Reviewerly/exHarmony huggingface dataset:
Moth-Flame Optimization-Bat Optimization: Map-Reduce Framework for Big Data Clustering Using the Moth-Flame Bat Optimization and Sparse Fuzzy C-Means

The technical advancements in big data have become popular and most desirable among users for storing, processing, and handling huge data sets. However, clustering using these big data sets has become a major challenge in big data analysis. The conventional clustering algorithms used scalable solutions for managing huge data sets. Thus, this study proposes a technique for big data clustering using the spark architecture. The proposed technique undergoes two steps for clustering the big data, involving feature selection and clustering, performed in the initial cluster nodes of spark architecture. At first, the initial cluster nodes read the big data from various distributed systems, and the optimal features are selected and placed in the feature vector based on the proposed moth-flame optimization-based bat (MFO-Bat) algorithm, which is designed by integrating MFO and Bat algorithms. Then, the selected features are fed to the final cluster nodes of spark, which uses the sparse-fuzzy C-means method for performing optimal clustering. The performance of proposed MFO-Bat outperformed other existing methods with a maximal classification accuracy of 95.806%, Dice coefficient of 99.181%, and Jaccard coefficient of 98.376%, respectively.
A CNN-based 3D human pose estimation based on projection of depth and ridge data

We propose a method that use a convolutional neural network (CNN) to estimate human pose by analyzing the projection of the depth and ridge data, which represent local maxima in a distance transform map. To fully utilize the 3D information of depth points, we propose a method to project the depth and ridge data on various directions. The proposed projection method can reduce the 3D information loss, the ridge data can avoid joint drift, and the CNN increases localization accuracy. The proposed method proceeds as follows. (1) We use depth data to segment the human from the background and extract ridge data from human silhouettes. (2) We project the depth and ridge data onto XY, XZ, and ZY planes. (3) ResNet-101 accepts six projected images and use 1 × 1 convolution layers to generate 2D heatmaps and offsets. (4) We generate 2D keypoints per plane by using the soft-argmax operation. (5) We obtain 3D joint positions by using the fully-connected layers. In experiments on the SMMC-10, EVAL, and ITOP datasets, the proposed method achieved the state-of-the-art pose estimation accuracies. The proposed method can eliminate the 3D information loss and drift of joint positions that can occur during estimation of human pose.
Software reuse cuts both ways: An empirical analysis of its relationship with security vulnerabilities

Software reuse is a widely adopted practice among both researchers and practitioners. The relation between security and reuse can go both ways: a system can become more secure by relying on mature dependencies, or more insecure by exposing a larger attack surface via exploitable dependencies. To follow up on a previous study and shed more light on this subject, we further examine the association between software reuse and security threats. In particular, we empirically investigate 1244 open-source projects in a multiple-case study to explore and discuss the distribution of security vulnerabilities between the code created by a development team and the code reused through dependencies. For that, we consider both potential vulnerabilities, as assessed through static analysis, and disclosed vulnerabilities, reported in public databases. The results suggest that larger projects in size are associated with an increase on the amount of potential vulnerabilities in both native and reused code. Moreover, we found a strong correlation between a higher number of dependencies and vulnerabilities. Based on our empirical investigation, it appears that source code reuse is neither a silver bullet to combat vulnerabilities nor a frightening werewolf that entail an excessive number of them.
A privacy preserve big data analysis system for wearable wireless sensor network

Big data and artificial intelligence develop rapidly. Big data analysis has been applied in many fields of smart healthcare. Once these data are leaked or modified during transmission, it will not only invade the privacy of patients, but also endanger their lives. Many researchers worked on encrypted personal health records (PHR). However, there are still some challenges, such as data leakage during deep learning and leakage of training models, and some users do not want their data to be leaked to the analysis organization. How to protect privacy while leveraging deep learning is a pressing issue. In this paper, we present a system for predicting disease and timely alarms by collecting data from sensors and using deep learning to analyze and monitor patient health data. In order to protect the privacy of health data, we adopt an assured data deletion approach which the data owner can choose to revoke some users' access to their health data. Extensive analysis and experimental results are presented that demonstrate the security, and efficiency of our proposed approach.
Fuzzy K-Means Clustering With Discriminative Embedding

