Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of image sharpness on quantitative eye movement data and on image quality evaluation while viewing natural images

The aim of the study is to test both customer image quality rating (subjective image quality) and physical measurement of user behavior (eye movements tracking) to find customer satisfaction differences in imaging technologies. Methodological aim is to find out whether eye movements could be quantitatively used in image quality preference studies. In general, we want to map objective or physically measurable image quality to subjective evaluations and eye movement data. We conducted a series of image quality tests, in which the test subjects evaluated image quality while we recorded their eye movements. Results show that eye movement parameters consistently change according to the instructions given to the user, and according to physical image quality, e.g. saccade duration increased with increasing blur. Results indicate that eye movement tracking could be used to differentiate image quality evaluation strategies that the users have. Results also show that eye movements would help mapping between technological and subjective image quality. Furthermore, these results give some empirical emphasis to top-down perception processes in image quality perception and evaluation by showing differences between perceptual processes in situations when cognitive task varies.
Data mining in the e‐learning domain

Higher education (HE) is becoming a big business, with huge investments in IT technology supporting online learning. With the awareness of the knowledge economy has come a growing consciousness that HE constitutes a large industry or economic sector in its own right. In a marketing fashion, we understand that some customers present much greater profit potential than others. But, how will we find those high‐potential customers in a database that contains hundreds of data items for each of millions of customers? Data mining software can help find the “high‐profit” gems buried in mountains of information. However, merely identifying the best prospects is not enough to improve customer value. One must somehow fit the data mining results into the execution of the content management system that enhances the profitability of customer relationships. However, data mining is not yet engaged into e‐learning systems. This paper describes how we can profit from the integration of data mining and the e‐learning technology.
Scene change detection and content-based sampling of video sequences

Digital images and image sequences (video) are a significant component of multimedia information systems, and by far the most demanding in terms of storage and transmission requirements. Content-based temporal sampling of video frames is proposed as an efficient method for representing the visual information contained in the video sequence by using only a small subset of the video frames. This involves the identification and retention of frames at which the contents of the scene is `significantly' different from the previously retained frames. It is argued that the criteria used to measure the significance of a change in the contents of the video frames are subjective, and performing the task of content-based sampling of image sequences, in general, requires a high level of image understanding. However, a significant subset of the points at which the contextual information in the video frames change significantly can be detected by a `scene change detection' method. The definition of a scene change is generalized to include not only the abrupt transitions between shots, but also gradual transitions between shots resulting from video editing modes, and inter-shot changes induced by camera operations. A method for detecting abrupt and gradual scene changes is discussed. The criteria for detecting camera-induced scene changes from camera operations are proposed. Scene matching is proposed as a means of achieving further reductions in the storage and transmission requirements.
An inner-enterprise wiki system integrated with semantic search for reuse of lesson-learned knowledge in product design

In the process of product design, it is usually difficult for engineers to find and reuse others’ lesson-learned knowledge, which is usually not well collected by an enterprise. This study proposes a model of an inner-enterprise wiki system integrated with a semantic search framework to support the accumulation and reuse of lesson-learned knowledge in product design. The accumulated wiki web pages in inner-enterprise wiki system can be viewed as a large lesson-learned knowledge repository, which stores engineers’ wisdom of product design and is the basis of semantic search for knowledge reuse. The semantic-based knowledge search mechanism of inner-enterprise wiki system is presented, which can overcome limitations of traditional keyword-based search in existing wiki systems. To validate our approach, an inner-enterprise wiki system implementation is introduced and some experiments are done. Effectiveness of semantic search mechanism in inner-enterprise wiki system is verified from several viewpoints such as comparison of precision and recall between proposed method and a keyword-based one. The performance of utilizing inner-enterprise wiki system for knowledge reuse is evaluated and the results show that inner-enterprise wiki system can improve the performance of learning. The promising results confirm the feasibility of our approach in helping engineers to better reuse needed lesson-learned knowledge.
Addressing issues in bioprocess monitoring and control

In order to improve the monitoring and control of an industrial fermentation process plant a fault detection and advisory system was developed. The need to utilise both algorithmic and rule based fault detection methods is discussed. Following this, the implementation strategy based on the use of G2 from Gensym is outlined. The KAT knowledge elicitation method was used to capture efficiently the fault detection rule set at the seed stage of the process. Examples of the method of elicitation and details of process performance estimation using algorithmic methods are given. Finally, the integration with multivariate data-based methods for fault detection and process application of the combined system is described.
Dithering with blue noise

