Reviewerly/exHarmony huggingface dataset:
PA-FEAT: Fast Feature Selection for Structured Data via Progress-Aware Multi-Task Deep Reinforcement Learning

Feature selection is an effective technique for structured data analytics, aiming to eliminate redundant features and irrelevant features for downstream tasks (e.g., classification). With the deepening of data-driven decision-making applications in various industries, the demand for real-time structured data analysis is constantly increasing. At this time, high requirements are placed on the time cost of feature selection. However, existing feature selection methods may easily fall into the dilemma of efficiency and effectiveness when faced with this situation due to the huge feature space. In this paper, we study a novel fast feature selection scenario, which is to generalize the knowledge of feature selection from historical structured data analytics tasks (seen tasks) and then quickly apply it to the process of feature selection for future structured data analytics tasks (unseen tasks). We propose a novel Progress-Aware multi-task deep reinforcement learning method for Fast fEAture selecTion (PA-FEAT), which makes full use of various progress-related information generated during the knowledge generalization process to achieve efficiency and effectiveness simultaneously. Extensive results on eight real-world datasets show that PA-FEAT consistently outperforms eight baselines in terms of efficiency and effectiveness.
The Little Seal Bug: Optical Sound Recovery from Lightweight Reflective Objects

In recent years, various studies have demonstrated methods to recover sound/speech with an optical sensor. Fortunately, each of these methods possess drawbacks limiting their utility (e.g., limited to recovering sounds at high volumes, utilize a sensor indicating their use, rely on objects not commonly found in offices, require preliminary data collection, etc.). One unaddressed method of recovering speech optically is via observing lightweight reflective objects (e.g., iced coffee can, smartphone stand, desk ornament) with a photodiode, an optical sensor used to convert photons to electricity. In this paper, we present the ‘little seal bug’ attack, an optical side-channel attack which exploits fluctuations in air pressure on the surface of a shiny object occurring in response to sound, to recover speech optically and passively using a photodiode. These air pressure fluctuations cause the shiny object to vibrate and reflect light modulated by the nearby sound; as a result, these objects can be used by eavesdroppers (e.g., private investigator, surveilling spouse) to recover the content of a victim's conversation when the victim is near such objects. We show how to determine the sensitivity specifications of the optical equipment (photodiode, ADC, etc.) needed to recover the minuscule vibrations of lightweight shiny objects caused by the surrounding sound waves. Given the optical measurements obtained from light reflected off shiny objects, we design and utilize an algorithm to isolate the speech contents from the optical measurements. In our evaluation of the ‘little seal bug’ attack, we compare its performance to that of related methods. We find eavesdroppers can exploit various lightweight shiny objects to optically recover the content of conversations at equal/higher quality than prior methods (fair-excellent intelligibility) while doing so from greater distances (up to 35 meters) and lower speech volumes (75 dB). We conclude that lightweight shiny objects are a potent attack vector for recovering speech optically, and can be harmful to victims being targeted for sensitive information conveyed in a spoken conversation (e.g., in cases of corporate espionage or intimate partner violence/surveillance) when seated at a desk near a lightweight reflective object.
Automatically Detecting Variability Bugs Through Hybrid Control and Data Flow Analysis

Subtle bugs that only manifest in certain software configurations are notoriously difficult to correctly trace. Sometimes called Heisenbugs, these runtime variability flaws can result from invoking undefined behavior in languages like C and C++, or from compiler flaws. In this paper, we present a novel analysis technique for detecting and correctly diagnosing variability bugs' impact on a program through comparing control-affecting data flow across differently compiled program variants. Our UBet prototype dynamically derives a runtime control flow trace while tracing universal data flow for a program processing a given input, operating at a level of tracing completeness not achievable through similar dynamic instrumentation means. Sans compiler bugs or undefined behavior, every compile-time program configuration (i.e., compiler flags vary) should be semantically equivalent. Thus, any input for which a program variant produces inconsistent output indicates a variability bug. Our analysis compares control-affecting data flow traces from disagreeing program version runs to identify related input bytes and determine where in the program the processing variability originates. Though we initially demonstrate our technique on C++ variability bugs in Nitro, the American Department of Defense NITF (National Imagery Transmission Format) reference implementation parser, our approach applies equally to other programs and input types beyond NITF parsers. Finally, we sketch a path toward completing this work and refining our analysis, including evaluating parsers of other input formats.
Photonic-accelerated AI for cybersecurity in sustainable 6G networks

