Miracle-dz/UniFAR-FTUs huggingface dataset:
‘I don’t think they realise how good we are’: Innovation, inclusion and exclusion in women’s Olympic boxing

Marginalisation and exclusion of women in elite boxing has emerged as a substantial international problem, threatening women’s democratic right to equal participation in sport. Since the London 2012 Olympic Games women’s boxing has been an Olympic event. However, only 3 of the 10 weight categories were included in the Olympic programme for women. Today, male boxers compete in 10 out of 10 possible weight categories. While female boxers have gained accessed to a previously closed realm, discrimination, exclusion and unequal treatment of female athletes remains a widespread problem in international elite boxing. This article explores how the introduction of women’s boxing into the Olympic Games involves practices of inclusion and exclusion in elite boxing. In particular, this analysis focuses on the following two research questions: (a) how practices of social exclusion and inclusion affect the involvement and engagement of women in boxing; and (b) how, and in what ways, female boxers have experienced recognition and acceptance in elite boxing after the introduction of women’s boxing into the Olympic Games. Using a qualitative approach including document analysis of official press releases from the International Olympic Committee (IOC) and the International Boxing Association (AIBA), as well as interviews with athletes and coaches, this study reveals that although women’s boxing has gained access to the Olympics, the structural and cultural changes that comprise the social inclusion of women in boxing are yet to be implemented.
Audio feature extraction for classification using relative transformation

Audio feature extraction plays a much important role in the areas of audio processing. This paper proposes a new audio feature extraction method using the relative transformation (RT). It begins with equally dividing an audio signal into a lot of segments. On each segment, the mel-frequency cepstral coefficients are extracted and combined by RT to generate a single feature vector. All these vectors are then combined by RT again to generate a single feature vector for the audio. This method can nicely deal with the noisy, sparse, and imbalance problems, while it has lower time complexity. It is purely data-driven and does not depend on particular audio characteristics. The experimental results suggest that the classifier with the proposed method often gives the better results in classification.
Online Multi-Object Tracking by Quadratic Pseudo-Boolean Optimization

Online multi-object tracking (MOT) is challenging: frame-by-frame matching of detection hypotheses to the correct trackers can be difficult. The Hungarian algorithm is the most commonly used online MOT data association method due to its rapid assignment; however, the Hungarian algorithm simply considers associations based on an affinity model. For crowded scenarios, frequently occurring interactions between objects complicate associations, and affinity-based methods usually fail in these scenarios. Here we introduce quadratic pseudo-Boolean optimization (QPBO) to an online MOT model to analyze frequent interactions. Specifically, we formulate two useful interaction types as pairwise potentials in QPBO, a design that benefits our model by exploiting informative interactions and allowing our online tracker to handle complex scenes. The auxiliary interactions result in a non-submodular QPBO, so we accelerate our online tracker by solving the model with a graph cut combined with a simple heuristic method. This combination achieves a reasonable local optimum and, importantly, implements the tracker efficiently. Extensive experiments on publicly available datasets from both static and moving cameras demonstrate the superiority of our method.
Multi-class power quality disturbances classification by using ensemble empirical mode decomposition based SVM

This paper presents performance comparisons of Support Vector Machine (SVM) and different classification method for power quality disturbance classification. The first goal of this study is to investigate EEMD (ensemble empirical mode decomposition) performance and to compare it with classical EMD for feature vector extraction and selection of power quality disturbances. Features are extracted from the power electrical signals by using Hilbert Huang Transform (HHT). This technique is a combination of ensemble empirical mode decomposition (EEMD) and Hilbert transform (HT). The outputs of HT are instantaneous frequency (IF) and instantaneous amplitude (IA). Characteristic features are obtained from first IMFs', IF and IA. The ten features, i.e. mean, standard deviation, singular values, maxima and minima of IF and IA, are then calculated. These features are normalized and the inputs of SVM and other classifiers.
FBG-based shape sensing tubes for continuum robots

Fiber Bragg gratings (FBG)-based optical sensors are a promising real-time technique for sensing the 3D curvature of continuum robots. Existing implementations, however, have relied on embedding optical fibers in small-diameter metal wires or needles. This paper proposes polymer tubes as an alternative substrate for the fibers. This approach separates the sensors from the robot structural components while using a minimal amount of the robot's tool lumen and providing the potential of inexpensive fabrication. Since the fibers are stiffer than the polymer substrate, however, design challenges arise in modeling strain transfer between the fibers and the tube substrate. To investigate the potential of this approach, a strain transfer model is derived and validated through simulation and experiment.
A New Thermal-Aware Voltage Island Formation for 3D Many-Core Processors

