nils-herrmann/clean_scirepeval huggingface dataset:
Modeling Rainfall Prediction Using Data Mining Method: A Bayesian Approach

Weather forecasting has been one of the most scientifically and technologically challenging problem around the world. Weather data is one of the meteorological data that is rich with important information, which can be used for weather prediction We extract knowledge from weather historical data collected from Indian Meteorological Department (IMD) Pune. From the collected weather data comprising of 36 attributes, only 7 attributes are most relevant to rainfall prediction. We made data preprocessing and data transformation on raw weather data set, so that it shall be possible to work on Bayesian, the data mining, prediction model used for rainfall prediction. The model is trained using the training data set and has been tested for accuracy on available test data. The meteorological centers uses high performance computing and supercomputing power to run weather prediction model. To address the issue of compute intensive rainfall prediction model, we proposed and implemented data intensive model using data mining technique. Our model works with good accuracy and takes moderate compute resources to predict the rainfall. We have used Bayesian approach to prove our model for rainfall prediction, and found to be working well with good accuracy.
Employee Satisfaction and Corporate Performance: Mining Employee Reviews on Glassdoor.com

In recent years, Big Data has created significant opportunities for academic research in a wide range of topics within the social sciences. We contribute to this growing field by exploiting the unique social media data from Glassdoor.com. We extract anonymous employee reviews for textual analysis to reveal the relation between employee satisfaction and company performance. Using categories from corporate value studies, our analysis not only provide a “bird’s eye view,” but also provide specific aspects of employee satisfaction are responsible for driving these correlations. We found that while Innovation is the most important category for technology industry, Quality category drives retailing and financial industry. We confirmed the significant correlation between overall employee satisfaction and corporate performance and discovered categories that are negatively correlated with performance: Safety, Communication and Integrity. We hope that this research encourages other researchers to consider the rich environthat a text analytics methodology makes possible.
Toward Continual Learning for Conversational Agents

While end-to-end neural conversation models have led to promising advances in reducing hand-crafted features and errors induced by the traditional complex system architecture, they typically require an enormous amount of data due to the lack of modularity. Previous studies adopted a hybrid approach with knowledge-based components either to abstract out domainspecific information or to augment data to cover more diverse patterns. On the contrary, we propose to directly address the problem using recent developments in the space of continual learning for neural models. Specifically, we adopt a domainindependent neural conversational model and introduce a novel neural continual learning algorithm that allows a conversational agent to accumulate skills across different tasks in a data-efficient way. To the best of our knowledge, this is the first work that applies continual learning to conversation systems. We verified the efficacy of our method through a conversational skill transfer from either synthetic dialogs or human-human dialogs to human-computer conversations in a customer support domain.
A BIASED RANDOM-KEY GENETIC ALGORITHM FOR JOB-SHOP SCHEDULING

This paper presents a local search, based on a new neighborhood for the job-shop scheduling problem, and its application within a biased random-key genetic algorithm. Schedules are constructed by decoding the chromosome supplied by the genetic algorithm with a procedure that generates active schedules. After an initial schedule is obtained, a local search heuristic, based on an extension of the graphical method of Akers (1956), is applied to improve the solution. The new heuristic is tested on a set of 205 standard instances taken from the job-shop scheduling literature and compared with results obtained by other approaches. The new algorithm improved the best known solution values for 57 instances.
High-order Graph-based Neural Dependency Parsing

In this work, we present a novel way of using neural network for graph-based dependency parsing, which fits the neural network into a simple probabilistic model and can be furthermore generalized to high-order parsing. Instead of the sparse features used in traditional methods, we utilize distributed dense feature representations for neural network, which give better feature representations. The proposed parsers are evaluated on English and Chinese Penn Treebanks. Compared to existing work, our parsers give competitive performance with much more efficient inference.
A NOVEL SOFT-SWITCHING BUCK CONVERTER WITH COUPLED INDUCTOR Josna

