nils-herrmann/clean_scirepeval huggingface dataset:
Characteristic sets: Accurate cardinality estimation for RDF queries with multiple joins

Accurate cardinality estimates are essential for a successful query optimization. This is not only true for relational DBMSs but also for RDF stores. An RDF database consists of a set of triples and, hence, can be seen as a relational database with a single table with three attributes. This makes RDF rather special in that queries typically contain many self joins. We show that relational DBMSs are not well-prepared to perform cardinality estimation in this context. Further, there are hardly any special cardinality estimation methods for RDF databases. To overcome this lack of appropriate cardinality estimation methods, we introduce characteristic sets together with new cardinality estimation methods based upon them. We then show experimentally that the new methods are-in the RDF context-highly superior to the estimation methods employed by commercial DBMSs and by the open-source RDF store
Application of Data Mining in Educational Database for Predicting Behavioural Patterns of the Students

Data mining refers to the technique of obtaining hidden, previously unknown and possibly significant knowledge from humongous amount of data. Data mining uses a combination of a vast knowledge base, advanced analytical skills, and domain knowledge to unveil hidden trends and patterns which can be applied in almost any sector ranging from business to medicine, then to Engineering. Nonetheless educational institutes can employ data mining to discover valuable information from their databases known as Educational Data Mining (EDM). Educational data mining requires transformation of existing or innovation of new approaches derived from statistics, machine learning, psychometrics, scientific computing etc. Current system is designed to justify that various data mining techniques which includes classification, can be used in educational databases to suggest career options for the high school students and also to predict the potentially violent behaviour among the students by including extra parameters other than academic details. RapidMiner has been used as Data mining tool.
Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder

We present an efficient method for detecting anomalies in videos. Recent applications of convolutional neural networks have shown promises of convolutional layers for object detection and recognition, especially in images. However, convolutional neural networks are supervised and require labels as learning signals. We propose a spatiotemporal architecture for anomaly detection in videos including crowded scenes. Our architecture includes two main components, one for spatial feature representation, and one for learning the temporal evolution of the spatial features. Experimental results on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of our method is comparable to state-of-the-art methods at a considerable speed of up to 140 fps.
A Survey of Opinion Mining and Sentiment Analysis

Sentiment analysis or opinion mining is the computational study of people’s opinions, appraisals, attitudes, and emotions toward entities, individuals, issues, events, topics and their attributes. The task is technically challenging and practically very useful. For example, businesses always want to find public or consumer opinions about their products and services. Potential customers also want to know the opinions of existing users before they use a service or purchase a product. With the explosive growth of social media (i.e., reviews, forum discussions, blogs and social networks) on the Web, individuals and organizations are increasingly using public opinions in these media for their decision making. However, finding and monitoring opinion sites on the Web and distilling the information contained in them remains a formidable task because of the proliferation of diverse sites. Each site typically contains a huge volume of opinionated text that is not always easily deciphered in long forum postings and blogs. The average human reader will have difficulty identifying relevant sites and accurately summarizing the information and opinions contained in them. Moreover, it is also known that human analysis of text information is subject to considerable biases, e.g., people often pay greater attention to opinions that are consistent with their own preferences. People also have difficulty, owing to their mental and physical limitations, producing consistent
Structured Topic Models for Language

This thesis introduces new methods for statistically modelling text using topic models. Topic models have seen many successes in recent years, and are used in a variety of applications, including analysis of news articles, topic-based search interfaces and navigation tools for digital libraries. Despite these recent successes, the field of topic modelling is still relatively new and there remains much to be explored. One noticeable absence from most of the previous work on topic modelling is consideration of language and document structure—from low-level structures, including word order and syntax, to higher-level structures, such as relationships between documents. The focus of this thesis is therefore structured topic models—models that combine latent topics with information about document structure, ranging from local sentence structure to inter-document relationships. These models draw on techniques from Bayesian statistics, including hierarchical Dirichlet distributions and processes, Pitman-Yor processes, and Markov chain Monte Carlo methods. Several methods for estimating the parameters of Dirichlet-multinomial distributions are also compared. The main contribution of this thesis is the introduction of three structured topic models. The first is a topic-based language model. This model captures both word order and latent topics by extending a Bayesian topic model to incorporate n-gram statistics. A bigram version of the new model does better at predicting future words than either a topic model or a trigram language model. It also provides interpretable topics. The second model arises from a Bayesian reinterpretation of a classic generative dependency parsing model. The new model demonstrates that parsing performance can be substantially improved by a careful choice of prior and by sampling hyperparameters. Additionally, the generative nature of the model facilitates the inclusion of latent state variables, which act as specialised part-of-speech tags or “syntactic topics”. The third is a model that captures high-level relationships between documents. This model uses nonparametric Bayesian priors and Markov chain Monte Carlo methods to infer topic-based document clusters. The model assigns a higher probability to unseen test documents than either a clustering model without topics or a Bayesian topic model without document clusters. The model can be extended to incorporate author information, resulting in finer-grained clusters and better predictive performance.
Inner Attention based Recurrent Neural Networks for Answer Selection

