Miracle-dz/UniFAR-FTUs huggingface dataset:
Opinion mining of product reviews based on semantic role labeling

Online product reviews are becoming increasingly available. Generally, potential customers usually wade through a lot of online reviews in order to make an informed decision. We tackle the problem of semantic understanding for consumer reviews based on semantic role labeling, which implements shallow semantic analysis. In this paper, a sentiment mining and retrieval system was proposed, which mines useful knowledge from product reviews. Furthermore, the sentiment orientation and comparison between positive and negative evaluation are presented visually in the system. Experimental results on a real-world data set have shown the system is both feasible and effective.
Classification trees for problems with monotonicity constraints

For classification problems with ordinal attributes very often the class attribute should increase with each or some of the explaining attributes. These are called classification problems with monotonicity constraints. Classical decision tree algorithms such as CART or C4.5 generally do not produce monotone trees, even if the dataset is completely monotone. This paper surveys the methods that have so far been proposed for generating decision trees that satisfy monotonicity constraints. A distinction is made between methods that work only for monotone datasets and methods that work for monotone and non-monotone datasets alike.
Periodic event-triggered dual-rate control for a networked control system

In this work, a Periodic Event-Triggered Sampling (PETS) strategy is implemented in a Networked Control System (NCS), which uses a gain-scheduled dual-rate PD controller in order to deal with time-varying network-induced delays and packet disorder. Compared to the Time-Triggered Sampling (TTS) strategy, this control solution is able to reduce network utilization (number of transmissions), while still guaranteeing control performance. Simulation and experimental results of an Unmanned Aerial Vehicle (UAV) illustrate the main benefits of the approach.
Effect of bilateral reaching on affected arm motor control in stroke – with and without loading on unaffected arm

Purpose. To investigate the effect of bilateral reaching, with/without inertial loading on the unaffected arm, on hemiparetic arm motor control in stroke. Methods. Twenty unilateral stroke patients were recruited. A three-dimensional optical motion capture system was used to measure the movement trajectory of the hemiparetic arm while performing three tasks: affected limb reaching forward; two-limb reaching forward; and two-limb reaching forward with inertia loading of 25% upper limb weight on the unaffected limb, respectively. Kinematical parameters were utilized to quantify the reaching performance of the affected arm. Results. No matter whether loading was applied on the unaffected arm or not, the bilateral reaching task did not significantly facilitate smoother and faster movement. Furthermore, during bilateral reaching task with/without loading on the unaffected arm, stroke patients showed slower movement, lower maximal movement velocity, feedback control dominant and discontinuous movements in the affected arm than the same task with unilateral reaching. Subjects showed the greatest active upper extremity range of motion in proximal joints during the bilateral reaching task without unaffected arm loading. The amount of trunk movement also increased during bilateral reaching either with or without loading on the unaffected arm. Patients with moderate upper extremity motor impairment performed more discontinuous movements and less active elbow range of motion during bilateral reaching tasks; however, those with mild upper extremity motor impairment performed smoother movements and demonstrated greater active elbow range of motion during bilateral reaching tasks. Conclusions. Bilateral reaching tasks with/without loading on the unaffected arm could be considered as adding challenges during motor control training. Training with bilateral arm movements may be considered as a treatment strategy, and can be incorporated in stroke rehabilitation to facilitate greater arm active movement and improve motor control performance in the affected arm.
Improved Fairness Using DCCP in Wireless Sensor Networks

Abstract—This paper studies TCP’s fairness issues in WSNs, and designs an improved congestion control algorithm based on the characteristics of the WSNs (Wireless Sensor Networks). The protocol is designed as extension to DCCP (Datagram Congestion Control Protocol) with a new congestion control component. We also implemented this congestion control algorithm in NS2. Simulation results show improvements on fairness achieved by using Extend congestion control algorithm.
FDTD/K-DWM SIMULATION OF 3D ROOM ACOUSTICS ON GENERAL PURPOSE GRAPHICS HARDWARE USING COMPUTE UNIFIE

