nils-herrmann/clean_scirepeval huggingface dataset:
CARET model checking for malware detection

The number of malware is growing significantly fast. Traditional malware detectors based on signature matching or code emulation are easy to get around. To overcome this problem, model-checking emerges as a technique that has been extensively applied for malware detection recently. Pushdown systems were proposed as a natural model for programs, since they allow to keep track of the stack, while extensions of LTL and CTL were considered for malicious behavior specification. However, LTL and CTL like formulas don't allow to express behaviors with matching calls and returns. In this paper, we propose to use CARET for malicious behavior specification. Since CARET formulas for malicious behaviors are huge, we propose to extend CARET with variables, quantifiers and predicates over the stack. Our new logic is called SPCARET. We reduce the malware detection problem to the model checking problem of PDSs against SPCARET formulas, and we propose efficient algorithms to model check SPCARET formulas for PDSs. We implemented our algorithms in a tool for malware detection. We obtained encouraging results.
Selected major risk factors and global and regional burden of disease

Reliable and comparable analysis of risks to health is key for preventing disease and injury. Causal attribution of morbidity and mortality to risk factors has traditionally been in the context of individual risk factors, often in a limited number of settings, restricting comparability. Our aim was to estimate the contributions of selected major risk factors to global and regional burden of disease in a unified framework.



For 26 selected risk factors, expert working groups undertook a comprehensive review of published work and other sources--eg, government reports and international databases--to obtain data on the prevalence of risk factor exposure and hazard size for 14 epidemiological regions of the world. Population attributable fractions were estimated by applying the potential impact fraction relation, and applied to the mortality and burden of disease estimates from the global burden of disease (GBD) database.



Childhood and maternal underweight (138 million disability adjusted life years [DALY], 9.5%), unsafe sex (92 million DALY, 6.3%), high blood pressure (64 million DALY, 4.4%), tobacco (59 million DALY, 4.1%), and alcohol (58 million DALY, 4.0%) were the leading causes of global burden of disease. In the poorest regions of the world, childhood and maternal underweight, unsafe sex, unsafe water, sanitation, and hygiene, indoor smoke from solid fuels, and various micronutrient deficiencies were major contributors to loss of healthy life. In both developing and developed regions, alcohol, tobacco, high blood pressure, and high cholesterol were major causes of disease burden.



Substantial proportions of global disease burden are attributable to these major risks, to an extent greater than previously estimated. Developing countries suffer most or all of the burden due to many of the leading risks. Strategies that target these known risks can provide substantial and underestimated public-health gains.
The Hologram in My Hand: How Effective is Interactive Exploration of 3D Visualizations in Immersive Tangible Augmented Reality?

We report on a controlled user study comparing three visualization environments for common 3D exploration. Our environments differ in how they exploit natural human perception and interaction capabilities. We compare an augmented-reality head-mounted display (Microsoft HoloLens), a handheld tablet, and a desktop setup. The novel head-mounted HoloLens display projects stereoscopic images of virtual content into a user's real world and allows for interaction in-situ at the spatial position of the 3D hologram. The tablet is able to interact with 3D content through touch, spatial positioning, and tangible markers, however, 3D content is still presented on a 2D surface. Our hypothesis is that visualization environments that match human perceptual and interaction capabilities better to the task at hand improve understanding of 3D visualizations. To better understand the space of display and interaction modalities in visualization environments, we first propose a classification based on three dimensions: perception, interaction, and the spatial and cognitive proximity of the two. Each technique in our study is located at a different position along these three dimensions. We asked 15 participants to perform four tasks, each task having different levels of difficulty for both spatial perception and degrees of freedom for interaction. Our results show that each of the tested environments is more effective for certain tasks, but that generally the desktop environment is still fastest and most precise in almost all cases.
China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up.

Large blood-based prospective studies can provide reliable assessment of the complex interplay of lifestyle, environmental and genetic factors as determinants of chronic disease.



The baseline survey of the China Kadoorie Biobank took place during 2004-08 in 10 geographically defined regions, with collection of questionnaire data, physical measurements and blood samples. Subsequently, a re-survey of 25,000 randomly selected participants was done (80% responded) using the same methods as in the baseline. All participants are being followed for cause-specific mortality and morbidity, and for any hospital admission through linkages with registries and health insurance (HI) databases.



