Reviewerly/exHarmony huggingface dataset:
Noise-Adaptive Non-Blind Image Deblurring

This work addresses the problem of non-blind image deblurring for arbitrary input noise. The problem arises in the context of sensors with strong chromatic aberrations, as well as in standard cameras, in low-light and high-speed scenarios. A short description of two common classical approaches to regularized image deconvolution is provided, and common issues arising in this context are described. It is shown how a pre-deconvolved deep neural network (DNN) based image enhancement can be improved by joint optimization of regularization parameters and network weights. Furthermore, a two-step approach to deblurring based on two DNNs is proposed, with the first network estimating deconvolution regularization parameters, and the second one performing image enhancement and residual artifact removal. For the first network, a novel RegParamNet architecture is introduced and its performance is examined for both direct and indirect regularization parameter estimation. The system is shown to operate well for input noise in a three orders of magnitude range (0.01-10.0) and a wide spectrum of 1D or 2D Gaussian blur kernels, well outside the scope of most previously explored image blur and noise degrees. The proposed method is found to significantly outperform several leading state-of-the-art approaches.
Team-Builder: Toward More Effective Lineup Selection in Soccer

Lineup selection is an essential and important task in soccer matches. To win a match, coaches must consider various factors and select appropriate players for a planned formation. Computation-based tools have been proposed to help coaches on this complex task, but they are usually based on over-simplified models on player performances, do not support interactive analysis, and overlook the inputs by coaches. In this article, we propose a method for visual analytics of soccer lineup selection by tackling two challenges: characterizing essential factors involved in generating optimal lineup, and supporting coach-driven visual analytics of lineup selection. We develop a lineup selection model that integrates such important factors, such as spatial regions of player actions and defensive interactions with opponent players. A visualization system, Team-Builder, is developed to help coaches control the process of lineup generation, explanation, and comparison through multiple coordinated views. The usefulness and effectiveness of our system are demonstrated by two case studies on a real-world soccer event dataset.
Evolution analysis of corporate governance structure based on blockchain network security and complex adaptive system

At present, the corporate governance structure of domestic enterprises is not sound, which directly affects the development of enterprise technological innovation. This study mainly discusses the evolution of corporate governance structure based on the complex adaptive system of blockchain network security. The blockchain is actually very similar to the accounting book. The account book formed by the summary of the accounting vouchers in each month of the enterprise is equivalent to each block, and the account book formed by the sequential connection is equivalent to the block chain formed by concatenating the blocks. This article believes that in the corporate governance system, the maximum fairness and the highest efficiency are always the basic goals pursued by the system, and the balance between fairness and efficiency is the basic principle. The research content of the corporate governance system mainly involves a series of issues such as the allocation of rights and responsibilities of all stakeholders, the arrangement of residual distribution rights, and the way of participating in governance with the principal-agent relationship between the internal owners and managers of the enterprise as the core. In the company's network communication structure, the first layer is the stakeholder level, that is, the interactive protocol layer (knowledge layer) between multiple agents. The specific technology is realized through networked audio and video. The second layer is the communication language layer between Agents. Define the grammar and semantics of the Agent communication language, and realize the message transfer between Agents. The third layer is the data transmission mechanism (communication layer) between Agents. The Internet (CORBA/Java) is used to realize the establishment of agent object communication between the server and the client, and the data transmission between Agents is realized through the TCP/IP protocol. The fourth layer is the data layer. The database structure is used to realize data storage, and the data storage, query and other functions are realized through database access. The fifth layer is the support layer. Provide physical connection and specific operation platform for company governance system data transmission and exchange, and other management software and application programs. The amount of funds raised for scientific and technological activities of large and medium-sized industrial enterprises rose from 104.665 billion yuan in 2017 to 330.079 billion yuan in 2019, an increase of 215%. This research will help the company's intelligent management.
An Implementation of Nonmonotonic Reasoning with System W

System W is a recently introduced reasoning method for conditional belief bases. While system W exhibits various desirable properties for nonmonotonic reasoning like extending rational closure and fully complying with syntax splitting, an implementation of it has been missing so far. In this paper, we present a first implementation of system W. The implementation is accessible via an extension of an online platform supporting a variety of nonmonotonic reasoning approaches.
Optimal Fixed-Premise Repairs of $$\mathcal{E}\mathcal{L}$$ TBoxes

