Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributional Semantics Meets Construction Grammar. towards a Unified Usage-Based Model of Grammar and Meaning

In this paper, we propose a new type of semantic representation of Construction Grammar that combines constructions with the vector representations used in Distributional Semantics. We introduce a new framework, Distributional Construction Grammar, where grammar and meaning are systematically modeled from language use, and finally, we discuss the kind of contributions that distributional models can provide to CxG representation from a linguistic and cognitive perspective.
DHT implementation on PIM-SM for an Inter-domain Multicast Routing

of Bachelor Thesis DHT Implementation on PIM-SM for an Inter-domain Multicast Routing This thesis proposes a design and implementation using DHT (Distributed Hash Table) on PIM-SM (Protocol Independent Multicast Sparse Mode) as a replacement to MSDP (Multicast Source Discovery Protocol) for inter-domain multicast routing. This implementation aims to solve multicast source discovery problem in inter-domain multicast as well as as reduce the scalability issue which lies in MSDP. With ability to scale extremely large number of nodes as well as provide efficient lookup service, instead of flooding the Source Active (SA) messages to inform the existence of Multicast sources between domains like MSDP, this research use DHT implementation as a sources-lookup operation which can lower network bandwidth consuming as well as improve scalability and robustness. With comparison from calculation and evaluation result, we expect that with this implementation, the number of communicating messages between inter-domain routers would be reduced significantly comparing to MSDP protocol in large-scale network.
Ipsilateral coordination features for automatic classification of Parkinson's disease

A reliable diagnosis of the Parkinson Disease lies on the objective evaluation of different motor sub-systems. Discovering specific motor patterns associated to the disease is fundamental for the development of unbiased assessments that facilitate the disease characterization, independently of the particular examiner. This paper proposes a new objective screening of patients with Parkinson, an approach that optimally combines ipsilateral global descriptors. These ipsilateral gait features are simple upper-lower limb relationships in frequency and relative phase spaces. These low level characteristics feed a simple SVM classifier with a polynomial kernel function. The strategy was assessed in a binary classification task, normal against Parkinson, under a leave-one-out scheme in a population of 16 Parkinson patients and 7 healthy control subjects. Results showed an accuracy of 94;6% using relative phase spaces and 82;1% with simple frequency relations.
The School Environment as a Source of Somatic Problems in Adolescents

Somatic symptoms are a prevalent health problem in children and adolescents. The specific cause often remains unidentified in paediatrics and cases are therefore frequently assigned to psychological aetiology. The most common cause of somatic symptoms is stress which can arise from a variety of areas of adolescent life. One significant factor affecting the mental health and wellbeing of youth is their school environment. The link between somatic symptoms and school has thus far been studied mainly from the perspective of the impact of health problems on academic performance, attendance or relationships with schoolmates. However, we can assume that the school environment and its characteristics can equally be a source of stress for adolescents and thereby also the reason for the occurrence of somatic issues. The main aim of this study is to investigate the presence of somatic symptoms in adolescents and above all, to determine if they can be caused by factors such as attachment to school, school climate, feeling of safety at school, teacher support, and self-academic motivation. To examine the factors of the school environment and the presence of individual somatic symptoms, we used the scales of the Social and Health Assessment (SAHA) questionnaire. Amongst the 1,961 adolescents observed, the most frequent somatic symptom was unspecified aches or pains (65.4%) followed by headaches (59.3%) and rashes/other skin problems (46.8%). Linear regression analysis showed that all examined factors of the school environment significantly lead to the occurrence of somatic symptoms.
Low Latency and Power Efficient Aproximate Multipliers using Compressors

:- Approximate computing has been considered to improve the accuracy-performance trade-off in error-tolerant applications. For many of these applications, multiplication is a key arithmetic operation. Given that approximate compressors are a key element in the design of power-efficient approximate multipliers, we first propose an initial approximate 4:2 compressor that introduces a rather large error to the output. According to the mean relative error distance (MRED), the most accurate of the proposed 16×16 unsigned designs has a 44% smaller power-delay product (PDP) compared to other designs with comparable accuracy. The radix-4 signed Booth multiplier constructed using the proposed compressor achieves a 52% reduction in the PDP-MRED product compared to other approximate Booth multipliers with comparable accuracy. The proposed multipliers outperform other approximate designs in image sharpening and joint photographic experts group (JPEG) applications by achieving higher quality outputs with lower power consumptions.
Grasping Algorithm for Varied Objects

