Reviewerly/exHarmony huggingface dataset:
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation

Federated learning (FL) can be essential in knowledge representation, reasoning, and data mining applications over multi-source knowledge graphs (KGs).A recent study FedE first proposes an FL framework that shares entity embeddings of KGs across all clients.However, entity embedding sharing from FedE would incur a severe privacy leakage.Specifically, the known entity embedding can be used to infer whether a specific relation between two entities exists in a private client.In this paper, we introduce a novel attack method that aims to recover the original data based on the embedding information, which is further used to evaluate the vulnerabilities of FedE.Furthermore, we propose a Federated learning paradigm with privacy-preserving Relation embedding aggregation (FEDR) to tackle the privacy issue in FedE.Besides, relation embedding sharing can significantly reduce the communication cost due to its smaller size of queries.We conduct extensive experiments to evaluate FEDR with five different KG embedding models and three datasets.Compared to FedE, FEDR achieves similar utility and significant improvements regarding privacy-preserving effect and communication efficiency on the link prediction task.
How to Create Personas: Three Persona Creation Methodologies with Implications for Practical Employment

Background: Personas are a technique for enhanced understanding of users and customers to improve the user-centered design of systems and products. Their creation can be categorized using three persona creation methodologies: Qualitative, Quantitative, and Mixed Methods. Despite the apparent differences in these methodologies, no previous review has systemically compared and contrasted the strengths and weaknesses of each of these methodologies for persona development. Method: This manuscript maps and navigates persona literature to identify the benefits and challenges of these three persona creation methodologies. Furthermore, the strategies and opportunities of the different methodologies are presented. Results: The results summarize the strengths and weaknesses of each of the three principal persona creation methodologies and offer suggestions of the benefits of their employment. Conclusion: In conclusion, we offer insights into the construction and usage of personas for practitioners and researchers, and we propose a framework to determine which persona creation methodology is most suitable for a given context. Keywords: Algorithmically-Generated Personas, Persona Analytics, Persona Science.
Changing the Rules? RDA and cataloguing in Europe

This paper provides an overview of plans to implement RDA: Resource Description &amp; Access in Europe to replace existing cataloguing rules. It is based on survey information gathered by EURIG and CILIP CIG. It includes background on the development of RDA as a replacement for AACR2.
Hash-Based Signatures

This note describes a digital signature system based on cryptographichash functions, following the seminal work in this area of Lamport,Diffie, Winternitz, and Merkle, as adapted by Leighton and Micali in1995. It specifies a one-time signature scheme and a general signaturescheme. These systems provide asymmetric authentication without usinglarge integer mathematics and can achieve a high security level. Theyare suitable for compact implementations, are relatively simple toimplement, and naturally resist side-channel attacks. Unlike mostother signature systems, hash-based signatures would still be secureeven if it proves feasible for an attacker to build a quantumcomputer. This document is a product of the Crypto Forum ResearchGroup (CFRG) in the IRTF.
Recurrent Shape Regression

An end-to-end network architecture, the Recurrent Shape Regression (RSR), is presented to deal with the task of facial shape detection, a crucial step in many computer vision problems. The RSR generalizes the conventional cascaded regression into a recurrent dynamic network through abstracting common latent models with stage-to-stage operations. Instead of invariant regression transformation, we construct shape-dependent dynamic regressors to attain the recurrence of regression action itself. The regressors can be stacked into a high-order regression network to represent more complex shape regression. By further integrating feature learning as well as global shape constraint, the RSR becomes more controllable in entire optimization of shape regression, where the gradient computation can be efficiently back-propagated through time. To handle the possible partial occlusions of shapes, we propose a mimic virtual occlusion strategy by randomly disturbing certain point cliques without the requirement of any annotations of occlusion information or even occluded training data. Extensive experiments on five face datasets demonstrate that the proposed RSR outperforms the recent state-of-the-art cascaded approaches.
FeatureLego: Volume Exploration Using Exhaustive Clustering of Super-Voxels

We present a volume exploration framework, FeatureLego, that uses a novel voxel clustering approach for efficient selection of semantic features. We partition the input volume into a set of compact super-voxels that represent the finest selection granularity. We then perform an exhaustive clustering of these super-voxels using a graph-based clustering method. Unlike the prevalent brute-force parameter sampling approaches, we propose an efficient algorithm to perform this exhaustive clustering. By computing an exhaustive set of clusters, we aim to capture as many boundaries as possible and ensure that the user has sufficient options for efficiently selecting semantically relevant features. Furthermore, we merge all the computed clusters into a single tree of meta-clusters that can be used for hierarchical exploration. We implement an intuitive user-interface to interactively explore volumes using our clustering approach. Finally, we show the effectiveness of our framework on multiple real-world datasets of different modalities.
Rectification from Radially-Distorted Scales

