nils-herrmann/clean_scirepeval huggingface dataset:
Developing an NLP and IR-based algorithm for analyzing gene-disease relationships.

High-throughput techniques such as cDNA microarray, oligonucleotide arrays, and serial analysis of gene expression (SAGE) have been developed and used to automatically screen huge amounts of gene expression data. However, researchers usually spend lots of time and money on discovering gene-disease relationships by utilizing these techniques. We prototypically implemented an algorithm that can provide some kind of predicted results for biological researchers before they proceed with experiments, and it is very helpful for them to discover gene-disease relationships more efficiently.



Due to the fast development of computer technology, many information retrieval techniques have been applied to analyze huge digital biomedical databases available worldwide. Therefore we highly expect that we can apply information retrieval (IR) technique to extract useful information for the relationship of specific diseases and genes from MEDLINE articles. Furthermore, we also applied natural language processing (NLP) methods to do the semantic analysis for the relevant articles to discover the relationships between genes and diseases.



We have extracted gene symbols from our literature collection according to disease MeSH classifications. We have also built an IR-based retrieval system, "Biomedical Literature Retrieval System (BLRS)" and applied the N-gram model to extract the relationship features which can reveal the relationship between genes and diseases. Finally, a relationship network of a specific disease has been built to represent the gene-disease relationships.



A relationship feature is a functional word that can reveal the relationship between one single gene and a disease. By incorporating many modern IR techniques, we found that BLRS is a very powerful information discovery tool for literature searching. A relationship network which contains the information on gene symbol, relationship feature, and disease MeSH term can provide an integrated view to discover gene-disease relationships.
From Treebank Conversion to Automatic Dependency Parsing for Vietnamese

This paper presents a new conversion method to automatically transform a constituent-based Vietnamese Treebank into dependency trees. On a dependency Treebank created according to our new approach, we examine two stateof-the-art dependency parsers: the MSTParser and the MaltParser. Experiments show that the MSTParser outperforms the MaltParser. To the best of our knowledge, we report the highest performances published to date in the task of dependency parsing for Vietnamese. Particularly, on gold standard POS tags, we get an unlabeled attachment score of 79.08% and a labeled attachment score of 71.66%.
Deep Learning Based Surveillance System for Open Critical Areas

How to automatically monitor wide critical open areas is a challenge to be addressed. Recent computer vision algorithms can be exploited to avoid the deployment of a large amount of expensive sensors. In this work, we propose our object tracking system which, combined with our recently developed anomaly detection system. can provide intelligence and protection for critical areas. In this work. we report two case studies: an international pier and a city parking lot. We acquire sequences to evaluate the effectiveness of the approach in challenging conditions. We report quantitative results for object counting, detection, parking analysis, and anomaly detection. Moreover, we report state-of-the-art results for statistical anomaly detection on a public dataset.
Segmentation and recognition of handwritten dates

This paper presents an HMM-MLP hybrid system to recognize complex date images written on Brazilian bank cheques. The system first segments implicitly a date image into sub-fields through the recognition process based on an HMM-based approach. Afterwards, the three obligatory date sub-fields are processed by the system (day, month and year). A neural approach has been adopted to work with strings of digits and a Markovian strategy to recognize and verify words. We also introduce the concept of meta-classes of digits, which is used to reduce the lexicon size of the day and year and improve the precision of their segmentation and recognition. Experiments show interesting results on date recognition.
Ten-year experience of bexarotene therapy for cutaneous T-cell lymphoma in Finland.

Bexarotene is an oral retinoid shown to be active against the cutaneous manifestations of cutaneous T-cell lym-phoma (CTCL). Literature on the efficacy, dosing and side-effects of bexarotene is sparse. We present here data on 37 Finnish patients with CTCL treated with bexarotene during the last 10 years. Bexarotene was equally effective as monotherapy or when combined with other treatment modalities, resulting in overall responses of approximately 75%. Early-stage CTCL responded better than advanced-stage CTCL (83% vs. 33%). The mean time to observable response was 3 months and the mean duration of the response was 21 months. The dose of bexarotene was generally lower than recommended due to side-effects. Abrupt elevation of liver transaminases, resulting in cessation of treatment, was observed in 4 (11%) patients. We conclude that the dose of bexarotene should be titrated individually to achieve optimal results. Maintenance therapy with low-dose bexarotene is a feasible alternative.
The Relationship between Burnout Syndrome and Emotional Intelligence in Healthcare Professionals

