Reviewerly/exHarmony huggingface dataset:
Cryptocurrency Crime: Behaviors of Malicious Smart Contracts in Blockchain

Blockchain and smart contracts can be used to facilitate almost any financial transaction. Thanks to these smart contracts, the settlement of dividends and coupons could be automated. The blockchain would allow all these transactions to be saved in a single ledger rather than in many databases through many organizations as is currently the case. Smart contracts have become lucrative and profitable targets for attackers because they can hold a large amount of money. This paper takes stock of cryptocurrency crime by assessing attacks due to smart contracts and the cost of losses. These losses are often the result of two types of malicious contracts: vulnerable contracts and criminal smart contracts. Studying the behavior of malicious contracts allows us to understand the root causes and consequences of attacks and the defense capabilities that exist although they do not definitively solve the crime problem. It makes it possible to approach new defense perspectives which will be concretized in future work.
Cat2Type: Wikipedia Category Embeddings for Entity Typing in Knowledge Graphs

The entity type information in Knowledge Graphs (KGs) such as DBpedia, Freebase, etc. is often incomplete due to automated generation. Entity Typing is the task of assigning or inferring the semantic type of an entity in a KG. This paper introduces an approach named Cat2Type which exploits the Wikipedia Categories to predict the missing entity types in a KG. This work extracts information from Wikipedia Category names and the Wikipedia Category graph which are the sources of rich semantic information about the entities. In Cat2Type, the characteristic features of the entities encapsulated in Wikipedia Category names are exploited using Neural Language Models. On the other hand, a Wikipedia Category graph is constructed to capture the connection between the categories. The Node level representations are learned by optimizing the neighbourhood information on the Wikipedia category graph. These representations are then used for entity type prediction via classification. The performance of Cat2Type is assessed on two real-world benchmark datasets DBpedia630k and FIGER. The experiments depict that Cat2Type obtained a significant improvement over state-of-the-art approaches.
Real‐time Denoising Using BRDF Pre‐integration Factorization

Abstract Path tracing has been used for real‐time renderings, thanks to the powerful GPU device. Unfortunately, path tracing produces noisy rendered results, thus, filtering or denoising is often applied as a post‐process to remove the noise. Previous works produce high‐quality denoised results, by accumulating the temporal samples. However, they cannot handle the details from bidirectional reflectance distribution function (BRDF) maps (e.g. roughness map). In this paper, we introduce the BRDF pre‐integration factorization for denoising to better preserve the details from BRDF maps. More specifically, we reformulate the rendering equation into two components: the BRDF pre‐integration component and the weighted‐lighting component. The BRDF pre‐integration component is noise‐free, since it does not depend on the lighting. Another key observation is that the weighted‐lighting component tends to be smooth and low‐frequency, which indicates that it is more suitable for denoising than the final rendered image. Hence, the weighted‐lighting component is denoised individually. Our BRDF pre‐integration demodulation approach is flexible for many real‐time filtering methods. We have implemented it in spatio‐temporal variance‐guided filtering (SVGF), ReLAX and ReBLUR. Compared to the original methods, our method manages to better preserve the details from BRDF maps, while both the memory and time cost are negligible.
Online Active Proposal Set Generation for weakly supervised object detection

To reduce the manpower consumption on box-level annotations, many weakly supervised object detection methods which only require image-level annotations, have been proposed recently. The training process in these methods is formulated into two steps. They firstly train a neural network under weak supervision to generate pseudo ground truths (PGTs). Then, these PGTs are used to train another network under full supervision. Compared with fully supervised methods, the training process in weakly supervised methods becomes more complex and time-consuming. Furthermore, overwhelming negative proposals are involved at the first step. This is neglected by most methods, which makes the training network biased towards to negative proposals and thus degrades the quality of the PGTs, limiting the training network performance at the second step. Online proposal sampling is an intuitive solution to these issues. However, lacking of adequate labeling, a simple online proposal sampling may make the training network stuck into local minima. To solve this problem, we propose an Online Active Proposal Set Generation (OPG) algorithm. Our OPG algorithm consists of two parts: Dynamic Proposal Constraint (DPC) and Proposal Partition (PP). DPC is proposed to dynamically determine different proposal sampling strategies according to the current training state. PP is used to score each proposal, part proposals into different sets and generate an active proposal set for the network optimization. Through experiments, our proposed OPG shows consistent and significant improvement on both datasets PASCAL VOC 2007 and 2012, yielding comparable performance to the state-of-the-art results.
An educational toolkit for teaching cloud computing

