Reviewerly/exHarmony huggingface dataset:
SLA-Based Group Tasks Max-Min (GTMax-Min) Algorithm for Task Scheduling in Multi-Cloud Environments

Cloud is a distributed heterogeneous computing paradigm that facilitates on-demand delivery of IT heterogeneous resources to the customer based on their needs over the Internet with a pay-as-per service they use. Service level agreement (SLA) specifies the customer's expected service levels through cloud service provider (CSP) and the remedies or penalties if any of the CSP does not meet agreed-on service levels. Before providing the requested services to the customer, CSP and customer negotiate and sign on an SLA. CSP earns money for the service provided to the customer on satisfying the agreed-on service levels. Otherwise, CSP pays the penalty cost to the customer for the violation of SLA. Task scheduling minimizes task execution time and maximizes resource usage rate. Scheduling objective tends to improve quality of service (QoS) parameters like resource usage, with a minimum execution time and cost (without violating SLA). The proposed algorithm SLA-GTMax-Min schedules the tasks efficiently to the heterogeneous multi-cloud environment satisfying SLA and balances makespan, gain, and penalty/violation cost. Proposed SLA-GTMax-Min represents three levels of SLA provided with three types of services expected by the customers. The services are namely tasks minimum execution time, tasks minimum gain cost, and tasks both minimum execution time and gain cost in percentage, respectively. Makespan is termed as tasks minimum execution time. Gain cost represents minimum execution cost for completing tasks execution. The proposed algorithm SLA-GTMax-Min incorporates the SLA gain cost for providing service successfully and SLA violation cost for providing service unsuccessfully. Performance analysis of algorithm SLA-GTMax-Min and existing algorithm is measured based on the benchmark dataset values. The experimental results of SLA-GTMax-Min algorithm and the existing scheduling algorithms, namely, SLA-MCT, Execution-MCT, Profit-MCT, SLA-Min-Min, Execution-Min-Min, and Profit-Min-Min, are compared by evaluation metrics. Evaluation measure considered for evaluating the performance of the proposed SLA-GTMax-Min algorithm are makespan, cloud utilization ratio, gain cost is the cost earned by the CSP for successful completion of the tasks, and penalty cost the CSP pays to the customer for violation of SLA. The experimental results illustrate clearly algorithm SLA-GTMax-Min performs a better balance among makespan, gain cost, and penalty cost than existing algorithms.
A Hybrid-Driven Optimization Framework for Fixed-Wing UAV Maneuvering Flight Planning

Performing autonomous maneuvering flight planning and optimization remains a challenge for unmanned aerial vehicles (UAVs), especially for fixed-wing UAVs due to its high maneuverability and model complexity. A novel hybrid-driven fixed-wing UAV maneuver optimization framework, inspired by apprenticeship learning and nonlinear programing approaches, is proposed in this paper. The work consists of two main aspects: (1) Identifying the model parameters for a certain fixed-wing UAV based on the demonstrated flight data performed by human pilot. Then, the features of the maneuvers can be described by the positional/attitude/compound key-frames. Eventually, each of the maneuvers can be decomposed into several motion primitives. (2) Formulating the maneuver planning issue into a minimum-time optimization problem, a novel nonlinear programming algorithm was developed, which was unnecessary to determine the exact time for the UAV to pass by the key-frames. The simulation results illustrate the effectiveness of the proposed framework in several scenarios, as both the preservation of geometric features and the minimization of maneuver times were ensured.
A systematic literature review on wearable health data publishing under differential privacy

Wearable devices generate different types of physiological data about the individuals. These data can provide valuable insights for medical researchers and clinicians that cannot be availed through traditional measures. Researchers have historically relied on survey responses or observed behavior. Interestingly, physiological data can provide a richer amount of user cognition than that obtained from any other sources, including the user himself. Therefore, the inexpensive consumer-grade wearable devices have become a point of interest for the health researchers. In addition, they are also used in continuous remote health monitoring and sometimes by the insurance companies. However, the biggest concern for such kind of use cases is the privacy of the individuals. A few privacy mechanisms, such as abstraction and k-anonymity, are widely used in information systems. Recently, differential privacy (DP) has emerged as a proficient technique to publish privacy sensitive data, including data from wearable devices. In this paper, we have conducted a systematic literature review (SLR) to identify, select and critically appraise researches in DP as well as to understand different techniques and exiting use of DP in wearable data publishing. Based on our study, we have identified the limitations of proposed solutions and provided future directions.
DataHunter: A System for Finding Datasets Based on Scientific Problem Descriptions

