Reviewerly/exHarmony huggingface dataset:
Support Vector Machine for Effective Robust Visual Tracking

Protest following is the route toward confining the moving object from the video courses of action. Following is essentially a planning issue being referred to following. There exist innumerable after strategies with revealing accomplishment. Nevertheless, the testing issue in visual after is to deal with the appearance changes of target question in perspective of its flexible limit. Difficulties in consequent things can appear due to non-inflexible development, quick advancement, broad assortment of stance and scale, obstacle and buoys etc. One of the standard reason behind such frustrations is that, the unsuccessful picture portrayal designs of various figuring. In this paper, we show a visual after methodology using Support Vector Machine (SVM) close by various piece computation. The Expectation Maximization estimation relies upon the check and score regard and empower portrayal using SVM. In protest following, select which question is taken after picking by customer. By then that challenge parts are evacuated. In perspective of the cutting edge and establishment stamp is set. By then this stamp and segments are given to the SVM getting ready. We moreover consolidate an update intend to speak to address appearance change. Our tracker can manage hindrance and performs against existing visual after figuring in dealing with various conditions. Unique and quantitative evaluations over various testing show the engaged implementation of our following figuring. The spine is question following and strange state affirmation is proposed for single dissent following, in which the target characterization is viably seen in the midst of following.
Deep template matching for offline handwritten Chinese character recognition

Just like its remarkable achievements in many computer vision tasks, the convolutional neural networks provide an end-to-end solution in handwritten Chinese character recognition (HCCR) with great success. However, the process of learning discriminative features for image recognition is difficult in cases where little data is available. In this study, the authors propose a novel method for learning siamese neural network which employs a special structure to predict the similarity between handwritten Chinese characters and template images. The optimisation of siamese neural network can be treated as a simple binary classification problem. When the training process finished, the powerful discriminative features will help to generalise the predictive power not just to new data, but to entirely new classes that never appear in the training set. Experiments performed on the ICDAR-2013 offline HCCR datasets have shown that the proposed method has a very promising generalisation ability for new classes that never appear in the training set.
On the Evolution of Mobile App Complexity

Android developers are known to frequently update their apps for fixing bugs and addressing vulnerabilities, but more commonly for introducing new features. This process leads a trail in the ecosystem with multiple successive app versions which record historical evolutions of a variety of apps. While the literature includes various works related to such evolutions, little attention has been paid to the research question on how quality evolves, in particular with regards to maintainability and code complexity. In this work, we fill this gap by presenting a large-scale empirical study: we leverage the AndroZoo dataset to obtain a significant number of app lineages (i.e., successive releases of the same Android apps), and rely on six well-established, maintainability-related complexity metrics commonly accepted in the literature on app quality, maintainability etc. Our empirical investigation eventually reveals that, overall, while Android apps become bigger in terms of code size as time goes by, the apps themselves appear to be increasingly maintainable and thus decreasingly complex.
Mobile Pervasive Augmented Reality Systems—MPARS

After briefly introducing aspects concerning Mobile Augmented Reality Systems, this article delves into the evolution of these systems as pervasive technology. The work debates also on acceptance of this technology in the context of outdoor applications. The need to develop context-aware, close-to-real-time feedback mechanisms that take into consideration a continuous measurement of Quality of Experience is also discussed. For this purpose, the work goes over how to integrate user preferences into context-aware feedback systems, proposing a theoretical model for measuring Quality of Experience. The model is derived from an analysis of previous technology adoption models and incorporates the knowledge of user preferences. This knowledge has been gathered via a public questionnaire.
Lose Yourself in VR: Exploring the Effects of Virtual Reality on Individuals’ Immersion

Virtual reality (VR) technology generates an interactive virtual environment (VE) creating unique users' experiences. A central part of VR experience isbeing immersed into a VE.Immersion factors, such as technological and perceptual features, are described in detail in non-immersive VR settings.However, advancements in VR technology, such as head-mounted displays with high resolution and precise motion tracking systems, that improve interactivity within the VE are not yet adequately considered.We conducted twelve semi-structured interviews immediately after respondents played highly immersive games using state-of-the-art VR equipment to identify novel immersion factors in this setting.The findings yield eleven immersion factors across three categories: (1) physical and physiological aspects, (2) cognitive and affective aspects, (3) social interaction and shared experience.Within these categories factors named "shared experience" and "translating actions from physical to virtual reality" were found as novel immersion factors in the VR context.
U-Net Fixed-Point Quantization for Medical Image Segmentation

