Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating IELTS Academic Writing Task 2 : Relationships between cognitive writing processes, text quality, and working memory

This project examined the cognitive processes and online behaviours of second language writers while performing IELTS Academic Writing Test Task 2, and the ways in which the online behaviours of test-takers relate to the quality of the text produced. An additional aim was to assess whether writing behaviours and text quality are influenced by individual differences in phonological short-term memory and executive control functions. Thirty participants, Mandarin users of L2 English from a UK university, performed a version of Task 2 of the IELTS Academic Writing Test. The online writing processes of the participants were captured by recordings of participants' eye-movements and logs of their keystrokes. After a short break, a subset of the participants took part in a stimulated recall session, as part of which participants were requested to describe their thought processes during task performance, prompted by the playback of the recordings of their keystrokes. Participants were administered an extensive battery of working memory tests (Chinese Digit Span, Chinese Non-word Span, Colour Shape Task, Corsi Block Forward-Backward, Stop Signal Task, and Operation Span). The essays produced were scored in terms of IELTS rating criteria, and analysed for linguistic complexity (lexical, syntactic and discourse complexity) and accuracy relying on computer-based and expert analyses. The results demonstrated that the IELTS Academic Writing Task 2 elicits a wide range of cognitive processes and writing behaviours, which are well aligned with the intended aim of the IELTS Academic Writing test. A number of links were also observed between the measures of writing behaviours and text quality, some of which included the IELTS total score and subscores. However, working memory was found to be related to only a few measures of writing behaviours and text quality indices.
Mining Using The Prosodic Information In The Spoken Utterance ’ s For Efficient Dialogue Management In A Spoken Dialogue System

Utterances used by a human while framing a response during the interaction with a software agent like Spoken Dialogue system(SDS) carries a lot of valuable information with regards to the internal mental state of the user. Opinion mining is an analysis of the mental state of a person, his opinion, appraisal or emotion towards an event, entities or their attributes. The users level of certainty about a topic could be determined by the analysis not only of the text used in the utterance but by studying the prosody information structure. Prosody reveals Information about the context by highlighting information structure and aspects of the speaker hearer relationship. Most often it is observed that the speaker‘s internal state is not depicted by the words he uses but by the tone of his utterance or facial expression of the user. In this paper we had analyzed a sample of student‘s after a lecture on operating system‘s subject and used the prosodic features of each dialogue with the student based on few questions. It was determined whether the students were certain, uncertain or neutral about their understanding of the lecture contents. This paper uses PRAAT a software tool for speech analysis which uses 15 acoustic features to determine the certainty of the responses of the user through classification by RAPIDMINER based on the prosody information which will actually aid the dialogue management component of the SDS in framing a better dialogue strategy.
Lipid Profiling following Intake of the Omega 3 Fatty Acid DHA Identifies the Peroxidized Metabolites F4-Neuroprostanes as the Best Predictors of Atherosclerosis Prevention

Abstract The anti-atherogenic effects of omega 3 fatty acids, namely eicosapentaenoic (EPA) and docosahexaenoic acids (DHA) are well recognized but the impact of dietary intake on bioactive lipid mediator profiles remains unclear. Such a profiling effort may offer novel targets for future studies into the mechanism of action of omega 3 fatty acids. The present study aimed to determine the impact of DHA supplementation on the profiles of polyunsaturated fatty acids (PUFA) oxygenated metabolites and to investigate their contribution to atherosclerosis prevention. A special emphasis was given to the non-enzymatic metabolites knowing the high susceptibility of DHA to free radical-mediated peroxidation and the increased oxidative stress associated with plaque formation. Atherosclerosis prone mice (LDLR−/−) received increasing doses of DHA (0, 0.1, 1 or 2% of energy) during 20 weeks leading to a dose-dependent reduction of atherosclerosis (R2 = 0.97, p = 0.02), triglyceridemia (R2 = 0.97, p = 0.01) and cholesterolemia (R2 = 0.96, p<0.01). Targeted lipidomic analyses revealed that both the profiles of EPA and DHA and their corresponding oxygenated metabolites were substantially modulated in plasma and liver. Notably, the hepatic level of F4-neuroprostanes, a specific class of DHA peroxidized metabolites, was strongly correlated with the hepatic DHA level. Moreover, unbiased statistical analysis including correlation analyses, hierarchical cluster and projection to latent structure discriminate analysis revealed that the hepatic level of F4-neuroprostanes was the variable most negatively correlated with the plaque extent (p<0.001) and along with plasma EPA-derived diols was an important mathematical positive predictor of atherosclerosis prevention. Thus, oxygenated n-3 PUFAs, and F4-neuroprostanes in particular, are potential biomarkers of DHA-associated atherosclerosis prevention. While these may contribute to the anti-atherogenic effects of DHA, further in vitro investigations are needed to confirm such a contention and to decipher the molecular mechanisms of action.
Peer-to-Peer Discovery of Semantic Associations

