Miracle-dz/UniFAR-FTUs huggingface dataset:
Educators' Perceptions of Effectiveness of Programs for Students with Emotional and Behavioral Disorders

Perceptions of staff in self-contained programs for students with emotional and behavioral disorders (EBD) in a district committed to inclusion, interagency coordination, and staff training were compared with perceptions in the same programs when the programs were newly developed and of staff working with children with EBD in mainstream settings. As self-contained programs for children with EBD became well-established, parent/school relationships noticeably improved. Even when relatively new, self-contained programs were perceived by staff to be superior to mainstream programs in resources and teaching strategies. Well established self-contained programs were perceived as superior to relatively new mainstream programs in resources, teaching strategies, program components, and parent/school relationships. Regardless of program, students with EBD were perceived as making moderate progress and as being moderately aggressive.
A Dynamic Gesture Recognition System based on CIPBR Algorithm

Dynamic gesture recognition has been studied actually for it big application in several areas such as virtual reality, games and sign language. But some problems have to be solved in computer applications, such as response time and classification rate, which directly affect the real-time usage. This paper proposes a novel algorithm called Convex Invariant Position Based on Ransac which improved the good results in dynamic gesture recognition problem. The proposed method is combined with a adapted PSO variation to reduce features and a HMM and three DTW variations as classifiers. Index Terms—Gesture recognition, computer vision, CIPBR, dynamic time wrapping, hidden Markov model.
Reliability-Based Decision Fusion in Multimodal Biometric Verification Systems

We present a methodology of reliability estimation in the multimodal biometric verification scenario. Reliability estimation has shown to be an efficient and accurate way of predicting and correcting erroneous classification decisions in both unimodal (speech, face, online signature) and multimodal (speech and face) systems. While the initial research results indicate the high potential of the proposed methodology, the performance of the reliability estimation in a multimodal setting has not been sufficiently studied or evaluated. In this paper, we demonstrate the advantages of using the unimodal reliability information in order to perform an efficient biometric fusion of two modalities. We further show the presented method to be superior to state-of-the-art multimodal decision-level fusion schemes. The experimental evaluation presented in this paper is based on the popular benchmarking bimodal BANCA database.
A study on spin-rate measurement using a uniquely marked moving ball

A method is proposed for measuring the spin rate of a ball moving through 3D space. The method uses only one high-speed camera and a uniquely marked ball. Five lines, each one a different color, are painted on the? ball's equator and on its perpendicular great circle, enabling measurement of both the rotational angular velocity (spin rate) and translational velocity. The proposed measurement algorithm consists of two steps. The first step is to measure the translational velocity, for which we developed a new semi-automatic translational speed measurement algorithm. The second step is to measure the ball spin rate employing a unique marker tracking method using a spin rate estimation algorithm based on a rotation matrix. Simulations and experiments were carried out to show the validity of the method, and reasonable results were obtained.
Implementing BRICKS, a Digital Library Management System

Digital Libraries (DLs) play a central role in the way information is produced, accessed and used in the Internet era. While the Web provides a universal access to uncontrolled information resources, DLs allow large, distributed repositories of multimedia content, annotated with possibly rich semantic structures and regulated by digital rights, to be created and managed to satisfy the needs of vast user communities. This paper describes the main features of BRICKS, an open-source DL management system based on an innovative peer-to-peer architecture and integrating advanced information management techniques, ranging from model-agnostic content and metadata management to distributed query processing.
An Information Theoretic Analysis of Single Transceiver Passive RFID Networks

