Reviewerly/exHarmony huggingface dataset:
On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings

We study Nesterov's accelerated gradient method with constant step-size and momentum parameters in the stochastic approximation setting (unbiased gradients with bounded variance) and the finite-sum setting (where randomness is due to sampling mini-batches). To build better insight into the behavior of Nesterov's method in stochastic settings, we focus throughout on objectives that are smooth, strongly-convex, and twice continuously differentiable. In the stochastic approximation setting, Nesterov's method converges to a neighborhood of the optimal point at the same accelerated rate as in the deterministic setting. Perhaps surprisingly, in the finite-sum setting, we prove that Nesterov's method may diverge with the usual choice of step-size and momentum, unless additional conditions on the problem related to conditioning and data coherence are satisfied. Our results shed light as to why Nesterov's method may fail to converge or achieve acceleration in the finite-sum setting.
When Pedestrian Detection Meets Nighttime Surveillance: A New Benchmark

Pedestrian detection at nighttime is a crucial and frontier problem in surveillance, but has not been well explored by the computer vision and artificial intelligence communities. Most of existing methods detect pedestrians under favorable lighting conditions (e.g. daytime) and achieve promising performances. In contrast, they often fail under unstable lighting conditions (e.g. nighttime). Night is a critical time for criminal suspects to act in the field of security. The existing nighttime pedestrian detection dataset is captured by a car camera, specially designed for autonomous driving scenarios. The dataset for nighttime surveillance scenario is still vacant. There are vast differences between autonomous driving and surveillance, including viewpoint and illumination. In this paper, we build a novel pedestrian detection dataset from the nighttime surveillance aspect: NightSurveillance1. As a benchmark dataset for pedestrian detection at nighttime, we compare the performances of state-of-the-art pedestrian detectors and the results reveal that the methods cannot solve all the challenging problems of NightSurveillance. We believe that NightSurveillance can further advance the research of pedestrian detection, especially in the field of surveillance security at nighttime.
Separating Local & Shuffled Differential Privacy via Histograms

Recent work in differential privacy has highlighted the shuffled model as a promising avenue to compute accurate statistics while keeping raw data in users' hands. We present a protocol in this model that estimates histograms with error independent of the domain size. This implies an arbitrarily large gap in sample complexity between the shuffled and local models. On the other hand, we show that the models are equivalent when we impose the constraints of pure differential privacy and single-message randomizers.
A New Crossover Mechanism for Genetic Algorithms for Steiner Tree Optimization

Genetic algorithms (GAs) have been widely applied in Steiner tree optimization problems. However, as the core operation, existing crossover operators for tree-based GAs suffer from producing illegal offspring trees. Therefore, some global link information must be adopted to ensure the connectivity of the offspring, which incurs heavy computation. To address this problem, this article proposes a new crossover mechanism, called leaf crossover (LC), which generates legal offspring by just exchanging partial parent chromosomes, requiring neither the global network link information, encoding/decoding nor repair operations. Our simulation study indicates that GAs with LC outperform GAs with existing crossover mechanisms in terms of not only producing better solutions but also converging faster in networks of varying sizes.
Communicating Artificial Intelligence (AI): Theory, Research, and Practice

In more than 60 years since the founding of artificial intelligence (AI) as a formal academic discipline, rapid advances in technology have driven an enormous increase in interest in the field of s...
Blockchain Technology Consensus Algorithms and Applications: A Survey

&lt;p class="0abstract"&gt;One of the new promising technologies for the future is blockchain. It has become one of the popular techniques for various transactions and applications in many different areas such as economy, business, and government. Blockchain technology started with cryptocurrency and bitcoin applications for a safe and transparent environment. Blockchain is a hybrid technology that incorporates various technologies and tools such as mathematics, peer-peer networking, cryptography algorithms, and consensus algorithms. This survey highlights the latest studies in blockchain and consensus algorithms. The study covers the most recent blockchain applications in various domains and sectors. Furthermore, the study gives an insight into the latest adoption of the blockchain in the real world.&lt;/p&gt;
Automating threat modeling using an ontology framework

Abstract Threat modeling is of increasing importance to IT security, and it is a complex and resource demanding task. The aim of automating threat modeling is to simplify model creation by using data that are already available. However, the collected data often lack context; this can make the automated models less precise in terms of domain knowledge than those created by an expert human modeler. The lack of domain knowledge in modeling automation can be addressed with ontologies. In this paper, we introduce an ontology framework to improve automatic threat modeling. The framework is developed with conceptual modeling and validated using three different datasets: a small scale utility lab, water utility control network, and university IT environment. The framework produced successful results such as standardizing input sources, removing duplicate name entries, and grouping application software more logically.
Laser and LIDAR in a System for Visibility Distance Estimation in Fog Conditions

