Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of perceived image sharpness with changes in the displayed image size

In this paper an evaluation of the degree of change in the perceived image sharpness with changes in displayed image size was carried out. This was achieved by collecting data from three psychophysical investigations that used techniques to match the perceived sharpness of displayed images of three different sizes. The paper first describes a method employed to create a series of frequency domain filters for sharpening and blurring. The filters were designed to achieve one just-noticeable-difference (JND) in quality between images viewed from a certain distance and having a certain displayed image size (and thus angle of subtense). During psychophysical experiments, the filtered images were used as a test series for sharpness matching. For the capture of test-images, a digital SLR camera with a quality zoom lens was used for recording natural scenes with varying scene content, under various illumination conditions. For the psychophysical investigation, a total of sixty-four original test-images were selected and resized, using bi-cubic interpolation, to three different image sizes, representing typical displayed sizes. Results showed that the degree of change in sharpness between images of different sizes varied with scene content.
Deployment and Performance Evaluation of Virtual Network based on OpenStack

In the context of the ever-developing virtual network, OpenStack officially launches its new network componentQuantum, but the comparative performance between singlehost and multi-host deployment is yet unknown. The paper introduces detailed deployment strategies of single-host and multi-host after analyzing Quantum work mechanism and the construction polices of virtual networks. And then, we probe into the influence of various deployments on the reliability of network service, and conduct connectivity and performance tests on different deployment plans. Experiment results show that our virtual network multi-host deployment plan improves virtual network service reliability and efficiency compared with single-node node. Keywords-Quantum; Single-host; Multi-host; Agent Scheduling
Regioselective Lithiation of Resorcinol Derivatives: Synthesis of Mono O-MOM- and O-Benzylresorcinols Prenylated at C-2 or C-4 Positions

: Resorcinol derivatives 3-benzyloxy-1-methoxymeth-oxybenzene ( 3b) and 4-benzyloxy-1-bromo-2-methoxymethoxy-benzene ( 3c) were regioselectively lithiated. The resulting aryllith-ium intermediates or their cuprate derivatives were reacted with allyl bromide ( 4a ), prenyl bromide ( 4b ) and ( E )-4-benzyl-1-bromo- 3-methylbut-2-ene ( 4c ), leading to C-2 and C-4 substituted resorci-nols 6 and 7 , respectively. The use of 2-methyl-2-vinyloxirane ( 5 ) as electrophile was also studied as another means of introduction of an oxygenated prenyl side chain on 3b or 3c . Prenylated compounds 6 and 7 could be transformed into the corresponding mono O -pro- tected resorcinols 8 , 9 and 10 by selective removal of either the benzyl or MOM group
Nanotechnology enables a new memory growth model

As we enter the nanotechnology era, a big shift in paradigm comes to the memory industry. The traditional computer industry for dynamic RAM is expected to mature its memory-bit consumption with a relatively low growth rate. Meanwhile, the memory consumption and high-density memory usage in mobile handsets and digital consumer applications will grow very fast. For these new applications, NAND Flash memory will be the key enabling technology and its easy scaling and multibit/cell capabilities require a new memory growth model. The well-known Moore's law still holds for most cases after the quarter-century history of the integrated circuit industry. However, the paradigm shift in the memory industry requires a new memory growth model: "a twofold increase per year in memory density. " This paper will cover some details of recent memory technologies, application trends, and the proposed new memory growth model.
Visual metaphors: 3D interfaces for knowledge workers

Between 1984 and 1987 a team of researchers in the Human Interface Program at the Microelectronics and Computer Technology Corporation (MCC) investigated innovative ways of building and using 3D user interfaces and graphic realism to support knowledge workers. Their research included knowledge-based applications of 3D user interfaces such as SemNet and was principally focused on a user interface development environment which they called the Metaphor Builder. This suite of tools included a representation scheme called GrafBag, a 3D editor called Visage, and a model editor called GrabGraf. The authors validated these tools by building the Visual Office, a 3D environment for managing work contexts. This research was accomplished in an advanced development environment created by integrating a Lisp machine with a 3D display engine. The authors describe how the Visual Office was designed and implemented using components of the Metaphor Builder. <<ETX>>
Hybrid Search over Korean Web Contents

Keyword search suffers from some limits: ambiguous keywords, synonymous words, and an inability to recognize semantic constraints. Semantic search helps resolve these issues but is limited by the quality of annotations which are likely to be incomplete or imprecise. Hybrid search, a search technique capitalizing the merits of both keyword and semantic search, appears to be a promising solution. In this paper we describe Hybrid search over Korean web contents by using HyKSS, a hybrid search system driven by extraction ontologies for both annotation creation and query interpretation.
A generic architecture for the development of intelligent programming tutors

