Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Advantages of Using Web & Grid Services for the Development of Collaborative Learning Management Systems

Modern on-line collaborative learning environments no longer depend on homogeneous groups, static content and resources, and single pedagogies, but high customization and flexibility are a must in this context. This means that collaborative learning practices need to be continuously adapted, adjusted, and personalized to each specific target learning group. Moreover, these environments are to enable and scale the involvement of an increasing large number of single/group participants who can geographically be widely distributed, and who need transparently share a huge variety of both software and hardware distributed resources. As a result, collaborative learning applications need to be designed in a way that overcome important nonfunctional requirements arisen in distributed contexts, such as scalability, flexibility, availability, interoperability, and integration of different, heterogeneous, and legacy collaborative learning systems. As a contribution towards this direction, in this paper, an approach based on Web and Grid services is proposed to guide developers in meeting these demanding requirements for the enhancement and improvement of the collaborative learning experience in highly distributed environments
Device And Method For The Biomechanical Analysis Of Articular Amplitude Using Micro-Sensors For The Measurement Of Movement

The present article describes the creation and operation of a device for biomechanical analysis, which is portable, precise, non-invasive, and which allows us to autonomously obtain the position, orientation and measurement of the articular amplitude of two body segments connected by a joint. The above is carried out with data obtained from a number of inertial and magnetic microsensors. The device, through the application of a series of methods, allows the recording of the movements made by a person while they go about their daily activities without the need of including a computing system that operates it. This can be done in real time or delayed-mode, and for long periods of time, depending on the battery charge of the device. Copyright © 2019 VBRI Press.
Understanding data traffic behaviour for smartphone video and audio apps

This poster is the first known attempt towards traffic engineering for smart-phone audio and video apps - it seeks to report network traffic characteristics, like packet size distribution, traffic burstiness and self-similarity in data traffic. We consider different candidate apps from three different groups - interactive apps, buffered apps and streaming apps, collect packet traces for three months with four customized Android smart-phones, and then analyze their internal patterns. We observe significant differences in traffic characteristics among various application groups, which can be explored in the future for the development of network level service provisioning and traffic management mechanisms.
Text-independent speaker verification using rank threshold in large number of speaker models

In this paper, we propose a novel speaker verification method which determines whether a claimer is accepted or rejected by the rank of the claimer in a large number of speaker models instead of score normalization, such as T-norm and Z-norm. The method has advantages over the standard T-norm in speaker verification accuracy. However, it needs much computation time as well as T-norm that needs calculating likelihoods for many cohort models. Hence, we also discuss the speed-up using the method that selects cohort subset for each target speaker in the training stage. This data driven approach can significantly reduce computation resulting in faster speaker verification decision. We conducted text-independent speaker verification experiments using large-scale Japanese speaker recognition evaluation corpus constructed by National Research Institute of Police Science. As a result, the proposed method achieved an equal error rate of 2.2 %, while T-norm obtained 2.7 %. Index Terms: speaker verification, cohort selection, normalization, rank threshold
Towards an Axiom System for Default Logic

Recently, Lakemeyer and Levesque proposed a logic of only-knowing which precisely captures three forms of nonmonotonic reasoning: Moore's Autoepistemic Logic, Konolige's variant based on moderately grounded expansions, and Reiter's default logic. Defaults have a uniform representation under all three interpretations in the new logic. Moreover, the logic itself is monotonic, that is, nonmonotonic reasoning is cast in terms of validity in the classical sense. While Lakemeyer and Levesque gave a model-theoretic account of their logic, a proof-theoretic characterization remained open. This paper fills that gap for the propositional subset: a sound and complete axiom system in the new logic for all three varieties of default reasoning. We also present formal derivations for some examples of default reasoning. Finally we present evidence that it is unlikely that a complete axiom system exists in the first-order case, even when restricted to the simplest forms of default reasoning.
Comparative epigenomics in distantly related teleost species identifies conserved cis-regulatory nodes active during the vertebrate phylotypic period

The complex relationship between ontogeny and phylogeny has been the subject of attention and controversy since von Baer’s formulations in the 19th century. The classic concept that embryogenesis progresses from clade general features to species-specific characters has often been revisited. It has become accepted that embryos from a clade show maximum morphological similarity at the so-called phylotypic period (i.e., during mid-embryogenesis). According to the hourglass model, body plan conservation would depend on constrained molecular mechanisms operating at this period. More recently, comparative transcriptomic analyses have provided conclusive evidence that such molecular constraints exist. Examining cis-regulatory architecture during the phylotypic period is essential to understand the evolutionary source of body plan stability. Here we compare transcriptomes and key epigenetic marks (H3K4me3 and H3K27ac) from medaka (Oryzias latipes) and zebrafish (Danio rerio), two distantly related teleosts separated by an evolutionary distance of 115–200 Myr. We show that comparison of transcriptome profiles correlates with anatomical similarities and heterochronies observed at the phylotypic stage. Through comparative epigenomics, we uncover a pool of conserved regulatory regions (≈700), which are active during the vertebrate phylotypic period in both species. Moreover, we show that their neighboring genes encode mainly transcription factors with fundamental roles in tissue specification. We postulate that these regulatory regions, active in both teleost genomes, represent key constrained nodes of the gene networks that sustain the vertebrate body plan.
Probabilistic Solution of the General Robin Boundary Value Problem on Arbitrary Domains