The Semantic Web vision promises an extension of the current Web in which all data is annotated with machine understandable metadata. The relationship-centric nature of this data has led to the definition of Semantic Associations, which are complex relationships between resources. Semantic Associations attempt to answer queries of the form “how are resource A and resource B related? ” Knowing how two entities are related is a crucial question in knowledge discovery applications. Much the same way humans collaborate and interact to form new knowledge, discovery of Semantic Associations across repositories on a peer-to-peer network can allow peers to share their local knowledge to collectively make new discoveries. In this paper we propose a method for computing Semantic Associations over distributed RDF data stores in a peer-to-peer setting. We follow a hierarchical peer / super-peer network topology, and we propose a novel query planning algorithm based on a notion of knowledgebase borders and minimum distances between borders.
Robot Manipulator Control and Computational Cost

In this paper robot control algorithms are considered from the point of view of computational complexity. All the algorithms provide for hybrid position and force control which is appropriate to unstructured environments. Both joint coordinate and generalized coordinate motion schemes are considered. The findings are interesting in that the peripheral processing involved in all methods, sometimes, swamps the orders of magnitude differences in computation involved in the inner loops. It is also interesting to note the similarity in complexity between direct methods and differential. Between joint coordinate methods and generalized coordinate methods there is a general two to one ratio in complexity. In general a 0.75 M Flop, 1.25 Mip machine is required to provide for a control rate of 250 hz. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-88-10. This technical report is available at ScholarlyCommons: http://repository.upenn. edu/cis_reports/621 ROBOT MANIPULATOR CONTROL AND COMPUTATIONAL COST YehongZhang Richard P. Paul MS-CIS-88-10 GRASP LAB 134 Department of Computer and Information Science School of Engineering and Applied Science University of Pennsylvania Philadelphia, PA 191 04
Fingerprinting based localisation revisited: A rigorous approach for comparing RSSI measurements coping with missed access points and differing antenna attenuations

Fingerprinting based localisation systems rely on taking a radio map of the environment and determine the position of the device by comparing this map to its current measurements. Therefore the performance of any such system heavily relies on the accuracy of this comparison. Typically two problems arise: first, access points can be missed in a scanning cycle both during the fingerprinting phase as well as during the localisation phase and second, if different devices are used for fingerprinting and for localisation their received signal strengths might not be the same due to differing antenna attenuation. A successful comparison function has to cope with both of these issues yielding repeatable high likelihoods for measurements taken in the same location while at the same time providing sufficiently high discrimination for measurements taken in different locations. In this paper we will propose such a likelihood observation function based on rigorous assumptions. Like most approaches we will compare RSSI values based on squared Euclidean distance of the log energies, which is essentially a Gaussian assumption on the distribution of the measurement error justifiable by arguments like maximum entropy or the law of large numbers acting on multiple additive error sources. However, this naive approach suffers from the varying dimensionality of the log-energy space caused by missing access points in the measurement. In order to overcome this in a rigorous manner we propose to model the access point pickup probabilities using Gibbs distributions enabling the introduction of rigorously motivated penalties for these dimension mismatches. As a further extension of the likelihood observation function we also propose to make it invariant to differences in antenna attenuation by estimating these explicitly from the log-energy observations and using the minimised squared observation residuals as invariant distance measure instead. We will discuss the properties of this improved likelihood observation function and compare its performance in a particle filter based WiFi localisation system.
A proposal for model–independent 3D wave field reconstruction from reflection data

With seismic interferometry one can retrieve the response to a virtual source inside an unknown medium, assuming there is a receiver at the position of the virtual source. In a companion paper, Broggini et al. show that for the 1D situation the requirement of having an actual receiver inside the medium can be circumvented. They show that the virtual source response can be obtained from reflection data only. This paper is a first step towards the generalization of their method to the 3D situation. We show how the full response to a virtual source inside the medium can be obtained from the reflection response at the surface. We also indicate how this method can be used to handle internal multiple scattering in migration.
Adaptive Bubble Burst (ABB): Mitigating DDoS attacks in Software-Defined Networks

The availability of online services is threatened by the Distributed Denial of Service (DDoS) attacks. As computing resources are getting cheaper, DDoS attacks are becoming more frequent and powerful. When DDoS attacks are directed towards a specific service, a small number of bots can overwhelm the service, making it unavailable to legitimate users. This paper presents Adaptive Bubble Burst (ABB), which takes advantage of the logically centralized architecture of Software-Defined Networks to provide mitigation against DDoS attacks that are targeted towards a specific service. ABB works by dispersing the incoming traffic in the network. Our experimental results show that ABB mitigates the DDoS attacks by enhancing the availability of a specific network resource.
Performance evaluation of distance measures for preprocessing of set-valued data in feature vector generated from LOD datasets