Attention based recurrent neural networks have shown advantages in representing natural language sentences (Hermann et al., 2015; Rocktäschel et al., 2015; Tan et al., 2015). Based on recurrent neural networks (RNN), external attention information was added to hidden representations to get an attentive sentence representation. Despite the improvement over nonattentive models, the attention mechanism under RNN is not well studied. In this work, we analyze the deficiency of traditional attention based RNN models quantitatively and qualitatively. Then we present three new RNN models that add attention information before RNN hidden representation, which shows advantage in representing sentence and achieves new stateof-art results in answer selection task.
Business Intelligence Capabilities as Facilitators to Achieve Organizational Agility

Today’s business environment is characterized by fast and unexpected changes, many of which are driven by technological advancement. In such environment, the ability to respond effectively and adapt to the new requirements is not only desirable but essential to survive. Comprehensive and quick understanding of intricacies of market changes facilitates firm’s faster and better response. Two concepts contribute to the success of this scenario; organizational agility and business intelligence (BI). As of today, despite BI’s capabilities to foster organizational agility and consequently improve organizational performance, a clear link between BI and organizational agility has not been established. In this paper we argue that BI solutions have the potential to be facilitators for achieving agility. We aim at showing how BI capabilities can help achieve agility at operational, portfolio, and strategic levels.
Transformer-Coupled Multiport ZVS Bidirectional DC–DC Converter With Wide Input Range

Multiport dc-dc converters are particularly interesting for sustainable energy generation systems where diverse sources and storage elements are to be integrated. This paper presents a zero-voltage switching (ZVS) three-port bidirectional dc-dc converter. A simple and effective duty ratio control method is proposed to extend the ZVS operating range when input voltages vary widely. Soft-switching conditions over the full operating range are achievable by adjusting the duty ratio of the voltage applied to the transformer winding in response to the dc voltage variations at the port. Keeping the volt-second product (half-cycle voltage-time integral) equal for all the windings leads to ZVS conditions over the entire operating range. A detailed analysis is provided for both the two-port and the three-port converters. Furthermore, for the three-port converter a dual-PI-loop based control strategy is proposed to achieve constant output voltage, power flow management, and soft-switching. The three-port converter is implemented and tested for a fuel cell and supercapacitor system.
Beliefs about emotional residue: the idea that emotions leave a trace in the physical environment.

Drawing upon the literatures on beliefs about magical contagion and property transmission, we examined people's belief in a novel mechanism of human-to-human contagion, emotional residue. This is the lay belief that people's emotions leave traces in the physical environment, which can later influence others or be sensed by others. Studies 1-4 demonstrated that Indians are more likely than Americans to endorse a lay theory of emotions as substances that move in and out of the body, and to claim that they can sense emotional residue. However, when the belief in emotional residue is measured implicitly, both Indians and American believe to a similar extent that emotional residue influences the moods and behaviors of those who come into contact with it (Studies 5-7). Both Indians and Americans also believe that closer relationships and a larger number of people yield more detectable residue (Study 8). Finally, Study 9 demonstrated that beliefs about emotional residue can influence people's behaviors. Together, these finding suggest that emotional residue is likely to be an intuitive concept, one that people in different cultures acquire even without explicit instruction.
RNN Simulations of Grammaticality Judgments on Long-distance Dependencies

