Reviewerly/exHarmony huggingface dataset:
MAD-SGCN: Multivariate Anomaly Detection with Self-learning Graph Convolutional Networks

Today's Cyber Physical Systems (CPSs) are large and complex data-intensive systems. Constant monitoring and analysis of the data generated by a multitude of interconnected sensors and actuators are required in order to detect anomalies due to possible intrusions or faults with high accuracy and timeliness. Recently, unsupervised anomaly detection techniques based on deep learning for multivariate time series have been proposed for detecting CPSs attacks with promising performance. However, the current methods are either limited by their representation learning methods in encoding the temporal and spatial information simultaneously and effectively, or cannot easily scale to other tasks without having explicit knowledge of the internal relationships between the different variables or sensors, which are both important for characterising CPSs data. In this paper, we propose a novel unsupervised anomaly detection method for multivariate time series MAD-SGCN which effectively captures the temporal and spatial correlations of the input sequences simultaneously using Long Short-Term Memory networks (LSTMs) and spectral-based Graph Convolutional Networks (GCNs). We design a self-supervised graph structure learning mechanism to minimize the usage of the prior knowledge about the network structures of the CPSs. Experiments on four CPS datasets demonstrate the superiority of the proposed method.
Cheaper Is Better: Exploring Price Competitiveness for Online Purchase Prediction

Price, a crucial factor determining whether a user will purchase an item, has attracted considerable attention in personalized ranking and recommendation. Existing studies commonly assume that only item price affects user online purchase decisions. However, in reality, users not only focus on the price of an item itself but also compare the price with the item's “comparison prices,” including its past prices, prices of similar items, and prices on other e-commerce platforms. Without carefully considering these comparison prices, methods fail to capture the purchase motivation attributable to prices comprehensively. To address this problem, in this paper, we introduce the concept of item price competitiveness. An item's price competitiveness measures the advantage of the item's price over its comparison prices. Then, a novel Price Competitiveness-aware Network (PCNet) is proposed to predict users' purchase behaviors by explicitly considering the price competitiveness of items. Specifically, PCNet consists of three key modules, and each module exploits one corresponding facet of price competitiveness. We leverage prior knowledge discovered from a real-world dataset to guide module designs, thus enhancing the performance and interpretability of the PCNet. Offline experiments show the superiority of the PCNet and verify the effectiveness of each module. Moreover, PCNet has been deployed online in a hotel search engine at Fliggy and benefits both the platform and users.
Towards Backdoor Attack on Deep Learning based Time Series Classification

As a fundamental task in modern data mining, time series classification is powering mission-critical tasks including stock price prediction and network traffic analysis. Due to the non-linear structure of deep neural networks (DNN), deep learning has established as a promising solution to time series classification. However, the excessive learning capacity of DNNs may make them prone to threats of backdoor attacks, where an attacker embeds hidden functionalities (i.e., backdoor) to DNNs and activates the backdoor by specially-designed inputs (i.e., triggers). Despite extensive studies concerning backdoor attacks on image and text domains, there is little known about the vulnerability of DNN based time series classifiers against backdoor attacks. Due to the unique characteristics of time series data, most existing backdoor attack techniques fail to threaten time series classifiers. In this paper, through analyzing the key factors which influence the effectiveness of a backdoor, we systematize a list of practical principles for designing triggers on time series data. In this light, we propose a novel framework called TimeTrojan, which aims to learn to form the trigger pattern through a constrained multi-objective optimization. To solve the hereafter challenging optimization issue, we further design an iterative learning algorithm. Remarkably, the proposed framework is agnostic to a wide range of DNN classifiers. Extensive empirical results on 6 representative DNN classifiers and 6 real-world datasets validate the effectiveness of the proposed attack framework. In most cases, TimeTrojan successfully injects backdoors with 100% attack success rate without affecting the model accuracy on clean samples, which implies the complete control of the behavior of the DNN classifiers by the adversary.
LAN: Learning-based Approximate k-Nearest Neighbor Search in Graph Databases