This paper introduces the first minimal solvers that jointly estimate lens distortion and affine rectification from repetitions of rigidly-transformed coplanar local features. The proposed solvers incorporate lens distortion into the camera model and extend accurate rectification to wide-angle images that contain nearly any type of coplanar repeated content. We demonstrate a principled approach to generating stable minimal solvers by the Gröbner basis method, which is accomplished by sampling feasible monomial bases to maximize numerical stability. Synthetic and real-image experiments confirm that the solvers give accurate rectifications from noisy measurements if used in a ransac-based estimator. The proposed solvers demonstrate superior robustness to noise compared to the state of the art. The solvers work on scenes without straight lines and, in general, relax strong assumptions about scene content made by the state of the art. Accurate rectifications on imagery taken with narrow focal length to fisheye lenses demonstrate the wide applicability of the proposed method. The method is automatic, and the code is published at https://github.com/prittjam/repeats .
Minimizing Node Expansions in Bidirectional Search with Consistent Heuristics

A* is optimally effective with regard to node expansions among unidirectional admissible algorithms—those that only assume that the heuristic used is admissible. Among bidirectional algorithms the Fractional MM algorithm is optimally effective (given the correct parameters) among admissible algorithms.This paper generalizes the bidirectional result to more complex settings where more information on the problem domain can be exploited: (1) When the cost of the minimal edge is known. (2) When the algorithm knows that the heuristics are consistent. This characterization uses a novel algorithm called MT. MT is similar to Fractional MM and is also optimally effective, but simpler to analyze.
Bayesian Distance Clustering.

Model-based clustering is widely used in a variety of application areas. However, fundamental concerns remain about robustness. In particular, results can be sensitive to the choice of kernel representing the within-cluster data density. Leveraging on properties of pairwise differences between data points, we propose a class of Bayesian distance clustering methods, which rely on modeling the likelihood of the pairwise distances in place of the original data. Although some information in the data is discarded, we gain substantial robustness to modeling assumptions. The proposed approach represents an appealing middle ground between distance- and model-based clustering, drawing advantages from each of these canonical approaches. We illustrate dramatic gains in the ability to infer clusters that are not well represented by the usual choices of kernel. A simulation study is included to assess performance relative to competitors, and we apply the approach to clustering of brain genome expression data.
An Efficient Lucas Sequence-Based Batch Auditing Scheme for the Internet of Medical Things

The Internet of Medical Things (IoMT) is one of the most promising applications of the newly emerging wearable devices and body-area networks. Generally, the IoMT devices are composed of lightweight facilities, which collect the health data (i.e., heart rate, body temperature, and respiratory rate) and upload them to the cloud. As the medical information is sensitive for the data owners (DOs), it is critical to ensure the integrity and confidentiality of these outsourced data. Normally, blinding (or encryption) and auditing are classical methods for achieving these goals. However, until to now, quite a few batch auditing schemes have been proposed, and they all share the following two shortcomings: 1) lacking of an efficient mechanism to identify the corrupted data files once the batch auditing task fails and 2) the communication overhead of audit phase increases linearly with the number of data files being audited or the number of data blocks being challenged. In order to overcome the above issues, in this paper, we propose an efficient batch auditing scheme based on the Lucas sequence for IoMT. To be specific, utilizing the self-query technique and polynomial commitment technique, we reduce the communication complexity of the batch auditing phase from linear to a constant scale. In addition, according to the recursion of the Lucas sequence, we design a novel search method for efficiently identifying the corrupted data files when the batch auditing task fails. The security of our scheme ensures that curious third-party auditor and cloud service provider cannot gain any real-data contents of the DO files. The detailed experiments show that our scheme is more efficient than the existing batch auditing schemes in terms of the communication overhead of the audit phase and the efficiency in identifying the corrupted files when the batch auditing task fails.
Type 2 constacyclic codes over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e19" altimg="si15.gif"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msub><mml:mrow><mml:mo>[</mml:mo><mml:mi>u</mml:mi><mml:mo>]</mml:mo></mml:mrow><mml:mo>∕</mml:mo><mml:mrow><mml:mo>〈</mml:mo><mml:msup><mml:…