In this paper, we study single transceiver passive RFID networks by modeling the underlying physical system as a special cascade of a certain broadcast channel (BCC) and a multiple access channel (MAC), using a "nested codebook" structure in between. The particular application differentiates this communication setup from an ordinary cascade of a BCC and a MAC, and requires certain structures such as "nested codebooks", impurity channels or additional power constraints. We investigate this problem both for discrete alphabets, where we characterize the achievable rate region, as well as for continuous alphabets with additive Gaussian noise, where we provide the capacity region. Hence, we establish the maximal achievable error free communication rates for this particular problem which constitutes the fundamental limit that is achievable by any TDMA based RFID protocol and the achievable rate region for any RFID protocol for the case of continuous alphabets under additive Gaussian noise.
On Threshold Circuits and Polynomial Computation

A Threshold Circuit consists of an acyclic digraph of unbounded fanin, where each node computes a threshold function or its negation. We investigate the computational power of Threshold Circuits. We discover a surprising relationship with another class of unbounded fanin circuits which we denote Finite Field ZP(n) Circuits where each node computes either multiple sums or products of integers modulo a prime P(n). In particular, we prove that all functions computed by Threshold Circuits of size S(n) ≥ n and depth D (n) can be also computed by ZP(n) Circuits of sixe 0(s(n) + nP(n)) and depth 0(D(n)). Furthermore, we prove that all functions computed by ZP(n) Circuits of size S(n) and depth D(n) can be computed by Threshold Circuits of sice (S(n)log P(n))0(1) and depth 0(D(n)). This is the main result of this paper. We get many useful and quite surprising consequences of this result. For example, integer reciprocal can be computed in size n0(1) and depth 0(1). More generally, any analytic function with a convergent rational polynomial power series (such as integer reciprocal, sine, cosine, exponentiation, square root, logarithm), can be computed within accuracy 2-nc for any constant c, by Threshold Circuits of polynomial sise and constant depth. Also, integer and polynomial division, FFT, polynomial interpolation, Chinese Remaindering, all the elementary symmetric functions, banded matrix inverses and triangular Toelplits matrix inverse can be exactly computed by Threshold Circuits of polynomial sise and constant depth. All these results and simulations hold for polytime uniform circuits. We give a corresponding simulation of logspace uniform Zp(n) Circuits by logspace uniform Threshold Circuits requiring an additional multiplying factor of 0(logloglog P(n)) depth. We also develop purely algebraic methods for lower bounds for Zp(n) Circuits. Using degree arguments, we prove a Depth Hierarchy Theorem for Zp(n) Circuits: for any S(n) ≥ n, D(n) < S(n)/2 and prime P(n) where 2n ≥ P(n) > 6 (S(n)/D(n))D(n), we can explicitly construct functions computable by Zp(n) Circuits of size S(n) and depth D(n), but provably not computable by Zp(n) Circuits of sise S(n)c and depth o(D(n)) for any constant c ≥ 1.
Multi Robot Learning by Demonstration

In this paper, we investigate the feasibility of a Multi Robot Learning by Demonstration system, which allows multiple teachers to give a demonstration to multiple robots simultaneously. A novel, complete end-to-end system was developed, which extracts data from a live human group demonstration, and allows the robots to imitate the demonstration by adapting the demonstration dataset to the current, possibly different environment. The complete system was evaluated using a series of increasingly difficult benchmark experiments, including a collaborative door opening experiment using a group of heterogeneous robots. The results showed, that the system is resistant to changes in the environment, as it was possible to give a demonstration in one environment, move the robots to a physically different but similar location, where the robots could still imitate the demonstration in this new context. The door opening experiment also shows that this system can be used to demonstrate and learn collaborative behaviour. Our results demonstrate a novel and promising method for teaching a group of robots to perform a joint task by human team demonstration.
Distributed Inference of Overlapping Communities

Overlapping community detection plays a key role in statistical network modeling. Despite the importance, popular models such as mixed membership stochastic blockmodels (MMSB) [2] are often not applicable to real world massive networks due to limited speed and memory of a single computing node. In this project, we develop distributed inference for models that can discover overlapping communities in real networks. Specifically, we address three key challenges in distributed network inference: 1) reduce O(N) pairwise parameters to O(N) by choosing constrained variational formulation described in [7]; 2) minimize communication cost between processes using a vertex-cut algorithm to partition the network; 3) synchronize global states between processes via a protocol designed for continuous aggregation and synchronization. Experimental results demonstrate our ability to tackle large scale network inference tasks.
Generalized polar varieties :

