nils-herrmann/clean_scirepeval huggingface dataset:
The development and maintenance of mental toughness: perceptions of elite performers.

Seven participants from a previous study (Jones, Hanton, & Connaughton, 2002) agreed to be interviewed about the development of mental toughness. We also aimed to determine whether mental toughness requires maintenance. Semistructured interviews were conducted to elicit the participants' perceptions of how mental toughness is cultivated and retained. Findings indicated that the development of mental toughness is a long-term process that encompasses a multitude of underlying mechanisms that operate in a combined, rather than independent, fashion. In general, these perceived underlying mechanisms related to many features associated with a motivational climate (e.g. enjoyment, mastery), various individuals (i.e. coaches, peers, parents, grandparents, siblings, senior athletes, sport psychologists, team-mates), experiences in and outside sport, psychological skills and strategies, and an insatiable desire and internalized motives to succeed. It was also reported that once mental toughness had been developed, three perceived underlying mechanisms were required to maintain this construct: a desire and motivation to succeed that was insatiable and internalized, a support network that included sporting and non-sporting personnel, and effective use of basic and advanced psychological skills. Practical implications and future avenues of research are discussed.
Human sensorimotor learning: adaptation, skill, and beyond

Recent studies of upper limb movements have provided insights into the computations, mechanisms, and taxonomy of human sensorimotor learning. Motor tasks differ with respect to how they weight different learning processes. These include adaptation, an internal-model based process that reduces sensory-prediction errors in order to return performance to pre-perturbation levels, use-dependent plasticity, and operant reinforcement. Visuomotor rotation and force-field tasks impose systematic errors and thereby emphasize adaptation. In skill learning tasks, which for the most part do not involve a perturbation, improved performance is manifest as reduced motor variability and probably depends less on adaptation and more on success-based exploration. Explicit awareness and declarative memory contribute, to varying degrees, to motor learning. The modularity of motor learning processes maps, at least to some extent, onto distinct brain structures.
Ensembles of Generative Adversarial Networks

Ensembles are a popular way to improve results of discrimina tive CNNs. The combination of several networks trained starting from diff erent initializations improves results significantly. In this paper we investigate t he usage of ensembles of GANs. The specific nature of GANs opens up several new ways t o construct ensembles. The first one is based on the fact that in the minima x game which is played to optimize the GAN objective the generator network k eeps on changing even after the network can be considered optimal. As such ens embles of GANs can be constructed based on the same network initialization but just taking models which have different amount of iterations. These so-cal led self ensembles are much faster to train than traditional ensembles. The second method, called cascade GANs, redirects part of the training data which is badly modeled by the first GAN to another GAN. In experiments on the CIFAR10 dataset we s how that ensembles of GANs obtain model probability distributions whi ch better model the data distribution. In addition, we show that these improved results can be obtained at little additional computational cost.
Predicting popular messages in Twitter

Social network services have become a viable source of information for users. In Twitter, information deemed important by the community propagates through retweets. Studying the characteristics of such popular messages is important for a number of tasks, such as breaking news detection, personalized message recommendation, viral marketing and others. This paper investigates the problem of predicting the popularity of messages as measured by the number of future retweets and sheds some light on what kinds of factors influence information propagation in Twitter. We formulate the task into a classification problem and study two of its variants by investigating a wide spectrum of features based on the content of the messages, temporal information, metadata of messages and users, as well as structural properties of the users' social graph on a large scale dataset. We show that our method can successfully predict messages which will attract thousands of retweets with good performance.
M3Express: A low-cost independently-mobile reconfigurable modular robot

This paper presents M3Express (Modular-Mobile-Multirobot), a new design for a low-cost modular robot. The robot is self-mobile, with three independently driven wheels that also serve as connectors. The new connectors can be automatically operated, and are based on stationary magnets coupled to mechanically actuated ferromagnetic yoke pieces. Extensive use is made of plastic castings, laser cut plastic sheets, and low-cost motors and electronic components. Modules interface with a host PC via Bluetooth® radio. An off-board camera, along with a set of modules and a control PC form a convenient, low-cost system for rapidly developing and testing control algorithms for modular reconfigurable robots. Experimental results demonstrate mechanical docking, connector strength, and accuracy of dead reckoning locomotion.
Augmented Reality : Applications , Challenges and Future Trends

