Reviewerly/exHarmony huggingface dataset:
New image denoising algorithm via improved deep convolutional neural network with perceptive loss

In order to remove the noise effectively and obtain the high quality denoised image, the new image denoising algorithm is proposed based on improved convolutional neural network with joint loss function (MP-DCNN). Firstly, the end to end adaptive residual convolutional neural network is constructed by using adaptive convolutional and residual learning for modeling the noisy image. Secondly, the input noise image features are extracted by adaptive convolution and leaky rectified linear units (Leaky ReLU) active function in the first half of the network, and then the image features reconstruction is performed by the other structure and convolutional operation of the network. Finally, the initial denoised image is obtained using the mean square error (MSE) loss function which can learn and optimize the extracted feature. Meanwhile, the initial denoising image is input to the pre-trained SegNet which can obtain a better learn of the image edge information. The final denoised image is obtained by using the perceptive loss function. To demonstrate the effectiveness of the proposed image denoising algorithm, the performance of the proposed algorithm is experimentally verified on a variety of noise levels. The experimental results show that the proposed algorithm can obtain superior performance both in visual quality and two evaluation indexes such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), compared with other state-of-the-art denoising algorithms. Especially for high noisy environment with a noise standard deviation of 60, the algorithm finally obtains 24.26 dB of average PSNR in the test dataset. Its average PSNR is about 0.5 dB and 1 dB higher than DnCNN and BM3D, respectively. At the same time, the proposed method obtain denoising image with no boundary artifact finally. The proposed algorithm can significantly improve the performance of the image denoising system from the perspective of expert and intelligent systems.
Cost optimized Hybrid Genetic-Gravitational Search Algorithm for load scheduling in Cloud Computing

In cloud computing, cost optimization is a prime concern for load scheduling. The swarm based meta-heuristics are prominently used for load scheduling in distributed computing environment. The conventional load scheduling approaches require a lot of resources and strategies which are non-adaptive and static in the computation, thereby increasing the response time, waiting time and the total cost of computation. The swarm intelligence-based load scheduling is adaptive, intelligent, collective, random, decentralized, self-collective, stochastic and is based on biologically inspired mechanisms than the other conventional mechanisms. The genetic algorithm schedules the particles based on mutation and crossover techniques. The force and acceleration acting on the particle helps in the finding the velocity and position of the next particle. The best position of the particles is assigned to cloudlets to be executed on the virtual machines in the cloud. The paper proposes a new load scheduling technique, Hybrid Genetic-Gravitational Search Algorithm (HG-GSA) for reducing the total cost of computation. The total computational cost includes cost of execution and transfer. It works on hybrid crossover technique based gravitational search algorithm for searching the best position of the particle in the search space. The best position of the particle is used calculating the force. The HG-GSA is compared to the existing approaches in the CloudSim simulator. By the convergence and statistical analysis of the results, the proposed HG-GSA approach reduces the total cost of computation considerably as compared to existing PSO, Cloudy-GSA and LIGSA-C approaches.
SOTorrent: Studying the Origin, Evolution, and Usage of Stack Overflow Code Snippets

Stack Overflow (SO) is the most popular question-and-answer website for software developers, providing a large amount of copyable code snippets. Like other software artifacts, code on SO evolves over time, for example when bugs are fixed or APIs are updated to the most recent version. To be able to analyze how code and the surrounding text on SO evolves, we built SOTorrent, an open dataset based on the official SO data dump. SOTorrent provides access to the version history of SO content at the level of whole posts and individual text and code blocks. It connects code snippets from SO posts to other platforms by aggregating URLs from surrounding text blocks and comments, and by collecting references from GitHub files to SO posts. Our vision is that researchers will use SOTorrent to investigate and understand the evolution and maintenance of code on SO and its relation to other platforms such as GitHub.
Lessons from Building Acoustic Models with a Million Hours of Speech

This is a report of our lessons learned building acoustic models from 1 Million hours of unlabeled speech, while labeled speech is restricted to 7,000 hours. We employ student/teacher training on unlabeled data, helping scale out target generation in comparison to confidence model based methods, which require a decoder and a confidence model. To optimize storage and to parallelize target generation, we store high valued logits from the teacher model. Introducing the notion of scheduled learning, we interleave learning on unlabeled and labeled data. To scale distributed training across a large number of GPUs, we use BMUF with 64 GPUs, while performing sequence training only on labeled data with gradient threshold compression SGD using 16 GPUs. Our experiments show that extremely large amounts of data are indeed useful; with little hyper-parameter tuning, we obtain relative WER improvements in the 10 to 20% range, with higher gains in noisier conditions.
The Motivation of Technological Scenarios in Augmented Reality (AR): Results of Different Experiments