Novice programmers experience a number of learning difficulties in grasping programming concepts. Research shows that individualised tutoring is the only means of assisting learners in overcoming these problems. However, both time and economic constraints make this infeasible. Intelligent tutoring systems have provided a solution to this problem. Studies conducted reveal that students using intelligent programming tutors were able to overcome their difficulties in a shorter time span compared to those students who did not use an intelligent programming tutor. In spite of this, the intelligent programming tutors developed thus far have certain limitations. Furthermore, the development costs, especially the time taken in designing and implementing such systems, are very high. There appears to be a need for a generic framework or architecture for the development of intelligent programming tutors. The main aim of this paper is to propose such an architecture. The paper firstly provides a brief account of the intelligent programming tutors developed thus far. A critical analysis of these intelligent programming tutors is then provided. Based on this analysis, a generic architecture for the development of intelligent programming tutors to teach the object-oriented and procedural programming paradigm is proposed. The paper describes the overall architecture, each module comprising the architecture, and the different artificial intelligence techniques used to represent information in each module. Finally, the paper examines future research that will be conducted in order to extend this architecture.
Convolutional Kernel Networks for Action Recognition in Videos

While convolutional neural networks (CNNs) have taken the lead for many learning tasks, action recognition in videos has yet to see this jump in performance. Many teams are working on the issue but so far there is no definitive answer how to make CNNs work well with video data. Recently, introduced convolutional kernel networks, a special case of CNNs which can be trained layer by layer in an unsupervised manner. This is done by approximating a kernel function in every layer with finite-dimensional descriptors. In this work we show the application of the CKN training to video, discuss the adjustments necessary and the influence of the type of data presented to the networks as well as the number of filters used.
Synthesis of Catalytic Materials on Multiple Length Scales: from Mesoporous to Macroporous Bulk Mixed Metal Oxides for Selective Oxidation of Hydrocarbons

A short review is provided of the principles guiding the synthesis of mesoporous and macroporous bulk mixed metal oxides on multiple length scales in the presence of surfactant mesophases and colloidal arrays of monodisperse spheres, respectively. Emerging catalytic applications of these novel bulk metal oxide phases in selective oxidation of hydrocarbons are described and discussed.
Bucket-brigade electronics: new possibilities for delay, time-axis conversion, and scanning

Deals with integrated analog shift register circuits. Analog information is stored in arrays or mosaics of very small capacitors and can be made to travel through the arrangement by charge transfer. The charge transfer is carried out by active elements located between the storage elements and controlled by a clock signal. The circuit presented is simple, feasible for integration, and suitable for various functions. Delay, time-axis conversion, and scanning are the most obvious applications. This first report is restricted to a description of basic principles, examples of integrated concepts, and a brief discussion of fields of application.
Chinese Text Classification Based On LDA and KSVM

With the rapid development of information technology and social networking, the amount of generated text data has increased enormously. As one of the crucial technologies for information organization and management, text classification has become much more significant in the area of machine learning and natural language processing. According to this paper, we present a text classification system. First, we apply LDA topic model to express the text instead of Boolean model or vector space model. Then, we choose KSVM which combines SVM with KNN as the classification algorithm. Finally, we choose documents with large amount of Chinese news for experiments. Compared with normal language models, these experimental data shows that our system gets higher classification accuracy.
An Agent-Centered Multi-Formalism Modeling Framework for SoS Behavior

The system of systems (SoS) possesses essential characteristics of autonomy, evolvement and emergency, which can only be studied from the aspect of SoS behavior. Modeling and Simulation (M&S) can cope with SoS complexity and study its behavior more efficiently at lower costs. Considering the multi-subsystem and multi-domain characteristic of SoS, this paper proposes an agent-centered multi-formalism modeling framework for SoS behavior which combines different modeling formalisms to describe different aspects of SoS behavior. The framework comprises two parts: cognitive behavior which is modeled using agent based modeling, and physical behavior which is modeled using classic modeling formalisms. Combing cognitive and physical behavior modeling, this framework supports simulation modeling of SoS behavior, and thus enables early verification & validation, automatic generation of SoS behavior simulation models, and human (SoS entity)-in-the- loop simulation of SoS behavior at variable resolution levels.
Extraction of System-Wide Sybil-Resistant Trust Value embedded in Online Social Network Graph

