nils-herrmann/clean_scirepeval huggingface dataset:
Current Advances in Detection and Treatment of Babesiosis

Babesiosis is a disease with a world-wide distribution affecting many species of mammals principally cattle and man. The major impact occurs in the cattle industry where bovine babesiosis has had a huge economic effect due to loss of meat and beef production of infected animals and death. Nowadays to those costs there must be added the high cost of tick control, disease detection, prevention and treatment. In almost a century and a quarter since the first report of the disease, the truth is: there is no a safe and efficient vaccine available, there are limited chemotherapeutic choices and few low-cost, reliable and fast detection methods. Detection and treatment of babesiosis are important tools to control babesiosis. Microscopy detection methods are still the cheapest and fastest methods used to identify Babesia parasites although their sensitivity and specificity are limited. Newer immunological methods are being developed and they offer faster, more sensitive and more specific options to conventional methods, although the direct immunological diagnoses of parasite antigens in host tissues are still missing. Detection methods based on nucleic acid identification and their amplification are the most sensitive and reliable techniques available today; importantly, most of those methodologies were developed before the genomics and bioinformatics era, which leaves ample room for optimization. For years, babesiosis treatment has been based on the use of very few drugs like imidocarb or diminazene aceturate. Recently, several pharmacological compounds were developed and evaluated, offering new options to control the disease. With the complete sequence of the Babesia bovis genome and the B. bigemina genome project in progress, the post-genomic era brings a new light on the development of diagnosis methods and new chemotherapy targets. In this review, we will present the current advances in detection and treatment of babesiosis in cattle and other animals, with additional reference to several apicomplexan parasites.
UNIFYing Cloud and Carrier Network Resources: An Architectural View

Cloud networks provide various services on top of virtualized compute and storage resources. The flexible operation and optimal usage of the underlying infrastructure are realized by resource orchestration methods and virtualization techniques developed during the recent years. In contrast, service deployment and service provisioning in carrier networks have several limitations in terms of flexibility, scalability or optimal resource usage as the built-in mechanisms are strongly coupled to the physical topology and special purpose hardware elements. Network Function Virtualization (NFV) opens the door between cloud and carrier networks by providing software-based telecommunication services which can run in virtualized environment on general purpose hardwares. Our main goal is to unify software and network resources in a common framework. In this paper, we propose a novel architecture supporting automated, dynamic service creation based on a fine-granular service chaining model, SDN and cloud virtualization techniques. First, we introduce the architecture with the main components. Second, the most important benefits are highlighted and compared to other state-of-the-art approaches. Finally, preliminary experiences with our proof-of-concept prototypes are presented.
Windows registry analysis for forensic investigation

Cyber attack comes in various approach and forms, either internally or externally. Remote access and spyware are forms of cyber attack leaving an organization to be susceptible to vulnerability. This paper investigates illegal activities and potential evidence of cyber attack through studying the registry on the Windows 7 Home Premium (32 bit) Operating System in using the application Virtual Network Computing (VNC) and keylogger application. The aim is to trace the registry artifacts left by the attacker which connected using Virtual Network Computing (VNC) protocol within Windows 7 Operating System (OS). The analysis of the registry focused on detecting unwanted applications or unauthorized access to the machine with regard to the user activity via the VNC connection for the potential evidence of illegal activities by investigating the Registration Entries file and image file using the Forensic Toolkit (FTK) Imager. The outcome of this study is the findings on the artifacts which correlate to the user activity.
How Topic Biases Your Results? A Case Study of Sentiment Analysis and Irony Detection in Italian

In this paper we present our approach to automatically identify the subjectivity, polarity and irony of Italian Tweets. Our system which reaches and outperforms the state of the art in Italian is well adapted for different domains since it uses abstract word features instead of bag of words. We also present experiments carried out to study how Italian Sentiment Analysis systems react to domain changes. We show that bag of words approaches commonly used in Sentiment Analysis do not adapt well to domain changes.
Age and Second Language Acquisition and Processing : A Selective Overview

This article provides a selective overview of theoretical issues and empirical findings relating to the question of age and second language acquisition (L2A). Both behavioral and brain-based data are discussed in the contexts of neurocognitive aging and cognitive neurofunction in the mature individual. Moving beyond the classical notion of “deficient” L2 processing and acquisition, we consider the complementary question of learner potential in postadolescent L2A.
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments

Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence. Among the most common approaches are algorithms based on gradient ascent of a score function representing discounted return. In this paper, we examine the role of these policy gradient and actor-critic algorithms in partially-observable multiagent environments. We show several candidate policy update rules and relate them to a foundation of regret minimization and multiagent learning techniques for the one-shot and tabular cases, leading to previously unknown convergence guarantees. We apply our method to model-free multiagent reinforcement learning in adversarial sequential decision problems (zero-sum imperfect information games), using RL-style function approximation. We evaluate on commonly used benchmark Poker domains, showing performance against fixed policies and empirical convergence to approximate Nash equilibria in self-play with rates similar to or better than a baseline model-free algorithm for zero-sum games, without any domain-specific state space reductions.
Surgical anatomy and anatomical surgery of the liver

The morphologic anatomy of the liver is described as 2 main and 2 accessory lobes. The more recent functional anatomy of the liver is based on the distribution of the portal pedicles and the location of the hepatic veins. The liver is divided into 4 sectors, some of them composed of 2 segments. In all, there are 8 segments. According to the anatomy, typical hepatectomies (or “réglées”) are those which are performed along anatomical scissurae. The 2 main technical conceptions of typical hepatectomies are those with preliminary vascular control (Lortat-Jacob's technique) and hepatectomies with primary parenchymatous transection (Ton That Tung's technique). A good knowledge of the anatomy of the liver is a prerequisite for anatomical surgery of this organ. L'anatomie morphologique du foie permet d'individualiser 2 lobes principaux et 2 lobes accessoires. L'anatomie fonctionnelle du foie, plus récemment décrite, est fondée sur la distribution des pédicules portaux et sur la localisation des veines sus-hépatiques. Le foie est divisé en 4 secteurs, eux-mÊmes composés en général de 2 segments. Au total, il y a 8 segments. Selon les données anatomiques, les hépatectomies typiques (ou réglées) sont celles qui sont réalisées le long des scissures anatomiques. Les deux conceptions principales des exérèses hépatiques typiques sont, du point de vue technique, les hépatectomies avec contrÔle vasculaire préalable (technique de Lortat-Jacob) et les hépatectomies avec abord transparenchymateux premier (technique de Ton That Tung). Une connaissance approfondie de l'anatomie du foie est une condition préalable à la réalisation d'une chirurgie anatomique de cet organe.
Points2Pix: 3D Point-Cloud to Image Translation using conditional Generative Adversarial Networks

We present the first approach for 3D point-cloud to image translation based on conditional Generative Adversarial Networks (cGAN). The model handles multi-modal information sources from different domains, i.e. raw point-sets and images. The generator is capable of processing three conditions, whereas the point-cloud is encoded as raw point-set and camera projection. An image background patch is used as constraint to bias environmental texturing. A global approximation function within the generator is directly applied on the point-cloud (Point-Net). Hence, the representative learning model incorporates global 3D characteristics directly at the latent feature space. Conditions are used to bias the background and the viewpoint of the generated image. This opens up new ways in augmenting or texturing 3D data to aim the generation of fully individual images. We successfully evaluated our method on the KITTI and SunRGBD dataset with an outstanding object detection inception score.
A Model of Plant Identification System Using GLCM, Lacunarity And Shen Features

Recently, many approaches have been introduced by several researchers to identify plants. Now, applications of texture, shape, color and vein features are common practices. However, there are many possibilities of methods can be developed to improve the performance of such identification systems. Therefore, several experiments had been conducted in this research. As a result, a new novel approach by using combination of Gray-Level Co-occurrence Matrix, lacunarity and Shen features and a Bayesian classifier gives a better result compared to other plant identification systems. For comparison, this research used two kinds of several datasets that were usually used for testing the performance of each plant identification system. The results show that the system gives an accuracy rate of 97.19% when using the Flavia dataset and 95.00% when using the Foliage dataset and outperforms other approaches.
Inverse Procedural Modeling by Automatic Generation of L-systems

We present an important step towards the solution of the problem of inverse procedural modeling by generating parametric context-free L-systems that represent an input 2D model. The L-system rules efficiently code the regular structures and the parameters represent the properties of the structure transformations. The algorithm takes as input a 2D vector image that is composed of atomic elements, such as curves and poly-lines. Similar elements are recognized and assigned terminal symbols of an L-system alphabet. The terminal symbols’ position and orientation are pair-wise compared and the transformations are stored as points in multiple 4D transformation spaces. By careful analysis of the clusters in the transformation spaces, we detect sequences of elements and code them as L-system rules. The coded elements are then removed from the clusters, the clusters are updated, and then the analysis attempts to code groups of elements in (hierarchies) the same way. The analysis ends with a single group of elements that is coded as an L-system axiom. We recognize and code branching sequences of linearly translated, scaled, and rotated elements and their hierarchies. The L-system not only represents the input image, but it can also be used for various editing operations. By changing the L-system parameters, the image can be randomized, symmetrized, and groups of elements and regular structures can be edited. By changing the terminal and non-terminal symbols, elements or groups of elements can be replaced.
Information Diffusion Analysis of Rumor Dynamics over a Social-Interaction Based Model

