Reviewerly/exHarmony huggingface dataset:
Granular Fuzzy Modeling Guided Through the Synergy of Granulating Output Space and Clustering Input Subspaces

As an augmentation of classic fuzzy models, granular fuzzy models (GFMs) have been applied to many fields being in rapport with experimental data, models, and users. However, most of the existing methods used to construct GFMs are based on the principle of optimal allocation of information granularity, which requires that a numeric model be provided in advance. In this paper, a straightforward and convincing modeling method is proposed to directly construct GFM on a basis of experimental data. The method first granulates the output space to form some interval information granules with distinct semantics and then uses them to partition the entire input space into a series of input subspaces. Subsequently, an initial GFM is emerged by using “If-Then” rules to relate with those interval information granules positioned in the output space and structures expressed in prototypes that are produced by clustering individual input subspaces. Further, the initial GFM is also refined by continuously migrating prototypes in individual input subspaces. The experimental studies using the synthetic dataset and several real-world datasets are reported. They offer a useful insight into the feasibility and effectiveness of the proposed modeling method and reveal the impact of parameters on the performance of the ensuing GFMs. An application example is also presented to exhibit the advantages of the resulting GFM.
Co-Performing Agent

Intelligent agents have become prevalent in everyday IT products and services. To improve an agent's knowledge of a user and the quality of personalized service experience, it is important for the agent to cooperate with the user (e.g., asking users to provide their information and feedback). However, few works inform how to support such user-agent co-performance from a human-centered perspective. To fill this gap, we devised Co-Performing Agent, a Wizard-of-Oz-based research probe of an agent that cooperates with a user to learn by helping users to have a partnership mindset. By incorporating the probe, we conducted a two-month exploratory study, aiming to understand how users experience co-performing with their agent over time. Based on the findings, this paper presents the factors that affected users' co-performing behaviors and discusses design implications for supporting constructive co-performance and building a resilient user-agent partnership over time.
Data Our Bodies Tell: Towards Critical Feminist Action in Fertility and Period Tracking Applications

This article situates reproductive applications as an emerging “do-it-yourself” health technology in need of feminist technical communication action. The authors focus on Glow, a fertility and period tracking application, and argue that though this application promises user’s self-empowerment over their reproductive health, individual agency is often reduced. The authors consider how technical communication scholars can intervene in fertility and period tracking applications through a redesign of how consent is obtained when collecting user’s personal health information.
An approach for recognition of human's daily living patterns using intention ontology and event calculus

This paper proposes a method of recognizing the intention of human activity based on percept sequences that represent the activities of daily living (ADL) in a residential space. Based on the activity intention ontology representing actions, poses, and nearby objects related to human activity intentions, the proposed method identifies a human activity intention by using the event calculus when a percept sequence is entered. It is very difficult to recognize ADL occurring in various places in a residence by using regular percept sequences without error. Furthermore, a human activity can have complex intentions. To solve these problems, this paper proposes an activity intention recognition process consisting of three steps. First, the activity intention inference step recognizes the intention of a given percept sequence based on the activity intention ontology. Second, the complex intention-identifying step determines whether to terminate the previously maintained activity intention or continue maintaining it in a complex manner based on the newly inferred activity intention. Third, the uncertainty handling step corrects an inaccurate activity intention caused by an error in the percept sequence. To evaluate the proposed method presented in this paper, activity intention recognition experiments were conducted based on the ADL data that were collected for six households of elderly persons older than 70 years. The results showed that the proposed method has a precision of 99.62% and recall of 92.83%.
Optimization and Hardware Implementation of Image and Video Watermarking for Low-Cost Applications

The prevalence of wireless networks has made the long-term need for communications security more imperative. In various wireless applications, images and/or video constitute critical data for transmission. For their copyright protection and authentication, watermarking can be used. In many cases, the cost of wireless nodes must be kept low, which means that their processing and/or power capabilities are very limited. In such cases, low-cost hardware implementations of digital image/video watermarking techniques are necessary. However, to end up with such implementations, proper selection of watermarking techniques is not enough. For this reason, in this paper, we introduce computation optimizations of the implemented algorithm to keep the integer part of arithmetic operations at optimal size, and, hence, arithmetic units as small as possible. In addition, further analysis is performed to reduce quantization error. Three different hardware-architecture variants, two for image watermarking and one for video (pipelined), are proposed, which reutilize the already small arithmetic units in different computation steps, to further reduce implementation cost. The proposed designs compare favorably to already existing implementations in terms of area, power, and performance. Moreover, the watermarked images'/frames' errors, compared to their floating point counterparts, are very small, while robustness to various attacks is high.
Managerial Visions

