Reviewerly/exHarmony huggingface dataset:
Enabling energy-efficient DNN training on hybrid GPU-FPGA accelerators

DNN training consumes orders of magnitude more energy than inference and requires innovative use of accelerators to improve energy-efficiency. However, despite having complementary features, GPUs and FPGAs have been mostly used independently for the entire training process, thus neglecting the opportunity in assigning individual but distinct operations to the most suitable hardware. In this paper, we take the initiative to explore new opportunities and viable solutions in enabling energy-efficient DNN training on hybrid accelerators. To overcome fundamental challenges including avoiding training throughput loss, enabling fast design space exploration, and efficient scheduling, we propose a comprehensive framework, Hype-training, that utilizes a combination of offline characterization, performance modeling, and online scheduling of individual operations. Experimental tests using NVIDIA V100 GPUs and Intel Stratix 10 FPGAs show that, Hype-training is able to exploit a mixture of GPUs and FPGAs at a fine granularity to achieve significant energy reduction, by 44.3% on average and up to 59.7%, without any loss in training throughput. Hype-training can also enforce power caps more effectively than state-of-the-art power management mechanisms on GPUs.
AES Sbox Acceleration Schemes for Low-Cost SoCs

Current solutions for low-cost and secure systems have ended up trading effective encryption schemes for lighter encryption schemes to ensure longevity in battery-powered applications. Here we demonstrate the potential to apply an effective and lighter encryption scheme, such as AES-256, in a low-cost battery-powered systems-on-chip (SoC) without demanding excessive energy. We accelerated AES-256 with a custom instruction along with an enhanced memory access scheme. Measurement results from a fabricated SoC featuring a RISC-V based 32-bit processor indicate a 900 fold improvement of AES-256 computing energy efficiency compared to pure-software implementations. The memory access improves the energy by 3 times an standard push-pull hardware implementation.
Anomalous object detection by active search with PTZ cameras

Due to the large amount of visual information generated daily, proposals that automatically analyze and process data are becoming increasingly necessary. This work focuses on the detection of anomalous objects in video sequences captured by PTZ (pan-tilt-zoom) cameras, considering as anomalies the objects which belong to categories that should not appear in a specific scene (e.g. pedestrians on a highway). There is a lack in the previous literature of a principled approach for the control of PTZ cameras that takes advantage of the recent developments in deep learning-based object detection. Our proposal aims to fill this gap by offering a probabilistic framework where the guidance of PTZ cameras is accommodated. The proposed methodology involves three different modules. An object detection stage, where deep learning networks are used to detect the objects that appear in the scene; an anomalous detection module, where a mixture of Dirichlet distributions is considered to detect automatically, and without supervised training, those detected objects which are likely to be anomalous; and finally, a PTZ camera controller which allows to follow and focus on the object considered as the most probably anomalous in the scene. The experimental results show the performance and viability of our proposal, which outperforms several competitors from qualitative and quantitative points of view.
Mixture of robust Gaussian processes and its hard-cut EM algorithm with variational bounding approximation

The Gaussian process is a powerful statistical learning model and has been applied widely in nonlinear regression and classification. However, it fails to model multi-modal data from a non-stationary source since a prior Gaussian process is generally stationary. Based on the idea of the mixture of experts, the mixture of Gaussian processes was established to increase the model flexibility. On the other hand, the Gaussian process is also sensitive to outliers and thus robust Gaussian processes have been suggested to own the heavy-tailed property. In practical applications, the datasets may be multi-modal and contain outliers at the same time. In order to overcome these two difficulties together, we propose a mixture of robust Gaussian processes (MRGP) model and establish a precise hard-cut EM algorithm for learning its parameters. Since the exact solving process is intractable due to the fact that non-Gaussian probability density functions of the noises are adopted into the likelihood of the proposed model on the dataset, we employ a variational bounding method to approximate the marginal likelihood functions so that the hard-cut EM algorithm can be implemented effectively. Moreover, we conduct various experiments on both synthetic and real-world datasets to evaluate and compare our proposed MRGP method with several competitive nonlinear regression methods. The experimental results demonstrate that our MRGP model with the hard-cut EM algorithm is much more effective and robust than the competitive nonlinear regression models.
High Throughput JPEG 2000 for Broadcast and IP-Based Applications