The problem of k-nearest neighbor (k-NN) search is fundamental in graph databases, which has numerous real-world applications, such as bioinformatics, computer vision, and software engineering. Graph edit distance (GED) and maximum common subgraph (MCS)-based distance are the most widely used distance measures in k-NN search. However, computing the exact k-NNs of a query graph <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> using these measures is prohibitively time-consuming, as a large number of graph distance computations is needed, and computing GED and MCS are both NP-hard. In this paper, we study the approximate k-nearest neighbor (k-ANN) search with the aim of trading efficiency with a slight decrease in accuracy. Greedy routing on the proximity graph (PG) index is a state-of-the-art method for k-ANN search. However, such routing algorithms are not designed for graph databases, and simple adoption is inefficient. The core reason is that the exhaustive neighbor exploration at each routing step incurs a large number of distance computations (NDC). In this paper, we propose a learning-based k-ANN search method to reduce NDC. First, we propose to prune unpromising neighbors from distance computations. We use a graph learning model to rank the neighbors at each routing step and explore only the top neighbors. For the accuracy of rank prediction, we propose a neighbor ranking model that works only in the neighborhood of Q. Second, we propose a learning-based method to select the initial node for the routing. The initial node selected has a high probability of being in the neighborhood of Q, such that the neighbor ranking model can be used. Third, we propose a compressed GNN-graph to accelerate the neighbor ranking model and the initial node selection model. We prove that learning efficiency is improved without degrading the accuracy. Our extensive experiments show that our method is about 3.6x to 18.6x faster than the state-of-the-art methods on real-world datasets.
PRISM: Prefix-Sum based Range Queries Processing Method under Local Differential Privacy

Range query over data cubes is a powerful tool for online analytical processing (OLAP). In this paper, we focus on answering range queries while satisfying local differential privacy (LDP). The key technical challenges come from the problem of noise aggregation and the curse of high dimensionality: multiple LDP noise will be aggregated when answering range queries and collecting high-dimensional data under LDP will further degrade the utility of the results. To this end, we present a novel method called Prefix-Sum based Range QuerIes ProceSsing Method (PRISM). Its main idea is to selectively collect a few prefix-sums in a data-dependent way, and answer range queries over prefix-sum-based cubes based on which any range query can be processed by using constant pieces of prefix-sums. In PRISM, we first alleviate the problem of noise aggregation by proposing a LDP mechanism called Range based Randomized Response (RRR) and a new type of prefix-sums-based cube called Grained Prefix-Sum (GPS) cube. We then alleviate the curse of high dimensionality by proposing a Data-Dependent Selective Prefix-Sum Collection Strategy (DELFT). We conduct experiments on both real-world datasets and synthetic datasets. Experimental results confirm the effectiveness of PRISM over existing methods.
Linking Entities across Relations and Graphs

This paper proposes a notion of parametric simulation to link entities across a relational database <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{D}$</tex> and a graph <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$G$</tex> . Taking functions and thresholds for measuring vertex close-ness, path associations and important properties as parameters, parametric simulation identifies tuples <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{D}$</tex> and vertices <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$v$</tex> in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$G$</tex> that refer to the same real-world entity, based on topological and semantic matching. We develop machine learning methods to learn the parameter functions and thresholds. We show that parametric simulation is in quadratic-time, by providing such an algorithm. Putting these together, we develop HER, a parallel system to check whether <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(t,v)$</tex> makes a match, find all vertex matches of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$G$</tex> , and compute all matches across <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{D}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$G$</tex> , all in quadratic-time. Using real-life and synthetic data, we empirically verify that HER is accurate with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{F}$</tex> -measure of 0.94 on average, and is able to scale with database <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{D}$</tex> and graph <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$G$</tex> .
Intent Mining: A Social and Semantic Enhanced Topic Model for Operation-Friendly Digital Marketing

In this paper, we study the digital marketing where marketing officers (MOs) have to commit to creating brand new promotion ads/contents based on understandings of users' needs or preferences. Users' behaviors are typically high dimensional and hard to understand. Therefore, dimension reduction of users' behaviors from high dimensions and explainability are important to help MOs launch operation-friendly marketings. As such, it is natural to exploit topic models to help MOs understand users' intents from users' behaviors (e.g., user-item visits) in case we treat each user as a document and users' behaviors of visiting an item as a word. However, users of low activities and items followed by power law distributions are common in user-item visit data, which pose significant challenges to traditional topic models. We present a social and semantic enhanced topic model (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TM) for users' intent mining. We optimize the user-intent estimates based on a graph neural network atop of a social network, and optimize the intent-item estimates based on a skip-gram word embedding approach by linking the semantics of items to pre-trained word embeddings. We propose an efficient stochastic vari-ational inference algorithm for the inference of latent variables and learning of parameters. Extensive experiments on real-world data show the effectivenesses of S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TM in terms of perplexities, topic coherence and semantic coherence compared with state-of-the-art topic models. We further show how MOs interact with our operation-friendly intent mining system, and results on real-world marketing campaigns in terms of click-through rate at Alipay.
Online Cardinality Estimation by Self-morphing Bitmaps