The power consumption of 3D many-core processors can be reduced, and the power delivery can be also improved by introducing voltage island design using on-chip voltage regulators. With the dramatic growth in the number of cores that are integrated in a processor, however, it is infeasible to adopt per-core voltage island design. We propose a 3D many-core processor architecture that consists of multiple voltage clusters, where each has a set of cores that share an on-chip voltage regulator. Based on the architecture that is fixed in pre-fabrication, the steady state temperature is analyzed so that the thermal characteristic of each voltage cluster is known. In the voltage scaling and task scheduling stages, the thermal characteristics are considered in addition to the communication between cores. The consideration of thermal characteristics enables the proposed voltage island formation to reduce the total energy consumption as well as the peak temperature and temperature gradients in 3D many-core processors.
Quantitative comparison of micro-vasculatures

Angiogenesis is a key component of ontogenesis, but also of tumor development or in some pathology repair (i.e. ischemia). Deciphering the underlying mechanisms of vessel formation is of importance. We aimed at identifying and characterizing the genetic components that are involved in this development. This requires to compare the effect of each gene with respect to the others, hence appeals for quantitative comparisons. To that end, we propose then in this paper a methodology that first transforms a vascular image into a tree and second quantitatively analyze 3D vascular trees. Our preliminary experiments are conducted with images of the renal arterial network of different mutant mice. A first set of quantitative measurements are proposed that allow for group study.
Study of MPEG-7 Sound Classification and Retrieval

In this paper, we present a comparison of three audio taxonomy methods for MPEG-7 sound classification. The MPEG-7 sound classification and indexing tools consist of both low-level and high-level description schemes. For the low-level descriptors that we use, low-dimensional features based on spectral basis descriptors are produced in three stages: normalized audio spectrum envelope, principal component analysis, and independent component analysis. High-level description schemes are used thereafter to describe the modeling of audio features, the procedure of audio classification, and retrieval. For classification we test three approaches: the direct approach, the hierarchical approach without hints, and the hierarchical approach with hints. Our experimental results show that the best approach is the hierarchical approach with hints, which results in a classification accuracy of around 99%. The direct approach produces the second best results, and the hierarchical approach without hints the third best results.
Research Session 1—choreography of Business Process Management Research Session 2—modeling and Abstraction of Services Process Research Session 3—business Oriented and Enabled Soa Detection of Semantically Equivalent Fragments for Business Process Model Research Session 4—cloud Services Research Ses

In a web service composition, an electronic contract (e-contract) regulates how the services participating in the composition should behave, including the restrictions that these services must fulfill, such as real-time constraints. In this work we present a visual model that allows us to specify e-contracts in a user friendly way, including conditional behavior and realtime constraints. A case study is presented to illustrate how this visual model defines e-contracts and a preliminary evaluation of the model is also done. Keywords-contracts; deontic specifications; visual models;
Toward building an efficient Application Layer Multicast tree

Link cost is very important in media distribution trees. A good cost function can provide information for media routing algorithms to find the best way to distribute the media on overlay networks. In this research, a bandwidth-type cost function is proposed. The proposed cost function can calculate links’ costs based on both network resources and application’s requirements. It can help Application Layer Multicast (ALM) routing algorithms to avoid congestion before building media distributing trees. The derivation process has also been explained in details so that it can be further applied in other conditions to build other cost functions suitable for different requirements. The newly proposed cost function will then be applied in a popular Application Layer Multicast (NICE) to replace its old cost function. Possible modifications to NICE’s algorithm will be discussed. Intensive simulation scenarios have also been carried out to validate the advanced performance of the newly proposed cost function in comparison with conventional functions. The simulation scenario has also been developed to show the adaptation of the cost function under real conditions when some peers use the WiMax access network to join the multicast group.
Massive star formation in the W49 giant molecular cloud: Implications for the formation of massive star clusters

We present results from JHKs imaging of the densest region of the W49 molecular cloud. In a recent paper (Alves & Homeier 2003, ApJ, 589, L45), we reported the detection of (previously unknown) massive stellar clusters in the well-known giant radio HII region W49A, and here we continue our analysis. We use the extensive line-of-sight extinction to isolate a population of objects associated with W49A. We constrain the slope of the stellar luminosity function by constructing an extinction-limited luminosity function, and use this to obtain a mass function. We find no evidence for a top-heavy MF, and the slope of the derived mass function is −1.6 ± 0.3. We identify candidate massive stars from our color-magnitude diagram, and we use these to estimate the current total stellar mass of 5−7 × 10 4 Min the region of the W49 molecular cloud covered by our survey. Candidate ionizing stars for several ultra-compact HII regions are detected, with many having multipe candidate sources. On the global molecular cloud scale in W49, massive star formation apparently did not proceed in a single concentrated burst, but in small groups, or subclusters. This may be an essential physical description for star formation in what will later be termed a "massive star cluster".
Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach

We study a model of a stochastic process with unobserved parameters which suddenly change at random times. The possible parameter values are assumed to be from a finite but unknown set. Using a Chinese restaurant process prior over parameters we develop an efficient MCMC procedure for Bayesian inference. We demonstrate the significance of our approach with an application to systems biology data.
Activity classification using a single wrist-worn accelerometer

Automatic identification of human activity has led to a possibility of providing personalised services in different domains i.e. healthcare, security and sport etc. With advancement in sensor technology, automatic activity recognition can be done in an unobtrusive and non-intrusive way. The placement of the sensor and wearability are ones of vital keys in the successful activity recognition of free space livings. Experiments were carried out to investigate the use of a single wrist-worn accelerometer for automatic activity classification. The performances of two classification algorithms namely Decision Tree C4.5 and Artificial Neural Network were compared using four different sets of features to classify five daily living activities. The result revealed that Decision Tree C4.5 has outperformed Neural Network regardless of the different sets of features used. The best classification result was achieved using the set containing the most popular and accurate features i.e. mean, minimum, energy and sample differences etc. The best accuracy of 94.13% was achieved using only wrist-worn accelerometer showing a possibility of automatic activity classification with no movement constrain, discomfort and stigmatisation caused by the sensor.
iDBMM: A Novel Algorithm to Model Dynamic Behavior in Large Evolving Graphs

In the dynamic social network, how to use data mining tools to find the hidden dynamic knowledge in the social network has become the focus of the study. It can be applied to a wide range of areas with good practical value and application significance. We propose a novel algorithm called iDBMM based on the improvement of DBMM algorithm. At first, iDBMM algorithm classifies the training set to obtain the basic characteristics of each role. Then it scores the test set relative to each role and distribute the role of the highest score to the corresponding node. Finally, the transition model is obtained by the statistical method. Experimental results show that new method determines the distribution of the roles of the nodes effectively to make up for the shortcoming of non-negative matrix factorization and improve the prediction accuracy.
Under review - comments welcome but please do not cite! Unsupervised Disambiguation for a Multilingual Medical Information System using UMLS

This paper describes techniques for unsupervised word sense disambiguation of English and German medical documents using the Unified Medical Language System (UMLS). We present both monolingual techniques which rely only on the structure of UMLS, and bilingual techniques which also rely on the availability of parallel corpora. The best results are obtained using relationships between terms given by UMLS, a method which achieves 74% precision, 66% coverage for English and 79% precision, 73% coverage for German on evaluation corpora and over 83% coverage over the whole corpus. The success of this technique for German shows that a lexical resource giving relationships between concepts used to index an English document collection can be used for high quality disambiguation in another language. Document retrieval experiments comparing models using semantic annotation and disambiguation show that a search engine which retreives documents using the semantic annotation with UMLS concepts is much more effective than a baseline search engine which indexes only tokens. In addition, adding the automatic disambiguation component to this search engine gave a small but reliable improvement in precision at high-recall document retrieval.
Explicating symbolic execution (xSymExe): An evidence-based verification framework

Previous applications of symbolic execution (Sym-Exe) have focused on bug-finding and test-case generation. However, SymExe has the potential to significantly improve usability and automation when applied to verification of software contracts in safety-critical systems. Due to the lack of support for processing software contracts and ad hoc approaches for introducing a variety of over/under-approximations and optimizations, most SymExe implementations cannot precisely characterize the verification status of contracts. Moreover, these tools do not provide explicit justifications for their conclusions, and thus they are not aligned with trends toward evidence-based verification and certification. We introduce the concept of explicating symbolic execution (xSymExe) that builds on a strong semantic foundation, supports full verification of rich software contracts, explicitly tracks where over/under-approximations are introduced or avoided, precisely characterizes the verification status of each contractual claim, and associates each claim with explications for its reported verification status. We report on case studies in the use of Bakar Kiasan, our open source xSymExe tool for Spark Ada.
Validation of dietary assessment methods in the UK arm of EPIC using weighed records, and 24-hour urinary nitrogen and potassium and serum vitamin C and carotenoids as biomarkers.

