nils-herrmann/clean_scirepeval huggingface dataset:
Simple Recurrent Units for Highly Parallelizable Recurrence

Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU achieves 5–9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets, and delivers stronger results than LSTM and convolutional models. We also obtain an average of 0.7 BLEU improvement over the Transformer model (Vaswani et al., 2017) on translation by incorporating SRU into the
Generating User Interface Code in a Model Based User Interface Development Environment

Declarative models play an important role in most software design activities, by allowing designs to be constructed that selectively abstract over complex implementation details. In the user interface setting, Model-Based User Interface Development Environments (MB-UIDEs) provide a context within which declarative models can be constructed and related, as part of the interface design process. However, such declarative models are not usually directly executable, and may be difficult to relate to existing software components. It is therefore important that MB-UIDEs both fit in well with existing software architectures and standards, and provide an effective route from declarative interface specification to running user interfaces. This paper describes how user interface software is generated from declarative descriptions in the Teallach MB-UIDE. Distinctive features of Teallach include its open architecture, which connects directly to existing applications and widget sets, and the generation of executable interface applications in Java. This paper focuses on how Java programs, organized using the model-view-controller pattern (MVC), are generated from the task, domain and presentation models of Teallach.
A Review on Prognosis of Rolling Element Bearings

Bearings are amongst the frequently encountered components to be found in rotating machinery. Though inexpensive, their failure can interrupt the production in a plant causing unscheduled downtime and production losses. So the bearing prognosis plays a significant role in reducing plant down time and enhanced operation safety, by estimating the Remaining Useful Life (RUL) of damaged bearing. Admitting the importance of bearing prognosis, this literature review attempts to summarize various techniques, methods and models used in the prognosis of bearing till date. The definition of bearing prognosis is discussed in the beginning, followed by classification of various prognostic methods, review of methods used by various investigators in this research domain and concluding the topic with the summary of future research directions. KeywordsBearing Prognosis, Remaining Useful Life (RUL).
WiFi signal strength-based robot indoor localization

Due to the unavailable GPS signals in indoor environments, indoor localization has become an increasingly heated research topic in recent years. Researchers in robotics community have tried many approaches, but this is still an unsolved problem considering the balance between performance and cost. The widely deployed low-cost WiFi infrastructure provides a great opportunity for indoor localization. In this paper, we develop a system for WiFi signal strength-based indoor localization and implement two approaches. The first is improved KNN algorithm-based fingerprint matching method, and the other is the Gaussian Process Regression (GPR) with Bayes Filter approach. We conduct experiments to compare the improved KNN algorithm with the classical KNN algorithm and evaluate the localization performance of the GPR with Bayes Filter approach. The experiment results show that the improved KNN algorithm can bring enhancement for the fingerprint matching method compared with the classical KNN algorithm. In addition, the GPR with Bayes Filter approach can provide about 2m localization accuracy for our test environment.
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs

Discriminatively trained undirected graphical models have had wide empirical success, and there has been increasing interest in toolkits that ease their application to complex relational data. The power in relational models is in their repeated structure and tied parameters; at issue is how to define these structures in a powerful and flexible way. Rather than using a declarative language, such as SQL or first-order logic, we advocate using an imperative language to express various aspects of model structure, inference, and learning. By combining the traditional, declarative, statistical semantics of factor graphs with imperative definitions of their construction and operation, we allow the user to mix declarative and procedural domain knowledge, and also gain significant efficiencies. We have implemented such imperatively defined factor graphs in a system we call FACTORIE, a software library for an object-oriented, strongly-typed, functional language. In experimental comparisons to Markov Logic Networks on joint segmentation and coreference, we find our approach to be 3-15 times faster while reducing error by 20-25%—achieving a new state of the art.
An Efficient Multi-User Searchable Encryption Scheme without Query Transformation over Outsourced Encrypted Data

Searchable Encryption (SE) schemes provide security and privacy to the cloud data. The existing SE approaches enable multiple users to perform search operation by using various schemes like Broadcast Encryption (BE), Attribute-Based Encryption (ABE), etc. However, these schemes do not allow multiple users to perform the search operation over the encrypted data of multiple owners. Some SE schemes involve a Proxy Server (PS) that allow multiple users to perform the search operation. However, these approaches incur huge computational burden on PS due to the repeated encryption of the user queries for transformation purpose so as to ensure that users' query is searchable over the encrypted data of multiple owners. Hence, to eliminate this computational burden on PS, this paper proposes a secure proxy server approach that performs the search operation without transforming the user queries. This approach also returns the top-k relevant documents to the user queries by using Euclidean distance similarity approach. Based on the experimental study, this approach is efficient with respect to search time and accuracy.
Laplacian-Steered Neural Style Transfer

