Miracle-dz/UniFAR-FTUs huggingface dataset:
‘Listening to myself’: improving oracy and literacy among children who fall behind

There is now a renewed emphasis in the UK on short intensive interventions to tackle reading failure. In this paper we report on the effect of a programme based on a view that reading problems are associated with the inability of the learner to deal with speech at the level of individual speech sounds even though they may be fully competent in the production and perception of oral language. We investigated the effect of a self‐voice feedback intervention programme on the word recognition abilities of pupils who were experiencing reading delay. Our sample was made up of 159 pupils aged 6–13 years, whose reading age was at least one year behind their chronological age, drawn from seven schools in England. We used a quasi‐experimental pre‐test/post‐test design with experimental and control groups using between‐subjects (randomly assigned) and within‐subjects (those waiting‐in‐line as controls before entering an intervention group) analysis. We found that those in the intervention condition made significantly greater gains in their word recognition abilities than their counterparts in control conditions or than they themselves had made prior to entering the intervention condition. We concluded that whilst the success of the programme suggests that pupils who display reading delay problems can have their word recognition abilities improved by an intense self‐voice feedback intervention, at least in the short term, further work is necessary to investigate how the intervention works procedurally and the longevity of its effect.
Hahn-Banach separation theorem for max-plus semimodules

We introduce max-plus analogues of basic Euclidian geometry notions: scalar product is replaced by a scalar division, and the associated distance is essentially Hilbert's projective distance. We introduce an orthogonal projection and prove a Hahn-Banach type theorem: a point can be separated from a semimodule by a hyperplane orthogonal to the direction of projection. We use these results to separate max-plus convex sets, and illustrate this new geometry by two-dimensional examples.
Adaptive myoelectric human-machine interface for video games

This paper proposes adaptive schemes for myoelectric based human-machine interface (HMI) applied to a video game. Adaptive schemes modify the classification criteria to keep a stable performance in long-term operations. Online support vector machine (SVM) is used as the core of classification to facilitate incremental training during run-time. Supervised and unsupervised methods are individually employed to update online training data set. The experimental results show that the proposed adaptive schemes increase the achieved scores and make a stable performance for myoelectric HMI.
Competence Based Educational Metadata for Supporting Lifelong Competence Development Programmes

In the context of the emerging paradigm of lifelong learning, competence-based learning is gradually attracting the attention of the technology-enhanced learning community, since it appears to meet the training expectations of both individuals and organisations. On the other hand, the paradigm of learning objects retains, to a large extent, its initial anticipations and, supported by educational metadata, it is still influencing technology-enhanced learning system design. Nevertheless, the IEEE LOM Specification does not directly support the description of learning resources in terms of their relevance to competence-based learning programmes. In this paper we target addressing this problem, that is, we identify and study the main issues related to the competence-relevant characteristics of learning resources and propose an IEEE LOM competence application profile that can be used for tagging educational resources in a competence-meaningful manner.
THE ASSESSMENT AND ANALYSIS OF HANDEDNESS

The need for a simply applied quantitative assessment of handedness is discussed and some previous forms reviewed. An inventory of 20 items with a set of instructions and response- and computational-conventions is proposed and the results obtained from a young adult population numbering some 1100 individuals are reported. The separate items are examined from the point of view of sex, cultural and socio-economic factors which might appertain to them and also of their inter-relationship to each other and to the measure com- puted from them all. Criteria derived from these considerations are then applied to eliminate 10 of the original 20 items and the results recomputed to provide frequency-distribution and cumulative frequency functions and a revised item-analysis. The difference of incidence of handedness between the sexes is discussed.
Eliminating conditional branches for enhancing instruction level parallelism in VLIW compiler

In VLIW (Very Long Installation Word) compilers, one of the most important issues is how to handle conditional branches, because control dependences are caused by conditional branches and limit the scope of scheduling. This paper proposes the efficient method of eliminating conditional branches. We use SSA (Static Single Assignment) information for preserving semantics. By using our methods, global scheduling techniques can be processed more efficiently and simply. We utilize /spl phi/-functions aggressively, thus computations for code motion are not required. We don't need complex hardware support. Our scheme also makes the performance independent on the result of branch outcomes.
Hippocampal neuronal damage in rats exposed to a double hit: irradiation and hyperthermia.

