nils-herrmann/clean_scirepeval huggingface dataset:
Intelligent Software Development Environments: Integrating Natural Language Processing with the Eclipse Platform

Software engineers need to be able to create, modify, and analyze knowledge stored in software artifacts. A significant amount of these artifacts contain natural language, like version control commit messages, source code comments, or bug reports. Integrated software development environments (IDEs) are widely used, but they are only concerned with structured software artifacts – they do not offer support for analyzing unstructured natural language and relating this knowledge with the source code. We present an integration of natural language processing capabilities into the Eclipse framework, a widely used software IDE. It allows to execute NLP analysis pipelines through the Semantic Assistants framework, a service-oriented architecture for brokering NLP services based on GATE. We demonstrate a number of semantic analysis services helpful in software engineering tasks, and evaluate one task in detail, the quality analysis of source code comments.
Identification and genetic mapping of PmAF7DS a powdery mildew resistance gene in bread wheat (Triticum aestivum L.)

Gene PmAF7DS confers resistance to wheat powdery mildew (isolate Bgt#211 ); it was mapped to a 14.6-cM interval ( Xgwm350 a– Xbarc184 ) on chromosome 7DS. The flanking markers could be applied in MAS breeding. Wheat powdery mildew (Pm) is caused by the biotrophic pathogen Blumeria graminis tritici (DC.) (Bgt). An ongoing threat of breakdown of race-specific resistance to Pm requires a continuous effort to discover new alleles in the wheat gene pool. Developing new cultivars with improved disease resistance is an economically and environmentally safe approach to reduce yield losses. To identify and characterize genes for resistance against Pm in bread wheat we used the (Arina × Forno) RILs population. Initially, the two parental lines were screened with a collection of 61 isolates of Bgt from Israel. Three Pm isolates Bgt#210 , Bgt#211 and Bgt#213 showed differential reactions in the parents: Arina was resistant (IT = 0), whereas Forno was moderately susceptible (IT = −3). Isolate Bgt#211 was then used to inoculate the RIL population. The segregation pattern of plant reactions among the RILs indicates that a single dominant gene controls the conferred resistance. A genetic map of the region containing this gene was assembled with DNA markers and assigned to the 7D physical bin map. The gene, temporarily designated PmAF7DS, was located in the distal region of chromosome arm 7DS. The RILs were also inoculated with Bgt#210 and Bgt#213. The plant reactions to these isolates showed high identity with the reaction to Bgt#211, indicating the involvement of the same gene or closely linked, but distinct single genes. The genomic location of PmAF7DS, in light of other Pm genes on 7DS is discussed.
Worth a Glance: Using Eye Movements to Investigate the Cognitive Neuroscience of Memory

Results of several investigations indicate that eye movements can reveal memory for elements of previous experience. These effects of memory on eye movement behavior can emerge very rapidly, changing the efficiency and even the nature of visual processing without appealing to verbal reports and without requiring conscious recollection. This aspect of eye movement based memory investigations is particularly useful when eye movement methods are used with special populations (e.g., young children, elderly individuals, and patients with severe amnesia), and also permits use of comparable paradigms in animals and humans, helping to bridge different memory literatures and permitting cross-species generalizations. Unique characteristics of eye movement methods have produced findings that challenge long-held views about the nature of memory, its organization in the brain, and its failures in special populations. Recently, eye movement methods have been successfully combined with neuroimaging techniques such as fMRI, single-unit recording, and magnetoencephalography, permitting more sophisticated investigations of memory. Ultimately, combined use of eye-tracking with neuropsychological and neuroimaging methods promises to provide a more comprehensive account of brain-behavior relationships and adheres to the "converging evidence" approach to cognitive neuroscience.
Comparison of novel coding techniques for a fixed wavelength hopping SAC-OCDMA