Digital halftoning, also referred to as spatial dithering is a method of rendering the illusion of continuous-tone pictures on displays that are capable of only producing binary picture elements. The concept of blue noise-high-frequency white noise-is introduced and found to have desirable properties for halftoning. Efficient algorithms for dithering with blue noise, based on perturbed error diffusion, are developed. The nature of dither patterns produced is extensively examined in the frequency domain. Metrics for analyzing the frequency content of aperiodic patterns for both rectangular and hexagonal grids are developed; blue-noise dithering is found to be ideally suited for rectangular grids. Several carefully selected digitally produced examples are included. >
Development of Selection Function for the Application in Cooperative Multiple Input Single Output at Energy Aware Wireless Sensor Network

Abstract Energy efficient data transfer is the key factor for the design of energy efficient wireless sensor network (WSN). IEEE 1451.5 standard explores the wireless communication schemes between multiple sensors and a data gathering node (DGN) emphasizing the multiple input single output (MISO) structure. Sensors participating in this MISO can be chosen selectively for more energy efficient data transfer and the selection is controlled by several parameters, which are not completely explored yet. In this paper, an energy efficient cooperative technique is proposed for a WSN where selected numbers of sensors are used to form a MISO structure wirelessly connected with a DGN. The selection of nodes is based on a selection function, which is a combination of channel gain, residual energy, inter-sensor distance in a cluster and geographical location. Data are sent by the sensors to a DGN using a multi-hop transmission. We are concentrating our design on the final hop where sensors transmit their data to the DGN. A mathematical model is developed to get the selection function.
Research on Constructing a Multi-Label Image Annotation and Retrieval System

In this paper, we design and construct a multi-label image annotation and retrieval system. Various MPEG-7 low level visual features are employed for representing images. For image annotation, a bi-coded genetic algorithm is employed to select optimal feature subsets and corresponding optimal weights for every one vs. one SVM classifiers. After an unlabelled image is segmented into several regions, pre-trained SVMs are used to annotate each region, final label is obtained by merging all the region labels. Based on multi-label of image, image retrieval system provides keyword-based image retrieval service. Multi-labels can provide abundant descriptions for image content in semantic level, high precision annotation algorithm further improve annotation performance.
Safe Collaboration in Extensible Operating Systems: A Study on Real-Time Extensions

Abstract This article proposes a solution to guarantee safe interaction to components that are willing to collaborate in an extensible operating system, dedicated to small embedded systems such as smart cards, that guarantees isolation. We propose a simple way to verify the behaviour of some components using an extension of the type system by adding information about argument-passing modes to the method signatures (e.g., is an argument read or written? ). We present a formalization of a PCC-like algorithm (off-card proof generator and on-card proof verifier) to statically check the passing modes of the components in the CAMILLE exokernel for smart cards. We apply our technique to ensure trust between collaborative real-time extensions with the aim of supporting safe dynamic loading of scheduling policy.
Performance Analysis of Path Relinking on Many-objective NK-Landscapes

Path relinking is a population-based heuristic that explores the trajectories in decision space between two elite solutions. It has been successfully used as a key component of several multi-objective optimizers, especially for solving bi-objective problems. Its unique characteristic of performing the search in the objective and decision spaces makes it interesting to study its behavior in many objective optimization. In this paper, we focus on the behavior of pure path relinking, propose several variants of the path relinking that vary on their strategies of selecting solutions, and analyze its performance using several many-objective NK-landscapes as instances. In general, results of the study show that the path relinking becomes more effective in improving the convergence of the algorithm as we increase the number of objectives. Also, it is shown that the selection strategy associated to path relinking plays an important role to emphasize either convergence or spread of the algorithm. This study provides useful insights for practitioners on how to exploit path relinking to enhance multi-objective evolutionary algorithms for complex combinatorial optimization problems.
Low-cost residue codes and their application to self-checking VLSI systems