To connect the robot with human life, we need an effective grasping algorithm. The robot should be able to grasp varied object in varied environment. The elements necessary for grasping algorithm are as follows: First, when grasping the object with diverse sizes and shapes, the fingers have to contact with them as widely as possible. Second, the robot should be able to control the force imposed on the object by finger knuckles. Third, if the size and position of object are not known accurately, the robot should be able to grasp it effectively. In this paper, we propose a grasping algorithm that can satisfy these conditions, showing the efficiency of this algorithm through the simulation.
Quantifying, generating and mitigating radio interference in Low-Power Wireless Networks

Radio interference affects the performance of low-power wireless networks (LPWN), leading to packet loss and reduced energy-efficiency, among other problems. Reliability of communications is key to expand application domains for LPWN. Since most LPWN operate in the license-free Industrial Scientific and Medical (ISM) bands and hence share the spectrum with other wireless technologies, addressing interference is an important challenge. In this context, we present JamLab: a low-cost infrastructure to augment existing LPWN testbeds with accurate interference generation in LPWN testbeds, useful to experimentally investigate the impact of interference on LPWN protocols. We investigate how interference in a shared wireless medium can be mitigated by performing wireless channel energy sensing in low-cost and low-power hardware. For this pupose, we introduce a novel channel quality metric— dubbed CQ—based on availability of the channel over time, which meaningfully quantifies interference. Using data collected from a number of Wi-Fi networks operating in a library building, we show that our metric has strong correlation with the Packet Reception Rate (PRR). We then explore dynamic radio resource adaptation techniques--namely packet size and error correction code overhead optimisations---based on instantaneous spectrum usage as quantified by our CQ metric. To conclude, we study emerging fast fading in the composite channel under constructive baseband interference, which has been recently introduced in low-power wireless networks as a promising technique. We show the resulting composite signal becomes vulnerable in the presence of noise, leading to significant deterioration of the link, whenever the carriers have similar amplitudes. Overall, our results suggest that the proposed tools and techniques have the potential to improve performance in LPWN operating in the unlicensed spectrum, improving coexistence while maintaining energy-efficiency. Future work includes implementation in next generation platforms, which provides superior computational capacity and more flexible radio chip designs.
Assessing the Impact of Embedding Online Academic and Information Literacy Resources into a First Year Business Course

Abstract Objectives – Literature supports the concept that embedding academic and information literacy support into first year university courses enables students to proceed more confidently with researching and writing their assignments, and thus contributes to student success in their course. A need was identified for academic and information literacy support for a cohort of first year business students as part of the development of online course content for Griffith Online, the institution’s online study degree option. This led to a collaboration between information literacy librarians, learning skills advisers, educational designers, and academic course convenors to develop and implement online resources. This paper will present findings on the impact of these online resources. Methods – Drawing on measures and methods identified in ISO16439 “Information and documentation: Methods and procedures for assessing the impact of libraries” (International Organisation for Standardization, 2014), in conjunction with the indicators offered by Lizzio’s (2006) Five Senses of Success framework, evidence was collected and combined from a variety of sources over semester 2, 2014, and semester 1, 2015 to assess the impact of the online resource. Inferred evidence was gathered from usage statistics (number of hits on the sites) and from performance measures (comparing student essay grade between those that did and did not use the resource). Solicited evidence was gathered from a survey of students, students in focus groups, and interviews with course lecturers, tutors and other stakeholders. Results – The inferred evidence showed a positive impact on the student success indicators of the sense of resourcefulness, capability, connection, purpose and identity. The solicited evidence suggests that students saw the online resource in a positive light and that staff were happy with the impact it had on students’ work and learning. It is believed that the gathered evidence indicates the Module did achieve the impact objective of a positive impact on the contribution to student success for these first year business students. Conclusions – The evidence has shown that this resource contributed to student success, and that staff and student satisfaction with the resource contributed to increased confidence with student academic skills and information literacy in respect to their assignment task. Assessing the impact of the online resource on student success has helped to demonstrate the value of the library at Griffith University to the wider community. The four-pronged collaboration relationship required for this approach was fostered with stakeholders outside of the library.
Pre-commitment and Updating Beliefs

