Reviewerly/exHarmony huggingface dataset:
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning

Although deep convolutional neural networks (CNNs) have demonstrated remarkable performance on multiple computer vision tasks, researches on adversarial learning have shown that deep models are vulnerable to adversarial examples, which are crafted by adding visually imperceptible perturbations to the input images. Most of the existing adversarial attack methods only create a single adversarial example for the input, which just gives a glimpse of the underlying data manifold of adversarial examples. An attractive solution is to explore the solution space of the adversarial examples and generate a diverse bunch of them, which could potentially improve the robustness of real-world systems and help prevent severe security threats and vulnerabilities. In this paper, we present an effective method, called Hamiltonian Monte Carlo with Accumulated Momentum (HMCAM), aiming to generate a sequence of adversarial examples. To improve the efficiency of HMC, we propose a new regime to automatically control the length of trajectories, which allows the algorithm to move with adaptive step sizes along the search direction at different positions. Moreover, we revisit the reason for high computational cost of adversarial training under the view of MCMC and design a new generative method called Contrastive Adversarial Training (CAT), which approaches equilibrium distribution of adversarial examples with only few iterations by building from small modifications of the standard Contrastive Divergence (CD) and achieve a trade-off between efficiency and accuracy. Both quantitative and qualitative analysis on several natural image datasets and practical systems have confirmed the superiority of the proposed algorithm.
Storage, partitioning, indexing and retrieval in Big RDF frameworks: A survey

Resource Description Framework (RDF) is increasingly being used to model data on the web. RDF model was designed to support easy representation and exchange of information on the web. RDF is queried using SPARQL, a standard query language recommended by W3C. The growth in acceptance of RDF format can be attributed to its flexible and reusable nature. The size of RDF data is steadily increasing as many government organizations and companies are using RDF for data representation and exchange. This resulted in the need for developing distributed RDF frameworks that can efficiently manage RDF data on large scale i.e. Big RDF data. These scalable distributed RDF data management systems competent enough to handle Big RDF data can also be termed as Big RDF frameworks. The proliferation of RDF data has made RDF data management a difficult task. In this survey, we provide an extensive literature on Big RDF frameworks from the aspect of storage, partitioning, indexing, query optimization and processing. A taxonomy of the tools and technologies used for storage and retrieval of Big RDF data in these systems has been presented. The comparative evaluation of some Big RDF frameworks based on query performance and our observations from this evaluation are described. The research challenges identified during the study of these systems are elaborated to suggest promising directions for future research.
When Bitstream Prior Meets Deep Prior

The standard paradigm of video super-resolution (SR) is to generate the spatial-temporal coherent high-resolution (HR) sequence from the corresponding low-resolution (LR) version which has already been decoded from the bitstream. However, a highly practical while relatively under-studied way is enabling the built-in SR functionality in the decoder, in the sense that almost all videos are compactly represented. In this paper, we systematically investigate the SR of compressed LR videos by leveraging the interactivity between decoding prior and deep prior. By fully exploiting the compact video stream information, the proposed bitstream prior embedded SR framework achieves compressed video SR and quality enhancement simultaneously in a single feed-forward process. More specifically, we propose a motion vector guided multi-scale local attention module that explicitly exploits the temporal dependency and suppresses coding artifacts with substantially economized computational complexity. Moreover, a scale-wise deep residual-in-residual network is learned to reconstruct the SR frames from the multi-scale fused features. To facilitate the research of compressed video SR, we also build a large-scale dataset with compressed videos of diverse content, including ready-made diversified kinds of side information extracted from the bitstream. Both quantitative and qualitative evaluations show that our model achieves superior performance for compressed video SR, and offers competitive performance compared to the sequential combinations of the state-of-the-art methods for compressed video artifacts removal and SR.
Towards Accuracy-Fairness Paradox

Machine learning fairness concerns about the biases towards certain protected or sensitive group of people when addressing the target tasks. This paper studies the debiasing problem in the context of image classification tasks. Our data analysis on facial attribute recognition demonstrates (1) the attribution of model bias from imbalanced training data distribution and (2) the potential of adversarial examples in balancing data distribution. We are thus motivated to employ adversarial example to augment the training data for visual debiasing. Specifically, to ensure the adversarial generalization as well as cross-task transferability, we propose to couple the operations of target task classifier training, bias task classifier training, and adversarial example generation. The generated adversarial examples supplement the target task training dataset via balancing the distribution over bias variables in an online fashion. Results on simulated and real-world debiasing experiments demonstrate the effectiveness of the proposed solution in simultaneously improving model accuracy and fairness. Preliminary experiment on few-shot learning further shows the potential of adversarial attack-based pseudo sample generation as alternative solution to make up for the training data lackage.
Logical Clifford Synthesis for Stabilizer Codes

