Reviewerly/exHarmony huggingface dataset:
Software defect prediction via cost-sensitive Siamese parallel fully-connected neural networks

Software defect prediction (SDP) has caused widespread concern among software engineering researchers, which aims to erect a software defect predictor according to historical data. However, it is still difficult to develop an effective SDP model on high-dimensional and limited data. In this study, a novel SDP model for this problem is proposed, called Siamese parallel fully-connected networks (SPFCNN), which combines the advantages of Siamese networks and deep learning into a unified method. And training this model is administered by AdamW algorithm for finding the best weights. The minimum value of a singular formula is the target of training for SPFCNN model. Significantly, we extensively compared SPFCNN method with the state-of-the-art SDP approaches using six openly available datasets from the NASA repository. Six indexes are used to evaluate the performance of the proposed method. Experimental results showed that the SPFCNN method contributes to significantly higher performance compared with benchmarked SDP approaches, indicating that a cost-sensitive neural network could be developed successfully for SDP.
A hybrid spatio-temporal forecasting of solar generating resources for grid integration

Recently, the installed solar generating resources have been increasing rapidly. Consequently, forecasting for solar generating resources are becoming an important work to integrate utility-scale solar generating resources into power systems. As solar generating resources are variable, uncontrollable, and uncertain, accurate and reliable forecasting enables higher penetrations of solar generating resources to be deployed on the electrical power grid. Accurate forecasting of solar resources contributes to evaluation of system reserves over large geographic area and to transmission system planning. To increase the penetration of solar generating resources on the electric power grid, the accurate power forecasting of geographically distributed solar generating resources is needed. In this paper, we propose a hybrid spatio-temporal forecasting of solar generating resources based on the naïve Bayesian classifier approach and spatial modelling approach. To validate our forecasting model, we use the empirical data from the practical solar farms in South Korea.
AgriKG: An Agricultural Knowledge Graph and Its Applications

Recently, with the development of information and intelligent technology, agricultural production and management have been significantly boosted. But it still faces considerable challenges on how to effectively integrate large amounts of fragmented information for downstream applications. To this end, in this paper, we propose an agricultural knowledge graph, namely AgriKG, to automatically integrate the massive agricultural data from internet. By applying the NLP and deep learning techniques, AgriKG can automatically recognize agricultural entities from unstructured text, and link them to form a knowledge graph. Moreover, we illustrate typical scenarios of our AgriKG and validate it by real-world applications, such as agricultural entity retrieval, and agricultural question answering, etc.
Charting Subtle Interaction in the HCI Literature

Human-computer interaction is replete with ways of talking about qualities of interaction or interfaces, including if they are expressive, rich, fluid, or playful. An example of such a quality is subtle. While this word is frequently used in the literature, we lack a coherent account of what it means to be subtle, how to achieve subtleness in an interface, and what theoretical backing subtleness has. To create such an account, we analyze a sample of 55 publications that use the word subtle. We describe the variants of subtle interaction in the literature, including claimed benefits, empirical approaches, and ethical considerations. Not only does this create a basis for thinking about subtleness as a quality of interaction, it also works to show how to solidify varieties of quality in HCI. We conclude by outlining some open empirical and conceptual questions about subtleness.
Our Friends Electric

Emerging technologies---such as the voice enabled internet---present many opportunities and challenges for HCI research and society as a whole. Advocating for better, healthier implementations of these technologies will require us to communicate abstract values, such as trust, to an audience that ranges from the general public to technologists and even policymakers. In this paper, we show how a combination of film-making and product design can help to illustrate these abstract values. Working as part of a wider international advocacy campaign, Our Friends Electric focuses on the voice enabled internet, translating abstract notions of Internet Health into comprehensible digital futures for the relationship between our voice and the internet. We conclude with a call for designers of physical things to be more involved with the development of trust, privacy and security in this powerful emerging technological landscape.
Explicating "Implicit Interaction"

