Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of the Use Effectiveness of Lighting Lamps Type for Household Needs

Lighting is very important in various activities in the household, so it becomes dominant in the review of electricity consumption needs. The problems faced by consumers in choosing the use of lighting generally only choose based on the type of lamp which is cheaper, without considering the quality of light and electrical energy used. This study aims to find profiles several types of lights based on the strength of the light produced, the use of electrical energy, and its sensitivity to lower voltage sources. The results of the study recommend a type of lamp that is considered efficient for users. The research method uses experimental approaches. The study was conducted by testing the strength of the light produced, the amount of electrical power used, and the sensitivity to lower voltage sources. Data collection techniques are carried out by measurement and testing. Data analysis was carried out with a quantitative approach and continued with qualitative analysis. The results showed that, (1) the types of Light Emitting Diode (LED) lamps produce better light power compared to Energy Saving Lights (ESL) at the same electrical power consumption, and (3) LED lighting components have better capability in facing a lower voltage source.
Cloud Server Oriented FPGA Accelerator for Long Short-Term Memory Recurrent Neural Networks

Long Short-Term Memory network(LSTM), which is the most widely used and representative recurrent neural network architecture, plays an important role in language modeling, machine translation, image captioning, etc. However, due to its recurrent nature, general-purpose processors like CPUs and GPUs cannot achieve high parallelism, not to mention their high power consumption. FPGA accelerators can outperform them by flexibility, energy-efficiency and more delicate optimization in each phase of the algorithm. In this paper, we present a cloud-oriented FPGA accelerator for LSTM based on OpenCL. Different from previous works which are designed for embedded systems, our FPGA accelerator performs multiple time series predictions in parallel. We provide a general matrix optimization model to optimize the computation of LSTM in the cloud environments. The performance of our implementation beats both the CPU implementation and other previous hardware implementations. We present and analyze the performance results of our work.
Examining User Experience of Conversational Agents in Hedonic Digital Services – Antecedents and the Role of
Psychological Ownership

Conversational agents (CA) that interact with users in human language have become increasingly popular over the past years. This study explores antecedents of the user experience with CAs in hedonic digital services, utilizing the example of music streaming services. Moreover, this study investigates whether a positive CA user experience increases users’ sense of psychological ownership towards the service, which in turn is supposed to positively influence users’ intention to use the service’s fee required premium version. Using structural equation modelling, the results indicate that perceived humanness and perceived personalization of the CA positively affect the user experience. The results also show that CAs can greatly benefit from higher humanness and personalization when users trust the hedonic digital service. Furthermore, psychological ownership has been identified as an underlying mechanism through which CA user experience leads to users’ premium usage intention, indicating that CAs might be valuable for hedonic digital services.
CNN-Based Refinement for Image Segmentation

Image segmentation is the area of computer vision that tries to partition an image into multiple parts, according to some semantic meaning. This is one of the core computer vision problems, and crucial to many applications. Medical imaging is one of such applications that can benefit from automatic image segmentation, due to the large amounts of experience necessary to properly evaluate the images and conditions, and even then facing ambiguity and sometimes leading to disagreement, even between medical professionals. Traditional image segmentation algorithms operate by iteratively working over an image, as if refining a segmentation until a stopping criterion is met, as the used algorithm determines. Deep learning is currently playing a crucial role in computer vision, replacing the traditional approaches of feature engineering with learned representations of data. The technology breakthroughs in processing power have allowed for the creation of deep neural networks that achieve state-of-the-art performance in many problems, one of them being image segmentation. However, the concept of segmentation refinement is not present anymore, since usually the images are segmented in a single step. This work focuses on the refinement of image segmentations using deep convolutional neural networks, first by exploring improvements for a method for image segmentation by quality inference. This approach tries to segment an image by first predicting the quality of an existing segmentation and then refining it through gradient descent, by modifying the input segmentation mask while trying to maximize the expected quality. Possible improvements include data augmentation, siamese networks, different quality metrics and possible stopping criteria. After that, a network for direct segmentation refinement with an extra quality output is introduced. We show that data augmentation tuned to the base model and the application of siamese networks can be used to improve the quality inference performance, despite not improving the segmentation refinement process, and that the quality concept can be used as a regularizer while training a network for direct segmentation refinement, achieving better performance results.
Effect of Feedback on eBay Sellers’ Business Using Markov Chain