Burnout syndrome (B.S.) affects millions of workers around the world, having a significant impact on their quality of life and the services they provide. It’s a psycho-social phenomenon, which can be handled through emotional management and psychological help. Emotional Intelligence (E.I) is very important to emotional management. This paper aims to investigate the relationship between Burnout syndrome and Emotional Intelligence in health professionals occupied in the sector of rehabilitation.  The data were collected from a sample of 148 healthcare professionals, workers in the field of rehabilitation, who completed Maslach Burnout Inventory questionnaire, Trait Emotional Intelligence Que-Short Form questionnaire and a questionnaire collecting demographic data as well as personal and professional information. Simple linear regression and multiple regression analyses were conducted to analyze the data.  The results indicated that there is a positive relationship between Emotional Intelligence and Burnout syndrome as Emotional Intelligence acts protectively against Burnout syndrome and even reduces it. In particular, it was found that the higher the Emotional Intelligence, the lower the Burnout syndrome. Also, among all factors of Emotional Intelligence, “Emotionality”, seems to influence Burnout syndrome the most, as, the higher the rate of Emotionality, the lower the rate of Burnout. At the same time, evidence was found on the variability of Burnout syndrome through various models of explanation and correlation between Burnout syndrome and Emotional Intelligence and also, Burnout syndrome and Emotional Intelligence factors.  Employers could focus on building emotional relationships with their employees, especially in the health care field. Furthermore, they could also promote some experimental seminars, sponsored by public or private institutions, in order to enhance Emotional Intelligence and to improve the workers’ quality of life and the quality of services they provide.
Top-K Ranking from Pairwise Comparisons: When Spectral Ranking is Optimal

We explore the top-K rank aggregation problem. Suppose a collection of items is compared in pairs repeatedly, and we aim to recover a consistent ordering that focuses on the top-K ranked items based on partially revealed preference information. We investigate the Bradley-Terry-Luce model in which one ranks items according to their perceived utilities modeled as noisy observations of their underlying true utilities. Our main contributions are two-fold. First, in a general comparison model where item pairs to compare are given a priori, we attain an upper and lower bound on the sample size for reliable recovery of the top-K ranked items. Second, more importantly, extending the result to a random comparison model where item pairs to compare are chosen independently with some probability, we show that in slightly restricted regimes, the gap between the derived bounds reduces to a constant factor, hence reveals that a spectral method can achieve the minimax optimality on the (order-wise) sample size required for top-K ranking. That is to say, we demonstrate a spectral method alone to be sufficient to achieve the optimality and advantageous in terms of computational complexity, as it does not require an additional stage of maximum likelihood estimation that a state-of-the-art scheme employs to achieve the optimality. We corroborate our main results by numerical experiments.
Implementation of a testbed with a hardware channel emulator for simulating the different atmospheric conditions to verify the transmitter and receiver of Optical Wireless systems

Related to different international activities in the Optical Wireless Communications (OWC) field Graz University of Technology (TUG) has high experience on developing different high data rate transmission systems and is well known for measurements and analysis of the OWC-channel. In this paper, a novel approach for testing Free Space Optical (FSO) systems in a controlled laboratory condition is proposed. Based on fibre optics technology, TUG testbed could effectively emulate the operation of real wireless optical communication systems together with various atmospheric perturbation effects such as fog and clouds. The suggested architecture applies an optical variable attenuator as a main device representing the tropospheric influences over the launched Gaussian beam in the free space channel. In addition, the current scheme involves an attenuator control unit with an external Digital Analog Converter (DAC) controlled by self-developed software. To obtain optimal results in terms of the presented setup, a calibration process including linearization of the non-linear attenuation versus voltage graph is performed. Finally, analytical results of the attenuation based on real measurements with the hardware channel emulator under laboratory conditions are shown. The implementation can be used in further activities to verify OWC-systems, before testing under real conditions.
Perceived determinants to alcohol consumption and misuse: a survey of university students.

Before an attempt is made to develop any population-specific behavioural change programme, it is important to know what the factors that influence behaviours are. The aim of this study was to identify what are the perceived determinants that attribute to young people's choices to both consume and misuse alcohol.



Using a descriptive survey design, a web-based questionnaire based on the Theory of Triadic Influence was administered to students aged 18-29 years at one university in Northern Ireland.



Out of the total respondents ( n = 595), knowledge scores on alcohol consumption and the health risks associated with heavy episodic drinking were high (92.4%, n = 550). Over half (54.1%, n = 322) cited the Internet as their main source for alcohol-related information. The three most perceived influential factors of inclination to misuse alcohol were strains/conflict within the family home ( M = 2.98, standard deviation ( SD) = 0.18, 98.7%, n = 587), risk taking/curiosity behaviour ( M = 2.97, SD = 0.27, 97.3%, n = 579) and the desire not to be socially alienated ( M = 2.94, SD = 0.33, 96%, n = 571). Females were statistically significantly more likely to be influenced by desire not to be socially alienated than males (  p = .029). Religion and personal reasons were the most commonly cited reasons for not drinking.