The term implicit interaction is often used to denote interactions that differ from traditional purposeful and attention demanding ways of interacting with computers. However, there is a lack of agreement about the term's precise meaning. This paper develops implicit interaction further as an analytic concept and identifies the methodological challenges related to HCI's particular design orientation. We first review meanings of implicit as unintentional, attentional background, unawareness, unconsciousness and implicature, and compare them in regards to the entity they qualify, the design motivation they emphasize and their constructive validity for what makes good interaction. We then demonstrate how the methodological challenges can be addressed with greater precision by using an updated, intentionality-based definition that specifies an input-effect relationship as the entity of implicit. We conclude by identifying a number of new considerations for design and evaluation, and by reflecting on the concepts of user and system agency in HCI.
Sentiment Analysis of Restaurant Reviews Using Machine Learning Techniques

Evolution of the Internet in the past decade resulted in generation of voluminous data in all sectors. Due to these advents, the people have new ways of expressing their opinions about anything in the form of tweets, blog posts, online discussion forums, status updates, etc. Sentiment analysis deals with the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude toward a particular topic is positive, negative, or neutral. Knowing the opinion of customers is very important for any business. Hence, in this paper, we analyze the reviews given by the customers of the restaurant with the help of machine learning classification algorithms. This paper mainly focuses on the implementation of various classification algorithms and their performance analysis. The simulation results showed that SVM classifier resulted in the highest accuracy of 94.56% for the given dataset.
Exploring Media Capture of Meaningful Experiences to Support Families Living with Dementia

Although designing interactive media experiences for people with dementia has become a growing interest in HCI, a strong focus on family members has rarely been recognised as worthy of design intervention. This paper presents a research through design (RTD) approach working closely with families living with dementia in order to create personalised media experiences. Three families took part in day trips, which they co-planned, with data collection during these days providing insights into their shared social experiences. Workshops were also held in order to personalise the experience of the media created during these days out. Our qualitative analysis outlines themes focusing on individuality, relationships, and accepting changed realities. Furthermore, we outline directions for future research focusing on designing for contested realities, the personhood of carers, and the ageing body and immersion.
Distributed and Efficient Object Detection via Interactions Among Devices, Edge, and Cloud

With the rapid development of Internet-of-Things and communication techniques, media transmission in surveillance applications is gradually relying on wireless networks. Meanwhile, the emergence of edge computing has pushed the media data analysis from the cloud to the edge of the network to achieve fast response for delay-sensitive media processing tasks. Object detection is a representative delay-sensitive image processing task in surveillance applications, but faces significant challenges in this context. For example, how to compress images for transmission in wireless environment without compromising the detection accuracy, and how to integrate and update local inference models online in an edge computing-based object detection system. In this paper, we propose an object detection architecture based on edge computing to achieve distributed and efficient object detection for surveillance applications. Under this architecture, we develop an adaptive Region-of-Interest-based image compression scheme for end devices to efficiently compress their captured images for wireless transmission but not to sacrifice the object detection accuracy of edge servers. Furthermore, we carefully design distributed and communication-efficient interactions among end devices, edge servers, and the cloud to dynamically optimize the object detection accuracy online. Extensive simulation results demonstrate that our proposed architecture not only achieves a competitive detection accuracy to traditional cloud-based objective detection solution with reduced response delay but also significantly improves the image transmission efficiency with adaptive image compression ratio.
Threats, Abuses, Flirting, and Blackmail

HCI4D researchers and practitioners have leveraged voice forums to enable people with literacy, socioeconomic, and connectivity barriers to access, report, and share information. Although voice forums have received impassioned usage from low-income, low-literate, rural, tribal, and disabled communities in diverse HCI4D contexts, the participation of women in these services is almost non-existent. In this paper, we investigate the reasons for the low participation of women in social media voice forums by examining the use of Sangeet Swara in India and Baang in Pakistan by marginalized women and men. Our mixed-methods approach spanning content analysis of audio posts, quantitative analysis of interactions between users, and qualitative interviews with users indicate gender inequity due to deep-rooted patriarchal values. We found that women on these forums faced systemic discrimination and encountered abusive content, flirts, threats, and harassment. We discuss design recommendations to create social media voice forums that foster gender equity in use of these services.
An Empirical Study on User Experience Evaluation and Identification of Critical UX Issues

