Miracle-dz/UniFAR-FTUs huggingface dataset:
Segmenting Green Tea Consumers by Purchase Motivation in South Korea

Since the 1990s green tea consumption in South Korea has been gradually increasing because consumers consider green tea as beneficial to health. It is, therefore, necessary to know the factors influencing each motivational cluster according to sociodemographic characteristics. This study aims to obtain an empirical understanding of the green tea market by using a segmentation approach to provide better information for green tea marketers in Korea. A self-administered survey was obtained from 595 consumers in Seoul, Korea. Four distinct segments from cluster analysis were identified based on motivation: low motivated (18.0%), social seekers (35.7%), want-it-all (25.3%), and refreshment (21.0%). A multinomial logit regression analysis was used to identify the characteristics of consumers who are most likely to opt for a suitable motivation segment. Results indicated that the determinants of consumer motivation are age, occupation, price, company, and purchasing place.
Driver Eye State Classification Based on Cooccurrence Matrix of Oriented Gradients

Accurate detection of driver's eye state by computer vision is critical to driver drowsiness monitoring. The histogram of oriented gradients (HOG) is commonly used as descriptive feature of eye image for state classification. However, HOG often suffers from the limit of local gradient information. This paper proposes a new HOG-like feature of eye image, called cooccurrence matrix of oriented gradients (CMOG), for the purpose of more effectively classifying the eye state. By introducing the cooccurrence matrix, the CMOG enhances the ability of describing global gradient information of eye images. The ZJU eye blink database is used as the baseline images for performance comparison. The classification results show that the accuracy of CMOG reaches up to 95.9% in comparison with 91.9% by HOG under this database.
Internet routing emulation system and stress testing

With the incessant growth of the Internet and the renewal of routing protocols, it becomes a demanding task to test the running characteristics of routing protocol implementations (RPI) in a real large-scale network. We propose a novel approach, named stress testing, to inspect the running characteristics. We compared stress testing with traditional protocol testing, and give the abstract test method. By combining Internet topology generation and RPI, we developed the Internet Routing Emulation System (IRES) as a testbed of stress testing. The architecture and some pivotal issues of IRES are presented. An example of stress testing is given, where IRES is the test bed and a CISCO2600 router is the router under test (RUT). The characteristics of OSPF routing interaction is measured and analyzed between the IRES and RUT. The computation complexity of OSPF RPI in the RUT and the upper bound of the network scale RUT supports are obtained. The experiment shows that stress testing can be widely used to inspect the stability and scalability of other RPI, e.g. RIP, BGP.
Real-time capturing and interactive synthesis of 3D scenes using integral photography

This paper proposes a system which can capture a dynamic 3D scene and synthesize its arbitrary views in real time. Our system consists of four components: a fresnel lens, a micro-lens array, an IEEE1394 digital camera, and a PC for rendering purpose. The micro-lens array forms an image which consists of a set of elemental images, in other words, multiple viewpoint images of the scene. The fresnel lens controls the depth of field by demagnifying the 3D scene. The problem is that the scene demagnified by the fresnel lens is compressed along its optical axis. Therefore, we propose a method for recovering the original scene from the compressed scene. The IEEE1394 digital camera captures multiple viewpoint images at 15 frames per second, and transfers these images to the PC. The PC synthesizes any perspective of the captured scene from the multiple viewpoint images using image-based rendering techniques. The proposed system synthesizes one perspective of the captured scene within 1/15 second. This means that a user can interactively move his/her viewpoint and observe even a moving object from various directions.
Research Trend of Physical Skill Science --Towards Elucidation of Physical Skill--

Physical skills and language skills are both fundamental intelligent abilities of human being. In this paper, we focus our attention to such sophisticated physical skills as playing sports and playing instruments and introduce research activities aiming at elucidating and verbalizing them. This research area has been launched recently. We introduce approaches from physical modeling, measurements and data analysis, cognitive science and human interface. We also discuss such issues as skill acquisition and its support systems. Furthermore, we consider a fundamental issue of individual differences occurring in every application of skill elucidation. Finally we introduce several attempts of skill elucidation in the fields of dancing, manufacturing, playing string instruments, sports science and medical care.
CryptoNET: a model of generic security provider

The model and design of a generic security provider provides a comprehensive set of security services, mechanisms, encapsulation methods, and security protocols for Java applications. The model is structured in four layers; each layer provides services to the upper layer and the top layer provide services to applications. The services reflect security requirements derived from a wide range of applications; from small desktop applications to large distributed enterprise environments. Based on the abstract model, this paper describes design and implementation of an instance of the provider comprising various generic security modules: symmetric key cryptography, asymmetric key cryptography, hashing, encapsulation, certificates management, creation and verification of signatures, and various network security protocols. This paper also describes the properties for extensibility, flexibility, abstraction, and compatibility of the Java security provider.
Accelerating Memory Access with Address Phase Skipping in LPDDR2-NVM

