Reviewerly/exHarmony huggingface dataset:
ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems

Recent research demonstrated that the superficially well-trained machine learning (ML) models are highly vulnerable to adversarial examples. As ML techniques are becoming a popular solution for cyber-physical systems (CPSs) applications in research literatures, the security of these applications is of concern. However, current studies on adversarial machine learning (AML) mainly focus on pure cyberspace domains. The risks the adversarial examples can bring to the CPS applications have not been well investigated. In particular, due to the distributed property of data sources and the inherent physical constraints imposed by CPSs, the widely-used threat models and the state-of-the-art AML algorithms in previous cyberspace research become infeasible.
Representing emotions with knowledge graphs for movie recommendations

Consumption of media, and movies in particular, is increasing and is influenced by a number of factors. One important and overlooked factor that affects the media consumption choices is the emotional state of the user and the decision making based on it. To include this factor in movie recommendation processes, we propose a knowledge graph representing human emotions in the domain of movies. The knowledge graph has been built by extracting emotions out of pre-existing movie reviews using machine learning techniques. To show how the knowledge graph can be used, a chatbot prototype has been developed. The chatbot’s reasoning mechanism derives movie recommendations for the user by combining the user’s emotions, which have been extracted from chat messages, with the knowledge graph. The developed approach for movie recommendations based on sentiment represented as a knowledge graph has been proven to be technically feasible, however, it requires more information about the emotions associated with the movies than currently available online.
Robust <i>k</i>-WTA Network Generation, Analysis, and Applications to Multiagent Coordination

In this article, a robust k -winner-take-all ( k -WTA) neural network employing the saturation-allowed activation functions is designed and investigated to perform a k -WTA operation, and is shown to possess enhanced robustness to disturbance compared to existing k -WTA neural networks. Global convergence and robustness of the proposed k -WTA neural network are demonstrated through analysis and simulations. An application studied in detail is competitive multiagent coordination and dynamic task allocation, in which k active agents [among ] are allocated to execute a tracking task with the static m-k ones. This is implemented by adopting a distributed k -WTA network with limited communication, aided with a consensus filter. Simulation results demonstrating the system's efficacy and feasibility are presented.
Medi-Block record: Secure data sharing using block chain technology

With the advances in the era of Artificial intelligence, block chain, cloud computing, big data has the need for secure, decentralized medical record storage and retrieval systems, while cloud storage resolves storage issues but it is difficult to realize the secure sharing of records over the network. The decentralized nature of block chain technology resolves the problem of authentication dependent on third party and imparts secure transmission. This paper proposed block chain based distributed authentication mechanism process and network architecture. Medi-Block presents a tamperproof and anonymous identity management model for medical record sharing for hospitals and patients, utilizing the concept of bilinear mapping for the authentication phase and eliminating third party trust issues. It implements two-way authentications between the patients and the hospital. The effectiveness of the proposed authentication scheme is analyzed by BAN logic, storage overhead and computing cost. The results presented in the paper demonstrate how a block chain platform based on BAN logic can enable medical data sharing while also meeting various security requirements during the authentication phase. Furthermore, reducing average communication time and cost in terms of accessibility meets the requirements of medical data record sharing for integrity, privacy, and availability.
Group recommender system based on genre preference focusing on reducing the clustering cost

The most significant advantage of the group recommender system over personalization is the low computational cost because the former analyzes the preferences of many users at once by integrating their preferences. The clustering step is the most time-consuming part of the entire process in a group recommender system. Existing studies either measured the similarities among all users or utilized a clustering algorithm based on the item preference vector to form the groups. However, these existing clustering methods overlooked the clustering cost, and the time complexity was not significantly better than that for personalized recommendations. Therefore, we propose a group recommender system based on the genre preferences of users to dramatically reduce the clustering cost. First, we define a genre preference vector and cluster the groups using this vector. Our group recommender system can reduce the time complexity more efficiently because the number of genres is significantly smaller than the number of items. In addition, we propose a new item preference along with genre weight to subdivide the preferences of users. The evaluation results show that the genre-based group recommender system significantly improves the time efficiency in terms of clustering. Clustering time was about five times faster when using k-means. In addition, for the Gaussian mixture model (GMM), it was about fifty times faster in MovieLens 100 k and about five hundred times faster in Last.fm. The normalized discounted cumulative gain (NDCG) (i.e., accuracy) is not much different from that of the item-based existing studies and is even higher when the number of users is low in a group in MovieLens 100 k.
Neural Style Palette: A Multimodal and Interactive Style Transfer From a Single Style Image

