nils-herrmann/clean_scirepeval huggingface dataset:
Influence of the Big Five personality traits on academic motivation among higher education students : Evidence from developing nation

This study examines the association between the personality traits and academic motivation among higher education institution students of Pakistan. The data is collected from the surveyquestionnaire using the convenience sampling technique. In total, 350 questionnaires are collected, however, the useful sample id 320. The technique through which the analysis was performed is PLS-SEM. The dependent variable of the study is academic motivation which was measured by using the LASSI scale. The independent variables of the study are personality traits i.e., Conscientiousness, agreeableness, neuroticism, extraversion, and openness which is measured by using NEO-FFI. The result shows that all the personality traits create a significant positive effect on academic motivation except for agreeableness which has an insignificant effect on academic motivation. This study highlights the importance of personality trait in forming academic motivation of the students and also help the educational institutions and teachers to develop strategies accordingly.
Semantic Word Clusters Using Signed Normalized Graph Cuts

Vector space representations of words capture many aspects of word similarity, but such methods tend to make vector spaces in which antonyms (as well as synonyms) are close to each other. We present a new signed spectral normalized graph cut algorithm, signed clustering, that overlays existing thesauri upon distributionally derived vector representations of words, so that antonym relationships between word pairs are represented by negative weights. Our signed clustering algorithm produces clusters of words which simultaneously capture distributional and synonym relations. We evaluate these clusters against the SimLex-999 dataset (Hill et al., 2014) of human judgments of word pair similarities, and also show the benefit of using our clusters to predict the sentiment of a given text.
Improved Boundary Equilibrium Generative Adversarial Networks

Boundary equilibrium generative adversarial networks (BEGANs) can generate impressively realistic face images, but there is a trade-off between the quality and the diversity of generated images. Based on BEGANs, we propose an effective approach to generate images with higher quality and better diversity. By adding a second loss function (a denoising loss) to the discriminator, the discriminator can learn more useful information about the distribution of real images. Naturally, the ability of discriminator in distinguishing between real and generated images is improved, which further guides the generator to produce more realistic images to confuse the discriminator. We also find that using technique of batch normalization in BEGANs architecture can improve the diversity of generated images. By using batch normalization and adding a denoising loss to the objective of discriminator, we achieve comparative generations on CIFAR-10 and CelebA data sets. In addition, we evaluate the effect of several techniques on BEGANs framework through ”Inception-Score”, a measure which has been found to correlate well with human assessment of generated samples.
HyPer: A hybrid OLTP&OLAP main memory database system based on virtual memory snapshots

The two areas of online transaction processing (OLTP) and online analytical processing (OLAP) present different challenges for database architectures. Currently, customers with high rates of mission-critical transactions have split their data into two separate systems, one database for OLTP and one so-called data warehouse for OLAP. While allowing for decent transaction rates, this separation has many disadvantages including data freshness issues due to the delay caused by only periodically initiating the Extract Transform Load-data staging and excessive resource consumption due to maintaining two separate information systems. We present an efficient hybrid system, called HyPer, that can handle both OLTP and OLAP simultaneously by using hardware-assisted replication mechanisms to maintain consistent snapshots of the transactional data. HyPer is a main-memory database system that guarantees the ACID properties of OLTP transactions and executes OLAP query sessions (multiple queries) on the same, arbitrarily current and consistent snapshot. The utilization of the processor-inherent support for virtual memory management (address translation, caching, copy on update) yields both at the same time: unprecedentedly high transaction rates as high as 100000 per second and very fast OLAP query response times on a single system executing both workloads in parallel. The performance analysis is based on a combined TPC-C and TPC-H benchmark.
Design of Highly Integrated Mechatronic Gear Selector Levers for Automotive Shift-by-Wire Systems

Increased requirements regarding ergonomic comfort, limited space, weight reduction, and electronic automation of functions and safety features are on the rise for future automotive gear levers. At the same time, current mechanical gear levers have restrictions to achieve this. In this paper, we present a monostable, miniaturized mechatronic gear lever to fulfill these requirements for automotive applications. This solution describes a gear lever for positioning in the center console of a car to achieve optimal ergonomics for dynamic driving, which enables both automatic and manual gear switching. In this paper, we describe the sensor and actuator concept, safety concept, recommended shift pattern, mechanical design, and the electronic integration of this shift-by-wire system in a typical automotive bus communication network. The main contribution of this paper is a successful system design and the integration of a mechatronic system in new applications for optimizing the human-machine interface inside road vehicles.
Why do they still use paper?: understanding data collection and use in Autism education