Future initiatives to reduce alcohol misuse and alcohol-related harms need to focus on changing social normative beliefs and attitudes around alcohol consumption and the family and environmental factors that influence the choice of young adult's alcohol drinking behaviour. Investment in multi-component interventions may be a useful approach.
Classification and Comparison of Range-Based Localization Techniques in Wireless Sensor Networks

—For the majority of the applications of WSNs, it is desirable to know the location of sensors. In WSNs, for obtaining this kind of information we need localization schemes. Localization techniques are used to determine the geographical position of sensor nodes. The position parameters of sensor nodes are also useful for many operations for network management, such as routing process, topology control, and security maintenance. So, it is very important that every node should reports its location information very accurately. As we know that GPS is very accurate in location determination but it is expensive in terms of cost and energy of nodes, so it is not useful in WSNs. There are so many localization techniques, including range-based and range-free, have been proposed to calculate positions for randomly deployed sensor nodes. Accuracy of localization techniques is most important before implementing it. With specific hardware, the range-based schemes typically achieve high accuracy based on either nodeto-node distances or angles. In this paper, our main focus is on the range-based localization. Precisely, in order to helps the network designers to find out which techniques/algorithms are suitable for their applications, the most popular range-based localization techniques are classified and compared.
Positivity Bias in Customer Satisfaction Ratings

Customer ratings are valuable sources to understand their satisfaction and are critical for designing better customer experiences and recommendations. The majority of customers, however, do not respond to rating surveys, which makes the result less representative. To understand overall satisfaction, this paper aims to investigate how likely customers without responses had satisfactory experiences compared to those respondents. To infer customer satisfaction of such unlabeled sessions, we propose models using recurrent neural networks (RNNs) that learn continuous representations of unstructured text conversation. By analyzing online chat logs of over 170,000 sessions from Samsung’s customer service department, we make a novel finding that while labeled sessions contributed by a small fraction of customers received overwhelmingly positive reviews, the majority of unlabeled sessions would have received lower ratings by customers. The data analytics presented in this paper not only have practical implications for helping detect dissatisfied customers on live chat services but also make theoretical contributions on discovering the level of biases in online rating platforms.  Kunwoo Park, Meeyoung Cha, and Eunhee Rhim. 2018. Positivity Bias in Customer Satisfaction Ratings. InWWW ’18 Companion: The 2018 Web Conference Companion, April 23–27, 2018, Lyon, France. ACM, New York, NY, USA, 8 pages.
Txt msg n school literacy : does texting and knowledge of text abbreviations adversely affect children ’ s literacy attainment ?

This paper reports on two studies which investigated the relationship between children’s texting behaviour, their knowledge of text abbreviations and their school attainment in written language skills. In Study One, 11–12-year-old children provided information on their texting behaviour. They were also asked to translate a standard English sentence into a text message and vice versa. The children’s standardised verbal and nonverbal reasoning scores were also obtained. Children who used their mobiles to send three or more text messages a day had significantly lower scores than children who sent none. However, the children who, when asked to write a text message, showed greater use of text abbreviations (‘textisms’) tended to have better performance on a measure of verbal reasoning ability, which is highly associated with Key Stage 2 (KS2) and 3 English scores. In Study Two, children’s performance on writing measures was examined more specifically. Ten to eleven-year-old children were asked to complete another English to text message translation exercise. Spelling proficiency was also assessed, and KS2 Writing scores were obtained. Positive correlations between spelling ability and performance on the translation exercise were found, and group-based comparisons based on the children’s writing scores also showed that good writing attainment was associated with greater use of textisms, although the direction of this association is nor clear. Overall, these findings suggest that children’s knowledge of textisms is not associated with poor written language outcomes for children in this age range.
Data Analytics based Deep Mayo Predictor for IPL-9

This paper presents a Deep Mayo Predictor model for predicting the outcomes of the matches in IPL 9 being played in April – May, 2016. The model has three components which are based on multifarious considerations emerging out of a deeper analysis of T20 cricket. The models are created using Data Analytics methods from machine learning domain. The prediction accuracy obtained is high as the Mayo Predictor Model is able to correctly predict the outcomes of 39 matches out of the 56 matches played in the league stage of the IPL IX tournament. Further improvement in the model can be attempted by using a larger training data set than the one that has been utilized in this work. No such effort at creating predictor models for cricket matches has been reported in the literature.
TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering

Trajectory classification, i.e., model construction for predicting the class labels of moving objects based on their trajectories and other features, has many important, real-world applications. A number of methods have been reported in the literature, but due to using the shapes of whole trajectories for classification, they have limited classification capability when discriminative features appear at parts of trajectories or are not relevant to the shapes of trajectories. These situations are often observed in long trajectories spreading over large geographic areas. Since an essential task for effective classification is generating discriminative features, a feature generation framework TraClass for trajectory data is proposed in this paper, which generates a hierarchy of features by partitioning trajectories and exploring two types of clustering: (1) region-based and (2) trajectory-based. The former captures the higher-level region-based features without using movement patterns, whereas the latter captures the lower-level trajectory-based features using movement patterns. The proposed framework overcomes the limitations of the previous studies because trajectory partitioning makes discriminative parts of trajectories identifiable, and the two types of clustering collaborate to find features of both regions and sub-trajectories. Experimental results demonstrate that TraClass generates high-quality features and achieves high classification accuracy from real trajectory data.
ANALYSIS OF THE SATURATION THROUGHPUT IN AN IEEE 802.11 NETWORK USING COGNITIVE RADIO

We present a Markov chain model to estimate the saturation throughput of an IEEE 802.11 network in the basic access mode using cognitive radio. IEEE 802.11 networks are very popular and likely more spectrum is needed for their future deployment and applications. On the other hand the cognitive radio idea has been proposed to deal with the spectrum shortage problem. It is thus reasonable to assume that future IEEE 802.11 network may operate in the context of cognitive radio. One paradigm is that IEEE 802.11 networks will act as secondary users sharing the spectrum of a primary use using the dynamic spectrum access approach. In this scenario it will be important to study how the physical layer of cognitive radio may impact on the IEEE 802.11 network as a whole. Based on a Markov chain model, we investigate the impact of the primary user’s arrivals on the IEEE 802.11 network’s saturation throughput. We validate our presented model with ns2 simulations. As shown by our numerical results, our model results are very close to the ns2 simulation results.
MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations.

With the advent of RFLPs, genetic linkage maps are now being assembled for a number of organisms including both inbred experimental populations such as maize and outbred natural populations such as humans. Accurate construction of such genetic maps requires multipoint linkage analysis of particular types of pedigrees. We describe here a computer package, called MAPMAKER, designed specifically for this purpose. The program uses an efficient algorithm that allows simultaneous multipoint analysis of any number of loci. MAPMAKER also includes an interactive command language that makes it easy for a geneticist to explore linkage data. MAPMAKER has been applied to the construction of linkage maps in a number of organisms, including the human and several plants, and we outline the mapping strategies that have been used.
Hypergraph-Based Image Representation

An appropriate image representation induces some good image treatment algorithms. Hypergraph theory is a theory of finite combinatorial sets, modeling a lot of problems of operational research and combinatorial optimization. Hypergraphs are now used in many domains such as chemistry, engineering and image processing. We present an overview of a hypergraph-based picture representation giving much application in picture manipulation, analysis and restoration: the Image Adaptive Neighborhood Hypergraph (IANH). With the IANH it is possible to build powerful noise detection an elimination algorithm, but also to make some edges detection or some image segmentation. IANH has various applications and this paper presents a survey of them.
Kernel Sparse Representation-Based Classifier

Sparse representation-based classifier (SRC), a combined result of machine learning and compressed sensing, shows its good classification performance on face image data. However, SRC could not well classify the data with the same direction distribution. The same direction distribution means that the sample vectors belonging to different classes distribute on the same vector direction. This paper presents a new classifier, kernel sparse representation-based classifier (KSRC), based on SRC and the kernel trick which is a usual technique in machine learning. KSRC is a nonlinear extension of SRC and can remedy the drawback of SRC. To make the data in an input space separable, we implicitly map these data into a high-dimensional kernel feature space by using some nonlinear mapping associated with a kernel function. Since this kernel feature space has a very high (or possibly infinite) dimensionality, or is unknown, we have to avoid working in this space explicitly. Fortunately, we can indeed reduce the dimensionality of the kernel feature space by exploiting kernel-based dimensionality reduction methods. In the reduced subspace, we need to find sparse combination coefficients for a test sample and assign a class label to it. Similar to SRC, KSRC is also cast into an ℓ1-minimization problem or a quadratically constrained ℓ1 -minimization problem. Extensive experimental results on UCI and face data sets show KSRC improves the performance of SRC.