Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Building a SentiWordNet for Tamil

Sentiment analysis is a discipline of Natural Language Processing which deals with analysing the subjectivity of the data. It is an important task with both commercial and academic functionality. Languages like English have several resources which assist in the task of sentiment analysis. SentiWordNet for English is one such important lexical resource that contains subjective polarity for each lexical item. With growing data in native vernacular, there is a need for language-speciﬁc SentiWordNet(s). In this paper, we discuss a generic approach followed for the development of a Tamil SentiWordNet us-ing currently available resources in English. For Tamil SentiWordNet, a substantial agreement Fleiss Kappa score of 0.663 was obtained after veriﬁcation from Tamil annotators. Such a resource would serve as a baseline for future improvements in the task of sentiment analysis speciﬁc to Tamil data.
Can a control model approach assist case formulation in psychotherapy?

Abstract This paper looks at issues regarding case formulation in psychotherapy. Case formulation is well recognized as being helpful in the conceptualizing of psychological problems and as a useful tool in the practice of cognitive therapy. Control, as opposed to behaviour, is increasingly being seen as that human process most relevant to psychopathology. We look at the diagnosis and treatment of a small number of people, treated in a naturalistic setting, who were selected without any specific criteria other than being people whose problems were treated using the Method of Levels, a form of cognitive therapy based on the principles of Perceptual Control Theory, and who completed both pre- and post-treatment questionnaires. We then consider how the problems these people presented with, and their treatment, might be formulated as a result of taking this approach.
Global Real-Time Semaphore Protocols: A Survey, Unified Analysis, and Comparison

All major real-time suspension-based locking protocols (or semaphore protocols) for global fixed-priority scheduling are reviewed and a new, unified response-time analysis framework applicable to all protocols is proposed. The newly proposed analysis, based on linear programming, is shown to be clearly preferable compared to all prior conventional approaches. Based on the new analysis, all protocols are directly compared with each other in a large-scale schedulability study. Interestingly, the Priority Inheritance Protocol (PIP) and the Flexible Multiprocessor Locking Protocol (FMLP), which are the two oldest and simplest of the considered protocols, are found to perform best.
Pixel-Wise Tamper Detection Under Generic Blur/Sharpen Attacks

In this paper robust image authentication integrated with semi-fragile pixel-wise tamper localization is analyzed. A new pixel-wise transformation robust to blurring/sharpening while fragile to all other image processing operations is proposed. A new method featuring binary and percentage measures with novel ability to integrate human opinion for image authenticity evaluation is presented. Protection for all bits in the pixel is advantage as well as small size of the signature  less than 10% of initial image.
Effectiveness of partitioning strategies of Fast Fourier Transform in GPU implementations

In this paper authors present the results of the effectiveness comparison between the variants of the Radix-2 Deci-mation in Time (DIT) Fast Fourier Transform (FFT) algorithm's implementations on graphics processing units (GPUs) which differ in the way the calculations are distributed among GPUs computational resources. The conducted experiments show that the partitioning of the FFT computational scheme into 4-point and 8-point subtransform blocks in which calculations are performed in a sequential, single instruction multiple thread (SIMT) manner results in significantly faster execution of the FFT calculations on the selected GPU architectures than the standard parallel 2-point butterfly base operation approach. Moreover the proposed partitioning scheme can be extended to arbitrary subtransform block size for a given N-point Radix-2 FFT implementation dedicated to particular GPU architectures.
Achievable DoF-Delay Trade-Offs for the $K$ -user MIMO Interference Channel With Delayed CSIT

The degrees of freedom (DoFs) of the K-user multiple-input multiple-output (MIMO) interference channel are studied when perfect, but delayed channel state information is available at the transmitter side (delayed CSIT). Recent works have proposed schemes improving the DoF knowledge of the interference channel, but at the cost of developing transmission involving many channel uses (long delay), thus increasing the complexity at both transmitter and receiver side. This paper proposes three linear precoding strategies, limited to at most three phases, based on the concept of interference alignment, and built upon three main ingredients: delayed CSIT precoding, user scheduling, and redundancy transmission. In this respect, the interference alignment is realized by exploiting delayed CSIT to align the interference at the non-intended receivers along the space-time domain. Moreover, a new framework is proposed where the number of transmitted symbols and duration of the phases is obtained as the solution of a maximization problem, and enabling the introduction of complexity constraints, which allows deriving the achievable DoF as a function of the transmission delay, i.e., the achievable DoF-delay trade-off. Finally, the latter part of this paper settles that the assumption of time-varying channels common along all the literature on delayed CSIT is indeed unnecessary.
Increasing Payload of Echo Hiding Scheme Using Dual Backward and Forward Delay Kernels

