Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Identity and Convergence: News Faculty and Student Perspectives on Web, Print, and Broadcast Skills

This research examines the role of intergroup bias in mass communication students and faculty perceptions of the importance of various media technology skills. This study differs from previous research because it compares the views of both print journalism and broadcasting students and faculty about media skills and convergence. A scale of social identity pinpoints a relationship between social psychology and attitudes about media skills. A weak negative correlation between the absolute value of the print-minus-broadcast skills-preference variable and web-skills preferences among all mass communication student and faculty participants was found, and the correlation strengthened when only broadcast students and faculty were analyzed, indicating that those inclined toward broadcasting were more likely to prefer traditional mass media skills training over cross-platform, new media, and Internet skills. Broadcast students and faculty also differed from other participants in how their preferences for the skills of their own profession relate to their openness to nontraditional media skills. Students reported a greater preference for skills training than faculty did. Comparisons of students and faculty preferences for print, broadcast, and web skills showed strong, statistically significant differences, with students rating all skills more highly than faculty did.
Sequential variational data assimilation

The algorithm of variational data assimilation in real time [1] is considered. It is a modification of the general algorithm of inverse modeling [2,3] under condition that the time interval of data assimilation is equal to one step of discretization of the model of processes and target functional. The purpose of work is to carry out investigation of the quality of numerical schemes and algorithms of this technique taking one and two-dimensional non-stationary models of advective - diffusive transport of pollutants in the atmosphere as examples. The comparison of data assimilation schemes based on the time-local adjoint problems with Kalman type algorithms show the advantage of the former in efficiency and labor cost. Some numerical results are presented.
Research on Developing Instructional Design Models for Flipped Learning

An emerging learning method, flipped learning, has gained much interest lately due to its process involving prior study followed by the students` classroom involvement, which direction matches that of the current educational policy that emphasizes self-directed learning. This study investigated the concept of flipped learning and explored ways to develop instructional design models that utilize it. Flipped learning is not a model that has been recently developed, as it uses the format of blended learning with the introduction of a new concept of prior learning that allows students to learn in advance through online lessons and video clips related with the classroom content to be covered. During class time, individualized supplementary or in-depth study is conducted on the basis of the students` prior learning. The main considerations for designing flipped learning are a flexible classroom environment, a shift in learning culture, intentional classroom content, and educators equipped with professional capability. The research proposes the development of instructional design models for flipped learning pursuant to such concept and considerations. Through this research, the concept of flipped learning can be comprehended; furthermore, flipped learning can be utilized more effectively in the teaching and learning environment.
An interactive method for the bi-objective resource-constrained project scheduling

In this paper, we propose an interactive method to solve a bi-objective resource-constrained project scheduling problem. The method is based on a double tabu search technique and generates a representative set of potentially efficient solutions to be considered in the scheduling of operations. The method proceeds in two steps. In the first step, the tabu mode, provides a good assignment of resources; in the second step, the tabu floating task, reschedules the tasks to obtain efficient solutions with regards the considered criteria. The approach is tested on a sample of problems ranging in size from 5 to 250 tasks and compared to other methods in the literature. The comparison shows the performance of the proposed approach.
Accelerating image processing capability using graphics processors

In the last few years, consumer Graphics Processor Units (GPUs) have been evolving from fixed-function display generators into general purpose parallel computers. We have explored the potential uses and limitations of this emerging technology as a video coprocessor for real-time image processing applications such as video enhancement, tracking, video stabilisation and multi-sensor fusion. We show how a GPU can be used to implement and accelerate some of these common tasks and show our results. We also address the problem of integrating a GPU into a rugged system in order to deploy this capability into the environments encountered in many defence and security applications.
Integrated Modeling and Control Based on Reinforcement Learning and Dynamic Programming

This is a summary of results with Dyna, a class of architectures for intelligent systems based on approximating dynamic programming methods. Dyna architectures integrate trial-and-error (reinforcement) learning and execution-time planning into a single process operating alternately on the world and on a learned forward model of the world. We describe and show results for two Dyna architectures, Dyna-AHC and Dyna-Q. Using a navigation task, results are shown for a simple Dyna-AHC system which simultaneously learns by trial and error, learns a world model, and plans optimal routes using the evolving world model. We show that Dyna-Q architectures (based on Watkins's Q-learning) are easy to adapt for use in changing environments.
Improvement in Moving Target Detection Based on Hough Transform and Wavelet