Let W be a closed algebraic subvariety of the n–dimensional projective space over the complex or real numbers and suppose that W is non–empty and equidimensional. In this paper we generalize the classic notion of polar variety of W associated with a given linear subvariety of the ambient space of W . As particular instances of this new notion of generalized polar variety we reobtain the classic ones and two new types of polar varieties, called dual and (in case that W is affine) conic. We show that for a generic choice of their parameters the generalized polar varieties of W are empty or equidimensional and smooth in any regular point of W . In the case that the variety W is affine and smooth and has a complete intersection ideal of definition, we are able, for a generic parameter choice, to describe locally the generalized polar varieties of W by explicit equations. Finally, we use this description in order to design a new, highly efficient elimination procedure for the following algorithmic task: In case, that the variety W is Q–definable and affine, having a complete intersection ideal of definition, and that the real trace of W is non–empty and smooth, find for each connected component of the real trace of W a representative point.
Carbon-Based Fibrous EDLC Capacitors and Supercapacitors

This paper investigates electrochemical double-layer capacitors (EDLCs) including two alternative types of carbon-based fibrous electrodes, a carbon fibre woven fabric (CWF) and a multiwall carbon nanotube (CNT) electrode, as well as hybrid CWF-CNT electrodes. Two types of separator membranes were also considered. An organic gel electrolyte PEO-LiCIO4-EC-THF was used to maintain a high working voltage. The capacitor cells were tested in cyclic voltammetry, charge-discharge, and impedance tests. The best separator was a glass fibre-fine pore filter. The carbon woven fabric electrode and the corresponding supercapacitor exhibited superior performance per unit area, whereas the multiwall carbon nanotube electrode and corresponding supercapacitor demonstrated excellent specific properties. The hybrid CWF-CNT electrodes did not show a combined improved performance due to the lack of carbon nanotube penetration into the carbon fibre fabric.
Cuticle Structure in Relation to Chemical Composition: Re-assessing the Prevailing Model

The surface of most aerial plant organs is covered with a cuticle that provides protection against multiple stress factors including dehydration. Interest on the nature of this external layer dates back to the beginning of the 19th century and since then, several studies facilitated a better understanding of cuticular chemical composition and structure. The prevailing undertanding of the cuticle as a lipidic, hydrophobic layer which is independent from the epidermal cell wall underneath stems from the concept developed by Brongniart and von Mohl during the first half of the 19th century. Such early investigations on plant cuticles attempted to link chemical composition and structure with the existing technologies, and have not been directly challenged for decades. Beginning with a historical overview about the development of cuticular studies, this review is aimed at critically assessing the information available on cuticle chemical composition and structure, considering studies performed with cuticles and isolated cuticular chemical components. The concept of the cuticle as a lipid layer independent from the cell wall is subsequently challenged, based on the existing literature, and on new findings pointing toward the cell wall nature of this layer, also providing examples of different leaf cuticle structures. Finally, the need for a re-assessment of the chemical and structural nature of the plant cuticle is highlighted, considering its cell wall nature and variability among organs, species, developmental stages, and biotic and abiotic factors during plant growth.
Production of fusaric acid by Fusarium species