In an educational context, experimenting with a real cloud computing platform is very important to let students understand the core concepts, methodologies and technologies of cloud computing. However, API heterogeneity of cloud providers complicates the experimentation by forcing students to focus on the use of different APIs, and by hindering the jointly use of different platforms. In this paper, we present EasyCloud, a toolkit enabling the easy and effective use of different cloud platforms. In particular, we describe its features, architecture, scalability, and use in our cloud computing courses, as well as the pedagogical insights we learnt over the years.
A Methodology for the Assessment of Operator 4.0 Skills based on Sentiment Analysis and Augmented Reality

Human-Cyber-Physical Systems are the key to the successful operation of manufacturing systems. Consequently, the need for adequate assessment of human operators and tracking of their skills and competencies evolution emerges. Additionally, the advances in digital technologies encourage the development of supportive and adaptive frameworks for the operation of flexible manufacturing systems. This paper presents an Augmented Reality based methodology for the detailed evaluation of human skills and competencies based on the processing of raw textual data with a Natural Language Processing algorithm, aiming at the provision of technician guidance. The developed framework is tested and validated in an industrial environment.
CNN-Based Machine Learning Channel on TDMR Drive Data

In this article, we demonstrate a convolutional neural network (CNN)-based data detection channel on real data from a commercial hard disk drive (HDD) employed with two-dimensional magnetic recording (TDMR) dual-reader technology. The neural network has two sets of input neurons and is trained using 485 4-kB sectors from the drive. Data detection is performed using the trained network and a one-to-one comparison of bit-error-rate (BER) performance with the HDD channel is systematically conducted. The trained neural network demonstrates comparable or slightly better performance than the state-of-the-art HDD channel. In addition, comparing to detection with a single reader, CNN-machine learning (ML) channel autonomously enables ~6% recording density gain when the second reader is available, and there is no physical modeling needed to manually combine or “fuse” signals from the multiple readers. We believe that the understanding elucidated in this article is crucial for developing more capable, robust, and effective ML-based data detection channels with the potential to realize even greater areal density gain in TDMR systems and higher capacity HDD.
Implementation of the Object Detection Algorithm (YOLOV3) on FPGA

From past to present, traffic has always been an important problem in society. Loss of life and material losses due to traffic accidents are quite high. The vast majority of these accidents are closely related to people who drive incorrectly and unconsciously. Therefore, researchers propose various methods to solve this problem. Recently, deep learning-based models have been widely used for this purpose. However, these models such as single shot detection (SSD), regions convolutional neural network (R-CNN) and fast regions convolutional neural network (Fast R-CNN) cannot perform real-time object recognition. Because, image processing time (speed) is too long. In this study, the you only look once version 3 (YOLOV3) model, which is one of the deep learning applications, is introduced in real-time to track cars in traffic. YOLOV3 can track objects in real-time. However, the hardware required for the model to give better and faster results is not sufficient today. For this reason, ZCU102 field programmable gate arrays (FPGA) card is used. It has been observed that faster and more accurate results are obtained with the YOLOV3 model run on the FPGA card.
Memory-Based Semantic Parsing

Abstract We present a memory-based model for context- dependent semantic parsing. Previous approaches focus on enabling the decoder to copy or modify the parse from the previous utterance, assuming there is a dependency between the current and previous parses. In this work, we propose to represent contextual information using an external memory. We learn a context memory controller that manages the memory by maintaining the cumulative meaning of sequential user utterances. We evaluate our approach on three semantic parsing benchmarks. Experimental results show that our model can better process context-dependent information and demonstrates improved performance without using task-specific decoders.
Bert-Based Chinese Medical Keyphrase Extraction Model Enhanced with External Features

Keyphrase extraction is a key natural language processing task and has widespread adoption in many information retrieval and text mining applications. In this paper, we construct nine Bert-based Chinese medical keyphrase extraction models enhanced with external features and present a thorough empirical evaluation to explore the impacts of feature types and feature fusion methods. The results show that encoding part-of-speech (POS) feature and lexicon feature generated from descriptive keyphrase metadata into the word embedding space improves the baseline Bert-SoftMax model for 4.82%, meaning that it’s beneficial to incorporate features into Chinese medical keyphrase extraction model. Furthermore, the results of the comparative evaluation experiments show that model performance is sensitive to both of feature types and feature fusion methods, so it’s advisable to consider these two factors when dealing with feature enhanced tasks. Our study also provides a feasible approach to employ metadata, aiming to help stakeholders of digital libraries to take full advantage of large quantities of metadata resources to boost the development of scholarly knowledge discovery.
A Novel Medical Prognosis System for Breast Cancer

