nils-herrmann/clean_scirepeval huggingface dataset:
Frontiers of biomedical text mining: current progress

It is now almost 15 years since the publication of the first paper on text mining in the genomics domain, and decades since the first paper on text mining in the medical domain. Enormous progress has been made in the areas of information retrieval, evaluation methodologies and resource construction. Some problems, such as abbreviation-handling, can essentially be considered solved problems, and others, such as identification of gene mentions in text, seem likely to be solved soon. However, a number of problems at the frontiers of biomedical text mining continue to present interesting challenges and opportunities for great improvements and interesting research. In this article we review the current state of the art in biomedical text mining or 'BioNLP' in general, focusing primarily on papers published within the past year.
Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales

www.frontiersinecology.org  Over the past decade, progress has been made in understanding how ecosystems provide services and how service provision translates into economic value (Daily 1997; MA 2005; NRC 2005). Yet, it has proven difficult to move from general pronouncements about the tremendous benefits nature provides to people to credible, quantitative estimates of ecosystem service values. Spatially explicit values of services across landscapes that might inform land-use and management decisions are still lacking (Balmford et al. 2002; MA 2005). Without quantitative assessments, and some incentives for landowners to provide them, these services tend to be ignored by those making land-use and land-management decisions. Currently, there are two paradigms for generating ecosystem service assessments that are meant to influence policy decisions. Under the first paradigm, researchers use broad-scale assessments of multiple services to extrapolate a few estimates of values, based on habitat types, to entire regions or the entire planet (eg Costanza et al. 1997; Troy and Wilson 2006; Turner et al. 2007). Although simple, this “benefits transfer” approach incorrectly assumes that every hectare of a given habitat type is of equal value – regardless of its quality, rarity, spatial configuration, size, proximity to population centers, or the prevailing social practices and values. Furthermore, this approach does not allow for analyses of service provision and changes in value under new conditions. For example, if a wetland is converted to agricultural land, how will this affect the provision of clean drinking water, downstream flooding, climate regulation, and soil fertility? Without information on the impacts of land-use management practices on ecosystem services production, it is impossible to design policies or payment programs that will provide the desired ecosystem services. In contrast, under the second paradigm for generating policy-relevant ecosystem service assessments, researchers carefully model the production of a single service in a small area with an “ecological production function” – how provision of that service depends on local ecological variables (eg Kaiser and Roumasset 2002; Ricketts et al. 2004). Some of these production function approaches also use market prices and non-market valuation methods to estimate the economic value of the service and how that value changes under different ecological conditions. Although these methods are superior to the habitat assessment benefits transfer approach, these studies lack both the scope (number of services) and scale (geographic and temporal) to be relevant for most policy questions. What is needed are approaches that combine the rigor of the small-scale studies with the breadth of broad-scale assessments (see Boody et al. 2005; Jackson et al. 2005; ECOSYSTEM SERVICES ECOSYSTEM SERVICES ECOSYSTEM SERVICES
Machine Learning for Information Extraction in Informal Domains

We consider the problem of learning to perform information extraction in domains where linguistic processing is problematic, such as Usenet posts, email, and finger plan files. In place of syntactic and semantic information, other sources of information can be used, such as term frequency, typography, formatting, and mark-up. We describe four learning approaches to this problem, each drawn from a different paradigm: a rote learner, a term-space learner based on Naive Bayes, an approach using grammatical induction, and a relational rule learner. Experiments on 14 information extraction problems defined over four diverse document collections demonstrate the effectiveness of these approaches. Finally, we describe a multistrategy approach which combines these learners and yields performance competitive with or better than the best of them. This technique is modular and flexible, and could find application in other machine learning problems.
SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining

In this work we present SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications. SENTIWORDNET 3.0 is an improved version of SENTIWORDNET 1.0, a lexical resource publicly available for research purposes, now currently licensed to more than 300 research groups and used in a variety of research projects worldwide. Both SENTIWORDNET 1.0 and 3.0 are the result of automatically annotating all WORDNET synsets according to their degrees of positivity, negativity, and neutrality. SENTIWORDNET 1.0 and 3.0 differ (a) in the versions of WORDNET which they annotate (WORDNET 2.0 and 3.0, respectively), (b) in the algorithm used for automatically annotating WORDNET, which now includes (additionally to the previous semi-supervised learning step) a random-walk step for refining the scores. We here discuss SENTIWORDNET 3.0, especially focussing on the improvements concerning aspect (b) that it embodies with respect to version 1.0. We also report the results of evaluating SENTIWORDNET 3.0 against a fragment of WORDNET 3.0 manually annotated for positivity, negativity, and neutrality; these results indicate accuracy improvements of about 20% with respect to SENTIWORDNET 1.0.
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

