Reviewerly/exHarmony huggingface dataset:
Sanity-Check: Boosting the Reliability of Safety-Critical Deep Neural Network Applications

The widespread usage of deep neural networks in autonomous driving necessitates a consideration of the safety arguments against hardware-level faults. This study confirms the possible catastrophic impact of hardware-level faults on DNN accuracy; the consequent need for low-cost fault tolerance methods can be met through a rigorous exploration of the mathematical properties of the associated computations. We propose Sanity-Check, which makes use of the linearity property and employs spatial and temporal checksums to protect fully-connected and convolutional layers in deep neural networks. Sanity-Check can be purely implemented on software and deployed on different execution platforms with no additional modification. We also propose Sanity-Check hardware which integrates seamlessly with modern DNN accelerators and neutralizes the small performance overhead in pure software implementations. Sanity-Check delivers perfect error-caused misprediction coverage in our experiments, which makes it a promising candidate for boosting the reliability of safety-critical deep neural network applications.
Face Image Age Estimation Based on Data Augmentation and Lightweight Convolutional Neural Network

Face images contain many important biological characteristics. The research directions of face images mainly include face age estimation, gender judgment, and facial expression recognition. Taking face age estimation as an example, the estimation of face age images through algorithms can be widely used in the fields of biometrics, intelligent monitoring, human-computer interaction, and personalized services. With the rapid development of computer technology, the processing speed of electronic devices has greatly increased, and the storage capacity has been greatly increased, allowing deep learning to dominate the field of artificial intelligence. Traditional age estimation methods first design features manually, then extract features, and perform age estimation. Convolutional neural networks (CNN) in deep learning have incomparable advantages in processing image features. Practice has proven that the accuracy of using convolutional neural networks to estimate the age of face images is far superior to traditional methods. However, as neural networks are designed to be deeper, and networks are becoming larger and more complex, this makes it difficult to deploy models on mobile terminals. Based on a lightweight convolutional neural network, an improved ShuffleNetV2 network based on the mixed attention mechanism (MA-SFV2: Mixed Attention-ShuffleNetV2) is proposed in this paper by transforming the output layer, merging classification and regression age estimation methods, and highlighting important features by preprocessing images and data augmentation methods. The influence of noise vectors such as the environmental information unrelated to faces in the image is reduced, so that the final age estimation accuracy can be comparable to the state-of-the-art.
Assessing augmented reality in production: remote-assisted maintenance with HoloLens

Digitalisation provides many opportunities for process improvements by using smart devices such as Augmented-reality (AR) displays in production. Nevertheless, in-depth studies about the effects of these applications on key performance process indicators are sparse. The goal of this study is to evaluate AR-based remote maintenance processes supported by cutting edge optical head-mounted display technology. A total of 12 test runs conducted with real-world maintenance engineers in a real-world industry setting were analysed based on video recordings and semi-structured interviews. The study identified both case-specific and general issues, and the inherent potential of the technology.
Scheduling Scientific Workflow Using Multi-Objective Algorithm With Fuzzy Resource Utilization in Multi-Cloud Environment

The provision of resources and services for scientific workflow applications using a multi-cloud architecture and a pay-per-use rule has recently gained popularity within the cloud computing research domain. This is because workflow applications are computation intensive. Most of the existing studies on workflow scheduling in the cloud mainly focus on finding an ideal makespan or cost. Nevertheless, there are other important quality of service metrics that are of critical concern in workflow scheduling such as reliability and resource utilization. In this respect, this paper proposes a new multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS) for scheduling scientific workflow based on particle swarm optimization (PSO) method. The algorithm minimizes cost and makespan while considering reliability constraint. The coding scheme jointly considers task execution location and data transportation order. Simulation experiments reveal that FR-MOS outperforms the basic MOS over the PSO algorithm.
Consistency Meets Inconsistency: A Unified Graph Learning Framework for Multi-view Clustering

Graph Learning has emerged as a promising technique for multi-view clustering, and has recently attracted lots of attention due to its capability of adaptively learning a unified and probably better graph from multiple views. However, the existing multi-view graph learning methods mostly focus on the multi-view consistency, but neglect the potential multi-view inconsistency (which may be incurred by noise, corruptions, or view-specific characteristics). To address this, this paper presents a new graph learning-based multi-view clustering approach, which for the first time, to our knowledge, simultaneously and explicitly formulates the multi-view consistency and the multi-view inconsistency in a unified optimization model. To solve this model, a new alternating optimization scheme is designed, where the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts of all views can be iteratively learned. It is noteworthy that our multi-view graph learning model is applicable to both similarity graphs and dissimilarity graphs, leading to two graph fusion-based variants, namely, distance (dissimilarity) graph fusion and similarity graph fusion. Experiments on various multi-view datasets demonstrate the superiority of our approach. The MATLAB source code is available at https://github.com/youweiliang/ConsistentGraphLearning.
TextRS: Deep Bidirectional Triplet Network for Matching Text to Remote Sensing Images

