Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy in Human-Robot Interaction : Survey and Future Work

Robots can cause privacy concerns for humans, partly because robots can gather data and move around in the real world, and partly because they can be perceived by humans as social actors. This is a growing issue as robots are increasingly located near humans. We propose a new area of research—“privacy-sensitive robotics”—to deal with this issue. The first part of this paper lays a foundation for new research with several broad literature surveys: of privacy in philosophy, U.S. law, the social sciences, medicine, and technology; of existing robotics technologies that could be used to protect privacy; and of existing work in “privacy-sensitive robotics” as defined herein. The second part presents a roadmap of future research directions by which to develop this new sub-field.
The Optimized Physical Model for Real Rover Vehicle

This paper presents the way to select appropriate features for rover vehicle model and to reduce the errors (differences) between two different vehicle models. The fundamental idea for selecting features is to compare the error rates when the specific group of features is removed and to choose the best case. Based on selected features and learning algorithm, I find the optimal coefficients and adjust the system properties using those values. Results from a relevant simulation experiment provide foundations to support and illustrate the benefit of the devised ways. The paper concludes with several promising directions for future research.
Classification of Alzheimer's Disease and Parkinson's Disease by Using Machine Learning and Neural Network Methods

Data mining is a fast evolving technology, is being adopted in biomedical sciences and research. Data mining in medicine is an emerging field of high importance for providing prognosis and a deeper understanding of the classification of neurodegenerative diseases. Given a data set of consists of 487 patients records collected from ADRC, USA. Around eight hundred and ninety patients were recruited to ADRC and diagnosed for AD (65%) and PD (40%), according to the established criteria. In our study we concentrated particularly on the major risk factors which are responsible for Alzheimer’s disease and Parkinson’s disease. This paper proposes a new model for the classification of Alzheimer’s disease and Parkinson’s disease by considering the most influencing risk factors. The main focus was on the selection of most influencing risk factors for both AD and PD using various attribute evaluation scheme with ranker search method. Different models for the classification of AD and PD using various classification techniques such as Neural Networks (NN) and Machine Learning (ML) methods were also developed. It was found that some specific genetic factors, diabetes, age and smoking were the strongest risk factors for Alzheimer’s disease. Similarly, for the classification of Parkinson’s disease, the risk factors such as stroke, diabetes, genes and age were the vital factors.
GISMO: a Graphical Interactive Student Monitoring Tool for Course Management Systems

This paper presents GISMO, a graphical interactive student monitor- ing and tracking system tool that extracts tracking data from an on-line course maintained with a Course Management System, and generates graphical repre- sentations that can be explored by course instructors to examine various aspects of distance students. GISMO uses techniques from Information Visualisation to render into an appropriate graphical manner the complex multidimensional stu- dent tracking data provided by the Course Management System. GISMO aims to help instructors to become aware of what is happening in their classes and provide a better support to their learners.
Performance Improving of Digital Video Broadcasting-terrestrial ( DVB-T 2 ) Using Alamouti Scheme with Cyclic Delay Diversity

The goal of the future terrestrial digital video broadcasting (DVB-T) standard is to employ diversity and spatial multiplexing in order to achieve the fully multiple-input multiple-output (MIMO) channel capacity. The DVB-T2 standard targets an improved system performance throughput by at least 30% over the DVB-T. The DVB-T2 enhances the performance using improved coding methods, modulation techniques and multiple antenna technologies. After a brief presentation of the antenna diversity technique and its properties, we introduce the fact of the well-known Alamouti decoding scheme cannot be simply used over the frequency selective channels. In other words, the Alamouti SpaceFrequency coding in DVB-T2 provides additional diversity. However, the performance degrades in highly frequencyselective channels, because the channel frequency response is not necessarily flat over the entire Alamouti block code. The objective of this work is to present an enhanced Alamouti space frequency block decoding scheme for MIMO and orthogonal frequency-division multiplexing (OFDM) systems using the delay diversity techniques over highly frequency selective channels. Also, we investigate the properties of the proposed scheme over different channels. Specifically, we show that the Alamouti scheme with using Cyclic Delay Diversity (CDD) over some particular channels has the better performance. Then, we exemplarity implement this scheme to the DVB-T2 system. Simulation results confirm that the proposed scheme has lower bit error rate (BER), especially for high SNRs, with respect to the standard Alamouti decoder over highly frequency-selective channels such as single frequency networks (SFN). Furthermore, the new scheme allows a high reliability and tolerability. The other advantages of the proposed method are its simplicity, flexibility and standard compatibility with respect to the conventional methods.
Computation of SAR in the simplified model of a pregnant woman exposed to RF radiation from 10 MHz to 1800 MHz