With the availability of large databases and recent improvements in deep learning methodology, the performance of AI systems is reaching or even exceeding the human level on an increasing number of complex tasks. Impressive examples of this development can be found in domains such as image classification, sentiment analysis, speech understanding or strategic game playing. However, because of their nested non-linear structure, these highly successful machine learning and artificial intelligence models are usually applied in a black box manner, i.e., no information is provided about what exactly makes them arrive at their predictions. Since this lack of transparency can be a major drawback, e.g., in medical applications, the development of methods for visualizing, explaining and interpreting deep learning models has recently attracted increasing attention. This paper summarizes recent developments in this field and makes a plea for more interpretability in artificial intelligence. Furthermore, it presents two approaches to explaining predictions of deep learning models, one method which computes the sensitivity of the prediction with respect to changes in the input and one approach which meaningfully decomposes the decision in terms of the input variables. These methods are evaluated on three classification tasks.
RedEye: Analog ConvNet Image Sensor Architecture for Continuous Mobile Vision

Continuous mobile vision is limited by the inability to efficiently capture image frames and process vision features. This is largely due to the energy burden of analog readout circuitry, data traffic, and intensive computation. To promote efficiency, we shift early vision processing into the analog domain. This results in RedEye, an analog convolutional image sensor that performs layers of a convolutional neural network in the analog domain before quantization. We design RedEye to mitigate analog design complexity, using a modular column-parallel design to promote physical design reuse and algorithmic cyclic reuse. RedEye uses programmable mechanisms to admit noise for tunable energy reduction. Compared to conventional systems, RedEye reports an 85% reduction in sensor energy, 73% reduction in cloudlet-based system energy, and a 45% reduction in computation-based system energy.
Generative Image Modeling Using Spatial LSTMs

Modeling the distribution of natural images is challenging, partly because of strong statistical dependencies which can extend over hundreds of pixels. Recurrent neural networks have been successful in capturing long-range dependencies in a number of problems but only recently have found their way into generative image models. We here introduce a recurrent image model based on multidimensional long short-term memory units which are particularly suited for image modeling due to their spatial structure. Our model scales to images of arbitrary size and its likelihood is computationally tractable. We find that it outperforms the state of the art in quantitative comparisons on several image datasets and produces promising results when used for texture synthesis and inpainting.
A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

Deep learning is recently showing outstanding results for solving a wide variety of robotic tasks in the areas of perception, planning, localization, and control. Its excellent capabilities for learning representations from the complex data acquired in real environments make it extremely suitable for many kinds of autonomous robotic applications. In parallel, Unmanned Aerial Vehicles (UAVs) are currently being extensively applied for several types of civilian tasks in applications going from security, surveillance, and disaster rescue to parcel delivery or warehouse management. In this paper, a thorough review has been performed on recent reported uses and applications of deep learning forUAVs, including themost relevant developments as well as their performances and limitations. In addition, a detailed explanation of the main deep learning techniques is provided. We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions.
A comparative analysis of synthetic genetic oscillators.

Synthetic biology is a rapidly expanding discipline at the interface between engineering and biology. Much research in this area has focused on gene regulatory networks that function as biological switches and oscillators. Here we review the state of the art in the design and construction of oscillators, comparing the features of each of the main networks published to date, the models used for in silico design and validation and, where available, relevant experimental data. Trends are apparent in the ways that network topology constrains oscillator characteristics and dynamics. Also, noise and time delay within the network can both have constructive and destructive roles in generating oscillations, and stochastic coherence is commonplace. This review can be used to inform future work to design and implement new types of synthetic oscillators or to incorporate existing oscillators into new designs.
DevOps - A Software Architect's Perspective

Our solutions was launched using a want to function as a full on-line digital local library that gives use of many PDF guide catalog. You may find many different types of e-guide as well as other literatures from my papers data bank. Specific popular topics that spread out on our catalog are famous books, answer key, assessment test questions and answer, guideline paper, training guideline, quiz test, consumer guide, consumer guidance, service instructions, restoration handbook, and so forth.
Antimicrobial potential of bacteriocins: in therapy, agriculture and food preservation.

