Reviewerly/exHarmony huggingface dataset:
FaceTouch: Practical Face Touch Detection with a Multimodal Wearable System for Epidemiological Surveillance

In this paper, we propose FaceTouch, a low-power and versatile method that enables accurate face touch detection with a multimodal wearable system. FaceTouch consists of two sensing components, an inertial sensor on the wrist and a novel vibration sensor on the finger. We leverage the wrist inertial sensor to detect the face-touch gesture that the hand moves towards the face area. To achieve this goal in a computation-efficient manner, we develop a cascading classification model including three classifiers to filter out irrelevant gestures to significantly extend the battery life while keeping a high recall. Once a face-touch gesture is triggered, we activate the vibration sensor to detect touch events. We implement FaceTouch using commercial off-the-shelf hardware components and evaluate its performance with various user activities and false-positive behaviors. FaceTouch achieves 93.5% F-1 score of face touch detection. The entire system only consumes 60.89 μ W power on average in normal daily usage and 209.15 μ W in extremely heavy usage, which is several magnitudes lower than the state-of-the-art systems, and FaceTouch can continuously detect face-touch events for 79 – 273 days using a small 400 mWh battery depending on usage.
CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval

Deep hashing has been extensively utilized in massive image retrieval because of its efficiency and effectiveness. However, deep hashing models are vulnerable to adversarial examples, making it essential to develop adversarial defense methods for image retrieval. Existing solutions achieved limited defense performance because of using weak adversarial samples for training and lacking discriminative optimization objectives to learn robust features. In this paper, we present a min-max based Center-guided Adversarial Training, namely CgAT, to improve the robustness of deep hashing networks through worst adversarial examples. Our key idea is to formulate a hash code (dubbed center code) as a discriminative semantic representation of the original sample, which can be used to guide the generation of the powerful adversarial example and as an accurate optimization objective for adversarial training. Specifically, we first formulate the center code as a semantically-discriminative representative of the input image content, which preserves the semantic similarity with positive samples and dissimilarity with negative examples. We prove that a mathematical formula can calculate the center code immediately. After obtaining the center codes in each optimization iteration of the deep hashing network, they are adopted to guide the adversarial training process. On the one hand, CgAT generates the worst adversarial examples as augmented data by maximizing the Hamming distance between the hash codes of the adversarial examples and the center codes. On the other hand, CgAT learns to mitigate the effects of adversarial samples by minimizing the Hamming distance to the center codes. Extensive experiments on the benchmark datasets demonstrate the effectiveness of our adversarial training algorithm in defending against adversarial attacks for deep hashing-based retrieval. Compared with the current state-of-the-art defense method, we significantly improve the defense performance by an average of 18.61%, 12.35%, and 11.56% on FLICKR-25K, NUS-WIDE, and MS-COCO, respectively. The code is available at https://github.com/xunguangwang/CgAT.
CoTel: Ontology-Neural Co-Enhanced Text Labeling

The success of many web services relies on the large-scale domain-specific high-quality labeled dataset. Insufficient public datasets motivate us to reduce the cost of data labeling while maintaining high accuracy in support of intelligent web applications. The rule-based method and the learning-based method are common techniques for labeling. In this work, we study how to utilize the rule-based and learning-based methods for resource-effective text labeling. We propose CoTel, the first ontology-neural co-enhanced framework for text labeling. We propose critical ontology extraction in the rule-based module and ontology-enhanced loss prediction in the learning-based module. CoTel can integrate explicit labeling rules and implicit labeling models and make them help each other to improve resource efficiency in text labeling tasks. We evaluate CoTel on both public datasets and real applications with three different tasks. Compared with the baseline, CoTel can reduce the time cost by 64.75% (a 2.84× speedup) and the number of labeling by 62.07%.
Response-act Guided Reinforced Dialogue Generation for Mental Health Counseling

Virtual Mental Health Assistants (VMHAs) have become a prevalent method for receiving mental health counseling in the digital healthcare space. An assistive counseling conversation commences with natural open-ended topics to familiarize the client with the environment and later converges into more fine-grained domain-specific topics. Unlike other conversational systems, which are categorized as open-domain or task-oriented systems, VMHAs possess a hybrid conversational flow. These counseling bots need to comprehend various aspects of the conversation, such as dialogue-acts, intents, etc., to engage the client in an effective and appropriate conversation. Although the surge in digital health research highlights applications of many general-purpose response generation systems, they are barely suitable in the mental health domain – the prime reason is the lack of understanding in the mental health counseling conversation. Moreover, in general, dialogue-act guided response generators are either limited to a template-based paradigm or lack appropriate semantics in dialogue generation. To this end, we propose READER – a REsponse-Act guided reinforced Dialogue genERation model for the mental health counseling conversations. READER is built on transformer to jointly predict a potential dialogue-act dt + 1 for the next utterance (aka response-act) and to generate an appropriate response (ut + 1). Through the transformer-reinforcement-learning (TRL) with Proximal Policy Optimization (PPO), we guide the response generator to abide by dt + 1 and ensure the semantic richness of the responses via BERTScore in our reward computation. We evaluate READER on HOPE, a benchmark counseling conversation dataset and observe that it outperforms several baselines across several evaluation metrics – METEOR, ROUGE, and BERTScore.
Anti-FakeU: Defending Shilling Attacks on Graph Neural Network based Recommender Model

