nils-herrmann/clean_scirepeval huggingface dataset:
Relational Deep Reinforcement Learning

We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning. It uses self-attention to iteratively reason about the relations between entities in a scene and to guide a model-free policy. Our results show that in a novel navigation and planning task called Box-World, our agent finds interpretable solutions that improve upon baselines in terms of sample complexity, ability to generalize to more complex scenes than experienced during training, and overall performance. In the StarCraft II Learning Environment, our agent achieves state-of-the-art performance on six mini-games – surpassing human grandmaster performance on four. By considering architectural inductive biases, our work opens new directions for overcoming important, but stubborn, challenges in deep RL.
Sentiment Analysis : A Review

The basic knowledge required to do sentiment analysis of Twitter is discussed in this review paper. Sentiment Analysis can be viewed as field of text mining, natural language processing. Thus we can study sentiment analysis in various aspects. This paper presents levels of sentiment analysis, approaches to do sentiment analysis, methodologies for doing it, and features to be extracted from text and the applications. Twitter is a microblogging service to which if sentiment analysis done one has to follow explicit path. Thus this paper puts overview about tweets extraction, their preprocessing and their sentiment analysis.
Rethink and Redesign Meta learning

Recently, meta-learning has shown as a promising way to improve the ability to learn from few data for many computer vision tasks. However, existing meta-learning approaches still fall behind human greatly, and like many deep learning algorithms, they also suffer from overfitting. We named this problem as task-over-fitting (TOF) problem1 that the meta-learner over-fit to the training tasks, not the training data. We human beings can learn from few data, mainly due to we are so smart to leverage past knowledge to understand the images of new categories rapidly. Furthermore, benefits from our flexible attention mechanism, we can accurately extract and select key features from images and further solve few-shot learning tasks with excellent performance. In this paper, we rethink the meta-learning algorithm and find that existing meta-learning approaches fail to consider attention mechanism and past knowledge. To this end, we present a novel paradigm of meta-learning approach and three methods to introduce attention mechanism and past knowledge step by step. In this way, we can narrow the problem space and improve the performance of metalearning, and the TOF problem can also be significantly reduced. Extensive experiments demonstrate the effectiveness of our designation and methods with state-of-the-art performance not only on several few-shot learning benchmarks but also on the Cross-Entropy across Tasks (CET) metric2.
Graph Based Decoding for Event Sequencing and Coreference Resolution

Events in text documents are interrelated in complex ways. In this paper, we study two types of relation: Event Coreference and Event Sequencing. We show that the popular tree-like decoding structure for automated Event Coreference is not suitable for Event Sequencing. To this end, we propose a graph-based decoding algorithm that is applicable to both tasks. The new decoding algorithm supports flexible feature sets for both tasks. Empirically, our event coreference system has achieved state-of-the-art performance on the TAC-KBP 2015 event coreference task and our event sequencing system beats a strong temporal-based, oracle-informed baseline. We discuss the challenges of studying these event relations.
Delta-encoder: an effective sample synthesis method for few-shot object recognition

Learning to classify new categories based on just one or a few examples is a long-standing challenge in modern computer vision. In this work, we propose a simple yet effective method for few-shot (and one-shot) object recognition. Our approach is based on a modified auto-encoder, denoted ∆-encoder, that learns to synthesize new samples for an unseen category just by seeing few examples from it. The synthesized samples are then used to train a classifier. The proposed approach learns to both extract transferable intra-class deformations, or "deltas", between same-class pairs of training examples, and to apply those deltas to the few provided examples of a novel class (unseen during training) in order to efficiently synthesize samples from that new class. The proposed method improves the state-of-the-art of one-shot object-recognition and performs comparably in the few-shot case.  The authors have contributed equally to this work  Leonid Karlinsky ( Visualization of two-way one-shot classification trained on synthesized examples. Correctly classified images are framed in magenta (Golden retriever) and yellow (African wild dog). The only two images seen at training time and used for sample synthesis are framed in blue. Note the non-trivial relative arrangement of examples belonging to different classes handled successfully by our approach. The figure is plotted using t-SNE applied to VGG features. Best viewed in color.  Work in progress. ar X iv :1 80 6. 04 73 4v 2 [ cs .C V ] 1 8 N ov 2 01 8
Two-photon targeted patching and electroporation in vivo.