AIM In utero irradiation models induce diffuse neuronal damage. Experimental studies have shown that hyperthermia induced seizures are easily elicited and have high mortality accompanied by neuronal loss. Neuronal damage and loss are the results of cell death coupled with cortical development in altered cellular development. The aim of the study was to investigate the changes in hippocampus that was exposed to irradiation and hyperthermia. MATERIAL AND METHODS Four groups were studied: 1) The irradiation group was exposed to 225 cGy irradiation on the 17th gestational day; 2) The hyperthermia group was exposed to hyperthermia on the 10th postnatal day; 3) The hyperthermia plus irradiation group was exposed to in utero irradiation and postnatal hyperthermia; 4) The control group was sham operated. Animals were examined 3 and 6 months later. RESULTS The hippocampus was atrophic with neuronal loss in CA regions and ectopic neurons were in irradiation group. Severe damage with the most atrophy was demonstrated in all regions of the irradiation plus hyperthermia group. In long term, damage was severe in all groups. CONCLUSION This study demonstrated more damage in hippocampi exposed to both irradiation and hyperthermia that may be taken as an evidence for the double hit hypothesis in the development of hippocampal damage.
Finite-time state estimation of Markovian jumping neural networks with time-varying and distributed delays

This paper is concerned with finite-time state estimation of a class of delayed Markovian jumping neural networks. Both time-varying and distributed delays are taken into consideration. The measured output is assumed to be related to the distributed term. By choosing a suitable stochastic Lyapunov functional with tripe integral terms, a delay- and mode-dependent condition is derived such that the error system is stochastically finite-time stable with respect to prescribed scalars. It is shown that the gain matrices of the finite-time state estimator can be obtained by solving some linear matrix inequalities. Finally, an illustrative example is presented to show the effectiveness of the developed result.
Prevalence of Depressive Symptoms and Related Factors in Japanese Employees: A Comparative Study between Surveys from 2007 and 2010

Aims. The aim of this study was to examine the prevalence of depressive symptoms and their related factors in Japan. The results were analyzed to identify the relationship between high scores on the CES-D, sociodemographic status, and employment-related variables. Methods. Employees in Akita prefecture completed the Center for Epidemiologic Studies Depression Scale (CES-D) during a survey period between November and December 2010. The cutoff point for the CES-D scores was 16 or above (high scorers). Results. Data from 1,476 employees indicated that 44.2% had high scores on the CES-D. Sociodemographic and occupation-related factors associated with a high risk of depression were being female, young age, fewer hours of sleep on weekdays, and working over 8 hours per day, whereas drinking alcohol one to two days per week, albeit only in men, was significantly associated with a low risk of depression. The present results were consistent with the results of a previous survey completed in 2007; however, the present results regarding job categories and smoking behavior were not significantly associated with depression and thus were inconsistent with the 2007 survey data. Conclusions. These results can be useful as benchmark values for the CES-D and might help predict depressive disorders.
6.9 A 1.1µm 33Mpixel 240fps 3D-stacked CMOS image sensor with 3-stage cyclic-based analog-to-digital converters