Using a capacity approach and the theory of the measure’s perturbation of the Dirichlet forms, we give the probabilistic representation of the general Robin boundary value problems on an arbitrary domain Ω, involving smooth measures, which give rise to a new process obtained by killing the general reflecting Brownian motion at a random time. We obtain some properties of the semigroup directly from its probabilistic representation, some convergence theorems, and also a probabilistic interpretation of the phenomena occurring on the boundary.
Automatic classification of eating conditions from speech using acoustic feature selection and a set of hierarchical support vector machine classifiers

The problem of automatic classification of seven types of eating conditions from speech is considered. Based on the confusion among different eating conditions from a seven class support vector machine (SVM) classifier, a hierarchical SVM classifier is designed. Experiments on the iHEARu-EAT database show that the hierarchical classifier results in a better classification accuracy compared to a seven class classifier. We also perform a feature selection for each of the classifiers in the hierarchical approach. This further improves the unweighted average recall (UAR) to 73.7% compared to an UAR of 60.9% obtained from the baseline scheme of a direct seven-way classification.
The Wiskott-Aldrich Syndrome: RESULTS OF TRANSFER FACTOR THERAPY

Abstract 12 patients with Wiskott-Aldrich syndrome were treated with therapeutic doses of transfer factor in an attempt to induce cellular immunity. Clinical improvement was noted after transfer factor therapy in 7 of the 12 patients treated. Because this disease has a variable course and temporary spontaneous improvement can occur, the observed improvement cannot necessarily be attributed to the transfer factor. However, in two patients repeated remissions consistently followed transfer factor administration on repeated occasions. This included freedom from infections, regression of splenomegaly, and clearing of eczema. An unexpected finding was a decrease in bleeding in 3 of the 10 patients who had bleeding. Conversion of skin reactivity was obtained in all seven patients who clinically seemed to respond to transfer factor. In vitro studies performed after the administration of transfer factor demonstrated that the lymphocytes of the patients now produced migration inhibitory factor in response to appropriate test antigens, but did not undergo increased radioactive thymidine incorporation in response to the same antigens. A defect in the monocyte IgG receptors has been found in certain patients with the disease, and the current study shows that all patients with defective monocyte IgG receptors responded to transfer factor, whereas only one patient with normal receptors showed any response. This test may thus prove to be useful in predicting the results of transfer factor therapy in patients with Wiskott-Aldrich syndrome, although evaluation of a larger series of patients will be necessary to confirm this point. We conclude that cellular immunity can be induced, that there appears to be clinical benefit in certain patients with Wiskott-Aldrich syndrome by the use of transfer factor, and that this mode of therapy warrents trial in these patients and others with defects of cellular immunity.
Eliminating or Minimizing the Effects of Cold Agglutinins on the Accuracy of Complete Blood Count Results

