Reviewerly/exHarmony huggingface dataset:
Neighborhood-based particle swarm optimization with discrete crossover for nonlinear equation systems

Solving nonlinear equation systems (NESs) is important in engineering and science. In general, NESs have more than one root, and it is a great challenge to locate multiple roots in a single run for numerical computation. In order to tackle this challenging task, we propose a neighborhood-based particle swarm optimization with discrete crossover (MNPSO). MNPSO has three main characteristics: (1) it adopts a two-stage framework to balance exploitation and exploration well; (2) a distance-based adaptive neighborhood technique is proposed, which can form an appropriate neighborhood for each particle; (3) a discrete crossover operator improves the performance of PSO in the high dimension problems. We choose thirty NESs with different features as the test suite to evaluate the performance of MNPSO. The experimental results reveal that MNPSO can locate multiple roots in a single run, and the root ratio and the success ratio of MNPSO are higher than other methods.
Meta-Path Based Neighbors for Behavioral Target Generalization in Sequential Recommendation

Click-through rate (CTR) prediction is a crucial task in recommender systems, which aims to model users’ dynamic preferences from their historical behaviors. To achieve this goal, most of the previous models adopt sequential neural networks (e.g., GRU) to encode the historical interactions into item representations for recommendations. Though these methods can perform well on recommending highly relevant items to users, we argue that such models are sub-optimal for the long-term user experience due to highly skewed recommendations: Monotonous items with similar subjects get more exposure because of inadequate interest explorations. Thus, some items which are not quite relevant to the users’ historical preferences should be considered. To address these limitations, we propose a Heterogeneous Graph Enhanced Sequential Neural Network, HGESNN, to explore the interests of users beyond their historical interactions by explicitly modeling item relations with meta-path constructions. We incorporate a transformer-based network to embed personalized user intents into sequential learning. In the experiments on both public and industrial datasets, HGESNN significantly outperforms the state-of-the-art solutions. Specifically, HGESNN has been deployed in the main traffic of our Image-Text feed recommender system, which obtains 6.28%, 6.82%, and 4.77% CTR gains on news, novels, and entertainment contents, respectively.
Automatic source code summarization with graph attention networks

Source code summarization aims to generate concise descriptions for code snippets in a natural language, thereby facilitates program comprehension and software maintenance. In this paper, we propose a novel approach–GSCS–to automatically generate summaries for Java methods, which leverages both semantic and structural information of the code snippets. To this end, GSCS utilizes Graph Attention Networks to process the tokenized abstract syntax tree of the program, which employ a multi-head attention mechanism to learn node features in diverse representation sub-spaces, and aggregate features by assigning different weights to its neighbor nodes. GSCS further harnesses an additional RNN-based sequence model to obtain the semantic features and optimizes the structure by combining its output with a transformed embedding layer. We evaluate our approach on two widely-adopted Java datasets; the experiment results confirm that GSCS outperforms the state-of-the-art baselines.
Quantum algorithm for neighborhood preserving embedding

Neighborhood preserving embedding (NPE) is an important linear dimensionality reduction technique that aims at preserving the local manifold structure. NPE contains three steps, i.e. , finding the nearest neighbors of each data point, constructing the weight matrix, and obtaining the transformation matrix. Liang et al . proposed a variational quantum algorithm (VQA) for NPE [ Phys. Rev. A 101 032323 (2020)]. The algorithm consists of three quantum sub-algorithms, corresponding to the three steps of NPE, and was expected to have an exponential speedup on the dimensionality n . However, the algorithm has two disadvantages: (i) It is not known how to efficiently obtain the input of the third sub-algorithm from the output of the second one. (ii) Its complexity cannot be rigorously analyzed because the third sub-algorithm in it is a VQA. In this paper, we propose a complete quantum algorithm for NPE, in which we redesign the three sub-algorithms and give a rigorous complexity analysis. It is shown that our algorithm can achieve a polynomial speedup on the number of data points m and an exponential speedup on the dimensionality n under certain conditions over the classical NPE algorithm, and achieve a significant speedup compared to Liang et al .’s algorithm even without considering the complexity of the VQA.
Iteratively Reweighted Algorithm for Fuzzy $c$-Means

Fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> -means method (FCM) is a popular clustering method, which uses alternating iteration algorithm to update membership matrix <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {F}$</tex-math></inline-formula> and center matrix <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {M}$</tex-math></inline-formula> of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d \times c$</tex-math></inline-formula> size. However, original FCM suffers from finding a suboptimal local minimum, which limits the performance of FCM. In this article, we propose a new optimization method for fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> -means problem. We first propose an equivalent minimization problem of FCM, then, a simple alternating iteration algorithm is proposed to solve the new minimization problem, which involves an effective and theoretically guaranteed Iteratively Reweighted (IRW) method, so we call the new optimization method IRW-FCM. Our IRW-FCM utilizes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> not <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$dc$</tex-math></inline-formula> intermediate variables to update <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {F}$</tex-math></inline-formula> , which can decrease space complexity. Extensive experiments including objective-function value comparison and clustering-performance comparison show that IRW-FCM can obtain better local minima than FCM with fewer iterations. And according to the time-complexity analysis, it is verified IRW-FCM has the same linear time complexity with FCM. What's more, compared with other clustering methods, IRW-FCM also shows its superiority.
An extensive study of C-SMOTE, a Continuous Synthetic Minority Oversampling Technique for Evolving Data Streams

Streaming Machine Learning (SML) studies algorithms that update their models, given an unbounded and often non-stationary flow of data performing a single pass. Online class imbalance learning is a branch of SML that combines the challenges of both class imbalance and concept drift. In this paper, we investigate the binary classification problem by rebalancing an imbalanced stream of data in the presence of concept drift, accessing one sample at a time. We propose an extensive comparative study of Continuous Synthetic Minority Oversampling Technique (C-SMOTE), inspired by the popular sampling technique Smote, as a meta-strategy to pipeline with SML classification algorithms. We benchmark C-SMOTE pipelines on both synthetic and real data streams, containing different types of concept drifts, different imbalance levels, and different class distributions. We bring statistical evidence that models learnt with C-SMOTE pipelines improve the minority class performance concerning both the baseline models and the state-of-the-art methods. We also perform a sensitivity analysis to detect the C-SMOTE impact on the majority class performance for the three types of concept drift and several class distributions. Moreover, we show a computational cost analysis in terms of time and memory consumption.
Fast and lean encrypted Internet traffic classification

Identifying the type of a network flow or a specific application has many advantages but becomes harder in recent years due to the use of encryption, e.g., by VPN. As a result, there is a recent wave of solutions that harness deep learning for traffic classification. These solutions either require a rather long time (15–60 Seconds) of flow data or rely on handcrafted features for solutions that classify flows faster. In this work, we suggest a novel approach for classification that extracts the most out of the two simple yet defining features of a flow: packet sizes and inter-arrival times. We employ a model that uses the inter-arrival times to parameterize the derivative of the flow hidden-state using a neural network (Neural ODE). We compare our results with a solution that uses the same data without the ODE solver and show the benefit of this approach. Our results can classify flows based on 20 or 30 consecutive packets taken from anywhere in one direction of a flow. This reduces the amount of traffic between the sampling point and the analyzer and does not require matching between two directions of the flow. As a result, our solution can classify traffic with good accuracy within a few seconds, and we show how to combine it with a more accurate (and a slower) classifier to achieve (mostly) fast and accurate classifications.
CG-ANER: Enhanced contextual embeddings and glyph features-based agricultural named entity recognition

In recent years, deep learning has greatly improved the performance of named entity recognition models in various fields, especially in the agricultural domain. However, most existing works only utilize word embedding models to generate the context-independent embeddings, which is limited in modeling polysemous words. Moreover, the abundant morphological information in agricultural texts has not been fully utilized. Besides, the local context information needs to be further extracted. To solve the aforementioned issues, a novel enhanced contextual embeddings and glyph features-based model was proposed. First, the contextual embeddings were dynamically generated by the fine-tuned Bidirectional Encoder Representation from Transformers (BERT) on the domain-specific corpus (e.g., agricultural texts), and then the multi-granularity information was obtained from the layers of BERT. Thus, the contextual embeddings not only contain domain-specific knowledge but also include multi-grained semantic information. Second, a novel 3-dimension convolutional neural network-based framework was designed to capture the contextual glyph features for each character from the image perspective. Third, a channel-wised fusion architecture was also introduced to further improve the ability of the convolutional neural network layer to capture local context features. Experimental results showed that our proposed model achieved the best F1-scores of 95.02% and 96.51% on AgCNER and Resume datasets, which indicated the effectiveness and generalization of our model to identify the entities in cross-domain texts. The ablation study in many aspects also demonstrated the better performance of the proposed model.
What is Data? - Exploring the Meaning of Data in Data Physicalisation Teaching