Reasoners can be used to derive implicit consequences from an ontology. Sometimes unwanted consequences are revealed, indicating errors or privacy-sensitive information, and the ontology needs to be appropriately repaired. The classical approach is to remove just enough axioms such that the unwanted consequences vanish. However, this is often too rough since mere axiom deletion also erases many other consequences that might actually be desired. The goal should not be to remove a minimal number of axioms but to modify the ontology such that only a minimal number of consequences is removed, including the unwanted ones. Specifically, a repair should rather be logically entailed by the input ontology, instead of being a subset. To this end, we introduce a framework for computing fixed-premise repairs of $$\mathcal{E}\mathcal{L}$$ TBoxes. In the first variant the conclusions must be generalizations of those in the input TBox, while in the second variant no such restriction is imposed. In both variants, every repair is entailed by an optimal one and, up to equivalence, the set of all optimal repairs can be computed in exponential time. A prototypical implementation is provided. In addition, we show new complexity results regarding gentle repairs.
On the security of fully homomorphic encryption for data privacy in Internet of Things

Summary To achieve data privacy in Internet of Things (IoT), fully homomorphic encryption (FHE) technique is used to encrypt the data while allowing others to compute on the encrypted data. However, there are many well‐known problems with FHE such as chosen‐ciphertext attack security and circuit privacy problem. In this article, we demonstrate that a famous FHE application named Brakerski/Fan–Vercauteren scheme, a circuit privacy application based on fast private set intersection, and an encoding application that encodes integer or floating point numbers based on Microsoft Simple Encryption Arithmetic Library homomorphic encryption library, are insecure against chosen ciphertext attacks due to insecurity of the underlying fully homomorphic schemes. These results show that using cryptographic primitives even with security proofs causes serious security vulnerabilities on the applications themselves. The results also give evidences that the security of adopted cryptographic primitives in IoT should be proved in appropriate formal security models as well as proof of the scheme itself.
An Open Source Virtual Reality Training Framework for the Criminal Justice System

We developed an open-source training framework to practice conversation skills in a controlled and immersive virtual reality (VR) environment. Virtual characters with different biographies were developed with which a conversation using natural language is possible. The virtual characters integrate a dialog management system (ChatScript) to provide different biographical memories. Natural language processing for the German language is integrated by using Kaldi, an open-source speech recognition toolkit. As the framework allows for interchangeable content there are many different possible application cases to apply within the criminal justice system. The VR frameworks code is available under an open-source license. In this article, an overview of the framework’s functionality is given as well as an outlook on possible areas of application. Statements about user acceptance and usability cannot yet be made, as relevant data have first to be gathered through a concrete application case.
Representing Camera Response Function by a Single Latent Variable and Fully Connected Neural Network

Abstract Modelling the mapping from scene irradiance to image intensity is essential for many computer vision tasks. Such mapping is known as the camera response. Most digital cameras use a nonlinear function to map irradiance, as measured by the sensor to an image intensity used to record the photograph. Modelling of the response is necessary for the nonlinear calibration. In this paper, a new high-performance camera response model that uses a single latent variable and fully connected neural network is proposed. The model is produced using unsupervised learning with an autoencoder on real-world (example) camera responses. Neural architecture searching is then used to find the optimal neural network architecture. A latent distribution learning approach was introduced to constrain the latent distribution. The proposed model achieved state-of-the-art CRF representation accuracy in a number of benchmark tests, but is over twice as fast as the best current models when performing the maximum likelihood estimation during camera response calibration due to the simple yet efficient model representation.
A distance of quantum mass function and its application in multi-source information fusion method based on discount coefficient

Distance measures provide a novel perspective for measuring the difference or consistency between bodies of evidence, which have been used in a wide range of fields. However, under the framework of quantum mass function, existing distances cannot measure the difference. Hence, this paper formulates a new distance measure, referred to as the distance of the quantum mass functions. The purpose of this distance measure is to quantify the difference between quantum mass functions. It can be demonstrated mathematically that it is a strict distance measure that satisfies the nonnegativity, symmetry, definiteness, triangle inequality. The proposed distance measure is a generalization of the classical evidence distance, and it introduces the concept of Minkowski distance as well. It is therefore not only able to reflects the difference of discord and non-specificity in the mass functions, but it also has the advantage of Minkowski distance, as well as high compatibility. Moreover, A number of numerical examples are also provided to illustrate its properties and advantages. Using the proposed distance measure, we design a new information fusion method based on the discount coefficient within a complex framework. As a further investigation, the proposed fusion method is applied to several data sets experiments and results indicate that compared to other methods, it has a certain potential in the field of multi-source information fusion under the framework of evidence theory.
The roles of hydrocarbons on the mineralization of Carlin-type gold deposits, Nanpanjiang Basin, South China