Fuzzy K-Means (FKM) clustering is of great importance for analyzing unlabeled data. FKM algorithms assign each data point to multiple clusters with some degree of certainty measured by the membership function. In these methods, the fuzzy membership degree matrix is obtained based on the calculation of the distance between data points in the original space. However, this operation may lead to suboptimal results because of the influence of noises and redundant features. Besides, some FKM clustering methods ignore the importance of the weighting exponent. In this paper, we propose a novel FKM method called Fuzzy K-Means Clustering With Discriminative Embedding. Within this method, we simultaneously conduct dimensionality reduction along with fuzzy membership degree learning. To retain most information in the embedding subspace and improve the robustness of this method, principal component analysis is incorporated into our framework. An iterative optimization algorithm is proposed to solve the model. To validate the efficacy of the proposed method, we perform comprehensive analyses, including convergence behavior, parameter determination and computational complexity. Moreover, we also match a appropriate weighting exponent for each data set. Experimental results on benchmark data sets show that the proposed method is more discriminative and effective for clustering tasks.
A graph-theoretic approach for the detection of phishing webpages

Over the years, various technical means have been developed to protect Internet users from phishing attacks. To enrich the anti-phishing efforts, we capitalise on concepts from graph theories, and propose a set of novel graph features to improve the phishing detection accuracy. The initial phase of the proposed technique involved the extraction of hyperlinks in the webpage under scrutiny and fetching the corresponding neighbourhood webpages. During this process, the page linking data were collected, and used to construct a web graph which models the overall hyperlink and network structure of the webpage. From the web graph, graph measures were computed and extracted as graph features to derive a classifier for detecting phishing webpages. Experimental results show that the proposed graph features achieve an improved overall accuracy of 97.8% when C4.5 was utilised as classifier, outperforming the existing conventional features derived from the same data samples. Unlike conventional features, the proposed graph features leverage inherent phishing patterns that are only visible at a higher level of abstraction, thus making it robust and difficult to be evaded by direct manipulations on the webpage contents. Our proposed graph-based technique also shows promising results when benchmarked against a prominent phishing detection technique. Hence, the proposed technique is an important contribution to the existing anti-phishing research towards improving the detection performance.
Summarization of biomedical articles using domain-specific word embeddings and graph ranking

Text summarization tools can help biomedical researchers and clinicians reduce the time and effort needed for acquiring important information from numerous documents. It has been shown that the input text can be modeled as a graph, and important sentences can be selected by identifying central nodes within the graph. However, the effective representation of documents, quantifying the relatedness of sentences, and selecting the most informative sentences are main challenges that need to be addressed in graph-based summarization. In this paper, we address these challenges in the context of biomedical text summarization. We evaluate the efficacy of a graph-based summarizer using different types of context-free and contextualized embeddings. The word representations are produced by pre-training neural language models on large corpora of biomedical texts. The summarizer models the input text as a graph in which the strength of relations between sentences is measured using the domain specific vector representations. We also assess the usefulness of different graph ranking techniques in the sentence selection step of our summarization method. Using the common Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, we evaluate the performance of our summarizer against various comparison methods. The results show that when the summarizer utilizes proper combinations of context-free and contextualized embeddings, along with an effective ranking method, it can outperform the other methods. We demonstrate that the best settings of our graph-based summarizer can efficiently improve the informative content of summaries and decrease the redundancy.
Enhancing Aspect-Based Sentiment Analysis With Capsule Network

Existing feature-based neural approaches for aspect-based sentiment analysis (ABSA) try to improve their performance with pre-trained word embeddings and by modeling the relations between the text sequence and the aspect (or category), thus heavily depending on the quality of word embeddings and task-specific architectures. Although the recently pre-trained language models, i.e., BERT and XLNet, have achieved state-of-the-art performance in a variety of natural language processing (NLP) tasks, they still subject to the aspect-specific, local feature-aware and task-agnostic challenges. To address these challenges, this paper proposes a XLNet and capsule network based model XLNetCN for ABSA. XLNetCN firstly constructs auxiliary sentence to model the sequence-aspect relation and generate global aspect-specific representations, which enables to enhance aspect-awareness and ensure the full pre-training of XLNet for improving task-agnostic capability. After that, XLNetCN also employs a capsule network with the dynamic routing algorithm to extract the local and spatial hierarchical relations of the text sequence, and yield its local feature representations, which are then merged with the global aspect-related representations for downstream classification via a softmax classifier. Experimental results show that XLNetCN outperforms significantly than the classical BERT, XLNet and traditional feature-based approaches on the two benchmark datasets of SemEval 2014, Laptop and Restaurant, and achieves new state-of-the-art results.
Supporting Self-Regulation of Children with ADHD Using Wearables

