Miracle-dz/UniFAR-FTUs huggingface dataset:
Injecting Multiple Psychological Features into Standard Text Summarisers

Automatic Text Summarisation is an essential technology to cope with the overwhelming amount of documents that are daily generated. Given an information source, such as a webpage or a news article, text summarisation consists of extracting content from it and present it in a condensed form for human consumption. Summaries are crucial to facilitate information access. The reader is provided with the key information in a concise and fluent way. This speeds up navigation through large repositories of data. With the rapid growth of online contents, creating manual summaries is not an option. Extractive summarisation methods are based on selecting the most important sentences from the input. To meet this aim, a ranking of candidate sentences is often built from a reduced set of sentence features. In this paper, we show that that many features derived from psychological studies are valuable for constructing extractive summaries. These features encode psychological aspects of communication and are a good guidance for selecting salient sentences. We use Quantitative Text Analysis tools for extracting these features and inject them into state-of-the-art extractive summarisers. Incorporating these novel components into existing extractive summarisers requires to combine and weight a high number of sentence features. In this respect, we show that Particle Swarm Optimisation is a viable approach to set the feature's weights. Following standard evaluation practice (DUC benchmarks), we also demonstrate that our novel summarisers are highly competitive.
Intrinsic Patterns of Coupling between Correlation and Amplitude of Low-Frequency fMRI Fluctuations Are Disrupted in Degenerative Dementia Mainly due to Functional Disconnection

Low frequency fluctuations (LFFs) of the BOLD signal are a major discovery in the study of the resting brain with functional magnetic resonance imaging (fMRI). Two fMRI-based measures, functional connectivity (FC), a measure of signal synchronicity, and the amplitude of LFFs (ALFF), a measure of signal periodicity, have been proved to be sensitive to changes induced by several neurological diseases, including degenerative dementia. In spite of the increasing use of these measures, whether and how they are related to each other remains to be elucidated. In this work we used voxel-wise FC and ALFF computed in different frequency bands (slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.073 Hz; and full-band: 0.01-0.073 Hz), in order to assess their relationship in healthy elderly as well as the relevant changes induced by Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI). We found that in healthy elderly subjects FC and ALFF are positively correlated in anterior and posterior cingulate cortex (full-band, slow-4 and slow-5), temporal cortex (full-band and slow-5), and in a set of subcortical regions (full-band and slow-4). These correlation patterns between FC and ALFF were absent in either AD or MCI patients. Notably, the loss of correlation between FC and ALFF in the AD group was primarily due to changes in FC rather than in ALFF. Our results indicate that degenerative dementia is characterized by a loss of global connection rather than by a decrease of fluctuation amplitude.
Classification of VHR Images Based on SVM and Multiobjective Evolutionary Optimization

This paper proposes an efficient classification system suitable for VHR images based on SVM and multiobjective optimization. The aim is to detect the optimal number of structural elements to be applied to the VHR image in addition to the optimal SVM parameters in a fully automatic way. To this end, the search process is guided by the simultaneous optimization of two different criteria. The first is the cross-validation accuracy computed on the training set, whereas the second one is related to class separability. In particular, we adopt the between and within class distance in the higher dimensional kernel space. To solve this problem, we use a multiobjective evolutionary algorithm based on decomposition (MOEA/D). MOEA/ED decomposes the multiobjective optimization problem into a number of scalar optimization sub-problems and optimizes them simultaneously. At convergence, a set of Pareto optimal solutions is obtained. For generating the final solution we propose to aggregate the Pareto optimal solutions by means of a simple majority voting (MV) rule. Experiments on a VHR image are reported and discussed.
Peak-to-average power ratio reduction method for OFDM signal by using permutation sequences

