nils-herrmann/clean_scirepeval huggingface dataset:
Risk as feelings.

Virtually all current theories of choice under risk or uncertainty are cognitive and consequentialist. They assume that people assess the desirability and likelihood of possible outcomes of choice alternatives and integrate this information through some type of expectation-based calculus to arrive at a decision. The authors propose an alternative theoretical perspective, the risk-as-feelings hypothesis, that highlights the role of affect experienced at the moment of decision making. Drawing on research from clinical, physiological, and other subfields of psychology, they show that emotional reactions to risky situations often diverge from cognitive assessments of those risks. When such divergence occurs, emotional reactions often drive behavior. The risk-as-feelings hypothesis is shown to explain a wide range of phenomena that have resisted interpretation in cognitive-consequentialist terms.
Outcome prediction of DOTA2 based on Naïve Bayes classifier

Although DOTA2 is a popular game around the world, no clear algorithm or software are designed to forecast the winning probability by analyzing the lineups. However, the author finds that Naive Bayes classifier, one of the most common classification algorithm, can analyze the lineups and predict the outcome according to the lineups and gives an improved Naive Bayes classifier. Using the DOTA2 data set published in the UCI Machine Learning Repository, we test Naive Bayes classifier's prediction of respective winning probability of both sides in the game. The results show that Naive Bayes classifier is a practical tool to analyze the lineups and predict the outcome based on players' choices.
Using neural network ensembles for bankruptcy prediction and credit scoring

Bankruptcy prediction and credit scoring have long been regarded as critical topics and have been studied extensively in the accounting and finance literature. Artificial intelligence and machine learning techniques have been used to solve these financial decision-making problems. The multilayer perceptron (MLP) network trained by the back-propagation learning algorithm is the mostly used technique for financial decision-making problems. In addition, it is usually superior to other traditional statistical models. Recent studies suggest combining multiple classifiers (or classifier ensembles) should be better than single classifiers. However, the performance of multiple classifiers in bankruptcy prediction and credit scoring is not fully understood. In this paper, we investigate the performance of a single classifier as the baseline classifier to compare with multiple classifiers and diversified multiple classifiers by using neural networks based on three datasets. By comparing with the single classifier as the benchmark in terms of average prediction accuracy, the multiple classifiers only perform better in one of the three datasets. The diversified multiple classifiers trained by not only different classifier parameters but also different sets of training data perform worse in all datasets. However, for the Type I and Type II errors, there is no exact winner. We suggest that it is better to consider these three classifier architectures to make the optimal financial decision.
Childhood Obesity Perceptions Among African American Caregivers in a Rural Georgia Community: A Mixed Methods Approach

Given the pivotal role of African American caregiver’s perceptions of childhood obesity in rural areas, the inclusion of caregiver’s perceptions could potentially reduce childhood obesity rates. The objective of the current study was to explore childhood obesity perceptions among African Americans in a rural Georgia community. This concurrent mixed methods study utilized two theoretical frameworks: Social Cognitive Theory and Social Ecological Model. Using a convenience sample, caregivers ages 22–65 years completed a paper-based survey (n = 135) and a face-to-face interview (n = 12) to explore perceptions of obesity risk factors, health complications, weight status, built environment features, and obesity prevention approaches. Descriptive statistics were generated and a six-step process was used for qualitative analysis. Participants commonly cited behavioral risk factors; yet, social aspects and appearance of the community were not considered contributing factors. Chronic diseases were reported as obesity health complications. Caregivers had a distorted view of their child’s weight status. In addition, analysis revealed that caregivers assessed child’s weight and height measurements by the child’s appearance or a recent doctor visit. Environmental barriers reported by caregivers included safety concerns and insufficient physical activity venues and programs. Also, caregivers conveyed parents are an imperative component of preventing obesity. Although this study found caregivers were aware of obesity risk factors, health complications, built environment features, and prevention approaches their obesity perceptions were not incorporated into school or community prevention efforts. Findings suggest that children residing in rural areas are in need of tailored efforts that address caregiver perceptions of obesity.
Jamming-resistant Key Establishment using Uncoordinated Frequency Hopping

