Reviewerly/exHarmony huggingface dataset:
Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM

For an intelligent detection of bearing failure in rotating machinery, this paper proposed a fault diagnosis method based on a probability box (p-box) and support vector machine (SVM) with a genetic algorithm (GA) algorithm.Firstly, based on vibration signals of the bearing, the different p-boxes are obtained and fused using the evidence theory.Then, the different bearing p-boxes can be classified by adopting SVM model; the GA algorithm is considered to optimize key parameters of the SVM model, i.e., GA-SVM.Finally, experimental results show that total recognition rate of this method is better than that of the traditional feature extraction method, which demonstrates the effectiveness of the current method.
Relational Neural Machines

Deep learning has been shown to achieve impressive results in several tasks where a large amount of training data is available. However, deep learning solely focuses on the accuracy of the predictions, neglecting the reasoning process leading to a decision, which is a major issue in life-critical applications. Probabilistic logic reasoning allows to exploit both statistical regularities and specific domain expertise to perform reasoning under uncertainty, but its scalability and brittle integration with the layers processing the sensory data have greatly limited its applications. For these reasons, combining deep architectures and probabilistic logic reasoning is a fundamental goal towards the development of intelligent agents operating in complex environments. This paper presents Relational Neural Machines, a novel framework allowing to jointly train the parameters of the learners and of a First--Order Logic based reasoner. A Relational Neural Machine is able to recover both classical learning from supervised data in case of pure sub-symbolic learning, and Markov Logic Networks in case of pure symbolic reasoning, while allowing to jointly train and perform inference in hybrid learning tasks. Proper algorithmic solutions are devised to make learning and inference tractable in large-scale problems. The experiments show promising results in different relational tasks.
Development of anti-phishing browser based on random forest and rule of extraction framework

Abstract Phishing is a technique under Social Engineering attacks which is most widely used to get user sensitive information, such as login credentials and credit and debit card information, etc. It is carried out by a person masquerading as an authentic individual. To protect web users from these attacks, various anti-phishing techniques are developed, but they fail to protect the user from these attacks in various ways. In this paper, we propose a novel technique to identify phishing websites effortlessly on the client side by proposing a novel browser architecture. In this system, we use the rule of extraction framework to extract the properties or features of a website using the URL only. This list consists of 30 different properties of a URL, which will later be used by the Random Forest Classification machine learning model to detect the authenticity of the website. A dataset consisting of 11,055 tuples is used to train the model. These processes are carried out on the client-side with the help of a redesigned browser architecture. Today Researches have come up with machine learning frameworks to detect phishing sites, but they are not in a state to be used by individuals having no technical knowledge. To make sure that these tools are accessible to every individual, we have improvised and introduced detection methods into the browser architecture named as ‘Embedded Phishing Detection Browser’ (EPDB), which is a novel method to preserve the existing user experience while improving the security. The newly designed browser architecture introduces a special segment to perform phishing detection operations in real-time. We have prototyped this technique to ensure maximum security, better accuracy of 99.36% in the identification of phishing websites in real-time.
A Comparative Performance Evaluation of Classification Algorithms for Clinical Decision Support Systems

Classification algorithms are widely taken into account for clinical decision support systems. However, it is not always straightforward to understand the behavior of such algorithms on a multiple disease prediction task. When a new classifier is introduced, we, in most cases, will ask ourselves whether the classifier performs well on a particular clinical dataset or not. The decision to utilize classifiers mostly relies upon the type of data and classification task, thus making it often made arbitrarily. In this study, a comparative evaluation of a wide-array classifier pertaining to six different families, i.e., tree, ensemble, neural, probability, discriminant, and rule-based classifiers are dealt with. A number of real-world publicly datasets ranging from different diseases are taken into account in the experiment in order to demonstrate the generalizability of the classifiers in multiple disease prediction. A total of 25 classifiers, 14 datasets, and three different resampling techniques are explored. This study reveals that the classifier that is likely to become the best performer is the conditional inference tree forest (cforest), followed by linear discriminant analysis, generalize linear model, random forest, and Gaussian process classifier. This work contributes to existing literature regarding a thorough benchmark of classification algorithms for multiple diseases prediction.
BERTnesia: Investigating the capture and forgetting of knowledge in BERT