The availability of huge variety of medical dataset coming from different sources is boon for automatic medical prognosis system. Breast cancer or breast carcinoma is one of the deadly diseases of modern times. Here in this paper, we emphasize on exploring unsupervised learning techniques. The objectives of this paper are to analyze the breast cancer dataset using different clustering methods to understand the correlations of the attributes present in the dataset and then investigate with different algorithm like random forest (RF), support vector machine (SVM), multilayer perceptron (MLP) and J48 to get the best model for our prediction and classification. So, we proposed a novel medical prognosis system (NMPS) which is an ensemble learning model combines all these algorithms and gives all possible results stated above in the purview of unsupervised learning classification with different clustering techniques.
Hypothesis testing in multivariate normal models with block circular covariance structures

In this article, we address the problem of simultaneous testing hypothesis about mean and covariance matrix for repeated measures data when both the mean vector and covariance matrix are patterned. In particular, tests about the mean vector under block circular and doubly exchangeable covariance structures have been considered. The null distributions are established for the corresponding likelihood ratio test statistics, and expressions for the exact or near-exact probability density and cumulative distribution functions are obtained. The application of the results is illustrated by both a simulation study and a real-life data example.
Secure Transmission of Medical Images using Improved Hybrid Cryptosystem: Authentication, Confidentiality and Integrity

In telemedicine applications, sensitive and private patients' information is collected and transmitted via a telecommunication system. To assure the security services of the exchanged medical images, improved cryptographic algorithms should be designed to protect private information against attacks. In this paper, we propose an efficient cryptosystem for medical image encryption and authentication. The cryptosystem is an Improved Advanced Encryption Standard (IAES) - Elliptic Curve Digital Signature Algorithm (ECDSA) hybrid scheme that uses symmetric and asymmetric approaches. The first one is used to encrypting the image. The second one is used to encrypting the initial secret key and owner's signature that permit authentication. The implementation on a Cyclone III FPGA uses 18.594 of total logic elements, 17.820 of total combinatorial functions, and 131.616 of total memory. It runs at a frequency of 147.16 MHz, consumes 171.16 mW and can achieve excellent throughput of 1.71 Gb / s. Results prove that the proposed cipher framework is appropriate for embedded systems respecting both real-time performance and resources constrained. The security analysis is successfully performed and experimental results prove that the suggested technique provides the basis of cryptography.
Coherent checking and updating of Bayesian models without specifying the model space: A decision-theoretic semantics for possibility theory

A Bayesian model has two parts. The first part is a family of sampling distributions that could have generated the data. The second part of a Bayesian model is a prior distribution over the sampling distributions. Both the diagnostics used to check the model and the process of updating a failed model are widely thought to violate the standard foundations of Bayesianism. That is largely because models are checked before specifying the space of all candidate replacement models, which textbook presentations of Bayesian model averaging would require. However, that is not required under a broad class of utility functions that apply when approximate model truth is an important consideration, perhaps among other important considerations. From that class, a simple criterion for model checking emerges and suggests a coherent approach to updating Bayesian models found inadequate. The criterion only requires the specification of the prior distribution up to ratios of prior densities of the models considered until the time of the check. That criterion, while justified by Bayesian decision theory, may also be derived under possibility theory from a decision-theoretic framework that generalizes the likelihood interpretation of possibility functions.
An Efficient Network Design for Face Video Super-resolution

Face video super-resolution algorithm aims to reconstruct realistic face details through continuous input video sequences. However, existing video processing algorithms usually contain redundant parameters to guarantee different super-resolution scenes. In this work, we focus on super-resolution of face areas in original video scenes, while rest areas are interpolated. This specific super-resolved task makes it possible to cut redundant parameters in general video super-resolution networks. We construct a dataset consisting entirely of face video sequences for network training and evaluation, and conduct hyper-parameter optimization in our experiments. We use three combined strategies to optimize the network parameters with a simultaneous train-evaluation method to accelerate optimization process. Results show that simultaneous train-evaluation method improves the training speed and facilitates the generation of efficient networks. The generated network can reduce at least 52.4% parameters and 20.7% FLOPs, achieve better performance on PSNR, SSIM compared with state-of-art video super-resolution algorithms. When processing 36 × 36 × 1 × 3 input video frame sequences, the efficient network provides 47.62 FPS real-time processing performance. We name our proposal as hyper-parameter optimization for face Video Super-Resolution (HO-FVSR), which is open-sourced at https://github.com/yphone/efficient-network-for-face-VSR.
Visual Linking of Feature Values in Immersive Graph Visualization Environment

