Reviewerly/exHarmony huggingface dataset:
Intelligent financial decision support system based on data mining

With the development of information technology, intelligent control technology is the comprehensive application of modern management techniques and methods. This paper mainly studies the intelligent financial decision support system based on data mining. This paper mainly introduces data mining technology, an intelligent financial decision support system and the application of data mining technology in an intelligent financial decision support system. The intelligent financial decision support system proposed in this paper uses a relational database to store massive business data, to improve the system expansion ability. By using mathematical model and data mining technology, an intelligent financial decision support system can automatically analyze data, discover the internal relationship between data, and mine the model that plays an important role in prediction and decision-making behavior, to establish a new business model, help decision-makers to make marketing strategies in line with the market and make correct decisions. The experimental results show that: the actual total profit of the company in 2019 is 43.37 million yuan, and the predicted total profit in 2019 is 43.38 million yuan. The similarity between the actual total profit in 2019 and the predicted total profit in 2019 is 99.98%. In 2019, the company’s sales revenue is 37.61 million yuan. The predicted sales revenue in 2019 is 37.62 million yuan, which is 99.97% similar to the actual sales revenue in 2019. The managers of the company can make marketing strategies and make correct decisions according to the sales revenue forecast in 2020.
DEPOSafe: Demystifying the Fake Deposit Vulnerability in Ethereum Smart Contracts

Cryptocurrency has seen an explosive growth in recent years, thanks to the evolvement of blockchain technology and its economic ecosystem.Besides Bitcoin, thousands of cryptocurrencies have been distributed on blockchains, while hundreds of cryptocurrency exchanges are emerging to facilitate the trading of digital assets.At the same time, it also attracts the attentions of attackers.Fake deposit, as one of the most representative attacks (vulnerabilities) related to exchanges and tokens, has been frequently observed in the blockchain ecosystem, causing large financial losses.However, besides a few security reports, our community lacks the understanding of this vulnerability, for example its scale and the impacts.In this paper, we take the first step to demystify the fake deposit vulnerability.Based on the essential patterns we have summarized, we implement DEPOSafe, an automated tool to detect and verify (exploit) the fake deposit vulnerability in ERC-20 smart contracts.DEPOSafe incorporates several key techniques including symbolic execution based static analysis and behavior modeling based dynamic verification.By applying DEPOSafe to 176,000 ERC-20 smart contracts, we have identified over 7,000 vulnerable contracts that may suffer from two types of attacks.Our findings demonstrate the urgency to identify and prevent the fake deposit vulnerability.
Forensic analysis of instant messaging apps: Decrypting Wickr and private text messaging data

Currently, obtaining digital evidence from instant messaging (IM) apps is being considered. IM apps store personal user data, e.g., user information, chat messages, and multimedia files, most IM apps protect the stored data using secure cryptographic algorithms such that only an authorized person can access the app data. Accordingly, it is important to develop a decryption method for IM app data for digital forensic investigations. In this paper, we present how the database and main files of IM apps are stored and encrypted, as well as how the apps verify passwords. We also present a decryption methodology for the databases and multimedia files of two IM apps, i.e., Wickr and Private Text Messaging, which provide various secure communications, e.g., text messages and multimedia files. The decrypted data of these IM apps was verified in simulations.
FaCT-LSTM: Fast and Compact Ternary Architecture for LSTM Recurrent Neural Networks

This article proposes a Fast and Compact Ternary LSTM (FaCTLSTM), which bridges the accuracy gap between the full-precision and quantized neural networks.
Active Learning of Discriminative Subgraph Patterns for API Misuse Detection

A common cause of bugs and vulnerabilities are the violations of usage constraints associated with Application Programming Interfaces (APIs).API misuses are common in software projects, and while there have been techniques proposed to detect such misuses, studies have shown that they fail to reliably detect misuses while reporting many false positives.One limitation of prior work is the inability to reliably identify correct patterns of usage.Many approaches confuse a usage pattern's frequency for correctness.Due to the variety of alternative usage patterns that may be uncommon but correct, anomaly detection-based techniques have limited success in identifying misuses.We address these challenges and propose ALP (Actively Learned Patterns), reformulating API misuse detection as a classification problem.After representing programs as graphs, ALP mines discriminative subgraphs.While still incorporating frequency information, through limited human supervision, we reduce the reliance on the assumption relating frequency and correctness.The principles of active learning are incorporated to shift human attention away from the most frequent patterns.Instead, ALP samples informative and representative examples while minimizing labeling effort.In our empirical evaluation, ALP substantially outperforms prior approaches on both MUBench, an API Misuse benchmark, and a new dataset that we constructed from real-world software projects.
Probabilistic domain-knowledge modeling of disorder pathogenesis for dynamics forecasting of acute onset