Despite the myriad of attributes found in a single style image, existing neural style transfer methods produce outputs with limited variety–typically only a single realization of the style image. They also do not provide an easy way to control the stylization process, limiting the creative freedom of users. In this paper, we propose Neural Style Palette ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NSP</i> ), a method for interactively generating a variety of stylized images from only a single style input. Our approach allows human influence in the stylization process, a design inspired by Hybrid Human-Artificial Intelligence. Like a color palette, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NSP</i> enables a meaningful interaction by presenting a collection of sub-textures, which we also refer to as anchor styles, that act as a visual guide for the users. These anchor styles capture different attributes in the single style image that the users can creatively blend to create their desired realizations. To offer a diversified selection in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NSP</i> , we constrain the anchor styles to be distant from one another while maintaining faithfulness to the original style image. This is possible through our two proposed novel losses: a style-separation loss that encourages the sub-textures to be distinct and a unification loss to ensure that the sub-textures center around the original style while encouraging additional diversity. We perform several experiments to prove the effectiveness of our method and generalize to improve existing methods.
Regularizing Neural Networks via Adversarial Model Perturbation

Effective regularization techniques are highly desired in deep learning for alleviating overfitting and improving generalization. This work proposes a new regularization scheme, based on the understanding that the flat local minima of the empirical risk cause the model to generalize better. This scheme is referred to as adversarial model perturbation (AMP), where instead of directly minimizing the empirical risk, an alternative "AMP loss" is minimized via SGD. Specifically, the AMP loss is obtained from the empirical risk by applying the "worst" norm-bounded perturbation on each point in the parameter space. Comparing with most existing regularization schemes, AMP has strong theoretical justifications, in that minimizing the AMP loss can be shown theoretically to favour flat local minima of the empirical risk. Extensive experiments on various modern deep architectures establish AMP as a new state of the art among regularization schemes. Our code is available at https://github.com/hiyouga/AMP-Regularizer.
Face Detection in Real Time Live Video Using Yolo Algorithm Based on Vgg16 Convolutional Neural Network

Face detection is not only one of the most studied topics in the computer vision field but also a very important task in many applications, such as security access control systems, video surveillance, human-computer interface, and image database management. Nowadays, various methods were developed for face detection systems like Viola-Jones, RCNN, SSD, and so on. Many researchers are still trying to improve face detection systems with various illustrations, poses, skin colors, and real-time detection. This paper intends to combine YOLO (You Only Look Once) algorithm with the VGG16 pre-trained convolutional neural network to propose an improvement for face detection systems. Experimental results show that proposed method has detected the test image set with over 95 % of average precision. Also, our proposed method considerably increased face detection speed in real-time live video. The experiment of this work was using the Image Processing Toolbox and the Deep Learning Toolbox in MATLAB.
Single Image Super-Resolution via Multi-Scale Information Polymerization Network

Recently, the performances of deep convolution neural networks (CNNs)-based single-image super-resolution (SISR) have been significantly improved. However, most of the existing CNN-based SISR methods mainly focus on wider or deeper networks and ignore the potential relationship between multi-scale features, leading to the limited representation ability of the reconstructed network. To address this problem, we propose a new multi-scale information polymerization network (MIPN). Specifically, we propose a multi-scale information polymerization block (MIPB), which uses convolution layers of different convolution kernel sizes to extract multi-scale image features, and effectively polymerizate the extracted features together to obtain fine image features. Moreover, we also propose a shallow residual block in MIPB. Compared with the traditional convolution layer, this proposed block can effectively extract image features without increasing the number of parameters. Extensive experiments show that the proposed method performs better than several state-of-the-art methods in quantitative and visual quality indicators.
A Search-Based Testing Framework for Deep Neural Networks of Source Code Embedding

