Reviewerly/exHarmony huggingface dataset:
The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure

Many modern machine learning classifiers are shown to be vulnerable to adversarial perturbations of the instances. Despite a massive amount of work focusing on making classifiers robust, the task seems quite challenging. In this work, through a theoretical study, we investigate the adversarial risk and robustness of classifiers and draw a connection to the well-known phenomenon of “concentration of measure” in metric measure spaces. We show that if the metric probability space of the test instance is concentrated, any classifier with some initial constant error is inherently vulnerable to adversarial perturbations.One class of concentrated metric probability spaces are the so-called Lévy families that include many natural distributions. In this special case, our attacks only need to perturb the test instance by at most O(√n) to make it misclassified, where n is the data dimension. Using our general result about Lévy instance spaces, we first recover as special case some of the previously proved results about the existence of adversarial examples. However, many more Lévy families are known (e.g., product distribution under the Hamming distance) for which we immediately obtain new attacks that find adversarial examples of distance O(√n).Finally, we show that concentration of measure for product spaces implies the existence of forms of “poisoning” attacks in which the adversary tampers with the training data with the goal of degrading the classifier. In particular, we show that for any learning algorithm that uses m training examples, there is an adversary who can increase the probability of any “bad property” (e.g., failing on a particular test instance) that initially happens with non-negligible probability to ≈ 1 by substituting only Õe(√m) of the examples with other (still correctly labeled) examples.
What Is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP Models

Despite the remarkable evolution of deep neural networks in natural language processing (NLP), their interpretability remains a challenge. Previous work largely focused on what these models learn at the representation level. We break this analysis down further and study individual dimensions (neurons) in the vector representation learned by end-to-end neural models in NLP tasks. We propose two methods: Linguistic Correlation Analysis, based on a supervised method to extract the most relevant neurons with respect to an extrinsic task, and Cross-model Correlation Analysis, an unsupervised method to extract salient neurons w.r.t. the model itself. We evaluate the effectiveness of our techniques by ablating the identified neurons and reevaluating the network’s performance for two tasks: neural machine translation (NMT) and neural language modeling (NLM). We further present a comprehensive analysis of neurons with the aim to address the following questions: i) how localized or distributed are different linguistic properties in the models? ii) are certain neurons exclusive to some properties and not others? iii) is the information more or less distributed in NMT vs. NLM? and iv) how important are the neurons identified through the linguistic correlation method to the overall task? Our code is publicly available as part of the NeuroX toolkit (Dalvi et al. 2019a). This paper is a non-archived version of the paper published at AAAI (Dalvi et al. 2019b).
Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification

Graph convolutional networks (GCNs) have been successfully applied in node classification tasks of network mining. However, most of these models based on neighborhood aggregation are usually shallow and lack the “graph pooling” mechanism, which prevents the model from obtaining adequate global information. In order to increase the receptive field, we propose a novel deep Hierarchical Graph Convolutional Network (H-GCN) for semi-supervised node classification. H-GCN first repeatedly aggregates structurally similar nodes to hyper-nodes and then refines the coarsened graph to the original to restore the representation for each node. Instead of merely aggregating one- or two-hop neighborhood information, the proposed coarsening procedure enlarges the receptive field for each node, hence more global information can be captured. The proposed H-GCN model shows strong empirical performance on various public benchmark graph datasets, outperforming state-of-the-art methods and acquiring up to 5.9% performance improvement in terms of accuracy. In addition, when only a few labeled samples are provided, our model gains substantial improvements.
Matching User with Item Set: Collaborative Bundle Recommendation with Deep Attention Network

Most recommendation research has been concentrated on recommending single items to users, such as the considerable work on collaborative filtering that models the interaction between a user and an item. However, in many real-world scenarios, the platform needs to show users a set of items, e.g., the marketing strategy that offers multiple items for sale as one bundle.In this work, we consider recommending a set of items to a user, i.e., the Bundle Recommendation task, which concerns the interaction modeling between a user and a set of items. We contribute a neural network solution named DAM, short for Deep Attentive Multi-Task model, which is featured with two special designs: 1) We design a factorized attention network to aggregate the item embeddings in a bundle to obtain the bundle's representation; 2) We jointly model user-bundle interactions and user-item interactions in a multi-task manner to alleviate the scarcity of user-bundle interactions. Extensive experiments on a real-world dataset show that DAM outperforms the state-of-the-art solution, verifying the effectiveness of our attention design and multi-task learning in DAM.
A Random Opposition-Based Learning Grey Wolf Optimizer