Many PAPR reduction techniques for OFDM signal have been proposed up to today, which can be classified into two major methods as the distortion and distortion-less methods. The most of distortion-less methods show better PAPR performance without degradation of BER performance. This paper proposes a novel distortion-less PAPR reduction method which employs the permutation sequence in the frequency domain with embedded side information. The feature of proposed method is to achieve the better PAPR performance with very few side information for the correct demodulation of data information at the receiver. This paper presents various computer simulation results to verify the effectiveness of proposed method as comparing with the conventional OFDM method in the non-linear channel.
Online Multi-Task Learning Using Biased Sampling

One of the long-standing challenges in Artiﬁ-cial Intelligence for goal-directed behavior is to build a single agent which can solve multiple tasks. Recent progress in multi-task learning for learning behavior in many goal-directed sequential tasks has been in the form of distillation based learning wherein a single student network learns from multiple task-speciﬁc teacher networks by mimicking the task-speciﬁc policies of the teacher networks. There has also been progress in the form of Progressive Networks which seek to overcome the catastrophic forgetting problem using gating mechanisms. We pro-pose a simple yet efﬁcient Multi-Tasking framework which solves many tasks in an online or active learning setup.
A novel framework for simultaneous localization and mapping

The six Degrees of freedom (6-Dof) Simultaneous Localization and Mapping (SLAM) aims to build a map of an unknown environment and simultaneously use this map to compute the location with 6-Dof poses. To solve this problem, probabilistic approaches such as Particle Filters (PF) have become dominant methods. PF suffers from certain problems (e.g. the need for large number of particles and so on) which induce high computational complexity. In this paper, an efficient SLAM framework is proposed and new ideas for each module are presented. By combining machine vision and a PF algorithm called the Exponential Natural Particle Filter (xNPF), the predicted results converge close to the true target states. Experimental results validate the potential of the proposed approach.
Syntax-based Attention Model for Natural Language Inference

Introducing attentional mechanism in neural network is a powerful concept, and has achieved impressive results in many natural language processing tasks. However, most of the existing models impose attentional distribution on a flat topology, namely the entire input representation sequence. Clearly, any well-formed sentence has its accompanying syntactic tree structure, which is a much rich topology. Applying attention to such topology not only exploits the underlying syntax, but also makes attention more interpretable. In this paper, we explore this direction in the context of natural language inference. The results demonstrate its efficacy. We also perform extensive qualitative analysis, deriving insights and intuitions of why and how our model works.
Applying a Common-Sense Approach to Fighting Obesity

The obesity epidemic is a threat to the health of millions and to the economic viability of healthcare systems, governments, businesses, and nations. A range of answers come to mind if and when we ask, “What can we, health professionals (physicians, nurses, nutritionists, behavioral psychologists), do about this epidemic? ” In this paper, we describe the Common-Sense Model of Self-Regulation as a framework for organizing existent tools and creating new tools to improve control of the obesity epidemic. Further, we explain how the Common-Sense Model can augment existing behavior-change models, with particular attention to the strength of the Common-Sense Model in addressing assessment and weight maintenance beyond initial weight loss.
Energy-efficient interconnect via Router Parking

The increase in on-chip core counts in Chip Multi Processors (CMPs) has led to the adoption of interconnects such as Mesh and Torus, which consume an increasing fraction of the chip power. Moreover, as technology and voltage continue to scale down, static power consumes a larger fraction of the total power; reducing it is increasingly important for energy proportional computing. Currently, processor designers strive to send under-utilized cores into deep sleep states in order to reduce idling power and improve overall energy efficiency. However, even in state-of-the-art CMP designs, when a core goes to sleep the router attached to it remains active in order to continue packet forwarding. In this paper, we propose Router Parking - selectively power-gating routers attached to parked cores. Router Parking ensures that network connectivity is maintained, and limits the average interconnect latency impact of packet detouring around parked routers. We present two Router Parking algorithms - an aggressive approach to park as many routers as possible, and a conservative approach that parks a limited set of routers in order to keep the impact on latency increase minimal. Further, we propose an adaptive policy to choose between the two algorithms at run-time. We evaluate our algorithms using both synthetic traffic as well as real workloads taken from SPEC CPU2006 and PARSEC 2.1 benchmark suites. Our evaluation results show that Router Parking can achieve significant savings in the total interconnect energy (average of 32%, 40% and 41% for the synthetic, SPEC CPU2006, and PARSEC 2.1 workloads, respectively).
Dye-sensitized solar cells using binary iodide-PVA gel electrolyte

