Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Irrigation System Using Wireless Sensor Networks

Wireless sensor networks have become an important tool for modern growers. Sensors provide the ability to monitor conditions from the farm-level down to individual plants. Such sensors include ambient temperature, humidity, soil water content, irrigation pressure, applied water volume, and fertilizer concentration. Commercially-available wireless sensor networks for agriculture have until recently focused on sensor monitoring, but not control. Growers using these systems have a wealth of crop information from sensors, but must implement control decisions using traditional means such as stand-alone irrigation controllers or manual labor. Wireless sensor networks (WSNs) find wide applications in environmental monitoring. MAC protocols play a vital role in controlling the energy consumption in a WSN. It tells the network when and how to access the medium. Time Division Multiple Accesses (TDMAs) are well suited for these real time applications. Because it prevents radio interference, and reduces energy consumptions. In this paper we reviewed the Basic Architecture of Automatic irrigation system using of WSN and some applications of WSN.
NetLogo: A simple environment for modeling complexity

NetLogo [Wilensky, 1999] is a multi-agent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disci-plines and education levels. In this paper we focus on NetLogo as a tool for research and for teaching at the undergraduate level and higher. We outline the principles behind our design and describe recent and planned enhancements
A robust and accurate camera pose determination method based on geometric optimization search using Internet of Things

We propose a robust and accurate camera pose determination method based on geometric optimization search using the Internet of Things (IoT). The central idea is to (1) obtain image information through Internet of Things technology, (2) obtain the first pose by minimizing the error function, and (3) use the geometric relationship and constraint condition to obtain the appropriate attitude angles as a new initial value for the next iteration calculation. The features of this method are as follows. First, this method can deal with a large amount of uncertain data, such as in the case of any shooting angle, in the case of any reference point, and in the case of a small number of feature points. Finally, because of using Internet of Things technology, our method can quickly complete data processing and transmission. Compared to state-of-the-art methods, the experimental results show that our approach performs well on both synthetic and real data and can be used to provide accurate and stable data for subsequent applications.
Graphical interpretation of text meaning

The text-to-picture system that synthesizes a picture from semi-general, (partially restricted) natural language text was presented. System using some AI methods, including natural language processing and soft computing and computer graphics. The effectiveness of the system was tested based on two simple scenarios. These results suggest that text-to-picture synthesis has potential in augmenting human-computer and human-human communication modalities, with applications e.g. in education.
An efficient Deep reinforcement learning with extended Kalman filter for device‐to‐device communication underlaying cellular network

In this paper, a novel Deep Q‐learning in addition with an extended Kalman filter (EKF) is proposed to solve the channel and power allocation issue for a device‐to‐device enabled cellular network, when the prior traffic information is not known to the base station. Furthermore, this paper work explores an optimal policy for resource and power allocation between users with the aim of maximizing the sum‐rate of the overall system. The proposed work comprises of four phases, ie, cell splitting, clustering, queuing model, channel allocation, and power allocation, simultaneously. The implementation of cell splitting with novel K‐means++ clustering technique increases the network coverage, reduces co‐channel cell interference, and minimizes the transmission power of nodes, whereas the M/M/C:N queuing model solves the issue of waiting time for users in a priority based data transmission. The difficulty with the Q‐learning and Deep Q‐learning environment is to achieve an optimal policy. This is because of the uncertainty of various parameters associated with the system, especially when the state space is extremely big. In order to improve the robustness of the learner, EKF together with the Deep Q‐network is presented in this paper, which incorporates weight uncertainty of the Q‐function as well as the state uncertainty during the transition. Furthermore, the use of EKF provides an improved version of the loss function that helps the learner in achieving an optimal policy. Through numerical simulation, the advantage of our resource sharing scheme over the other existing schemes is also verified.
Domain Category Information as a Guide for Sentence Ranking to Support Medical Text Summarization

DOMAIN CATEGORY INFORMATION AS A GUIDE FOR SENTENCE RANKING TO SUPPORT MEDICAL TEXT SUMMARIZATION Kasiemobi Esther Maduabunachukwu University of Guelph, 2019 Advisor(s): Professor Fei Song Professor Andrew Hamilton-Wright Medical professionals are required to pursue evidence-based practice by including the best available evidence from published research in their decision-making process. However, the exponential growth of biomedical resources makes it difficult for them to follow this requirement, and as a result, there is a need for automatic systems that can generate short, evidence-based summaries to assist them in the process. This thesis contributes to existing research by proposing sentence ranking strategies to incorporate domain-specific categorial information to support medical text summarization. Particularly, both structure-related categories (e.g., Population, Intervention, Background, other, Study, Outcome) and evidence-based categories (e.g., Strength of Recommendation Taxonomy) are considered. For the evidence-based categories, we developed our own SVM classifier so that we can compute the categorial information for all sentences in our dataset. These contributions are evaluated experimentally against an existing baseline system, along with a description about their potential applications.
One approach in the time domain in detecting copy-move of speech recordings with the similar magnitude

