Miracle-dz/UniFAR-FTUs huggingface dataset:
Licensed under Creative Commons Attribution Cc by Modified Constraint Induced Movement Therapy (mcimt) for Children with Hemiplegic Cerebral Palsy to Improve Upper Extremity Function: Pilot Study

The purpose of this study was to identify effectiveness of modified Constraint Induced Movement Therapy (mCIMT) to improve upper extremity function in children with hemiplegic Cerebral Palsy. Ten children with hemiplegic cerebral palsy were included through convenience sampling procedure. The results found that there is statistically significant difference (t =-4.68, p < 0.01) between pre test & post test total score of QUEST scale. Further it found that there is statistically significant difference between pre test & post test scores of all QUEST components (t=-3.07, p<0.05 for dissociate moment, t=-2.93; p<0.05 for grasps; t=-5.20, p<0.01 for weight bearing; t=-22.90, p<0.05 for protective extension). Finally, this study concluded mCIMT for small children with hemiplegic cerebral palsy seems to be an important intervention method for improving upper extremity function.
A Solution to the Adaptive Visual Servoing Problem

In this paper we address, and solve, a basic adaptive visual servoing problem whose solution was hampered by the nonlinear dependence of the system dynamics on the unknown parameters. Namely, we present a globally convergent vision{based position controller for a planar two-links manipulator in the so-called xedcamera con guration, where the camera orientation and scale factor are considered unknown. The controller design technique of immersion and invariance, recently proposed in the literature, is used to derive a smooth adaptive scheme that ensures global asymptotic regulation without overparametrization, projections or persistency of excitation assumptions. In the case of tracking, we establish error bounds that depend on the speed of the reference trajectory, and can be reduced (eventually to zero) with improved prior knowledge on the camera scale factor. The eÆcacy of the approach is shown through simulations.
Documents de Travail du Centre d’Economie de la Sorbonne

This paper develops the method for pricing bivariate contingent claims under General Autoregressive Conditionally Heteroskedastic (GARCH) process. In order to provide a general framework being able to accommodate skewness, leptokurtosis, fat tails as well as the time varying volatility that are often found in ﬁnancial data, generalized hyperbolic (GH) distribution is used for innovations. As the association between the underlying assets may vary over time, the dynamic copula approach is considered. Therefore, the proposed method proves to play an important role in pricing bivariate option. The approach is illustrated for Chinese market with one type of better-of-two-markets claims: call option on the better performer of Shanghai Stock Composite Index and Shenzhen Stock Composite Index. Results show that the option prices obtained by the GARCH-GH model with time-varying copula diﬀer substantially from the prices implied by the GARCH-Gaussian dynamic copula model. Moreover, the empirical work displays the advantage of the suggested method.
Touch Interaction for Corpus-based Audio–Visual Synthesis

Audio–visual corpus-based synthesis extends the principle of concatenative sound synthesis to the visual domain, where, in addition to the sound corpus (i.e. a collection of segments of recorded sound with a perceptual description of their sound character), the artist uses a corpus of still images with visual perceptual description (colour, texture, brightness), in order to create an audio–visual musical performance by navigating in real-time through these descriptor spaces, i.e. through the collection of sound grains in a space of perceptual audio descriptors, and at the same time through the visual descriptor space, i.e. selecting images from the visual corpus for rendering, and thus navigate in parallel through both corpora interactively with gestural control via touch sensing. The artistic–scientiﬁc question that is explored here is how to control at the same time the navigation through the audio and the image descriptor spaces with gestures, in other words, how to link the touch input to both descriptor spaces in order to create a multi-modal embodied audio–visual experience.
ScholarlyCommons ScholarlyCommons