Beliefs help individuals make predictions about the world. When those predictions are incorrect, it may be useful to update beliefs. However, motivated cognition and biases (notably, hindsight bias and confirmation bias) can instead lead individuals to reshape interpretations of new evidence to seem more consistent with prior beliefs. Pre-committing to a prediction or evaluation of new evidence before knowing its results may be one way to reduce the impact of these biases and facilitate belief updating. I first examined this possibility by having participants report predictions about their performance on a challenging anagrams task before or after completing the task. Relative to those who reported predictions after the task, participants who pre-committed to predictions reported predictions that were more discrepant from actual performance and updated their beliefs about their verbal ability more (Studies 1a and 1b). The effect on belief updating was strongest among participants who directly tested their predictions (Study 2) and belief updating was related to their evaluations of the validity of the task (Study 3). Furthermore, increased belief updating seemed to not be due to faulty or shifting memory of initial ratings of verbal ability (Study 4), but rather reflected an increase in the discrepancy between predictions and observed outcomes (Study 5). In a final study (Study 6), I examined pre-commitment as an intervention to reduce confirmation bias, finding that pre-committing to evaluations of new scientific studies eliminated the relation between initial beliefs and evaluations of evidence while also increasing belief updating. Together, these studies suggest that pre-commitment can reduce biases and increase belief updating in light of new evidence.
Maternal Child-Feeding Practices: Relationship with the BMI and Body Fat Percentage of Mexican children

Objectives: 1) To assess the reliability of the Child Feeding Questionnaire (CFQ), 2) To describe maternal child feeding practices (MCFP) and 3) To associate the MCFP with the child’s body mass index (BMI) and body fat percentage (BFP). Methods: A cross-sectional correlational study. Participants: 786 pairs (mother and preschooler / schooler child). The mothers answered the CFQ, which consists of 31 items, distributed among seven factors. The mother’s and child’s weight and height were measured, and the child’s BFP. Descriptive statistics and Spearman’s correlation coefficient were applied. Results: The CFQ obtained a 0.75 Cronbach’s alpha coefficient, 283 children (36.01%) were overweight or obese, BFP mean was 25.90 (SD = 9.92) for boys and 27.20 (SD = 8.79) for girls. Related to MCFP, the perceived responsibility factor obtained the highest score (Mean = 84.35, SD = 16.99), while the lowest scores were for perceived child weight (Mean = 50.06, SD = 8.38) and concern about child weight (Mean = 44.69, SD = 29.34). The factor, perceived child weight was associated with the child’s BMI (rs = 564, p = .001) and BFP (rs = 535, p = .001).
Using Web-based laboratories for control engineering education

This paper describes the experience of the Department of Computer Sciences and Automatic Control of the Spanish University of Distance Learning (UNED) about the use of Web-based laboratories in a distance course on automatic control. To begin with , we present the approach followed to transform three conventional didactical setups (a three-tank system, a heat-flow apparatus and an electrical drive servo motor) into virtual/ remote control laboratories. This app roach differentiates two layers in the construction of We b- based laboratories: the experimentation layer and t he e- learning layer. For the experimentation layer, LabV IEW and data acquisition boards from National Instruments are used to create the server-side applications, an d Easy Java Simulations for the client-side interfaces. Fo r the e- learning layer, the eMersion environment is used to support the required flexible educational scheme.
Asymptotically good convolutional codes with feedback encoders

The results of a computer search for convolutional encoders for the binary symmetric channel and the additive white Gaussian noise channel are reported. Both feedforward and feedback encoders are considered, and it is shown that when the bit error rate is the design criteria feedback encoders may outperform the best feed-forward encoders at no additional complexity. Thesis Supervisor: Amos Lapidoth Title: Assistant Professor
n Masjienleerbenadering tot woordafbreking in Afrikaans

The aim of this study was to determine the level of success achievable with a purely pattern based approach to hyphenation in Afrikaans. The machine learning techniques artificial neural networks, decision trees and the TEX algorithm were investigated since they can be trained with patterns of letters from word lists for syllabification and decompounding. A lexicon of Afrikaans words was extracted from a corpus of electronic text. To obtain lists for syllabification and decompounding, words in the lexicon were respectively syllabified and compound words were decomposed. From each list of ±183 000 words, ±10 000 words were reserved as testing data and the rest was used as training data. A recursive algorithm for decompounding was developed. In this algorithm all words corresponding with a reference list (the lexicon) are extracted by string fitting from beginning and end of words. Splitting points are then determined based on the length of reassembled words. The algorithm was expanded by addressing shortcomings of this basic procedure. Artificial neural networks and decision trees were trained and variations of both were examined to find optimal syllabification and decompounding models. Patterns for the TEX algorithm were generated by using the program OPatGen. Testing showed that the TEX algorithm performed best on both syllabification and decompounding tasks with 99,56% and 99,12% accuracy, respectively. It can therefore be used for hyphenation in Afrikaans with little risk of hyphenation errors in printed text. The performance of the artificial neural network was lower, but still acceptable, with 98,82% and 98,42% accuracy for syllabification and decompounding, respectively. The decision tree with accuracy of 97,91% on syllabification and 90,71% on decompounding was found to be too risky to use for either of the tasks A combined algorithm was developed where words are first decompounded by using the TEX algorithm before syllabifying them with both the TEX algoritm and the neural network and combining the results. This algoritm reduced the number of errors made by the TEX algorithm by 1,3% but missed more hyphens. Testing the algorithm on Afrikaans publications showed the risk for hyphenation errors to be ±0,02% for text assumed to have an average of ten words per line.
Breathing Monitoring and Pattern Recognition with Wearable Sensors

