Miracle-dz/UniFAR-FTUs huggingface dataset:
An evaluation of transmission capability of air interface for PDC packet communication system

In Japan, a circuit-switched voice and data communication service has been provided as the PDC (Personal Digital Cellular) system. Especially in the data communication service, a 9.6 kbps transmission rate service is now offered. Moreover, to meet the demand for a more efficient communication system for interactive data communication, the commercial service of the PDC packet communication system namely PDC-P started in March, 1997 in Tokyo. The main features of the PDC-P system are a higher throughput of user packet data compared to the current PDC system. This paper shows a detailed algorithm and features of three improved techniques for the PDC-P system and qualitively clarifies the effect of throughput characteristics by computer simulation. Then, the paper describes the results of field tests carried out by using Internet access via our commercial network in the Tokyo area. Based on the results of both simulations and the field tests, the paper concludes with an evaluation of the total transmission capability, such as the throughput of the PDC-P air interface.
Classification of Cloud Types Based on Spatial Textural Measures Using Noaa-Avhrr Data

The United States Navy has a requirement for real time cloud analysis and classification as part of a nowcasting capability. The use of texture me asures in addition to standard Advanced Very High Resolution Radiometer (AVHRR) channel radiances is explored to provide an improved cloud analysis. Nowcasting delivers a very short term (2 to 4 hours) weather forecast for operational use. Therefore, speed and accuracy of computation are both critical. This research effort resulted in the development of multi-spectral textural cloud type detection algorithm. Several statistical textural measures were investigated in order to select the most appropriate subset of textures suitable for cloud classification. The algorithm was tested on a NOAA-AVHRR data set over the Pacific ocean near the coast of California.
Webbasierte linguistische Forschung: Möglichkeiten und Begrenzungen beim Umgang mit Massendaten

Over the past ten to fifteen years, web-based methods of sociological research have emerged alongside classical methods such as interviews, observations and experiments, and linguistic research is increasingly relying upon them as well. This paper provides an overview of three web-based approaches, i.e. online surveys, crowd-sourcing and web-based corpus analyses. Examples from specific projects serve to reflect upon these methods, address their potential and limitations, and make a critical appraisal. Internet-based empirical research produces vast and highly diverse quantities of (speaker-based or textual) data, presenting linguistic research with new opportunities and challenges. New procedures are required to make effective use of these resources.
Crop Planning Models with Symmetric Risk Measures

Abstract: In this paper the financial risk of crop plans is measured by two symmetric risk measures: variance and mean absolute deviation of the return. Several crop planning models with symmetric risk measures, based on the financial portfolio theory, are formulated. Among them minimum risk and maximum expected return models are of interest. The decision variables are the land areas allocated to crops. The models belong to mathematical programming with continuous variables. Some numerical examples for the minimum financial risk model are studied and efficient frontiers of the models are displayed.
The Minnesota Imaging Project research application for understanding an emerging technology

The Minnesota Imaging Project (MIP) is a joint academic-practitioner research program at the Carlson School of Management. The MIP research team consists of individuals with diverse and significant experience with imaging, supported by group decision support systems (GDSS) technology. The MIP team is implementing a research application as the means to investigate several research issues. The project is evaluating a new phased, joint development methodology designed for new technologies. The research application has several characteristics that will permit it to be developed and studied on a small scale, and yet generalized to a broad set of applications: groups of knowledge workers that work together, frequently dispersed in time and place, focusing on the paper-based information they use, with modest amounts of file folder and workflow, and with important indexing and retrieval requirements. The research subjects are: the GDSS and the Rhetoric faculty teams, and the MIP research team. <<ETX>>
Automatic signature verification: making it work

The issues discussed, when considered in the broader context of the practical design of an automatic signature verification system, point to a number of important considerations which should ideally inform and guide the design and implementation processes. In particular, the introduction of a "toolkit" concept for the specification of signature verification systems matches well to these requirements, and has led to the implementation of the KAPPA system which is capable of meeting relevant performance criteria while remaining flexible and easily adaptable across a wide range of applications. The design process should involve interactive manipulation of design data within an integrated and interdependent network of design considerations relating specifically to a flexible modular toolkit structure, and encompassing system optimisation and evaluative studies. Such an approach, while clearly not displacing the principal system requirement for reliable and robust verification algorithms can, however, have a powerful impact on the degree of optimality achieved in designing a signature verification system for use in a practical environment.
A 3-D wafer scale architecture for early vision processing

