nils-herrmann/clean_scirepeval huggingface dataset:
Assigning personality/identity to a chatting machine for coherent conversation generation

Endowing a chatbot with personality or an identity is quite challenging but critical to deliver more realistic and natural conversations. In this paper, we address the issue of generating responses that are coherent to a pre-specified agent profile. We design a model consisting of three modules: a profile detector to decide whether a post should be responded using the profile and which key should be addressed, a bidirectional decoder to generate responses forward and backward starting from a selected profile value, and a position detector that predicts a word position from which decoding should start given a selected profile value. We show that general conversation data from social media can be used to generate profile-coherent responses. Manual and automatic evaluation shows that our model can deliver more coherent, natural, and diversified responses.
Haploview: analysis and visualization of LD and haplotype maps

Research over the last few years has revealed significant haplotype structure in the human genome. The characterization of these patterns, particularly in the context of medical genetic association studies, is becoming a routine research activity. Haploview is a software package that provides computation of linkage disequilibrium statistics and population haplotype patterns from primary genotype data in a visually appealing and interactive interface.


AVAILABILITY
http://www.broad.mit.edu/mpg/haploview/
What leads developers towards the choice of a JavaScript framework?

The increasing popularity of JavaScript has lead to a variety of frameworks that aim to help developers to address programming tasks. However, the number of JavaScript Frameworks has risen rapidly to thousands and more. It is difficult for practitioners to identify the frameworks that best fit to their needs and to develop new frameworks that fit such needs. Existing research has focused in proposing software metrics for the frameworks, which do not carry a high value to practitioners. While benchmarks, technical reports, and experts’ opinions are available, they suffer the same issue that they do not carry much value. In particular, there is a lack of knowledge regarding the processes and reasons that drive developers towards the choice. ∗Corresponding author Email addresses: amantia.pano2@unibz.it (Amantia Pano), daniel.graziotin@informatik.uni-stuttgart.de (Daniel Graziotin), pekkaa@ntnu.edu (Pekka Abrahamsson) 1 ar X iv :1 60 5. 04 30 3v 1 [ cs .S E ] 1 3 M ay 2 01 6  This paper explores the human aspects of software development behind the decision-making process that leads to a choice of a JavaScript Framework.  We conducted a qualitative interpretive study, following the grounded theory data analysis methodology. We interviewed 18 participants who are decision makers in their companies or entrepreneurs, or are able to motivate the JavaScript Framework decision-making process.  We offer a model of factors that are desirable to be found in a JavaScript Framework and a representation of the decision makers involved in the frameworks selection. The factors are usability (attractiveness, learnability, understandability), cost, efficiency (performance, size), and functionality (automatisation, extensibility, flexibility, isolation, modularity, suitability, updated). These factors are evaluated by a combination of four possible decision makers, which are customer, developer, team, and team leader.  Our model contributes to the body of knowledge related to the decision-making process when selecting a JavaScript framework. As a practical implication, we believe that our model is useful for (1) Web developers and (2) JavaScript framework developers.
A Soft Strain Sensor Based on Ionic and Metal Liquids

A novel soft strain sensor capable of withstanding strains of up to 100% is described. The sensor is made of a hyperelastic silicone elastomer that contains embedded microchannels filled with conductive liquids. This is an effort of improving the previously reported soft sensors that uses a single liquid conductor. The proposed sensor employs a hybrid approach involving two liquid conductors: an ionic solution and an eutectic gallium-indium alloy. This hybrid method reduces the sensitivity to noise that may be caused by variations in electrical resistance of the wire interface and undesired stress applied to signal routing areas. The bridge between these two liquids is made conductive by doping the elastomer locally with nickel nanoparticles. The design, fabrication, and characterization of the sensor are presented.
Airport Gate Assignment: New Model and Implementation

airport operations. In this paper, we study the Airport Gate Assignment Problem (AGAP), propose a new model and implement the model with Optimization Programming language (OPL). With the objective to minimize the number of conflicts of any two adjacent aircrafts assigned to the same gate, we build a mathematical model with logical constraints and the binary constraints, which can provide an efficient evaluation criterion for the Airlines to estimate the current gate assignment. To illustrate the feasibility of the model we construct experiments with the data obtained from Continental Airlines, Houston Gorge Bush Intercontinental Airport IAH, which indicate that our model is both energetic and effective. Moreover, we interpret experimental results, which further demonstrate that our proposed model can provide a powerful tool for airline companies to estimate the efficiency of their current work of gate assignment.
Serverless: beyond the cloud