Low power double data rate 2 non-volatile memory (LPDDR2-NVM) has been deemed the standard interface to connect non-volatile memory devices such as phase-change memory (PCM) directly to the main memory bus. However, most of the previous literature does not consider or overlook this standard interface. In this paper, we propose address phase skipping by reforming the way of interfacing with LPDDR2-NVM. To verify effectiveness and functionality, we also develop a system-level prototype that includes our customized LPDDR2-NVM controller and commercial PCM devices. Extensive simulations and measurements demonstrate up to a 3.6% memory access time reduction for commercial PCM devices and a 31.7% reduction with optimistic parameters of the PCM research prototypes in industries.
Implementation of a 64-point adaptive filter using an analogue c.c.d. programmable filter

This letter describes the construction and operation of an adaptive filter based on the Widrow least-mean-square adaption algorithm using a 64-point analogue programmable c.c. d. transversal filter as the main processing element. Initial test results are presented to confirm the principle of the system and these results may be readily compared with previously published simulation results.
Selection of Suitable Turbulence Models for Numerical Modelling of Hydrocyclones

The capability of Computational Fluid Dynamics (CFD) alternates the interest of researcher from the empirical models into the numerical approaches for studying hydrocyclones. This paper presents a comprehensive survey on the influences of turbulence model options in the 3D simulation of the hydrocyclone flow pattern. The required grid resolution was selected through a grid independency study. Four categories of turbulence models involving models based on the Boussinesq hypothesis, the Reynolds Stress Model (RSM), the Large Eddy Simulation (LES) model, and the Detached Eddy Simulation (DES) model were investigated for prediction of velocity components within the hydrocyclone. The methodology was validated by experimental data. The results confirm that both RSM and LES models are efficient turbulent model choices for the simulation of swirling flow of hydrocyclones.
Single-class classifier learning using neural networks: an application to the prediction of mineral deposits

Single-class classifier learning is the problem of learning a classifier from a set of training examples in which only examples of the target class are present. Most existing approaches to this problem are based on density estimation and hence suffer from the usual problems associated with estimating probability densities in high dimensional spaces. This paper describes how feedforward neural networks can be used to learn a classifier from a dataset consisting of (labeled) examples of the target class (positive examples) together with a corpus of unlabeled (positive and negative) examples. An application of the technique to the prediction of mineral deposit location is provided, and empirical results are presented.
A new method for decision making in multi-objective optimization problems

Many engineering sectors are challenged by multi-objective optimization problems. Even if the idea behind these problems is simple and well established, the implementation of any procedure to solve them is not a trivial task. The use of evolutionary algorithms to find candidate solutions is widespread. Usually they supply a discrete picture of the non-dominated solutions, a Pareto set. Although it is very interesting to know the non-dominated solutions, an additional criterion is needed to select one solution to be deployed. To better support the design process, this paper presents a new method of solving non-linear multi-objective optimization problems by adding a control function that will guide the optimization process over the Pareto set that does not need to be found explicitly. The proposed methodology differs from the classical methods that combine the objective functions in a single scale, and is based on a unique run of non-linear single-objective optimizers.
Intelligent sensing: neural network based health diagnosis for sensor arrays

The paper discusses the appropriateness of local sensor health monitoring/sensor fault diagnosis and suggests an artificial intelligence (AI) based solution to the diagnosis of sensor fault, in the context of sensor arrays. In contradistinction with the "centralized" methods currently used process/system fault detection, the work presented here aims to establish a process independent and sensor specific methodology for detecting and isolating sensor failures and fault recovery. The intelligent system proposed integrates the adaptability and learning ability of artificial neural networks with effective inter-sensor communication protocols. Micromachined accelerometers are considered as a case study.
A Grid‐enabled problem‐solving environment for advanced reservoir uncertainty analysis