Graph neural network (GNN) based recommendation models are observed to be more vulnerable against carefully-designed malicious records injected into the system, i.e., shilling attacks, which manipulate the recommendation to common users and therefore impair user trust. In this paper, we for the first time conduct a systematic study on the vulnerability of GNN based recommendation model against the shilling attack. With the aid of theoretical analysis, we attribute the root cause of the vulnerability to its neighborhood aggregation mechanism, which could make the negative impact of attacks propagate rapidly in the system. To restore the robustness of GNN based recommendation model, the key factor lies in detecting malicious records in the system and preventing the propagation of misinformation. To this end, we construct a user-user graph to capture the patterns of malicious behaviors and design a novel GNN based detector to identify fake users. Furthermore, we develop a data augmentation strategy and a joint learning paradigm to train the recommender model and the proposed detector. Extensive experiments on benchmark datasets validate the enhanced robustness of the proposed method in resisting various types of shilling attacks and identifying fake users, e.g., our proposed method fully mitigating the impact of popularity attacks on target items up to , and improving the accuracy of detecting fake users on the Gowalla dataset by .
BlinkViz: Fast and Scalable Approximate Visualization on Very Large Datasets using Neural-Enhanced Mixed Sum-Product Networks

Web-based online interactive visual analytics enjoys popularity in recent years. Traditionally, visualizations are produced directly from querying the underlying data. However, for a very large dataset, this way is so time-consuming that it cannot meet the low-latency requirements of interactive visual analytics. In this paper, we propose a learning-based visualization approach called BlinkViz, which uses a learned model to produce approximate visualizations by leveraging mixed sum-product networks to learn the distribution of the original data. In such a way, it makes visualization faster and more scalable by decoupling visualization and data. In addition, to improve the accuracy of approximate visualizations, we propose an enhanced model by incorporating a neural network with residual structures, which can refine prediction results, especially for visual requests with low selectivity. Extensive experiments show that BlinkViz is extremely fast even on a large dataset with hundreds of millions of data records (over 30GB), responding in sub-seconds (from 2ms to less than 500ms for different requests) while keeping a low error rate. Furthermore, our approach remains scalable on latency and memory footprint size regardless of data size.
HybridEval: A Human-AI Collaborative Approach for Evaluating Design Ideas at Scale

Evaluating design ideas is necessary to predict their success and assess their impact early on in the process. Existing methods rely either on metrics computed by systems that are effective but subject to errors and bias, or experts’ ratings, which are accurate but expensive and long to collect. Crowdsourcing offers a compelling way to evaluate a large number of design ideas in a short amount of time while being cost-effective. Workers’ evaluation is, however, less reliable and might substantially differ from experts’ evaluation.
Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge Graph

In the last few years, the solution to Knowledge Graph (KG) completion via learning embeddings of entities and relations has attracted a surge of interest. Temporal KGs(TKGs) extend traditional Knowledge Graphs (KGs) by associating static triples with timestamps forming quadruples. Different from KGs and TKGs in the transductive setting, constantly emerging entities and relations in incomplete TKGs create demand to predict missing facts with unseen components, which is the extrapolation setting. Traditional temporal knowledge graph embedding (TKGE) methods are limited in the extrapolation setting since they are trained within a fixed set of components. In this paper, we propose a Meta-Learning based Temporal Knowledge Graph Extrapolation (MTKGE) model, which is trained on link prediction tasks sampled from the existing TKGs and tested in the emerging TKGs with unseen entities and relations. Specifically, we meta-train a GNN framework that captures relative position patterns and temporal sequence patterns between relations. The learned embeddings of patterns can be transferred to embed unseen components. Experimental results on two different TKG extrapolation datasets show that MTKGE consistently outperforms both the existing state-of-the-art models for knowledge graph extrapolation and specifically adapted KGE and TKGE baselines.
CAM: A Large Language Model-based Creative Analogy Mining Framework