A novel topology for a soft-switching buck dc– dc converter with a coupled inductor is proposed. The softswitching buck converter has advantages over the traditional hardswitching converters. The most significant advantage is that it offers a lower switching loss. This converter operates under a zero-current switching condition at turn on and a zerovoltage switching condition at turn off. It presents the circuit configuration with a least components for realizing soft switching. Because of soft switching, the proposed converter can attain a high efficiency under heavy load conditions. Likewise, a high efficiency is also attained under light load conditions, which is significantly different from other soft switching buck converters.  Buck converter, coupled inductor, soft switching, zero-current switching (ZCS), zero-voltage switching (ZVS).
Recent Research on Human Learning Challenges Conventional Instructional Strategies

There has been a recent upsurge of interest in exploring how choices of methods and timing of instruction affect the rate and persistence of learning. The authors review three lines of experimentation—all conducted using educationally relevant materials and time intervals— that call into question important aspects of common instructional practices. First, research reveals that testing, although typically used merely as an assessment device, directly potentiates learning and does so more effectively than other modes of study. Second, recent analysis of the temporal dynamics of learning show that learning is most durable when study time is distributed over much greater periods of time than is customary in educational settings. Third, the inter-leaving of different types of practice problems (which is quite rare in math and science texts) markedly improves learning. The authors conclude by discussing the frequently observed dissociation between people's perceptions of which learning procedures are most effective and which procedures actually promote durable learning. T he experimental study of human learning and memory began more than 100 years ago and has developed into a major enterprise in behavioral science. Although this work has revealed some striking laboratory phenomena and elegant quantitative principles, it is disappointing that it has not thus far given teachers, learners, and curriculum designers much in the way of concrete and nonobvious advice that they can use to make learning more efficient and durable. In the past several years, however, there has been a new burst of effort by researchers to identify and test concrete principles that have this potential, yielding a slew of recommended strategies that have been listed in recent reports (e. Some of the most promising results involve the effects of testing on learning and different ways of scheduling study events. Those skeptical of behavioral research might assume that principles of learning would already be fairly obvious to anyone who has been a student, yet the results of recent experimentation challenge some of the most widely used study practices. We discuss three topics, focusing on the effects of testing, the role of temporal spacing, and the effects of interleaving different types of materials. Tests of student mastery of content material are customarily viewed as assessment devices, used to provide incentives for students (and in some cases teachers and school systems as well). However, memory research going back some years has revealed that a test that requires a learner to retrieve some piece of …
OpenRAVE: A Planning Architecture for Autonomous Robotics

One of the challenges in developing real-world autonomous robots is the need for integrating and rigorously testing high-level scripting, motion planning, perception, and control algorithms. For this purpose, we introduce an open-source cross-platform software architecture called OpenRAVE, the Open Robotics and Animation Virtual Environment. OpenRAVE is targeted for real-world autonomous robot applications, and includes a seamless integration of 3-D simulation, visualization, planning, scripting and control. A plugin architecture allows users to easily write custom controllers or extend functionality. With OpenRAVE plugins, any planning algorithm, robot controller, or sensing subsystem can be distributed and dynamically loaded at run-time, which frees developers from struggling with monolithic code-bases. Users of OpenRAVE can concentrate on the development of planning and scripting aspects of a problem without having to explicitly manage the details of robot kinematics and dynamics, collision detection, world updates, and robot control. The OpenRAVE architecture provides a flexible interface that can be used in conjunction with other popular robotics packages such as Player and ROS because it is focused on autonomous motion planning and high-level scripting rather than low-level control and message protocols. OpenRAVE also supports a powerful network scripting environment which makes it simple to control and monitor robots and change execution flow during run-time. One of the key advantages of open component architectures is that they enable the robotics research community to easily share and compare algorithms.
Elderly activities recognition and classification for applications in assisted living

Assisted living systems can help support elderly persons with their daily activities in order to help them maintain healthy and safety while living independently. However, most current systems are ineffective in actual situation, difficult to use and have a low acceptance rate. There is a need for an assisted living solution to become intelligent and also practical issues such as user acceptance and usability need to be resolved in order to truly assist elderly people. Small, inexpensive and low-powered consumption sensors are now available which can be used in assisted living applications to provide sensitive and responsive services based on users current environments and situations. This paper aims to address the issue of how to develop an activity recognition method for a practical assisted living system in term of user acceptance, privacy (non-visual) and cost. The paper proposes an activity recognition and classification method for detection of Activities of Daily Livings (ADLs) of an elderly person using small, low-cost, non-intrusive non-stigmatize wrist worn sensors. Experimental results demonstrate that the proposed method can achieve a high classification rate (>90%). Statistical tests are employed to support this high classification rate of the proposed method. Also, we prove that by combining data from temperature sensor and/or altimeter with accelerometer, classification accuracy can be improved.
The State of Electronic Word-Of-Mouth Research: A Literature Analysis