By combining patch-clamp methods with two-photon microscopy, it is possible to target recordings to specific classes of neurons in vivo. Here we describe methods for imaging and recording from the soma and dendrites of neurons identified using genetically encoded probes such as green fluorescent protein (GFP) or functional indicators such as Oregon Green BAPTA-1. Two-photon targeted patching can also be adapted for use with wild-type brains by perfusing the extracellular space with a membrane-impermeable dye to visualize the cells by their negative image and target them for electrical recordings, a technique termed "shadowpatching." We discuss how these approaches can be adapted for single-cell electroporation to manipulate specific cells genetically. These approaches thus permit the recording and manipulation of rare genetically, morphologically, and functionally distinct subsets of neurons in the intact nervous system.
Building Brand Community

A brand community from a customer-experiential perspective is a fabric of relationships in which the customer is situated. Crucial relationships include those between the customer and the brand, between the customer and the firm, between the customer and the product in use, and among fellow customers. The authors delve ethnographically into a brand community and test key findings through quantitative methods. Conceptually, the study reveals insights that differ from prior research in four important ways: First, it expands the definition of a brand community to entities and relationships neglected by previous research. Second, it treats vital characteristics of brand communities, such as geotemporal concentrations and the richness of social context, as dynamic rather than static phenomena. Third, it demonstrates that marketers can strengthen brand communities by facilitating shared customer experiences in ways that alter those dynamic characteristics. Fourth, it yields a new and richer conceptualization of customer loyalty as integration in a brand community.
Python probabilistic type inference with natural language support

We propose a novel type inference technique for Python programs. Type inference is difficult for Python programs due to their heavy dependence on external APIs and the dynamic language features. We observe that Python source code often contains a lot of type hints such as attribute accesses and variable names. However, such type hints are not reliable. We hence propose to use probabilistic inference to allow the beliefs of individual type hints to be propagated, aggregated, and eventually converge on probabilities of variable types. Our results show that our technique substantially outperforms a state-of-the-art Python type inference engine based on abstract interpretation.
The renaming of mental retardation: understanding the change to the term intellectual disability.

There is considerable and intense discussion in the field of intellectual disability/mental retardation about the construct of disability, how intellectual disability fits within the general construct of disability, and the use of the term intellectual disability (Glidden, 2006; Greenspan, 2006; MacMillan, Siperstein, & Leffert, 2006; Schalock & Luckasson, 2004; Switzky & Greenspan, 2006b). This discussion is occurring within the context of competing world views of the philosophical and epistemological underpinnings of the conceptions of intellectual disability/mental retardation (Switzky & Greenspan, 2006a). Increasingly, the term intellectual disability is being used instead of mental retardation. This transition in terminology is exemplified by organization names (e.g., the American Association on Intellectual and Developmental Disabilities—AAIDD, International Association for the Scientific Study of Intellectual Disabilities, President’s Committee for People With Intellectual Disabilities), journal titles, and published research (Parmenter, 2004; Schroeder, Gertz, & Velazquez, 2002). A number of questions have emerged with the increased use of the term intellectual disability:
Do type B malleolar fractures need a positioning screw?

Type B malleolar fractures (AO/ASIF classification) are usually stable ankle joint fractures. Nonetheless, some show a residual instability after internal fixation requiring further stabilization. How often does such a situation occur and can these unstable fractures be recognized beforehand?From 1995 to 1997, 111 malleolar fractures (three type A, 90 type B, 18 type C) were operated on. Seventeen out of 90 patients (19%) with a type B fracture showed residual instability after internal fixation (one unilateral, four bimalleolar and 12 trimalleolar fractures). Five of these patients showed a dislocation in the sagittal plane (anteroposterior) clinically or on the radiographs, five a dislocation in the coronal plane with dislocation of the tibia on the medial aspect of the ankle joint, and four an incongruency on the medial aspect of the joint. In three cases, no preoperative abnormality indicating instability was found. The fractures were all fixed using an additional positioning screw. In 11 patients, the positioning screw was removed after 8-12 weeks, in six patients removal was performed after 1 year along with removal of the plate. All 17 patients were reviewed 1 year after internal fixation, 16/17 showed a good or excellent result with identical or only minor impairment of range of motion of the ankle joint.



