nils-herrmann/clean_scirepeval huggingface dataset:
Tracking Multiple Social Media for Stock Market Event Prediction

The problem of modeling the continuously changing trends in finance markets and generating real-time, meaningful predictions about significant changes in those markets has drawn considerable interest from economists and data scientists alike. In addition to traditional market indicators, growth of varied social media has enabled economists to leverage microand real-time indicators about factors possibly influencing the market, such as public emotion, anticipations and behaviors. We propose several specific market related features that can be mined from varied sources such as news, Google search volumes and Twitter. We further investigate the correlation between these features and financial market fluctuations. In this paper, we present a Delta Naive Bayes (DNB) approach to generate prediction about financial markets. We present a detailed prospective analysis of prediction accuracy generated from multiple, combined sources with those generated from a single source. We find that multi-source predictions consistently outperform single-source predictions, even though with some limitations.
Recursive Neural Network Rule Extraction for Data With Mixed Attributes

In this paper, we present a recursive algorithm for extracting classification rules from feedforward neural networks (NNs) that have been trained on data sets having both discrete and continuous attributes. The novelty of this algorithm lies in the conditions of the extracted rules: the rule conditions involving discrete attributes are disjoint from those involving continuous attributes. The algorithm starts by first generating rules with discrete attributes only to explain the classification process of the NN. If the accuracy of a rule with only discrete attributes is not satisfactory, the algorithm refines this rule by recursively generating more rules with discrete attributes not already present in the rule condition, or by generating a hyperplane involving only the continuous attributes. We show that for three real-life credit scoring data sets, the algorithm generates rules that are not only more accurate but also more comprehensible than those generated by other NN rule extraction methods.
Noisy Activation Functions

Common nonlinear activation functions used in neural networks can cause training difficulties due to the saturation behavior of the activation function, which may hide dependencies that are not visible to vanilla-SGD (using first order gradients only). Gating mechanisms that use softly saturating activation functions to emulate the discrete switching of digital logic circuits are good examples of this. We propose to exploit the injection of appropriate noise so that the gradients may flow easily, even if the noiseless application of the activation function would yield zero gradient. Large noise will dominate the noise-free gradient and allow stochastic gradient descent to explore more. By adding noise only to the problematic parts of the activation function, we allow the optimization procedure to explore the boundary between the degenerate (saturating) and the well-behaved parts of the activation function. We also establish connections to simulated annealing, when the amount of noise is annealed down, making it easier to optimize hard objective functions. We find experimentally that replacing such saturating activation functions by noisy variants helps training in many contexts, yielding state-of-the-art or competitive results on different datasets and task, especially when training seems to be the most difficult, e.g., when curriculum learning is necessary to obtain good results.
Influence of Gender and Age in Aggressive Dream Content of Spanish Children and Adolescents

This study investigated the aggressive components of the dream content of 120 Spanish children and adolescents of 4 different age groups. The C. S. Hall and R. L. Van de Castle (1966) coding system was used to rate the number of dream characters and aggressions, and the content findings were analyzed via the indicators presented by G. W. Domhoff (1993, 1996, 2003). Results confirm the findings of previous studies of gender and age differences in dream content: Boys tend to have more aggressive dream content, which tends to decrease with age until reaching a pattern similar to the normative group; younger children, especially boys, tend to be victims of aggression more frequently than do older children. In addition, a data analysis procedure involving cumulative scoring of the aggression scale as well as nonparametric statistics yielded significant differences between boys and girls of the youngest group for severity of aggression.
Heterogeneity of IT employees: an analysis of a model of perceived organizational support by job type