Quantum error-correcting codes are used to protect qubits involved in quantum computation. This process requires logical operators, acting on protected qubits, to be translated into physical operators (circuits) acting on physical quantum states. We propose a mathematical framework for synthesizing physical circuits that implement logical Clifford operators for stabilizer codes. Circuit synthesis is enabled by representing the desired physical Clifford operator in $\mathbb{C}^{N \times N}$ as a partial $2m \times 2m$ binary symplectic matrix, where $N = 2^m$. We state and prove two theorems that use symplectic transvections to efficiently enumerate all binary symplectic matrices that satisfy a system of linear equations. As a corollary of these results, we prove that for an $[\![ m,k ]\!]$ stabilizer code every logical Clifford operator has $2^{r(r+1)/2}$ symplectic solutions, where $r = m-k$, up to stabilizer degeneracy. The desired physical circuits are then obtained by decomposing each solution into a product of elementary symplectic matrices, that correspond to elementary circuits. This enumeration of all physical realizations enables optimization over the ensemble with respect to a suitable metric. Furthermore, we show that any circuit that normalizes the stabilizer of the code can be transformed into a circuit that centralizes the stabilizer, while realizing the same logical operation. Our method of circuit synthesis can be applied to any stabilizer code, and this paper discusses a proof of concept synthesis for the $[\![ 6,4,2 ]\!]$ CSS code. Programs implementing the algorithms in this paper, which includes routines to solve for binary symplectic solutions of general linear systems and our overall LCS (logical circuit synthesis) algorithm, can be found at: https://github.com/nrenga/symplectic-arxiv18a
DMVOS

Though recent methods on semi-supervised video object segmentation (VOS) have achieved an appreciable improvement of segmentation accuracy, it is still hard to get an adequate speed-accuracy balance when facing real-world application scenarios. In this work, we propose Discriminative Matching for real-time Video Object Segmentation (DMVOS), a real-time VOS framework with high-accuracy to fill this gap. Based on the matching mechanism, our framework introduces discriminative information through the Isometric Correlation module and the Instance Center Offset module. Specifically, the isometric correlation module learns a pixel-level similarity map with semantic discriminability, and the instance center offset module is applied to exploit the instance-level spatial discriminability. Experiments on two benchmark datasets show that our model achieves state-of-the-art performance with extremely fast speed, for example, J&F of 87.8% on DAVIS-2016 validation set with 35 milliseconds per frame.
Real-time and private spatio-temporal data aggregation with local differential privacy

Technology and usage advances in wireless communication and smart mobile devices with localization capabilities enable a large number of emerging applications of location-based services, e.g. mobile crowdsourcing applications, which are facilitating our daily life. However, collecting and sharing location data to service providers of applications will give rise to mobile users' concerns on their privacy, especially location privacy. In this paper, we investigate the problem of real-time spatio-temporal data aggregation with privacy preservation in the local setting. In response to this, we propose a systematic solution based on stringent local differential privacy preservation technique to deal with the problem. Firstly, we introduce a novel definition of (ε, δ)-local differential privacy with the ability to capture the temporal correlation in spatio-temporal data and provide differential privacy preservation. Secondly, we develop an efficient framework of generalized randomized response (GRR) based real-time and private spatio-temporal data aggregation with an untrusted server. Finally, we conduct experiments on two real-world datasets to evaluate our framework. The results show that our GRR based framework significantly outperforms that based on PIM and LRM in data utility and demonstrate its superiority to achieve better trade-off of privacy and utility for real-time spatio-temporal data aggregation with stringent privacy preservation.
Comparative Sentiment Analysis of App Reviews

Google app market captures the school of thought of users via ratings and text reviews. The critique's viewpoint regarding an app is proportional to their satisfaction level. Consequently, this helps other users to gain insights before downloading or purchasing the apps. The potential information from the reviews can't be extracted manually, due to its exponential growth. Sentiment analysis, by machine learning algorithms employing NLP, is used to explicitly uncover and interpret the emotions. This study aims to perform the sentiment classification of the app reviews and identify the university students' behavior towards the app market. We applied machine learning algorithms using TF-IDF text representation scheme and the performance was evaluated on ensemble learning method. Our model was trained on Google reviews and tested on students' reviews. SVM recorded the maximum accuracy(93.37%), Fscore(0.88) on trigram+TF-IDF scheme. Bagging enhanced the performance of LR and NB with accuracy of 87.80% and 85.5% respectively.
Learner’s Dilemma: IoT Devices Training Strategies in Collaborative Deep Learning