Augmented reality, in which virtual content is seamlessly integrated with displays of real-world scenes, is a growing area of interactive design. With the rise of personal mobile devices capable of producing interesting augmented reality environments, the vast potential of AR has begun to be explored. This paper surveys the current state-of-the-art in augmented reality. It describes work performed in different application domains and explains the exiting issues encountered when building augmented reality applications considering the ergonomic and technical limitations of mobile devices. Future directions and areas requiring further research are introduced and discussed.
Scientific workflow design for mere mortals

Recent years have seen a dramatic increase in research and development of scientific workflow systems. These systems promise to make scientists more productive by automating data-driven and computeintensive analyses. Despite many early achievements, the long-term success of scientific workflow technology critically depends on making these systems useable by ‘‘mere mortals’’, i.e., scientists who have a very good idea of the analysis methods they wish to assemble, but who are neither software developers nor scripting-language experts. With these users in mind, we identify a set of desiderata for scientific workflow systems crucial for enabling scientists to model and design the workflows they wish to automate themselves. As a first step towards meeting these requirements, we also show how the collection-oriented modeling and design (comad) approach for scientific workflows, implemented within the Kepler system, can help provide these critical, design-oriented capabilities to scientists.
Low self-esteem is related to aggression, antisocial behavior, and delinquency.

The present research explored the controversial link between global self-esteem and externalizing problems such as aggression, antisocial behavior, and delinquency. In three studies, we found a robust relation between low self-esteem and externalizing problems. This relation held for measures of self-esteem and externalizing problems based on self-report, teachers' ratings, and parents' ratings, and for participants from different nationalities (United States and New Zealand) and age groups (adolescents and college students). Moreover, this relation held both cross-sectionally and longitudinally and after controlling for potential confounding variables such as supportive parenting, parent-child and peer relationships, achievement-test scores, socioeconomic status, and IQ. In addition, the effect of self-esteem on aggression was independent of narcissism, an important finding given recent claims that individuals who are narcissistic, not low in self-esteem, are aggressive. Discussion focuses on clarifying the relations among self-esteem, narcissism, and externalizing problems.
Multi-fingered telemanipulation - mapping of a human hand to a three finger gripper

If a teleoperation scenario foresees complex and fine manipulation tasks a multi-fingered telemanipulation system is required. In this paper a multi-fingered telemanipulation system is presented, whereby the human hand controls a three-finger robotic gripper and force feedback is provided by using an exoskeleton. Since the human hand and robotic grippers have different kinematic structures, appropriate mappings for forces and positions are applied. A point-to-point position mapping algorithm as well as a simple force mapping algorithm are presented and evaluated in a real experimental setup.
A new method for design and implementation of intelligent traffic control system based on fuzzy logic using FPGA

In order to manage the traffic at the intersection, the traffic lights are often used. These lights are turned on and off at the predetermined time. Intelligent traffic control systems are designed to dynamically treat the problem of traffic and reduce traffic, pollution and transit time of vehicles at the intersection. In this paper we present a design of intelligent traffic control based on fuzzy logic. The input parameters for intelligent controller are selected with the various modes of intersections to be a true simulation of the intersection environment. In order to facilitate the hardware implementation and increasing the computational speed decision algorithm state machine in this system is written in Verilog language and has capability to implement in the Field Programmable Gate Array (FPGA). The simulation results show that the proposed traffic signal controller system (that has been) designed with fuzzy logic has better performance than other designed systems.
Novel low cost compact phased array antenna for millimeter-wave 3D beam scanning applications

A novel 3D scanning antenna is presented for 5G mobile wireless communication applications. The proposed antenna employs 8 phase shifters for a 16-element phased array. It provides a 3D scanning window of Phi from 165° to 200° and from 245 ° to 270 ° while Theta can go up to 20 °. The size of the antenna including the feeding network is 2.5cm × 2.5cm. The Antenna gain is 16.5dB at 30GHz, and the gain variation is less than 1dB over the scanning window.
ParaDrop: a multi-tenant platform for dynamically installed third party services on home gateways

The landscape of computing capabilities within the home has seen a recent shift from persistent desktops to mobile platforms, which has led to the use of the cloud as the primary computing platform implemented by developers today. Cloud computing platforms, such as Amazon EC2 and Google App Engine, are popular for many reasons including their reliable, always on, and robust nature. The capabilities that centralized computing platforms provide are inherent to their implementation, and unmatched by previous platforms (e.g., Desktop applications). Thus, third-party developers have come to rely on cloud computing platforms to provide high quality services to their end-users.
Fast Homomorphic Evaluation of Deep Discretized Neural Networks

