Reviewerly/exHarmony huggingface dataset:
CNN Based Multi-view Image Quality Enhancement

We propose a CNN-based multi-view image quality enhancement (MVIQE) to improve the quality of a target image using adjacent multi-view images. Differing from the conventional single frame quality enhancement, our approach aims to improve the quality by transferring a higher quality of other input views in multi-view images as references. Our network contains an optical flow estimation module, warping layers, and image synthesis module to enhance the quality of a target image with quantization noise. Experimental results show that our method outperforms previous studies on image quality enhancement in terms of peak signal-to-noise ratio performance.
Ethereum Based Storage Aware Mining for Permissioned Blockchain Network

This work aims to reduce transaction time by integrating the mining process with the sending of transactions to reduce overall transaction time. This work also decreased the overhead of mining in a private network, by reducing the production of empty blocks in the network which saves energy, storage space, network bandwidth and computational complexity. We proposed an Auto Integrated Mining (AIM) algorithm which starts the mining process only when there is at least one pending transaction in the network, and stops mining as soon as the pending transactions are mined. The results show that the AIM algorithm reduced the number of mined blocks in a 12 hour period by producing only 24% of the original number of blocks. The proposed algorithm is also able to reduce the storage used to save chaindata by 16%. The experiment shows that the mining latency of AIM varied between 200-650ms when the number of pending transactions was between 1-1,000, and had a latency between 350-1,350ms when there were 1,000-10,000 pending transactions in the private blockchain network.
Spectrum Based Wireless Radio Traffic Classification using Hybrid Deep Neural Network

In recent years, traffic classification (TC) represents an important issue in managing and optimizing the wireless network capacity. With the growth of numerous wireless technologies, it has become more challenging to develop an efficient TC system. Deep learning (DL) based architecture provides feasible solution in today’s complex and modern scenarios where even traffic is encrypted. Traditional TC using DL based architecture exploits the byte/protocol representation of the packet at the link layer (L2) or above on the same radio network domain. It limits the efficacy of the TC systems in wireless networks using shared spectrum. Therefore, designing TC based on spectrum band generated physical layer (L1) packet using DL based architecture has received significant research attention more recently. In this article, we propose a deep hybrid neural network that incorporates a deep convolutional network with a recurrent network to classify traffic at layer 7 (L7) (e.g., application characterization and application identification) of the radio network stack using L1 packets. The proposed network can capture spatio-temporal feature correlation and use multiscale feature map to avoid vanishing gradient problem. From the simulation, it is seen that the proposed classifier can achieve 98.25% accuracy and 86.28% accuracy for the task of application characterization and application detection, respectively. Simulation results unveil that our proposed network is very promising for classifying traffic at L7 using the L1 packet.
Optimum control method of workload placement and air conditioners in a GPU server environment

High-performance computing datacenters have been rapidly growing, both in number and size. In addition to the conventional CPU servers, more GPU servers are being placed in datacenters to achieve high-speed processing such as image recognition. From our experiments, the power consumption during GPU operation is greatly affected by changes in the temperature of the server intake port. Therefore, to reduce the total power consumption of datacenters, thermal management of datacenters can address dominant problems associated with cooling such as the recirculation of hot air from the server outlets to their inlets and the appearance of hot spots. In this paper, we propose a workload placement method for environments where CPU servers and GPU servers coexist, and an optimum air-conditioning control method that cooperates with the workload placement method to reduce the total power consumption of the servers and air conditioners in datacenters. Experiment results in an actual machine environment showed that our proposed method has valid power-saving effects by adjusting cooling capacity tradeoffs between GPU servers and air conditioners.
3D Map Reconstruction From Single Satellite Image Using a Deep Monocular Depth Network

In this paper, we propose a 3D reconstruction scheme from single image with deep monocular depth estimation network, BTS (From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation) [1]. Furthermore, we expand it to height estimation focused building from remote sensing images. To address these issues, we substitute depth estimation loss function with height estimation loss function. Moreover, considering improving the quality of the building height map and looking as similar as possible to the ground-truth view, we apply building adaptive loss function.
Slardar: Scheduling information incomplete inter-datacenter deadline-aware coflows with a decentralized framework