Overall, 512,891 adults aged 30-79 years were recruited, including 41% men, 56% from rural areas and mean age was 52 years. The prevalence of ever-regular smoking was 74% in men and 3% in women. The mean blood pressure was 132/79 mmHg in men and 130/77 mmHg in women. The mean body mass index (BMI) was 23.4 kg/m(2) in men and 23.8 kg/m(2) in women, with only 4% being obese (>30 kg/m(2)), and 3.2% being diabetic. Blood collection was successful in 99.98% and the mean delay from sample collection to processing was 10.6 h. For each of the main baseline variables, there is good reproducibility but large heterogeneity by age, sex and study area. By 1 January 2011, over 10,000 deaths had been recorded, with 91% of surviving participants already linked to HI databases.



This established large biobank will be a rich and powerful resource for investigating genetic and non-genetic causes of many common chronic diseases in the Chinese population.
A Statistical Model for Slurry Thickening

Thickening is the first step in the design of sustainable (cost effective, environmentally friendly, and socially viable) tailings management solutions for surface deposition, mine backfilling, and sub-aqueous discharge. The high water content slurries are converted to materials with superior dewatering properties by adding long-chain synthetic polymers. Given the solid and liquid composition of a slurry, a high settling rate alongside a high solids content can be achieved by optimizing the various polymers parameters: ionic type (T), charge density (C), molecular weight (M), and dosage (D). This paper developed a statistical model to predict field performance of a selected metal mine slurry using laboratory test data. Results of sedimentationconsolidation tests were fitted using the method of least squares. A newly devised polymer characteristic coefficient (Cp) that combined the various polymer parameters correlated well with the observed dewatering behavior as the R equalled 0.95 for void ratio and 0.84 for hydraulic conductivity. The various combinations of polymer parameters resulted in variable slurry performance during sedimentation and were found to converge during consolidation. Further, the void ratio-effective stress and the hydraulic conductivity-void ratio relationships were found to be e = a σ′ b and k = 10 (c + e , respectively.
Biclustering neighborhood-based collaborative filtering method for top-n recommender systems

We propose a novel collaborative filtering method for top- $$n$$ n recommendation task using bicustering neighborhood approach. Our method takes advantage of local biclustering structure for a more precise and localized collaborative filtering. Using several important properties from the field of Formal Concept Analysis, we build user-specific biclusters that are “more personalized” to the users of interest. We create an innovative rank scoring of candidate items that combines local similarity of biclusters with global similarity. Our method is parameter-free, thus removing the need for tuning parameters. It is easily scalable and can efficiently make recommendations. We demonstrate the performance of our algorithm using several standard benchmark datasets and two paypal (in-house) datasets. Our experiments show that our method generates better recommendations compared to several state-of-the-art algorithms, especially in the presence of sparse data. Furthermore, we also demonstrated the robustness of our approach to increasing data sparsity and the number of users.
Exercise-induced neuroplasticity: Balance training increases cortical thickness in visual and vestibular cortical regions

Physical exercise has been shown to induce structural plasticity in the human brain and to enhance cognitive functions. While previous studies focused on aerobic exercise, suggesting a link between increased cardiorespiratory fitness and exercise-induced neuroplasticity, recent findings have suggested that whole-body exercise with minor metabolic demands elicits beneficial effects on brain structure as well. In the present study, we tested if balance training, challenging the sensory-motor system and vestibular self-motion perception, induces structural plasticity. Thirty-seven healthy adults aged 19-65 years were randomly assigned to either a balance training or a relaxation training group. All participants exercised twice a week for 12 weeks. Assessments before and after the training included a balance test and the acquisition of high-resolution T1-weighted images to analyze morphological brain changes. Only the balance group significantly improved balance performance after training. Cortical thickness was increased in the superior temporal cortex, in visual association cortices, in the posterior cingulate cortex, in the superior frontal sulcus, and in the precentral gyri in the balance group, compared to the relaxation group. Moreover, there was evidence that the balance training resulted in decreased putamen volume. Improved balance performance correlated with the increase of precentral cortical thickness and the decrease in putamen volume. The results suggest that balance training elicits neuroplasticity in brain regions associated with visual and vestibular self-motion perception. As these regions are known for their role in spatial orienting and memory, stimulating visual-vestibular pathways during self-motion might mediate beneficial effects of physical exercise on cognition.
Angewandte Mathematik Und Informatik Universit at Zu K Oln Level Planar Embedding in Linear Time