Exploring the relevance between images and their respective natural language descriptions, due to its paramount importance, is regarded as the next frontier in the general computer vision literature. Thus, recently several works have attempted to map visual attributes onto their corresponding textual tenor with certain success. However, this line of research has not been widespread in the remote sensing community. On this point, our contribution is three-pronged. First, we construct a new dataset for text-image matching tasks, termed TextRS, by collecting images from four well-known different scene datasets, namely AID, Merced, PatternNet, and NWPU datasets. Each image is annotated by five different sentences. All the five sentences were allocated by five people to evidence the diversity. Second, we put forth a novel Deep Bidirectional Triplet Network (DBTN) for text to image matching. Unlike traditional remote sensing image-to-image retrieval, our paradigm seeks to carry out the retrieval by matching text to image representations. To achieve that, we propose to learn a bidirectional triplet network, which is composed of Long Short Term Memory network (LSTM) and pre-trained Convolutional Neural Networks (CNNs) based on (EfficientNet-B2, ResNet-50, Inception-v3, and VGG16). Third, we top the proposed architecture with an average fusion strategy to fuse the features pertaining to the five image sentences, which enables learning of more robust embedding. The performances of the method expressed in terms Recall@K representing the presence of the relevant image among the top K retrieved images to the query text shows promising results as it yields 17.20%, 51.39%, and 73.02% for K = 1, 5, and 10, respectively.
Wearable Bits

Smart garment and wearable e-textile prototypes are difficult to co-design because of the variety of expertise needed (garment design, sewing skills, hardware prototyping, and software programming). To help with this, we developed a toolkit for prototyping wearable e-textiles, named Wearable Bits, which enables co-design with non-expert users without demanding sewing, hardware or software skills. We developed a low-fidelity and medium-fidelity experience prototype of the toolkit and ran a series of workshops where non-expert users designed their own e-textile wearables using Wearable Bits. In this paper, we discuss the ideas they developed, their construction techniques, the roles individuals took on while building, and suggestions for future toolkits.
Managing IoT Cyber-Security Using Programmable Telemetry and Machine Learning

Cyber-security risks for Internet of Things (IoT) devices sourced from a diversity of vendors and deployed in large numbers, are growing rapidly. Therefore, management of these devices is becoming increasingly important to network operators. Existing network monitoring technologies perform traffic analysis using specialized acceleration on network switches, or full inspection of packets in software, which can be complex, expensive, inflexible, and unscalable. In this paper, we use SDN paradigm combined with machine learning to leverage the benefits of programmable flow-based telemetry with flexible data-driven models to manage IoT devices based on their network activity. Our contributions are three-fold: (1) We analyze traffic traces of 17 real consumer IoT devices collected in our lab over a six-month period and identify a set of traffic flows (per-device) whose time-series attributes computed at multiple timescales (from a minute to an hour) characterize the network behavior of various IoT device types, and their operating states (i.e., booting, actively interacted with user, or being idle); (2) We develop a multi-stage architecture of inference models that use flow-level attributes to automatically distinguish IoT devices from non-IoTs, classify individual types of IoT devices, and identify their states during normal operations. We train our models and validate their efficacy using real traffic traces; and (3) We quantify the trade-off between performance and cost of our solution, and demonstrate how our monitoring scheme can be used in operation for detecting behavioral changes (firmware upgrade or cyber attacks).
Player Experience of Needs Satisfaction (PENS) in an Immersive Virtual Reality Exercise Platform Describes Motivation and Enjoyment

Recent research suggests that virtual reality (VR) games can engage players in physical activity with high levels of enjoyment. Understanding users’ motivation to engage and enjoy immersive VR exercise platforms is thus important to designers. We designed a VR exercise platform and conducted an experiment with two conditions, one with a static user interface (UI) and the other with an open world environment. Across participants there was significantly (p = 0.03*) greater enjoyment reported in an open world compared to static UI. Enjoyment in both static UI and open world conditions was positively correlated wih user’s psychological needs and experience; autonomy and immersion. Participants’ future play intention was also predicted by autonomy and immersion, but only within the open world condition. Our findings also suggest players can be classified into entertainment-focused and exercise-focused with different expectations and therefore different engagement behaviors with each VR exercise environment. The study highlights the value of informing VR design with measures of psychological need satisfaction.
Blockchain Technology-Future Of IoT: Including Structure, Limitations And Various Possible Attacks