Due to the appearance of antibiotic resistance and the toxicity associated with currently used antibiotics, peptide antibiotics are the need of the hour. Thus, demand for new antimicrobial agents has brought great interest in new technologies to enhance safety. One such antimicrobial molecule is bacteriocin, synthesised by various micro-organisms. Bacteriocins are widely used in agriculture, veterinary medicine as a therapeutic, and as a food preservative agent to control various infectious and food-borne pathogens. In this review, we highlight the potential therapeutic and food preservative applications of bacteriocin.
PAIGE: PAirwise Image Geometry Encoding for improved efficiency in Structure-from-Motion

Large-scale Structure-from-Motion systems typically spend major computational effort on pairwise image matching and geometric verification in order to discover connected components in large-scale, unordered image collections. In recent years, the research community has spent significant effort on improving the efficiency of this stage. In this paper, we present a comprehensive overview of various state-of-the-art methods, evaluating and analyzing their performance. Based on the insights of this evaluation, we propose a learning-based approach, the PAirwise Image Geometry Encoding (PAIGE), to efficiently identify image pairs with scene overlap without the need to perform exhaustive putative matching and geometric verification. PAIGE achieves state-of-the-art performance and integrates well into existing Structure-from-Motion pipelines.
Robust Downbeat Tracking Using an Ensemble of Convolutional Networks

In this paper, we present a novel state-of-the-art system for automatic downbeat tracking from music signals. The audio signal is first segmented in frames which are synchronized at the tatum level of the music. We then extract different kind of features based on harmony, melody, rhythm, and bass content to feed convolutional neural networks that are adapted to take advantage of the characteristics of each feature. This ensemble of neural networks is combined to obtain one downbeat likelihood per tatum. The downbeat sequence is finally decoded with a flexible and efficient temporal model which takes advantage of the assumed metrical continuity of a song. We then perform an evaluation of our system on a large base of nine datasets, compare its performance to four other published algorithms and obtain a significant increase of 16.8% points compared to the second-best system, for altogether a moderate cost in test and training. The influence of each step of the method is studied to show its strengths and shortcomings.
Network biology concepts in complex disease comorbidities

The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being collected electronically, disease co-occurrences are starting to be quantitatively characterized. Linking network dynamics to the real-life, non-ideal patient in whom diseases co-occur and interact provides a valuable basis for generating hypotheses on molecular disease mechanisms, and provides knowledge that can facilitate drug repurposing and the development of targeted therapeutic strategies.
Online computerized Hotel Management System

The purpose of this research, computerized hotel management system with Satellite Motel Ilorin, Nigeria as the case study is to understand and make use of the computer to solve some of the problems which are usually encountered during manual operations of the hotel management. Finding an accommodation or a hotel after having reached a particular destination is quite time consuming as well as expensive. Here comes the importance of online hotel booking facility. Online hotel booking is one of the latest techniques in the arena of internet that allows travelers to book a hotel located anywhere in the world and that too according to your tastes and preferences. In other words, online hotel booking is one of the awesome facilities of the internet. Booking a hotel online is not only fast as well as convenient but also very cheap. Nowadays, many of the hotel providers have their sites on the web, which in turn allows the users to visit these sites and view the facilities and amenities offered by each of them. So, the proposed computerized of an online hotel management system is set to find a more convenient, well organized, faster, reliable and accurate means of processing the current manual system of the hotel for both near and far customer.
Manuka honey-impregnated dressings in the treatment of neuropathic diabetic foot ulcers.

In this study, we investigate the effect of manuka honey-impregnated dressings (MHID) on the healing of neuropathic diabetic foot ulcers (NDFU). A total of 63 Caucasians, type 2 diabetic patients followed up in the diabetic foot outpatient clinic comprised the study population. Patients were randomised in two groups as follows: group I patients were treated with MHID and group II patients were treated with conventional dressings (CD). The patients were followed up on a weekly basis for 16 weeks. Mean healing time was 31 ± 4 days in group I versus 43 ± 3 days in group II (P < 0·05). In group I patients 78·13% of ulcers became sterile during the first week versus 35·5% in group II patients; the corresponding percentages for weeks 2, 4 and 6 were 15·62% versus 38·7%, 6·25% versus 12·9% and 0% versus 12·9% respectively. The percent of ulcers healed did not differ significantly between groups (97% for MHID and 90% for CD). MHID represent an effective treatment for NDFU leading to a significant reduction in the time of healing and rapid disinfection of ulcers.
An FPGA Move Generator for the Game of Chess