Analogies inspire creative solutions to problems, and facilitate the creative expression of ideas and the explanation of complex concepts. They have widespread applications in scientific innovation, creative writing, and education. The ability to discover creative analogies that are not explicitly mentioned but can be inferred from the web is highly desirable to power all such applications dynamically and augment human creativity. Recently, Large Pre-trained Language Models (PLMs), trained on massive Web data, have shown great promise in generating mostly known analogies that are explicitly mentioned on the Web. However, it is unclear how they could be leveraged for mining creative analogies not explicitly mentioned on the Web. We address this challenge and propose Creative Analogy Mining (CAM), a novel framework for mining creative analogies, which consists of the following three main steps: 1) Generate analogies using PLMs with effectively designed prompts, 2) Evaluate their quality using scoring functions, and 3) Refine the low-quality analogies by another round of prompt-based generation. We propose both unsupervised and supervised instantiations of the framework so that it can be used even without any annotated data. Based on human evaluation using Amazon Mechanical Turk, we find that our unsupervised framework can mine 13.7% highly-creative and 56.37% somewhat-creative analogies. Moreover, our supervised scores are generally better than the unsupervised ones and correlate moderately with human evaluators, indicating that they would be even more effective at mining creative analogies. These findings also shed light on the creativity of PLMs 1.
Confident Action Decision via Hierarchical Policy Learning for Conversational Recommendation

Conversational recommender systems (CRS) aim to acquire a user’s dynamic interests for a successful recommendation. By asking about his/her preferences, CRS explore current needs of a user and recommend items of interest. However, previous works may not determine a proper action in a timely manner which leads to the insufficient information gathering and the waste of conversation turns. Since they learn a single decision policy, it is difficult for them to address the general decision problems in CRS. Besides, existing methods do not distinguish whether the past behaviors inferred from the historical interactions are closely related to the user’s current preference. To address these issues, we propose a novel Hierarchical policy learning based Conversational Recommendation framework (HiCR). HiCR formulates the multi-round decision making process as a hierarchical policy learning scheme, which consists of both a high-level policy and a low-level policy. In detail, the high-level policy aims to determine what type of action to take, such as a recommendation or a query, by observing the comprehensive conversation information. According to the decided action type, the low-level policy selects a specific action, such as which attribute to ask or which item to recommend. The hierarchical conversation policy enables CRS to decide an optimal action, resulting in reducing the unnecessary consumption of conversation turns and the continuous failure of recommendations. Furthermore, in order to filter out the unnecessary historical information when enriching the current user preference, we extract and utilize the informative past behaviors that are attentive to the current needs. Empirical experiments on four real-world datasets show the superiority of our approach against the current state-of-the-art methods.
Learning Mixtures of Markov Chains with Quality Guarantees

A large number of modern applications ranging from listening songs online and browsing the Web to using a navigation app on a smartphone generate a plethora of user trails. Clustering such trails into groups with a common sequence pattern can reveal significant structure in human behavior that can lead to improving user experience through better recommendations, and even prevent suicides [14]. One approach to modeling this problem mathematically is as a mixture of Markov chains. Recently, Gupta, Kumar and Vassilvitski [10] introduced an algorithm () based on the singular value decomposition (SVD) that under certain conditions can perfectly recover a mixture of L chains on n states, given only the distribution of trails of length 3 (3-trail).
Hashtag-Guided Low-Resource Tweet Classification

Social media classification tasks (e.g., tweet sentiment analysis, tweet stance detection) are challenging because social media posts are typically short, informal, and ambiguous. Thus, training on tweets is challenging and demands large-scale human-annotated labels, which are time-consuming and costly to obtain. In this paper, we find that providing hashtags to social media tweets can help alleviate this issue because hashtags can enrich short and ambiguous tweets in terms of various information, such as topic, sentiment, and stance. This motivates us to propose a novel Hashtag-guided Tweet Classification model (HashTation), which automatically generates meaningful hashtags for the input tweet to provide useful auxiliary signals for tweet classification. To generate high-quality and insightful hashtags, our hashtag generation model retrieves and encodes the post-level and entity-level information across the whole corpus. Experiments show that HashTation achieves significant improvements on seven low-resource tweet classification tasks, in which only a limited amount of training data is provided, showing that automatically enriching tweets with model-generated hashtags could significantly reduce the demand for large-scale human-labeled data. Further analysis demonstrates that HashTation is able to generate high-quality hashtags that are consistent with the tweets and their labels. The code is available at https://github.com/shizhediao/HashTation.
The Chameleon on the Web: an Empirical Study of the Insidious Proactive Web Defacements