Abstract This paper presents NoSQ (short for No Store Queue), a microarchitecture that performs store-load communication without a store queue and without executing stores in the out-of-order engine. NoSQ implements store-load communication using speculative memory bypassing (SMB), the dynamic short-circuiting of DEF-store-load-USE chains to DEF-USE chains. Whereas previous proposals used SMB as an opportunistic complement to conventional store queue-based forwarding, NoSQ uses SMB as a store queue replacement. NoSQ relies on two supporting mechanisms. The first is an advanced store-load bypassing predictor that for a given dynamic load can predict whether that load will bypass and the identity of the communicating store. The second is an efficient verification mechanism for both bypassed and non-bypassed loads using in-order load re-execution with an SMB-aware store vulnerability window (SVW) filter. The primary benefit of NoSQ is a simple, fast datapath that does not contain store-load forwarding hardware; all loads get their values either from the data cache or from the register file. Experiments show that this simpler design - despite being more speculative - slightly outperforms a conventional store-queue based design on most benchmarks (by 2% on average).
Convolutional Embedded Networks for Population Scale Clustering and Bio-ancestry Inferencing.

The study of genetic variants can help find correlating population groups to identify cohorts that are predisposed to common diseases and explain differences in disease susceptibility and how patients react to drugs. Machine learning algorithms are increasingly being applied to identify interacting GVs to understand their complex phenotypic traits. Since the performance of a learning algorithm not only depends on the size and nature of the data but also on the quality of underlying representation, deep neural networks can learn non-linear mappings that allow transforming GVs data into more clustering and classification friendly representations than manual feature selection. In this paper, we proposed convolutional embedded networks in which we combine two DNN architectures called convolutional embedded clustering and convolutional autoencoder classifier for clustering individuals and predicting geographic ethnicity based on GVs, respectively. We employed CAE-based representation learning on 95 million GVs from the 1000 genomes and Simons genome diversity projects. Quantitative and qualitative analyses with a focus on accuracy and scalability show that our approach outperforms state-of-the-art approaches such as VariantSpark and ADMIXTURE. In particular, CEC can cluster targeted population groups in 22 hours with an adjusted rand index of 0.915, the normalized mutual information of 0.92, and the clustering accuracy of 89%. Contrarily, the CAE classifier can predict the geographic ethnicity of unknown samples with an F1 and Mathews correlation coefficient(MCC) score of 0.9004 and 0.8245, respectively. To provide interpretations of the predictions, we identify significant biomarkers using gradient boosted trees(GBT) and SHAP. Overall, our approach is transparent and faster than the baseline methods, and scalable for 5% to 100% of the full human genome.
Évaluation du dispositif PiGaT (PilotGazeTrainer) pour l'entraînement des stratégies visuelles d'élèves pilotes

Les Glass cockpits favorisent le developpement de certaines mauvaises habitudes oculaires, comme de trop regarder a l'interieur du cockpit [17]. Ce comportement nuit a la securite des vols a vue (VFR) puisqu'il reduit la surveillance du ciel. Afin d'eliminer au plus tot cette mauvaise habitude, nous avons developpe un nouveau dispositif d'entrainement de strategies visuelles, PilotGazeTrainer (PiGaT). Ce dispositif est base sur l’utilisation d’un eye-tracker et repose sur deux principes : (i) l’analyse en temps-reel du comportement oculaire d’une personne et (ii) le declenchement automatique de notifications (audio ou visuelle). Cet article presente en detail le dispositif PiGaT ainsi que la tache de pilotage abstraite (Abstract Flying Task, AFT) qui a servi a realiser une validation experimentale du dispositif. Les resultats de cette experimentation ad-hoc, realisee avec 81 participants, montrent que ce dispositif permet de modifier durablement le comportement oculaire sans degrader la performance sur l'AFT. Les utilisations plus larges rendues possibles par le dispositif PiGaT sont finalement discutees.
Faktor risiko stunting pada anak umur 12-24 bulan