 Abstract — In this study, we focused on copy-move detection in the content of audio files. We have many ways to identify a forged audio, in this article we focus on comparing the pitch of samples, compare the similarity of sound intensity in the time domain together. The goal of this method is to focus detecting copy-move speech in an audio file, which it has archived high speed processing and improved accuracy.
Extractive and Abstractive Summarization for Multiple-sentence Questions

cannot be summarized by extractive approaches, but abstractive approaches are required. We created a dataset by regarding the question-title pairs posted on a CQA site as question-summary pairs. By using the data, we trained extractive and ab-stractive summarization models, and compared them based on the ROUGE score and manual evaluation. Our experimental results show an abstractive method, the encoder-decoder with the copying mechanism, achieves better scores both on ROUGE-2 F-measure and the evaluation by human judges.
Propulsion system

Summary form only given. The propulsion system of low-voltage dc (-600 V) transit cars is complicated by the continual breaking of heavy dc currents in the typical multistep motor controller. Semiconductor controllers have two difficulties: they are vulnerable to high voltage impulses, and they require the ministrations of electronic technicians who may be hard to attract to the railroad industry. The author suggests an arrangement using two old and well-proven devices to control the flow of power to the traction motors. One is the venerable synchronous converter running inverted, that is, converting dc to polyphase ac, for example, 12 phase ac. The second device is the polyphase variable autotransformer, receiving its power from the converter and driving separately excited dc traction motors through diodes. Coasting is effected by simply backing off the variable autotransformer.
Web Service Recommendation Technology Based on Knowledge Graph Representation Learning

This paper proposed a recommendation algorithm based on knowledge graph representation learning (RABKGRL). The algorithm embeds the entities and relationships of the knowledge graph into the low-dimensional vector space. The relationship information of services is incorporated into the recommendation algorithm by calculating the distance between the service entities. The association between services that is not considered when using the collaborative filtering algorithm can be supplemented, and the recommendation effect is enhanced. The experimental results show that this algorithm can not only effectively improve the accuracy rate, recall rate and coverage rate of recommendation, but also solve the cold start problem to some extent.
Image saliency detection based on geodesic‐like and boundary contrast maps

Image saliency detection is the basis of perceptual image processing, which is significant to subsequent image processing methods. Most saliency detection methods can detect only a single object with a high‐contrast background, but they have no effect on the extraction of a salient object from images with complex low‐contrast backgrounds. With the prior knowledge, this paper proposes a method for detecting salient objects by combining the boundary contrast map and the geodesics‐like maps. This method can highlight the foreground uniformly and extract the salient objects efficiently in images with low‐contrast backgrounds. The classical receiver operating characteristics (ROC) curve, which compares the salient map with the ground truth map, does not reflect the human perception. An ROC curve with distance (distance receiver operating characteristic, DROC) is proposed in this paper, which takes the ROC curve closer to the human subjective perception. Experiments on three benchmark datasets and three low‐contrast image datasets, with four evaluation methods including DROC, show that on comparing the eight state‐of‐the‐art approaches, the proposed approach performs well.
Neurostimulator with Waveforms Inspired by Nature for Wearable Electro-Acupuncture

Follow this and additional works at: https://digitalcommons.lsu. edu/gradschool_dissertations Part of the Alternative and Complementary Medicine Commons, Bioelectrical and Neuroengineering Commons, Biomedical Devices and Instrumentation Commons, Electrical and Electronics Commons, Other Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, and the Small or Companion Animal Medicine Commons
Detecting Mobile Crowdsensing Context in the Wild

Understanding the sensing context of raw data is crucial for assessing the quality of large crowdsourced spatio-temporal datasets. Detecting sensing contexts in the wild is a challenging task and requires features from smartphone sensors that are not always available. In this paper, we propose three heuristic algorithms for detecting sensing contexts such as in/out-pocket, under/over-ground, and in/out-door for crowdsourced datasets that are destined for human mobility mining. These are unsupervised binary classifiers with a small memory footprint and execution time. Using a segment of the Ambiciti real dataset – a feature-limited crowdsourced dataset – we report that our algorithms perform equally well in terms of balanced accuracy (within 4.3%) when compared to machine learning (ML) models reported by an AutoML tool.
Development of pedagogical design in technology-rich environments for language teaching and learning