This thesis details the use of a programmable logic device to increase the playing strength of a chess program. The time–consuming task of generating chess moves is relegated to hardware in order to increase the processing speed of the search algorithm. A simpler inter–square connection protocol reduces the number of wires between chess squares, when compared to the Deep Blue design. With this interconnection scheme, special chess moves are easily resolved. Furthermore, dynamically programmable arbiters are introduced for optimal move ordering. Arbiter centrality is also shown to improve move ordering, thereby creating smaller search trees. The move generator is designed to allow the integration of crucial move ordering heuristics. With its new hardware move generator, the chess program’s playing ability is noticeably improved.
Analysis and Modeling of Urban Land Cover Change in Setúbal and Sesimbra, Portugal

The expansion of cities entails the abandonment of forest and agricultural lands, and these lands‘ conversion into urban areas, which results in substantial impacts on ecosystems. Monitoring these changes and planning urban development can be successfully achieved using multitemporal remotely sensed data, spatial metrics, and modeling. In this paper, urban land use change analysis and modeling was carried out for the Concelhos of Setúbal and Sesimbra in Portugal. An existing land cover map for the year 1990, together with two derived land cover maps from multispectral satellite images for the years 2000 and 2006, were utilized using an object-oriented classification approach. Classification accuracy assessment revealed satisfactory results that fulfilled minimum standard accuracy levels. Urban land use dynamics, in terms of both patterns and quantities, were studied using selected landscape metrics and the Shannon Entropy index. Results show that urban areas increased by 91.11% between 1990 and 2006. In contrast, the change was only 6.34% between 2000 and 2006. The entropy value was 0.73 for both municipalities in 1990, indicating a high rate of urban sprawl in the area. In 2006, this value, for both Sesimbra and Setúbal, reached almost 0.90. This is demonstrative of a tendency toward intensive urban sprawl. Urban land use change for the year 2020 was modeled using a Cellular Automata based approach. The predictive power of the model was successfully validated using Kappa variations. Projected land cover changes show a growing tendency in urban land use, which might threaten areas that are currently reserved for natural parks and agricultural lands.
Experiments on Learning by Back Propagation.

Rumelhart, Hinton and Williams [Rumelhart et al. 86] describe a learning procedure for layered networks of deterministic, neuron-like units. This paper describes further research on the learning procedure. We start by describing the units, the way they are connected, the learning procedure, and the extension to iterative nets. We then give an example in which a network learns a set of filters that enable it to discriminate formant-like patterns in the presence of noise. The speed of learning is strongly dependent on the shape of the surface formed by the error measure in "weight space." We give examples of the shape of the error surface for a typical task and illustrate how an acceleration method speeds up descent in weight space. The main drawback of the learning procedure is the way it scales as the size of the task and the network increases. We give some preliminary results on scaling and show how the magnitude of the optimal weight changes depends on the fan-in of the units. Additional results illustrate the effects on learning speed of the amount of interaction between the weights. A variation of the learning procedure that back-propagates desired state information rather than error gradients is developed and compared with the standard procedure. Finally, we discuss the relationship between our iterative networks and the "analog" networks described by Hopfield and Tank [Hopfield and Tank 85]. The learning procedure can discover appropriate weights in their kind of network, as well as determine an optimal schedule for varying the nonlinearity of the units during a search.
Deep Predictive Coding Network for Object Recognition

Based on the predictive coding theory in neuroscience, we designed a bi-directional and recurrent neural net, namely deep predictive coding networks (PCN), that has feedforward, feedback, and recurrent connections. Feedback connections from a higher layer carry the prediction of its lower-layer representation; feedforward connections carry the prediction errors to its higher-layer. Given image input, PCN runs recursive cycles of bottom-up and top-down computation to update its internal representations and reduce the difference between bottom-up input and top-down prediction at every layer. After multiple cycles of recursive updating, the representation is used for image classification. With benchmark datasets (CIFAR-10/100, SVHN, and MNIST), PCN was found to always outperform its feedforward-only counterpart: a model without any mechanism for recurrent dynamics, and its performance tended to improve given more cycles of computation over time. In short, PCN reuses a single architecture to recursively run bottom-up and top-down processes to refine its representation towards more accurate and definitive object recognition.