Reviewerly/exHarmony huggingface dataset:
Code Duplication and Reuse in Jupyter Notebooks

Duplicating one's own code makes it faster to write software. This expediency is particularly valuable for users of computational notebooks. Duplication allows notebook users to quickly test hypotheses and iterate over data. In this paper, we explore how much, how and from where code duplication occurs in computational notebooks, and identify potential barriers to code reuse. Previous work in the area of computational notebooks describes developers' motivations for reuse and duplication but does not show how much reuse occurs or which barriers they face when reusing code. To address this gap, we first analyzed GitHub repositories for code duplicates contained in a repository's Jupyter notebooks, and then conducted an observational user study of code reuse, where participants solved specific tasks using notebooks. Our findings reveal that repositories in our sample have a mean self-duplication rate of 7.6%. However, in our user study, few participants duplicated their own code, preferring to reuse code from online sources.
Soft-Ranking Label Encoding for Robust Facial Age Estimation

Automatic facial age estimation can be used in a wide range of real-world applications.However, this process is challenging due to the randomness and slowness of the aging process.Accordingly, in this paper, we propose a novel method aimed at overcoming the challenges associated with facial age estimation.First, we propose a novel age encoding method, referred to as 'Soft-ranking', which encodes two important properties of facial age, i.e., the ordinal property and the correlation between adjacent ages.Therefore, Soft-ranking provides a richer supervision signal for training deep models.Moreover, we carefully analyze existing evaluation protocols for age estimation, finding that the overlap in identity between the training and testing sets affects the relative performance of different age encoding methods.Moreover, we achieve state-of-the-art performance on four most popular age databases, i.e., Morph II, AgeDB, CLAP2015, and CLAP2016.
IoTrace: A Flexible, Efficient, and Privacy-Preserving IoT-Enabled Architecture for Contact Tracing

Contact tracing promises to help fight the spread of Covid-19 via an early detection of possible contagion events. To this end, most existing solutions share the following architecture: smartphones continuously broadcast random beacons that are intercepted by nearby devices and stored into their local contact logs. In this paper, we propose an IoT-enabled architecture for contact tracing that relaxes the smartphone-centric assumption, and provide a solution that enjoys the following features: (i) it reduces the overhead on the end-user to the bare minimum -- the mobile device only broadcasts its beacons; (ii) it provides the user with a degree of privacy not achieved by competing solutions -- even in the most privacy adverse scenario, the solution provides k-anonymity; and, (iii) it is flexible: the same architecture can be configured to support several models -- ranging from the fully decentralized to the fully centralized ones -- and the system parameters can be tuned to support the tracing of several social interaction models. We also highlight open issues and discuss a number of future research directions.
Character-level neural network model based on Nadam optimization and its application in clinical concept extraction

Clinical concept extraction aims to quickly and effectively extract available data from complex and diverse clinical information, which is a crucial task for medical diagnosis using electronic medical records. Named entity recognition (NER) accurately marks essential information in clinical records based on the characteristics of the target entity, providing a way to extract clinical concepts. In the clinical concept extraction task, the existing methods are not satisfactory to obtain accurate labelling results in the face of large-scale and complex clinical information. To solve this problem, we improve and optimize a named entity recognition method based on the LSTM-CRF model. First, the improved deep neural network model uses two optional configurations of Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BLSTM) to achieve character-level representation. Then the BLSTM layer obtains the context information of the target word, and the Conditional Random Field (CRF) gives constraints to ensure the standardization of the label. On this basis, Nadam is used to optimize the training process of the network. The experimental results show that our new method has an F1 score of 84.61 on the public dataset of 2010 i2b2/VA concept extraction task, which exceeds the LSTM-CRF model. And the recall of 85.41 is ahead of all the methods evaluated on this data set.
Joint Optimization of Data Sampling and User Selection for Federated Learning in the Mobile Edge Computing Systems

Federated learning is a model-level aggregation learning paradigm, which can generate high quality models without collecting the local private data of users. As a distributed coordination learning method, it can be deployed at the edge devices in mobile edge computing (MEC) systems, and provides an applicable solution of implementing network edge intelligence. However, the performance of federated learning cannot be guaranteed in the MEC systems, since the quality of local training data and wireless channels is not always satisfactory. To tackle with this problem, the joint optimization of data sampling and user selection is studied in this paper. First, to capture the key features of deploying federated learning in the MEC systems, we formulate an optimization problem to minimize the accuracy loss and cost, considering the computation and communication resource constraints. Then, an optimization algorithm is designed to jointly optimize the data sampling and user selection strategies, which can approach the stationary optimal solution efficiently. Finally, the numerical simulation and experiment results are provided to evaluate the performance of our proposed optimization scheme, which show that our proposed algorithm can significantly improve the performance of federated learning in the MEC systems.
Improving Transaction Speed and Scalability of Blockchain Systems via Parallel Proof of Work