This paper presents a novel audio watermarking scheme based on echo hiding technique. This method is different from conventional echo hiding methods which embed only one bit in each audio section. In this method by using different echo delays we can embed more than one bit in each audio sections. For enhancing robustness of proposed algorithm against various types of attacks, we exploit dual echo kernels. Experimental results show that proposed method has good performance in robustness and inaudibility point of view.
Ethical issues in the design and conduct of cluster randomised controlled trials

In most randomised controlled trials, individual patients are randomised to a treatment or control group, but sometimes this is undesirable or even impossible and groups (clusters) of people may be randomised instead. These are called cluster randomised controlled trials, and although they have been around for a long time, the need for them is likely to increase in line with growing concern to evaluate the delivery of health services, public education, and policy on social care. ### Summary points Need for cluster trials will increase with concern over health service evaluation, but issues of ethics and guardianship must be addressed In some cluster trials the intervention can be targeted at individuals (individual-cluster); where this would be too difficult or expensive the intervention is targeted at the whole group (cluster-cluster) Autonomy is important in individual-cluster trials, while the utilitarian welfare of the cluster as a whole is of paramount importance in cluster-cluster trials In individual-cluster trials the participants should give consent; cluster-cluster trials need procedural safeguards appropriate to the risks carried by the cluster intervention Guardians should sign a consent form that sets out their duties before they volunteer a cluster for a trial The ethical aspects of medical practice and medical research are most often discussed in the context of two main moral traditions—utilitarianism and Kantian ethics. Broadly speaking, utilitarianism is concerned with increasing social utility (value), which usually means that the individuals maximise their expected utility and so act in their own best interests. In the long run social utility will not be served by demanding that individuals be self sacrificing for the common good. This leads to matters of distributive justice whereby utility and disutility, benefits and costs, are distributed as fairly and evenly as possible across society. The Kantian tradition shows why we are duty bound to respect a person's …
Primary Anterior Cruciate Ligament Reconstruction Using the Contralateral Autogenous Patellar Tendon

We studied patients who underwent primary anterior cruciate ligament reconstruction using either the contralateral (N 434) or ipsilateral (N 228) autogenous patellar tendon graft to determine the difference between groups for the return of range of motion, quadriceps muscle strength, and return to sports. The contralateral group had statistically significantly more flexion than the ipsilateral group at 1 week and 2 weeks postoperatively. The contralateral group had statistically significantly greater quadriceps muscle strength in the reconstructed knee at 1, 2, and 4 months postoperatively and in the donor knee at 1 and 2 months postoperatively. Mean KT-1000 arthrometer results were 1.9 1.3 mm for the contralateral group and 2.2 1.1 mm for the ipsilateral group. The mean time to return to sports at full capability in a competitive subgroup was 4.1 months for contralateral patients and 5.5 months for ipsilateral patients. Overall, 49% of patients in the contralateral group and 12% of patients in the ipsilateral group returned to their preinjury levels of activity by 4 months postoperatively. Our results indicate that the contralateral patellar tendon can be used to restore range of motion and strength sooner than an ipsilateral patellar tendon graft. Patients can also have a faster return to full capability in sports without compromising ultimate stability.
Analysis of high-throughput plant image data with the information system IAP

This work presents a sophisticated information system, the Integrated Analysis Platform (IAP), an approach supporting large-scale image analysis for different species and imaging systems. In its current form, IAP supports the investigation of Maize, Barley and Arabidopsis plants based on images obtained in different spectra. Several components of the IAP system, which are described in this work, cover the complete end-to-end pipeline, starting with the image transfer from the imaging infrastructure, (grid distributed) image analysis, data management for raw data and analysis results, to the automated generation of experiment reports.
Extended Release of an Anti–Heparan Sulfate Peptide From a Contact Lens Suppresses Corneal Herpes Simplex Virus-1 Infection

Purpose To prolong the release of a heparan sulfate binding peptide, G2-C, using a commercially available contact lens as a delivery vehicle and to demonstrate the ability of the released peptide to block herpes simplex virus-1 (HSV-1) infection using in vitro, ex vivo, and in vivo models of corneal HSV-1 infection. Methods Commercially available contact lenses were immersed in peptide solution for 5 days prior to determining the release of the peptide at various time points. Cytotoxicity of the released samples was determined by MTT and cell cycle analysis, and the functional activity of the released samples were assessed by viral entry, and viral spread assay using human corneal epithelial cells (HCE). The ability to suppress infection in human and pig cornea ex vivo and mouse in vivo models were also assessed. Results Peptide G2-C was released through the contact lens. Following release for 3 days, the peptide showed significant activity by inhibiting HSV-1 viral entry and spread in HCE cells. Significant suppression of infection was also observed in the ex vivo and in vivo experiments involving corneas. Conclusions Extended release of an anti–HS peptide through a commercially available contact lens can generate significant anti–HSV-1 activity and provides a new and effective way to control corneal herpes.
Evaluation of efficient high quality depth upsampling methods for 3DTV

