Reviewerly/exHarmony huggingface dataset:
Source Domain Selection for Cross-House Human Activity Recognition with Ambient Sensors

Human activity recognition using ambient sensors has become particularly important due to social demands of applications in smart homes. To address the problem of labeling sensing data for every individual house, cross-house human activity recognition is proposed to use available labeled houses (source domains) to train recognition models for applying to unlabeled houses (target domains). In this paper, we propose a method of source domain selection for cross-house human activity recognition. We first improve the method for representing semantic relationships of sensors. To select the best similar source houses for a target house, we then propose a method for calculating similarity score between two houses. Using 19 houses of the CASAS dataset, we evaluate the recognition performance in target houses using models trained by several similar source houses, randomly selected houses, dissimilar source houses, and all source houses without selection. Experimental results illustrate that the average accuracy of models trained from the small number of the best similar houses achieve the best performance, and thus they confirm the effectiveness of our proposed method.
Zero-Shot Text Matching for Automated Auditing using Sentence Transformers

Natural language processing methods have several applications in automated auditing, including document or passage classification, information retrieval, and question answering. However, training such models requires a large amount of annotated data which is scarce in industrial settings. At the same time, techniques like zero-shot and unsupervised learning allow for application of models pre-trained using general domain data to unseen domains.In this work, we study the efficiency of unsupervised text matching using Sentence-Bert, a transformer-based model, by applying it to the semantic similarity of financial passages. Experimental results show that this model is robust to documents from in- and out-of-domain data.
An Empirical Study of Automatic Social Media Content Labeling and Classification based on BERT Neural Network

Web flow now is a very important success factor for social media marketing and thus more and more approaches for creating high web flow have been proposed in recent years. Automatic content generation (ACG) website is one of the possible approaches which can help to create web flow. In order to achieve the idea of automatic content generation website, web article classification has been considered the most important task. Therefore, we have development an empirical study to test the content labeling and article classification performance, which is based on the technique of BERT neural network. The performance evaluation including accuracy performance and time performance that are important for us to understand the possibility for implementing the ACG website in real environment, especially the possibility when dealing with large amount of data.
TrADe Re-ID – Live Person Re-Identification using Tracking and Anomaly Detection

Person Re-Identification (Re-ID) aims to search for a person of interest (query) in a network of cameras. In the classic Re-ID setting the query is sought in a gallery containing properly cropped images of entire bodies. Recently, the live Re-ID setting was introduced to represent the practical application context of Re-ID better. It consists in searching for the query in short videos, containing whole scene frames. The initial live Re-ID baseline used a pedestrian detector to build a large search gallery and a classic Re-ID model to find the query in the gallery. However, the galleries generated were too large and contained low-quality images, which decreased the live Re-ID performance. Here, we present a new live Re-ID approach called TrADe, to generate lower high-quality galleries. TrADe first uses a Tracking algorithm to identify sequences of images of the same individual in the gallery. Following, an Anomaly Detection model is used to select a single good representative of each tracklet. TrADe is validated on the live Re-ID version of the PRID-2011 dataset and shows significant improvements over the baseline.
PRISTINE: Semi-supervised Deep Learning Opioid Crisis Detection on Reddit

The drug abuse epidemic has been on the rise in the past few years, particularly after the start of COVID-19 pandemic. Our preliminary observations on Reddit alone show that discussions on drugs from 2018 to 2020 increased between a range of 45% to 200%, and so has the number of unique users participating in those discussions. Existing efforts focused on utilizing social media to distinguish potential drug abuse chats from unharmful chats regardless of what drug is being abused. Others focused on understanding the trends and causes of drug abuse from social media. To this end, we introduce PRISTINE (opioid crisis detection on reddit), our work dynamically detects-and extracts evolving misleading drug names from Reddit comments using reinforced Dynamic Query Expansion (DQE) and constructs a textual Graph Convolutional Network with the aid of powerful pre-trained embeddings to detect which type of drug class a Reddit comment corresponds to. Further, we perform extensive experiments to investigate the effectiveness of our model.
Characteristics Analysis of Moving Conversations to Detect Events on Twitter