Disease pathogenesis, a type of domain knowledge about biological mechanisms leading to diseases, has not been adequately encoded in machine-learning-based medical diagnostic models because of the inter-patient variabilities and complex dependencies of the underlying pathogenetic mechanisms. We propose 1) a novel pathogenesis probabilistic graphical model (PPGM) to quantify the dynamics underpinning patient-specific data and pathogenetic domain knowledge, 2) a Bayesian-based inference paradigm to answer the medical queries and forecast acute onsets. The PPGM model consists of two components: a Bayesian network of patient attributes and a temporal model of pathogenetic mechanisms. The model structure was reconstructed from expert knowledge elicitation, and its parameters were estimated using Variational Expectation-Maximization algorithms. We benchmarked our model with two well-established hidden Markov models (HMMs) - Input-output HMM (IO-HMM) and Switching Auto-Regressive HMM (SAR-HMM) - to evaluate the computational costs, forecasting performance, and execution time. Two case studies on Obstructive Sleep Apnea (OSA) and Paroxysmal Atrial Fibrillation (PAF) were used to validate the model. While the performance of the parameter learning step was equivalent to those of IO-HMM and SAR-HMM models, our model forecasting ability was outperforming those two models. The merits of the PPGM model are its representation capability to capture the dynamics of pathogenesis and perform medical inferences and its interpretability for physicians. The model has been used to perform medical queries and forecast the acute onset of OSA and PAF. Additional applications of the model include prognostic healthcare and preventive personalized treatments.
Multi AGV Coordination Tolerant to Communication Failures

Most path planning algorithms used presently in multi-robot systems are based on offline planning. The Timed Enhanced A* (TEA*) algorithm gives the possibility of planning in real time, rather than planning in advance, by using a temporal estimation of the robot’s positions at any given time. In this article, the implementation of a control system for multi-robot applications that operate in environments where communication faults can occur and where entire sections of the environment may not have any connection to the communication network will be presented. This system uses the TEA* to plan multiple robot paths and a supervision system to control communications. The supervision system supervises the communication with the robots and checks whether the robot’s movements are synchronized. The implemented system allowed the creation and execution of paths for the robots that were both safe and kept the temporal efficiency of the TEA* algorithm. Using the Simtwo2020 simulation software, capable of simulating movement dynamics and the Lazarus development environment, it was possible to simulate the execution of several different missions by the implemented system and analyze their results.
Visualization of Tensor Fields in Mechanics

Abstract Tensors are used to describe complex physical processes in many applications. Examples include the distribution of stresses in technical materials, acting forces during seismic events, or remodeling of biological tissues. While tensors encode such complex information mathematically precisely, the semantic interpretation of a tensor is challenging. Visualization can be beneficial here and is frequently used by domain experts. Typical strategies include the use of glyphs, color plots, lines, and isosurfaces. However, data complexity is nowadays accompanied by the sheer amount of data produced by large‐scale simulations and adds another level of obstruction between user and data. Given the limitations of traditional methods, and the extra cognitive effort of simple methods, more advanced tensor field visualization approaches have been the focus of this work. This survey aims to provide an overview of recent research results with a strong application‐oriented focus, targeting applications based on continuum mechanics, namely the fields of structural, bio‐, and geomechanics. As such, the survey is complementing and extending previously published surveys. Its utility is twofold: (i) It serves as basis for the visualization community to get an overview of recent visualization techniques. (ii) It emphasizes and explains the necessity for further research for visualizations in this context.
Bayesian Learning of LF-MMI Trained Time Delay Neural Networks for Speech Recognition

Discriminative training techniques define state-of-the-art performance for automatic speech recognition systems. However, they are inherently prone to overfitting, leading to poor generalization performance when using limited training data. In order to address this issue, this paper presents a full Bayesian framework to account for model uncertainty in sequence discriminative training of factored TDNN acoustic models. Several Bayesian learning based TDNN variant systems are proposed to model the uncertainty over weight parameters and choices of hidden activation functions, or the hidden layer outputs. Efficient variational inference approaches using as few as one single parameter sample ensure their computational cost in both training and evaluation time comparable to that of the baseline TDNN systems. Statistically significant word error rate (WER) reductions of 0.4%-1.8% absolute (5%-11% relative) were obtained over a state-of-the-art 900 h speed perturbed Switchboard corpus trained baseline LF-MMI factored TDNN system using multiple regularization methods including F-smoothing, L2 norm penalty, natural gradient, model averaging and dropout, in addition to i-Vector plus learning hidden unit contribution (LHUC) based speaker adaptation and RNNLM rescoring. The efficacy of the proposed Bayesian techniques is further demonstrated in a comparison against the state-of-the-art performance obtained on the same task using the most recent hybrid and end-to-end systems reported in the literature. Consistent performance improvements were also obtained on a 450-h HKUST conversational Mandarin telephone speech recognition task. On a third cross domain adaptation task requiring rapidly porting a 1000-h LibriSpeech data trained system to a small DementiaBank elderly speech corpus, the proposed Bayesian TDNN LF-MMI systems outperformed the baseline system using direct weight fine-tuning by up to 2.5% absolute WER reduction.
YOLOv3-Based Human Activity Recognition as Viewed from a Moving High-Altitude Aerial Camera

