Reviewerly/exHarmony huggingface dataset:
Guaranteed Safe Reachability-based Trajectory Design for a High-Fidelity Model of an Autonomous Passenger Vehicle

Trajectory planning is challenging for autonomous cars since they operate in unpredictable environments with limited sensor horizons. To incorporate new information as it is sensed, planning is done in a loop, with the next plan being computed as the previous plan is executed. Reachability-based Trajectory Design (RTD) is a recent, provably safe, realtime algorithm for trajectory planning. RTD consists of an offline Forward Reachable Set (FRS) computation of the vehicle tracking parameterized trajectories; and online trajectory optimization using the FRS to map obstacles to constraints in a provably-safe way. In the literature, RTD has only been applied to small mobile robots. The contribution of this work is RTD on a passenger vehicle in CarSim, with a full powertrain model, chassis and tire dynamics. RTD operates the vehicle safely at up to 15 m/s on a two-lane road around randomly-placed obstacles only known to the vehicle when detected within its sensor horizon. RTD is compared with a Nonlinear Model-Predictive Control (NMPC) and a Rapidly-exploring Random Tree (RRT) approach. The experiment demonstrates RTD's ability to plan safe trajectories in real time, in contrast to the existing state-of-the-art approaches.
AdaWFPA: Adaptive Online Website Fingerprinting Attack for Tor Anonymous Network: A Stream-wise Paradigm

Nowadays, network traffic analysis is quite pervasive in human practice. Website fingerprinting attack, which is a new variant of traffic analysis attacks, identifies the websites visited by clients in encrypted and anonymized Tor connections by observing patterns in packet flows. Previous website fingerprinting attacks focus on static models in which the classifier is trained within a time period and then it is utilized to identify targeted websites. Static attacks cannot handle the time effect on the accuracy since their classifiers are not trained on the newest versions of the websites. Consequently, their accuracy drops drastically when tested on captured traffic traces of websites, days after training. This time effect is known as concept drift. In order to maintain the performance of the classifier, the classifier must be updated over time. In static attacks, updating the classifier includes updating the whole dataset again along with retraining the classifier. Recollecting, maintaining, and updating datasets as well as updating and retraining the classifier are very time and memory consuming. To deal with the emerging issues arising from concept drift and expensive retraining phases, this paper proposes AdaWFPA; an adaptive online website fingerprinting attack which is based on adaptive stream mining algorithms. AdaWFPA avoids concept drift by updating its model over time. Our empirical analyses and experiments over real world datasets indicate the superiority of our approach; as it offers better accuracy, precision, and recall in comparison with other state-of-the-art methods in the literature.
Self-Encrypting Deception: Weaknesses in the Encryption of Solid State Drives

We have analyzed the hardware full-disk encryption of several solid state drives (SSDs) by reverse engineering their firmware. These drives were produced by three manufacturers between 2014 and 2018, and are both internal models using the SATA and NVMe interfaces (in a M.2 or 2.5" traditional form factor) and external models using the USB interface. In theory, the security guarantees offered by hardware encryption are similar to or better than software implementations. In reality, we found that many models using hardware encryption have critical security weaknesses due to specification, design, and implementation issues. For many models, these security weaknesses allow for complete recovery of the data without knowledge of any secret (such as the password). BitLocker, the encryption software built into Microsoft Windows will rely exclusively on hardware full-disk encryption if the SSD advertises support for it. Thus, for these drives, data protected by BitLocker is also compromised. We conclude that, given the state of affairs affecting roughly 60% of the market, currently one should not rely solely on hardware encryption offered by SSDs and users should take additional measures to protect their data.
Two Secure Privacy-Preserving Data Aggregation Schemes for IoT

As the next generation of information and communication infrastructure, Internet of Things (IoT) enables many advanced applications such as smart healthcare, smart grid, smart home, and so on, which provide the most flexibility and convenience in our daily life. However, pervasive security and privacy issues are also increasing in IoT. For instance, an attacker can get health condition of a patient via analyzing real-time records in a smart healthcare application. Therefore, it is very important for users to protect their private data. In this paper, we present two efficient data aggregation schemes to preserve private data of customers. In the first scheme, each IoT device slices its actual data randomly, keeps one piece to itself, and sends the remaining pieces to other devices which are in the same group via symmetric encryption. Then, each IoT device adds the received pieces and the held piece together to get an immediate result, which is sent to the aggregator after the computation. Moreover, homomorphic encryption and AES encryption are employed to guarantee secure communication. In the second scheme, the slicing strategy is also employed. Noise data are introduced to prevent the exchanged actual data of devices from disclosure when the devices blend data each other. AES encryption is also employed to guarantee secure communication between devices and aggregator, compared to homomorphic encryption, which has significantly less computational cost. Analysis shows that integrity and confidentiality of IoT devices’ data can be guaranteed in our schemes. Both schemes can resist external attack, internal attack, colluding attack, and so on.
Distributed Deep Learning Optimized System over the Cloud and Smart Phone Devices