This paper examines the entangled development of governance strategies and networked technologies in the pervasive but under-examined domain of public restrooms. Drawing on a mix of archival materials, participant observation, and interviews within and beyond the city of Seattle, Washington, we look at the motivations of public restroom facilities managers as they introduce (or consider introducing) networked technology in the spaces they administer. Over the course of the research, we found internet of things technologies-or, connected devices imbued with computational capacity-became increasingly tied up with cost-reducing efficiencies and exploitative regulatory techniques. Drawing from this case study, we develop the concept of managerial visions: ways of seeing that structure labor, enforce compliance, and define access to resources. We argue that these ways of seeing prove increasingly critical to HCI research as it attends to computer-mediated collaboration beyond white-collar settings.
Disaster-and-Evacuation-Aware Backup Datacenter Placement Based on Multi-Objective Optimization

Backup datacenters provide massive data storage and access services, and their failure may result in huge economic losses. So their location selection requires low damage risk and high evacuation capability simultaneously. But previous works on backup datacenter placement have not jointly considered these two factors from the viewpoint of traffic engineering and might result in the unnecessary loss in case of disaster. In this paper, with the global view of network resources in the software defined network scenarios, we propose a new disaster-and-evacuation-aware backup datacenter placement strategy. To reduce backup loss risk and apply rapid post-disaster evacuation, we jointly consider expected disaster loss and evacuation latency and formulate a new disaster-and-evacuation-aware facility location problem (NP-hard) which is multi-objective. To obtain the solution according to the disaster situation assessment, we propose a disaster-and-evacuation-aware multi-objective optimization algorithm. We optimize multiple objectives owning different coefficients in different disaster situations. We introduce location-output-capability, backup-evacuation-latency, Pareto-recommendation-degree, and node-damage-loss to guide solution searching. We prune the external set according to fitness-deviation-ratio to improve convergence speed and computation efficiency of the algorithm. Through extensive simulations, we demonstrate that our algorithm is efficient and promising with less expected disaster loss and higher evacuation capability simultaneously.
Turn to the Self in Human-Computer Interaction

Everyday life is increasingly mediated by technology. Technology is rapidly growing capacity and complexity, especially evident in developments in artificial intelligence and big data analytics. As human-computer interaction (HCI) endeavors to examine and theorize how people act and interact with the ever-evolving technology, an important, emerging concern is how the self-the totality of internal qualities such as consciousness and agency-plays out in relation to the technology-mediated external world. To analyze this question, we draw from Michel Foucault's ethics of "care of the self," which examines how the self is constituted through conscious and reflective work on self-transformation. We present three case studies to illustrate how individuals carry out practices of the self to reflect upon and negotiate their relationship with technology. We discuss the importance of examining the self and foreground the notion of care of the self in HCI research and design.
Quantum Lc-circuit satisfying the Schrodinger-Fisher-Kolmogorov equation and quantization of Dc–Pumped Josephson parametric amplifier

In this study, we have discussed the quantization of the electric LC-circuit based on the generalized Heisenberg's uncertainty relation. The new electric system is governed by the Schrödinger-Fisher-Kolmogorov equation where its Lagrangian and Hamiltonian structures are obtained. As an application, we have analyzed the DC-pumped Josephson parametric amplifier. Several new properties were analyzed and discussed accordingly.
Modeling of systematic errors in stereo-digital image correlation due to camera self-heating

Abstract Errors in strain measurements in stereo-digital image correlation (stereo-DIC) caused by camera self-heating have been experimentally observed in previous research, and have been shown to widely vary from one system configuration to another. Such “thermal errors” are sometimes so large that they strongly compromise the accuracy of the measurements. Despite correcting such errors is crucial when aiming at high-accuracy measurements, the mechanism of the thermal error generation and how it relates to the camera parameters in stereo-DIC are still not clear. In this paper, we first explain in detail how self-heating can introduce large artifacts in the strains measured by stereo-DIC. Using a simplified stereovision model, we provide the main equations that describe the theoretical errors in 3D coordinate reconstruction and 3D deformation measurement. Through several sets of simulations, the effect of camera self-heating on the 3D coordinate, displacement and strain measurements, and the effect of camera parameters on the thermal errors in stereo-DIC were explicitly presented based on the derived theoretical formulas. Finally, several real self-heating tests using a smartphone-based single-camera stereo-DIC system confirm the correctness of theoretical analyses and simulation results.
Part-of-Speech-Based Long Short-Term Memory Network for Learning Sentence Representations