Autism education programs for children collect and use large amounts of behavioral data on each student. Staff use paper almost exclusively to collect these data, despite significant problems they face in tracking student data in situ, filling out data sheets and graphs on a daily basis, and using the sheets in collaborative decision making. We conducted fieldwork to understand data collection and use in the domain of autism education to explain why current technology had not met staff needs. We found that data needs are complex and unstandardized, immediate demands of the job interfere with staff ability to collect in situ data, and existing technology for data collection is inadequate. We also identified opportunities for technology to improve sharing and use of data. We found that data sheets are idiosyncratic and not useful without human mediation; improved communication with parents could benefit children's development; and staff are willing, and even eager, to incorporate technology. These factors explain the continued dependence on paper for data collection in this environment, and reveal opportunities for technology to support data collection and improve use of collected data.
Anomaly Detection in Time Series Data Based on Holt-Winters Method

In today’s world the amount of collected data increases every day, this is a trend which is likely to continue. At the same time the potential value of the data does also increase due to the constant development and improvement of hardware and software. However, in order to gain insights, make decisions or train accurate machine learning models we want to ensure that the data we collect is of good quality. There are many definitions of data quality, in this thesis we focus on the accuracy aspect. One method which can be used to ensure accurate data is to monitor for and alert on anomalies. In this thesis we therefore suggest a method which, based on historic values, is able to detect anomalies in time series as new values arrive. The method consists of two parts, forecasting the next value in the time series using Holt-Winters method and comparing the residual to an estimated Gaussian distribution. The suggested method is evaluated in two steps. First, we evaluate the forecast accuracy for Holt-Winters method using different input sizes. In the second step we evaluate the performance of the anomaly detector when using different methods to estimate the variance of the distribution of the residuals. The results indicate that the suggested method works well most of the time for detection of point anomalies in seasonal and trending time series data. The thesis also discusses some potential next steps which are likely to further improve the performance of this method.
Charge and discharge characteristics of lead-acid battery and LiFePO4 battery

The charge and discharge characteristics of lead-acid battery and LiFePO4 battery is proposed in this paper. The purpose of this paper lies in offering the pulse current charger of higher peak value which can shorten the charging time to reach the goal of charging fast and also avoids the polarization phenomena produced while charging the voltage and current signal simultaneously, supervising whole charging course of the battery, avoiding the situation of excessive charging, and ensuring the life of battery. The hardware circuit of this pulse current charger adopts the power electronic elements for the main structure, uses Digital Signal Processor as the core of the controller, and substantially decreases the volume of charger and the loss of circuit. Besides, the input power supply of this charger is utility, greatly facilitating its using or carrying, which contributes to the development of variety of electric equipments in the future.
Vaginoplasty using autologous in vitro cultured vaginal tissue in a patient with Mayer-von-Rokitansky-Kuster-Hauser syndrome.

Mayer-von-Rokitansky-Küster-Hauser syndrome (MRKHS) is characterized by vaginal agenesis with variable Müllerian duct abnormalities. The Abbè-McIndoe technique is considered a valid treatment option for vaginoplasty but no consensus has been reached on what material should be used for the neovagina canal wall lining. We report the first case of autologous vaginal tissue transplantation in a 28-year-old women with MRKHS. The patient was subjected to a 1 cm2 full-thickness mucosal biopsy from the vaginal vestibule. Following enzymatic dissociation, cells were inoculated onto collagen IV-coated plates and cultured for 2 weeks. The patient was subjected to a vaginoplasty with a modified Abbè-McIndoe vaginoplasty with 314 cm2 autologous in vitro cultured vaginal tissue for the canal lining. At 1 month from surgery, the vagina appeared normal in length and depth and a vaginal biopsy revealed normal vaginal tissue. The use of autologous in vitro cultured vaginal tissue to create a neovagina appears as an easy, minimally invasive and useful method.
SensePlace2: GeoTwitter analytics support for situational awareness

Geographically-grounded situational awareness (SA) is critical to crisis management and is essential in many other decision making domains that range from infectious disease monitoring, through regional planning, to political campaigning. Social media are becoming an important information input to support situational assessment (to produce awareness) in all domains. Here, we present a geovisual analytics approach to supporting SA for crisis events using one source of social media, Twitter. Specifically, we focus on leveraging explicit and implicit geographic information for tweets, on developing place-time-theme indexing schemes that support overview+detail methods and that scale analytical capabilities to relatively large tweet volumes, and on providing visual interface methods to enable understanding of place, time, and theme components of evolving situations. Our approach is user-centered, using scenario-based design methods that include formal scenarios to guide design and validate implementation as well as a systematic claims analysis to justify design choices and provide a framework for future testing. The work is informed by a structured survey of practitioners and the end product of Phase-I development is demonstrated / validated through implementation in SensePlace2, a map-based, web application initially focused on tweets but extensible to other media.
Nationalism and patriotism: national identification and out-group rejection.

