nils-herrmann/clean_scirepeval huggingface dataset:
Actor-Critic Sequence Training for Image Captioning

Generating natural language descriptions of images is an important capability for a robot or other visual-intelligence driven AI agent that may need to communicate with human users about what it is seeing. Such image captioning methods are typically trained by maximising the likelihood of ground-truth annotated caption given the image. While simple and easy to implement, this approach does not directly maximise the language quality metrics we care about such as CIDEr. In this paper we investigate training image captioning methods based on actor-critic reinforcement learning in order to directly optimise non-differentiable quality metrics of interest. By formulating a per-token advantage and value computation strategy in this novel reinforcement learning based captioning model, we show that it is possible to achieve the state of the art performance on the widely used MSCOCO benchmark.
Providing Database as a Service

In this paper, we explore a new paradigm for data management in which a third party service provider hosts ”database as a service” providing its customers seamless mechanisms to create, store, and access their databases at the host site. Such a model alleviates the need for organizations to purchase expensive hardware and software, deal with software upgrades, and hire professionals for administrative and maintenance tasks which are taken over by the service provider. We have developed and deployed a database service on the Internet, called NetDB2, which is in constant use. In a sense, data management model supported by NetDB2 provides an effective mechanism for organizations to purchase data management as a service, thereby freeing them to concentrate on their core businesses. Among the primary challenges introduced by ”database as a service” are additional overhead of remote access to data, an infrastructure to guarantee data privacy , and user interface design for such a service. These issues are investigated in the study. We identify data privacy as a particularly vital problem and propose alternative solutions based on data encryption. This paper is meant as a challenges paper for the database community to explore a rich set of research issues that arise in developing such a
Shuffled frog-leaping algorithm : a memetic metaheuristic for discrete optimization

A memetic meta-heuristic called the shuffled frog-leaping algorithm (SFLA) has been developed for solving combinatorial optimization problems. The SFLA is a population-based cooperative search metaphor inspired by natural memetics. The algorithm contains elements of local search and global information exchange. The SFLA consists of a set of interacting virtual population of frogs partitioned into different memeplexes. The virtual frogs act as hosts or carriers of memes where a meme is a unit of cultural evolution. The algorithm performs simultaneously an independent local search in each memeplex. The local search is completed using a particle swarm optimization-like method adapted for discrete problems but emphasizing a local search. To ensure global exploration, the virtual frogs are periodically shuffled and reorganized into new memplexes in a technique similar to that used in the shuffled complex evolution algorithm. In addition, to provide the opportunity for random generation of improved information, random virtual frogs are generated and substituted in the population.The algorithm has been tested on several test functions that present difficulties common to many global optimization problems. The effectiveness and suitability of this algorithm have also been demonstrated by applying it to a groundwater model calibration problem and a water distribution system design problem. Compared with a genetic algorithm, the experimental results in terms of the likelihood of convergence to a global optimal solution and the solution speed suggest that the SFLA can be an effective tool for solving combinatorial optimization problems.
Cluster-based approach to improve affect recognition from passively sensed data

Negative affect is a proxy for mental health in adults. By being able to predict participants' negative affect states unobtrusively, researchers and clinicians will be bet­ter positioned to deliver targeted, just-in-time mental health interventions via mobile applications. This work attempts to personalize the passive recognition of negative affect states via group-based modeling of user behavior patterns captured from mobility, communication, and activity patterns. Results show that group models outperform generalized models in a dataset based on two weeks of users' daily lives.
Stochastic choice of basis functions in adaptive function approximation and the functional-link net

A theoretical justification for the random vector version of the functional-link (RVFL) net is presented in this paper, based on a general approach to adaptive function approximation. The approach consists of formulating a limit-integral representation of the function to be approximated and subsequently evaluating that integral with the Monte-Carlo method. Two main results are: (1) the RVFL is a universal approximator for continuous functions on bounded finite dimensional sets, and (2) the RVFL is an efficient universal approximator with the rate of approximation error convergence to zero of order O(C/ radicaln), where n is number of basis functions and with C independent of n. Similar results are also obtained for neural nets with hidden nodes implemented as products of univariate functions or radial basis functions. Some possible ways of enhancing the accuracy of multivariate function approximations are discussed.
Understanding network failures in data centers: measurement, analysis, and implications