Dear Editor, I have read the interesting paper by Rim et al. [1] regarding the effects of cold agglutinins (CAs) on the accuracy of complete blood count (CBC) results and believe that a number of critical aspects deserve discussion. Based on a single case of cold agglutinin disease (CAD), the authors propose two optimal sample pre-treatment protocols for eliminating interferences caused by the presence of CAs in the CBC. For this purpose, 16 whole blood dipotassium EDTA (K2EDTA) samples from the patient were analyzed using four hematological analyzers under different temperature and storage conditions: Sysmex XE-2100 and XN-1000 (Sysmex Corporation, Kobe, Japan), Siemens ADVIA 2120i (Siemens Diagnostics, Tarrytown, NY, USA), and Unicel DxH 800 (Beckman Coulter Inc., Fullerton, CA, USA). Based on their results, the authors propose the following protocols: (i) measuring CBC within one hour if the sample is stored at 37°C after collection and (ii) measuring CBC after incubation at 37°C for one hour if the sample is stored at 4°C for a short period. The first critical aspect is that neither of these “protocols” is novel or original. Sample collection at 37°C and/or pre-heating before counting was already proposed in 1945 by Finland et al. [2] as a method for eliminating CA interferences in cellular counting. Although other methods have been proposed, such as the replacement of plasma with a warm isotonic solution [3], the Finland method is one of the most widely employed to date. More recently, in some hematological analyzers, including Sysmex XE-2100 and XN-1000, the counting in the reticulocyte (RET) channel is sufficient to provide accurate red blood cell (RBC) counts, as well as Wintrobe indices, without thermal pre-treatment of the sample [4-8]. Similar observations have not been described for Unicel DxH 800 and ADIVA 2120i, in which the environmental conditions in the RET channel do not lead to the spontaneous resolution of cold agglutination. Pre-heating at 41°C for one minute has resulted in RBC counts similar to that of the RET channel and that of pre-heating at 37°C for two hours [9]. Apart from the non-originality of the proposed protocols, two other critical aspects should be noted: (i) The study is based on a single case; this preempts general conclusions and operative suggestions. Clinical features and analytical interferences caused by CA presence in blood depend on many variables such as avidity and the ability to initiate the classic complement pathway. In addition, the thermal amplitude of CA and titer in the blood determine the degree of hemolysis and amplitude of analytical “errors” [10]. Therefore, each CA-containing sample
Network anomaly detection by continuous hidden markov models: An evolutionary programming approach

Information security is an important and growing need. The most common schemes used for detection systems include pattern-or signature-based and anomaly-based. Anomaly-based schemes use a set of metrics, which outline the normal system behavior and any significant deviation from the established profile will be treated as an anomaly. This paper contributes with an anomaly-based scheme that monitors the bandwidth consumption of a subnetwork, at the Universidad Michoacana, in Mexico. A normal behavior model is based on bandwidth consumption of the subnetwork. The presence of an anomaly indicates that something is misusing the network viruses, worms, denial of service, or any other kind of attack. This work also presents a scheme for an automatic architecture design and parameters optimization of Hidden Markov Models HMMs, based on Evolutionary Programming EP. The variables to be used by the HMMs are: the bandwidth consumption of network IN and OUT, and the associated time where the network activity occurs. The system was tested with univariate and bivariate observation sequences to analyze and detect anomaly behavior. The HMMs, designed and trained by EP, were compared against semi-random HMMs trained by the Baum-Welch algorithm. On a second experiment, the HMMs, designed and trained by EP, were compared against HMMs created by an expert user. The HMMs outperformed the other methods in all cases. Finally, we made the HMMs time-aware, by including time as another variable. This inclusion made the HMMs capable of detecting activity patterns that are normal during a period of time but anomalous at other times. For instance, a heavy load on the network may be completely normal during working times, but anomalous at nights or weekends.
Multi-file proofs of retrievability for cloud storage auditing

Cloud storage allows clients to store a large amount of data with the help of storage service providers (SSPs). Proof-of-retrievability(POR) protocols allow one server to prove to a verifier the availability of data stored by some client. Shacham et al. presented POR protocols based on homomorphic authenticators and proved security of their schemes under a stronger security model, which requires the existence of an extractor to retrieve the original file by receiving the program of a successful prover. When using their POR protocol with public verifiability to verify the availability of multiple files separately, the number of pairing operations computed by a verifier is linear with the number of files. To improve the heavy burden on the verifier, we introduce a notion called multi-proof-of-retrievability(MPOR), allowing one verifier to verify the availability of multiple files stored by a server in one pass. We also design a MPOR protocol with public verifiability by extending the work of Shacham et al. The advantage of our MPOR scheme is that computational overhead of a verifier in our scheme is constant, independent of the number of files. Nevertheless, the soundness of our MPOR protocol is proved under a relatively weak security notion. In particular, analysis of our MPOR protocol shows that each file can be extracted in expected polynomial time under certain restriction on the size of processed files.
Exploiting Class Hierarchies for Knowledge Transfer in Hyperspectral Data

Obtaining ground truth for classification of remotely sensed data is time consuming and expensive, resulting in poorly represented signatures over large areas. In addition, the spectral signatures of a given class vary with location and/or time. Therefore, successful adaptation of a classifier designed from the available labeled data to classify new hyperspectral images acquired over other geographic locations or subsequent times is difficult, if minimal additional labeled data are available. In this paper, the binary hierarchical classifier is used to propose a knowledge transfer framework that leverages the information extracted from the existing labeled data to classify spatially separate and multitemporal test data. Experimental results show that in the absence of any labeled data in the new area, the approach is better than a direct application of the original classifier on the new data. Moreover, when small amounts of the labeled data are available from the new area, the framework offers further improvements through semisupervised learning mechanisms and compares favorably with previously proposed methods
An Endogenous Inhibitor of [ 3 H ] Estradiol Binding to Nuclear Type Estrogen Binding Sites in Normal and Malignant Tissues 1