Probing complex language models has recently revealed several insights into linguistic and semantic patterns found in the learned representations.In this paper, we probe BERT specifically to understand and measure the relational knowledge it captures.We utilize knowledge base completion tasks to probe every layer of pre-trained as well as fine-tuned BERT (ranking, question answering, NER).Our findings show that knowledge is not just contained in BERT's final layers.Intermediate layers contribute a significant amount (17-60%) to the total knowledge found.Probing intermediate layers also reveals how different types of knowledge emerge at varying rates.When BERT is fine-tuned, relational knowledge is forgotten but the extent of forgetting is impacted by the fine-tuning objective but not the size of the dataset.We found that ranking models forget the least and retain more knowledge in their final layer.We release our code on github 1 to repeat the experiments.
BlinKey

Virtual Reality (VR) has shown promising potentials in many applications, such as e-business, healthcare, and social networking. Rich information regarding user's activities and their online accounts is stored in VR devices. If it is carelessly unattended, then attackers, including insiders, can make use of the stored information to, for example, perform in-app purchases at the legitimate owner's expenses. Current solutions, mostly following schemes designed for general personal devices, have been proved vulnerable to shoulder-surfing attacks due to the sight blocking caused by the headset. Although there have been efforts trying to fill this gap, they either rely on some highly advanced equipment, such as electrodes to read brainwaves, or introduce heavy cognitive load that has users perform a series of cumbersome authentication tasks. Therefore, an authentication method for VR devices that is robust and convenient is in dire need. In this paper, we present the design, implementation, and evaluation of a two-factor user authentication scheme, BlinKey, for VR devices that are equipped with an eye tracker. A user's secret passcode is a set of recorded rhythms when he/she blinks, together with the unique pupil size variation pattern. We call this passcode as a blinkey, which can be jointly characterized by knowledge-based and biometric features. To examine the performances, BlinKey is implemented on an HTC Vive Pro with a Pupil Labs eye tracker. Through extensive experimental evaluations with 52 participants, we show that our scheme can achieve the average EER as low as 4.0% with only 6 training samples. Besides, it is robust against various types of attacks. BlinKey also exhibits satisfactory usability in terms of login attempts, memorability, and impact of user motions. We also carry out questionnaire-based pre-/post-studies. The survey result indicates that BlinKey is well accepted as a user authentication scheme for VR devices.
Don’t Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities

In this paper, we introduce a new annotated dataset which is aimed at supporting the development of NLP models to identify and categorize language that is patronizing or condescending towards vulnerable communities (e.g. refugees, homeless people, poor families). While the prevalence of such language in the general media has long been shown to have harmful effects, it differs from other types of harmful language, in that it is generally used unconsciously and with good intentions. We furthermore believe that the often subtle nature of patronizing and condescending language (PCL) presents an interesting technical challenge for the NLP community. Our analysis of the proposed dataset shows that identifying PCL is hard for standard NLP models, with language models such as BERT achieving the best results.
Detection of Genuine and Posed Facial Expressions of Emotion: Databases and Methods

Facial expressions of emotion play an important role in human social interactions. However, posed expressions of emotion are not always the same as genuine feelings. Recent research has found that facial expressions are increasingly used as a tool for understanding social interactions instead of personal emotions. Therefore, the credibility assessment of facial expressions, namely, the discrimination of genuine (spontaneous) expressions from posed (deliberate/volitional/deceptive) ones, is a crucial yet challenging task in facial expression understanding. With recent advances in computer vision and machine learning techniques, rapid progress has been made in recent years for automatic detection of genuine and posed facial expressions. This paper presents a general review of the relevant research, including several spontaneous vs. posed (SVP) facial expression databases and various computer vision based detection methods. In addition, a variety of factors that will influence the performance of SVP detection methods are discussed along with open issues and technical challenges in this nascent field.
IFC-BD: An Interpretable Fuzzy Classifier for Boosting Explainable Artificial Intelligence in Big Data