High quality 3D content generation requires high quality depth maps. In practice, depth maps generated by stereo-matching, depth sensingcameras, or decoders, have a low resolution and suffer from unreliable estimates and noise. Therefore depth post-processing is necessary. In this paper we benchmark state-of-the-art filter based depth upsampling methods on depth accuracy and interpolation quality by conducting a parameter space search to find the optimum set of parameters for various upscale factors and noise levels. Additionally, we analyze each method’s computational complexity with the big O notation and we measure the runtime of the GPU implementation that we built for each method.
Evaluating Entropic Based Clustering Algorithms on Biomedical Data

Clustering algorithms are being widely used on biomedical data. They aim to extract important information that can be used to improve life conditions by helping specialized technicians on the decision process. Clustering algorithms based on information theory concepts claim that by using higher order statistic they are able to extract more information from the data and therefore provide much better results. In this work we try to verify this claim by comparing the performance of some entropic clustering algorithms against more conventional ones. Results of the performed experiments are not conclusive but they seem to indicate that this kind of entropic algorithms may provide some improvements when clustering biomedical data.
Thymic Stromal Lymphopoietin Is Produced by Dendritic Cells

Thymic stromal lymphopoietin (TSLP) is a type 1 cytokine that contributes to lymphopoiesis and the development of asthma and atopic dermatitis. TSLP acts on multiple lineages, including dendritic cells (DCs), T cells, NKT cells, eosinophils, and mast cells, mediating proliferation and survival and linking innate and adaptive immune responses. TSLP is produced by a range of cells, including epithelial cells, fibroblasts, stromal cells, and keratinocytes. DCs are important primary targets of TSLP, and we unexpectedly demonstrated that DCs also produce TSLP in response to TLR stimulation and that this is augmented by IL-4. Moreover, we demonstrated that when mice were challenged with house dust mite extract, lung CD11c+ DCs expressed TSLP mRNA at an even higher level than did epithelial cells. These data suggested that DCs not only respond to TSLP but also are a source of TSLP during pathogen and/or allergen encounter.
Análise Fatorial de Uma Medida de Estratégias de Enfrentamento

The goal of this study was to investigate the factorial structure of the Brazilian version of the Ways of Coping Scale (Gimenes & Queiroz, 1997) that measures coping strategies people use when facing specific stressors. The sample of 409 male and female adults was divided into two groups: 252 reported one major stressor related to a real problem in their everyday activities and the other 157 reported and described the suffering of a chronic disease. A factor analysis with principal axis method and orthogonal rotation, extracted four factors: coping strategies based on the stressor, coping strategies based on the emotion, religiosity/fantasy thinking and search for social support. Comparisons between the two samples suggested different possibilities for the development of research and intervention in the clinical work aimed at the management of stress by different type of patients.
An improved unsupervised learning probabilistic model of word sense disambiguation

Unsupervised learning can address the general limitation of supervised learning that sense-tagged text is not available for most domains and is expensive to create. However, the existing unsupervised learning probabilistic models are computationally expensive and convergence slowly because of large numbers and random initialization of model parameters. This paper reduces the noise jamming and the dimensionality of the models by using proposed feature selection and initial parameter estimation. Experimental result shows the accuracy and efficiency of the proposed probabilistic model are obviously improved.
Speller performance prediction for query autocorrection

Query speller is an indispensable part of any modern search engine. In this paper we define the problem of speller performance prediction and apply it to the task of query spelling autocorrection. As candidates for query autocorrection we used the suggestions generated by a query speller. To determine their reliability we used a binary classifier trained on manually labeled data. In addition to the basic standard lexical and statistical features we utilized a number of new click-based features, what allowed to significantly outperform the algorithm trained on the basic set of features.
Teachers’ perceptions of school connectedness and risk-taking in adolescence

School connectedness has been shown to be an important protective factor in adolescent development, which is associated with reduced risk-taking behavior. Interventions to increase students’ connectedness to school commonly incorporate aspects of teacher training. To date, however, research on connectedness has largely been based on student survey data, with no reported research addressing teachers’ perceptions of students’ connectedness and its association with student behavior. This research attempted to address this gap in the literature through in-depth interviews with 14 school teachers and staff from two Australian high schools. Findings showed that teachers perceived students’ connectedness to be important in regard to reducing problem behavior, and discussed aspects of connectedness, including fairness and discipline, feeling valued, belonging and having teacher support, and being successfully engaged in school, as being particularly important. This research enables the development of school-based intervention programs that are based on both student- and teacher-focused research.
Unbiased estimation of risk Marcin Pitera