Model quantization is leveraged to reduce the memory consumption and the computation time of deep neural networks. This is achieved by representing weights and activations with a lower bit resolution when compared to their high precision floating point counterparts. The suitable level of quantization is directly related to the model performance. Lowering the quantization precision (e.g. 2 bits), reduces the amount of memory required to store model parameters and the amount of logic required to implement computational blocks, which contributes to reducing the power consumption of the entire system. These benefits typically come at the cost of reduced accuracy. The main challenge is to quantize a network as much as possible, while maintaining the performance accuracy. In this work, we present a quantization method for the U-Net architecture, a popular model in medical image segmentation. We then apply our quantization algorithm to three datasets: (1) the Spinal Cord Gray Matter Segmentation (GM), (2) the ISBI challenge for segmentation of neuronal structures in Electron Microscopic (EM), and (3) the public National Institute of Health (NIH) dataset for pancreas segmentation in abdominal CT scans. The reported results demonstrate that with only 4 bits for weights and 6 bits for activations, we obtain 8 fold reduction in memory requirements while loosing only $$2.21\%$$ , $$0.57\%$$ and $$2.09\%$$ dice overlap score for EM, GM and NIH datasets respectively. Our fixed point quantization provides a flexible trade-off between accuracy and memory requirement, which is not provided by previous quantization methods for U-Net (Our code is released at https://github.com/hossein1387/U-Net-Fixed-Point-Quantization-for-Medical-Image-Segmentation ).
Annotating German Clinical Documents for De-Identification.

We devised annotation guidelines for the de-identification of German clinical documents and assembled a corpus of 1,106 discharge summaries and transfer letters with 44K annotated protected health information (PHI) items. After three iteration rounds, our annotation team finally reached an inter-annotator agreement of 0.96 on the instance level and 0.97 on the token level of annotation (averaged pair-wise F1 score). To establish a baseline for automatic de-identification on our corpus, we trained a recurrent neural network (RNN) and achieved F1 scores greater than 0.9 on most major PHI categories.
RM2PT: Requirements Validation through Automatic Prototyping

Prototyping is an effective and efficient way of requirements validation to avoid introducing errors in the early stage of software development. Our previous work presents a tool RM2PT to automatically generate prototypes from requirements models. The stakeholders can easily check whether the requirements reflect their real needs by investigating the executions of use cases in the generated prototypes. However, the conflict and contradictory of the requirements are hard to be discovered. In this paper, we enhance RM2PT by introducing consistency checking and state observations in the generated prototypes. Requirements inconsistency can be automatically detected and further fixed through carefully analyzing the contracts of system operations and system state observations. We have conducted four case studies with over 50 use cases. The experimental result shows that 107 requirements inconsistency are founded in requirements validations. Overall, the result is satisfiable, and the enhanced RM2PT can be further applied to the software industry for requirements validation. The tool can be downloaded at http://rm2pt.mydreamy.net and a demo video casting its features is at https://youtu.be/Y7GNa57WGfA.
Model of information multi-stage process in structured system

The information process consisting of the stages of collection, processing, storage and protection of information is considered in this paper. The process is formalized in the form of a structurally logical scheme consisting of stages of the information process. It is assumed that the process of collecting, processing and storing information is carried out by special equipment. The analysis of the process under investigation is presented. A numerical example is given.
Optical Mouse Sensor for Eye Blink Detection and Pupil Tracking: Application in a Low-Cost Eye-Controlled Pointing Device

In this paper, a new application of the optical mouse sensor is presented. The optical mouse is used as a main low-cost infrared vision system of a new proposal of a head-mounted human-computer interaction (HCI) device controlled by eye movements. The default optical mouse sensor lens and illumination source are replaced in order to improve its field of view and capture entire eye images. A complementary 8-bit microcontroller is used to acquire and process these images with two optimized algorithms to detect forced eye blinks and pupil displacements which are translated to computer pointer actions. This proposal introduces an inexpensive and approachable plug and play (PnP) device for people with severe disability in the upper extremities, neck, and head. The presented pointing device performs standard computer mouse actions with no extra software required. It uses the human interface device (HID) standard class of the universal serial bus (USB) increasing its compatibility for most computer platforms. This new device approach is aimed at improving comfortability and portability of the current commercial devices with simple installation and calibration. Several performance tests were done with different volunteer users obtaining an average pupil detection error of 0.34 pixels with a successful detection in 82.6% of all mouse events requested by means of pupil tracking.
Can 'More Speech' Counter Ignorant Speech?