In this work, polyvinyl alcohol (PVA)-salt gel polymer electrolytes (GPE) have been prepared with ethylene carbonate (EC) and propylene carbonate (PC) dissolved in dimethyl sulfoxide (DMSO). Potassium iodide (KI) and tetrapropyl ammonium iodide (TPAI) salts have been used to provide the conducting ions. The single salt system (containing KI only) with composition 15.20 wt. % PVA - 31.73 wt. % KI - 22.67 wt. % EC - 30.40 wt. % PC has the highest room temperature conductivity of 1.25 × 10-2 S cm-1. With addition of TPAI, conductivity of 9.79×10-3 S·cm-1 has been obtained for 15.21 wt. % PVA - 15.86 wt. % KI - 15.86 wt. % TPAI - 22.67 wt. % EC - 30.40 wt. % PC and is the highest conducting electrolyte in the binary salt system. The dye-sensitized solar cell (DSSC) using the electrolyte with only KI salt exhibits an efficiency of 2.64% whereas DSSC utilizing binary salt electrolyte system has efficiency of 3.12%.
Environmental factors affecting sampling success of artificial cover objects

Artificial cover objects (ACOs) can be useful for surveying an area for snake abundance. However, very little is known about the correlation between environmental conditions, time of day, and ACO capture success rates. We studied the effects of time of day, temperature, humidity, wind speed, and sky cover variables in relation to ACO sampling capture rates of two colubrid species, Thamnophis butleri and Thamnophis sirtalis. We found that time of day, temperature, and sky cover best explained capture with a quadratic function of temperature being a significant variable. An optimal temperature of 26°C with increasing cloudiness was found to produce the highest ACO sampling capture rate. These observations provide some of the first quantitative information regarding temporal and environmental correlates of ACO methodology, and may help improve the efficiency of the ACO technique for surveying snake abundance.
How Metformin Acts in PCOS Pregnant Women

OBJECTIVE Metformin has been reported to reduce the risk of gestational diabetes (GD) in women with polycystic ovarian syndrome (PCOS). However, little is known about the mechanisms of action of this drug during pregnancy. In the attempt to fill this gap, we performed a prospective longitudinal study providing a detailed examination of glucose and insulin metabolism in pregnant women with PCOS undergoing metformin therapy. RESEARCH DESIGN AND METHODS We enrolled 60 women with PCOS who conceived while undergoing metformin treatment. An oral glucose tolerance test and a euglycemic-hyperinsulinemic clamp were performed at each trimester of gestation in 47 ongoing pregnancies. RESULTS Twenty-two of the study subjects had development of GD despite the treatment. At baseline, insulin sensitivity was comparable between women who had development of GD and women who did not. A progressive decline in this parameter occurred in all subjects, independently of the trimester of GD diagnosis. Insulin secretion was significantly higher during the first trimester in patients with an early failure of metformin treatment. Women with third trimester GD and women with no GD exhibited a significant increase in insulin output as gestation proceeded. All newborns were healthy and only one case of macrosomia was observed. CONCLUSIONS Women with PCOS who enter pregnancy in a condition of severe hyperinsulinemia have development of GD earlier, independently of metformin treatment. The physiologic deterioration of insulin sensitivity is not affected by the drug and does not predict the timing and severity of the glycemic imbalance. Despite the high incidence of GD observed, the drug itself or the intensive monitoring probably accounted for the good neonatal outcome.
THE MEREOLOGICAL SELF A Multisensory Description of Self-Plasticity

