Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Resource Provisioning in Cloud based on Queuing Model

One of the main aim of cloud computing is to provide bigger data center that will carter the storage needs of end user. In a data centre, server clusters are used to provide the required processing capability to get acceptable response time for interactive applications. In this paper, an interactive system based on queuing model is presented in which the cloud customer (CC) initially establishes the session to access the resources. The proposed model uses banker’s algorithm for resource allocation due to which deadlock for resource allocation among various processes is not possible. Moreover, by putting restriction on number of login users, resources are not choked out even in case of heavy demand of resources. The concept of resource allocation matrix helps the cloud service provider to predict the resource requirements in advance. Since new sessions can be established and existing sessions can be terminated, the number of logon users can change over time. Resources are dynamically allocated according to the requirements of the user. The results obtained are accurate in terms of predicting the minimum number of processor nodes required to meet the performance goal of an interaction application.
Semantic classifier based on compressed sensing for image and video annotation

A new semantic classification approach for image and video annotation is proposed, which fits a semantic classification task into theory of a compressed sensing framework. The proposed approach first utilises training samples to create a dictionary matrix and then uses a matching pursuit algorithm to find the sparse vector. The final annotations are determined according to the reconstruction value from the positive samples and the sparse vector. A systematic performance study on TRECVID 2008 video dataset and Corel image dataset shows the proposed approach is more effective than the traditional support vector machine scheme.
Array geometries, signal type, and sampling conditions for the application of compressed sensing in MIMO radar

MIMO radar utilizes the transmission and reflection of multiple independent waveforms to construct an image approximating a target scene. Compressed sensing (CS) techniques such as total variation (TV) minimization and greedy algorithms can permit accurate reconstructions of the target scenes from undersampled data. The success of these CS techniques is largely dependent on the structure of the measurement matrix. A discretized inverse scattering model is used to examine the imaging problem, and in this context the measurement matrix consists of array parameters regarding the geometry of the transmitting and receiving arrays, signal type, and sampling rate. We derive some conditions on these parameters that guarantee the success of these CS reconstruction algorithms. The effect of scene sparsity on reconstruction accuracy is also addressed. Numerical simulations illustrate the success of reconstruction when the array and sampling conditions are satisfied, and we also illustrate erroneous reconstructions when the conditions are not satisfied.
Energy Efficient Protocols for Wireless Sensor Networks: A Survey and Approach

Wireless Sensor Networks have limited resources with traditional data gathering techniques. One of the limitations of wireless sensor nodes is its inherent limited energy resource. Therefore, designing an effective wireless sensor network to maximizing the lifetime of sensor node in order to minimize maintenance and maximize overall system performance becomes important. In this article, we have outlined the design factors and challenges in sensor networks. Then, we describe several MAC layer and routing layer protocols proposed for sensor networks. In this paper, we propose an adaptive approach to find an optimal routing path from source to sink when the sensor nodes are deployed randomly deployed in a restricted service area with single sink. Our analysis says our approach reduces the message communication to find a optimal routing path. Hence, the network consumes less energy and increases the lifetime of the network. General Terms Wireless Sensor Network, Communication Protocol.
Connected word talker verification using whole word hidden Markov models

A speaker verification system using connected word verification phrases has been implemented and studied. Verification utterances are represented as concatenated speaker-dependent whole-word hidden Markov models (HMMs). Verification phrases are specified as strings of words drawn from a small fixed vocabulary, such as the digits. Phrases can either be individualized or randomized for greater security. Training techniques to create speaker-dependent models for verification are used in which initial word models are created by bootstrapping from existing speaker-independent models. The system has been evaluated on a 20-speaker telephone database of connected digital utterances. Using approximately 66 s of connected digit training utterances per speaker, the verification equal-error rate is approximately 3.5% for 1.1 s test utterances and 0.3% for 4.4 s test utterances. In comparison, the performance of a template-based system using the same amount of training data is 6.7% and 1.5%, respectively. <<ETX>>
Approximate Packet Delay Analysis for IEEE 802.16 Networks