The optical code division multiple access (OCDMA), the most advanced multiple access technology in optical communication has become significant and gaining popularity because of its asynchronous access capability, faster speed, efficiency, security and unlimited bandwidth. Many codes are developed in spectral amplitude coding optical code division multiple access (SAC-OCDMA) with zero or minimum cross-correlation properties to reduce the multiple access interference (MAI) and Phase Induced Intensity Noise (PIIN). This paper compares two novel SAC-OCDMA codes in terms of their performances such as bit error rate (BER), number of active users that is accommodated with minimum cross-correlation property, high data rate that is achievable and the minimum power that the OCDMA system supports to achieve a minimum BER value. One of the proposed novel codes referred in this work as modified random diagonal code (MRDC) possesses cross-correlation between zero to one and the second novel code referred in this work as modified new zero cross-correlation code (MNZCC) possesses cross-correlation zero to further minimize the multiple access interference, which are found to be more scalable compared to the other existing SAC-OCDMA codes. In this work, the proposed MRDC and MNZCC codes are implemented in an optical system using the optisystem version-12 software for the SAC-OCDMA scheme. Simulation results depict that the OCDMA system based on the proposed novel MNZCC code exhibits better performance compared to the MRDC code and former existing SAC-OCDMA codes. The proposed MNZCC code accommodates maximum number of simultaneous users with higher data rate transmission, lower BER and longer traveling distance without any signal quality degradation as compared to the former existing SAC-OCDMA codes.
Object-Level Representation Learning for Few-Shot Image Classification

Few-shot learning that trains image classifiers over few labeled examples per category is a challenging task. In this paper, we propose to exploit an additional big dataset with different categories to improve the accuracy of few-shot learning over our target dataset. Our approach is based on the observation that images can be decomposed into objects, which may appear in images from both the additional dataset and our target dataset. We use the object-level relation learned from the additional dataset to infer the similarity of images in our target dataset with unseen categories. Nearest neighbor search is applied to do image classification, which is a non-parametric model and thus does not need fine-tuning. We evaluate our algorithm on two popular datasets, namely Omniglot and MiniImagenet. We obtain 8.5% and 2.7% absolute improvements for 5-way 1-shot and 5-way 5-shot experiments on MiniImagenet, respectively. Source code will be published upon acceptance.
Embedded Real-Time Fall Detection Using Deep Learning For Elderly Care

This paper proposes a real-time embedded fall detection system using a DVS(Dynamic Vision Sensor)(Berner et al. [2014]) that has never been used for traditional fall detection, a dataset for fall detection using that, and a DVSTN(DVS-Temporal Network). The first contribution is building a DVS Falls Dataset, which made our network to recognize a much greater variety of falls than the existing datasets that existed before and solved privacy issues using the DVS. Secondly, we introduce the DVS-TN : optimized deep learning network to detect falls using DVS. Finally, we implemented a fall detection system which can run on low-computing H/W with real-time, and tested on DVS Falls Dataset that takes into account various falls situations. Our approach achieved 95.5% on the F1-score and operates at 31.25 FPS on NVIDIA Jetson TX1 board.
Support Vector Regression to Forecast the Demand and Supply of Pulpwood

regression is the natural extension of large margin kernel methods used for classification to regression analysis. In this paper Support Vector Regression is used to forecast the demand and supply of pulpwood. The usage of paper increases day to day. Wood Pulp is the most common raw material in paper making. On account of steady increase in paper demand, the forecast on demand and supply of pulp wood is considered to improve the socio economic development of India. Forecasting is done in Libsvm a library for support vector machines by integrating it with MATLAB.
A programming language for implementing computational models