5 The Mereological Self 6 The Embodied Self 10 Somatosensory Mereology 11 The Acting Self 13 Motor Mereology 14 The Plastic Self 15 Parthood 15 Modification 16 Separation 17 Composition 18 Selfhood 22 Duplication 23 Projection 26 Simulation 31 Discussion 34 References 39 A MULTISENSORY DESCRIPTION OF SELF-PLASTICITY 5
Multiculturality Concept and Its Reflections on Education: The Case of Turkey

The current study investigated the multiculturality attitudes of high school students using the Multiculturality Attitude Scale. The determination of the dynamics affecting students' multiculturality attitudes will provide opportunities for reconstructing the educational settings accordingly. The sample of the study was 2237 students from 24 high schools from 14 provincial centres in 7 geographical regions of Turkey. The data analysis was carried out using descriptive and inferential statistics. The analyses examined the relationships between the multiculturality attitudes of the students and their gender, class level, family income status, education level of the parents, and the geographic region of residence. The findings indicated significant differences between all dimensions of the scale (i.e., anxiety, richness, tolerance, threat, and discrimination) and the variables of the study. The findings indicated that the female students’ attitude mean scores were more positive than those of the male students. Students in Central Anatolia, Southeastern Anatolia, and Marmara regions had higher attitude scores than students from other regions. As students’ family income level increases, they regard multiculturality as a threat (ethnicity-based). Students whose parents have a high level of education regard multiculturality as a threat (ethnicity-based). In order to minimize these negative attitudes towards multiculturality in the society, people’s awareness of different life practices by various groups within a country or community offers richness that should be recognized. To achieve this, the regulation of educational experiences considering cultural diversity and normalizing transitions, the differences in the educational institutions that serve as foundation in the society-building process are of the utmost importance.
Exploring in the weblog space by detecting informative and affective articles

Weblogs have become a prevalent source of information for people to express themselves. In general, there are two genres of contents in weblogs. The first kind is about the webloggers' personal feelings, thoughts or emotions. We call this kind of weblogs affective articles. The second kind of weblogs is about technologies and different kinds of informative news. In this paper, we present a machine learning method for classifying informative and affective articles among weblogs. We consider this problem as a binary classification problem. By using machine learning approaches, we achieve about 92% on information retrieval performance measures including precision, recall and F1. We set up three studies on the applications of above classification approach in both research and industrial fields. The above classification approach is used to improve the performance of classification of emotions from weblog articles. We also develop an intent-driven weblog-search engine based on the classification techniques to improve the satisfaction of Web users. Finally, our approach is applied to search for weblogs with a great deal of informative articles.
Scalable Techniques for Performance Analysis

The scalability of performance tools in high performance computing has been lagging behind the growth in the sizes of supercomputers and the applications that run on them. It is recognized that performance event traces still provide the best and most useful source of information for analysis. However, the volume of performance trace data generated easily becomes unmanagable without appropriate controls as we scale upwards. At the same time, the amount of information that has to be presented to a human analyst can also become overwhelming. We present techniques used to address the above problems and enhance the scalability of Projections, a performance instrumentation and visualization framework for the migratable object programming model CHARM++. Projections provides multiple resolutions of performance data. We couple this feature with the use of heuristics and clustering algorithms at application runtime to provide powerful mechanisms to reduce performance data volume and analysis time while preserving data relevance. We employ similar heuristics and algorithms for enhanced interactive post-mortem visualization and analysis assistance to a human expert. To demonstrate the continued effectiveness of the analysis process using the above techniques while showing that the data volume is significantly reduced, we present experimental results on simulation benchmarks based on NAMD , a popular molecular dynamics application.
Bonding Strength Effects in Hydro-Mechanical Coupling Transport in Granular Porous Media by Pore-Scale Modeling