It is argued that the differentiation between nationalism and patriotism proposed in the literature can be seen as analogous to judgments based on different types of comparisons: intergroup comparisons with other nations are associated with intergroup behaviour that corresponds to nationalism, whereas temporal or standard comparisons are linked with behaviour that corresponds to patriotism. Four studies (N = 103, 107, 96 and 105) conducted in Germany and Britain examined the hypothesis that national identification and in-group evaluation only show a reliable relationship with out-group rejection under an intergroup comparison orientation. Participants were primed with either an intergroup comparison, a temporal comparison or no explicit comparison orientation. A subsequent questionnaire assessed in-group (own country) identification, in-group evaluation (i.e. national pride) and rejection of national out-groups. Across all four studies, both in-group identification and in-group evaluation show a stronger correlation with out-group derogation if participants were primed with an intergroup comparison orientation compared to temporal and control conditions. Results are discussed with regard to nationalism and patriotism as well as Hinkle and Brown's (1990) model on relational vs. autonomous orientations.
A keyword extraction method from twitter messages represented as graphs

Twitter is a microblog service that generates a huge amount of textual content daily. All this content needs to be explored by means of text mining, natural language processing, information retrieval, and other techniques. In this context, automatic keyword extraction is a task of great usefulness. A fundamental step in text mining techniques consists of building a model for text representation. The model known as vector space model, VSM, is the most well-known and used among these techniques. However, some difficulties and limitations of VSM, such as scalability and sparsity, motivate the proposal of alternative approaches. This paper proposes a keyword extraction method for tweet collections that represents texts as graphs and applies centrality measures for finding the relevant vertices (keywords). To assess the performance of the proposed approach, three different sets of experiments are performed. The first experiment applies TKG to a text from the Time magazine and compares its performance with that of the literature. The second set of experiments takes tweets from three different TV shows, applies TKG and compares it with TFIDF and KEA, having human classifications as benchmarks. Finally, these three algorithms are applied to tweets sets of increasing size and their computational running time is measured and compared. Altogether, these experiments provide a general overview of how TKG can be used in practice, its performance when compared with other standard approaches, and how it scales to larger data instances. The results show that TKG is a novel and robust proposal to extract keywords from texts, particularly from short messages, such
[Psychological dimension of cancer genetics: Doctor-patient communication].

Cancer is a disease that threatens life and body integrity. A method of cancer prediction is genetic testing, thus the primary detection of individuals who have oncogenes and are at risk of a hereditary cancer. Genetic counseling is the translation of technical information by physicians and the analysis of the problems subsequent to this information, into the therapeutic relationship. Generally, the communication between medical staff and patient must follow certain rules. There are two goals; the physician's comfort when he is dealing with giving news, especially bad news and the maintenance of patient's hope during making decisions for his health and his life. Communication skills focus on emotional reaction and empathy towards the patient.
18F-Fluciclovine PET/MRI for preoperative lymph node staging in high-risk prostate cancer patients

To investigate the diagnostic potential of simultaneous 18F-fluciclovine PET/MRI for pelvic lymph node (LN) staging in patients with high-risk prostate cancer. High-risk prostate cancer patients (n=28) underwent simultaneous 18F-fluciclovine PET/MRI prior to surgery. LNs were removed according to a predefined template of eight regions. PET and MR images were evaluated for presence of LN metastases according to these regions. Sensitivity/specificity for detection of LN metastases were calculated on patient and region basis. Sizes of LN metastases in regions with positive and negative imaging findings were compared with linear mixed models. Clinical parameters of PET-positive and -negative stage N1 patients were compared with the Mann-Whitney U test. Patient- and region-based sensitivity/specificity for detection of pelvic LN metastases was 40 %/87.5 % and 35 %/95.7 %, respectively, for MRI and 40 %/100 % and 30 %/100 %, respectively, for PET. LN metastases in true-positive regions were significantly larger than metastases in false-negative regions. PET-positive stage N1 patients had higher metastatic burden than PET-negative N1 patients. Simultaneous 18F-fluciclovine PET/MRI provides high specificity but low sensitivity for detection of LN metastases in high-risk prostate cancer patients. 18F-Fluciclovine PET/MRI scan positive for LN metastases indicates higher metastatic burden than negative scan. • 18 F-Fluciclovine PET/MRI has high specificity for detection of lymph node metastasis. • 18 F-Fluciclovine PET/MRI lacks sensitivity to replace ePLND. • 18 F-Fluciclovine PET/MRI may be used to aid surgery and select adjuvant therapy. • 18 F-Fluciclovine PET-positive patients have more extensive disease than PET-negative patients. • Size of metastatic lymph nodes is an important factor for detection.
Android interface based GSM home security system