The number of datasets is steadily rising, making it increasingly difficult for researchers and practitioners in the various scientific disciplines to be aware of all datasets, particularly of the most relevant datasets for a given research problem. To this end, dataset search engines have been proposed. However, they are based on the users' keywords and thus have difficulties in determining precisely fitting datasets for complex research problems. In this paper, we propose the system at http://data-hunter.io that recommends suitable datasets to users based on given research problem descriptions. It is based on fastText for the text representation and text classification, the Data Set Knowledge Graph (DSKG) with metadata about almost 1,700 unique datasets, as well as 88,000 paper abstracts as research problem descriptions for training the model. Overall, our system demonstrates that recommending datasets facilitates data provisioning and reuse according to the FAIR principles and that dataset recommendation is a promising future research direction.
Artificially Time-Varying Differential MIMO for Achieving Practical Physical Layer Security

In this paper, we propose a differential multiple-input multiple-output (MIMO) scheme based on the novel concept of chaos-based time-varying unitary matrices to demonstrate—for the first time in the literature—the ability of differential encoding in achieving practical physical layer security even without the need for using channel estimation. In the proposed scheme, an erroneous secret key, which is extracted from the wireless nature, is used to initialize a chaos sequence that is responsible for generating artificially time-varying unitary matrices capable of obfuscating the transmitted data symbols from illegitimate eavesdroppers. Contrary to conventional studies, the key agreement ratio in this study is assumed to be imperfect, which is often true and very realistic in high-mobility scenarios. Following this, we conceive a new calibration algorithm for reconciling the chaotic sequence generated at the legitimate parties, thus making this calibration algorithm a unique, novel solution to the key sharing problem of conventional chaos-based communication techniques, which has been overlooked over the past few decades. It is found out that differential encoding obviates additional complexity and insecurity in dealing with channel estimation, whereas an eavesdropper must tackle the complicated differentially encoded patterns, which have an exponentially increasing complexity order. In addition, the obtained simulation results demonstrate that the proposed scheme can outperform conventional chaos-based MIMO schemes that assume perfect channel knowledge.
The Capacity Region of Distributed Multi-User Secret Sharing

In this paper, we study the problem of distributed multi-user secret sharing, including a trusted master node, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N\in \mathbb {N}$ </tex-math></inline-formula> storage nodes, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> users, where each user has access to the contents of a subset of storage nodes. Each user has an independent secret message with certain rate, defined as the size of the message normalized by the size of a storage node. Having access to the secret messages, the trusted master node places encoded shares in the storage nodes, such that (i) each user can recover its own message from the content of the storage nodes that it has access to, (ii) each user cannot gain any information about the message of any other user. We characterize the capacity region of the distributed multi-user secret sharing, defined as the set of all achievable rate tuples, subject to the correctness and privacy constraints. In the achievable scheme, for each user, the master node forms a polynomial with the degree equal to the number of its accessible storage nodes minus one, where the value of this polynomial at certain points are stored as the encoded shares. The message of that user is embedded in some of the coefficients of the polynomial. The remaining coefficients are determined such that the content of each storage node serves as the encoded shares for all users that have access to that storage node.
The development and validation of a teacher education digital literacy and digital pedagogy evaluation

K-12 educators' digital literacy skills have been designated as a national and state priority by education and accreditation agencies in Texas, resulting in the need for curriculum alignments to the 2017 ISTE Standards for Educators and implementation of a digital literacy evaluation. The purpose of this validation study was to develop a Digital Literacy Evaluation as a diagnostic tool for digital literacy and digital pedagogy of pre-service teachers. Using a convergent mixed methods design, data were collected through a quantitative pilot study and a qualitative expert panel review. The results indicated that the instrument exhibited strong internal consistency and content validity. Next steps include the implementation of the Digital Literacy Evaluation and the development of a digital literacy curriculum for pre-service teachers.
Parameter estimation of continuous variable quantum key distribution system via artificial neural networks

Continuous-variable quantum key distribution (CVQKD) allows legitimate parties to extract and exchange secret keys. However, the tradeoff between the secret key rate and the accuracy of parameter estimation still around the present CVQKD system. In this paper, we suggest an approach for parameter estimation of the CVQKD system via artificial neural networks (ANN), which can be merged in post-processing with less additional devices. The ANN-based training scheme, enables key prediction without exposing any raw key. Experimental results show that the error between the predicted values and the true ones is in a reasonable range. The CVQKD system can be improved in terms of the secret key rate and the parameter estimation, which involves less additional devices than the traditional CVQKD system.
Boosting Intelligent Data Analysis in Smart Sensors by Integrating Knowledge and Machine Learning