The paper explores the ability of LSTM networks trained on a language modeling task to detect linguistic structures which are ungrammatical due to extraction violations (extra arguments and subject-relative clause island violations), and considers its implications for the debate on language innatism. The results show that the current RNN model can correctly classify (un)grammatical sentences, in certain conditions, but it is sensitive to linguistic processing factors and probably ultimately unable to induce a more abstract notion of grammaticality, at least in the domain we tested. Title and Abstract in Italian RNN Simulazioni di giudizi di grammaticità sulle dipendenze a distanza L’articolo studia la capacità delle reti neurali LSTM addestrate su un compito di modellazione linguistica di rilevare strutture linguistiche che sono agrammaticali a causa di violazioni nella estrazione di argomenti (dovute alla presenza di argomenti di troppo, o alla presenza di isole del soggetto e delle frasi relative), esplorando le implicazioni per il dibattito sull’innatismo linguistico. I risultati mostrano che l’attuale modello RNN può classificare correttamente frasi grammaticali, in certe condizioni, ma è eccessivamente sensibile a fattori di elaborazione linguistica e probabilmente non in grado di indurre una nozione più astratta di grammaticalità, almeno nel dominio da noi testato.
Automatic detection and tracking of oil spills in SAR imagery with level set segmentation

Automatic detection and monitoring of oil spills and illegal oil discharges is of fundamental importance in ensuring compliance with marine legislation and protection of the coastal environments, which are under considerable threat from intentional or accidental oil spills, uncontrolled sewage and wastewater discharged. In this paper the level set based image segmentation was evaluated for the real-time detection and tracking of oil spills from SAR imagery. The developed processing scheme consists of a preprocessing step, in which an advanced image simplification is taking place, followed by a geometric level set segmentation for the detection of the possible oil spills. Finally a classification was performed, for the separation of lookalikes, leading to oil spill extraction. Experimental results demonstrate that the level set segmentation is a robust tool for the detection of possible oil spills, copes well with abrupt shape deformations and splits and outperforms earlier efforts which were based on different types of threshold or edge detection techniques. The developed algorithm’s efficiency for real-time oil spill detection and monitoring was also tested.
A Vector Space Approach for Aspect Based Sentiment Analysis

Vector representations for language have been shown to be useful in a number of Natural Language Processing (NLP) tasks. In this thesis, we aim to investigate the effectiveness of word vector representations for the research problem of Aspect-Based Sentiment Analysis (ABSA), which attempts to capture both semantic and sentiment information encoded in user generated content such as product reviews. In particular, we target three ABSA sub-tasks: aspect term extraction, aspect category detection, and aspect sentiment prediction. We investigate the effectiveness of vector representations over different text data, and evaluate the quality of domain-dependent vectors. We utilize vector representations to compute various vector-based features and conduct extensive experiments to demonstrate their effectiveness. Using simple vector-based features, we achieve F1 scores of 79.9% for aspect term extraction, 86.7% for category detection, and 72.3% for aspect sentiment prediction. Co Thesis Supervisor: James Glass Title: Senior Research Scientist Co Thesis Supervisor:  Postdoctoral Associate 3
ANYmal - a highly mobile and dynamic quadrupedal robot

This paper introduces ANYmal, a quadrupedal robot that features outstanding mobility and dynamic motion capability. Thanks to novel, compliant joint modules with integrated electronics, the 30 kg, 0.5 m tall robotic dog is torque controllable and very robust against impulsive loads during running or jumping. The presented machine was designed with a focus on outdoor suitability, simple maintenance, and user-friendly handling to enable future operation in real world scenarios. Performance tests with the joint actuators indicated a torque control bandwidth of more than 70 Hz, high disturbance rejection capability, as well as impact robustness when moving with maximal velocity. It is demonstrated in a series of experiments that ANYmal can execute walking gaits, dynamically trot at moderate speed, and is able to perform special maneuvers to stand up or crawl very steep stairs. Detailed measurements unveil that even full-speed running requires less than 280 W, resulting in an autonomy of more than 2 h.
Adjacent Vehicle Collision Warning System using Image Sensor and Inertial Measurement Unit

Advanced driver assistance systems are the newest addition to vehicular technology. Such systems use a wide array of sensors to provide a superior driving experience. Vehicle safety and driver alert are important parts of these system. This paper proposes a driver alert system to prevent and mitigate adjacent vehicle collisions by proving warning information of on-road vehicles and possible collisions. A dynamic Bayesian network (DBN) is utilized to fuse multiple sensors to provide driver awareness. It detects oncoming adjacent vehicles and gathers ego vehicle motion characteristics using an on-board camera and inertial measurement unit (IMU). A histogram of oriented gradient feature based classifier is used to detect any adjacent vehicles. Vehicles front-rear end and side faces were considered in training the classifier. Ego vehicles heading, speed and acceleration are captured from the IMU and feed into the DBN. The network parameters were learned from data via expectation maximization(EM) algorithm. The DBN is designed to provide two type of warning to the driver, a cautionary warning and a brake alert for possible collision with other vehicles. Experiments were completed on multiple public databases, demonstrating successful warnings and brake alerts in most situations.
EFFECTIVENESS OF JASMINE OIL MASSAGE ON REDUCTION OF LABOR PAIN AMONG PRIMIGRAVIDA MOTHERS 1 2

