Reviewerly/exHarmony huggingface dataset:
Mobile and Wearable Computing in Patagonian Wilderness

Recent advances in mobile and wearable technology in the last few years have made the optimization of data collection processes possible in diverse fields. Users currently have access to small portable devices that are not only sensitive to their activity, but also to their interaction with their environment. These growing technological advances are in constant development, and have given way to the study and redesign of processes that can be tailored to fit any particular needs. Even users that are far from urbanization, without access to electricity can make use of these possibilities. These technologies can substantially improve their productivity, by allowing them to concentrate solely on their own tasks instead of on the interactions with the computational method used to support their activities. This study presents results and indicators relating to the application these tools within the field of Flora information retrieval, in areas far from urban centers.
Analysis of Still Image IP Transmission in the Tactical Communication Network

This paper presents analysis of still image IP transmission in the tactical network. The analysis is provided through the delivery time, packets sent/received/lost and the display of the images after the reconstruction on the receiving side. Image transmission is analyzed through multiple scenarios - without and with compression, without and in the presence of jamming. It has been shown that delivery time can be significantly reduced by data compression, without significant loss of visual information. It has also been shown that for analyzing the quality of transmission and usefulness of received information, it is better to use the display of reconstructed images than the degree of packet losses. Furthermore, it has been shown that in the tactical networks of limited bandwidth, it is very important to comply bit rates from source of information to the end user, and in this way to avoid buffer memory overflow and packet losses.
Spoken Dialogue Systems for Medication Management

The interest towards spoken dialogue systems has been rapidly growing in the last few years, including the field of health care. There is a growing need for automated systems that can do more than order airline and movie tickets, find restaurants and hotels, or find information on the internet. Eliciting information from patients about their current health and medications using natural language at the point of care is a task currently performed by skilled nurses during the intake interview in both inpatient and outpatient settings. This routine task lends itself well to automation and a well-crafted dialogue system with state management can enable standardized yet individually tailored interactions with the patient using natural language. The need for extensive domain knowledge (e.g. medications, dosages, disorders, symptoms, etc.) in order to achieve broad coverage makes this task particularly challenging. In this project, we explore the use of the PyDial framework and a medication-oriented knowledge base containing information from RxNorm to create a dialogue system capable of eliciting medication history information from patients.
Amharic Question Answering for Biography, Definition, and Description Questions

A broad range of information needs can often be stated as a question. Question Answering (QA) systems attempt to provide users concise answer(s) to natural language questions. The existing Amharic QA systems handle fact-based questions that usually take named entities as an answer. To deal with more complex information needs we developed an Amharic non-factoid QA for biography, definition, and description questions. A hybrid approach has been used for the question classification. For document filtering and answer extraction we have used lexical patterns. On the other hand to answer biography questions we have used a summarizer and the generated summary is validated using a text classifier. Our QA system is evaluated and has shown a promising result.
Studying the Performance Characteristics of the Electric Vehicle

Electric cars come into use more and more often. One of the reasons for this is the decrease of the quantities of liquid fuels, and another, the striving to reduce the harmful emissions. However, the distance run on a single battery recharge is still a major disadvantage of electric cars. One way to extend the distance is by monitoring the current power consumption, as well as the electrical braking with energy return into the battery. Research in the present work is in this direction.
Data Augmentation for Signature Images in Online Verification Systems

One of the main problems of designing a handwritten signature online verification system is a small number of signatures committed by the user for training. To solve this problem, ways of expanding dataset size based on existing authenticated signatures might be proposed. The research proposes a new technique for generating dynamic signatures based on the original sample. The resulting sample simulates real signature forms and letter-style characteristics. Artificially created genuine and fake samples based on the author’s and intruder’s signatures are used to train the classifier, which can improve the accuracy of training on the original sample of a small size. Handwritten signature data augmentation methods were investigated with the aim of further development in more efficient handwritten verification algorithm.
New methodology for solar irradiance calculation using Meteosat satellite imagery

Solar Resource assessment is necessary for concentrated solar power plants development, it is important to choose a suitable location, to ensure the project viability, to improve operation and maintenance procedures and to optimise the energy production. Satellite imagery is a good choice to elaborate this kind of studies due to its historical database that allows to compare many locations and determine reliable meteorological characteristics. The new methodology developed to calculate solar irradiance using MSG imagery enable users to analyse the plant production with high accuracy, MAE 32% lower than previous works, and easily comprehensible presentation of the results.
Usability of Direct Manipulation Interaction Methods for Augmented Reality Environments Using Smartphones and Smartglasses

