Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditional random field-based gesture recognition with depth information

Abstract. Gesture recognition is useful for human-computer interaction. The difficulty of gesture recognition is that instances of gestures vary both in motion and shape in three-dimensional (3-D) space. We use depth information generated using Microsoft’s Kinect in order to detect 3-D human body components and apply a threshold model with a conditional random field in order to recognize meaningful gestures using continuous motion information. Body gesture recognition is achieved through a framework consisting of two steps. First, a human subject is described by a set of features, encoding the angular relationship between body components in 3-D space. Second, a feature vector is recognized using a threshold model with a conditional random field. In order to show the performance of the proposed method, we use a public data set, the Microsoft Research Cambridge-12 Kinect gesture database. The experimental results demonstrate that the proposed method can efficiently and effectively recognize body gestures automatically.
Temporal aggregation and systematic sampling in structural time-series models

Given a structural time-series model specified at a basic time interval, this paper deals with the problems of forecasting efficiency and estimation accuracy generated when the data are collected at a timing interval which is a multiple of the time unit chosen to build the basic model. Results are presented for the simplest structural models, the trend plus error models, under the assumption that the parameters of the model are known. It is shown that the gains in forecasting efficiency and estimation accuracy for having data at finer intervals are considerable for both stock and flow variables with only one exception. No gain in forecasting efficiency is achieved in the case of a stock series that follows a random walk.
Criteria for acceptable abstracts: A survey of abstracters' instructions

The need for criteria by which to judge the adequacy of an abstract is felt most strongly when evaluating machine‐produced abstracts. In order to develop a set of criteria, a survey was conducted of the instructions prepared by various scientific publications as a guide to their abstracters in the preparation of copy. One‐hundred‐and‐thirty sets of instructions were analyzed and compared as to their function, content, and form. It was concluded that, while differences in subject matter do not necessarily require different kinds of abstracts, there are significant variations between the informative and the indicative abstracts. A set of criteria for the writing of an acceptable abstract of science literature was derived. The adequacy of these criteria is still to be validated, and the authors' plans for future research in this area are specified.
Position sensorless control for ultrasonic motors based on input voltage information

This paper presents a method of position sensorless control of ultrasonic motors (USM). The position sensorless control method is useful for reducing the cost and size of drive system. The position estimation method is based on the input voltage information of the ultrasonic motor. The characteristic of input voltage versus rotor position is expressed by a quadratic function which is used to estimate a rotor position based on input voltage information. This estimation method, which uses only the drive voltage of the ultrasonic motor, is a very simple algorithm and shows good estimation ability. The parameters of the quadratic function are adjusted by using the recursive least squares method and are obtained on‐line. Since it can correspond to parameter change, the estimated rotor position shows good agreement with the measured rotor position against load torque and motor temperature changes. Sensorless position control is achieved by using the estimated rotor position instead of the measured rotor position. The validity of the proposed method is confirmed by experimental results. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 163(1): 57–64, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20626
Design of learning spaces in 3D virtual worlds: an empirical investigation of Second Life

Second Life (SL) is a three‐dimensional (3D) virtual world, and educational institutions are adopting SL to support their teaching and learning. Although the question of how 3D learning spaces should be designed to support student learning and engagement has been raised among SL educators and designers, there is hardly any guidance or research in this area. In this article, we report an empirical study in which we have elicited educators’, designers’ and students’ perceptions of learning spaces within SL. Based on this empirical research, we have presented some design considerations for SL educators and designers who are involved in designing learning activities and spaces in SL. We hope that the design guidance and examples described in this article will support educators and designers to design learning spaces that foster students’ socialisation, informal learning, collaboration and creativity.
Human Facial Feature Point Detection is Based on Improved ASM Method

In view of the traditional Active Shape Model (ASM) method’s flaw and insufficiency, the paper proposed several improvements methods. The traditional ASM method carries on the characteristic search using of the initial point which provided using the person face examination；in the improvement algorithm, the paper has used the method of pupil orientation on the initialization and adopted improvement methods of edge restraint, auto-adapted length of stride to effectively improved the ASM method performance. The experimental result indicated that, the improvement ASM method has a bigger enhancement in the accuracy and robustness.
Radar based detection and tracking of a walking human