The presented paper proposes a hybrid neural architecture that enables intelligent data analysis efficacy to be boosted in smart sensor devices, which are typically resource-constrained and application-specific. The postulated concept integrates prior knowledge with learning from examples, thus allowing sensor devices to be used for the successful execution of machine learning even when the volume of training data is highly limited, using compact underlying hardware. The proposed architecture comprises two interacting functional modules arranged in a homogeneous, multiple-layer architecture. The first module, referred to as the knowledge sub-network, implements knowledge in the Conjunctive Normal Form through a three-layer structure composed of novel types of learnable units, called L-neurons. In contrast, the second module is a fully-connected conventional three-layer, feed-forward neural network, and it is referred to as a conventional neural sub-network. We show that the proposed hybrid structure successfully combines knowledge and learning, providing high recognition performance even for very limited training datasets, while also benefiting from an abundance of data, as it occurs for purely neural structures. In addition, since the proposed L-neurons can learn (through classical backpropagation), we show that the architecture is also capable of repairing its knowledge.
Deep Feature Learning to Quantitative Prediction of Software Defects

Defect prediction forecasts defect proneness or the number of defects contained in software systems. It is frequently employed to efficiently prioritize and allocate the limited testing resources to the modules that are more likely to be defective during the process of software development and maintenance. Consequently, a number of defect prediction approaches have been proposed. Most of the existing approaches on defect prediction regard defect prediction as a classification problem in which programs are classified as buggy or non-buggy. However, identifying the defect proneness of a given software module is not sufficient in practical software testing. The research on predicting the number of defects is limited and the performances of these approaches are constantly being optimized and improved. Therefore, in this paper, we propose a novel approach that leverages a convolutional neural network to predict the number of defects in software systems automatically. First, we preprocess the PROMISE dataset, which involves performing natural logarithm transformation and data normalization. Second, we feed the preprocessed dataset to a specially designed convolutional neural network-based model to predict the number of defects. Third, we rank the software modules according to the corresponding predicted number of defects in descending order. We also evaluate the proposed approach on a well-known dataset by cross-validation. The evaluation results suggest that the proposed approach is both accurate and robust, and it improves the state of the art. On average, it significantly improves the Kendall correlation coefficient by 16% and the fault-percentile-average by 4%.
Embedding Extra Knowledge and A Dependency Tree Based on A Graph Attention Network for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis analyses the fine-grained sentiment polarity of a particular attribute in a sentence. In addition to the fact that most papers have applied attention mechanisms and neural networks to fine-grained sentiment analysis, some studies have reported outstanding performance from graph-structured neural networks in aspect-based sentiment analysis. In this paper, we propose the use of a graph attention network (GAT) to embed external knowledge and grammatical relations (KD-GAT). First, we employ a GAT to extract the nodes and edges related to sentences in the knowledge graph. Second, we consider the influence of conjunctions and parts of speech based on the aspect-oriented syntactic dependency tree. Finally, we emphasize the effect of aspect position in the mechanism of multiple attention. Experiments on the SemEval 2014 and Twitter datasets show that our model can improve the ability to analyze sentiment and that the graph structure method can better integrate grammatical relations to understand a sentence.
Application of Convolution Neural Network (CNN) Model Combined with Pyramid Algorithm in Aerobics Action Recognition

In order to realize high-accuracy recognition of aerobics actions, a highly applicable deep learning model and faster data processing methods are required. Therefore, it is a major difficulty in the field of research on aerobics action recognition. Based on this, this paper studies the application of the convolution neural network (CNN) model combined with the pyramid algorithm in aerobics action recognition. Firstly, the basic architecture of the convolution neural network model based on the pyramid algorithm is proposed. Combined with the application strategy of the common recognition model in aerobics action recognition, the traditional aerobics action capture information is processed. Through the characteristics of different aerobics actions, different accurate recognition is realized, and then, the error of the recognition model is evaluated. Secondly, the composite recognition function of the convolution neural network model in this application is constructed, and the common data layer effect recognition method is used in the optimization recognition. Aiming at the shortcomings of the composite recognition function, the pyramid algorithm is used to improve the convolution neural network recognition model by deep learning optimization. Finally, through the effectiveness comparison experiment, the results show that the convolution neural network model based on the pyramid algorithm is more efficient than the conventional recognition method in aerobics action recognition.
A Secure Revocable Fine-Grained Access Control and Data Sharing Scheme for SCADA in IIoT Systems

