Reviewerly/exHarmony huggingface dataset:
Story Forest

Extracting events accurately from vast news corpora and organize events logically is critical for news apps and search engines, which aim to organize news information collected from the Internet and present it to users in the most sensible forms. Intuitively speaking, an event is a group of news documents that report the same news incident possibly in different ways. In this article, we describe our experience of implementing a news content organization system at Tencent to discover events from vast streams of breaking news and to evolve news story structures in an online fashion. Our real-world system faces unique challenges in contrast to previous studies on topic detection and tracking (TDT) and event timeline or graph generation, in that we (1) need to accurately and quickly extract distinguishable events from massive streams of long text documents, and (2) must develop the structures of event stories in an online manner, in order to guarantee a consistent user viewing experience. In solving these challenges, we propose Story Forest , a set of online schemes that automatically clusters streaming documents into events, while connecting related events in growing trees to tell evolving stories. A core novelty of our Story Forest system is EventX , a semi-supervised scheme to extract events from massive Internet news corpora. EventX relies on a two-layered, graph-based clustering procedure to group documents into fine-grained events. We conducted extensive evaluations based on (1) 60 GB of real-world Chinese news data, (2) a large Chinese Internet news dataset that contains 11,748 news articles with truth event labels, and (3) the 20 News Groups English dataset, through detailed pilot user experience studies. The results demonstrate the superior capabilities of Story Forest to accurately identify events and organize news text into a logical structure that is appealing to human readers.
Hierarchical Bipartite Graph Neural Networks: Towards Large-Scale E-commerce Applications

The e-commerce appeals to a multitude of online shoppers by providing personalized experiences and becomes indispensable in our daily life. Accurately predicting user preference and making a recommendation of favorable items plays a crucial role in improving several key tasks such as Click Through Rate (CTR) and Conversion Rate (CVR) in order to increase commercial value. Some state-of-the-art collaborative filtering methods exploiting non-linear interactions on a user-item bipartite graph are able to learn better user and item representations with Graph Neural Networks (GNNs), which do not learn hierarchical representations of graphs because they are inherently flat. Hierarchical representation is reportedly favorable in making more personalized item recommendations in terms of behaviorally similar users in the same community and a context of topic-driven taxonomy. However, some advanced approaches, in this regard, are either only considering linear interactions, or adopting single-level community, or computationally expensive. To address these problems, we propose a novel method with Hierarchical bipartite Graph Neural Network (HiGNN) to handle large-scale e-commerce tasks. By stacking multiple GNN modules and using a deterministic clustering algorithm alternately, HiGNN is able to efficiently obtain hierarchical user and item embeddings simultaneously, and effectively predict user preferences on a larger scale. Extensive experiments on some real-world e-commerce datasets demonstrate that HiGNN achieves a significant improvement compared to several popular methods. Moreover, we deploy HiGNN in Taobao, one of the largest e-commerces with hundreds of million users and items, for a series of large-scale prediction tasks of item recommendations. The results also illustrate that HiGNN is arguably promising and scalable in real-world applications.
SONG: Approximate Nearest Neighbor Search on GPU

Approximate nearest neighbor (ANN) searching is a fundamental problem in computer science with numerous applications in (e.g.,) machine learning and data mining. Recent studies show that graph-based ANN methods often outperform other types of ANN algorithms. For typical graph-based methods, the searching algorithm is executed iteratively and the execution dependency prohibits GPU adaptations. In this paper, we present a novel framework that decouples the searching on graph algorithm into 3 stages, in order to parallel the performance-crucial distance computation. Furthermore, to obtain better parallelism on GPU, we propose novel ANN-specific optimization methods that eliminate dynamic GPU memory allocations and trade computations for less GPU memory consumption. The proposed system is empirically compared against HNSW–the state-of-the-art ANN method on CPU–and Faiss–the popular GPU-accelerated ANN platform–on 6 datasets. The results confirm the effectiveness: SONG has around 50-180x speedup compared with single-thread HNSW, while it substantially outperforms Faiss.
<i>R²PEDS</i>: A Recoverable and Revocable Privacy-Preserving Edge Data Sharing Scheme