With the growth of Internet of Things (IoT) and mobile edge computing, billions of smart devices are interconnected to develop applications used in various domains including smart homes, healthcare and smart manufacturing. Deep learning has been extensively utilized in various IoT applications which require huge amount of data for model training. Due to privacy requirements, smart IoT devices do not release data to a remote third party for their use. To overcome this problem, collaborative approach to deep learning, also known as Collaborative Deep Learning (CDL) has been largely employed in data-driven applications. This approach enables multiple edge IoT devices to train their models locally on mobile edge devices. In this paper, we address IoT device training problem in CDL by analyzing the behavior of mobile edge devices using a game-theoretic model, where each mobile edge device aims at maximizing the accuracy of its local model at the same time limiting the overhead of participating in CDL. We analyze the Nash Equilibrium in an N-player static game model. We further present a novel clusterbased fair strategy to approximately solve the CDL game to enforce mobile edge devices for cooperation. Our experimental results and evaluation analysis in a real-world smart home deployment show that 80% mobile edge devices are ready to cooperate in CDL, while 20% of them do not train their local models collaboratively.
RETRACTED: Sports image detection based on particle swarm optimization algorithm

Athletes can still insist on the practicality of sports practice in today's sports environment by locating image detection technology, video and video files, as well as broadcast data detection key format. However, from the most advanced of the situation in the country, goal can confirm that the light detection dong area is still immature. So let's take a look at the video cloth applied to detect motion pictures in ages to search for software. Method are looking at the side of detection, grayscale processing, grasping the project, target identification, and see the light detection era. The actual sports programs and the desire to meet the different needs of the sports image detection mix. At the same time, in the present study, found that the popularity of athletes and judge, motion recognition of motor behavior. Along with affirmed the effectiveness of this method of research, have created a platform for inspection. Results show that the research method is always practical, can provide a theoretical basis for future research. A set of rules contained in a hierarchy: First, the Particle Swarm Optimize [PSO] algorithm is used to the annotation image per day. In the experiment, 3 sets of data were used to evaluate a set of rules, the results show that the performance of the algorithm.
Performance Analysis of Augmented Reality Based on Vuforia Using 3D Marker Detection

In the field of education, especially engineering, there is evidence of skills gaps. According to a survey in the UK covers 2017-2024, the demand for electricity projected to increase. In fact, at the same time, 23.7% of electricity workers are predicted to experience retirement. Therefore, skills gaps need to be minimized through the development of effective and credible learning media. One of the innovations in developing instructional media is applying augmented reality. However, augmented reality performance needs testing to find out the factors that influence the success of object detection to provide maximum results when implemented in learning media. Several existing studies analyze the performance of augmented reality based on the NyARToolkit library, template matching, and the Metaio Mobile SDK. In this research, the performance of 3D object detection performed on augmented reality based on the Vuforia. The research scenarios based on the results of the analysis of the Vuforia working principle. The study conducted with three angles of shooting and several variations of light intensity and distance of the object. The research also conducted by covering part of the object's surface. The results showed that the Vuforia was able to detect objects well in several scenarios that applied with a success rate of 87.5%. The success rate of object detection strongly influenced by the surface area of the detected object and the intensity of the light space.
Ultrahigh vacuum packaging and surface cleaning for quantum devices

We describe design, implementation, and performance of an ultra-high vacuum (UHV) package for superconducting qubit chips or other surface sensitive quantum devices. The UHV loading procedure allows for annealing, ultra-violet light irradiation, ion milling, and surface passivation of quantum devices before sealing them into a measurement package. The package retains vacuum during the transfer to cryogenic temperatures by active pumping with a titanium getter layer. We characterize the treatment capabilities of the system and present measurements of flux tunable qubits with an average T1 = 84 µs and T2echo=134 μs after vacuum-loading these samples into a bottom loading dilution refrigerator in the UHV-package.
REIN the RobuTS: Robust DNN-Based Image Recognition in Autonomous Driving Systems