The estimation of risk measured in terms of a risk measure is typically done in two steps: in the first step, the distribution is estimated by statistical methods, either parametric or nonparametric. In the second step, the estimated distribution is considered as true distribution and the targeted risk-measure is computed. In the parametric case this is achieved by using the formula for the risk-measure in the model and inserting the estimated parameters. It is wellknown that this procedure is not efficient because the highly nonlinear mapping from model parameters to the risk-measure introduces an additional biases. Statistical experiments show that this bias leads to a systematic underestimation of risk. In this regard we introduce the concept of unbiasedness to the estimation of risk. We show that an appropriate bias correction is available for many well known estimators. In particular, we consider value-at-risk and tail value-at-risk (expected shortfall). In the special case of normal distributions, closed-formed solutions for unbiased estimators are given. For the general case we propose a bootstrapping algorithm and illustrate the outcomes by several data experiments.
Finding Syntactic Structures from Human Motion Data

We present a new approach to motion rearrangement that preserves the syntactic structures of an input motion automatically by learning a context‐free grammar from the motion data. For grammatical analysis, we reduce an input motion into a string of terminal symbols by segmenting the motion into a series of subsequences, and then associating a group of similar subsequences with the same symbol. To obtain the most repetitive and precise set of terminals, we search for an optimial segmentation such that a large number of subsequences can be clustered into groups with little error. Once the input motion has been encoded as a string, a grammar induction algorithm is employed to build up a context‐free grammar so that the grammar can reconstruct the original string accurately as well as generate novel strings sharing their syntactic structures with the original string. Given any new strings from the learned grammar, it is straightforward to synthesize motion sequences by replacing each terminal symbol with its associated motion segment, and stitching every motion segment sequentially. We demonstrate the usefulness and flexibility of our approach by learning grammars from a large diversity of human motions, and reproducing their syntactic structures in new motion sequences.
Purchasing Ipos with Commissions

We find direct evidence that institutions increase round-trip stock trades, increase average commissions per share, and pay unusually high commissions on some trades in order to send abnormally high commissions to the lead underwriters of profitable initial public offerings (IPOs). These excess commission payments are a particularly effective way for transient investors to receive lucrative IPO allocations. Our results suggest that the underwriter’s concern for their long-term client relationships limits the payment-for-IPO practice. We estimate that abnormal commission payments are large for the most profitable issues, and that an additional $1 excess commission payment to the lead underwriter results in $2.21 in investor profits from allocated shares.
Preference-Implicated Personal Data Model for Personalization Service Collaboration

Nowadays Personalized Service Collaboration (PSC) becomes more feasible due to the increasing availability of mobile application and web service. The semantic user model comprised of Resource Description Framework (RDF) plays a significant role in PSC. In practice, preferences within (personal data/model) often change with different time, context and dimension, which are dynamic information and contribute to PSC. Therefore, personal data model which represents time, context and dimension is increasingly required to provide a more complete user model view and more value-added user information to all relevant applications and services for PSC. In this paper, a novel personal data model based on RDF is proposed, which takes into account how to represent implicated and varied preference that corresponding to different time, context and dimension. The presented model aims to solve the issues that store, organize and represent unified personal data formally in service collaboration environment which harbors different kinds of personal date from various mobile applications and web services. Finally, the comparison and analysis between proposed personal data model and conventional user model are presented. The experiment result on real data set demonstrate the feasibility and effectiveness of proposed personal data model.
Towards Semantic Web Support for Writing Project Proposals

Writing technical project proposals is a task that needs convenient access to a wide range of information. The Web provides some support for this activity in the form of search engines (to discover relevant material) and links (to make reference to supporting evidence). This paper establishes some requirements for supporting proposal authoring and describes how they can be satisfied by a Semantic Web environment.
Robust Discontinuity Preserving Optical Flow Methods

In this work, we present an implementation of discontinuity-preserving strategies in TV-L 1 optical ﬂow methods. These are based on exponential functions that mitigate the regularization at image edges, which usually provide precise ﬂow boundaries. Nevertheless, if the smoothing is not well controlled, it may produce instabilities in the computed motion ﬁelds. We present an algorithm that allows three regularization strategies: the ﬁrst one uses an exponential function together with a TV process; the second one combines this strategy with a small constant that ensures a minimum isotropic smoothing; the third one is a fully automatic approach that adapts the diﬀusion depending on the histogram of the image gradients. The last two alternatives are aimed at reducing the eﬀect of instabilities. In the experiments, we observe that the pure exponential function is highly unstable while the other strategies preserve accurate motion contours for a large range of parameters.