Reviewerly/exHarmony huggingface dataset:
A Learned Sketch for Subgraph Counting

Subgraph counting, as a fundamental problem in network analysis, is to count the number of subgraphs in a data graph that match a given query graph by either homomorphism or subgraph isomorphism. The importance of subgraph counting derives from the fact that it provides insights of a large graph, in particular a labeled graph, when a collection of query graphs with different sizes and labels are issued. The problem of counting is challenging. On one hand, exact counting by enumerating subgraphs is NP-hard. % On the other hand, approximate counting by subgraph isomorphism can only support 3/5-node query graphs over unlabeled graphs. % Another way for subgraph counting is to specify it as an \SQL query and estimate the cardinality of the query in \rdbm. Existing approaches for cardinality estimation can only support subgraph counting by homomorphism up to some extent, as it is difficult to deal with sampling failure when a query graph becomes large. A question that arises is if subgraph counting can be supported by machine learning (ML) and deep learning (DL). The existing DL approach for subgraph isomorphism can only support small data graphs. The ML/DL approaches proposed in \rdbm context for approximate query processing and cardinality estimation cannot be used, as subgraph counting is to do complex self-joins over one relation, whereas existing approaches focus on multiple relations. In this paper, we propose an Active Learned Sketch for Subgraph Counting (\ALSS) with two main components: a sketch learned (ŁSS) and an active learner (\AL). The sketch is learned by a neural network regression model, and the active learner is to perform model updates based on new arrival test query graphs. % We conduct extensive experimental studies to confirm the effectiveness and efficiency of \ALSS using large real labeled graphs. Moreover, we show that \ALSS can assist query optimizers to find a better query plan for complex multi-way self-joins.
Mobile Augmented Reality Applications in Teaching: A Proposed Technology Acceptance Model

This study proposed MARAM, a mobile augmented reality acceptance model that determines the factors that affect teachers' intention to use AR applications in their teaching. MARAM extends TAM by adding the variables of perceived relative advantage, perceived enjoyment, facilitating conditions, and mobile self - efficacy. MARAM was tested in a pilot empirical study with 127 teachers who used educational mobile AR applications and developed their own ones. The results of regression analysis showed that MARAM can predict a satisfactory percentage of the variance in teachers' intention, attitude, perceived usefulness and perceived ease of use. Attitude, perceived usefulness, and facilitating conditions affected intention. Both perceived usefulness and perceived enjoyment affected attitude. Furthermore, perceived relative advantage and perceived enjoyment affected perceived usefulness. In addition, mobile self-efficacy and facilitating conditions affected perceived ease of use. However, perceived ease of use did not have any effect on attitude and perceived usefulness. MARAM could serve as the basis for future studies on teachers' acceptance of mobile AR applications and be expanded through the addition of other variables.
Context-Aware Layout to Image Generation with Enhanced Object Appearance

A layout to image (L2I) generation model aims to generate a complicated image containing multiple objects (things) against natural background (stuff), conditioned on a given layout. Built upon the recent advances in generative adversarial networks (GANs), existing L2I models have made great progress. However, a close inspection of their generated images reveals two major limitations: (1) the object-to-object as well as object-to-stuff relations are often broken and (2) each object’s appearance is typically distorted lacking the key defining characteristics associated with the object class. We argue that these are caused by the lack of context-aware object and stuff feature encoding in their generators, and location-sensitive appearance representation in their discriminators. To address these limitations, two new modules are proposed in this work. First, a context-aware feature transformation module is introduced in the generator to ensure that the generated feature encoding of either object or stuff is aware of other coexisting objects/stuff in the scene. Second, instead of feeding location-insensitive image features to the discriminator, we use the Gram matrix computed from the feature maps of the generated object images to preserve location-sensitive information, resulting in much enhanced object appearance. Extensive experiments show that the proposed method achieves state-of-the-art performance on the COCO-Thing-Stuff and Visual Genome benchmarks. Code available at: https://github.com/wtliao/layout2img.
Computation Offloading in Untrusted MEC-Aided Mobile Blockchain IoT Systems