The security of one's belongings when a person leaves his/her house is always a concern with increasing number of incidents of theft, robbery etc. Many automated systems has been developed which informs the owner in a remote location about any intrusion or attempt to intrude in the house. 8051 has been extensively used in past projects. However, this paper looks into the development of an ANDROID application which interprets the message a mobile device receives on possible intrusion and subsequently a reply (Short Message Service) SMS which triggers an alarm/buzzer in the remote house making others aware of the possible intrusion.
Reading and Writing as a Creative Cycle: the Need for a Computational Model

The field of computational narratology has produced many efforts aimed at generating narrative by computational means. In recent times, a number of such efforts have considered the task of modelling how a reader might consume the story. Whereas all these approaches are clearly different aspects of the task of generating narrative, so far the efforts to model them have occurred as separate and disjoint initiatives. There is an enormous potential for improvement if a way was found to combine results from these initiatives with one another. The present position paper provides a breakdown of the activity of creating stories into five stages that are conceptually different from a computational point of view and represent important aspects of the overall process as observed either in humans or in existing systems. These stages include a feedback loop that builds interpretations of an ongoing composition and provides feedback based on these to inform the composition process. This model provides a theoretical framework that can be employed first to understand how the various aspects of the task of generating narrative relate to one another, second to identify which of these aspects are being addressed by the different existing research efforts, and finally to point the way towards possible integrations of these aspects within progressively more complex systems.
An adaptive peer-to-peer collision warning system

Cooperative vehicle collision warning system is made possible by recent progress in advanced positioning systems and wireless communication technology. In this paper we report our work in developing a cooperative intersection collision warning system that is not limited by the requirement of line-of-sight. The novel elements of the system include the use of a dynamic ad hoc wireless network for peer-to-peer data sharing, a new intersection collision warning algorithm, and a flexible and extensible software architecture and system design. The very same system is also capable of collision warning for other scenarios such as frontal and rear-end. Further, the tunable parameters in the algorithm make it possible for the system to adapt to the preference and capability of each individual driver.
Detection of Outliers in Time Series Data

DETECTION OF OUTLIERS IN TIME SERIES DATA Samson Kiware, B.A. Marquette University, 2010 This thesis presents the detection of time series outliers. The data set used in this work is provided by the GasDay Project at Marquette University, which produces mathematical models to predict the consumption of natural gas for Local Distribution Companies (LDCs). Flow with no outliers is required to develop and train accurate models. GasDay is using statistical approaches motivated by normally distributed samples such as the 3 − σ rule and the 5 − σ rule to aid the experts in detecting outliers in residuals from the models. However, the Jarque-Bera statistical test shows that the residuals from the GasDay models are not normally distributed. We present an explanation of Density Based Spatial Clustering of Applications with Noise (DBSCAN) and how it is used to detect time series outliers. We have introduced a new application for the DBSCAN algorithm by adapting it to detect outliers in natural gas flow. The performance of DBSCAN is compared with GasDay’s existing technique. Five data sets from temperature-sensitive operating areas with identified outliers and 1000 data sets with synthetic outliers are used in the evaluation process. The 1000 synthetic data sets are prepared using the same empirical distribution as one of the identified data set. This work indicates that DBSCAN has shown some improvement in detecting outliers over GasDays existing technique and merits further exploration.
A High-Efficiency Multimode Li–Ion Battery Charger With Variable Current Source and Controlling Previous-Stage Supply Voltage

A high-efficiency multimode Li-ion battery charger with variable current source and controlling previous-stage supply voltage is presented in this paper. Using variable current source can achieve the goal of constant-current mode to charge the battery and control previous-stage supply voltage which could increase the efficiency of the multimode battery charger. Moreover, the charging mode adopted in this charger is applied by two types of dual-mode strategy decided by the value of the equivalent series resistance of the Li-ion battery. This technique will reduce the damage of Li-ion battery. The Li-ion battery charger is designed with 0.35-mum CMOS double-poly four-metal processes. The experimental results show that the charger works well and the theoretical analysis can be confirmed. The average power efficiency of the multimode Li-Ion battery charger can be up to 91.2% under the average power of 1.24 W, and the accuracy of the adaptive reference voltage is up to 97.3%. The chip area is only 1.32 times 0.95 mm2.