We present the first large-scale analysis of failures in a data center network. Through our analysis, we seek to answer several fundamental questions: which devices/links are most unreliable, what causes failures, how do failures impact network traffic and how effective is network redundancy? We answer these questions using multiple data sources commonly collected by network operators. The key findings of our study are that (1) data center networks show high reliability, (2) commodity switches such as ToRs and AggS are highly reliable, (3) load balancers dominate in terms of failure occurrences with many short-lived software related faults,(4) failures have potential to cause loss of many small packets such as keep alive messages and ACKs, and (5) network redundancy is only 40% effective in reducing the median impact of failure.
Advanced Transport Options for the Dynamic Adaptive Streaming over HTTP

Multimedia streaming over HTTP is no longer a niche research topic as it has entered our daily live. The common assumption is that it is deployed on top of the existing infrastructure utilizing application (HTTP) and transport (TCP) layer protocols as is. Interestingly, standards like MPEG’s Dynamic Adaptive Streaming over HTTP (DASH) do not mandate the usage of any specific transport protocol allowing for sufficient deployment flexibility which is further supported by emerging developments within both protocol layers. This paper investigates and evaluates the usage of advanced transport options for the dynamic adaptive streaming over HTTP. We utilize a common test setup to evaluate HTTP/2.0 and Google’s Quick UDP Internet Connections (QUIC) protocol in the context of DASH-based services.
Study of Brightness Preservation Histogram Equalization Techniques

Image captured under various environment conditions with low contrast results in visual deterioration. To enhance the contrast of images a number of techniques have been proposed over the years. Histogram equalization is one of those methods which could be used for this purpose. For the contrast enhancement purpose histogram equalization (HE) attempts reduction in the number of gray levels. Contrast enhancement by histogram equalization method involve mapping of gray levels on the basis of changeover function that is derived from the probability distribution of gray levels embedded in input image. For this, the neighbouring gray levels in image having low probability density values are combined into single gray level value. Whereas for neighbouring gray levels having high probability density value the gap between them is increased, HE often makes over-enhancement of images with frequent gray level region resulting in loss of contrast for less frequent area of the images. Thus, the HE technique is not able to preserve image brightness. So wherever the preservation of image brightness is required this method is not preferred. This paper review various histogram equalization techniques used in preserving image brightness as well as the contrast enhancement is presented. The key approach for this aim is to use image segmentation. The criteria used are decomposition of images and the presentation of the basic difference between these techniques. For comparative study of performance of these techniques, absolute mean brightness error (AMBE) is used to determine degree of brightness preservation while peak signal to noise ratio (PSNR) have been used to fine\ degree of contrast enhancement.
Data Security and Privacy Protection Issues in Cloud Computing

It is well-known that cloud computing has many potential advantages and many enterprise applications and data are migrating to public or hybrid cloud. But regarding some business-critical applications, the organizations, especially large enterprises, still wouldn't move them to cloud. The market size the cloud computing shared is still far behind the one expected. From the consumers' perspective, cloud computing security concerns, especially data security and privacy protection issues, remain the primary inhibitor for adoption of cloud computing services. This paper provides a concise but all-round analysis on data security and privacy protection issues associated with cloud computing across all stages of data life cycle. Then this paper discusses some current solutions. Finally, this paper describes future research work about data security and privacy protection issues in cloud.
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications

With the advent of Web 2.0, people became more eager to express and share their opinions on web regarding day-to-day activities and global issues as well. Evolution of social media has also contributed immensely to these activities, thereby providing us a transparent platform to share views across the world. These electronic Word of Mouth (eWOM) statements expressed on the web are much prevalent in business and service industry to enable customer to share his/her point of view. In the last one and half decades, research communities, academia, public and service industries are working rigorously on sentiment analysis, also known as, opinion mining, to extract and analyze public mood and views. In this regard, this paper presents a rigorous survey on sentiment analysis, which portrays views presented by over one hundred articles published in the last decade regarding necessary tasks, approaches, and applications of sentiment analysis. Several sub-tasks need to be performed for sentiment analysis which in turn can be accomplished using various approaches and techniques. This survey covering published literature during 2002-2015, is organized on the basis of sub-tasks to be performed, machine learning and natural language processing techniques used and applications of sentiment analysis. The paper also presents open issues and along with a summary table of a hundred and sixty one articles.
Panelrama : A Framework for Cross-Device Applications

We introduce Panelrama, a web framework for the construction of distributed user interfaces (DUI). Our implementation provides developers with low migration costs through built-in mechanisms for the synchronization of UI state and minimal changes to existing languages. Additionally, we describe a solution to categorize device characteristics and dynamically change UI allocation to best-fit devices. Finally, we illustrate the use of Panelrama through a sample application which demonstrates multidevice interaction techniques and collaborative potential.
DaTuM: Dynamic tone mapping technique for OLED display power saving based on video classification