Deploying a mobile edge computing (MEC) server in the mobile blockchain-enabled Internet of things (IoT) system is a promising approach to improve the system performance, however, it imposes a significant challenge on the trust of the MEC server. To address this problem, we first propose an untrusted MEC proof of work (PoW) scheme in mobile blockchain networks where plenty of nonce hash computing demands can be offloaded to the MEC server. Then, we design a nonce ordering algorithm for this scheme to provide fairer computing resource allocation for all mobile IoT devices/users. Specifically, we formulate the user’s nonce selection strategy as a non-cooperative game, where utilities of the individual user are maximized in the untrusted MEC-aided mobile blockchain networks. We also prove the existence of Nash equilibrium and analyze that the cooperation behavior is unsuitable for blockchain-enabled IoT devices by using the repeated game. Finally, we design the blockchain’s difficulty adjustment mechanism to ensure stable block times during a long period of time. Compared with the weighted round-robin algorithm, our proposed nonce ordering algorithm can provide fairer computation resources and optimal nonce selection strategies for all mobile users. Network stability is gained through the proposed blockchain’s difficulty adjustment mechanism. The analysis and optimization results provide valuable design insights for practical mobile blockchain IoT systems.
From Semantic Categories to Fixations: A Novel Weakly-supervised Visual-auditory Saliency Detection Approach

Thanks to the rapid advances in the deep learning techniques and the wide availability of large-scale training sets, the performances of video saliency detection models have been improving steadily and significantly. However, the deep learning based visual-audio fixation prediction is still in its infancy. At present, only a few visual-audio sequences have been furnished with real fixations being recorded in the real visual-audio environment. Hence, it would be neither efficiency nor necessary to re-collect real fixations under the same visual-audio circumstance. To address the problem, this paper advocate a novel approach in a weakly-supervised manner to alleviating the demand of large-scale training sets for visual-audio model training. By using the video category tags only, we propose the selective class activation mapping (SCAM), which follows a coarse-to-fine strategy to select the most discriminative regions in the spatial-temporal-audio circumstance. Moreover, these regions exhibit high consistency with the real human-eye fixations, which could subsequently be employed as the pseudo GTs to train a new spatial-temporal-audio (STA) network. Without resorting to any real fixation, the performance of our STA network is comparable to that of the fully supervised ones. Our code and results are publicly available at https://github.com/guotaowang/STANet.
Robust visual object tracking using context-based spatial variation via multi-feature fusion

With the emergence of camera technology, visual tracking has witnessed great attention in the field of computer vision. For instance, numerous discriminative correlation filter (DCF) methods are broadly used in tracking, nevertheless, most of them fail to efficiently find the target in challenging situations which leads to tracking failure throughout the sequences. In order to handle these issues, we propose contextual information based spatial variation with a multi-feature fusion method (CSVMF) for robust object tracking. This work incorporates the contextual information of the target to determine the location of the target accurately, which utilizes the relationship between the target and its surroundings to increase the efficiency of the tracker. In addition, we integrate the spatial variation information which measures the second-order difference of the filter to avoid the over-fitting problem caused by the changes in filter coefficient. Furthermore, we adopt multi-feature fusion strategy to enhance the target appearance by using different metrics. The tracking results from different features are fused by employing peak-to-sidelobe ratio (PSR) which measures the peak strength of the response. Finally, we conduct extensive experiments on TC128, DTB70, [email protected], and UAV123 datasets to demonstrate that the proposed method achieves a favorable performance over the existing ones.
Gradient Descent Effects on Differential Neural Architecture Search: A Survey

Gradient Descent, an effective way to search for the local minimum of a function, can minimize training and validation loss of neural architectures and also be incited in an appropriate order to decrease the searching cost of neural architecture search. In recent trends, the neural architecture search (NAS) is enormously used to construct an automatic architecture for a specific task. Mostly well-performed neural architecture search methods have adopted reinforcement learning, evolutionary algorithms, or gradient descent algorithms to find the best-performing candidate architecture. Among these methods, gradient descent-based architecture search approaches outperform all other methods in terms of efficiency, simplicity, computational cost, and validation error. In view of this, an in-depth survey is necessary to cover the usefulness of gradient descent method and how this can benefit neural architecture search. We begin our survey with basic concepts of neural architecture search, gradient descent, and their unique properties. Our survey then delves into the impact of gradient descent method on NAS and explores the effect of gradient descent in the search process to generate the candidate architecture. At the same time, our survey reviews mostly used gradient-based search approaches in NAS. Finally, we provide the current research challenges and open problems in the NAS-based approaches, which need to be addressed in future research.
Blockchain-IoT Sensor (BIoTS): A Solution to IoT-Ecosystems Security Issues

Sensor devices that act in the IoT architecture perception layer are characterized by low data processing and storage capacity. These reduced capabilities make the system ubiquitous and lightweight, but considerably reduce its security. The IoT-based Food Traceability Systems (FTS), aimed at ensuring food safety and quality, serve as a motivating scenario for BIoTS development and deployment; therefore, security challenges and gaps related with data integrity are analyzed from this perspective. This paper proposes the BIoTS hardware design that contains some modules built-in VHDL (SHA-256, PoW, and SD-Memory) and other peripheral electronic devices to provide capabilities to the perception layer by implementing the blockchain architecture’s security requirements in an IoT device. The proposed hardware is implemented on FPGA Altera DE0-Nano. BIoTS can participate as a miner in the blockchain network through Smart Contracts and solve security issues related to data integrity and data traceability in an Blockchain-IoT system. Blockchain algorithms implemented in IoT hardware opens a path to IoT devices’ security and ensures participation in data validation inside a food certification process.
A Deep Dive into Deep Learning Approaches for Text-to-SQL Systems