Serverless computing is widely known as an event-driven cloud execution model. In this model, the client provides the code and the cloud provider manages the life-cycle of the execution environment of that code. The idea is based on reducing the life span of the program to execute functionality in response to an event. Hence, the program's processes are born when an event is triggered and are killed after the event is processed. This model has proved its usefulness in the cloud as it reduced the operational cost and complexity of executing event-driven workloads. In this paper we argue that the serverless model does not have to be limited the to the cloud. We show how the same model can be applied at the micro-level of a single machine. In such model, certain operating system commands are treated as events that trigger a serverless reaction. This reaction consists of deploying and running code only in response to those events. Thus, reducing the attack surface and complexity of managing single machines.
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning

Deep Learning has recently become hugely popular in machine learning for its ability to solve end-to-end learning systems, in which the features and the classifiers are learned simultaneously, providing significant improvements in classification accuracy in the presence of highly-structured and large databases.
 Its success is due to a combination of recent algorithmic breakthroughs, increasingly powerful computers, and access to significant amounts of data.
 Researchers have also considered privacy implications of deep learning. Models are typically trained in a centralized manner with all the data being processed by the same training algorithm. If the data is a collection of users' private data, including habits, personal pictures, geographical positions, interests, and more, the centralized server will have access to sensitive information that could potentially be mishandled. To tackle this problem, collaborative deep learning models have recently been proposed where parties locally train their deep learning structures and only share a subset of the parameters in the attempt to keep their respective training sets private. Parameters can also be obfuscated via differential privacy (DP) to make information extraction even more challenging, as proposed by Shokri and Shmatikov at CCS'15.
 Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper. In particular, we show that a distributed, federated, or decentralized deep learning approach is fundamentally broken and does not protect the training sets of honest participants. The attack we developed exploits the real-time nature of the learning process that allows the adversary to train a Generative Adversarial Network (GAN) that generates prototypical samples of the targeted training set that was meant to be private (the samples generated by the GAN are intended to come from the same distribution as the training data). Interestingly, we show that record-level differential privacy applied to the shared parameters of the model, as suggested in previous work, is ineffective (i.e., record-level DP is not designed to address our attack).
Stacks: an analysis tool set for population genomics.

Massively parallel short-read sequencing technologies, coupled with powerful software platforms, are enabling investigators to analyse tens of thousands of genetic markers. This wealth of data is rapidly expanding and allowing biological questions to be addressed with unprecedented scope and precision. The sizes of the data sets are now posing significant data processing and analysis challenges. Here we describe an extension of the Stacks software package to efficiently use genotype-by-sequencing data for studies of populations of organisms. Stacks now produces core population genomic summary statistics and SNP-by-SNP statistical tests. These statistics can be analysed across a reference genome using a smoothed sliding window. Stacks also now provides several output formats for several commonly used downstream analysis packages. The expanded population genomics functions in Stacks will make it a useful tool to harness the newest generation of massively parallel genotyping data for ecological and evolutionary genetics.
Forest annual carbon cost: a global-scale analysis of autotrophic respiration.

Forest autotrophic respiration (R(a)) plays an important role in the carbon balance of forest ecosystems. However, its drivers at the global scale are not well known. Based on a global forest database, we explore the relationships of annual R(a) with mean annual temperature (MAT) and biotic factors including net primary productivity (NPP), total biomass, stand age, mean tree height, and maximum leaf area index (LAI). The results show that the spatial patterns of forest annual R(a) at the global scale are largely controlled by temperature. R(a) is composed of growth (R(g)) and maintenance respiration (R(m)). We used a modified Arrhenius equation to express the relationship between R(a) and MAT. This relationship was calibrated with our data and shows that a 10 degrees C increase in MAT will result in an increase of annual R(m) by a factor of 1.9-2.5 (Q10). We also found that the fraction of total assimilation (gross primary production, GPP) used in R(a) is lowest in the temperate regions characterized by a MAT of approximately 11 degrees C. Although we could not confirm a relationship between the ratio of R(a) to GPP and age across all forest sites, the R(a) to GPP ratio tends to significantly increase in response to increasing age for sites with MAT between 8 degrees and 12 degrees C. At the plant scale, direct up-scaled R(a) estimates were found to increase as a power function with forest total biomass; however, the coefficient of the power function (0.2) was much smaller than that expected from previous studies (0.75 or 1). At the ecosystem scale, R(a) estimates based on both GPP - NPP and TER - R(h) (total ecosystem respiration - heterotrophic respiration) were not significantly correlated with forest total biomass (P > 0.05) with either a linear or a power function, implying that the previous individual-based metabolic theory may be not suitable for the application at ecosystem scale.
A comprehensive introduction to label noise