Uncertainty analysis is critical for conducting reservoir performance prediction. However, it is challenging because it relies on (1) massive modeling‐related, geographically distributed, terabyte, or even petabyte scale data sets (geoscience and engineering data), (2) needs to rapidly perform hundreds or thousands of flow simulations, being identical runs with different models calculating the impacts of various uncertainty factors, (3) an integrated, secure, and easy‐to‐use problem‐solving toolkit to assist uncertainty analysis. We leverage Grid computing technologies to address these challenges. We design and implement an integrated problem‐solving environment ResGrid to effectively improve reservoir uncertainty analysis. The ResGrid consists of data management, execution management, and a Grid portal. Data Grid tools, such as metadata, replica, and transfer services, are used to meet massive size and geographically distributed characteristics of data sets. Workflow, task farming, and resource allocation are used to support large‐scale computation. A Grid portal integrates the data management and the computation solution into a unified easy‐to‐use interface, enabling reservoir engineers to specify uncertainty factors of interest and perform large‐scale reservoir studies through a web browser. The ResGrid has been used in petroleum engineering. Copyright © 2008 John Wiley & Sons, Ltd.
An object-oriented refinement methodology through the design of a settop-box

We present an object-oriented approach to optimize the design now of embedded systems targeted for the fast growing multimedia application market. We start with a high-level model of the system written in SystemC and propose design guidelines to optimize the top-down design flow leading to the system implementation. We highlight the usefulness of C++ expressiveness in mixed hardware-software systems design. We show how a strict separation of the internal behavior from the interface behavior of the design components combined with SystemC and CoWare solves many important codesign issues such as providing seamless IP reuse and a clear refinement methodology to achieve the final synthesizable hardware and compiled software blocks with minimal re-coding effort. We propose to explore those possibilities through a settop-box case study.
Traffic volume data outlier recovery via tensor model

Traffic volume data is already collected and used for a variety of purposes in intelligent transportation system (ITS). However, the collected data might be abnormal due to the problem of outlier data caused by malfunctions in data collection and record systems. To fully analyze and operate the collected data, it is necessary to develop a validate method for addressing the outlier data. Many existing algorithms have studied the problem of outlier recovery based on the time series methods. In this paper, a multiway tensor model is proposed for constructing the traffic volume data based on the intrinsic multilinear correlations, such as day to day and hour to hour. Then, a novel tensor recovery method, called ADMM-TR, is proposed for recovering outlier data of traffic volume data. The proposed method is evaluated on synthetic data and real world traffic volume data. Experimental results demonstrate the practicability, effectiveness, and advantage of the proposed method, especially for the real world traffic volume data.
More Human Participation Chinese Character CAPTCHA: Generating Strong Text-Based CAPTCHA Based on Adding More Human Participation

Many websites use CAPTCHAs ("Completely Automated Public Turing tests to tell Computers and Humans Apart") in an attempt to block automated interactions with their sites. However, widely deployed schemes can be broken with simple but novel attacks. As safety problem has always been very important, in order to meet safety requirements, we through reading many related articles and finding the existing problem of the CAPTCHA. To solve these problems, we developed a new chinese character scheme adding more human participation. This new scheme can increase the robustness of CAPTCHA and effectively prevent the automatic program attacks.
FEROM: Feature Extraction and Refinement for Opinion Mining

Opinion mining involves the analysis of customer opinions using product reviews and provides meaningful information including the polarity of the opinions. In opinion mining, feature extraction is important since the customers do not normally express their product opinions holistically but separately according to its individual features. However, previous research on feature‐based opinion mining has not had good results due to drawbacks, such as selecting a feature considering only syntactical grammar information or treating features with similar meanings as different. To solve these problems, this paper proposes an enhanced feature extraction and refinement method called FEROM that effectively extracts correct features from review data by exploiting both grammatical properties and semantic characteristics of feature words and refines the features by recognizing and merging similar ones. A series of experiments performed on actual online review data demonstrated that FEROM is highly effective at extracting and refining features for analyzing customer review data and eventually contributes to accurate and functional opinion mining.
SPEEDES - A multiple-synchronization environment for parallel discrete-event simulation

Synchronous Parallel Environment for Emulation and Discrete-Event Simulation (SPEEDES) is a unified parallel simulation environment. It supports multiple-synchronization protocols without requiring users to recompile their code. When a SPEEDES simulation runs on one node, all the extra parallel overhead is removed automatically at run time. When the same executable runs in parallel, the user preselects the synchronization algorithm from a list of options. SPEEDES currently runs on UNIX networks and on the California Institute of Technology/Jet Propulsion Laboratory Mark III Hypercube. SPEEDES also supports interactive simulations. Featured in the SPEEDES environment is a new parallel synchronization approach called Breathing Time Buckets. This algorithm uses some of the conservative techniques found in Time Bucket synchronization, along with the optimism that characterizes the Time Warp approach. A mathematical model derived from first principles predicts the performance of Breathing Time Buckets. Along with the Breathing Time Buckets algorithm, this paper discusses the rules for processing events in SPEEDES, describes the implementation of various other synchronization protocols supported by SPEEDES, describes some new ones for the future, discusses interactive simulations, and then gives some performance results. 20 refs.
The Role of Age-Related Neural Timing Variability in Speech Processing