This chapter introduces the anatomy and physiology of the respiratory system, and the reasons for measuring breathing events, particularly, using wearable sensors. Respiratory monitoring is vital including detection of sleep apnea and measurement of respiratory rate. The automatic detection of breathing patterns is equally important in other respiratory rehabilitation therapies, for example, magnetic resonance exams for respiratory triggered imaging, and synchronized functional electrical stimulation. In this context, the goal of many research groups is to create wearable devices able to monitor breathing activity continuously, under natural physiological conditions in different environments. Therefore, wearable sensors that have been used recently as well as the main signal processing methods for breathing analysis are discussed. The following sensor technologies are presented: acoustic, resistive, inductive, humidity, acceleration, pressure, electromyography, impedance, and infrared. New technologies open the door to future methods of noninvasive breathing analysis using wearable sensors associated with machine learning techniques for pattern detection.
Outlier Detection And Data Association For Data Mining Criminal Incidents

Outlier detection has been extensively studied in the field of statistics, and a number of discordancy tests have been developed. Most of these studies treat outliers as “noise” and they try to eliminate the effects of outliers by removing outliers or develop some outlier-resistant methods. However, in data mining, we consider outliers “meaningful input signals” rather than “noise”. In some cases, outliers represent unique characteristics of the objects, which are important to an organization. Law enforcement is one area where outlier detection is critically important. In law enforcement, we want to associate criminal incidents caused by the same persoflgroup and detect outliers from this behavior. The purpose of this paper is two-folded: to describe an outlier detection technique and to propose a data association method based upon this technique. We focus our analysis on categorical data since these data are typically found in crime analysis. First, we develop an outlier score function, which measures the “extremeness” level for observations. Then we dkcuss some of the properties of this outlier score function. Finally, we describe a data association method based on the outlier score function. We apply our data association method to a real robbery data from the Richmond, Virginia, USA. Results show that the approach is promising.
Modeling, Analysis, and Optimization of Robustness in Interdependent Networks against Cascading Failures

Critical infrastructures, including those that concern the nation's economy and security such as electrical power systems, water distribution systems and transportation systems, are becoming more and more interdependent with each other. Although they bring unprecedented improvements on efficiency and flexibility, the interdependent relations enable failures in one network to propagate and impact the performance of other coupled networks. Cascading failures is one such phenomenon that creates dramatic damages to critical infrastructures, where a small initial shock can get escalated due to the intricate dependencies and result in system-wide collapses. This dissertation aims to understand and mitigate the root cause of the seemingly unexpected large-scale cascading failures by characterizing and modeling the inherent dependencies between and within different networks. A main finding is that allocating the available redundancies uniformly across the system maximizes the robustness against random failures. We support this thesis statement with different networks and attack types: flow-carrying networks under random and targeted attack, interdependent flow-carrying networks under random attacks, and interdependent cyber-physical networks under random attacks. In the flow redistribution network, we propose a global and equal flow redistribution model to capture the cascading failure dynamics. In the case of random attacks, we derive the final system size and critical attack size, and prove that the optimal robustness is reached when system redundancy is allocated uniformly. For targeted attacks, we propose the optimization problem of finding the best k lines to attack so as to minimize the number of alive lines at the steady-state, to reveal the worst-case attack vulnerability of the system. In interdependent flow-carrying networks, we study a model where the flow of a failed line is redistributed partially within the network that the failed line belongs to, with the rest being shed to other coupled networks. Analyzing the cascading failures in this model, we show that interdependence has a multi-faceted impact on system robustness in that as the level of coupling increases, the chance for both networks to survive or collapse concurrently increases, whereas it becomes more difficult for each component network to survive on its own. To understand the robustness of integrated cyber-physical systems (CPSs), we develop a novel interdependent system model to capture the inherently different failure cascade characteristics of each component network; i.e., the cyber and the physical networks are governed by different cascade rules to be able to function. We demonstrate the ability of our model to capture the unexpected nature of large-scale cascading failures in CPSs, and provide insights on improving system robustness by proposing optimal redundancy allocation schemes.