Grey wolf optimizer (GWO) algorithm is a swarm intelligence optimization technique that is recently developed to mimic the hunting behavior and leadership hierarchy of grey wolves in nature.It has been successfully applied to many real world applications.In the GWO algorithm, ''C'' is an important parameter which favoring exploration.At present, the researchers are few study the parameter ''C'' in GWO algorithm.In addition, during the evolution process, the other individuals in the population move towards to the α, β, and δ wolves which are to accelerate convergence.However, GWO is easy to trap in the local optima.This paper presents a modified parameter ''C'' strategy to balance between exploration and exploitation of GWO.Simultaneously, a new random opposition-based learning strategy is proposed to help the population jump out of the local optima.The experiments on 23 widely used benchmark test functions with various features, 30 benchmark problems from IEEE CEC 2014 Special Session, and three engineering design optimization problems.The results reveal that the proposed algorithm shows better or at least competitive performance against other compared algorithms on not only global optimization but also engineering design optimization problems.
DEBUG: A Dense Bottom-Up Grounding Approach for Natural Language Video Localization

In this paper, we focus on natural language video localization: localizing (i.e., grounding) a natural language description in a long and untrimmed video sequence.All currently published models for addressing this problem can be categorized into two types: (i) top-down approach: it does classification and regression for a set of pre-cut video segment candidates; (ii) bottom-up approach: it directly predicts probabilities for each video frame as the temporal boundaries (i.e., start and end time point).However, both two approaches suffer several limitations: the former is computationintensive for densely placed candidates, while the latter has trailed the performance of the top-down counterpart thus far.To this end, we propose a novel dense bottom-up framework: DEnse Bottom-Up Grounding (DEBUG).DE-BUG regards all frames falling in the ground truth segment as foreground, and each foreground frame regresses the unique distances from its location to bi-directional ground truth boundaries.Extensive experiments on three challenging benchmarks (TACoS, Charades-STA, and ActivityNet Captions) show that DE-BUG is able to match the speed of bottom-up models while surpassing the performance of the state-of-the-art top-down models.* Chujie Lu and Long Chen are co-first
Diffusion Improves Graph Learning

Graph convolution is the core of most Graph Neural Networks (GNNs) and usually approximated by message passing between direct (one-hop) neighbors. In this work, we remove the restriction of using only the direct neighbors by introducing a powerful, yet spatially localized graph convolution: Graph diffusion convolution (GDC). GDC leverages generalized graph diffusion, examples of which are the heat kernel and personalized PageRank. It alleviates the problem of noisy and often arbitrarily defined edges in real graphs. We show that GDC is closely related to spectral-based models and thus combines the strengths of both spatial (message passing) and spectral methods. We demonstrate that replacing message passing with graph diffusion convolution consistently leads to significant performance improvements across a wide range of models on both supervised and unsupervised tasks and a variety of datasets. Furthermore, GDC is not limited to GNNs but can trivially be combined with any graph-based model or algorithm (e.g. spectral clustering) without requiring any changes to the latter or affecting its computational complexity. Our implementation is available online.
Hierarchical Attention Prototypical Networks for Few-Shot Text Classification

Most of the current effective methods for text classification task are based on large-scale labeled data and a great number of parameters, but when the supervised training data are few and difficult to be collected, these models are not available.In this paper, we propose a hierarchical attention prototypical networks (HAPN) for few-shot text classification.We design the feature level, word level, and instance level multi cross attention for our model to enhance the expressive ability of semantic space.We verify the effectiveness of our model on two standard benchmark fewshot text classification datasets -FewRel and CSID, and achieve the state-of-the-art performance.The visualization of hierarchical attention layers illustrates that our model can capture more important features, words, and instances separately.In addition, our attention mechanism increases support set augmentability and accelerates convergence speed in the training stage.
A GAN-Based Anomaly Detection Approach for Imbalanced Industrial Time Series