The Scientific Computation Language (SCL) was designed mainly for developing computational models in education and research. This paper presents the justification for such a language, its relevant features, and a case study of a computational model implemented with the SCL.
 Development of the SCL language is part of the OOPsim project, which has had partial NSF support (CPATH). One of the goals of this project is to develop tools and approaches for designing and implementing computational models, emphasizing multi-disciplinary teams in the development process.
 A computational model is a computer implementation of the solution to a (scientific) problem for which a mathematical representation has been formulated. Developing a computational model consists of applying Computer Science concepts, principles and methods.
 The language syntax is defined at a higher level of abstraction than C, and includes language statements for improving program readability, debugging, maintenance, and correctness. The language design was influenced by Ada, Pascal, Eiffel, Java, C, and C++.
 The keywords have been added to maintain full compatibility with C. The SCL language translator is an executable program that is implemented as a one-pass language processor that generates C source code. The generated code can be integrated conveniently with any C and/or C++ library, on Linux and Windows (and MacOS). The semantics of SCL is informally defined to be the same C semantics.
Endoscopic Surgery for Omohyoid Muscle Syndrome.

Omohyoid muscle syndrome (OMS) (not omohyoid syndrome) is a rare clinical condition that has a characteristic feature of a protruding lateral neck mass during swallowing. The use of endoscopic surgery on the neck is now pretty well established for thyroid and parathyroid glands. Patients with OMS usually undergo simple surgical transection of the omohyoid muscle. The procedure leaves operative scars on the neck, and most patients worry about the cosmetic problems. We report here the first use of an endoscopic procedure instead of traditional surgery for treatment of OMS.



We present a rare case of a 26-year-old Chinese man who noted a protruding mass involving the right side of his neck during the past 10 years. OMS was diagnosed. Laparoscopic simple transection of the omohyoid muscle by an ultrasonically activated scalpel was performed.



After laparoscopic transection of the omohyoid muscle, the neck mass completely disappeared during swallowing, and there were no operative scars on the neck.



To our knowledge, this is the first report of laparoscopy for treatment of OMS. We believe that the laparoscopic procedure is made acceptable for this unusual disease because of the cosmetic result.
Electric-field visualization technique for evaluating radiation from automotive millimeter-wave radar

Pushing accuracy and reliability of radar systems to higher levels is a requirement to realize autonomous driving. To maximize its performance, the millimeter-wave radar has to be designed in consideration of its surroundings such as emblems, bumpers and so on, because the electric-field distortion will degrade the performance. We propose electro-optic (EO) measurement system to visualize amplitude and phase distribution of millimeter waves, aiming at the evaluation of the disturbance of car components with the radar module equipped inside a vehicle. Visualization of 76-GHz millimeter waves passing through plastic plates is presented to demonstrate our system's capability of diagnosing a local cause of the field disturbance.
Vibration and motion control design and trade-off for high-performance mechatronic systems

This paper discusses the H∞-based design of a vibration controller for an industrial pick-and-place machine. A vibration controller is added to the classical motion control scheme with the purpose of improving positioning behavior by reducing the vibration level and settling time. It is shown that a trade-off is required between vibration reduction and motion control. The approach is validated experimentally and the results clearly illustrate the benefit of the proposed method.
Discovering Commonsense Entailment Rules Implicit in Sentences

Reasoning about ordinary human situations and activities requires the availability of diverse types of knowledge, including expectations about the probable results of actions and the lexical entailments for many predicates. We describe initial work to acquire such a collection of conditional (if–then) knowledge by exploiting presuppositional discourse patterns (such as ones involving ‘but’, ‘yet’, and ‘hoping to’) and abstracting the matched material into general rules.
An effective and efficient defect inspection system for TFT-LCD polarised films using adaptive thresholds and shape-based image analyses

An effective and efficient defect inspection system for TFT-LCD polarised films using adaptive thresholds and shape-based image analyses Chung-Ho Noha; Seok-Lyong Leea; Deok-Hwan Kimb; Chin-Wan Chungc; Sang-Hee Kimd a School of Industrial and Management Engineering, , Korea b School of Electronics Engineering, Inha University, Yonghyun-dong, Incheon-shi, Korea c Division of Computer Science, KAIST, Daejeon-shi, Korea d Key Technology Research Center, Agency for Defense Development, Daejeon-shi, Korea
Solution of Inverse Kinematics for SCARA Manipulator Using Adaptive Neuro-Fuzzy Network