Web defacement is one of the major promotional channels for online underground economies. It regularly compromises benign websites and injects fraudulent content to promote illicit goods and services. It inflicts significant harm to websites’ reputations and revenues and may lead to legal ramifications. In this paper, we uncover proactive web defacements, where the involved web pages (i.e., landing pages) proactively deface themselves within browsers using JavaScript (i.e., control scripts). Proactive web defacements have not yet received attention from research communities, anti-hacking organizations, or law-enforcement officials. To detect proactive web defacements, we designed a practical tool, PACTOR. It runs in the browser and intercepts JavaScript API calls that manipulate web page content. It takes snapshots of the rendered HTML source code immediately before and after the intercepted API calls and detects proactive web defacements by visually comparing every two consecutive snapshots. Our two-month empirical study, using PACTOR, on 2,454 incidents of proactive web defacements shows that they can evade existing URL safety-checking tools and effectively promote the ranking of their landing pages using legitimate content/keywords. We also investigated the vendor network of proactive web defacements and reported all the involved domains to law-enforcement officials and URL-safety checking tools.
Catch: Collaborative Feature Set Search for Automated Feature Engineering

Feature engineering often plays a crucial role in building mining systems for tabular data, which traditionally requires experienced human experts to perform. Thanks to the rapid advances in reinforcement learning, it has offered an automated alternative, i.e. automated feature engineering (AutoFE). In this work, through scrutiny of the prior AutoFE methods, we characterize several research challenges that remained in this regime, concerning system-wide efficiency, efficacy, and practicality toward production. We then propose Catch, a full-fledged new AutoFE framework that comprehensively addresses the aforementioned challenges. The core to Catch composes a hierarchical-policy reinforcement learning scheme that manifests a collaborative feature engineering exploration and exploitation grounded on the granularity of the whole feature set. At a higher level of the hierarchy, a decision-making module controls the post-processing of the attained feature engineering transformation. We extensively experiment with Catch on 26 academic standardized tabular datasets and 9 industrialized real-world datasets. Measured by numerous metrics and analyses, Catch establishes a new state-of-the-art, from perspectives performance, latency as well as its practicality towards production. Source code1 can be found at https://github.com/1171000709/Catch.
Some Observations on the FGH Theorem

Abstract We investigate the Friedman–Goldfarb–Harrington theorem from two perspectives. Firstly, in the frameworks of classical and modal propositional logics, we study the forms of sentences whose existence is guaranteed by the FGH theorem. Secondly, we prove some variations of the FGH theorem with respect to Rosser provability predicates.
FPGA Implementation of Gauss Gradient Edge Detectors for Medical Images

Boundary recognition plays a vital role in real-world scenarios like medical imaging and surveillance. The classical edge detector fails to preserve minute details in the image. The abdominal CT images were first analyzed using two edge detection techniques based on gauss gradient are proposed here for real-time data processing. The performance of the edge detectors is validated by performance metrics and verified for benchmark dataset images. The results reveal that the gauss gradient edge detector was efficient for the boundary extraction in benchmark and medical images. The VLSI implementation of the proposed gauss gradient edge detectors was done using the Kintex 7-FPGA board, hardware implementation also generates efficient results with reduced execution time.
A Robust Continuous Authentication System Using Smartphone Sensors and Wasserstein Generative Adversarial Networks

Since the continuous authentication (CA) system based on smartphone sensors has been facing the challenge of the low-data regime under some practical scenarios, which leads to low accuracy of CA, it needs to be solved urgently. To this end, currently, the generative adversarial networks (GAN) provide a powerful method to train the result generative model that could generate very convincing verisimilar data. The framework of the GAN and its variants shed much light on improving the performance of CA. Therefore, in this article, we propose a continuous authentication system on smartphones based on a Wasserstein generative adversarial network (WGAN) for sensor data augmentation, which utilizes accelerometers, gyroscopes, and magnetometers of smartphone sensors to sense phone movements caused by user operation behavior. Specifically, based on sensor data under different user activities, the WGAN is used to create additional data in training data for data augmentation. With the augmented data, we design a convolutional neural network to learn and extract deep features from sensor data, and then use four classifiers of RF, OCSVM, DT, and KNN to train these features. Finally, we train and test on the HMOG dataset, and the results show that the EER of the authentication system is between 3.68% and 6.39% on the sensor data with a time window of 2 s.
Formation and Development of the Phenomenon of Pedagogical Expertise in Domestic Education of the XX – early XXI century

The need of modern education for a systematic analysis and comprehensive assessment of the professional activities carried out by teachers actualizes the problem of finding and substantiating methods and means of pedagogical expertise. A purposeful historical and genetic study of the processes of formation and development of the phenomenon of pedagogical expertise in Russian education allowed us to determine its socio-historical prerequisites, identify key priorities, as well as identify and comprehend the main features and specific features of the examination of the work of Russian teachers during the XX – first quarter of the XXI century. The genesis of the functional purpose of the examination of pedagogical work is revealed, the priority of the humanitarian function of expert evaluation is justified, the experience of modern study and use of pedagogical expertise in the domestic system of education and upbringing is summarized. The trends of further study and application of expertise in the conditions of Russian education are outlined.