The efficient application of error-detecting codes to VLSI designs requires a study of their cost and effectiveness to supplement the knowledge of their theoretical properties. The paper discusses the tradeoffs for a low-cost residue code applied to common types of VLSI hardware. Comparisons are made between the kinds of residue codes available and their suitability for use in VLSI designs. The code is then applied to a typical VLSI system for comparison with other test strategies.
Task Scheduling Algorithm Based on Improved Min-Min Algorithm in Cloud Computing Environment

Task schedule algorithms directly related to the speed and quality of schedule. Min-Min algorithm always completes the shortest total completion time task first, and has the characteristic of simple and shortest completion time. This paper research scheduling algorithm based on Min—Min algorithm. The result shows that the proposed algorithm is efficient in the cloud computing environment.
Multi-stage supply chain scheduling with non-preemptive continuous operations and execution control

An integrated multi-stage scheduling and routing problem with alternative machines at each supply chain (SC) stage and non-preemptive operations is studied. The peculiarity of the considered problem is the consideration of both continuous flows in operations and discrete assignments. Such problems are typical in the process industry. Taking into account the basic aspects that pertain to this case, a dynamic decomposition of the problem on the basis of optimal programme control is proposed. A particular feature and novelty of the presented study is the consideration of non-preemptive operations and their execution dynamics. The solution is based on a combined application of discrete and continuous optimization. With the special properties of the proposed scheduling model, it becomes possible to develop an optimal solution to the integrated scheduling and routing of jobs. Since the schedule generation is formulated in control terms, the models of scheduling and adaptation can be integrated. From the practical point of view, such integration can be advantageous subject to further dynamic analysis of the robustness, stability, and adaptability of SC schedules.
Keyword Recognition Based on MFCC

After about 50 years of development, speech recognition technology has been able to achieve large vocabulary, non-specific human continuous speech recognition system. On account of Chinese pronunciation features, we research the small vocabulary, non-specific Chinese speech recognition based on continuous Hidden Markov Model approach. With comparing the datasets of VQ/DTW, VQ/DHMM, CHMM state-1 recognition algorithm and CHMM state-2 recognition algorithm, the results of our experiment show that: (1) CHMM state-2 branch method performs primely in reduction of the recognition time; and (2) the recognition accuracy is improved eventually.
Multi-level approaches to low power 16-bit ALU design

A low power 16-b ALU has been designed at the transistor level. The designed ALU performs 16 instructions and has a two-stage pipelined architecture. For low power consumption we propose a new ALU architecture which has efficient propagation (P) and generation (G) block schemes of ELM adder. The adder of the proposed ALU is disabled while the logic operation is performed and vice versa. Outputs of each P block are separated to the dual output bus to reduce switching capacitance during the ALU operation. The proposed ALU was simulated with O.6/spl mu/m single-poly triple-metal CMOS process. As a result of post-layout simulations, addition time of the core is about 5ns with 3. 3 V supply voltage and the average power consumption of the core was 54 mW at 200 MHz.
DDC Design for Multi-Frequency Receiver Based on RF Sampling

Multi-frequency receivers rely on GNSS to achieve high-precision positioning, high sensitivity and other functions. GNSS signal frequencies are widely distributed around 1.1GHz to 1.6GHz. Good performance RF front-end receiver will provide high-quality acquisition and tracking environment, reducing the SER to ensure CNR. RF sampling technique is widely used in software radio for its simple structure, flexible configuration, and other advantages, but rarely used in satellite navigation, because the high signal carrier frequency and sampling rate, down-conversion based on RF sampling becomes a big challenge. So, to solve the problems of widely distributed we design three signal bands, using different local oscillator to implement signal down-conversion; to solve the problems of high sampling rate, we propose a down-conversion program of using a cascade of CIC filter’s ployphase and FIR compensation filter. Through the simulation of MATLAB after tracking tests, analysis of CNR and other indicators of programs. Ultimately, we implement the program with Verilog language on the multi-frequency GNSS receiver. The results show that this scheme can achieve digital down-conversion and stable performance of tracking loop with less consumption of resources.
A High Resolution, High Speed 3-D Laser Line Scan Camera For Inspection And Measurement