A massively parallel SIMD cellular computer is designed for processing early vision algorithms based on regularization theory and Markov random field (MRF) models. Algorithmic requirements and implementation issues are reviewed in detail for edge detection/surface reconstruction. The development of 3-D wafer scale integration (WSI) technologies that offer an ideal medium for implementing many early vision algorithms is discussed. An edge detection algorithm is mapped to the 3-D WSI computer that consists of a 128*128 array of processors formed by stacking 15 four inch CMOS wafers. This mapping is used as the basis for an enhanced array processor tailored for multiresolution MRF processing. Enhancements are proposed that would boost peak performance to over a trillion operations per second, using a stack of 40 wafers, with a total system volume of 820 cm/sup 3/ and consuming about 370 W. <<ETX>>
Validity and Decision Issues in Selecting a CAT Measurement Model

This paper discusses validity and decision issues that should be addressed in selecting a computerized adaptive testing (CAT) model. The paper begins with a historical perspective on the expected benefits of computer-based testing. It summarizes six computer-based testing innovations by the authors and shows how these innovations extend traditional theory and practice of measurement science andCAT. These six innovations include performance work models, job analysis and synthesis, continuous learning progress pathways, validity-centered design and documentation, and logical measurement opportunities in performance tasks and simulations. The paper then discusses specific decision issues relevant to the selection of a CAT model and provides current research related to those decision issues. Three illustrated examples are provided for using item response theory and CAT elements leading toward a performance testing measurement model. Conclusions and recommendations for future research are presented.
Making Thinking Visible: Using Online Tools to Initiate Teachers' and Students' Thinking.

This paper shares findings from a formative evaluation that explores a professional development program, Intel® Teach to the Future Workshop on Interactive Thinking Tools. The Workshop trains K-12 teachers how to use online tools with their students, which are designed to foster higher-order thinking skills, and combine technology and disciplinary content, with projectbased learning activities. We found that the range in project types that were developed demonstrated that teachers, like their students, are learners in the process of understanding the higher-order thinking skills and concepts targeted by the tools. This research has implications for in-service teachers, teacher educators, and student learning.
Noise detection in classification problems

Large volumes of data have been produced in many application domains. Nonetheless, when data quality is low, the performance of Machine Learning techniques is harmed. Real data are frequently affected by the presence of noise, which, when used in the training of Machine Learning techniques for predictive tasks, can result in complex models, with high induction time and low predictive performance. Identiﬁcation and removal of noise can improve data quality and, as a result, the induced model. This thesis proposes new techniques for noise detection and the development of a recommendation system based on meta-learning to recommend the most suitable ﬁlter for new tasks. Experiments using artiﬁcial and real datasets show the relevance of this research.
A comparison of SIMULA and GPSS for simulating sparse traffic

This paper describes the features needed in a simula tion language to model a network with sparse traffic. GPSS does not provide two necessary features, inter process communication and list processing, in con venient forms. SIMULA's built-in list-handling features and its powerful class construct can be used to model such a traffic network conveniently. The differences between the languages are illustrated by modelling a road network in two versions of GPSS and one of SIMULA.
Research on one Intelligent Open Online Course Based on Big Data

Open online courses integrate high-quality education resources and play active roles in the education system. This paper presents an intelligent open online course based on big data. This online course provides a variety of resources and massive exercises. Based on the recommendersystem, the course recommends the most appropriate resources to each student. And based on the neural network, the course develops a suitable review plan for each student. Introduction In 2012, the Massachusetts Institute of Technology launched an open online class. Since then, Massive Open Online Courses (MOOCs) have been popular in the world. Open online courses integrate high-quality education resources and play active roles in the education system. With the development of open online courses, high-quality teaching resources have accumulated more and more. In an online course, each knowledge point often corresponds to a number of videos, animations, coursewares, texts and other teaching resources, as well as a large number of exercises. The increase of types and quantities of resources enriched the teaching forms, but is also brings new problems: how do students choose resources? Excellent open online courses should guide students to learn the most effective teaching resources. With the rapid development of new information technology, the informationization construction in colleges and universities is also facing great challenges. How to achieve intelligent online course is worth studying. Through the excavation and analysis of big data on campus, we can understand each student's living habits and interest in learning. Intelligent online courses can provide the most suitable teaching resources for each student, and can make the most effective learning plan for each student. Design of the Intelligent Online Course. The intelligent online course proposed in this paper needs to be integrated with big data. Its design is shown in Fig. 1. Figure 1. Design of the intelligent online course In Fig. 1, the teaching resources database contains a wealth of teaching resources. According to the analysis of big data, intelligent learning recommendation system will filter the content of resources, big data on campus The most suitable teaching resources
Low-cost home monitoring using a Java-based embedded computer