A level graph G = (V; E;) is a directed acyclic graph with a mapping : V ! for i 6 = j, such that (v) (u) + 1 for each edge (u; v) 2 E. The level planarity testing problem is to decide if G can be drawn in the plane such that for each level V i , all v 2 V i are drawn on the line l i = f(x; k ? i) j x 2 Rg, the edges are drawn monotonically with respect to the vertical direction, and no edges intersect except at their end vertices. In order to draw a level planar graph without edge crossings, a level planar embedding of the level graph has to be computed. Level planar embeddings are characterized by linear orderings of the vertices in each V i (1 i k). We present an O(jV j) time algorithm for embedding level planar graphs. This approach is based on a level planarity test by J unger, Leipert, and Mutzel 1998].
An evolutionary theory of human motivation.

The authors review psychology's historical, competing perspectives on human motivation and propose a new comprehensive theory. The new theory is based on evolutionary principles as proposed by C. Darwin (1859) and modified by W. D. Hamilton (1964, 1996), R. L. Trivers (1971, 1972), and R. Dawkins (1989). The theory unifies biological, behavioral, and cognitive approaches to motivation. The theory is neuropsychological and addresses conscious and nonconscious processes that underlie motivation, emotion, and self-control. The theory predicts a hierarchical structure of motives that are measurable as individual differences in human behavior. These motives are related to social problem domains (D. B. Bugental, 2000; D. T. Kenrick, N. P. Li, & J. Butner, 2003), and each is hypothesized to solve a particular problem of human inclusive fitness.
Why Does the Neocortex Have Layers and Columns, A Theory of Learning the 3D Structure of the World

Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensor data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed.
Membership Inference Attack against Differentially Private Deep Learning Model

The unprecedented success of deep learning is largely dependent on the availability of massive amount of training data. In many cases, these data are crowd-sourced and may contain sensitive and confidential information, therefore, pose privacy concerns. As a result, privacy-preserving deep learning has been gaining increasing focus nowadays. One of the promising approaches for privacy-preserving deep learning is to employ differential privacy during model training which aims to prevent the leakage of sensitive information about the training data via the trained model. While these models are considered to be immune to privacy attacks, with the advent of recent and sophisticated attack models, it is not clear how well these models trade-off utility for privacy. In this paper, we systematically study the impact of a sophisticated machine learning based privacy attack called the membership inference attack against a state-of-the-art differentially private deep model. More specifically, given a differentially private deep model with its associated utility, we investigate how much we can infer about the model’s training data. Our experimental results show that differentially private deep models may keep their promise to provide privacy protection against strong adversaries by only offering poor model utility, while exhibit moderate vulnerability to the membership inference attack when they offer an acceptable utility. For evaluating our experiments, we use the CIFAR-10 and MNIST datasets and the corresponding classification tasks.
Effective Pattern Discovery for Text Mining

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.
Feature-rich Encoder Memory-based Answer Pointer Iterative Aligner

In this paper, we introduce the Reinforced Mnemonic Reader for machine comprehension (MC) task, which aims to answer a query about a given context document. We propose several novel mechanisms that address critical problems in MC that are not adequately solved by previous works, such as enhancing the capacity of encoder, modeling long-term dependencies of contexts, refining the predicted answer span, and directly optimizing the evaluation metric. Extensive experiments on TriviaQA and Stanford Question Answering Dataset (SQuAD) show that our model achieves state-of-theart results.
Generating Textual Adversarial Examples for Deep Learning Models: A Survey

