Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the reasons for aberrant response patterns in classroom maths tests

This study has focused on the investigation of the reasons for aberrant response patterns in classroom maths tests. Data were collected from high schools in Cyprus over two academic years. The assessment instruments used included: three Maths Tests, a Test Anxiety Inventory (TAI) and a shorter version of it, an Attention Deficit Hyperactivity Disorder (ADHD) scale and a Maths Self-Esteem Scale. Results showed no associations between any of the factors investigated and misfit when tests with polytomous items were used. Factors investigated included: student and teacher gender, item order, different schools, different teachers, ability, test anxiety, ADHD, maths self-esteem, motivation, language competency, interest in maths, private tuition in maths, study time and class revision. This finding has led to the investigation whether misfit is an inherent characteristic of students and the conclusion that it is not. The only factors that showed some association with misfit were ability (p = 0.022), the interaction of gender with test anxiety (p = 0.018) and different teachers (p = 0.027), and the first two were only for the test containing 12 (out of 16) dichotomous items. Further investigation of these factors is suggested. Analyses of interviews of 21 misfitting students showed that the main reason given for unexpected responses among high ability students was, as expressed by them, carelessness and among low scorers prior knowledge and to a lesser degree cheating and special preference. The two mean square statistics, infit and outfit were also investigated, and an explanation is given for why high infit is considered more of a threat to measurement than high outfit. The researcher finally argues that students with misfitting patterns with high outfit values should not be considered as invalidly measured without further investigation. Similarly, items with high outfit should not be considered as malfunctioning and removed without further investigation
Consonant discrimination in elicited and spontaneous speech: a case for signal-adaptive front ends in ASR

The constant frame length in typical ASR front ends is too long to capture transient phenomena in speech, such as stop bursts. However, current HMM systems have consistently outperformed systems based solely on non-uniform units. This work investigates an approach to “add back” such transient information to a speech recognizer, without losing the robustness of the standard a coustic models. We demonstrate a set of phonetically-motivated acoustic features that discriminate a preliminary test set of highly ambiguous voiceless stops in CV contexts. The features are automatically computed from data that had been hand-marked for consonant burst location and voicing onset (extension to automatic marking is also proposed). Two corpora are processed using a parallel set of features: conversational speech over the telephone (Switchboard), and a corpus of carefully elicited speech. The latter provides a n upper bound on discrimination, and allows for comparison of feature usage across speaking style. We explore data-driven approaches to obtaining variable-length time-localized features compatible with an HMM statistical framework. We also suggest techniques for extension to automatic annotation of burst location, for computation of features at such points, and for augmentation of an HMM system with the added information.
Influence of Prosodic Information on the Processing of Split Particles: ERP Evidence from Spoken German

Spoken language comprehension involves the use of different sources of linguistic information such as prosodic, syntactic, lexical, and semantic information. The question, however, of when and how these sources of information are exploited by the language processing system still remains unanswered. In the present study, we used event-related brain potentials (ERPs) to investigate the interaction between prosodic, syntactic, and lexical information during the processing of spoken German sentences. The sentence structure was manipulated by positioning a split particle at the end of the sentences after the occurrence of inflected verb whose lexical entry does not contain a split particle (e.g., Sie alarmierte den Detektiv an [She alerted at the detective]) [According to linguistic convention, incorrect sentences are marked by an asterisk. ]. The prosodic contour of the verb stems was manipulated such that it marked either the presence of a split particle at a later position in the sentence or not. Participants performed an off-line probe-detection task. ERP data indicate that prosodic information of German-inflected verb stems is consulted on-line by the language processing system (parser) in order to predict the presence of a split particle at a later position in the sentence. An N400 effect was observed for the processing of split particles following verb stems which do not take a particle. However, this effect was only observed when the prosody of the verb stem did signal the presence of a split particle. We argue that the N400 component reflects the high costs associated with the lexical search that the language processing system has to perform when confronted with nonexisting words such as these resulting from the combination of the split particle and the verb stem in the present study. Furthermore, as a general reflection of prosodic processes, a Closure Positive Shift (CPS) was found at intonational phrase boundaries. In sum, the present findings provide strong evidence that prosodic information is a good predictor of upcoming information during the auditory processing of German sentences.
A Neural Network Model for Attribute-Based Decision Processes

We propose a neural model of multiattribute-decision processes, based on an attractor neural network with dynamic thresholds. The model may be viewed as a generalization of the elimination by aspects model, whereby simultaneous selection of several aspects is allowed. Depending on the amount of synaptic inhibition, various kinds of scanning strategies may be performed, leading in some cases to vacillations among the alternatives. The model predicts that decisions of a longer time duration exhibit a lower violation of the simple scalability law, as opposed to shorter decisions. Furthermore, the model is suggested as a general attribute-based decision module. Accordingly, various decision strategies are manifested depending on the module's parameters.
Cloaker Catcher: A Client-based Cloaking Detection System