Over the past few years, deep neural networks (DNNs) have been continuously expanding their real-world applications for source code processing tasks across the software engineering domain, e.g., clone detection, code search, comment generation. Although quite a few recent works have been performed on testing of DNNs in the context of image and speech processing, limited progress has been achieved so far on DNN testing in the context of source code processing, that exhibits rather unique characteristics and challenges.In this paper, we propose a search-based testing framework for DNNs of source code embedding and its downstream processing tasks like Code Search. To generate new test inputs, we adopt popular source code refactoring tools to generate the semantically equivalent variants. For more effective testing, we leverage the DNN mutation testing to guide the testing direction. To demonstrate the usefulness of our technique, we perform a large-scale evaluation on popular DNNs of source code processing based on multiple state-of-the-art code embedding methods (i.e., Code2vec, Code2seq and CodeBERT). The testing results show that our generated adversarial samples can on average reduce the performance of these DNNs from 5.41% to 9.58%. Through retraining the DNNs with our generated adversarial samples, the robustness of DNN can improve by 23.05% on average. The evaluation results also show that our adversarial test generation strategy has the least negative impact (median of 3.56%), on the performance of the DNNs for regular test data, compared to the other methods.
HyAdamC: A New Adam-Based Hybrid Optimization Algorithm for Convolution Neural Networks

As the performance of devices that conduct large-scale computations has been rapidly improved, various deep learning models have been successfully utilized in various applications. Particularly, convolution neural networks (CNN) have shown remarkable performance in image processing tasks such as image classification and segmentation. Accordingly, more stable and robust optimization methods are required to effectively train them. However, the traditional optimizers used in deep learning still have unsatisfactory training performance for the models with many layers and weights. Accordingly, in this paper, we propose a new Adam-based hybrid optimization method called HyAdamC for training CNNs effectively. HyAdamC uses three new velocity control functions to adjust its search strength carefully in term of initial, short, and long-term velocities. Moreover, HyAdamC utilizes an adaptive coefficient computation method to prevent that a search direction determined by the first momentum is distorted by any outlier gradients. Then, these are combined into one hybrid method. In our experiments, HyAdamC showed not only notable test accuracies but also significantly stable and robust optimization abilities when training various CNN models. Furthermore, we also found that HyAdamC could be applied into not only image classification and image segmentation tasks.
Quantum Hypothesis Testing for Exoplanet Detection

Detecting the faint emission of a secondary source in the proximity of the much brighter one has been the most severe obstacle for using direct imaging in searching for exoplanets. Using quantum state discrimination and quantum imaging techniques, we show that one can significantly reduce the probability of error for detecting the presence of a weak secondary source, especially when the two sources have small angular separations. If the weak source has intensity ε≪1 relative to the bright source, we find that the error exponent can be improved by a factor of 1/ε. We also find linear-optical measurements that are optimal in this regime. Our result serves as a complementary method in the toolbox of optical imaging, with applications ranging from astronomy to microscopy.Received 3 June 2021Accepted 2 August 2021DOI:https://doi.org/10.1103/PhysRevLett.127.130502© 2021 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum channelsQuantum metrologyQuantum sensingQuantum Information
Delegitimizing the media?

Abstract A growing literature on the impact of “fake news“ accusations on legacy news outlets suggests that the use of this term is part of a much larger trend of increased and delegitimizing media criticism by political actors. However, so far, there is very little empirical evidence on how prevailing politicians’ delegitimizing media criticism really is and under which conditions it occurs. To fill these gaps, we present results of a content analysis of media-related Facebook postings by Austrian and German politicians in 2017 ( N = 2,921). The results suggest that media criticism, in general, is actually rare and that about half of it can be described as delegitimizing (i.e., characterized by incivility or absence of argumentation). Most often, media criticism is used by populist politicians, who accuse “the media” in general of bias and falsehoods.
Instance GNN: A Learning Framework for Joint Symbol Segmentation and Recognition in Online Handwritten Diagrams

Online handwritten diagram recognition (OHDR) has attracted considerable attention for its potential applications in many areas, but it is a challenging task due to the complex 2D structure, writing style variation, and lack of annotated data. Existing OHDR methods often have limitations in modeling and learning complex contextual relationships. To overcome these challenges, we propose an OHDR method based on graph neural networks (GNNs) in this paper. In particular, we formulate symbol segmentation and symbol recognition as node clustering and node classification problems on stroke graphs and solve the problems jointly under a unified learning framework with a GNN model. This GNN model is denoted as Instance GNN since it gives the symbol instance label as well as the semantic label. Extensive experiments on two flowchart datasets and a finite automata dataset show that our method consistently outperforms previous methods with large margins and achieves state-of-the-art performance. In addition, we release a large-scale annotated online handwritten flowchart dataset, CASIA-OHFC, and provide initial experimental results as a baseline.
WuDaoCorpora: A super large-scale Chinese corpora for pre-training language models