Jalkanen, Juha Development of pedagogical design in technology-rich environments for language teaching and learning Jyväskylä: University of Jyväskylä, 2015, 87 p. (Jyväskylä Studies in Humanities ISSN 1459-4323; 265 (nid); ISSN 1459-4331; 265 (PDF)) ISBN 978-951-39-6355-2 (nid.) ISBN 978-951-39-6356-9 (PDF) This study explores the development of pedagogical design for language teaching and learning in increasingly technology-rich environments. More specifically, it focuses on the process of design, enactment and analysis of language and literacy pedagogies in technology-rich environments. Two substudies are reported in five articles, each of which approaches pedagogical design from a different perspective. The first substudy examined (a) what pedagogical choices language students make in regard to learning objectives, working modes, materials and assessment, and (b) what kind of experiences, attitudes and perceptions were behind these choices. The second substudy focused on developing a design-based framework for collaborative pedagogical development and investigating the enactment of pedagogical designs in the context of higher education language teaching. The results show that digital technologies increase the complexity and unpredictability of the pedagogical design in language teaching. Digital environments expand the possibilities for languaging and create spaces for emerging types of literacies to take place. These literacies are social by nature and operate across different languages, spaces, and timeframes. However, to address these emerging language and literacy practices in formal education, the notion of design must be revisited. It was also found that the literacy practices in language teacher students’ pedagogical designs are mainly static and do not meet needs of the contemporary and future society. In these designs, expertise appears as a property of the teacher and the discursive focus is on teaching instead of learning. The use of digital technologies is mostly disconnected from other areas of pedagogical design. Finally, the results indicate that in collaborative pedagogical development, expertise appears as shared and negotiated around different tasks. A structured and design-based development process creates a spectrum of pedagogical questions for further exploration.
iTest: A novel online testing system based on the WeChat platform

Timely feedback is important for both teachers and students in teaching and learning practice. Teachers rely on feedback from students to gain a better understanding of their learning effects. Students also need feedback to identify gaps in their knowledge. Conducting in‐class tests is considered an effective way to obtain such feedback. However, traditional testing methods are inflexible and inefficient. In this paper, we introduce iTest, a novel online testing system developed on the popular WeChat platform. With the help of iTest, the teacher organizes an in‐class test conveniently, and the students take the test with their smartphones regardless of which operating systems the smartphones use. When the student answers questions in a test, abundant behavior data for the student are collected. According to the analysis of the behavior data and the testing results, the teacher gains a deeper insight into the learning effect for each student. A weighted knowledge graph is also constructed for each student by virtue of the prerequisite relationships among the knowledge points and the behavior data. On the basis of the weighted knowledge graph, iTest recommends personalized exercises in an appropriate order to each student after the test, which helps to improve the learning effect. Teaching practice in five courses verifies that iTest is effective and efficient in promoting teaching and learning. Both teachers and students have high praise for iTest.
Motive, gesture and the analysis of performance

‘Musical gestures are musical acts, and our perception and understanding of gestures involves understanding the physicality involved in their production’ (Cox 2006: 45). Arnie Cox’s provocative statement serves as the point of departure for this chapter.1 One of its main premises is that music’s gestural properties are neither captured by nor fully encoded within musical notation, but instead require the agency of performance to achieve their full realization. The performances in question need not be live: recordings too have distinctly gestural properties even if the visual dimension and experiential character of live music-making are lacking. This chapter also reverses a common tendency to assign the status of musical gestures to conventional musical motives.2 In contrast, we regard the gestures created in and through performance as potentially having motivic functions within the performed music. Such ‘motives’ are defined not in terms of pitch, harmony or rhythm, however, but as expressive patterns in timing, dynamics, articulation, timbre and/or other performative parameters that maintain their identity upon literal or varied repetition.3 The essential point has to do with the nature and function of the given patterns. By way of example, the following discussion focuses on select
HAPTIC FEEDBACK IN A VIRTUAL REALITY EXERCISE GAME: DESIGN OF A STUDY AND IMPLEMENTATION OF A SYSTEM