Abstract In this paper, a radar based approach for pedestrian detection and tracking is presented. The approach utilizes automotive radar for tracking the targets and a separate Doppler radar to provide extra information about the classes of the targets. The classification of objects as humans is based on analysis of the Doppler spectrum of targets as well as the speed of the targets gained through the tracking process. The presented method is able to make a difference between a non-human target and a human target if the human target is moving. At the moment the measurement platform needs to be stationary or move at very low speed.
Investigation of blind source separation methods for extraction of fetal ECG

Extraction of fetal electrocardiogram (FECG) from maternal skin electrode measurements needs a robust technique. This problem can be modeled from the perspective of blind source separation (BSS) and so the problem reduces to the estimation of the independent sources of fetal and maternal cardiac activity. In this paper, analysis and study of some major algorithms like Bell and Sejnowski's infomax algorithm, Cardoso's joint approximate diagonalization of eigen matrices (JADE) algorithm, Hyvarinen's fixed-point algorithm and Comon's algorithm, was made for this important biomedical application. For robustness, two scenarios, i.e, (a) different amplitude ratios of simulated maternal and fetal ECG and (b) different values of additive white Gaussian noise, were investigated. It was observed that if the ratio of the amplitude of maternal to fetal ECG is 10:1 with an input SNR of 2 dB, all four algorithms were able to extract the fetal ECG. The signal-to-error (SER) ratios of the extracted maternal and fetal ECG were around 3 dB and 1 dB, respectively.
Finite Gamma mixture modelling using minimum message length inference: application to SAR image analysis

This paper discusses the unsupervised learning problem for finite mixtures of Gamma distributions. An important part of this problem is determining the number of clusters which best describes a set of data. We apply the Minimum Message Length (MML) criterion to the unsupervised learning problem in the case of finite mixtures of Gamma distributions. The MML and other criteria in the literature are compared in terms of their ability to estimate the number of clusters in a data set. The comparison utilizes synthetic and RADARSAT SAR images. The performance of our method is also tested by contextual evaluations involving SAR image segmentation and change detection.
Technology as a Tool for Leadership Development: Effectiveness of Automated Web-Based Systems in Facilitating Tacit Knowledge Acquisition

The effectiveness of emerging technology in helping to develop the tacit or experience-based knowledge needed for effective leadership performance was demonstrated in an on-line environment for discussion and training. One hundred and twenty-seven military students participated in three 20-minute discussions in one of three learning environments: standard classroom; standard on-line discussion; and discussion assisted by semantic technology. Consistent with expectations, semantic technology-supported learning resulted in greater discussion participation and training performance with discussion participation mediating the relationship between the learning environment and training satisfaction. An interaction between learning goal orientation (LGO) and learning environment on tacit knowledge performance showed that face-to-face conditions may help those with low LGO. Implications for improving tacit knowledge acquisition and technology-supported leadership development practices are discussed.
Optimal approach of achieving group-based mobile multimedia-on-demand services

This paper addresses a group-based bandwidth-efficient mobile multimedia-on-demand (GMMOD) service over wireless networks, e.g. multimedia broadcast multicast system (MBMS) and digital video broadcasting network (DVB). In this service model, user requests are first aggregated or batched and then served over a shared multicast or broadcast channel to achieve the objective of high system satisfactory ratio. We first analyze the tradeoff between service blocking probability and user reneging probability for a basic timeout-based batching approach. A theoretical model is developed from which we propose an optimal timeout-based approach that is able to identify the optimal tradeoff point that maximizes the system satisfactory ratio at a particular cell status, traffic and user profile. The optimal timeout-based approach is then compared with previously studied approaches and the simulation results demonstrate that the proposed approach gives better performance in improving the satisfactory ratio and reducing the reneging probability while maintaining a small blocking probability.
A novel approach to unsupervised robust clustering using genetic niching