In contrast with past, the blooming of Internet of Things is enlarging swiftly. But providing security in IoT devices is still a challenging issue and the solution is “Blockchaln”. Blockchain is a synthesis technology that integrates an innovative and well-built eye-sight to mould all-inclusive clarification to IoT device securities. Blockchain is used to furnish IoT device in a way of security, as it fixed a route to modify our job by cloud build evolution. Blockchainis speedily growing to be the upcoming interrupted revolution for secured connection or association, hopefully provide positive changes towards our working and living upcoming century.
Stable computation of higher order Charlier moments for signal and image reconstruction

The computation of high-order discrete orthogonal moments is very complex and unstable due to fluctuations in numerical polynomial values. In this paper, we will present new algorithms for the stable computation of high-order discrete orthogonal Charlier polynomials and moments. These algorithms are based on the use of modified recurrence relations of Charlier polynomials with respect to the order n and with respect to the variable x, and on the use of the modified Gram-Schmidt orthonormalization process (GSOP). The algorithms in question allow the cancellation of terms that cause the numerical fluctuations of Charlier polynomial values during the recursive calculations. They also preserve the orthogonality property of high-order Charlier polynomials, which enables an efficient analysis of large-size bio-signals and 2D/3D images. Therefore, the simulation results prove the efficiency of the proposed algorithms when it comes to reconstructing large-size bio-signals and images.
Modified hybrid bat algorithm with genetic crossover operation and smart inertia weight for multilevel image segmentation

Multilevel thresholding is one of the most commonly used methods in image segmentation. However, the exhaustive search method is computationally expensive for selecting the optimal thresholds. Therefore, a hybrid bat algorithm with genetic crossover operation and smart inertia weight (SGA-BA) is proposed to choose the optimal thresholds. Furthermore, between-class variance (the Otsu method) and Kapur's entropy are used as objective functions. In the novel SGA-BA, the smart inertia weight balances the SGA-BA's exploration and exploitation based on the number of iterations and fitness values. Moreover, the local search capability of SGA-BA is strengthened by the crossover operation of the genetic algorithm. Meanwhile, the random vector is replaced by the beta distribution, which updates the frequency of bat in a smart way. The proposed SGA-BA was evaluated by a set of benchmark images with various levels of thresholds. Additionally, SGA-BA was compared with some well-known and recent heuristic algorithms, such as the genetic algorithm (GA), gravitational search algorithm (GSA), particle swarm optimization (PSO), whale optimization algorithm (WOA), improved salp swarm algorithm (LSSA) and basic bat algorithm (BA). The experimental results show that the proposed SGA-BA provides better outcomes than the other algorithms.
A Design of Continuous User Verification for Online Exam Proctoring on M-Learning

The use of m-learning or other remote education continue to increase due to its ability to reach people who don't have access to campus. Exams are important components of educational programs as well as on an online learning program. In an exam, a proctoring method to detect and reduce the cheating possibility is very important to ensure that the students have learned the material given. Various methods had been proposed to provide an efficient, comfortable online exam proctoring. Start with implementing an exam design with hard constraints in a no proctoring exam, a remote proctoring using a webcam, a machine based proctoring and finally research on automated online proctoring. A visual verification for the whole exam session is needed in an online exam, therefore a face verification is needed. A remaining problem in face recognition area is the system robustness for pose and lighting variations. In this paper, we proposed a method to enhance the robustness for pose and lighting variations by doing an incremental training process using the training data set obtained from m-learning online lecture sessions. As a result, the design of the proposed method is presented in this paper.
Statistical inference for model parameters in stochastic gradient descent