Augmented Reality (AR) is an emergent technology that is acquiring more and more relevance in teaching every day. Together with mobile technology, this combination arises as one of the most effective binomials to support significant and ubiquitous learning. Nevertheless, this binomial can only prove valid if the student is motivated to use it during the learning process. An attempt was made through the implementation of Keller’s Instructional Material Motivational Survey model o determine the degree of motivation of Pedagogy, Medicine and Art students from the University of Seville for using AR-enriched notes available by means of mobile devices in the classroom. Three applications designed for the subjects of Educational Technology, Anatomy and Art served to assess it positively in terms of the motivation raised by the participation in the experiment, as well as regarding academic performance improvement. It can additionally be stated that our main finding was a link between students’ motivation to use the enriched notes and the performance obtained in the subject in which they use them. Evidence was also found that the utilization of Augmented Reality benefits the learning process.
Large-scale multiview 3D hand pose dataset

Accurate visual hand pose estimation at joint level has several applications for human-robot interaction, natural user interfaces and virtual/augmented reality applications. However, it is still an open problem being addressed by the computer vision community. Recent novel deep learning techniques may help circumvent the limitations of standard approaches. However, they require large amounts of accurate annotated data. Hand pose datasets that have been released so far present issues such as limited number of samples, inaccurate data or high-level annotations. Moreover, most of them are focused on depth-based approaches, providing only depth information (missing RGB data). In this work, we present a novel multiview hand pose dataset in which we provide hand color images and different kind of annotations for each sample, i.e. the bounding box and the 2D and 3D location on the joints in the hand. Furthermore, we introduce a simple yet accurate deep learning architecture for real-time robust 2D hand pose estimation. Then, we conduct experiments that show how the use of the proposed dataset in the training stage produces accurate results for 2D hand pose estimation using a single color camera.
GhostVLAD for Set-Based Face Recognition

The objective of this paper is to learn a compact representation of image sets for template-based face recognition. We make the following contributions: first, we propose a network architecture which aggregates and embeds the face descriptors produced by deep convolutional neural networks into a compact fixed-length representation. This compact representation requires minimal memory storage and enables efficient similarity computation. Second, we propose a novel GhostVLAD layer that includes ghost clusters, that do not contribute to the aggregation. We show that a quality weighting on the input faces emerges automatically such that informative images contribute more than those with low quality, and that the ghost clusters enhance the network’s ability to deal with poor quality images. Third, we explore how input feature dimension, number of clusters and different training techniques affect the recognition performance. Given this analysis, we train a network that far exceeds the state-of-the-art on the IJB-B face recognition dataset. This is currently one of the most challenging public benchmarks, and we surpass the state-of-the-art on both the identification and verification protocols.
Convergence of the Iterates in Mirror Descent Methods

We consider centralized and distributed mirror descent (MD) algorithms over a finite-dimensional Hilbert space, and prove that the problem variables converge to an optimizer of a possibly nonsmooth function when the step sizes are square summable but not summable. Prior literature has focused on the convergence of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">function value</i> to its optimum. However, applications from distributed optimization and learning in games require the convergence of the variables to an optimizer, which is generally not guaranteed without assuming strong convexity of the objective function. We provide numerical simulations comparing entropic MD and standard subgradient methods for the robust regression problem.
π-Net

Sequential Recommendation (SR) is the task of recommending the next item based on a sequence of recorded user behaviors. We study SR in a particularly challenging context, in which multiple individual users share a single account (shared-account) and in which user behaviors are available in multiple domains (cross-domain). These characteristics bring new challenges on top of those of the traditional SR task. On the one hand, we need to identify different user behaviors under the same account in order to recommend the right item to the right user at the right time. On the other hand, we need to discriminate the behaviors from one domain that might be helpful to improve recommendations in the other domains.
Multimodal Image Super-Resolution via Joint Sparse Representations Induced by Coupled Dictionaries

