Reviewerly/exHarmony huggingface dataset:
On the Integration of Blockchain to the Internet of Things for Enabling Access Right Delegation

With the advancement of the Internet of Things (IoT) in recent years, there is a bigger potential to use online services than ever before. The use of the IoT brings numerous opportunities for both service providers and end users, however, it faces critical questions of security and privacy. Toward this, access control is one of the significant security challenges for the IoT, in particular, considering the characteristics of such IoT systems. To develop a secure access control architecture for the IoT, the propagation of access right delegation is a major issue. Many proposals present access control issues for the IoT but given the specific context of access right delegation, it is still in its infancy. This article presents an approach to address such a delegation issue for the IoT using the blockchain technology. We propose a delegation model that employv the critical issues, e.g., the use of nonunique identities, asynchronous and flexible delegation nature of communication for the IoT without the need of a centralized system. The goal of our primitive is to use attributes for validating the identity of an entity instead of relying on a concrete unique identity of an entity. To provide privacy for the attributes, we propose a dual blockchain architecture that moves the attribute storage and access of the public blockchain and onto a secure private blockchain. To demonstrate the feasibility of our proposed approach, we evaluate the system performances using the Ethereum blockchain network.
Medical Image Classification Using Deep Learning

Image classification is to assign one or more labels to an image, which is one of the most fundamental tasks in computer vision and pattern recognition. In traditional image classification, low-level or mid-level features are extracted to represent the image and a trainable classifier is then used for label assignments. In recent years, the high-level feature representation of deep convolutional neural networks has proven to be superior to hand-crafted low-level and mid-level features. In the deep convolutional neural network, both feature extraction and classification networks are combined together and trained end-to-end. Deep learning techniques have also been applied to medical image classification and computer-aided diagnosis. In this chapter, we first introduce fundamentals of deep convolutional neural networks for image classification and then introduce an application of deep learning to classification of focal liver lesions on multi-phase CT images. The main challenge in deep-learning-based medical image classification is the lack of annotated training samples. We demonstrate that fine-tuning can significantly improve the accuracy of liver lesion classification, especially for small training samples.
Ontology, Ontologies and the “I” of FAIR

According to the FAIR guiding principles, one of the central attributes for maximizing the added value of information artifacts is interoperability. In this paper, I discuss the importance, and propose a characterization of the notion of Semantic Interoperability. Moreover, I show that a direct consequence of this view is that Semantic Interoperability cannot be achieved without the support of, on one hand, (i) ontologies, as meaning contracts capturing the conceptualizations represented in information artifacts and, on the other hand, of (ii) Ontology, as a discipline proposing formal meth- ods and theories for clarifying these conceptualizations and articulating their representations. In particular, I discuss the fundamental role of formal ontological theories (in the latter sense) to properly ground the construction of representation languages, as well as methodological and computational tools for supporting the engineering of ontologies (in the former sense) in the context of FAIR.
Social network services for academic libraries: A study based on social capital and social proof

Despite the potential of social networking services (SNS) as a tool for communication between academic libraries and users, many academic libraries are yet to successfully optimize their SNS. As a result, their social proof and social capital of various SNS do not perform well. This research aims to evaluate the SNS effectiveness of the University of Hong Kong Libraries (HKUL) based on social capital and social proof concepts. We hope that our recommendations according to our findings will be applicable to other academic library contexts. We have found that: (i) there are no major differences between undergraduate and postgraduate students in their attitudes and behaviors regarding the SNS of HKUL on various platforms; (ii) low social proof is related to a lack of user interaction and promotion; (iii) low satisfaction with SNS contents may lead to low social capital. As such, understanding user information need, setting goals and metrics for each SNS, and formulating a formal SNS policy are the keys to further develop library SNS.
Detecting Fake Accounts in Online Social Networks at the Time of Registrations

Online social networks are plagued by fake information. In particu- lar, using massive fake accounts (also called Sybils), an attacker can disrupt the security and privacy of benign users by spreading spam, malware, and disinformation. Existing Sybil detection methods rely on rich content, behavior, and/or social graphs generated by Sybils. The key limitation of these methods is that they incur significant delays in catching Sybils, i.e., Sybils may have already performed many malicious activities when being detected. In this work, we propose Ianus, a Sybil detection method that leverages account registration information. Ianus aims to catch Sybils immediately after they are registered. First, using a real- world registration dataset with labeled Sybils from WeChat (the largest online social network in China), we perform a measurement study to characterize the registration patterns of Sybils and benign users. We find that Sybils tend to have synchronized and abnormal registration patterns. Second, based on our measurement results, we model Sybil detection as a graph inference problem, which allows us to integrate heterogeneous features. In particular, we extract synchronization and anomaly based features for each pair of accounts, use the features to build a graph in which Sybils are densely connected with each other while a benign user is isolated or sparsely connected with other benign users and Sybils, and finally detect Sybils via analyzing the structure of the graph. We evaluate Ianus using real-world registration datasets of WeChat. Moreover, WeChat has deployed Ianus on a daily basis, i.e., WeChat uses Ianus to analyze newly registered accounts on each day and detect Sybils. Via manual verification by the WeChat security team, we find that Ianus can detect around 400K per million new registered accounts each day and achieve a precision of over 96% on average.
Towards a model for spoken conversational search