We consider the following problem: how can two devices that do not share any secrets establish a shared secret key over a wireless radio channel in the presence of a communication jammer? An inherent challenge in solving this problem is that known anti-jamming techniques (e.g., frequency hopping or direct-sequence spread spectrum) which should support device communication during the key establishment require that the devices share a secret spreading key (or code) prior to the start of their communication. This requirement creates a circular dependency between antijamming spread-spectrum communication and key establishment, which has so far not been addressed. In this work, we propose an uncoordinated frequency hopping (UFH) scheme that breaks this dependency and enables key establishment in the presence of a communication jammer. We perform a detailed analysis of our UFH scheme and show its feasibility, both in terms of execution time and resource requirements.
Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications

Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded in subspaces of high dimensional data, scalability, end-user comprehensibility of the results, non-presumption of any canonical data distribution, and insensitivity to the order of input records. We present CLIQUE, a clustering algorithm that satisfies each of these requirements. CLIQUE identifies dense clusters in subspaces of maximum dimensionality. It generates cluster descriptions in the form of DNF expressions that are minimized for ease of comprehension. It produces identical results irrespective of the order in which input records are presented and does not presume any specific mathematical form for data distribution. Through experiments, we show that CLIQUE efficiently finds accurate cluster in large high dimensional datasets.
What Your Images Reveal: Exploiting Visual Contents for Point-of-Interest Recommendation

The rapid growth of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which facilitates the study of point-of-interest (POI) recommendation. The majority of the existing POI recommendation methods focus on four aspects, i.e., temporal patterns, geographical influence, social correlations and textual content indications. For example, user’s visits to locations have temporal patterns and users are likely to visit POIs near them. In real-world LBSNs such as Instagram, users can upload photos associating with locations. Photos not only reflect users’ interests but also provide informative descriptions about locations. For example, a user who posts many architecture photos is more likely to visit famous landmarks; while a user posts lots of images about food has more incentive to visit restaurants. Thus, images have potentials to improve the performance of POI recommendation. However, little work exists for POI recommendation by exploiting images. In this paper, we study the problem of enhancing POI recommendation with visual contents. In particular, we propose a new framework Visual Content Enhanced POI recommendation (VPOI), which incorporates visual contents for POI recommendations. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework.
Pfp: parallel fp-growth for query recommendation

Frequent itemset mining (FIM) is a useful tool for discovering frequently co-occurrent items. Since its inception, a number of significant FIM algorithms have been developed to speed up mining performance. Unfortunately, when the dataset size is huge, both the memory use and computational cost can still be prohibitively expensive. In this work, we propose to parallelize the FP-Growth algorithm (we call our parallel algorithm PFP) on distributed machines. PFP partitions computation in such a way that each machine executes an independent group of mining tasks. Such partitioning eliminates computational dependencies between machines, and thereby communication between them. Through empirical study on a large dataset of 802,939 Web pages and 1,021,107 tags, we demonstrate that PFP can achieve virtually linear speedup. Besides scalability, the empirical study demonstrates that PFP to be promising for supporting query recommendation for search engines.
Effectiveness of occupational performance coaching in improving children's and mothers' performance and mothers' self-competence.

This study examined the effectiveness of occupational performance coaching in improving children's and mothers' occupational performance and mothers' parenting self-competence.



A one-group time-series design was used to evaluate changes in children's (n = 29) and mothers' (n = 8) occupational performance at four time points: (1) pre-wait list, (2) preintervention, (3) postintervention, and (4) follow-up.



Significant improvements in occupational performance occurred postintervention for children, F(1, 78) = 153.72, p < .001, η² = .86, and mothers, F(1, 78) = 153.72, p < .001, η² = .86, that were maintained 6 wk after intervention. Mothers' self-competence in parenting also improved, F(1, 72) = 17.36, p < .001, η² = .42.



Findings provide preliminary evidence supporting the effectiveness of occupational performance coaching in improving children's and mothers' occupational performance and mothers' parenting self-competence. Improvements were sustained and appeared to generalize to other areas of performance.
Socially Aware Conference Participant Recommendation With Personality Traits

As a result of the importance of academic collaboration at smart conferences, various researchers have utilized recommender systems to generate effective recommendations for participants. Recent research has shown that the personality traits of users can be used as innovative entities for effective recommendations. Nevertheless, subjective perceptions involving the personality of participants at smart conferences are quite rare and have not gained much attention. Inspired by the personality and social characteristics of users, we present an algorithm called Socially and Personality Aware Recommendation of Participants (SPARP). Our recommendation methodology hybridizes the computations of similar interpersonal relationships and personality traits among participants. SPARP models the personality and social characteristic profiles of participants at a smart conference. By combining the aforementioned recommendation entities, SPARP then recommends participants to each other for effective collaborations. We evaluate SPARP using a relevant data set. Experimental results confirm that SPARP is reliable and outperforms other state-of-the-art methods.
A gut (microbiome) feeling about the brain.