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">High-Throughput JPEG 2000 (HTJ2K) is a royalty-free image compression standard published in 2019 that enhances JPEG 2000 by replacing its slow block coder with a fast block coder. The resulting speedup (e.g., &gt;30× for lossless coding) accelerates the encoding and decoding of images, which can have a great impact on users. A less obvious benefit of the speedup is the enabling of low-latency applications, which was not possible with the original JPEG 2000 standard. Examples of these new potential applications are live, high-quality, low-latency broadcast video contribution, remote production, and Internet Protocol (IP)-based production on-premises and on cloud. This paper examines various HTJ2K encoding parameters and their impact on quality, bitrate, latency, and multigeneration encode/decode cycle performance in the context of requirements of broadcast and IP-based applications. Configurations include a number of different wavelet filters, code-block sizes, and wavelet decomposition structures that are available with HTJ2K. The performance of low-latency HTJ2K is also compared to other low-latency wavelet codecs like VC-2, JPEG XS, and JPEG 2000 Part-1. The JPEG 2000 Part-1 comparisons use the full-frame broadcast profile as well as the ultra-low latency (ULL) configuration as per Video Services Forum (VSF) Technical Recommendation (TR)-01:2018. A tradeoff between latency and compression quality is discussed</i> .
Vulnerability disclosure mechanisms: A synthesis and framework for market-based and non-market-based disclosures

Vulnerability disclosure has been a controversial topic among scholars and practitioners. Most scholars agree on adopting the responsible disclosure practices for vulnerability disclosures, which give firms a protected period to address the vulnerability before public disclosure is made. However, the firms may not fully utilize the protected period resulting in financial and reputational losses. The recent popularity in market-based disclosure methods such as bug bounty programs has provided new methods to control ethical hackers and effectively manage the disclosure timelines. Through a systematic literature review, we investigate and identify various vulnerability disclosure mechanisms and elaborate the disclosure process of each mechanism. We synthesize and compare the antecedents and consequences of the vulnerability disclosure under market- and non-market-based disclosure mechanisms by proposing two research frameworks. Our analysis suggests that incentivizing hackers in market mechanisms change hackers' motivations, leading to behavioral changes and eventually giving firms more control over the disclosure process. Additionally, our research frameworks provide a basis for further theorizing in this area. We also identify several open research questions addressing issues and challenges in the market-based disclosures. The research has important implications for firms, hackers, policymakers, and researchers in this area.
Targeting deep-seated gold deposits: A study from the qujia gold deposit, Shandong province, China

The Jiaodong gold (Au) province is the largest gold ore cluster in China and is an important world-class Au cluster area. Due to the exploitation of shallow Au deposits, prospecting for deep-seated Au deposits (>500 m) become increasingly important. This study uses the Qujia Au deposit to illustrate how to target deep-seated Au deposits by integrating soil and borehole geochemical data into a three-dimensional (3-D) geochemical model based on nine section lines with 652 surface soil samples and 823 rock samples from 30 boreholes. The additive log-ratio (alr)-factor analysis (FA) was adopted for soil geochemical data to identify element associations, and key element associations were extracted: F5 (Au–Ag association) indicates Au mineralization, and F3 (Hg–As–Br–I–Se association) represents the mineralizing agents. Combined with Au, Ag and Hg distribution by 3-D modelling based on borehole geochemical data, Hg is extremely competent as a vector for transportation channels of Au-bearing hydrothermal fluids in shallow space and ore-hosting space in deep space. Meanwhile, Au and Ag in primary halos not only form secondary halos near the primary halos but also remobilize, migrate, and deposit to form secondary anomalies at Lower-lying areas far away from primary halos due to their high mobility, while Hg is more stable and forms anomalies near the primary halos in supergene environments. Finally, two indicators for deep-seated Au ore bodies are constructed: one is the composite anomaly of Au, Ag, and Hg derived from soil geochemical data, and the other is the Hg anomaly derived from borehole geochemical data. Overall, this study successfully constructed indicators for deep-seated Au ore bodies by integrating soil and borehole geochemical data which is significant for geochemical exploration of deep-seated Au ore bodies in the same mineralization belt of the Jiaodong district.
Colorectal Cancer Segmentation Using Atrous Convolution and Residual Enhanced UNet

Colorectal cancer is a leading cause of death worldwide. However, early diagnosis dramatically increases the chances of survival, for which it is crucial to identify the tumor in the body. Since its imaging uses high-resolution techniques, annotating the tumor is time-consuming and requires particular expertise. Lately, methods built upon Convolutional Neural Networks(CNNs) have proven to be at par, if not better in many biomedical segmentation tasks. For the task at hand, we propose another CNN-based approach, which uses atrous convolutions and residual connections besides the conventional filters. The training and inference were made using an efficient patch-based approach, which significantly reduced unnecessary computations. The proposed AtResUNet was trained on the DigestPath 2019 Challenge dataset for colorectal cancer segmentation with results having a Dice Coefficient of 0.748. Its ensemble, with its simpler version, achieved a Dice Coefficient of 0.753.
Ship Detection in Complex Weather Based on CNN