In this paper the design and development of a low-cost homecare architecture for remote patient telemetry built upon a Java-based embedded computer is presented. The proposed architecture has been validated by experiencing real-time ECG monitoring by means of desktop PCs equipped with Web browsers, Personal Digital Assistants and WAP-enabled GSM/GPRS mobile phones. The modular approach followed in the development of the home station allows additional medical devices to be easily integrated to fulfil the requirements of heterogeneous homecare scenarios. We expect that the availability of such a low-cost telemedicine device will contribute to further extend healthcare reach to the homecare environment.
Detection and Classification of MRI Brain Images For Head/Brain Injury Using Soft Computing Techniques

It is essential to have a rigorous computerized system for Magnetic Resonance Images (MRI) of the brain for medical perception and clinical analysis. This article focuses on our proposed method of hybrid approach for classification of normal and abnormalities in magnetic resonance brain images. Wavelet and PCA were functioning feature extraction and reduction from image respectively. The featured images finally were linked to Neuro-Fuzzy Classifier (NFC) for classification. The proposed methodology, including three basic steps, namely preprocessing, training and classified output. It extracts and reduced the dimension of features from the set of scan brain MR images of patients. Once preprocessing done, the featured image trained by soft computing based fuzzy neural tool and finally fed to the Neuro-Fuzzy Classifier (NFC) for detection of abnormalities in new MR images. The Hybrid NFC is combined with K- fold fuzzy C-means Neuro-Fuzzy Classifier which is used to enhance abstraction of NFC. We focus on common brain diseases such as meningioma, Alzheimer's and visual agnosia as an abnormal brain. K-Fold Neuro Fuzzy Classifier provides the accuracy around 98% with minimum computational time.
Development of AAC Vocabulary·Symbol System for the Users with Language Impairments

국내에서 중도의 의사소통장애학생에게 보완․대체의사소통 체계를 사용하는 경우가 점차로 증가하는데 비하여, 실제로 이를 위해 적합한 어휘나 상징체계를 선택하는데 필요한 진단 과정이나 고려할 변인들에 대한 기초연구는 많이 이루어지지 않았다. 이에 본 논문은 먼저 최근 AAC 분야에서 많이 사용되는 세 가지 진단 모델(참여 모델, 사회-의사소통.
Analysis and enhancement of locally harmonic signals using adaptive multi-kernel methods

This paper is concerned with the analysis/synthesis and enhancement of signals that can be efficiently modeled as "quasi-harmonic". We first provide a formal definition of quasi-harmonic signals and show that known estimation methods for such signals can be interpreted in a non-parametric local approximation framework. We then investigate the possibility of using "adaptive multi-kernel" estimation methods for which the duration of the analysis frame is tuned independently for each harmonic depending on the local behavior of the signal.
OFDM for wireless communications: techniques for capacity improvement

Orthogonal frequency division multiplexing (OFDM) modulation is a promising technique for achieving the high-bit-rates required for a wireless multimedia service. We describe three techniques to improve the capacity of OFDM systems, which are robust channel estimation for coherent detection, adaptive antenna arrays for interference suppression, and transmitter diversity for high efficiency transmission.
Error Correction of Continuous Handwriting Recognition by Multimodal Fusion

In recognition-based user interface,users'satisfaction is determined not only by recognition accuracy but also by effort to correct recognition errors.In this paper,an error correction technique based on multimodal fusion is introduced.It allows a user to correct errors of Chinese handwriting recognition by repeating the handwriting in speech.A multimodal fusion algorithm is the key of the technique.By constraining the search for the best handwriting recognition result by speech input,the algorithm can correct errors in both character extraction and recognition of handwriting.The experimental result indicates that the algorithm is effective and efficient in computation.Moreover,evaluation also shows the correction technique can help users to correct errors in handwriting recognition more efficiently than the other two error correction techniques.
SERSCIS-Ont Evaluation of a Formal Metric Model using Airport Collaborative Decision Making

— In the Future Internet, programs will run on a dynamically changing collection of services, entailing the consumption of a more complex set of resources including financial resources. The von Neumann model offers no useful abstractions for such resources, even with refinements to address parallel and distributed computing devices. In this paper we detail the specification for a post-von Neumann model of metrics where program performance and resource consumption can be quantified and encoding of the behaviour of processes that use these resources is possible. Our approach takes a balanced view between service provider and service consumer requirements, supporting service management and protection as well as non-functional specifications for service discovery and composition. The approach is evaluated using a case study based on an airport-based collaborative decision-making scenario. Two experimental approaches are presented: the first based on stochastic process simulation, the second on discrete event-based simulation.
Practice-Based Learning in Higher Education