The linked open data cloud has evolved as a huge repository of data with data from various domains. A lot of work has been done in generating these datasets and enhancing the LOD cloud, whereas a little work is being done in the consumption of the available data from the LOD. There are several types of applications that have been developed using the data from the LOD cloud; of which, one of the areas that has attracted the researchers and developers most is the use of these data for machine learning and knowledge discovery. Using the available, state of the art knowledge discovery and machine learning algorithms requires conversion of the heterogeneous interlinked RDF graph datasets, available in LOD cloud, to a feature vector. This conversion is performed with the subject set as instances; the predicates set as attributes and object set as attribute values in a feature vector. The converted feature vector may contain set-valued attributes as there can be more than one object for a subject with the same predicate name. These set-valued data in the attribute of the feature vector needs to be pre-processed so that the feature vector contains attributes with single values only and can be used directly in machine learning algorithms. The pre-processing approach involves distance calculation and application of Fastmap algorithm for transformation of set-valued data attribute into k columns, which are replaced in the feature vector making the feature vector appropriate to be used as input for knowledge discovery and machine learning. However choosing the most suitable distance measures of the different distance measures available is a problem that needs to be catered. This paper provides a performance study to select the most suitable distance measure that can be used in pre-processing by building the feature vector with the different distance measures for set-valued data attributes and applying transformation with Fastmap. The evaluation of the distance measures is done using clustering of the transformed feature vector table with pre-identified class labels and getting micro-precision values for the clustering results. Performing the experimental analysis with LMDB data it has been found that the Hausdorff and RIBL distance measures are the most suitable distance measures that can be used to pre-process the created feature vector with set-valued data from the linked open data cloud.
BoPi - a distributed machine for experimenting Web services technologies

BoPi is a programming language with a runtime support that allows the distribution and the execution of programs over the network. The language is a process calculus with XML values and datatypes, and with a pattern matching mechanism for deconstructing values. The compiler gives a typesafe bytecode in the form of an XML document, that may be deployed on the network. What comes out is a simple, statically typed, and formally defined core BPEL language with a basic query mechanism supplied by patterns.
Morphology, Ultrastructure and Possible Functions of Antennal Sensilla of Sitodiplosis mosellana Géhin (Diptera: Cecidomyiidae)

To better understand the olfactory receptive mechanisms involved in host selection and courtship behavior of Sitodiplosis mosellana (Diptera: Cecidomyiidae), one of the most important pests of wheat, scanning and transmission electron microscopy were used to examine the external morphology and ultrastructure of the antennal sensilla. The moniliform antennae exhibit obvious sexual dimorphism: antennae of the males are markedly longer than those of the females. Furthermore, each male flagellomere consists of two globular nodes, whereas each female flagellomere is cylindrical. Seven types of sensilla were identified in both sexes. Two types of s. chaetica have a lumen without dendrites and thick walls, suggesting that they are mechanoreceptors. S. trichodea and s. circumfila are typical chemoreceptors, possessing thin multiporous walls encircling a lumen with multiple dendrites. There are significantly more s. trichodea in female than in male, which may be related to host plant localization. In contrast, male s. circumfila are highly elongated compared to those of females, perhaps for pheromone detection. Peg-shaped s. coeloconica are innervated with unbranched dendrites extending from the base to the distal tip. Type 1 s. coeloconica, which have deep longitudinal grooves and finger-like projections on the surface, may serve as olfactory or humidity receptors, whereas type 2 s. coeloconica, smooth with a terminal pore, may be contact chemoreceptors. Also, this is the first report of Böhm’ bristles at proximal scape on antennae of Cecidomyiid species potentially functioning as mechanoreceptors.
Periodicity and Jumps in Cohomology of R-Torsion-Free Groups

A discrete group G has periodic cohomology over R if there is an element α ∈ Ext RG (R, R) of positive degree and an integer n ≥ 0 such that the cup product map by α induces an isomorphism of Ext RG (R,−) and Ext i+|α| RG (R,−) for i ≥ n. Adem and Smith [1] showed if R = Z, then this condition is equivalent to the existence of a finite dimensional free-G-CW-complex homotopy equivalent to a sphere. It was conjectured by Olympia Talelli in [14], that if G is also torsion-free then it must have finite cohomological dimension. In this paper we investigate the implied condition of jump cohomology over R: G has jump cohomology of height k over R if any subgroup H with finite cohomological dimension over R, has cdR(H) ≤ k. We prove that all solvable and HF groups with jump cohomology over R must have finite cohomological dimension over R if they are R-torsion-free.
Identifying Emotion Labels from Psychiatric Social Texts Using Independent Component Analysis

