create a communication bridge between the community and deaf people, the use of technology that can be used is the use of image processing technology as a translator tool. Image processing can translate images into text. In the implementation of digital image processing, it will use the hand key point library, where the hand key point library is a library that will detect the location of the hand in each image, but as it is known, image processing cannot stand alone as a data processor but requires an algorithm that functions as a classification tool. The Convolutional Neural Network (CNN) algorithm in the Deep Learning method can be a classification tool, with the ability of the Convolutional Neural Network (CNN) to learn several things. And according to several previous studies that combining several algorithms can increase the accuracy value. In this study, a trial of combining CNN models using the Ensemble method has been successfully carried out with the results being able to increase the accuracy value to 99.4%. So that the results of the research can be summarized that using Ensemble can increase the higher accuracy value.
Push &amp; Pull: Transferable Adversarial Examples With Attentive Attack

Targeted attack aims to mislead the classification model to a specific class, and it can be further divided into black-box and white-box targeted attack depending on whether the classification model is known. A growing number of approaches rely on disrupting the image representations to craft adversarial examples. However, this type of methods often suffer from either low <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">white-box targeted</i> attack success rate or poor <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">black-box targeted</i> attack transferability. To address these problems, we propose a Transferable Attentive Attack (TAA) method which adds perturbation to clean images based on the attended regions and features. This is motivated by one important observation that deep-learning based classification models (or even shallow-learning based models like SIFT) make the prediction mainly based on the informative and discriminative regions of an image. Specifically, the corresponding features of the informative regions are firstly extracted, and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">anchor</i> image’s features are iteratively “pushed” away from the source class and simultaneously “pulled” closer to the target class along with attacking. Moreover, we introduce a new strategy that the attack selects the centroids of source and target class cluster as the input of triplet loss to achieve high transferability. Experimental results demonstrate that our method improves the transferability of adversarial example, while maintaining higher success rate for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">white-box targeted</i> attacks compared with the state-of-the-arts. In particular, TAA attacks on image-representation based task like VQA also result in a significant performance drop in terms of accuracy.
TexYZ: Embroidering Enameled Wires for Three Degree-of-Freedom Mutual Capacitive Sensing

In this paper, we present TexYZ, a method for rapid and effortless manufacturing of textile mutual capacitive sensors using a commodity embroidery machine. We use enameled wire as a bobbin thread to yield textile capacitors with high quality and consistency. As a consequence, we are able to leverage the precision and expressiveness of projected mutual capacitance for textile electronics, even when size is limited. Harnessing the assets of machine embroidery, we implement and analyze five distinct electrode patterns, examine the resulting electrical features with respect to geometrical attributes, and demonstrate the feasibility of two promising candidates for small-scale matrix layouts. The resulting sensor patches are further evaluated in terms of capacitance homogeneity, signal-to-noise ratio, sensing range, and washability. Finally, we demonstrate two use case scenarios, primarily focusing on continuous input with up to three degrees-of-freedom.
Commonsense visual sensemaking for autonomous driving – On generalised neurosymbolic online abduction integrating vision and semantics

We demonstrate the need and potential of systematically integrated vision and semantics solutions for visual sensemaking in the backdrop of autonomous driving. A general neurosymbolic method for online visual sensemaking using answer set programming (ASP) is systematically formalised and fully implemented. The method integrates state of the art in visual computing, and is developed as a modular framework that is generally usable within hybrid architectures for realtime perception and control. We evaluate and demonstrate with community established benchmarks KITTIMOD, MOT-2017, and MOT-2020. As use-case, we focus on the significance of human-centred visual sensemaking —e.g., involving semantic representation and explainability, question-answering, commonsense interpolation— in safety-critical autonomous driving situations. The developed neurosymbolic framework is domain-independent, with the case of autonomous driving designed to serve as an exemplar for online visual sensemaking in diverse cognitive interaction settings in the backdrop of select human-centred AI technology design considerations.
Siri, Write the Next Method

