nils-herrmann/clean_scirepeval huggingface dataset:
Understanding the Self-Directed Online Learning Preferences, Goals, Achievements, and Challenges of MIT OpenCourseWare Subscribers

This research targeted the learning preferences, goals and motivations, achievements, challenges, and possibilities for life change of self-directed online learners who subscribed to the monthly OpenCourseWare (OCW) e-newsletter from MIT. Data collection included a 25-item survey of 1,429 newsletter subscribers; 613 of whom also completed an additional 15 open-ended survey items. The 25 close-ended survey findings indicated that respondents used a wide range of devices and places to learn for their self-directed learning needs. Key motivational factors included curiosity, interest, and internal need for self-improvement. Factors leading to success or personal change included freedom to learn, resource abundance, choice, control, and fun. In terms of achievements, respondents were learning both specific skills as well as more general skills that help them advance in their careers. Science, math, and foreign language skills were the most desired by the survey respondents. The key obstacles or challenges faced were time, lack of high quality open resources, and membership or technology fees. Several brief stories of life change across different age ranges are documented. Among the chief implications is that learning something new to enhance one’s life or to help others is often more important than course transcript credit or a certificate of completion.
An automated method of penetration testing

An automated method of penetration testing was proposed, which is deigned to solve the problems such as high cost and low efficiency in the traditional penetration testing. This method consists of two parts: the automatic generating method of the penetration testing scheme and the automatic executing method of penetration testing scheme. We design and implement an original system which can execute penetration testing automatically, it was named AEPT (automatic executing penetration testing). The system integrates the necessary functions of penetration testing and can execute the penetration testing automatically. The experimental results showed that this method can overcome the problems exist in traditional penetration testing and can dramatically improve the efficiency and the accuracy of penetration testing, greatly reduce the cost of penetration testing.
Long-term functional and radiographic outcomes in 243 operated ankle fractures

Large comparative studies that have evaluated long-term functional outcome of operatively treated ankle fractures are lacking. This study was performed to analyse the influence of several combinations of malleolar fractures on long-term functional outcome and development of osteoarthritis.



Retrospective cohort-study on operated (1995-2007) malleolar fractures. Results were assessed with use of the AAOS- and AOFAS-questionnaires, VAS-pain score, dorsiflexion restriction (range of motion) and osteoarthritis. Categorisation was determined using the number of malleoli involved.



243 participants with a mean follow-up of 9.6 years were included. Significant differences for all outcomes were found between unimalleolar (isolated fibular) and bimalleolar (a combination of fibular and medial) fractures (AOFAS 97 vs 91, p = 0.035; AAOS 97 vs 90, p = 0.026; dorsiflexion restriction 2.8° vs 6.7°, p = 0.003). Outcomes after fibular fractures with an additional posterior fragment were similar to isolated fibular fractures. However, significant differences were found between unimalleolar and trimalleolar (a combination of lateral, medial and posterior) fractures (AOFAS 97 vs 88, p < 0.001; AAOS 97 vs 90, p = 0.003; VAS-pain 1.1 vs 2.3 p < 0.001; dorsiflexion restriction 2.9° vs 6.9°, p < 0.001). There was no significant difference in isolated fibular fractures with or without additional deltoid ligament injury. In addition, no functional differences were found between bimalleolar and trimalleolar fractures. Surprisingly, poor outcomes were found for isolated medial malleolar fractures. Development of osteoarthritis occurred mainly in trimalleolar fractures with a posterior fragment larger than 5 %.



The results of our study show that long-term functional outcome is strongly associated to medial malleolar fractures, isolated or as part of bi- or trimalleolar fractures. More cases of osteoarthritis are found in trimalleolar fractures.
Adolescents ’ Music Preferences and Personality Characteristics

The present paper examined the structure of Dutch adolescents’ music preferences, the stability of music preferences and the relations between Big-Five personality characteristics and (changes in) music preferences. Exploratory and confirmatory factor analyses of music-preference data from 2334 adolescents aged 12–19 revealed four clearly interpretable music-preference dimensions: Rock, Elite, Urban and Pop/Dance. One thousand and forty-four randomly selected adolescents from the original sample filled out questionnaires on music preferences and personality at three follow-up measurements. In addition to being relatively stable over 1, 2 and 3-year intervals, music preferences were found to be consistently related to personality characteristics, generally confirming prior research in the United States. Personality characteristics were also found to predict changes in music preferences over a 3-year interval.
Detecting Hate Speech Against Women in English Tweets

