Reviewerly/exHarmony huggingface dataset:
Robust steganography resisting JPEG compression by improving selection of cover element

Through minimizing embedding cost, modern adaptive steganography has gained unprecedented success in terms of its undetectability. However, due to Joint Photographic Experts Group (JPEG) compression, the secret bits hidden in the image transmitted over social network platform fail to be perfectly extracted, that remarkably limits its wide application in the real world. In this paper, by improving selection of cover element, we propose to design an enhanced robust steganographic algorithm to resist against JPEG compression. First, since that before and after JPEG compression the sign of Discrete Cosine Transform (DCT) coefficient is not easy to change, we devise cover element based on the sign of DCT coefficient. Furthermore, the selection of cover element is successfully improved with the help of post-processing operation analysis. Second, the embedding cost for each candidate DCT coefficient is calculated. Finally, dependent of both error correction algorithm and syndrome-trellis codes, a compression-resistant stego image is generated with minimum distortion. Numerical results verify that the robustness of our proposed steganographic algorithm is superior to that of current arts; meanwhile, the effectiveness of the algorithm is also verified over social network platform.
Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection

The increasing size of machine learning models, especially deep neural network models, can improve the model generalization capability. However, large models require more training data and more computing resources (such as GPU clusters) to train. In distributed training, the communication overhead of exchanging gradients or models among workers becomes a potential system bottleneck that limits the system scalability. Recently, many research works aim to reduce communication time of two types of distributed deep learning architectures, centralized and decentralized.
FlexDNN: Input-Adaptive On-Device Deep Learning for Efficient Mobile Vision

Mobile vision systems powered by the recent advancement in Deep Neural Networks (DNNs) are enabling a wide range of on-device video analytics applications. Considering mobile systems are constrained with limited resources, reducing resource demands of DNNs is crucial to realizing the full potential of these applications. In this paper, we present FlexDNN, an input-adaptive DNN-based framework for efficient on-device video analytics. To achieve this, FlexDNN takes the intrinsic dynamics of mobile videos into consideration, and dynamically adapts its model complexity to the difficulty levels of input video frames to achieve computation efficiency. FlexDNN addresses the key drawbacks of existing systems and pushes the state-of-the-art forward. We use FlexDNN to build three representative on-device video analytics applications, and evaluate its performance on both mobile CPU and GPU platforms. Our results show that FlexDNN significantly outperforms status quo approaches in accuracy, average CPU/GPU processing time per frame, frame drop rate, and energy consumption.
Deep Residual Network in Network

Deep network in network (DNIN) model is an efficient instance and an important extension of the convolutional neural network (CNN) consisting of alternating convolutional layers and pooling layers. In this model, a multilayer perceptron (MLP), a nonlinear function, is exploited to replace the linear filter for convolution. Increasing the depth of DNIN can also help improve classification accuracy while its formation becomes more difficult, learning time gets slower, and accuracy becomes saturated and then degrades. This paper presents a new deep residual network in network (DrNIN) model that represents a deeper model of DNIN. This model represents an interesting architecture for on-chip implementations on FPGAs. In fact, it can be applied to a variety of image recognition applications. This model has a homogeneous and multilength architecture with the hyperparameter "L" ("L" defines the model length). In this paper, we will apply the residual learning framework to DNIN and we will explicitly reformulate convolutional layers as residual learning functions to solve the vanishing gradient problem and facilitate and speed up the learning process. We will provide a comprehensive study showing that DrNIN models can gain accuracy from a significantly increased depth. On the CIFAR-10 dataset, we evaluate the proposed models with a depth of up to L = 5 DrMLPconv layers, 1.66x deeper than DNIN. The experimental results demonstrate the efficiency of the proposed method and its role in providing the model with a greater capacity to represent features and thus leading to better recognition performance.
What blockchain can do for power grids?