Supporting QoS is one of notable features in IEEE 802.16 networks. In this study, we used the queuing-theoretic approach to model the uplink air interface of IEEE 802.16 networks as M/Db/1 queues. The justiﬁcation for this modeling argument was given in the paper. The average queuing delay and system delay were derived with embedded Markov chain technique. We also pointed out that the created Markov chain exhibits geometric tail behavior so that the Markov chain can be solved more eﬃcient. The analytical results were veriﬁed through computer simulations. The derived results would be supporting the design of packet scheduling or admission control.
Some Conditions under Which Integrated Computer-Based Training Software Can Facilitate Learning

When students are required to learn a new computer application program, frequently they need to split their attention between material in a manual and material on the screen because neither is self-contained. Previous work has indicated that split-attention can interfere with learning because the need to mentally integrate material imposes an extraneous cognitive load. Alternatively, even if the screen-based material is self-contained, the material of a redundant manual, if processed by learners, can also impose an extraneous cognitive load. Under these circumstances, learning may be facilitated by the use of self-contained, screen-based material alone. In Experiment 1, the first author developed a windows spreadsheet, computer-based training package with an integrated format, to test these hypotheses. The split-attention effect was investigated by comparing the performance of a group of students using the integrated computer-based training software with that of a group using a conventional manual plus the computer software to be learned. The redundancy effect was investigated by comparing a group using the computer-based training software with a group using the same computer-based training software plus a hard-copy of this training software. Results from Experiment 1 supported the above cognitive load hypotheses. Specifically, in areas of high information complexity, the integrated computer-based training software group clearly outperformed both the conventional manual plus computer software group, and the computer-based training software plus hard-copy group, thus demonstrating both split-attention and redundancy effects. Experiment 2 partially replicated this result, as well as providing support for a cognitive load explanation of results by measuring mental load. In the light of these findings and previous research, it is suggested that cognitive load is a major factor in all aspects of instructional design. Computer training software that reduces extraneous cognitive load by adopting integrated computer-based training formats can considerably facilitate learning.
A novel modular product unit neural network for modelling constrained spatial interaction flows

A novel product unit neural network approach is presented for modelling origin constrained spatial interaction flows. We adopt the Alopex procedure, a global search procedure, in combination with the bootstrapping pairs approach to address the issue of maximum likelihood estimation of the parameters. A benchmark comparison illustrates the generalisation performance of the model approach in terms of Kullback and Leibler's information criterion.
Filtering method for 3D laser scanning point cloud

In recent years, with the rapid development of the hardware and software of the three-dimensional model acquisition, three-dimensional laser scanning technology is utilized in various aspects, especially in space exploration. The point cloud filter is very important before using the data. In the paper, considering both the processing quality and computing speed, an improved mean-shift point cloud filter method is proposed. Firstly, by analyze the relevance of the normal vector between the upcoming processing point and the near points, the iterative neighborhood of the mean-shift is selected dynamically, then the high frequency noise is constrained. Secondly, considering the normal vector of the processing point, the normal vector is updated. Finally, updated position is calculated for each point, then each point is moved in the normal vector according to the updated position. The experimental results show that the large features are retained, at the same time, the small sharp features are also existed for different size and shape of objects, so the target feature information is protected precisely. The computational complexity of the proposed method is not high, it can bring high precision results with fast speed, so it is very suitable for space application. It can also be utilized in civil, such as large object measurement, industrial measurement, car navigation etc. In the future, filter with the help of point strength will be further exploited.
A three-tier ICT access model for intention to participate online

The digital divide between countries is a critical phenomenon separating the countries that ‘have’ (mostly developed countries) from the countries that ‘have not’ (mostly developing countries). It places developing countries in a disadvantaged position economically, politically, and socially that in turns affects the life and social well-being of the citizens. Current understanding of the digital divide is limited by usage of secondary data, which prompts scholars to call for more theoretically-based empirical research. Since the key to reducing the digital divide is to increase people’s access to ICT, this study developed and empirically tested a 3-tier ICT access model to determine the effect on ICT development of intention to participate in online activities among citizens in developed and developing countries. The model was founded on three existing research frameworks. In addition, this study theoretically clarified the relationship between four types of ICT access and how they contribute to intention to participate online. Survey data of university students from Korea and Cambodia was collected. The results confirm the effect of ICT development on citizens’ intention and isolate key factors that impact the two countries differently. Technological access was found to impact extrinsic motivational access and skills access. Social access influenced extrinsic and intrinsic motivational access and skills access. Implications for research and practice are discussed.
Automatic human detecting and tracking using stereo vision technique