This study evaluates the current status of electronic word-of-mouth (eWOM) research through an exhaustive literature review of relevant articles. We have identified a total of 83 eWOM research articles published from 2001 through 2010. Published research into eWOM first appeared in peerreviewed journals about ten years ago, and research has been steadily increasing. Among research topic area, the impact of eWOM communication was the most researched topic in the last decade. We also found that individual and message were the two mostly used unit of analysis in eWOM studies. Survey, secondary data analysis, and mathematical modeling were the three main streams of research method. Finally, we found diverse theoretical approaches in understanding eWOM communication. We conclude this paper by identifying important trends in the eWOM literature to provide future research directions.
EX 2 : Exploration with Exemplar Models for Deep Reinforcement Learning

Efficient exploration in high-dimensional environments remains a key challenge in reinforcement learning (RL). Deep reinforcement learning methods have demonstrated the ability to learn with highly general policy classes for complex tasks with high-dimensional inputs, such as raw images. However, many of the most effective exploration techniques rely on tabular representations, or on the ability to construct a generative model over states and actions. Both are exceptionally difficult when these inputs are complex and high dimensional. On the other hand, it is comparatively easy to build discriminative models on top of complex states such as images using standard deep neural networks. This paper introduces a novel approach, EX, which approximates state visitation densities by training an ensemble of discriminators, and assigns reward bonuses to rarely visited states. We demonstrate that EX achieves comparable performance to the state-of-the-art methods on lowdimensional tasks, and its effectiveness scales into high-dimensional state spaces such as visual domains without hand-designing features or density models.
Still too far to walk: Literature review of the determinants of delivery service use

Skilled attendance at childbirth is crucial for decreasing maternal and neonatal mortality, yet many women in low- and middle-income countries deliver outside of health facilities, without skilled help. The main conceptual framework in this field implicitly looks at home births with complications. We expand this to include "preventive" facility delivery for uncomplicated childbirth, and review the kinds of determinants studied in the literature, their hypothesized mechanisms of action and the typical findings, as well as methodological difficulties encountered.



We searched PubMed and Ovid databases for reviews and ascertained relevant articles from these and other sources. Twenty determinants identified were grouped under four themes: (1) sociocultural factors, (2) perceived benefit/need of skilled attendance, (3) economic accessibility and (4) physical accessibility.



There is ample evidence that higher maternal age, education and household wealth and lower parity increase use, as does urban residence. Facility use in the previous delivery and antenatal care use are also highly predictive of health facility use for the index delivery, though this may be due to confounding by service availability and other factors. Obstetric complications also increase use but are rarely studied. Quality of care is judged to be essential in qualitative studies but is not easily measured in surveys, or without linking facility records with women. Distance to health facilities decreases use, but is also difficult to determine. Challenges in comparing results between studies include differences in methods, context-specificity and the substantial overlap between complex variables.



Studies of the determinants of skilled attendance concentrate on sociocultural and economic accessibility variables and neglect variables of perceived benefit/need and physical accessibility. To draw valid conclusions, it is important to consider as many influential factors as possible in any analysis of delivery service use. The increasing availability of georeferenced data provides the opportunity to link health facility data with large-scale household data, enabling researchers to explore the influences of distance and service quality.
Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering

In this paper, we focus on the problem of answer triggering addressed by Yang et al. (2015), which is a critical component for a real-world question answering system. We employ a hierarchical gated recurrent neural tensor (HGRNT) model to capture both the context information and the deep interactions between the candidate answers and the question. Our result on F value achieves 42.6%, which surpasses the baseline by over 10 %.
A unified approach to mapping and clustering of bibliometric networks