The spatial association of hydrocarbons with metalliferous ore deposits, like MVT lead–zinc deposits, sandstone uranium deposits and various type gold deposits (epithermal Au-Ag (-Hg) deposits, orogenic gold deposits and Carlin-type gold deposits) is found worldwide. However, the genetic relationship between hydrocarbon evolution and mineralization is still under controversy. Integrating isotope dates related to timing of petroleum evolution and gold mineralization from published data, as well as high-resolution transmission electron microscopy (TEM) analysis of pyrobitumen, electron probe microanalysis (EPMA) on both pyrobitumen and pyrite, the In-situ S isotope analyses of the gold-bearing pyrite in this study, we consider different type hydrocarbons (liquid oil and gas) played different roles on the mineralization of Carlin type gold deposits. As the residue of liquid oil cracking, the widespread high maturity pyrobitumen within the reservoir indicate both oil and gas has ever accumulated in the Nanpanjiang Basin. The high gold abundance (EPMA: ∼120 ppm; TEM: ∼1500 ppm) within the pyrobitumen in agreement with the metal solubility experiment suggest liquid oils could have acted as gold carrier before metal precipitation. The late Triassic timing overlapping between gas generation and gold mineralization and the δ34S features with gold bearing pyrite ∼ 10 ‰ lighter than the coeval seawater sulfate indicate gas dominated TSR process may play an important role on the supply of gold bisulfide complex (Au(HS)2−) in the ore-forming fluid and directly involved the precipitation of the Carlin-type gold deposit.
Toward Zero-Shot and Zero-Resource Multilingual Question Answering

In recent years, multilingual question answering has been an emergent research topic and has attracted much attention. Although systems for English and other rich-resource languages that rely on various advanced deep learning-based techniques have been highly developed, most of them in low-resource languages are impractical due to data insufficiency. Accordingly, many studies have attempted to improve the performance of low-resource languages in a zero-shot or few-shot manner based on multilingual bidirectional encoder representations from transformers (mBERT) by transferring knowledge learned from rich-resource languages to low-resource languages. Most methods require either a large amount of unlabeled data or a small set of labeled data for low-resource languages. In Wikipedia, 169 languages have less than 10,000 articles, and 48 languages have less than 1,000 articles. This reason motivates us to conduct a zero-shot multilingual question answering task under a zero-resource scenario. Thus, this study proposes a framework to fine-tune the original mBERT using data from rich-resource languages, and the resulting model can be used for low-resource languages in a zero-shot and zero-resource manner. Compared to several baseline systems, which require millions of unlabeled data for low-resource languages, the performance of our proposed framework is not only highly comparative but is also better for languages used in training.
A Progressive Difference Method for Capturing Visual Tempos on Action Recognition

Visual tempos show the dynamics of action instances, characterizing the diversity of the actions, such as walking slowly and running quickly. To facilitate action recognition, it is essential to capture visual tempos. To this end, previous methods sample raw videos at multiple frame rates or integrate multi-scale temporal features. These methods inevitably introduce two-stream networks or feature-level pyramid structures, leading to expensive computation. In this work, we propose a progressive difference method to capture visual tempos for efficient action recognition, by computing coarse-to-fine motion information within a small neighborhood around temporal frames. Specifically, the uniform sampling method is first applied to each video, and then first-order temporal differences around each frame are calculated to describe local motions. On the basis of differences, further computing the variations of differences, namely second-order differences, can gradually capture fine-grained spatiotemporal features and characterize the areas where the motion cues are more prominent. On one hand, multi-order motion differences can be combined with raw input to describe the diversity of the actions. On the other hand, the variations of first-order differences information can be used to activate first-order salient motion regions, thereby facilitating the discrimination of finer-grained actions. Our method can be combined with existing backbones in a plug-and-play manner. Extensive experiments are conducted on several video benchmarks, including Kinetics400, HMDB51, UCF101, UAV-Human, Something-Something V1 and V2. We also give detailed analysis and qualitative experiments to demonstrate the effectiveness of our method.
Digital Stereotypes in HMI—The Influence of Feature Quantity Distribution in Deep Learning Models Training