Previous studies from our laboratory have demonstrated that nonneoplastic tissues, such as rat uterus, contain an endoge nous inhibitor of [3H]estradiol binding to nuclear type II sites. Since the stimulation of nuclear type II estrogen binding sites in the rat uterus is highly correlated with the stimulation of uterine growth and DMA synthesis, we have suggested that this inhibitor may be an endogenous ligand for the type II site which may antagonize estrogenic response. The present studies demon strate that normal mouse mammary gland cytosol contains very high levels of this inhibitor activity, whereas cytosol from mouse mammary tumors and human breast cancer contains very low quantities of this inhibitor. These results are consistent with the observations that rapidly proliferating neoplastic tissue contains very high quantities of nuclear type II sites and that this may be directly related to lower levels of this endogenous inhibitor in these tissues.
Estimating the Population Mean From a Simple Random Sample When Some Responses are Missing

We develop a design-based prediction approach to estimate the finite population mean in a simple setting where some responses are missing. The approach is based on indicator sampling random variables that operate on labeled units (subjects). Missing data mechanisms are defined that may depend on a subject, or on a selection (such as when the study design assigns groups of selected subjects to different interviewers). Using an approach usually reserved for model-based inference, we develop a predictor that equals the sample total divided by the expected sample size. The methods are direct extensions of best linear unbiased prediction (BLUP) in finite population mixed models. When the probability of missing is estimated from the sample, the empirical estimator simplifies to the mean of the realized non-missing responses. The different missing data mechanisms are revealed by the notation that accounts for the labels and sample selections. The mean squared error (MSE) of the empirical estimator, counterintuitively, is smaller than the MSE if the probability of missing is known.
The splab at the NTCIR-12 Short Text Conversation Task

The splab team participated in the Chinese subtask of the NTCIR-12 on Short Text Conversation Task. This task assumes that the existing comments in a post-comment repository can be reused as suitable responses to a new short text. Our task is to return 10 most appropriate comments to such a short text. In our system, we attempt to employ advanced IR methods and the recent deep learning techniques to tackle the problem. We develop a three-tier ranking framework to promote the most suitable comments in top position as much as possible. It consists of three components, i.e., search, lexical ranking and semantic ranking. In the search component, three different query generation methods are employed to boost the system’s recall. In the lexical ranking, we exploit the training data of labelled post-comment pairs to score the comments in the candidate pool. In the final semantic ranking, we apply the deep learning techniques to convert the comment string or a short text string to a continuous, low-dimensional feature vector, re-score the final candidate comments and provide the 10 most reasonable comments to a short text. The evaluation of submitted results empirically shows our framework is effective in terms of mean nDCG@1, mean P+ and mean nERR@10.
Brainy: effective selection of data structures

Data structure selection is one of the most critical aspects of developing effective applications. By analyzing data structures' behavior and their interaction with the rest of the application on the underlying architecture, tools can make suggestions for alternative data structures better suited for the program input on which the application runs. Consequently, developers can optimize their data structure usage to make the application conscious of an underlying architecture and a particular program input. This paper presents the design and evaluation of Brainy, a new program analysis tool that automatically selects the best data structure for a given program and its input on a specific microarchitecture. The data structure's interface functions are instrumented to dynamically monitor how the data structure interacts with the application for a given input. The instrumentation records traces of various runtime characteristics including underlying architecture-specific events. These generated traces are analyzed and fed into an offline model, constructed using machine learning, to select the best data structure. That is, Brainy exploits runtime feedback of data structures to model the situation an application runs on, and selects the best data structure for a given application/input/architecture combination based on the constructed model. The empirical evaluation shows that this technique is highly accurate across several real-world applications with various program input sets on two different state-of-the-art microarchitectures. Consequently, Brainy achieved an average performance improvement of 27% and 33% on both microarchitectures, respectively.
Simple and efficient importance sampling scheme for a tandem queue with server slow-down

This paper considers importance sampling as a tool for rare-event simulation. The system at hand is a so-called tandem queue with slow-down, which essentially means that the server of the first queue (or: upstreanm queue) switches to a lower speed when the second queue (downstream queue) exceeds some threshold. The goal is to assess to what extent such a policy succeeds in protecting the first queue, and therefore we focus on estimating the probability of overflow in the downstream queue. It is known that in this setting importance sampling with traditional state-independent distributions performs poorly. More sophisticated state-dependent schemes can be shown to be asymptotically efficient, but their implementation may be problematic, as for each state the new measure has to be computed. This paper presents an algorithm that is considerably simpler than the fully state-dependent scheme; it requires low computational effort, but still has high efficiency.