Accessing the web has been an efficient and effective means to acquire self-help knowledge when suf- fering from depressive problems. Many mental health websites have developed community-based ser- vices such as web forums and blogs for Internet users to share their depressive problems with other us- ers and health professionals. Other users or health professionals can then make recommendations in re- sponse to these problems. Such communications produce a large number of documents called psychiat- ric social texts containing rich emotion labels representing different depressive problems. Automatically identify such emotion labels can make online psychiatric services more effective. This study proposes a framework combining latent semantic analysis (LSA) and independent component analysis (ICA) to ex- tract concept-level features for emotion label identification. LSA is used to discover latent concepts that do not frequently occur in psychiatric social texts, and ICA is used to extract independent components by minimizing the term dependence among the concepts. By combining LSA and ICA, more useful la- tent concepts can be discovered for different emotion labels, and the dependence between them can also be minimized. The discriminant power of classifiers can thus be improved by training them on the inde- pendent components with minimized term overlap. Experimental results show that the use of concept- level features yielded better performance than the use of word-level features. Additionally, combining LSA and ICA improved the performance of using each LSA and ICA alone.
Teaching multidisciplinary engineering using concepts and technology of WSN

This paper discusses teaching and learning strategies of Wireless Sensor Networks technology in a new postgraduate subject run at the Faculty of Engineering and IT, University of Technology, Sydney. The aim is to present the role of using practice based and multidisciplinary methodologies in the context of new ICT technologies. This includes shared experiences, observations and common problems experienced in teaching new concepts and paradigms, standards, protocols and algorithms, embedded systems and sensor technologies. The theory of WSN is applied as a driver of system development for the group projects that students undertake in the subject.
Reference-Dependent Preferences: Evidence from Marathon Runners

Models of reference-dependent preferences propose that individuals evaluate outcomes as gains or losses relative to a neutral reference point. We test for reference dependence in a large dataset of marathon finishing times (n = 9,524,071). Models of reference-dependent preferences such as prospect theory predict bunching of finishing times at reference points. We provide visual and statistical evidence that round numbers (e.g., a four-hour marathon) serve as reference points in this environment and as a result produce significant bunching of performance at these round numbers. Bunching is driven by planning and adjustments in effort provision near the finish line and cannot be explained by explicit rewards (e.g., qualifying for the Boston Marathon), peer effects, or institutional features (e.g., pacesetters). We calibrate a simple model of prospect theory as well as other models of reference dependence and show that the basic qualitative shape of the empirical distribution of finishing times is consistent with parameters that have previously been estimated in the laboratory.
Tuning garbage collection in an embedded Java environment

Traditionally, the Java virtual machine (JVM), the cornerstone of Java technology, is tuned for performance, taking into account that the energy consumption requires re-evaluation, and possibly re-design of the virtual machine. This motivates us to tune specific components of the virtual machine for a battery-operated architecture. As embedded JVMs are designed to run for long periods of time on limited-memory embedded systems, creating and managing Java objects is of critical importance. The garbage collector (GC) is an important part of the JVM responsible for the automatic reclamation of unused memory. This paper shows that the GC is not only important for limited-memory systems but also for energy-constrained architectures. In particular, we present a GC-controlled leakage energy optimization technique that shuts off memory banks that do not hold live data. A variety of parameters, such as bank size, the garbage collection frequency, object allocation style, compaction style, and compaction frequency are tuned for energy saving.
Host carbon sources modulate cell wall architecture, drug resistance and virulence in a fungal pathogen

The survival of all microbes depends upon their ability to respond to environmental challenges. To establish infection, pathogens such as Candida albicans must mount effective stress responses to counter host defences while adapting to dynamic changes in nutrient status within host niches. Studies of C. albicans stress adaptation have generally been performed on glucose‐grown cells, leaving the effects of alternative carbon sources upon stress resistance largely unexplored. We have shown that growth on alternative carbon sources, such as lactate, strongly influence the resistance of C. albicans to antifungal drugs, osmotic and cell wall stresses. Similar trends were observed in clinical isolates and other pathogenic Candida species. The increased stress resistance of C. albicans was not dependent on key stress (Hog1) and cell integrity (Mkc1) signalling pathways. Instead, increased stress resistance was promoted by major changes in the architecture and biophysical properties of the cell wall. Glucose‐ and lactate‐grown cells displayed significant differences in cell wall mass, ultrastructure, elasticity and adhesion. Changes in carbon source also altered the virulence of C. albicans in models of systemic candidiasis and vaginitis, confirming the importance of alternative carbon sources within host niches during C. albicans infections.