Computer-based modelling is becoming standard practice in modern room acoustics. Its applications range from prediction of design parameters, visualization of sound propagation and auralisation [1]. Traditional architectural acoustic modelling methods, such as Ray-Tracing [2] and Image-Source models [3], draw on geometrical assumptions and therefore may be confidently applied only to an explicit class of design problems. Wave-based methods, on the other hand, attempt to solve the acoustic wave equation under specific boundary conditions, and are therefore more precise in predicting sound propagation, albeit at a much higher computational cost. Common methods such as Boundary Element [4] and Finite Element [5] analyses are considered highly accurate; however they impose two major limitations: a) their computational requirements grow exponentially with frequency, thus rendering them inadequate for wide band problems; and b) traditionally, their solution correspond to a steady-state analysis which neglects discrete timedomain transient responses. Throughout the past decade much attention has been shifted towards numerical time domain methods, particularly the Finite Difference Time Domain (FDTD) [6] and the Digital Waveguide Mesh (DWM) [7]. Even as being able to provide discrete time-space approximations to the N-dimensional wave equation, these methods have two major drawbacks. First, the entire acoustic space must be modelled resulting in large computational domains, hence higher calculation times. Second, as finite difference approximations are employed, these methods exhibit dispersion errors that increase with frequency and vary with direction of propagation [8]; thus imposing a high-frequency calculation limit. Various methods for reducing these errors have been studied, most noticeably by considering non-rectangular mesh topologies [9], [10], by spatial interpolation and frequency warping [11]. One feasible trade-off is to oversample the mesh, consequently extending the high frequency limit at the expense of a considerably large computational problem. Evidently, a way to efficiently solve such large problems at reasonable calculation times would diminish the aforementioned restrictions.
Exact and Approximate Maximum Inner Product Search with LEMP

We study exact and approximate methods for maximum inner product search, a fundamental problem in a number of data mining and information retrieval tasks. We propose the LEMP framework, which supports both exact and approximate search with quality guarantees. At its heart, LEMP transforms a maximum inner product search problem over a large database of vectors into a number of smaller cosine similarity search problems. This transformation allows LEMP to prune large parts of the search space immediately and to select suitable search algorithms for each of the remaining problems individually. LEMP is able to leverage existing methods for cosine similarity search, but we also provide a number of novel search algorithms tailored to our setting. We conducted an extensive experimental study that provides insight into the performance of many state-of-the-art techniques—including LEMP—on multiple real-world datasets. We found that LEMP often was significantly faster or more accurate than alternative methods.
Zinc supplementation as an adjunct to standard therapy in childhood nephrotic syndrome - a systematic review

AIM To evaluate the role of zinc as add on treatment to the “recommended treatment” of nephrotic syndrome (NS) in children. METHODS All the published literature through the major databases including Medline/Pubmed, Embase, and Google Scholar were searched till 31st December 2015. Reference lists from the articles were reviewed to identify additional pertinent articles. Retrieved papers concerning the role of zinc in childhood NS were reviewed by the authors, and the data were extracted using a standardized data collection tool. Randomized trials (RCTs) comparing zinc vs placebo was included. Effect of zinc was studied in both steroid sensitive and steroid dependent/frequent relapsing NS. The primary outcome measure was the risk of relapse in 12 mo. The secondary outcome measures were mean relapse rate per patient in 12 mo, mean relapse rate per patient in 6 mo, risk of infection associated relapse in 12 mo, cumulative dose of steroids in two groups, mean length of time to next relapse, adverse effects of therapy, and change in serum zinc levels. RESULTS Of 54 citations retrieved, a total of 6 RCTs were included. Zinc was used at a dose of 10-20 mg/d, for the duration that varied from 6-12 mo. Compared to placebo, zinc reduced the frequency of relapses, induced sustained remission/no relapse, reduced the proportion of infection episodes associated with relapse with a mild adverse event in the form of metallic taste. The GRADE evidence generated was of “very low-quality”. CONCLUSION Zinc may be a useful additive in the treatment of childhood NS. The evidence generated mostly was of “very low-quality”. We need more good quality RCTs in different country setting as well different subgroups of children before any firm recommendation can be made.
Human–Robot Collaboration Based on Motion Intention Estimation

