Reviewerly/exHarmony huggingface dataset:
RUM: Network Representation Learning Using Motifs

We bring the novel idea of exploiting motifs into network embedding, in a dual-level network representation learning model called RUM (network Representation learning Using Motifs). Towards the leveraging of graph motifs that constitute higher-order organizations in a network, we propose two strategies, namely MotifWalk and MotifRe-weighting for learning motif-aware network embeddings. Motif-based and node-based representations are simultaneously generated, so that both the high-order structures and each node's individual properties are preserved in the final embeddings. We demonstrate that RUM has strong and well-balanced capability of preserving lowerorder proximities while discovering and capturing higher-order network structures. In empirical evaluation, RUM is tested on multiple public datasets, that range from small to medium citation networks to a large social network with more than a million nodes. Results show that the use of motifs in the representation learning process brings substantial benefits in reallife tasks, resulting in up to 12% microF1 and 8% macroF1 relative gains for node classification performance over the bestperforming competing methods.
Extended co-citation search: Graph-based document retrieval on a co-citation network containing citation context information

This study proposes a novel extended co-citation search technique, which is graph-based document retrieval on a co-citation network containing citation context information. The proposed search expands the scope of the target documents by repetitively spreading the relationship of co-citation in order to obtain relevant documents that are not identified by traditional co-citation searches. Specifically, this search technique is a combination of (a) applying a graph-based algorithm to compute the similarity score on a complicated network, and (b) incorporating co-citation contexts into the process of calculating similarity scores to reduce the negative effects of an increasing number of irrelevant documents. To evaluate the search performance of the proposed search, 10 proposed methods (five representative graph-based algorithms applied to co-citation networks weighted with/without contexts) are compared with two kinds of baselines (a traditional co-citation search with/without contexts) in information retrieval experiments based on two test collections (biomedicine and computer linguistic articles). The experiment results showed that the scores of the normalized discounted cumulative gain ([email protected]) of the proposed methods using co-citation contexts tended to be higher than those of the baselines. In addition, the combination of the random walk with restart (RWR) algorithm and the network weighted with contexts achieved the best search performance among the 10 proposed methods. Thus, it is clarified that the combination of graph-based algorithms and co-citation contexts are effective in improving the performance of co-citation search techniques, and that sole use of a graph-based algorithm is not enough to enhance search performances from the baselines.
Deep topology network: A framework based on feedback adjustment learning rate for image classification

Convolutional Neural Network (CNN) has demonstrated its superior ability to achieve amazing accuracy in computer vision field. However, due to the limitation of network depth and computational complexity, it is still difficult to obtain the best classification results for the specific image classification tasks. In order to improve classification performance without increasing network depth, a new Deep Topology Network (DTN) framework is proposed. The key idea of DTN is based on the iteration of multiple learning rate feedback. The framework consists of multiple sub-networks and each sub-network has its own learning rate. After the determined iteration period, these learning rates can be adjusted according to the feedback of training accuracy, in the feature learning process, the optimal learning rate is updated iteratively to optimize the loss function. In practice, the proposed DTN framework is applied to several state-of-the-art deep networks, and its performance is tested by extensive experiments and comprehensive evaluations of CIFAR-10 and MNIST benchmarks. Experimental results show that most deep networks can benefit from the DTN framework with an accuracy of 99.5% on MINIST dataset, which is 5.9% higher than that on the CIFAR-10 benchmark.
State of Public and Private Blockchains

It has been a decade since the concept of blockchain was invented as the underlying core data structure of the permissionless or public Bitcoin cryptocurrency network. Since then, several cryptocurrencies, tokens and ICOs have emerged. After much speculation and hype, significant number of them have become problematic or worthless! The public blockchain system Ethereum emerged by generalizing the use of blockchains to manage any kind of asset, be it physical or purely digital, with the introduction of Smart Contracts. Over the years, numerous myths have developed with respect to the purported utility and the need for public blockchains. The adoption and further adaptation of blockchains and smart contracts for use in the permissioned or private environments is what I consider to be useful and of practical consequence. Hence, the technical aspects of only private blockchain systems will be the focus of my SIGMOD 2019 keynote. Along the way, I will bust many myths associated with public blockchains. I will also compare traditional database technologies with blockchain systems' features and identify desirable future research topics.
Towards Social Choice-based Explanations in Group Recommender Systems