English. Hate speech is prevalent in social media platforms. Systems that can automatically detect offensive content are of great value to assist human curators with removal of hateful language. In this paper, we present machine learning models developed at UW Tacoma for detection of misogyny, i.e. hate speech against women, in English tweets, and the results obtained with these models in the shared task for Automatic Misogyny Identification (AMI) at EVALITA2018. Italiano. Commenti offensivi nei confronti di persone con diversa orientazione sessuale o provenienza sociale sono oggigiorno prevalenti nelle piattaforme di social media. A tale fine, sistemi automatici in grado di rilevare contenuti offensivi nei confronti di alcuni gruppi sociali sono importanti per facilitare il lavoro dei moderatori di queste piattaforme a rimuovere ogni commento offensivo usato nei social media. In questo articolo, vi presentiamo sia dei modelli di apprendimento automatico sviluppati all’Università di Washington in Tacoma per il rilevamento della misoginia, ovvero discorsi offensivi usati nei tweet in lingua inglese contro le donne, sia i risultati ottenuti con questi modelli nel processo per l’identificazione automatica della misoginia in EVALITA2018.
Walk and Learn: Facial Attribute Representation Learning from Egocentric Video and Contextual Data

The way people look in terms of facial attributes (ethnicity, hair color, facial hair, etc.) and the clothes or accessories they wear (sunglasses, hat, hoodies, etc.) is highly dependent on geo-location and weather condition, respectively. This work explores, for the first time, the use of this contextual information, as people with wearable cameras walk across different neighborhoods of a city, in order to learn a rich feature representation for facial attribute classification, without the costly manual annotation required by previous methods. By tracking the faces of casual walkers on more than 40 hours of egocentric video, we are able to cover tens of thousands of different identities and automatically extract nearly 5 million pairs of images connected by or from different face tracks, along with their weather and location context, under pose and lighting variations. These image pairs are then fed into a deep network that preserves similarity of images connected by the same track, in order to capture identity-related attribute features, and optimizes for location and weather prediction to capture additional facial attribute features. Finally, the network is fine-tuned with manually annotated samples. We perform an extensive experimental analysis on wearable data and two standard benchmark datasets based on web images (LFWA and CelebA). Our method outperforms by a large margin a network trained from scratch. Moreover, even without using manually annotated identity labels for pre-training as in previous methods, our approach achieves results that are better than the state of the art.
A catalog of stream processing optimizations

Various research communities have independently arrived at stream processing as a programming model for efficient and parallel computing. These communities include digital signal processing, databases, operating systems, and complex event processing. Since each community faces applications with challenging performance requirements, each of them has developed some of the same optimizations, but often with conflicting terminology and unstated assumptions. This article presents a survey of optimizations for stream processing. It is aimed both at users who need to understand and guide the system’s optimizer and at implementers who need to make engineering tradeoffs. To consolidate terminology, this article is organized as a catalog, in a style similar to catalogs of design patterns or refactorings. To make assumptions explicit and help understand tradeoffs, each optimization is presented with its safety constraints (when does it preserve correctness?) and a profitability experiment (when does it improve performance?). We hope that this survey will help future streaming system builders to stand on the shoulders of giants from not just their own community.
In search of principles for a Theory of Organisms

Lacking an operational theory to explain the organization and behaviour of matter in unicellular and multicellular organisms hinders progress in biology. Such a theory should address life cycles from ontogenesis to death. This theory would complement the theory of evolution that addresses phylogenesis, and would posit theoretical extensions to accepted physical principles and default states in order to grasp the living state of matter and define proper biological observables. Thus, we favour adopting the default state implicit in Darwin’s theory, namely, cell proliferation with variation plus motility, and a framing principle, namely, life phenomena manifest themselves as non-identical iterations of morphogenetic processes. From this perspective, organisms become a consequence of the inherent variability generated by proliferation, motility and self-organization. Morphogenesis would then be the result of the default state plus physical constraints, like gravity, and those present in living organisms, like muscular tension.
A Deep Learning Approach for Credit Scoring of Peer-to-Peer Lending Using Attention Mechanism LSTM