ABSTRACT Anonymity is the one of main reasons for substantial improvement of Internet. It encourages various users to express their opinion freely and helps Internet based distributed systems vitalize. But, anonymity can cause unexpected threats because personal information of an online user is hidden. Especially, distributed systems are threatened by Sybil attack, where one malicious user creates and manages multiple fake online identities. To prevent Sybil attack, the traditional solutions include increasing the complexity of identity generation and mapping online identities to real-world identities. But, even though the high complexity of identity generation increases the generation cost of Sybil identities, eventually they are generated and there is no further way to suppress their activity. Also, the mapping between online identities and real identities may cause high possibility of losing anonymity. Recently, some methods using online social network to prevent Sybil attack are researched. In this paper, a new method is proposed for extracting a user’s system-wide Sybil-resistant trust value by using the properties embedded in online social network graphs. The proposed method can be categorized into 3 types based on sampling and decision strategies. By using graphs sampled from Facebook, the performance of the 3 types of the proposed method is evaluated. Moreover, the impact of Sybil attack on nodes with different characteristics is evaluated in order to understand the behavior of Sybil attack.Keywords:Sybil Attack, Sybil-Resistant Trust Value, Fast Mixing Graph, Online Social Network
Assessment of Learners' Attention to E-Learning by Monitoring Facial Expressions for Computer Network Courses

Recognition of students' facial expressions can be used to understand their level of attention. In a traditional classroom setting, teachers guide the classes and continuously monitor and engage the students to evaluate their understanding and progress. Given the current popularity of e-learning environments, it has become important to assess the degree of attention during the online learning process. In this study, we used interactive video-capture facial-recognition technology to automatically detect the facial expressions of students as a means of analyzing their attention state during the e-learning process. Participants were divided into three different learning-strategy groups for a course on computer networks. An attention-detection feedback module evaluated participants' attention span during the learning sessions and initiated a response to redirect the participants' attention when they became distracted. The three groups of participants showed significant differences in their course achievement; this was attributed to the different learning strategies used for content presentation. A positive correlation was found between learning improvement and attention, indicating that video-capture facial-recognition technology can be used to provide timely learning assistance and appropriate stimulation to enhance the educational benefits of e-learning.
Collaborative Semantic Annotation of Images : Ontology-Based Model

can interpret it differently. The purpose of this paper is to propose a collaborative annotation system that brings out the meaning of images from the different interpretations of annotators. The different works carried out in this paper lead to a semantic model of an image, i.e. the different means that a picture may have. This method relies on the different tools of the Semantic Web, especially ontologies.
Cloud Computing—New Web-based Training Mode for Art and Design Teachers in College

With the development of the technology of cloud computing, its application in education field has become a hot issue. This paper discuses the building of cloud computing training mode for art and design teachers, and analyzes the urgency to train teachers as well as the advantages of cloud computing in web-based training mode. A new web-based training mode based on cloud computing is proposed for art and design teachers, which will play a valuable role in related research.
Color- and texture-based image segmentation using local feature analysis approach

Image segmentation is one of the most challenging problems in image processing. While significant progress has been made in gray-scale texture segmentation and color segmentation problem separately, the combined color and texture segmentation problem is less considered. In this paper, we use independent component analysis to extract local color and texture features for segmentation. Experiments compared with gray-scale texture analysis method show that the proposed method is more effective in segmenting complex color and texture images.
Use of heuristics: Insights from forecasting research

Tversky and Kahneman (1974) originally discussed three main heuristics: availability, representativeness, and anchoring-and-adjustment. Research on judgemental forecasting suggests that the type of information on which forecasts are based is the primary factor determining the type of heuristic that people use to make their predictions. Specifically, availability is used when forecasts are based on information held in memory; representativeness is important when the value of one variable is forecast from explicit information about the value of another variable; and anchoring-and-adjustment is employed when the value of a variable is forecast from explicit information about previous values of that same variable. Although there has been increased emphasis on the adaptiveness of heuristics and increased interest in specifying their use in terms of computational models, this way of structuring our knowledge about judgemental forecasting continues to be a useful one. I use it to frame discussion of some recent debates in the area.
Impacts Of Ventilation: Studies On\“environmental Tobacco Smoke”

A number of legislative bodies in Europe have already made or are currently considering making policy decisions on the issue of smoking in public places. Policy alternatives have been discussed in Town & Country Planning (2004 and 2008). Scientific evidence relating to this debate has been reported in a diverse range of publications such as the British Medical Journal, Indoor Air and the Chartered Institution of Building Services Engineers Journal. On inspection much of this reporting concludes negatively on the performance of ventilation systems. In this paper a critical review is undertaken of three “Environmental tobacco smoke” study papers, to supplement the overview provided by the authors in their paper in the International Journal of Innovative Computing, Information and Control (IJICIC) in 2007.
A general configurable architecture for WSI implementation for neural nets