In the analysis of bibliometric networks, researchers often use mapping and clustering techniques in a combined fashion. Typically, however, mapping and clustering techniques that are used together rely on very different ideas and assumptions. We propose a unified approach to mapping and clustering of bibliometric networks. We show that the VOS mapping technique and a weighted and parameterized variant of modularity-based clustering can both be derived from the same underlying principle. We illustrate our proposed approach by producing a combined mapping and clustering of the most frequently cited publications that appeared in the field of information science in the period 1999–2008.
Predictors of amygdala activation during the processing of emotional stimuli: A meta-analysis of 385 PET and fMRI studies

Although amygdala activity has been purported to be modulated by affective and non-affective factors, considerable controversy remains on its precise functional nature. We conducted a meta-analysis of 385 functional neuroimaging studies of emotional processing, examining the effects of experimental characteristics on the probability of detecting amygdala activity. All emotional stimuli were associated with higher probability of amygdala activity than neutral stimuli. Comparable effects were observed for most negative and positive emotions, however there was a higher probability of activation for fear and disgust relative to happiness. The level of attentional processing affected amygdala activity, as passive processing was associated with a higher probability of activation than active task instructions. Gustatory-olfactory and visual stimulus modalities increased the probability of activation relative to internal stimuli. Aversive learning increased the probability of amygdala activation as well. There was some evidence of hemispheric specialization with a relative left-lateralization for stimuli containing language and a relative right-lateralization for masked stimuli. Methodological variables, such as type of analysis and magnet strength, were also independent predictors of amygdala activation.
Beyond NER: Towards Semantics in Clinical Text

While clinical text NLP systems have become very effective in recognizing named entities in clinical text and mapping them to standardized terminologies in the normalization process, there remains a gap in the ability of extractors to combine entities together into a complete semantic representation of medical concepts that contain multiple attributes each of which has its own set of allowed named entities or values. Furthermore, additional domain knowledge may be required to determine the semantics of particular tokens in the text that take on special meanings in relation to this concept. This research proposes an approach that provides ontological mappings of the surface forms of medical concepts that are of the UMLS semantic class signs/symptoms. The mappings are used to extract and encode the constituent set of named entities into interoperable semantic structures that can be linked to other structured and unstructured data for reuse in research and analysis.
Implantable Neural Microsystems in Medical Industry

Medical devices have a great impact but rigorous production and quality norms to meet, which pushes manufacturing technology to its limits in several fields, such as electronics, optics, communications, among others. This paper briefly explores how the medical industry is absorbing many of the technological developments from other industries, and making an effort to translate them into the healthcare requirements. An example is discussed in depth: implantable neural microsystems used for brain circuits mapping and modulation. Conventionally, light sources and electrical recording points are placed on silicon neural probes for optogenetic applications. The active sites of the probe must provide enough light power to modulate connectivity between neural networks, and simultaneously ensure reliable recordings of action potentials and local field activity. These devices aim at being a flexible and scalable technology capable of acquiring knowledge about neural mechanisms. Moreover, this paper presents a fabrication method for 2-D LED-based microsystems with high aspect-ratio shafts, capable of reaching up to 20 mm deep neural structures. In addition, PDMS  $\mu $ lenses on LEDs top surface are presented for focusing and increasing light intensity on target structures.
Protecting Visual Secrets Using Adversarial Nets

Protecting visual secrets is an important problem due to the prevalence of cameras that continuously monitor our surroundings. Any viable solution to this problem should also minimize the impact on the utility of applications that use images. In this work, we build on the existing work of adversarial learning to design a perturbation mechanism that jointly optimizes privacy and utility objectives. We provide a feasibility study of the proposed mechanism and present ideas on developing a privacy framework based on the adversarial perturbation mechanism.
Building Virtual Reality Environments for Distance Education on the Web: A Case Study in Medical Education

The paper presents an investigation into the role of virtual reality and web technologies in the field of distance education. Within this frame, special emphasis is given on the building of web-based virtual learning environments so as to successfully fulfill their educational objectives. In particular, basic pedagogical methods are studied, focusing mainly on the efficient preparation, approach and presentation of learning content, and specific designing rules are presented considering the hypermedia, virtual and educational nature of this kind of applications. The paper also aims to highlight the educational benefits arising from the use of virtual reality technology in medicine and study the emerging area of web-based medical simulations. Finally, an innovative virtual reality environment for distance education in medicine is demonstrated. The proposed environment reproduces conditions of the real learning process and enhances learning through a real-time interactive simulator. Distance education, medicine, virtual reality, web.