ABSTRACT The most fundamental problem in radar signal is the detection of a moving object or a physical phenomenon. This problem became more challenging to the signal processing and radar communities under severe interference. In order to effectively detect moving targets in strong noise conditions, we proposed a new method based on wavelet and Hough transform for multiple moving targets detection. First, the noise is removed from the received signal by the wavelet technique. We propose an improved wavelet threshold function. The new threshold function not only reduces constant error of soft thresholding but also improves the discontinuity of hard thresholding. Second, the image processing technique of the Hough transform is used. The benefits of this method include improved performance of detection and implementation flexibility, which indicates the effectiveness and robustness of the algorithm.
QoS over Multihop Wireless Networks

The aim of this work is to understand the requirements behind Quality of Service (QoS) for Multihop Wireless Networks and evaluate the performance of different such strategies. This work starts by establishing the basis for requirement of QoS and evaluates different approaches for providing QoS. Bandwidth is selected as the most important resource amongst the resources identified for ensuring QoS. The problem is modeled as an optimization problem that tries to maximize the amount of bandwidth available in the system while providing bounds over the bandwidth available over a route. Other QoS parameters are bound by hard limits and are not involved in the optimization problem. The existence of spatial reuse rules has been established through simulations for a TCP based network. This establishes that the reuse rules are independent of the MAC and network layer protocols used. This idea is used in designing the simulations for strategies that use controlled spatial reuse and give known bounds for QoS. Simulations take the network and a set of connections to generate the best possible schedule that guarantees bandwidth to individual connections and maximizes the total number of slots used in the entire system. The total number of slots used is a measure of the bandwidth in use. The set of graphs and connections is generated by a random graph and connection generator and the data set is large enough to average the results. There are two different approaches used for scheduling the connections. The first approach uses graph coloring and provides a simpler implementation in terms of network deployments. Second approach uses on-demand slot allocation. The approaches are compared for their pros and cons.
Access flow control scheme for ATM networks using neural-network-based traffic prediction

The authors propose a new approach to the problem of congestion control arising at the user network interface (UNI) of ATM-based broadband networks. The access flow control mechanism operates on the principle of feedback control. It uses a finite impulse response (FIR) neural network to accurately predict the traffic arrival patterns. The predicted output in conjunction with the current queue information of the buffer can be used as a measure of congestion. When the congestion level is reached, a control signal is generated to throttle the input arrival rate. The FIR multilayer perceptron model and its training algorithm are discussed. Simulation results presented in the paper suggest that the scheme provides a simple and efficient traffic management for ATM networks.
Extracting the interpretive characteristics of translations based on the asymptotic correspondence vocabulary presumption method

翻訳の背景にある解釈については古くから翻訳の比較や背景的な思想の分析が行われてきたが，人文学的手法によるものであるため客観性の担保が困難で，大規模な比較分析には適さなかった．本研究では情報技術を活用し，計量的な翻訳の比較と背景的思想の特徴抽出を行う手法を提案する．まず，原文と翻訳文の対応語彙の特定・抽出アルゴリズムを考案した．単語の一対一対応の仮定，被特定単語の除去による相互情報量の再計算，閾値の漸近的低減を組み合わせることで従来の手法に比べて再現率が20%程度向上し，一つの単語の複数の単語への対応も抽出可能となった．次に，語彙の対応関係を用いて二種類のネットワークを作成し中心性の計算から各翻訳での特徴的な概念を示す単語を抽出した．抽出結果は，翻訳作成の背景的な思想と合致しており，分析手法の有効性が確認された．
A Novel Model of Computation for Software Synthesis Based on Data Frame Driving