Visibility is a critical factor for transportation, even if we refer to air, water, or ground transportation. The biggest trend in the automotive industry is autonomous driving, the number of autonomous vehicles will increase exponentially, prompting changes in the industry and user segment. Unfortunately, these vehicles still have some drawbacks and one, always in attention and topical, will be treated in this paper—visibility distance issue in bad weather conditions, particularly in fog. The way and the speed with which vehicles will determine objects, obstacles, pedestrians, or traffic signs, especially in bad visibility, will determine how the vehicle will behave. In this paper, a new experimental set up is featured, for analyzing the effect of the fog when the laser and LIDAR (Light Detection And Ranging) radiation are used in visibility distance estimation on public roads. While using our experimental set up, in the laboratory, the information offered by these measurement systems (laser and LIDAR) are evaluated and compared with results offered by human observers in the same fog conditions. The goal is to validate and unitarily apply the results regarding visibility distance, based on information arrives from different systems that are able to estimate this parameter (in foggy weather conditions). Finally, will be notifying the drivers in case of unexpected situations. It is a combination of stationary and of moving systems. The stationary system will be installed on highways or express roads in areas prone to fog, while the moving systems are, or can be, directly installed on the vehicles (autonomous but also non-autonomous).
Recommender systems: an overview, research trends, and future directions

Recommender system (RS) has emerged as a major research interest that aims to help users to find items online by providing suggestions that closely match their interest. This paper provides a comprehensive study on the RS covering the different recommendation approaches, associated issues, and techniques used for information retrieval. Thanks to its widespread applications, it has induced research interest among a significant number of researchers around the globe. The main purpose of this paper is to spot the research trend in RS. More than 1,000 research papers, published by ACM, IEEE, Springer, and Elsevier since 2011 to the first quarter of 2017, have been considered. Several interesting findings have come out of this study, which will help the current and future RS researchers to assess and set their research roadmap. Furthermore, this paper also envisions the future of RS which may open up new research directions in this domain.
FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters

Deep Neural Networks (DNNs) have revolutionized numerous applications, but the demand for ever more performance remains unabated. Scaling DNN computations to larger clusters is generally done by distributing tasks in batch mode using methods such as distributed synchronous SGD. Among the issues with this approach is that to make the distributed cluster work with high utilization, the workload distributed to each node must be large, which implies nontrivial growth in the SGD mini-batch size. In this paper, we propose a framework called FPDeep, which uses a hybrid of model and layer parallelism to configure distributed reconfigurable clusters to train DNNs. This approach has numerous benefits. First, the design does not suffer from batch size growth. Second, novel workload and weight partitioning leads to balanced loads of both among nodes. And third, the entire system is a fine-grained pipeline. This leads to high parallelism and utilization and also minimizes the time features need to be cached while waiting for back-propagation. As a result, storage demand is reduced to the point where only on-chip memory is used for the convolution layers. We evaluate FPDeep with the Alexnet, VGG-16, and VGG-19 benchmarks. Experimental results show that FPDeep has good scalability to a large number of FPGAs, with the limiting factor being the FPGA-to-FPGA bandwidth. With 6 transceivers per FPGA, FPDeep shows linearity up to 83 FPGAs. Energy efficiency is evaluated with respect to GOPs/J. FPDeep provides, on average, 6.36x higher energy efficiency than comparable GPU servers.
A New Video-Based Crash Detection Method: Balancing Speed and Accuracy Using a Feature Fusion Deep Learning Framework

Quick and accurate crash detection is important for saving lives and improved traffic incident management. In this paper, a feature fusion-based deep learning framework was developed for video-based urban traffic crash detection task, aiming at achieving a balance between detection speed and accuracy with limited computing resource. In this framework, a residual neural network (ResNet) combined with attention modules was proposed to extract crash-related appearance features from urban traffic videos (i.e., a crash appearance feature extractor), which were further fed to a spatiotemporal feature fusion model, Conv-LSTM (Convolutional Long Short-Term Memory), to simultaneously capture appearance (static) and motion (dynamic) crash features. The proposed model was trained by a set of video clips covering 330 crash and 342 noncrash events. In general, the proposed model achieved an accuracy of 87.78% on the testing dataset and an acceptable detection speed (FPS &gt; 30 with GTX 1060). Thanks to the attention module, the proposed model can capture the localized appearance features (e.g., vehicle damage and pedestrian fallen-off) of crashes better than conventional convolutional neural networks. The Conv-LSTM module outperformed conventional LSTM in terms of capturing motion features of crashes, such as the roadway congestion and pedestrians gathering after crashes. Compared to traditional motion-based crash detection model, the proposed model achieved higher detection accuracy. Moreover, it could detect crashes much faster than other feature fusion-based models (e.g., C3D). The results show that the proposed model is a promising video-based urban traffic crash detection algorithm that could be used in practice in the future.
Communication in health work during the COVID-19 pandemic