Deep learning has been becoming a promising focus in data mining research. With deep learning techniques, researchers can discover deep properties and features of events from quantitative mobile sensor data. However, many data sources are geographically separated and have strict privacy, security, and regulatory constraints. Upon releasing the privacy-sensitive data, these data sources generally no longer physically possess their data and cannot interfere with the way their personal data being used. Therefore, it is necessary to explore distributed data mining architecture which is able to conduct consensus learning based on needs. Accordingly, we propose a distributed deep learning optimized system which contains a cloud server and multiple smartphone devices with computation capabilities and each device is served as a personal mobile data hub for enabling mobile computing while preserving data privacy. The proposed system keeps the private data locally in smartphones, shares trained parameters, and builds a global consensus model. The feasibility and usability of the proposed system are evaluated by three experiments and related discussion. The experimental results show that the proposed distributed deep learning system can reconstruct the behavior of centralized training. We also measure the cumulative network traffic in different scenarios and show that the partial parameter sharing strategy does not only preserve the performance of the trained model but also can reduce network traffic. User data privacy is protected on two levels. First, local private training data do not need to be shared with other people and the user has full control of their personal training data all the time. Second, only a small fraction of trained gradients of the local model are selected for sharing, which further reduces the risk of information leaking.
MNO-OTT Collaborative Video Streaming in 5G: The Zero-Rated QoE Approach for Quality and Resource Management

The Quality of Experience (QoE) management procedures for multimedia services benefit from an effective collaboration between the Mobile Network Operators (MNOs) and the Over-The-Top (OTT) service providers as the former can allocate the appropriate network resources to the users and the latter has access to key influence factors for having a proper view of the provided QoE. One successful collaboration model is the zero-rated data rate approach, according to which the MNO limits the data rate of the users towards the collaborating OTT applications with the benefit for the user that the generated traffic is not counted in her monthly contract data limit. Accordingly, the MNO may reduce the network congestion, and the users are encouraged to select the collaborating OTT applications. Though, this approach does not consider the resulting QoE, which may vary significantly from one user to another even if the same throughput is given. Based on this consideration, in this paper, we proposed the zero-rated QoE approach, according to which the limit is introduced in terms of QoE rather than throughput. This clearly requires a stronger collaboration between the OTT and the MNO, as the first has to give access to the second to quality influence factors and the latter to allocate resources according to the predicted QoE. The contributions of this paper are: the introduction of the novel zero-rated QoE approach with particular reference to video streaming services; the definition of novel components in the 3GPP architecture so that this approach can be introduced; the definition of the algorithm for the allocation of the appropriate radio resources to each user; a simulation analysis where the proposed approach is compared with respect to the former zero-rated approach, which shows significant improvements in terms of average provided quality and quality fairness at the same overall throughput.
Embedding Model Design for Producing Book Recommendation

Internet services often recommend contents to users in order to maintain the interaction. Recommendation system plays a major role to formulate and produce a series of recommendation based on users' behavior. Surprisingly, user-generated scoring or known as ratings are the main raw materials to learn the pattern of favorable contents of each user. As a part of collaborative filtering strategy, rating is considerable to be included in formulating recommended contents. In this research, the basic formulation to recommend books is discovered. The recommendation system has been tested on one random-picked user behavior and successfully has generated five recommended books by analyzing prior activities. The embedding model produced the recommended books with 59% of accuracy. This research is done to provide an insightful experience for developing content recommendation system in Binus University's corporate learning system.
Image quality recognition technology based on deep learning

Image plays an important role in today's society and is an important information carrier. However, due to the problems in shooting or processing, image quality is often difficult to be guaranteed, and low-quality images are often difficult to identify, which results in the waste of information. How to effectively identify low-quality images has become a hot research topic in today's society. Deep learning has a good application in image recognition. In this paper, it is applied to low-quality image recognition. An image quality recognition technology based on deep learning is studied to effectively realize low-quality image recognition. Firstly, in the stage of image preprocessing, a low-quality image enhancement method is proposed, which uses non-linear transformation to enhance image contrast image, restore image details and enhance image quality. Secondly, the convolutional neural network is used to extract image features, and the L2 regularization method is introduced to optimize the over-fitting problem. Finally, SVM is used to recognize the output of convolutional neural network to realize low quality image recognition. Through simulation analysis, it is found that the image enhancement method proposed in the pre-processing stage can effectively enhance the image quality, and deep learning can effectively realize the recognition of the enhanced image and improve the recognition accuracy.
Teaching and Learning Chemistry via Augmented and Immersive Virtual Reality

