Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Ranked Spatial Keyword Query in Mobile Cloud-Assisted Fog Computing

With the increasing popularity of GPS-equipped mobile devices in cloud-assisted fog computing scenarios, massive spatio-textual data is generated and outsourced to cloud servers for storage and analysis. Existing privacy-preserving range query or ranked keyword search schemes does not support a unified index, and are just applicable for the symmetric environment where all users sharing the same secret key. To solve this issue, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> rivacy-preserving <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> anked <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> patial keyword <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</u> uery in mobile cloud-assisted <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> og computing (PRSQ-F). Specifically, we design a novel comparable product encoding strategy that combines both spatial and textual conditions tightly to retrieve the objects in query range and with the highest textual similarity. Then, we use a new conversion protocol and attribute-based encryption to support privacy-preserving retrieval and malicious user traceability in the asymmetric environment where different query users have different keys. Furthermore, we construct an R-tree-based index to achieve faster-than-linear retrieval. Our formal security analysis shows that data security can be guaranteed. Our empirical experiments using a real-world dataset demonstrate the efficiency and feasibility of PRSQ-F.
Automated feature engineering for prediction of victories in online computer games

An accurate evaluation of player-win likelihoods during online games is critical for controlling the attractiveness and immersion of the gameplay, especially if played against an AI bot. Predicting game victories is a very challenging problem heavily depends on the complexity and the stage of the game. With the multitude of ways and formats that the players, the gameplay, individual state and history can be encoded and utilized by machine learning (ML) models, it is fascinating to take part in the IEEE Big Data 2021 Cup and explore which data representations, feature engineering methods and prediction models work better than others and what levels of predictability they can achieve in the specific use case of predicting victories in Tactical Troops: Anthracite Shift online computer game. Our explorations throughout the game and a high 4th place in the Cup’s final indicate that a careful and comprehensive feature engineering methodology numerically capturing the last state of the game paired with the variants of the gradient boosting models offer a robust and competitive solution for this challenge.
Quantized Distributed Federated Learning for Industrial Internet of Things

Federated learning (FL) enables multiple devices to collaboratively train a shared machine learning (ML) model while keeping all the local data private, which is a crucial enabler to implement artificial intelligence (AI) at the edge of the Industrial Internet of Things (IIoT) scenario. Distributed FL (DFL) based on Device-to-Device (D2D) communications can solve the single point of failure and scaling issue of centralized FL, but subject to the communication resource limitation of D2D links. Thus, it is crucial to reduce the data transmission volume of FL models between devices. In this article, we propose a quantization-based DFL (Q-DFL) mechanism in a D2D network and prove its convergence. Q-DFL contains two phases: 1) in phase I, a local model is trained with the stochastic gradient descent (SGD) algorithm on each IIoT device and then exchanges the quantified model parameters between neighboring nodes and 2) in phase II, a quantitative consensus mechanism is designed to ensure the local models converge to the same global model. We also propose an adaptive stopping mechanism and a synchronization protocol to fulfill the phase transition from phase I to phase II. Simulation results reveal that with Q-DFL, a 1-bit quantizer can be employed without affecting the model convergence at the price of slight accuracy reduction, which achieves significant transmission bandwidth saving. Further simulation of Q-DFL for the MobileNet model is fulfilled with different quantization bit levels, which reveals their performance tradeoff among the system information flow consumption, the system time delay, and the system energy cost.
High-accuracy simultaneous phase extraction and unwrapping method for single interferogram based on convolutional neural network

The high-precision dynamic measurement of optical surfaces in unstable environments is a critical problem in the process of fabrication and application. In order to solve this problem, a high-precision ultra-fast phase demodulation method based on convolutional neural network is proposed in this paper. The wrapped phase and corresponding wrap count map can be obtained from one interferogram at the same time, so the phase extraction and unwrapping are performed simultaneously. It only takes 0.02 seconds to demodulate single-frame interferogram. Simulation and experimental results show that the root-mean-square error between this algorithm and phase-shifting algorithm is better than 0.01λ (λ=632.8nm). It indicates that the proposed method has excellent performance in measurement accuracy and efficiency.
WP-GBDT: An Approach for Winner Prediction using Gradient Boosting Decision Tree

