nils-herrmann/clean_scirepeval huggingface dataset:
Semantic Textual Similarity Methods, Tools, and Applications: A Survey

Measuring Semantic Textual Similarity (STS), between words/ terms, sentences, paragraph and document plays an important role in computer science and computational linguistic. It also has many applications over several fields such as Biomedical Informatics and Geoinformation. In this paper, we present a survey on different methods of textual similarity and we also reported about the availability of different software and tools those are useful for STS. In natural language processing (NLP), STS is a important component for many tasks such as document summarization, word sense disambiguation, short answer grading, information retrieval and extraction. We split out the measures for semantic similarity into three broad categories such as (i) Topological/Knowledge-based (ii) Statistical/ Corpus Based (iii) String based. More emphasis is given to the methods related to the WordNet taxonomy. Because topological methods, plays an important role to understand intended meaning of an ambiguous word, which is very difficult to process computationally. We also propose a new method for measuring semantic similarity between sentences. This proposed method, uses the advantages of taxonomy methods and merge these information to a language model. It considers the WordNet synsets for lexical relationships between nodes/words and a uni-gram language model is implemented over a large corpus to assign the information content value between the two nodes of different classes.
An adaptive hysteresis band current controller for inverter base DG with reactive power compensation

In this paper the three-phase grid connected inverter has been investigated. The inverter’s control strategy is based on the adaptive hysteresis current controller. Inverter connects the DG (distributed generation) source to the grid. The main advantages of this method are constant switching frequency, better current control, easy filter design and less THD (total harmonic distortion). Since a constant and ripple free dc bus voltage is not ensured at the output of alternate energy sources, the main aim of the proposed algorithm is to make the output of the inverter immune to the fluctuations in the dc input voltage This inverter can be used to connect the medium and small-scale wind turbines and solar cells to the grid and compensate local load reactive power. Reactive power compensating improves SUF (system usage factor) from nearly 20% (in photovoltaic systems) to 100%. The simulation results confirm that switching frequency is constant and THD of injected current is low.
Linearization of photonic components for digital and analog optical fiber communication systems

The need for photonic components with linear electric field has increased for both analog and digital communication systems as these systems evolve toward faster speed, higher spectral efficiency (SE), and wider bandwidth environments. Here, we present a common linearization platform for electric field coming from any Mach-Zehnder (MZ)-based devices. We show how this common platform has applications and advantages as linear Frequency Discriminator (FD) device for phase-modulated direct-detection Microwave Photonic Link (MPLs), and as linear Electric Field Modulator (LOFM) for multilevel coherent transmitter in digital optical communication systems.
Towards a Model-Driven-Architecture Process for Smart Grid Projects

The complexity in electrical power systems is continuously increasing due to its advancing distribution. This affects the topology of the grid infrastructure as well as the IT-infrastructure, leading to various heterogeneous systems, data models, protocols, and interfaces. This in turn raises the need for appropriate processes and tools that facilitate the management of the systems architecture on different levels and from different stakeholders’ view points. In order to achieve this goal, a common understanding of architecture elements and means of classification shall be gained. The Smart Grid Architecture Model (SGAM) proposed in context of the European standardization mandate M/490 provides a promising basis for domainspecific architecture models. The idea of following a Model-Driven-Architecture (MDA)-approach to create such models, including requirements specification based on Smart Grid use cases, is detailed in this contribution. The SGAM-Toolbox is introduced as tool-support for the approach and its utility is demonstrated by two real-world case studies.
IncQuery-D: A Distributed Incremental Model Query Framework in the Cloud

Queries are the foundations of data intensive applications. In model-driven software engineering (MDE), model queries are core technologies of tools and transformations. As software models are rapidly increasing in size and complexity, traditional tools exhibit scalability issues that decrease productivity and increase costs . While scalability is a hot topic in the database community and recent NoSQL efforts have partially addressed many shortcomings, this happened at the cost of sacrificing the ad-hoc query capabilities of SQL. Unfortunately, this is a critical problem for MDE applications due to their inherent workload complexity. In this paper, we aim to address both the scalability and ad-hoc querying challenges by adapting incremental graph search techniques – known from the EMF-IncQuery framework – to a distributed cloud infrastructure. We propose a novel architecture for distributed and incremental queries, and conduct experiments to demonstrate that IncQuery-D, our prototype system, can scale up from a single workstation to a cluster that can handle very large models and complex incremental queries efficiently.
Regulation of WASH-Dependent Actin Polymerization and Protein Trafficking by Ubiquitination

