Reviewerly/exHarmony huggingface dataset:
Identity Preservability and Detectability of IDInvert GAN Model

GAN inversion is a type of generative adversarial networks (GAN) models that can regenerate realistic images from real face photos and further perform image manipulation. While GAN inversion models can be useful for many purposes, it can be abused to generate harmfully fake contents also. This paper evaluates the performance of the recent In-domain GAN inversion model (IDInvert) regarding identity preservability and detectability of its generated face images. The experiments are conducted to answer the two main questions; how well IDInvert can imitate real face photos and how well existing image classification techniques can distinguish its generated images from the real ones. The results show that generated images do not preserve personal identity and thus significantly loss similarity to their reference photos. In addition, common machine learning classifiers can modestly distinguish these generated images from real photos with 0.87 accuracy. This indicates that the recent IDInvert model’s ability to imitate real faces is not yet perfect and hazardous, and its generated images are still simply detected.
High-Throughput Vector Similarity Search in Knowledge Graphs

There is an increasing adoption of machine learning for encoding data into vectors to serve online recommendation and search use cases. As a result, recent data management systems propose augmenting query processing with online vector similarity search. In this work, we explore vector similarity search in the context of Knowledge Graphs (KGs). Motivated by the tasks of finding related KG queries and entities for past KG query workloads, we focus on hybrid vector similarity search (hybrid queries for short) where part of the query corresponds to vector similarity search and part of the query corresponds to predicates over relational attributes associated with the underlying data vectors. For example, given past KG queries for a song entity, we want to construct new queries for new song entities whose vector representations are close to the vector representation of the entity in the past KG query. But entities in a KG also have non-vector attributes such as a song associated with an artist, a genre, and a release date. Therefore, suggested entities must also satisfy query predicates over non-vector attributes beyond a vector-based similarity predicate. While these tasks are central to KGs, our contributions are generally applicable to hybrid queries. In contrast to prior works that optimize online queries, we focus on enabling efficient batch processing of past hybrid query workloads. We present our system, HQI, for high-throughput batch processing of hybrid queries. We introduce a workload-aware vector data partitioning scheme to tailor the vector index layout to the given workload and describe a multi-query optimization technique to reduce the overhead of vector similarity computations. We evaluate our methods on industrial workloads and demonstrate that HQI yields a 31× improvement in throughput for finding related KG queries compared to existing hybrid query processing approaches.
ML2DAC: Meta-Learning to Democratize AutoML for Clustering Analysis

Analysts often struggle with the combined algorithm selection and hyperparameter optimization problem, a.k.a. CASH problem in literature. Typically, they execute several algorithms with varying hyperparameter settings to find configurations that show valuable results. Efficiently finding these configurations is a major challenge. In clustering analyses, analysts face the additional challenge to select a cluster validity index that allows them to evaluate clustering results in a purely unsupervised fashion. Many different cluster validity indices exist and each one has its benefits depending on the dataset characteristics. While experienced analysts might address these challenges using their domain knowledge and experience, especially novice analysts struggle with them. In this paper, we propose a new meta-learning approach to address these challenges. Our approach uses knowledge from past clustering evaluations to apply strategies that experienced analysts would exploit. In particular, we use meta-learning to (a) select a suitable clustering validity index, (b) efficiently select well-performing clustering algorithm and hyperparameter configurations, and (c) reduce the search space to suitable clustering algorithms. In the evaluation, we show that our approach significantly outperforms state-of-the-art approaches regarding accuracy and runtime.
FEC: Efficient Deep Recommendation Model Training with Flexible Embedding Communication

Embedding-based deep recommendation models (EDRMs), which contain small dense models and large embedding tables, are widely used in industry. Embedding communication constitutes the main cost for the distributed training of EDRMs, and thus we propose two strategies to improve its efficiency, i.e.,embedding tiering andpre-fetching. In particular, embedding tiering uses AllReduce to communicate popular embeddings that are accessed frequently. This is counter-intuitive as embeddings belong to the sparse embedding tables, but reasonable because the access pattern of popular embeddings resembles dense models. Pre-fetching starts communication early for embeddings that receive no updates such that they are removed from the critical path of training. We implement embedding tiering and pre-fetching in a system called FEC and compare it with the state-of-the-art systems on real datasets. The results show that FEC consistently outperforms the existing methods on all datasets, and its speed can be up to 6.65x and 2.42x in terms of embedding communication time and training throughput compared with the best performing baseline.
DVFaaS: Leveraging DVFS for FaaS Workflows