We introduce an approach that supports researchers and practitioners to determine the quality of first-time user experience (FTUX) and long-term user experience (LTUX), as well as to identify critical issues with these two types of UX. The product we chose to study is a mobile fitness application. Mobile apps tend to have a much shorter service life than most other products; thus, the developers/designers need to pay great attention to both first-time and long-term user experience. This study is based on a multi-method approach. We employed the AttrakDiff questionnaire to assess users’ first impressions of the app, and the UX Curve method to evaluate how users’ experience of the app has changed over time. Besides the quantitative data, which helped to determine the quality of user experience, we also collected qualitative data during two interviews with participants, and focused on the issues that predominantly deteriorated user experience. A four-coordinate plane tool was designed later in the data analysis process that combined the two kinds of user experience data at the same time, which led to a qualitative positioning of the user experience status of a certain product. The model was further successfully adopted in the identification of user experience issues of an online fitness application.
Reliable image matching via photometric and geometric constraints structured by Delaunay triangulation

Image matching is a basic task in the field of photogrammetry and remote sensing. By using the advantages of the Delaunay triangulation, this paper proposes a novel image matching method. First, neighboring structures of randomly distributed feature points are formed with the assistance of the Delaunay triangulation and its corresponding graph, and the image planes are simultaneously divided into patches of near-regular triangles. Second, two constraints, a photometric constraint and a geometric constraint, are implemented based on the constructed neighboring structures, which incorporate the hierarchical elimination and left-right checking strategies to deliver the influences of outliers on the decision of inliers and ensure the high precision of the final matches. The former utilizes a line descriptor as a second-order photometric constraint, and the latter adopts the spatial angular order (SAO) to achieve a geometric constraint for the calculation of dissimilarity scores between correspondences. In addition, with the constraints between triangles of the refined Delaunay triangulation and its corresponding graph, a match expansion is designed to exploit as many inliers as possible. Finally, a reliable image matching algorithm is proposed by sequentially executing the three constraints for outlier elimination and match expansion. Under comprehensive analysis and comparison with five state-of-the-art algorithms, the performance of the proposed method is verified by using both rigid and non-rigid datasets. The experimental results demonstrate that the Delaunay triangulation is sufficient to construct neighboring structures for the implementation of local photometric and geometric constraints, and the proposed method can achieve good performance in terms of the precision, recall and number of inliers, and provide reliable matches for stereo image pairs with both rigid and non-rigid transformations.
Collaborative Variational Deep Learning for Healthcare Recommendation

Healthcare recommender system (HRS) has shown the great potential of targeting medical experts or patients, and plays a key role in improving an individual's health by providing insightful recommendations. The HRSs generate recommendations based on a successful and widely applied method known as collaborative filtering (CF). Despite its success, the CF suffers from data sparsity and cold-start problem, which results in the poor quality of recommendations. In particular, it is a great challenge to seeking information relevant to patients' condition, and understanding the medical terms and relationships between them in HRSs. To address these problems, we design a novel collaborative variational deep learning model (CVDL) to exploit multi-sourced information for providing appropriate healthcare recommendations in primary care service. CVDL employs additional variational autoencoder (VAE) to learn deep latent representations for item contents (the description of primary care doctors) in latent space, instead of observation space through an inference network. Meanwhile, the CVDL extracts latent user (patient) features by incorporating user profile in a VAE neural network. Therefore, the CVDL can learn better implicit relationships between items and users from item content, user profile, and rating matrix. In addition, a Stochastic Gradient Variational Bayes (SGVB) approach is proposed to calculate the maximum posterior estimates for learning model parameters. The experiments conducted on three datasets have indicated that our method significantly outperforms the state-of-the-art hybrid CF methods.
A Study on the Applications of the Biomedical Image Encryption Methods for Secured Computer Aided Diagnostics

Computer aided diagnostic is one of the most active research area and has huge impact on the health care industry.With the advent of intelligent methods, biomedical data processing becomes easier and less error prone.Moreover, remote health care is also possible using the IoT infrastructure.However, data security over the network is always considered as a challenge.Biomedical data are generally sensitive to external disturbances and small manipulation in the data may cause huge difference in the ultimate result.Wrong diagnosis can be life threatening in some scenarios or can be severe in almost every instance.Therefore, biomedical data security is one of the major challenge and necessary for remote health care.In this work, some application of cryptographic methods have been discussed which are used to protect biomedical images from unwanted modifications and access.This work can be highly beneficial in future research in the field of biomedical image protection as well as multimedia data security.
Invited Paper: Improving Data Center Efficiency Through Holistic Scheduling In Kubernetes