In classification, it is often difficult or expensive to obtain completely accurate and reliable labels. Indeed, labels may be polluted by label noise, due to e.g. insufficient information, expert mistakes, and encoding errors. The problem is that errors in training labels that are not properly handled may deteriorate the accuracy of subsequent predictions, among other effects. Many works have been devoted to label noise and this paper provides a concise and comprehensive introduction to this research topic. In particular, it reviews the types of label noise, their consequences and a number of state of the art approaches to deal with label noise.
Towards End-to-End Text Spotting with Convolutional Recurrent Neural Networks

In this work, we jointly address the problem of text detection and recognition in natural scene images based on convolutional recurrent neural networks. We propose a unified network that simultaneously localizes and recognizes text with a single forward pass, avoiding intermediate processes, such as image cropping, feature re-calculation, word separation, and character grouping. In contrast to existing approaches that consider text detection and recognition as two distinct tasks and tackle them one by one, the proposed framework settles these two tasks concurrently. The whole framework can be trained end-to-end, requiring only images, ground-truth bounding boxes and text labels. The convolutional features are calculated only once and shared by both detection and recognition, which saves processing time. Through multi-task training, the learned features become more informative and improves the overall performance. Our proposed method has achieved competitive performance on several benchmark datasets.
Edge and line oriented contour detection: State of the art

a r t i c l e i n f o We present an overview of various edge and line oriented approaches to contour detection that have been proposed in the last two decades. By edge and line oriented we mean methods that do not rely on segmentation. Distinction is made between edges and contours. Contour detectors are divided in local and global operators. The former are mainly based on differential analysis, statistical approaches, phase congruency, rank order filters, and combinations thereof. The latter include computation of contour saliency, perceptual grouping, relaxation labeling and active contours. Important aspects are covered, such as preprocessing aimed to suppress texture and noise, multiresolution techniques, connections between computational models and properties of the human visual system, and use of shape priors. An overview of procedures and metrics for quantitative performance evaluation is also presented. Our main conclusion is that contour detection has reached high degree of sophistication, taking into account multimodal contour definition (by luminance, color or texture changes), mechanisms for reducing the contour masking influence of noise and texture, perceptual grouping, multiscale aspects and high-level vision information.
5.2 Third Generation Multimedia Applications

11 generating contextual information which can be used to make applications which are adaptive and far more exible. The third generation of multimedia applications will not only exploit improvements in the network technology, but more importantly will take advantage of any contextual or location information made available. As mentioned earlier, this information will come from agents interpreting streams, as well as from resource and active location databases. We have experimented with these applications in both Pandora and Medusa, although the integration of location system services has in both cases been retrospective. We consider provision of such information to be a necessary underlying component of any future systems we might build-it should be intrinsic to the system, and available at all levels within it. The Teleporting System Bennett94] is an example of an application that may be able to exploit third generation networks. It ooers users the ability to interact with existing computer applications from any suitably equipped networked terminal. The system presents a means by which we can experiment with applications that adapt and reconngure based on any contextual and location information available. At present, multimedia applications do not function within the teleport-ing system, but the system's integration with third generation networks may make this possible. A subject of ongoing research is the provision of systems support, at all levels, for multimedia applications which adapt to the kind of network they are reign routed on, the kind of peripherals they are using, as well as the environment within which they are operating. 6  We recognise two important details which will innuence the further development and wider acceptance of the technologies outlined above. These are cost and scalability. While networks improve and begin to support the underlying QoS requirements of future multimedia applications, each of these details must be considered. The cost aspect involves charging for network use, which will have to change signiicantly. There will be a blur between the notions of long and short distance, prompted particularly due to mobile networks which force a relaxation in this distinction. There is also the cost of a network connection itself, which must not limit us in our choice of networked peripherals. The wide-area propagation of these third generation networks will see their use on many diierent scales. It will extend from the household level, with perhaps even with an ATM doorbell, through to the wide area where we will see …
Experimental Studies of Automotive Disc Brake Noise and Vibration : A Review