This paper proposes a concept of Digital Stereotypes, observed during research on quantitative overrepresentation of one class over others, and its impact on the results of the training of Deep Learning models. The real-life observed data classes are rarely of the same size, and the intuition of presenting multiple examples of one class and then showing a few counterexamples may be very misleading in multimodal classification. Deep Learning models, when taught with overrepresentation, may produce incorrect inferring results, similar to stereotypes. The generic idea of stereotypes seems to be helpful for categorisation from the training point of view, but it has a negative influence on the inferring result. Authors evaluate a large dataset in various scenarios: overrepresentation of one or two classes, underrepresentation of some classes, and same-size (trimmed) classes. The presented research can be applied to any multiclassification applications, but it may be especially important in AI, where the classification, uncertainty and building new knowledge overlap. This paper presents specific 'decreases in accuracy' observed within multiclassification of unleveled datasets. The 'decreases in accuracy', named by the authors 'stereotypes', can also bring an inspiring insight into other fields and applications, not only multimodal sentiment analysis.
Towards explainable model extraction attacks

One key factor able to boost the applications of artificial intelligence (AI) in security-sensitive domains is to leverage them responsibly, which is engaged in providing explanations for AI. To date, a plethora of explainable artificial intelligence (XAI) has been proposed to help users interpret model decisions. However, given its data-driven nature, the explanation itself is potentially susceptible to a high risk of exposing privacy. In this paper, we first show that the existing XAI is vulnerable to model extraction attacks and then present an XAI-aware dual-task model extraction attack (DTMEA). DTMEA can attack a target model with explanation services, that is, it can extract both the classification and explanation tasks of the target model. More specifically, the substitution model extracted by DTMEA is a multitask learning architecture, consisting of a sharing layer and two task-specific layers for classification and explanation. To reveal which explanation technologies are more vulnerable to expose privacy information, we conduct an empirical evaluation of four major explanation types in the benchmark data set. Experimental results show that the attack accuracy of DTMEA outperforms the predicted-only method with up to 1.25%, 1.53%, 9.25%, and 7.45% in MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100, respectively. By exposing the potential threats on explanation technologies, our research offers the insights to develop effective tools that are able to trade off security-sensitive relationships.
Static Analysis of the Source Code of Python Applications

One of the popular methods of software code analysis is the static analysis method. This method allows not only to check the code for compliance with the language specification, but also to find potential vulnerabilities. The work performs a static analysis of a corpus of open source Python application. Using the Bandit library, statistical indicators of various categories of potential vulnerabilities are found, a rating table of vulnerabilities found in the studied data set is built. A qualitative analysis of threats is carried out according to their danger based on the CWE catalog data. The purpose of this work is to analyze a corpus of open source Python listings for potential vulnerabilities using a static analyzer and rank threats based on the potential vulnerabilities found. Thus, the study should answer the following questions: Q1. What categories of potential vulnerabilities in the CWE catalog are most common in Python open source projects? Q2. What categories of tests (checks) of the Bandit library found the most vulnerabilities? Q3. Is there a correlation between the obtained statistical data and the OWASP rating data?
UEFPN: Unified and Enhanced Feature Pyramid Networks for Small Object Detection

Object detection models based on feature pyramid networks have made significant progress in general object detection. However, small object detection is still a challenge for the existing models. In this paper, we think that two factors in the existing feature pyramid networks inhibit the performance of small object detection. The first one is that the different feature domains of shallow and deep layer features inhibit the model performance. The second one is that the accumulation of upper layer features leads to feature aliasing effect on the lower layer features, which interferes with the representations of small object features. Therefore, we propose Unified and Enhanced Feature Pyramid Networks (UEFPN) to improve the APs and ARs of small object detection. It has the following three characteristics: (1) Using the deep features of high-resolution image and original image to form the multi-scale features of unified domain. (2) In multi-scale features fusion, we learn the importance of upper layer features with the Channel Attention Fusion module (CAF) , to optimize feature aliasing effect and enhance the context information of shallow layer features. (3) UEFPN can be quickly applied to different models. The results of many experiments show that the models with UEFPN achieve significant performance improvement in small object detection compared with the baseline models.