Endosomal protein trafficking is an essential cellular process that is deregulated in several diseases and targeted by pathogens. Here, we describe a role for ubiquitination in this process. We find that the E3 RING ubiquitin ligase, MAGE-L2-TRIM27, localizes to endosomes through interactions with the retromer complex. Knockdown of MAGE-L2-TRIM27 or the Ube2O E2 ubiquitin-conjugating enzyme significantly impaired retromer-mediated transport. We further demonstrate that MAGE-L2-TRIM27 ubiquitin ligase activity is required for nucleation of endosomal F-actin by the WASH regulatory complex, a known regulator of retromer-mediated transport. Mechanistic studies showed that MAGE-L2-TRIM27 facilitates K63-linked ubiquitination of WASH K220. Significantly, disruption of WASH ubiquitination impaired endosomal F-actin nucleation and retromer-dependent transport. These findings provide a cellular and molecular function for MAGE-L2-TRIM27 in retrograde transport, including an unappreciated role of K63-linked ubiquitination and identification of an activating signal of the WASH regulatory complex.
Tumour stem cells and drug resistance

The contribution of tumorigenic stem cells to haematopoietic cancers has been established for some time, and cells possessing stem-cell properties have been described in several solid tumours. Although chemotherapy kills most cells in a tumour, it is believed to leave tumour stem cells behind, which might be an important mechanism of resistance. For example, the ATP-binding cassette (ABC) drug transporters have been shown to protect cancer stem cells from chemotherapeutic agents. Gaining a better insight into the mechanisms of stem-cell resistance to chemotherapy might therefore lead to new therapeutic targets and better anticancer strategies.
Foraging strategies of black kites (Milvus migrans govinda) in urban garbage dumps

Large numbers of black kites (Milvus migrans govinda) forage with house crows (Corvus splendens) at garbage dumps in many Indian cities. Such aggregation of many individuals results in aggressiveness where adoption of a suitable behavioral approach is crucial. We studied foraging behavior of black kites in dumping sites adjoining two major corporation markets of Kolkata, India. Black kites used four different foraging tactics which varied and significantly influenced foraging attempts and their success rates. Kleptoparasitism was significantly higher than autonomous foraging events; interspecific kleptoparasitism was highest in occurrence with a low success rate, while ‘autonomous-ground’ was least adopted but had the highest success rate.
Bromelain: an overview of industrial application and purification strategies

This review highlights the use of bromelain in various applications with up-to-date literature on the purification of bromelain from pineapple fruit and waste such as peel, core, crown, and leaves. Bromelain, a cysteine protease, has been exploited commercially in many applications in the food, beverage, tenderization, cosmetic, pharmaceutical, and textile industries. Researchers worldwide have been directing their interest to purification strategies by applying conventional and modern approaches, such as manipulating the pH, affinity, hydrophobicity, and temperature conditions in accord with the unique properties of bromelain. The amount of downstream processing will depend on its intended application in industries. The breakthrough of recombinant DNA technology has facilitated the large-scale production and purification of recombinant bromelain for novel applications in the future.
Design of Unequal Dual-Band Gysel Power Divider With Arbitrary Termination Resistance

This paper presents an exact closed-form design method for dual-band unequal Gysel power divider with arbitrary termination resistance. To obtain the unequal property, branch lines with different characteristic impedances attached to a short-circuit terminated stub and an open-circuit terminated stub are needed. Arbitrary termination resistance matching can be achieved without additional output transformers. Line impedance analysis shows the proposed structure can support large power-dividing ratio (up to 6) with wide frequency ratio range (1.5-2.4). Power operation analysis shows that it maintains Gysel divider's high power-handling advantage over Wilkinson divider.
Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction

Short-term traffic forecasting based on deep learning methods, especially long short-term memory (LSTM) neural networks, has received much attention in recent years. However, the potential of deep learning methods in traffic forecasting has not yet fully been exploited in terms of the depth of the model architecture, the spatial scale of the prediction area, and the predictive power of spatial-temporal data. In this paper, a deep stacked bidirectional and unidirectional LSTM (SBU- LSTM) neural network architecture is proposed, which considers both forward and backward dependencies in time series data, to predict network-wide traffic speed. A bidirectional LSTM (BDLSM) layer is exploited to capture spatial features and bidirectional temporal dependencies from historical data. To the best of our knowledge, this is the first time that BDLSTMs have been applied as building blocks for a deep architecture model to measure the backward dependency of traffic data for prediction. The proposed model can handle missing values in input data by using a masking mechanism. Further, this scalable model can predict traffic speed for both freeway and complex urban traffic networks. Comparisons with other classical and state-of-the-art models indicate that the proposed SBU-LSTM neural network achieves superior prediction performance for the whole traffic network in both accuracy and robustness.
Heart rate estimation in photoplethysmogram signals using nonlinear model-based preprocessing

