Reviewerly/exHarmony huggingface dataset:
Typing on a Smartwatch While Mobile: A Comparison of Input Methods

The user experience of typing on a smartwatch was evaluated with three unique input methods (tap, trace, and handwriting) while standing and while walking.Despite widespread development within the technology industry, smartwatches have had a relatively slow adoption worldwide compared to smartphones. One limiting factor of smartwatches has been the lack of an efficient means of text entry. The 2017 release of Android Wear addressed this issue by providing support for native text entry (i.e., tap, trace, and handwriting). Determining how user performance and subjective ratings compare across these input methods is essential to understanding their contribution to smartwatch user experience.Twenty college-age individuals typed phrases using tap, trace, and handwriting input on a smartwatch in three different mobility scenarios (standing, walking a simple course, walking a complex course).Participants typed faster with trace (30 words per minute; WPM) than with tap (20 WPM) and handwriting (18 WPM), regardless of mobility. Trace also outperformed tap and handwriting across all subjective metrics, regardless of mobility.Trace input appears to be especially well suited for typing on a smartwatch as it was found to be objectively and subjectively superior to tap and handwriting regardless of user mobility. Objectively, typing speeds with trace are shown to be nearly two times faster than most alternative input methods described in the literature.Results suggest smartwatch manufacturers should include QWERTY keyboards with trace input as a standard feature in order to provide the best overall typing experience for their users.
MIFTel: a multimodal interactive framework based on temporal logic rules

Human-computer interfaces and multimodal interaction are increasingly used in everyday life. Environments equipped with sensors are able to acquire and interpret a wide range of information, thus assisting humans in several application areas, such as behaviour understanding, event detection, action recognition, and many others. In these areas, the suitable processing of this information is a key factor to properly structure multimodal data. In particular, heterogeneous devices and different acquisition times can be exploited to improve recognition results. On the basis of these assumptions, in this paper, a multimodal system based on Allen’s temporal logic combined with a prevision method is proposed. The main target of the system is to correlate user’s events with system’s reactions. After the post-processing data coming from different acquisition devices (e.g., RGB images, depth maps, sounds, proximity sensors), the system manages the correlations between recognition/detection results and events, in real-time, thus creating an interactive environment for users. To increase the recognition reliability, a predictive model is also associated with the method. Modularity of the system grants a full dynamic development and upgrade with customized modules. Finally, comparisons with other similar systems are shown, thus underlining the high flexibility and robustness of the proposed event management method.
LeadLets: Towards a Pattern Language for Leadership Development of Human and AI Agents

Advances in artificial intelligence (AI) technologies have inspired businesses and researchers to identify new ways in which AI can improve our way of life.One such quest lies in giving AIs complex human capabilities -like leadership.We take the first step towards that goal and propose a pattern-based approach to leadership.We argue that leadership best practices are actually a series of mini interventions each of which results in a consistent and desired response from the followers.When codified, these repeatable interventions can serve as foundational blocks for AI algorithms.To this end, we introduce LeadLets: A pattern language that codifies named, scripted, and repeatable leadership techniques that have a predictable influence causing a purposeful effect on one or more individuals.We argue that a pattern-based approach such as LeadLets can create leadership templates that inform programing leadership behavior into AI artifacts and designing leaders development programs.
The Business Process Model and Notation Used for the Representation of Alzheimer’s Disease Patients Care Process

Currently, the number of patients with neurological diseases is increasing, especially those older than 65 suffering from Alzheimer’s disease. This development increases the emphasis on understanding and mapping treatment and care processes, not only for the elderly. Service providers (of both treatment and care) are under general pressure to decrease charges and maintain or improve existing levels of care. This situation is significantly influenced by a comprehensive knowledge of the whole process and its values. This publication therefore aims to describe the fundamental procedural aspects of caring for patients with Alzheimer’s disease, using Business Process Model and Notation (BPMN). It also aims to show the possibilities of using BPMN in the description of treatment and care. Modeling of the business process is more frequently being applied not only by businesses but also by scientists involved in process models. It is used to model medical topics, with approximately 10% of its publications only, and most of these publications deal only with clinical pathways, not with overall treatment and care processes. However, the BPMN model allows the whole process of medical and nonmedical care for patients with Alzheimer’s disease to be described, including the decomposition of partial activities into individual threads and sub-processes or atomic tasks. This paper presents the BPMN modeling and mapping of the specific care path for neurodegenerative patients. The text provides a new perspective on the BPMN modeling of Alzheimer’s disease. The presented model offers the option of expanding treatment cost calculation to simulate the process using graphical tools and languages. The overall view of this system creates a much more complex concept of the system and its surroundings.
Weight enumerators, intersection enumerators, and Jacobi polynomials