Conversation is the natural mode for information exchange in daily life, a spoken conversational interaction for search input and output is a logical format for information seeking. However, the conceptualisation of user–system interactions or information exchange in spoken conversational search (SCS) has not been explored. The first step in conceptualising SCS is to understand the conversational moves used in an audio-only communication channel for search. This paper explores conversational actions for the task of search. We define a qualitative methodology for creating conversational datasets, propose analysis protocols, and develop the SCSdata. Furthermore, we use the SCSdata to create the first annotation schema for SCS: the SCoSAS, enabling us to investigate interactivity in SCS. We further establish that SCS needs to incorporate interactivity and pro-activity to overcome the complexity that the information seeking process in an audio-only channel poses. In summary, this exploratory study unpacks the breadth of SCS. Our results highlight the need for integrating discourse in future SCS models and contributes the advancement in the formalisation of SCS models and the design of SCS systems.
Key influencing factors of the Kubernetes auto-scaler for computing-intensive microservice-native cloud-based applications

Nowadays, different types of computing-intensive services such as mechanical, aerospace, civil and environmental applications are often deployed on the cloud since it offers a convenient on-demand model for renting resources and easy-to-use elastic infrastructures. Moreover, the modern software engineering disciplines exploit orchestration tools such as Kubernetes to run cloud applications based on a set of microservices packaged in containers. On the one hand, in order to ensure the users’ experience, it is necessary to allocate enough number of container instances before the workload intensity surges at runtime. On the other hand, renting expensive cloud-based resources can be unaffordable over a long period of time. Therefore, the choice of a reactive auto-scaling method may significantly affect both response time and resource utilisation. This paper presents a set of key factors which should be considered in the development of auto-scaling methods. Through a set of experiments, a discussion follows to help shed light on how such factors influence the performance of auto-scaling methods under different workload conditions such as on-and-off, predictable and unpredictable bursting workload patterns. Due to suitable results, the proposed set of key factors are exploited in the PrEstoCloud software system for microservice-native cloud-based computationally-intensive applications.
Accelerate CNN via Recursive Bayesian Pruning

Channel Pruning, widely used for accelerating Convolutional Neural Networks, is an NP-hard problem due to the inter-layer dependency of channel redundancy. Existing methods generally ignored the above dependency for computation simplicity. To solve the problem, under the Bayesian framework, we here propose a layer-wise Recursive Bayesian Pruning method (RBP). A new dropout-based measurement of redundancy, which facilitate the computation of posterior assuming inter-layer dependency, is introduced. Specifically, we model the noise across layers as a Markov chain and target its posterior to reflect the inter-layer dependency. Considering the closed form solution for posterior is intractable, we derive a sparsity-inducing Dirac-like prior which regularizes the distribution of the designed noise to automatically approximate the posterior. Compared with the existing methods, no additional overhead is required when the inter-layer dependency assumed. The redundant channels can be simply identified by tiny dropout noise and directly pruned layer by layer. Experiments on popular CNN architectures have shown that the proposed method outperforms several state-of-the-arts. Particularly, we achieve up to 5.0x, 2.2x and 1.7x FLOPs reduction with little accuracy loss on the large scale dataset ILSVRC2012 for VGG16, ResNet50 and MobileNetV2, respectively.
Unsupervised Learning of Landmarks by Descriptor Vector Exchange

Equivariance to random image transformations is an effective method to learn landmarks of object categories, such as the eyes and the nose in faces, without manual supervision. However, this method does not explicitly guarantee that the learned landmarks are consistent with changes between different instances of the same object, such as different facial identities. In this paper, we develop a new perspective on the equivariance approach by noting that dense landmark detectors can be interpreted as local image descriptors equipped with invariance to intra-category variations. We then propose a direct method to enforce such an invariance in the standard equivariant loss. We do so by exchanging descriptor vectors between images of different object instances prior to matching them geometrically. In this manner, the same vectors must work regardless of the specific object identity considered. We use this approach to learn vectors that can simultaneously be interpreted as local descriptors and dense landmarks, combining the advantages of both. Experiments on standard benchmarks show that this approach can match, and in some cases surpass state-of-the-art performance amongst existing methods that learn landmarks without supervision. Code is available at www.robots.ox.ac.uk/~vgg/research/DVE/.
Making History Matter: History-Advantage Sequence Training for Visual Dialog