This paper presents a method to classify human activities as normal or suspicious using YOLOv3 to automatically process video footages taken from a high altitude moving aerial camera, such as the one attached to a drone. We consider four human activities namely, jogging, walking, fighting, and chasing. Objects generally appear much smaller, with less visible features, when viewed from high altitudes. The reduced visible features make automatic human activity detection from ground surveillance cameras not applicable to the high altitude case. Through transfer learning, we modified existing pre-trained YOLOv3 convolutional neural networks (CNN`s) and retrained with our own high aerial human action dataset. By so doing, we were able to customize YOLOv3 to detect, localize, and recognize aerial human activities in real-time as normal or suspicious. The proposed approach achieves a promising average precision accuracy of 82.30%, and average F1 score of 88.10% on classifying high aerial human activities. We demonstrated that YOLOv3 is a powerful approach and relatively fast for the recognition and localization of human subjects as seen from above.
Revisiting Indexes for Assessing Interpretability of Fuzzy Systems

Interpretability is one of the most valuable properties of fuzzy systems. Despite the effort made by the research community for characterizing interpretability, there is not a consensus about how to measure interpretability yet. It is admitted that the analysis of interpretability is subjective because it depends on the background of the person who makes the assessment. Accordingly, an index flexible enough to fit with preferences and expectations would be appreciated from the designer but also from the user viewpoints. Hence, subjective indexes become essential for customization purposes. However, they are not enough for measuring interpretability in the broad sense. There is also a need of objective indexes to make feasible fair comparisons among different fuzzy systems designed for solving a given problem. Thus, it is necessary to look for two kinds of complementary indexes, subjective and objective ones. Moreover, they should tackle with interpretability from readability and comprehensibility viewpoints. They also must consider both structural and semantic interpretability properties. In this chapter, we give an overview on existing indexes for assessing all aspects related to interpretability of fuzzy systems. In addition, we present a conceptual framework for assessing interpretability with a customizable index which combines subjective and objective ones.
Low-Autocorrelation Random Number Generator Based on Adiabatic Quantum-Flux-Parametron Logic

We propose a true random number generator (TRNG) based on adiabatic quantum-flux-parametron (AQFP) logic. This AQFP TRNG corresponds to an AQFP buffer chain with the input current removed, which enables the generation of random bits via thermal fluctuation. To realize ultralow autocorrelation, several isolation inductors are inserted between adjacent buffers in the AQFP TRNG, thereby reducing the impact of the back action between buffers. We design and fabricate an AQFP TRNG using the AIST 10 kA/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Nb high-speed standard process and evaluate the quality of the generated random bit streams. For a 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">17</sup> -bit random stream obtained from the AQFP TRNG at 100 kHz, the autocorrelation function is 5.00 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> . Furthermore, we evaluate a 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">20</sup> -bit random stream from the AQFP TRNG using a statistical test suite provided by the National Institute of Standards and Technology. The bit stream passed 14 out of 15 tests, which suggests that the proposed AQFP TRNG can be used for applications such as stochastic computing and stochastic local search algorithms.
Dense Gaussian Processes for Few-Shot Segmentation

Few-shot segmentation is a challenging dense prediction task, which entails segmenting a novel query image given only a small annotated support set. The key problem is thus to design a method that aggregates detailed information from the support set, while being robust to large variations in appearance and context. To this end, we propose a few-shot segmentation method based on dense Gaussian process (GP) regression. Given the support set, our dense GP learns the mapping from local deep image features to mask values, capable of capturing complex appearance distributions. Furthermore, it provides a principled means of capturing uncertainty, which serves as another powerful cue for the final segmentation, obtained by a CNN decoder. Instead of a one-dimensional mask output, we further exploit the end-to-end learning capabilities of our approach to learn a high-dimensional output space for the GP. Our approach sets a new state-of-the-art on the PASCAL-5 $$^i$$ and COCO-20 $$^i$$ benchmarks, achieving an absolute gain of $$+8.4$$ mIoU in the COCO-20 $$^i$$ 5-shot setting. Furthermore, the segmentation quality of our approach scales gracefully when increasing the support set size, while achieving robust cross-dataset transfer. Code and trained models are available at https://github.com/joakimjohnander/dgpnet .
End-to-End Rain Removal Network Based on Progressive Residual Detail Supplement