Imbalanced time series are universally found in industrial applications, where the number of normal samples is far larger than that of abnormal cases. Traditional machine learning algorithms, such as support vector machine and convolutional neural networks, are struggling to attain high classification accuracies for class-imbalanced problems, because they tend to ensure the accuracy of the majority class. Hereby, this paper proposes a novel anomaly detection approach based on generative adversarial networks (GAN) to overcome this problem. In particular, an encoder-decoder-encoder three-sub-network generator is trained involving the elaborately extracted features from normal samples alone. Anomaly scores for anomaly detection are made up of apparent loss and latent loss. Without having any knowledge of the abnormal samples, our approach can diagnose faults by generating much higher anomaly scores when a fault sample is fed into the trained model. Experimental studies are conducted to verify the validity and feasibility of our approach, including a benchmark rolling bearing dataset acquired by Case Western Reserve University and another rolling bearing dataset which is acquired by our laboratory. Our approach can distinguish abnormal samples from normal samples with 100% accuracies on both datasets.
Blockchain-based ubiquitous manufacturing: a secure and reliable cyber-physical system

With product customisation and emerging business opportunities, small and medium manufacturing enterprises (SMEs) must find ways to collaborate and share competency in a trustable manner to survive a turbulent market. Therefore, service industry turns to the manufacturing industry and SMEs migrate to cloud manufacturing (CM) and ubiquitous manufacturing. However, existing platforms use centralised networking, which suffers from security, scalability and big-data problems. In this paper, we propose a blockchain-based platform as a trustable network to eradicate third-party problems, which can improve the scalability, security and big-data problems for SMEs. Our proposed platform is developed based on a consortium blockchain which provides a peer-to-peer communication network between the end user and the service provider. We improve existing consensus mechanism and communication protocol based on a cyber-physical system (CPS), via an autonomous agent. Firstly, we provide a review of cloud manufacturing, ubiquitous manufacturing and blockchain-based manufacturing approaches by highlighting the main problems. Then, the proposed platform, blockchain ubiquitous manufacturing (BCUM), is explained, based on its architecture, consensus algorithm and CPS, with the help of autonomous agent communication. The proposed platform has been developed for 3D printing companies which are geographically distributed and tested based on network performance and three practical scenarios.
Temporal phase unwrapping using deep learning

The multi-frequency temporal phase unwrapping (MF-TPU) method, as a classical phase unwrapping algorithm for fringe projection techniques, has the ability to eliminate the phase ambiguities even while measuring spatially isolated scenes or the objects with discontinuous surfaces. For the simplest and most efficient case in MF-TPU, two groups of phase-shifting fringe patterns with different frequencies are used: the high-frequency one is applied for 3D reconstruction of the tested object and the unit-frequency one is used to assist phase unwrapping for the wrapped phase with high frequency. The final measurement precision or sensitivity is determined by the number of fringes used within the high-frequency pattern, under the precondition that its absolute phase can be successfully recovered without any fringe order errors. However, due to the non-negligible noises and other error sources in actual measurement, the frequency of the high-frequency fringes is generally restricted to about 16, resulting in limited measurement accuracy. On the other hand, using additional intermediate sets of fringe patterns can unwrap the phase with higher frequency, but at the expense of a prolonged pattern sequence. With recent developments and advancements of machine learning for computer vision and computational imaging, it can be demonstrated in this work that deep learning techniques can automatically realize TPU through supervised learning, as called deep learning-based temporal phase unwrapping (DL-TPU), which can substantially improve the unwrapping reliability compared with MF-TPU even under different types of error sources, e.g., intensity noise, low fringe modulation, projector nonlinearity, and motion artifacts. Furthermore, as far as we know, our method was demonstrated experimentally that the high-frequency phase with 64 periods can be directly and reliably unwrapped from one unit-frequency phase using DL-TPU. These results highlight that challenging issues in optical metrology can be potentially overcome through machine learning, opening new avenues to design powerful and extremely accurate high-speed 3D imaging systems ubiquitous in nowadays science, industry, and multimedia.
Public-key authenticated encryption with keyword search revisited: Security model and constructions

In cloud era, it is necessary to store sensitive data in an encrypted form. This arises the interesting and challenging problem of searching on encrypted data. However, previous Public-key Encryption with Keyword Search (PEKS) inherently cannot resist against inside keyword guessing attacks. To alleviate this issue, recently Huang and Li proposed the notion of Public-key Authenticated Encryption with Keyword Search (PAEKS), which requires the data sender not only encrypting a keyword using the receiver’s public key, but also authenticating it using his secret key. This paper first revisits HL-PAEKS security model and finds that it did not capture a realistic threat, called (outside) chosen multi-ciphertext attacks. That is, an outside adversary can decide whether two encrypted files share some identical keywords or not. To resolve this issue, we propose a new PAEKS security model that captures both (outside) chosen multi-ciphertext attacks and (inside) keyword guessing attacks. Then, we give a concrete PAEKS scheme and prove its security in the new PAEKS security model. We also propose a method to simplify data sender’s key management using identity-based key exchange protocol. Finally, we provide implementation results of our schemes to show the comparable efficiency of our schemes with previous PEKS/PAEKS schemes.
Filtration and Distillation: Enhancing Region Attention for Fine-Grained Visual Categorization