The pregnant woman model is particularly complex structure involving numerous tissues with different electrical parameters. Such complex structures are generally handled by numerical methods implemented in simulation software packages. The structures involving high discretization density (mainly based on Magnetic Resonance Imaging and handled by Finite Difference Time Domain method) consume tremendously high computational resources. On the other hand, oversimplified numerical models, with coarser segmentation, will inevitably generate less accurate results. The aim of this study was to develop a simplified 3D model of a pregnant woman with the reasonable tradeoff between computing demands and result accuracy. The exposure to the plane wave and short antenna was examined and the SAR distribution, peak localized 10g-averaged SAR and volume-averaged SAR were determined in a broad frequency range (from 10 MHz to 1800 MHz). The results were compared with relevant exposure limits defined by ICNIRP. The simulations were carried out using available commercial simulation software based on the Finite Element Method (FEKO) and the Finite Difference Time Domain Method (SEMCAD X).
Identification of Crowding Stress Tolerance Co-Expression Networks Involved in Sweet Corn Yield

Tolerance to crowding stress has played a crucial role in improving agronomic productivity in field corn; however, commercial sweet corn hybrids vary greatly in crowding stress tolerance. The objectives were to 1) explore transcriptional changes among sweet corn hybrids with differential yield under crowding stress, 2) identify relationships between phenotypic responses and gene expression patterns, and 3) identify groups of genes associated with yield and crowding stress tolerance. Under conditions of crowding stress, three high-yielding and three low-yielding sweet corn hybrids were grouped for transcriptional and phenotypic analyses. Transcriptional analyses identified from 372 to 859 common differentially expressed genes (DEGs) for each hybrid. Large gene expression pattern variation among hybrids and only 26 common DEGs across all hybrid comparisons were identified, suggesting each hybrid has a unique response to crowding stress. Over-represented biological functions of DEGs also differed among hybrids. Strong correlation was observed between: 1) modules with up-regulation in high-yielding hybrids and yield traits, and 2) modules with up-regulation in low-yielding hybrids and plant/ear traits. Modules linked with yield traits may be important crowding stress response mechanisms influencing crop yield. Functional analysis of the modules and common DEGs identified candidate crowding stress tolerant processes in photosynthesis, glycolysis, cell wall, carbohydrate/nitrogen metabolic process, chromatin, and transcription regulation. Moreover, these biological functions were greatly inter-connected, indicating the importance of improving the mechanisms as a network.
Fine Time Course Expression Analysis Identifies Cascades of Activation and Repression and Maps a Putative Regulator of Mammalian Sex Determination

In vertebrates, primary sex determination refers to the decision within a bipotential organ precursor to differentiate as a testis or ovary. Bifurcation of organ fate begins between embryonic day (E) 11.0–E12.0 in mice and likely involves a dynamic transcription network that is poorly understood. To elucidate the first steps of sexual fate specification, we profiled the XX and XY gonad transcriptomes at fine granularity during this period and resolved cascades of gene activation and repression. C57BL/6J (B6) XY gonads showed a consistent ∼5-hour delay in the activation of most male pathway genes and repression of female pathway genes relative to 129S1/SvImJ, which likely explains the sensitivity of the B6 strain to male-to-female sex reversal. Using this fine time course data, we predicted novel regulatory genes underlying expression QTLs (eQTLs) mapped in a previous study. To test predictions, we developed an in vitro gonad primary cell assay and optimized a lentivirus-based shRNA delivery method to silence candidate genes and quantify effects on putative targets. We provide strong evidence that Lmo4 (Lim-domain only 4) is a novel regulator of sex determination upstream of SF1 (Nr5a1), Sox9, Fgf9, and Col9a3. This approach can be readily applied to identify regulatory interactions in other systems.
Enhancing information retrieval efficiency using semantic-based-combined-similarity-measure

Most of the knowledge intensive organizations are having their information resided in large text document repositories and most of these text repositories and databases are either unstructured or semi-structured. Recently various soft computing techniques have been used to improve information retrieval efficiency. More specifically genetic algorithms have been used for various information retrieval components like matching function learning, documents clustering, information extraction, query optimization [1 – 6]. In most of the cases in information retrieval matching function is based on term frequency. But the problem with this approach is that the syntactic information of the text document is lost and phrases are also not considered, so results in poor accuracy. In this paper we have proposed a new semantic based similarity measure in which each term can be a phrase or a single word and the weight assigned to each term is based on its semantic importance considering each sentence. We have used this semantic similarity measure along with other standard similarity measure as Jaccard and cosine to form the semantic-based-combined-similarity-measure. Standard genetic algorithm has been used to optimize the weight given for each similarity measure.
Use of an anatomical scalar to control for sex-based size differences in measures of hyoid excursion during swallowing.