In this paper, adaptive impedance control is proposed for a robot collaborating with a human partner, in the presence of unknown motion intention of the human partner and unknown robot dynamics. Human motion intention is defined as the desired trajectory in the limb model of the human partner, which is extremely difficult to obtain considering the nonlinear and time-varying property of the limb model. Neural networks are employed to cope with this problem, based on which an online estimation method is developed. The estimated motion intention is integrated into the developed adaptive impedance control, which makes the robot follow a given target impedance model. Under the proposed method, the robot is able to actively collaborate with its human partner, which is verified through experiment studies.
Towards robust computerised marking of free-text responses

This paper describes and exemplifies an application of AutoMark, a software system developed in pursuit of robust computerised marking of free-text answers to open-ended questions. AutoMark employs the techniques of Information Extraction to provide computerised marking of short free-text responses. The system incorporates a number of processing modules specifically aimed at providing robust marking in the face of errors in spelling, typing, syntax, and semantics. AutoMark looks for specific content within free-text answers, the content being specified in the form of a number of mark scheme templates. Each template represents one form of a valid (or a specifically invalid) answer. Student answers are first parsed, and then intelligently matched against each mark scheme template, and a mark for each answer is computed. The representation of the templates is such that they can be robustly mapped to multiple variations in the input text. The current paper describes AutoMark for the first time, and presents the results of a brief quantitative and qualitative study of the performance of the system in marking a range of free-text responses in one of the most demanding domains: statutory national curriculum assessment of science for pupils at age 11. This particular domain has been chosen to help identify the strengths and weaknesses of the current system in marking responses where errors in spelling, syntax, and semantics are at their most frequent. Four items of varying degrees of open-endedness were selected from the 1999 tests. These items are drawn from the real-world of so-called ‘high stakes’ testing experienced by cohorts of over half a million pupils in England each year since 1995 at ages 11 and 14. A quantitative and qualitative study of the performance of the system is provided, together with a discussion of the potential for further development in reducing these errors. The aim of this exploration is to reveal some of the issues which need to be addressed if computerised marking is to play any kind of reliable role in the future development of such test regimes.
CLAP: Closely link associated pixel based extraction of brain tumor in MR images

One of the most severe diseases in the field of medical science is brain tumor. A proper diagnosis is required in the early phase of tumor growth. In the past various methods had applied on brain MR (Magnetic Resonance) imaging to figure out the proper abnormality region from overall volume of the brain. The literature helps to identify that various bi-clustering algorithms had cluster out the region based on some predefined threshold value which results in generation of cluster which was dependent on specific threshold value only. In this paper a new bi-clustering algorithm has been proposed to cluster out the maximum abnormality area from the brain MR image without any predefined threshold. The algorithm is based on the closely link associated pixel (CLAP) mechanism for tumor segmentation.
Improving Telugu Dependency Parsing using Combinatory Categorial Grammar Supertags

We show that Combinatory Categorial Grammar (CCG) supertags can improve Telugu dependency parsing. In this process, we first extract a CCG lexicon from the dependency treebank. Using both the CCG lexicon and the dependency treebank, we create a CCG treebank using a chart parser. Exploring different morphological features of Telugu, we develop a supertagger using maximum entropy models. We provide CCG supertags as features to the Telugu dependency parser (MST parser). We get an improvement of 1.8% in the unlabelled attachment score and 2.2% in the labelled attachment score. Our results show that CCG supertags improve the MST parser, especially on verbal arguments for which it has weak rates of recovery.
RouteLite: One-hop path splicing with path migration

