Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Network Parameter Optimization Based on Genetic Algorithm for Software Defect Prediction

Software fault prediction approaches are much more efficient and effective to detect software faults compared to software reviews. Machine learning classification algorithms have been applied for software defect prediction. Neural network has strong fault tolerance and strong ability of nonlinear dynamic processing of software defect data. However, practicability of neural network is affected due to the difficulty of selecting appropriate parameters of network architecture. Software fault prediction datasets are often highly imbalanced class distribution. Class imbalance will reduce classifier performance. A combination of genetic algorithm and bagging technique is proposed for improving the performance of the software defect prediction. Genetic algorithm is applied to deal with the parameter optimization of neural network. Bagging technique is employed to deal with the class imbalance problem. The proposed method is evaluated using the datasets from NASA metric data repository. Results have indicated that the proposed method makes an improvement in neural network prediction performance.
EXPERIMENTAL CHARACTERIZATION OF A LOW-COST FAST FREQUENCY-HOPPING SPREAD-SPECTRUM SYSTEM FOR WIRELESS IN-HOUSE OPTICAL COMMUNICATIONS F. Delgaoo. R. PBrez-Jimenez (Member), J. RabadAn (Member). M. A. Bacallado and F J. Lopez-Hernandez (Member)

In this paper, the design and experimental characterization of a Wireless Infrared Fast Frequency Hopping Spread Spectrum FIRFFHSS' system is presented. The use of spread spectrum techniques on wireless infrared indoor communications reduces the effect ofinterference present~in the infrared channel. It also improves the system performance versus multipath propagation. The use of FHSS techniques allow the use of Code Division Multiple Access (CDMA) strategies for sharing the channel. We have developed and tested a prototype with a bit rate of I Mb/s. but it can be easily risen up to 4Mb/s. We also present some results on a simplified synchronization structure so as to be used on reduced-cost domestic equipment. These systems are suitable for different applications at low and medium rates.
Search engines evaluation using precision and document-overlap measurements at 10-50 cutoff points

The Internet has become a huge store of distributed documents. A user of the Internet, at times, seeks information which he may not know to solve a problem. He therefore has to express his information needs as a request for information in one form or another using a search engine. The search engine then tries to infer and retrieve relevant documents and presents the results in a hit list. But, the relevant documents from the hit list can only be determined by the user. The quality of hit lists very depending on the effectiveness of the indexing process which generate the surrogates from the original documents. Usually, the quality of the hit list can be measured by the precision measure, i.e. the ratio of the number of retrieved and relevant documents over the number of retrieved documents. This measure has been used to evaluate ten major search engines using ten queries at cutoff points of 10, 20, 30, 40 and 50. We have also introduced an overlap measure to determine the commonality of documents between the hit lists of various search engines. With these two measures we can evaluate the performance of the search engines. The search engines chosen for study are Altavista, Hotbot, Excite, Lycos, Webcrawler, Infoseek, Magellan, Northernlight, SavvySearch and Metacrawler.
Word segmentation, word recognition, and word learning: a computational model of first language acquisition

Many word boundaries are not marked acoustically in fluent speech (Lehiste, 1960), a fact that is immediately apparent from listening to speech in an unfamiliar language, and which poses a special problem for infants. The acquisition literature shows that infants begin to segment speech (identify word boundaries) between 6 and 10.5 months (Saffran, Aslin, & Newport, 1996; Jusczyk, Hohne, & Baumann, 1999; Jusczyk, Houston, & Newsome, 1999; Mattys & Jusczyk, 2001; Bortfeld, Morgan, Golinkoff, & Rathbun, 2005) although they possess minuscule receptive vocabularies at this age (Dale & Fenson, 1996). Thus, word segmentation largely appears before and supports word learning (Aslin, Woodward, LaMendola, & Bever, 1996; van de Weijer, 1998; Brent & Siskind, 2001; Davis, 2004), rather than the other way around. These results raise several further questions. How do infants begin to find word boundaries in speech when they don't know most of the words they hear? How are word segmentation, word recognition, and word learning linked in development? I propose DiBS – x *Di*phone-* B*ased *S*egmentation – as a computational model of word segmentation. The core idea of DiBS is to recover word boundaries in speech based on the immediate phonotactic context, by estimating the probabilities of a word boundary within every possible sequence of two speech sounds (diphone, e.g. [ba]). As a proof of concept, a supervised DiBS model is tested on English and Russian data, yielding a consistent pattern of high accuracy with some undersegmentation. Next, a learning theory is developed, by which DiBS can be estimated from information that is observable to infants, including the distribution of speech sounds at phrase edges and any words they have managed to learn; these models achieve superior segmentation relative to other prelexical statistical proposals such as segmentation based on Saffran et al's (1996) transitional probability. Finally, this learning model is integrated with a model of lexical access and word-learning to form a full bootstrapping model, which achieves a relatively high degree of success in word segmentation, but only partial success in word learning. The successes and failures of this model are discussed, as they highlight the need for additional research on wordform learning.
Wild data: Collaborative e-research and university libraries