The retention of existing IT employees is crucial due to the expected shortage of the IT labor force in the U.S., Canada, and European countries. While much of the extant IT turnover literature implicitly assumes that IT employees are homogeneous, we contend that they are a diverse group and that exploring the group in depth would reveal further insights into why employees turnover. We examined a sample of employees by IT job type in a turnover model of the antecedents and impacts of perceived organizational support (POS), which is another infrequently studied concept in the literature but is a potentially important predictor of turnover. A survey of 302 IT employees at a large U.S.-based company showed that these employees are in fact diverse. The relationships between role ambiguity and POS and work schedule flexibility and POS were found to be significant for managerial employees, but not for technically-oriented employees. The relationship between career accommodations and POS, however, was found to be significant for technically-oriented employees, but not managerial employees. As a whole, this study suggests that by combining all IT employees together in our analyses, we may forego some of the unique insights about these employees that we can otherwise cultivate to strengthen the bond between the organization and its employees and to enhance our existing IT turnover literature. The results of this study provide implications for organizations on how they can better balance the tactics they use to retain their valued IT employees. IT managers can be in a better position to focus on building relationships with their employees based on what is generally important to those employees.
Software architecture: a travelogue

Over the past two and a half decades software architecture has emerged as an important subfield of software engineering. During that time there has been considerable progress in developing the technological and methodological base for treating architectural design as an engineering discipline. However, much still remains to be done to achieve that. Moreover, the changing face of technology raises a number of challenges for software architecture. This travelogue recounts the history of the field, its current state of practice and research, and speculates on some of the important emerging trends, challenges, and aspirations.
CREDBANK: A Large-Scale Social Media Corpus With Associated Credibility Annotations

Social media has quickly risen to prominence as a news source, yet lingering doubts remain about its ability to spread rumor and misinformation. Systematically studying this phenomenon, however, has been difficult due to the need to collect large-scale, unbiased data along with in-situ judgements of its accuracy. In this paper we present CREDBANK, a corpus designed to bridge this gap by systematically combining machine and human computation. Specifically, CREDBANK is a corpus of tweets, topics, events and associated human credibility judgements. It is based on the real-time tracking of more than 1 billion streaming tweets over a period of more than three months, computational summarizations of those tweets, and intelligent routings of the tweet streams to human annotators—within a few hours of those events unfolding on Twitter. In total CREDBANK comprises more than 60 million tweets grouped into 1049 real-world events, each annotated by 30 human annotators. As an example, with CREDBANK one can quickly calculate that roughly 24% of the events in the global tweet stream are not perceived as credible. We have made CREDBANK publicly available, and hope it will enable new research questions related to online information credibility in fields such as social science, data mining and health.
SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning

Systems for declarative large-scale machine learning (ML) algorithms aim at high-level algorithm specification and automatic optimization of runtime execution plans. State-ofthe-art compilers rely on algebraic rewrites and operator selection, including fused operators to avoid materialized intermediates, reduce memory bandwidth requirements, and exploit sparsity across chains of operations. However, the unlimited number of relevant patterns for rewrites and operators poses challenges in terms of development effort and high performance impact. Query compilation has been studied extensively in the database literature, but ML programs additionally require handling linear algebra and exploiting algebraic properties, DAG structures, and sparsity. In this paper, we introduce Spoof, an architecture to automatically (1) identify algebraic simplification rewrites, and (2) generate fused operators in a holistic framework. We describe a snapshot of the overall system, including key techniques of sum-product optimization and code generation. Preliminary experiments show performance close to hand-coded fused operators, significant improvements over a baseline without fused operators, and moderate compilation overhead.
A Neural Network Approach to Jointly Modeling Social Networks and Mobile Trajectories

Two characteristics of location-based services are mobile trajectories and the ability to facilitate social networking. The recording of trajectory data contributes valuable resources towards understanding users’ geographical movement behaviors. Social networking is possible when users are able to quickly connect to anyone nearby. A social network with location based services is known as location-based social network (LBSN). As shown in Cho et al. [2013], locations that are frequently visited by socially related persons tend to be correlated, which indicates the close association between social connections and trajectory behaviors of users in LBSNs. To better analyze and mine LBSN data, we need to have a comprehensive view of each of these two aspects, i.e., the mobile trajectory data and the social network.
 Specifically, we present a novel neural network model that can jointly model both social networks and mobile trajectories. Our model consists of two components: the construction of social networks and the generation of mobile trajectories. First we adopt a network embedding method for the construction of social networks: a networking representation can be derived for a user. The key to our model lies in generating mobile trajectories. Second, we consider four factors that influence the generation process of mobile trajectories: user visit preference, influence of friends, short-term sequential contexts, and long-term sequential contexts. To characterize the last two contexts, we employ the RNN and GRU models to capture the sequential relatedness in mobile trajectories at the short or long term levels. Finally, the two components are tied by sharing the user network representations. Experimental results on two important applications demonstrate the effectiveness of our model. In particular, the improvement over baselines is more significant when either network structure or trajectory data is sparse.
Effects of mobile phone use on academic performance of college going young adults in India