Explanations help users to better understand why a set of items has been recommended. Compared to single user recommender systems, explanations in group recommender systems have further goals. Examples thereof are fairness which helps to take into account as much as possible group members' preferences and consensus which persuades group members to agree on a decision. This paper proposes different explanation types and investigates which explanation best helps to increase the fairness perception, consensus perception, and satisfaction of group members with regard to group recommendations. We conducted a user study to evaluate the proposed explanations. The results show that explanations which take into account preferences of all or the majority of group members achieve the best results in terms of the mentioned aspects. Moreover, there exist positive correlations among these aspects, i.e., as the perceived fairness (or the perceived consensus) of explanations increases, so does the satisfaction of users with regard to group recommendations. In addition, in the context of repeated decisions, the inclusion of group members' satisfaction from previous decisions in the explanations helps to improve the fairness perception of users with regard to group recommendations.
Modelling an Adjustable Autonomous Multi-agent Internet of Things System for Elderly Smart Home

Internet of Things (IoT) introduces many intelligent applications that are closely attached to humans' daily activities. This advanced technology attempts to bridge the gap between the information world and the physical world. Recent studies investigate efficient, flexible, scalable and reliable IoT systems that not only control things and devices on behalf of humans but adaptable to humans' preferences. However, the autonomous control of the IoT in a smart home or healthcare environment subjects to many factors such as human health, time and date. For example, peoples' needs and behaviours during workdays differ from weekends or a young person needs and behaviours differs from an elderly person. Hence, the practical setting of a smart home entails flexible management to the autonomous control of IoT systems. This paper proposes an architecture of Adjustable-Autonomous Multi-agent IoT (AAMA-IoT) system to resolve a number of the IoT management of control and application interface challenges. The AAMA-IoT is applied in an elderly smart home simulation in which autonomous agents control passive things such as a chair or door and active things such as a television or an air conditioner. The test results show that the AAMA-IoT system controls 14 things with average activities recognition accuracy of 96.97%.
Deep class-skewed learning for face recognition

Face datasets often exhibit highly-skewed class distribution, i.e., rich classes contain a plenty amount of instances, while only few images belong to poor classes. To mitigate this issue, we explore deep class-skewed learning from two aspects in this paper: feature augmentation and feature normalization. To deal with the imbalance distribution problem, we put forward a novel feature augmentation method termed Large Margin Feature Augmentation (LMFA) to augment hard features and equalize class distribution, leading to balanced classification boundaries between rich and poor classes. By considering the distribution gap between training and testing features, A novel feature normalization called Transferable Domain Normalization (TDN) is proposed to normalize domain-specific features to obey an identical Gaussian distribution, and enhance the feature generalization. Extensive experiments are conducted on five popular face recognition datasets including LFW, YTF, CFP, AgeDB and MegaFace. We achieve remarkable results on par with or better than the state-of-the-art methods, which demonstrate the effectiveness of our proposed learning class-balanced features.
Challenges with Responding to Static Analysis Tool Alerts

Static analysis tool alerts can help developers detect potential defects in the code early in the development cycle. However, developers are not always able to respond to the alerts with their preferred action and may turn away from using the tool. In this paper, we qualitatively analyze 280 Stack Overflow (SO) questions regarding static analysis tool alerts to identify the challenges developers face in understanding and responding to these alerts. We find that the most prevalent question on SO is how to ignore and filter alerts, followed by validation of false positives. Our findings confirm prior researchers' findings related to notification communication theory as 44.6% of the SO questions that we analyzed indicate developers face communication challenges.
Investigating Next Steps in Static API-Misuse Detection

Application Programming Interfaces (APIs) often impose constraints such as call order or preconditions. API misuses, i.e., usages violating these constraints, may cause software crashes, data-loss, and vulnerabilities. Researchers developed several approaches to detect API misuses, typically still resulting in low recall and precision. In this work, we investigate ways to improve API-misuse detection. We design MUDetect, an API-misuse detector that builds on the strengths of existing detectors and tries to mitigate their weaknesses. MUDetect uses a new graph representation of API usages that captures different types of API misuses and a systematically designed ranking strategy that effectively improves precision. Evaluation shows that MUDetect identifies real-world API misuses with twice the recall of previous detectors and 2.5x higher precision. It even achieves almost 4x higher precision and recall, when mining patterns across projects, rather than from only the target project.
A Brief Survey on Nature-Inspired Metaheuristics for Feature Selection in Classification in this Decade