In the last few decades, there have been extensive studies on analysis and investigation of disc brake vibrations done by many researchers around the world on the possibility of eliminating brake vibration to improve vehicle users’ comfort. Despite these efforts, still no general solution exists. Therefore, it is one of the most important issues that require a detailed and in-depth study for investigation brake noise and vibration. Research on brake noise and vibration has been conducted using theoretical, experimental and numerical approaches. Experimental methods can provide real measured data and they are trustworthy. This paper aims to focus on experimental investigations and summarized recent studies on automotive disc brake noise and vibration for measuring instable frequencies and mode shapes for the system in vibration and to verify possible numerical solutions. Finally, the critical areas where further research directions are needed for reducing vibration of disc brake are suggested in the conclusions.
Hybrid Mode Wideband Patch Antenna Loaded With a Planar Metamaterial Unit Cell

A wideband patch antenna loaded with a planar metamaterial unit cell is proposed. The metamaterial unit cell is composed of an interdigital capacitor and a complementary split-ring resonator (CSRR) slot. A dispersion analysis of the metamaterial unit cell reveals that an increase in series capacitance can decrease the half-wavelength resonance frequency, thus reducing the electrical size of the proposed antenna. In addition, circulating current distributions around the CSRR slot with increased interdigital finger length bring about the TM01 mode radiation, while the normal radiation mode is the TM10 mode. Furthermore, the TM01 mode can be combined with the TM10 mode without a pattern distortion. The hybridization of the two modes yields a wideband property (6.8%) and a unique radiation pattern that is comparable with two independent dipole antennas positioned orthogonally. Also, the proposed antenna achieves high efficiency (96%) and reasonable gain (3.85 dBi), even though the electrical size of the antenna is only 0.24λ0×0.24λ0×0.02λ0.
A hybrid lidar-based indoor navigation system enhanced by ceiling visual codes for mobile robots

Localization and navigation are fundamental issues to autonomous mobile robotics. In the case of the environmental map has been built, the traditional two-dimensional (2D) lidar localization and navigation system can't be matched to the initial position of the robot in dynamic environment and will be unreliable when kidnapping occurs. Moreover, it relies on high-cost lidar for high accuracy and long range. In view of this, the paper presents a low cost navigation system based on a low cost lidar and a cheap webcam. In this approach, 2D-codes are attached to the ceiling, to provide reference points to aid the indoor robot localization. The mobile robot is equipped with webcam pointing to the ceiling to identify 2D-codes. On the other hand, a low-cost 2D laser scanner is applied to build a map in unknown environment and detect obstacles. Adaptive Monte Carlo Localization (AMCL) is implements for lidar positioning, A* and Dynamic Window Approach (DWA) are applied in path planning based on a 2D grid map. The error analysis and experiments has validated the proposed method.
Human-robot proxemics: Physical and psychological distancing in human-robot interaction

To seamlessly integrate into the human physical and social environment, robots must display appropriate proxemic behavior - that is, follow societal norms in establishing their physical and psychological distancing with people. Social-scientific theories suggest competing models of human proxemic behavior, but all conclude that individuals' proxemic behavior is shaped by the proxemic behavior of others and the individual's psychological closeness to them. The present study explores whether these models can also explain how people physically and psychologically distance themselves from robots and suggest guidelines for future design of proxemic behaviors for robots. In a controlled laboratory experiment, participants interacted with Wakamaru to perform two tasks that examined physical and psychological distancing of the participants. We manipulated the likeability (likeable/dislikeable) and gaze behavior (mutual gaze/averted gaze) of the robot. Our results on physical distancing showed that participants who disliked the robot compensated for the increase in the robot's gaze by maintaining a greater physical distance from the robot, while participants who liked the robot did not differ in their distancing from the robot across gaze conditions. The results on psychological distancing suggest that those who disliked the robot also disclosed less to the robot. Our results offer guidelines for the design of appropriate proxemic behaviors for robots so as to facilitate effective human-robot interaction.
Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery.

Neuroanatomy has entered a new era, culminating in the search for the connectome, otherwise known as the brain's wiring diagram. While this approach has led to landmark discoveries in neuroscience, potential neurosurgical applications and collaborations have been lagging. In this article, the authors describe the ideas and concepts behind the connectome and its analysis with graph theory. Following this they then describe how to form a connectome using resting state functional MRI data as an example. Next they highlight selected insights into healthy brain function that have been derived from connectome analysis and illustrate how studies into normal development, cognitive function, and the effects of synthetic lesioning can be relevant to neurosurgery. Finally, they provide a précis of early applications of the connectome and related techniques to traumatic brain injury, functional neurosurgery, and neurooncology.