Nowadays, geographically distributed edge clusters and datacenters generate large volumes of wide-area network (WAN) traffic. Therefore, it is critical to schedule inter-datacenter flows, especially coflows that imply application-level semantics, to improve the performance of geo-distributed applications. However, coflows often have a mixture of soft and hard deadline requirements, which cannot be efficiently guaranteed by existing methods. In this paper, a decentralized deadline-ware coflow scheduling framework, called Slardar, is proposed. It schedules inter-datacenter coflows while considering both hard and soft deadline requirements. However, it is challenging to design such a framework without complete coflow information. Hence, we first model the intra-coflow bandwidth allocation problem as a mean-field game where each datacenter aims to minimize its own cost with only local coflow information. We further prove its mean-field equilibrium is also the optimal bandwidth allocation strategy and propose a deep reinforcement learning based algorithm to find an approximate optimal numerical solution. Then, we investigate the offline inter-coflow bandwidth allocation problem by modeling it as a linear programming problem, and an online greedy heuristic algorithm is proposed to find an approximate optimal solution. Finally, we compare Slardar with state-of-art methods by large-scale simulation experiments. Experimental results show that our proposed method can greatly improve the transmission revenue and increase the soft and hard deadline guarantee ratios by 7.5% and 10% at least.
A Java typestate checker supporting inheritance

Detecting programming errors in software is increasingly important, and building tools that help developers with this task is a crucial area of investigation on which the industry depends. Leveraging on the observation that in Object-Oriented Programming (OOP) it is natural to define stateful objects where the safe use of methods depends on their internal state, we present Java Typestate Checker (JATYC), a tool that verifies Java source code with respect to typestates. A typestate defines the object's states, the methods that can be called in each state, and the states resulting from the calls. The tool statically verifies that when a Java program runs: sequences of method calls obey to object's protocols; objects' protocols are completed; null-pointer exceptions are not raised; subclasses' instances respect the protocol of their superclasses. To the best of our knowledge, this is the first OOP tool that simultaneously tackles all these aspects.
Learning on the Job: Long-Term Behavioural Adaptation in Human-Robot Interactions

In this work, we propose a framework for allowing autonomous robots deployed for extended periods of time in public spaces to adapt their own behaviour online from user interactions. The robot behaviour planning is embedded in a Reinforcement Learning (RL) framework, where the objective is maximising the level of overall user engagement during the interactions. We use the Upper-Confidence-Bound Value-Iteration (UCBVI) algorithm, which gives a helpful way of managing the exploration-exploitation trade-off for real-time interactions. An engagement model trained end-to-end generates the reward function in real-time during policy execution. We test this approach in a public museum in Lincoln (U.K.), where the robot is deployed as a tour guide for the visitors. Results show that after a couple of months of exploration, the robot policy learned to maintain the engagement of users for longer, with an increase of 22.8% over the initial static policy in the number of items visited during the tour and a 30% increase in the probability of completing the tour. This work is a promising step toward behavioural adaptation in long-term scenarios for robotics applications in social settings.
Crowd counting in complex scenes based on an attention aware CNN network

Crowd counting with density estimation has been an active research community due to its significant applications in the fields of public security, video surveillance, traffic monitoring. However, Crowd counting for congested scenes often suffers from some obstacles including severe occlusions, large scale variations, noise interference, etc. In this paper, using the first ten layers of a modified VGG16 and dilated convolution layers as the framework, we have proposed a CNN based crowd counting and density estimation model improved by the attention aware modules with residual connections. To tackle the problem of noise interference, convolutional block attention modules have been introduced into the deep network to segment the foreground and background to focus on interest information, refining deeper features of the input image. To improve information transmission and reuse, residual connections are utilized to link 3 attention blocks. Meanwhile, dilated convolution layers keep larger reception fields and obtain high-resolution density maps. The proposed method has been evaluated on three public benchmarks, i.e. Shanghai Tech A & B, UCF-QNRF and MALL, achieving the mean absolute errors of 64.6 & 8.3, 113.8 and 1.68, respectively. The results outperform some existing excellent approaches. This indicates that the proposed model has high accuracy and better robustness, which is suitable for crowd counting and density estimation in various congested scenes.
RZee: Cryptographic and statistical model for adversary detection and filtration to preserve blockchain privacy

