Reviewerly/exHarmony huggingface dataset:
Joint Optimization of Task Offloading and Resource Allocation Based on Differential Privacy in Vehicular Edge Computing

In the Internet of Vehicles (IoVs), task offloading is necessary to ensure the low-response delay due to the limitation of vehicular computational capacity. Task offloading involving social behavior can improve the utilization of computational resources in IoVs. To offload tasks effectively, the connected vehicles (CVs) need to upload context information, such as speed and location to road side unit (RSU) and base station (BS), which brings dramatic threat and risk for CVs&#x2019; privacy security. To solve the above-mentioned issue, we propose a privacy-preserving vehicular edge computing (PP-VEC) system architecture in this article. In the PP-VEC, the vehicular tasks can be offloaded to RSUs and adjacent CVs with adequate computing resources. Privacy mechanism disturbs the context information of CVs based on differential privacy technology before uploading it to the BS for offloading decisions to protect the CVs&#x2019; privacy. This article adopts the local differential privacy algorithm based on histogram algorithm and proposes a K-neighbor joint optimization of task offloading and resource allocation algorithm (K-NJTA) to optimize the global delay of task execution. We demonstrate the effectiveness of the proposed methods by simulation experiments. The results demonstrate K-NJTA on task execution delay and our local differential privacy algorithm can protect CVs&#x2019; privacy while has less effect on the task offloading algorithm due to the context information distribution.
Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images

The diagnosis and the subtyping of non-Hodgkin lymphoma (NHL) are challenging and require expert knowledge, great experience, thorough morphological analysis, and often additional expensive immunohistological and molecular methods. As these requirements are not always available, supplemental methods supporting morphological-based decision making and potentially entity subtyping are required. Deep learning methods have been shown to classify histopathological images with high accuracy, but data on NHL subtyping are limited. After annotation of histopathological whole-slide images and image patch extraction, we trained and optimized an EfficientNet convolutional neuronal network algorithm on 84,139 image patches from 629 patients and evaluated its potential to classify tumor-free reference lymph nodes, nodal small lymphocytic lymphoma/chronic lymphocytic leukemia, and nodal diffuse large B-cell lymphoma. The optimized algorithm achieved an accuracy of 95.56% on an independent test set including 16,960 image patches from 125 patients after the application of quality controls. Automatic classification of NHL is possible with high accuracy using deep learning on histopathological images and routine diagnostic applications should be pursued.
Omnidirectional Image Quality Assessment by Distortion Discrimination Assisted Multi-Stream Network

Omnidirectional image (OI) quality assessment is crucial to facilitate the development of virtual reality (VR) related technology. In this work, a distortion discrimination assisted multi-stream network is proposed for OI quality assessment. The multi-stream architecture is constructed by generating the viewport images received by the retina at one point to simulate the characteristics of humans perceiving VR contents. Additionally, the strategy of generating several viewport image sets from one OI is proposed for data augmentation. Furthermore, the facts that the human brain has the ability for both quality assessment and distortion type distinguishment, and the process of human brain handling two tasks exists information interaction inspire us to employ an auxiliary distortion discrimination task to facilitate the quality assessment task learning. Extensive experiments conducted on two public OI databases demonstrate the superiority of the proposed method to both traditional 2D quality metrics and existing metrics specific for OIs. Moreover, utilizing the assistant task is proven to be more effective than the single task learning for OI quality evaluation. Better generalization performance is also verified to be another valuable trait of the proposed method.
Predicting Human Intentions in Human–Robot Hand-Over Tasks Through Multimodal Learning

In human–robot shared manufacturing contexts, product parts or tools hand-over between the robot and the human is an important collaborative task. Facilitating the robot to figure out and predict human hand-over intentions correctly to improve the task efficiency in human–robot collaboration is therefore a necessary issue to be addressed. In this study, a teaching-learning-prediction (TLP) framework is proposed for the robot to learn from its human partner’s multimodal demonstrations and predict human hand-over intentions. In this approach, the robot can be programmed by the human through demonstrations utilizing natural language and wearable sensors according to task requirements and the human’s working preferences. Then the robot learns from human hand-over demonstrations online via extreme learning machine (ELM) algorithms to update its cognition capacity, allowing the robot to use its learned policy to predict human intentions actively and assist its human companion in hand-over tasks. Experimental results and evaluations suggest that the human may program the robot easily by the proposed approach when the task changes, as the robot can effectively predict hand-over intentions with competitive accuracy to complete the hand-over tasks. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by human–robot hand-over problems in smart manufacturing contexts. Product parts or tools delivery in worker–robot partnerships is an important collaborative task. We develop a teaching-learning-prediction (TLP) framework for the robot to learn from its human partner’s multimodal demonstrations and predict human hand-over intentions. The robot can be taught by human through natural language and wearable sensing information. The extreme learning machine (ELM) approach is employed for the robot to build its cognition capacity to predict human intentions actively and assist its human companion in hand-over tasks. We demonstrate that the proposed approach presents distinct and effective advantages to facilitate human–robot hand-over tasks in collaborative manufacturing contexts.
Single Image Dehazing via Dual-Path Recurrent Network