Cloaking has long been exploited by spammers for the purpose of increasing the exposure of their websites. In other words, cloaking has long served as a major malicious technique in search engine optimization (SEO). Cloaking hides the true nature of a website by delivering blatantly different content to users versus web crawlers. Recently, we have also witnessed a rising trend of employing cloaking in search engine marketing (SEM). However, detecting cloaking is challenging. Existing approaches cannot detect IP cloaking and are not suitable for detecting cloaking in SEM because their search-and-visit method leads to click fraud. In addition, they focus on detecting and measuring cloaking on the server side, but the results are not visible to users to help them avoid frauds. Our work focuses on mitigating IP cloaking and SEM cloaking, and providing client-based real-time cloaking detection services. To achieve these goals, we first propose the Simhash-based Website Model (SWM), a condensed representation of websites, which can model natural page dynamics. Based on SWM, we design and implement Cloaker Catcher, an accurate, efficient and privacy-preserving system, that consists of a server that crawls websites visited by users on demand and a client-side extension that fetches spider views of websites from the server and compares them with user views to detect cloaking. Since Cloaker Catcher checks on the client side for each real user, IP cloaking can be detected whenever it occurs and click fraud in SEM can also be prevented. Using our system, we conducted the first analysis of SEM cloaking and found that the main purpose of SEM cloakers is to provide illicit services.
Diagnosis and Treatment of Diabetic Kidney Disease

Abstract:Diabetic kidney disease (DKD) is the most common cause of chronic kidney disease in the United States. In the last several years, there have been several new developments in the field of the DKD. In 2007, the National Kidney Foundation and Kidney Disease Outcomes Quality Initiative released clinical practice guidelines that included new definitions and summarized diagnostic and therapeutic approaches for DKD. The results of several recent randomized controlled trials provided novel insights regarding effects of glycemic and lipid control on vascular and renal outcomes in patients with diabetes. Additionally, the findings of the Action to Control Cardiovascular Risk in Diabetes—Blood Pressure trial played a critical role in the revision of blood pressure target guidelines in patients with diabetes. The goal of this review article is to summarize recent updates and recommendations for the diagnosis and treatment of DKD.
Physical Synthesis Comes of Age

Physical synthesis, the integration of logic synthesis with physical design information, was born in the mid to late 1990s, which means it is about to enter its teenage years. Today, physical synthesis tools are a major part of the EDA industry, accounting for hundreds of millions of dollars in revenue. This work looks at how technology and design trends have affected physical synthesis over the last decade and also how physical synthesis will continue to evolve on its way to adulthood.
The University of Sheffield and Harbin Engineering University at TRECVID 2012: Instance Search

This paper describes our contribution to instance search (INS) task for TRECVID 2012. We present four approaches for this task, (i) histograms of SIFT features as feature vectors and Bhatacharya distance for similarity detection (ii) feature vector is combination of SIFT features alone, while for matching we used a basic descriptor matching algorithm (iii) IR based approach using SIFT features and (iv) afﬁne invariant SIFT features as feature vectors.
Characterization of the maize lipoxygenase gene family in relation to aflatoxin accumulation resistance

Maize (Zea mays L.) is a globally important staple food crop prone to contamination by aflatoxin, a carcinogenic secondary metabolite produced by the fungus Aspergillus flavus. An efficient approach to reduce accumulation of aflatoxin is the development of germplasm resistant to colonization and toxin production by A. flavus. Lipoxygenases (LOXs) are a group of non-heme iron containing dioxygenase enzymes that catalyze oxygenation of polyunsaturated fatty acids (PUFAs). LOX derived oxylipins play critical roles in plant defense against pathogens including A. flavus. The objectives of this study were to summarize sequence diversity and expression patterns for all LOX genes in the maize genome, and map their effect on aflatoxin accumulation via linkage and association mapping. In total, 13 LOX genes were identified, characterized, and mapped. The sequence of one gene, ZmLOX10, is reported from 5 inbred lines. Genes ZmLOX1/2, 5, 8, 9, 10 and 12 (GRMZM2G156861, or V4 numbers ZM00001D042541 and Zm00001D042540, GRMZM2G102760, GRMZM2G104843, GRMZM2G017616, GRMZM2G015419, and GRMZM2G106748, respectively) fell under previously published QTL in one or more mapping populations and are linked to a measurable reduction of aflatoxin in maize grains. Association mapping results found 28 of the 726 SNPs tested were associated with reduced aflatoxin levels at p ≤ 9.71 x 10−4 according to association statistics. These fell within or near nine of the ZmLOX genes. This work confirms the importance of some lipoxygenases for resistance to aflatoxin accumulation and may be used to direct future genetic selection in maize.
Effect of social capital on emotion, language style and latent topics in online depression community