PURPOSE Traditional methods for measuring hyoid excursion from dynamic videofluoroscopy recordings involve calculating changes in position in absolute units (mm). This method shows a high degree of variability across studies but agreement that greater hyoid excursion occurs in men than in women. Given that men are typically taller than women, the authors hypothesized that controlling for participant size might neutralize apparent sex-based differences in hyoid excursion. METHOD Hyoid excursion in 20 young (<45) healthy volunteers (10 male), stratified by height, was measured in a tightly controlled videofluoroscopic protocol. RESULTS The study identified an anatomical scalar (C2-C4 length), visible on the videofluoroscopic image, correlated with participant height. This scalar differed significantly between men and women. By incorporating the anatomical scalar as a continuous covariate in repeated measures mixed-model analyses of variance of hyoid excursion, apparent sex-based differences were neutralized. Transforming measures of hyoid excursion into anatomically scaled units achieved the same result, reducing variation attributable to sex-based differences in participant size. CONCLUSIONS Hyoid excursion during swallowing is dependent on a person's size. If measurements do not control for this source of variation, apparent sex differences in hyoid excursion are seen.
The strategic role evolution of foreign automotive parts subsidiaries in China: A case study from the perspective of capabilities evolution

Purpose – The purpose of this paper is to investigate the strategic role evolution of client‐following local subsidiaries of foreign automotive parts suppliers in China.Design/methodology/approach – The units of analysis are five local subsidiaries of foreign automotive parts suppliers dealing with Beijing Hyundai Motor Corporation in China; a case study methodology based on interviews with managers of the subsidiaries was employed.Findings – First, a modified version of Ferdows's model can be utilized to aptly analyze the strategic role changes of subsidiaries of foreign automotive parts suppliers which have followed their major client into the emerging market. Second, the development of a subsidiary's functional capabilities varies from subsidiary to subsidiary, depending on the headquarters' (HQ) global strategy and the nature of its interactions with external players. Third, the strategic role evolution of a subsidiary is critically influenced by the levels of its functional capabilities. Finally, the.
The Non-Operative Treatment of Anterior Knee Pain

Anterior knee pain is a common presenting complaint, and in many cases no identifiable cause can be found. In these circumstances it is commonly known as anterior knee pain syndrome or patellofemoral pain syndrome. The management for this condition is most commonly non-operative. Treatment strategies include physiotherapy, pharmacotherapy, orthoses and combinations of the above. There are many described methods in the literature with a wide spectrum of outcomes, which in itself is testimony to the lack of any generally accepted gold standard of care for these patients. It is thus unclear to the health care professional treating these patients which is the best treatment to offer. In this review we aim to summarise historical and most up to date literature on the subject and in so doing allow the health care professional pick whichever treatment strategy they feel most beneficial and also provide a guide for appropriate patient education.
A method for dead reckoning parameter correction in pedestrian navigation system

This paper presents a method for correcting dead reckoning parameters, which are heading and step size, for a pedestrian navigation system. In this method, the compass bias error and the step size error can be estimated during the period that the GPS signal is available. The errors are used for correcting those parameters to improve the accuracy of position determination using only the dead reckoning system when the GPS signal is not available. The results show that the parameters can be estimated with reasonable accuracy. Moreover, the method also helps increase the positioning accuracy when the GPS signal is available.
Central European Countries’ Accessions to EMU - Costs and Benefits for EMU-Insiders and Outsiders

During the process of economic integration in Western Europe formerly unstable peripheral countries (Ireland, Portugal, Spain) achieved higher real economic growth than Germany and France provided their stabilization efforts are treated as credible in the long run. The reason was that the shift to stability reduced uncertainties for investors. Central European Countries have a better chance to pursue a stabilization policy without painful real effects like unemployment because their political will to join EMU and their economic advantages in case of an accession are credible. Therefore they should follow a strict stabilization strategy combined with consequent institutional and legal adjustments. (JEL F 31; F 33; F 36) When the West European integration process started many experts expected a relative advantage of the core, stable, big countries like Germany and France. Peripheral, unstable economies like Ireland, Portugal and Spain were expected to have more difficulties during the specific process of integration laid down in the Maastricht treaty because necessary stabilization policies in order to fullfil the Maastricht criteria are far more painful for them. More basic reforms are required in those economies reaching from the reduction of large fiscal deficits, dramatic changes in monetary and labour market policies to legal and institutional reforms like an independent central bank and the removal of capital controls. As long as these efforts are not credible for private transactors inflationary expectations remain high resulting in recessions and high unemployment. When authorities then reduce their stabilization efforts in order to reduce unemployment this
How Children Move: Activity Pattern Characteristics in Lean and Obese Chinese Children