Mobile phones have become the most popular way to communicate with other individuals. While cell phones have become less of a status symbol and more of a fashion statement, they have created an unspoken social dependency. Adolescents and young adults are more likely to engage in SMS messing, making phone calls, accessing the internet from their phone or playing a mobile driven game. Once pervaded by boredom, teenagers resort to instant connection, to someone, somewhere. Sensation seeking behavior has also linked adolescents and young adults to have the desire to take risks with relationships, rules and roles. Individuals seek out entertainment and avoid boredom at all times be it appropriate or inappropriate. Cell phones are used for entertainment, information and social connectivity. It has been demonstrated that individuals with low self – esteem use cell phones to form and maintain social relationships. They form an attachment with cell phone which molded their mind that they cannot function without their cell phone on a day-to-day basis. In this context, the study attempts to examine the extent of use of mobile phone and its influence on the academic performance of the students. A face to face survey using structured questionnaire was the method used to elicit the opinions of students between the age group of 18-25 years in three cities covering all the three regions the State of Andhra Pradesh in India. The survey was administered among 1200 young adults through two stage random sampling to select the colleges and respondents from the selected colleges, with 400 from each city. In Hyderabad, 201 males and 199 females participated in the survey. In Visakhapatnam, 192 males and 208 females participated. In Tirupati, 220 males and 180 females completed the survey. Two criteria were taken into consideration while choosing the participants for the survey. The participants are college-going and were mobile phone users. Each of the survey responses was entered and analyzed using SPSS software. The Statistical Package for Social Sciences (SPSS 16) had been used to work out the distribution of samples in terms of percentages for each specified parameter.
Twelve tips for giving feedback effectively in the clinical environment.

Feedback is an essential element of the educational process for clinical trainees. Performance-based feedback enables good habits to be reinforced and faulty ones to be corrected. Despite its importance, most trainees feel that they do not receive adequate feedback and if they do, the process is not effective.



The authors reviewed the literature on feedback and present the following 12 tips for clinical teachers to provide effective feedback to undergraduate and graduate medical trainees. In most of the tips, the focus is the individual teacher in clinical settings, although some of the suggestions are best adopted at the institutional level.



Clinical educators will find the tips practical and easy to implement in their day-to-day interactions with learners. The techniques can be applied in settings whether the time for feedback is 5 minutes or 30 minutes.



Clinical teachers can improve their skills for giving feedback to learners by using the straightforward and practical tools described in the subsequent sections. Institutions should emphasise the importance of feedback to their clinical educators, provide staff development and implement a mechanism by which the quantity and quality of feedback is monitored.
Interventions to Break and Create Consumer Habits

Interventions to change everyday behaviors often attempt to change people’s beliefs and intentions. As the authors explain, these interventions are unlikely to be an effective means to change behaviors that people have repeated into habits. Successful habit change interventions involve disrupting the environmental factors that automatically cue habit performance. The authors propose two potential habit change interventions. “Downstream-plus” interventions provide informational input at points when habits are vulnerable to change, such as when people are undergoing naturally occurring changes in performance environments for many everyday actions (e.g., moving households, changing jobs). “Upstream” interventions occur before habit performance and disrupt old environmental cues and establish new ones. Policy interventions can be oriented not only to the change of established habits but also to the acquisition and maintenance of new behaviors through the formation of new habits.
Cell type discovery and representation in the era of high-content single cell phenotyping