The literature speaks of a ‘deluge’ of scientific and research data and the importance of capturing and managing it for use beyond its original creating community, purpose, and time. Data value increases as it is interconnected, networked, shared, used, and re-used. This paper extends the conversation about data sharing to ‘wild data’: that is, data generated and held outside of ‘academic’ or ‘professional’ science, as in the case of environmental voluntary groups (EVGs). Currently, important data generated by these groups are likely to be inaccessible to the academic community, or any community or body outside those often-small EVGs. Although large quantities of data are often generated by EVGs, management of thesedata may be poor or non-existent; and quality control of data may be haphazard and spasmodic. This article reports on a pilot project which explored the data sought, generated, stored, and shared by members of EVGs. The project also investigated members’ views about data management and sharing .
A Purposeful Dynamic Stretching Routine

May/June 2012 Dynamic stretching, which involves moving parts of the body and gradually increases range of motion, speed of movement, or both through controlled, sport-specific movements, has become the popular choice of pre-exercise warm-up (Woods, Bishop, & Jones, 2007). This type of warm-up has evolved to encompass several variations, but at its core is the principle theme that preparing the body for exercise can be achieved best through continuous, coordinated movement. Dynamic stretching improves flexibility, while simultaneously increasing body heat and blood flow, all of which are important to warming up for a race (McMillian, Moore, Hatler, & Taylor, 2006). By increasing range of motion, dynamic stretching serves as a possible preventative measure against injuries. Furthermore, dynamic stretching is a more functional way to prepare the body for exercise, because it also increases the core temperature of the body (Mann & Whedon, 2001). For the majority of sports, everything that is needed in a proper warm-up can be found through dynamic stretching. Dynamic stretching has been defined as large body movements often combined with typical running drills in multiple directions (i.e., lunging and squatting, change-indirection) (McMillian et al. 2006). For distance running and other sports with a running component, an increased range of motion can potentially increase stride length, which will allow runners to be able to cover more ground over a shorter period of time. For distance running in particular, the debate exists as to what stretching method (static or dynamic) should be used as a pre-run warm up. Static stretching is the stretching of a muscle or muscle group to its furthest point and holding that position for 10-30 seconds (Mann & Whedon, 2001). Static stretching has long been a staple in many runners’ prerun routine and is viewed as the easiest and safest way to stretch (Mann & Whedon, 2001). However, recent developments in scientific research have indicated that static stretching might not be the most efficient or effective way to prepare distance runners for their upcoming event (McMillian et al. 2006). A recent publication in Strategies (Vardiman, Carrand, & Gallagher, 2010) addressed dynamic stretching but not specific to distance runners. Therefore, the purpose of this article are twofold: 1) to review the benefits of dynamic stretching for distance runners, and 2) to provide distance runners and coaches with a detailed dynamic stretching routine that can be adopted. All suggestions made within this article can easily be transferred to other sports, especially those that contain a running component.
Creating visual aids with graphic organisers on an infinite canvas – the impact on the presenter

Instead of the traditional set of slides, the visual aids of a presentation can now be graphic organisers (concept maps, knowledge maps, mind maps) on an infinite canvas. Constructing graphic organisers has a beneficial impact on learning, but this topic has not been studied in the context of giving a presentation. The present study examined this issue by having 199 students prepare a presentation. The control sample created visual aids that are similar to a deck of slides with text and pictures. In the first experimental condition, graphic organisers were inserted in this deck of slides, and in the second experimental condition, visuals aids with interconnected graphic organisers were produced. There were no significant differences in self-reported self-efficacy related to giving a presentation and in motivational variables. To detect if the presentations met the principles set by the conditions, the participants’ products were coded qualitatively, but this analysis also yielded no differences in variables. Further research should therefore explore the interaction between software and presenter. Keywords: visual communication; teaching practices; presentation skills; self-efficacy; prezi (Published: 20 February 2015) Citation : Research in Learning Technology 2015, 23 : 25161 - http://dx.doi. org/10.3402/rlt.v23. 25161
A Novel Human Action Recognition Algorithm Based on Edit Distance

