nils-herrmann/clean_scirepeval huggingface dataset:
Systems Chronotherapeutics

Chronotherapeutics aim at treating illnesses according to the endogenous biologic rhythms, which moderate xenobiotic metabolism and cellular drug response. The molecular clocks present in individual cells involve approximately fifteen clock genes interconnected in regulatory feedback loops. They are coordinated by the suprachiasmatic nuclei, a hypothalamic pacemaker, which also adjusts the circadian rhythms to environmental cycles. As a result, many mechanisms of diseases and drug effects are controlled by the circadian timing system. Thus, the tolerability of nearly 500 medications varies by up to fivefold according to circadian scheduling, both in experimental models and/or patients. Moreover, treatment itself disrupted, maintained, or improved the circadian timing system as a function of drug timing. Improved patient outcomes on circadian-based treatments (chronotherapy) have been demonstrated in randomized clinical trials, especially for cancer and inflammatory diseases. However, recent technological advances have highlighted large interpatient differences in circadian functions resulting in significant variability in chronotherapy response. Such findings advocate for the advancement of personalized chronotherapeutics through interdisciplinary systems approaches. Thus, the combination of mathematical, statistical, technological, experimental, and clinical expertise is now shaping the development of dedicated devices and diagnostic and delivery algorithms enabling treatment individualization. In particular, multiscale systems chronopharmacology approaches currently combine mathematical modeling based on cellular and whole-body physiology to preclinical and clinical investigations toward the design of patient-tailored chronotherapies. We review recent systems research works aiming to the individualization of disease treatment, with emphasis on both cancer management and circadian timing system-resetting strategies for improving chronic disease control and patient outcomes.
Self-tuning for cloud database clusters

The well known approaches of tuning and self-tuning of data management systems are essential in the context of the Cloud environment, which promises self management properties, such as elasticity, scalability, and fault tolerance. Moreover, the intricate Cloud storage systems criteria, such as their modular, distributed, and multi-layered architecture, add to the complexity of the tuning process and necessity of the self-tuning process. Furthermore, if we have one or more applications with one or more workloads with contradicting and possibly changing optimization goals, we are faced with the question of how to tune the underlying storage system cluster to best achieve the optimization goals of all workloads. Based on that, we define the tuning problem as finding the cluster configuration out of a set of possible configurations that would minimize the aggregated cost value for all workloads while still fulfilling their performance thresholds. In order to solve such a problem, we investigate the design and implementation of a Cloud storage system agnostic (self-)tuning framework. This framework consists of components to observe, and model different performance criteria of the underlying Cloud storage system. It also includes a decision model to configure tuning parameters based on applications requirements. To model the performance of the underlying Cloud storage system, we use statistical machine learning techniques. The statistical data that is needed to model the performance can be generated in a training phase. For that we designed a training component that generates workloads and automates the testing process with different cluster configurations. As part of our evaluation, we address the essential problem of tuning the cluster size of the Cloud storage system while minimizing the latency for the targeted workloads. In order to do that, we model the latency in relation to cluster size and workload characteristics. The predictive models can then be used by the decision component to search for the optimal allocation of nodes to workloads. We also evaluate different alternatives for the search algorithms as part of the decision component implementation. These alternatives include brute-force, and genetic algorithm approaches.
Predicting the Price of Bitcoin Using Machine Learning

The goal of this paper is to ascertain with what accuracy the direction of Bitcoin price in USD can be predicted. The price data is sourced from the Bitcoin Price Index. The task is achieved with varying degrees of success through the implementation of a Bayesian optimised recurrent neural network (RNN) and a Long Short Term Memory (LSTM) network. The LSTM achieves the highest classification accuracy of 52% and a RMSE of 8%. The popular ARIMA model for time series forecasting is implemented as a comparison to the deep learning models. As expected, the non-linear deep learning methods outperform the ARIMA forecast which performs poorly. Finally, both deep learning models are benchmarked on both a GPU and a CPU with the training time on the GPU outperforming the CPU implementation by 67.7%.
On the universality and cultural specificity of emotion recognition: a meta-analysis.

