Reviewerly/exHarmony huggingface dataset:
Fast Adaption for Multi Motor Anomaly Detection via Meta Learning and deep unsupervised learning

For PHM(Predictive Health Management) in an industrial scenario, motor vibration anomaly detection requires a large number of labeled data. The problem is that the anomaly vibration signal data is scarce and difficult to be obtained, especially the problem of insufficient data in the building and deployment of multi-machine models. This paper proposes a Meta-learning method based on deep unsupervised learning(Autoencoder). We use non-labeled and a small amount of vibration signal data which is converted into 52 key physical and statistical features to build models of different motors. By inputting these 52 features into designed models, other 52 features can be reconstructed as output. We use input and output features to calculate reconstruction error which is used as the criterion for the performance of models. We also use accuracy as the criterion for the performance of models. In the actual industrial situation, the improvement of accuracy by our proposed method is about 33.50% better than the method only with unsupervised learning for the new sensor model with few vibration data.
Digital Gesture Recognition based on Deep Capsule Network

Because gesture recognition is more and more useful in life, gesture recognition should not only consider the changes of gestures, but also be affected by the environment. Therefore, there are high requirements for the gesture recognition algorithms. Previously, image processing methods were used for gesture recognition, but the recognition accuracy of traditional methods was not high. The Capsule Network Model (CapsNet) proposed by Hinton has better recognition performance than traditional networks due to the use of dynamic routing algorithms. Based on CapsN et, this paper designs and realizes a deep capsule network model (DCNet), change the size of the convolution kernel to reduce the amount of parameters, and adds a convolution to the original capsule network structure to extract more feature information, and increase the dimension of the capsule from 8 to 16 to improve the decision-making ability of the capsule. Make the recognition accuracy of the network on a SIGN digital gesture data set higher. Using DCNet to test on the SIGN data set, the detection accuracy of training learning can reach 99.62%. The experimental show that the test accuracy of DCN et is higher than CapsN et for testing with digital gesture data set.
Ares: A System-Oriented Wargame Framework for Adversarial ML

Since the discovery of adversarial attacks against machine learning models nearly a decade ago, research on adversarial machine learning has rapidly evolved into an eternal war between defenders, who seek to increase the robustness of ML models against adversarial attacks, and adversaries, who seek to develop better attacks capable of weakening or defeating these defenses. This domain, however, has found little buy-in from ML practitioners, who are neither overtly concerned about these attacks affecting their systems in the real world nor are willing to trade off the accuracy of their models in pursuit of robustness against these attacks.In this paper, we motivate the design and implementation of Ares, an evaluation framework for adversarial ML that allows researchers to explore attacks and defenses in a realistic wargame-like environment. Ares frames the conflict between the attacker and defender as two agents in a reinforcement learning environment with opposing objectives. This allows the introduction of system-level evaluation metrics such as time to failure and evaluation of complex strategies such as moving target defenses. We provide the results of our initial exploration involving a white-box attacker against an adversarially trained defender.
SaaS Platform for Blockchain Based E-Document Authentication applications

Technological developments are increasingly sophisticated where every day, these developments have directions and goals to make it easier for humans in all things. In society 5.0, technology is judged not only by its intelligence but also by its benefits in helping humans. In a company, documents are precious objects, so that various things are very concerning, such as ratification, storage, and sharing of documents. 2 (two) problems were identified, namely that many companies still use the manual method where documents must be printed to be ratified by meeting directly and conventionally storing printed documents, so it is considered ineffective and efficient. These problems can be overcome with a Software as a Service (SaaS)-based digital signature system with distributed Blockchain support, making it easier for companies to validate and store documents. By using the SDLC waterfall development method and six literature studies. Researchers hope that digital signatures make it easier for everyone to validate digitally without having to print documents and do not need to think about the validity of the digital documents so that they can be considered effective and efficient to facilitate work.
Direct Gibbs posterior inference on risk minimizers: Construction, concentration, and calibration

Real-world problems, often couched as machine learning applications, involve quantities of interest that have real-world meaning, independent of any statistical model. To avoid potential model misspecification bias or over-complicating the problem formulation, a direct, model-free approach is desired. The traditional Bayesian framework relies on a model for the data-generating process so, apparently, the desired direct, model-free, posterior-probabilistic inference is out of reach. Fortunately, likelihood functions are not the only means of linking data and quantities of interest. Loss functions provide an alternative link, where the quantity of interest is defined, or at least could be defined, as a minimizer of the corresponding risk, or expected loss. In this case, one can obtain what is commonly referred to as a Gibbs posterior distribution by using the empirical risk function directly. This manuscript explores the Gibbs posterior construction, its asymptotic concentration properties, and the frequentist calibration of its credible regions. By being free from the constraints of model specification, Gibbs posteriors create new opportunities for probabilistic inference in modern statistical learning problems.
Extracting Information from Large Scale Graph Data: Case Study on Automated UI Testing