There are a variety of graphs where multidimensional feature values are assigned to the nodes. Visualization of such datasets is not an easy task since they are complex and often huge. Immersive Analytics is a powerful approach to support the interactive exploration of such large and complex data. Many recent studies on graph visualization have applied immersive analytics frameworks; however, there have been few studies on immersive analytics for visualization of multidimensional attributes associated with the input graphs. This paper presents a new immersive analytics system that supports the interactive exploration of multidimensional feature values assigned to the nodes of input graphs. The presented system displays "label-axes" corresponding to the dimensions of feature values, and "label-edges" that connect label-axes and corresponding to the nodes. The system supports visual linking operations which controls the display of edges that connect a label-axis and nodes of the graph. This paper introduces visualization examples with a graph dataset of Twitter users and reviews by experts on graph data analysis.
Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator

Recent advances in algorithm-hardware co-design for deep neural networks (DNNs) have demonstrated their potential in automatically designing neural architectures and hardware designs. Nevertheless, it is still a challenging optimization problem due to the expensive training cost and the time-consuming hardware implementation, which makes the exploration on the vast design space of neural architecture and hardware design intractable. In this paper, we demonstrate that our proposed approach is capable of locating designs on the Pareto frontier. This capability is enabled by a novel three-phase co-design framework, with the following new features: (a) decoupling DNN training from the design space exploration of hardware architecture and neural architecture, (b) providing a hardware-friendly neural architecture space by considering hardware characteristics in constructing the search cells, (c) adopting Gaussian process to predict accuracy, latency and power consumption to avoid time-consuming synthesis and place-and-route processes. In comparison with the manually-designed ResNet101, InceptionV2 and MobileNetV2, we can achieve up to 5% higher accuracy with up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\times$</tex> speed up on the ImageNet dataset. Compared with other state-of-the-art co-design frameworks, our found network and hardware configuration can achieve 2% (~ 6% higher accuracy, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2\times\sim 26\times$</tex> smaller latency and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$8.5\times$</tex> higher energy efficiency.
Self-Sufficient Feature Enhancing Networks for Video Salient Object Detection

Detecting salient objects in videos is a very challenging task. Current state-of-the-art methods are dominated by motion based deep neural networks, among which optical flow is often leveraged as motion representation. Though with robust performance, these optical flow-based video salient object detection methods face at least two problems that may hinder their generalization and application. First, computing optical flow as a pre-processing step does not support direct end-to-end learning; second, little attention has been given to the quality of visual features due to high computational cost of spatiotemporal feature encoding. In this paper we propose a novel self-sufficient feature enhancing network (SFENet) for video salient object detection, which leverages optical flow estimation as an auxiliary task while being end-to-end trainable. With a joint training scheme of both salient object detection and optical flow estimation, its multi-task architecture can be totally self-sufficient for achieving good performance without any pre-processing. Furthermore, for improving feature quality, we design four lightweight modules in spatial and temporal domains, including cross-layer fusion, multi-level warping, spatial-channel attention and boundary-aware refinement. The proposed method is evaluated through extensive experiments on five video salient object detection datasets. Experimental results show that our SFENet can be easily trained with fast convergence speed. It significantly outperforms previous methods in terms of various evaluation metrics. Moreover, with optical flow estimation and unsupervised video object segmentation as example applications, our method also yields state-of-the-art results on standard datasets.
Deep Learning With Weak Supervision for Disaster Scene Description in Low-Altitude Imagery

Pictures or videos captured from a low-altitude aircraft or an unmanned aerial vehicle are a fast and cost-effective way to survey the affected scene for the quick and precise assessment of a catastrophic event’s impacts and damages. Using advanced techniques, such as deep learning, it is now possible to automate the description of disaster scenes and identify features in captured images or recorded videos to gain situational awareness. However, building a large-scale, high-quality dataset with annotated disaster-related features for supervised model training is time-consuming and costly. In this article, we propose a weakly supervised approach to train a deep neural network on low-altitude imagery with highly imbalanced and noisy crowd-sourced labels. We further make use of the rich spatiotemporal data obtained from the pictures and its sequence information to enhance the model’s performance during training via label propagation. Our approach achieves the highest score among all the submitted runs in the TRECVID2020 Disaster Scene Description and Indexing (DSDI) Challenge, indicating its superior capabilities in retrieving disaster-related video clips compared to other proposed methods.