Miracle-dz/UniFAR-FTUs huggingface dataset:
Extraction of Tumor Site from Cancer Pathology Reports using Deep Filters

\beginabstract Purpose: Pathology reports are the primary source of information concerning the millions of cancer cases across the United States. % Cancer registries manually process the pathology reports to extract the pertinent information including primary tumor site, behavior, histology, laterality, and grade. % Processing a large volume of the pathology reports in a timely manner is a continuing challenge for cancer registries. % The purpose of this study is to develop an information extraction pipeline to reliably and efficiently extract reportable information. \\ Method: % We have developed a novel inverse-regression (IR) based information extraction pipeline. % The inverse-regression based supervised filter has been successfully applied to many application domains. % However, its application to the information extraction from unstructured text is hindered primarily by the extreme high-dimensionality of n-gram representations of text. % In this study, we attempt to overcome the obstacles by a novel bootstrapping strategy. % First, we use an information-theoretic mutual information based filter to discard the excessive and redundant n-gram features. % This step reduces the size and improves the condition number of the sample covariance matrix, thus reducing the computational cost and improving the numerical stability of the subsequent inverse-regression step. % Then we use localized sliced inverse-regression (LSIR) to learn a low-dimensional discriminatory subspace for information inference. % In particular, we use the k-nearest neighbors of an unlabeled pathology report in the learned representation to infer the desired information from the labeled data in a supervised manner. % \\ % Results: The experiments were conducted on a set of de-identified pathology reports with human expert labels as the ground truth. % Our pipeline consistently performed better than or comparable to the best performing state-of-the-art methods while reducing the training and inference times substantially. \\ Conclusion: Our results demonstrate the potential of \emergencystretch 3em inverse-regression based information extraction pipeline for reliable and efficient information extraction from unstructured text. % The information extracted from the pathology reports can be used along with clinical information, medical imaging, and genomic information to instigate discoveries in cancer research. % \endabstract
A Comprehensive Review of Adaptive Noise Cancellation Techniques in the Internet of Things

Internet of things (IoT) has made possible the sharing of data wirelessly among everyday objects and physical devices. However, distortion and noise can significantly degrade the signaling performance of IoT. Adaptive Noise Cancellation (ACN) technology plays an important role not only in IoT but also in other numerous fields such as system identification, noise cancellation, communication, signal control, etc. At present, IoT is an advance trend and need to deal with upcoming issues in a way to deal properly in enjoying this technology. Due to the presence of noise in the signal, the quality of the signal is reduced, so the quality of communication (signal) can be improved by adaptive noise (acoustic) cancellation technology. This paper aims to provide the performance analysis of adaptive algorithm and the application of adaptive noise cancellation technique. Further, use cases of three-noise cancellation filter in IoT are introduced. In wireless communication, quality of services relies on signal quality and thus noise cancellation plays a vital role in the signaling of IoT. At the end of this paper performance analysis of different ANC algorithms including least mean square (LMS), Normalized least mean square (NLMS), recursive least square (RLS) will be done based on simulation results.
Keeping a finger in the pie?

In fully automated vehicles, human participation in the driving task is unnecessary but some involvement is desired from a user acceptance and experience perspective. A new collaborative relationship will need to be established, thus this study explores how users perceive different relationships between vehicle and human and which preferences they hold. For the study, four prototypes of interaction designs were developed, each embodying one hypothesis for a collaborative relationship. They were tested with 24 drivers in a scenario-based within-subject study using a very simple driving simulator. Each participant tested two prototypes, and half of participants noticed a difference between the pair they experienced. Differences were seen relating to the dimension of adaptation, like how involved the vehicle invited them to be, how it presented options and how the interaction made them feel more or less responsible. The different interpretations of control appear to have played a central role in the participants’ experiences of the different relationships. In conclusion, the study reinforces the importance of explicitly designing the collaborative relationship between human and vehicle, as well as provides formative insight on which criteria that will need to inform the design of future human – vehicle relationships.
The role of virtual communities of practice in overcoming isolation and improving knowledge sharing in general practice training in Australia

15 THE ROLE OF VIRTUAL COMMUNITIES OF PRACTICE IN OVERCOMING ISOLATION AND IMPROVING KNOWLEDGE SHARING IN GENERAL PRACTICE TRAINING 15 INTRODUCTION 15 METHODS 15 RESULTS 16 DISCUSSION 17 CHAPTER ONE: INTRODUCTION AND OVERVIEW OF THE RESEARCH 19 THE IMPORTANCE OF PRIMARY CARE AND GENERAL PRACTICE 19 GENERAL PRACTICE ORIGINS 19 GENERAL PRACTICE IN AUSTRALIA 20 COMMUNITIES OF PRACTICE 23 THE INTERNET AND KNOWLEDGE SHARING 23 VIRTUAL COMMUNITIES OF PRACTICE 24 RESEARCH QUESTIONS 24 PRESENTATION OF THESIS 25 RESEARCH STRUCTURE AND METHODOLOGY 25 REFERENCES 30 CHAPTER 2: GENERAL PRACTICE TRAINING AND VIRTUAL COMMUNITIES OF PRACTICE – A REVIEW OF THE LITERATURE 34
Is Technology Killing Human Emotion?: How Computer-Mediated Communication Compares to Face-to-Face Interactions