We propose a frequency domain algorithm for heart rate estimation from photoplethysmographic signals (PPG) recorded during intense physical activity. Starting from the Beer-Lambert law we model the effect of movement artifacts on the PPG signal as an amplitude modulation. Using tri-axis accelerometer recordings to demodulate the PPG signal, we are able to extract the heart rate even when motion artifacts are within the frequency band of interest. The average heart rate error using two-channel PPG recordings from 12 subjects was 1:6 BPM with a standard deviation of 3 BPM.
RankME: Reliable Human Ratings for Natural Language Generation

Human evaluation for natural language generation (NLG) often suffers from inconsistent user ratings. While previous research tends to attribute this problem to individual user preferences, we show that the quality of human judgements can also be improved by experimental design. We present a novel rank-based magnitude estimation method (RankME), which combines the use of continuous scales and relative assessments. We show that RankME significantly improves the reliability and consistency of human ratings compared to traditional evaluation methods. In addition, we show that it is possible to evaluate NLG systems according to multiple, distinct criteria, which is important for error analysis. Finally, we demonstrate that RankME, in combination with Bayesian estimation of system quality, is a cost-effective alternative for ranking multiple NLG systems.
Acquaintance to Artificial Neural Networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review.

Tuberculosis [TB] has afflicted numerous nations in the world. As per a report by the World Health Organization [WHO], an estimated 1.4 million TB deaths in 2015 and an additional 0.4 million deaths resulting from TB disease among people living with HIV, were observed. Most of the TB deaths can be prevented if it is detected at an early stage. The existing processes of diagnosis like blood tests or sputum tests are not only tedious but also take a long time for analysis and cannot differentiate between different drug resistant stages of TB. The need to find newer prompt methods for disease detection has been aided by the latest Artificial Intelligence [AI] tools. Artificial Neural Network [ANN] is one of the important tools that is being used widely in diagnosis and evaluation of medical conditions. This review aims at providing brief introduction to various AI tools that are used in TB detection and gives a detailed description about the utilization of ANN as an efficient diagnostic technique. The paper also provides a critical assessment of ANN and the existing techniques for their diagnosis of TB. Researchers and Practitioners in the field are looking forward to use ANN and other upcoming AI tools such as Fuzzy-logic, genetic algorithms and artificial intelligence simulation as a promising current and future technology tools towards tackling the global menace of Tuberculosis. Latest advancements in the diagnostic field include the combined use of ANN with various other AI tools like the Fuzzy-logic, which has led to an increase in the efficacy and specificity of the diagnostic techniques.
AUTONOMOUS MISSION PLANNING FOR UAVS : A COGNITIVE APPROACH

Autonomous operation is becoming an increasingly important factor for UAVs. It enables a vehicle to decide on the most appropriate action under consideration of the current vehicle and environment state. We investigated the decision-making process using the cognitive agent-based architecture Soar, which uses techniques adapted from human decision-making. Based on Soar an agent was developed which enables UAVs to autonomously make decisions and interact with a dynamic environment. One or more UAV agents were then tested in a simulation environment which has been developed using agent-based modelling. By simulating a dynamic environment, the capabilities of a UAV agent can be tested under defined conditions and additionally its behaviour can be visualised. The agent’s abilities were demonstrated using a scenario consisting of a highly dynamic border-surveillance mission with multiple autonomous UAVs. We can show that the autonomous agents are able to execute the mission successfully and can react adaptively to unforeseen events. We conclude that using a cognitive architecture is a promising approach for modelling autonomous behaviour.
State-of-the-Art and Open Challenges in RTS Game-AI and Starcraft

This paper presents a review of artificial intelligence for different approaches used in real-time strategy games. Real-time strategy (RTS) based games are quick combat games in which the objective is to dominate and destroy the opposing enemy such as Rome-total war, Starcraft, the age of empires, and command & conquer, etc. In such games, each player needs to utilize resources efficiently, which includes managing different types of soldiers, units, equipment’s, economic status, positions and the uncertainty during the combat in real time. Now the best human players face difficulty in defeating the best RTS games due to the recent success and advancement of deep mind technologies. In this paper, we explain state-of-the-art and challenges in artificial intelligence (AI) for RTS games and Starcraft, describing problems and issues carried out by RTS based games with some solutions that are addressed to them. Finally, we conclude by emphasizing on game ‘CIG & AIIDE’ competitions along with open research problems and questions in the context of RTS Game-AI, where some of the problems and challenges are mostly considered improved and solved but yet some are open for further research. Real Time Strategy (RTS); Game-AI; Starcraft; MMOG; AIIDE; CIG; MOBA
Turnover of new graduate nurses in their first job using survival analysis.