Many techniques for 3-D data acquisition exist and have been reviewed in the literature. Figures of merit have been published based upon key parameters. This paper first reviews the performance of high speed imagers. Then the performance of a high speed, high resolution 3-D laser scanning sensor developed at SVS will be examined. Results will be shown which illustrate the applicability of this imaging sensor for inspection of electronic components, solder paste, machined parts, etc.
The Rapid Development of User Interfaces: Experience with the Wizard of OZ Method

The Wizard of Oz technique is an efficient way to examine user interaction with computers and facilitate rapid iterative development of dialog wording and logic. The technique requires two machines linked together, one for the subject and one for the experimenter. In this implementation the experimenter (the “Wizard”), pretending to be a computer, types in complete replies to user queries or presses function keys to which common messages have been assigned (e.g., Fl=“Help is not available”). The software automatically records the dialog and its timing. This paper provides a detailed description of the first implementation of the Oz paradigm for the IBM Personal Computer. It also includes application guidelines, information which is currently missing from the literature.
Support for persistent objects: two architectures

Describes the architecture of the IBM RISC System/6000 and the IBM AS/400 from the standpoint of their suitability for implementing persistent object systems. Both systems provide facilities in their hardware, microcode and operating system that, carefully used, can simplify the implementation of persistent object systems and improve their performance. The facilities provided are not, however, perfectly matched to the requirements of persistent object systems. The authors discuss design trade-offs and identify recommended extensions. <<ETX>>
Study on Effective Classifier for Software Engineering Data Sets

A wide range of important software engineering problems need solutions that involve accurately predicting outcomes, such as the number of defects in a module, the estimated cost, or deciding the best software development process to use. Mathematically a classifier is a function that maps an N-dimensional attribute space to a discrete set of labels of the class variable. We proposed an effective classifier for software engineering data sets, the results show that the accurate predictions can have an enormous positive impact on reducing these problems and ensure projects’ successes.
New proxy replacement algorithm for multimedia streaming

Proxy servers play an important role in between servers and clients in various multimedia systems on the Internet. Since proxy servers do not have an infinite-capacity cache for keeping all the continuous media data, the challenge for the replacement policy is to determine which streams should be cached or removed from the proxy server. In this paper, a new proxy replacement algorithm, named the Least Popular Used (LPU) caching algorithm, is proposed for layered encoded multimedia streams in the Internet. The LPU method takes both the short-term and long-term popularity of the video into account in determining the replacement policy. Simulation evaluation shows that our proposed scheme achieves better results than some existing methods in term of the cache efficiency and replacement frequency under both static and dynamic access environments.
Sub-1-V CMOS proportional-to-absolute temperature references

Presents a new all-MOS circuit technique for very-low-voltage proportional-to-absolute temperature (PTAT) references. Optimization of supply scaling below the sum of threshold voltages is based on log companding and implemented by operating the MOSFET in weak inversion. The key design equations for current (/spl mu/A) and voltage (sub-100 mV) references and their standard deviations (around 5%) are derived by analytical analysis. Two sub-1-V sub-5-/spl mu/W integrated PTAT references are presented and exhaustively tested for 1.2- and 0.35-/spl mu/m very large scale integration technologies. Both designs report good agreement between analytical, simulated, and experimental data, exhibiting PSRR(DC)+>60 dB. Hence, the resulting PTAT circuits are suitable for very-low-voltage system-on-a-chip applications in digital CMOS technologies.
New insights into correlation-based template matching

Correction-based template matching has been used extensively in computer vision for object recognition and also for other tasks such as edge detection, stereo, motion and inspection. It has also found wide application in character recognition. A deeper understanding of the performance of this technique for such tasks would help predict when it will succeed or fail. Previous work on this problem has examined correlation-based template matching using signal processing techniques. Our approach is different: we dissect it employing concepts from geometry and physics. This leads to new insights into correlation-based template matching. We study the performance of correlation between images for different lighting conditions, viewpoints and scales of a scene, obtaining new results for scale variation and viewpoint change for binary images. We analyze gray level images for changes in lighting alone and obtain useful and novel formulae. Knowing how correlation behaves with these changes helps to strategically distribute templates for a given recognition task. We then develop a method to compute the probability of confusion for recognition by template matching. We obtain a closed form solution for the probability of confusion in the two template case. We conclude by noting that template matching encounters difficulties in tasks such as object recognition because of its strong dependence on viewing conditions, although it can be useful in some situations when templates are chosen and positioned judiciously.