Edge servers (ESs) are utilized to achieve the storage and sharing of IoT data. However, even if ES brings us much benefit, it also leads to many serious privacy leakage issues because users' data in ESs are out of control. Moreover, ES providers may also disclose user's private-sensitive data. Hence, in this article, we present a privacy-preserving, recoverable, and revocable edge data sharing scheme. In this scheme, we propose a novel attribute revocation chain based on the blockchain technology to achieve attribute revocation in ciphertext-policy attribute-based encryption (CP-ABE). Meanwhile, a secret sharing scheme (SSS) is introduced to assist the data recovery. Especially, for the situation that a single ES is hijacked, we also propose a corresponding efficient detection mechanism and key updating policy to promise the subsequent security of the whole system. Moreover, this scheme also resists Economic Denial-of-Sustainability (EDoS) attacks which are launched by some malicious users. The analysis shows that the proposed scheme can protect user's privacy and resist many attacks. Additionally, relevant experimental results demonstrate that our scheme has low computational overhead on the user side.
Social Boundaries for Personal Agents in the Interpersonal Space of the Home

The presence of voice activated personal assistants (VAPAs) in people's homes rises each year [31]. Industry efforts are invested in making interactions with VAPAs more personal by leveraging information from messages and calendars, and by accessing user accounts for 3rd party services. However, the use of personal data becomes more complicated in interpersonal spaces, such as people's homes. Should a shared agent access the information of many users? If it does, how should it navigate issues of privacy and control? Designers currently lack guidelines to help them design appropriate agent behaviors. We used Speed Dating to explore inchoate social mores around agent actions within a home, including issues of proactivity, interpersonal conflict, and agent prevarication. Findings offer new insights on how more socially sophisticated agents might sense, make judgements about, and navigate social roles and individuals. We discuss how our findings might impact future research and future agent behaviors.
Automatic View Generation with Deep Learning and Reinforcement Learning

Materializing views is an important method to reduce redundant computations in DBMS, especially for processing large scale analytical queries. However, many existing methods still need DBAs to manually generate materialized views, which are not scalable to a large number of database instances, especially on the cloud database. To address this problem, we propose an automatic view generation method which judiciously selects "highly beneficial" subqueries to generate materialized views. However, there are two challenges. (1) How to estimate the benefit of using a materialized view for a query? (2) How to select optimal subqueries to generate materialized views? To address the first challenge, we propose a neural network based method to estimate the benefit of using a materialized view to answer a query. In particular, we extract significant features from different perspectives and design effective encoding models to transform these features into hidden representations. To address the second challenge, we model this problem to an ILP (Integer Linear Programming) problem, which aims to maximize the utility by selecting optimal subqueries to materialize. We design an iterative optimization method to select subqueries to materialize. However, this method cannot guarantee the convergence of the solution. To address this issue, we model the iterative optimization process as an MDP (Markov Decision Process) and use the deep reinforcement learning model to solve the problem. Extensive experiments show that our method outperforms existing solutions by 28.4%, 8.8% and 31.7% on three real-world datasets.
The Blockchain and the Commons: Dilemmas in the Design of Local Platforms

This paper addresses the design dilemmas that arise when distributed ledger technologies (DLT) are to be applied in the governance of artificial material commons. DLTs, such as blockchain, are often presented as enabling technologies for self-governing communities, provided by their consensus mechanisms, transparent administration, and incentives for collaboration and cooperation. Yet, these affordances may also undermine public values such as privacy and displace human agency in governance procedures. In this paper, the conflicts regarding the governance of communities which collectively manage and produce a commons are discussed through the case of a fictional energy community. Three mechanisms are identified in this process: tracking use of and contributions to the commons; managing resources, and negotiating the underlying rule sets and user rights. Our effort is aimed at contributing to the HCI community by introducing a framework of three mechanisms and six design dilemmas that can aid in balancing conflicting values in the design of local platforms for commons-based resource management.
Hybrid of Harmony Search Algorithm and Ring Theory-Based Evolutionary Algorithm for Feature Selection

Feature Selection (FS) is an important pre-processing step in the fields of machine learning and data mining, which has a major impact on the performance of the corresponding learning models. The main goal of FS is to remove the irrelevant and redundant features, resulting in optimized time and space requirements along with enhanced performance of the learning model under consideration. Many meta-heuristic optimization techniques have been applied to solve FS problems because of its superiority over the traditional optimization approaches. Here, we have introduced a new hybrid meta-heuristic FS model based on a well-known meta-heuristic Harmony Search (HS) algorithm and a recently proposed Ring Theory based Evolutionary Algorithm (RTEA), which we have named as Ring Theory based Harmony Search (RTHS). Effectiveness of RTHS has been evaluated by applying it on 18 standard UCI datasets and comparing it with 10 state-of-the-art meta-heuristic FS methods. Obtained results prove the superiority of RTHS over the state-of-the-art methods considered here for comparison.
A survey of machine learning-based solutions to protect privacy in the Internet of Things