The hydro-mechanical coupling transport process of sand production is numerically investigated with special attention paid to the bonding effect between sand grains. By coupling the lattice Boltzmann method (LBM) and the discrete element method (DEM), we are able to capture particles movements and fluid flows simultaneously. In order to account for the bonding effects on sand production, a contact bond model is introduced into the LBM-DEM framework. Our simulations first examine the experimental observation of “initial sand production is evoked by localized failure” and then show that the bonding or cement plays an important role in sand production. Lower bonding strength will lead to more sand production than higher bonding strength. It is also found that the influence of flow rate on sand production depends on the bonding strength in cemented granular media, and for low bonding strength sample, the higher the flow rate is, the more severe the erosion found in localized failure zone becomes.
A spectroscopic atlas of post-AGB stars and planetary nebulae selected from the IRAS point source catalogue

Aims. We study the optical spectral properties of a sample of stars showing far infrared colours similar to those of well-known planetary nebulae. The large majority of them were unidentified sources or poorly known in the literature at the time when this spectroscopic survey started, some 15 years ago. Methods. We present low-resolution optical spectroscopy, finding charts and improved astrometric coordinates of a sample of 253 IRAS sources. Results. We have identified 103 sources as post-AGB stars, 21 as "transition sources", and 36 as planetary nebulae, some of them strongly reddened. Among the rest of sources in the sample, we were also able to identify 38 young stellar objects, 5 peculiar stars, and 2 Seyfert galaxies. Up to 49 sources in our spectroscopic sample do not show any optical counterpart, and most of them are suggested to be heavily obscured post-AGB stars, rapidly evolving on their way to becoming planetary nebulae. Conclusions. An analysis of the galactic distribution of the sources identified as evolved stars in the sample is presented together with a study of the distribution of these stars in the IRAS two-colour diagram. Finally, the spectral type distribution and other properties of the sources identified as post-AGB in this spectroscopic survey are discussed in the framework of stellar evolution.
The Efficient Design and Modification of Cascaded Integrator Comb Filter

The traditional Cascaded Integrator Comb (CIC) filter is introduced at first, and its pass-band attenuation and mirror-aliasing can not meet the requirements of the GSM radio remote unit (RRU) system. Then a compensation technique, Interpolated Second Order Polynomial (ISOP) method, is studied to improve the frequency response of the traditional CIC filter, which can enhance the performance in pass-band. However, this kind of filter will reduce the attenuation in stop-band at the same time. In order to deal with this problem, this paper proposes Cosine filter, which is cascaded before CIC-ISOP to implement pre-filtering. By analyzing frequency characteristics, this cascaded filter enlarges the attenuation in stop-band and sharpens the transition band. With this method, we can obtain the improved and ideal magnitude response, and the results of this approach come up to the requirements of GSM RRU system.
Interprofessional role boundaries in diabetes education in Australia

ABSTRACT Diabetes presents a challenge to healthcare services worldwide. Diabetes educators work with individuals and communities to reduce the impact of diabetes. In Australia, diabetes educators derive from one of several primary qualifications including nursing, medicine or a specified allied health background, and have an accredited postgraduate qualification in diabetes education. The peak professional body, the Australian Diabetes Educators Association (ADEA), promotes equivalence of all diabetes educators in terms of their scope of practice. However, in practice, there is evidence of inequities, particularly between those from nursing and allied health backgrounds. This paper uses a neo-Weberian lens to explore the interprofessional role dynamics of a ‘postprofessional’ group of practitioners, who adopt a common role and title to create a professional identity at post-qualifying level. Data were collected via individual interviews with 19 stakeholders and analysed using an abductive template approach. Differential role boundaries between nurse and allied health diabetes educators were established and reinforced in several ways. Diabetes education is considered a sub-specialty of nursing only; access to education and credentialing has been restricted for allied health; reinforcement of professional stereotypes and perceived professional values; and perceived legislative differences in access to medication management for nurse and allied health diabetes educators.