Even though a large-scale graph structure is a powerful model to solve several challenging problems in various applications’ domains today, it can also preserve various raw essences regarding user behavior, especially in the e-commerce domain. Information extraction is a promising research area in deep learning algorithms using large-scale graph data. This study focuses on understanding users’ implicit navigational behavior on an e-commerce site that we can represent with the large-scale graph data. We propose a GAN-based e-business workflow by leveraging the large-scale browsing graph data and the footprints of navigational users’ behavior on the e-commerce site. With this method, we have discovered various frequently repeated clickstream data sequences, which do not appear in training data at all. Therefore, We developed a prototype application to demonstrate performance tests on the proposed business e-workflow. The experimental studies we conducted show that the proposed methodology produces noticeable and reasonable outcomes for our prototype application.
BAM: a balanced attention mechanism to optimize single image super-resolution

Recovering texture information from the aliasing regions has always been a major challenge for single image super-resolution (SISR) task. These regions are often submerged in noise so that we have to restore texture details while suppressing noise. To address this issue, we propose an efficient Balanced Attention Mechanism (BAM), which consists of Avgpool Channel Attention Module (ACAM) and Maxpool Spatial Attention Module (MSAM) in parallel. ACAM is designed to suppress extreme noise in the large-scale feature maps, while MSAM preserves high-frequency texture details. Thanks to the parallel structure, these two modules not only conduct self-optimization, but also mutual optimization to obtain the balance of noise reduction and high-frequency texture restoration during the back propagation process, and the parallel structure makes the inference faster. To verify the effectiveness and robustness of BAM, we applied it to 10 state-of-the-art SISR networks. The results demonstrate that BAM can efficiently improve the networks' performance, and for those originally with attention mechanism, the substitution with BAM further reduces the amount of parameters and increases the inference speed. Information multi-distillation network (IMDN), a representative lightweight SISR network with attention, when the input image size is 200 × 200, the FPS of proposed IMDN-BAM precedes IMDN {8.1%, 8.7%, 8.8%} under the three SR magnifications of × 2, × 3, × 4, respectively. Densely residual Laplacian network (DRLN), a representative heavyweight SISR network with attention, when the scale is 60 × 60, the proposed DRLN-BAM is {11.0%, 8.8%, 10.1%} faster than DRLN under × 2, × 3, × 4. Moreover, we present a dataset with rich texture aliasing regions in real scenes, named realSR7. Experiments prove that BAM achieves better super-resolution results on the aliasing area.
A Correlation Analysis Method for Geographical Object Flows from a Geoeconomic Perspective

Geographic object flow is the reason behind the relationship of geographic units. There are interactions in the process of dynamic change of a geographic object flow, and its regularity, which can reflect the relationship or pattern of geographic units in a region. In this paper, an association rule mining method for the geographic object flow linkage process is studied from a geoeconomics perspective. Additionally, an association rule mining algorithm with hierarchical constraints is proposed. Data segmentation is performed according to the time series characteristics of geographic object flow data. The basic attributes for the association rule mining are determined based on the basic parameters of geographic object flows, and a database for the association rule mining is formed according to the characteristics of the hierarchical structure of the geographic object flows. Based on the obtained data, the association rule mining algorithm with hierarchical constraints obtained using a parent–child matrix is improved by adding the Apriori algorithm. With the Indo-Pacific region as an example, the trade flow association rules for 25 countries in the region from 2010 to 2021 are selected. In addition, a mathematical statistical analysis of the strongly associated mined trade flows and geoeconomic factors is conducted in terms of both a basic feature analysis of trade flow associations and a country-oriented trade flow association analysis by considering domain knowledge. The effectiveness of the method has been evaluated from various perspectives such as correlation analysis, mathematical statistics, and comparison with the findings of existing studies and proved the validity of the method.
An Enhanced YOLOv5 Model with Attention Module for Vehicle-Pedestrian Detection