The Internet of things (IoT) aims to connect everything and everyone around the world to provide diverse applications that improve quality of life. In this technology, the preservation of data privacy plays a crucial role. Recently, many studies have leveraged machine learning (ML) as a strategy to address the privacy issues of IoT including scalability, interoperability, and resource limitation such as computation and energy. In this paper, we aim to review these studies and examine opportunities and concerns related to utilizing data in ML-based solutions for privacy in IoT. We, first, explore and introduce different data sources in IoT and categorize them. Then, we review existing ML-based solutions that are designed and developed to protect privacy in IoT. Finally, we examine the extent to which some data categories have been used with ML-based solutions to preserve privacy and propose other novel opportunities for ML-based solutions to leverage these data sources in the IoT ecosystem.
Symmetry-Protected Privacy: Beating the Rate-Distance Linear Bound Over a Noisy Channel

There are two main factors limiting the performance of quantum key distribution---channel transmission loss and noise. Previously, a linear bound was believed to put an upper limit on the rate-transmittance performance. Remarkably, the recently proposed twin-field and phase-matching quantum key distribution schemes have been proven to overcome the linear bound. In practice, due to the intractable phase fluctuation of optical signals in transmission, these schemes suffer from large error rates, which renders the experimental realization extremely challenging. Here, we close this gap by proving the security based on a different principle---encoding symmetry. With the symmetry-based security proof technique, we can decouple the privacy from the channel disturbance, and eventually remove the limitation of secure key distribution on bit error rates. As a direct application, we show that the phase-matching scheme can yield positive key rates even with high bit error rates up to 50%. In the simulation, with typical experimental parameters, the key rate is able to break the linear bound with an error rate of 13%. Meanwhile, we provide a simple finite-data size analysis for the phase-matching scheme under this symmetry-based analysis, which can break the bound with a reasonable data size of ${10}^{12}$. Encouraged by high loss and error tolerance, we expect the approach based on symmetry-protected privacy will provide a different insight into the security of quantum key distribution.
Transferring Dense Pose to Proximal Animal Classes

Recent contributions have demonstrated that it is possible to recognize the pose of humans densely and accurately given a large dataset of poses annotated in detail. In principle, the same approach could be extended to any animal class, but the effort required for collecting new annotations for each case makes this strategy impractical, despite important applications in natural conservation, science and business. We show that, at least for proximal animal classes such as chimpanzees, it is possible to transfer the knowledge existing in dense pose recognition for humans, as well as in more general object detectors and segmenters, to the problem of dense pose recognition in other classes. We do this by (1) establishing a DensePose model for the new animal which is also geometrically aligned to humans (2) introducing a multi-head R-CNN architecture that facilitates transfer of multiple recognition tasks between classes, (3) finding which combination of known classes can be transferred most effectively to the new animal and (4) using self-calibrated uncertainty heads to generate pseudo-labels graded by quality for training a model for this class. We also introduce two benchmark datasets labelled in the manner of DensePose for the class chimpanzee and use them to evaluate our approach, showing excellent transfer learning performance.
Exploring Data Aggregation in Policy Learning for Vision-Based Urban Autonomous Driving

Data aggregation techniques can significantly improve vision-based policy learning within a training environment, e.g., learning to drive in a specific simulation condition. However, as on-policy data is sequentially sampled and added in an iterative manner, the policy can specialize and overfit to the training conditions. For real-world applications, it is useful for the learned policy to generalize to novel scenarios that differ from the training conditions. To improve policy learning while maintaining robustness when training end-to-end driving policies, we perform an extensive analysis of data aggregation techniques in the CARLA environment. We demonstrate how the majority of them have poor generalization performance, and develop a novel approach with empirically better generalization performance compared to existing techniques. Our two key ideas are (1) to sample critical states from the collected on-policy data based on the utility they provide to the learned policy in terms of driving behavior, and (2) to incorporate a replay buffer which progressively focuses on the high uncertainty regions of the policy's state distribution. We evaluate the proposed approach on the CARLA NoCrash benchmark, focusing on the most challenging driving scenarios with dense pedestrian and vehicle traffic. Our approach improves driving success rate by 16% over state-of-the-art, achieving 87% of the expert performance while also reducing the collision rate by an order of magnitude without the use of any additional modality, auxiliary tasks, architectural modifications or reward from the environment.
Learn2Perturb: An End-to-End Feature Perturbation Learning to Improve Adversarial Robustness