In this letter, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DVFaaS</i> , a per-core DVFS framework that utilizes control systems theory to assign <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">just-enough</i> frequency for the purpose of addressing the QoS requirements on serverless workflows comprising unseen functions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DVFaaS</i> exploits the intermittent nature of serverless workflows, which enables staged control on distinguishable functions, which jointly contribute to the end-to-end latency. Our results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DVFaaS</i> considerably outperforms related work, reducing power consumption by up to 22%, with 2x fewer QoS violations.
QaaD (Query-as-a-Data): Scalable Execution of Massive Number of Small Queries in Spark

Spark big data processing platform is heavily used in today's IT services for various critical applications such as machine learning tasks for service recommendations or massive volumes of raw sales data analysis. Spark is designed to deliver high performance by enabling a high degree of parallelism while processing various heavy-weight queries that require homogeneous operations on large data. However, it has been observed that workloads made of small and short-running queries coming from various sources are becoming dominant in practice. Unfortunately, the current Spark architecture is unfit to process workloads made of a large number of small queries optimally due to excessive I/Os with small computations. We present a technique, called QaaD, that addresses this problem fundamentally by applying i) transparent conversion of workloads made of small queries into one with large queries and ii) dynamic partition size adjustment for runtime overhead minimization. For this, we introduce a new abstraction, microRDD, to support our design of query merging, the embedding of queries as part of data, and an opportunistic sharing of common input data among queries. Comprehensive evaluation using real-world data shows that QaaD is able to deliver 10.6x to 36.6x speed-up against standard Spark executions for small query workloads.
Maestro: Automatic Generation of Comprehensive Benchmarks for Question Answering Over Knowledge Graphs

Recently, there has been an upsurge in the number of knowledge graphs (KG) that can only be accessed by experts. Non-expert users lack an adequate understanding of the queried knowledge graph's vocabulary and structure, as well as the syntax of the structured query language used to express the user's information needs. To increase the user base of these KGs, a set of Question Answering (QA) systems that use natural language to query these knowledge graphs have been introduced. However, finding a benchmark that accurately evaluates the quality of a QA system is a difficult task due to (1) the high degree of variation in the fine-grained properties among the existing benchmarks, (2) the static nature of the existing benchmarks versus the evolving nature of KGs, and (3) the limited number of KGs targeted by existing benchmarks, which hinders the usability of QA systems in real-world deployment over KGs that are different from those that were used in the evaluation of the QA systems. In this paper, we introduce Maestro, a benchmark generation system for question answering over knowledge graphs. Maestro can generate a new benchmark for any KG given the KG and, optionally, a text corpus that covers this KG. The benchmark generated by Maestro is guaranteed to cover all the properties of the natural language questions and queries that were encountered in the literature as long as the targeted KG includes these properties. Maestro also generates high-quality natural language questions with various utterances that are on par with manually-generated ones to better evaluate QA systems.
Theories and Principles Matter: Towards Visually Appealing and Effective Abstraction of Property Graph Queries

Existing visual abstraction of a property graph query by representing it as a labeled atomic graph (LAG) has great potential to democratize the usage of property graph databases as it enables user-friendly visual query formulation without demanding the need to learn a property graph query language e.g., Cypher. Unfortunately, existing LAG-based query interfaces do not embrace HCI principles and psychology theories to inform their design and as a result may have adverse impact on their usability and aesthetics. In this paper, we depart from the classical theory- and principles-oblivious LAG abstraction to present a novel theory-informed visual abstraction called labeled composite graph (LCG) to address this limitation. It realizes a novel and extensible visual shape definition language called VEDA to create and maintain an LCG systematically, guided by a variety of theories and principles from HCI, visualization and psychology. We build a novel LCG-based visual property graph query interface for Cypher called SIERRA and demonstrate through a user study its superiority to an industrial-strength LAG-based query interface for property graphs w.r.t. usability, aesthetics and efficient query formulation.
Shortest Paths Discovery in Uncertain Networks via Transfer Learning