In current Data Science applications, the course of action has derived to adapt the system behavior for the human cognition, resulting in the emerging area of explainable artificial intelligence. Among different classification paradigms, those based on fuzzy rules are suitable solutions to stress the interpretability of the global systems. However, in case of addressing Big Data analytics, they may comprise an excessive number of rules and/or linguistic labels that not only may cause losing the system performance but also may affect the system semantic as well as the system interpretability. In this article, we propose IFC-BD, an interpretable fuzzy classifier for Big Data, aiming at boosting the horizons of explainability by learning a compact yet accurate fuzzy model. IFC-BD is developed in a cell-based distributed framework through the three working stages of initial rule learning, rule generalization, and heuristic rule selection. This whole procedure allows reaching from a high number of specific rules to less number of more general and confident rules. Additionally, in order to resolve possible rules conflict, a new estimated rule weight is proposed specifically for big data problems. IFC-BD was evaluated in comparison to the state-of-the-art approaches of the fuzzy classification paradigm, considering interpretability, accuracy, and running time. The findings of the experiments revealed that the proposed algorithm was able to improve the explainability of fuzzy rule-based classifiers as well as their predictive performance.
Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Evidence

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Transformer is All You Need: Multimodal Multitask Learning with a Unified Transformer

We propose UniT, a Unified Transformer model to simultaneously learn the mostprominent tasks across different domains, ranging from object detection tolanguage understanding and multimodal reasoning. Based on the transformerencoder-decoder architecture, our UniT model encodes each input modality withan encoder and makes predictions on each task with a shared decoder over theencoded input representations, followed by task-specific output heads. Theentire model is jointly trained end-to-end with losses from each task. Comparedto previous efforts on multi-task learning with transformers, we share the samemodel parameters to all tasks instead of separately fine-tuning task-specificmodels and handle a much higher variety of tasks across different domains. Inour experiments, we learn 7 tasks jointly over 8 datasets, achieving comparableperformance to well-established prior work on each domain under the samesupervision with a compact set of model parameters. Code will be released inMMF at https://mmf.sh.
A survey on training and evaluation of word embeddings

Word embeddings have proven to be effective for many natural language processing tasks by providing word representations integrating prior knowledge. In this article, we focus on the algorithms and models used to compute those representations and on their methods of evaluation. Many new techniques were developed in a short amount of time, and there is no unified terminology to emphasise strengths and weaknesses of those methods. Based on the state of the art, we propose a thorough terminology to help with the classification of these various models and their evaluations. We also provide comparisons of those algorithms and methods, highlighting open problems and research paths, as well as a compilation of popular evaluation metrics and datasets. This survey gives: (1) an exhaustive description and terminology of currently investigated word embeddings, (2) a clear segmentation of evaluation methods and their associated datasets, and (3) high-level properties to indicate pros and cons of each solution.
SAR image de-noising via grouping-based PCA and guided filter

A novel synthetic aperture radar (SAR) image de-noising method based on the local pixel grouping (LPG) principal component analysis (PCA) and guided filter is proposed. This method contains two steps. In the first step, we process the noisy image by coarse filters, which can suppress the speckle effectively. The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction. Then, we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching. The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation, so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain. In the second step, we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering. Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signal-to-noise ratio (PSNR), the structural similarity (SSIM) index and the equivalent number of looks (ENLs), and is of perceived image quality.
Interactive Visual Exploration of Longitudinal Historical Career Mobility Data

The increased availability of quantitative historical datasets has provided new research opportunities for multiple disciplines in social science. In this article, we work closely with the constructors of a new dataset, CGED-Q (China Government Employee Database-Qing), that records the career trajectories of over 340,000 government officials in the Qing bureaucracy in China from 1760 to 1912. We use these data to study career mobility from a historical perspective and understand social mobility and inequality. However, existing statistical approaches are inadequate for analyzing career mobility in this historical dataset with its fine-grained attributes and long time span, since they are mostly hypothesis-driven and require substantial effort. We propose CareerLens, an interactive visual analytics system for assisting experts in exploring, understanding, and reasoning from historical career data. With CareerLens, experts examine mobility patterns in three levels-of-detail, namely, the macro-level providing a summary of overall mobility, the meso-level extracting latent group mobility patterns, and the micro-level revealing social relationships of individuals. We demonstrate the effectiveness and usability of CareerLens through two case studies and receive encouraging feedback from follow-up interviews with domain experts.
Lord of the Rings: Hanoi Pooling and Self-Knowledge Distillation for Fast and Accurate Vehicle Reidentification