Using large-scale training data to build a pre-trained language model (PLM) with a larger volume of parameters can significantly improve downstream tasks. For example, OpenAI trained the GPT3 model with 175 billion parameters on 570 GB English training data, enabling downstream applications building with only a small number of samples. However, there is a lack of Chinese corpus to support large-scale PLMs. This paper introduces a super large-scale Chinese corpora WuDaoCorpora, containing about 3 TB training data and 1.08 trillion Chinese characters. We also release the base version of WuDaoCorpora, containing about 200 GB training data and 72 billion Chinese characters. As a baseline, we train a model transformer-XL with 3 billion parameters on the base version to test the corpora's effect. The results show that the models trained on this corpora can achieve excellent performance in Chinese. The data and model are available at https://data.wudaoai.cn and https://github.com/THUDM/Chinese-Transformer-XL, respectively.
Computer vision and photosensor based hybrid control strategy for a two-axis solar tracker - Daylighting application

This paper deals with a computer vision and photosensor based two-axis solar tracking system for an active daylighting system. The real-time image processing was performed by using a Raspberry Pi 4 controller, and processed data were used as an input of an ATmega128 microcontroller to track the sun’s path. An active daylighting system requires a higher concentration of sunlight with an acceptable accuracy in different weather conditions. Alone, an image based or photosensor based control strategy cannot fulfill both requirements. Successful integration of two different feedback mechanisms could overcome the tracking difficulties. The system was composed of Raspberry Pi 4 and ATmega128 controllers, a camera, electronic circuits, and stepper motors. The proposed control system could distinguish the objects (e.g., sun or clouds) in front of the camera, and process the data required to run the solar tracker. The integration of extra photosensors and the camera was able to avoid the cloud disturbance. To investigate the efficacy of the solar tracker, it was integrated with the optical fiber cable to transmit harvested daylight for an indoor illumination.
Quantum Federated Learning with Quantum Data

Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore complex machine learning problems. Recently, some QML models were proposed for performing classification tasks, however, they rely on centralized solutions that cannot scale well for distributed quantum networks. Hence, it is apropos to consider more practical quantum federated learning (QFL) solutions tailored towards emerging quantum networks to allow for distributing quantum learning. This paper proposes the first fully quantum federated learning frame-work that can operate over purely quantum data. First, the proposed framework generates the first quantum federated dataset in literature. Then, quantum clients share the learning of quantum circuit parameters in a decentralized manner. Extensive experiments are conducted to evaluate and validate the effectiveness of the proposed QFL solution, which is the first implementation combining Google’s TensorFlow Federated and TensorFlow Quantum.
How do the current auditing standards fit the emergent use of blockchain?

Purpose Blockchain is expected to impact reporting and auditing processes. Indeed, the increasing use of blockchain could affect the nature and extent of information available to auditors and how audits are performed. This paper aims to investigate how auditors are assessing the relevance of the current auditing standards in light of the emergent use of blockchain technology. Design/methodology/approach Based on qualitative content analysis, this paper analyzed semi-structured interviews with auditors to understand their shared perception of how the current auditing standards address blockchain’s emergence. Findings The findings reveal a growing demand for information technology (IT) auditing standards, as well as a mismatch in timing between the quickly changing IT environment and the regulators’ slowness in releasing new standards or updating standards. Research limitations/implications The findings reflect the external auditors’ points of view and cannot be generalized to all countries, but future studies should address the development of specific IT-related auditing standards to better fit the fast-evolving technology environment in ways that consider the other stakeholders’ points of view, including those of the standard setters. Practical implications The results of this study show that auditors consider the current auditing standards for IT to be too vague, and they need more guidance on both auditing blockchain and using technologies as audit tools. Originality/value The original contribution of this study lies in the in-depth understanding it provides of the adequacy of the current auditing standards to audit companies using blockchain, which is an under-researched topic.