There is an increasing demand for high-reality video systems. The ITU-R has standardized video parameters for ultra-high-definition TV (UHDTV), and the full-specification video signal stated in this international standard is prescribed as having a 7,680 (H) × 4,320 (V) pixel count, 120Hz frame frequency with progressive scanning, 12b tone reproduction, and wide color gamut. A 33Mpixel 120fps CMOS image sensor with a 12b column-parallel analog-to-digital converter (ADC) is reported in [1]. In addition to standard operation, higher sensitivity, smaller pixels, and a higher frame rate of 240fps or more are required for CMOS image sensors. Backside-illuminated stacked CMOS image sensors [2] are effective for simultaneously achieving both high sensitivity for small pixels and high operation speed. However, these stacked structures are still insufficient for high-frame-rate UHDTV image sensors since the pixel wafer and the ASIC wafer are connected by through-silicon vias in the peripheral area.
Superstition in Strategic Decision Making: A Two-Level Study

This study explores the role of superstition in strategic decision making. We propose that, in addition to rational and intuitional approaches, managers sometimes rely on supernatural or nonphysical causality, i.e., superstition. On the personal/decision-maker level, we examine how personal traits are linked to superstitious belief and practice in business decision. On the incident/decision level, we examine how decision characteristics are linked to the use of superstition and consequent decision outcomes. Using a sample of senior managers in China, we confirm the existence of superstition in strategic decision making and its distinctiveness from rationality and intuition. Our findings show that core self-evaluation is negatively related to superstitious belief whilst promotional orientation is positive; mangers are more likely to use superstition when they face important, uncertain, and urgent decisions. Contrary to common knowledge, superstition shows positive impact on decision effectiveness and satisfaction. A rare systematic empirical investigation of superstition in strategic decision making, this study makes important contribution to the management literature.
Student reflections on preference and use of lecture notes and recordings

This study examines feedback from students about the use of Tablet PC technology in material science lectures to help us understand how students use available learning resources and to inform the creation of future materials. Students commented on their preferences for being given full notes or partial notes which were annotated during the lectures and also on how they used notes and recordings in their learning. Students presented conflicting views on which style of note-taking they preferred with a varied range of reasons for their preferences. Feedback indicated that students perceive that live lectures are important and that the distribution of complete notes and recordings were useful as revision aids and if missing a lecture was unavoidable. Suggestions were made that the technology could also be used to produce podcasts of key points and videos of demonstrations performed in lectures.
Wave Propagation in Elastic Media with Micro/Nano-Structures

Microand nano-scale materials and structures such as plateor beam-like structures with submicron or nano thicknesses have attracted considerable interest from the scientific community due to the increasingly strong demands of miniaturization in the fields of microelectronics and nanotechnology. More and more nano-structures, e.g. ultra-thin films, nanowires and nanotubes, have been fabricated and served as the basic building blocks for microelectromechanical systems (MEMS) and nanoelectromechanical systems (NEMS) (Jin et al., 1998; Craighead, 2000; Husain et al., 2003; Feng et al., 2007). For long-term stability and reliability of various devices at nanoscale, researchers should possess a deep understanding and knowledge of mechanical properties of nano materials and structures, especially for dynamic properties. Among many techniques, high-frequency acoustic wave technique has been regarded as one of very efficient nondestructive methods to characterize elastic media with microor nanostructures. (Hernaandez et al., 2002) used high-frequency laser-excited guided acoustic waves to estimate the in-plane mechanical properties of silicon nitride membranes. Mechanical properties and residual stresses in the membranes were evaluated from measured acoustic dispersion curves. The mean values of the Young’s modulus and density of three nanocrystalline diamond films and a free standing diamond plate were determined by analyzing the dispersion of laser-generated surface waves (Philip et al., 2003). Recently, growing interest of using terahertz (THz) waves in nanoscale materials and nano-photonic or nano-phononic devices has opened a new topic on the wave characteristics of nanomaterials (Schneider et al., 2000, Vollmannn et al., 2004; Ramprasad & Shi, 2005; Sampathkumar et al., 2006). As dimensions of the material become smaller, however, their resistance to deformation is increasingly determined by internal or external discontinuities (such as surfaces, grain boundary, strain gradient, and dislocation). Although many sophisticated approaches for predicting the mechanical properties of nanomaterials have been reported, few addressed the challenges posed by interior nanostructures such as the surfaces, interfaces, structural discontinuities and deformation gradient of the nanomaterials under extreme loading conditions. The use of atomistic simulation may be a potential solution in the long run. However, it is well known that the capability of this approach is
Solving a new multi-objective hybrid flexible flowshop problem with limited waiting times and machine-sequence-dependent set-up time constraints