In recent years, the neural network (NN) has shown its great potential in image recognition tasks of autonomous driving systems, such as traffic sign recognition, pedestrian detection, etc. However, theoretically well-trained NNs usually fail their performance when facing real-world scenarios. For example, adverse real-world conditions, e.g., bad weather and lighting conditions, can introduce different physical variations and cause considerable accuracy degradation. As for now, the generalization capability of NNs is still one of the most critical challenges for the autonomous driving system. To facilitate the robust image recognition tasks, in this work, we build the RobuTS dataset: a comprehensive Robust Traffic Sign Recognition dataset, which includes images with different environmental variations, e.g., rain, fog, darkening, and blurring. Then to enhance the NN's generalization capability, we propose two generalization-enhanced training schemes: 1) REIN for robust training without data in adverse scenarios and 2) Self-Teaching (ST) for robust training with unlabeled adverse data. The great advantages of such two training schemes are they are data-free (REIN) and label-free (ST), thus effectively reducing the huge human efforts/cost of on-road driving data collection, as well as the expensive manual data annotation. We conduct extensive experiments to validate our methods' performance on both classification and detection tasks. For classification tasks, our proposed training algorithms could consistently improve model performance by +15%-25% (REIN) and +16%-30% (ST) in all adverse scenarios of our RobuTS datasets. For detection tasks, our ST could also improve the detector's performance by +10.1 mean average precision (mAP) on Foggy-Cityscapes, outperforming previous state-of-the-art works by +2.2 mAP.
The role of the data paper in scholarly communication

Abstract Data sharing and reuse promise many benefits to science, but many researchers are reluctant to share and reuse data. Data papers, published as peer‐reviewed articles that provide descriptive information about specific datasets, are a potential solution as they may incentivize sharing by providing a mechanism for data producers to get citation credit and support reuse by providing contextual information about dataset production. Data papers can receive many citations. However, does citation of a data paper mean reuse of the underlying dataset? This paper presents preliminary findings from a content‐based citation analysis of data papers ( n = 103) published in two specialized data journals, one in earth sciences and one in physical and chemical sciences. We conclude that while the genre of data papers facilitates some data sharing and reuse, they fail to live up to their full potential. Further, practices of reuse of datasets from data papers vary considerably between disciplines. We propose measures for academic publishers to enhance the data paper's role in scholarly communication to attract more attention from researchers and to inform discipline‐specific policy and practices related to data publication.
On the Minimization of Sobolev Norms of Time-Varying Graph Signals: Estimation of New Coronavirus Disease 2019 Cases

ever, this model does not take into account the underlying spatial information of the problem, i.e., the model does not associate the number of new cases in nearby localities.In the same way, not all countries are able to effectively sample the number of infected people in their population.In this work, we model the number of new COVID-19 cases as a reconstruction of time-varying graph signals [5][6][7].Our algorithm associates a graph to the geographical localization of cities or countries, and the number of new COVID-19 cases is represented by a graph signal that evolves in time [8][9][10].Intuitively, the prior assumption of this work is that the number of new COVID-19 cases is smooth both in the graph and in time, i.e., the number of new COVID-19 cases should be similar in nearby localities, and the change in time is progressive.Unlike previous mathematical models of infectious diseases, this paper takes into account the underlying spatial information of the problem.In the same way, we propose a new time-varying graph signals reconstruction algorithm based on the minimization of the Sobolev norm of Graph Signal Processing (GSP) [11].Our method outperforms state-of-the-art algorithms in time-varying graph signals, while showing convergence rate benefits based on the Hessian of the underlying problem.The main contributions of this paper are summarized as follows:• To the best of our knowledge, concepts of GSP are introduced for the first time in the domain of infectious diseases modeling.• A new algorithm based on the minimization of the Sobolev norm is introduced in the problem of timevarying graph signals reconstruction.• We show the convergence rate benefits on the minimization of the Sobolev norm for the reconstruction of time-varying graph signals.The rest of the paper is organized as follows.Section 2 explains the basic concepts and the proposed method.Section 3 introduces the experimental framework.Finally, Sections 4 and 5 present the results and conclusions, respectively.
QoE-Aware Rendering Service Allocation in Fog-Assisted Cloud Gaming Environments

Cloudification of heterogeneous networks along with the virtualization of gaming resources, allow the game providers to move into the cloud gaming era. Cloud gaming offloads computation-intensive tasks to the cloud in order to alleviate the computational burden of end-users’ devices, but introduces delay which deteriorates their Quality of Experience (QoE). To solve the optimization problem of rendering service allocation with QoE guarantees and reduce the operational expenditures of game providers, fog computing is deemed a promising solution. However, the optimization problem is NP-hard since it falls into the category of the well-known Facility Location Problems. In the current paper, a distributed policy is proposed, named QoE-Aware Rendering Service Allocation (QoERSA), which takes advantage of strictly local network information, to reallocate the rendering services inside the fog computing network, towards an optimal delay-sensitive placement, given the QoE gaming constraints and the need for capital investment reduction. To this end, it employs Mean Opinion Score objective QoE assessment and simple mechanics that move, copy and merge services with high elasticity and scalability. QoERSA is evaluated through simulations that highlight its efficiency and showcase its capability of overall service access and deployment cost reduction.