Fusaric acid is a mycotoxin with low to moderate toxicity, which is of concern since it might be synergistic with other cooccurring mycotoxins. Fusaric acid is widespread on corn and corn-based food and feeds and is frequently found in grain, where Fusarium spp. are also isolated. We surveyed 78 strains of Fusarium moniliforme, F. crookwellense, F. subglutinans, F. sambucinum, F. napiforme, F. heterosporum, F. oxysporum, F. solani, and F. proliferatum for their ability to produce fusaric acid. Strains in Fusarium section Liseola also were assigned to mating population of the Gibberella fujikuroi species complex. The fungi could be divided into three classes, low (< 100 micrograms/g), moderate (100 to 500 micrograms/g), and high (> 500 micrograms/g), based on the amounts of this mycotoxin produced in culture on autoclaved corn. Strains of mating populations C from rice consistently produced moderate to high concentrations of fusaric acid. Two isolates, one each from mating populations C and D, produced fusaric acid in excess of 1,000 micrograms/g of corn. No isolates of any of the Fusarium species examined were negative for the production of fusaric acid on autoclaved corn.
Caspase-3-dependent Apoptosis in Vascular Smooth Muscle Cell by Proteasome Inhibition

The effects of a number of substances on neointima formation following angioplasty have been investigated in animal models. It was suggested that delivering of proteasome inhibitor to the site of vascular injury would be a potential therapeutic approach in prevention of vascular restenosis. But the mechanisms underlying biologic activities of proteasome inhibition in vascular smooth muscle cells (VSMCs) are largely unknown. We have investigated effects of proteasome inhibition on VSMCs using proteasome inhibitor MG115. MG115 induced apoptotic death in VSMCs as determined by viability, morphology, and DNA fragmentation. Proteasome inhibition was accompanied by up-regulation of p53, p21, and p27. In contrast, there were no appreciable alterations in the levels of Bcl-2 and Bax. Proteasome inhibition was followed by activation of caspase-3 but not of –8. The induction of apoptosis was suppressed by treatment with a selective inhibitor of the caspase-3 family, z-DEVD-fmk but not by NG-monomethyl-L-arginine. These results indicate that proteasome inhibition induces apoptosis in VSMCs by activation of caspase-3.
Nuclear Imaging Techniques for the Assessment of Hepatic Function in Liver Surgery and Transplantation

This review describes the application of 2 nuclear imaging techniques for assessment of hepatic function in the setting of liver surgery and transplantation. The biochemical and technical background, as well as the clinical applications, of 99mTc-labeled diethylenetriaminepentaacetic acid galactosyl human serum albumin (GSA) scintigraphy and hepatobiliary scintigraphy (HBS) with 99mTc-labeled iminodiacetic acid derivates is discussed. 99mTc-mebrofenin is considered the most suitable iminodiacetic acid agent for 99mTc-HBS. 99mTc-GSA scintigraphy and 99mTc-mebrofenin HBS are based on 2 different principles. 99mTc-GSA scintigraphy is a receptor-mediated technique whereas HBS represents hepatic uptake and excretion function. Both techniques are noninvasive and provide visual and quantitative information on both total and regional liver function. They can be used for preoperative assessment of future remnant liver function, follow-up after preoperative portal vein embolization, and evaluation of postoperative liver regeneration. In liver transplantation, these methods are used to assess graft function and biliary complications.
On the Use of Formal Techniques for Analysis of Fault-tolerant Real-time Protocols

Traditionally, in the development of critical (dependable real-time) systems, scheduling theory has received considerable attention, due to the need to ensure satisfaction of stringent timing and operational constraints. However, timing constraints for a given task are usually derived constraints, and there is often exibility in the values selected. The greater precision of formal methods can potentially allow these requirements to be calculated more precisely. This paper develops an approach in using formal methods for this purpose by developing exact case analyses along with formally derived precise timing bounds. We discuss the insights gained and the aws being identiied in the hand analysis in an attempt to formally analyze and verify the correctness of an existing fault-tolerant real-time scheduling algorithm.
Minimum outage probability transmission with imperfect feedback for MISO fading channels