The sixth generation (6G) of mobile communications, expected to be deployed around the year 2030, is predicted to be characterized by ubiquitous connected intelligence. With Artificial Intelligence (AI) operations being deployed in every aspect of future network infrastructure, network security will also evolve from current solutions to intelligent architectures. To meet the massive amount of operations computed by AI models, photonic hardware can be exploited, delivering higher processing speed and computing density and lower power consumption with respect to electronic counterparts. In this paper, we propose a photonic-based Convolutional Neural Network (CNN) solution able to work on real-time traffic, capable of identifying Denial of Service (DoS) Hulk attacks with 99.73 mean F1-score when exploiting 4 bits. We also compared photonic accelerators with their electronic counterparts, showing limited F1-score degradation, especially in the 4 and 8 bit scenarios.
GenSql: A Generative Natural Language Interface to Database Systems

To make databases more accessible to a much broader audience of non-technical users, many applications, such as chatbots and search engines, have developed a natural language (NL) interface for the underlying databases (NLIDB). With the advances in machine learning techniques, most recent research employs language translation models to build NLIDB systems. In this demonstration, we introduce GenSql, a generative NLIDB system that enables users to query databases using NL. Unlike most existing NLIDB systems that attempt to leverage a generalized language translation model to convert NL to SQL queries (NL2SQL) for any database, GenSql utilizes a set of sample queries to capture the specific structure and semantics of a given database and thus to provide more accurate translation results. The underlying NL2SQL model in GenSql is a novel generate-and-rank model named Gar designed by the authors, which first generates a set of generalized SQL queries with corresponding NL expressions from the given sample queries, and ranks the NL expressions to get the best matching one, and hence the SQL query. This demonstration shows the effectiveness of GenSql, especially in answering complex queries, which proves its utility in practice.
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning

Interactive data exploration (IDE) is an effective way of comprehending big data, whose volume and complexity are beyond human abilities. The main goal of IDE is to discover user interest regions from a database through multi-rounds of user labelling. Existing IDEs adopt active-learning framework, where users iteratively discriminate or label the interestingness of selected tuples. The process of data exploration can be viewed as the process of training of a classifier, which determines whether a database tuple is interesting to a user. An efficient exploration thus takes very few iterations of user labelling to reach the data region of interest. In this work, we consider the data exploration as the process of few-shot learning, where the classifier is learned with only a few training examples, or exploration iterations. To this end, we propose a learning-to-explore framework, based on meta-learning, which learns how to learn a classifier with automatically generated meta-tasks, so that the exploration process can be much shortened. Extensive experiments on real datasets show that our proposal outperforms existing explore-by-example solutions in terms of accuracy and efficiency.
Fed-SC: One-Shot Federated Subspace Clustering over High-Dimensional Data

Recent work has explored federated clustering and developed an efficient k-means based method. However, it is well known that k-means clustering underperforms in high-dimensional space due to the so-called "curse of dimensionality". In addition, high-dimensional data (e.g., generated from healthcare, medical, and biological sectors) are pervasive in the big data era, which poses critical challenges to federated clustering in terms of, but not limited to, clustering effectiveness and communication efficiency. To fill this significant gap in federated clustering, we propose a one-shot federated subspace clustering scheme Fed-SC that can achieve remarkable clustering effectiveness on high-dimensional data while keeping communication cost low using only one round of communication for each local device. We further establish theoretical guarantees on the clustering effectiveness of one-shot Fed-SC and exploit the benefits of statistical heterogeneity across distributed data. Extensive experiments on synthetic and real-world datasets demonstrate significant effectiveness gains of Fed-SC compared with both subspace clustering and one-shot federated clustering methods.
Hierarchical Interest Modeling of Long-tailed Users for Click-Through Rate Prediction

Click-through rate (CTR) prediction, whose purpose is to predict the probability of a user clicking on an item, plays a pivotal role in recommender systems. Capturing users’ accurate preferences from their historical interactions (e.g., clicks) is an essential step for handling this task and has aroused wide concern in both academia and industry. However, most of the previous methods focus on the users with abundant clicks and ill-serve the users who rarely click or purchase items. Though the ratio of these long-tailed users may be small on popular platforms, such as Amazon and Taobao, they are the majority on the newborn e-commerce company like Lazada. To extract the interests of long-tailed users, several works attempt to integrate the side information, such as demographic features. Nevertheless, these features are usually not available and may even lead to privacy concerns. Therefore, how to utilize the noisy and limited clicks becomes the key challenge.In this paper, we propose a novel model called Hierarchical Interest Modeling (HIM). It hierarchically utilizes long-tailed users’ limited behaviors and captures their preferences from both personalized and group-wise perspectives. HIM consists of two main components, including User Behavior Pyramid (UBP) and User Behavior Clustering (UBC). The UBP module utilizes additional negative feedback to reduce the noises in positive feedback, thus obtaining reliable user personalized representations. Then, the UBC module automatically discovers latent user groups with self-supervised reconstruction loss and learns another interest representation for each user in a group-wise aspect. Extensive experiments on both public and industrial datasets verify the superiority of HIM compared with the state-of-the-art baselines. Moreover, HIM has already been deployed on Lazada recommendation scenario and gains 3.38% on CTR prediction on average on the online A/B test. Our codes are available in https://github.com/xiaojin-nj/HIM.
Divergent Representations: When Compiler Optimizations Enable Exploitation