Technology is a vital part of day-to-day life for people all around the globe, but the effects of taking our communication online remain unclear, especially in terms of interpersonal communication. Human interaction and social connectedness are key to our happiness, and technology has the power to influence our well-being. This research explores how 10 aspects of interpersonal communication differ in virtual and face-to-face environments. An online questionnaire was created where participants assessed their interpersonal communication in virtual and real-life environments. Our findings show that face-to-face communication is able to convey five out of ten dimensions of interpersonal communication competence significantly better than virtual communication because important implicit information and nonverbal cues are transferred, whereas in a virtual environment they are either not sent or not received.
Towards a Comprehensive Definition of Second Screen

Second screening is a widespread activity among users, although many are not aware of the term, and second screen applications are becoming increasingly popular in research and commercial use, from well-known examples such as Netflix, Amazon Prime Video or YouTube to applications that enable novel uses cases and have the potential to change the current usage behavior of the content on the first screen. Despite the increasing prevalence of this phenomenon, there are different interpretations in the relevant literature and in existing applications about what exactly is meant by the term second screen. In order to evaluate these different interpretations, we conducted a structured review of literature (SRL) on 65 publications in this area and reflected the results on an analysis of currently available second screen applications to precisely delineate the term and its characteristics. Furthermore, we derived a content and technical classification that will make it easier to manage the various benefits and characteristics of second screen applications in the future, to create a framework for the appropriate positioning of new research activities and reasoned basis for discussion.
ThumbStroke

The QWERTY keyboard on mobile devices usually requires users’ full visual attention and both hands, which is not always possible. We propose a thumb-stroke-based keyboard, ThumbStroke, to support both sight-free and one-handed text entry. Text entry via ThumbStroke completely relies on the directions of thumb strokes at any place on the screen of a mobile device. It does not require physical press on any specific keys, thus eliminating the need for visual attention and reducing errors due to tiny key size, fat thumbs, limited thumb reachability, and visual occlusion. We empirically evaluated ThumbStroke through a 20-session longitudinal controlled lab experiment. ThumbStroke shows advantages in typing accuracy and user perceptions in comparison to the Escape and QWERTY keyboards and results in faster typing speed than QWERTY in sight-free and one-handed text entry. This study provides novel research contributions to mobile HCI, advancing the design of soft keyboards for one-handed interaction with mobile devices and mobile accessibility.
What causes the adoption failure of service robots?: a case of Henn-na hotel in japan

With the emergence of AI-powered products and services, the hospitality industry has started to adopt service robots to transform the guest experience. Despite this growing interest, Henn-na Hotel, the world's first robot hotel, recently announced to abandon half of its robots. This study aims to unveil factors leading to the adoption failure of service robots in the hospitality context using Henn-na Hotel as the case study. Through mining online guest reviews from four different leading online booking sites, we conducted thematic content analysis on a total of 250 negative online reviews. A total of six themes emerged from our data (e.g., human intervention, usefulness, embodiment), illustrating various factors resulting in the adoption failure. Based on this, we come up with six design implications for future researchers and designers to re-think about the interaction process between human and robots, as well as how service robots could be better designed and used in hospitality settings to fulfill guest needs.
Bringing open data into danish schools and its potential impact on school pupils

Private and public institutions are using open and public data to provide better services, which increases the impact of open data on daily life. With the advancement of technology, it becomes also important to equip our younger generation with the essential skills for future challenges. In order to bring up a generation equipped with 21st century skills, open data could facilitate educational processes at school level as an educational resource. Open data could acts as a key resource to enhance the understanding of data through critical thinking and ethical vision among the youth and school pupils. To bring open data into schools, it is important to know the teacher's perspective on open data literacy and its possible impact on pupils. As a research contribution, we answered these questions through a Danish public school teacher's survey where we interviewed 10 Danish public school teachers of grade 5-7th and analyzed their views about the impact of open data on pupils' learning development. After analyzing Copenhagen city's open data, we identified four open data educational themes that could facilitate different subjects, e.g. geography, mathematics, basic science and social science. The survey includes interviews, open discussions, questionnaires and an experiment with the grade 7th pupils, where we test the pupils' understanding with open data. The survey concluded that open data cannot only empower pupils to understand real facts about their local areas, improve civics awareness and develop digital and data skills, but also enable them to come up with the ideas to improve their communities.
Visual Aesthetics and Performance: A First Meta-Analysis