We study the multi-round response generation in visual dialog, where a response is generated according to a visually grounded conversational history. Given a triplet: an image, Q&A history, and current question, all the prevailing methods follow a codec (i.e., encoder-decoder) fashion in a supervised learning paradigm: a multimodal encoder encodes the triplet into a feature vector, which is then fed into the decoder for the current answer generation, supervised by the ground-truth. However, this conventional supervised learning does NOT take into account the impact of imperfect history, violating the conversational nature of visual dialog and thus making the codec more inclined to learn history bias but not contextual reasoning. To this end, inspired by the actor-critic policy gradient in reinforcement learning, we propose a novel training paradigm called History Advantage Sequence Training (HAST). Specifically, we intentionally impose wrong answers in the history, obtaining an adverse critic, and see how the historic error impacts the codec's future behavior by History Advantage - a quantity obtained by subtracting the adverse critic from the gold reward of ground-truth history. Moreover, to make the codec more sensitive to the history, we propose a novel attention network called History-Aware Co-Attention Network (HACAN) which can be effectively trained by using HAST. Experimental results on three benchmarks: VisDial v0.9&v1.0 and GuessWhat?!, show that the proposed HAST strategy consistently outperforms the state-of-the-art supervised counterparts.
Boosting the secret key rate in a shared quantum and classical fibre communication system

During the last 20 years, the advance of communication technologies has generated multiple exciting applications. However, classical cryptography, commonly adopted to secure current communication systems, can be jeopardized by the advent of quantum computers. Quantum key distribution (QKD) is a promising technology aiming to solve such a security problem. Unfortunately, current implementations of QKD systems show relatively low key rates, demand low channel noise and use ad hoc devices. In this work, we picture how to overcome the rate limitation by using a 37-core fibre to generate 2.86 Mbit/s per core that can be space multiplexed into the highest secret key rate of 105.7 Mbit/s to date. We also demonstrate, with off-the-shelf equipment, the robustness of the system by co-propagating a classical signal at 370 Gbit/s, paving the way for a shared quantum and classical communication network.
Notice of Violation of IEEE Publication Principles: Recent Advances in 3D Object Detection in the Era of Deep Neural Networks: A Survey

With the rapid development of deep learning technology and other powerful tools, 3D object detection has made great progress and become one of the fastest growing field in computer vision. Many automated applications such as robotic navigation, autonomous driving, and virtual or augmented reality system require estimation of accurate 3D object location and detection. Under this requirement, many methods have been proposed to improve the performance of 3D object localization and detection. Despite recent efforts, 3D object detection is still a very challenging task due to occlusion, viewpoint variations, scale changes, and limited information in 3D scenes. In this paper, we present a comprehensive review of recent state-of-the-art approaches in 3D object detection technology. We start with some basic concepts, then describe some of the available datasets that are designed to facilitate the performance evaluation of 3D object detection algorithms. Next, we will review the state-of-the-art technologies in this area, highlighting their contributions, importance, and limitations as a guide for future research. Finally, we provide a quantitative comparison of the results of the state-of-the-art methods on the popular public datasets.
Robust Control of an Aerial Manipulator Based on a Variable Inertia Parameters Model

Aerial manipulator, which is composed of an UAV (Unmanned Aerial Vehicle) and a multi-link manipulator and can perform aerial manipulation, has shown great potential of applications. However, dynamic coupling between the UAV and the manipulator makes it difficult to control the aerial manipulator with high performance. In this paper, system modeling and control problem of the aerial manipulator are studied. Firstly, an UAV dynamic model is proposed with consideration of the dynamic coupling from an attached manipulator, which is treated as disturbance for the UAV. In the dynamic model, the disturbance is affected by the variable inertia parameters of the aerial manipulator system. Then, based on the proposed dynamic model, a disturbance compensation robust $H_{\infty}$ controller is designed to stabilize flight of the UAV while the manipulator is in operation. Finally, experiments are conducted and the experimental results demonstrate the feasibility and validity of the proposed control scheme.
Phrase2Vec: Phrase embedding based on parsing