Predicting victories in video games from rich history of gameplay logs is a severe challenge to game developers. It is hard for humans to evaluate the real-time game situation and predict who will win the video game. In this paper, we propose an approach to this problem by following the sequence of machine learning steps which consist of feature engineering, feature selection, and model construction. We conduct a detailed analysis of the game logs and generate effective features from different granularity gameplay logs in the feature engineering phase. Then, we design a group based recursive feature elimination method for feature selection. In model construction, we present an ensemble approach that combines stacking and averaging for prediction to improve the generalization performance of models. The proposed approach achieves AUC scores of 0.8997 on the test set, which is the highest final score in the competition.
GLE-Net: A Global and Local Ensemble Network for Aerial Object Detection

Abstract Recent advances in camera-equipped drone applications increased the demand for visual object detection algorithms with deep learning for aerial images. There are several limitations in accuracy for a single deep learning model. Inspired by ensemble learning can significantly improve the generalization ability of the model in the machine learning field, we introduce a novel integration strategy to combine the inference results of two different methods without non-maximum suppression. In this paper, a global and local ensemble network (GLE-Net) was proposed to increase the quality of predictions by considering the global weights for different models and adjusting the local weights for bounding boxes. Specifically, the global module assigns different weights to models. In the local module, we group the bounding boxes that corresponding to the same object as a cluster. Each cluster generates a final predict box and assigns the highest score in the cluster as the score of the final predict box. Experiments on benchmarks VisDrone2019 show promising performance of GLE-Net compared with the baseline network.
Most Cited Works on Cloud Computing: The ‘Citation Classics’ as Viewed through Dimensions.ai

Cloud computing is an interdisciplinary field which has witnessed exponential growth in recent years and there is a need to consolidate the research efforts. This study used citation analysis method to identify most cited papers which have achieved the status of citation classics. It is the first study using Dimensions.ai database to identify classics in cloud computing literature. Using a search strategy data was extracted from Dimensions database from 2010 and 2020. Finally 96 papers were identified as citation classics which were cited with at least 400 citations. Data was finally analyzed through Biblioshiny package of R Studio. It was found that Future Generation Computer Systems, IEEE Communication Survey and Tutorials, IEEE Transaction on Parallel and Distributed Systems, Computer and IEEE Access are the key journals contributing to classics. Most papers were contributed from China and USA. R. Buyya is the prolific author who has contributed nine citation classics.
Multi-objective optimization of data deployment and scheduling based on the minimum cost in geo-distributed cloud

Nowadays, the amount of data coming from terminal devices is increasing on a vast scale. Data centers in different geographical locations store vast amounts of data. In this paper, to optimize the data deployment problem, a data layout algorithm that can be satisfied with the load balancing of the cluster is introduced. Under the three constraints of cost, capacity, and load balance, this paper presents the idea of ant colony optimization and uses the Lagrangian relaxation method to verify the value of the optimal solution. For improving the execution efficiency of various cloud data centers, a task scheduling method is proposed. This method solves the problems of delay and transmission cost and then uses the gray Markov-based prediction method to predict the dynamic changes of resources of different cluster nodes and select the most suitable task scheduling node. As shown in the experiment, the completion time of the application can be shortened by this algorithm. Meanwhile, in this paper, the consumption of cluster resources is reduced, and the throughput of the cluster is improved.
Domain-Specific Keyword Extraction Using Joint Modeling of Local and Global Contextual Semantics

Domain-specific keyword extraction is a vital task in the field of text mining. There are various research tasks, such as spam e-mail classification, abusive language detection, sentiment analysis, and emotion mining, where a set of domain-specific keywords (aka lexicon) is highly effective. Existing works for keyword extraction list all keywords rather than domain-specific keywords from a document corpus. Moreover, most of the existing approaches perform well on formal document corpuses but fail on noisy and informal user-generated content in online social media. In this article, we present a hybrid approach by jointly modeling the local and global contextual semantics of words, utilizing the strength of distributional word representation and contrasting-domain corpus for domain-specific keyword extraction. Starting with a seed set of a few domain-specific keywords, we model the text corpus as a weighted word-graph. In this graph, the initial weight of a node (word) represents its semantic association with the target domain calculated as a linear combination of three semantic association metrics, and the weight of an edge connecting a pair of nodes represents the co-occurrence count of the respective words. Thereafter, a modified PageRank method is applied to the word-graph to identify the most relevant words for expanding the initial set of domain-specific keywords. We evaluate our method over both formal and informal text corpuses (comprising six datasets), and show that it performs significantly better in comparison to state-of-the-art methods. Furthermore, we generalize our approach to handle the language-agnostic case, and show that it outperforms existing language-agnostic approaches.
Business as usual through contact tracing app: what influences intention to download?

