nils-herrmann/clean_scirepeval huggingface dataset:
How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance

Past research in the area of information systems acceptance has primarily focused on initial adoption under the implicit assumption that IS usage is mainly determined by intention. Carol Saunders was the accepting senior editor for this paper. Sue Brown was the associate editor. Anol Bhattacherjee and Likoebe Maruping served as reviewers. While plausible in the case of initial IS adoption, this assumption may not be as readily applicable to continued IS usage behavior since it ignores that frequently performed behaviors tend to become habitual and thus automatic over time. This paper is a step forward in defining and incorporating the “habit” construct into IS research. Specifically, the purpose of this study is to explore the role of habit and its antecedents in the context of continued IS usage. Building on previous work in other disciplines, we define habit in the context of IS usage as the extent to which people tend to perform behaviors (use IS) automatically because of learning. Using recent work on the continued usage of IS (IS continuance), we have developed a model suggesting that continued IS usage is not only a consequence of intention, but also of habit. In particular, in our research model, we propose IS habit to moderate the influence of intention such that its importance in determining behavior decreases as the behavior in question takes on a more habitual nature. Integrating past research on habit and IS continuance further, we suggest how antecedents of behavior/behavioral intention as identified by IS continuance research relate to drivers of habitualization. We empirically tested the model in the context of voluntary continued WWW usage. Our results support the argument that habit acts as a moderating variable of the relationship between intentions and IS continuance behavior, which may put a boundary condition on the explanatory power of intentions in the context of continued IS usage. The data also support that satisfaction, frequency of past behavior, and comprehensiveness of usage are key to habit formation and Limayem et al./Limits to the Predictive Power of Intention 706 MIS Quarterly Vol. 31 No. 4/December 2007 thus relevant in the context of IS continuance behavior. Implications of these findings are discussed and managerial guidelines presented.
Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network

Recent years have witnessed great success of convolutional neural network (CNN) for various problems both in low and high level visions. Especially noteworthy is the residual network which was originally proposed to handle high-level vision problems and enjoys several merits. This paper aims to extend the merits of residual network, such as skip connection induced fast training, for a typical low-level vision problem, i.e., single image super-resolution. In general, the two main challenges of existing deep CNN for supper-resolution lie in the gradient exploding/vanishing problem and large numbers of parameters or computational cost as CNN goes deeper. Correspondingly, the skip connections or identity mapping shortcuts are utilized to avoid gradient exploding/vanishing problem. In addition, the skip connections have naturally centered the activation which led to better performance. To tackle with the second problem, a lightweight CNN architecture which has carefully designed width, depth and skip connections was proposed. In particular, a strategy of gradually varying the shape of network has been proposed for residual network. Different residual architectures for image super-resolution have also been compared. Experimental results have demonstrated that the proposed CNN model can not only achieve state-of-the-art PSNR and SSIM results for single image super-resolution but also produce visually pleasant results. This paper has extended the mmm 2017 oral conference paper with a considerable new analyses and more experiments especially from the perspective of centering activations and ensemble behaviors of residual network.
Policy Optimization with Model-based Explorations

Model-free reinforcement learning methods such as the Proximal Policy Optimization algorithm (PPO) have successfully applied in complex decision-making problems such as Atari games. However, these methods suffer from high variances and high sample complexity. On the other hand, model-based reinforcement learning methods that learn the transition dynamics are more sample efficient, but they often suffer from the bias of the transition estimation. How to make use of both model-based and model-free learning is a central problem in reinforcement learning. In this paper, we present a new technique to address the tradeoff between exploration and exploitation, which regards the difference between model-free and model-based estimations as a measure of exploration value. We apply this new technique to the PPO algorithm and arrive at a new policy optimization method, named Policy Optimization with Modelbased Explorations (POME). POME uses two components to predict the actions’ target values: a model-free one estimated by Monte-Carlo sampling and a model-based one which learns a transition model and predicts the value of the next state. POME adds the error of these two target estimations as the additional exploration value for each state-action pair, i.e, encourages the algorithm to explore the states with larger target errors which are hard to estimate. We compare POME with PPO on Atari 2600 games, and it shows that POME outperforms PPO on 33 games out of 49 games.
A tutorial on a practical Bayesian alternative to null-hypothesis significance testing.