A meta-analysis examined emotion recognition within and across cultures. Emotions were universally recognized at better-than-chance levels. Accuracy was higher when emotions were both expressed and recognized by members of the same national, ethnic, or regional group, suggesting an in-group advantage. This advantage was smaller for cultural groups with greater exposure to one another, measured in terms of living in the same nation, physical proximity, and telephone communication. Majority group members were poorer at judging minority group members than the reverse. Cross-cultural accuracy was lower in studies that used a balanced research design, and higher in studies that used imitation rather than posed or spontaneous emotional expressions. Attributes of study design appeared not to moderate the size of the in-group advantage.
Location Based Data Aggregation with Energy Aware Scheduling at RSU for Effective Message Dissemination in VANET

Vehicular adhoc networks (VANETs) are relegated as a subgroup of Mobile adhoc networks (MANETs), with the incorporation of its principles. In VANET the moving nodes are vehicles which are self-administrated, not bounded and are free to move and organize themselves in the network. VANET possess the potential of improving safety on roads by broadcasting information associated with the road conditions. This results in generation of the redundant information been disseminated by vehicles. Thus bandwidth issue becomes a major concern. In this paper, Location based data aggregation technique is been proposed for aggregating congestion related data from the road areas through which vehicles travelled. It also takes into account scheduling mechanism at the road side units (RSUs) for treating individual vehicles arriving in its range on the basis of first-cum-first order. The basic idea behind this work is to effectually disseminate the aggregation information related to congestion to RSUs as well as to the vehicles in the network. The Simulation results show that the proposed technique performs well with the network load evaluation parameters.
Organizational culture and knowledge sharing: critical success factors

This research aims at investigating the role of certain factors in organizational culture in the success of knowledge sharing. Such factors as interpersonal trust, communication between staff, information systems, rewards and organization structure play an important role in defining the relationships between staff and in turn, providing possibilities to break obstacles to knowledge sharing. This research is intended to contribute in helping businesses understand the essential role of organizational culture in nourishing knowledge and spreading it in order to become leaders in utilizing their know-how and enjoying prosperity thereafter.  The conclusions of this study are based on interpreting the results of a survey and a number of interviews with staff from various organizations in Bahrain from the public and private sectors.  The research findings indicate that trust, communication, information systems, rewards and organization structure are positively related to knowledge sharing in organizations.  The authors believe that further research is required to address governmental sector institutions, where organizational politics dominate a role in hoarding knowledge, through such methods as case studies and observation.  Previous research indicated that the Bahraini society is influenced by traditions of household, tribe, and especially religion of the Arab and Islamic world. These factors define people’s beliefs and behaviours, and thus exercise strong influence in the performance of business organizations. This study is motivated by the desire to explore the role of the national organizational culture on knowledge sharing, which may be different from previous studies conducted abroad.
Dissemination of Antimicrobial Resistance in Microbial Ecosystems through Horizontal Gene Transfer

The emergence and spread of antibiotic resistance among pathogenic bacteria has been a rising problem for public health in recent decades. It is becoming increasingly recognized that not only antibiotic resistance genes (ARGs) encountered in clinical pathogens are of relevance, but rather, all pathogenic, commensal as well as environmental bacteria-and also mobile genetic elements and bacteriophages-form a reservoir of ARGs (the resistome) from which pathogenic bacteria can acquire resistance via horizontal gene transfer (HGT). HGT has caused antibiotic resistance to spread from commensal and environmental species to pathogenic ones, as has been shown for some clinically important ARGs. Of the three canonical mechanisms of HGT, conjugation is thought to have the greatest influence on the dissemination of ARGs. While transformation and transduction are deemed less important, recent discoveries suggest their role may be larger than previously thought. Understanding the extent of the resistome and how its mobilization to pathogenic bacteria takes place is essential for efforts to control the dissemination of these genes. Here, we will discuss the concept of the resistome, provide examples of HGT of clinically relevant ARGs and present an overview of the current knowledge of the contributions the various HGT mechanisms make to the spread of antibiotic resistance.
Attacking convolutional neural network using differential evolution

The output of convolutional neural networks (CNNs) has been shown to be discontinuous which can make the CNN image classifier vulnerable to small well-tuned artificial perturbation. That is, images modified by conducting such alteration (i.e., adversarial perturbation) that make little difference to the human eyes can completely change the CNN classification results. In this paper, we propose a practical attack using differential evolution (DE) for generating effective adversarial perturbations. We comprehensively evaluate the effectiveness of different types of DEs for conducting the attack on different network structures. The proposed method only modifies five pixels (i.e., few-pixel attack), and it is a black-box attack which only requires the miracle feedback of the target CNN systems. The results show that under strict constraints which simultaneously control the number of pixels changed and overall perturbation strength, attacking can achieve 72.29%, 72.30%, and 61.28% non-targeted attack success rates, with 88.68%, 83.63%, and 73.07% confidence on average, on three common types of CNNs. The attack only requires modifying five pixels with 20.44, 14.28, and 22.98 pixel value distortion. Thus, we show that current deep neural networks are also vulnerable to such simpler black-box attacks even under very limited attack conditions.
Deep Learning and Convolutional Neural Networks in the Aid of the Classification of Melanoma