Objective. Report on communication and qualified listening in nursing work in the face of the COVID-19 pandemic.&#x0D; Methods. This descriptive, theoretical and reflexive report was developed by nurses between March 20th and May 25th 2020 at Emergency Care Services in the city of Fortaleza, Ceará, Brazil. Health communication served as the theoretical background for this research.&#x0D; Results. Two main thematic categories were highlighted: (i) Resignifications of communication in the work relationships of the health team and (ii) Guided listening to users by nurses at the Emergency Care Services during the pandemic.&#x0D; Conclusion. The experience revealed an excerpt of what is found under the conditions of the current situation resulting from COVID-19. Communication turned into an essential tool to maintain professional relationships and culminate in collaboration and cooperation of theteam in order to provide a close relationship with the user and promote the quality of health care processes.
Composite Adversarial Attacks

Adversarial attack is a technique for deceiving Machine Learning (ML) models, which provides a way to evaluate the adversarial robustness. In practice, attack algorithms are artificially selected and tuned by human experts to break a ML system. However, manual selection of attackers tends to be sub-optimal, leading to a mistakenly assessment of model security. In this paper, a new procedure called Composite Adversarial Attack (CAA) is proposed for automatically searching the best combination of attack algorithms and their hyper-parameters from a candidate pool of 32 base attackers. We design a search space where attack policy is represented as an attacking sequence, i.e., the output of the previous attacker is used as the initialization input for successors. Multi-objective NSGA-II genetic algorithm is adopted for finding the strongest attack policy with minimum complexity. The experimental result shows CAA beats 10 top attackers on 11 diverse defenses with less elapsed time (6 × faster than AutoAttack), and achieves the new state-of-the-art on linf, l2 and unrestricted adversarial attacks.
RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving

Although the recent image-based 3D object detection methods using Pseudo-LiDAR representation have shown great capabilities, a notable gap in efficiency and accuracy still exist compared with LiDAR-based methods. Besides, over-reliance on the stand-alone depth estimator, requiring a large number of pixel-wise annotations in the training stage and more computation in the inferencing stage, limits the scaling application in the real world. In this paper, we propose an efficient and accurate 3D object detection method from stereo images, named RTS3D. Different from the 3D occupancy space in the Pseudo-LiDAR similar methods, we design a novel 4D feature-consistent embedding (FCE) space as the intermediate representation of the 3D scene without depth supervision. The FCE space encodes the object's structural and semantic information by exploring the multi-scale feature consistency warped from stereo pair. Furthermore, a semantic-guided RBF (Radial Basis Function) and a structure-aware attention module are devised to reduce the influence of FCE space noise without instance mask supervision. Experiments on KITTI benchmark show that RTS3D is the first true real-time system (FPS&gt;24) for stereo image 3D detection meanwhile achieves 10% improvement in average precision comparing with the previous state-of-the-art method.
The value of federated learning during and post-COVID-19

Abstract Federated learning (FL) as a distributed machine learning (ML) technique has lately attracted increasing attention of healthcare stakeholders as FL is perceived as a promising decentralized approach to address data privacy and security concerns. The FL approach stores and maintains the privacy-sensitive data locally while allows multiple sites to train ML models collaboratively. We aim to describe the most recent real-world cases using the FL in both COVID-19 and non-COVID-19 scenarios and also highlight current limitations and practical challenges of FL.
Managing charity 4.0 with Blockchain: a case study at the time of Covid-19