A novel human action recognition algorithm based on edit distance is proposed in this paper. In the method, the mesh feature of each image in human action sequence is firstly calculated; then the feature vectors are quantized through a rival penalized competitive neural network; and through this processing, the time-sequential image sequences are converted into symbolic sequences. For human action recognition, the observed action is firstly vector quantized with the former competitive neural network; then the normalized edit distances to the training samples are calculated and the action which best matches the observed sequence is chosen as the final category. The experiments on Weizmann dataset demonstrate that our method is effective for human action recognition. The average recognition accuracy can reach above 94%.
A radially-Gaussian, signal-dependent time-frequency representation

An optimization formulation for designing signal-dependent kernels that are based on radially Gaussian functions is presented. The method is based on optimality criteria and is not ad hoc. The procedure is automatic. The optimization criteria are formulated so that the resulting time-frequency distribution (TFD) is insensitive to the time scale and orientation of the signal in time-frequency. Examples demonstrate that the optimal-kernel TFD offers excellent performance for a larger class of signals than any current fixed-kernel representation. The technique performs well in the presence of substantial additive noise, which suggests that it may prove useful for automatic detection of unknown signals in noise. The cost of this technique is only a few times greater than that of the fixed-kernel methods and the 1/0 optimal kernel method. <<ETX>>
Overview of State-of-the-Art in Digital Image Forensics

Digital images can now be easily created, altered, and manipulated with no obvious traces of having been subjected to any of these operations. There are currently no established methodologies to verify the authenticity and integrity of digital images in an automatic manner. Digital image forensics is an emerging research field with important implications for ensuring the credibility of digital images. In an attempt to assist these efforts, this chapter surveys the recent developments in the field of digital image forensics. Proposed techniques in the literature are categorized into three primary areas based on their focus: image source identification, discrimination of synthetic images, and image forgery detection. The main idea of the proposed approaches in each category is described in detail, and reported results are discussed to evaluate the potential of the methods.
PRESUPPOSITION AND LINGUISTIC CONTEXT

According to a pragmatic view, the presuppositions of a sentence detrmine the class of contexts in which the sentence could be felicitously uttered. Complex sentences present a difficult problem in this framework. No simple "projection method" has been found by which we could compute their presuppositions from those of their constituent clauses. This paper presents a way to eliminate the projection problem. A recursive definition of "satisfaction of presuppositions" is proposed that makes it unnecessary to have any explicit method for assigning presuppositions to compound sentences. A theory of presuppositions becomes a theory of contraints on successive contexts in a fully explicit discourse.
Performance & packet traffic dynamics of Packet Switching Network model

Dynamics of packet traffic in data communication networks can be complex and often not well understood. Understanding of these complex dynamics is important for their control, prediction purposes and for the data networks design. The engineering community has described wired data networks architectures and studied them by means of a layered, hierarchical abstraction called ISO OSI (International Standard Organization Open System Interconnect) Reference Model. The Network Layer of the ISO OSI Reference Model is responsible for routing packets across the network from their sources to their destinations and for control of congestion in data networks. Using an abstraction of the Network Layer that we developed, we investigate packet traffic dynamics in our data network models of data communication networks of packet switching type, in particular near the phase transition point from free flow to congestion. We explore how these dynamics and network performance indicators are affected by network connection topology and routing algorithms. We consider static and adaptive routing algorithms. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Accelerating Pseudo‐Random Number Generator for MCNP on GPU

Pseudo‐random number generators (PRNG) are intensively used in many stochastic algorithms in particle simulations, artificial neural networks and other scientific computation. The PRNG in Monte Carlo N‐Particle Transport Code (MCNP) requires long period, high quality, flexible jump and fast enough. In this paper, we implement such a PRNG for MCNP on NVIDIA’s GTX200 Graphics Processor Units (GPU) using CUDA programming model. Results shows that 3.80 to 8.10 times speedup are achieved compared with 4 to 6 cores CPUs and more than 679.18 million double precision random numbers can be generated per second on GPU.
Long‐term trends of zonally averaged aerosol optical thickness observed from operational satellite AVHRR instrument

Atmospheric aerosols are solid and liquid particles suspended in air. Aerosols have important consequences for global climate, ecosystem processes and human health due to their various sizes and chemical compositions. In this paper, nearly 23 years of aerosol optical thickness (AOT) data from operational satellite AVHRR observations over the global ocean have been used to study the latitudinal variations of the linear long‐term trends (LLT) of zonal mean AOT. For the LLT of zonally averaged AOT over the global ocean, their significance generally falls below the 95% confidence level. However, the LLT of zonal mean AOT for the individual oceans, such as the Atlantic Ocean (AO), Indian Ocean (IO), and West Pacific Ocean (WPO), can easily pass 95% confidence level in some latitude belts (e.g. 5–25°N for IO, 33–40°N for WPO, 30–50°N for AO) and may produce zonally averaged solar dimming/brightening accordingly in cloudless conditions. The results will help us to understand the latitudinal features of the decadal changes in aerosol loadings, as well as surface solar radiation, better. Published in 2011 by John Wiley & Sons, Ltd.
Co-forming real space blends in tactile signed language dialogues