The rise of machine learning as a service multiplies scenarios where one faces a privacy dilemma: either sensitive user data must be revealed to the entity that evaluates the cognitive model (e.g., in the Cloud), or the model itself must be revealed to the user so that the evaluation can take place locally. Fully Homomorphic Encryption (FHE) offers an elegant way to reconcile these conflicting interests in the Cloudbased scenario and also preserve non-interactivity. However, due to the inefficiency of existing FHE schemes, most applications prefer to use Somewhat Homomorphic Encryption (SHE), where the complexity of the computation to be performed has to be known in advance, and the efficiency of the scheme depends on this global complexity. In this paper, we present a new framework for homomorphic evaluation of neural networks, that we call FHE–DiNN, whose complexity is strictly linear in the depth of the network and whose parameters can be set beforehand. To obtain this scale-invariance property, we rely heavily on the bootstrapping procedure. We refine the recent FHE construction by Chillotti et al. (ASIACRYPT 2016) in order to increase the message space and apply the sign function (that we use to activate the neurons in the network) during the bootstrapping. We derive some empirical results, using TFHE library as a starting point, and classify encrypted images from the MNIST dataset with more than 96% accuracy in less than 1.7 seconds. Finally, as a side contribution, we analyze and introduce some variations to the bootstrapping technique of Chillotti et al. that offer an improvement in efficiency at the cost of increasing the storage requirements.
Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks

Malignant melanoma has one of the most rapidly increasing incidences in the world and has a considerable mortality rate. Early diagnosis is particularly important since melanoma can be cured with prompt excision. Dermoscopy images play an important role in the non-invasive early detection of melanoma . However, melanoma detection using human vision alone can be subjective, inaccurate and poorly reproducible even among experienced dermatologists. This is attributed to the challenges in interpreting images with diverse characteristics including lesions of varying sizes and shapes, lesions that may have fuzzy boundaries, different skin colors and the presence of hair . Therefore, the automatic analysis of dermoscopy images is a valuable aid for clinical decision making and for image-based diagnosis to identify diseases such as melanoma [1-4].
JDeodorant: Identification and Removal of Type-Checking Bad Smells

In this demonstration, we present an Eclipse plug-in that automatically identifies type-checking bad smells in Java source code, and resolves them by applying the "replace conditional with polymorphism" or "replace type code with state/strategy " refactorings. To the best of our knowledge there is a lack of tools that identify type-checking bad smells. Moreover, none of the state-of-the-art IDEs support the refactorings that resolve such kind of bad smells.
Bottom-Up and Top-Down Attention for Image Captioning and VQA

Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and topdown attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the task, improving the best published result in terms of CIDEr score from 114.7 to 117.9 and BLEU-4 from 35.2 to 36.9. Demonstrating the broad applicability of the method, applying the same approach to VQA we obtain a new state-of-the-art on the VQA v2.0 dataset with 70.2% overall accuracy.
BlogPulse: Automated Trend Discovery for Weblogs

Over the past few years, weblogs have emerged as a new communication and publication medium on the Internet. In this paper, we describe the application of data mining, information extraction and NLP algorithms for discovering trends across our subset of approximately 100,000 weblogs. We publish daily lists of key persons, key phrases, and key paragraphs to a public web site, BlogPulse.com. In addition, we maintain a searchable index of weblog entries. On top of the search index, we have implemented trend search, which graphs the normalized trend line over time for a search query and provides a way to estimate the relative buzz of word of mouth for given topics over time.
Object detection in high resolution satellite images

Given the abundance of various types of satellite imagery of almost any region on the globe we are faced with a challenge of interpreting this data to extract useful information. In this thesis we look at a way of automating the detection of ships to track maritime traffic in a desired port or region. We propose a machine learning approach using deep neural networks and explore the development, implementation and evaluation of such a pipeline, as well as methods and dataset used to train the neural network classifier. We also take a look at a graphical approach to computation using TensorFlow [13] which offers easy massive parallelization and deployment to cloud. The final result is an algorithm which is capable of receiving images from various providers at various resolutions and outputs a binary pixelwise mask over all detected ships.
The effects of divided attention on encoding and retrieval processes in human memory.