The design of wearable applications supporting children with Attention Deficit Hyperactivity Disorders (ADHD) requires a deep understanding not only of what is possible from a clinical standpoint but also how the children might understand and orient towards wearable technologies, such as a smartwatch. Through a series of participatory design workshops with children with ADHD and their caregivers, we identified tensions and challenges in designing wearable applications supporting the self-regulation of children with ADHD. In this paper, we describe the specific challenges of smartwatches for this population, the balance between self-regulation and co-regulation, and tensions when receiving notifications on a smartwatch in various contexts. These results indicate key considerations-from both the child and caregiver viewpoints-for designing technological interventions supporting children with ADHD.
Culture in Action: Unpacking Capacities to Inform Assets-Based Design

The field of Human-Computer Interaction (HCI) aims at securing a lasting impact for technology-based interventions in the context of social inequities. Increasingly, HCI scholars are proposing assets-based design as an effective approach towards this issue. Rather than starting from people's needs and deficits, this approach posits that design should start from a deep understanding of people's assets. A pending issue, however, is how to account for the situated nature of assets; that is, how to decide which asset to leverage and for what design purpose. Drawing from cultural sociology and shifting the emphasis from assets to capacities, we propose Swidler's theory of culture-in-action as an analytical lens for unpacking the complex relationship between capacities, goals, and structural limitations. Leveraging findings from a Participatory Design engagement with 35 Latino immigrant parents for envisioning parent-education technologies, we demonstrate the applicability of this lens. We contribute to HCI scholarship by further discussing 1) how to analyze capacities' design potential, and 2) the methodological particularities for collecting them.
An Efficient Cloud Data Center Allocation to the Source of Requests

A Cloud data center is a network of virtualized resources, namely virtualized servers. They provision on-demand services to the source of requests ranging from virtual machines to virtualized storage and virtualized networks. The cloud data center service requests can come from different sources across the world. It is desirable for enhancing Quality of Service (QoS), which is otherwise known as a service level agreement (SLA), an agreement between cloud service requester and cloud service consumer on QoS, to allocate the cloud data center closest to the source of requests. This article models a Cloud data center network as a graph and proposes an algorithm, modified Breadth First Search where the source of requests assigned to the Cloud data centers based on a cost threshold, which limits the distance between them. Limiting the distance between Cloud data centers and the source of requests leads to faster service provisioning. The proposed algorithm is tested for various graph instances and is compared with modified Voronoi and modified graph-based K-Means algorithms that they assign source of requests to the cloud data centers without limiting the distance between them. The proposed algorithm outperforms two other algorithms in terms of average time taken to allocate the cloud data center to the source of requests, average cost and load distribution.
GUIComp: A GUI Design Assistant with Real-Time, Multi-Faceted Feedback

Users may face challenges while designing graphical user interfaces, due to a lack of relevant experience and guidance. This paper aims to investigate the issues users face during the design process, and how to resolve them. To this end, we conducted semi-structured interviews, based on which we built a GUI prototyping assistance tool called GUIComp. This tool can be connected to GUI design software as an extension, and it provides real-time, multi-faceted feedback on a user's current design. Additionally, we conducted two user studies, in which we asked participants to create mobile GUIs with or without GUIComp, and requested online workers to assess the created GUIs. The experimental results show that GUIComp facilitated iterative designs and the participants with GUIComp had better a user experience and produced more acceptable designs than those who did not use it.
Rapid Iron-On User Interfaces: Hands-on Fabrication of Interactive Textile Prototypes

Rapid prototyping of interactive textiles is still challenging, since manual skills, several processing steps, and expert knowledge are involved. We present Rapid Iron-On User Interfaces, a novel fabrication approach for empowering designers and makers to enhance fabrics with interactive functionalities. It builds on heat-activated adhesive materials consisting of smart textiles and printed electronics, which can be flexibly ironed onto the fabric to create custom interface functionality. To support rapid fabrication in a sketching-like fashion, we developed a handheld dispenser tool for directly applying continuous functional tapes of desired length as well as discrete patches. We introduce versatile compositions techniques that allow for creating complex circuits, utilizing commodity textile accessories and sketching custom-shaped I/O modules. We further contribute a comprehensive library of components for input, output, wiring and computing. Three example applications, results from technical experiments and expert reviews demonstrate the functionality, versatility and potential of this approach.
Cross-Domain Authorship Attribution Using Pre-trained Language Models