The aim of this work is to provide an up-to-date comprehensive review of the peer-reviewed articles, the research projects and the entrepreneurial efforts that consider the utilization of blockchain technology in the energy sector in general and the power grid in particular. Through our review study we systematically classify existing applications of blockchain technology in the energy sector according to their field of activity. The comprehensive and holistic picture provided aims to contribute to the body of knowledge of the applicability of blockchain technology within the energy sector and pave the way for further research in this field.
Toward Adaptive Disk Failure Prediction via Stream Mining

We explore machine learning for accurately predicting imminent disk failures and hence providing proactive fault tolerance for modern storage systems. Current disk failure prediction approaches are mostly offline and assume that the disk logs required for training learning models are available a priori. However, in large-scale disk deployment, disk logs are often continuously generated as an evolving data stream, in which the statistical patterns vary over time (also known as concept drift). Such a challenge motivates the need of online techniques that perform training and prediction on the incoming stream of disk logs in real time, while being adaptive to concept drift.We present StreamDFP, a general stream mining framework for disk failure prediction with concept-drift adaptation. We start with a measurement study and demonstrate the existence of concept drift on various disk models based on the datasets from Backblaze and Alibaba Cloud. Motivated by our study, we design StreamDFP with three key techniques, namely (i) online labeling, (ii) concept-drift-aware training, and (iii) general prediction, with a primary objective of making StreamDFP support various machine learning algorithms as a general frame-work. Our evaluation shows that StreamDFP improves the prediction accuracy significantly compared to without concept-drift adaptation under various settings, and achieves reasonably high stream processing performance.
A New Metaheuristic Optimization Algorithms for Brushless Direct Current Wheel Motor Design Problem

The Equilibrium Optimizer (EO), Grey Wolf Optimizer (GWO), and Whale Optimizer (WO) algorithms are being recently developed for engineering optimization problems. In this paper, the EO, GWO, and WO algorithms are applied indi... | Find, read and cite all the research you need on Tech Science Press
Autonomous planning based on spatial concepts to tidy up home environments with service robots

Tidy-up tasks by service robots in home environments are challenging in robotics applications because they involve various interactions with the environment. In particular, robots are required not only to grasp, move, and release various home objects but also to plan the order and positions for placing the objects. In this paper, we propose a novel planning method that can efficiently estimate the order and positions of the objects to be tidied up by learning the parameters of a probabilistic generative model. The model allows a robot to learn the distributions of the co-occurrence probability of the objects and places to tidy up using the multimodal sensor information collected in a tidied environment. Additionally, we develop an autonomous robotic system to perform the tidy-up operation. We evaluate the effectiveness of the proposed method by an experimental simulation that reproduces the conditions of the Tidy Up Here task of the World Robot Summit 2018 international robotics competition. The simulation results show that the proposed method enables the robot to successively tidy up several objects and achieves the best task score among the considered baseline tidy-up methods.
Security Issues and Threats Facing the Electronic Enterprise Leadership

The explosion if e.-commerce businesses have seen the growth if number if the online businesses worldwide. Consumers nowadays are opted with inline business commerce which is inconsiderably inconvenience compared to traditional transactions. Deniably, e.-commerce enterprises offer more benefits, comparing to “bricks-andmortal” traditional enterprises, but not to be missed the issues and challenges faced by the e-commerce enterprises. This study highlighted the issues and challenges of e-commerce and a set of proposed solutions to those issues and challenges. Among the issues are trust, readiness and security. For each of the issues and challenges, proposed solutions are presented.
DeM-CoD: Novel Access-Control-Based Cash on Delivery Mechanism for Decentralized Marketplace