Compiler optimizations can introduce unexpected security weaknesses in programs. In this paper, we introduce a newly discovered form of optimization-introduced security weakness that can benefit attackers, called divergent representations. We show that when divergent representations appear near vulnerabilities, they can enable attackers to create more powerful exploits. We provide a case study of a publicly disclosed SQLite CVE that becomes exploitable because of a divergent representation. We show that divergent representations are prevalent in software by searching for code patterns that may produce divergent representations, and found candidate patterns in 44 % of scanned repositories.
Scripted Henchmen: Leveraging XS-Leaks for Cross-Site Vulnerability Detection

The key security principle that browsers adhere to, such as the same-origin policy and site isolation, ensure that when visiting a potentially untrusted website, the web page is loaded in an isolated environment. These security measures aim to prevent a malicious site from extracting information about cross-origin resources. However, in recent years, several techniques have been discovered that leak potentially sensitive information from responses sent by other sites. In this paper, we show that these XS-Leaks can be used to force an unwitting visitor to detect prevalent web vulnerabilities in other websites during a visit to a malicious web page. This lets an adversary leverage the computing and network resources of visitors and send malicious requests from a large variety of trustworthy IP addresses originating from residential networks. Finally, we find that currently deployed security measures are inadequate to thwart the realistic threat of cross-origin vulnerability detection.
Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM

Feature learning is a crucial phase machine learning [1] – [3] . How to obtain appropriate features distribution without any background is still a hard problem in machine learning. In this paper, we present a novel unsupervised feature learning architecture (see Fig. 1 ), which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering integration RBM (MIRBM). In the multi-clustering integration module, we choose three clusterers to obtain three different global clustering partitions (CPs). Then, an unanimous voting strategy is used to generate the local clustering partition (LCP) of visible layer data. Hence, the LCP only has partial visible layer data. The novel MIRBM model is a core feature encoding part of the proposed unsupervised feature learning architecture. The novelty of it is that the LCP as an unsupervised guidance is integrated into the CD 1 learning to guide the distribution of the hidden layer features. For the instance in the same LCP cluster, the hidden and reconstructed hidden layer features of the MIRBM model in the proposed architecture tend to constrict together in the training process. Meanwhile, each LCP center tends to disperse from each other as much as possible in the hidden and reconstructed hidden layer during training. This work has three main contributions: 1) A novel unsupervised feature learning architecture is proposed, which consists of a multi-clustering integration module and an MIRBM model. 2) In the multi-clustering integration module of the proposed architecture, three unsupervised algorithms are employed to obtain three different global CPs without any background knowledge or label. 3) The MIRBM model in the proposed architecture uses the LCP as an unsupervised guidance to guide the distribution of the hidden layer features by integrating the LCP into the CD 1 learning.
A Holistic Approach for Answering Logical Queries on Knowledge Graphs

Logical queries on Knowledge Graphs (KGs) is a fundamental sub-task of knowledge graph reasoning. A promising paradigm for answering logical queries, recently, has been proposed based on versatile deep learning techniques. In this line, the query is first broken down into a series of first-order logical predicates, and then both the query and knowledge graph entities are jointly encoded in the same embedding space. Some approaches are able to support the full range of traditional First-Order Logic (FOL) operations for complex queries in real-world scenarios, while others have attempted to create a new combination of FOL operations by replacing the negation operation with the difference operation due to the poor performance of the negation operation. Our empirical observations show that the difference operator is more effective for multi-hop reasoning, while the negation operator is better suited for use as the final operation in the query, particularly in single-hop settings. In addition, other fundamental limitations such as linear transformation assumption for negation operator and the fixed-lossy problem for difference operator further degrade the performance of these methods. In light of these, we propose the HaLk, a holistic approach for answering logical queries that, to our knowledge, is the first to support a full set of logical operators in a unified end-to-end framework. In this approach, we propose specific neural models for each operator by considering their own intrinsic properties, based on which HaLk effectively mitigates the cascading error of projection and negation operators as well as delicately provides closed-formed solutions for difference operator. Extensive experimental results on three datasets demonstrate that HaLk outperforms all competitors and achieves up to 32% improvement in accuracy.
Reachability Queries with Label and Substructure Constraints on Knowledge Graphs (Extended abstract)