An image can be decomposed into two parts: the basic content and details, which usually correspond to the low-frequency and high-frequency information of the image. For a hazy image, these two parts are often affected by haze in different levels, e.g., high-frequency parts are often affected more serious than low-frequency parts. In this paper, we approach the single image dehazing problem as two restoration problems of recovering basic content and image details, and propose a Dual-Path Recurrent Network (DPRN) to simultaneously tackle these two problems. Specifically, the core structure of DPRN is a dual-path block, which uses two parallel branches to learn the characteristics of the basic content and details of hazy images. Each branch consists of several Convolutional LSTM blocks and convolution layers. Moreover, a parallel interaction function is incorporated into the dual-path block, thus enables each branch to dynamically fuse the intermediate features of both the basic content and image details. In this way, both branches can benefit from each other, and recover the basic content and image details alternately, therefore alleviating the color distortion problem in the dehazing process. Experimental results show that the proposed DPRN outperforms state-of-the-art image dehazing methods in terms of both quantitative accuracy and qualitative visual effect.
SyNet: An Ensemble Network for Object Detection in UAV Images

Recent advances in camera equipped drone applications and their widespread use increased the demand on vision based object detection algorithms for aerial images. Object detection process is inherently a challenging task as a generic computer vision problem, however, since the use of object detection algorithms on UAVs (or on drones) is relatively a new area, it remains as a more challenging problem to detect objects in aerial images. There are several reasons for that including: (i) the lack of large drone datasets including large object variance, (ii) the large orientation and scale variance in drone images when compared to the ground images, and (iii) the difference in texture and shape features between the ground and the aerial images. Deep learning based object detection algorithms can be classified under two main categories: (a) single-stage detectors and (b) multi-stage detectors. Both single-stage and multi-stage solutions have their advantages and disadvantages over each other. However, a technique to combine the good sides of each of those solutions could yield even a stronger solution than each of those solutions individually. In this paper, we propose an ensemble network, SyNet, that combines a multi-stage method with a single-stage one with the motivation of decreasing the high false negative rate of multi-stage detectors and increasing the quality of the single-stage detector proposals. As building blocks, CenterNet and Cascade R-CNN with pretrained feature extractors are utilized along with an ensembling strategy. We report the state of the art results obtained by our proposed solution on two different datasets: namely MS-COCO and visDrone with %52.1 mAP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IoU=0.75</sub> is obtained on MS-COCO val2017 dataset and %26.2 mAP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IoU=0.75</sub> is obtained on VisDrone test - set. Our code is available at: https://github.com/mertalbaba/SyNet.
Shorter Lattice-Based Zero-Knowledge Proofs via One-Time Commitments

There has been a lot of recent progress in constructing efficient zero-knowledge proofs for showing knowledge of an \(\vec {\varvec{s}}\) with small coefficients satisfying \(\varvec{A}\vec {\varvec{s}}=\vec {\varvec{t}}\). For typical parameters, the proof sizes have gone down from several megabytes to a bit under 50KB (Esgin et al., Asiacrypt 2020). These are now within an order of magnitude of the sizes of lattice-based signatures, which themselves constitute proof systems which demonstrate knowledge of something weaker than the aforementioned equation. One can therefore see that this line of research is approaching optimality. In this paper, we modify a key component of these proofs, as well as apply several other tweaks, to achieve a further reduction of around \(30\%\) in the proof output size. We also show that this savings propagates itself when these proofs are used in a general framework to construct more complex protocols.
Session-aware recommendation: A surprising quest for the state-of-the-art