A conversation is an exchange of thoughts, news, or ideas about a particular topic between two or more people. On Twit-ter, hashtags allow its users to collate all conversations pertaining to a particular topic. The progressions that occur in such conversations through the geographic space, the time, or the thematic contexts, create trajectories of conversations on Twitter, and they can give us valuable insights into interesting events that take place around us. In this paper we develop an approach based on data analysis and visualisation, to (1) construct such conversation trajectories for chosen popular hashtags, (2) analyse the various geospatial- and content-characteristics of the conversation trajectories (e.g., distance variance, speed of propagation, topic diversity, or credibility) to determine co-located events, and (3) rank and sort the resulting conversation trajectories according to a user-defined interestingess-measure, to narrow down the search space for interesting conversation trajectories. Our approach is among the first to introduce the us-age of movement of conversations across geographic space and time for the exploratory detection and analysis of events, whereas most existing works use keyword-based text analysis to detect events on Twitter. All the three stages of the approach (construct, analyse, rank & sort) are presented in a visual-interactive interface that allows us to explore Twitter text data without extensive prior knowledge, and benefit from the pure exploratory capabilities of the tool. The usefulness of our approach is demonstrated as a proof-of-concept to detect sports-related events, where we were able to identify the outcome of a contest for Major League Baseball sportsmen on Twitter.
Faster Greedy Optimization of Resistance-based Graph Robustness

The total effective resistance, also called the Kirchhoff index, provides a robustness measure for a graph <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$G$</tex> . We consider the optimization problem of adding <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> new edges to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$G$</tex> such that the resulting graph has minimal total effective resistance (i. e., is most robust). The total effective resistance and effective resistances between nodes can be computed using the pseudoinverse of the graph Laplacian. The pseudoinverse may be computed explicitly via pseudoinversion; yet, this takes cubic time in practice and quadratic space. We instead exploit combinatorial and algebraic connections to speed up gain computations in established generic greedy heuristics. Moreover, we leverage existing randomized techniques to boost the performance of our approaches by introducing a sub-sampling step. Our different graph- and matrix-based approaches are indeed significantly faster than the state-of-the-art greedy algorithm, while their quality remains reasonably high and is often quite close. Our experiments show that we can now process large graphs for which the application of the state-of-the-art greedy approach was infeasible before. As far as we know, we are the first to be able to process graphs with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$100K+$</tex> nodes in the order of minutes.
URLytics: Profiling Forum Users from their Posted URLs

Online forums contain a substantial amount of data, but very few studies have focused on mining the URLs posted by users. How can we fully leverage these posted URLs to extract as much information as possible about forum users? We perform a systematic study for extracting as much information as possible about forum users via their URL posting behavior. Within this study we develop a series of tools to analyze the data. Given a forum, we extract the following information: (a) basic statistics and a profile of the forum, (b) a profile for each user based on their referral to accounts in other platforms, (c) identification of communities within the forum, and (d) detection of malicious behavior. Most prior works focus on analyzing the text found in user posts rather than on URLs themselves, as we do here. In our study, we analyze three online security forums and find interesting results: (a) we identify 7% of the users posting social media links on other platforms, (b) we detect 148 groups of users that engage in communities on external social media platforms, (c) we expose 139 malicious users that collectively posted 328 malicious URLs. Additionally, we identify 17 groups with membership spanning across multiple forums, and discover numerous other groups that engage in coordinated malicious behavior. Our work is a significant step towards an all-encompassing system for profiling forum users at large.
PriMe: A Novel Privacy Measuring Framework for Online Social Networks

Online Social Networks are responsible for disclosing a large amount of sensitive information. Users unintentionally reveal their sensitive information and are unaware of the privacy risks involved. But the users should be well informed about their privacy quotient and should know where they stand on the privacy measuring scale. In this paper, we proposed an adaptive privacy measuring framework called PriMe that can measure the privacy leakage score for each action of a user in an OSN and subsequently adjust the privacy settings based on the preferred privacy scopes and boundaries. Various types of data, actions, and personal characteristics of each user have been considered to ensure the calculated privacy leakage score is accurate.
#WashTheHate: Understanding the Prevalence of Anti-Asian Prejudice on Twitter During the COVID-19 Pandemic

Prejudice and hate directed toward Asian individuals has increased in prevalence and salience during the COVID-19 pandemic, with notable rises in physical violence. Concurrently, as many governments enacted stay-at-home mandates, the spread of anti-Asian content increased in online spaces, including social media. In the present study, we investigated temporal and geographical patterns in social media content relevant to anti-Asian prejudice during the COVID-19 pandemic. Using the Twitter Data Collection API, we queried over 13 million tweets posted between January 30, 2020, and April 30, 2021, for both negative (e.g., #kungflu) and positive (e.g., #stopAAPIhate) hashtags and keywords related to anti-Asian prejudice. In a series of descriptive analyses, we found differences in the frequency of negative and positive keywords based on geographic location. Using burst detection, we also identified distinct increases in negative and positive content in relation to key political tweets and events. These largely exploratory analyses shed light on the role of social media in the expression and proliferation of prejudice as well as positive responses online.
A Machine Learning Approach to Identify Toxic Language in the Online Space

