Reviewerly/exHarmony huggingface dataset:
Fractional chaotic maps based short signature scheme under human-centered IoT environments

The Internet of Things (IoT) comprises of various smart devices for the sharing of sensed data through online services. People will be directly contacted to check their health parameters and the reports will be collected centrally through smart devices. The requirement is protection of messages during the exchange of data between sender and receiver in order to tackle human malicious attacks. Various signature-based schemes are discussed in the literature to provide secure communication. Smart devices however require lightweight tasks by ensuring critical safety strengths. An important problem in the signature based method is that it incurs more computational expenses for signing and verification process in large numbers. In this study, we introduced an efficient Short Signature Scheme (SSS) that uses Fractional Chaotic Map (FCM) for secure communication in IoT based smart devices, the security of which is closely related to a random oracle based on FCM assumption. In this study, we have designed new short signature scheme using FCM. The presented scheme consist of four sub-algorithm as follows: setup, key generation, signing and verification. We have used less rigorous operations based on the FCM to carry out signing and verification procedures, similar to human signing on valid documents and then verifying them as per witness. The proposed SSS offers a better security assurance than currently established signature schemes. The key advantage of the SSS over the DSA schemes is that at the verification stage and signing period it takes less computation; it retains the degree of protection. The presented SSS takes less bandwidth for storage, communication, and computing resources; particularly applicable to wireless devices and smart cards. We concluded that the uses of fractional chaotic maps is more effective for secure communication in human-centered IoT to present a provably secure short signature technique.
Attacking and Protecting Data Privacy in Edge–Cloud Collaborative Inference Systems

Benefiting from the advance of deep learning (DL) technology, Internet-of-Things (IoT) devices and systems are becoming more intelligent and multifunctional. They are expected to run various DL inference tasks with high efficiency and performance. This requirement is challenged by the mismatch between the limited computing capability of edge devices and large-scale deep neural networks. Edge-cloud collaborative systems are then introduced to mitigate this conflict, enabling resource-constrained IoT devices to host arbitrary DL applications. However, the introduction of third-party clouds can bring potential privacy issues to edge computing. In this article, we conduct a systematic study about the opportunities of attacking and protecting the privacy of edge-cloud collaborative systems. Our contributions are twofold: 1) we first devise a set of new attacks for an untrusted cloud to recover arbitrary inputs fed into the system, even if the attacker has no access to the edge device's data or computations, or permissions to query this system and 2) we empirically demonstrate that solutions that add noise fail to defeat our proposed attacks, and then propose two more effective defense methods. This provides insights and guidelines to develop more privacy-preserving collaborative systems and algorithms.
Accurate and Lightweight Image Super-Resolution With Model-Guided Deep Unfolding Network

Deep neural networks (DNNs) based methods have achieved great success in single image super-resolution (SISR). However, existing state-of-the-art SISR techniques are designed like black boxes lacking transparency and interpretability. Moreover, the improvement in visual quality is often at the price of increased model complexity due to black-box design. In this paper, we present and advocate an explainable approach toward SISR named model-guided deep unfolding network (MoG-DUN). Targeting at breaking the coherence barrier, we opt to work with a well-established image prior named nonlocal auto-regressive model and use it to guide our DNN design. By integrating deep denoising and nonlocal regularization as trainable modules within a deep learning framework, we can unfold the iterative process of model-based SISR into a multi-stage concatenation of building blocks with three interconnected modules (denoising, nonlocal-AR, and reconstruction). The design of all three modules leverages the latest advances including dense/skip connections as well as fast nonlocal implementation. In addition to explainability, MoG-DUN is accurate (producing fewer aliasing artifacts), computationally efficient (with reduced model parameters), and versatile (capable of handling multiple degradations). The superiority of the proposed MoG-DUN method to existing state-of-the-art image SR methods including RCAN, SRMDNF, and SRFBN is substantiated by extensive experiments on several popular datasets and various degradation scenarios.
The effects of negative information-related incidents on social media discontinuance intention: Evidence from SEM and fsQCA