Unstable ankle joints after internal fixation of type B malleolar fractures exist. Residual instability most often occurs after trimalleolar fractures with initial joint dislocation. Treatment with an additional positioning screw generally produced a satisfactory result.
Automatic Music Transcription: An Overview

The capability of transcribing music audio into music notation is a fascinating example of human intelligence. It involves perception (analyzing complex auditory scenes), cognition (recognizing musical objects), knowledge representation (forming musical structures), and inference (testing alternative hypotheses). Automatic music transcription (AMT), i.e., the design of computational algorithms to convert acoustic music signals into some form of music notation, is a challenging task in signal processing and artificial intelligence. It comprises several subtasks, including multipitch estimation (MPE), onset and offset detection, instrument recognition, beat and rhythm tracking, interpretation of expressive timing and dynamics, and score typesetting.
Intrusion Detection System in Cloud Computing Environment

The Cloud computing system can be easily threatened by various attacks, because most of the cloud computing systems provide service to so many people who are not proven to be trustworthy. Due to their distributed nature, cloud computing environment are easy targets for intruders[1]. There are various Intrusion Detection Systems having various specifications to each. Cloud computing have two approaches i. e. Knowledge-based IDS and Behavior-Based IDS to detect intrusions in cloud computing. Behavior-Based IDS assumes that an intrusion can be detected by observing a deviation from normal to expected behavior of the system or user[2]s. Knowledge-based IDS techniques apply knowledge
How Dreams And Memory May Be Related

I present a theory of dreams and long term memory structure that proposes that both entities are closely related. It is based on a variation of Freud's dream theory: (1) I re-label Freud's "Unconscious" the “Long Term Memory Structure” (LTMS), (2) I propose that dreams are ever present excitational responses to perturbations of perceptions and thought, during waking life as well as sleep, which only become conscious when the executive function of waking life ceases, and (3) I reinterpret Freud’s “Dream Work” as describing the pre-dream Storage Transformation of perceptions and thought into the LTMS. I make one further conjecture: Memories are stored in the LTMS according to what is already in the LTMS. The observables of Freud's theory remain the same. The new theory is also consistent with recent experimental findings and suggests a partial basis for personality: the selection process of the Storage Transformation. “... each of us remembers and forgets in a pattern whose labyrinthing windings are an identification mark no less distinctive than a fingerprint” (American Pastoral, Philip Roth)
Six weeks of whole-body vibration exercise improves pain and fatigue in women with fibromyalgia.

The aim of this study was to investigate the effectiveness of a 6-week traditional exercise program with supplementary whole-body vibration (WBV) in improving health status, physical functioning, and main symptoms of fibromyalgia (FM) in women with FM.



Thirty-six (36) women with FM (mean +/- standard error of the mean age 55.97 +/- 1.55) were randomized into 3 treatment groups: exercise and vibration (EVG), exercise (EG), and control (CG). Exercise therapy, consisting of aerobic activities, stretching, and relaxation techniques, was performed twice a week (90 min/day). Following each exercise session, the EVG underwent a protocol with WBV, whereas the EG performed the same protocol without vibratory stimulus. The Fibromyalgia Impact Questionnaire (FIQ) was administered at baseline and 6 weeks following the initiation of the treatments. Estimates of pain, fatigue, stiffness, and depression were also reported using the visual analogue scale.



A significant 3 x 2 (group x time)-repeated measures analysis of variance interaction was found for pain (p = 0.018) and fatigue (p = 0.002) but not for FIQ (p = 0.069), stiffness (p = 0.142), or depression (p = 0.654). Pain and fatigue scores were significantly reduced from baseline in the EVG, but not in the EG or CG. In addition, the EVG showed significantly lower pain and fatigue scores at week 6 compared to the CG, whereas no significant differences were found between the EG and CG (p > 0.05).



Results suggest that a 6-week traditional exercise program with supplementary WBV safely reduces pain and fatigue, whereas exercise alone fails to induce improvements.
A model of the relationship between psychological characteristics, mobile phone addiction and use of mobile phones by Taiwanese university female students

While many researches have analyzed the psychological antecedents of mobile phone addiction and mobile phone usage behavior, their relationship with psychological characteristics remains mixed. We investigated the relationship between psychological characteristics, mobile phone addiction and use of mobile phones for 269 Taiwanese female university students who were administered Rosenberg’s selfesteem scale, Lai’s personality inventory, and a mobile phone usage questionnaire and mobile phone addiction scale. The result showing that: (1) social extraversion and anxiety have positive effects on mobile phone addiction, and self-esteem has negative effects on mobile phone addiction. (2) Mobile phone addiction has a positive predictive effect on mobile phone usage behavior. The results of this study identify personal psychological characteristics of Taiwanese female university students which can significantly predict mobile phone addiction; female university students with mobile phone addiction will make more phone calls and send more text messages. These results are discussed and suggestions for future research for school and university students are provided.
Personalising learning with dynamic prediction and adaptation to learning styles in a conversational intelligent tutoring system