Solution of inverse kinematic equations is complex problem, the complexity comes from the nonlinearity of joint space and Cartesian space mapping and having multiple solution. In this work, four adaptive neurofuzzy networks ANFIS are implemented to solve the inverse kinematics of 4-DOF SCARA manipulator. The implementation of ANFIS is easy, and the simulation of it shows that it is very fast and give acceptable error.
Sustainable urban agriculture in developing countries. A review

The population living in cities is continuously increasing worldwide. In developing countries, this phenomenon is exacerbated by poverty, leading to tremendous problems of employment, immigration from the rural areas, transportation, food supply and environment protection. Simultaneously with the growth of cities, a new type of agriculture has emerged; namely, urban agriculture. Here, the main functions of urban agriculture are described: its social roles, the economic functions as part of its multi-functionality, the constraints, and the risks for human consumption and the living environment. We highlight the following major points. (1) Agricultural activity will continue to be a strong contributor to urban households. Currently, differences between rural and urban livelihood households appear to be decreasing. (2) Urban agricultural production includes aquaculture, livestock and plants. The commonest crops are perishable leafy vegetables, particularly in South-east Asia and Africa. These vegetable industries have short marketing chains with lower price differentials between farmers and consumers than longer chains. The city food supply function is one of the various roles and objectives of urban agriculture that leads to increasing dialogue between urban dwellers, city authorities and farmers. (3) One of the farmers’ issues is to produce high quality products in highly populated areas and within a polluted environment. Agricultural production in cities faces the following challenges: access to the main agricultural inputs, fertilizers and water; production in a polluted environment; and limitation of its negative impact on the environment. Urban agriculture can reuse city wastes, but this will not be enough to achieve high yields, and there is still a risk of producing unsafe products. These are the main challenges for urban agriculture in keeping its multi-functional activities such as cleansing, opening up the urban space, and producing fresh and nutritious food.
On hierarchical clustering for speech phonetic segmentation

In this paper, we face the problem of phonetic segmentation under the hierarchical clustering framework. We extend the framework with an unsupervised segmentation algorithm based on a divisive clustering technique and compare both approaches: agglomerative nesting (Bottom-up) against divisive analysis (Top-down). As both approaches require prior knowledge of the number of segments to be estimated, we present a stopping criterion in order to make these algorithms become standalone. This criterion provides an estimation of the underlying number of segments inside the speech acoustic data. The evaluation of both approaches using the stopping criterion reveals good compromise between boundary estimation (Hit rate) and number of segments estimation (over-under segmentation).
Structure and function of the global ocean microbiome

Microbes are dominant drivers of biogeochemical processes, yet drawing a global picture of functional diversity, microbial community structure, and their ecological determinants remains a grand challenge. We analyzed 7.2 terabases of metagenomic data from 243 Tara Oceans samples from 68 locations in epipelagic and mesopelagic waters across the globe to generate an ocean microbial reference gene catalog with >40 million nonredundant, mostly novel sequences from viruses, prokaryotes, and picoeukaryotes. Using 139 prokaryote-enriched samples, containing >35,000 species, we show vertical stratification with epipelagic community composition mostly driven by temperature rather than other environmental factors or geography. We identify ocean microbial core functionality and reveal that >73% of its abundance is shared with the human gut microbiome despite the physicochemical differences between these two ecosystems.
An on-chip self-test architecture with test patterns recorded in scan chains