Physical activity and sedentary behavior are central components of lifetime weight control; however, our understanding of dimensions of these behaviors in childhood is limited. This study investigated free-living activity pattern characteristics and the individual variability of these characteristics in 84 lean and obese Chinese children (7–9 y) during the school day and over the weekend. Activity pattern characteristics were established from triaxial accelerometry (StayHealthy RT3). Results indicated that children's free-living activity is characterized by many short-duration, low-intensity bouts of movement. Obese children take longer rest intervals between bouts and engage in fewer activity bouts both at school and at home. Intraindividual variability in activity patterns was low during school days but high for the rest intervals between bouts and number of activity bouts per day at the weekend. Finding ways to reduce the rest time between bouts of movement and increase the number of movement bouts a child experiences each day is an important next step.
Time course of neuromuscular alterations during a prolonged running exercise.

PURPOSE This study investigated the time course of contractile and neural alterations of knee extensor (KE) muscles during a long-duration running exercise. METHODS Nine well-trained triathletes and endurance runners sustained 55% of their maximal aerobic velocity (MAV) on a motorized treadmill for a period of 5 h. Maximal voluntary contraction (MVC), maximal voluntary activation level (%VA), and electrically evoked contractions (single and tetanic stimulations) of KE muscles were evaluated before, after each hour of exercise during short (10 min) interruptions, and at the end of the 5-h period. Oxygen uptake was also measured at regular intervals during the exercise. RESULTS Reductions of MVC and %VA were significant after the 4th hour of exercise and reached -28% (P < 0.001) and -16% (P < 0.01) respectively at the end of the exercise. The reduction in MVC was highly correlated with the decline of %VA (r = 0.98, P < 0.001). M-wave was also altered after the fourth hour of exercise (P < 0.05) in both vastus lateralis and rectus femoris muscles. Peak twitch was potentiated at the end of the exercise (+18%, P = 0.01); 20- and 80-Hz maximal tetanic forces were not altered by the exercise. Oxygen uptake increased linearly during the running period (+18% at 5 h, P < 0.001). CONCLUSION These findings suggest that KE maximal voluntary force generating capability is depressed in the final stages of a 5-h running exercise. Central activation failure and alterations in muscle action potential transmission were important mechanisms contributing to the impairment of the neuromuscular function during prolonged running.
Annotating user-viewed objects for wearable AR systems

By realizing augmented reality on wearable computers, it is possible to overlay annotations on the real world based on the user's current position and orientation. However, it is difficult for the user to understand links between annotations and real objects intuitively when the scene is complicated or many annotations are overlaid at the same time. This paper describes a view management method which emphasizes user-viewed real objects and their annotations using 3D models of the real scene. The proposed method highlights the objects viewed by the user. In addition, when the viewed object is occluded by other real objects, the object is complemented by using an image, which is made from 3D models, on the overlaid image.
Hybrid acoustic models for distant and multichannel large vocabulary speech recognition

We investigate the application of deep neural network (DNN)-hidden Markov model (HMM) hybrid acoustic models for far-field speech recognition of meetings recorded using microphone arrays. We show that the hybrid models achieve significantly better accuracy than conventional systems based on Gaussian mixture models (GMMs). We observe up to 8% absolute word error rate (WER) reduction from a discriminatively trained GMM baseline when using a single distant microphone, and between 4-6% absolute WER reduction when using beamforming on various combinations of array channels. By training the networks on audio from multiple channels, we find the networks can recover significant part of accuracy difference between the single distant microphone and beamformed configurations. Finally, we show that the accuracy of a network recognising speech from a single distant microphone can approach that of a multi-microphone setup by training with data from other microphones.
Forecasting exchange rate of Solomon Islands dollar against Euro using artificial neural network

With continual changes made and reviews of the exchange rate regime of Solomon Islands it is imperative that a proper forecasting modelling tool is established. The use of neural network models in exchange rate forecasting has received much attention in recent research. In this paper we propose an artificial neural network (ANN) model for forecasting exchange rates of the Solomon Islands dollar (SBD) against Euro (EUR). We use daily exchange rate data during the period of January 5, 1998 to June 30, 2014. The proposed model is compared with a naive method as a benchmarked method. Further, it is compared with single exponential smoothing; double exponential smoothing with trend; and Holt-Winter multiplicative and additive seasonal and multiple linear regression models. The performance of the models was measured by using various error functions such as root mean square error, mean absolute error, and mean absolute percentage error. The validation tests of the models were also carried out using different goodness of fit measures such as R-square, bias and tracking signal. The empirical result reveals that the proposed model is an efficient tool for forecasting SBD against Euro more accurately than are regression and time series models.