The stochastic gradient descent (SGD) algorithm has been widely used in statistical estimation for large-scale data due to its computational and memory efficiency. While most existing works focus on the convergence of the objective function or the error of the obtained solution, we investigate the problem of statistical inference of true model parameters based on SGD when the population loss function is strongly convex and satisfies certain smoothness conditions. Our main contributions are twofold. First, in the fixed dimension setup, we propose two consistent estimators of the asymptotic covariance of the average iterate from SGD: (1) a plug-in estimator, and (2) a batch-means estimator, which is computationally more efficient and only uses the iterates from SGD. Both proposed estimators allow us to construct asymptotically exact confidence intervals and hypothesis tests. Second, for high-dimensional linear regression, using a variant of the SGD algorithm, we construct a debiased estimator of each regression coefficient that is asymptotically normal. This gives a one-pass algorithm for computing both the sparse regression coefficients and confidence intervals, which is computationally attractive and applicable to online data.
Identifying groundwater contaminant sources based on a KELM surrogate model together with four heuristic optimization algorithms

Identifying groundwater contaminant sources involves a reverse determination of the source characteristics by monitoring contaminant concentrations in a few observation wells. However, due to the ill-posed nature and high time consumption of identification, an efficient identification process with accurate estimated results is particularly important. To improve the efficiency of identifying groundwater contaminant sources, a kernel-based extreme learning machine was used as a surrogate for the time-consuming simulation model. Four heuristic search algorithms were used to improve the accuracy of the identification results. The proposed approach was tested in both hypothetical and actual cases. The conclusions are: 1. By forward and backward calculation of the surrogate model, the time cost of identifying groundwater sources can be reduced significantly; 2. When a traditional genetic algorithm and a particle swarm optimization algorithm are combined with quantum computing, computational efficiency and accuracy are both improved; and 3. By using various search algorithms to identify unknown contaminant sources in the actual case, the range of release histories of each contaminant source can be obtained, decreasing the ill-posed nature of the identification result obtained by a single algorithm and improving the reliability of the identification results.
Lower Bound on Transmission Using Non-Linear Bounding Function in Single Image Dehazing

The visibility of an image captured in poor weather (such as haze, fog, mist, smog) degrades due to scattering of light by atmospheric particles. Single image dehazing (SID) methods are used to restore visibility from a single hazy image. The SID is a challenging problem due to its ill-posed nature. Typically, the atmospheric scattering model (ATSM) is used to solve SID problem. The transmission and atmospheric light are two prime parameters of ATSM. The accuracy and effectiveness of SID depends on accurate value of transmission and atmospheric light. The proposed method translates transmission estimation problem into estimation of the difference between minimum color channel of hazy and haze-free image. The translated problem presents a lower bound on transmission and is used to minimize reconstruction error in dehazing. The lower bound depends upon the bounding function (BF) and a quality control parameter. A non-linear model is then proposed to estimate BF for accurate estimation of transmission. The proposed quality control parameter can be utilized to tune the effect of dehazing. The accuracy obtained by the proposed method for transmission is compared with state of the art dehazing methods. Visual comparison of dehazed images and objective evaluation further validates the effectiveness of the proposed method.
LDC: A lightweight dada consensus algorithm based on the blockchain for the industrial Internet of Things for smart city applications

A lightweight data consensus algorithm for the Industrial Internet of Things (IIoT) based on blockchain technology is proposed to ensure secure data transmission in the IIoT for smart city applications. The algorithm uses a distributed ledger on multiple edge gateways. Data consistency during data transmission is achieved using a two-path routing transmission strategy. The lightweight data block structure is an improvement over the traditional blockchain technology. The simulation results show that the proposed lightweight data consensus algorithm reduces the average hop count of data transmission, thereby reducing the probability of data being stolen. The accuracy of the data and the low energy consumption and delay are verified. The algorithm achieves high data accuracy and ensures the safety and reliability of the IIoT.
TTH-RNN: Tensor-Train Hierarchical Recurrent Neural Network for Video Summarization

Although a recurrent neural network (RNN) has achieved tremendous advances in video summarization, there are still some problems remaining to be addressed. In this article, we focus on two intractable problems when applying an RNN to video summarization: first the extremely large feature-to-hidden matrices. Since video features are usually in a high-dimensional space, it leads to extremely large feature-to-hidden mapping matrices in the RNN model, which increases the training difficulty. Second, the deficiency in long-range temporal dependence exploration. Most videos contain thousands of frames at least, which is such a long sequence that traditional RNNs cannot deal well with. Facing the abovementioned two problems, we develop a tensor-train hierarchical recurrent neural network (TTH-RNN) for the video summarization task. It contains a tensor-train embedding layer to avert the large feature-to-hidden matrices, together with a hierarchical structure of an RNN to explore the long-range temporal dependence among video frames. Practically, the experimental results on four benchmark datasets, including SumMe, TVsum, MED, and VTW, have demonstrated the excellent performance of a TTH-RNN in video summarization.