Social media have experienced a decline in their number of users. Understanding the antecedents of users’ discontinuance intention can help social media platforms in mitigating them. It is necessary to enrich the theoretical account of social media discontinuance intention from the aspect of negative information-related incidents. With this realization, underpinned by stimulus–organism–response (SOR) framework, the current study examined the interrelationships of negative information-related incidents, social media fatigue, perceived information overload, and social media discontinuance intention. The proposed hypotheses were verified by analyzing the responses of 328 Weibo users through structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). According to the SEM results, all negative-related information incidents can explain perceived information overload and social media fatigue, which in turn lead to discontinuance intention. The fsQCA findings also confirmed the significance of discontinuance intention determinants and provided the configurations that enhanced discontinuance intention. These findings can assist social media managers in developing unique strategies to address the specific needs of social media users.
A PID-Type Fuzzy Logic Controller-Based Approach for Motion Control Applications

Motion control is widely used in industrial applications since machinery, robots, conveyor bands use smooth movements in order to reach a desired position decreasing the steady error and energy consumption. In this paper, a new Proportional-Integral-Derivative (PID) -type fuzzy logic controller (FLC) tuning strategy that is based on direct fuzzy relations is proposed in order to compute the PID constants. The motion control algorithm is composed by PID-type FLC and S-curve velocity profile, which is developed in C/C++ programming language; therefore, a license is not required to reproduce the code among embedded systems. The self-tuning controller is carried out online, it depends on error and change in error to adapt according to the system variations. The experimental results were obtained in a linear platform integrated by a direct current (DC) motor connected to an encoder to measure the position. The shaft of the motor is connected to an endless screw; a cart is placed on the screw to control its position. The rise time, overshoot, and settling time values measured in the experimentation are 0.124 s, 8.985% and 0.248 s, respectively. These results presented in part 6 demonstrate the performance of the controller, since the rise time and settling time are improved according to the state of the art. Besides, these parameters are compared with different control architectures reported in the literature. This comparison is made after applying a step input signal to the DC motor.
SberQuAD – Russian Reading Comprehension Dataset: Description and Analysis

The paper presents SberQuAD – a large Russian reading comprehension (RC) dataset created similarly to English SQuAD. SberQuAD contains about 50K question-paragraph-answer triples and is seven times larger compared to the next competitor. We provide its description, thorough analysis, and baseline experimental results. We scrutinized various aspects of the dataset that can have impact on the task performance: question/paragraph similarity, misspellings in questions, answer structure, and question types. We applied five popular RC models to SberQuAD and analyzed their performance. We believe our work makes an important contribution to research in multilingual question answering.
Visual Mis/disinformation in Journalism and Public Communications: Current Verification Practices, Challenges, and Future Opportunities

Social media platforms and news organisations alike are struggling with identifying and combating visual mis/disinformation presented to their audiences. Such processes are complicated due to the enormous number of media items being produced, how quickly media items spread, and the often-subtle or sometimes invisible-to-the-naked-eye nature of deceptive edits. Despite knowing little about the provenance and veracity of the visual content they encounter, journalists have to quickly determine whether to re-publish or amplify this content, with few tools and little time available to assist them in such an evaluation. With the goal of equipping journalists with the mechanisms, skills, and knowledge to be effective gatekeepers and stewards of the public trust, this study reviews current journalistic image verification practices, examines a number of existing and emerging image verification technologies that could be deployed or adapted to aid in this endeavour, and identifies the strengths and limitations of the most promising extant technical approaches. While oriented towards practical and achievable steps in combating visual mis/disinformation, the study also contributes to discussions on fact-checking, source-checking, verification, debunking and journalism training and education.
Contextual Chatbot for Healthcare Purposes (using Deep Learning)

As the demand in Machine Learning & AI keeps growing, new technologies will keep coming in the market which will impact our day-to-day activities, and one such technology is Virtual Assistant Bots or simply Chatbots. Chatbots have evolved from being Menu/Button based, to Keywords based and now Contextual based. The most advanced among all of the above is contextual based because it uses Machine Learning and Artificial Intelligence techniques to store and process the training models which help the chatbot to give better and appropriate response when user asks domain specific questions to the bot. In this paper we will be not only discussing about the working of our model but also the applications and relevant work conducted in this domain, also there will be discussion about the challenges and future scope of this technology. For this work, neural networks have been used to train data and various packages which help us in giving better results. In this chatbot we will be integrating the concepts of Natural Language Processing with Deep Learning for getting better results. Healthcare plays a wide role in our daily lives, whenever a person feels sick he/she visits their family doctor or any nearby clinic just to get to know what issues they are facing, in the recent years many companies & institutions have collaborated with hospitals to provide support which can help doctors and medical staff to deal with patients in better way and reduce their efforts with the help of technology. Chatbots can play a major role in reshaping the healthcare industry by providing either predictive diagnosis or any other assistantships like booking an appointment.
The Impact of the COVID-19 on Organizational and Information Systems Agility