Abstract The Covid-19 emergency is demonstrating the need to follow new solutions that can support the important role played by non-profit organizations around the world. Contrary to what should have happened to further combat the effect of pandemic, the majority of philanthropic organisations had a negative impact on fundraising, suffering a substantial decrease. Today, the Blockchain can play a pivotal role to re-establish pre-pandemic standards and enhance the development of global philanthropy. However, it is still too little considered due to the criticalities encountered during the launch and development of the initiatives as well as for a general incomprehension of its technology. Therefore, this work aims to demonstrate the Blockchain impact on the development of charity 4.0, especially in an extremely dramatic historical moment marked by the Covid-19 pandemic. The objective is achieved through the case study of Charity Wall, an emerging Italian social marketplace appreciated by important business associations for its innovative solutions in the charity 4.0 sector and for the important support provided to NPOs during their traditional function as well as against Covid-19 in Italy. Through a benchmark analysis, this work succeeds in highlighting the innovative solutions proposed by Charity Wall compared to the charity 4.0 systems on the market. More specifically, through the Charity Wall case study it is possible to demonstrate which aspects of Blockchain technology can be used to strengthen the philanthropic system by avoiding cases of fraud to the detriment of beneficiaries, receivers and donors as well as to create a closer network between the various philanthropic players to support charitable initiatives against the Covid-19.
Current Research Trends in IoT Security: A Systematic Mapping Study

The smart mobile Internet-of-things (IoT) network lays the foundation of the fourth industrial revolution, the era of hyperconnectivity, hyperintelligence, and hyperconvergence. As this revolution gains momentum, the security of smart mobile IoT networks becomes an essential research topic. This study aimed to provide comprehensive insights on IoT security. To this end, we conducted a systematic mapping study of the literature to identify evolving trends in IoT security and determine research subjects. We reviewed the literature from January 2009 to August 2020 to identify influential researchers and trends of keywords. We additionally performed structural topic modeling to identify current research topics and the most promising ones via topic trend estimation. We synthesized and interpreted the results of the systematic mapping study to devise future research directions. The results obtained from this study are useful to understand current trends in IoT security and provide insights into research and development of IoT security.
Community Detection in Blockchain Social Networks

In this work, we consider community detection in blockchain networks. We specifically take the Bitcoin network and Ethereum network as two examples, where community detection serves in different ways. For the Bitcoin network, we modify the traditional community detection method and apply it to the transaction social network to cluster users with similar characteristics. For the Ethereum network, on the other hand, we define a bipartite social graph based on the smart contract transactions. A novel community detection algorithm which is designed for low-rank signals on graph can help find users' communities based on user-token subscription. Based on these results, two strategies are devised to deliver on-chain advertisements to those users in the same community. We implement the proposed algorithms on real data. By adopting the modified clustering algorithm, the community results in the Bitcoin network are basically consistent with the ground-truth of the betting site community which has been announced to the public. Meanwhile, we run the proposed strategy on real Ethereum data, visualize the results and implement an advertisement delivery on the Ropsten test net.
An Efficient Method for Generating UAV-Based Hyperspectral Mosaics Using Push-Broom Sensors

Hyperspectral sensors mounted in unmanned aerial vehicles offer new opportunities to explore high-resolution multitemporal spectral analysis in remote sensing applications. Nevertheless, the use of hyperspectral data still poses challenges mainly in postprocessing to correct from high geometric deformation of images. In general, the acquisition of high-quality hyperspectral imagery is achieved through a time-consuming and complex processing workflow. However, this effort is mandatory when using hyperspectral imagery in a multisensor data fusion perspective, such as with thermal infrared imagery or photogrammetric point clouds. Push-broom hyperspectral sensors provide high spectral resolution data, but its scanning acquisition architecture imposes more challenges to create geometrically accurate mosaics from multiple hyperspectral swaths. In this article, an efficient method is presented to correct geometrical distortions on hyperspectral swaths from push-broom sensors by aligning them with an RGB photogrammetric orthophoto mosaic. The proposed method is based on an iterative approach to align hyperspectral swaths with an RGB photogrammetric orthophoto mosaic. Using as input preprocessed hyperspectral swaths, apart from the need of introducing some control points, the workflow is fully automatic and consists of: adaptive swath subdivision into multiple fragments; detection of significant image features; estimation of valid matches between individual swaths and the RGB orthophoto mosaic; and calculation of the best geometric transformation model to the retrieved matches. As a result, geometrical distortions of hyperspectral swaths are corrected and an orthomosaic is generated. This methodology provides an expedite solution able to produce a hyperspectral mosaic with an accuracy ranging from two to five times the ground sampling distance of the high-resolution RGB orthophoto mosaic, enabling the hyperspectral data integration with data from other sensors for multiple applications.
An Empirical Study on Neural Keyphrase Generation

Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them.Model performance on KPG tasks has increased significantly with evolving deep learning research.However, there lacks a comprehensive comparison among different model designs, and a thorough investigation on related factors that may affect a KPG system's generalization performance.In this empirical study, we aim to fill this gap by providing extensive experimental results and analyzing the most crucial factors impacting the generalizability of KPG models.We hope this study can help clarify some of the uncertainties surrounding the KPG task and facilitate future research on this topic.