This work proposes a novel test architecture that combines the advantages of both scan-based and built-in self-test (BIST) designs. The main idea is to record (store) all required compressed test data in a novel scan chain structure such that the stored data can be extracted, reconstructed and decompressed into required deterministic patterns using an on-chip test controller with a test pattern decompressor. The recording of test data is achieved by modifying the connections between scan cells. Techniques to extract test data from the modified scan cells and to deliver decompressed test patterns to the modified scan cells are presented. The on-chip test controller can automatically generate all required control signals for the whole test procedure. This significantly reduces the requirements on external ATE. Experimental results on OpenSPARC T2, a publicly accessible 8-core processor containing 5.7M gates, show that all required test data for 100% testable stuck-at fault coverage can be stored in the scan chains of the processor with less than 3% total area overhead for the whole test architecture.
Joint Generative-Discriminative Aggregation Model for Multi-Option Crowd Labels

Although some crowdsourcing aggregation models have been introduced to aggregate noisy crowd labels, these models mostly consider single-option (i.e. discrete) crowd labels as the input variables, and are not compatible with multi-option (i.e. non-deterministic) crowd data. In this paper, we propose a novel joint generative-discriminative aggregation model, which is able to efficiently deal with both single-option and multi-option crowd labels. Considering the confidence of workers for each option as the input data, we first introduce a new discriminative aggregation model, called Constrained Weighted Majority Voting (CWMVL1), which improves the performance of majority voting method. CWMVL1 considers flexible reliability parameters for crowd workers, employs L1-norm loss function to deal with noisy crowd data, and includes optimization constraints to have probabilistic outputs. We prove that our object is convex, and derive an efficient optimization algorithm. Moreover, we integrate the discriminative CWMVL1 model with a generative model, resulting in a powerful joint aggregation model. Combination of these sub-models is obtained in a probabilistic framework rather than a heuristic way. For our joint model, we derive an efficient optimization algorithm, which alternates between updating the parameters and estimating the potential true labels. Experimental results indicate that the proposed aggregation models achieve superior or competitive results in comparison with the state-of-the-art models on single-option and multi-option crowd datasets, while having faster convergence rates and more reliable predictions.
Formation of a ZnO overlayer in industrial Cu/ZnO/Al2 O3 catalysts induced by strong metal-support interactions.

In industrially relevant Cu/ZnO/Al2 O3 catalysts for methanol synthesis, the strong metal support interaction between Cu and ZnO is known to play a key role. Here we report a detailed chemical transmission electron microscopy study on the nanostructural consequences of the strong metal support interaction in an activated high-performance catalyst. For the first time, clear evidence for the formation of metastable "graphite-like" ZnO layers during reductive activation is provided. The description of this metastable layer might contribute to the understanding of synergistic effects between the components of the Cu/ZnO/Al2 O3 catalysts.
The amygdala: vigilance and emotion

Here we provide a review of the animal and human literature concerning the role of the amygdala in fear conditioning, considering its potential influence over autonomic and hormonal changes, motor behavior and attentional processes. A stimulus that predicts an aversive outcome will change neural transmission in the amygdala to produce the somatic, autonomic and endocrine signs of fear, as well as increased attention to that stimulus. It is now clear that the amygdala is also involved in learning about positively valenced stimuli as well as spatial and motor learning and this review strives to integrate this additional information. A review of available studies examining the human amygdala covers both lesion and electrical stimulation studies as well as the most recent functional neuroimaging studies. Where appropriate, we attempt to integrate basic information on normal amygdala function with our current understanding of psychiatric disorders, including pathological anxiety.
A New Heuristic Reduct Algorithm Base on Rough Sets Theory

Real world data sets usually have many features, which increases the complexity of data mining task. Feature selection, as a preprocessing step to the data mining, has been shown very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving comprehensibility. To find the optimal feature subsets is the aim of feature selection. Rough sets theory provides a mathematical approach to find optimal feature subset, but this approach is time consuming. In this paper, we propose a novel heuristic algorithm based on rough sets theory to find out the feature subset. This algorithm employs appearing frequency of attribute as heuristic information. Experiment results show in most times our algorithm can find out optimal feature subset quickly and efficiently.