A fully automatic system for human detection and tracking in front of an Interactive Whiteboard is presented. When a person is between a projector and the projection area, deleterious effects can be created from light shining on the face. We developed a stereo vision system that can be used to mitigate problems arising from this issue by accurately detecting the human body and masking the face. We present two main parts of this system: namely, automatic system calibration and the human detection and tracking. We use a checkerboard pattern that is projected on the whiteboard at start-up for automatic calibration. Grid patterns from two images are processed, and points between them are detected and localized. A projective transform is used to set the homography between the two images. Testing shows precise automatic calibration, with an average RMS error of 0.4 pixels in the off-line test. Human detection and tracking is accomplished using a similarity measure, foreground segmentation, principle component analysis, body shape feature extraction, disparity measure, and location estimation. We achieved an average detection rate of 97.7 % in the off-line tests. The method was fully implemented in a real-time system and testing showed the system to be very robust.
An Evaluation of Nonresponse and Coverage Errors in a Prerecruited Probability Web Panel Survey

This study examines nonresponse and coverage errors separately in a probability Web panel survey by applying traditional postsurvey adjustments. This was done by using variables whose estimates were obtainable at both the survey respondent and the full survey sample levels and whose values were known for both the full survey sample and the target population. Nonresponse error measured by the differences between the estimates from the respondents and the known full sample values was not found to be large, implying that nonresponse error in this Web survey data may not be critical. However, coverage properties of the full survey sample show some problems, and traditional postsurvey adjustments were limited in alleviating the unequal coverage of the survey sample. This coverage problem was more evident for the subpopulation-level estimates.
Fiber optic displacement sensor for honey purity detection in distilled water

The aim of the symposium is to provide an interdisciplinary forum for scientist engaged in the full spectrum of research and development (R&D) activities. The meeting intends to bring together researchers, scientists, and scholars to exchange and share their experiences, new ideas, and research results in related fields and discuss the practical challenges encountered and the solutions adopted.
Low-power high-performance 2D transform coprocessor for H.264 video compression standard

This paper presents a VLSI architecture and an efficient implementation of an embedded transform coprocessor for H.264 video compression standard. The proposed coprocessor was designed to work with an ARM946E-S processor. To enhance the performance, both data parallelism and pipelined architecture are utilized in the design. In this study, coprocessor was synthesized with 0.18 μm CMOS technology and its footprint is only 0.0838 mm2. Coprocessor can calculate 2-D transform for a macroblock in 30 clock cycles. The 2-D transform coprocessor dissipates 529 μW with 1.55-volt power supply at 10 MHz clock rate.
Theory in human-computer interaction?

There is no (formal) theory of HCl-there is rather only an HCl theory vacuum. However, the requirements for a sensible understanding of the question are becoming clearer. There is no sensible understanding of theory in HCl outside an understanding of: HCl as a design discipline, the ontology of HCl, as including humans and computers, tasks and effectiveness, the conception of HCl as an expression of the general design problem which HCl theory is intended to solve, and HCl theory (and practice) differentiation. Within this understanding of HCl, which encompasses discipline, ontology, conception and epistemology, two types of progress concerning theory in HCl are described. The first concerns formal HCl theory and is currently associated with formalising, formulating and operationalising the general HCl design problem. The second concerns informal HCl theory and is currently associated with models and guidelines, and methods and tools.
A fuzzy logic-based gain tuner for PID controllers

A fuzzy logic-based gain tuner for tuning gains of PID controllers is proposed. The tuning is completed by the fine tune following the rough tune of gains under the stabilized operation established by the Lyapunov function-based stabilizer. Simulation results on a speed controller of induction motors are presented to show the effectiveness of the proposed gain tuner.
The past is important: a method for determining memory structure in NARX neural networks