The intersection enumerator and the Jacobi polynomial in an arbitrary genus for a binary code are introduced. Adding the weight enumerator into our discussion, we give the explicit relations among them and give some of their basic properties.
Data-driven Discovery of a Sepsis Patients Severity Prediction in the ICU via Pre-training BiLSTM Networks

Sepsis is the third-highest mortality disease in intensive care units(ICU) and expensive treatment costs, but the best treatment strategy remains uncertain. In this paper, we proposed a pre-training bidirectional LSTM Networks to predict the Sepsis severity of patients in ICU. Most previous models for severity prediction rely on the multi-task recurrent neural networks. In addition, state-of-the-art neural models based on attention mechanisms do not fully utilize information of organ systems that may be the most crucial features for severity prediction. To address these issues, we propose an end-to-end recurrent neural model which incorporates simultaneously analyses different organ systems and intuitively reflect the condition of the patients in a timely fashion. Specifically, we apply a pre-training technique in our model to combines it with labeled data via multi-task learning. Experimental results on the real-world clinical dataset (MIMIC-III), one of the most popular sepsis severity prediction tasks, demonstrate that our model outperforms existing state-of-the-art models.
Small area estimation of receiver operating characteristic curves for ordinal data under stochastic ordering

There has been a recent increase in the diagnosis of diseases through radiographic images such as x‐rays, magnetic resonance imaging, and computed tomography. The outcome of a radiological diagnostic test is often in the form of discrete ordinal data, and we usually summarize the performance of the diagnostic test using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The ROC curve will be concave and called proper when the outcomes of the diagnostic test in the actually positive subjects are higher than in the actually negative subjects. The diagnostic test for disease detection is clinically useful when a ROC curve is proper. In this study, we develop a hierarchical Bayesian model to estimate the proper ROC curve and AUC using stochastic ordering in several domains when the outcome of the diagnostic test is discrete ordinal data and compare it with the model without stochastic ordering. The model without stochastic ordering can estimate the improper ROC curve with a nonconcave shape or a hook when the true ROC curve of the population is a proper ROC curve. Therefore, the model with stochastic ordering is preferable over the model without stochastic ordering to estimate the proper ROC curve with clinical usefulness for ordinal data.
Vision-Language Navigation Policy Learning and Adaptation

Vision-language navigation (VLN) is the task of navigating an embodied agent to carry out natural language instructions inside real 3D environments. In this paper, we study how to address three critical challenges for this task: the cross-modal grounding, the ill-posed feedback, and the generalization problems. First, we propose a novel Reinforced Cross-Modal Matching (RCM) approach that enforces cross-modal grounding both locally and globally via reinforcement learning (RL). Particularly, a matching critic is used to provide an intrinsic reward to encourage global matching between instructions and trajectories, and a reasoning navigator is employed to perform cross-modal grounding in the local visual scene. Evaluation on a VLN benchmark dataset shows that our RCM model significantly outperforms baseline methods by 10 percent on Success Rate weighted by Path Length (SPL) and achieves the state-of-the-art performance. To improve the generalizability of the learned policy, we further introduce a Self-Supervised Imitation Learning (SIL) method to explore and adapt to unseen environments by imitating its own past, good decisions. We demonstrate that SIL can approximate a better and more efficient policy, which tremendously minimizes the success rate performance gap between seen and unseen environments (from 30.7 to 11.7 percent).
An Energy-Efficient Systolic Pipeline Architecture for Binary Convolutional Neural Network

Memory and computation cost limit the applications of Convolutional Neural Networks (CNN) on mobile devices. Binary CNN (BCNN) is a quantized neural network, which can reduce memory requirement and achieve multiplication-free computation. This paper focuses on BCNN. First, we proposed a hardware friendly CNN model to decrease the inference accuracy loss by eliminating the decimal part of each floating number. Second, we presented a fully pipelined on-chip BCNN architecture. The architecture has systolic data flow and an interlayer pipeline, which ensures weights reuse and high throughputs. The results show that we achieve an inference accuracy of 99.04% for the MNIST dataset on the Pytorch platform and 98.91% in the hardware architecture, which means the inference accuracy loss is only 0.13%; the inference accuracy loss without this model is 0.41%. Besides, this architecture can achieve 23.08k: qfps and 353.5 GOP/s/W at 120MHz with small resource use while processing the BCNN.
Multi-feature Fusion for Ultrasound Breast Image Classification of Benign and Malignant