Estimating the cardinality of a data stream is a fundamental problem underlying numerous applications such as traffic monitoring in a network or a datacenter, popularity tracking on social media, and cache optimization in proxy servers. Existing solutions suffer from high processing/query overhead or memory in-efficiency, which prevents them from operating online for data streams with very high arrival rates. This paper takes a new solution path different from the prior art and proposes a self-morphing bitmap, which combines operational simplicity with structural dynamics, allowing the bitmap to be morphed in a series of steps with an evolving sampling probability that automatically adapts to different stream sizes. We evaluate the self-morphing bitmap theoretically and experimentally. The results demonstrate that it significantly outperforms the prior art.
Collecting Triangle Counts with Edge Relationship Local Differential Privacy

Counting subgraphs in decentralized settings has drawn increasing attention for graph analysis, wherein triangle count is one of the fundamental statistics. However, triangle counts may breach edge privacy, such as sensitive relations of individuals. Protecting edge privacy in triangle counts collection is a challenging problem due to the strong correlations among data from different clients. Decentralized Differential Privacy (DDP), as a possible option, protects edge privacy on correlated data to some extent. However, DDP provides a weak privacy guarantee by only hiding one edge in global. Unlike DDP, Local Differential Privacy (LDP) is a widely adopted standard for data collection which hides multiple data points in global at a time. But the LDP notion does not consider data correlations. With the understanding of these limitations, we introduce Edge Relationship Local Differential Privacy (Edge-RLDP), which provides a strong privacy guarantee as LDP and considers data correlations simultaneously. Based on Edge-RLDP, a baseline framework for triangle counts collection is proposed, as well as an improved two-phase framework, which strikes a better balance between privacy and data utility. Our improved framework fully utilizes the privacy budget by asking each client to only report the count of randomly sampled triangles after measuring the global data correlation. Theoretically, we rigorously prove that our framework satisfies ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon, \delta$</tex> ) -Edge-RLDP. Experimentally, we demonstrate our framework outperforms the state-of-art methods in terms of triangle count accuracy under a stricter privacy definition.
Implementation via Information Design using Global Games

For binary-action supermodular games with a continuum of symmetric players, we show that simple global game information structures can be used to implement an optimal outcome under adversarial equilibrium selection.
Practical Implementation Method of Reinforcement Learning for Power Converter

Reinforcement Learning (RL) is a field of machine learning that is widely used to control complex systems such as games. It has the advantage of finding optimal control input at a specific state through the iterative learning process. Recently, RL has been considered for the power electronics application having nonlinearities with multiple inputs due to empirically finding the optimal control input. This paper is about the DC/DC converter control using the DDPG (Deep Deterministic Policy Gradient). During the DDPG training, permanent damage to the experimental setup may occur. Thus, an offline training environment is created, and the neural network training for the DDPG is performed using iterative PC simulations. The acquired artificial neural network gains are directly applied to the experimental setup, and it improves the control performance compared to the conventional PI controller. Also, unlike previous papers that were performed on the costly FPGAs to solve artificial neural networks, in this paper, the size of neural networks is reduced sufficiently. Thus, the RL algorithm can be implemented with only a cost-effective microcontroller.
An Adaptive IPM-Based HEVC Video Steganography via Minimizing Non-Additive Distortion