We study optimal transmission strategies in terms of minimum outage probability for a fading channel with multiple transmit antennas and a single receive antenna. We consider two cases of imperfect channel state information (CSI) feedback: mean feedback and covariance feedback. In both cases, the optimum strategy is shown to be transmission of multiple data streams to the same directions as when maximizing the ergodic channel capacity, but with different power allocation strategies which are closely related to the target rate. In the mean feedback case, the optimal power allocation strategy is also related to the accuracy of the feedback CSI, which affects significantly the achievable outage capacity.
Intrapartum ultrasound: A useful method for evaluating labor progress and predicting operative vaginal delivery

The last step of a successful pregnancy is the safe delivery of the fetus. An important question is if the delivery should vaginal or operative. In addition to the use of conventional antenatal ultrasound, the use of intrapartum ultrasound to evaluate fetal head station, position, cervical ripening, and placental separation is promising. This review evaluates and summarizes the usefulness of intrapartum ultrasound for the evaluation of labor progress and predicting successful operative vaginal delivery.
Structural Bioinformatics-Based Design of Selective, Irreversible Kinase Inhibitors

The active sites of 491 human protein kinase domains are highly conserved, which makes the design of selective inhibitors a formidable challenge. We used a structural bioinformatics approach to identify two selectivity filters, a threonine and a cysteine, at defined positions in the active site of p90 ribosomal protein S6 kinase (RSK). A fluoromethylketone inhibitor, designed to exploit both selectivity filters, potently and selectively inactivated RSK1 and RSK2 in mammalian cells. Kinases with only one selectivity filter were resistant to the inhibitor, yet they became sensitized after genetic introduction of the second selectivity filter. Thus, two amino acids that distinguish RSK from other protein kinases are sufficient to confer inhibitor sensitivity.
Tensor completion via group-sparse regularization

To enable low-rank tensor completion and factorization, this paper puts forth a novel tensor rank regularization method based on the ℓ1,2-norm of the tensor's parallel factor analysis (PARAFAC) factors. Specifically, for an N-way tensor, upon collecting the magnitudes of its rank-1 components in a vector, the proposed regularizer controls the tensor's rank by inducing sparsity in the vector of magnitudes through ℓ1/N (pseudo)-norm regularization. Our approach favors sparser magnitude vectors than existing ℓ2/N- and ℓ1-based alternatives. With an eye towards large-scale tensor mining applications, we also develop efficient and highly scalable solvers for tensor factorization and completion using the proposed criterion. Extensive numerical tests using both synthetic and real data demonstrate that the proposed criterion is better in terms of revealing the correct number of components and estimating the underlying factors than competing alternatives.
Parameter Tuning using the Out-of-Bootstrap Generalisation Error Estimate for Stochastic Discrimination and Random Forests

Stochastic discrimination is a machine learning algorithm with strong theoretical underpinnings and good published results on UCI datasets. However, it has not been popular amongst practitioners. We look at some of the issues involved in its use, propose the out-of-bootstrap error estimator as a means of tuning stochastic discrimination's and other classifiers' performance and contrast stochastic discrimination's utility with that of a related classification technique of random forests
epSICAR: An Emerging Patterns based approach to sequential, interleaved and Concurrent Activity Recognition

Recognizing human activities from sensor readings has recently attracted much research interest in pervasive computing. This task is particularly challenging because human activities are often performed in not only a simple (i.e., sequential), but also a complex (i.e., interleaved and concurrent) manner in real life. In this paper, we propose a novel Emerging Patterns based approach to Sequential, Interleaved and Concurrent Activity Recognition (epSICAR). We exploit Emerging Patterns as powerful discriminators to differentiate activities. Different from other learning-based models built upon the training dataset for complex activities, we build our activity models by mining a set of Emerging Patterns from the sequential activity trace only and apply these models in recognizing sequential, interleaved and concurrent activities. We conduct our empirical studies in a real smart home, and the evaluation results demonstrate that with a time slice of 15 seconds, we achieve an accuracy of 90.96% for sequential activity, 87.98% for interleaved activity and 78.58% for concurrent activity.