A blockchain is a distributed ledger forming a distributed consensus on a history of transactions, and is the underlying technology for the Bitcoin cryptocurrency. Its applications are far beyond the financial sector. The transaction verification process for cryptocurrencies is much slower than traditional digital transaction systems. One approach to scalability or the speed at which transactions are processed is to design a solution that offers faster Proof of Work. In this paper, we propose a method for accelerating the process of Proof of Work based on parallel mining rather than solo mining. The goal is to ensure that no more than two or more miners put the same effort into solving a specific block. The proposed method includes a process for selection of a manager, distribution of work and a reward system. This method has been implemented in a test environment that contains all the characteristics needed to perform Proof of Work for Bitcoin and has been tested, using a variety of case scenarios, by varying the difficulty level and number of validators. Experimental evaluations were performed locally and in a cloud environment, and experimental results demonstrate the feasibility the proposed method.
Smart Contract Centric Inference Engine For Intelligent Electric Vehicle Transportation System

The provision of electric vehicles (EVs) is increasing due to the need for ecological green energy. The increment in EVs leads to an intelligent electric vehicle transportation system’s need instead of cloud-based systems to manage privacy and security issues. Collecting and delivering the data to current transportation systems means disclosing personal information about vehicles and drivers. We have proposed a secure and intelligent electric vehicle transportation system based on blockchain and machine learning. The proposed method utilizes the state of the art smart contract module of blockchain to build an inference engine. This system takes the sensors’ data from the vehicle control unit of EV, stores it in the blockchain, makes decisions using an inference engine, and executes those decisions using actuators and user interface. We have utilized a double-layer optimized long short term memory (LSTM) algorithm to predict EV’s stator temperature. We have also performed an informal analysis to demonstrate the proposed system’s robustness and reliability. This system will resolve the security issues for both information and energy interactions in EVs.
GGS: General Gradient Sparsification for Federated Learning in Edge Computing

Federated learning is an emerging concept that trains the machine learning models with the distributed datasets, without sending the raw data to the data center. But in an edge computing enviroment where the wireless network resource is constrained, the key problem of federated learning is the communication overhead for parameters synchronization, which wastes bandwidth, increases training time, and even impacts the model accuracy. Gradient sparsification has received increasing attention, which only updates significant gradients and accumulates insignificant gradients locally. However, how to preserve the accuracy after a high ratio sparsification has always been ignored. In this paper, a General Gradient Sparsification (GGS) framework is proposed for adaptive optimizers, to correct the sparse gradient update process. It consists of two important mechanisms: gradient correction and batch normalization update with local gradients (BN-LG). With gradient correction, the optimizer can properly treat the accumulated insignificant gradients, which makes the model converge better. Furthermore, updating the batch normalization layer with local gradients can relieve the impact of delayed gradients without increasing the communication overhead. We have conducted experiments on LeNet-5, CifarNet, DenseNet-121, and AlexNet with adaptive optimizers. Results show that when 99.9% gradients are sparsified, validation datasets are maintained with top-l accuracy.
Market efficiency and volatility persistence of cryptocurrency during pre‐ and <scp>post‐crash</scp> periods of Bitcoin: Evidence based on fractional integration

Abstract This article investigates both market efficiency and volatility persistence in 12 cryptocurrencies during pre‐crash and post‐crash periods. The article contributes to the debate on the market efficiency of cryptocurrencies in the presence of volatility, considering robust fractional integration methods in both linear and nonlinear setups. We find that markets of Bitcoin and most altcoins considered in our study can be dubbed as efficient, and are also highly volatile, particularly, in the post‐crash period that we are experiencing now. The volatilities are more likely to persist for a shorter period than volatilities in the pre‐crash period. Our work, therefore, renders important information to cryptocurrency market participants and portfolio managers.
A Blockchain-Based Traceable Self-Tallying E-Voting Protocol in AI Era