With the rapid development of the peer-to-peer lending industry in China, it has been a crucial task to evaluate the default risk of each loan. Motivated by the research in natural language processing, we make use of the online operation behavior data of borrowers and propose a consumer credit scoring method based on attention mechanism LSTM, which is a novel application of deep learning algorithm. Inspired by the idea of Word2vec, we treat each type of event as a word, construct the Event2vec model to convert each type of event transformation into a vector and, then, use an attention mechanism LSTM network to predict the probability of user default. The method is evaluated on the real dataset, and the results show that the proposed solution can effectively increase the predictive accuracy compared with the traditional artificial feature extraction method and the standard LSTM model.
PAMR: Passive aggressive mean reversion strategy for portfolio selection

This article proposes a novel online portfolio selection strategy named “Passive Aggressive Mean Reversion” (PAMR). Unlike traditional trend following approaches, the proposed approach relies upon the mean reversion relation of financial markets. Equipped with online passive aggressive learning technique from machine learning, the proposed portfolio selection strategy can effectively exploit the mean reversion property of markets. By analyzing PAMR’s update scheme, we find that it nicely trades off between portfolio return and volatility risk and reflects the mean reversion trading principle. We also present several variants of PAMR algorithm, including a mixture algorithm which mixes PAMR and other strategies. We conduct extensive numerical experiments to evaluate the empirical performance of the proposed algorithms on various real datasets. The encouraging results show that in most cases the proposed PAMR strategy outperforms all benchmarks and almost all state-of-the-art portfolio selection strategies under various performance metrics. In addition to its superior performance, the proposed PAMR runs extremely fast and thus is very suitable for real-life online trading applications. The experimental testbed including source codes and data sets is available at  http://www.cais.ntu.edu.sg/~chhoi/PAMR/ .
A Gentle Introduction to Deep Learning in Medical Image Processing

This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modeling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep ()learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.
Graph-boosted convolutional neural networks for semantic segmentation

This paper investigates the problem of weakly-supervised semantic segmentation, where image-level labels are used as weak supervision. Inspired by the successful use of Convolutional Neural Networks (CNNs) for fully-supervised semantic segmentation, we choose to directly train the CNNs over the oversegmented regions of images for weakly-supervised semantic segmentation. Although there are a few studies on CNNs-based weakly-supervised semantic segmentation, they have rarely considered the noise issue, i.e., the initial weak labels (e.g., social tags) may be noisy. To cope with this issue, we thus propose graph-boosted CNNs (GB-CNNs) for weakly-supervised semantic segmentation. In our GB-CNNs, the graph-based model provides the initial supervision for training the CNNs, and then the outcomes of the CNNs are used to retrain the graph-based model. This training procedure is iteratively implemented to boost the results of semantic segmentation. Experimental results demonstrate that the proposed model outperforms the state-of-the-art weakly-supervised methods. More notably, the proposed model is shown to be more robust in the noisy setting for weakly-supervised semantic segmentation.
Effective Self-Training for Parsing

We present a simple, but surprisingly effective, method of self-training a twophase parser-reranker system using readily available unlabeled data. We show that this type of bootstrapping is possible for parsing when the bootstrapped parses are processed by a discriminative reranker. Our improved model achieves an f -score of 92.1%, an absolute 1.1% improvement (12% error reduction) over the previous best result for Wall Street Journal parsing. Finally, we provide some analysis to better understand the phenomenon.
Power Management Strategies for a Microgrid With Multiple Distributed Generation Units

This paper addresses real and reactive power management strategies of electronically interfaced distributed generation (DG) units in the context of a multiple-DG microgrid system. The emphasis is primarily on electronically interfaced DG (EI-DG) units. DG controls and power management strategies are based on locally measured signals without communications. Based on the reactive power controls adopted, three power management strategies are identified and investigated. These strategies are based on 1) voltage-droop characteristic, 2) voltage regulation, and 3) load reactive power compensation. The real power of each DG unit is controlled based on a frequency-droop characteristic and a complimentary frequency restoration strategy. A systematic approach to develop a small-signal dynamic model of a multiple-DG microgrid, including real and reactive power management strategies, is also presented. The microgrid eigen structure, based on the developed model, is used to 1) investigate the microgrid dynamic behavior, 2) select control parameters of DG units, and 3) incorporate power management strategies in the DG controllers. The model is also used to investigate sensitivity of the design to changes of parameters and operating point and to optimize performance of the microgrid system. The results are used to discuss applications of the proposed power management strategies under various microgrid operating conditions
Study of Phishing Attacks and Preventions