ADVERTISEMENT RETURN TO BOOKPREVChapterNEXTTeaching and Learning Chemistry via Augmented and Immersive Virtual RealityZulma A. Jiménez*Zulma A. JiménezNotre Dame of Maryland University, 4701 North Charles Street, Baltimore, Maryland 21210, United States*E-mail: [email protected]More by Zulma A. JiménezDOI: 10.1021/bk-2019-1318.ch003Publication Date (Web):September 26, 2019Publication History Published online26 September 2019Published inprint 1 January 2019RIGHTS & PERMISSIONSTechnology Integration in Chemistry Education and Research (TICER)Chapter 3pp 31-52ACS Symposium SeriesVol. 1318ISBN13: 9780841234390eISBN: 9780841234383 Copyright © 2019 American Chemical SocietyChapter Views244Citations15LEARN ABOUT THESE METRICSChapter Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit Read OnlinePDF (3 MB) Get e-Alerts
The Effect of Task on Visual Attention in Interactive Virtual Environments

Virtual environments for gaming and simulation provide dynamic and adaptive experiences, but, despite advances in multisensory interfaces, these are still primarily visual experiences. To support real-time dynamic adaptation, interactive virtual environments could implement techniques to predict and manipulate human visual attention. One promising way of developing such techniques is to base them on psychophysical observations, an approach that requires a sound understanding of visual attention allocation. Understanding how this allocation of visual attention changes depending on a user’s task offers clear benefits in developing these techniques and improving virtual environment design. With this aim, we investigated the effect of task on visual attention in interactive virtual environments. We recorded fixation data from participants completing freeview, search, and navigation tasks in three different virtual environments. We quantified visual attention differences between conditions by identifying the predictiveness of a low-level saliency model and its corresponding color, intensity, and orientation feature-conspicuity maps, as well as measuring fixation center bias, depth, duration, and saccade amplitude. Our results show that task does affect visual attention in virtual environments. Navigation relies more than search or freeview on intensity conspicuity to allocate visual attention. Navigation also produces fixations that are more central, longer, and deeper into the scenes. Further, our results suggest that it is difficult to distinguish between freeview and search tasks. These results provide important guidance for designing virtual environments for human interaction, as well as identifying future avenues of research for developing “attention-aware” virtual worlds.
Progressive Fusion for Unsupervised Binocular Depth Estimation Using Cycled Networks

Recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance. However, they require costly ground truth annotations during training. To cope with this issue, in this paper we present a novel unsupervised deep learning approach for predicting depth maps. We introduce a new network architecture, named Progressive Fusion Network (PFN), that is specifically designed for binocular stereo depth estimation. This network is based on a multi-scale refinement strategy that combines the information provided by both stereo views. In addition, we propose to stack twice this network in order to form a cycle. This cycle approach can be interpreted as a form of data-augmentation since, at training time, the network learns both from the training set images (in the forward half-cycle) but also from the synthesized images (in the backward half-cycle). The architecture is jointly trained with adversarial learning. Extensive experiments on the publicly available datasets KITTI, Cityscapes and ApolloScape demonstrate the effectiveness of the proposed model which is competitive with other unsupervised deep learning methods for depth prediction.
Ratio-Based Nonlocal Anisotropic Despeckling Approach for SAR Images

Although the first filtering algorithms have been proposed more than 30 years ago, despeckling of synthetic aperture radar images is still an open issue. A new boost has been provided by nonlocal (NL) means filters. The idea of NL filters is to move from the exploitation of spatial neighboring pixels to the exploitation of similar pixels found across the image. The difference between the NL algorithms is mainly related to the definition of the similarity between pixels and how similar pixels are exploited in the restoration process. Generally, to define the similarity, the patches are adopted. In this paper, a new similarity criterion for selecting similar pixels is presented. It is based on the definition of the ratio patch between the patch containing the pixel to be restored and the patch containing a candidate similar pixel. If the two pixels are similar, it is expected that the corresponding ratio patch will follow a specific statistical distribution. A modified version of the Kolmogorov-Smirnov distance is introduced to decide whether the statistical distribution of the ratio patch follows the expected one. To reduce the possible artifacts, anisotropy is exploited. Considering the proposed approach, the designed algorithm turns to be unbiased, able to provide the restored solution without any thresholding procedure, in which the tuning is substantially unsupervised and able to work with both single-look and multilook images. The algorithm has been tested on different simulated and real data. Qualitative and quantitative analyses validate the proposed approach, showing very good despeckling capabilities.
Privbus: A privacy-enhanced crowdsourced bus service via fog computing