Real-world data processing problems often involve various image modalities associated with a certain scene, including RGB images, infrared images or multi-spectral images. The fact that different image modalities often share certain attributes, such as certain edges, textures and other structure primitives, represents an opportunity to enhance various image processing tasks. This paper proposes a new approach to construct a high-resolution (HR) version of a low-resolution (LR) image given another HR image modality as reference, based on joint sparse representations induced by coupled dictionaries. Our approach, which captures the similarities and disparities between different image modalities in a learned sparse feature domain in \emph{lieu} of the original image domain, consists of two phases. The coupled dictionary learning phase is used to learn a set of dictionaries that couple different image modalities in the sparse feature domain given a set of training data. In turn, the coupled super-resolution phase leverages such coupled dictionaries to construct a HR version of the LR target image given another related image modality. One of the merits of our sparsity-driven approach relates to the fact that it overcomes drawbacks such as the texture copying artifacts commonly resulting from inconsistency between the guidance and target images. Experiments on real multimodal images demonstrate that incorporating appropriate guidance information via joint sparse representation induced by coupled dictionary learning brings notable benefits in the super-resolution task with respect to the state-of-the-art. Of particular relevance, the proposed approach also demonstrates better robustness than competing deep-learning-based methods in the presence of noise.
LPTD: Achieving lightweight and privacy-preserving truth discovery in CIoT

In recent years, cognitive Internet of Things (CIoT) has received considerable attention because it can extract valuable information from various Internet of Things (IoT) devices. In CIoT, truth discovery plays an important role in identifying truthful values from large scale data to help CIoT provide deeper insights and value from collected information. However, the privacy concerns of IoT devices pose a major challenge in designing truth discovery approaches. Although existing schemes of truth discovery can be executed with strong privacy guarantees, they are not efficient or cannot be applied in real-life CIoT applications. This article proposes a novel framework for lightweight and privacy-preserving truth discovery called LPTD-I, which is implemented by incorporating fog and cloud platforms, and adopting the homomorphic Paillier encryption and one-way hash chain techniques. This scheme not only protects devices’ privacy, but also achieves high efficiency. Moreover, we introduce a fault tolerant (LPTD-II) framework which can effectively overcome malfunctioning CIoT devices. Detailed security analysis indicates the proposed schemes are secure under a comprehensively designed threat model. Experimental simulations are also carried out to demonstrate the efficiency of the proposed schemes.
A survey on zero knowledge range proofs and applications

In last years, there has been an increasing effort to leverage distributed ledger technology (DLT), including blockchain. One of the main topics of interest, given its importance, is the research and development of privacy mechanisms, as for example is the case of zero knowledge proofs (ZKP). ZKP is a cryptographic technique that can be used to hide information that is put into the ledger, while still allowing to perform validation of this data. In this work we describe different strategies to construct zero knowledge range proofs (ZKRP), as for example the scheme proposed by Boudot (in: Bart (ed) Advances in cryptology—EUROCRYPT 2000, Springer, Berlin, 2000) in 2001; the one proposed by Camenisch et al. (in: Josef (ed) Advances in cryptology—ASIACRYPT 2008, Springer, Berlin, 2008), and bulletproofs (Bünz et al., in: 2018 IEEE symposium on security and privacy (SP), 2018), proposed in 2017. We also compare these strategies and discuss possible use cases. Since bulletproofs (Bünz et al. 2018) is the most efficient construction, we will give a detailed description of its algorithms and optimizations. Bulletproofs is not only more efficient than previous schemes, but also avoids the trusted setup, which is a requirement that is not desirable in the context of DLT and blockchain. In case of cryptocurrencies, if the setup phase is compromised, it would be possible to generate money out of thin air. Interestingly, bulletproofs can also be used to construct generic ZKP, in the sense that it can be used to prove generic statements, and thus it is not only restricted to ZKRP, but it can be used for any kind of proof of knowledge. Hence Bulletproofs leads to a more powerful tool to provide privacy for DLT. Here we describe in detail the algorithms involved in Bulletproofs protocol for ZKRP. Also, we present our implementation, which was open sourced (Morais et al., in: Zero knowledge range proof implementation, 2018. https://github.com/ing-bank/zkrangeproof ).
Can We Track Targets From Space? A Hybrid Kernel Correlation Filter Tracker for Satellite Video