Preserving anonymity and confidentiality of transactions has become crucial with widespread of the blockchain technology. Despite of the increased efforts for retaining privacy in blockchain networks, invasion attacks are still surfacing. Most of these attacks do not come from outsiders, but from the resident adversarial nodes. Existence of these insider adversaries lead to damaging of an organization’s internal network system and information leakage. Consequently, transaction anonymity and confidentiality are compromised. Hence, adversary detection and filtration play a vital role in protecting networks against unforeseen privacy and security threats. Therefore, in this paper, we propose RZee, a cryptographic and statistical privacy preserving model for adversary detection and filtering in blockchain networks. Firstly, RZee exploits zero-knowledge proofs to cryptographically secure the data. Secondly, based on certain identified conditions, RZee captures node behavior and blacklists malicious nodes to restrict those from injecting harmful data into the chain or viewing transactions as they propagate across the network. This adds an additional layer of protecting transactions from unauthorized and malicious intervention. The proposed framework is evaluated based on various privacy attributes as identified by literature. For this evaluation, 4 different types of experiments have been conducted. Further, the comparison of privacy perseverance of RZee with existing benchmark privacy-preserving frameworks is also done. The results depict that performance and privacy preservation in RZee exceeds the rest with an attribute score of 6.774 and a gain margin of 46.5%.
Matching wide-baseline stereo images with weak texture using the perspective invariant local feature transformer

The development of remote sensing sensor techniques allows us to now readily capture many types of indoor and outdoor scene images, which often include many weak texture regions with notable geometric distortions. Obtaining qualified matches from these difficult stereo images using existing methods is challenging. The recent achievements of deep-learning models have shown that the convolutional neural network (CNN) is adept at the image matching task. However, in practical applications, the following challenges remain: first, it is difficult to detect features in the weak texture regions of an image, and existing CNNs fail to extract discriminative image information from the quantized features of weak texture; second, as a result of the complex distortion across wide-baseline stereo images, it is difficult to match feature primitives detected in the image pair. To solve these problems, we propose the perspective invariant local feature transformer (PILFT) algorithm. Our method includes four main steps. (1) The affine scale-invariant feature transform is proposed to automatically extract the corresponding features from images, and then the perspective of the matched image is corrected to eliminate as much geometric deformation as possible. (2) The residual network is used to extract potential features from stereo images to obtain coarse and fine feature maps at different scales. (3) Using an attention mechanism, location and context information are added to the coarse level features, which are predicted by a dual-softmax function layer. (4) The features are precisely predicted on the fine feature map using the coarse reference, and the final matching results are determined by calculating the matching probability. A large number of experiments on wide-baseline weak texture images demonstrate that the proposed method has advantages over the existing algorithms in the number of matches, correct match rate, and matching accuracy. The pseudocodes of PILFT are available at https://github.com/KiltAB/PILFT.
Return of the normal distribution: Flexible deep continual learning with variational auto-encoders

Learning continually from sequentially arriving data has been a long standing challenge in machine learning. An emergent body of deep learning literature suggests various solutions, through introduction of significant simplifications to the problem statement. As a consequence of a growing focus on particular tasks and their respective benchmark assumptions, these efforts are thus becoming increasingly tailored to specific settings. Whereas approaches that leverage Variational Bayesian techniques seem to provide a more general perspective of key continual learning mechanisms, they however entail their own caveats. Inspired by prior theoretical work on solving the prevalent mismatch between prior and aggregate posterior in deep generative models, we return to a generic variational auto-encoder based formulation and investigate its utility for continual learning. Specifically, we propose to adapt a two-stage training framework towards a context conditioned variant for continual learning, where we then formulate mechanisms to alleviate catastrophic forgetting through choices of generative rehearsal or well-motivated extraction of data exemplar subsets. Although the proposed generic two-stage variational auto-encoder is not tailored towards a particular task and allows for flexible amounts of supervision, we empirically demonstrate it to surpass task-tailored methods in both supervised classification, as well as unsupervised representation learning.
Research on the Variable Selection Methods Based on Random Forests

In modern statistical models, the problem of variable selection has been paid much attention because it can enhance the explanatory rate of models and reduce the amount of calculation. This paper studies variable selection based on random forest, and compares random forest with regularization penalty methods. The specific research steps are as follows: First, the related theories of random forest and regularization penalty methods are explained. The applicability and validity of each method are compared by simulation in linear and Logistic classification models. Finally, the advantages and disadvantages of each method on different datasets are obtained through simulation and data analysis. That is, random forest has better performance when data sample is large and random forest is more stable than regularization penalty methods.
Unsupervised Anomaly Detection in Traffic Surveillance Based on Global Foreground Modeling

