Reviewerly/exHarmony huggingface dataset:
An efficient public key secure scheme for cloud and IoT security

According to the National Institute of Standard and Technology (NIST), the security level of RSA is safe when it is N-bit modulus ≥2048 bits. Because of this, the processing time to generate asymmetric keys also increases. Taking this into account, an efficient and non-shareable Public Key Exponent Secure Scheme (ENPKESS) is proposed by using a non-linear Diophantine equation to have high security against side-channel attacks like timing attacks. This scheme has three-stage of encryption and two-stage of decryption, whereas other schemes like ESR and RSA has one level in encryption and decryption. Due to this, extraction of the secret key is extremely hard to determine from our public exponents α,R,N. Our methodology is well suited for secure cloud computing environments used in the Internet of Things (IoT). Here we have also applied the Knapsack method to encrypt our ENPKESS keys to enrich high security in cloud systems. We show a strong performance evaluation on standard RSA, Enhanced and Secured RSA Key Generation Scheme (ESRKGS), and ENPKESS on its key generation, encryption and decryption by varying the N-bit moduli size up to 10K bits. From the overall result, ENPKESS consumes 89% of standard RSA and 27% of ESRKGS.
Unsupervised High-Resolution Depth Learning From Videos With Dual Networks

Unsupervised depth learning takes the appearance difference between a target view and a view synthesized from its adjacent frame as supervisory signal. Since the supervisory signal only comes from images themselves, the resolution of training data significantly impacts the performance. High-resolution images contain more fine-grained details and provide more accurate supervisory signal. However, due to the limitation of memory and computation power, the original images are typically down-sampled during training, which suffers heavy loss of details and disparity accuracy. In order to fully explore the information contained in high-resolution data, we propose a simple yet effective dual networks architecture, which can directly take high-resolution images as input and generate high-resolution and high-accuracy depth map efficiently. We also propose a Self-assembled Attention (SA-Attention) module to handle low-texture region. The evaluation on the benchmark KITTI and Make3D datasets demonstrates that our method achieves state-of-the-art results in the monocular depth estimation task.
DeepFall: Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders

Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. Due to the rarity of falls, it is difficult to employ supervised classification techniques to detect them. Moreover, in these highly skewed situations, it is also difficult to extract domain-specific features to identify falls. In this paper, we present a novel framework, DeepFall, which formulates the fall detection problem as an anomaly detection problem. The DeepFall framework presents the novel use of deep spatio-temporal convolutional autoencoders to learn spatial and temporal features from normal activities using non-invasive sensing modalities. We also present a new anomaly scoring method that combines the reconstruction score of frames across a temporal window to detect unseen falls. We tested the DeepFall framework on three publicly available datasets collected through non-invasive sensing modalities, thermal camera and depth cameras, and show superior results in comparison with traditional autoencoder methods to identify unseen falls.
Location-Based PVO and Adaptive Pairwise Modification for Efficient Reversible Data Hiding

Pixel-value-ordering (PVO) is an efficient technique of reversible data hiding (RDH). By PVO, the maximum and minimum in each cover image block are first predicted and then modified to embed data. Actually, many PVO-based methods are essentially based on high-dimensional histogram modification. For these methods, a two-dimensional (2D) prediction-error histogram (PEH) is first generated and then modified based on a 2D mapping. However, these methods have two drawbacks. On one hand, the generated 2D PEH is irregular so that it is difficult to design suitable histogram modification strategy. On the other hand, the employed 2D mapping is empirically designed, and thus the embedding performance is far from optimal. Based on these considerations, a new PVO-based RDH scheme is proposed in this paper. By considering both pixel value orders and pixel locations, a new predictor is proposed so that the generated 2D PEH is regular in shape and suitable for reversible embedding. Moreover, instead of manually designing 2D mappings, to optimize the embedding performance, a self-learning mechanism is proposed to adaptively select the 2D mapping according to the image content. With the new predictor and the self-learning mechanism for 2D mapping selection, the proposed method works well with a good marked image quality, e.g., the PSNR of the image Lena is as high as 61.53 dB for an embedding capacity of 10 000 bits. Besides, compared with some state-of-the-art RDH methods, the superiority of the proposed method is experimentally verified.
Integrating TANBN with cost sensitive classification algorithm for imbalanced data in medical diagnosis