Delicate attention of the discriminative regions plays a critical role in Fine-Grained Visual Categorization (FGVC). Unfortunately, most of the existing attention models perform poorly in FGVC, due to the pivotal limitations in discriminative regions proposing and region-based feature learning. 1) The discriminative regions are predominantly located based on the filter responses over the images, which can not be directly optimized with a performance metric. 2) Existing methods train the region-based feature extractor as a one-hot classification task individually, while neglecting the knowledge from the entire object. To address the above issues, in this paper, we propose a novel “Filtration and Distillation Learning” (FDL) model to enhance the region attention of discriminate parts for FGVC. Firstly, a Filtration Learning (FL) method is put forward for discriminative part regions proposing based on the matchability between proposing and predicting. Specifically, we utilize the proposing-predicting matchability as the performance metric of Region Proposal Network (RPN), thus enable a direct optimization of RPN to filtrate most discriminative regions. Go in detail, the object-based feature learning and region-based feature learning are formulated as “teacher” and “student”, which can furnish better supervision for region-based feature learning. Accordingly, our FDL can enhance the region attention effectively, and the overall framework can be trained end-to-end without neither object nor parts annotations. Extensive experiments verify that FDL yields state-of-the-art performance under the same backbone with the most competitive approaches on several FGVC tasks.
Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions

The Internet of Things (IoT) has gained significant recognition to become a novel sensing paradigm to interact with the physical world in this Industry 4.0 era. The IoTs are being used in many diverse applications that are part of our life and is growing to become the global digital nervous systems. It is quite evident that in the near future, hundreds of millions of individuals and businesses with billions will have smart-sensors and advanced communication technology, and these things will expand the boundaries of current systems. This will result in a potential change in the way we work, learn, innovate, live and entertain. The heterogeneous smart sensors within the Internet of Things are indispensable parts, which capture the raw data from the physical world by being the first port of contact. Often the sensors within the IoT are deployed or installed in harsh environments. This inevitably means that the sensors are prone to failure, malfunction, rapid attrition, malicious attacks, theft and tampering. All of these conditions cause the sensors within the IoT to produce unusual and erroneous readings, often known as outliers. Much of the current research has been done in developing the sensor outlier and fault detection models exclusively for the Wireless Sensor Networks (WSN), and adequate research has not been done so far in the context of the IoT. Wireless sensor network’s operational framework differ greatly when compared to IoT’s operational framework, using some of the existing models developed for WSN cannot be used on IoT’s for detecting outliers and faults. Sensor faults and outlier detection is very crucial in the IoT to detect the high probability of erroneous reading or data corruption, thereby ensuring the quality of the data collected by sensors. The data collected by sensors are initially pre-processed to be transformed into information and when Artificially Intelligent (AI), Machine Learning (ML) models are further used by the IoT, the information is further processed into applications and processes. Any faulty, erroneous, corrupted sensor readings corrupt the trained models, which thereby produces abnormal processes or outliers that are significantly distinct from the normal behavioural processes of a system. In this paper, we present a comprehensive review of the detecting sensor faults, anomalies, outliers in the Internet of Things and the challenges. A comprehensive guideline to select an adequate outlier detection model for the sensors in the IoT context for various applications is discussed.
Virtual Reality in Museums: Exploring the Experiences of Museum Professionals

The past few years have seen an increase in the use of virtual reality (VR) in museum environments in an attempt for museums to embrace technological innovations and adapt to the challenges of the digital era. While there are studies that examine the advantages of VR in museums and visitors’ experiences with it, there are no studies examining the experiences of museum professionals who are responsible for a museum’s objects and narratives. The aim of this paper is to explore the practices, experiences, and perceptions of museum professionals on the use of VR technology in museums, their perceived advantages and challenges of such technologies, and their vision for the future of technology in museums. The paper provides an in-depth analysis of interviews with museum professionals from a number of countries around the world who worked with particular VR projects in their own institutions. The ultimate aim is to offer a more critical and holistic examination and assessment of the use of VR in museums and provide suggestions for designing and developing VR projects in the future.