One of the main challenges facing foreground detection methods in the applications of video analytic, is the continuous detection of the objects after they have stopped moving. In this paper, a novel global foreground modeling (GFM) guided background subtraction method, which is capable of detecting stopped foreground objects, is presented. Specifically, the GFM method, which detects both the moving and stopped foreground objects, guides the background modeling method, which detects only the moving objects, for the purpose of adaptive background updating. The main contributions of the paper are two-fold. First, a novel GFM guided background modeling method is proposed by adaptively updating the background model based on the differences between the two foreground masks produced by the GFM method and a background modeling method, such as Zivkovic's method, respectively. As a result, the proposed method both improves the foreground detection and detects the stopped foreground objects. Second, a boosting strategy is integrated into the proposed method for eliminating the false alarms caused by noise. Real traffic videos are used to evaluate the effectiveness of the proposed method. In particular, experimental results using the real traffic videos from the New Jersey Department of Transportation (NJDOT) show that the proposed GFM guided background modeling method is able to detect stopped foreground objects, such as stopped vehicles, in real time.
Real-Time Hysteresis Foreground Detection in Video Captured by Moving Cameras

Foreground detection is an important first step in video analytics. While the stationary cameras facilitate the foreground detection due to the apparent motion between the moving foreground and the still background, the moving cameras make such a task more challenging because both the foreground and the background appear in motion in the video. To tackle this challenging problem, an innovative real-time foreground detection method is presented, that models the foreground and the background simultaneously and works for both moving and stationary cameras. In particular, first, each input video frame is partitioned into a number of blocks. Then, assuming the background takes the majority of each video frame, the iterative pyramidal implementation of the Lucas-Kanade optical flow approach is applied on the centers of the background blocks in order to estimate the global motion and compensate for the camera movements. Subsequently, each block in the background is modeled by a mixture of Gaussian distributions and a separate Gaussian mixture model is constructed for the foreground in order to enhance the classification. However, the errors in motion compensation can contaminate the foreground model with background values. The novel idea of the proposed method matches a set of background samples to their corresponding block for the most recent frames in order to avoid contaminating the foreground model with background samples. The input values that do not fit into either the statistical or the sample-based background models are used to update the foreground model. Finally, the foreground is detected by applying the Bayes classification technique to the major components in the background and foreground models, which removes the false positives caused by the hysteresis effect. Experimental evaluations demonstrate the feasibility of the proposed method in the foreground segmentation when applied to videos in public datasets.
Pareto-Optimal Covert Channels in Sensor Data Transmission

If a stream of sensor data values is transmitted as floating-point numbers, a covert channel can be realized by re-coding the numbers into a more compact number format and using the freed bits to transmit a secret message. Yet, re-coding may introduce rounding errors. By applying a uniform description for a large family of number formats, we compute a Pareto front of optimal covert channel bandwidth versus average relative error. Our only assumption is the knowledge about the statistical distribution of the exponent values in the original floating-point numbers. We exemplify such Pareto fronts for several distributions and identify number formats with relevant frequency of appearance.
PedRecNet: Multi-task deep neural network for full 3D human pose and orientation estimation

We present a multitask network that supports various deep neural network based pedestrian detection functions. Besides 2D and 3D human pose, it also supports body and head orientation estimation based on full body bounding box input. This eliminates the need for explicit face recognition. We show that the performance of 3D human pose estimation and orientation estimation is comparable to the state-of-the-art. Since very few data sets exist for 3D human pose and in particular body and head orientation estimation based on full body data, we further show the benefit of particular simulation data to train the network. The network architecture is relatively simple, yet powerful, and easily adaptable for further research and applications.
ARAGAN: A dRiver Attention estimation model based on conditional Generative Adversarial Network

Predicting driver’s attention in complex driving scenarios is becoming a hot topic due to it helps the design of some autonomous driving tasks, optimizing visual scene understanding and contributing knowledge to the decision making. We introduce ARAGAN, a driver attention estimation model based on a conditional Generative Adversarial Network (cGAN). This architecture uses some of the most challenging and novel deep learning techniques to develop this task. It fuses adversarial learning with Multi-Head Attention mechanisms. To the best of our knowledge, this combination has never been applied to predict driver’s attention. Adversarial mechanism learns to map an attention image from an RGB traffic image while mapping the loss function. Attention mechanism contributes to the deep learning paradigm finding the most interesting feature maps inside the tensors of the net. In this work, we have adapted this concept to find the saliency areas in a driving scene. An ablation study with different architectures has been carried out, obtained the results in terms of some saliency metrics. Besides, a comparison with other state-of-the-art models has been driven, outperforming results in accuracy and performance, and showing that our proposal is adequate to be used on real-time applications. ARAGAN has been trained in BDDA and tested in BDDA and DADA2000, which are two of the most complex driver attention datasets available for research.