nils-herrmann/clean_scirepeval huggingface dataset:
Supervised and Unsupervised Transfer Learning for Question Answering

Although transfer learning has been shown to be successful for tasks like object and speech recognition, its applicability to question answering (QA) has yet to be well-studied. In this paper, we conduct extensive experiments to investigate the transferability of knowledge learned from a source QA dataset to a target dataset using two QA models. The performance of both models on a TOEFL listening comprehension test (Tseng et al., 2016) and MCTest (Richardson et al., 2013) is significantly improved via a simple transfer learning technique from MovieQA (Tapaswi et al., 2016). In particular, one of the models achieves the state-of-the-art on all target datasets; for the TOEFL listening comprehension test, it outperforms the previous best model by 7%. Finally, we show that transfer learning is helpful even in unsupervised scenarios when correct answers for target QA dataset examples are not available.
Pursuing Pleasure or Virtue : The Differential and Overlapping Well-Being Benefits of Hedonic and Eudaimonic Motives

Hedonia (seeking pleasure and comfort) and eudaimonia (seeking to use and develop the best in oneself) are often seen as opposing pursuits, yet each may contribute to well-being in different ways. We conducted four studies (two correlational, one experience-sampling, and one intervention study) to determine outcomes associated with activities motivated by hedonic and eudaimonic aims. Overall, results indicated that: between persons (at the trait level) and within persons (at the momentary state level), hedonic pursuits related more to positive affect and carefreeness, while eudaimonic pursuits related more to meaning; between persons, eudaimonia related more to elevating experience (awe, inspiration, and sense of connection with a greater whole); within persons, hedonia related more negatively to negative affect; between and within persons, both pursuits related equally to vitality; and both pursuits showed some links with life satisfaction, though hedonia’s links were more frequent. People whose lives were high in both eudaimonia and hedonia had: higher degrees of most well-being variables than people whose lives were low in both pursuits (but did not differ in negative affect or carefreeness); higher positive affect and carefreeness than predominantly eudaimonic individuals; and higher meaning, elevating experience, and vitality than predominantly hedonic individuals. In the intervention study, hedonia produced more well-being benefits at short-term follow-up, while eudaimonia produced more at 3-month follow-up. The findings show that hedonia and eudaimonia occupy both overlapping and distinct niches within a complete picture of wellbeing, and their combination may be associated with the greatest well-being.
Modular meta-learning

Many prediction problems, such as those that arise in the context of robotics, have a simplifying underlying structure that, if known, could accelerate learning. In this paper, we present a strategy for learning a set of neural network modules that can be combined in different ways. We train different modular structures on a set of related tasks and generalize to new tasks by composing the learned modules in new ways. By reusing modules to generalize we achieve combinatorial generalization, akin to the ”infinite use of finite means” displayed in language. Finally, we show this improves performance in two robotics-related problems.
Credit Card Fraud Detection using Big Data Analytics: Use of PSOAANN based One-Class Classification

Banking and financial industries are facing severe challenges in the form of fraudulent transactions. Credit card fraud is one example of them. In order to detect credit card fraud, we employed one-class classification approach in big data paradigm. We implemented a hybrid architecture of Particle Swarm Optimization and Auto-Associative Neural Network for one-class classification in Spark computational framework. In this paper, we implemented parallelization of the auto-associative neural network in the hybrid architecture.
Teaching tools, teachers’ rules: exploring the impact of teaching styles on young children’s programming knowledge in ScratchJr

Computer programming tools for young children are being created and used in early childhood classrooms more than ever. However, little is known about the relationship between a teacher’s unique instructional style and their students’ ability to explore and retain programming content. In this mixed-methods study, quantitative and qualitative data were collected from N = 6 teachers and N = 222 Kindergarten through second grade students at six schools across the United States. These teachers and students participated in an investigation of the relationship between teaching styles and student learning outcomes. All participants engaged in a minimum of two lessons and a maximum of seven lessons using the ScratchJr programming environment to introduce coding. Teachers reported on their classroom structure, lesson plan, teaching style and comfort with technology. They also administered ScratchJr Solve It assessments to capture various aspects of students’ programming comprehension, which were analyzed for trends in learning outcomes. Results from this descriptive, exploratory study show that all students were successful in attaining foundational ScratchJr programming comprehension. Statistically significant findings revealed higher programming achievement in students whose educators demonstrated flexibility in lesson planning, responsiveness to student needs, technological content expertise, and concern for developing students’ independent thinking. Implications for research in the development of computational thinking strategies are discussed, as well as & Amanda Strawhacker  Melissa Lee leemel@iu.edu Marina Umaschi Bers marina.bers@tufts.edu 1 DevTech Research Group, Eliot Pearson Department of Child Study and Human Development, Tufts University, 105 College Ave., Medford, MA 02155, USA 2 Present Address: Department of Curriculum Studies, Indiana University School of Education, 201 N. Rose Avenue, Bloomington, IN 47405-1006, USA 123  Educ DOI 10.1007/s10798-017-9400-9
Deep Reinforcement Learning: A Brief Survey

Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higherlevel understanding of the visual world. Currently, deep learning is enabling reinforcement learning (RL) to scale to problems that were previously intractable, such as learning to play video games directly from pixels. DRL algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of RL, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep RL, including the deep Q-network (DQN), trust region policy optimization (TRPO), and asynchronous advantage actor critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via RL. To conclude, we describe several current areas of research within the field.
Instagram photos reveal predictive markers of depression

Using Instagram data from 166 individuals, we applied machine learning tools to successfully identify markers of depression. Statistical features were computationally extracted from 43,950 participant Instagram photos, using color analysis, metadata components, and algorithmic face detection. Resulting models outperformed general practitioners’ average unassisted diagnostic success rate for depression. These results held even when the analysis was restricted to posts made before depressed individuals were first diagnosed. Human ratings of photo attributes (happy, sad, etc.) were weaker predictors of depression, and were uncorrelated with computationally-generated features. These results suggest new avenues for early screening and detection of mental illness.
Agricultural Aid for Mango cutting (AAM)

Mango cultivation methods being adopted currently are ineffective and low productive despite consuming huge man power. Advancements in robust unmanned aerial vehicles (UAV's), high speed image processing algorithms and machine vision techniques, reinforce the possibility of transforming agricultural scenario to modernity within prevailing time and energy constraints. Present paper introduces Agricultural Aid for Mango cutting (AAM), an Agribot that could be employed for precision mango farming. It is a quadcopter empowered with vision and cutter systems complemented with necessary ancillaries. It could hover around the trees, detect the ripe mangoes, cut and collect them. Paper also sheds light on the available Agribots that have mostly been limited to the research labs. AAM robot is the first of its kind that once implemented could pave way to the next generation Agribots capable of increasing the agricultural productivity and justify the existence of intelligent machines.
MOBILE LANGUAGE LEARNING : MORE THAN JUST “ THE PLATFORM ”

Mobile-assisted language learning (MALL) is attracting a great deal of attention at present (Stockwell & Sotillo, 2011). However, it is important that teacher and researcher exploitation of technological developments be guided by more than just enthusiasm. Language Learning & Technology's commitment to empirical studies is admirable but, for conclusions based on empirical research to be valid, it is important to be clear about exactly what any data being gathered pertains to. In many ways, Stockwell (2010) presents a solid piece of research on the effects of the mobile phone platform on vocabulary activities; however, it could be argued that efforts made to ensure the comparability of the data gathered resulted in the data gathered having little relevance to genuine MALL activities. To some extent, this can be explained as a consequence of the speed of technological innovation in this area, but in other respects, it suggests that more care needs to be taken to develop a research framework within which the platform is not artificially separated from the learning activity. If such a framework can be developed, the scope of findings would have considerably greater validity and represent a far greater contribution to an empirical account of MALL. Computer assisted language learning (CALL) has been around since the early 1960s, and it has generally been technology-driven, as opposed to driving the technology (Beatty, 2003). Indeed, early CALL programs can be seen to have had their roots in the audio-lingual method, or else were often little more than alternative platforms for the delivery of what could just have easily been pen and paper activities: reviewing the first 30 years of CALL, Fox declares that ―the essential conservatism and unimaginativeness of many of the programs is depressing‖ (as cited in ibid., p. 2). However, with the rise of the Internet, CALL began to step out of the language lab and into the world. Hence, recent research on CALL has often focused on issues of learner autonomy, and in particular, distance-learning: Internet-enabled CALL is seen as an opportunity to engage language learners in communicative tasks outside the classroom. In the last decade, CALL literature has begun to address the potentials of MALL: ―Mobile-assisted language learning,‖ or ―M-learning‖ (Godwin-Jones, 2008; 2011). MALL has opened new directions in CALL, as the flexibility offered to users has the potential to greatly exceed that of non-mobile CALL. However, the pace of technological innovation can have the effect …
Extracting More Concurrency from Distributed Transactions