The adoption of the latest OLED (organic light emitting diode) technology does not change the fact that screen is still one of the most energy-consuming modules in modern smartphones. In this work, we found that video streams from the same video category share many common power consumption features on OLED screens. Therefore, we are able to build a Hidden Markov Model (HMM) classifier to categorize videos based on OLED screen power characteristics. Using this HMM classifier, we propose a video classification based dynamic tone mapping (DTM) scheme, namely, DaTuM, to remap output color range and minimize the power-hungry color compositions on OLED screens for power saving. Experiment shows that DaTuM scheme averagely reduces OLED screen power by 17.8% with minimum display quality degradation. Compared to DTM scheme based on official category info provided by the video sources and one state-of-the-art scheme, DaTuM substantially enhances OLED screens' power efficiency and display quality controllability.
Inhalation/Exhalation ratio modulates the effect of slow breathing on heart rate variability and relaxation.

Slow breathing is widely applied to improve symptoms of hyperarousal, but it is unknown whether its beneficial effects relate to the reduction in respiration rate per se, or, to a lower inhalation/exhalation (i/e) ratio. The present study examined the effects of four ventilatory patterns on heart rate variability and self-reported dimensions of relaxation. Thirty participants were instructed to breathe at 6 or 12 breaths/min, and with an i/e ratio of 0.42 or 2.33. Participants reported increased relaxation, stress reduction, mindfulness and positive energy when breathing with the low compared to the high i/e ratio. A lower compared to a higher respiration rate was associated only with an increased score on positive energy. A low i/e ratio was also associated with more power in the high frequency component of heart rate variability, but only for the slow breathing pattern. Our results show that i/e ratio is an important modulator for the autonomic and subjective effects of instructed ventilatory patterns.
Arthrodesis with Intramedular Fixation in Posttraumatic Arthrosis of the Midfoot: A Case Report

We present two middle-aged men with posttraumatic arthrosis of the midfoot. Both patients suffered from severe pain and their foot was unable to bear weight. Both were operated using new fusion bolt 6.5 mm and additional screws. In their case, arthrodesis was mandatory and effective intervention. After surgical treatment both patients were pain free and able to walk without crutches and return to daily work.
Characterizing Variation in Crowd-Sourced Data for Training Neural Language Generators to Produce Stylistically Varied Outputs

One of the biggest challenges of endto-end language generation from meaning representations in dialogue systems is making the outputs more natural and varied. Here we take a large corpus of 50K crowd-sourced utterances in the restaurant domain and develop text analysis methods that systematically characterize types of sentences in the training data. We then automatically label the training data to allow us to conduct two kinds of experiments with a neural generator. First, we test the effect of training the system with different stylistic partitions and quantify the effect of smaller, but more stylistically controlled training data. Second, we propose a method of labeling the style variants during training, and show that we can modify the style of the generated utterances using our stylistic labels. We contrast and compare these methods that can be used with any existing large corpus, showing how they vary in terms of semantic quality and
Towards Ontology Learning from Folksonomies

A folksonomy refers to a collection of user-defined tags with which users describe contents published on the Web. With the flourish of Web 2.0, folksonomies have become an important mean to develop the Semantic Web. Because tags in folksonomies are authored freely, there is a need to understand the structure and semantics of these tags in various applications. In this paper, we propose a learning approach to create an ontology that captures the hierarchical semantic structure of folksonomies. Our experimental results on two different genres of real world data sets show that our method can effectively learn the ontology structure from the folksonomies.
Predicting degree-completion time with data mining

Data mining in academic databases nowadays used for analyzing patterns and gaining new useful knowledge. This paper tries to predict the degree-completion time of bachelor's degree students using data mining technique and algorithms especially C4.5 and naive Bayes classifier algorithm, and measure the algorithms accuracy, precision, and recall percentages for both algorithms also exploring some factors that assume in theory have some impact on the model. The result from given dataset to build the models shows that C4.5 algorithm better than naive Bayes classifier algorithm with 78% accuracy, 85% weighted mean class precision, and 65% weighted mean class recall. This research can be expanded with different data mining algorithms or other related attributes that have some effects to the degree-completion time.
A Survey on Multipath Routing Protocols for QoS Assurances in Real-Time Wireless Multimedia Sensor Networks