Recently, almost all the proposed adaptive video steganographic schemes are based on minimizing an additive embedding distortion. However, they ignore the hard fact that the additive embedding distortion is not quite suitable for video steganography because of the interplay of cover elements in video steganography. In this article, an adaptive intra prediction mode based (IPM-based) video steganography is proposed by minimizing the non-additive distortion in HEVC. To reduce the complexity of minimizing the non-additive distortion, a multi-layered embedding structure combined with a proposed embedding distortion updating strategy is adopted to approximate the non-additive distortion in an additive form. First, all IPMs are decomposed into multiple layers based on the distortion drift graph to offer multi-layered embedding. Each IPM in the same layer is considered to be independent, and syndrome-trellis code (STC) can be applied to embed the message segment into each layer with an additive distortion function sequentially. Then, a distortion function composed of self-distortion and drift-distortion is proposed to initialize the distortion of modifying each IPM. Finally, after embedding the first message segment into the IPMs in the first layer with the initialized distortions, an embedding distortion updating strategy is applied to update the distortions of the IPMs in the remaining layers dynamically. Experimental results demonstrate that the proposed adaptive IPM-based video steganography can achieve much better perceptual quality and security performance than the state-of-the-art.
Comprehensive AI Model Development for Gleason Grading: From Scanning, Cloud-Based Annotation to Pathologist-AI Interaction

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A novel deep neural network based marine predator model for effective classification of big data from social Internet of Things

Summary Social Internet of Things (SIoT) is considered as one of the most recently evolved topics that connects people and object, object and object as well as people and people. The SIoT and big data provide an exact representation of IoT and social system for human progression characterization. Numerous machine learning techniques are employed to classify the data gathered from SIoT in a more powerful way. In this article, a deep neural network based marine predator (DRNN‐MP) algorithm is proposed in classifying big data. Here, an adaptive Savitzky–Golay filter is employed for selecting the subset and to eliminate undesirable data, as well as different noises. The big data databases are reduced using a Hadoop map reducing framework thereby enhancing the performances of the proposed approach. In addition to this, a modified relief technique is employed to select optimal features thereby performing better classification. The testing and training process based on the proposed approach for optimal classification of features from the big data considers four different databases namely coronary illness, GPS trajectories, localization data, and water treatment plant obtained from UCI machine learning repository. In addition to this, the proposed approach is evaluated for diverse performance measures namely accuracy, precision, specificity, sensitivity, throughput, and energy consumption. Finally, the proposed approach is compared for various metrics to illustrate the effectiveness of the system and the results demonstrated that the accuracy of our work is 98.25%.
Two-Qubit Geometric Gates Based on Ground-State Blockade of Rydberg Atoms

We achieve the robust nonadiabatic holonomic two-qubit controlled gate in one step based on the ground-state blockade mechanism between two Rydberg atoms. By using the Rydberg-blockade effect and the Raman transition mechanism, we can produce the blockade effect of double occupation of the corresponding ground state, i.e., ground-state blockade, to encode the computational subspace into the ground state, thus effectively avoiding the spontaneous emission of the excited Rydberg state. On the other hand, the feature of geometric quantum computation independent of the evolutionary details makes the scheme robust to control errors. In this way, the controlled quantum gate constructed by our scheme not only greatly reduces the gate infidelity caused by spontaneous emission but is also robust to control errors.
AiRS: A Large-Scale Recommender System at NAVER News

Online news providers such as Google News, Bing News, and NAVER News collect a large number of news articles from a variety of presses and distribute these articles to users via their portals. Dynamic nature of a news domain causes the problem of information overload that makes it difficult for a user to find her preferable news articles. Motivated by this situation, NAVER Corp., the largest portal company in South Korea, identified four design considerations (DCs) for news recommendation that reflect the unique characteristics of a news domain. In this paper, we introduce a large-scale news recommender system named as AiRS, present how it jointly leverages the four DCs for NAVER News service. Specifically, AiRS first generates candidate articles for recommendation to a target user based on collaborative filtering (CF), quality estimation (QE), and social impact (SI) models; then, it ranks the candidate articles based on the scores computed by considering their multi-type feature scores (e.g., user's section preference and article's recency), finally recommending the top- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> news articles that a target user is likely to prefer. Also, we present how to build the architecture for online deployment of AiRS at NAVER News. Through extensive offline and online A/B tests using the real-world datasets, we validate that AiRS successfully reflects all of the DCs into the news recommendation process, all design choices employed in AiRS help improve the recommendation accuracy, and AiRS significantly outperforms five state-of-the-art news recommendation approaches in terms of accuracy.