There is an increasing realization that the microorganisms which reside within our gut form part of a complex multidirectional communication network with the brain known as the microbiome-gut-brain axis. In this review, we focus on recent findings which support a role for this axis in modulating neurodevelopment and behavior.



A growing body of research is uncovering that under homeostatic conditions and in response to internal and external stressors, the bacterial commensals of our gut can signal to the brain through a variety of mechanisms to influence processes such neurotransmission, neurogenesis, microglia activation, and modulate behavior. Moreover, the mechanisms underlying the ability of stress to modulate the microbiota and also for microbiota to change the set point for stress sensitivity are being unraveled. Dysregulation of the gut microbiota composition has been identified in a number of psychiatric disorders, including depression. This has led to the concept of bacteria that have a beneficial effect upon behavior and mood (psychobiotics) being proposed for potential therapeutic interventions.



Understanding the mechanisms by which the bacterial commensals of our gut are involved in brain function may lead to the development of novel microbiome-based therapies for these mood and behavioral disorders.
A generalized uncertainty principle and sparse representation in pairs of bases

An elementary proof of a basic uncertainty principle concerning pairs of representations of RN vectors in different orthonormal bases is provided. The result, slightly stronger than stated before, has a direct impact on the uniqueness property of the sparse representation of such vectors using pairs of orthonormal bases as overcomplete dictionaries. The main contribution in this paper is the improvement of an important result due to Donoho and Huo concerning the replacement of the l0 optimization problem by a linear programming minimization when searching for the unique sparse representation.
The Complementarity of Information Technology Infrastructure and E-Commerce Capability: A Resource-Based Assessment of Their Business Value

This study seeks to assess the business value of e-commerce capability and information technology (IT) infrastructure in the context of electronic business at the firm level. Grounded in the IT business-value literature and enhanced by the resource-based theory of the firm, we developed a research framework in which both the main effects and the interaction effects of e-commerce and IT on firm performance were tested. Within this theoretical framework, we formulated several hypotheses. We then developed a multidimensional e-commerce capability construct, and after establishing its validity and reliability, tested the hypotheses with empirical data from 114 companies in the retail industry. Controlling for variations of firm size and subindustry effects, our empirical analysis found a strong positive interaction effect between IT infrastructure and e-commerce capability. This suggests that their complementarity positively contributes to firm performance in terms of sales per employee, inventory turnover, and cost reduction. The results are consistent with the resource-based theory, and provide empirical evidence to the complementary synergy between front-end e-commerce capability and back-end IT infrastructure. Combined together, they become more effective in producing business value. Yet the value of this synergy has not been recognized in the IT payoff literature. The “productivity paradox” observed in various studies has been attributed to variation in methods and measures, yet we offer an additional explanation: ignoring complementarities in business value measurement implies that the impact of IT was seriously underestimated. Our results emphasized the integration of resources as a feasible path to e-commerce value—companies need to enhance the integration between front-end e-commerce capability and back-end IT infrastructure in order to reap the benefits of e-commerce investments.
How (and where) does moral judgment work?

Moral psychology has long focused on reasoning, but recent evidence suggests that moral judgment is more a matter of emotion and affective intuition than deliberate reasoning. Here we discuss recent findings in psychology and cognitive neuroscience, including several studies that specifically investigate moral judgment. These findings indicate the importance of affect, although they allow that reasoning can play a restricted but significant role in moral judgment. They also point towards a preliminary account of the functional neuroanatomy of moral judgment, according to which many brain areas make important contributions to moral judgment although none is devoted specifically to it.
Visualizing Data using t-SNE

We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large datasets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of datasets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the datasets.
Supporting Fast Iteration in Model Building