Vehicle and pedestrian detection is one of the vital elements of autonomous driving environmental perception. Although enormous methods have been proposed for detection tasks, the demand for detecting specialized categories is still noteworthy. In this paper, we propose a model which fuses an attention module by optimizing YOLOv5 algorithm and adjusts the sizes of anchor boxes to further improve the performance of vehicle-pedestrian detection. An attention module can effectively collect discriminative features from dense contextual information, which contributes to the better detection of distant objects. An anchor box adjustment makes the model more familiar with the dataset. The qualitative and quantitative experimental results on KITTI benchmark show that our model outperforms the state-of-the-art methods. The preliminary experiment with an embedded device, Nvidia Jetson TX2, further confirms the realtime capability of our proposed model.
Document similarity detection based on multi-feature semantic fusion and concept graph

Document Similarity Detection (DSD) is important for checking about plagiarism and duplicate project approval. Abundant word-based detection methods are not enough for actual requirements. The semantic level DSD has recently become a challenging direction because semantic information needs mine. Firstly, we propose a Contextual Multi-feature Semantic Fusion (CMSF) module to purify keyword recognition, and then propose a Concept Graph (CG) structure for document entity. CMSF employs multi-feature enhanced semantics to represent entity, and CG leverages the text interaction graph and text semantic representation vector as initial features, and then Graph Convolutional Network (GCN) uses the features to match document. Extensive experiments based on the real dataset show that CMSF-GCN may converge quickly and get a high accuracy rate compared with recent methods about natural language matching.
The transformation of artificial intelligence in the 5G era and the impact on education

This paper summarizes the development of artificial intelligence and 5G technology in China in recent years, analyzes the changes of artificial intelligence technology in various fields under the background of 5G,and discusses the impact of “5G + AI” on higher education. In recent years, China's artificial intelligence and 5G technology have developed rapidly, and the deep integration of the two is an inevitable trend.which has an important impact on the social economy and various fields of life. With the support of 5G technology, the intelligence of consumption, transportation, tourism, medical care,education and other fields has been further improved. Among them.in the field of higher education, the integration of “5G+AI”has contributed to the construction of the “smart education” environment,the update of the training model.and the innovation of teaching models and learning methods.
Anomaly Detection with Neural Parsers That Never Reject

Reinforcement learning has recently shown promise as a technique for training an artificial neural network to parse sentences in some unknown format, through a body of work known as RL-GRIT. A key aspect of the RL-GRIT approach is that rather than explicitly inferring a grammar that describes the format, the neural network learns to perform various parsing actions (such as merging two tokens) over a corpus of sentences, with the goal of maximizing the estimated frequency of the resulting parse structures. This can allow the learning process to more easily explore different action choices, since a given choice may change the optimality of the parse (as expressed by the total reward), but will not result in the failure to parse a sentence. However, this also presents a limitation: because the trained neural network can successfully parse any sentence, it cannot be directly used to identify sentences that deviate from the format of the training sentences, i.e., that are anomalous. In this paper, we address this limitation by presenting procedures for extracting production rules from the neural network, and for using these rules to determine whether a given sentence is nominal or anomalous. When a sentence is anomalous, an attempt is made to identify the location of the anomaly. We empirically demonstrate that our approach is capable of grammatical inference and anomaly detection for both non-regular formats and those containing regions of high randomness/entropy. While a format with high randomness typically requires large sets of production rules, we propose a two pass grammatical inference method to generate parsimonious rule sets for such formats. By further improving parser learning, and leveraging the presented rule extraction and anomaly detection algorithms, one might begin to understand common errors, either benign or malicious, in practical formats.
Misleading Deep-Fake Detection with GAN Fingerprints

Generative adversarial networks (GANs) have made remarkable progress in synthesizing realistic-looking images that effectively outsmart even humans. Although several detection methods can recognize these deep fakes by checking for image artifacts from the generation process, multiple counterattacks have demonstrated their limitations. These attacks, however, still require certain conditions to hold, such as interacting with the detection method or adjusting the GAN directly. In this paper, we introduce a novel class of simple counterattacks that overcomes these limitations. In particular, we show that an adversary can remove indicative artifacts, the GAN fingerprint, directly from the frequency spectrum of a generated image. We explore different realizations of this removal, ranging from filtering high frequencies to more nuanced frequency-peak cleansing. We evaluate the performance of our attack with different detection methods, GAN architectures, and datasets. Our results show that an adversary can often remove GAN fingerprints and thus evade the detection of generated images.
"It builds trust with the customers" - Exploring User Perceptions of the Padlock Icon in Browser UI