Computer-aided diagnosis (CAD) system for breast ultrasound can effectively assist doctors in classifying benign and malignant of breast tumors. CAD system increases the work efficiency and diagnostic accuracy, and it also reduces the rate of misdiagnosis. This paper proposes a method based on a histogram of oriented gradients (HOG), local binary pattern (LBP) and gray-level co-occurrence matrix (GLCM) feature extraction combined with machine learning classifier - support vector machine (SVM) to classify benign and malignant breast tumor ultrasound images. Besides, due to the lack of the latest and comprehensive ultrasonic image dataset, this work collected a latest ultrasound image dataset, which considers 350 benign lesions and 257 malignant tumors proven by biopsy. The ability of the classifier to predict the pathological classes is measured by the receiver operating characteristic (ROC) curve and obtain good classification result.
Smart City Transportation Technologies: Automatic No-Helmet Penalizing System

With the concept of Smart City, the concept of a Smart Traffic Management System comes up automatically. Traffic chaos has increased a lot nowadays. There are now more vehicles than there are men on road. Due to this boom in urbanization and the number of vehicles on road, the problem to keep a check on the riders has also become very difficult for the policemen. People take traffic safety measures for granted and do not take them seriously. As a result, a lot of accidents happen heavily due to not wearing helmet on motorcycles, and not following the safety measures prescribed by the traffic police department of the nation. To overcome this challenge, this solution will act as a helping hand for the policemen in controlling the traffic. The solution will detect the riders for the helmet, and help the policemen to get an actual glimpse of the helmet-wearing status in the city and better control the traffic accidents and enforce the traffic rules. If the rider is found to be not wearing a helmet then his or her number plate will be scanned and stored in the fine database, from where a fine will be generated based on the vehicle registration number that has been captured by the surveillance camera.
Examining the Acceptance of Virtual Assistant - Vanika for University Students

Finding the right information in easily manner is a need of the community. Virtual assistant is a mobile application to fill this necessity in easily, effective, and efficient manners. Vanika was the virtual assistant developed by Soegijapranata Catholic University (SCU) to serve students and prospective students in finding the right information about the SCU. This study aims to examine the acceptance of virtual assistant-Vanika among them. The answers of two hundred questionnaires were collected from students and prospective students of SCU after they utilized Vanika at least twice. The result reveals that all of factors Information Service Quality, Perceived Ease of Use, and Perceived Usefulness have statistically correlation on Behavioral Intention and each other. Only Information Service Quality has statistically significant direct effect on Behavioral Intention. Prospective students frequently spend more time in searching university information than students, conversely, student perceived easier in using Vanika than prospective students. The last, males spend more time in searching university information than females, and females have more intention to use Vanika than males. The practical implications derived from the findings are the actions to increase individual's acceptance to use Vanika by means of increasing factors relating to Information Service Quality, Perceived Ease of Use, Perceived Usefulness, Age, and Gender.
Analysis of GPGPU-Based Brute-Force and Dictionary Attack on SHA-1 Password Hash

Password data in a system usually stored in hash. Various human-caused negligence and system vulnerability can make those data fall in the hand of those who isn't entitled to or even those who have malicious purpose. Attacks which could be done on the hashed password data using GPGPU-based machine are for example: brute-force, dictionary, mask-attack, and word-list. This research explains about effectivity of brute-force and dictionary attack which done on SHA-l hashed password using GPGPU-based machine. Result is showing that brute-force effectively crack more password which has lower set of character, with over 11% of 7 or less characters passwords vs mere 3 % in the dictionary attack counterpart. Whereas dictionary attack is more effective on cracking password which has unsecure character pattern with 5,053 passwords vs 491 on best brute-force attack scenario. Usage of combined attack method (brute-force + dictionary) gives more balanced approach in terms of cracking whether the password is long or secure patterned string.
Mobile-based In-Room Check-in System for Optimizing Check-in Process at The Sultan Hotel &amp; Residence Jakarta