A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology. In this paper, we provide examples of state-of-the-art cellular biomarker characterization using high-content cytometry and single cell RNA sequencing, and present strategies for standardized cell type representations based on the data outputs from these cutting-edge technologies, including “context annotations” in the form of standardized experiment metadata about the specimen source analyzed and marker genes that serve as the most useful features in machine learning-based cell type classification models. We also propose a statistical strategy for comparing new experiment data to these standardized cell type representations. The advent of high-throughput/high-content single cell technologies is leading to an explosion in the number of distinct cell types being identified. It will be critical for the bioinformatics community to develop and adopt data standard conventions that will be compatible with these new technologies and support the data representation needs of the research community. The proposals enumerated here will serve as a useful starting point to address these challenges.
Audio-Visual Speech Modeling for Continuous Speech Recognition

This paper describes a speech recognition system that uses both acoustic and visual speech information to improve the recognition performance in noisy environments. The system consists of three components: 1) a visual module; 2) an acoustic module; and 3) a sensor fusion module. The visual module locates and tracks the lip movements of a given speaker and extracts relevant speech features. This task is performed with an appearance-based lip model that is learned from example images. Visual speech features are represented by contour information of the lips and grey-level information of the mouth area. The acoustic module extracts noise-robust features from the audio signal. Finally, the sensor fusion module is responsible for the joint temporal modeling of the acoustic and visual feature streams and is realized using multistream hidden Markov models (HMMs). The multistream method allows the definition of different temporal topologies and levels of stream integration and hence enables the modeling of temporal dependencies more accurately than traditional approaches. We present two different methods to learn the asynchrony between the two modalities and how to incorporate them in the multistream models. The superior performance for the proposed system is demonstrated on a large multispeaker database of continuously spoken digits. On a recognition task at 15 dB acoustic signal-to-noise ratio (SNR), acoustic perceptual linear prediction (PLP) features lead to 56% error rate, noise robust RASTA-PLP (Relative Spectra) acoustic features to 7.2% error rate and combined noise robust acoustic features and visual features to 2.5% error rate.
Engaging Students in Learning Activities : It Is Not Autonomy Support or Structure but Autonomy Support and Structure

We investigated 2 engagement-fostering aspects of teachers’ instructional styles—autonomy support and structure—and hypothesized that students’ engagement would be highest when teachers provided high levels of both. Trained observers rated teachers’ instructional styles and students’ behavioral engagement in 133 public high school classrooms in the Midwest, and 1,584 students in Grades 9 –11 reported their subjective engagement. Correlational and hierarchical linear modeling analyses showed 3 results: (a) Autonomy support and structure were positively correlated, (b) autonomy support and structure both predicted students’ behavioral engagement, and (c) only autonomy support was a unique predictor of students’ self-reported engagement. We discuss, first, how these findings help illuminate the relations between autonomy support and structure as 2 complementary, rather than antagonistic or curvilinear, engagement-fostering aspects of teachers’ instructional styles and, second, the somewhat different results obtained for the behavioral versus self-report measures of students’ classroom engagement.
Survey of Visual Question Answering: Datasets and Techniques

Visual question answering (or VQA) is a new and exciting problem that combines natural language processing and computer vision techniques. We present a survey of the various datasets and models that have been used to tackle this task. The first part of this survey details the various datasets for VQA and compares them along some common factors. The second part of this survey details the different approaches for VQA, classified into four types: non-deep learning models, deep learning models without attention, deep learning models with attention, and other models which do not fit into the first three. Finally, we compare the performances of these approaches and provide some directions for future work.
A survey on sentiment analysis and opinion mining for social multimedia

Social media sentiment analysis (also known as opinion mining) which aims to extract people’s opinions, attitudes and emotions from social networks has become a research hotspot. Conventional sentiment analysis concentrates primarily on the textual content. However, multimedia sentiment analysis has begun to receive attention since visual content such as images and videos is becoming a new medium for self-expression in social networks. In order to provide a reference for the researchers in this active area, we give an overview of this topic and describe the algorithms of sentiment analysis and opinion mining for social multimedia. Having conducted a brief review on textual sentiment analysis for social media, we present a comprehensive survey of visual sentiment analysis on the basis of a thorough investigation of the existing literature. We further give a summary of existing studies on multimodal sentiment analysis which combines multiple media channels. We finally summarize the existing benchmark datasets in this area, and discuss the future research trends and potential directions for multimedia sentiment analysis. This survey covers 100 articles during 2008–2018 and categorizes existing studies according to the approaches they adopt.