Artificial intelligence (AI) has demonstrated huge potential in a variety of real-world applications. However, some significant considerations like fairness, transparency and trustworthiness are still challenging when applying AI to trust-oriented applications such as E-voting. E-voting plays a significant role in democratic societies, which requires voters and initiators have strong mutual trust. In this paper, we aim to facilitate the consolidation of AI ecosystems by developing a blockchain-based traceable self-tallying e-voting system. We take advantage of an event-oriented linkable group signature and a homomorphic time-lock puzzle to balance the anonymity and accountability, and the voting scale and efficiency of an e-voting system. The proposed e-voting protocol supports additional functions like multi-choice and self-tallying. We prove that the proposed protocol satisfies anonymity, time-bounded privacy, linkability and full-traceability. We also evaluate the time cost of off-chain operations and the gas cost of on-chain operations, which show the proposed e-voting protocol is practical and can be adopted in real-world applications.
Recommendation by Users’ Multimodal Preferences for Smart City Applications

As an essential role in smart city applications, personalized recommender systems help users to find their potentially interested items from their historically generated data. Recently, researchers have started to utilize the massive user-generated multimodal contents to improve recommendation performance. However, previous methods have at least one of the following drawbacks: 1) employing shallow models, which cannot well capture high-level conceptual information; 2) failing to capture personalized user visual preference. In this article, we present a deep users’ multimodal preferences-based recommendation (UMPR) method to capture the textual and visual matching of users and items for recommendation. We extract textual matching from historical reviews. We construct users’ visual preference embeddings to model users’ visual preference and match them with items’ visual embeddings to obtain the visual matching. We apply UMPR on two applications related to smart city: restaurant recommendation and product recommendation. Experiments show that UMPR outperforms competitive baseline methods.
A Hybrid Improved Dragonfly Algorithm for Feature Selection

Feature selection, which eliminates irrelevant and redundant features, is one of the most efficient classification methods. However, searching for an optimal subset from the original set is still a challenging problem. This paper proposes a novel feature selection algorithm named hybrid improved dragonfly algorithm (HIDA) which combines the advantages of both mRMR and improved dragonfly algorithm (IDA) in order to generate promising candidate subset and achieve higher classification accuracy rate. Firstly, to generate promising subset, features with small weight have chance to be selected into candidate subset with a small probability in mRMR. Secondly, to balance the exploitation and exploration capabilities of IDA, dynamic swarming factors are proposed to balance global and local capability. Lastly, to enhance the exploitation capability of IDA, quantum local optimum and global optimum are introduced in the position updating mechanism. The performance of HIDA is investigated on ten gene expression datasets and eight UCI data sets from the UCI Machine Learning Data Repository. Results show that the performance of HIDA is superior to BBA, BDA, CDA, LBPSO, MPMDWOA and MSMCCS.
A New Under-Sampling Method to Face Class Overlap and Imbalance

Class overlap and class imbalance are two data complexities that challenge the design of effective classifiers in Pattern Recognition and Data Mining as they may cause a significant loss in performance. Several solutions have been proposed to face both data difficulties, but most of these approaches tackle each problem separately. In this paper, we propose a two-stage under-sampling technique that combines the DBSCAN clustering algorithm to remove noisy samples and clean the decision boundary with a minimum spanning tree algorithm to face the class imbalance, thus handling class overlap and imbalance simultaneously with the aim of improving the performance of classifiers. An extensive experimental study shows a significantly better behavior of the new algorithm as compared to 12 state-of-the-art under-sampling methods using three standard classification models (nearest neighbor rule, J48 decision tree, and support vector machine with a linear kernel) on both real-life and synthetic databases.
It Is All About Location: Smartphones and Tracking the Spread of COVID-19

Mobile phone location data have become tied to understandings of and responses to the COVID-19 pandemic. Data visualizations have used mobile phone data to inform people about how mobility practices may be linked to the spread of the virus, and governments have explored contact tracing that relies upon mobile phone data. This article examines how these uses of location data implicate three particular issues that have been present in the growing body of locative media research: (1) anonymized data are often not anonymous, (2) location data are not always representative and can exacerbate inequality, and (3) location data are a key part of the extension of the surveillance state.
Kernelized Mahalanobis Distance for Fuzzy Clustering