BACKGROUND In the UK EPIC validation studies, the accuracy of several methods was assessed by comparison with to-day weighed records and the biomarkers, 24-hour urine nitrogen (N) and potassium (K), plasma carotenoids and plasma vitamin C. METHODS Comparisons between methods were made on 156 women, studied over 1 year at 3-monthly intervals at home. On each of four occasions, volunteers completed 4 days of weighed records and provided two 24-hour urine collections and a fasting blood sample. RESULTS In comparison with the 16 days of weighed records, a food frequency questionnaire (FFQ) yielded higher values mainly due to greater reported consumption of milk and of vegetables. A 24-hour recall was as good as the FFQ in placing individuals in the distribution of habitual diet from weighed records. Results obtained from a 7-day estimated record were closest to those obtained from the weighed record. Correlations between 24-hour urine excretion and dietary N intake from weighed records were high (0.78-0.87) as were those with estimated food diaries (0.60-0.70). Correlations between urine N and the FFQ and 24-hour recall were lower (0.10 to 0.27), but improved by energy adjustment using residuals for N and K which are correlated with total energy intake. Comparisons between dietary estimates and urinary K and serum carotenoids and vitamin C showed broadly similar results. Limited biomarker information amongst 200 UK EPIC participants supported the findings of the validation study. CONCLUSIONS UK EPIC uses three methods (the 7-day diary, an improved FFQ, and the 24-hour recall) to assess diet. 93% of first food diaries are returned completed by participants. Repeated diaries are the main dietary assessment method for nested case-control analyses.
Predicting Support for Procedurally Just Treatment: The Case of the Israel National Police

The importance of police treating citizens with procedural justice is well recognized. Recently, scholars have begun exploring officers’ views and beliefs that are associated with support for procedurally fair policing, but have not relied on a consistent conceptual framework. In the present study, we propose such a framework, focusing on three core realms of the policing environment: officers’ affiliation with their supervisors, officers’ perceptions of their authority and powers, and officers’ relationship with the public. We then use this framework to predict Israeli police officers’ endorsement of procedurally just policing. We find positive, direct effects for perceived public support, self-legitimacy, years of experience, and being a minority officer. In contrast to previous findings, internal procedural and distributive justice did not show significant effects. We discuss these findings and their implications, and stress that the relationship between attitudes toward procedural justice and actual behavior continues to be explored.
Risk of Adverse Obstetric and Neonatal Outcomes by Maternal Age: Quantifying Individual and Population Level Risk Using Routine UK Maternity Data

Objective The objective of this study was to investigate whether moderately increased maternal age is associated with obstetric and neonatal outcome in a contemporary population, and to consider the possible role of co-morbidities in explaining any increased risk. Study Design Secondary analysis of routinely collected data from a large maternity unit in London, UK. Data were available on 51,225 singleton deliveries (≥22 weeks) occurring to women aged ≥20 between 2004 and 2012. Modified Poisson regression was used to estimate risk ratios for the association between maternal age and obstetric and neonatal outcome (delivery type, postpartum haemorrhage, stillbirth, low birthweight, preterm birth, small for gestational age, neonatal unit admission), using the reference group 20–24 years. Population attributable fractions were calculated to quantify the population impact. Results We found an association between increasing maternal age and major postpartum haemorrhage (≥1000ml blood loss) (RR 1.36 95% CI 1.18–1.57 for age 25–29 rising to 2.41 95% CI 2.02–2.88 for age ≥40). Similar trends were observed for caesarean delivery, most notably for elective caesareans (RR 1.64 95% CI 1.36–1.96 for age 25–29 rising to 4.94 95% CI 4.09–5.96 for age ≥40). There was evidence that parity modified this association, with a higher prevalence of elective caesarean delivery in older nulliparous women. Women aged ≥35 were at increased risk of low birthweight and preterm birth. We found no evidence that the risk of stillbirth, small for gestational age, or neonatal unit admission differed by maternal age. Conclusions Our results suggest a gradual increase in the risk of caesarean delivery and postpartum haemorrhage from age 25, persisting after taking into account maternal BMI, hypertension and diabetes. The risk of low birthweight and preterm birth was elevated in women over 35. Further research is needed to understand the reasons behind the high prevalence of elective caesarean delivery in nulliparous older mothers.
Leakage Power and Circuit Aging Cooptimization by Gate Replacement Techniques

As technology scales, the aging effect caused by negative bias temperature instability (NBTI) has become a major reliability concern. In the mean time, reducing leakage power remains to be one of the key design goals. Because both NBTI-induced circuit degradation and standby leakage power have a strong dependency on the input vectors, input vector control (IVC) technique could be adopted to reduce the leakage power and mitigate NBTI-induced degradation. The IVC technique, however, is ineffective for larger circuits. Consequently, in this paper, we propose two gate replacement algorithms [direct gate replacement (DGR) algorithm and divide and conquer-based gate replacement (DCBGR) algorithm], together with optimal input vector selection, to simultaneously reduce the leakage power and mitigate NBTI-induced degradation. Our experimental results on 23 benchmark circuits reveal the following. 1) Both DGR and DCBGR algorithms outperform pure IVC technique by 15%-30% with 5% delay relaxation for three different design goals: leakage power reduction only, NBTI mitigation only, and leakage/NBTI cooptimization. 2) The DCBGR algorithm leads to better optimization results and save on average more than 10 runtime compared to the DGR algorithm. 3) The area overhead for leakage reduction is much more than that for NBTI mitigation.