Background : In 2013, the prevalence of stunting and severe stunting in Brebes reached 26.9 % and 16.8 %. These prevalences of stunting were higher than the stunting prevlence in Central Java Province (11.0%). This study aim ed to determine risk factors of stunting among children aged 12-24 months in Br ebes District. Methods : This research was conducted with a case-control design o n 77 cases (stunting) and 77 controls (normal) in Brebes Subdistrict . Data on birth weight, b irth length, infection history, pesticide exposure were obtained through interviews , using structured questionnaires . The analysis was conducted by calculating Odd Ratios and logistic regression s . Results : Multivariate results showed that the risk factors of stunting in children aged 12-24 months in B rebes subdistrict were low energy adequacy levels ( OR = 7.71 ; 95% CI : 3.63 - 16.3 ; p = 0.00 1 ) , low protein adequacy levels ( OR=7.65 ; 95% CI : 3.67 - 15.9 , p = 0.001) ; low zinc adequacy levels ( OR = 8.78 ; 95% CI : 3.53 - 21.5 , p=0,001 ) ; low birth weight ( OR = 3.63 ; 95% CI : 1.65 - 7.96 ; p = 0.00 2 ) and high exposure to pesticides ( OR = 8.48 ; 95% CI : 3.93 - 18.28 ; p=0,001 ) . These three variables are contributing to stunting of 45 %. Low compliance of vitamin A capsule consumption , the frequencies of diarrhea respiratory infection were not the risk factors for stunting in this study . Conclusions : The risk factors of stunting among children aged 12-24 months were low energy adequacy levels , low protein adequacy levels, low zinc adequac y levels , low birth weight and high exposure to pesticides. The highest risk was the high pesticide exposure.
POLITECNICO DI TORINO Repository ISTITUZIONALE

—Packet ﬁltering is a technology at the foundation of many trafﬁc analysis tasks. While languages and tools for packet ﬁltering have been available for many years, none of them supports ﬁlters operating on the encapsulation relationships found in each packet. This represents a problem as the number of possible encapsulations used to transport trafﬁc is steadily increasing and we cannot deﬁne exactly which packets have to be captured. Thispaper presents our early work on an algorithm that models protocol ﬁltering patterns (including encapsulation constraints) as Finite State Automata and supports the composition of multiple expressions within the same ﬁlter. The resulting, optimized ﬁlter is then translated into executable code. The above ﬁltering algorithms are available in the NetBee open source library, which provides some basic tools for handling network packets (e.g., a tcpdump -like program) and APIs to build more advanced tools.
Factors Influencing Customers' Online Shopping Behaviour: A Study of an E-Commerce in Malaysia

Shopee is one of the top-notch companies in the e-commerce field. It recorded 343 million visitors per month and its gross merchandise value was even aggregated to USD 19 billion in the second quarter of 2022. Shopee also expanded its business operations aggressively to new regions, namely Europe, Latin America, and South Korea. This study intends to examine the variables that influence customers’ online shopping behaviour in Shopee. A survey questionnaire is conducted to collect data from 150 Shopee’s users in Malaysia. The findings depicted that perceived trust and security, perceived service quality and perceived ease of use had influenced the customers’ online shopping behaviour towards Shopee in Malaysia. Perceived usefulness and perceived convenience did not greatly impact the customers’ online buying intention on Shopee in Malaysia. This study can provide Shopee with useful business insights about customers’ online shopping behaviour. It is recommended for Shopee Malaysia to prioritize the enhancement of three variables that exhibit a favorable correlation with customers' online shopping behavior, thereby ensuring business growth and expansion.
OCR for Script Identification of Hindi ( Devnagari ) Numerals using Feature Sub Selection by Means of End-Point with Neuro-Memetic Model

challenging problems. In Optical Character Recognition [OCR], a character or symbol to be recognized can be machine printed or handwritten characters/numerals. There are several approaches that deal with problem of recognition of numerals/character depending on the type of feature extracted and different way of extracting them. This paper proposes a recognition scheme for handwritten Hindi (devnagiri) numerals; most admired one in Indian subcontinent. Our work focused on a technique in feature extraction i.e. global based approach using end-points information, which is extracted from images of isolated numerals. These feature vectors are fed to neuro-memetic model [18] that has been trained to recognize a Hindi numeral. The archetype of system has been tested on varieties of image of numerals. In proposed scheme data sets are fed to neuro-memetic algorithm, which identifies the rule with highest fitness value of nearly 100 % & template associates with this rule is nothing but identified numerals. Experimentation result shows that recognition rate is 92-97 % compared to other models.
Delft University of Technology Evaluating firms ’ R & D performance using best worst method