Based on observations of the guest check-in process at The Sultan Hotel & Residence Jakarta, it was found that the length of the check-in process causes a queue of guests at the reception. At this time, the check-in process takes more than 4 minutes on average. It often causes quite long queues and creates inconvenience for guests. Therefore, business process improvement is needed to overcome the problem of the length of the check-in process. In this study, it is proposed to improve the check-in process by implementing a mobile-based “In Room Check-in” system. The system developed is the same as the check-in system that runs at the moment, but has the advantage of being accessible via mobile devices such as smartphones or tablets. With these advantages, the check-in process becomes more effective and flexible. The system is also directly connected to the main hotel system. The results of testing the “In Room Check-in” system showed an increase in the speed of the check-in process up to 3 minutes per guest.
A new redefined model of Firefly Algorithm with application to strategic bidding problem in Power Sector

Real-life optimization problems require an effective mechanism that completely utilizes the search space to obtain optimal solutions. A designer has always an opportunity to propose a new effective solution technique to address this issue. In this order, this paper presents a new upgraded redefined model of nature-inspired state of art meta-heuristic firefly algorithm (FA). FA is centered on swarm intelligence, which is motivated by the flashing pattern and behavior of fireflies. However, for a few instances, FA has a tendency to trap in local optima and it exhibits slow convergence. The proposed model is enabled through Time-varying Inertia Weight (TIW), Opposition Based Learning (OBL) and hybridized with sine cosine operators to get updated positions of search agents. A set of twenty-two classical benchmark function problems with numerous range and features are employed to prove the efficacy of the proposed model. Also, the effects of the above-mentioned modifications during the whole optimization routine are analyzed through different statistical and numerical analyses. The result analysis proves that the proposed modifications make FA more compatible. The proposed model is also tested on the strategic bidding problem of the power market of two different test systems with single and multi-trading hours. All the reported results confirm the supremacy of the proposed redefined model of FA.
Security and privacy issues of electronic healthcare system: A survey

In today’s digital environment, the paper-based healthcare system shifts towards the Electronic Healthcare System (EHS). The EHS features will allow its user to access the healthcare data and resources anywhere in the world-wide with the help of the Internet. This will improve the access and sharing, healthcare quality, treatment procedure, efficiency and reduce the care cost and time of the EHS. Regardless of the advantages, the transformation from a paper-based healthcare system to EHS has brought many security and privacy issues such as access control, trust, authentication, transmission of data, sharing, delegation, abuse use of healthcare data, data confidentiality and integrity, and many more. Due to such issues, there is a need to be the focus on the security and privacy issues in the EHS. Thus, in this paper, we focus on many security and privacy issues present in the modern EHS. The paper discussed the architecture of the modern EHS. Additionally, we have discussed the security analysis of the EHS. The given security analysis and solutions of EHS will help the EHS research people to design a more secure and robust healthcare system.
White-Light Interference Microscopy Image Super-Resolution Using Generative Adversarial Networks

To reduce external disturbances and achieve high vertical resolution, the scanning time for white-light interference microscopy is very short. Because capturing high-resolution (HR) images is time consuming, low-resolution (LR) images are acquired instead. However, HR images are more desirable because they contain more details. To ensure high vertical resolution and high image resolution, one feasible solution is to process the scanned LR images to HR images by single image super-resolution (SISR). In this paper, an interference image super-resolution (IISR) model based on a generative adversarial network (GAN) is proposed. The generator is based on the enhanced super-resolution generative adversarial network (ESRGAN) architecture. With the aim of acquiring more realistic images, the discriminator network is designed using a modified DenseNet architecture, in which the pooling layers are replaced with dilated convolutional layers. The perceptual loss is optimized, and the content loss is upgraded to a continuously differentiable piecewise function. Various microscopy images are tested, including images with and without interference fringes. The IISR model has been proven to restore LR images to HR images. The comparative experiments prove that the proposed model achieves better visual quality than other models, preserving more realistic details.
ForceStamps

We present ForceStamps, fiducial markers for supporting rapid prototyping of physical control interfaces on pressure-sensitive touch surfaces. We investigate marker design options for supporting various physical controls, with focusing on creating dedicated footprints and maintaining the structural stability. ForceStamps can be persistently tracked on surfaces along with the force information and other attributes. Designers without knowledge of electronics can rapidly prototype physical controls by attaching mechanisms to ForceStamps, while manipulating the haptic feedback with buffer materials. The created control widgets can be spatially configured on the touch surface to make an interface layout. We showcase a variety of example controls created with ForceStamps. In addition, we report on our analysis of a two-day musical instrument design workshop to explore the affordances of ForceStamps for making novel instruments with diverse interaction designs.