While deep neural networks have been achieving state-of-the-art performance across a wide variety of applications, their vulnerability to adversarial attacks limits their widespread deployment for safety-critical applications. Alongside other adversarial defense approaches being investigated, there has been a very recent interest in improving adversarial robustness in deep neural networks through the introduction of perturbations during the training process. However, such methods leverage fixed, pre-defined perturbations and require significant hyper-parameter tuning that makes them very difficult to leverage in a general fashion. In this study, we introduce Learn2Perturb, an end-to-end feature perturbation learning approach for improving the adversarial robustness of deep neural networks. More specifically, we introduce novel perturbation-injection modules that are incorporated at each layer to perturb the feature space and increase uncertainty in the network. This feature perturbation is performed at both the training and the inference stages. Furthermore, inspired by the Expectation-Maximization, an alternating back-propagation training algorithm is introduced to train the network and noise parameters consecutively. Experimental results on CIFAR-10 and CIFAR-100 datasets show that the proposed Learn2Perturb method can result in deep neural networks which are 4-7% more robust on l_inf FGSM and PDG adversarial attacks and significantly outperforms the state-of-the-art against l_2 C\&W attack and a wide range of well-known black-box attacks.
A weighted hybrid ensemble method for classifying imbalanced data

In real datasets, most are unbalanced. Data imbalance can be defined as the number of instances in some classes greatly exceeds the number of instances in other classes. Whether in the field of data mining or machine learning, data imbalance can have adverse effects. At present, the methods to solve the problem of data imbalance can be divided into data-level methods, algorithm-level methods and hybrid methods. In this paper, we propose a weighted hybrid ensemble method for classifying imbalanced data in binary classification tasks, called WHMBoost. In the framework of the boosting algorithm, the presented method combines two data sampling methods and two base classifiers, and each sampling method and each base classifier is assigned corresponding weights, which makes them have better complementary advantages. The performance of WHMBoost has been evaluated on 40 benchmark imbalanced datasets with state of the art ensemble methods like AdaBoost, RUSBoost, SMOTEBoost using AUC, F-Measure and Geometric Mean as the performance evaluation criteria. Experimental results show significant improvement over the other methods and it can be concluded that WHMBoost is a promising and effective algorithm to deal with imbalance datasets.
Blockchain Solution for IoT-based Critical Infrastructures: Byzantine Fault Tolerance

Providing an acceptable level of security for Internet of Things (IoT)-based critical infrastructures, such as the connected vehicles, considers as an open research issue. Nowadays, blockchain overcomes a wide range of network limitations. In the context of IoT and blockchain, Byzantine Fault Tolerance (BFT)-based consensus protocol, that elects a set of authenticated devices/nodes within the network, considers as a solution for achieving the desired energy efficiency over the other consensus protocols. In BFT, the elected devices are responsible for ensuring the data blocks’ integrity and preventing the concurrently appended blocks that might contain some malicious data. In this paper, we evaluate the fault-tolerance with different network settings, i.e., the number of connected vehicles. We verify and validate the proposed model with MATLAB/Simulink package simulations. The results show that our proposed hybrid scenario performed over the non-hybrid scenario taking throughput and latency in the consideration as the evaluated metrics.
PADS: Policy-Adapted Sampling for Visual Similarity Learning

Learning visual similarity requires to learn relations, typically between triplets of images. Albeit triplet approaches being powerful, their computational complexity mostly limits training to only a subset of all possible training triplets. Thus, sampling strategies that decide when to use which training sample during learning are crucial. Currently, the prominent paradigm are fixed or curriculum sampling strategies that are predefined before training starts. However, the problem truly calls for a sampling process that adjusts based on the actual state of the similarity representation during training. We, therefore, employ reinforcement learning and have a teacher network adjust the sampling distribution based on the current state of the learner network, which represents visual similarity. Experiments on benchmark datasets using standard triplet-based losses show that our adaptive sampling strategy significantly outperforms fixed sampling strategies. Moreover, although our adaptive sampling is only applied on top of basic triplet-learning frameworks, we reach competitive results to state-of-the-art approaches that employ diverse additional learning signals or strong ensemble architectures. Code can be found under https://github.com/Confusezius/CVPR2020_PADS.
You2Me: Inferring Body Pose in Egocentric Video via First and Second Person Interactions

The body pose of a person wearing a camera is of great interest for applications in augmented reality, healthcare, and robotics, yet much of the person's body is out of view for a typical wearable camera. We propose a learning-based approach to estimate the camera wearer's 3D body pose from egocentric video sequences. Our key insight is to leverage interactions with another person---whose body pose we can directly observe---as a signal inherently linked to the body pose of the first-person subject. We show that since interactions between individuals often induce a well-ordered series of back-and-forth responses, it is possible to learn a temporal model of the interlinked poses even though one party is largely out of view. We demonstrate our idea on a variety of domains with dyadic interaction and show the substantial impact on egocentric body pose estimation, which improves the state of the art.