In this study, we trained three machine learning models to detect toxic language on social media. These models were trained using data from diverse sources to ensure that the models have a broad understanding of toxic language. Next, we evaluate the performance of our models on a dataset with samples of data from a large number of diverse online forums. The test dataset was annotated by three independent annotators. We also compared the performance of our models with Perspective API - a toxic language detection model created by Jigsaw and Google's Counter Abuse Technology team. The results showed that our classification models performed well on data from the domains they were trained on (Fl = 0.91, 0.91, & 0.84, for the RoBERTa, BERT, & SVM respectively), but the performance decreased when they were tested on annotated data from new domains (Fl = 0.80, 0.61, 0.49, & 0.77, for the RoBERTa, BERT, SVM, & Google perspective, respectively). Finally, we used the best-performing model on the test data (RoBERTa, ROC = 0.86) to examine the frequency (/proportion) of toxic language in 21 diverse forums. The results of these analyses showed that forums for general discussions with moderation (e.g., Alternate history) had much lower proportions of toxic language compared to those with minimal moderation (e.g., 8Kun). Although highlighting the complexity of detecting toxic language, our results show that model performance can be improved by using a diverse dataset when building new models. We conclude by discussing the implication of our findings and some directions for future research.
BAN - MPR: Defending against Membership Inference Attacks with Born Again Networks and Membership Privacy Regularization

Membership inference attack (MIA) is one of the data security issues faced by machine learning, that is, an attacker infers whether a specific sample exists in the training set based on the output of the model. There are quite a few methods have been proposed to defend against MIAs such as differential privacy, distillation and adversarial regularization, etc. However, due to the addition of too conservative noise, differential privacy leads to a sharp drop in model utility, which cannot achieve the trade-off between utility and security. For defending against MIAs, distillation often needs external data. Adversarial regularization makes strong assumptions about the attack model, so such this defense is usually limited to specific attacks. To overcome these deficiencies, in this work, we adopt the architecture of BAN (Born Again Neural Networks) as our defending framework to preserve model utility, and design MPR (Membership Privacy Regularization) to resist various forms of MIAs. In short, we propose a new method to defend against MIAs in the black-box setting, named BAN-MPR (a membership inference attacks defense method with Born Again Network and Membership Privacy Regularization). First of all, we create multiple subsets through data augmentation and partition, then use the distillation method of BAN, combined with MPR to train a series of student models on their respective subsets, and finally the results of the current sample in all the student models are simply integrated as the final output of the prediction stage. Our experiments show BAN-MPR achieves a better trade-off between model utility and security. It can mitigate the risks of MIAs (near random guess), and can achieve this with a negligible drop in model's utility (less than 3.5%).
Fer-MLP: Fast Expression Recognition Network Based on MLP

Facial expression recognition (FER) is an important research topic of image classification in computer vision. However, due to the complexity and locality of facial expressions, there are still challenges in FER. The effective methods for FER were mainly based on traditional CNN networks, such as VGG and ResNet. Unfortunately, the CNN-based methods brought heavy computation. They led to low speed and the inability to recognize expressions in real-time and efficiency-critical conditions. In this paper, we propose Fast expression recognition MLP(Fer-MLP), which is a network only based on multi-layer perceptions(MLP). We design a tokenized MLP block, which can tokenize the features and model the representation well. The proposed tokenized MLP block can extract more compelling features to help classification with fewer parameters and computational complexity. Experiments on the FER2013 dataset show that the proposed Fer-MLP outperforms the CNN baselines. Compared to average, the Fer-MLP decreases the parameter amount by 10%, reduces the calculation amount by 69%, and accelerates inference by 96% without much loss of accuracy.
Generating Synthetic Data for Credit Card Fraud Detection Using GANs

Deep learning-based classifiers for object classification and recognition have been utilized in various sectors. However according to research papers deep neural networks achieve better performance using balanced datasets than imbalanced ones. It's been observed that datasets are often imbalanced due to less fraud cases in production environments. Deep generative approaches, such as GANs have been applied as an efficient method to augment high-dimensional data. In this research study, the classifiers based on a Random Forest, Nearest Neighbor, Logistic Regression, MLP, Adaboost were trained utilizing our novel K-CGAN approach and compared using other oversampling approaches achieving higher F1 score performance metrics. Experiments demonstrate that the classifiers trained on the augmented set achieved far better performance than the same classifiers trained on the original data producing an effective fraud detection mechanism. Furthermore, this research demonstrates the problem with data imbalance and introduces a novel model that's able to generate high quality synthetic data.
The Prevalence of Digital Currency in E-commerce: A Case Study