A growing body of work focuses on physicalisations based on personal, everyday data. Despite growing interest, little is known about how to educate people on their creation. We designed a teaching method of ’Data Diaries’, which consists of five representation assignments that move from visualising to physicalising personal data. The Data Diaries were used in a semester project, with the aim of creating an interactive physicalisation. We analysed the Data Diaries, written reports, and participant interviews. Our analysis shows that people need to overcome the challenge of using materiality to communicate data, which happens in four stages. Moreover, the materiality made participants realise that physicalisations do not focus on efficiency and accuracy, but on the story of the data, by referring to its origin, use of personal mappings, and reduction. As physicalisations blur the line between quantitative and qualitative, designing them engenders a change in our notion of ’data’.
Data-Driven Personas

Data-driven personas are a significant advancement in the fields of human-centered informatics and human-computer interaction. Data-driven personas enhance user understanding by combining the empathy inherent with personas with the rationality inherent in analytics using computational methods. Via the employment of these computational methods, the data-driven persona method permits the use of large-scale user data, which is a novel advancement in persona creation. A common approach for increasing stakeholder engagement about audiences, customers, or users, persona creation remained relatively unchanged for several decades. However, the availability of digital user data, data science algorithms, and easy access to analytics platforms provide avenues and opportunities to enhance personas from often sketchy representations of user segments to precise, actionable, interactive decision-making tools—data-driven personas! Using the data-driven approach, the persona profile can serve as an interface to a fully functional analytics system that can present user representation at various levels of information granularity for more task-aligned user insights. We trace the techniques that have enabled the development of data-driven personas and then conceptually frame how one can leverage data-driven personas as tools for both empathizing with and understanding of users. Presenting a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes, we illustrate applying this framework via practical use cases in areas of system design, digital marketing, and content creation to demonstrate the application of data-driven personas in practical applied situations. We then present an overview of a fully functional data-driven persona system as an example of multi-level information aggregation needed for decision making about users. We demonstrate that data-driven personas systems can provide critical, empathetic, and user understanding functionalities for anyone needing such insights.
Architecture of a Hybrid Video/Optical See-through Head-Mounted Display-Based Augmented Reality Surgical Navigation Platform

In the context of image-guided surgery, augmented reality (AR) represents a ground-breaking enticing improvement, mostly when paired with wearability in the case of open surgery. Commercially available AR head-mounted displays (HMDs), designed for general purposes, are increasingly used outside their indications to develop surgical guidance applications with the ambition to demonstrate the potential of AR in surgery. The applications proposed in the literature underline the hunger for AR-guidance in the surgical room together with the limitations that hinder commercial HMDs from being the answer to such a need. The medical domain demands specifically developed devices that address, together with ergonomics, the achievement of surgical accuracy objectives and compliance with medical device regulations. In the framework of an EU Horizon2020 project, a hybrid video and optical see-through augmented reality headset paired with a software architecture, both specifically designed to be seamlessly integrated into the surgical workflow, has been developed. In this paper, the overall architecture of the system is described. The developed AR HMD surgical navigation platform was positively tested on seven patients to aid the surgeon while performing Le Fort 1 osteotomy in cranio-maxillofacial surgery, demonstrating the value of the hybrid approach and the safety and usability of the navigation platform.
Telepresence Robots for People with Special Needs: A Systematic Review

Telepresence robots are increasingly used to support remote social interaction. Telerobots allow the user to move a camera and a microphone at a remote location in real time – often with a display of the user’s face at the robot. These robots can increase the quality of life for people with special needs, who are, for instance, bed bound. However, interface accessibility barriers have made them difficult to use for some people. Still, no state-of-the-art literature review has been made of research on telerobots for people with disabilities. We used Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines for a review. Web of Science (WoS), ACM Digital Library, IEEE Xplore, PubMed, and Scopus were searched, and a supplemental by hand examination of reference lists was done. The search includes studies published between 2009 and 2019.A total of 871 articles were included in this review, 42 of which were eligible for the analysis. These articles were further characterized in terms of problems addressed, objectives, types of special needs considered, features of the devices, features of solutions, and the evaluation methods applied. Based on the review, future research directions are being proposed, addressing issues like use-cases; user conditions; universal accessibility; safety; privacy and security; independence and autonomy; evaluation methods; and user training programs. The review provides an overview of existing research, a summary of common research directions, and a summary of issues, which need to be considered in future research.
Blockchain-Enabled Efficient Dynamic Cross-Domain Deduplication in Edge Computing