Data is a prevalent part of every business and scientific domain,but its explosive volume and increasing complexity make data querying challenging even for experts. For this reason, numerous text-to-SQL systems have been developed that enable querying relational databases using natural language. The recent advances on deep neural networks along with the creation of two large datasets specifically made for training text-to-SQL systems, have paved the path for a novel and very promising research area. The purpose of this tutorial is a deep dive into this area, covering state-of-the-art techniques for natural language representation in neural networks,benchmarks that sparked research and competition, recent text-to-SQL systems using deep learning techniques, as well as open problems and research opportunities.
Detecting Malicious Model Updates from Federated Learning on Conditional Variational Autoencoder

In federated learning, the central server combines local model updates from the clients in the network to create an aggregated model. To protect clients' privacy, the server is designed to have no visibility into how these updates are generated. The nature of federated learning makes detecting and defending against malicious model updates a challenging task. Unlike existing works that struggle to defend against Byzantine clients, the paper considers defending against targeted model poisoning attack in the federated learning setting. The adversary aims to reduce the model performance on targeted subtasks while maintaining the main task's performance. This paper proposes Fedcvae, a robust and unsupervised federated learning framework where the central server uses conditional variational autoencoder to detect and exclude malicious model updates. Since the reconstruction error of malicious updates is much larger than that of benign ones, it can be used as an anomaly score. We formulate a dynamic threshold of reconstruction error to differentiate malicious updates from normal ones based on this idea. Fedcvae is tested with extensive experiments on IID and non-IID federated benchmarks, showing a competitive performance over existing aggregation methods under Byzantine attack and targeted model poisoning attack.
Interactive Dimensionality Reduction for Comparative Analysis

Finding the similarities and differences between groups of datasets is a fundamental analysis task. For high-dimensional data, dimensionality reduction (DR) methods are often used to find the characteristics of each group. However, existing DR methods provide limited capability and flexibility for such comparative analysis as each method is designed only for a narrow analysis target, such as identifying factors that most differentiate groups. This paper presents an interactive DR framework where we integrate our new DR method, called ULCA (unified linear comparative analysis), with an interactive visual interface. ULCA unifies two DR schemes, discriminant analysis and contrastive learning, to support various comparative analysis tasks. To provide flexibility for comparative analysis, we develop an optimization algorithm that enables analysts to interactively refine ULCA results. Additionally, the interactive visualization interface facilitates interpretation and refinement of the ULCA results. We evaluate ULCA and the optimization algorithm to show their efficiency as well as present multiple case studies using real-world datasets to demonstrate the usefulness of this framework.
Implementing evolutionary optimization on actual quantum processors

This paper introduces a new evolutionary algorithm with the support of an actual quantum processor, a computing device which uses phenomena from quantum mechanics to enable a considerable speed-up in computation. In particular, the proposed approach uses quantum superposition and entanglement to implement quantum evolutionary concepts such as quantum chromosome, entangled crossover, rotation mutation, and quantum elitism, to efficiently perform genetic evolution on quantum devices, and converge towards proper sub-optimal solutions of a given optimization problem. The proposed quantum genetic algorithm has been implemented by using a hybrid hardware architecture, where classical processors interact with the family of quantum processors provided by the IBM Q Experience® initiative. As shown in the experimental section, the proposed quantum genetic algorithm's performance highlights that the synergy between quantum and evolutionary computation results in a new and promising bio-inspired optimization strategy.
SADPonzi: Detecting and Characterizing Ponzi Schemes in Ethereum Smart Contracts