This paper develops a new multi-objective hybrid flexible flowshop problem with some useful constraints including of the limited waiting times (LWT) between every two successive operations, unrelated parallel machines at least one stage, sequence- and machine-dependent set-up times and due dates of jobs. A mathematical model for this problem as a mixed-integer programming is provided to simultaneously minimise the total weighted tardiness and maximum completion times. We applied two metaheuristic methods based on the Pareto approach, multi-objective Particle swarm optimisation and strength Pareto evolutionary algorithm II, to solve our problem and achieve the non-dominated Pareto frontier. To assess the performances of the presented algorithms and compare their results, a set of test problems in small and large sizes are created and then executed on the algorithms. Owing to the sensitivity of the values of parameters in the metaheuristic algorithms, a response surface methodology (RSM) as a strength statistical tool adjusts the parameters of both algorithms separately for small- and large-sized problems. The computational evaluation demonstrated that multi-objective Particle swarm optimisation is an effective and appropriate algorithm for our investigated problem.
DAISY: a simulation-based high-level synthesis tool for /spl Delta//spl Sigma/ modulators

An integrated tool called DAISY (Delta-Sigma Analysis and Synthesis) is presented for the high-level synthesis of /spl Delta//spl Sigma/ modulators. The approach determines both the optimum modulator topology and the required building block specifications, such that the system specifications mainly accuracy and signal bandwidth-are satisfied at the lowest possible power consumption. A genetic-based differential evolution algorithm is used in combination with a fast dedicated behavioral simulator that includes the major nonidealities of the building blocks to realistically analyze and optimize the modulator performance. Experimental results illustrate the effectiveness of the approach. Also, an overview of optimized topologies as a function of the modulator specifications for a wide range of values shows the capabilities and performance range covered by the tool.
Token-passing Optimal Reduction with Embedded Read-back

We introduce a new interaction net implementation of optimal reduction for the pure untyped lambda calculus. Unlike others, our implementation allows to reach normal form regardless of the interaction net reduction strategy using the approach of so-called token-passing nets and a non-deterministic extension for interaction nets. Another new feature is the read-back mechanism implemented without leaving the formalism of interaction nets.
Modular expansion of the hidden layer in Single Layer Feedforward neural Networks

We present a neural network architecture and a training algorithm designed to enable very rapid training, and that requires low computational processing power, memory and time. The algorithm is based on a modular architecture, which expands the output weights layer constructively, so that the final network can be visualised as a Single Layer Feedforward Network (SLFN) with a large hidden-layer. The method does not use backpropagation, and consequently offers very fast training and very few trainable parameters in each module. It is therefore potentially a useful method for applications which require frequent retraining, or which rely on reduced hardware capability, such as mobile robots or Internet of Things (IoT). We demonstrate the efficacy of the method in two benchmark image classification datasets, MNIST and CIFAR-10. The network produces very favourable results for a SLFN on these benchmarks, with an average of 99.07% correct classification rate on MNIST and nearly 82% on CIFAR-10 when applied to convolutional features. Code for the method has been made available online.
Incidence and Speciation of Candida Species among Non-gravid young Females in Ilorin, North Central, Nigeria