The unfortunate COVID-19 pandemic serendipitously might have evoked an era of agility. In information systems development (ISD), agility has been exemplified by agile software development. ISD researchers have proposed that the term agility be operationalized by the ability to create, embrace, and learn from change. The dynamic capabilities approach is similar and focuses on sensing, seizing, and reconfiguring opportunities. Risk and opportunity intelligence, aligned decision-making, IT flexibility, and employee capability are critical antecedents of agility.
New method for fingerprint images encryption using DNA sequence and chaotic tent map

The security of data transfer by the web and network is very important, especially in medicine. So encryption is a challenging and important task in information security. Recently, several algorithms have been presented for image encryption and security based on different methods, specifically based on encryption by DNA sequencing and chaotic systems. In this paper, a hybrid algorithm is presented using the tent chaotic mapping and DNA sequencing techniques. In this method first, the original image and the chaotic mapping are encrypted separately using DNA sequencing. Then, the logical XOR operator is applied to them and the encrypted image is produced using chaotic systems. Finally the average entropy for fingerprint images after encryption was obtained 7.9882. The results obtained from the conducted experiments and computer simulations indicate that the proposed method performs the encryption have a good result. The proposed system has also been evaluated against common attacks and has shown good results.
Fully-Occluded Target Selection in Virtual Reality

The presence of fully-occluded targets is common within virtual environments, ranging from a virtual object located behind a wall to a datapoint of interest hidden in a complex visualization. However, efficient input techniques for locating and selecting these targets are mostly underexplored in virtual reality (VR) systems. In this paper, we developed an initial set of seven techniques techniques for fully-occluded target selection in VR. We then evaluated their performance in a user study and derived a set of design implications for simple and more complex tasks from our results. Based on these insights, we refined the most promising techniques and conducted a second, more comprehensive user study. Our results show how factors, such as occlusion layers, target depths, object densities, and the estimation of target locations, can affect technique performance. Our findings from both studies and distilled recommendations can inform the design of future VR systems that offer selections for fully-occluded targets.
Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-Efficient Cardiac Segmentation

Medical image annotations are prohibitively time-consuming and expensive to obtain. To alleviate annotation scarcity, many approaches have been developed to efficiently utilize extra information, e.g., semi-supervised learning further exploring plentiful unlabeled data, domain adaptation including multi-modality learning and unsupervised domain adaptation resorting to the prior knowledge from additional modality. In this paper, we aim to investigate the feasibility of simultaneously leveraging abundant unlabeled data and well-established cross-modality data for annotation-efficient medical image segmentation. To this end, we propose a novel semi-supervised domain adaptation approach, namely Dual-Teacher, where the student model not only learns from labeled target data (e.g., CT), but also explores unlabeled target data and labeled source data (e.g., MR) by two teacher models. Specifically, the student model learns the knowledge of unlabeled target data from intra-domain teacher by encouraging prediction consistency, as well as the shape priors embedded in labeled source data from inter-domain teacher via knowledge distillation. Consequently, the student model can effectively exploit the information from all three data resources and comprehensively integrate them to achieve improved performance. We conduct extensive experiments on MM-WHS 2017 dataset and demonstrate that our approach is able to concurrently utilize unlabeled data and cross-modality data with superior performance, outperforming semi-supervised learning and domain adaptation methods with a large margin.
Green blasting policy: Simultaneous forecast of vertical and horizontal distribution of dust emissions using artificial causality-weighted neural network

The environmental impacts of the mining industry as well as reduction strategies are important issues that have been highlighted as a result of the environmentally-friendly policy of green mining. As part of that policy, this study developed an approach based on a multi-layer artificial neural network and fuzzy cognitive map to predict the vertical and horizontal distribution of blast-induced dust emissions, simultaneously. Hence, a fuzzy cognitive map based on the cause-and-effect analysis concept was first designed to extract inputs' weights. An optimal network with two hidden layers was then implemented to predict the vertical and horizontal dust distributions in a mine close to residential and agricultural areas. The performance evaluation of the approach indicated good results by the R2 of 0.9933 and 0.9267 between the measured and predicted values for the horizontal and vertical distributions, respectively. Furthermore, the predicted and measured outputs over every blasting round were in accordance due to the slight mean absolute and root mean square errors of 0.009 and 0.018, respectively. A sensitivity analysis revealed that all inputs, excluding air humidity on the vertical distribution, had acceptable effects on the dust distribution outputs by the strength of higher than 0.7. Based on a reduction solution that we suggested, one round was blasted by taking water capsules together with stemming material in 40% of the whole blast holes. The results indicated that the maximum horizontal distribution of blast-induced dust was decreased about six times. The approach can be straightforwardly updated and used for other mining cases.
Stochastic Fine-Grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition

In this paper, we propose novel stochastic modeling of various components of a continuous sign language recognition (CSLR) system that is based on the transformer encoder and connectionist temporal classification (CTC). Most importantly, We model each sign gloss with multiple states, and the number of states is a categorical random variable that follows a learned probability distribution, providing stochastic fine-grained labels for training the CTC decoder. We further propose a stochastic frame dropping mechanism and a gradient stopping method to deal with the severe overfitting problem in training the transformer model with CTC loss. These two methods also help reduce the training computation, both in terms of time and space, significantly. We evaluated our model on popular CSLR datasets, and show its effectiveness compared to the state-of-the-art methods.
Personalized treatment for coronary artery disease patients: a machine learning approach

Current clinical practice guidelines for managing Coronary Artery Disease (CAD) account for general cardiovascular risk factors. However, they do not present a framework that considers personalized patient-specific characteristics. Using the electronic health records of 21,460 patients, we created data-driven models for personalized CAD management that significantly improve health outcomes relative to the standard of care. We develop binary classifiers to detect whether a patient will experience an adverse event due to CAD within a 10-year time frame. Combining the patients' medical history and clinical examination results, we achieve 81.5% AUC. For each treatment, we also create a series of regression models that are based on different supervised machine learning algorithms. We are able to estimate with average R2 = 0.801 the outcome of interest; the time from diagnosis to a potential adverse event (TAE). Leveraging combinations of these models, we present ML4CAD, a novel personalized prescriptive algorithm. Considering the recommendations of multiple predictive models at once, the goal of ML4CAD is to identify for every patient the therapy with the best expected TAE using a voting mechanism. We evaluate its performance by measuring the prescription effectiveness and robustness under alternative ground truths. We show that our methodology improves the expected TAE upon the current baseline by 24.11%, increasing it from 4.56 to 5.66 years. The algorithm performs particularly well for the male (24.3% improvement) and Hispanic (58.41% improvement) subpopulations. Finally, we create an interactive interface, providing physicians with an intuitive, accurate, readily implementable, and effective tool.
Phishing Attacks Detection using Deep Learning Approach

In the COVID-19 pandemic, people are enforced to adopt ‘work from home’ policy. The Internet has become an effective channel for social interactions nowadays. Peoples' immense dependence on digital platform opens doors for fraud. Phishing is a type of cybercrime to steal users' credentials from online platforms such as online banking, online business, e-commerce, online classroom, digital marketplaces, etc. Phishers develop fake webpages alike the original one and send spam emails to hook the users. Phishers seize users' credentials when an online user visits the counterfeit webpages through the spams. Researchers have introduced enormous tools like blacklist, white-list, and antivirus software to detect phishing webpages. Attackers always devise creative ways to exploit human and network weakness to penetrate cyber defense. This paper presents a data-driven framework for detecting phishing webpages using deep learning approach. More precisely, a multilayer perceptron, which is also referred as a feed-forward neural network is used to predict the phishing webpages. The dataset was collected from Kaggle and contains information of ten thousand webpages. It consists of ten attributes. The proposed model has achieved 95% training accuracy and 93% test accuracy.
Feature Reintegration over Differential Treatment: A Top-down and Adaptive Fusion Network for RGB-D Salient Object Detection

Most methods for RGB-D salient object detection (SOD) utilize the same fusion strategy to explore the cross-modal complementary information at each level. However, this may ignore different feature contributions from two modalities on different levels towards prediction. In this paper, we propose a novel top-down multi-level fusion structure where different fusion strategies are utilized to effectively explore the low-level and high-level features. This is achieved by designing the interweave fusion module (IFM) to effectively integrate the global information and designing the gated select fusion module (GSFM) to discriminatively select useful local information by filtering out the unnecessary one from RGB and depth data. Moreover, we propose an adaptive fusion module (AFM) to reintegrate the fused cross-modal features of each level to predict a more accurate result. Comprehensive experiments on 7 challenging benchmark datasets demonstrate that our method achieves the competitive performance over 14 state-of-the-art RGB-D alternative methods.