As Internet applications demand high flexibility and reliability, giving an end-user control over routing while not breaking the Internet has been attractive to both end-users and Internet Services Providers (ISPs). In this paper, we present RouteLite, a lightweight routing scheme that achieves “controlled flexibility”, i.e., provide users with flexibility in selecting routes while only behaving users are granted such flexibility. RouteLite consists of one-hop path splicing and path migration, where we can migrate traffic seamlessly between different paths during transmission based on an end-user's behavior. RouteLite can not only grant an end-user control over routing through her behavior but also help ISPs operate their network efficiently and securely while achieving flexibility on the part of the users. The controlled flexibility can be realized through a credit-based accounting mechanism, through measurement based on the feedback on end-user's past behavior to control and trigger the path migration. We implement a prototype of such a RouteLite system and the experimental and simulation results show the efficiency of our proposed RouteLite scheme even with malicious users attempting to abuse the system causing DDoS-type congestion.
Genomic diversity of the Avian leukosis virus subgroup J gp85 gene in different organs of an infected chicken

The genomic diversity of Avian leukosis virus subgroup J (ALV-J) was investigated in an experimentally infected chicken. ALV-J variants in tissues from four different organs of the same bird were re-isolated in DF-1 cells, and their gp85 gene was amplified and cloned. Ten clones from each organ were sequenced and compared with the original inoculum strain, NX0101. The minimum homology of each organ ranged from 96.7 to 97.6%, and the lowest homology between organs was only 94.9%, which was much lower than the 99.1% homology of inoculum NX0101, indicating high diversity of ALV-J, even within the same bird. The gp85 mutations from the left kidney, which contained tumors, and the right kidney, which was tumor-free, had higher non-synonymous to synonymous mutation ratios than those in the tumor-bearing liver and lungs. Additionally, the mutational sites of gp85 gene in the kidney were similar, and they differed from those in the liver and lung, implying that organ- or tissue-specific selective pressure had a greater influence on the evolution of ALV-J diversity. These results suggest that more ALV-J clones from different organs and tissues should be sequenced and compared to better understand viral evolution and molecular epidemiology in the field.
Histology of “placoderm” dermal skeletons: Implications for the nature of the ancestral gnathostome

The vertebrate dermal skeleton has long been interpreted to have evolved from a primitive condition exemplified by chondrichthyans. However, chondrichthyans and osteichthyans evolved from an ancestral gnathostome stem‐lineage in which the dermal skeleton was more extensively developed. To elucidate the histology and skeletal structure of the gnathostome crown‐ancestor we conducted a histological survey of the diversity of the dermal skeleton among the placoderms, a diverse clade or grade of early jawed vertebrates. The dermal skeleton of all placoderms is composed largely of a cancellar architecture of cellular dermal bone, surmounted by dermal tubercles in the most ancestral clades, including antiarchs. Acanthothoracids retain an ancestral condition for the dermal skeleton, and we record its secondary reduction in antiarchs. We also find that mechanisms for remodeling bone and facilitating different growth rates between adjoining plates are widespread throughout the placoderms. J. Morphol. , 2013. © 2013 Wiley Periodicals, Inc.
Pattern Kits

Compressed full-text indexes have been one of pattern matching’s most important success stories of the past decade. We can now store a text in nearly the information-theoretic minimum of space, such that we can still quickly count and locate occurrences of any given pattern. However, some files or collections of files are so huge that, even compressed, they do not all fit in one machine’s internal memory. One solution is to break the file or collection into pieces and create a distributed index spread across many machines (e.g., a cluster, grid or cloud). Suppose we want to search such an index for many patterns. Since each pattern is to be sought on each machine, it is worth spending a reasonable amount of time to preprocess the patterns if that leads to faster searches. In this paper we show that if the concatenation of the patterns can be compressed well with LZ77, then we can take advantage of their similarities to speed up searches in BWT-based indexes. More specifically, if we are searching for t patterns of total length m in a distributed index for a text of total length n, then we spend O(m + (g + t) logm) time preprocessing the patterns on one machine and then O ( (g + t) log m log n ) time searching for them on each machine, where g is the size of the smallest straight-line program for the concatenation of the patterns. Thus, if the concatenation of the patterns has a small straight-line program — plausible if the patterns are similar — and the number of machines is large, we achieve a theoretically significant speed-up. The techniques we use seem likely to be of independent interest and we show how they can be applied to pattern matching with wildcards and parallel pattern matching.
Non-Steroidal Anti-inflammatory Drugs Decrease E2F1 Expression and Inhibit Cell Growth in Ovarian Cancer Cells