Ship detection methods which can be used in all kinds of complex weather is of great significance to the waterborne traffic. This paper uses the algorithm of dark channel estimation to simulate a data set under complex weather conditions naturally by small-scale dataset contain images mostly taken on sunny days. Besides, this paper compares several detectors based on CNN and different structure of feature extraction network. Finally, this paper gets a ship detection model that can achieve great performance under both original small-scale dataset and extended dataset with simulated complex weather condition images.
Discovering accurate deep learning based predictive models for automatic customer support ticket classification

Ticket Management Systems are widespread in disparate kinds of companies and organizations, as they represent a fundamental tool for handling customer requests and issues in an efficient and effective manner. In particular, accurately categorizing incoming tickets is a key task in real-life application settings (e.g., helpdesk/CRM systems and bug tracking systems), in order to improve ticket processing efficiency and effectiveness (e.g., in terms of customer satisfaction). In this work, we propose a comprehensive ticket-categorization analysis that relies on inducing and exploiting a heterogeneous ensemble of deep learning architectures, in addition to a range of functionalities for acquiring, integrating and pre-processing ticket-related information coming from different channels (e.g. mail, chat, web form, etc.). Experimental results conducted on the specific application scenario concerning the data of a publicly available ticket-mining dataset have proven the effectiveness of the framework in different ticket categorization tasks.
Covid-19 Pandemic: A New Era Of Cyber Security Threat And Holistic Approach To Overcome

In this paper, we present cyber security threat created worldwide has been discussed during the current COVID-19 situation. To stop the spread of Coronavirus, almost all the countries of the world declared lockdown. People are now doing their office, schooling, and business remotely. For that reason, the use of computers and the internet has increased a lot and made cyber criminals more active. Recently the number of cyber-attacks has increased a lot, and everyone has become a target the cyber criminals. Lack of personal safety precautions in using the internet is causing significant damage to everyone. COVID-19 has made it easy for cyber criminals to have easy access to everyone's data. Many banking sectors and government and non-government organizations have faced several cyberattacks during this time. This paper focuses on some safety precautions to safeguard the personal and organizational data from cyber criminals.
Epitomic Variational Graph Autoencoder

Variational autoencoder (VAE) is a widely used generative model for learning latent representations. Burda et al. [3] in their seminal paper showed that learning capacity of VAE is limited by over-pruning. It is a phenomenon where a significant number of latent variables fail to capture any information about the input data and the corresponding hidden units become inactive. This adversely affects learning diverse and interpretable latent representations. As variational graph autoencoder (VGAE) extends VAE for graph-structured data, it inherits the over-pruning problem. In this paper, we adopt a model based approach and propose epitomic VGAE (EVGAE), a generative variational framework for graph datasets which successfully mitigates the over-pruning problem and also boosts the generative ability of VGAE. We consider EVGAE to consist of multiple sparse VGAE models, called epitomes, that are groups of latent variables sharing the latent space. This approach aids in increasing active units as epitomes compete to learn better representation of the graph data. We verify our claims via experiments on three benchmark datasets. Our experiments show that EVGAE has a better generative ability than VGAE. Moreover, EVGAE outperforms VGAE on link prediction task in citation networks.
Analysis and Probing of Parallel Channels in the Lightning Network

Bitcoin can process only a few transactions per second, which is insufficient for a global payment network. The Lightning Network (LN) aims to address this challenge. The LN allows for low-latency bitcoin transfers through a network of payment channels. In contrast to regular Bitcoin transactions, payments in the LN are not globally broadcast. Thus it may improve not only Bitcoin’s scalability but also privacy. However, the probing attack allows an adversary to discover channel balances, threatening users’ privacy. Prior work on probing did not account for the possibility of multiple (parallel) channels between two nodes. Naive probing algorithms yield false results for parallel channels. In this work, we develop a new probing model that accurately accounts for parallel channels. We describe jamming-enhanced probing that allows for full balance information extraction in multi-channel hops, which was impossible with earlier probing methods. We quantify the attacker’s information gain and propose an optimized algorithm for choosing probe amounts for multi-channel hops. We demonstrate its efficiency based on real-world data using our own probing-focused LN simulator. Finally, we discuss countermeasures such as new forwarding strategies, intra-hop payment split, rebalancing, and unannounced channels.
LinkNet: 2D-3D linked multi-modal network for online semantic segmentation of RGB-D videos