Data samples of complicated geometry and nonlinear separability are considered as common challenges to clustering algorithms. In this article, we first construct Mahalanobis distance in the kernel space and then propose a novel fuzzy clustering model with a kernelized Mahalanobis distance, namely KMD-FC. The key contributions of KMD-FC include: first, the construction of KMD matrix is innovatively transformed from the Euclidean distance kernel matrix, which is able to effectively avoid the problem of “curse of dimensionality” posed by explicitly calculating the sample covariance matrix in the kernel space; second, for the first time, the kernelized Gustafson–Kessel (GK) fuzzy C-means algorithm is achieved, which is critically important to extend the applications of the GK algorithm to the nonlinear classification tasks; finally, taking account of the overall distribution of samples in the kernel space after kernel mapping to improve the generalizability of the proposed KMD-FC clustering method. Comprehensive experiments conducted on a wide range of datasets, including synthetic datasets and machine learning repository (UCI) datasets, have validated that the proposed clustering algorithm outperformed the state-of-the-art methods in comparison.
Scoping the potential for earthquake early warning in Aotearoa New Zealand: A sectoral analysis of perceived benefits and challenges

Earthquake Early Warning (EEW) systems are used to detect earthquakes and provide advanced notice of strong shaking intensity, allowing people to take pre-emptive actions that benefit the health of infrastructure and the life-safety of the public. With the advancement of technical research of EEWs, there is also an increasing need to understand EEW's potential impacts on society. In this study, we investigated the perspectives of various sectors in Aotearoa New Zealand on EEW. We used focus groups and interviews with multiple sectors, including but not limited to utilities, infrastructure, emergency management, health and education. We found that participants generally supported the idea of EEW. Participants perceived EEW to provide benefits to (1) life-safety and health & safety (i.e. death and injury); (2) psychological preparedness; (3) activation of emergency plans and situational assessment; and (4) organisational and site-specific actions to reduce impacts and aid response and recovery. Furthermore, our findings also showed that despite support from the sectors, there are challenges in introducing and applying EEW. Participants acknowledged challenges that need to be addressed, such as: (1) users' confidence in the EEW system; (2) the threshold and delivery for sending warnings; and (3) the content and comprehension of an EEW message that would result in people taking appropriate or inappropriate action. As EEW technological research and deployment is rapidly advancing internationally, future research should keep in mind the societal benefits and challenges of EEW when looking to develop useful sectoral applications.
A unified view of modalities in type systems

We propose to unify the treatment of a broad range of modalities in typed lambda calculi. We do so by defining a generic structure of modalities, and show that this structure arises naturally from the structure of intuitionistic logic, and as such finds instances in a wide range of type systems previously described in literature. Despite this generality, this structure has a rich metatheory, which we expose.
Transductive semi-supervised metric learning for person re-identification

Semi-supervised learning is important and has become more widespread because obtaining labeled data is expensive and labor-intensive. In this paper, we focus on the challenging semi-supervised person Re-identification (ReID) task, which is a metric learning problem based on the assumption that unlabeled data is open-set. To address this problem, we propose the Transductive Semi-Supervised Metric Learning (TSSML) framework. In TSSML, we propose a graph-based transductive hard mining method for deeply mining hard triplets in unlabeled data and a degree-based relationship confidence scoring method for further reducing incorrect triplets. Moreover, we investigate the feature consistency loss (FCL) and adopt the curriculum learning strategy to improve the representation learning for semi-supervised ReID. Extensive experiments have been conducted on three large-scale ReID datasets and demonstrate the effectiveness of our TSSML framework.
COVID-19 Mobile Contact Tracing Apps (MCTA): A Digital Vaccine or a Privacy Demolition?

The COVID-19 global pandemic emerged in the late 2019 causing so far a massive global health disruption with many fatalities and huge economy impact, enforcing most if not all governments to a global lockdown.Besides the battle on the medical front-line, governments and the industry also massively explored the deployment of information and communication technologies to track and curb the spread of the virus.On the front-line of these efforts, have been the so-called Mobile Contact Tracing Applications (MCTA).These refer to mobile apps that exploit the rich ecosystem of mobile sensors (e.g., location, proximity) as well as social networks to facilitate the process of identification of persons who may have been previously into contact with a covid infected person and subsequent collection of further information about these contacts.Although MCTA can in theory help governments fight the rapid spread of diseases like COVID-19, there are important privacy considerations and many claim that these technologies will put in place a massive global surveillance infrastructure that will survive even when a vaccine for the COVID-19 disease has been found.This panel aims to discuss the major challenges and open topics surrounding MCTA.The panelists are expected to bring wealth of experience and vision from the academic, governmental and industrial sector to answer a set of challenging questions that are currently open to public debate as well as the global benefits one can expect when fighting the COVID-19 spread.