Null-hypothesis significance testing remains the standard inferential tool in cognitive science despite its serious disadvantages. Primary among these is the fact that the resulting probability value does not tell the researcher what he or she usually wants to know: How probable is a hypothesis, given the obtained data? Inspired by developments presented by Wagenmakers (Psychonomic Bulletin & Review, 14, 779-804, 2007), I provide a tutorial on a Bayesian model selection approach that requires only a simple transformation of sum-of-squares values generated by the standard analysis of variance. This approach generates a graded level of evidence regarding which model (e.g., effect absent [null hypothesis] vs. effect present [alternative hypothesis]) is more strongly supported by the data. This method also obviates admonitions never to speak of accepting the null hypothesis. An Excel worksheet for computing the Bayesian analysis is provided as supplemental material.
The Toyota way of global knowledge creation the ‘ learn local , act global ’ strategy

This paper presents insights from two case studies of Toyota Motor Corporation and its way of strategic global knowledge creation. We will show how Toyota’s knowledge creation has moved from merely transferring knowledge from Japan to subsidiaries abroad to a focus of creating knowledge in foreign markets by local staff. Toyota’s new strategy of ‘learn local, act global’ for international business development proved successful for tapping rich local knowledge bases, thus ensuring competitive edge. In fact, this strategy finally turned Toyota from simply being a global projector to a truly metanational company.
Deep Discriminative Latent Space for Clustering

Clustering is one of the most fundamental tasks in data analysis and machine learning. It is central to many data-driven applications that aim to separate the data into groups with similar patterns. Moreover, clustering is a complex procedure that is affected significantly by the choice of the data representation method. Recent research has demonstrated encouraging clustering results by learning effectively these representations. In most of these works a deep auto-encoder is initially pre-trained to minimize a reconstruction loss, and then jointly optimized with clustering centroids in order to improve the clustering objective. Those works focus mainly on the clustering phase of the procedure, while not utilizing the potential benefit out of the initial phase. In this paper we propose to optimize an auto-encoder with respect to a discriminative pairwise loss function during the auto-encoder pre-training phase. We demonstrate the high accuracy obtained by the proposed method as well as its rapid convergence (e.g. reaching above 92% accuracy on MNIST during the pre-training phase, in less than 50 epochs), even with small networks.
Measuring the effectiveness of ISO26262 compliant self test library

Automotive SoCs are constantly being tested for correct functional operation, even long after they have left fabrication. The testing is done at the start of operation (car ignition) and repeatedly during operation (during the drive) to check for faults. Faults can result from, but are not restricted to, a failure in a part of a semiconductor circuit such as a failed transistor, interconnect failure due to electromigration, or faults caused by soft errors (e.g., an alpha particle switching a bit in a RAM or other circuit element). While the tests can run long after the chip was taped-out, the safety definition and test plan effort is starting as early as the specification definitions. In this paper we give an introduction to functional safety concentrating on the ISO26262 standard and we touch on a couple of approaches to functional safety for an Intellectual Property (IP) part such as a microprocessor, including software self-test libraries and logic BIST. We discuss the additional effort needed for developing a design for the automotive market. Lastly, we focus on our experience of using fault grading as a method for developing a self-test library that periodically tests the circuit operation. We discuss the effect that implementation decisions have on this effort and why it is important to start with this effort early in the design process.
Parameter estimation techniques: a tutorial with application to conic fitting

Almost all problems in computer vision are related in one form or an other to the problem of estimating parameters from noisy data. In this tutorial, we present what is probably the most commonly used techniques for parameter es timation. These include linear least-squares (pseudo-inverse and eigen analysis); orthogonal least-squares; gradient-weighted least-squares; bias-corrected renormal ization; Kalman ltering; and robust techniques (clustering, regression diagnostics, M-estimators, least median of squares). Particular attention has been devoted to discussions about the choice of appropriate minimization criteria and the robustness of the di erent techniques. Their application to conic tting is described.  Parameter estimation, Least-squares, Bias correction, Kalman lter ing, Robust regression Updated on April 15, 1996 To appear in Image and Vision
A Miniaturized Semantic Segmentation Method for Remote Sensing Image

In order to save the memory, we propose a miniaturization method for neural network to reduce the parameter quantity existed in remote sensing (RS) image semantic segmentation model. The compact convolution optimization method is first used for standard U-Net to reduce the weights quantity. With the purpose of decreasing model performance loss caused by miniaturization and based on the characteristics of remote sensing image, fewer down-samplings and improved cascade atrous convolution are then used to improve the performance of the miniaturized U-Net. Compared with U-Net, our proposed Micro-Net not only achieves 29.26 times model compression, but also basically maintains the performance unchanged on the public dataset. We provide a Keras and Tensorflow hybrid programming implementation for our model:
Cultural Proximity and The Type of Korean Television Programs in Asian Market