This contribution presents a study examining five different interaction methods for manipulating objects in augmented reality (AR). Three of them were implemented on a smartglass (virtual buttons, swipe pad of a smartglass, remote control via the touchscreen of a smartwatch) and two of them on a smartphone (virtual buttons, touch interaction). 32 participants were asked to scale and rotate a virtual 3D object. We studied the usability of the interaction methods by measuring effectiveness, efficiency, and satisfaction of the users. The results of the study showed that smartphone interaction is superior to any of the studied smartglass interaction methods. Of the interaction methods implemented for the smartglass, the interaction via smartwatch shows the highest usability. Our findings suggest that smartwatches offer higher grades of usability when interacting with virtual objects rather than using the swipe pad of the smartglass or virtual buttons.
Comparing of K-Means, K-Medodis and Fuzzy C Means Cluster Method for Analog Modulation Recognition

A modulation process is required to transmit analog signals with higher quality. Modulation is the process of transporting the signal by another carrier signal. This study aims to process analog signals. Using 200 samples of each of the six types of analog modulation modules. Nowadays these are Amplitude Modulation (AM), Double Side Band (DSB), Upper Side Band (USB), Lower Side Band (LSB), Frequency Modulation (FM) and Phase Modulation(PM) respectively. In the study an intelligent clustering method has been developed. The 5th level Discrete Wavelet Transform (DWT), Norm Entropy and Energy properties of AM, DSB, USB, LSB, FM and PM analog modulated signals have been removed during feature extraction phase. The results have been compared using K-Means, k-Medoid and Fuzzy C Mean (FCM) algorithms using a feature vector of 6x2x1200 obtained at the feature extraction stage and carrying out smart intelligent clustering for recognition. The most successful result has been obtained with FCM of 85.75%.00
Distinguisher and non‐randomness of Grain‐v1 for 112, 114 and 116 initialisation rounds with multiple‐bit difference in IVs

In this study, the authors construct two different distinguishers on Grain-v1 with 112 and 114 initialisation rounds. Their first distinguisher can distinguish Grain-v1 with 112 initialisation rounds from a uniform random source for 99% of the randomly chosen keys from full key space. The second one can distinguish Grain-v1 from a random source for 73% of the randomly chosen keys for one-fourth of the total key space (278 keys out of 280 keys). Our results improve upon the earlier distinguishers. The technique used for the distinguishers is conditional differential cryptanalysis. The existing works in this direction considered only one bit difference in the initialisation vector. However, for the first time, they could handle complicated conditions for the 2-bit difference to obtain better cryptanalytic results. Extending their technique by allowing the 1-bit difference in the pair of keys (i.e. related keys) and the 4-bit difference in IVs, they could observe the non-randomness till 116 initialisation rounds with a success in 62% cases.
Autonomous 3-D Aerial Navigation System for Precision Agriculture

The ever increase of human population brings the problem of how to accommodate the required food production and maintain system sustainability. The goal of precision agriculture is to offer farmers better management systems that improve production and the use of resources like water and fertiliser, resulting in better crop yields. This paper presents a 3-D navigation system for an unmanned aerial vehicle to be used in precision agriculture as a more reliable, flexible and affordable solution to gather useful data about the crops, compared to current methods such as satellite imagery. The solution was developed in the ROS platform and coupled with external software components, with the goal of making an autonomous multi-rotor aerial vehicle capable of operating in these kind of environments, completely exploring and surveying an agricultural field. Finally a simulator in Unity was constructed to support the development and testing of this navigation system.
Review on Real-time Detection of a Panic Behavior in Crowded Scenes

Due to the rapidly increasing number of requests from the surveillance and security industries, detecting a panic behavior in human crowd have become an active research in the field of computer vision in recent years. The automatic and real-time analysis of video sequences in order to detect a panic behavior is one of the most challenging tasks for computer vision experts. This stems from the fact that a panic is mostly defined as a sudden change in the pedestrian movements and that motion estimation is computationally expensive. The aim of this paper is to give an overview of the reported real-time techniques in the literature by focusing on how these techniques overcame the heavy computations of the motion estimation. Moreover, the present work summarizes the accuracy and the execution time of those techniques to highlight the direction to future studies.
Delegated updates in epistemic graphs for opponent modelling