Vehicle reidentification has seen increasing interest, thanks to its fundamental impact on intelligent surveillance systems and smart transportation. The visual data acquired from monitoring camera networks come with severe challenges, including occlusions, color and illumination changes, as well as orientation issues (a vehicle can be seen from the side/front/rear due to different camera viewpoints). To deal with such challenges, the community has spent much effort in learning robust feature representations that hinge on additional visual attributes and part-driven methods, but with the side effects of requiring extensive human annotation labor as well as increasing computational complexity. In this article, we propose an approach that learns a feature representation robust to vehicle orientation issues without the need for extra-labeled data and adding negligible computational overheads. The former objective is achieved through the introduction of a Hanoi pooling layer exploiting ring regions and the image pyramid approach yielding a multiscale representation of vehicle appearance. The latter is tackled by transferring the accuracy of a deep network to its first layers, thus reducing the inference effort by the early stop of a test example. This is obtained by means of a self-knowledge distillation framework encouraging multiexit network decisions to agree with each other. Results demonstrate that the proposed approach significantly improves the accuracy of early (i.e., very fast) exits while maintaining the same accuracy of a deep (slow) baseline. Moreover, our solution obtains the best existing performance on three benchmark datasets. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label><sup>1</sup></label> [Online]. Available: <uri>https://github.com/iN1k1/</uri>. </fn>
Explaining and Improving BERT Performance on Lexical Semantic Change Detection

Type-and token-based embedding architectures are still competing in lexical semantic change detection.The recent success of typebased models in SemEval-2020 Task 1 has raised the question why the success of tokenbased models on a variety of other NLP tasks does not translate to our field.We investigate the influence of a range of variables on clusterings of BERT vectors and show that its low performance is largely due to orthographic information on the target word, which is encoded even in the higher layers of BERT representations.By reducing the influence of orthography we considerably improve BERT's performance.
Real Estate Data Marketplace

Real estate financing, construction, and management are being revolutionised by the data science and infrastructure technologies of Artificial Intelligence (AI), Internet of Things (IoT), Big Data, Digital Object Identifiers (DOI) and Blockchain. The key to automation and integration is a Real Estate Data Marketplace across the lifespan of finance, planning, construction, regulation, management, even refurbishment and demolition. Integration of digital 4.0 and industry 4.0. Real estate is becoming increasingly digital, automated and integrated; embracing connected-buildings, wearable personal devices, integrated transportation systems, buildings and infrastructure data management and smart cities. The key to promoting the use of data-driven techniques in the real estate sector is the introduction of a Data Marketplace that promotes validation of data and tokenisation to create value for the owners of the data, Blockchain security and governance for users of the data, the application of Artificial Intelligence for data analytics and ethics for governance. The technical capability exists today for a complete end-to-end reshaping of the real estate market based on data and ethics. This is data-driven real estate (cf. Google, Amazon, Alibaba, Tencent). The contributions of this review paper are fourfold: (a) to provide a vision of a fully digital real estate marketplace; (b) to brief the industry on digital technology and also stimulate engagement with the computer science community; (c) to encourage debate amongst professionals, academics and politicians of the benefits of full automation and integration of real estate; (d) present ethics for the Real Estate Data Marketplace based on lawfulness, fairness and transparency.
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety

The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability to problems with malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from safety concerns. In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged. This work provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern ML methods. We moreover hope that our contribution fuels discussions on desiderata for ML systems and strategies on how to propel existing approaches accordingly.
Application of Deep Learning Using Convolutional Neural Network (CNN) Method For Women’s Skin Classification

Facial skin is skin that protects the inside of the face such as the eyes, nose, mouth, and others. Facial skin consists of several types, including normal skin, oily skin, dry skin, and combination skin. This is a problem for women because it is difficult to recognize and distinguish their skin types this is what causes some women to find it difficult to determine the right make-up and care products for their skin types. In this study, the Convolutional Neural Network (CNN) method is the right method for classifying women's skin types from the age of 20-30 years by following several stages using Python 3.5 programming with a depth of three layers and the results of this research using the CNN method get the results of the accuracy value good at 67%