Aesthetics has become a central construct in Human-Computer Interaction (HCI), and it has clear beneficial effects on users' perceptions and attitudes. Yet, do attractive interfaces actually enhance user performance? In the light of the importance of the construct and the mixed findings in various studies on the matter a systematic approach is urgently needed. Thus, the present meta-analysis examines in detail the effects of visual interface aesthetics of websites, software and other interactive systems on objective user performance. A systematic literature search yielded 25 eligible studies with 101 observed effects and a total of 3,025 participants. The conducted meta-analysis revealed a small significant overall effect of interface aesthetics on user performance (g = 0.12), while a high heterogeneity of effects was observed. As potential moderators, we tested the type of used interaction medium, task, goal orientation, measure of performance, measure of aesthetics, and aesthetics manipulation. None showed a significant moderating influence. Thus, aesthetics can be considered to have a small but heterogeneous influence on user performance that so far cannot be further resolved by moderating variables reported in eligible studies. Therefore, the discussion sketches avenues for future research and encloses a call to action for the HCI community.
Anticipating response to predictive genetic testing for Alzheimer's disease: a survey of first-degree relatives.

: Two hundred and three children and siblings of patients with Alzheimer's disease (AD) (age range: 30-92 years, 75% female) were surveyed regarding potential predictive testing options for the disorder. A mailed questionnaire posed various hypothetical scenarios and assessed the following variables: interest in testing, perceptions of its pros and cons, and psychological and demographic predictors of test intentions. In 5 of 6 scenarios, a majority of participants expressed intentions to pursue testing, with perceived pros outweighing cons. The most important reasons for seeking testing were informing later-life decisions and planning future AD care. Predictors of test intentions were male gender, information-seeking style, higher perceived AD threat, and appraisal of test pros versus cons. Situational factors such as available treatment options and certainty of test information also affected responses. Results suggest a positive view of predictive testing, with its limitations and risks underrated. Study findings may inform AD genetic counseling and health education efforts.
A wider picture of trust: confronting multi-contextual social traits for trust inference

With the advent of social networks, problems related to information veracity and people trustworthiness naturally arise and become more acute as the numbers of participants and the volumes of exchanged information grow. Predicting and recommending trust (and distrust) levels is essential for a healthier online presence. Although many research efforts are being devoted to these tasks, most studies that have been undertaken in this area are constrained by two facts. 1) Distrust, contrary to trust, is not necessarily propagative, thus is harder to infer using propagative approaches. 2) Most publicly available datasets fail to capture the essence of trust for they ignore one of its most important aspects: its context. In this paper, we propose a multidimensional trust model that allows us to describe some social traits that affect trust, and which could not be described with a unidimensional model. We, then, use these traits to propose a straightforward trust prediction approach that does not rely on trust propagation. Our work aims to provide a usable foundation and to ignite more advanced researches on trust and its applications.
How Time Constraints in a Creativity Support Tool Affect the Creative Writing Experience

Being able to write ‘on cue’ is critical in text-based, creative domains, since time is often a limited resource. Although numerous digital-interactive Creativity Support Tools (CSTs) are available, writing applications with features to support creative writing as a time-constrained activity have scarcely evolved since the 1960s. This is striking, since studies show that time constraints may benefit creativity. We present an exploratory survey-based, qualitative user experience study of how a writing application prototype designed to accelerate text production by imposing time constraints affects a creative writing task among high school students (n=45). Using thematic analysis, we report how implicit and explicit time constraints built into the GUI (graphical user interface) influence how users experience time pressure and the quantity vs. quality, initiation, and revision of their creative writing process. We discuss how writing applications may develop to incorporate time constraints to support creative writing, as well as the need for increased tool literacy.
Adoption, Use and Diffusion of Crisis Apps in Germany: A Representative Survey

The research field of crisis informatics examines the potentials and limitations of information and communication technology in crises, disasters, and emergencies. Although ICT plays an increasingly large role in crisis response and management, in-depth studies on crisis apps and similar technology in the context of an emergency have been missing. Based on responses by 1024 participants in Germany, we examine the diffusion, usage, perception and adoption of mobile crisis apps as well as required functions and improvements. We conclude that crisis apps are still a little-known form of disaster ICT, but have potential for enhancing communication, keeping users up to date and providing a more effective crisis management as supplement to other media channels dependent on different underlying infrastructures. However, they should be adaptable to user characteristics, consider privacy, allow communication and offer valuable information to raise awareness of potential disasters without creating an overload. Also, the familiarity with and trust in crisis apps should be addressed to maximize their beneficial impact on crisis communication and management. We discuss further implications as well as directions for future research with larger target groups and specific usage scenarios.
Identifying causal patterns from mobile sensing data: a case study on blood glucose inference