We performed a large-scale online survey (n=1,880) to study the padlock icon, an established security indicator in web browsers that denotes connection security through HTTPS. In this paper, we evaluate users’ understanding of the padlock icon, and how removing or replacing it might influence their expectations and decisions. We found that the majority of respondents (89%) had misconceptions about the padlock’s meaning. While only a minority (23%-44%) referred to the padlock icon at all when asked to evaluate trustworthiness, these padlock-aware users reported that they would be deterred from a hypothetical shopping transaction when the padlock icon was absent. These users were reassured after seeing secondary UI surfaces (i.e., Chrome Page Info) where more verbose information about connection security was present.We conclude that the padlock icon, displayed by browsers in the address bar, is still misunderstood by many users. The padlock icon guarantees connection security, but is often perceived to indicate the general privacy, security, and trustworthiness of a website. We argue that communicating connection security precisely and clearly is likely to be more effective through secondary UI, where there is more surface area for content. We hope that this paper boosts the discussion about the benefits and drawbacks of showing passive security indicators in the browser UI.
Clairvoyance: Exploiting Far-field EM Emanations of GPU to "See" Your DNN Models through Obstacles at a Distance

Deep neural networks (DNNs) are becoming increasingly popular in real-world applications, and they are considered valuable assets of enterprises. In recent years, a number of model extraction attacks have been formulated that can be mounted to successfully steal proprietary DNN models. Nevertheless, previous model extraction attacks require either logical access to the target models or physical access to the victim machines, and thus are not suitable for performing model stealing in scenarios where an outside attacker is in the proximity but at a distance.In this paper, we propose a new model extraction attack named Clairvoyance that exploits certain far-field electromagnetic signals emanated from a GPU to steal DNN models at a distance of several meters away from the victim machine even with some obstacles in-between. Using Clairvoyance, an attacker can effectively deduce DNN architectures (e.g., the number of layers and their types) and layer configurations (e.g., the number of kernels, sizes of layers, and sizes of strides). We use several case studies (e.g., VGG and ResNet) to demonstrate its effectiveness.
Secure System Medical Record with Blockchain System: Recchain Framework

Medical records are very important and provide benefits that are felt by patients, doctors and other health workers in terms of clinical decision making. Seeing the importance of the presence of medical records, Indonesia has also implemented innovation in the use of technology in the application of digital medical records. However, until now there are still problems that occur in the application of the existing medical record technology which is centralized so it is still difficult to get the trust of patients. Therefore, in this case the research will have the object of research in storing medical record databases without the involvement of third parties. (Decentralized) So as a solution, this research will present a schematic system of architecture RecChain(Record Blockchain) with the advantages of being transparent and decentralized for the purpose of increasing patient trust in medical records with a decentralized and transparent database. In RecChain&#x0027;s research, it is not limited to decentralization, external provider medical record data, and smart contract networks. The results of the research obtained, the RecChain system can minimize data misuse, and the proposed scheme design will not depend on the Blockchain platform which makes the scheme have the potential for wider application in other health electronics to increase the tightness of system data protection.
Classification of Multi-Labeled Text Articles with Reuters Dataset using SVM

In text mining problems, text classification is one of the common tasks. Several real-world document classification involves imbalanced text data. This research investigates the behavior of Support Vector Machines classifiers on textual news data. The set of features is developed using the Count Vectorizer and Term Frequency-Inverse Document Frequency to train and test the model. We have observed the quality of the SVM classification algorithm with both linear and polynomial kernel on benchmark UCI News datasets: Reuters. The result shows that SVM with linear performs better and achieves 94.10&#x0025; accuracy where SVM with polynomial kernel achieves 93.28&#x0025; accuracy on the benchmark dataset.
Latent Representation Guided Multi-view Clustering

Multi-view clustering aims to reveal the correlation between different input modalities in an unsupervised way. Similarity between data samples can be described by a similarity graph, which governs the quality of multi-view clustering. However, existing multi-view graph learning methods mainly construct similarity graph based on raw features, which are unreliable as real-world datasets usually contain noises, outliers, or even redundant information. In this paper, we formulate a novel model to simultaneously learn a robust structured similarity graph and perform multi-view clustering. The similarity graph is adaptively learned based on a latent representation that is invulnerable to noises and outliers. Furthermore, the similarity graph is enforced to contain a clear structure, i.e., the number of connected components of the target graph is exactly equal to the ground-truth class number. Consequently, the label to each data sample can be directly assigned without any postprocessing. As a result, our model aims at accomplishing three subtasks: latent representation extraction, similarity graph learning, and cluster label allocation, in a unified framework. These three subtasks are seamlessly integrated and can be mutually boosted by each other towards the overall optimal solution. An efficient alternation algorithm is proposed to solve the optimization problem. Experimental results on several benchmark datasets illustrate the effectiveness of the proposed model.