With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications. However, previous efforts have shown that DNNs were vulnerable to strategically modified samples, named adversarial examples. These samples are generated with some imperceptible perturbations, but can fool the DNNs to give false predictions. Inspired by the popularity of generating adversarial examples for image DNNs, research efforts on attacking DNNs for textual applications emerges in recent years. However, existing perturbation methods for images cannot be directly applied to texts as text data is discrete. In this article, we review research works that address this difference and generate textual adversarial examples on DNNs. We collect, select, summarize, discuss and analyze these works in a comprehensive way and cover all the related information to make the article self-contained. Finally, drawing on the reviewed literature, we provide further discussions and suggestions on this topic.
Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks

Recently, realistic image generation using deep neural networks has become a hot topic in machine learning and computer vision. Images can be generated at the pixel level by learning from a large collection of images. Learning to generate colorful cartoon images from black-and-white sketches is not only an interesting research problem, but also a potential application in digital entertainment. In this paper, we investigate the sketch-to-image synthesis problem by using conditional generative adversarial networks (cGAN). We propose the auto-painter model which can automatically generate compatible colors for a sketch. The new model is not only capable of painting hand-draw sketch with proper colors, but also allowing users to indicate preferred colors. Experimental results on two sketch datasets show that the auto-painter performs better that existing image-to-image methods.
Pulmonary nodule classification with deep residual networks

Lung cancer has the highest death rate among all cancers in the USA. In this work we focus on improving the ability of computer-aided diagnosis (CAD) systems to predict the malignancy of nodules from cropped CT images of lung nodules. We evaluate the effectiveness of very deep convolutional neural networks at the task of expert-level lung nodule malignancy classification. Using the state-of-the-art ResNet architecture as our basis, we explore the effect of curriculum learning, transfer learning, and varying network depth on the accuracy of malignancy classification. Due to a lack of public datasets with standardized problem definitions and train/test splits, studies in this area tend to not compare directly against other existing work. This makes it hard to know the relative improvement in the new solution. In contrast, we directly compare our system against two state-of-the-art deep learning systems for nodule classification on the LIDC/IDRI dataset using the same experimental setup and data set. The results show that our system achieves the highest performance in terms of all metrics measured including sensitivity, specificity, precision, AUROC, and accuracy. The proposed method of combining deep residual learning, curriculum learning, and transfer learning translates to high nodule classification accuracy. This reveals a promising new direction for effective pulmonary nodule CAD systems that mirrors the success of recent deep learning advances in other image-based application domains.
EFFECT OF CUSTOMER RELATIONSHIP MANAGEMENT ON PERCEIVED ORGANIZATIONAL PERFORMANCE- A CASE STUDY OF FAMILY BANK LIMITED JULIUS MBUGUA WACHIRA, DR. SUSAN WERE

Customer satisfaction is central to achieving a competitive advantage and should be viewed as an integral part of relationship building that is underpinned by CRM. A defined organizational culture can provide the necessary leadership that ensures that staffs attain high levels of performance. Customer service should be placed in a strategic marketing context, which means that marketers can develop a high quality customer service policy and the organization can put a customer response system in place which ensures that customer service is seen as customer focused. There is an increased need to constantly reengineer business strategies that improve the customer experience and increase profitability for the company. The study sought to determine the effect of customer relationship management (CRM) on the perceived organisational performance with a case of Family Bank Limited. Based on the research topic, the study addressed the research problem by addressing the following research objectives; to determine the effect of changes in the organizational culture on the perceived organizational performance of Family bank limited, to establish the effect of customer retention strategies on the perceived organizational performance of family bank limited, to determine the effect of technology on the perceived organizational performance of family bank limited, and to establish the effect of service quality management on the perceived organizational performance of family bank limited. The study reviewed related literature under theoretical and empirical review to facilitate in identifying the research gap. The study adopted a descriptive research design to address the research objectives. In this case sampling was done using a systematic random sampling technique to select the participating staff in all the bank branches covered. Data was collected in form of both primary and secondary data. The collected data was analysed using both qualitative and quantitative data analysis techniques using the Statistical Package for Social Sciences (SPSS  22).The study found out that customer relationship management has a positive effect on the performance of the bank.The study found out that service quality management was the dimension of CRM that affected the performance of the bank to a very great extent. Therefore, the study recommended that there was a need for organizations to have training and development that are directed at building enduring customer relationships.