The Internet is a great discovery for ordinary citizens correspondence. People with criminal personality have found a method for taking individual data without really meeting them and with minimal danger of being gotten. It is called Phishing. Phishing represents a huge threat to the web based business industry. Not just does it smash the certainty of clients towards online business, additionally causes electronic administration suppliers colossal financial misfortune. Subsequently, it is fundamental to think about phishing. This paper gives mindfulness about Phishing assaults and hostile to phishing apparatuses.
Value or Bubble ? A Decomposition of the Bitcoin Price

In this study, I analyze Bitcoin transaction data and build an economic model on Bitcoin traders incentives to decompose the Bitcoin price into a utility-driven component, a speculative component, and a friction component. The model I build extends the LDA (Latent-Dirichlet-Allocation) model, and I perform a paralleled collapsed Gibbs Sampling method to estimate the realized incentives of each individual trader at each time point. For post-estimation analysis, I look into major headline news to see which how information or rumor affects the different components of the Bitcoin price. The preliminary results show interesting patterns of trading and pricing in the Bitcoin market for the first time.
Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization

Chinese social media text summarization Problem: Current Chinese social media text summarization models are based on an encoder-decoder framework. Although its generated summaries are similar to source texts literally, they have low semantic relevance. Proposal: a Semantic Relevance Based neural model to encourage high semantic similarity between texts and summaries. Example of RNN Generated  因天气原因,飞机备降太原机场。有乘客要求重新安检,机长决 定继续飞行,引起机组人员与未吸烟乘客冲突。 Last night, several people were caught to smoke on a flight of China United Airlines from Chendu to Beijing. Later the flight temporarily landed on Taiyuan Airport. Some passengers asked for a security check but were denied by the captain, which led to a collision between crew and passengers. RNN: 中联航空机场发生爆炸致多人死亡。 China United Airlines exploded in the airport, leaving several people dead. Gold: 航班多人吸烟机组人员与乘客冲突。 Several people smoked on a flight which led to a collision between crew and passengers.
Reinterpreting the development of reading skills

184 L earning to read is one of the greatest accomplishments in childhood because it is the foundation for learning and academic achievement. Therefore, it is not surprising that debates among educators about how best to help children learn to read have been heated, polarized, and unsettled for many years. The intensity of the debates , coupled with enormous political pressures and commercial interests, has made learning to read a contentious public issue in the United States. What makes the debates at the beginning of the new century different than similar debates during the past 50 years is a greater than ever reliance on scientific evidence to guide educational policies for assessment and instruction. The attention and new credibility given to reading research have been hard won by the academic community and have great promise, but there are also pitfalls to avoid in the rush to use basic research for legislated policies and educational prescriptions (Berliner, 2002; Feuer, Towne, & Shavelson, 2002). The purpose of this article is to reveal flaws in traditional research on developing reading skills that have wide ramifications. My goal is to stimulate researchers to reconsider how to conceptualize, research, and interpret the development of reading skills. This reinterpretation is necessary because traditional reading research has ignored fundamental differences in the developmental trajec-tories of reading skills. These different trajectories are manifested in different times of skill onset, different durations of acquisition, and different asymptotic levels of performance. What is most important is that there are some skills that are more constrained than others; they are learned quickly, mastered entirely, and should not be conceptualized as enduring individual difference variables. Constrained skills have been analyzed with the same research tools and parametric statistical analyses as unconstrained skills. I claim that this is a mistake that can lead to spurious claims about early reading skills. The theoretical focus of this article is on construct validity and the developmental differences among reading skills. The methodologi-cal focus is on the distributions of longitudinal data and the methods to examine 185 THEORIES ABOUT reading have neglected basic differences in the developmental trajectories of skills related to reading. This essay proposes that some reading skills, such as learning the letters of the alphabet, are constrained to small sets of knowledge that are mastered in relatively brief periods of development. In contrast, other skills, such as vocabulary, are unconstrained by the knowledge to …
Pedestrian Detection

A program that detects people in images has a multitude of potential applications, including tracking for biomedical applications or surveillance, activity recognition for person-device interfaces (device control, video games), organizing personal picture collections, and much more. However, detecting people is difficult, as the appearance of a person can vary enormously because of changes in viewpoint or lighting, clothing style, body pose, individual traits, occlusion, and more. It then makes sense that the first people detectors were really detectors of pedestrians, that is, people walking at a measured pace on a sidewalk, and viewed from a fixed camera. Pedestrians are nearly always upright, their arms are mostly held along the body, and proper camera placement relative to pedestrian traffic can virtually ensure a view from the front or from behind . These factors reduce variation of appearance, although clothing, illumination, background, occlusions, and somewhat limited variations of pose still present very significant challenges.