Recommender systems are designed to help users in situations of information overload. In recent years, we observed increased interest in session-based recommendation scenarios, where the problem is to make item suggestions to users based only on interactions observed in an ongoing session. However, in cases where interactions from previous user sessions are available, the recommendations can be personalized according to the users' long-term preferences, a process called session-aware recommendation. Today, research in this area is scattered and many existing works only compare session-aware with session-based models. This makes it challenging to understand what represents the state-of-the-art. To close this research gap, we benchmarked recent session-aware algorithms against each other and against a number of session-based recommendation algorithms and trivial extensions thereof. Our comparison, to some surprise, revealed that (i) item simple techniques based on nearest neighbors consistently outperform recent neural techniques and that (ii) session-aware models were mostly not better than approaches that do not use long-term preference information. Our work therefore not only points to potential methodological issues where new methods are compared to weak baselines, but also indicates that there remains a huge potential for more sophisticated session-aware recommendation algorithms.
Evaluation of how users interface with holographic augmented reality surgical scenes: Interactive planning MR‐Guided prostate biopsies

Abstract Background User interfaces play a vital role in the planning and execution of an interventional procedure. The objective of this study is to investigate the effect of using different user interfaces for planning transrectal robot‐assisted MR‐guided prostate biopsy (MRgPBx) in an augmented reality (AR) environment. Method End‐user studies were conducted by simulating an MRgPBx system with end‐ and side‐firing modes. The information from the system to the operator was rendered on HoloLens as an output interface. Joystick, mouse/keyboard, and holographic menus were used as input interfaces to the system. Results The studies indicated that using a joystick improved the interactive capacity and enabled operator to plan MRgPBx in less time. It efficiently captures the operator's commands to manipulate the augmented environment representing the state of MRgPBx system. Conclusions The study demonstrates an alternative to conventional input interfaces to interact and manipulate an AR environment within the context of MRgPBx planning.
Global path planning for mobile robot based on Artificial Bee Colony and Dijkstra’s algorithms

The global path planning for mobile robot in variable robot space is not a trivial matter, since generation of optimal path is very time-consuming. A variable workspace may result in infeasible pre-optimized path, due to new obstacles or worker position. In this paper, a hybrid approach to path planning for mobile robot in variable space is proposed. It consists of off-line global path optimization algorithm and online path planning schemes. The first part requires the definition of most common situations of workers and obstacles positions, and then global optimization of the path for a mobile robot. It is made using Artificial Bee Colony algorithm. The control path points generated from off-line part are used to create a graph in on-line part. Next, the shortest path is selected using Dijkstra's algorithm. The proposed approach ensures an at least satisfactory path in variable workspace with small computational effort in real-time path planning. The examination was made in Matlab/Simulink environment and the supplementary material, incl. visualization of the considered example, is included.
Optimizing Depthwise Separable Convolution Operations on GPUs

The depthwise separable convolution is commonly seen in convolutional neural networks (CNNs), and is widely used to reduce the computation overhead of a standard multi-channel 2D convolution. Existing implementations of depthwise separable convolutions target accelerating model training with large batch sizes with a large number of samples to be processed at once. Such approaches are inadequate for small-batch-sized model training and the typical scenario of model inference where the model takes in a few samples at once. This article aims to bridge the gap of optimizing depthwise separable convolutions by targeting the GPU architecture. We achieve this by designing two novel algorithms to improve the column and row reuse of the convolution operation to reduce the number of memory operations performed on the width and the height dimensions of the 2D convolution. Our approach employs a dynamic tile size scheme to adaptively distribute the computational data across GPU threads to improve GPU utilization and to hide the memory access latency. We apply our approach on two GPU platforms: an NVIDIA RTX 2080Ti GPU and an embedded NVIDIA Jetson AGX Xavier GPU, and two data types: 32-bit floating point (FP32) and 8-bit integer (INT8). We compared our approach against cuDNN that is heavily tuned for the NVIDIA GPU architecture. Experimental results show that our approach delivers over 2× (up to 3×) performance improvement over cuDNN. We show that, when using a moderate batch size, our approach averagely reduces the end-to-end training time of MobileNet and EfficientNet by 9.7 and 7.3 percent respectively, and reduces the end-to-end inference time of MobileNet and EfficientNet by 12.2 and 11.6 percent respectively.
Data-to-text Generation with Macro Planning

Abstract Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof. These models generate text that is fluent (but often imprecise) and perform quite poorly at selecting appropriate content and ordering it coherently. To overcome some of these issues, we propose a neural model with a macro planning stage followed by a generation stage reminiscent of traditional methods which embrace separate modules for planning and surface realization. Macro plans represent high level organization of important content such as entities, events, and their interactions; they are learned from data and given as input to the generator. Extensive experiments on two data-to-text benchmarks (RotoWire and MLB) show that our approach outperforms competitive baselines in terms of automatic and human evaluation.
SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events