Let F2m be a finite field of cardinality 2m, n be an odd positive integer, and denote R=F2m[u]∕〈u3〉. Let δ,α∈F2m×. Then (δ+αu2)-constacyclic codes over R are called constacyclic codes over R of Type 2. In this paper, an explicit representation and a complete description for all distinct (δ+αu2)-constacyclic codes over R of length 2n and their dual codes are given. Moreover, explicit formulas for the number of codewords in each code and the number of all such codes are provided respectively. In particular, all distinct self-dual (1+αu2)-constacyclic codes over R of length 2n are presented precisely. In addition, a complement to a result in Cao et al. (2017) is given.
Weakly Supervised Hierarchical Text Classification

The dimensions in a text cube often have taxonomic class structures. When allocating documents into a text cube, it is highly desirable to organize text documents into a class hierarchy instead of performing flat classification. In this chapter, we introduce how to extend our previous weakly-supervised text classification approach to hierarchical settings, for enabling multi-granular document classification.
Find Me if You Can: Deep Software Clone Detection by Exploiting the Contest between the Plagiarist and the Detector

Code clone is common in software development, which usually leads to software defects or copyright infringement. Researchers have paid significant attention to code clone detection, and many methods have been proposed. However, the patterns for generating the code clones do not always remain the same. In order to fool the clone detection systems, the plagiarists, known as the clone creator, usually conduct a series of tricky modifications on the code fragments to make the clone difficult to detect. The existing clone detection approaches, which neglects the dynamics of the “contest” between the plagiarist and the detectors, is doomed to be not robust to adversarial revision of the code. In this paper, we propose a novel clone detection approach, namely ACD, to mimic the adversarial process between the plagiarist and the detector, which enables us to not only build strong a clone detector but also model the behavior of the plagiarists. Such a plagiarist model may in turn help to understand the vulnerability of the current software clone detection tools. Experiments show that the learned policy of plagiarist can help us build stronger clone detector, which outperforms the existing clone detection methods.
On Structured Argumentation with Conditional Preferences

We study defeasible knowledge bases with conditional preferences (DKB). A DKB consists of a set of undisputed facts and a rule-based system that contains different types of rules: strict, defeasible, and preference. A major challenge in defining the semantics of DKB lies in determining how conditional preferences interact with the attack relations represented by rebuts and undercuts, between arguments.&#x0D; We introduce the notions of preference attack relations as sets of attacks between preference arguments and the rebuts or undercuts among arguments as well as of preference attack relation assignments which map knowledge bases to preference attack relations. We present five rational properties (referred to as regular properties), the inconsistency-resolving, effective rebuts, context-independence, attack monotonicity and link-orientation properties generalizing the properties of the same names for the case of unconditional preferences.&#x0D; Preference attack relation assignment are defined as regular if they satisfy all regular properties. We show that the set of regular assignments forms a complete lower semilattice whose least element is referred to as the canonical preference attack relation assignment. Canonical attack relation assignment represents the semantics of preferences in defeasible knowledge bases as intuitively, it could be viewed as being uniquely identified by the regular properties together with the principle of minimal removal of undesired attacks. We also present the normal preference attack relation assignment as an approximation of the canonical attack relation assignment.
Inter-Class Angular Loss for Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have shown great power in various classification tasks and have achieved remarkable results in practical applications. However, the distinct learning difficulties in discriminating different pairs of classes are largely ignored by the existing networks. For instance, in CIFAR-10 dataset, distinguishing cats from dogs is usually harder than distinguishing horses from ships. By carefully studying the behavior of CNN models in the training process, we observe that the confusion level of two classes is strongly correlated with their angular separability in the feature space. That is, the larger the inter-class angle is, the lower the confusion will be. Based on this observation, we propose a novel loss function dubbed “Inter-Class Angular Loss” (ICAL), which explicitly models the class correlation and can be directly applied to many existing deep networks. By minimizing the proposed ICAL, the networks can effectively discriminate the examples in similar classes by enlarging the angle between their corresponding class vectors. Thorough experimental results on a series of vision and nonvision datasets confirm that ICAL critically improves the discriminative ability of various representative deep neural networks and generates superior performance to the original networks with conventional softmax loss.
Deep Learning for Smartphone-Based Human Activity Recognition Using Multi-sensor Fusion