Neural Style Transfer based on Convolutional Neural Networks (CNN) aims to synthesize a new image that retains the high-level structure of a content image, rendered in the low-level texture of a style image. This is achieved by constraining the new image to have high-level CNN features similar to the content image, and lower-level CNN features similar to the style image. However in the traditional optimization objective, low-level features of the content image are absent, and the low-level features of the style image dominate the low-level detail structures of the new image. Hence in the synthesized image, many details of the content image are lost, and a lot of inconsistent and unpleasing artifacts appear. As a remedy, we propose to steer image synthesis with a novel loss function: the Laplacian loss. The Laplacian matrix ("Laplacian" in short), produced by a Laplacian operator, is widely used in computer vision to detect edges and contours. The Laplacian loss measures the difference of the Laplacians, and correspondingly the difference of the detail structures, between the content image and a new image. It is flexible and compatible with the traditional style transfer constraints. By incorporating the Laplacian loss, we obtain a new optimization objective for neural style transfer named Lapstyle. Minimizing this objective will produce a stylized image that better preserves the detail structures of the content image and eliminates the artifacts. Experiments show that Lapstyle produces more appealing stylized images with less artifacts, without compromising their "stylishness".
The MGB challenge: Evaluating multi-genre broadcast media recognition

This paper describes the Multi-Genre Broadcast (MGB) Challenge at ASRU 2015, an evaluation focused on speech recognition, speaker diarization, and "lightly supervised" alignment of BBC TV recordings. The challenge training data covered the whole range of seven weeks BBC TV output across four channels, resulting in about 1,600 hours of broadcast audio. In addition several hundred million words of BBC subtitle text was provided for language modelling. A novel aspect of the evaluation was the exploration of speech recognition and speaker diarization in a longitudinal setting - i.e. recognition of several episodes of the same show, and speaker diarization across these episodes, linking speakers. The longitudinal tasks also offered the opportunity for systems to make use of supplied metadata including show title, genre tag, and date/time of transmission. This paper describes the task data and evaluation process used in the MGB challenge, and summarises the results obtained.
An Overview of Digital Video Watermarking

The illegal distribution of a digital movie is a common and significant threat to the film industry. With the advent of high-speed broadband Internet access, a pirated copy of a digital video can now be easily distributed to a global audience. A possible means of limiting this type of digital theft is digital video watermarking whereby additional information, called a watermark, is embedded in the host video. This watermark can be extracted at the decoder and used to determine whether the video content is watermarked. This paper presents a review of the digital video watermarking techniques in which their applications, challenges, and important properties are discussed, and categorizes them based on the domain in which they embed the watermark. It then provides an overview of a few emerging innovative solutions using watermarks. Protecting a 3D video by watermarking is an emerging area of research. The relevant 3D video watermarking techniques in the literature are classified based on the image-based representations of a 3D video in stereoscopic, depth-image-based rendering, and multi-view video watermarking. We discuss each technique, and then present a survey of the literature. Finally, we provide a summary of this paper and propose some future research directions.
GcsDecolor: Gradient Correlation Similarity for Efficient Contrast Preserving Decolorization

This paper presents a novel gradient correlation similarity (Gcs) measure-based decolorization model for faithfully preserving the appearance of the original color image. Contrary to the conventional data-fidelity term consisting of gradient error-norm-based measures, the newly defined Gcs measure calculates the summation of the gradient correlation between each channel of the color image and the transformed grayscale image. Two efficient algorithms are developed to solve the proposed model. On one hand, due to the highly nonlinear nature of Gcs measure, a solver consisting of the augmented Lagrangian and alternating direction method is adopted to deal with its approximated linear parametric model. The presented algorithm exhibits excellent iterative convergence and attains superior performance. On the other hand, a discrete searching solver is proposed by determining the solution with the minimum function value from the linear parametric model-induced candidate images. The non-iterative solver has advantages in simplicity and speed with only several simple arithmetic operations, leading to real-time computational speed. In addition, it is very robust with respect to the parameter and candidates. Extensive experiments under a variety of test images and a comprehensive evaluation against existing state-of-the-art methods consistently demonstrate the potential of the proposed model and algorithms.
Recognition memory for emotionally negative and neutral words: an ERP study