Pattern recognition in digital images is a major limitation in machine learning area. But, in recent years, deep learning has rapidly been diffused, providing large advancements in visual computing by solving the main problems that machine learning imposes. Based on these advances, this study aims to improve results of a problem well-known by visual computing, the classification of melanoma, this one is classified as a malignant tumor, highly invasive and easily confused with other skin diseases. To achieve this, we use some techniques of deep learning to try to get better results in the task of classifying whether a melanotic lesion is the malignant type (melanoma) or not (nevus). In this work we present a training approach using a custom dataset of skin diseases, transfer learning, convolutional neural networks and data augmentation of the deep network ResNet (Deep Residual Network). Keywords-deep learning; convolutional neural networks; melanoma classification;
PROCESS : A Versatile Computational Tool for Observed Variable Mediation , Moderation , and Conditional Process Modeling 1

Statistical mediation and moderation analysis are widespread throughout the behavioral sciences. Increasingly, these methods are being integrated in the form of the analysis of ―mediated moderation‖ or ―moderated mediation,‖ or what Hayes and Preacher (in press) call conditional process modeling. In this paper, I offer a primer on some of the important concepts and methods in mediation analysis, moderation analysis, and conditional process modeling prior to describing PROCESS, a versatile modeling tool freely-available for SPSS and SAS that integrates many of the functions of existing and popular published statistical tools for mediation and moderation analysis as well as their integration. Examples of the use of PROCESS are provided, and some of its additional features as well as some limitations are described.
Towards a scientific blockchain framework for reproducible data analysis

Publishing reproducible analyses is a long-standing and widespread challenge  for the scientific community, funding bodies and publishers [2, 3, 4]. Although a definitive solution is still elusive [5], the problem is recognized to affect all disciplines [6, 7, 8] and lead to a critical system inefficiency [9]. Here, we propose a blockchain-based approach to enhance scientific reproducibility, with a focus on life science studies and precision medicine. While the interest of encoding permanently into an immutable ledger all the study key information–including endpoints, data and metadata, protocols, analytical methods and all findings–has been already highlighted, here we apply the blockchain approach to solve the issue of rewarding time and expertise of scientists that commit to verify reproducibility. Our mechanism builds a trustless ecosystem of researchers, funding bodies and publishers cooperating to guarantee digital and permanent access to information and reproducible results. As a natural byproduct, a procedure to quantify scientists’ and institutions’ reputation for ranking purposes is obtained.
A Dynamic Window Neural Network for CCG Supertagging

Combinatory Category Grammar (CCG) supertagging is a task to assign lexical categories to each word in a sentence. Almost all previous methods use fixed context window sizes to encode input tokens. However, it is obvious that different tags usually rely on different context window sizes. This motivates us to build a supertagger with a dynamic window approach, which can be treated as an attention mechanism on the local contexts. We find that applying dropout on the dynamic filters is superior to the regular dropout on word embeddings. We use this approach to demonstrate the state-ofthe-art CCG supertagging performance on the standard test set.  Combinatory Category Grammar (CCG) provides a connection between syntax and semantics of natural language. The syntax can be specified by derivations of the lexicon based on the combinatory rules, and the semantics can be recovered from a set of predicate-argument relations. CCG provides an elegant solution for a wide range of semantic analysis, such as semantic parsing (Zettlemoyer and Collins 2007; Kwiatkowski et al. 2010; 2011; Artzi, Lee, and Zettlemoyer 2015), semantic representations (Bos et al. 2004; Bos 2005; 2008; Lewis and Steedman 2013), and semantic compositions, all of which heavily depend on the supertagging and parsing performance. All these motivate us to build a more accurate CCG supertagger. CCG supertagging is the task to predict the lexical categories for each word in a sentence. Existing algorithms on CCG supertagging range from point estimation (Clark and Curran 2007; Lewis and Steedman 2014) to sequential estimation (Xu, Auli, and Clark 2015; Lewis, Lee, and Zettlemoyer 2016; Vaswani et al. 2016), which predict the most probable supertag of the current word according to the context in a fixed size window. This fixed size window assumption is too strong to generalize. We argue this from two perspectives. One perspective comes from the inputs. For a particular word, the number of its categories may vary from 1 to 130 in CCGBank 02-21 (Hockenmaier and Steedman 2007). We ∗Corresponding author.
Multi-scale Pyramid Feature Maps for Object Detection