Age-related difficulties in speech processing remain a concern, especially as technology continues to depend heavily on successful speech comprehension on the part of users. Event-related potentials (ERPs) have frequently been used to assess age-related changes in the processing of language. Specifically, the amplitude of the ERP is often compared between conditions or groups of interest. In constructing ERPs, many neurophysiologic responses are averaged together to reduce the contribution of uncorrelated background activity in the electroencephalogram (EEG). However, if variability in the timing of each potential on a trial-by-trial basis (i.e., “latency jitter”) is confounded with a variable of interest, then the presence of amplitude differences observed in the average ERP might not be solely the result of genuine amplitude differences, but also timing variability. We examined the role latency jitter may play in the well-established observation of age-related changes in the processing of natural speech as indexed by the N400. Older and younger adults were presented with sentences that ended in either expected or unexpected final words. In agreement with previous findings, a reduction in N400 amplitude was observed in the older adults compared with the younger adults in response to the unexpected final words. However, analyzing ERPs on a single trial basis reveals the older adults to have significantly greater intra-subject variability in N400 latency for incongruent speech stimuli, in comparison to younger adults. This increased intra-individual latency variability may contribute to the smaller N400 amplitudes observed in each subject’s average ERP. Age-related reductions in N400 amplitudes may indicate less precise timing of neurological processes. Conversely, they may indicate that the older brain exhibits greater specificity in the processing of individual sentence stimuli.
The Knowledge Acquisition Bottleneck: Time for Reassessment?

: Knowledge acquisition has long been considered to be the major constraint in the development of expert systems. Conventional wisdom also maintains that the major problem encountered in knowledge acquisition is in identifying the varying structures and characteristics of domain knowledge and matching these to suitable acquisition techniques. With the aid of the first substantial systematic analysis of a sample of expert systems applications developed in the real world, the authors describe what is actually going on in terms of knowledge acquisition. In the light of the evidence, it is argued that a reappraisal of the conventional approach to knowledge acquisition is necessary.
Considering video as a volume

We present an alternative approach to viewing video information. Video frames can be combined to form a volume in much the same way that medical CT images can be processed. Once the volume has been formed, techniques from medical imaging can be applied and novel views and data visualization of the original video data obtained. (1 page)
Review of Z39.50 servers and Z39.50 environment in Russia

The article covers the five‐year history of the implementation of Z39.50‐type products in Russia. All existing projects are mentioned in the order of their development. Since the authors are from RUSLANet, that project is described in greater detail. The architecture of a distributed library system, as well as its basic components, are presented, and the place and role of Z39.50 facilities in Russian libraries are highlighted. The obstacles the Russian library community faces in moving to a networked environment are covered. The need to develop uniquely Russian Z39.50 databases grew from specific document‐ processing features within the libraries. Some prospects for future development and use of Z39.50 environment in Russian libraries are also given.
Automatic 3D power line reconstruction of multi-angular imaging power line inspection system

We develop a multi-angular imaging power line inspection system. Its main objective is to monitor the relative distance between high voltage power line and around objects, and alert if the warning threshold is exceeded. Our multi-angular imaging power line inspection system generates DSM of the power line passage, which comprises ground surface and ground objects, for example trees and houses, etc. For the purpose of revealing the dangerous regions, where ground objects are too close to the power line, 3D power line information should be extracted at the same time. In order to improve the automation level of extraction, reduce labour costs and human errors, an automatic 3D power line reconstruction method is proposed and implemented. It can be achieved by using epipolar constraint and prior knowledge of pole tower's height. After that, the proper 3D power line information can be obtained by space intersection using found homologous projections. The flight experiment result shows that the proposed method can successfully reconstruct 3D power line, and the measurement accuracy of the relative distance satisfies the user requirement of 0.5m.
Combining Ontologies and Peer-to-Peer Technologies for Inter-Organizational Knowledge Management

Purpose – The purpose of the paper is to review the characteristics of systems that combine P2P technology with explicit ontologies and assess the benefits of these technologies for inter‐organizational knowledge management.Design/methodology/approach – We characterize existing technologies with respect to a number of aspects that are relevant to knowledge management on a technical level. We further provide an example of an existing system and categorize it according to the aspects.Findings – We conclude that ontology‐based P2P systems are in general beneficial for distributed knowledge management systems and that the design of such systems can be guided using the aspects we distinguish.Originality/value – The paper presents the first attempt to rigorously identify and discuss the design space of ontology‐based P2P systems.