ABSTRACT Software synthesis is a useful technology to accelerate the design of digital systems, and the MoC (model of computation) is the foundation of software synthesis. Current researches on MoC mainly focus on the situations that the produced data frames are used only once by its downstream module. However, for some applications with loops, the data frame produced by upstream module needs to be reused and thus the current MoCs cannot be directly used to synthesize these applications’ DFG (data flow graph). To solve this problem, a novel MoC based on data frame driving is proposed for software synthesis in this paper. In our MoC, the DFG with loops is first transformed into an acyclic DFG and modules in the acyclic DFG are divided into different levels that define the priority of modules. Then, the schedule can be generated according to the modules’ level. The proposed MoC is verified under both random generated DFG and real-world application's DFG. The experimental results show that by using the proposed MoC, the schedule for DFG with reused data frame can be generated for both single processor system and multi-processors system, and it thus can be used as a useful tool to aid the software synthesis.
Analysis of conductive target influence in plasma jet experiments through helium metastable and electric field measurements

The use of cold atmospheric pressure plasma jets for in vivo treatments implies most of the time plasma interaction with conductive targets. The effect of conductive target contact on the discharge behavior is studied here for a grounded metallic target and compared to the free jet configuration. In this work, realized with a plasma gun, we measured helium metastable HeM (23S1) concentration (by laser absorption spectroscopy) and electric field (EF) longitudinal and radial components (by electro-optic probe). Both diagnostics were temporally and spatially resolved. Mechanisms after ionization front impact on the target surface have been identified. The remnant conductive ionized channel behind the ionization front electrically transiently connects the inner high voltage electrode to the target. Due to impedance mismatching between the ionized channel and the target, a secondary ionization front is initiated and rapidly propagates from the target surface to the inner electrode through this ionized channel. This leads to a greatly enhanced HeM production inside the plasma plume and the capillary. Forward and reverse dynamics occur with further multi reflections of more or less damped ionization fronts between the inner electrode and the target as long as the ionized channel is persisting. This phenomenon is very sensitive to parameters such as target distance and ionized channel conductivity affecting electrical coupling between these two and evidenced using positive or negative voltage polarity and nitrogen admixture. In typical operating conditions for the plasma gun used in this work, it has been found that after the secondary ionization front propagation, when the ionized channel is conductive enough, a glow like discharge occurs with strong conduction current. HeM production and all species excitation, especially reactive ones, are then driven by high voltage pulse evolution. The control of forward and reverse dynamics, impacting on the production of the glow like discharge, will be useful for biomedical applications on living tissues.
Wide‐band characteristics of fin ferrite electromagnetic wave absorber

Fin electromagnetic wave absorbers are proposed in which the sintered ferrite plates are placed in the direction perpendicular to the electric field of the incident electromagnetic field. By numerical calculations, it is shown that this structure has a broadband reflection suppressing characteristic. The characteristics of the absorbers in terms of the structural parameters of the absorber and ferrite permeability are shown, and it is found that the optimum structure of the absorber for a broadband operation depends on the permeability of the ferrite. Further, by means of the model experiment, the broadband characteristics of the proposed absorber are demonstrated.
The relationship between innovation orientation and organisational performance

Organisation performance is related to several factors including the organisation's mission, vision, strategy, and its innovation orientation. An innovation orientation describes an organisation's degree of innovativeness. This exploratory study examines the relationships between innovation and organisational performance in a sample of Canadian organisations. The results suggest that an innovation orientation is related to overall organisational performance. The high innovating firms had a positive relationship with the top line growth, and customer satisfaction, and bottom line growth, and profitability. There was a positive relationship between return on investment and innovation orientation. Those organisations possessing low innovation orientations had lower firm performance and enterprise value. Significant negative correlations were identified with return on investment, overall firm performance and the overall enterprise value.
Tracking players and a ball in soccer games

In ball games, understanding the movement of players and a ball is essential to analyze matches and tactics. In this paper, we present a system to track soccer players and a ball by using color information from video images. First, our system extracts regions of shirt and pants of each player. It tracks soccer players based on the regions. It copes with the variations of posture and occlusion of players. The ball is too small and fast to extract regions only by color. The system extracts candidate regions for a ball based on both color and time difference image, and tracks the ball using constraint of motion. It maps the position of players and the ball in the image onto that on the field. Experimental results on real image sequences show the effectiveness of this system.
Fusing binary support vector machines (SVM) into multiclass SVM

