Miracle-dz/UniFAR-FTUs huggingface dataset:
Image schemata in the brain

A focus on the brain as an organic biological entity that grows and develops as the organism does is a prerequisite to a neurally-plausible theory of how image schemata structure language. Convergent evidence from the cognitive neurosciences has begun to establish the neural basis of image schemata as dynamic activation patterns that are shared across the neural maps of the sensorimotor cortex. First, I discuss the numerous experimental studies on normal subjects that, coupled with recent neurological studies of body-part language deficits in patients, have begun to establish that the sensorimotor cortices are crucial to the semantic comprehension of bodily action terms and sentences. Second, by tracing the cognitive and neural development of image schemata through both animal neuroanatomical studies and human neuroimaging studies, I review the neurobiologically plausible bases for image schemata. I propose that Edelman’s theory of secondary neural repertoires is the likeliest process to account for how integrative areas of the sensorimotor cortex can develop both sensorimotor and image schematic functions. Third, I assess the evidence from recent fMRI and ERP experiments showing that literal and metaphoric language stimuli activate areas of sensorimotor cortex consonant with the image schemata hypothesis. I conclude that these emerging bodies of evidence show how the image schematic functions of the sensorimotor cortex structure linguistic expression and metaphor.
A novel glutamate transporter blocker, LL-TBOA, attenuates ischaemic injury in the isolated, perfused rat heart despite low transporter levels.

OBJECTIVES Loss of glutamate from cardiomyocytes during ischaemia may aggravate ischaemia-reperfusion injury in open heart surgery. This may be due to reversal of excitatory amino acid transporters (EAATs). However, the expression of such transporters in cardiomyocytes is ambiguous and quantitative data are lacking. Our objective was to study whether EAATs were expressed in the rat heart and to study whether blocking of transporter operation during cardiac ischaemia could be beneficial. METHODS We used TaqMan real-time PCR and immunoisolation followed by western blotting to unequivocally identify EAAT subtypes in rat hearts. We used a novel high-affinity non-transportable competitive inhibitor, named LL-TBOA [(2S,3S)-3-(3-(6-(6-(2-(2-(2-(2-(2-aminoethoxy)ethoxy)-ethoxy)ethoxy) acetamido)hexanamido)- hexanamido)-5-(4-(trifluoromethyl)benzamido)benzyloxy) aspartic acid], to block EAAT-mediated transport during global ischaemia and reperfusion of isolated rat hearts. RESULTS Rat hearts expressed EAAT subtypes 1 and 3, while subtypes 2 and 4 were not detected. Hearts were isolated and perfused with 1.6 µM LL-TBOA for 5 min before 30 min of induced global ischaemia and 60 min of reperfusion (n = 8). Control hearts were perfused either with the solvent dimethylsulfoxide 3.5 mM (n = 7) or with no pretreatment (n = 8). Infarct size was evaluated by triphenyl tetrazolium chloride (TTC) staining. LL-TBOA reduced infarct size from 33 ± 14 to 20 ± 5% (mean ± SD) (P = 0.015). Dimethylsulfoxide alone had no effect (35 ± 2%). Reperfusion arrhythmias were reduced by LL-TBOA (P = 0.009), but not by dimethylsulfoxide alone. CONCLUSION Rat hearts express EAAT1 and EAAT3, but the mRNA levels are, respectively, ∼ 25 and 200 times lower than in the brain. Addition of LL-TBOA has a beneficial effect against ischaemia-reperfusion injury.
Spiritual Wellness Among Aggressive Adolescents: Efficacy of Raga Bhairavi

The objective of the present study was to investigate the efficacy of Raga Bhairavi for the promotion of spiritual wellness among aggressive adolescents. It was hypothesized that postintervention scores on spiritual wellness would be significantly higher as compared with preintervention scores. A pre- and postassessment design was adopted. The Spiritual Wellness Inventory (Elliott Ingersoll, 1995) and Aggression Questionnaire (Buss & Perry, 1992) were used to identify 32 adolescents with low spirituality and high aggression. A flute version of Raga Bhairavi was played for eight sessions. Scores were subjected to t test statistical comparison. Postintervention mean scores on subscales of spiritual wellness were comparatively higher than preintervention scores. Results revealed that raga therapy enhanced connectedness (t = 3.04; p < .01), meaning (t = 2.02; p < .05) and hope (t = 2.11; p < .05) dimensions of spiritual wellness. The difference between pre- and postintervention scores on aggression was also observed.
An Experience-Based Approach for Test Execution Effort Estimation