Text is one of the most common unstructured data, and usually, the most primary task in text mining is to transfer the text into a structured representation. However, the existing text representation models split the complete semantic unit and neglect the order of words, finally lead to understanding bias. In this paper, we propose a novel phrase-based text representation method that takes into account the integrity of semantic units and utilizes vectors to represent the similarity relationship between texts. First, we propose HPMBP (Hierarchical Phrase Mining Based on Parsing) which mines hierarchical phrases by parsing and uses BOP (Bag Of Phrases) to represent text. Then, we put forward three phrase embedding models, called Phrase2Vec, including Skip-Phrase, CBOP (Continuous Bag Of Phrases), and GloVeFP (Global Vectors For Phrase Representation). They learn the phrase vector with semantic similarity, further obtain the vector representation of the text. Based on Phrase2Vec, we propose PETC (Phrase Embedding based Text Classification) and PETCLU (Phrase Embedding based Text Clustering). PETC utilizes the phrase embedding to get the text vector, which is fed to a neural network for text classification. PETCLU gets the vectorization expression of text and cluster center by Phrase2Vec, furthermore extends the K-means model for text clustering. To the best of our knowledge, it is the first work that focuses on the phrase-based English text representation. Experiments show that the introduced Phrase2Vec outperforms state-of-the-art phrase embedding models in the similarity task and the analogical reasoning task on Enwiki, DBLP, and Yelp dataset. PETC is superior to the baseline text classification methods in the F1-value index by about 4%. PETCLU is also ahead of the prevalent text clustering methods in entropy and purity indicators. In summary, Phrase2Vec is a promising approach to text mining.
Extrinsic Calibration between Camera and LiDAR Sensors by Matching Multiple 3D Planes

This paper proposes a simple extrinsic calibration method for a multi-sensor system which consists of six image cameras and a 16-channel 3D LiDAR sensor using a planar chessboard. The six cameras are mounted on a specially designed hexagonal plate to capture omnidirectional images and the LiDAR sensor is mounted on the top of the plates to capture 3D points in 360 degrees. Considering each camera-LiDAR combination as an independent multi-sensor unit, the rotation and translation between the two sensor coordinates are calibrated. The 2D chessboard corners in the camera image are reprojected into 3D space to fit to a 3D plane with respect to the camera coordinate system. The corresponding 3D point data that scan the chessboard are used to fit to another 3D plane with respect to the LiDAR coordinate system. The rotation matrix is calculated by aligning normal vectors of the corresponding planes. In addition, an arbitrary point on the 3D camera plane is projected to a 3D point on the LiDAR plane, and the distance between the two points are iteratively minimized to estimate the translation matrix. At least three or more planes are used to find accurate external parameters between the coordinate systems. Finally, the estimated transformation is refined using the distance between all chessboard 3D points and the LiDAR plane. In the experiments, quantitative error analysis is done using a simulation tool and real test sequences are also used for calibration consistency analysis.
Classifying Multilingual User Feedback using Traditional Machine Learning and Deep Learning

With the rise of social media like Twitter and of software distribution platforms like app stores, users got various ways to express their opinion about software products. Popular software vendors get user feedback thousandfold per day. Research has shown that such feedback contains valuable information for software development teams such as problem reports or feature and support inquires. Since the manual analysis of user feedback is cumbersome and hard to manage many researchers and tool vendors suggested to use automated analyses based on traditional supervised machine learning approaches. In this work, we compare the results of traditional machine learning and deep learning in classifying user feedback in English and Italian into problem reports, inquiries, and irrelevant. Our results show that using traditional machine learning, we can still achieve comparable results to deep learning, although we collected thousands of labels.
Attention by Selection: A Deep Selective Attention Approach to Breast Cancer Classification

Deep learning approaches are widely applied to histopathological image analysis due to the impressive levels of performance achieved. However, when dealing with high-resolution histopathological images, utilizing the original image as input to the deep learning model is computationally expensive, while resizing the original image to achieve low resolution incurs information loss. Some hard-attention based approaches have emerged to select possible lesion regions from images to avoid processing the original image. However, these hard-attention based approaches usually take a long time to converge with weak guidance, and valueless patches may be trained by the classifier. To overcome this problem, we propose a deep selective attention approach that aims to select valuable regions in the original images for classification. In our approach, a decision network is developed to decide where to crop and whether the cropped patch is necessary for classification. These selected patches are then trained by the classification network, which then provides feedback to the decision network to update its selection policy. With such a co-evolution training strategy, we show that our approach can achieve a fast convergence rate and high classification accuracy. Our approach is evaluated on a public breast cancer histopathological image database, where it demonstrates superior performance compared to state-of-the-art deep learning approaches, achieving approximately 98% classification accuracy while only taking 50% of the training time of the previous hard-attention approach.