Feature selection (FS) has been a hot topic in the past few decades. More and more researches spring up due to the fast development of data and computer science. Feature selection as a preprocessing technique plays an important role in improving the efficiency of data mining and analysis over substantial real-world applications. This paper provides a brief survey on feature selection based on nature-inspired metaheuristics for classification in this decade to detail the newest researches. The main challenges facing researchers are concluded as accuracy, stability, scalability and computational cost. Related work trying to solve these challenges is discussed.
An Identity-Based Encryption Scheme for Data Security in Fog Computing

Fog computing provides better integration facilities between cloud computing and Internet of things with real-time interactions. It delivers faster computing functionalities sitting close to user applications and has local storage options. Despite these potentials in fogging, maintaining secure data communication is still a challenging issue and needs further development. In this paper, we have presented an Identity-Based Encryption (IBE) scheme that ensures secure data transmission to authorized users. To provide data security, we have proposed a four-level Hierarchical Identity-Based Architecture for Fog Computing(HIBAF). In addition, we have evaluated our scheme to analyze the performance in terms of user load, memory utilization, response time, and delay time over different sizes of datasets. Finally, we have compared our results with other cryptography systems to figure out the effectiveness of our scheme and found overall 30% efficient.
Cognitive Cybersecurity for CPS-IoT Enabled Healthcare Ecosystems

Cyber Physical Systems (CPS)-Internet of Things (IoT) enabled healthcare services and infrastructures improve human life, but are vulnerable to a variety of emerging cyber-attacks. Cybersecurity specialists are finding it hard to keep pace of the increasingly sophisticated attack methods. There is a critical need for innovative cognitive cybersecurity for CPS-IoT enabled healthcare ecosystem. This paper presents a cognitive cybersecurity framework for simulating the human cognitive behaviour to anticipate and respond to new and emerging cybersecurity and privacy threats to CPS-IoT and critical infrastructure systems. It includes the conceptualisation and description of a layered architecture which combines Artificial Intelligence, cognitive methods and innovative security mechanisms.
Exposing Library API Misuses Via Mutation Analysis

Misuses of library APIs are pervasive and often lead to software crashes and vulnerability issues. Various static analysis tools have been proposed to detect library API misuses. They often involve mining frequent patterns from a large number of correct API usage examples, which can be hard to obtain in practice. They also suffer from low precision due to an over-simplified assumption that a deviation from frequent usage patterns indicates a misuse. We make two observations on the discovery of API misuse patterns. First, API misuses can be represented as mutants of the corresponding correct usages. Second, whether a mutant will introduce a misuse can be validated via executing it against a test suite and analyzing the execution information. Based on these observations, we propose MutApi, the first approach to discovering API misuse patterns via mutation analysis. To effectively mimic API misuses based on correct usages, we first design eight effective mutation operators inspired by the common characteristics of API misuses. MutApi generates mutants by applying these mutation operators on a set of client projects and collects mutant-killing tests as well as the associated stack traces. Misuse patterns are discovered from the killed mutants that are prioritized according to their likelihood of causing API misuses based on the collected information. We applied MutApi on 16 client projects with respect to 73 popular Java APIs. The results show that MutApi is able to discover substantial API misuse patterns with a high precision of 0.78. It also achieves a recall of $0.49$ on the MuBench benchmark, which outperforms the state-of-the-art techniques.
Multiple Model Kalman and Particle Filters and Applications: A Survey

Kalman Filters (KF) is a recursive estimation algorithm, a special case of Bayesian estimators under Gaussian, linear and quadratic conditions. For non-linear systems, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) provide first and higher order linearization approximations. Particle Filters (PF), on the other hand, are sequential Monte Carlo methods to provide estimations for non-linear non-Gaussian problems. For complex systems, Kalman or Particle Filter based single model filters may not be sufficient to model the system behaviour. Multiple Model (MM) Filters achieve more reliable estimates by using more than one filter with different models in parallel and the outputs of each filter are fused by assigning a probability to each filter. The most common methods used in the literature for multiple model estimation are Multiple Model Adaptive Estimation (MMAE) and Interacting Multiple Model (IMM). This paper presents an overview of the recent research on multiple model filters.
CheaPay: An Optimal Algorithm for Fee Minimization in Blockchain-Based Payment Channel Networks