Abstract Crowdsourced bus service provides the customized bus for a group of users with similar itineraries by designing the route based on the users’ trip plans. With crowdsourced bus service, the users with similar trips can enjoy the customized bus route efficiently and inexpensively. However, serious privacy concerns (e.g., the exposure of users’ current and future locations) have become a major barrier. To protect users’ itineraries, we propose Privbus, a privacy-enhanced crowdsourced bus service without hampering the functionality of bus route planning. Specifically, Privbus improves the performance of clustering itineraries due to the assistance of fogs. Then, Privbus executes the fog-assisted density peaks clustering operations on ciphertexts of users’ travel plans to protect the users’ trips. By doing so, the clustering operation is removed from users’ smart devices to fog nodes, so as to enable the users to be offline after they submit their travel plans. According to the clustering results, Privbus uses a route planning method to optimize the bus routes. The optimization reduces the time cost on travel of users, while guaranteeing the good profit and the wide coverage of crowdsourced bus service. Finally, through the performance evaluation and extensive experiments, we demonstrate that Privbus has the advantage of low computational and communication overhead, while providing high security and precision guarantees.
The Analysis of Mainlobe-Slumping Quantum Effect of the Cube in the Scattering Characteristics of Quantum Radar

As the core characters in the scattering characteristics of quantum radar, the quantum radar cross section (QRCS) has some distinct properties from the classical radar cross section (CRCS) on two-dimensional (2D) targets, such as the quantum interference effect of sidelobe. However, due to the ability of the algorithm only for 2D targets basically, there is not sufficient work on the quantum effect in the scattering characteristics of quantum radar for three-dimensional (3D) targets, especially for other effects. Here we report a novel mainlobe-slumping quantum effect of the cube in the scattering characteristics of quantum radar with an improved algorithm for the 3D targets. In addition, it has a completely different physical origin from the classic radar scattering. Further, we analyze the source of this sudden decreasing in the curve of QRCS based on the quantum theory and simulation data. We conclude that this kind of new macroscopic quantum phenomenon can be used in the detection and discrimination of stealthy weapons and biomedicine.
Tree Gradient Coding

Scaling up distributed machine learning systems face two major bottlenecks - delays due to stragglers and limited communication bandwidth. Recently, a number of coding theoretic strategies have been proposed for mitigating these bottlenecks. In particular, the Gradient Coding (GC) scheme was proposed to speed up distributed gradient descent algorithm in a synchronous master-worker setting by providing robustness to stragglers. A major drawback of the master-worker architecture for distributed learning is however, the bandwidth contention at the master, which can significantly deteriorate the performance as the cluster size increases. In this paper, we propose a new framework named Tree Gradient Coding (TGC) for distributed gradient aggregation, which parallelizes communication over a tree topology while providing straggler robustness. As our main contribution, we characterize the minimum computation load for TGC for a given tree topology and straggler resiliency, and design a tree gradient coding algorithm that achieves this optimal computation load. Furthermore, we provide results from experiments over Amazon EC2, where TGC speeds up the training time by up to 18.8× in comparison to GC.
A hybrid convolution network for serial number recognition on banknotes

As the sole identity of banknote, serial number has played a crucial role in monitoring the circulation of currencies. Serial number recognition plays an important role in financial market, which requires fast and accurate performances in real applications. In this paper, a hybrid convolution network model has been proposed, in which a dilated-based convolution neural network is employed to improve the recognition accuracy and a quantitative neural network method is developed to speed up the identification process. In dilated-based convolution neural network, the convolution layer and the pooling layer have been replaced by dilated convolution, which can reduce the computation cost. The quantitative neural network based method quantizes the weight parameters to an integer power of two, which transforms the original multiplication operation to a shift operation and can greatly reduce the time. The proposed model was examined and tested on four different banknotes with 35,000 banknote images including RMB, HKD, USD and GBP. The experimental results show that, the proposed model can efficiently improve the recognition accuracy to 99.89% and reduce the recognition time to less than 0.1 ms, and it outperforms the other algorithms on both recognition accuracy and recognition speed.