Traffic event cognition and reasoning in videos is an important task that has a wide range of applications in intelligent transportation, assisted driving, and autonomous vehicles. In this paper, we create a novel dataset, SUTD-TrafficQA (Traffic Question Answering), which takes the form of video QA based on the collected 10,080 in-the-wild videos and annotated 62,535 QA pairs, for benchmarking the cognitive capability of causal inference and event understanding models in complex traffic scenarios. Specifically, we propose 6 challenging reasoning tasks corresponding to various traffic scenarios, so as to evaluate the reasoning capability over different kinds of complex yet practical traffic events. Moreover, we propose Eclipse, a novel Efficient glimpse network via dynamic inference, in order to achieve computation-efficient and reliable video reasoning. The experiments show that our method achieves superior performance while reducing the computation cost significantly. The project page: https://github.com/SUTDCV/SUTD-TrafficQA.
ICLSTM: Encrypted Traffic Service Identification Based on Inception-LSTM Neural Network

The wide application of encryption technology has made traffic classification gradually become a major challenge in the field of network security. Traditional methods such as machine learning, which rely heavily on feature engineering and others, can no longer fully meet the needs of encrypted traffic classification. Therefore, we propose an Inception-LSTM(ICLSTM) traffic classification method in this paper to achieve encrypted traffic service identification. This method converts traffic data into common gray images, and then uses the constructed ICLSTM neural network to extract key features and perform effective traffic classification. To alleviate the problem of category imbalance, different weight parameters are set for each category separately in the training phase to make it more symmetrical for different categories of encrypted traffic, and the identification effect is more balanced and reasonable. The method is validated on the public ISCX 2016 dataset, and the results of five classification experiments show that the accuracy of the method exceeds 98% for both regular encrypted traffic service identification and VPN encrypted traffic service identification. At the same time, this deep learning-based classification method also greatly simplifies the difficulty of traffic feature extraction work.
How do app icon color and border shape influence visual search efficiency and user experience? Evidence from an eye-tracking study

Icon color and icon border shape are two key factors that affect search efficiency and user experience but have previously been studied separately. This study aimed to ascertain their separate and combined effects on smartphone interfaces. We conducted an experiment using eye tracking in addition to performance and experience measures to understand the effects of app icon color and border shape on visual efficiency and user experience. The results identified both features as essential attributes with interactive effects in the process of searching app icons on a smartphone interface. The study confirmed that varied colors across icons and a rounded square border shape helped to improve search efficiency, decrease cognitive effort, and lead to a more positive user experience. Users of smartphones are often confronted with the problem of selecting a single app from a great number of apps. Visual design of app icons plays a key role in influencing visual search efficiency and user experience. The results of this study have implications for designing app icons on the interface of smartphones to improve search efficiency and elicit positive user experience.
Assessing new intra-daily temperature-based machine learning models to outperform solar radiation predictions in different conditions

• Solar radiation values were predicted using only intra-daily temperature-based data. • Several machine learning models were assessed in different geo-climatic conditions. • The RMSE improvements ranged from 7.56% (arid site) to 45.65% (humid site). • Compared to empirical methods, the mean NSE was increased up to 60% (in summer). • The RMSE was reduced up to 32.27% when the models were used in a non-trained site. The measure of solar radiation is costly, as well as its maintenance and calibration needs; therefore, reliable datasets are scarce. In this work, several machine learning models to predict solar radiation have been developed and assessed at nine locations (Southern Spain and North Carolina in the USA), representing different geo-climatic conditions (aridity, sea distance, and elevation). As a novelty, due to the ease of providing air temperature measurements, different new input variables from intra-daily temperature datasets were used. According to the results, all the models highly outperformed self-calibrated empirical methods such as Hargreaves-Samani and Bristow-Campbell, with improvements in RMSE ranging from 7.56% in arid climate to 45.65% in humid. Moreover, regarding mean NSE and R 2 values, several inland locations obtained values above 0.9. In summer, the highest statistics for all sites (more than a 60% improvement in NSE and R 2 ) were obtained, whereas the worst were given in winter (more than an 18% improvement in NSE and R 2 ). Besides, when assessing the models in different non-used locations with similar climatic characteristics, the reduction in RMSE was from 0.305 W m −2 to 0.252 W m −2 in a semiarid coastal climate and from 0.344 W m −2 to 0.233 W m −2 in dry sub-humid climate, compared to Hargreaves-Samani method. Overall, the MLP obtained the highest performance using the new proposed variables in all locations with medium aridity values, whereas in the aridest and most humid sites, SVM and RF models were preferred. Therefore, the temperature-based models developed in this work can predict solar radiation more accurately than the current ones. This is crucial in locations with no available datasets or missing/low quality and can be used to optimize the determination of the potential locations for solar power plants' construction.