The cash on delivery (CoD) is currently one of the significant payment mechanism in many developing countries' E-commerce systems. A transaction between a seller and a purchaser is completed when the seller has agreed to exchange packaged goods for a payment from the purchaser. Building highly trustworthy, accountable, credible, and decentralized CoD systems to trace and track physical items is a very challenging task. Several technologies and models for deploying CoD-based applications have been proposed in the literature. In most scenarios, these models have been created with the aim of accommodating collaborative processes involving multiple participants, e.g. seller, purchasers, and shippers, that belong to independent organizations. However, these approaches face several limitations and require appropriate improvement to sustain CoD-based systems' adoption further. First of all, there are no incentives for participants to act honestly. Secondly, the shippers are often kept out of the delivery chain and are affected by or not involved in any incentives or logs. Last but not least, users' privacy can be easily compromised and sensitive data collected and generated during the transactions can be easily accessed and abused. Building upon these critical insights, we propose a novel decentralized marketplace mechanism using the smart contract via blockchain technology. Our approach operates by incentivizing all the participants to act honestly and fulfill their obligations without resorting to a trusted third party. It also integrates adapted access control protocols to protect user privacy. The model's architecture shows that our approach guarantees integrity and robustness. It contributes to effectively addressing the issues listed above. A complete code solution is publicized on the authors' GitHub repository to engage further reproducibility and improvement.
Complex Human Action Recognition Using a Hierarchical Feature Reduction and Deep Learning-Based Method

Automated human action recognition is one of the most attractive and practical research fields in computer vision. In such systems, the human action labelling is based on the appearance and patterns of the motions in the video sequences; however, majority of the existing research and most of the conventional methodologies and classic neural networks either neglect or are not able to use temporal information for action recognition prediction in a video sequence. On the other hand, the computational cost of a proper and accurate human action recognition is high. In this paper, we address the challenges of the preprocessing phase, by an automated selection of representative frames from the input sequences. We extract the key features of the representative frame rather than the entire features. We propose a hierarchical technique using background subtraction and HOG, followed by application of a deep neural network and skeletal modelling method. The combination of a CNN and the LSTM recursive network is considered for feature selection and maintaining the previous information; and finally, a Softmax-KNN classifier is used for labelling the human activities. We name our model as "Hierarchical Feature Reduction & Deep Learning"-based action recognition method, or HFR-DL in short. To evaluate the proposed method, we use the UCF101 dataset for the benchmarking which is widely used among researchers in the action recognition research field. The dataset includes 101 complicated activities in the wild. Experimental results show a significant improvement in terms of accuracy and speed in comparison with eight state-of-the-art methods.
Developing Teachers’ Digital Competence: Results From a Pilot in Portugal

This article analyzes the contribution of three continuous professional development sessions for the improvement of teachers' digital competence. The contribution was measured using a pre- and post-test. It uses the European Framework for the Digital Competence of Educators (DigCompEdu) as a guiding document and the DigCompEdu Check-In questionnaire as a self-assessment tool. The results show that the contribution was positive, with proficiency levels improving in all competences and competence areas explored during the sessions. Implications are discussed.
$A^3$-FKG: Attentive Attribute-Aware Fashion Knowledge Graph for Outfit Preference Prediction

With the booming development of the online fashion industry, effective personalized recommender systems have become indispensable for the convenience they brought to the customers and the profits to the e-commercial platforms. Estimating the user’s preference towards the outfit is at the core of a personalized recommendation system. Existing works on fashion recommendation are largely centering on modelling the clothing compatibility without considering the user factor or characterizing the user’s preference over the single item. However, how to effectively model the outfits with either few or even none interactions, is yet under-explored. In this paper, we address the task of personalized outfit preference prediction via a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ttentive <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ttribute- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> ashion <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</b> nowledge <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> raph ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$A^3$</tex-math></inline-formula> -FKG), which is incorporated to build the association between different outfits with both outfit- and item- level attributes. Additionally, a two-level attention mechanism is developed to capture the user’s preference: 1) User-specific relation-aware attention layer, which captures the user’s fine-grained preferences with different focus on relations for learning outfit representation; 2) Target-aware attention layer, which characterizes the user’s latent diverse interests from his/her behavior sequences for learning user representation. Extensive experiments conducted on a large-scale fashion outfit dataset demonstrate significant improvements over other methods, which verify the excellence of our proposed framework.
Exploring complex graphs using three-dimensional quantum walks of correlated photons

Correlated photons undergo quantum walks on 3D graphs governed by their spatial and polarization degrees of freedom.
Exploring Temporal and Spatial Features for Next POI Recommendation in LBSNs