Authorship attribution attempts to identify the authors behind texts and has important applications mainly in cyber-security, digital humanities and social media analytics. An especially challenging but very realistic scenario is cross-domain attribution where texts of known authorship (training set) differ from texts of disputed authorship (test set) in topic or genre. In this paper, we modify a successful authorship verification approach based on a multi-headed neural network language model and combine it with pre-trained language models. Based on experiments on a controlled corpus covering several text genres where topic and genre is specifically controlled, we demonstrate that the proposed approach achieves very promising results. We also demonstrate the crucial effect of the normalization corpus in cross-domain attribution.
StoryMakAR: Bringing Stories to Life With An Augmented Reality &amp; Physical Prototyping Toolkit for Youth

Makerspaces can support educational experiences in prototyping for children. Storytelling platforms enable high levels of creativity and expression, but have high barriers of entry. We introduce StoryMakAR, which combines making and storytelling. StoryMakAR is a new AR-IoT system for children that uses block programming, physical prototyping, and event-based storytelling to bring stories to life. We reduce the barriers to entry for youth (Age=14-18) by designing an accessible, plug-and-play system through merging both electro-mechanical devices and virtual characters to create stories. We describe our initial design process, the evolution and workflow of StoryMakAR, and results from multiple single-session workshops with 33 high school students. Our preliminary studies led us to understand what students want to make. We provide evidence of how students both engage and have difficulties with maker-based storytelling. We also discuss the potential for StoryMakAR to be used as a learning environment for classrooms and younger students.
The Social Network: How People with Visual Impairment use Mobile Phones in Kibera, Kenya

Living in an informal settlement with a visual impairment can be very challenging resulting in social exclusion. Mobile phones have been shown to be hugely beneficial to people with sight loss in formal and high-income settings. However, little is known about whether these results hold true for people with visual impairment (VIPs) in informal settlements. We present the findings of a case study of mobile technology use by VIPs in Kibera, an informal settlement in Nairobi. We used contextual interviews, ethnographic observations and a co-design workshop to explore how VIPs use mobile phones in their daily lives, and how this use influences the social infrastructure of VIPs. Our findings suggest that mobile technology supports and shapes the creation of social infrastructure. However, this is only made possible through the existing support networks of the VIPs, which are mediated through four types of interaction: direct, supported, dependent and restricted.
G-CARE: A Framework for Performance Benchmarking of Cardinality Estimation Techniques for Subgraph Matching

Despite the crucial role of cardinality estimation in query optimization, there has been no systematic and in-depth study of the existing cardinality estimation techniques for subgraph matching queries. In this paper, for the first time, we present a comprehensive study of the existing cardinality estimation techniques for subgraph matching queries, scaling far beyond the original experiments. We first introduce a novel framework called g-care that enables us to realize all existing techniques on top of it and that provides insights on their performance. By using g-care, we then reimplement representative cardinality estimation techniques for graph databases as well as relational databases. We next evaluate these techniques w.r.t accuracy on rdf and non-rdf graphs from different domains with subgraph matching queries of various topologies so far considered. Surprisingly, our results reveal that all existing techniques have serious problems in accuracy for various scenarios and datasets. Intriguingly, a simple sampling method based on an online aggregation technique designed for relational data, consistently outperforms all existing techniques.
Ethics in Movement

How is ethics shaped by the particularities of a design? Through a detailed video analysis, we explore how ethicality is shaped in interaction between a choreographer, a performer and a choir of five drones, performing together on the opera stage. We pinpoint how movements enabled by the human-drone assemblage may limit or liberate artistic expressions vis-à-vis the norms of operatic performance. From a somaesthetics perspective on ethics, we show how the process of crafting rich experiences together with drones can deepen sensory appreciation skills, leading to an increased understanding of underlying somatic drivers and imposed norms. Somatic awareness thereby enables a richer repertoire of movements, expanding the ability to freely choose how to act, and cultivating empathy towards others. This shifts our understanding of ethics in HCI as solely about abstract rules or policies 'out there' to also concern the specifics of how technology informs or dictates movement and experience.
Blockchain Adoption from an Interorganizational Systems Perspective – A Mixed-Methods Approach

This study investigates the potential influence of blockchain technology adoption on a company's competitive performance from an interorganizational systems perspective. A research framework is derived based on expert interviews and tested with a quantitative survey. The results show, that by offering traceability and immutability of transactions, blockchain technology could positively impact a company's competitive performance. The study further identified a positive influence of smart contract technology on partnering flexibility and competitive performance.