Ponzi schemes are financial scams that lure users under the promise of high profits. With the prosperity of Bitcoin and blockchain technologies, there has been growing anecdotal evidence that this classic fraud has emerged in the blockchain ecosystem. Existing studies have proposed machine-learning based approaches for detecting Ponzi schemes, i.e., either based on the operation codes (opcodes) of the smart contract binaries or the transaction patterns of addresses. However, state-of-the-art approaches face several major limitations, including lacking interpretability and high false positive rates. Moreover, machine-learning based methods are susceptible to evasion techniques, and transaction-based techniques do not work on smart contracts that have a small number of transactions. These limitations render existing methods for detecting Ponzi schemes ineffective. In this paper, we propose SADPonzi, a semantic-aware detection approach for identifying Ponzi schemes in Ethereum smart contracts. Specifically, by strictly following the definition of Ponzi schemes, we propose a heuristic-guided symbolic execution technique to first generate the semantic information for each feasible path in smart contracts and then identify investor-related transfer behaviors and the distribution strategies adopted. Experimental result on a well-labelled benchmark suggests that SADPonzi can achieve 100% precision and recall, outperforming all existing machine-learning based techniques. We further apply SADPonzi to all 3.4 million smart contracts deployed by EOAs in Ethereum and identify 835 Ponzi scheme contracts, with over 17 million US Dollars invested by victims. Our observations confirm the urgency of identifying and mitigating Ponzi schemes in the blockchain ecosystem.
Deep Multi-Task Learning for Joint Localization, Perception, and Prediction

Over the last few years, we have witnessed tremendous progress on many subtasks of autonomous driving including perception, motion forecasting, and motion planning. However, these systems often assume that the car is accurately localized against a high-definition map. In this paper we question this assumption, and investigate the issues that arise in state-of-the-art autonomy stacks under localization error. Based on our observations, we design a system that jointly performs perception, prediction, and localization. Our architecture is able to reuse computation between the three tasks, and is thus able to correct localization errors efficiently. We show experiments on a large-scale autonomy dataset, demonstrating the efficiency and accuracy of our proposed approach.
Topic recommendation for software repositories using multi-label classification algorithms

Many platforms exploit collaborative tagging to provide their users with faster and more accurate results while searching or navigating. Tags can communicate different concepts such as the main features, technologies, functionality, and the goal of a software repository. Recently, GitHub has enabled users to annotate repositories with topic tags. It has also provided a set of featured topics, and their possible aliases, carefully curated with the help of the community. This creates the opportunity to use this initial seed of topics to automatically annotate all remaining repositories, by training models that recommend high-quality topic tags to developers. In this work, we study the application of multi-label classification techniques to predict software repositories’ topics. First, we map the large-space of user-defined topics to those featured by GitHub. The core idea is to derive more information from projects’ available documentation. Our data contains about 152K GitHub repositories and 228 featured topics. Then, we apply supervised models on repositories’ textual information such as descriptions, README files, wiki pages, and file names. We assess the performance of our approach both quantitatively and qualitatively. Our proposed model achieves Recall@5 and LRAP scores of 0.890 and 0.805, respectively. Moreover, based on users’ assessment, our approach is highly capable of recommending correct and complete set of topics. Finally, we use our models to develop an online tool named Repository Catalogue, that automatically predicts topics for GitHub repositories and is publicly available1.
Requirement Engineering Challenges for AI-intense Systems Development

Availability of powerful computation and communication technology as well as advances in artificial intelligence enable a new generation of complex, AI-intense systems and applications. Such systems and applications promise exciting improvements on a societal level, yet they also bring with them new challenges for their development. In this paper we argue that significant challenges relate to defining and ensuring behaviour and quality attributes of such systems and applications. We specifically derive four challenge areas from relevant use cases of complex, AI-intense systems and applications related to industry, transportation, and home automation: understanding, determining, and specifying (i) contextual definitions and requirements, (ii) data attributes and requirements, (iii) performance definition and monitoring, and (iv) the impact of human factors on system acceptance and success. Solving these challenges will imply process support that integrates new requirements engineering methods into development approaches for complex, AI-intense systems and applications. We present these challenges in detail and propose a research roadmap.
Comparison of machine learning techniques for the identification of human activities from inertial sensors available in a mobile device after the application of data imputation techniques

Human activity recognition (HAR) is a significant research area due to its wide range of applications in intelligent health systems, security, and entertainment games. Over the past few years, many studies have recognized human daily living activities using different machine learning approaches. However, the performance of a machine learning algorithm varies based on the sensing device type, the number of sensors in that device, and the position of the underlying sensing device. Moreover, the incomplete activities (i.e., data captures) in a dataset also play a crucial role in the performance of machine learning algorithms. Therefore, we perform a comparative analysis of eight commonly used machine learning algorithms in different sensor combinations in this work. We used a publicly available mobile sensors dataset and applied the k-Nearest Neighbors (KNN) data imputation technique for extrapolating the missing samples. Afterward, we performed a couple of experiments to figure out which algorithm performs best at which sensors' data combination. The experimental analysis reveals that the AdaBoost algorithm outperformed all machine learning algorithms for recognizing five different human daily living activities with both single and multi-sensor combinations. Furthermore, the experimental results show that AdaBoost is capable to correctly identify all the activities presented in the dataset with 100% classification accuracy.