With the increasing popularity of Location-Based Social Networks (LBSNs), a significant volume of check-in data of users has been generated. Such massive data brings difficulties for the users to efficiently retrieve their desired point-of-interest (POI). As a result, POI recommendation systems have received extensive attention from academia and industry. Currently, most existing POI recommendation approaches only provide users with a fixed set of recommended POIs based on the historical check-in records of the users, and cannot achieve flexible and feasible recommendations according to different spatial and temporal situations of the users. In this paper, we propose a next POI recommendation model that will predict POIs to be visited by users in the next few hours according to their historical check-in data and current contextual information (such as the current time and locations of the users). In our model, we propose a unified approach to calculate context-aware similarities between different users by investigating the influences of both temporal and spatial features for the users. We also propose an approach to dynamically generate different POI recommendation lists for a particular user according to different current context information of the user. Compared with the state-of-the-art POI recommendation approaches, the experimental results demonstrate that our system achieves much better performance.
Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.
Mitigating Packet Reordering for Random Packet Spraying in Data Center Networks

Modern data center networks are usually constructed in multi-rooted tree topologies, which require the highly efficient multi-path load balancing to achieve high link utilization. Recent packet-level load balancer obtains high throughput by spraying packets to all paths, but it easily leads to the packet reordering under network asymmetry. The flow-level or flowlet-level load balancer avoids the packet reordering, while reducing the link utilization due to their inflexibility. To solve these problems, we design a Queueing Delay Aware Packet Spraying (QDAPS), that effectively mitigates the packet reordering for packet-level load balancer. QDAPS selects paths for packets according to the queueing delay of output buffer, and lets the packet arriving earlier be forwarded before the later packets to avoid packet reordering. Moreover, we adopt the “power-of- n-choices” paradigm on QDAPS to alleviate the impact of herd behavior under multiple forwarding engines. We compare QDAPS with ECMP, LetFlow and RPS through NS2 simulation and Mininet implementation. The test results show that QDAPS reduces flow completion time (FCT) by ~30%-50% over the state-of-the-art load balancing mechanism.
Dual-JPEG-image reversible data hiding

Because of the widespread popularity of JPEG image compression format, reversible data hiding (RDH) for JPEG images has practical application value with increasing research attention. This paper proposes a dual-image RDH method based on a modification of the discrete cosine transform (DCT) coefficients. Because of the limited embedding capacity, a dual-image strategy is introduced and improved according to the characteristics of JPEG. In our method, all nonzero DCT coefficients are embedded without the additional distortion caused by invalid modifications, and the spatial distortion caused by the modification of DCT coefficients with different frequencies varies. A dynamic allocation method is proposed to arrange the embedding capacity reasonably to achieve less distortion. The nonzero DCT coefficients can be embedded flexibly with different bits of secret data to significantly improve the embedding capacity. As far as we know, this is the first work to achieve RDH in JPEG images in a dual-image manner. Furthermore, we also apply the proposed method to the reversible authentication of JPEG images. Experimental results demonstrate the efficacy of the proposed method with high embedding capacity and satisfactory visual quality while suppressing file size expansion.
Indirectly Supervised Anomaly Detection of Clinically Meaningful Health Events from Smart Home Data

Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly Supervised Detector of Relevant Anomalies from time series data. Isudra employs Bayesian optimization to select time scales, features, base detector algorithms, and algorithm hyperparameters that increase true positive and decrease false positive detection. This optimization is driven by a small amount of example anomalies, driving an indirectly supervised approach to anomaly detection. Additionally, we enhance the approach by introducing a warm-start method that reduces optimization time between similar problems. We validate the feasibility of Isudra to detect clinically relevant behavior anomalies from over 2M sensor readings collected in five smart homes, reflecting 26 health events. Results indicate that indirectly supervised anomaly detection outperforms both supervised and unsupervised algorithms at detecting instances of health-related anomalies such as falls, nocturia, depression, and weakness.