This study investigated the incidence and speciation of Candida species among non-gravid young females, using commercially available chromogenic Candida speciation media (CHROM agar) for the identification/speciation of medically important yeast and yeastlike organisms in a routine clinical mycology laboratory. High Vaginal Swabs (HVS) were randomly collected from consenting non-gravid-young females for the study. The participants also completed a structured questionnaire assessing demographic data, symptoms, and risk factors of candidiasis. A total of 120 females between the ages of 17 and 31 years were randomly recruited for the study. Standard microbiological techniques such as Gram’s stain, wet mount and culture on Sabouraud Dextrose Agar (SDA) and CHROM agar were used to analyze the swabs. Candida species was isolated from 64 of the 120 females, representing 53.3%. The highest incidence rate of 25.0% was recorded in the 23-25 years age group, followed by 12.5% in the 26 28 years while the lowest incidence (1.7%) was observed in the 17-19 years age group. Candida albicans (35.0%) was the most common Candida species , followed by C. tropicalis (8.3%), C. glabrata (6.7%) and C. krusie (3.3%), whereas, non-Candida infection constituted 46.7% (Negative cultures). Vaginal discharge (85.5%) and itching (52.5%) recorded the high values, with respect to symptoms. The prevalent risk factors associated with vaginal candidiasis in this study were washing of vagina with soap (Vaginal douching) (72.5%) and unprotected casual sex (16.7%). Whitish vaginal discharge (78.3%) was most prevalent followed by creamish vagina discharge (56.7%). In addition to Candida albicans , non-albicans Candida spp were isolated from HVS specimens; therefore, public health education is vital. CHROM agar is a simple, rapid and inexpensive method with good sensitivity and specificity for identification and speciation of Candida species thus, allowing an early and appropriate antifungal therapy. The results of the study will eliminate the ambiguities concerning Candida identification in this country and will contribute to better management and proper treatment of patients. Keywords: Candidiasis, vulvovaginitis, Incidence, Speciation, CHROM agar, Risk Factors
A comparative study on machine learning algorithms for indoor positioning

Fingerprinting based positioning is commonly used for indoor positioning. In this method, initially a radio map is created using Received Signal Strength (RSS) values that are measured from predefined reference points. During the positioning, the best match between the observed RSS values and existing RSS values in the radio map is established as the predicted position. In the positioning literature, machine learning algorithms have widespread usage in estimating positions. One of the main problems in indoor positioning systems is to find out appropriate machine learning algorithm. In this paper, selected machine learning algorithms are compared in terms of positioning accuracy and computation time. In the experiments, UJIIndoorLoc indoor positioning database is used. Experimental results reveal that k-Nearest Neighbor (k-NN) algorithm is the most suitable one during the positioning. Additionally, ensemble algorithms such as AdaBoost and Bagging are applied to improve the decision tree classifier performance nearly same as k-NN that is resulted as the best classifier for indoor positioning.
SOME MARINE SPONGES OF NORTHEAST BRAZIL

The Porifera in this study, with the exception of one specimen, are described from dry material collected over a period of several years by various individuais. No records are available concerning the color or consistencyof the living specimens, or in some cases, the depth from which the sponge was taken. Many of the specimens were incomplete and often only one sample of a particular species was retained. Thus, in most cases, the sponges received only a tentative identification.
Face clustering based on graph embedded semi-NMF with Kernels

Non-negative Matrix Factorization (NMF) has been widely studied and applied to variant computer vision tasks, such as image clustering and pattern classification. Meanwhile, real world stimuli for human neural system (e.g., face images) are usually represented as high-dimensional data vectors rely on graph embedding in original Euclidean space. Thus, the traditional NMF and its variants exhibit weakness for such clustering tasks. In this paper, we propose a Graph Embedding based Semi-NMF on Reproducing Kernel Hillbert Space (RKHS-SNMF) for human face clustering. This algorithm jointly considers the efficient dimension reduction and effective locally geometric preservation based on the exceptional property of measurability and differentiability of Reproducing Kernel Hillbert Space (RKHS). Extensive experimental results show that our RKHS-SNMF clustering algorithm outperforms the traditional clustering methods and the state-of-the-art NMF based clustering approaches on four benchmark human face databases.