Practice-based learning (PBL) has been a staple in higher education from inception; however, increasing challenges faced by institutions of higher learning to ensure that students receive value for their investment of time and money and are adequately prepared to meet the demands of the twenty-first-century workplace have fostered renewed interest in the implementation of PBL. This article provides a brief overview of the major characteristics, principles, and manifestations of the state of PBL across disciplines in universities and colleges.
Application domain knowledge acquisition and use in requirements engineering for computerized systems

F-cube ('From Fuzzy to Formal') is an ESPRIT-funded project (No.6612) to create methods and tools to support requirements engineers in the acquisition, analysis, validation, specification, and prototyping of requirements for computerized systems. It is intended that tools under the general umbrella of enterprise and environment modelling be part of the tool-kit. The authors have carried out knowledge acquisition with requirements engineers and end-users at the sites of the user partners in the project consortium to model the user (the requirements engineer) and user-tasks. The results of the task-analysis emphasize the importance of acquisition and use by the requirements engineer of a critical minimum subset of application domain knowledge. The authors intend to construct a knowledge-based tool to support the requirements engineer in carrying out early requirements capture, analysis, and validation (ERCAV)-the process of converting a set of requirements, obtained from requirement holder(s) and end-users or their representatives, which may be inexact, incomplete, contradictory, ambiguous, inconcise, and badly structured, into a requirements specification which can form a useful basis for the cost-effective design and construction of a system, comprising software and hardware, which adequately satisfies the needs of the requirement holder(s) and end-users. The tool will prompt the requirements engineer to acquire and input application domain knowledge and will use it to reason over an appropriate representation of the requirements, to assist the requirements engineer in detecting problems and resolving them. >
Bioengineering & Biomedical Science

This research study reports on the effective band of EEG signal to be used in seizure prediction, such as gamma, beta, alpha etc. The exercises were performed on a patient-specific framework for Electroencephalography (EEG) channel selection and seizure prediction, based on statistical probability distributions of the EEG signals. This framework is an enhanced method consists of two major phases, training and testing. Our objective was to distinguish between predicted and normal EEG signals. We achieved high prediction efficiency in reasonable time with low false alarm rate considering the parameters of seizure prediction techniques. Overall, we reached an efficiency of 96.2485% with prediction time of 54.012 min and false alarm rate of 0.10526/h. This approach is having considerable significance. It is a simple method which depends on all filtering technique. This method can be implemented easily in future work and it
Multi-thread implementation of a fuzzy neural network for automatic ECG arrhythmia detection

A fuzzy neural network was implemented using a multithreading approach for detection of atrial fibrillation, bigeminy, and normal sinus rhythm in the MIT-BIH Arrhythmia Database. The feedforward multilayer perceptron neural network produces fuzzy outputs due to a modification of the learning algorithm that changes the crisp target labels for fuzzy target labels. The input data to the neural network consisted of nine inputs: Seven contiguous RR intervals, their average and their standard deviation. The trained fuzzy neural network was implemented using concurrent thread synchronization with critical sections for mutual exclusion and process synchronization with semaphores. Concurrent process synchronisation is slower but allows data sharing among different process. Sensitivity and positive predictivity rates above 90% for atrial fibrillation episode and duration detection were reached in the database.
Heap-size analysis for assembly programs

Our objective is to propose methods for resource-aware compilation inspired by the implicit complexity community. We consider a small assembly-like language and we build abstract finite machine models in order to predict a bound on the maximal heap usage of a program. We propose a polynomial time procedure to detect and certify a broad and meaningful class of non-size increasing programs, which run in a constant size heap. We end by discussing about programs running in linear heap space and discussing how to capture logarithmic space computation.
IoT Based Power Theft Detection and Monitoring System

: Electricity theft is a major concern for the utilities. Many times power theft has been major impact on the economy as well as the development of the country. At present to monitor the various parameters like power consumption, the amount of load and to prevent electricity siphoning, an intelligent device would come handy to solve the problem for the power company and the clients. Application of techniques of power monitoring allows to power monitoring systems to receive the information remotely and in relation to the coordinates and time. In this project we are using smart power meter which are fitted on both at the transmission and load side. These meters are capable of measuring power sent over the load sand power consumed by the load over the time respectively. Both the parameters are sent to the base station wirelessly. Whenever there is a mismatch above the tolerance level parameters, then power theft is detected. The system will trigger the alarm to intimate to the concern authority so that they take necessary legal action and prevent power theft in the future.