Presents a solution that allows flexible mapping of neural nets (such as multi-layered ones) onto uncommitted processing arrays in which a large number of processing elements are interconnected by a switched-bus network. The basic algorithms leading to such mapping are outlined, providing a balance between structure simplicity and parallelism of operation speed. A protocol by which the array can be configured (and, therefore, initialized) is presented: nominal operation is then described, and it is seen that the same solution providing for initialization supports also subsequent algorithms. The structure of the basic elements of the architecture (switches and processing elements) is detailed, so as to allow an evaluation of complexity as regards silicon requirements in CMOS. <<ETX>>
Investigations into model-reference adaptive control systems

The paper considers the behaviour of model-reference adaptive control systems when the system is very simple. Theoretical analysis of the behaviour of the adaptive loop is followed by analogue-computer studies. A second-order system with a second-order model and then a third-order system with a second-order model are considered. Effects of various inputs are investigated, including a ramp, square wave and white noise. The results demonstrate that the model-reference adaptive loop is capable of very fast response. It is also shown that, where the system and the model are of a different order, the frequency spectrum of the input has to be carefully considered to avoid large errors. It is also shown that one effect of the presence of noise may be to keep the loop parameters at or around their correct value in the absence of demand signal.
Trustworthy Embedded Computing for Cyber-Physical Control

A cyber-physical controller (CPC) uses computing to control a physical process. Example CPCs can be found in self-driving automobiles, unmanned aerial vehicles, and other autonomous systems. They are also used in large-scale industrial control systems (ICSs) manufacturing and utility infrastructure. CPC operations rely on embedded systems having real-time, high-assurance interactions with physical processes. However, recent attacks like Stuxnet have demonstrated that CPC malware is not restricted to networks and generalpurpose computers, rather embedded components are targeted as well. General-purpose computing and network approaches to security are failing to protect embedded controllers, which can have the direct effect of process disturbance or destruction. Moreover, as embedded systems increasingly grow in capability and find application in CPCs, embedded leaf node security is gaining priority. This work develops a root-of-trust design architecture, which provides process resilience to cyber attacks on, or from, embedded controllers: the Trustworthy Autonomic Interface Guardian Architecture (TAIGA). We define five trust requirements for building a fine-grained trusted computing component. TAIGA satisfies all requirements and addresses all classes of CPC attacks using an approach distinguished by adding resilience to the embedded controller, rather than seeking to prevent attacks from ever reaching the controller. TAIGA provides an on-chip, digital, security version of classic mechanical interlocks. This last line of defense monitors all of the communications of a controller using configurable or external hardware that is inaccessible to the controller processor. The interface controller is synthesized from C code, formally analyzed, and permits run-time checked, authenticated updates to certain system parameters but not code. TAIGA overrides any controller actions that are inconsistent with system specifications, including prediction and preemption of latent malwares attempts to disrupt system stability and safety. Trustworthy Embedded Computing for Cyber-Physical Control
Redundancy and novelty mining in the business blogosphere

Purpose – The paper aims to explore the performance of redundancy and novelty mining in the business blogosphere, which has not been studied before.Design/methodology/approach – Novelty mining techniques are implemented to single out novel information out of a massive set of text documents. This paper adopted the mixed metric approach which combines symmetric and asymmetric metrics.Findings – The results show that the novelty mining system can detect novel and redundant blogs in the dataset of business blogs with a very high accuracy.Originality/value – This paper shows that novelty mining techniques can be applied to business blogs to help organizations filter redundant information, and that the cosine and mixed metrics approaches produce better results.
Soft Discretization in a Classification Model for Modeling Adaptive Route Choice with a Fuzzy ID3 Algorithm

This study introduces a way to overcome the sensitivity of decision trees used for route choice behavior studies by using fuzzy logic while preserving the advantages of decision trees and the C4.5 algorithm, namely, comprehensibility and ease of application. Soft discretization of continuous values in fuzzy decision trees can provide a more robust classification. Also, the use of fuzzy logic makes it possible to accommodate qualitative attributes describing route characteristics. Apart from these features, fuzzy decision tree learning algorithms are also capable of assigning numeric values on decisions about the degree of certainty of each recommendation emanating from the fuzzy reasoning. This feature makes it possible to solve the multiple suggestion problem whereby the classic decision tree may suggest that more than one route is optimal. To investigate improvements resulting from the application of fuzzy decision tree learning algorithms, software for an adaptive route choice model using a fuzzy decision tree learning algorithm, fuzzy ID3, was developed and simulation experiments with the model were carried out. The comparison of results with the nonfuzzy adaptive route choice model indicates better predictive accuracy and more effective applicability for the fuzzy model in practice.