Scalp recorded event-related potentials were used to investigate the neural activity elicited by emotionally negative and emotionally neutral words during the performance of a recognition memory task. Behaviourally, the principal difference between the two word classes was that the false alarm rate for negative items was approximately double that for the neutral words. Correct recognition of neutral words was associated with three topographically distinct ERP memory 'old/new' effects: an early, bilateral, frontal effect which is hypothesised to reflect familiarity-driven recognition memory; a subsequent left parietally distributed effect thought to reflect recollection of the prior study episode; and a late onsetting, right-frontally distributed effect held to be a reflection of post-retrieval monitoring. The old/new effects elicited by negative words were qualitatively indistinguishable from those elicited by neutral items and, in the case of the early frontal effect, of equivalent magnitude also. However, the left parietal effect for negative words was smaller in magnitude and shorter in duration than that elicited by neutral words, whereas the right frontal effect was not evident in the ERPs to negative items. These differences between neutral and negative words in the magnitude of the left parietal and right frontal effects were largely attributable to the increased positivity of the ERPs elicited by new negative items relative to the new neutral items. Together, the behavioural and ERP findings add weight to the view that emotionally valenced words influence recognition memory primarily by virtue of their high levels of 'semantic cohesion', which leads to a tendency for 'false recollection' of unstudied items.
Helium-Oxygen Therapy for Infants With Bronchiolitis

Patients initially received nebulized albuterol treatment driven by 100% oxygen. Patients were randomized to the helium-oxygen or oxygen group and received nebulized racemic epinephrine via a face mask. After nebulization, humidified helium-oxygen or oxygen was delivered by HFNC. After 60 minutes of inhalation therapy, patients with an M-WCAS of 2 or higher received a second delivery of nebulized racemic epinephrine followed by helium-oxygen or oxygen delivered by HFNC.  Degree of improvement of M-WCAS for 240 minutes or until emergency department discharge.
Personality Traits , Self-Esteem and Academic Achievement in Secondary School Students in Campania , Italy

For years educators have attempted to identify the effective predictors of scholastic achievement and several personality variables were described as significantly correlated with grade performance. Since one of the crucial practical implications of identifying the factors involved in academic achievement is to facilitate the teaching-learning process, the main variables that have been associated with achievement should be investigated simultaneously in order to provide information as to their relative merit in the population examined. In contrast with this premise, limited research has been conducted on the importance of personality traits and self-esteem on scholastic achievement. To this aim in a sample of 439 subjects (225 males) with an average age of 12.36 years (SD= .99) from three first level secondary school classes of Southern Italy, personality traits, as defined by the Five Factor Model, self-esteem and socioeconomic status were evaluated. The academic results correlated significantly both with personality traits and with some dimensions of self-esteem. Moreover, hierarchical regression analyses brought to light, in particular, the predictive value of openness to experience on academic marks. The results, stressing the multidimensional nature of academic performance, indicate a need to adopt complex approaches for undertaking action addressing students’ difficulties in attaining good academic achievement.
Surgical robotics. Evaluation of the Computer Motion AESOP 3000 robotic endoscope holder.

The use of robots to manipulate surgical instruments inside the patient has already moved from the world of fiction to fact. While the widespread use of full-function surgical robots is still many years away, less sophisticated robots that perform very specific surgical functions are already at a stage where the typical hospital can consider their use. Currently, the most affordable and commonly used type of "surgical-assist" robot is the robotic endoscope holder, which is used to hold and position rigid endoscopes during minimally invasive surgery. In this study, we introduce readers to the topic of surgical robotics, focusing specifically on robotic endoscope holders. The study includes a Technology Management Guide, in which we discuss who should and who shouldn't consider implementing such robots, and it includes our evaluation protocol and findings for one such robot, the Computer Motion AESOP 3000. We judged the evaluated system based on its performance relative to the human scope holders it is designed to replace, as well as its safety and ease of use. While we found the AESOP 3000 to be an acceptable, and sometimes preferred, alternative to the use of a human scope holder, we caution that many healthcare facilities won't see sufficient clinical benefit to warrant its purchase at this time.
New bipyridine gold(III) dithiocarbamate-containing complexes exerted a potent anticancer activity against cisplatin-resistant cancer cells independent of p53 status