For the imbalanced classification problems, most traditional classification models only focus on searching for an excellent classifier to maximize classification accuracy with the fixed misclassification cost, not take into consideration that misclassification cost can change with sample probability distribution. So far as we know, cost-sensitive learning method can be effectively utilized to solve imbalanced data classification problems. In this regards, we propose an integrated TANBN with cost-sensitive classification algorithm (AdaC-TANBN) to overcome the above drawback and improve classification accuracy. The AdaC-TANBN algorithm employs variable misclassification cost determined by samples distribution probability to train classifier, then implements classification for imbalanced data in medical diagnosis. The effectiveness of our proposed approach is examined on the Cleveland heart dataset (Heart), Indian liver patient dataset (ILPD), Dermatology dataset and Cervical cancer risk factors dataset (CCRF) from the UCI learning repository. The experimental results indicate that the AdaC-TANBN algorithm can outperform other state-of-the-art comparative methods.
Scientist’s guide to developing explanatory statistical models using causal analysis principles

Abstract Recent discussions of model selection and multimodel inference highlight a general challenge for researchers: how to convey the explanatory content of a hypothesized model or set of competing models clearly. The advice from statisticians for scientists employing multimodel inference is to develop a well‐thought‐out set of candidate models for comparison, though precise instructions for how to do that are typically not given. A coherent body of knowledge, which falls under the general term causal analysis , now exists for examining the explanatory scientific content of candidate models. Much of the literature on causal analysis has been recently developed, and we suspect may not be familiar to many ecologists. This body of knowledge comprises a set of graphical tools and axiomatic principles to support scientists in their endeavors to create “well‐formed hypotheses,” as statisticians are asking them to do. Causal analysis is complementary to methods such as structural equation modeling, which provides the means for evaluation of proposed hypotheses against data. In this paper, we summarize and illustrate a set of principles that can guide scientists in their quest to develop explanatory hypotheses for evaluation. The principles presented in this paper have the capacity to close the communication gap between statisticians, who urge scientists to develop well‐thought‐out coherent models, and scientists, who would like some practical advice for exactly how to do that.
Modulation Recognition With Graph Convolutional Network

In most non-cooperative communication systems, modulation recognition is a fundamental and critical technique. Traditional methods of modulation recognition can be categorized as maximum likelihood hypothesis algorithms and pattern recognition algorithms. However, these methods have high complexities or need additional data preprocessing. Recently, neural network algorithms have shown great potential in modulation recognition. In this letter, we propose a method of modulation recognition by exploiting the graph convolutional network (GCN). However, GCNs cannot be directly used to perform modulation recognition since modulated signals are not graphs. To convert signals to graphs, the modulation dataset is divided into multiple subsets. We design a feature extraction convolutional neural network (CNN) and a graph mapping CNN to extract signal features and map subsets into graphs, respectively. Then we input the graphs into the GCN to predict modulation modes of unlabeled signals. The experimental results show that the proposed GCN algorithm achieves higher recognition accuracy than CNN algorithm and K-nearest neighbor (KNN) algorithm, especially when SNR is low.
Cross-validation Metrics for Evaluating Classification Performance on Imbalanced Data

Imbalanced data was often a classification issue, because a training process using the data would make model too suitable for the majority class. Meanwhile, ensemble technique was one alternative to deal with imbalanced data. The paper aimed to compare metrics, measuring classification performance for imbalanced data through an empirical study on cabbage image classification. Metrics used were accuracy, F1 score, g-mean, MCC, Cohen's Kappa statistics, and AUC. We used three ensembles i.e. bagging, Breiman boosting, and Freund boosting. The empirical study result indicated that accuracy, F1 score, and g-mean gave values not reflecting the actual confusion cases. Accuracy, F1 score, g-mean, MCC, and Kappa showed the same values in different confusion matrix conditions, but AUC gave the different values in different confusion matrix. Based on the result, AUC become the robust metrics to measure on imbalanced condition.
Preserving Privacy in the Internet of Connected Vehicles

Today's vehicles are advancing from stand-alone transportation means to vehicle-to-vehicle, and vehicle-to-infrastructure communications enabled devices which are able to exchange data through the transportation communication infrastructure. As the IoT and data remain intrinsically linked together, the fast-changing mobility landscape of intent-based networking for the Internet of connected vehicles comes with a great risk of data security and privacy violations. This paper considers the privacy issues in the distributed edge computing, in which the data is communicated between a number of vehicles in the IoT layer and potentially untrusted edge controllers at the edge of the network. The sensory data communicated by the vehicles contain sensitive information, such as location and speed, which could violate the users' privacy if they are leaked with no perturbation. Recent studies suggest mechanisms for randomizing the stream of data to ensure individuals' privacy. Although the past works on differential privacy provide a strong privacy guarantee, they are limited to applications where communication parties are trusted and/or there is no correlation between the users or the featured of sensory data. In this paper, we address this gap by proposing a differentially private data streaming system that adds a correlated noise in the vehicle's side (IoT layer) rather than the transportation infrastructure. Also, our system is able to ensure a strong privacy level over time. The proposed mechanism is data-adaptive and scales the noise with respect to the data correlation. Our extensive experiments demonstrate that the utility of the output generated by our method outperforms the recent approaches.
DeepMutation++: A Mutation Testing Framework for Deep Learning Systems