Methods of rain removal based on deep learning have rapidly developed, and the image quality after rain removal is continuously improving. However, the results of most methods have some common problems, including a loss of details, a blurring of edges, and the existence of artifacts. To remove rain-related information more thoroughly and retain more edge details, this paper proposes an end-to-end rain removal network based on the progressive residual detail supplement (ERRN-PRDS) approach. The entire network structure is designed in an iterative manner to obtain higher-quality rain removal images from coarse to fine. In the network, a diamond residual block is constructed as the main module of iteration to learn the feature information of the background layer. Meanwhile, to keep more texture details in the background layer, a detail supplement mechanism is designed between the iterative layers to transfer more information to the next iterative operation. Experimental results show that this method can remove the rain information more completely and better retain the image edges compared with previous state-of-the-art methods. In addition, because of the sparsity of the detail injection, our network also achieves high-quality results for image denoising tasks.
Secure Image Authentication Using Watermarking and Blockchain

Image authentication is an important field that employs many different approaches and has several significant applications. In the proposed approach, we used a combination of two techniques to achieve authentication. Image watermarking is one of the techniques that has been used in many studies but the authentication field still needs to be studied. Blockchain technology is a relatively new technology that has significant research potential related to image authentication. The watermark is embedded into the third-level discrete wavelet transform (DWT) in the middle frequency regions to achieve security and imperceptibility goals. Peak signal-to-noise ratio PSNR, structural similarity matrix (SSIM), normalized correlation coefficient (NCC), and bit error rate (BER) are used to measure the performance of image watermarking. We used blockchain technology to avoid involving a trusted third party for authentication. Secure Hash Algorithm 256 (SHA-256) is applied on the watermark to save it into the blockchain. The watermark is encrypted using Advanced Encryption Standard (AES) and embedded into the image. The proposed method is tested on the USP SICI database and the MedPix medical image database. Ethereum blockchain is used to provide security, anonymity, and integrity of data with no third-party intervention. The proposed solution demonstrates enhanced security for image authentication compared with the state-of-the-art.
Generative Deep Learning in Digital Pathology Workflows

Many modern histopathology laboratories are in the process of digitizing their workflows. Digitization of tissue images has made it feasible to research the augmentation or automation of clinical reporting and diagnosis. The application of modern computer vision techniques, based on deep learning, promises systems that can identify pathologies in slide images with a high degree of accuracy. Generative modeling is an approach to machine learning and deep learning that can be used to transform and generate data. It can be applied to a broad range of tasks within digital pathology, including the removal of color and intensity artifacts, the adaption of images in one domain into those of another, and the generation of synthetic digital tissue samples. This review provides an introduction to the topic, considers these applications, and discusses future directions for generative models within histopathology.
A comparison of Gaussian processes and neural networks for computer model emulation and calibration

Abstract The Department of Energy relies on complex physics simulations for prediction in domains like cosmology, nuclear theory, and materials science. These simulations are often extremely computationally intensive, with some requiring days or weeks for a single simulation. In order to assure their accuracy, these models are calibrated against observational data in order to estimate inputs and systematic biases. Because of their great computational complexity, this process typically requires the construction of an emulator , a fast approximation to the simulation. In this paper, two emulator approaches are compared: Gaussian process regression and neural networks. Their emulation accuracy and calibration performance on three real problems of Department of Energy interest is considered. On these problems, the Gaussian process emulator tends to be more accurate with narrower, but still well‐calibrated uncertainty estimates. The neural network emulator is accurate, but tends to have large uncertainty on its predictions. As a result, calibration with the Gaussian process emulator produces more constrained posteriors that still perform well in prediction.
A Knowledge-Enriched and Span-Based Network for Joint Entity and Relation Extraction

The joint extraction of entities and their relations from certain texts plays a significant role in most natural language processes.For entity and relation extraction in a specific domain, we propose a hybrid neural framework consisting of two parts: a span-based model and a graph-based model.The span-based model can tackle overlapping problems compared with BILOU methods, whereas the graph-based model treats relation prediction as graph classification.Our main contribution is to incorporate external lexical and syntactic knowledge of a specific domain, such as domain dictionaries and dependency structures from texts, into end-to-end neural models.We conducted extensive experiments on a Chinese military entity and relation extraction corpus.The results show that the proposed framework outperforms the baselines with better performance in terms of entity and relation prediction.The proposed method provides insight into problems with the joint extraction of entities and their relations.