Due to various reasons such as noisy measurement and privacy preservation, a network/graph is often uncertain such that each edge in the network has a probability of existence. In this paper, we study finding the most probable shortest path which has the highest probability of being the shortest path between a given pair of nodes in an uncertain network. Despite significant progress being made, this problem still suffers from the efficiency and scalability issue. To solve this problem, the state-of-the-art adopts a two-phase approach where Phase 1 generates some candidate paths and Phase 2 estimates their probabilities of being the shortest path and returns the one with the highest probability as the solution. Notably, Phase 2 requires a large number of simulations over all edges in the network and can easily dominate the cost of the whole process. In this paper, we aim to resolve the efficiency and scalability issue by optimizing Phase 2. Specifically, we first propose a non-learning based fast approximation technique which significantly reduces the number of samples for the probability estimation in each simulation. Afterwards, we further propose a learning-based method which can directly estimate the probability of each candidate path without costly simulations. Extensive experiments show that (1) compared to the state-of-the-art, our fast approximation technique and learning-based method can achieve up to 5x and 210x speedups in Phase 2 respectively while maintaining highly competitive or even equivalent results, (2) the training process is highly scalable and (3) the prediction function can work effectively under the problem settings different from the one it was trained.
Exploring the Virtual Space in the Metaverse: A Case Study Analysis

Metaverse platforms, such as Second Life, VR Chat, and Sansar, have gained significant popularity as immersive, customizable virtual environments. This study aimed to analyze and compare these platforms based on immersion, customization, and user experience, utilizing a mixed-methods approach, including personal experience, observation, data collection from journals, research proceedings, and available documentation. The findings reveal that each platform has unique strengths and weaknesses, including performance and stability issues, limited customization options, complex interfaces, cost and accessibility, realism, and community and social features. The study proposes several potential future improvements, including enhancing realism and immersion, improving customization options, enhancing social and community aspects, examining potential uses and applications, and investigating ethical and societal implications. In conclusion, this study provides valuable insights for designers, developers, and researchers seeking to enhance user experience in virtual environments. The proposed future improvements have the potential to enhance the functionality and design of Metaverse platforms, leading to a better user experience. These findings can contribute to the development of high-quality virtual environments, with implications for a wide range of applications, including education, entertainment, and social interaction.
Fall Detection Approach Using Variational Autoencoders with Self-Attention Features

In this paper, we propose an alternative method for fall detection using variational autoencoders (VAEs) with an attention mechanism on an existing dataset. The dataset consists of 6 different fall cases from 21 people. For effective fall detection, we introduce the use of the magnitude of the acceleration vector (MAV) of wearable gyroscope data and apply fast-Fourier transform (FFT) to create new features. These FFT features are then passed through attention modules with self-combination to form attention features. Our experimental results show that the VAE with self-attention features achieved an average accuracy of 90.7% and an F1 score of 93.8% in fall detection, demonstrating the effectiveness of the proposed method in utilizing gyroscope sensors for fall detection in the context of threshold criteria.
Practical Differentially Private and Byzantine-resilient Federated Learning

Privacy and Byzantine resilience are two indispensable requirements for a federated learning (FL) system. Although there have been extensive studies on privacy and Byzantine security in their own track, solutions that consider both remain sparse. This is due to difficulties in reconciling privacy-preserving and Byzantine-resilient algorithms. In this work, we propose a solution to such a two-fold issue. We use our version of differentially private stochastic gradient descent (DP-SGD) algorithm to preserve privacy and then apply our Byzantine-resilient algorithms. We note that while existing works follow this general approach, an in-depth analysis on the interplay between DP and Byzantine resilience has been ignored, leading to unsatisfactory performance. Specifically, for the random noise introduced by DP, previous works strive to reduce its impact on the Byzantine aggregation. In contrast, we leverage the random noise to construct an aggregation that effectively rejects many existing Byzantine attacks. We provide both theoretical proof and empirical experiments to show our protocol is effective: retaining high accuracy while preserving the DP guarantee and Byzantine resilience. Compared with the previous work, our protocol 1) achieves significantly higher accuracy even in a high privacy regime; 2) works well even when up to 90% of distributive workers are Byzantine.
EARLY: Efficient and Reliable Graph Neural Network for Dynamic Graphs