The authors examined the effects of divided attention (DA) at encoding and retrieval in free recall, cued recall, and recognition memory in 4 experiments. Lists of words or word pairs were presented auditorily and recalled orally; the secondary task was a visual continuous reaction-time (RT) task with manual responses. At encoding, DA was associated with large reductions in memory performance, but small increases in RT; trade-offs between memory and RT were under conscious control. In contrast, DA at retrieval resulted in small or no reductions in memory, but in comparatively larger increases in RT, especially in free recall. Memory performance was sensitive to changes in task emphasis at encoding but not at retrieval. The results are discussed in terms of controlled and automatic processes and speculatively linked to underlying neuropsychological mechanisms.
Marker-less Tracking for AR: A Learning-Based Approach

Estimating the pose of a camera (virtual or real) in which some augmentation takes place is one of the most important parts of an augmented reality (AR) system. Availability of powerful processors and fast frame grabbers have made vision-based trackers commonly used due to their accuracy as well as flexibility and ease of use. Current vision-based trackers are based on tracking of markers. The use of markers increases robustness and reduces computational requirements. However, their use can be very complicated, as they require certain maintenance. Direct use of scene features for tracking, therefore, is desirable. To this end, we describe a general system that tracks the position and orientation of a camera observing a scene without any visual markers. Our method is based on a two-stage process. In the first stage, a set of features is learned with the help of an external tracking system while in action. The second stage uses these learned features for camera tracking when the system in the first stage decides that it is possible to do so. The system is very general so that it can employ any available feature tracking and pose estimation system for learning and tracking. We experimentally demonstrate the viability of the method in real-life examples.
SegEM: Efficient Image Analysis for High-Resolution Connectomics

Progress in electron microscopy-based high-resolution connectomics is limited by data analysis throughput. Here, we present SegEM, a toolset for efficient semi-automated analysis of large-scale fully stained 3D-EM datasets for the reconstruction of neuronal circuits. By combining skeleton reconstructions of neurons with automated volume segmentations, SegEM allows the reconstruction of neuronal circuits at a work hour consumption rate of about 100-fold less than manual analysis and about 10-fold less than existing segmentation tools. SegEM provides a robust classifier selection procedure for finding the best automated image classifier for different types of nerve tissue. We applied these methods to a volume of 44 × 60 × 141 μm(3) SBEM data from mouse retina and a volume of 93 × 60 × 93 μm(3) from mouse cortex, and performed exemplary synaptic circuit reconstruction. SegEM resolves the tradeoff between synapse detection and semi-automated reconstruction performance in high-resolution connectomics and makes efficient circuit reconstruction in fully-stained EM datasets a ready-to-use technique for neuroscience.
Salt-and-pepper noise detection and reduction using fuzzy switching median filter

This paper presents a new fuzzy switching median (FSM) filter employing fuzzy techniques in image processing. The proposed filter is able to remove salt-and-pepper noise in digital images while preserving image details and textures very well. By incorporating fuzzy reasoning in correcting the detected noisy pixel, the low complexity FSM filter is able to outperform some well known existing salt-and-pepper noise fuzzy and classical filters.
Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data

Recent work in learning vector-space embeddings for multi-relational data has focused on combining relational information derived from knowledge bases with distributional information derived from large text corpora. We propose a simple approach that leverages the descriptions of entities or phrases available in lexical resources, in conjunction with distributional semantics, in order to derive a better initialization for training relational models. Applying this initialization to the TransE model results in significant new stateof-the-art performances on the WordNet dataset, decreasing the mean rank from the previous best of 212 to 51. It also results in faster convergence of the entity representations. We find that there is a tradeoff between improving the mean rank and the hits@10 with this approach. This illustrates that much remains to be understood regarding performance improvements in relational models.
Whoo.ly: facilitating information seeking for hyperlocal communities using social media

Social media systems promise powerful opportunities for people to connect to timely, relevant information at the hyper local level. Yet, finding the meaningful signal in noisy social media streams can be quite daunting to users. In this paper, we present and evaluate Whoo.ly, a web service that provides neighborhood-specific information based on Twitter posts that were automatically inferred to be hyperlocal. Whoo.ly automatically extracts and summarizes hyperlocal information about events, topics, people, and places from these Twitter posts. We provide an overview of our design goals with Whoo.ly and describe the system including the user interface and our unique event detection and summarization algorithms. We tested the usefulness of the system as a tool for finding neighborhood information through a comprehensive user study. The outcome demonstrated that most participants found Whoo.ly easier to use than Twitter and they would prefer it as a tool for exploring their neighborhoods.