(2021). Business as usual through contact tracing app: what influences intention to download? Journal of Marketing Management: Vol. 37, Understanding the effects of social distancing on consumer and business practices during a pandemic: Marketing and management implications, pp. 1903-1932.
Learning to Generate Levels From Nothing

Machine learning for procedural content generation has recently become an active area of research. Levels vary in both form and function and are mostly unrelated to each other across games. This has made it difficult to assemble suitably large datasets to bring machine learning to level design in the same way as it's been used for image generation. Here we propose Generative Playing Networks which design levels for itself to play. The algorithm is built in two parts; an agent that learns to play game levels, and a generator that learns the distribution of playable levels. As the agent learns and improves its ability, the space of playable levels, as defined by the agent, grows. The generator targets the agents playability estimates to then update its understanding of what constitutes a playable level. We call this process of learning the distribution of data found through self-discovery with an environment, self-supervised inductive learning. Unlike previous approaches to procedural content generation, Generative Playing Networks are end-to-end differentiable and does not require human-designed examples or domain knowledge. We demonstrate the capability of this framework by training an agent and level generator for a 2D dungeon crawler game.
Artificial intelligence for knowledge graphs of cryptocurrency anti-money laundering in fintech

Cryptocurrency anti-money laundering has become an important research topic in recent years. Legal empirical research combined with AI technology has received considerable attention. How to construct a knowledge graph of cryptocurrency anti-money laundering in a small sample of international cases and judgments on the prevention and control of cryptocurrency money laundering has become an essential issue for a better understanding of the relationship between the crime patterns and emerging financial technologies. In this study, we proposed artificial intelligence meta-learning with a few-shot learning model to construct a cryptocurrency anti-money laundering knowledge graph. The research method of this study aims at the abuse of electronic payment tools and cryptocurrency in various crimes by analyzing the causes and background, the amount of money, the type of crime, and the growth trend in recent years. The contribution of this study is that the proposed AI cryptocurrency anti-money laundering knowledge graphs in fintech can be applied to the content analysis and question-and-answer system of legal documents.
Bayesian Optimization for auto-tuning GPU kernels

Finding optimal parameter configurations for tunable GPU kernels is a non-trivial exercise for large search spaces, even when automated. This poses an optimization task on a nonconvex search space, using an expensive to evaluate function with unknown derivative. These characteristics make a good candidate for Bayesian Optimization, which has not been applied to this problem before. However, the application of Bayesian Optimization to this problem is challenging. We demonstrate how to deal with the rough, discrete, constrained search spaces, containing invalid configurations. We introduce a novel contextual variance exploration factor, as well as new acquisition functions with improved scalability, combined with an informed acquisition function selection mechanism. By comparing the performance of our Bayesian Optimization implementation on various test cases to the existing search strategies in Kernel Tuner, as well as other Bayesian Optimization implementations, we demonstrate that our search strategies generalize well and consistently outperform other search strategies by a wide margin.
R-SWAP: Relay Based Atomic Cross-Chain Swap Protocol

In this paper, we consider the problem of cross-chain transactions where parties that do not trust each other safely exchange digital assets across blockchains. Open blockchains models are decentralized ledgers that keep records of transactions. They are comparable with distributed account books. While they have proven their potential as a store of value, exchanging assets across several blockchains remains a challenge. Our paper proposes a new protocol, R-SWAP, for cross-chain swaps that outperforms existing solutions. Our protocol is built on top of two abstractions: relays and adapters that we formalize for the first time in this paper. Furthermore, we prove the correctness of R-SWAP and analytically evaluate its performances, in terms of cost and latency. Moreover, we evaluate the performances of R-SWAP in two case studies showing the generality of our approach: atomic swaps between Ethereum and Bitcoin (two popular permissionless blockchains) and atomic swaps between Ethereum and Tendermint (one permissionless and one permissioned blockchain).
PartitionChain: A Scalable and Reliable Data Storage Strategy for Permissioned Blockchain