Building real-world machine learning models involves an iterative process of engineering features, fitting models, and tuning parameters until a model that meets specific acceptance criteria is identified. Over the course of a modeling session, tens to hundreds of models may be built and evaluated. Although the modeling process is iterative, current tools are optimized for individual models as opposed to modeling sessions. In this paper, we argue the need for a system to support the entire modeling process by making it cheap to run and track experiments. We describe the desiderata for such a system spanning systems optimizations to visual interfaces, and describe the ongoing implementation of SHERLOCK, a system optimized for iterative model building.
Design & Check Cyclic Redundancy Code using VERILOG HDL

1096 Abstract-the CRC or cyclic redundancy check is a widely used technique for error checking in many protocols used in data transmission. The aim of this project is to design the CRC RTL generator or a tool that calculates the CRC equations for the given CRC polynomials and generates the Verilog RTL code .This block deals with the calculation of equations for standard polynomials like CRC-4, CRC-8, CRC-16, CRC-32 and CRC-48, CRC-64 and also user defined proprietary polynomial. To use PERL as the platform it also aims at having a simpler user interface. To generate the RTLs for any data width and for any standard polynomial or user defined polynomial, this design aims to be complete generic. The RTLs generated by this tool are verified by System Verilog constrained random testing to make it more robust and reliable. ACRC (Cyclic Redundancy Check)  is a popular error detecting code computed through binary polynomial division. To generate a CRC, the sender treats binary data as a binary polynomial and performs the modulo-2 division of the polynomial by a standard generator (e.g., CRC-32[2]). The remainder of this division becomes the CRC of the data, and it is attached to the original data and transmitted to the receiver. Receiving the data and CRC, the receiver also performs the modulo-2 division with the received data and the same generator polynomial. Errors are detected by comparing the computed CRC with the received one. The CRC algorithm only adds a small number of bits (32 bits in the case of CRC-32) to the message regardless of the length of the original data, and shows good performance in detecting a single error as well as an error burst. Because the CRC algorithm is good at detecting errors and is simple to implement in hardware, CRCs are widely used today for detecting corruption in digital data which may have occurred during production, transmission, or storage. And CRCs have recently found a new application in universal mobile telecommunications system standard for message length detection of variable-length message communications . Traditionally, the LFSR (Linear Feedback Shift Register) circuit is implemented in VLSI (Very-Large-Scale Integration) to perform CRC calculation which can only process one bit per cycle . In this project the method used for the generation of CRC polynomials is based on the LFSR CRC implementation, where the CRC is calculated by passing each data bit every cycle, feeding the …
Fundamental limits on adversarial robustness

The goal of this paper is to analyze an intriguing phenomenon recently discovered in deep networks, that is their instability to adversarial perturbations (Szegedy et al., 2014). We provide a theoretical framework for analyzing the robustness of classifiers to adversarial perturbations, and establish fundamental limits on the robustness of some classifiers in terms of a distinguishability measure between the classes. Our result implies that in tasks involving small distinguishability, no classifier in the considered set will be robust to adversarial perturbations, even if a good accuracy is achieved. Moreover, we show the existence of a clear distinction between the robustness of a classifier to random noise and its robustness to adversarial perturbations. Specifically, in high dimensions, the former is shown to be much larger than the latter for linear classifiers. This result gives a theoretical explanation for the discrepancy between the two robustness properties, which was empirically observed in (Szegedy et al., 2014) in the context of neural networks. Our theoretical framework shows that the adversarial instability is a phenomenon that goes beyond deep networks, and affects all classifiers. Unlike the initial belief that adversarial examples are caused by the high non-linearity of neural networks, our results suggest instead that this phenomenon is due to the low flexibility of classifiers, compared to the difficulty of the classification task, which is captured by the distinguishability measure. We believe these results ICML 2015 Workshop on Deep Learning, Lille, France.
IT Governance Mechanisms in Multisourcing--A Business Group Perspective

Many international corporations are adopting multisourcing strategies. Multisourcing is defined as the blending of services from multiple company-internal and external suppliers. To date, performance measurement and governance-related aspects have been scarcely covered in the academic literature of IT outsourcing. With this study, we intend to broaden understanding of how business groups utilize governance mechanisms in a multi-sourced IT environment. This article contributes to the body of knowledge in three ways. First, it describes established IT governance mechanisms, second, it presents insights into a real-life example of IT governance mechanisms at a leading financial services provider, and third, it extends current literature toward multisourcing in a group-context by proposing a framework of IT governance mechanisms targeting the different relationships of multisourcing in business groups. In addition, this research is useful for corporations facing similar challenges.