Graph neural networks have been widely used to learn node representations for many real-world static graphs. In general, they learn node representations by recursively aggregating information from neighbors. However, graphs in many applications are dynamic, evolving with continuous graph events, such as node feature and graph structure updates. These events require the node representations to be updated accordingly. Currently, due to the real-time requirement, how to efficiently and reliably update node representations under continuous graph events is still an open problem. Recent studies propose two solutions to partially address this problem, but their performance is still limited. First, local-based GNNs only update the nodes directly involved in events, suffering from the quality-deficit issue, since they neglect the other nodes affected by these events. Second, neighbor-sampling GNNs propose to sample neighbors to accelerate neighbor aggregation computations, encountering the neighbor-redundant issue. These sampled neighbors may be similar and cannot reflect the distribution of all neighbors, leading that node representations aggregated on these redundant neighbors may differ from those aggregated on all neighbors. In this paper, we propose an efficient and reliable graph neural network, namely EARLY, to update node representations for dynamic graphs. We first identify the top-k influential nodes that are most affected by graph events. Then, to sample neighbors diversely, we propose a diversity-aware layer-wise sampling technique. We theoretically demonstrate that this technique can decrease the sampling expectation error and learn more reliable node representations. Therefore, the top-k nodes selection and diversity-aware sampling enable EARLY to efficiently update node representations in a reliable way. Extensive experiments on the five real-world graphs demonstrate the effectiveness and efficiency of our proposed EARLY.
Vineyard: Optimizing Data Sharing in Data-Intensive Analytics

Modern data analytics and AI jobs become increasingly complex and involve multiple tasks performed on specialized systems. Sharing of intermediate data between different systems is often a significant bottleneck in such jobs. When the intermediate data is large, it is mostly exchanged through files in standard formats (e.g., CSV and ORC), causing high I/O and (de)serialization overheads. To solve these problems, we develop Vineyard, a high-performance, extensible, and cloud-native object store, trying to provide an intuitive experience for users to share data across systems in complex real-life workflows. Since different systems usually work on data structures (e.g., dataframes, graphs, hashmaps) with similar interfaces, and their computation logic is often loosely-coupled with how such interfaces are implemented over specific memory layouts, it enables Vineyard to conduct data sharing efficiently at a high level via memory mapping and method sharing. Vineyard provides an IDL named VCDL to facilitate users to register their own intermediate data types into Vineyard such that objects of the registered types can then be efficiently shared across systems in a polyglot workflow. As a cloud-native system, Vineyard is designed to work closely with Kubernetes, as well as achieve fault-tolerance and high performance in production environments. Evaluations on real-life datasets and data analytics jobs show that the above optimizations of Vineyard can significantly improve the end-to-end performance of data analytics jobs, by reducing their data-sharing time up to 68.4x.
Quantum algorithm for the covariance matrix preparation and its application

Abstract Performing the eigendecomposition of the covariance matrix of the dataset is of great significance in the field of machine learning. However, classical operations will become time-consuming when involving large data sets. In this paper, in order to address this problem, we design an efficient quantum algorithm to prepare the covariance matrix state by means of quantum amplitude estimation. After that, we research on its application in principal component analysis and Mahalanobis distance calculation. Specifically, we obtain the transformation matrix for quantum principal component analysis based on the singular value estimation algorithm and the amplitude amplification algorithm. Furthermore, we invoke the quantum matrix inversion algorithm to calculate the Mahalanobis distance. The final complexity analysis shows that our proposed algorithms can achieve speedup compared to their classical counterparts under certain conditions.
Data Stream Clustering: An In-depth Empirical Study

Data Stream Clustering (DSC) plays an important role in mining continuous and unlabeled data streams in real-world applications. Over the last decades, numerous DSC algorithms have been proposed with promising clustering accuracy and efficiency. Despite the significant differences among existing DSC algorithms, they are commonly built around four key design aspects: summarizing data structure, window model, outlier detection mechanism, and offline refinement strategy. However, there is a lack of empirical studies on these key design aspects in the same codebase using real-world workloads with distinct characteristics. As a result, it is difficult for researchers to improve upon the state-of-the-art. In this paper, we conduct such a study of DSC on its four key design aspects. We implemented state-of-the-art variants of all of these design choices in an open-sourced platform from scratch and evaluated them using both real-world and synthetic workloads. Our analysis identifies the fundamental issues and trade-offs of each design choice in terms of both accuracy and efficiency. We even find that combining flexible design choices led to the development of a new algorithm called Benne, which can be tuned to achieve either better accuracy or better efficiency compared to the state-of-the-art.