Epidemiological studies have shown that the regular use of non-steroidal anti-inflammatory (NSAIDs) drugs is associated with a reduced risk of various cancers. In addition, in vitro and experiments in mouse models have demonstrated that NSAIDs decrease tumor initiation and/or progression of several cancers. However, there are limited preclinical studies investigating the effects of NSAIDs in ovarian cancer. Here, we have studied the effects of two NSAIDs, diclofenac and indomethacin, in ovarian cancer cell lines and in a xenograft mouse model. Diclofenac and indomethacin treatment decreased cell growth by inducing cell cycle arrest and apoptosis. In addition, diclofenac and indomethacin reduced tumor volume in a xenograft model of ovarian cancer. To identify possible molecular pathways mediating the effects of NSAID treatment in ovarian cancer, we performed microarray analysis of ovarian cancer cells treated with indomethacin or diclofenac. Interestingly, several of the genes found downregulated following diclofenac or indomethacin treatment are transcriptional target genes of E2F1. E2F1 was downregulated at the mRNA and protein level upon treatment with diclofenac and indomethacin, and overexpression of E2F1 rescued cells from the growth inhibitory effects of diclofenac and indomethacin. In conclusion, NSAIDs diclofenac and indomethacin exert an anti-proliferative effect in ovarian cancer in vitro and in vivo and the effects of NSAIDs may be mediated, in part, by downregulation of E2F1.
Intensified and protective CD4+ T cell immunity in mice with anti–dendritic cell HIV gag fusion antibody vaccine

Current human immunodeficiency virus (HIV) vaccine approaches emphasize prime boost strategies comprising multiple doses of DNA vaccine and recombinant viral vectors. We are developing a protein-based approach that directly harnesses principles for generating T cell immunity. Vaccine is delivered to maturing dendritic cells in lymphoid tissue by engineering protein antigen into an antibody to DEC-205, a receptor for antigen presentation. Here we characterize the CD4+ T cell immune response to HIV gag and compare efficacy with other vaccine strategies in a single dose. DEC-205–targeted HIV gag p24 or p41 induces stronger CD4+ T cell immunity relative to high doses of gag protein, HIV gag plasmid DNA, or recombinant adenovirus-gag. High frequencies of interferon (IFN)-γ– and interleukin 2–producing CD4+ T cells are elicited, including double cytokine-producing cells. In addition, the response is broad because the primed mice respond to an array of peptides in different major histocompatibility complex haplotypes. Long-lived T cell memory is observed. After subcutaneous vaccination, CD4+ and IFN-γ–dependent protection develops to a challenge with recombinant vaccinia-gag virus at a mucosal surface, the airway. We suggest that a DEC-targeted vaccine, in part because of an unusually strong and protective CD4+ T cell response, will improve vaccine efficacy as a stand-alone approach or with other modalities.
On the Coding of Functions of Correlated Sources

In their seminal work [1], Slepian and Wolf consider the network information theoretic problem of compressing two correlated sources. The sources are independent and identically distributed according to p(x, y) taking values in X×Y . Denote an instance of the source by the random variables X and Y . Specifically, they consider a general setting in which there is a source encoder for each source and a source decoder for each source. They consider all possible permutations of the information available to each encoder and each decoder as shown in Figure 1 (Figure 3 from [1]). They determine the set of all achievable rates (R x, Ry) in this general setting (where rates are achievable