The high-dimensional and co-evolved data streams sensed by mobile devices typically exists time delays that form the "causal-and-effect" patterns. Understanding the informative causal patterns from the multivariate time series is critical but challenging for the inference tasks with the sensing data. While a large scope of statistical learning methods has undergone great advances in the causal pattern recognition problem, most of them are still limited in the unreliable causal analysis, high computational complexity and the environmental noise interruption. To this end, we propose a novel directed information (DI)-aided approach to efficiently select the casual patterns from a set of feature streams collected from mobile devices. The proposed approach has been evaluated on a real blood glucose sensing dataset. The results demonstrate our proposed approach outperforms the traditional methods in cost efficiency and inference accuracy.
Effects of sorting prior to feeding Zilpaterol hydrochloride and effects of different implant strategies on feedlot performance and carcass characteristics of steers and heifers

Two studies were conducted to evaluate sorting and feeding Zilpaterol hydrochloride (ZH) on feedlot performance and carcass characteristics. Cross-bred beef steers were used in a randomized block design finishing trial. In Exp. 1, (initial BW 342 ± 10 kg, n = 1,000) 5 treatments included an unsorted non-ZH fed negative control (CON), unsorted ZH fed positive control (+CON); and three treatments where the heaviest 20% within the pen were sorted and marketed 28 d early and the remaining 80% were fed ZH. The 20% were identified at the beginning (EARLY), 100 d from slaughter (MIDDLE), or 50 d from slaughter (LATE). Because of sorting, remaining steers in sorted treatments were fed 14 d longer than -CON and +CON. Average days on feed for control treatments were 165 d and 173 d for the EARLY, MIDDLE, and LATE treatments. In Exp. 2 (initial BW 376 ± 29 kg, n = 1,400) the 4 treatments included CON, +CON, an early weight sort fed ZH (1-SORT) with heaviest 20% identified at d 1, and sorted 50 d from harvest and marketed 14 d prior to –CON and +CON, with the remaining 80% of the pen fed 7 d longer than –CON and +CON, and a four-way sort 50 d from harvest fed ZH (4-SORT) with steers sorted into a HEAVY, MID-HEAVY, MIDLIGHT, and LIGHT group marketed -14 d, 0 d, +7 d, and +28 d from –CON and +CON, respectively. Average days on feed for control treatments were 154 d, 157 d for the 1SORT, and 159 d for 4-SORT. Steers were fed Zilmax at 8.3 mg/kg DM for 20 d followed by a 3 d withdrawal. In Exp. 1, steers fed +CON had 13 kg greater HCW than CON. Steers sorted EARLY, MIDDLE, and LATE had 28, 25, and 24 kg heavier (P < 0.01) HCW than -CON, respectively. Carcass weight SD was greater (P = 0.01) for +CON than -CON, but not different (P = 0.17) between -CON and ZH sorted treatments.
Automated inference of cognitive performance by fusing multimodal information acquired by smartphone

Recognizing human cognitive performance is important for preserving working efficiency and preventing human error. This paper presents a method for estimating cognitive performance by leveraging multiple information available in a smartphone. The method employs the Go-NoGo task to measure cognitive performance, and fuses contextual and behavioral features to identify the level of performance. It was confirmed that the proposed method could recognize whether cognitive performance was high or low with an average accuracy of 71%, even when only referring to inertial sensor logs. Combining sensing modalities improved the accuracy up to 74%.
Personal thermal perception models using skin temperatures and HR/HRV features: comparison of smartwatch and professional measurement devices

In order to detect a person's individual momentary thermal sensation and comfort, an increased number of models are developed that involve physiological data, especially skin temperatures and features based on the heart activity. In this paper, we investigate the feasibility of Machine Learning (ML) models which include physiological data based on two data sources: (1) a smartwatch and (2) a portable chest belt device. Further, we investigate the difference between thermal sensation and comfort votes and propose a new combined ground truth label. Data were collected in lab-like studies in a single-bed hotel room. Our study focuses on the detection of cold-induced thermal discomfort by varying the room air temperature between 24 °C and 20 °C. Results show that ML based approaches lead to accuracies up to 83.1% when using a chest belt device. Using a smartwatch, accuracies drop down to 79.8%. Our investigation shows that even though Heart Rate (HR) based features improve the prediction accuracy, the highest feature importances have distal skin temperature features.