Blockchain, a specific distributed database which maintains a list of data records against tampering and corruption, has aroused wide interests and become a hot topic in the real world. Nevertheless, the increasingly heavy storage consumption brought by the full-replication data storage mechanism, becomes a bottleneck to the system scalability. To address this problem, a reliable storage scheme named BFT-Store (Qi <i>et al.</i> 2020), integrating erasure coding with Byzantine Fault Tolerance (BFT), was proposed recently. While, three critical problems are still left open: (i) The complex re-initialization process of the blockchain when the number of nodes varies; (ii) The high computational overload of downloading data; (iii) The massive communication on the network. This paper proposes a better trade-off for blockchain storage scheme termed PartitionChain which addresses the above three problems, maintaining the merits of BFT-Store. First, our scheme allows the original nodes to merely update a single aggregate signature (e.g., 320 bits) when the number of nodes varies. Using aggregate signatures as the proof of the encoded data not only saves the storage costs but also gets rid of the trusted third party. Second, the computational complexity of retrieving data by decoding, compared to BFT-Store, is greatly reduced by about <inline-formula><tex-math notation="LaTeX">$2^{18}$</tex-math></inline-formula> times on each node. Third, the amount of transmitted data for recovering each block is reduced from <inline-formula><tex-math notation="LaTeX">$O(n)$</tex-math></inline-formula> (assuming <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of nodes) to <inline-formula><tex-math notation="LaTeX">$O(1)$</tex-math></inline-formula> , by partitioning each block into smaller pieces and applying Reed-Solomon coding to each block. Furthermore, this paper also introduces a reputation ranking system where the malicious behaviors of the nodes can be detected and marked, enabling PartitionChain to check the credits of each node termly and expel the nodes with misbehavior to the specific extent. Comparing with BFT-Store, our scheme allows blockchain system to suit dynamic network with higher efficiency and scalability.
An Image Dehazing Approach With Adaptive Color Constancy for Poor Visible Conditions

The presence of suspended particles in turbid media not only superimposes a veil on the scene but also changes the color of the scene, resulting in poor visibility and low contrast of images. It is essential to overcome these effects for further applications of the images. However, these effects are difficult to eliminate simultaneously and thus the restoration for the images becomes multitasked. In this letter, we propose an extended image formation model that introduces color constancy theory to discuss the change of light color when natural light penetrates the medium to the object. Based on the model, the image restoration task is divided into two parts: dehazing and color correction, and for these two parts, scene depth fusion-based dehazing and adaptive color constancy are proposed. The dehazing approach introduces a weighted fusion strategy to estimate scene depth to achieve a better dehazing effect. The color constancy approach improves the Gray World hypothesis with gain factors to adaptively compensate for the chromatic loss and extends the color deviation solved by color constancy from color temperature to medium. Extensive experiments on images of different scenes prove the effectiveness of the proposed method in image restoration.
Neural Acoustic-Phonetic Approach for Speaker Verification With Phonetic Attention Mask

Traditional acoustic-phonetic approach makes use of both spectral and phonetic information when comparing the voice of speakers. While phonetic units are not equally informative, the phonetic context of speech plays an important role in speaker verification (SV). In this paper, we propose a neural acoustic-phonetic approach that learns to dynamically assign differentiated weights to spectral features for SV. Such differentiated weights form a phonetic attention mask (PAM). The neural acoustic-phonetic framework consists of two training pipelines, one for SV and another for speech recognition. Through the PAM, we leverage the phonetic information for SV. We evaluate the proposed neural acoustic-phonetic framework on the RSR2015 database Part III corpus, that consists of random digit strings. We show that the proposed framework with PAM consistently outperforms baseline with an equal error rate reduction of 13.45% and 10.20% for female and male data, respectively.
Contrastive Learning for Mitochondria Segmentation

Mitochondria segmentation in electron microscopy images is essential in neuroscience. However, due to the image degradation during the imaging process, the large variety of mitochondrial structures, as well as the presence of noise, artifacts and other sub-cellular structures, mitochondria segmentation is very challenging. In this paper, we propose a novel and effective contrastive learning framework to learn a better feature representation from hard examples to improve segmentation. Specifically, we adopt a point sampling strategy to pick out representative pixels from hard examples in the training phase. Based on these sampled pixels, we introduce a pixel-wise label-based contrastive loss which consists of a similarity loss term and a consistency loss term. The similarity term can increase the similarity of pixels from the same class and the separability of pixels from different classes in feature space, while the consistency term is able to enhance the sensitivity of the 3D model to changes in image content from frame to frame. We demonstrate the effectiveness of our method on MitoEM dataset as well as FIB-SEM dataset and show better or on par with state-of-the-art results.