Since research and development (R & D) is the most critical determinant of the productivity, growth and competitive advantage of firms, measuring R &D performance has become the core of attention of R & D managers, and an extensive body of literature has examined and identified different R & D measurements and determinants of R &D performance. However, measuring R & D performance and assigning the same level of importance to different R & D measures, which is the common approach in existing studies, can oversimplify the R & D measuring process, which may result in misinterpretation of the performance and consequently fallacy R & D strategies. The aim of this study is to measure R &D performance taking into account the different levels of importance of R &D measures, using a multi-criteria decision-making method called Best Worst Method (BWM) to identify the weights (importance) of R &D measures and measure the R & D performance of 50 high-tech SMEs in the Netherlands using the data gathered in a survey among SMEs and from R&D experts. The results show how assigning different weights to different R & D measures (in contrast to simple mean) results in a different ranking of the firms and allow R&D managers to formulate more effective strategies to improve their firm’s R &D performance by applying knowledge regarding the importance of different R & D measures.
Improving English and Chinese Ad-hoc Retrieval: Tipster Text Phase 3 Final Report

We investigated both English and Chinese ad-hoc information retrieval (IR). Part of our objectives is to study the use of term, phrasal and topical concept level evidence, either individually or in combination, to improve retrieval accuracy. For short queries, we studied five term level techniques that together lead to improvements over standard ad-hoc 2-stage retrieval some 20% to 40% for TREC5 & 6 experiments. For long queries, we studied linguistic phrases as evidence to re-rank outputs of term level retrieval. It brings small improvements in both TREC5 & 6 experiments, but needs further confirmation. We also investigated clustering of output documents from term level retrieval. Our aim is to separate relevant and irrelevant documents into different clusters, and to rerank the output list by groups based on query and cluster-profile matching. Investigation is still on-going. For Chinese IR, many results were confirmed or discovered. For example, accurate word segmentation is not as important as first thought, but short-word segmentation is preferable to long-word (phrase). Simple bigram representation can give very good retrieval. A stopword list is not necessary; and presence of non-content terms does not hurt evaluation results much. One only needs screening out statistical stopwords of high frequency. Character indexing by itself is not competitive, but is useful for augmenting short-words or bigrams. Best results were obtained by combining retrievals of bigram and short-word with character representation. Chinese IR retums better precision than English, and it is not clear if this is a language-related, or collection-related phenomenon.
A cooperative top-down/bottom-up technique for motion field segmentation

The segmentation of video sequences into regions underlying a coherent motion is one of the most useful processing for video analysis and coding. In this paper, we propose an algorithm that exploits the advantages of both top-down and bottom-up techniques for motion field segmentation. To remove camera motion, a global motion estimation and compensation is first performed. Local motion estimation is then carried out relying on a traslational motion model. Starting from this motion field, a two-stage analysis based on affine models takes place. In the first stage, using a top-down segmentation technique, macro-regions with coherent affine motion are extracted. In the second stage, the segmentation of each macro-region is refined using a bottom-up approach based on a motion vector clustering. In order to further improve the accuracy of the spatio-temporal segmentation, a Markov Random Field (MRF)-inspired motion-and-intensity based refinement step is performed to adjust objects boundaries.
Featherweight generic confinement

Existing approaches to object encapsulation either rely on ad hoc syntactic restrictions or require the use of specialised type systems. Syntactic restrictions are difficult to scale and to prove correct, while specialised type systems require extensive changes to programming languages. We demonstrate that confinement can be enforced cheaply in Featherweight Generic Java, with no essential change to the underlying language or type system. This result demonstrates that polymorphic type parameters can simultaneously act as ownership parameters and should facilitate the adoption of confinement and ownership type systems in general-purpose programming languages.
Instrumental Radiation Patterns as Models for Corpus-Based Spatial Sound Synthesis: Cosmologies for Piano and 3D Electronics