Despite the great success of correlation filter-based trackers in visual tracking, it is questionable whether they can still perform on the satellite video data, acquired by a satellite or space station very high above the earth. The difficulty lies in that the targets usually occupy only a few pixels compared with the image size of over one million pixels and almost melt into the similar background. Since correlation filter models strongly depend on the quality of features and the spatial layout of the tracked object, they would probably fail on satellite video tracking tasks. In this paper, we propose a hybrid kernel correlation filter (HKCF) tracker employing two complementary features adaptively in a ridge regression framework. One feature is the optical flow that can detect variation pixels of the target. The other one is the histogram of oriented gradient that can capture the contour and texture information in the target, and an adaptive fusion strategy is proposed to employ the strengths of both features in different satellite videos. Quantitative evaluations are performed on six real satellite video data sets. The results show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames/s.
Enterprise cybersecurity training and awareness programs: Recommendations for success

To help employees recognize and change their computing security behavior, organizations need to invest in cybersecurity training and awareness programs to encourage their employees’ active engagement in complying with their security policies. However, many organizations’ cybersecurity training and awareness programs fail to achieve their goals as employees feel bored with such training programs and lack enthusiasm to participate in them. Highlighting the criticality of the success of cybersecurity training and awareness programs in organizations, this paper identifies best practices and provides actionable insights (relating cyber awareness to employees’ personal life, reinforcing security procedures and guidelines, instilling a “relaxed alert” state of employees, and minimizing security fatigue for employees) that will help enterprises develop and implement economical, effective, and engaging cybersecurity training and awareness programs.
HWNet v2: an efficient word image representation for handwritten documents

We present a framework for learning an efficient holistic representation for handwritten word images. The proposed method uses a deep convolutional neural network with traditional classification loss. The major strengths of our work lie in: (i) the efficient usage of synthetic data to pre-train a deep network, (ii) an adapted version of the ResNet-34 architecture with the region of interest pooling (referred to as HWNet v2) which learns discriminative features for variable sized word images, and (iii) a realistic augmentation of training data with multiple scales and distortions which mimics the natural process of handwriting. We further investigate the process of transfer learning to reduce the domain gap between synthetic and real domain and also analyze the invariances learned at different layers of the network using visualization techniques proposed in the literature. Our representation leads to a state-of-the-art word spotting performance on standard handwritten datasets and historical manuscripts in different languages with minimal representation size. On the challenging iam dataset, our method is first to report an mAP of around 0.90 for word spotting with a representation size of just 32 dimensions. Furthermore, we also present results on printed document datasets in English and Indic scripts which validates the generic nature of the proposed framework for learning word image representation.
Polarization Multiplexing Reservoir Computing Based on a VCSEL With Polarized Optical Feedback

We propose to realize the parallel tasks processing by reservoir computing (RC) using two polarization-resolved modes in a single vertical-cavity surface-emitting laser (VCSEL). Two feedback cases, parallelly-polarized optical feedback (PPOF) and orthogonally-polarized optical feedback, are considered in the VCSEL. A time series prediction task and a waveform recognition task are employed to examine the parallel tasks processing performance in two polarization-resolved modes of the VCSEL-based RC system. Through numerical simulations, the dependences of parallel tasks processing performance of the VCSEL-based RC system on the feedback strength, injection strength are analyzed carefully and compared for the two feedback cases. Besides, the effects of injection current, frequency detuning, spontaneous emission noise, and the number of virtual nodes are also examined. It is found that, for both feedback cases, good performance of parallel tasks processing in two polarization modes of the VCSEL-based RC system could be obtained in broad parameter regions, and in these regions a better performance is achieved for the PPOF case. This proposed polarization multiplexing RC based on VCSEL permits parallel tasks processing in two polarization modes in a single VCSEL, and hence is interesting and valuable for low power consumption neuromorphic photonics systems.
Cooperative Localization Under Limited Connectivity

In this article, we report two decentralized multiagent cooperative localization algorithms in which, to reduce the communication cost, interagent state estimate correlations are not maintained but accounted for implicitly. In our first algorithm, to guarantee filter consistency, we account for unknown interagent correlations via an upper bound on the joint covariance matrix of the agents. In the second method, we use an optimization framework to estimate the unknown interagent cross-covariance matrix. In our algorithms, each agent localizes itself in a global coordinate frame using a local filter driven by local dead reckoning and occasional absolute measurement updates, and opportunistically corrects its pose estimate whenever it can obtain relative measurements with respect to other mobile agents. To process any relative measurement, only the agent that has taken the measurement and the agent the measurement is taken from need to communicate with each other. Consequently, our algorithms are decentralized algorithms that do not impose restrictive network-wide connectivity condition. Moreover, we make no assumptions about the type of agents or relative measurements. We demonstrate our algorithms in simulation and a robotic experiment.