Effects of price, sold items, negative feedbacks, positive feedbacks, or no feedbacks are studied for online market engines mainly through eBay. Random walk type model is established to measure the duration that sellers with business effected negatively in order to turn it around. This study was based on data from eBay where not only prices play a role in selling items but also the impact factor that buyers depend on in general. This impact factor was studied for five different types of sellers over the same duration selling the same commodity.
A Hybrid Path Planning Algorithm for Unmanned Surface Vehicles in Complex Environment With Dynamic Obstacles

Unmanned surface vesssel (USV) has been widely applied due to its advantages in the military reconnaissance and resources exploration. Path planning is one of the critical issues for USV applications, which usually includes global and local path planning methods. However, individual global path planning algorithms may not be easy to detect the dynamic obstacles in the environment, and individual local path planning algorithms may not always guarantee the existence of the feasible solution for the complex environment. Therefore, a hybrid algorithm which effectively combines global and local path planning is proposed in this paper to overcome these drawbacks. The A* algorithm is used in the global path planning to generate a global path for USV to reach the target point. The dynamic window algorithm (DWA) is used in the local path planning to avoid the dynamic obstacles and track the global path by following the local target point which is the intersection of the global and local path planning. The weight coefficient considering sea state is added in the objective function of DWA, where the security of USV can be guaranteed by reducing the weight of velocity and increasing the weight of distance when the sea state level becomes high. Thus, USV can get a global optimal path and reach the target point in complex environment with dynamic obstacles and ocean currents via the proposed hybrid algorithm, and the comparative simulation is carried out to verify the effectiveness and advantage of the proposed method.
Estudio de los rasgos lingüísticos de la mentira en el medio escrito: un análisis contrastivo inglés-español

espanolEl objetivo principal de esta tesis doctoral es el analisis de las caracteristicas linguisticas de la mentira en el lenguaje escrito en ingles y en espanol, para lo cual se ha llevado a cabo un analisis contrastivo entre ambas lenguas. Se han realizado diversos experimentos de clasificacion automatica sobre dos corpora ad-hoc para testar la clasificacion de los textos segun su valor de verdad. En el primer experimento se han aplicado tecnicas de aprendizaje automatico y se han comparado los resultados con un modelo Bag-of-Words, obteniendo una tasa de exito maxima de 78,5% para ingles y de 84,5% para espanol. El segundo experimento ha incluido dos tecnicas estadisticas: analisis discriminante y regresion logistica binaria, siendo los resultados de clasificacion igualmente satisfactorios. Ademas de ello, se ha confirmado el papel fundamental en la configuracion de la mentira escrita de parametros tales como la longitud del texto, referencias propias, entendimiento y exclusiones. Palabras clave: linguistica computacional, deteccion de la mentira, analisis contrastivo, clasificacion automatica. EnglishThe main aim of this PhD thesis is to analyse the linguistic cues to deception in written language both in English and Spanish, performing a contrastive analysis between both languages. For this purpose, several automatic classification experiments have been performed on two ad-hoc corpora in both languages, in order to check whether the texts could be successfully classified on the basis of their truth value. In the first set of experiments, a machine learning technique has been applied on the data and compared to a Bag-of-Words model, obtaining a maximum rate of 78.5% for English and 84.5% for Spanish. The second experiment involved statistical techniques, namely discriminant function analysis and binary logistic regression, and the results obtained proved remarkably successful too. In addition, they confirm the leading role in deception detection of parameters such as text length, self-references, insight and exclusive words. Keywords: computational linguistics, deception detection, contrastive analysis, automatic classification.
Computational Design and Evaluation of a Smart Material Morphing Building Surface Tile

The objective of the present work is to develop and numerically evaluate a novel concept for a shape-changing smart material building surface tile. This concept is based on a unique objective to reduce the area of the facade exposed to solar irradiance, thereby reducing thermal gains during high-temperature periods, by intelligently changing the shape of the facade surface depending upon the surface location, time of year, and other environmental factors. Moreover, a particularly high level of control, and therefore functionality is achieved for this tile concept by using a combination of localized smart material activation and mechanical actuation. First, an evaluation of the self-shading capabilities of the tile concept is presented. Of particular importance is that a morphing tile leads to an increase in shaded area on a building facade in comparison to a static tile. Next, a computational strategy for the design of the morphing tile concept that includes a numerical representation of the tile concept combined with a non-linear optimization process is presented. The computational design approach is shown to be capable of accurately determining design solutions for various target shapes while also minimizing energy usage. Furthermore, it is shown that utilization of a localized material activation (as opposed to global material activation) parameterization leads to more accurate and energy efficient solutions. Finally, the development and quantification of the capabilities of a benchtop prototype of the tile concept is presented. The results indicate that if provided the proper material activation parameters the shape-changing smart material tile can provide a significant decrease in tile area exposed to solar irradiance for various times of day. Furthermore, results indicate that the morphing frequency (i.e., monthly, daily, hourly, etc.) and the control method of an array of tiles (independent vs dependent) have a significant effect on the area of the tile exposed to solar irradiance.
Digital divide: addressing Internet skills. Educational implications in the validation of a scale