The past several years have witnessed an explosive growth in cryptocurrencies, but the blockchain-based cryptocurrencies have also raised many concerns, among which a crucial one is the scalability issue. Suffering from the large overhead of global consensus and security assurance, even the leading cryptocurrencies can only handle up to tens of transactions per second, which largely limits their applications in real-world scenarios. Among many proposals to improve the cryptocurrency scalability, one of the most promising and mature solutions is the payment channel network (PCN), which offers the off-chain settlement of transactions with minimal involvement of expensive blockchain operations. In this paper, we investigate the problem of payment routing in PCNs from an optimization perspective, which is to minimize the transaction fee of a payment path, subject to the timeliness and feasibility constraints. We present an optimal distributed algorithm CheaPay for this problem. Extensive simulations demonstrate that CheaPay significantly outperforms baseline algorithms in terms of the success ratio and the average accepted value.
Review of Intent Detection Methods in the Human-Machine Dialogue System

Abstract Spoken language understanding is an important part of the human-machine dialogue system, intent detection is a sub-task of spoken language understanding, and it is very important. The accuracy of intent detection is directly related to the performance of semantic slot filling, and it is helpful to the following research of the dialogue system. Considering the difficulty of intent detection in human-machine dialogue system, the traditional machine learning method cannot understand the deep semantic information of user’s discourse. This paper mainly analyzes, compares and summarizes the deep learning methods applied in the research of intent detection in recent years, and further considers how to apply deep learning model to multi-intent detection task, so as to promote the research of multi-intent detection methods based on deep neural network.
Daxing Smartphone Identification Dataset

Over the past few years, the imaging device has changed from digital cameras to smartphone cameras. With the popularity of mobile Internet applications, there explode massive digital images and videos captured by such smartphones, which are nearly held one per person. Consequently, the capturing source of images/videos delivers valuable identity information for criminal investigations and critical forensic evidence. It is significant to address the source identification of smartphone images/videos. In this paper, we build a Daxing smartphone identification dataset, which collects images and videos from extensive smartphones of different brands, models and devices. Specifically, the dataset includes 43 400 images and 1,400 videos captured by 90 smartphones of 22 models belonging to 5 brands. For example, there are 23 smartphone devices for the iPhone 6S (Plus) model. To the best of our knowledge, Daxing dataset uses the largest amount of smartphones for image/video source identification compared with other related datasets, as well as the highest numbers of devices per model and captured images/videos. The dataset has been released as a free and open-source for scientific researchers and criminal investigators.
Breast mass detection from the digitized X-ray mammograms based on the combination of deep active learning and self-paced learning

Breast mass detection is a challenging task in mammogram, since mass is usually embedded and surrounded by various normal tissues with similar density. Recently, deep learning has achieved impressive performance on this task. However, most deep learning methods require large amounts of well-annotated datasets. Generally, the training datasets is generated through manual annotation by experienced radiologists. However, manual annotation is very time-consuming, tedious and subjective. In this paper, for the purpose of minimizing the annotation efforts, we propose a novel learning framework for mass detection that incorporates deep active learning (DAL) and self-paced learning (SPL) paradigm. The DAL can significantly reduce the annotation efforts by radiologists, while improves the efficiency of model training by obtaining better performance with fewer overall annotated samples. The SPL is able to alleviate the data ambiguity and yield a robust model with generalization capability in various scenarios. In detail, we first employ a few of annotated easy samples to initialize the deep learning model using Focal Loss. In order to find out the most informative samples, we propose an informativeness query algorithm to rank the large amounts of unannotated samples. Next, we propose a self-paced sampling algorithm to select a number of the most informative samples. Finally, the selected most informative samples are manually annotated by experienced radiologists, which are added into the annotated samples for the model updating. This process is looped until there are not enough most informative samples in the unannotated samples. We evaluate the proposed learning framework on 2223 digitized mammograms, which are accompanied with diagnostic reports containing weakly supervised information. The experimental results suggest that our proposed learning framework achieves superior performance over the counterparts. Moreover, our proposed learning framework dramatically reduces the requirement of the annotated samples, i.e., about 20% of all training data.
Designing Animated Transitions to Convey Aggregate Operations

Abstract Data can be aggregated in many ways before being visualized in charts, profoundly affecting what a chart conveys. Despite this importance, the type of aggregation is often communicated only via axis titles. In this paper, we investigate the use of animation to disambiguate different types of aggregation and communicate the meaning of aggregate operations. We present design rationales for animated transitions depicting aggregate operations and present the results of an experiment assessing the impact of these different transitions on identification tasks. We find that judiciously staged animated transitions can improve subjects' accuracy at identifying the aggregation performed, though sometimes with longer response times than with static transitions. Through an analysis of participants' rankings and qualitative responses, we find a consistent preference for animation over static transitions and highlight visual features subjects report relying on to make their judgments. We conclude by extending our animation designs to more complex charts of aggregated data such as box plots and bootstrapped confidence intervals.