Software testing is becoming more and more important as it is a widely used activity to ensure software quality. Testing is now an essential phase in software development life cycle. Test execution becomes an activity in the critical path of project development. In this case, a good test execution effort estimation approach can benefit both tester managers and software projects.This paper proposes an experience-based approach for test execution effort estimation. In the approach, we characterize a test suite as a 3-dimensions vector which combines test case number, test execution complexity and its tester together. Based on the test suite execution vector model, we set up an experience database, and then a machine learning algorithm is applied to estimate efforts for given test suite vectors. We evaluate the approach through an empirical study on projects from a financial software company.
Multi-object tracking at intersections using the cardinalized probability hypothesis density filter

A large percentage of accidents with body injuries in urban areas occur at intersections. Thus, improving safety at intersections using infrastructure based perception systems is desirable. In order to recognize and track the moving objects, a network of laserscanners is used to observe the intersection. In this contribution, a robust object recognition algorithm for vehicles and pedestrians is proposed. Further, the Cardinalized Probability Hypothesis Density with integrated estimation of the clutter density is applied to track vehicles and pedestrians at an intersection. The performance of the system is evaluated using real world sensor data of an intersection.
Fractional factorial designs that restrict the number of treatment combinations for factor subsets

Designs are constructed that limit the number of treatment combinations within subsets of factors. Such designs came to our attention in the context of machine assembly experiments [1,2]. For example, to improve a stepper motor, we plan a 2 design to investigate four rotor factors and a similar 2 design to investigate four stator factors. The product of these two arrays to study the eight factors of these motors would involve 64 combinations. However, a resolution IV design is possible in only 16 treatment combinations. Others have cited the benefits of such design efficiencies for assembly experiments. We propose two methods for constructing restricted subset designs and catalog the possible designs with factors partitioned into two or more subsets. We also suggest alternative applications of these designs and cite related literature.
$\mathcal{G}$-SELC: Optimization by sequential elimination of level combinations using genetic algorithms and Gaussian processes

Identifying promising compounds from a vast collection of feasible compounds is an important and yet challenging problem in the pharmaceutical industry. An efficient solution to this problem will help reduce the expenditure at the early stages of drug discovery. In an attempt to solve this problem, Mandal, Wu and Johnson [Technometrics 48 (2006) 273―283] proposed the SELC algorithm. Although powerful, it fails to extract substantial information from the data to guide the search efficiently, as this methodology is not based on any statistical modeling. The proposed approach uses Gaussian Process (GP) modeling to improve upon SELC, and hence named G-SELC. The performance of the proposed methodology is illustrated using four and five dimensional test functions. Finally, we implement the new algorithm on a real pharmaceutical data set for finding a group of chemical compounds with optimal properties.
Hyper documents with quality for distance learning: cognitive strategies to help teachers in the navigational project and content organization

The main goal of this work was to evaluate the use of a group of Cognitive Strategies as a help in the structure and organization of the instructional material content by the interface, targeting to increase the hyper document usability and so its quality. For an effective process of distance learning it is necessary to assist teachers during the design of instructional material, helping them to design the material structure, navigation, organization and layout and considering pedagogical aspects that facilitate the student knowledge building. Considering usability as the needed effort to use the software, and considering the material as an interface to students, so if the material is made even easier to be understood, teachers are reducing the students' effort and so increasing this material usability. The case studies realized suggested that the Cognitive Strategies proposed enhanced the hyper documents usability.
FPGA Implementation of 64 Point FFT for Passive RADAR Applications

A power efficient Fast Fourier Transform (FFT) processor for use in the Direction of Arrival (DOA) estimation of a wideband waveform is presented. The target device for implementation is a Xilinx Spartan-3 Xc3s200 Field Programmable Gate Array (FPGA). The FFT processor was developed using the Xilinx ISE in Verilog code. Although the parallel and pipelined architecture uses a large portion of the available FPGA resources, the architecture does yield a high throughput.
An Adaptive Timeout Strategy for Profiling UDP Flows