We synthesized, characterized and tested in a panel of cancer cell lines, nine new bipyridine gold(III) dithiocarbamate-containing complexes. In vitro studies demonstrated that compounds 1, 2, 4, 5, 7 and 8 were the most cytotoxic in prostate, breast, ovarian cancer cell lines and in Hodgkin lymphoma cells with IC50 values lower than the reference drug cisplatin. The most active compound 1 was more active than cisplatin in ovarian (A2780cis and 2780CP-16) and breast cancer cisplatin-resistant cells. Compound 1 determined an alteration of the cellular redox homeostasis leading to increased ROS levels, a decrease in the mitochondrial membrane potential, cytochrome-c release from the mitochondria and activation of caspases 9 and 3. The ROS scavenger NAC suppressed ROS generation and rescued cells from damage. Compound 1 resulted more active in tumor cells than in normal human Mesenchymal stromal cells. Gold compounds were active independent of p53 status: exerted cytotoxic effects on a panel of non-small cell lung cancer cell lines with different p53 status and in the ovarian A2780 model where the p53 was knocked out. In conclusion, these promising results strongly indicate the need for further preclinical evaluation to test the clinical potential of these new gold(III) complexes.
CLUSTERING CORRELATED , SPARSE DATA STREAMS TO ESTIMATE A LOCALIZED HOUSING PRICE INDEX

Understanding how housing values evolve over time is important to policy makers, consumers and real estate professionals. Existing methods for constructing housing indices are computed at a coarse spatial granularity, such as metropolitan regions, which can mask or distort price dynamics apparent in local markets, such as neighborhoods and census tracts. A challenge in moving to estimates at, for example, the census tract level is the scarcity of spatiotemporally localized house sales observations. Our work aims to address this challenge by leveraging observations from multiple census tracts discovered to have correlated valuation dynamics. Our proposed Bayesian nonparametric approach builds on the framework of latent factor models to enable a flexible, data-driven method for inferring the clustering of correlated census tracts. We explore methods for scalability and parallelizability of computations, yielding a housing valuation index at the level of census tract rather than zip code, and on a monthly basis rather than quarterly. Our analysis is provided on a large Seattle metropolitan housing dataset.
The influence of essential oils on human attention. I: alertness.

Scientific research on the effects of essential oils on human behavior lags behind the promises made by popular aromatherapy. Nearly all aspects of human behavior are closely linked to processes of attention, the basic level being that of alertness, which ranges from sleep to wakefulness. In our study we measured the influence of essential oils and components of essential oils [peppermint, jasmine, ylang-ylang, 1,8-cineole (in two different dosages) and menthol] on this core attentional function, which can be experimentally defined as speed of information processing. Substances were administered by inhalation; levels of alertness were assessed by measuring motor and reaction times in a reaction time paradigm. The performances of the six experimental groups receiving substances (n = 20 in four groups, n = 30 in two groups) were compared with those of corresponding control groups receiving water. Between-group analysis, i.e. comparisons between experimental groups and their respective control groups, mainly did not reach statistical significance. However, within-group analysis showed complex correlations between subjective evaluations of substances and objective performance, indicating that effects of essentials oils or their components on basic forms of attentional behavior are mainly psychological.
Action Video Games Make Dyslexic Children Read Better

Learning to read is extremely difficult for about 10% of children; they are affected by a neurodevelopmental disorder called dyslexia [1, 2]. The neurocognitive causes of dyslexia are still hotly debated [3-12]. Dyslexia remediation is far from being fully achieved [13], and the current treatments demand high levels of resources . Here, we demonstrate that only 12 hr of playing action video games-not involving any direct phonological or orthographic training-drastically improve the reading abilities of children with dyslexia. We tested reading, phonological, and attentional skills in two matched groups of children with dyslexia before and after they played action or nonaction video games for nine sessions of 80 min per day. We found that only playing action video games improved children's reading speed, without any cost in accuracy, more so than 1 year of spontaneous reading development and more than or equal to highly demanding traditional reading treatments. Attentional skills also improved during action video game training. It has been demonstrated that action video games efficiently improve attention abilities [14, 15]; our results showed that this attention improvement can directly translate into better reading abilities, providing a new, fast, fun remediation of dyslexia that has theoretical relevance in unveiling the causal role of attention in reading acquisition.