Ignorant speech, which spreads falsehoods about people and policies, is pervasive in public discourse. A popular response to this problem recommends countering ignorant speech with more speech, rather than legal regulations. However, Mary Kate McGowan has influentially argued that this ‘counterspeech’ response is flawed, as it overlooks the asymmetric pliability of conversational norms: the phenomenon whereby some conversational norms are easier to enact than subsequently to reverse.&#x0D; After demonstrating that this conversational ‘stickiness’ is an even broader concern for counterspeech than McGowan suggests—it applies not just to oppressive or hateful speech, but also to ordinary policy-related misinformation—I argue that a more sophisticated account of counterspeech can nevertheless overcome it.&#x0D; First, the stickiness objection overlooks the distinction between ‘negative’ and ‘positive’ counterspeech. Instead of directly negating a distorted proposition, positive counterspeech affirms a correct proposition that is inconsistent with the falsehoods at hand. This, I contend, allows it to counter ignorant speech without triggering the properties that render it sticky.&#x0D; Second, the stickiness objection presupposes an unrefined conception of counterspeech’s temporality. Counterspeech should be understood as a diachronic process, which not only follows, but also pre-empts, ignorant utterances. Drawing on speech-act theories of silencing, I argue that pre-emptive counterspeech can condition the conversational context so as to prevent subsequent ignorant utterances from enacting sticky conversational norms.&#x0D; Thus, this theoretically-refined conception of counterspeech helps appreciate how verbal responses might overcome the stickiness of conversational norms; and, in doing so, it reveals that this stickiness need not provide reasons to prefer legal remedies to counterspeech.
Chatbots for Enterprises: Outlook

Chatbots are going to be the main tool for automated conversations with customers. Still, there is no consistent methodology for choosing a suitable chatbot platform for a particular business. This paper proposes a new method for chatbot platform evaluation. To describe the current state of chatbot platforms, two high-level approaches to chatbot platform design are discussed and compared. WYSIWYG platforms aim to simplicity but may lack some advanced features. All-purpose chatbot platforms require extensive technical skills and are more expensive but give their users more freedom in chatbot design. We provide an evaluation of six major chatbot solutions. The proposed method for the chatbot selection is demonstrated on two sample businesses - a large bank and a small taxi service.
Implementation of CNN on Zynq based FPGA for Real-time Object Detection

The aim of this work is to implement a Convolutional Neural Network (CNN) using a Python framework on Xilinx® Zynq® based Field Programmable Gate Array (FPGA). And the prototype is used to tackle the challenging problem of real-time object detection in computer vision. Pre-trained CNN models are implemented using Tensorflow Application Programming Interface (API). The models are then implemented on Xilinx® Zynq® based FPGA using Python productivity for Zynq (PYNQ). The versatility of this approach is tested on four state-of-the-art object detectors based on the classifiers i.e. mobilenet v1 and inception v2 and the meta-architectures i.e. SSD and Faster R-CNN which have been trained on MS-COCO dataset. System functionality is compared on the basis of system latency, detection accuracy and ease of implementation on ARM® embedded mobile platforms. It is observed from the results and real-time frame rate, SSD with inception v2 model is suitable for the intended application. This hardware software co-design approach forms the basis of FPGA based hardware accelerators.
State-of-the-Art Reinforcement Learning Algorithms

This research paper brings together many different aspects of the current research on several fields associated to Reinforcement Learning which has been growing rapidly, providing a wide variety of learning algorithms like Markov Decision Processes (MDPs), Temporal Difference (TD) Learning, Advantage Actor-Critic (A2C), Asynchronous Advantage Actor-Critic (A3C), Deep Q Networks (DQNs), Deep Deterministic Policy Gradient (DDPG) and Evolution Strategies (ES) for different applications.In this paper, the computations and procedures involved in Reinforcement Learning algorithms are briefly discussed.Reinforcement Learning can be used is almost every field for its automation and advancement.Nowadays, Meta-Learning, Automated Machine Learning and Self-Learning Systems have become very popular.Metalearning which is an application of evolution strategies is an exciting area of research that tackles the problem of learning to learn faster with being generalizable to many tasks.Automated machine learning is the process of automating end-to-end the process of applying machine learning to real-world problems.
Estimating Sunshine Duration Using Hourly Total Cloud Amount Data from a Geostationary Meteorological Satellite