Deep neural networks (DNNs) are increasingly expanding their real-world applications across domains, e.g., image processing, speech recognition and natural language processing. However, there is still limited tool support for DNN testing in terms of test data quality and model robustness. In this paper, we introduce a mutation testing-based tool for DNNs, DeepMutation++, which facilitates the DNN quality evaluation, supporting both feed-forward neural networks (FNNs) and stateful recurrent neural networks (RNNs). It not only enables to statically analyze the robustness of a DNN model against the input as a whole, but also allows to identify the vulnerable segments of a sequential input (e.g. audio input) by runtime analysis. It is worth noting that DeepMutation++ specially features the support of RNNs mutation testing. The tool demo video can be found on the project website https://sites.google.com/view/deepmutationpp.
Rapid and automatic optimal exposure control for digital fringe projection technique

This paper presents a method that can determine the optimal exposure time for high-quality three-dimensional (3D) shape measurement with digital fringe projection technique. The proposed method only requires capture fringe image(s) with one exposure time to automatically determine the global optimal exposure time for high-quality 3D shape measurement; and we further propose to automatically achieve high-dynamic range (HDR) 3D shape measurement by capture image(s) with the optimized global single exposure. Experimental results demonstrated the proposed method can successful work on a static complex scene and can also be applicable for real-time applications.
Delphi

Many companies provide neural network prediction services to users for a wide range of applications. However, current prediction systems compromise one party's privacy: either the user has to send sensitive inputs to the service provider for classification, or the service provider must store its proprietary neural networks on the user's device. The former harms the personal privacy of the user, while the latter reveals the service provider's proprietary model.
Digital transformation in higher education: the use of communication technologies by students

Considering the growing number of students accessing the Internet through mobile devices, universities have been increasingly interested in accepting new communication technologies in the teaching context, enhancing the digital transformation of these institutions. Thus, it becomes relevant to have instruments that allow the characterization of the use of these technologies in the context of higher education. This paper is the result of a more comprehensive research project and aims to analyze the students’ perspective on the use of those technologies to communicate with their teachers, to investigate with what goals and functions students use the technologies, and to understand what their expectations are. To support the study, an analysis model was conceptualized and deployed in a case study conducted at a Portuguese university. Data collection was carried out through the administration of an online questionnaire to students in 2018. The results show that applications that allow interpersonal communication as well as publishing and sharing technologies are preferred by students to communicate with their teachers. In addition to the use, we analyzed the answers regarding the ease of use, expectation, attitude toward using, satisfaction, frequency of use, among other variables considered in the analysis model and presented in this paper.
Artificial electric field algorithm for engineering optimization problems

Nature-inspired optimization algorithms have attracted significant attention from researchers during the past decades due to their applicability to solving the challenging optimization problems, efficiently. Many intelligent systems require an excellent constrained optimization scheme to act as an artificially intelligent system. Artificial electric field algorithm (AEFA) is an intelligently designed artificial system that deals with the purpose of function optimization. AEFA works on the principle of Coulombs' law of electrostatic force and Newtons' law of motion. The present article extends the AEFA algorithm for constrained optimization problems by introducing the new velocity and position bound strategies. These bounds lead the particle to interact with each other within the domain of the problem, and they are allowed to learn from the problem space individually. They also help to make a better balance between exploration and exploitation by controlling the position update of the particles. The challenging IEEE CEC 2017 constrained benchmark set of 28 problems, and five multidimensional non-linear structural design optimization problems are solved using AEFA-C, which tests the effectiveness and the efficiency of the proposed scheme. The comparative study of AEFA-C is performed with nine state-of-art algorithms, including some IEEE CEC 2017 competitors. The comparative study, statistical analysis, and the findings suggest that the proposed AEFA-C is an efficient constrained optimizer.
Sofware engneering challenges for machine learning applications: A literature review

Machine learning techniques, especially deep learning, have achieved remarkable breakthroughs over the past decade. At present, machine learning applications are deployed in many fields. However, the outcomes of software engineering researches are not always easily utilized in the development and deployment of machine learning applications. The main reason for this difficulty is the many differences between machine learning applications and traditional information systems. Machine learning techniques are evolving rapidly, but face inherent technical and non-technical challenges that complicate their lifecycle activities. This review paper attempts to clarify the software engineering challenges for machine learning applications that either exist or potentially exist by conducting a systematic literature collection and by mapping the identified challenge topics to knowledge areas defined by the Software Engineering Body of Knowledge (Swebok).
Multilingual End-to-End Speech Translation