Since knowledge graphs (KGs) describe and model the relationships between entities and concepts in the real world, reasoning on KGs often corresponds to the r eachability queries with l abel and s ubstructure c onstraints (LSCR queries). Specifically, for a search path p , LSCR queries not only require that the labels of the edges passed by p are in a label set, but also claim that a vertex in p could satisfy a substructure constraint.
Efficient and Effective Multi-Modal Queries through Heterogeneous Network Embedding (Extended Abstract)

Recent information retrieval (IR) systems answer a multi-modal query by considering it as a set of separate uni-modal queries. However, depending on the chosen operationalisation, such an approach is inefficient or ineffective. It either requires multiple passes over the data or leads to inaccuracies since the relations between data modalities are neglected in the relevance assessment. To mitigate these challenges, we present an IR system that has been designed to answer genuine multi-modal queries. It relies on a heterogeneous network embedding, so that features from diverse modalities can be incorporated when representing both, a query and the data over which it shall be evaluated. An experimental evaluation using diverse real-world and synthetic datasets illustrates that our approach returns twice the amount of relevant information compared to baseline techniques, while scaling to large multi-modal databases.
Plug-and-play Text-based Emotion Recognition for Chatbots as Virtual Companions for Older People

Emotional support chatbots are becoming increasingly important as they can understand and potentially support people’s emotional health. This study focuses on developing text-based, lightweight plug-and-play models for emotion recognition using machine learning (ML) and natural language processing (NLP), which are an essential component of emotional support chatbots. Our experiments show that the DistilBERT model is able to detect emotions in conversations faster and with comparable accuracy to other state-of-the-art methods. Our implementation of a Streamlit APP visualizes the emotion recognition results for an input utterance. This facilitates the application of the language model in a real chatbot as a virtual companion for active assisted living (AAL) for older people.
FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge

Deep Neural Networks (DNNs) have been ubiquitously adopted in internet of things and are becoming an integral of our daily life. When tackling the evolving learning tasks in real world, such as classifying different types of objects, DNNs face the challenge to continually retrain themselves according to the tasks on different edge devices. Federated continual learning is a promising technique that offers partial solutions but yet to overcome the following difficulties: the significant accuracy loss due to the limited on-device processing, the negative knowledge transfer caused by the limited communication of non-IID data, and the limited scalability on the tasks and edge devices. In this paper, we propose FedKNOW, an accurate and scalable federated continual learning framework, via a novel concept of signature task knowledge. FedKNOW is a client side solution that continuously extracts and integrates the knowledge of signature tasks which are highly influenced by the current task. Each client of FedKNOW is composed of a knowledge extractor, a gradient restorer and, most importantly, a gradient integrator. Upon training for a new task, the gradient integrator ensures the prevention of catastrophic forgetting and mitigation of negative knowledge transfer by effectively combining signature tasks identified from the past local tasks and other clients’ current tasks through the global model. We implement FedKNOW in PyTorch and extensively evaluate it against state-of-the-art techniques using popular federated continual learning benchmarks. Extensive evaluation results on heterogeneous edge devices show that FedKNOW improves model accuracy by 63.24% without increasing model training time, reduces communication cost by 34.28%, and achieves more improvements under difficult scenarios such as large numbers of tasks or clients, and training different complex networks.
Wind-Bell Index: Towards Ultra-Fast Edge Query for Graph Databases

Graphs are good at presenting relational and structural information, making it powerful in the representation of various data. For the efficient storage and processing of graph-like data, graph databases have been rapidly developed and extensively studied. However, graph databases mostly use adjacency lists as their basic data structure (e.g., Neo4j), which could result in poor performance of edge due to the skewed degree distribution of graphs.We design the Wind-Bell Index to address this problem. Wind-Bell Index is a memory-efficient index data structure, which can be attached to existing graph databases to speed up the edge. We have fully implemented our data structure in Neo4j, the most popular graph database today, and conduct theoretical and experimental analysis to evaluate the performance. Theoretical results prove the high query efficiency of our algorithm. And experimental results show that the average edge query speed is increased by hundreds of times compared with the original query interface of Neo4j. We believe that the excellent performance and scalability of Wind-Bell Index make it suitable for the application in a variety of graph databases.