Sentence representations play an important role in the field of natural language processing. While word representation has been applied to many natural language processing tasks, sentence representation has not been applied as widely due to the more complex structure and richer syntactic information of sentences. To learn sentence representations with structural and syntactic information, we propose a new model called the part-of-speech-based Long Short-Term Memory network (pos-LSTM) model. The pos-LSTM model generates a structural representation using the standard LSTM model and a syntactic representation using the part of speech; then, the pos-LSTM model obtains the final sentence representation by combining these two representations. The experimental results from 20 sentence similarity tasks and an entailment classification task show that the pos-LSTM model can better capture the syntactic information of sentences and generate higher quality sentence representations than traditional models.
Demonstrational Interaction for Data Visualization

Recently, there has been an increasing trend to extend the demonstrational interaction paradigm to visualization tools. As more analytic operations can be performed by demonstration, new user tasks can be supported. In this paper, we discuss the properties of tasks where the by-demonstration paradigm can be effective and describe the main components needed to implement the demonstrational paradigm in visualization tools.
Anomaly detection over differential preserved privacy in online social networks

The massive reach of social networks (SNs) has hidden their potential concerns, primarily those related to information privacy. Users increasingly rely on social networks for more than merely interactions and self-representation. However, social networking environments are not free of risks. Users are often threatened by privacy breaches, unauthorized access to personal information, and leakage of sensitive data. In this paper, we propose a privacy-preserving model that sanitizes the collection of user information from a social network utilizing restricted local differential privacy (LDP) to save synthetic copies of collected data. This model further uses reconstructed data to classify user activity and detect abnormal network behavior. Our experimental results demonstrate that the proposed method achieves high data utility on the basis of improved privacy preservation. Moreover, LDP sanitized data are suitable for use in subsequent analyses, such as anomaly detection. Anomaly detection on the proposed method's reconstructed data achieves a detection accuracy similar to that on the original data.
ViePEP-C: A Container-Based Elastic Process Platform

Business Process Management Systems (BPMS) need to be able to take into account the fluctuating demand for computational resources during the execution of business process activities. Today, BPMS rely on the leasing and releasing of virtual machines (VMs) on cloud resources, which leads to a rather coarse-grained allocation of computational resources. This may result in an increase in the execution cost, flexibility restrictions, and a negative impact on the Quality of Service. In order to overcome these drawbacks, we introduce the Vienna Platform for Elastic Processes on Containers (ViePEP-C). ViePEP-C is an elastic BPMS that uses containers instead of VMs for the execution of business process activities on cloud resources, leading to a more fine-grained execution environment. To achieve this, ViePEP-C offers cloud controller, monitoring and business process execution functionalities and provides a platform for different resource and task scheduling algorithms. To evaluate the benefits of ViePEP-C, we further present a resource and task scheduling algorithm and show that, by using containers as execution environment, the execution cost can be decreased by over 20 percent (compared to a state-of-the-art VM-based scheduling algorithm) while considering a high service level.
Self-Similarity and Symmetry With SIFT for Multi-Modal Image Registration

This paper presents a novel feature-based multi-modal image registration technique called self-similarity and symmetry with a scale invariant feature transform (3S-SIFT). The proposed technique has the following two main components. First, a ubiquitous problem existing in registering multi-modal images, gradient reversal, is well studied and addressed. Second, the proposed technique takes into account self-similarity information between keypoint triangles, which is conducive to enhancing the registration accuracy. Moreover, a simplified version of 3S-SIFT called 4S-SIFT is proposed as a pruning technique for feature matching. The proposed techniques are generally applicable to the registration of multi-modal images with changes in scale, rotation, and translation. The experiments have been conducted on a set of benchmark datasets in the domain of image registration, demonstrating that the proposed techniques achieve the state-of-the-art performance in both matching accuracy and recall.
Movie Recommendation System

Recommender systems are called information filtering tools, which use big data to recommend likes of the user according to their preference and interest. Moreover, they also help in matching users with similar tastes and interests. Due to this, a central part of websites and e-commerce applications is taken up by the recommender systems. Systems using recommendation algorithms like Collaborative Filtering, Content-Based Filtering, etc., are called Recommendation systems. Recommendation systems are transforming the way quiescent websites corresponds with their users. Rather than providing an orthodox experience in which users search for the products which they want and potentially buy products, recommender systems increase communication to provide a better experience. The work would be to implement both collaborative as well as content-based recommenders available and try to extend the knowledge obtained to a more efficient hybrid model. Then benchmark these hybrid algorithms for accuracy and computation time as well.
Artificial Playfulness