Social capital is linked to mental illness. It has been proposed that higher social capital is associated with better mental well-being in both individuals and groups in offline setting. However, in online settings, the association between online social capital and mental health conditions has not yet been explored. Social media offer us a rich opportunity to determine the link between social capital and aspects of mental wellbeing. In this paper, we examine social capital based on levels of social connectivity of bloggers can be connected to aspects of depression in individuals and online depression community. We explore apparent properties of textual contents, including expressed emotions, language styles and latent topics, of a large corpus of blog posts, to analyze the aspect of social capital in the community. Using data collected from online Livejoumal depression community, we apply both statistical tests and machine learning approaches to examine how predictive factors vary between low and high social capital groups. Significant differences are found between low and high social capital groups when characterized by a set of latent topics, language features derived from blog posts, suggesting discriminative features, proved to be useful in the classification task. This shows that linguistic styles are better predictors than latent topics as features. The findings indicate the potential of using social media as a sensor for monitoring mental well-being in online settings.
A Bioinspired Neurodynamics-Based Approach to Tracking Control of Mobile Robots

Tracking control is a fundamentally important issue for robot and motor systems, where smooth velocity commands are desirable for safe and effective operation. In this paper, a novel biologically inspired tracking control approach to real-time navigation of a nonholonomic mobile robot is proposed by integrating a backstepping technique and a neurodynamics model. The tracking control algorithm is derived from the error dynamics analysis of the mobile robot and the stability analysis of the closed-loop control system. The stability of the robot control system and the convergence of tracking errors to zeros are guaranteed by a Lyapunov stability theory. Unlike some existing tracking control methods for mobile robots whose control velocities suffer from velocity jumps, the proposed neurodynamics-based approach is capable of generating smooth continuous robot control signals with zero initial velocities. In addition, it can deal with situations with a very large tracking error. The effectiveness and efficiency of the proposed neurodynamics-based tracking control of mobile robots are demonstrated by experimental and comparison studies.
A three phase approach for mental task classification using EEG

The Brain Computer Interface provides a channel of communication to physically challenged individuals who have fully or partially lost the power to interact with their surroundings. The strength of a BCI system lies in its ability to determine the appropriate features for a given task and to be able to correctly classify a task amid many other mental tasks. The electroencephalogram (EEG) is a cost effect and efficient means to capture the intent of a person with motor disorder. Wavelet decomposition for feature extraction from EEG is suitable for the analysis of non-linear and non-stationary time series. Hence, Wavelet decomposition of EEG is used in this study to form a feature set. Of the extracted features, a subset of discriminatory and relevant features is selected using ratio of scatter matrices, Chernoff distance measure and linear regression. The performance of different mental tasks using the selected features is compared and evaluated in terms of classification accuracy and the dimension of features. Experimental results on publicly available data show that there is significant improvement in classification accuracies with different feature selection methods. Linear regression performs better in comparison to other two methods and Support Vector Machine requires lesser number of features to build the model.
Co-located MIMO radar with orthogonal waveform coding : Cramér-Rao Lower Bound

In this paper, we derive and analyze closed form (nonmatrix) expression of the Cramer-Rao lower bound (CRB) for the location and the velocity of moving targets contaminated by a structured noise (clutter echoes) and a background noise. The context of our analysis is the co-located MIMO (multiple-input multiple-output) radar system, ie. , we dispose of multiple antennas in transmission and reception which are close in space. In addition as it is standard in modern MIMO radar, several noncoherent orthogonal waveforms are transmitted. The analysis of the proposed expressions of the CRB allows to better understand the characterisation of the target. In particular, we prove (1) the estimation of the direction parameter and the velocity of the target are cross-linked ; (2) it is more difficult to estimate the target velocity if the radar station is moving ; (3) if the radar station is not moving, the estimation of the target velocity is isotropic ; (4) it is more difficult to accurately localize fixed or slow moving targets and finally, the CRB for a MIMO radar is a lower bound of the one for the SIMO (single-input multiple-output) radar.
Relationship Marketing : Is It a Paradigm Shift ?