Research in Learning Technology 2019. © 2019 A. Surian and A. Sciandra. Research in Learning Technology is the journal of the Association for Learning Technology (ALT), a UK-based professional and scholarly society and membership organisation. ALT is registered charity number 1063519. http://www.alt. ac.uk/. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.
Wireless Body Area Network Sensor Faults and Anomalous Data Detection and Classification using Machine Learning

Sensor Networks are very much vulnerable and prone to faults and external attacks. Sensor networks used for Healthcare Monitoring are termed as Wireless Body Area Networks (WBAN), which is used for collecting various vital physiological parameters of patients from remote locations. However, WBAN sensors are prone to failures because of noise, hardware misplacement, patient‘s sweating. Sensed data from these sensors are sent from the Local Processing Unit to Medical Professionals. It would be very difficult for the Medical Professionals to diagnose correctly if the sensed data from these sensors are faulty or effected by the malicious third party. At times, even faulty data might lead to misdiagnosis or death of a patient. It motivated us to address this challenge by proposing a Machine Learning Paradigm to distinguish this anomalous data from the genuine sensed data. Firstly, we classify the health parameters as normal records or abnormal record. After the classification, we propose to apply regression technique for identifying the anomalous data and actual critical data. We use real patient‘s vital physiological parameters for validating the robustness and reliability of our proposed approach.
Classification of EEG-based motor imagery BCI by using ECOC

AbstractAccuracy in identifying the subjects’ intentions for moving their different limbs from EEG signals is regarded as an important factor in the studies related to BCI. In fact, the complexity of motor-imagination and low amount of signal-to-noise ratio for EEG signal makes this identification as a difficult task. In order to overcome these complexities, many techniques such as various feature extraction methods, learning algorithms, and classifier schemes have been developed in this regard. However, conducting more research is necessary for improvement. The present study aimed to use an ensemble learning approach to improve the performance of MI-BCI systems. Therefore, filter bank common spatial pattern (FBCSP), as a well-known feature extraction method, was used to produce separable features from EEG signals. Accordingly, error correcting output codes (ECOC) was applied on several learning algorithms to classify four classes of motor imagery tasks. The proposed ECOC ensemble technique was tested on the data set 2a from BCI competition IV. Based on the results, the ECOC can lead to an improvement by using the naive Bayesian parzen window algorithm, compared to the winner algorithm of BCI competition IV, which is superior to other selected state of the art algorithms.
Resolution and depth perception in laparoscopic surgery

Training in laparoscopic surgery involves adapting to operate using a two-dimensional video image instead of the three-dimensional view of open surgery. Surgeons must overcome the lack of depth perception and reduced tactile feedback. These factors make laparoscopic surgery technically difficult, and advanced imaging systems are required to improve performance and reduce the risk of complications. Two laparoscopic systems are at the cutting edge of surgical video technology: 3D and 4K. 3D systems utilise passive polarising glasses to restore binocular depth perception, whilst 4K systems provide a 2D image with four times the number of pixels of high definition (HD) systems. Limited evidence supports the clinical use of 3D over 2D HD, however some surgeons cannot perceive depth when using them, and they are expensive. Whilst 4K is a 2D technology, it has been suggested that the ultra high resolution may provide comparable depth perception to 3D. This study aims to determine whether 3D systems provide better depth perception, and quicker, safer surgery, when compared to 4K systems. It is the first clinical study to involve an ultra high definition, 4K system. This thesis involved benchtop optical testing, a crossover trial of simulated laparoscopic tasks, and a clinical trial involving patients undergoing laparoscopic cholecystectomy. Whilst 4K systems demonstrated high resolution on optical testing, pixel resolution appears to be only one of several determinants of resolving power. Performance benefits in terms of reduced time, errors and workload scores were observed for novices and experts when performing simulated depth perception tasks with 3D when compared to a non-validated 4K prototype. There was no increase in side effects. However, no improvement in operative time, error score, complications or reattendance rates were seen with 3D compared to 4K in the clinical trial of laparoscopic cholecystectomy, as performed by Consultant surgeons. The conflicting findings of the simulator and clinical trials likely reflect the differing tasks used. Existing simulator tasks predominantly test depth perception, rather than other qualities of laparoscopic systems. The development of new assessment measures is required to draw strong conclusions about 3D and 4K technologies.
The Influence of Natural Contamination on Pollution Flashover Voltage Waveform of Porcelain Insulators in Heavily Polluted Area