To examine factors related to turnover of new graduate nurses in their first job.



Data were obtained from a 3-year panel survey (2006-2008) of the Graduates Occupational Mobility Survey that followed-up college graduates in South Korea. The sample consisted of 351 new graduates whose first job was as a full-time registered nurse in a hospital.



Survival analysis was conducted to estimate survival curves and related factors, including individual and family, nursing education, hospital, and job dissatisfaction (overall and 10 specific job aspects).



The estimated probabilities of staying in their first job for 1, 2, and 3 years were 0.823, 0.666, and 0.537, respectively. Nurses reporting overall job dissatisfaction had significantly lower survival probabilities than those who reported themselves to be either neutral or satisfied. Nurses were more likely to leave if they were married or worked in small (vs. large), nonmetropolitan, and nonunionized hospitals. Dissatisfaction with interpersonal relationships, work content, and physical work environment was associated with a significant increase in the hazards of leaving the first job.



Hospital characteristics as well as job satisfaction were significantly associated with new graduates' turnover.



The high turnover of new graduates could be reduced by improving their job satisfaction, especially with interpersonal relationships, work content, and the physical work environment.
Acute phase Peyronie's disease management with traction device: a nonrandomized prospective controlled trial with ultrasound correlation.

Outcome data of penile traction therapy (PTT) for the acute phase (AP) of Peyronie's disease (PD) have not been specifically studied.



The aim of this study was to assess the effectiveness of a penile extender device for the treatment of patients with AP of PD.



A total of 55 patients underwent PTT for 6 months and were compared with 41 patients with AP of PD who did not receive active treatment ("no intervention group" .



Pre- and posttreatment variables included degree of curvature, penile length and girth, pain by 0-10 cm visual analog scale (VAS), erectile function (EF) domain of the International Index of Erectile Function questionnaire, Erection Hardness Scale, Sexual Encounter Profile 2 question, and penile sonographic evaluation (only patients in the intervention group).



The mean curvature decreased from 33° at baseline to 15° at 6 months and 13° at 9 months with a mean decrease 20° (P < 0.05) in the PTT group. VAS score for pain decreased from 5.5 to 2.5 after 6 months (P < 0.05). EF and erection hardness also improved significantly. The percentage of patients who were not able to achieve penetration decreased from 62% to 20% (P < 0.03). In the NIG, deformity increased significantly, stretched flaccid penile length decreased, VAS score for pain increased, and EF and erection hardness worsened. PTT was associated with the disappearance of sonographic plaques in 48% of patients. Furthermore, the need for surgery was reduced in 40% of patients who would otherwise have been candidates for surgery and simplified the complexity of the surgical procedure (from grafting to plication) in one out of every three patients.



PTT seems an effective treatment for the AP of PD in terms of pain reduction, penile curvature decrease, and improvement in sexual function.
A SECURITY FRAMEWORK IN CLOUD COMPUTING INFRASTRUCTURE

In a typical cloud computing diverse facilitating components like hardware, software, firmware, networking, and services integrate to offer different computational facilities, while Internet or a private network (or VPN) provides the required backbone to deliver the services. The security risks to the cloud system delimit the benefits of cloud computing like “on-demand, customized resource availability and performance management”. It is understood that current IT and enterprise security solutions are not adequate to address the cloud security issues. This paper explores the challenges and issues of security concerns of cloud computing through different standard and novel solutions. We propose analysis and architecture for incorporating different security schemes, techniques and protocols for cloud computing, particularly in Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) systems. The proposed architecture is generic in nature, not dependent on the type of cloud deployment, application agnostic and is not coupled with the underlying backbone. This would facilitate to manage the cloud system more effectively and provide the administrator to include the specific solution to counter the threat. We have also shown using experimental data how a cloud service provider can estimate the charging based on the security service it provides and security-related cost-benefit analysis can be estimated.
NLPReViz: an interactive tool for natural language processing on clinical text

The gap between domain experts and natural language processing expertise is a barrier to extracting understanding from clinical text. We describe a prototype tool for interactive review and revision of natural language processing models of binary concepts extracted from clinical notes. We evaluated our prototype in a user study involving 9 physicians, who used our tool to build and revise models for 2 colonoscopy quality variables. We report changes in performance relative to the quantity of feedback. Using initial training sets as small as 10 documents, expert review led to final F1scores for the "appendiceal-orifice" variable between 0.78 and 0.91 (with improvements ranging from 13.26% to 29.90%). F1for "biopsy" ranged between 0.88 and 0.94 (-1.52% to 11.74% improvements). The average System Usability Scale score was 70.56. Subjective feedback also suggests possible design improvements.