Relationship marketing emerged as a contestant to traditional marketing theories since the early 1990s. Proponents of relationship marketing as a paradigm shift to traditional marketing theories criticized the transactional nature of traditional marketing concept. They argued that the positivist nature of theorizing marketing based on microeconomic models ignored the factor of relationship in a marketing process and its strategic implications in human interactions in an exchange process. This paper outlines their major contentions and provides a critique on the subject.
Assembly of the Paraflagellar Rod and the Flagellum Attachment Zone Complex During the Trypanosoma brucei Cell Cycle

ABSTRACT Trypanosomes possess a single flagellum that is attached to their cell body via the flagellum attachment zone (FAZ). The FAZ is composed of two structures: a cytoplasmic filament complex and four microtubules situated next to it. There is a complex transmembrane crosslinking of this FAZ to the paraflagellar rod (PFR) and axoneme within the flagellum. We have partially purified the FAZ complex and have produced monoclonal antibodies both against the FAZ and the paraflagellar rod. The two antibodies against the FAZ (L3B2 and L6B3) recognise the cytoplasmic filament in immunofluorescence and in immunoelectron microscopy. On Western blot, they detect a doublet of high molecular weight (M, 200,000). Two anti‐PFR antibodies (L13D6 and L8C4) recognise the paraflagellar rod in immunofluorescence, but show a difference on Western blot: L13D6 recognises both major PFR proteins, whereas L8C4 is specific for only one of them. Using these new antibodies we have shown that although the growth of both cytoplasmic FAZ filament and external PFR are related, their growth initiates at different time points during the cell cycle and the two structures elongate at distinct rates.
Learning image based surrogate relevance criterion for atlas selection in segmentation

Picking geometrically relevant atlases from the whole training set is crucial to multi-atlas based image segmentation, especially with extensive data of heterogeneous quality in the Big Data era. Unfortunately, there is very limited understanding of how currently used image similarity criteria reveal geometric relevance, let alone the optimization of them. This paper aims to develop a good image based surrogate relevance criterion to best reflect the underlying inaccessible geometric relevance in a learning context. We cast this surrogate learning problem into an optimization framework, by encouraging the image based surrogate to behave consistently with geometric relevance during training. In particular, we desire a criterion to be small for image pairs with similar geometry and large for those with significantly different segmentation geometry. Validation experiments on corpus callosum segmentation demonstrate the improved quality of the learned surrogate compared to benchmark surrogate candidates.
Physicians

This paper describes the Higoumenakis sign, enlargement of the sternal end of the clavicle in patients with late congenital syphilis and the dermatologist after whom it is named. Several professors and doctors from the Medical School of the University of Athens opposed his actions especially at the University in Greece. His persistence led him to productive scientific activity in syphilis, leishmaniasis and psoriasis. He became a member of the Greek Parliament from 1964 to 1967 and eventually Minister of Hygiene – even though this may have been an imprudent political choice, due to the unstable socio-political status of that period. He died on 27 December 1983 at the age of 88.
HUMAN RESOURCES MANAGEMENT AS A DETERMINING FACTOR IN ORGANISATIONAL LEARNING

The aim of this paper is to analyse the relationship between HR practices and organisational learning. The hypotheses proposed are contrasted with a sample of 195 Spanish companies employing over 200 people, and using the modelling of structural equations as a statistical technique. The results show that the introduction of techniques such as selective hiring, strategic training, employee participation in decision making, and contingent reward increases the ability of the organisation to learn.
Things I Have Learned ( So Far )

This is an account of what I have learned (so far) about the application of statistics to psychology and the other sociobiomedical sciences. It includes the principles "less is more" (fewer variables, more highly targeted issues, sharp rounding off), "simple is better" (graphic representation, unit weighting for linear composites), and "some things you learn aren't so. " I have learned to avoid the many misconceptions that surround Fisherian null hypothesis testing. I have also learned the importance of power analysis and the determination of just how big (rather than how statistically significant) are the effects that we study. Finally, I have learned that there is no royal road to statistical induction, that the informed judgment of the investigator is the crucial element in the interpretation of data, and that things take time.
Connectors in Software Architectures

Nowadays, to allow for rapid software evolution, more and more software developers are starting to construct their products from reusable software components. In this approach, the architecture of a system is described as a collection of components along with the interactions among those components. Even though the main building blocks of the system are components, the properties of the system also strongly depend on the character of the component interactions. This fact gave birth to a “connector” as an abstraction capturing the nature of these interactions. Even though the notion of connectors originates in the earliest papers on software architectures [54, 47], connectors are currently far from being a typical first class entity in contemporary component-based systems. The main problem of current component-based systems and their respective ADLs is that they either do not capture component interactions at all, or that they only focus on application design stages. By articulating the problem of deployment anomaly, this thesis identifies a role that connectors should play when the distribution and deployment of a component-based application are considered. As none of the related component-based systems does so, a new connector model that allows a variety of (possibly complex) component interactions to be reflected at all the key stages of an application lifecycle (ADL specification, implementation, deployment, and runtime) is further introduced.