Over the previous decade, cryptocurrency, along with the current trend of digital wallets, has gained popularity as a cashless, more convenient, and fast payment option for doing ecommerce transactions. In both public and private sector computer applications, blockchain (a popular cryptocurrency technology) promises to be hugely disruptive and liberating. Blockchain provides a consensus record with an encrypted audit trail that can be maintained and validated by different nodes as a mechanism to rank transactions in a distributed ledger. The use of blockchain and bitcoin as an alternative payment system has been unquestionably broad, not just in e-commerce but also in other areas of finance. Major businesses who adopted cryptocurrency have benefited from the boom, but there is a risk of value depreciation because cryptocurrency is notoriously volatile. While bitcoin enables for anonymous purchases via encrypted wallet addresses, it's essentially the same as carrying about unmarked cash. This allows your customers to purchase things without exposing their personal information, and it allows you to conduct business without fear of violating any privacy laws. Crypto payments, on the other hand, come with no user protections or redress, akin to the security vulnerabilities that can arise with a wire transfer.
Discussion on Smart Contract Under Blockchains Technology

At present, blockchains has become a major trend at home and abroad. From the government’s announcement to the establishment of the blockchain infrastructure in Xiongan New Area, the development trend of blockchain cannot be ignored. With the development of blockchain,a new blockchain platform of Ethereum has emerged.This paper makes a further introduction to the core of Ethereum platform - Smart contract. Smart contract is a famous concept proposed by Nick Szabo in 1996. The emergence of blockchain and Ethereum makes it possible to use on a large scale. This paper also discusses the development of smart contract and its existing problems.
Security-Alert Screening with Oversampling Based on Conditional Generative Adversarial Networks

Imbalanced class distribution can cause information loss and missed/false alarms for deep learning and machine-learning algorithms. The detection performance of traditional intrusion detection systems tend to degenerate due to skewed class distribution caused by the uneven allocation of observations in different kinds of attacks. To combat class imbalance and improve network intrusion detection performance, we adopt the conditional generative adversarial network (CTGAN) that enables the generation of samples of specific classes of interest. CTGAN builds on the generative adversarial networks (GAN) architecture to model tabular data and generate high quality synthetic data by conditionally sampling rows from the generated model. Oversampling using CTGAN adds instances to the minority class such that both data in the majority and the minority class are of equal distribution. The generated security alerts are used for training classifiers that realize critical alert detection. The proposed scheme is evaluated on a real-world dataset collected from security operation center of a large enterprise. The experiment results show that detection accuracy can be substantially improved when CTGAN is adopted to produce a balanced security-alert dataset. We believe the proposed CTGAN-based approach can cast new light on building effective systems for critical alert detection with reduced missed/false alarms.
GAMC: An Oversampling Method Based on Genetic Algorithm and Monte Carlo Method to Solve the Class Imbalance Issue in Industry

Due to the characteristics of long-term steady in industrial production processes, there is a large amount of imbalanced data in it, which can make it difficult for detection models to accurately capture abnormal events and cause property damage. In this paper, a data oversampling method is proposed to balance the industrial process data from the perspective of sample distribution. This oversampling method uses the crossover and mutation processes of genetic algorithm to generate offspring samples, and then uses the monte carlo acceptance-rejection sampling method to determine whether the new attribute values match the distribution of original sample attribute values. This method can effectively enrich the information carried in the minority class samples and improve the effectiveness of classification model. In the experimental section, the results of the comparison with existing two classical oversampling methods on six publicly available datasets show that the algorithm in this paper outperforms the existing oversampling methods in terms of overall performance.
Accurate Prediction of Electric Fields of Nanoparticles With Deep Learning Methods

Three different deep learning models were designed in this paper, to predict the electric fields of single nanoparticles, dimers, and nanoparticle arrays. For single nanoparticles, the prediction error was 4.4%. For dimers with strong couplings, a sample self-normalization method was proposed, and the error was reduced by an order of magnitude compared with traditional methods. For nanoparticle arrays, the error was reduced from 28.8% to 5.6% compared with previous work. Numerical tests proved the validity of the proposed deep learning models, which have potential applications in the design of nanostructures.