As the rapid proliferation of Internet of Things (IoT) and edge computing, large amounts of data are needed to be stored and transmitted in the online storage system. Data deduplication can be adopted to improve communication efficiency and minimize storage space. However, in edge computing, data deduplication brings security and functionality requirements that are still unsatisfied. Most existing schemes are vulnerable to brute-force attacks and single-point attacks. Moreover, they impose a heavy burden on resource-constrained edge nodes and do not support cross-domain deduplication. Blockchain is a promising technology because the programmable smart contract can be utilized to perform cross-domain deduplication and guarantee the traceability of data. In this article, an efficient dynamic cross-domain deduplication scheme in blockchain-enabled edge computing is proposed to solve the above problems. Specifically, the smart contract is employed to assist cross-domain deduplication, which also can reduce the storage pressure of edge nodes. Meanwhile, a hash proof system-based oblivious pseudorandom function is created to reduce the time cost of key generation and achieve the security requirements of resistance to brute-force attacks and single-point attacks. The technology of accumulators is adopted to achieve Proofs of Ownership (PoO), which can prevent duplicate-faking attacks. The security analysis demonstrates that the proposed scheme has a higher security level. The performance evaluation shows that the proposed scheme significantly reduces computation cost and communication overhead, compared with other existing schemes. The smart contract is implemented in the Ethereum test network (i.e., Rinkeby), which shows acceptable gas cost even the functions are called frequently.
Continuous User Authentication Using Mouse Dynamics, Machine Learning, and Minecraft

Mouse dynamics has grown in popularity as a novel, irreproducible behavioral biometric. Datasets which contain general, unrestricted mouse movements from users are sparse in the current literature. The Balabit mouse dynamics dataset, produced in 2016, was made for a data science competition and despite some of its shortcomings, is considered to be the first publicly available mouse dynamics dataset. Collecting mouse movements in a dull, administrative manner, as Balabit does, may unintentionally homogenize data and is also not representative of real-world application scenarios. This paper presents a novel mouse dynamics dataset that has been collected while 10 users play the video game Minecraft on a desktop computer. Binary Random Forest (RF) classifiers are created for each user to detect differences between a specific user&#x0027;s movements and an imposter&#x0027;s movements. Two evaluation scenarios are proposed to evaluate the performance of these classifiers; one scenario outperformed previous works in all evaluation metrics, reaching average accuracy rates of 92%, while the other scenario successfully reported reduced instances of false authentications of imposters.
Spacecraft Telemetry Anomaly Detection Based on Parametric Causality and Double-Criteria Drift Streaming Peaks over Threshold

Most of the spacecraft telemetry anomaly detection methods based on statistical models suffer from the problems of high false negatives, long time consumption, and poor interpretability. Besides, complex interactions, which may determine the propagation of anomalous mode between telemetry parameters, are often ignored. To discover the complex interaction between spacecraft telemetry parameters and improve the efficiency and accuracy of anomaly detection, we propose an anomaly detection framework based on parametric causality and Double-Criteria Drift Streaming Peaks Over Threshold (DCDSPOT). We propose Normalized Effective Transfer Entropy (NETE) to reduce the error and noise caused by nonstationarity of the data in the calculation of transfer entropy, and then apply NETE to improve the Multivariate Effective Source Selection (MESS) causal inference algorithm to infer parametric causality. We define the Weighted Source Parameter (WSP) of the target parameter to be detected, then DSPOT is employed to set multi-tier thresholds for target parameter and WSP. At last, two criteria are formulated to determine anomalies. Additionally, to cut the time consumption of the DCDSPOT, we apply Probability Weighted Moments (PWM) for parameter estimation of Generalized Pareto Distribution (GPD). Experiments on real satellite telemetry dataset shows that our method has higher recall and F1-score than other commonly used methods, and the running time is also significantly reduced.