In the field of ubiquitous computing, machines need to be aware of the present context to enable anticipatory communication with humans. This leads to human-centric applications that have the primary objective of improving the Quality-of-Life (QoL) of its users. One important type of context information for these applications is the current activity of the user, which can be derived from environmental and wearable sensors. Due to the processing capabilities and the number of sensors embedded in a smartphone, this device exhibits the most promise among other existing technologies in human activity recognition (HAR) research. While machine learning-based solutions have been successful in past HAR studies, several design struggles can be easily resolved with deep learning. In this paper, we investigated Convolutional Neural Networks and Long Short-Term Memory Networks in dealing with common challenges in smartphone-based HAR, such as device location and subject dependency, and manual feature extraction. We showed that the CNN model accomplished location- and subject-independent recognition with overall accuracy of 98.38% and 90.61%, respectively. The LSTM model also performed location-independent recognition with an accuracy of 97.17% but has a subject-independent recognition accuracy of only 80.02%. Finally, optimal performance of the network was achieved by performing Bayesian Optimization using Gaussian Processes in tuning the design hyperparameters.
Study of a Plateau Surface Evaluation Method Using a Fast M-Estimation Type Hough Transform

A surface with stratified functional properties (hereafter, referred to as the “plateau surface”) improves the tribological characteristics of sliding surfaces in machine tools, automobile parts, engines, etc. The evaluation methods of the plateau surface are listed in the ISO 13565 standard. However, since the evaluation methods listed in ISO 13565-3 are associated with several problems, these evaluation methods have been rarely used in production management. A plateau surface evaluation method that applied image recognition technology was developed in previous research studies to solve these problems. However, this method is time consuming with a calculation time of approximately 20 s. Therefore, in this research, we developed a new plateau surface evaluation method that applied the fast M-estimation type Hough transformation to achieve a computational time of a few seconds. It is expected that this method will be used in mass production processes.
Balancing People Re-Identification Data for Deep Parts Similarity Learning

The learning of a distance metric, to measure the degree of appearance similarity between a pair of person images, becomes a remarkable challenge in person re-identification due to the unbalanced nature of the datasets, where the number of instances of a query identity is very limited against the vast quantity of different people representations, among which it must be identified. This article presents two network models, a Siamese and a Triplet one, which exploit the multiple possible combinations of training samples in pairs and triplets, respectively. Both models have been used to learn features for nine different body parts, which has been simultaneously analyzed to embed the inter-view variations in a Mahalanobis distance. The influence of the model and training data in the features learning has been evaluated through several tests over the challenging PRID2011 dataset, besides of the proposed system re-identification performance in comparison with other state-of-the-art approaches.
Identification of anomaly behavior of ships based on KNN and LOF combination algorithm

On the issue of low precision of ship anomaly behavior detection method based on global variable and calculation complexity of ship anomaly detection based on local variable, a combination of K Nearest Neighbor (KNN) and Local Outlier Factor (LOF) algorithm for ship anomaly behavior detection is proposed in this paper. Firstly, ship anomaly data candidate set is filtered by K nearest neighbor, then calculating local deviation index by LOF algorithm, lastly setting threshold value to judge ship anomaly behavior, so as to achieve rapid, effective ship anomaly behavior detection. To a certain extent, it helps the maritime safety supervision department to identify the potential risks of their ship, and improve regulatory efficiency.
Deep Learning Architectures for Object Detection and Classification

Object detection and classification have observed large amount of transformation and research after the advances in machine learning algorithms. The advancement in the computing power and data availability is complimenting this transformation in object detection. In recent times, research in the field of object detection is dominated by special type of neural network called Convolutional Neural Network (CNN). The object detection system has to localize objects in an image and accurately classify it. CNN is well suited for this task as it can accurately find features like edges, corners and even more advanced features needed to detect object. This chapter provides detailed overview on how CNN works and how it is useful in object detection and classification task. After that popular deep networks based on CNN like ResNet, VGG16, VGG19, GoogleNet and MobileNet are explained in detail. These networks worked well for object classification task but needed sliding window technique for localizing object in an image. It worked slowly as it needed to process many windows for a single image. This led to more advanced algorithms for object detection based on CNN like Convolutional Neural Network with Region proposals (R-CNN), fast R-CNN, faster R-CNN, Single shot multi-box detector (SSD) and You Only Look Once (YOLO). This chapter provides a detail explanation of how these algorithms work and comparison between them. Most of the deep learning algorithms require large amount of data and dedicated hardware like GPUs to train. To overcome this, the concept of transfer learning is discovered. In that pre-trained models of popular CNN architecture are used to solve new problems. So in the last part of the chapter this concept of transfer learning and when it is useful is explained.