Code completion is one of the killer features of Integrated Development Environments (IDEs), and researchers have proposed different methods to improve its accuracy. While these techniques are valuable to speed up code writing, they are limited to recommendations related to the next few tokens a developer is likely to type given the current context. In the best case, they can recommend a few APIs that a developer is likely to use next. We present FeaRS, a novel retrieval-based approach that, given the current code a developer is writing in the IDE, can recommend the next complete method (i.e., signature and method body) that the developer is likely to implement. To do this, FeaRS exploits "implementation patterns" (i.e., groups of methods usually implemented within the same task) learned by mining thousands of open source projects. We instantiated our approach to the specific context of Android apps. A large-scale empirical evaluation we performed across more than 20k apps shows encouraging preliminary results, but also highlights future challenges to overcome.
Linked Data and Cultural Heritage

The cultural heritage sector has traditionally been concerned with sharing resources and furthering human knowledge, with particular interest to the issues associated with metadata and interoperability, especially when it comes to the use of technology. These goals and interests in the cultural heritage sector are natural alignments with those of linked data; hence. there has been an increasing interest in the application of linked data in this sector. This article studies the implementation of linked data in the cultural heritage sector, through a systematic literature review of case studies of linked data implementation projects in this sector. The results reflect on the parties involved, the level of collaboration, and the motivation behind these projects. The study suggests that universities and national institutions were the main players in implementing such technologies in the cultural heritage sector, suggesting that there may be some barriers preventing smaller GLAM institutions from implementing linked data projects. The results further suggest that many linked data projects in this sector were primarily exploratory projects, and often performed in a collaborative manner. They further indicate that the most common motivating factors behind these projects were research needs, a desire to contribute to linked data as a movement, and other specific user needs. Reflecting on this systematic literature review, this article makes a set of recommendations for future work to increase the use of linked data in the cultural heritage sector and to remove barriers to adoption.
Mobile Robot Path Planning Based on Improved Ant Colony Algorithm

Aiming at the problem that traditional ant colony algorithm has path redundancy in mobile robots and is easy to fall into local optimal solutions, an improved ant colony algorithm is designed. First of all, analysis in the process of the ants moving, because they choose the path according to the roulette, the path has redundancy, retreat, and wave-like progress. It is difficult to find the optimal path. To solve this problem, this paper adopts path correction. The method to modify the path to the target point can effectively improve the convergence of the ant colony algorithm, and the optimal path obtained is shorter, while avoiding the pheromone update of some redundant paths from affecting the probability of the ant path selection in the later circle. Aiming at the obtained optimal path, optimize the path node to improve the path smoothness. It is verified by simulation that the improved ant colony algorithm has better convergence than traditional ant colony algorithm, reduces the number of nodes, and is more in line with the actual demands of robot movement.
A Case Study of Onboarding in Software Teams: Tasks and Strategies

Developers frequently move into new teams or environments across software companies. Their onboarding experience is correlated with productivity, job satisfaction, and other short-term and long-term outcomes. The majority of the onboarding process comprises engineering tasks such as fixing bugs or implementing small features. Nevertheless, we do not have a systematic view of how tasks influence onboarding. In this paper, we present a case study of Microsoft, where we interviewed 32 developers moving into a new team and 15 engineering managers onboarding a new developer into their team - to understand and characterize developers' onboarding experience and expectations in relation to the tasks performed by them while onboarding. We present how tasks interact with new developers through three representative themes: learning, confidence building, and socialization. We also discuss three onboarding strategies as inferred from the interviews that managers commonly use unknowingly, and discuss their pros and cons and offer situational recommendations. Furthermore, we triangulate our interview findings with a developer survey (N = 189) and a manager survey (N = 37) and find that survey results suggest that our findings are representative and our recommendations are actionable. Practitioners could use our findings to improve their onboarding processes, while researchers could find new research directions from this study to advance the understanding of developer onboarding. Our research instruments and anonymous data are available at https://zenodo.org/record/4455937#.YCOQCs 0lFd.