Recurrent networks have become popular models for system identification and time series prediction. NARX (nonlinear autoregressive models with exogenous inputs) network models are a popular subclass of recurrent networks and have been used in many applications. Though embedded memory can be found in all recurrent network models, it is particularly prominent in NARX models. We show that the use of intelligent memory order selection through pruning and good initial heuristics significantly improves the generalization and predictive performance of these nonlinear systems on problems as diverse as grammatical inference and time series prediction.
Adaptive semi-soft handoff for Cellular IP networks

Various advances in technologies have led to a myriad of wireless mobile devices to ubiquitously connect to the internet. This creates a significant demand for seamless wireless network connectivity. The Cellular IP semi-soft handoff protocol has been proposed to satisfy this demand. Studies have shown that it performs better than the conventional hard handoff protocol. However, these studies are based on unrealistic assumptions of symmetrical network topologies and fixed loads. In practice, network topology can be asymmetric and network load fluctuates. Semi-soft handoff uses fixed handoff parameters for stream synchronization and mobile host's tune-in time, which may work well for the symmetrical network setups, but performs poorly with unbalanced and dynamically changing networks. This paper proposes adaptive semi-soft handoff protocol (Adaptive-SS) to address this issue by dynamically changing handoff parameters based on network conditions during handoff. Our simulation study shows that Adaptive-SS significantly reduces network traffic and packet losses/duplications during handoff, while still minimising handoff latency.
Welding quality monitoring and management system based on data mining technology

In allusion to automatic arc welding production, this paper focuses on the establishment of on-line quality monitoring system based on data mining (DB) technology. The system is set up with client/server architecture. In the server, Shewhart control chart and decision tree are applied to monitor the production and supply decision-making based on space series DB; whilst Shewhart control chart and multivariate statistical process control (MSPC) are applied to give quality indication of welding process based on time series DB. The system realizes the on-line quality monitoring for each produced parts as well as the quality management for the whole workshop production.
Lossy encoding of document images with the continuous skeleton

We examine a new approach to document image compression based on the continuous skeleton shape representation. In particular, we exploit the structure-extracting property of the skeleton transformation to devise a code which allows graceful degradation of reproduction quality in exchange for dramatic increases in compression. At the source, the contours which make up the skeleton-based shape description are approximated by B-spline functions and then quantized and encoded. At the receiver, the skeleton contours are reconstructed and then used to regenerate the document. We apply the method to a standard ensemble of facsimile documents and compare the compression results with conventional lossless methods. The result is that significant compression gains can be achieved while incurring nearly imperceptible loss in the reproduction. This approach is motivated by the principle that the proper level at which to encode a document is at the object or component level, rather than at the pixel level. Approximations made to the descriptions of these objects are much less noticeable than comparable approximations made to the raw pixel data. Thus, adopting this object-centered viewpoint toward document compression allows us to introduce errors progressively, and often unobtrusively.
Structure of a scene from two and three projections

A study of 3-D vision is presented where real distances among some points of a scene are evaluated. To this end, two or three images taken at unknown positions are considered, and the "eight point algorithm" is applied. An experimental way to test the accuracy of estimations is considered, and an extension of a linear method to three frames is discussed. Simulations and actual experiments have been carried out and results point out the validity of the three view approach.
Requirements and techniques for an automated change detection system

To enhance the ability of remote sensing system to provide accurate, timely, and complete geospatial information at regional and/or global scale, an automated change detection system has been and will continue to be one of the important yet challenging problems in remote sensing. This research was designed to evaluate the requirements and prototype the algorithms for an automated change detection system at landscape level using various geospatial data sources including multisensor remotely sensed imagery and ancillary data layers. In this paper, the requirements for an operational change information extraction system and its associated techniques are discussed. These techniques are included in three subsystems: automated computer image understanding, multisource data fusion, and database updating and visualization.
Explicit Instruction in Decoding Benefits Children High in Phonemic Awareness and Alphabet Knowledge

This article reports a study of 32 children who had been trained to a high level in phonemic awareness and alphabet knowledge over a 12-week period at preschool. During their first 6 weeks in kindergarten, these phonemically aware children were taught to read 10 real words, using either decoding and encoding techniques or a whole word method. At posttesting the children taught by the decoding and encoding method were superior in reading and writing both novel real words and pseudowords, compared to the whole word group. The results support the contention that explicit instruction in decoding is helpful even when children have high levels of phonemic awareness.