Data centers are the infrastructure that underpins modern distributed service-oriented systems. They are complex systems-of-systems, with many interacting elements, that consume vast amounts of power. Demand for such facilities is growing rapidly, leading to significant global environmental impact. The data center industry has conducted much research into efficiency improvements, but this has mostly been at the physical infrastructure level. Research into software-based solutions for improving efficiency is greatly needed. However, most current research does not take a holistic view of the data center that considers virtual and physical infrastructures as well as business process. This is crucial if a solution is to be applied in a realistic setting. This paper describes the complex, system-of-systems nature of data centers, and discusses the service models used in the industry. We describe a holistic scheduling system that replaces the default scheduler in the Kubernetes container system, taking into account both software and hardware models. We discuss the initial results of deploying this scheme in a real data center, where power consumption reductions of 10-20% were observed. We show that by introducing hardware modelling into a software-based solution, an intelligent scheduler can make significant improvements in data center efficiency. We conclude by looking at some of the future work that needs to be performed in this area.
Orthodontics in the era of big data analytics

The objective of this report was to provide an overview of the current landscape of big data analytics in the healthcare sector, introduce various approaches of machine learning and discuss potential implications in the field of orthodontics. With the increasing availability of data from various sources, the traditional analytical methods may not be conducive anymore for examining clinical outcomes. Machine-learning approaches, which are algorithms trained to identify patterns in large data sets, are ideally suited to facilitate data-driven decision making. The field of orthodontics is particularly ripe for embracing the big data analytics platform to improve decision making in clinical practice. The availability of omics data, state-of-the-art imaging and potential for establishing large clinical data repositories have favourably positioned the specialty of orthodontics to deliver personalized and precision orthodontic care. Specifically, we discuss about next-generation sequencing, radiomics in the context of CBCT imaging, and how centralized data repositories can enable real-time data pooling from multiple sources.
A framework for group activity detection and recognition using smartphone sensors and beacons

Understanding occupant activities in a building is essential for building management systems to provide occupants with comfort and intelligent indoor environment. However, current occupant activity recognition mainly focuses on individual activity. Group activity recognition indoors has gained little attention, but remains of paramount importance, such as working together, taking classes, and discussions. In this paper, we propose a framework for group activity detection and recognition (i.e., GADAR framework) using smartphone sensors and Bluetooth beacons data. This framework consists of the following four layers: user layer, data package layer, processing layer, and output layer. As individuals within the group show similarity in motion, audio, and proximity, such similarity values are calculated and clustered into groups using hierarchical clustering. The framework then extracts the role, motion, speaking and location features from the clustered groups to distinguish different group activities. Decision tree classifier was selected to recognize the group activity that the group is engaged in. An experiment was conducted to identify the following three common group activities: taking class, seminar, and discussion. The result shows that the proposed GADAR framework could provide more than 89% accuracy in group detection and 89% accuracy in recognizing group activity.
Image encryption scheme based on a new secure variant of Hill cipher and 1D chaotic maps

This article suggests a new image encryption algorithm based on a secure variant of Hill Cipher (HC) and three improved one-dimensional (1D) chaotic maps. The proposed scheme fully satisfies the two basic concepts of security, namely confusion and diffusion. The confusion is ensured by the product of a vector consisting of the key-pixel couple and a 2 × 2 Hill matrix on the one hand, and the addition of another pseudo-random translation vector on the other hand. And the diffusion is ensured by a strong avalanche effect that links each encrypted pixel to its adjacent. The chaotic sequences used come from 1D chaotic maps with excellent statistical proprieties. The proposed algorithm aims to encrypt pixel-by-pixel all types of images, even those with a high correlation of adjacent pixels or a black background, and to make the encryption system more secure. So, the simulation results performed on a database of color and grayscale images show that the histogram of a cipher image produced a uniform distribution, a higher entropy, a correlation among image pixels that was significantly decreased; Also, the proposed algorithm has great performance in terms of sensitivity, UACI and NPCR.