In this paper, we propose a simple yet effective framework for multilingual end-to-end speech translation (ST), in which speech utterances in source languages are directly translated to the desired target languages with a universal sequence-to-sequence architecture. While multilingual models have shown to be useful for automatic speech recognition (ASR) and machine translation (MT), this is the first time they are applied to the end-to-end ST problem. We show the effectiveness of multilingual end-to-end ST in two scenarios: one-to-many and many-to-many translations with publicly available data. We experimentally confirm that multilingual end-to-end ST models significantly outperform bilingual ones in both scenarios. The generalization of multilingual training is also evaluated in a transfer learning scenario to a very low-resource language pair. All of our codes and the database are publicly available to encourage further research in this emergent multilingual ST topic <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> Available at https://github.com/espnet/espnet..
Online Hyper-Parameter Learning for Auto-Augmentation Strategy

Data augmentation is critical to the success of modern deep learning techniques. In this paper, we propose Online Hyper-parameter Learning for Auto-Augmentation (OHL-Auto-Aug), an economical solution that learns the augmentation policy distribution along with network training. Unlike previous methods on auto-augmentation that search augmentation strategies in an offline manner, our method formulates the augmentation policy as a parameterized probability distribution, thus allowing its parameters to be optimized jointly with network parameters. Our proposed OHL-Auto-Aug eliminates the need of re-training and dramatically reduces the cost of the overall search process, while establishes significantly accuracy improvements over baseline models. On both CIFAR-10 and ImageNet, our method achieves remarkable on search accuracy, 60x faster on CIFAR-10 and 24x faster on ImageNet, while maintaining competitive accuracies.
Few-Shot Learning for Domain-Specific Fine-Grained Image Classification

Learning to recognize novel visual categories from a few examples is a challenging task for machines in real-world industrial applications. In contrast, humans have the ability to discriminate even similar objects with little supervision. This article attempts to address the few-shot fine-grained image classification problem. We propose a feature fusion model to explore discriminative features by focusing on key regions. The model utilizes the focus-area location mechanism to discover the perceptually similar regions among objects. High-order integration is employed to capture the interaction information among intraparts. We also design a center neighbor loss to form robust embedding space distributions. Furthermore, we build a typical fine-grained and few-shot learning dataset miniPPlankton from the real-world application in the area of marine ecological environments. Extensive experiments are carried out to validate the performance of our method. The results demonstrate that our model achieves competitive performance compared with state-of-the-art models. Our work is a valuable complement to the model domain-specific industrial applications.
The Future of Blockchain Technology in Healthcare Internet of Things Security

The recent emergence of Internet of Things (IoT) brings a whole new class of applications and higher efficiency for existing areas of services. One such popular service that is trying to reap the benefits of IoT is the Healthcare sector. Application-specific requirements, as well as connectivity and communication ability of devices have introduced new challenges for these Healthcare IoT applications. Specifically, we have seen a surge of healthcare IoT applications that have manifested in recent years. Almost simultaneously to the surge in IoT, there has been potential venues for Blockchain Technology to find a home outside of the financial sector in many different applicable scenarios, including those in Healthcare. This chapter provides an overview of IoT health technologies with a specific emphasis on those proposed making use of Blockchain Technology. We investigate both the positive aspects of such implementations for the future, as well the shortcomings of known designs and tackle future challenges of this novel area of research. This chapter surveys the latest works in this very specific field, and gives an unbiased vantage point to assist researchers navigate this new horizon for Healthcare, IoT, and Blockchain Technology towards the future.
A Blockchain Model for Fair Data Sharing in Deregulated Smart Grids

The emergence of smart home appliances has generated a high volume of data on smart meters belonging to different customers. However, customers can not share their data in deregulated smart grids due to privacy concern. Although, these data are important for the service provider in order to provide an efficient service. To encourage the customers' participation, this paper proposes an access control mechanism by fairly compensating customers for their participation in data sharing via blockchain using the concept of differential privacy. We addressed the computational issues of existing ethereum blockchain by proposing a proof of authority consensus protocol through the Pagerank mechanism in order to derive the reputation scores. Experimental results show the efficiency of the proposed model to minimize privacy risk, and maximize aggregator's profit. In addition, gas consumption, as well as the cost of the computational resources, is reduced.