The emerging Asian market of Korean broadcasting programs is pushing forward a new phase of cultural marketing. The Korean trend in Asia brought issues such as cultural proximity, and the issues have been analyzed by structural analysis. This article suggests which kind of program Asians adopted as the favorites based on the factors of cultural frame in the aspect of performance. The results of analysis shows that Korean programs satisfy Asian emotional needs as being easy to assimilate to similar life styles, cultural proximity and expressiveness. The preference of Korean programs shows that Asians express sympathy for Asian culture frames including family morals, high morality, love and sacrifice. Additionally, as a case study this paper analyzes the characteristics of the most favorite Korean programs in Asia using five categories: harmony, tension, compromise, participation and agreement. The result of the case study showed that Asian people have a similar culture frame and like stories dealing with love, harmony oriented stories, stories with tension in daily life, low participation and the agreement and reinforcement with their traditional values.
Continual Learning with Neural Networks: A Review

Continual learning broadly refers to the algorithms which aim to learn continuously over time across varying domains, tasks or data distributions. This is in contrast to algorithms restricted to learning a fixed number of tasks in a given domain, assuming a static data distribution. In this survey we aim to discuss a wide breadth of challenges faced in a continual learning setup and review existing work in the area. We discuss parameter regularization techniques to avoid catastrophic forgetting in neural networks followed by memory based approaches and the role of generative models in assisting continual learning algorithms. We discuss how dynamic neural networks assist continual learning by endowing neural networks with a new capacity to learn further. We conclude by discussing possible future directions.
Deep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back. (L'apprentissage profond sur graphes attribués: Un voyage aller-retour aux plongements des graphes)

A graph is a powerful concept for representation of relations between pairs of entities. Data with underlying graph structure can be found across many disciplines, describing chemical compounds, surfaces of three-dimensional models, social interactions, or knowledge bases, to name only a few. There is a natural desire for understanding such data better. Deep learning (DL) has achieved significant breakthroughs in a variety of machine learning tasks in recent years, especially where data is structured on a grid, such as in text, speech, or image understanding. However, surprisingly little has been done to explore the applicability of DL on arbitrary graph-structured data directly. The goal of this thesis is to investigate architectures for DL on graphs and study how to transfer, adapt or generalize concepts that work well on sequential and image data to this domain. We concentrate on two important primitives: embedding graphs or their nodes into a continuous vector space representation (encoding) and, conversely, generating graphs from such vectors back (decoding). To that end, we make the following contributions. First, we introduce Edge-Conditioned Convolutions (ECC), a convolution-like operation on graphs performed in the spatial domain where filters are dynamically generated based on edge attributes. The method is used to encode graphs with arbitrary and varying structure. Second, we propose SuperPoint Graph, an intermediate point cloud representation with rich edge attributes encoding the contextual relationship between object parts. Based on this representation, ECC is employed to segment large-scale point clouds without major sacrifice in fine details. Third, we present GraphVAE, a graph generator allowing us to decode graphs with variable but upper-bounded number of nodes making use of approximate graph matching for aligning the predictions of an autoencoder with its inputs. The method is applied to the task of molecule generation.
Image Generation from Scene Graphs

To truly understand the visual world our models should be able not only to recognize images but also generate them. To this end, there has been exciting recent progress on generating images from natural language descriptions. These methods give stunning results on limited domains such as descriptions of birds or flowers, but struggle to faithfully reproduce complex sentences with many objects and relationships. To overcome this limitation we propose a method for generating images from scene graphs, enabling explicitly reasoning about objects and their relationships. Our model uses graph convolution to process input graphs, computes a scene layout by predicting bounding boxes and segmentation masks for objects, and converts the layout to an image with a cascaded refinement network. The network is trained adversarially against a pair of discriminators to ensure realistic outputs. We validate our approach on Visual Genome and COCO-Stuff, where qualitative results, ablations, and user studies demonstrate our method's ability to generate complex images with multiple objects.
Who compares and despairs ? The effect of social comparison orientation on social media use and its outcomes