In an epistemic graph, belief in arguments is represented by probability distributions. Furthermore, the influence that belief in arguments can have on the belief in other arguments is represented by constraints on the probability distributions. Different agents may choose different constraints to describe their reasoning, thus making epistemic graphs extremely flexible tools. A key application for epistemic graphs is modelling participants in persuasion dialogues, with the aim of modelling the change in beliefs as each move in the dialogue is made. This requires mechanisms for updating the model throughout the dialogue. In this paper, we introduce the class of delegated update methods, which harness existing, simpler update methods in order to produce more realistic outputs. In particular, we focus on hypothesized updates, which capture agent's reluctance or susceptibility to belief updates that can be caused by certain factors, such as time of the day, fatigue, dialogue length, and more. We provide a comprehensive range of options for modelling different kinds of agents and we explore a range of properties for categorising the options.
Enabling Low Cost Elderly Monitoring for Connected Communities in Depopulated Area

By introducing the state of the art sensing technologies to the home, monitoring the daily activities of elders living alone or with husband/wife only, will be common soon to support their life from remote sites. We have developed a portable monitoring system to estimate daily activities just using simple, low-cost binary sensors and power monitors using MLN (Markov Logic Network), and installed in real elders' homes for monitoring. However, such monitoring systems usually require the Internet connection, which is not often available in depopulated areas due to the blind zone of 3G/LTE or installation cost which incurs extra investment for those people who have no interest in Internet services. In this paper, we propose a method of collecting daily activity monitoring data, which is obtained by our monitoring system working standalone at home, via LPWA (Low Power Wide Area). The idea is to utilize vehicles such as community buses and mail trucks which drive daily along almost fixed routes. An LPWA onboard receiver is mounted to collect data from each LPWA transmitter embedded into our monitoring system at home. Since not all the houses may be covered by the LPWA, LPWA-based "house clusters" are automatically formed to deliver the data in the cluster via a home contacted by the receiver vehicle. We have tested the system prototype in our university campus and installed it to the five real houses in the mountainous areas in cooperation with the Toyooka city municipal government, which has to manage broad depopulated areas.
Medical Information Digital Right Management on the Information-Centric Networking

In this article, we described the digital rights management of medical information in an information-centric networking (ICN). ICN is a new Internet 3.0-based network architecture that is a way to transform a host-centric internet infrastructure into an information-centric mobile environment. In this new era, the privacy and copyright protection of personal medical information is an important issue of information security. How to use the ICN architecture to establish cross-domain sharing and personal medical information copyright authorization is the most important contribution of this paper. Specially, we used blockchain technology to establish authorization and used zero knowledge key agreement authentication mechanisms to establish each blockchain and data communication. Finally, we designed a mechanism to create and authorize the information sharing of personal medical media using the ICN architecture. The personal medical records that we designed for ICN environment can be securely accessed anytime, anywhere, and can be securely shared with different healthcare professionals to provide data certified by different medical units in the upcoming high-bandwidth network.
Finite Two-Dimensional Beta Mixture Models: Model Selection and Applications

Finite mixture models are powerful and progressively important probabilistic tools in machine learning. The practicality of these inference engines is widely acknowledged by employing them in various areas of science and technology which involve the statistical modeling of multimodal and complex data. One of the crucial tasks that should be addressed in mixture models and unsupervised learning problem is defining the number of clusters which best describe the data. This article proposes a clustering framework for learning a finite mixture model based on a bivariate Beta distribution with three parameters and the proper number of clusters is determined by Minimum Message Length (MML). The feasibility and effectiveness of our work are demonstrated by real world challenging applications such as image segmentation and occupancy estimation in smart buildings.
HealthSEA: Towards Improving the Search Engine of KAAHE Arabic Health Encyclopedia

KAAHE is an online Arabic Health Encyclopedia that received tremendous efforts in promoting health awareness and, consequently, was awarded with multiple national certifications. However, the website's search engine does not come up to its high standards. Therefore, in this paper, we experiment with combinations of stemming and query expansion on articles collected from KAAHE, in order to create an improved Arabic Health Encyclopedia Search Engine (HealthSEA).
CBR Insight: Measure and Visualize Source Code Quality