The Cosmologies project aims to situate the listener inside a virtual grand piano by enabling computer processes to learn from the spatial presence of the live instrument and performer. We propose novel techniques that leverage measurements of natural acoustic phenomena to inform spatial sound composition and synthesis. Measured radiation patterns of acoustic instruments are applied interactively in response to a live input to synthesize spatial forms in real time. We implement this with software tools for the ﬁrst time connecting audio descriptor analysis and corpus-based synthesis to spatialization using Higher-Order Ambisonics and machine learning. The resulting musical work, Cosmologies for piano and 3D electronics, explodes the space inside the grand piano out to the space of the concert hall, allowing the listener to experience its secret inner life.
in Computer

-- Face recognition is an active area of research since 1980s. It is one of the most successful and important applications of image analysis and processing. Eigenface approach is one of the earliest appearance-based face recognition methods, which was developed by M. Turk and A. Pentland in 1991 [1]. This method utilizes the idea of the Principal Component Analysis (PCA) which decomposes a face image into a small set of characteristic feature images called eigenfaces and recognition is performed by projecting a new face onto a low dimensional linear “face space” defined by the eigenfaces, followed by computing the distance between the resultant position in the face space and those of known face classes. A number of experiments are done at different conditions to evaluate the performance of the face recognition system. The results demonstrate that the face recognition using eigenfaces is quite robust to head/face orientation and illumination.
in Computer

:-In this work we present an overview of image alignment, describing most of the algorithms and their extensions in a consistent framework. We concentrate on the inverse compositional algorithm, an efficient algorithm that we recently proposed. We cover the quantity approximated, the warp update rule, and the gradient descent approximation. In this work, we propose the use of a modified version of the correlation coefficient as a performance criterion for the image alignment problem. The proposed modification has the desirable characteristic of being invariant with respect to photometric distortions. Since the resulting similarity measure is a nonlinear function of the warp parameters, we develop two iterative sche mes for its maximization, one based on the forward additive approach and the second on the inverse compositional method. As is customary in iterative optimization, in each iteration, the nonlinear objective function is approximated by an alternative expression for which the corresponding optimization is simple. In our case, we propose an efficient approximation that leads to a closed-form solution (per iteration) which is of low computational complexity, the latter property being particularly strong in our inverse version. The proposed schemes are tested against the Forward Additive Lucas-Kanade and the Simultaneous Inverse Compositional (SIC) algorithm through simulations. Under noisy conditions and photometric distortions, our forward version achieves more accurate alignments and exhibits faster convergence, whereas our inverse version has similar performance as the SIC algorithm but at a lower computational complexity.
New remains of the aquatic turtle Hydromedusa casamayorensis (Pleurodira, Chelidae) from the middle Eocene of Patagonia: taxonomic validation and phylogenetic relationships

Twenty‐seven new specimens recovered from lower levels of the Sarmiento Formation (Casamayoran SALMA, middle Eocene) in the ‘Cañadón Hondo’ area, Chubut (Argentina), bring new information on the Palaeogene long‐necked chelid Hydromedusa casamayorensis. The discovery of cranial and postcranial remains allows a complete description of the only known extinct species of the genus Hydromedusa recognizing diagnostic characters that revalidate the taxonomic status of this species. Hydromedusa casamayorensis is incorporated into a cladistic framework of chelid turtles. The unconstrained and constrained phylogenetic analyses recovered H. casamayorensis as a sister taxon of Hydromedusa maximiliani plus H. tectifera. A Danian age is proposed at least for the origin of the Hydromedusa clade, while a Late Cretaceous age is suggested for the split from the Yaminuechelys clade. Great differences in size are documented for H. casamayorensis based on the various specimens recovered from this study. The osteohistological study in H. casamayorensis and H. tectifera shows similarities in the internal cortex and cancellous bone of both taxa, but some differences are observed in the external cortex of H. casamayorensis (e.g. a parallel fibred bone on the more external surface of this cortex).