This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student‟s learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and personalising the tutorial to boost confidence and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic. The Oscar CITS methodology and architecture are independent of the learning styles model and tutoring subject domain. Oscar CITS was implemented using the Index of Learning Styles (ILS) model (Felder & Silverman 1988) to deliver an SQL tutorial. Empirical studies involving real students have validated the prediction of learning styles in a real-world teaching/learning environment. The results showed that all learning styles in the ILS model were successfully predicted from a natural language tutoring conversation, with an accuracy of 61-100%. Participants also found Oscar‟s tutoring helpful and achieved an average learning gain of 13%.
Gender differences in kindergarteners ’ robotics and programming achievement

Early childhood is a critical period for introducing girls to traditionally masculine fields of science and technology before more extreme gender stereotypes surface in later years. This study looks at the TangibleK Robotics Program in order to determine whether kindergarten boys and girls were equally successful in a series of building and programming tasks.  Although previous research has found that males outperform females in robotics and programming related fields, it was hypothesized that the young age of participants and their limited cultural indoctrination regarding gender stereotypes would allow boys and girls to have equal success in this program. Although boys had a higher mean score than girls on more than half of the tasks, very few of these differences were statistically significant. Boys scored significantly higher than girls only in two areas: properly attaching robotic materials, and programming using Ifs. Overall, both boys and girls were able to successfully complete the program.
Mining and Ranking Biomedical Synonym Candidates from Wikipedia

Biomedical synonyms are important resources for Natural Language Processing in Biomedical domain. Existing synonym resources (e.g., the UMLS) are not complete. Manual efforts for expanding and enriching these resources are prohibitively expensive. We therefore develop and evaluate approaches for automated synonym extraction from Wikipedia. Using the inter-wiki links, we extracted the candidate synonyms (anchor-text e.g., “increased thirst”) in a Wikipedia page and the title (e.g., “polyuria”) of its corresponding linked page. We rank synonym candidates with word embedding and pseudo-relevance feedback (PRF). Our results show that PRF-based reranking outperformed word embedding based approach and a strong baseline using interwiki link frequency. A hybrid method, Rank Score Combination, achieved the best results. Our analysis also suggests that medical synonyms mined from Wikipedia can increase the coverage of existing synonym resources such
Instance-Level Human Parsing via Part Grouping Network

Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing multiple instances in a single pass. Several related works all follow the “parsing-by-detection” pipeline that heavily relies on separately trained detection models to localize instances and then performs human parsing for each instance sequentially. Nonetheless, two discrepant optimization targets of detection and parsing lead to suboptimal representation learning and error accumulation for final results. In this work, we make the first attempt to explore a detection-free Part Grouping Network (PGN) for efficiently parsing multiple people in an image in a single pass. Our PGN reformulates instance-level human parsing as two twinned sub-tasks that can be jointly learned and mutually refined via a unified network: 1) semantic part segmentation for assigning each pixel as a human part (e.g ., face, arms); 2) instance-aware edge detection to group semantic parts into distinct person instances. Thus the shared intermediate representation would be endowed with capabilities in both characterizing fine-grained parts and inferring instance belongings of each part. Finally, a simple instance partition process is employed to get final results during inference. We conducted experiments on PASCAL-Person-Part dataset and our PGN outperforms all state-of-the-art methods. Furthermore, we show its superiority on a newly collected multi-person parsing dataset (CIHP) including 38,280 diverse images, which is the largest dataset so far and can facilitate more advanced human analysis. The CIHP benchmark and our source code are available at http://sysu-hcp.net/lip/.
A data mining approach to employee turnover prediction ( case study : Arak automotive parts manufacturing )

Training and adaption of employees are time and money consuming. Employees’ turnover can be predicted by their organizational and personal historical data in order to reduce probable loss of organizations. Prediction methods are highly related to human resource management to obtain patterns by historical data. This article implements knowledge discovery steps on real data of a manufacturing plant. We consider many characteristics of employees such as age, technical skills and work experience. Different data mining methods are compared based on their accuracy, calculation time and user friendliness. Furthermore the importance of data features is measured by Pearson ChiSquare test. In order to reach the desired user friendliness, a graphical user interface is designed specifically for the case study to handle knowledge discovery life cycle.