It is significant to reveal the characteristics of voltage waveform of porcelain insulator with heavy industry pollution in flashover test. In this study, the contamination of insulator samples came from heavy industry pollution areas was measured. The voltage and leakage current signals in the pollution flashover test were recorded completely. A parameter, Uw, was formed by summing the variation slope of the peaks of voltage on insulator of each power frequency cycle. It quantified the fluctuant accumulation of voltage signal waveform. Another parameter, Kfv, was formed by calculating the drop slope of voltage at flashover. It was analyzed that the effects of the pollution severity and contaminant content of insulator on fluctuant accumulation of voltage signal and the drop slope of voltage at flashover. It is found there is a positive correlation between the ratio of non-soluble to soluble deposit on insulator, RNE, and the pre-flashover voltage fluctuant accumulation, Uwb. Moreover, the very heavily polluted insulators have ultra-high value of Uwb, the medium and light polluted insulators have the outstanding value of Kfv.
BASIC SIGNAL PROCESSING PRINCIPLES FOR MONITORING OF PERSONS USING UWB SENSORS – AN OVERVIEW

The paper offers an overview of signal processing approaches that are suitable for monitoring of persons by means of ultrawideband sensors. The “Monitoring of persons” is related to their detection, localization, and tracking and in the case of static persons, also to the monitoring of their vital signs such as the breathing frequency and the heart rate. This paper discusses basic principles in the monitoring of persons who can be moving, static, or changing the character of their movement. In the case of static persons, the paper describes signal processing methods suitable for contactless estimation of their breathing frequency and the heart rate. Monitoring of persons using UWB sensors is attractive in many application scenarios such as emergency, rescue, medical, etc. Therefore, we believe that this comprehensive survey brings a lot of interesting information to researchers and specialists working in the field of contactless monitoring of persons.
Does Feedback Influence Student Postings to Online Discussions

Feedback theory proposes that feedback influences the behavior of a system and its parts and that is governed by rules. This exploratory study attempts to test this theory in a graduate-level class on leadership theory. Twelve students were asked to participate in five online discussions, each lasting one week. The questions for each discussion were selected to be provocative and rich, without having any correct answer. At the end of the discussion, students were asked to indicate which posting and poster they felt was “best” or most valuable and why they felt the posting was “best. ” There is mixed evidence that the voting influenced subsequent postings; some individuals did improve while others were consistently good or poor posters. Students selected postings that were (in declining frequency of occurrence): thoughtful or thought-provoking, well written or justified, uplifting, presented new information, same as their own opinions, changed me, or complex. These reasons are similar to those of the instructor, although the students’ difficulty in choosing and rationalizing a choice and the frequency at which students chose posts that captured their own thoughts and opinions are of some concern. This study provides some evidence that these graduate students could evaluate their own discussions without the instructor intruding or dictating an evaluation scheme, although this may not be true for other groups of online students. The Journal of Educators Online, Volume 4, Number 1, January 2007
Extensively drug-resistant tuberculosis at a tuberculosis specialist hospital in Shanghai, China: Clinical characteristics and treatment outcomes

Abstract Background: Extensively drug-resistant tuberculosis (XDR-TB) has recently emerged as a global public health problem. The objective of this study was to investigate the clinical characteristics, management and outcomes of human immunodeficiency virus-negative patients with XDR-TB at a specialist TB hospital in Shanghai, China. Methods: From July 2007 to June 2009 we analyzed TB patients with culture-proven XDR-TB at a specialist TB hospital in Shanghai. Results: Among 1156 TB cases, all culture-positive for Mycobacterium tuberculosis complex, 494 cases (42.7%) were classified as MDR-TB; 126 cases (10.9%) were XDR-TB. At least 3 lung fields were involved in 90.5% of XDR-TB patients and in 80.7% of other MDR-TB patients (p = 0.008). Of the XDR-TB cases, 40.5% were complicated by diabetes and other diseases, significantly higher compared with the other MDR-TB cases (p = 0.002). The rates of resistance to all drugs except isoniazid and rifampicin were significantly higher in patients with XDR-TB than in patients with other MDR-TB (p < 0.001). Treatment failure was more common in patients with XDR-TB than in those with other MDR-TB (p < 0.001), whereas the mortality and default rates did not differ significantly. Conclusions: The prevalence of XDR-TB is high in some areas of China. The clinical treatment outcome for XDR-TB is usually very poor.