The study was conducted to assess the effectiveness of jasmine oil massage on labour pain during first stage of labour among 40 primigravida women. The study design adopted was true experimental approach with pre-test post-test control group design. The demographic Proforma were collected from the women by interview and Visual analogue scale was used to measure the level of labour pain in both the groups. Data obtained in these areas were analysed by descriptive and inferential statistics. A significant difference was found in the experimental group( t 9.869 , p<0.05) . A significant difference was found between experimental group and control group.
Programmable aperture photography: multiplexed light field acquisition

In this paper, we present a system including a novel component called programmable aperture and two associated post-processing algorithms for high-quality light field acquisition. The shape of the programmable aperture can be adjusted and used to capture light field at full sensor resolution through multiple exposures without any additional optics and without moving the camera. High acquisition efficiency is achieved by employing an optimal multiplexing scheme, and quality data is obtained by using the two post-processing algorithms designed for self calibration of photometric distortion and for multi-view depth estimation. View-dependent depth maps thus generated help boost the angular resolution of light field. Various post-exposure photographic effects are given to demonstrate the effectiveness of the system and the quality of the captured light field.
Deep Representation Learning With Part Loss for Person Re-Identification

Learning discriminative representations for unseen person images is critical for person re-identification (ReID). Most of the current approaches learn deep representations in classification tasks, which essentially minimize the empirical classification risk on the training set. As shown in our experiments, such representations easily get over-fitted on a discriminative human body part on the training set. To gain the discriminative power on unseen person images, we propose a deep representation learning procedure named part loss network, to minimize both the empirical classification risk on training person images and the representation learning risk on unseen person images. The representation learning risk is evaluated by the proposed part loss, which automatically detects human body parts and computes the person classification loss on each part separately. Compared with traditional global classification loss, simultaneously considering part loss enforces the deep network to learn representations for different body parts and gain the discriminative power on unseen persons. Experimental results on three person ReID datasets, i.e., Market1501, CUHK03, and VIPeR, show that our representation outperforms existing deep representations.
Natural Language Comprehension with the EpiReader

We present the EpiReader, a novel model for machine comprehension of text. Machine comprehension of unstructured, real-world text is a major research goal for natural language processing. Current tests of machine comprehension pose questions whose answers can be inferred from some supporting text, and evaluate a model’s response to the questions. The EpiReader is an end-to-end neural model comprising two components: the first component proposes a small set of candidate answers after comparing a question to its supporting text, and the second component formulates hypotheses using the proposed candidates and the question, then reranks the hypotheses based on their estimated concordance with the supporting text. We present experiments demonstrating that the EpiReader sets a new state-of-the-art on the CNN and Children’s Book Test machine comprehension benchmarks, outperforming previous neural models by a significant margin.
Utilization of Data Mining Techniques for Prediction and Diagnosis of Tuberculosis Disease Survivability

The prediction and diagnosis of Tuberculosis survivability has been a challenging research problem for many researchers. Since the early dates of the related research, much advancement has been recorded in several related fields. For instance, thanks to innovative biomedical technologies, better explanatory prognostic factors are being measured and recorded; thanks to low cost computer hardware and software technologies, high volume better quality data is being collected and stored automatically; and finally thanks to better analytical methods, those voluminous data is being processed effectively and efficiently. Tuberculosis is one of the leading diseases for all people in developed countries including India. It is the most common cause of death in human being. The high incidence of Tuberculosis in all people has increased significantly in the last years. In this paper we have discussed various data mining approaches that have been utilized for Tuberculosis diagnosis and prognosis. This study paper summarizes various review and technical articles on Tuberculosis diagnosis and prognosis also we focus on current research being carried out using the data mining techniques to enhance the Tuberculosis diagnosis and prognosis. Here, we took advantage of those available technological advancements to develop the best prediction model for Tuberculosis survivability.
Basic tasks of sentiment analysis

Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment. So, it is desired for a sentiment analysis engine to find and separate the objective sentences for further analysis, e.g., polarity detection. In subjective sentences, opinions can often be expressed on one or multiple topics. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about.