The vision of wireless multimedia sensor networks (WMSNs) is to provide real-time multimedia applications using wireless sensors deployed for long-term usage. Quality of service assurances for both best effort data and real-time multimedia applications introduced new challenges in prioritizing multipath routing protocols in WMSNs. Multipath routing approaches with multiple constraints have received considerable research interest. In this paper, a comprehensive survey of both best effort data and real-time multipath routing protocols for WMSNs is presented. Results of a preliminary investigation into design issues affecting the development of strategic multipath routing protocols that support multimedia data in WMSNs are also presented and discussed from the network application perspective.
Non-Intrusive Load Monitoring Using Prior Models of General Appliance Types

Non-intrusive appliance load monitoring is the process of disaggregating a household’s total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances can be iteratively separated from an aggregate load. Unlike existing approaches, our approach does not require training data to be collected by sub-metering individual appliances, nor does it assume complete knowledge of the appliances present in the household. Instead, we propose an approach in which prior models of general appliance types are tuned to specific appliance instances using only signatures extracted from the aggregate load. The tuned appliance models are then used to estimate each appliance’s load, which is subsequently subtracted from the aggregate load. This process is applied iteratively until all appliances for which prior behaviour models are known have been disaggregated. This paper summarises the contribution described in full in .
Risk factors for body dissatisfaction in adolescent girls: a longitudinal investigation.

Because few prospective studies have examined predictors of body dissatisfaction--an established risk factor for eating disorders--the authors tested whether a set of sociocultural, biological, interpersonal, and affective factors predicted increases in body dissatisfaction using longitudinal data from adolescent girls (N = 496). Elevated adiposity, perceived pressure to be thin, thin-ideal internalization, and social support deficits predicted increases in body dissatisfaction, but early menarche, weight-related teasing, and depression did not. There was evidence of 2 distinct pathways to body dissatisfaction--1 involving pressure to be thin and 1 involving adiposity. Results support the contention that certain sociocultural, biological, and interpersonal factors increase the risk for body dissatisfaction, but suggest that other accepted risk factors are not related to this outcome.
A weighted additive fuzzy programming approach for multi-criteria supplier selection

In supply chain management, to build strategic and strong relationships, firms should select best suppliers by applying appropriate method and selection criteria. In this paper, to handle ambiguity and fuzziness in supplier selection problem effectively, a new weighted additive fuzzy programming approach is developed. Firstly, linguistic values expressed as trapezoidal fuzzy numbers are used to assess the weights of the factors. By applying the distances of each factor between Fuzzy Positive Ideal Rating and Fuzzy Negative Ideal Rating, weights are obtained. Then applying suppliers’ constraints, goals and weights of the factors, a fuzzy multi-objective linear model is developed to overcome the selection problem and assign optimum order quantities to each supplier. The proposed model is explained by a numerical example.
Compression of multiple depth maps for IBR

Image-based rendering techniques include those with geometry and those without. Geometric information in the form of a depth map aligned with the image holds a lot of promise for IBR due to the several methods available to capture it. It can improve the quality of generated views using a limited number of views. Compression of light fields or multiple images has attracted a lot of attention in the past. Compression of multiple depth maps of the same scene has not been explored much in the literature. We propose a method for compressing multiple depth maps in this paper using geometric proxy. Different quality of rendering and compression ratio can be achieved by varying different parameters. Experiments show the effectiveness of the compression technique on several model data.
Unsupervised feature selection for multi-cluster data

In many data analysis tasks, one is often confronted with very high dimensional data. Feature selection techniques are designed to find the relevant feature subset of the original features which can facilitate clustering, classification and retrieval. In this paper, we consider the feature selection problem in unsupervised learning scenario, which is particularly difficult due to the absence of class labels that would guide the search for relevant information. The feature selection problem is essentially a combinatorial optimization problem which is computationally expensive. Traditional unsupervised feature selection methods address this issue by selecting the top ranked features based on certain scores computed independently for each feature. These approaches neglect the possible correlation between different features and thus can not produce an optimal feature subset. Inspired from the recent developments on manifold learning and L1-regularized models for subset selection, we propose in this paper a new approach, called Multi-Cluster Feature Selection (MCFS), for unsupervised feature selection. Specifically, we select those features such that the multi-cluster structure of the data can be best preserved. The corresponding optimization problem can be efficiently solved since it only involves a sparse eigen-problem and a L1-regularized least squares problem. Extensive experimental results over various real-life data sets have demonstrated the superiority of the proposed algorithm.