We present a new unsupervised robust clustering algorithm that can successfully find dense areas (clusters) in feature space and determine their number. The clustering problem is converted to a multimodal function optimization problem within the context of genetic niching. The niche peaks, which constitute the final cluster centers, are identified based on deterministic crowding (DC). The problem of crossover interactions in DC is eliminated by restricting the mating to members of the same niche only. Finally, the correct number of niche peaks or cluster centers is extracted from the final population. Genetic optimization makes our approach much less prone to suboptimal solutions than other objective function based approaches, and frees it from the necessity of an analytical derivation of the prototypes. As a result, our approach can handle a vast array of general subjective, even non-metric dissimilarities, and is thus useful in many applications such as Web and data mining. Additionally, the use of robust weights makes it less sensitive to the presence of noise than most traditional unsupervised clustering techniques.
Cloud Computing and Creativity: Learning on a Massive Open Online Course

This paper explores cloud computing and how it might advance learning and teaching, particularly in terms of social creativity and collaborative learning. We present a study of a Massive Open Online Course (MOOC) – a semi-autonomous learning environment mainly distributed on the cloud – in which Open Educational Resources were produced, researched and shared by participants worldwide. The objective of this research was to explore the level of importance of creativity for learning and then to closely investigate how this creativity might be fostered in such a ‘vast’ educational setting and what factors might be of importance to enhance creativity in open networked learning. Through the participants’ experiences, we discuss the various dynamics and profiles of the participants as they move from being consumers on the environment to becoming ‘producers’ and take creative steps in their learning. More importantly, we identify the elements of the course that need to be in place to encourage and support this move towards more effective creativity and learning. Finally further discussions and conclusions are presented.
On improving IED object detection by exploiting scene geometry using stereo processing

Detecting changes in the environment with respect to an earlier data acquisition is important for several applications, such as finding Improvised Explosive Devices (IEDs). We explore and evaluate the benefit of depth sensing in the context of automatic change detection, where an existing monocular system is extended with a second camera in a fixed stereo setup. We then propose an alternative frame registration that exploits scene geometry, in particular the ground plane. Furthermore, change characterization is applied to localized depth maps to distinguish between 3D physical changes and shadows, which solves one of the main challenges of a monocular system. The proposed system is evaluated on real-world acquisitions, containing geo-tagged test objects of 18 18 9 cm up to a distance of 60 meters. The proposed extensions lead to a significant reduction of the false-alarm rate by a factor of 3, while simultaneously improving the detection score with 5%.
Information theory with applications

A few properties of conditional entropy are listed below: • H(Y |X) = 0 implies that either PX,Y (a, b) = 0 or lnW(b|a) = 0. So, if PX,Y (a, b) 6= 0 then W(b|a) = 1. Thus there is a map f : A → B such that Y = f(X) with probability one. So, Y almost surely depends on X in a deterministic fashion. • H(Y |X) ≤ H(Y ) This property implies that knowing X decreases the entropy of Y . We would like to show this property and we prepare this with lemma below.
The small world of human language

Words in human language interact in sentences in non–random ways, and allow humans to construct an astronomic variety of sentences from a limited number of discrete units. This construction process is extremely fast and robust. The co–occurrence of words in sentences reflects language organization in a subtle manner that can be described in terms of a graph of word interactions. Here, we show that such graphs display two important features recently found in a disparate number of complex systems. (i) The so called small–world effect. In particular, the average distance between two words, d (i.e. the average minimum number of links to be crossed from an arbitrary word to another), is shown to be d≈ 2–3, even though the human brain can store many thousands. (ii) A scale–free distribution of degrees. The known pronounced effects of disconnecting the most connected vertices in such networks can be identified in some language disorders. These observations indicate some unexpected features of language organization that might reflect the evolutionary and social history of lexicons and the origins of their flexibility and combinatorial nature.
State-Model-Based Regression Test Reduction for Component-Based Software

We present a novel regression test selection approach based on analysis of state and dependence models of components. Our technique targets to select a smaller regression test suite compared to the pure dependence-based RTS approaches while maintaining the fault revealing effectiveness. In our approach, after a modification, control and data dependencies are analyzed to identify the potentially affected statements. Subsequently, the state model of the component is analyzed to compute a precise publishable change information to support efficient regression test selection by the application developers.
An Optimizing Method of Competitive Neural Network

This paper presents an optimizing method of competitive neural network(CNN):During clustering analysis fixed on the optimum number of output neurons according to the change of DB value，and then adjusted connected weight including increasing ,dividing , delete. Each neuron had the different variety trend of learning rate according with the change of the probability of neurons. The optimizing method made classification more accurate. Simulation results showed that optimized network structure had a strong ability to adjust the number of clusters dynamically and good results of classification.
Thin film sensor for infant respiration