Sunshine duration is an important indicator of the amount of solar radiation received in a region and an important input parameter for the study of atmospheric energy balance, climate change, ecosystem evolution, and social sustainability. Currently, extrapolation and interpolation of data from meteorological stations are the most common methods used to calculate sunshine duration on a regional scale. However, it is difficult to obtain high precision sunshine duration in areas lacking ground observation or where sunshine duration is highly heterogeneous on the ground. In this paper, a new method is proposed to estimate sunshine duration with hourly total cloud amount (CTA) data from sunrise to sunset derived from the Fengyun-2G geostationary meteorological satellite (FY-2G). This method constructs a new index known as daytime mean total cloud coverage amount and provides quadratic equations relating daytime mean total cloud coverage amount to relative sunshine duration in different seasons. The method was validated with ground observation data for 2016 from 18 meteorological stations in the Three-River Headwaters Region of Qinghai Province, China. For individual stations, the coefficient of determination (R2) between estimated and measured sunshine was at least 0.894, the RMSE (root mean square error) was 0.977 h/day or less, the MAE (mean absolute error) was 0.824 h/day or less, the RE (relative error) was 0.150 or lower, and the value of d was 0.963 or greater, which validated that the proposed method can effectively predict daily sunshine duration. These equations can also provide higher precision estimates of regional-scale sunshine duration. This was demonstrated by comparing, for the entire study region, the spatial distribution of sunshine duration estimated from season-based equations with results from three different interpolation methods based on ground observations. Overall, the study confirms that total cloud amount measures from a geostationary satellite can be used to successfully estimate sunshine duration.
COMPARISON OF TRADITIONAL AND AGILE SOFTWARE DEVELOPMENT METHODOLOGY: A SHORT SURVEY

Software Development Methodologies (SDM) are used for every activity performed on a software product from initiation to maintenance.There are a variety of software development methodologies (waterfall, spiral and iterative) that are available to develop software products.One of the key challenges faced by the software developer is the selection of SDM in a software product.No single methodology is ideal to work effectively in all scenarios.Therefore, software product features play an important role in the SDM selection.This paper aims to explain different features, characteristics, critical practices, advantages, disadvantages of different methodologies related to the software product.We have used six models including waterfall, unified process, spiral, extreme programming, scrum, and feature-driven development.This paper also summarized the limitations and cost control factors of SDM while developing software products.
Implementing Nationwide Facility-based Electronic Disease Surveillance in Sierra Leone: Lessons Learned

The Global Health Security Agenda aims to improve countries' ability to prevent, detect, and respond to infectious disease threats by building or strengthening core capacities required by the International Health Regulations (2005). One of those capacities is the development of surveillance systems to rapidly detect and respond to occurrences of diseases with epidemic potential. Since 2015, the US Centers for Disease Control and Prevention (CDC) has worked with partners in Sierra Leone to assist the Ministry of Health and Sanitation in developing an Integrated Disease Surveillance and Response (IDSR) system. Beginning in 2016, CDC, in collaboration with the World Health Organization and eHealth Africa, has supported the ministry in the development of Android device mobile data entry at the health facility for electronic IDSR (eIDSR), also known as health facility–based eIDSR. Health facility–based eIDSR was introduced via a pilot program in 1 district, and national rollout began in 2018. With more than 1,100 health facilities now reporting, the Sierra Leone eIDSR system is substantially larger than most mobile-device health (mHealth) projects found in the literature. Several technical innovations contributed to the success of health facility–based eIDSR in Sierra Leone. Among them were data compression and dual-mode (internet and text) message transmission to mitigate connectivity issues, user interface design tailored to local needs, and a continuous-feedback process to iteratively detect user or system issues and remediate challenges identified. The resultant system achieved high user acceptance and demonstrated the feasibility of an mHealth-based surveillance system implemented on a national scale.
Modeling morphological learning, typology, and change: What can the neural sequence-to-sequence framework contribute?

We survey research using neural sequence-to-sequence models as compu-tational models of morphological learning and learnability. We discusstheir use in determining the predictability of inflectional exponents, inmaking predictions about language acquisition and in modeling languagechange. Finally, we make some proposals for future work in these areas.
Flow in VR: A Study on the Relationships Between Preconditions, Experience and Continued Use

Flow is characterized as an autotelic experience where action and awareness merge, there is high concentration on task and little attention is paid to time or self.It is believed that VR has a powerful affordance for inducing the flow state, as VR is, at least anecdotally, a technology that transports users to immersive realities, which can facilitate flow.However, VR imposes usability challenges that may inhibit flow.This research investigates flow in VR and its characteristics, determinants and outcomes through a survey (n = 681) and Structural Equation Modeling (SEM).The results indicate that flow in VR is positively association with intentions to continue VR use and longer VR sessions.