this paper presents how we can achieve the state-of-the-art accuracy in multi-scale objection detection task while adopting and combining the recent technical innovation in deep learning. Following the common pipeline of CNN feature extraction, we mainly design the architecture of feature extraction which exploits the idea of feature pyramid. We further add an extra 1*1 convolution layer to benefit feature extraction, via the batch normalization. In addition, the designed network architecture for feature extraction combines low-resolution and high-resolution feature layers to predict the category of the object in images. The new architecture is trained with the help of batch normalization, mean pooling based on plateau detection. The proposed architecture shows competitive results compared to some state-of-the-art algorithms both in accuracy and in speed on some datasets.
Automatic Keyword Extraction for Text Summarization: A Survey

In recent times, data is growing rapidly in every domain such as news, social media, banking, education, etc. Due to the excessiveness of data, there is a need of automatic summarizer which will be capable to summarize the data especially textual data in original document without losing any critical purposes. Text summarization is emerged as an important research area in recent past. In this regard, review of existing work on text summarization process is useful for carrying out further research. In this paper, recent literature on automatic keyword extraction and text summarization are presented since text summarization process is highly depend on keyword extraction. This literature includes the discussion about different methodology used for keyword extraction and text summarization. It also discusses about different databases used for text summarization in several domains along with evaluation matrices. Finally, it discusses briefly about issues and research challenges faced by researchers along with future direction.
Computer Vision – ECCV 2016 Workshops

This paper presents a robust multi-class multi-object tracking (MCMOT) formulated by a Bayesian filtering framework. Multiobject tracking for unlimited object classes is conducted by combining detection responses and changing point detection (CPD) algorithm. The CPD model is used to observe abrupt or abnormal changes due to a drift and an occlusion based spatiotemporal characteristics of track states. The ensemble of convolutional neural network (CNN) based object detector and Lucas-Kanede Tracker (KLT) based motion detector is employed to compute the likelihoods of foreground regions as the detection responses of different object classes. Extensive experiments are performed using lately introduced challenging benchmark videos; ImageNet VID and MOT benchmark dataset. The comparison to state-of-the-art video tracking techniques shows very encouraging results.
Weakly- and Semi-Supervised Object Detection with Expectation-Maximization Algorithm

Object detection when provided image-level labels instead of instance-level labels (i.e., bounding boxes) during training is an important problem in computer vision, since large scale image datasets with instance-level labels are extremely costly to obtain. In this paper, we address this challenging problem by developing an ExpectationMaximization (EM) based object detection method using deep convolutional neural networks (CNNs). Our method is applicable to both the weakly-supervised and semisupervised settings. Extensive experiments on PASCAL VOC 2007 benchmark show that (1) in the weakly supervised setting, our method provides significant detection performance improvement over current state-of-the-art methods, (2) having access to a small number of strongly (instance-level) annotated images, our method can almost match the performace of the fully supervised Fast RCNN. We share our source code at https://github.com/
Multi-View Clustering and Feature Learning via Structured Sparsity

Combining information from various data sources has become an important research topic in machine learning with many scientific applications. Most previous studies employ kernels or graphs to integrate different types of features, which routinely assume one weight for one type of features. However, for many problems, the importance of features in one source to an individual cluster of data can be varied, which makes the previous approaches ineffective. In this paper, we propose a novel multi-view learning model to integrate all features and learn the weight for every feature with respect to each cluster individually via new joint structured sparsity-inducing norms. The proposed multi-view learning framework allows us not only to perform clustering tasks, but also to deal with classification tasks by an extension when the labeling knowledge is available. A new efficient algorithm is derived to solve the formulated objective with rigorous theoretical proof on its convergence. We applied our new data fusion method to five broadly used multi-view data sets for both clustering and classification. In all experimental results, our method clearly outperforms other related state-of-the-art methods.