With the increase of network bandwidth, more and more new applications such as audio, video and online games have become the main body in network traffic. Based on real time considerations, these new applications mostly use UDP as transport layer protocol, which directly increase UDP traffic. However, traditional studies believe that TCP dominates the Internet traffic and previous traffic measurements were generally based on it while UDP was ignored. In view of this, we mainly discuss the adaptive timeout strategy of UDP flows in this paper. Firstly, due to the significant differences in flow characteristics between the TCP flows and UDP flows, we expound and prove that the existing adaptive timeout strategies are not appropriate for UDP flows. Secondly, we present our adaptive strategy using Support Vector Machine techniques. We build six classifiers to accurately predict its corresponding maximum packet inter-arrival time and adapt its timeout value within the flow duration. Limited to its accurate rating, we present another concept of adjust accuracy rating which can probability-guaranteed(90%,95%,98%) to avoid long flow to be cut into short flows. The experiment result reveals that our adaptive strategy has the potential to achieve significant performance advantages over other widely used fixed and other adaptive timeout schemes.
Built-in Regression Testing for Component-based Software Systems

Some specialties of component, such as high evolvability, implementation transparent, and limited access support, bring a great challenge for testing the systems built by externally-provided components, especially for regression testing. Built-in test design is a fairly effective way to improve component's testability. In this paper, we present a built-in regression testing method to validate the change and its impact of component-based software, which needs the mutual collaboration between the component developers and component users. Through employing preliminary experiments on some medium scale systems, our regression testing method based on built-in test design has been proven to be feasible and practical. Although our method indicates the same precision as Orso et al. 's method at statement level, it needs less exchanged information (i.e., meta-data) and test scripts, so it is more cost-effective.
Detecting bots via incremental LS-SVM learning with dynamic feature adaptation

As botnets continue to proliferate and grow in sophistication, so does the need for more advanced security solutions to effectively detect and defend against such attacks. In particular, botnets such as Conficker have been known to encrypt the communication packets exchanged between bots and their command-and-control server, making it costly for existing botnet detection systems that rely on deep packet inspection (DPI) methods to identify compromised machines. In this paper, we argue that, even in the face of encrypted traffic flows, botnets can still be detected by examining the set of server IP-addresses visited by a client machine in the past. However there are several challenges that must be addressed. First, the set of server IP-addresses visited by client machines may evolve dynamically. Second, the set of client machines used for training and their class labels may also change over time. To overcome these challenges, this paper presents a novel incremental LS-SVM algorithm that is adaptive to both changes in the feature set and class labels of training instances. To evaluate the performance of our algorithm, we have performed experiments on two large-scale datasets, including real-time data collected from peering routers at a large Tier-1 ISP. Experimental results showed that the proposed algorithm produces classification accuracy comparable to its batch counterpart, while consuming significantly less computational resources.
The role of person in subject auxiliary inversion in English wh-questions: Evidence from Korean-English bilingual children

Aims: The present study investigates whether Korean-English (K-E) bilingual children develop subject auxiliary inversion (SAI) in English wh-questions differently from monolingual English (M-E) speaking children. Specifically, an experimental study was designed to determine whether there is an effect of subject person in their acquisition of SAI in English wh-questions, a factor that had been suggested but not tested as a contributing factor in SAI in a previous study. Design: Twenty-six K-E bilingual and 20 M-E preschool-aged children were recruited for an elicitation study testing the effect of person on the use of SAI in wh-questions in English. The experiment consisted of having children interact with puppets in a guessing game that elicited first, second, and third person object-what questions in English. Data and analysis: Data from the experiment were analyzed through a binomial logistic regression, which accounts for the binary nature of the data (auxiliary inversion: present or absent) and identified which variables contribute significantly to the presence and absence of SAI. Findings/conclusions: Results indicated a significant difference between K-E and M-E groups and a significant main effect for person on SAI for the K-E group. The paper proposes that the [prs] feature is part of the bundle of features that drives the movement of the features in T to C. Originality: The paper contributes evidence for a link between the person feature and SAI, a link that has not been seen before for wh-question research. Significance/implications: The new evidence of a possible link between subject person and auxiliary inversion may open a window to new avenues for wh-question acquisition research.
Efficient Sparse Linear System Solution of the PageRank Problem

UPGRADE is the anchor point for UPENET (UPGRADE European NETwork), the network of CEPIS member societies’ publications, that currently includes the following ones: • Informatik-Spektrum, journal published by Springer Verlag on behalf of the CEPIS societies GI, Germany, and SI, Switzerland • ITNOW, magazine published by Oxford University Press on behalf of the British CEPIS society BCS • Mondo Digitale, digital journal from the Italian CEPIS society AICA • Novática, journal from the Spanish CEPIS society ATI • OCG Journal, journal from the Austrian CEPIS society OCG • Pliroforiki, journal from the Cyprus CEPIS society CCS • Pro Dialog, journal from the Polish CEPIS society PTI-PIPS
An evaluated semantic query expansion and structure-based approach for enhancing Arabic question/answering