Multi-class support vector machine by fusing a class of binary support vector machines is proposed. The classifier fusion approaches include simple combination method such as Maximum, Minimum, Product, Mean, Median and Major Voting. Dempster-Shafer fusion method is also presented as well as KNN and Neural network approaches. The proposed algorithms are applied to the facial expression recognition applications for both the Japanese female facial expression database and the Cohn-Kanade AU-coded facial expression database. The results show that it is effective to combine binary support vector machines (SVM) to a multi-class SVM.
Comparison of Source and Artifact Characterization Data Using a Generalized Distance Measure

Abstract A major difficulty arises in sourcing studies when more than two or three elements are used in the comparison of artifacts with a number of potential sources. Multivariate statistical analysis can be used to define source groups and to allocate artifacts to their most likely sources. In this study of North Island, New Zealand, obsidian sources used in prehistory, the generalized distance measure of Mahalanobis and Rao is applied in three phases: first, to define source groups in terms of their inter- and intrasource variation using characterization data from X-ray fluorescence spectrography of five trace element constituents; second, the five-dimensional array was reduced to a three-dimensional figure for visual presentation; and finally, the highest probability of the association of an artifact data-set with that of a source group is used to allocate archaeologically derived material to its most likely source of raw material.
Using class-based feature selection for the classification of hyperspectral data

The rapid advances in hyperspectral sensing technology have made it possible to collect remote-sensing data in hundreds of bands. However, the data-analysis methods that have been successfully applied to multispectral data are often limited in achieving satisfactory results for hyperspectral data. The major problem is the high dimensionality, which deteriorates the classification due to the Hughes Phenomenon. In order to avoid this problem, a large number of algorithms have been proposed, so far, for feature reduction. Based on the concept of multiple classifiers, we propose a new schema for the feature selection procedure. In this framework, instead of using feature selection for whole classes, we adopt feature selection for each class separately. Thus different subsets of features are selected at the first step. Once the feature subsets are selected, a Bayesian classifier is trained on each of these feature subsets. Finally, a combination mechanism is used to combine the outputs of these classifiers. Experiments are carried out on an Airborne Visible/Infrared Imaging Spectroradiometer (AVIRIS) data set. Encouraging results have been obtained in terms of classification accuracy, suggesting the effectiveness of the proposed algorithms.
A 40 /spl mu/A/channel compensated 18-channel strain gauge measurement system for stress monitoring in dental implants

This paper presents an ASIC of an autonomous monitoring system that is capable of measuring 18 strain gauges simultaneously. The sensor interface chip is implemented in a 0.7 /spl mu/m CMOS technology and includes a PTAT-current reference, an 8-bit DAC, together with a digital interface for multi-gauge nulling, an SC instrumentation amplifier, an SC S/H and a 9-bit successive approximation ADC. The accuracy is better than 20 /spl mu/strain, while consuming only 40 /spl mu/A/channel.
Issues in training SVM classifications

SVM classification has great potential in remote sensing. The nature of SVM classification also provides opportunities for accurate classification from relatively small training sets, especially if interest is focused on a single class. Five approaches to reducing training set size from that suggested by conventional heuristics are discussed: intelligent selection of the most informative training samples, selective class exclusion, acceptance of imprecise descriptions for spectrally distinct classes, the adoption of a one-class classifier and a focus on boundary regions. All five approaches were able to reduce the training set size required considerably below that suggested by conventional widely used heuristics without significant impact on the accuracy with which the class of interest was classified. For example, reductions in training set size of ~90% from that suggested by a conventional heuristic are reported with the accuracy of the class of interest remaining nearly constant at ~95% and ~97% from the user's and producer's perspectives respectively.
Impact of Fin Aspect Ratio on Short-Channel Control and Drivability of Multiple-Gate SOI MOSFET's

This paper puts forward an advanced consideration on the design of scaled multiple-gate FET (MuGFET); the aspect ratio (R h/w ) of the fin height (h) to fin width (w) of MuGFET is considered with the aid of 3-D device simulations. Since any change in the aspect ratio must consider the trade-off between drivability and short-channel effects, it is shown that optimization of the aspect ratio is essential in designing MuGFET’s. It is clearly seen that the triple-gate (TG) FET is superior to the conventional FinFET from the viewpoints of drivability and short-channel effects as was to be expected. It can be concluded that the guideline of w ＜ L/3, where L is the channel length, is essential to suppress the short-channel effects of TG-FET.