Distributed storage systems run transactions across machines to ensure serializability. Traditional protocols for distributed transactions are based on two-phase locking (2PL) or optimistic concurrency control (OCC). 2PL serializes transactions as soon as they conflict and OCC resorts to aborts, leaving many opportunities for concurrency on the table. This paper presents ROCOCO, a novel concurrency control protocol for distributed transactions that outperforms 2PL and OCC by allowing more concurrency. ROCOCO executes a transaction as a collection of atomic pieces, each of which commonly involves only a single server. Servers first track dependencies between concurrent transactions without actually executing them. At commit time, a transaction’s dependency information is sent to all servers so they can re-order conflicting pieces and execute them in a serializable order. We compare ROCOCO to OCC and 2PL using a scaled TPC-C benchmark. ROCOCO outperforms 2PL and OCC in workloads with varying degrees of contention. When the contention is high, ROCOCO’s throughput is 130% and 347% higher than that of 2PL and OCC.
A review on deep learning for recommender systems: challenges and remedies

Recommender systems are effective tools of information filtering that are prevalent due to increasing access to the Internet, personalization trends, and changing habits of computer users. Although existing recommender systems are successful in producing decent recommendations, they still suffer from challenges such as accuracy, scalability, and cold-start. In the last few years, deep learning, the state-of-the-art machine learning technique utilized in many complex tasks, has been employed in recommender systems to improve the quality of recommendations. In this study, we provide a comprehensive review of deep learning-based recommendation approaches to enlighten and guide newbie researchers interested in the subject. We analyze compiled studies within four dimensions which are deep learning models utilized in recommender systems, remedies for the challenges of recommender systems, awareness and prevalence over recommendation domains, and the purposive properties. We also provide a comprehensive quantitative assessment of publications in the field and conclude by discussing gained insights and possible future work on the subject.
The Role of Energy Storage in Development of Smart Grids

The adoption of Smart Grid devices throughout utility networks will effect tremendous change in grid operations and usage of electricity over the next two decades. The changes in ways to control loads, coupled with increased penetration of renewable energy sources, offer a new set of challenges in balancing consumption and generation. Increased deployment of energy storage devices in the distribution grid will help make this process happen more effectively and improve system performance. This paper addresses the new types of storage being utilized for grid support and the ways they are integrated into the grid.
Improving Semi-Supervised Learning with Auxiliary Deep Generative Models

Deep generative models based upon continuous variational distributions parameterized by deep networks give state-of-the-art performance. In this paper we propose a framework for extending the latent representation with extra auxiliary variables in order to make the variational distribution more expressive for semi-supervised learning. By utilizing the stochasticity of the auxiliary variable we demonstrate how to train discriminative classifiers resulting in state-of-the-art performance within semi-supervised learning exemplified by an 0.96% error on MNIST using 100 labeled data points. Furthermore we observe empirically that using auxiliary variables increases convergence speed suggesting that less expressive variational distributions, not only lead to looser bounds but also slower model training.
A Pencil-and-Paper Algorithm for Solving Sudoku Puzzles

T he puzzle Sudoku has become the passion of many people the world over in the past few years. The interesting fact about Sudoku is that it is a trivial puzzle to solve. The reason it is trivial to solve is that an algorithm exists for Sudoku solutions. The algorithm is a tree-based search algorithm based on backtracking in a tree until a solution is found. If all a person needs to do is sit down at their personal computer, punch in the numbers given in the puzzle, and then watch a computer program compute the solution, we can reasonably ask why a person would bother to struggle to solve Sudoku puzzles. The reason is that people enjoy struggling with pencil and paper to work out Sudoku solutions. Herzberg and Murty (2007, p. 716) give two reasons for the enjoyment of this struggle: First, it is sufficiently difficult to pose a serious mental challenge for anyone attempting to do the puzzle. Secondly, simply by scanning rows and columns, it is easy to enter the “missing colors”, and this gives the solver some encouragement to persist. This paper develops an algorithm for solving any Sudoku puzzle by pencil and paper, especially the ones classified as diabolical.
PupilScreen: Using Smartphones to Assess Traumatic Brain Injury

Before a person suffering from a traumatic brain injury reaches a medical facility, measuring their pupillary light reflex (PLR) is one of the few quantitative measures a clinician can use to predict their outcome. We propose PupilScreen, a smartphone app and accompanying 3D-printed box that combines the repeatability, accuracy, and precision of a clinical device with the ubiquity and convenience of the penlight test that clinicians regularly use in emergency situations. The PupilScreen app stimulates the patient's eyes using the smartphone's flash and records the response using the camera. The PupilScreen box, akin to a head-mounted virtual reality display, controls the eyes' exposure to light. The recorded video is processed using convolutional neural networks that track the pupil diameter over time, allowing for the derivation of clinically relevant measures. We tested two different network architectures and found that a fully convolutional neural network was able to track pupil diameter with a median error of 0.30 mm. We also conducted a pilot clinical evaluation with six patients who had suffered a TBI and found that clinicians were almost perfect when separating unhealthy pupillary light reflexes from healthy ones using PupilScreen alone.
From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities

Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals and the availability of extensive set of medical images through internal/external communication systems, have resulted in a recent surge of significant interest in “Radiomics”. Radiomics is an emerging and relatively new research field, which refers to extracting semi-quantitative and/or quantitative features from medical images with the goal of developing predictive and/or prognostic models, and is expected to become a critical component for integration of image-derived information for personalized treatment in the near future. The conventional Radiomics workflow is typically based on extracting pre-designed features (also referred to as hand-crafted or engineered features) from a segmented region of interest. Nevertheless, recent advancements in deep learning have caused trends towards deep learning-based Radiomics (also referred to as discovery Radiomics). Capitalizing on the advantageous of these two approaches, there are also hybrid solutions developed to exploit the potentials of multiple data sources. Considering the variety of approaches to Radiomics, further improvements require a comprehensive and integrated sketch, which is the goal of this article. This manuscript provides a unique interdisciplinary perspective on Radiomics by discussing state-of-the-art signal processing solutions in the context of cancer Radiomics.
Hierarchical Reinforcement Learning for Adaptive Text Generation

We present a novel approach to natural language generation (NLG) that applies hierarchical reinforcement learning to text generation in the wayfinding domain. Our approach aims to optimise the integration of NLG tasks that are inherently different in nature, such as decisions of content selection, text structure, user modelling, referring expression generation (REG), and surface realisation. It also aims to capture existing interdependencies between these areas. We apply hierarchical reinforcement learning to learn a generation policy that captures these interdependencies, and that can be transferred to other NLG tasks. Our experimental results—in a simulated environment—show that the learnt wayfinding policy outperforms a baseline policy that takes reasonable actions but without optimization.
CariGANs: unpaired photo-to-caricature translation

Facial caricature is an art form of drawing faces in an exaggerated way to convey humor or sarcasm. In this paper, we propose the first Generative Adversarial Network (GAN) for unpaired photo-to-caricature translation, which we call "CariGANs". It explicitly models geometric exaggeration and appearance stylization using two components: CariGeoGAN, which only models the geometry-to-geometry transformation from face photos to caricatures, and CariStyGAN, which transfers the style appearance from caricatures to face photos without any geometry deformation. In this way, a difficult cross-domain translation problem is decoupled into two easier tasks. The perceptual study shows that caricatures generated by our CariGANs are closer to the hand-drawn ones, and at the same time better persevere the identity, compared to state-of-the-art methods. Moreover, our CariGANs allow users to control the shape exaggeration degree and change the color/texture style by tuning the parameters or giving an example caricature.
A taxonomy of DDoS attack and DDoS defense mechanisms

Distributed denial-of-service (DDoS) is a rapidly growing problem. The multitude and variety of both the attacks and the defense approaches is overwhelming. This paper presents two taxonomies for classifying attacks and defenses, and thus provides researchers with a better understanding of the problem and the current solution space. The attack classification criteria was selected to highlight commonalities and important features of attack strategies, that define challenges and dictate the design of countermeasures. The defense taxonomy classifies the body of existing DDoS defenses based on their design decisions; it then shows how these decisions dictate the advantages and deficiencies of proposed solutions.
Nasal bone hypoplasia in trisomy 21 at 15-22 weeks' gestation.

To investigate the potential value of ultrasound examination of the fetal profile for present/hypoplastic fetal nasal bone at 15-22 weeks' gestation as a marker for trisomy 21.



This was an observational ultrasound study in 1046 singleton pregnancies undergoing amniocentesis for fetal karyotyping at 15-22 (median, 17) weeks' gestation. Immediately before amniocentesis the fetal profile was examined to determine if the nasal bone was present or hypoplastic (absent or shorter than 2.5 mm). The incidence of nasal hypoplasia in the trisomy 21 and the chromosomally normal fetuses was determined and the likelihood ratio for trisomy 21 for nasal hypoplasia was calculated.



All fetuses were successfully examined for the presence of the nasal bone. The nasal bone was hypoplastic in 21/34 (61.8%) fetuses with trisomy 21, in 12/982 (1.2%) chromosomally normal fetuses and in 1/30 (3.3%) fetuses with other chromosomal defects. In 3/21 (14.3%) trisomy 21 fetuses with nasal hypoplasia there were no other abnormal ultrasound findings. In the chromosomally normal group hypoplastic nasal bone was found in 0.5% of Caucasians and in 8.8% of Afro-Caribbeans. The likelihood ratio for trisomy 21 for hypoplastic nasal bone was 50.5 (95% CI 27.1-92.7) and for present nasal bone it was 0.38 (95% CI 0.24-0.56).



Nasal bone hypoplasia at the 15-22-week scan is associated with a high risk for trisomy 21 and it is a highly sensitive and specific marker for this chromosomal abnormality.