This paper describes an approach for improving the re- ranking of passages for Arabic Question/Answering (Q/A). This approach implements a process performing a semantic Query Expansion (QE) based on the Arabic WordNet (AWN) ontology with a structure-based Passage Retrieval (PR) based on the Distance Density n-gram model. Experiments with a set of translated CLEF and TREC questions have shown that the accuracy, the Mean Reciprocal Rank and the number of answered questions have been significantly improved using our approach. These experiments have been conducted in an open domain by querying the web as document collection. An analysis of the obtained performances is discussed in this paper.
Why Prices Need Algorithms

Understanding when equilibria are guaranteed to exist is a central theme in economic theory, seemingly unrelated to computation. This paper shows that the existence of pricing equilibria is inextricably connected to the computational complexity of related optimization problems: demand oracles, revenue-maximization, and welfare-maximization. This relationship implies, under suitable complexity assumptions, a host of impossibility results. We also suggest a complexity-theoretic explanation for the lack of useful extensions of the Walrasian equilibrium concept: such extensions seem to require the invention of novel polynomial-time algorithms for welfare-maximization.
Stock Market Prediction Using Artificial Neural Networks

Prediction of stock market returns is an important issue in finance. Artificial neural networks have been used in stock market prediction during the last decade. Studies were performed for the prediction of stock index values as well as daily direction of change in the index. In some applications it has been specified that artificial neural networks have limitations for learning the data patterns or that they may perform inconsistently and unpredictable because of the complex financial data used. In Turkey artificial neural networks are mostly used in predicting financial failures. There has been no specific research for prediction of Turkish stock market values. The aim of this paper is to use artificial neural networks to predict Istanbul Stock Exchange (ISE) market index value. Preliminary research performed on Turkish stock market has suggested that the inputs to the system may be taken as: previous day’s index value, previous day’s TL/USD exchange rate, previous day’s overnight interest rate and 5 dummy variables each representing the working days of the week. After the inputs have been determined, the data have been gathered for the period of July 1, 2001 through February 28, 2003 from the Central Bank of Republic of Turkey. Training set is determined to include about 90% of the data set and the rest 10% will be used for testing purposes. Network architecture is determined to be Multi Layer Perceptron and Generalized Feed Forward networks. Training and testing is performed using these two network architectures. However, subsystems are considered, which had different number of hidden layers (1, 2 and 4) for a mean-squared error value of 0.003. The results are then compared with the results of moving averages for 5 and 10-day periods, which showed that artificial neural networks have better performances than moving averages.
Korean mothers' beliefs and practices on early English education in ESL and EFL contexts

This study examines the relationship between the beliefs and practices of mothers and children’s learning experiences. By examining maternal beliefs and practices in different sociocultural contexts, the researcher identifies how the contexts of families and children’s learning experiences are related. Specifically, the purpose of the study is to describe and discuss Korean mothers’ beliefs and reported practices in early English education in ESL and EFL contexts. To examine Korean parents’ enthusiastic attitudes toward early English education, the researcher describes their subjective beliefs and selfreported practices. The present study is designed to employ both qualitative methodology and Q-sort methodology. To collect data, the researcher met 30 Korean mothers individually, some in Korea and some in the United States. Among them, eight mothers in Korea and eight mothers in the United States were randomly selected and interviewed. The purpose of the interviews was to elaborate on the mothers’ beliefs and practices through narratives that would complement the data from the Q-sorts. Using Q-sort methodology, the researcher examined mothers’ subjective beliefs concerning early English education and conducted semi-structured interviews using a standardized, open-ended strategy to describe and compare mothers’ beliefs in different sociocultural contexts. The results of this study were the following: (1) Similarities and differences exist in mothers’ beliefs concerning early English education in different contexts. Mothers in both contexts showed positive beliefs concerning early English education, even though their satisfaction was higher in the EFL context. Mothers in Korea clearly showed more
Mapping between image regions and caption concepts of captioned depictive photographs

We discuss the obstacles to inference of correspondences b tween objects within photographic images and their counterpart concepts in descriptive captions of those images. This is important for information retrieval of photographic data since its content analysis is much harder than linguistic analysis of its captions. We argue that the key mapping is between certain caption concepts representing the "linguistic focus" and certain image regions representing the "visual focus". The mapping is one-to-many, however, and many image regions and captions concepts are not mapped at all. We discuss some domain-independent co straints that can restrict potential mappings. We also report on experiments testing our criteria for visual focus of images.