A compliant strain gauge sensor has been developed to monitor infant respiration, and to detect episodes of apnea. Previous clinical studies have indicated that an abdominally placed mercury strain gauge yields a more efficacious respiration signal than a transthoracic impedance monitor. The thin-film sensor is designed to supplant the mercury strain gauge in this application. Test results for three sensors are shown. One-way analysis of variance for apnea duration shows no significant difference among them. <<ETX>>
The Research of Chinese Name Entity Disambiguation Based On Word Sense Disambiguation

Named Entity Disambiguation (NED) refers to the processing of determining the real entity concept of a given name with some necessary context. This paper introduces a calculation model of the NED based on the word sense disambiguation in order to solve the problem of Chinese name disambiguation which we will face in the text automatic processing module of the software platform called "Shanghai Academic Degree and Postgraduate Education Information Web". The main idea of the model is to calculate the similarity between the all person templates with same name in the person entity library (PEL) and the real person template extracted from the context which is based on the method of word sense disambiguation and finally determine the most possible person after the matching process.
Color pictorial serpentine halftone for secure embedded data

This paper introduces a new rotatable glyph shape for trusted printing applications that has excellent image rendering, data storage and counterfeit deterrence properties. Referred to as a serpentine because it tiles into a meandering line screen, it can produce high quality images independent of its ability to embed data. The hafltone cell is constructed with hyperbolic curves to enhance its dynamic range, and generates low distortion because of rotational tone invariance with its neighbors. An extension to the process allows the data to be formatted into human readable text patterns, viewable with a magnifying glass, and therefore not requiring input scanning. The resultant embedded halftone patterns can be recognized as simple numbers (0 - 9) or alphanumerics (a - z). The pattern intensity can be offset from the surrounding image field intensity, producing a watermarking effect. We have been able to embed words such as 'original' or license numbers into the background halftone pattern of images which can be readily observed in the original image, and which conveniently disappear upon copying. We have also embedded data blocks with self-clocking codes and error correction data which are machine-readable. Finally, we have successfully printed full color images with both the embedded data and text, simulating a trusted printing application.
Improved AdaBoost Based Infrared Object Detection Algorithm

The traditional AdaBoost based object detection algorithm can get perfect performance for visible images, but not for infrared images because of the worse description of Haar features. In this paper, a novel infrared object detection algorithm based on improved AdaBoost with powerful histogram features is proposed. Firstly, HOG histogram with fourth grid area of image gradient is used for the input of classifiers. And then, AdaBoost with weighted FLD as weak classifiers is employed to select the multi-dimension features. Finally, the infrared object detection is realized by multi-scale sliding windows and non-maxima suppression algorithm. The experimental results demonstrate the better performance of the improved AdaBoost detection algorithm which can detect multi-scale infrared targets accurately.
Software engineering globalization and localization

The development of software under the constraints of different natural languages and local customs and cultures is addressed. Three questions are examined: how to make the core of the software program capable of handling data given in a different natural language without any loss of functionality, how to adapt the user interface so that it becomes a natural vehicle of communicating information with the user of a given language, taking into account the user's local customs and culture, and how to provide the user with the software capability of effectively working in a multilingual environment where two or more natural languages are simultaneously used. An example that shows what language conventions need to be considered and how to use these conventions at the implementation level is given. <<ETX>>
Reusable Ni-Catalysts for the Highly Chemoselective Synthesis of Primary Amines

The synthesis of aromatic amines from nitroarenes through hydrogenation is an industrially and academically important reaction. In addition, the employment of base metal catalysts in reactions that are preferentially mediated by rare noble metals is a desirable aim in catalysis and an attractive element‐conservation strategy. Especially appealing is the observation of novel selectivity patterns with such inexpensive metal catalysts. Herein, we report a novel mesostructured Ni nanocomposite catalyst. It is the first example of a reusable Ni catalyst that is able to hydrogenate nitroarenes selectively to anilines in the presence of highly sensitive functional groups such as C-C bonds and nitrile, aldehyde, and iodo substituents.
NNSYSID and NNCTRL tools for system identification and control with neural networks

Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview of the design of NNSYSID and NNCTRL.