a r t i c l e i n f o People vary in their tendencies to compare themselves to others, an individual difference variable called social comparison orientation (SCO). Social networking sites provide information about others that can be used for social comparison. The goal of the present set of studies was to explore the relationship between SCO, Facebook use, and negative psychological outcomes. Studies 1a and 1b used correlational approaches and showed that participants high (vs. low) in SCO exhibited heavier Facebook use. Study 2 used an experimental approach and revealed that participants high in SCO had poorer self-perceptions, lower self-esteem, and more negative affect balance than their low-SCO counterparts after engaging in brief social comparisons on Facebook. SCO did not have as strong or consistent effects for participants engaging in control tasks. Results are discussed in the context of extant literature and the impact of social media use on well-being. In their daily interactions, people are afforded many opportunities to learn about others' opinions, abilities, and lives. Such social comparative information can be useful for a multitude of purposes, including self-Importantly, people differ in their tendencies to engage in social comparison and in the psychological consequences incurred. Social comparison orientation (SCO) is a trait that reflects these individual differences (Gibbons & Buunk, 1999). Individuals high in SCO have a chronic sensitivity to and awareness of others, and experience more uncertainty and instability regarding their self-concepts (Buunk & Gibbons, 2006; Gibbons & Buunk, 1999). For the current research, we suggest that social media offers a novel medium in which social comparison can take place—and that people high in SCO might be more drawn to social media and more affected by the comparisons made therein. 1.1. Social media and social comparison opportunities Social media use has become ubiquitous in many societies, with popular social network sites (SNSs) such as Facebook.com having 1.4 billion active users worldwide (Facebook, 2015). SNSs not only allow users to maintain friendships, form new relationships, and connect with others allow people to construct their own personal profiles and present a rich set of information about themselves (e.g. the rich information we can learn about others and the expansive network of people from which we can learn it (Acar, 2008), SNSs offer up an ideal platform for social comparison to take place. Indeed, it appears that people are quite interested in learning about others on SNSs, …
Progresses towards safe and efficient gene therapy vectors

The emergence of genetic engineering at the beginning of the 1970's opened the era of biomedical technologies, which aims to improve human health using genetic manipulation techniques in a clinical context. Gene therapy represents an innovating and appealing strategy for treatment of human diseases, which utilizes vehicles or vectors for delivering therapeutic genes into the patients' body. However, a few past unsuccessful events that negatively marked the beginning of gene therapy resulted in the need for further studies regarding the design and biology of gene therapy vectors, so that this innovating treatment approach can successfully move from bench to bedside. In this paper, we review the major gene delivery vectors and recent improvements made in their design meant to overcome the issues that commonly arise with the use of gene therapy vectors. At the end of the manuscript, we summarized the main advantages and disadvantages of common gene therapy vectors and we discuss possible future directions for potential therapeutic vectors.
The efficacy and safety of Ginkgo biloba extract as an adjuvant in type 2 diabetes mellitus patients ineffectively managed with metformin: a double-blind, randomized, placebo-controlled trial

Type 2 diabetes mellitus (T2DM) is one of the major diseases confronting the health care systems. In diabetes mellitus (DM), combined use of oral hypoglycemic medications has been shown to be more effective than metformin (Met) alone in glycemic control. This study determined the effects of Ginkgo biloba (GKB) extract as an adjuvant to Met in patients with uncontrolled T2DM.



Sixty T2DM patients were recruited in a randomized, placebo-controlled, double-blinded, and multicenter trial. The patients, currently using Met, were randomly grouped into those treated with either GKB extract (120 mg/day) or placebo (starch, 120 mg/day) for 90 days. Blood glycated hemoglobin (HbA1c), fasting serum glucose, serum insulin, body mass index (BMI), waist circumference (WC), insulin resistance, and visceral adiposity index (VAI) were determined before (baseline) and after 90 days of GKB extract treatment.



GKB extract significantly decreased blood HbA1c (7.7%±1.2% vs baseline 8.6%±1.6%, P<0.001), fasting serum glucose (154.7±36.1 mg/dL vs baseline 194.4±66.1 mg/dL, P<0.001) and insulin (13.4±7.8 μU/mL vs baseline 18.5±8.9 μU/mL, P=0.006) levels, BMI (31.6±5.1 kg/m2 vs baseline 34.0±6.0 kg/m2, P<0.001), waist WC (102.6±10.5 cm vs baseline 106.0±10.9 cm, P<0.001), and VAI (158.9±67.2 vs baseline 192.0±86.2, P=0.007). GKB extract did not negatively impact the liver, kidney, or hematopoietic functions.



GKB extract as an adjuvant was effective in improving Met treatment outcomes in T2DM patients. Thus, it is suggested that GKB extract is an effective dietary supplement for the control of DM in humans.