Exercise is an important activity which many people do not include in their lives, leading to health issues as they age. A commonly cited reason for not exercising is lack of motivation. One approach to increase motivation to exercise is to combine exercise with video games, called exergames. My research presents an experiment designed to determine how an exergame with vibrotactile feedback affects a users’ intrinsic motivation, immersion, and interest in the game and a system designed for the experiment. I conducted a user study with students between the ages of 20 to 30 who played the same exergame with vibrotactile feedback for between 3 to 5 minutes and without vibrotactile feedback for between 3 to 5 minutes. The results and analysis of the data collected indicate that the system functions properly and that vibrotactile feedback can increase intrinsic motivation in an exergame user. No evidence was found that supported vibrotactile feedback increasing a user’s immersion in the game. Further study is warranted due to the small sample size of the study.
Android behind the scenes: revealing hidden malware with AndroMEDA

With the unprecedented growth in the diversity of digital distribution platforms, there has been an increasing concern about privacy of the content being produced by the different types of applications users are installing across a variety of mobile platforms. The mobile devices average users own generally have inbuilt hardware interfaces capable of gathering different types of rich information including temperature, accelerometer, as well as personal identifiable information such as phone numbers, personal communication messages, and location information. Some digital distribution platforms, like Google’s Play Store (GPStore), created a permission-based security model in which users are prompted with a list of permissions they must accept in order to download and install an application. In recent years many researchers have presented attacks compromising the previous security model using different types of techniques including malicious applications such as malware and trojans. To better understand mobile malware, we introduce the concept of the User-App Agreement (UAA) — a conceptual framework for a user consenting to and trusting specific actions an app may perform. Using UAA we examine the Android Permission system with Android Census — a comprehensive app metadata database. We conclude the main shortcoming lies in the Permission system’s lack of addressing context and use, presenting an opportunity for Info Theft Malware — malicious software that profits off of users’ personal information. Finally, we present Android Malware Evaluation Detection and Analysis (AndroMEDA), an Android Security Extension which forms a novel feedback loop, providing users with a method for understanding the context and use of actions an app performs, thus allowing them to identify suspicious behavior that violates users’ trust.
ARCHER, a New Monte Carlo Software Tool for Emerging Heterogeneous Computing Environments

The Monte Carlo radiation transport community faces a number of challenges associated with peta- and exa-scale computing systems that rely increasingly on heterogeneous architectures involving hardware accelerators such as GPUs. Existing Monte Carlo codes and methods must be strategically upgraded to meet emerging hardware and software needs. In this paper, we describe the development of a software, called ARCHER (Accelerated Radiation-transport Computations in Heterogeneous EnviRonments), which is designed as a versatile testbed for future Monte Carlo codes. Preliminary results from five projects in nuclear engineering and medical physics are presented.
SentScore: Autonomous Text Sentiment scoring and Summarizing System related to Complaint Management

In traditional markets, customer complaints are considered as an important source of information. Since complaint management is recognized as a central for customer satisfaction, any measure of complaint behaviour should consider the degree and quality of the underlying customer satisfaction. Therefore, analyzing customer complaints is part of the process of a business. A prompt, reasonable and efficient response to a complaint can win you a loyal customer, and develop your business's reputation for top quality service. This project would be analysing customer complaints, in order to improve customer experience. As the solution to solve this issue, the proposed solution would address issues with respect to consumer complaint data in a textual format (complaint by phones), which are identified with the IT field (Technical Support Complaints). Furthermore, literary data written in English dialect will be considered. Moreover, SentScore ought to be savvy enough to interpret data identified with complaints efficiently and effectively, classify and analyse sentiment score precisely, summarise them into aspects, and distinguish how the customer feels about those aspects. With this proposed solution the Customer Complaint Operators are able to extract a summarized analysis of the complaint solution by assigning weights to the complaint and aspects including Internet, Television and Facility, which are the main aspect categories considered when analyzing the customer complaint. The system makes utilization of Natural Language Processing, Machine Learning and Sentiment Analysis concepts, to provide the highest accurate sentiments or opinions expressed by the customer in complaints, to present the end users with accurate and effective summarized outcome of the customer complaints and aspect of it.
An Interdisciplinary Methodology to Validate Formal Representations of Legal Text Applied to the GDPR

The modelling of a legal text into a machine-processable form, such as a list of logic formulæ, enables a semi-automatic reasoning about legal compliance but might entail some anticipation of legal interpretation in the modelling. The formulæ need therefore to be validated by legal experts, but it is unlikely that they are familiar with the formalism used. This calls for an interdisciplinary validation methodology to ensure that the model is legally coherent with the text it aims to represent but that could also close the communication gap between formal modellers and legal evaluators. This paper discusses such a methodology, providing an human-readable representation that preserves the formulæ’s meaning but that presents them in a way that is usable by non-experts. We exemplify the methodology on a use case where Articles of the GDPR are translated in the Reiﬁed I/O logic encoded in LegalRuleML.