Usertesting is commonly employed in games user research (GUR) to understand the experience of players interacting with digital games. However, recruitment and testing with human users can be laborious and resource-intensive, particularly for independent developers. To help mitigate these obstacles, we are developing a framework for simulated testing sessions with agents driven by artificial intelligence (AI). Specifically, we aim to imitate the navigation of human players in a virtual world. By mimicking the tendency of users to wander, explore, become lost, and so on, these agents may be used to identify basic issues with a game's world and level design, enabling informed iteration earlier in the development process. Here, we detail our progress in developing a framework for configurable agent navigation and simple visualization of simulated data. Ultimately, we hope to provide a basis for the development of a tool for simulation-driven usability testing in games.
Mobile Persuasive Technology

My research examines new ways of persuading citizens to change their behavior towards waste management, and protect the environment. As a first step towards contributing to research, I conducted a user-based study, to find what strategies could be used to motivate national citizens to adopt positive waste disposal behaviors. Mapping the results to their matching persuasive technology techniques and operationalizing them in a mobile web platform, I show how mobile persuasive system interventions could be designed to promote positive and environmentally responsible behaviors and protect the environment from pollution.
HCI and Menopause

With growing concern for the intimate dimensions of technology development, HCI scholars have begun to grapple with who wields power in design around the body. However, beyond menstruation, few studies have sought to examine the role of technology design in the later stages of life for menstruating people. This paper considers menopause as a central, but overlooked life phase informing the design of future intimate technologies. We review empirical analysis of menopausal experiences and design provocations that emerged from our work. We end with a reflection on the opportunities and pitfalls around menopause design.
Successful Engagement of Practitioners and Software Engineering Researchers: Evidence From 26 International Industry&amp;ndash;Academia Collaborative Projects

There has been a push to increase the practical relevance and impact of software engineering research. Although many practitioners and researchers agree that this change is desirable, thus far, only a few concrete actions have been taken by the community. In this article, we present our experiences with a large number of collaborative research projects that have had a practical (industrial) impact.
Low Cost Eye Gaze Tracker Using Web Camera

The traditional gaze tracking systems are of invasive, expensive or not of a standard hardware. To address this problem, research has been focused onto systems with simple hardware with gaze tracking systems. We propose a system which uses web camera and the free open source Computer Vision Library Open CV. Gaze estimation plays a major role in predicting human attention and understanding human activities. It is also used in market analysis, gaze driven interactive displays, medical research, usability research, packaging research, gaming research, psychology research, and other human-machine interfaces. This paper describes how to develop a low cost eye gaze system using a simple web camera. The system captures real time video of the person to detect the eyes in the initial frames and extract the features of eyes. Once the features are extracted, the eyes are tracked in the subsequent frames and the gaze direction is estimated using the computational intelligence techniques.
HCI for Accurate, Impartial and Transparent Journalism

While new media technologies hold the potential to serve journalism's dual goals of informing and engaging the public, these technologies also challenge the journalistic norms of accuracy, impartiality and transparency. The key question in this workshop is: How can HCI support accurate, impartial and transparent journalism? This question is ever more timely as the need for accurate and credible journalism is growing amid the proliferation of disinformation and opinion manipulation. In this workshop, we will identify challenges and solutions in the design of user interfaces, user experiences and production processes in journalism. We bring together researchers and practitioners designing, deploying and studying new technologies in journalism. The goal of the workshop is to harness the potential of HCI for supporting accurate, impartial and transparent journalism.
Dynamic Container-based Resource Management Framework of Spark Ecosystem

Apache Spark is known for its robustness in processing large-scale datasets in a distributed computing environment. This form of efficiency is highly observing because of the direct use of Random-Access Memory (RAM) in processing its resilient distributed datasets across the ecosystem. Recently, it is observed that, the memory utilization in computing spark jobs is mainly dependent on job containers, which are closely associated to persistent storage media components. Thus, spark jobs processing relevancy is tightly coupled to the type of storage container and in case of any dynamic resource allocation, the job loses its ratio of resource computation in existing container and increases a functional issue of processing large-scale datasets in spark ecosystem. In this paper, we propose dynamic container-based resource management framework, that shifts coupled associations of job profiles to dynamically available resource containers. Also, it relieves static container allocations and presumes them as a fresh piece of resource allocation for new job profile. The experimental evaluation shows that the proposed dynamic framework reduces wastage of resource allocations and increase ecosystem performance than default job profile in spark ecosystem.