A critical aspect of software development is creating high-quality source code that is reliable, maintainable, and has limited technical debt. Software development teams generally employ a variety of design techniques, processes, and tools to continually work towards quality code while balancing the overall time and budget demands of the project. The goal of CBR Insight (CBRI) is to provide an objective and understandable measure of software quality that can help guide decisions and direct limited resources during software acquisition, development, and sustainment. CBRI supports the ability of technical and non-technical decision makers to verify that a project's software implementation follows through on promises around developing and sustaining reliable and maintainable software while managing technical debt.
Improved Self-Adaption Matched Filter for Moving Target Detection

In this paper, an improved self-adaption matched filter (ISMF) is proposed to detect the small moving target in complex environments. The power factor is optimized using the smooth random substitution technique in order to improve the performance of the self-adaption matched filter (SMF). The small moving target can be detected with higher probability when the small target close to the strong interference compared with the traditional SMF. The proposed ISMF is validated through simulations and experiments.
A URL-based Analysis of WWW Structure and Dynamics

Understanding the evolving characteristics of the World Wide Web is challenging due to its immense size and diversity. In this paper, we investigate Web structure and dynamics by analyzing over 1 trillion URLs requested during Web browsing by a 2 million person user panel over a period of 12 months. We begin by examining the lifetime of URLs and find that in contrast to early studies, the set of URLs visited is highly dynamic and well-modeled by a gamma distribution. Next, we analyze URL-traversal patterns and find that browsing behaviors differ substantially from hyperlink connectivity. One consequence of this is that the structure of the Web that is derived from hyperlink connectivity does not extend directly to actual user behavior. Finally, we consider the commonly used path and query portions of URLs and highlight their characteristics when used by different website genres. These semantic differences suggest that URL structure can broadly classify the kind of resource that a URL references. Our analyses lead to a set of proposed enhancements to the URL standard that would improve Web manageability and transparency and make a step toward the semantic web.
A Study on Female Students’ Attitude Towards the Use of Augmented Reality to Learn Atoms and Molecules Reactions in Palestinian Schools

The chemical reaction between different molecules is an important learning subject in a chemistry course. Especially for elementary school students, this can be an abstract concept and therefore difficult to understand. One way to facilitate this learning process is to use Augmented Reality (AR) technology, which is considered as an added value compared to classical learning materials such as textbooks, 2D images, video, etc. Among the different advantages of using AR technology in the context of educational domain is the fact that 3D technology is offering a safe environment especially if the students have to perform critical tasks such as simulating chemical reactions. This work investigates the students' attitude towards the use of a mobile AR application for learning atoms and molecules reactions. In particular, we focused on female students as in general female students show less interest in science and technology than male students. We were keen to investigate whether the use of AR technology could change this attitude. The students are able to interact with the different AR components to explore the possible reactions in a 3D interface, to see the structure and shape of atoms and molecules, and to view related descriptions in their native language, i.e. the Arabic language. The mobile AR application was evaluated by a class of 12–13 years old (7th grade) students in a Palestinian primary school. The number of participants was 50, all female students. After analyzing the results, we can conclude that the female students had a positive attitude toward the use of this AR application in their learning process.
Game Theory for Eye Robot Movement: Approach and Hardware Implementation

Robotic eyes imitate human eye appearance, but, they lack human-like movements. Also, there are no criteria to select one eye as the leader in eyes simultaneous movement. To solve these challenges, this paper proposes two algorithms to coordinate the movement of two separately controlled eyes, and, to select the leader eye in their synchronous movement. In the first algorithm-called Gazist-eyes apply the concept of bi-matrix games in noncooperative game theory to decide between their vertical and horizontal options. In the second algorithm-called Leaf-we consider game trees for sequential eye movements. Nash equilibrium for each game tree (found by backward induction) is our proposed criteria to choose the leader eye. Our algorithms are validated through experiments on a 3D printed eye robot that tracks the face for a person walking before it. To the best of our knowledge, Gazist algorithm constitutes one of the first examples of coordinated movement for robotic eyes by game theory. Also, Leaf algorithm proposes the first criteria for choosing the leader in eyes synchronous movement.
Comparison of Various Wearable Activity Trackers

With the various activity trackers available in the market, customers often find themselves confused between choosing one amongst them all. Due to the advancements in technology there are several specifications and features to choose from but the main challenge is that there is not one activity tracker that has it all, hence the need of comparison between the several activity trackers and their specifications. Existing research focuses mostly on giving the specifications of all the activity trackers out there, and not comparing different activity trackers to help the customer make the correct choice. This research paper has outlined the need, challenges and comparison of the specification between the different activity trackers available in the market.