The supervisory control and data acquisition (SCADA) system is widely used in industrial control and the contemporary Industrial Internet of Things (IIoT). Unfortunately, due to its relatively weak design in terms of data security and access control, SCADA systems are becoming a favorite target for attackers. End-to-end encryption, such as SSL/TLS protocol, is used to protect the data transmission, but it cannot guarantee security in third-party cloud platforms. In this article, we propose a secure revocable fine-grained access control and data sharing scheme. This scheme not only ensures the confidentiality of the data but also enhances the access control of the SCADA system. Our scheme is based on three key observations. The common communication architecture of SCADA systems cannot protect data security itself. The security supports provided by industrial control protocols are limited. Moreover, the third-party cloud platforms are semitrusted. In addition, we have introduced digital signature technology to assure the integrity of the data in the SCADA system. We prove that our scheme is secure. This scheme has been experimentally evaluated to introduce negligible performance losses while improving data security in the SCADA system.
Enhancement of Robustness in Object Detection Module for Advanced Driver Assistance Systems

A unified system integrating a compact object detector and a surrounding environmental condition classifier for enhancing the robustness of object detection scheme in advanced driver assistance systems (ADAS) is proposed in this paper. ADAS are invented to improve traffic safety and effectiveness in autonomous driving systems where object detection plays an extremely important role. However, modern object detectors integrated into ADAS are still unstable due to high latency and the variation of the environmental contexts in the deployment phase. Our system is proposed to address the aforementioned problems. The proposed system includes two main components: (1) a compact one-stage object detector which is expected to be able to perform at a comparable accuracy compared to state-of-the-art object detectors, and (2) an environmental condition detector that helps to send a warning signal to the cloud in case the self-driving car needs human actions due to the significance of the situation. The empirical results prove the reliability and the scalability of the proposed system to realistic scenarios.
UCP-Net: Unstructured Contour Points for Instance Segmentation

The goal of interactive segmentation is to assist users in producing segmentation masks as fast and as accurately as possible. Interactions have to be simple and intuitive and the number of interactions required to produce a satisfactory segmentation mask should be as low as possible. In this paper, we propose a novel approach to interactive segmentation based on unconstrained contour clicks for initial segmentation and segmentation refinement. Our method is class-agnostic and produces accurate segmentation masks (IoU > 85%) for a lower number of user interactions than state-of-the-art methods on popular segmentation datasets (COCO MVal, SBD and Berkeley).
Unsupervised person re-identification by Intra–Inter Camera Affinity Domain Adaptation

Person re-identification (re-ID) based on unsupervised domain adaptation intends to distill knowledge from annotated source dataset to identify target persons in another dataset. Although the advanced UDA re-ID models are dominated by pseudo-label methods, they almost transform images from various camera views into the same feature space, without considering the camera distribution gaps, which may lead to generate considerably noisy pseudo-labels. In this study, we develop an Intra–Inter Camera Affinity Domain Adaptation (I2CADA) to tackle these problems for UDA person re-ID. Precisely, I2CADA framework is composed of two modules. The first one is generative adversarial learning module, aiming to train a feature extractor that can map target data to source feature space by supervised learning and adversarial learning, which can relieve the distribution gap between different datasets (domains). The second one is affinity transfer learning module, which simultaneously considers intra-camera clustering and inter-camera separation among persons with similar appearances in the target domain, thus mitigating the distribution inconsistency among person images collected from multiple target camera views. Besides, comprehensive experiments exhibit that I2CADA outperforms the existing UDA person re-identification approaches.
“I Never Thought About Securing My Machine Learning Systems”: A Study of Security and Privacy Awareness of Machine Learning Practitioners

Machine learning (ML) models have become increasingly important components of many software systems. Therefore, ensuring their privacy and security is a crucial task. Current research mainly focuses on the development of security and privacy methods. However, ML practitioners, as the individuals in charge of translating the theory into practical applications, have not yet received much attention. In this paper, the security and privacy awareness and practices of ML practitioners are studied through an online survey with the aim of (1) gaining insight into the current state of awareness, (2) identifying influencing factors, and (3) exploring the actual use of existing methods and tools. The results indicate a relatively low general privacy and security awareness among the ML practitioners surveyed. In addition, they are less familiar with ML privacy protection methods than with general security methods or ML-related ones. Moreover, awareness correlates with the years of working with ML but not with the level of academic education or the field of occupation. Finally, the practitioners in this study seem to experience uncertainties in implementing legal frameworks, such as the European General Data Protection Regulation, into their ML workflows.