Abstract This article reports on a linguistic study examining the use of real space blending in the tactile signed languages of Norwegian and Swedish signers who are both deaf and blind. Tactile signed languages are typically produced by interactants in contact with each other’s hands while signing. Of particular interest to this study are utterances which not only consist of the signer producing signs with his or her own hands (or other body parts), but which also recruit the other interactant’s hands (or another body part). These utterances, although perhaps less frequent, are co-constructed, in a very real sense, and they illustrate meaning construction during emerging, embodied discourse. Here, we analyze several examples of these types of utterances from a cognitive linguistic and cognitive semiotic perspective to explore how interactants prompt meaning construction through touch and the involvement of each other’s bodies during a particular type of co-regulation.
Real-Time Video-Based Rendering from Multiple Cameras

In recent years, many Image-Based Rendering techniques have advanced from static to dynamic scenes and thus became Video-Based Rendering (VBR) methods. But actually, only few of them can render new views in live. We present a new VBR system that creates new views of a dynamic scene in live. This system provides high quality images and does not require any background extraction. Our method follows a plane-sweep approach and reaches realtime rendering using consumer graphic hardware (GPU). Only one computer is used for both acquisition and rendering. The video stream acquisition is performed by at least 3 webcams. We propose an additional video stream management that extends the number of webcams to 10 or more. These considerations make our system low-cost and hence accessible for everyone.
Introduction: Cognitive Tools for Collaborative Communities

The notion of computers as cognitive tools is not new—but increasingly, they are powerful tools for creating communities and supporting collaborative learning. As a number of researchers point out, cognitive tools can be designed to extend classroom experiences beyond the physical classroom to enable learning in virtual spaces (Lajoie & Derry, 1993, Jonassen & Reeves, 1996, Lajoie, Garcia, Berdugo, & Ramirez, 2005). Such tools, often designed based on strong theoretical commitments, can change the nature of teaching and learning as they enable new forms of activity. “Cognitive tools refer to technologies, tangible or intangible, that enhance the power of human beings during thinking, problem solving, and learning” (Jonassen & Reeves, 1996, p. 693). This is similar to Salomon’s (1993) notion of effects with technology, that is, those tools that allow the learner to accomplish different kinds of cognitive activities while using technology. In this conception, computers serve as intellectual partners that enable higher-order thinking and learning as lower-level activities are offloaded to the technology. But Salomon also noted the importance of the effects of technology—the cognitive effects that last beyond the use of technology. For both kinds of effects to occur, computers need to be integrated into meaningful activities that afford constructive processing. Cognitive tools actively engage learners in constructing new understanding and making their own conceptualizations visible. Jonassen & Reeves (1996) argue that cognitive tools are best used to represent knowledge and solve problems within the contexts of personally meaningful problems and investigations. They enable mindful, challenging learning and can be used to support deep reflection. Some examples of cognitive tools include programming
Ultra‐thin Polarization Independent Absorber Using Hexagonal Interdigital Metamaterial

An ultra‐thin hexagonal microwave metamaterial absorber is described. It can absorb any polarized transverse electromagnetic wave because of its hexagonal shape. In spite of its very thin structure, almost 0. 028λg, the absorber achieved 99% absorptivity at 11.35 GHz in experimental results because of the increased coupling losses, showing good agreement with simulation results. In addition, this high absorbance is unchanged for any polarized waves with the same frequency.
Learning to Collaborate: An Instructional Approach to Promoting Collaborative Problem Solving in Computer-Mediated Settings

Effective collaboration in computer-mediated settings among spatially distributed people is a precondition for success in many new learning and working contexts but it is hard to achieve. We have developed two instructional approaches to improve collaboration in such settings by promoting people's capabilities to collaborate in a fruitful way and furthering their understanding of what characterizes good collaboration. The rationale is that strategies necessary for a good and effective computer-mediated collaboration may be conveyed to people by exposing them to an elaborated worked-out collaboration example (observational learning) or by giving them the opportunity to learn from scripted collaborative problem-solving. An experimental study was conducted that compared learning from observing a worked-out collaboration example with the learning effects of scripted collaborative problem-solving, the effects of unscripted collaborative problem-solving, and a control condition without a learning phase. The experimental design provided clearly separated phases for the instructional treatments (learning phase) and for applying and testing the acquired skills (application phase). Both observing a worked-out collaboration example and collaborating with a script during the learning phase showed positive effects on process and outcome of the second collaboration in the application phase.