This paper proposes LinkNet, a 2D-3D linked multi-modal network served for online semantic segmentation of RGB-D videos, which is essential for real-time applications such as robot navigation. Existing methods for RGB-D semantic segmentation usually work in the regular image domain, which allows efficient processing using convolutional neural networks (CNNs). However, RGB-D videos are captured from a 3D scene, and different frames can contain useful information of the same local region from different views. Working solely in the image domain fails to utilize such crucial information. Our novel approach is based on joint 2D and 3D analysis. The online process is realized simultaneously with 3D scene reconstruction, from which we set up 2D-3D links between continuous RGB-D frames and 3D point cloud. We combine image color and view-insensitive geometric features generated from the 3D point cloud for multi-modal semantic feature learning. Our LinkNet further uses a recurrent neural network (RNN) module to dynamically maintain the hidden semantic states during 3D fusion, and refines the voxel-based labeling results. The experimental results on SceneNet [1] and ScanNet [2] demonstrate that the semantic segmentation results of our framework are stable and effective.
Distributed Ciphertext-Policy Attribute-Based Encryption With Enhanced Collusion Resilience and Privacy Preservation

In recent years, numerous incidents of information leakage have not only violated the privacy of users but also been a key issue hindering the development of cloud computing. How to guarantee the confidentiality of shared data while protecting the privacy of users has become a hot topic of research. In this article, we propose a decentralized attribute-based access control mechanism. Compared with the existing well-known schemes, the proposed scheme enhances private security and the confidentiality of shared data. An anonymous key extraction protocol is introduced to help users prove their identities with zero knowledge and help the authorities to generate legitimate private keys without knowing the users global identifiers and specific attributes. We also eliminate linear relationships among the keys appearing in the previous reported works, which makes unauthorized users unable to combine their private keys to generate a legal secret key or combine their respective decryption results to recover the plaintext message. Performance and security analyses show that the new work is secure against collusion attacks, thus protecting users&#x2019; privacy and security. Besides, the security of the proposed scheme is reduced to the q-PBDHE assumption in the standard model.
Application of Augmented Reality in the field of Virtual Labs

An application of augmented reality (AR) for virtual labs is implemented. The emphasis is to demonstrate the use of different buttons and knobs of a Cathode Ray Oscilloscope (CRO) using an application Unity 3D which will make it easier for the students to get a better understanding of the CRO and its working. Using Vuforia engine, which is used to create AR applications in Unity 3D, an application is created which takes the picture of a CRO and superimposes the description of different knobs and buttons present on it. The AR camera does this by detecting specific features of the image. Realizing the impact of a stable and flexible AR virtual lab on the present education system, a more relevant application has been introduced in this paper.
The Extended Integrated Particle Filter Method (IPFx) as a High-Performance Earthquake Early Warning System

ABSTRACT An earthquake early warning (EEW) system rapidly analyzes seismic data to report the occurrence of an earthquake before strong shaking is felt at a site. In Japan, the integrated particle filter (IPF) method, a new source-estimation algorithm, was recently incorporated into the EEW system to improve the source-estimation accuracy during active seismicity. The problem of the current IPF method is that it uses the trigger information computed at each station in a specific format as the input and is therefore applicable to only limited seismic networks. This study proposes the extended IPF (IPFx) method to deal with continuous waveforms and merge all Japanese real-time seismic networks into a single framework. The new source determination algorithm processes seismic waveforms in two stages. The first stage (single-station processing) extracts trigger and amplitude information from continuous waveforms. The second stage (network processing) accumulates information from multiple stations and estimates the location and magnitude of ongoing earthquakes based on Bayesian inference. In 10 months of continuous online experiments, the IPFx method showed good performance in detecting earthquakes with maximum seismic intensity ≥3 in the Japan Meteorological Agency (JMA) catalog. By merging multiple seismic networks into a single EEW system, the warning time of the current EEW system can be improved further. The IPFx method provides accurate shaking estimation even at the beginning of event detection and achieves seismic intensity error &amp;lt;0.25 s after detecting an event. This method correctly avoided two major false alarms on 5 January 2018 and 30 July 2020. The IPFx method offers the potential of expanding the JMA IPF method to global seismic networks.