Rumors may potentially cause undesirable effect such as the widespread panic in the general public. Especially, with the unprecedented growth of different types of social and enterprise networks, rumors could reach a larger audience than before. Many researchers have proposed different approaches to analyze and detect rumors in social networks. However, most of them either study on theoretical models without real data experiments or use content-based analysis and limited information diffusion analysis without fully considering social interactions. In this paper, we propose a social interaction based model FAST by taking four major properties of social interactions into account including familiarity, activeness, similarity, and trustworthiness. Also, we evaluate our model on real data from Sina Weibo (Twitter-like social network in China), which contains around 200 million tweets and 14 million Weibo users. Based on our model, we create a new metrics Fractional Directed Power Community Index (FD-PCI) derived from μ-PCI to identify the influential spreaders in social networks. FD-PCI shows better performance than conventional metrics such as K-core index and PageRank. Moreover, we obtain interesting influential features to detect rumors by the comparison between rumor and real news dynamics.
Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour

In this paper we have described a solution for supporting independent living of the elderly by means of equipping their home with a simple sensor network to monitor their behaviour. Standard home automation sensors including movement sensors and door entry point sensors are used. By monitoring the sensor data, important information regarding any anomalous behaviour will be identified. Different ways of visualizing large sensor data sets and representing them in a format suitable for clustering the abnormalities are also investigated. In the latter part of this paper, recurrent neural networks are used to predict the future values of the activities for each sensor. The predicted values are used to inform the caregiver in case anomalous behaviour is predicted in the near future. Data collection, classification and prediction are investigated in real home environments with elderly occupants suffering from dementia.
Bending the Curve: Sensitivity to Bending of Curved Paths and Application in Room-Scale VR

Redirected walking (RDW) promises to allow near-natural walking in an infinitely large virtual environment (VE) by subtle manipulations of the virtual camera. Previous experiments analyzed the human sensitivity to RDW manipulations by focusing on the worst-case scenario, in which users walk perfectly straight ahead in the VE, whereas they are redirected on a circular path in the real world. The results showed that a physical radius of at least 22 meters is required for undetectable RDW. However, users do not always walk exactly straight in a VE. So far, it has not been investigated how much a physical path can be bent in situations in which users walk a virtual curved path instead of a straight one. Such curved walking paths can be often observed, for example, when users walk on virtual trails, through bent corridors, or when circling around obstacles. In such situations the question is not, whether or not the physical path can be bent, but how much the bending of the physical path may vary from the bending of the virtual path. In this article, we analyze this question and present redirection by means of bending gains that describe the discrepancy between the bending of curved paths in the real and virtual environment. Furthermore, we report the psychophysical experiments in which we analyzed the human sensitivity to these gains. The results reveal encouragingly wider detection thresholds than for straightforward walking. Based on our findings, we discuss the potential of curved walking and present a first approach to leverage bent paths in a way that can provide undetectable RDW manipulations even in room-scale VR.
Convolutional Neural Network based Age Estimation from Facial Image and Depth Prediction from Single Image

Convolutional neural network (CNN), one of the most commonly used deep learning methods, has been applied to various computer vision and pattern recognition tasks, and has achieved state-of-the-art performance. Most recent research work on CNN focuses on the innovations of the structure. This thesis explores both the innovation of structure and final label encoding of CNN. To evaluate the performance of our proposed network structure and label encoding method, two computer vision tasks are conducted, namely age estimation from facial image and depth estimation from a single image. For age estimation from facial image, we propose a novel hierarchical aggregation based deep network to learn aging features from facial images and apply our encoding method to transfer the discrete aging labels into a possibility label, which enables the CNN to conduct a classification task rather than regression task. In contrast to traditional aging features, where identical filter is applied to the entire facial image, our deep aging feature can capture both local and global cues in aging. Under our formulation, convolutional neural network (CNN) is employed to extract region specific features at lower layers. Then, low layer features are hierarchically aggregated by using fully connected way to consecutive higher layers. The resultant aging feature is of dimensionality 110, which achieves both good discriminative ability and efficiency. Experimental results of age prediction on the MORPH-II and the FG-NET databases show that the proposed deep aging feature outperforms state-of-the-art aging features by a margin. Depth estimation from a single image is an essential component toward understanding the 3D geometry of a scene. Compared with depth estimation from stereo images, depth map estimation from a single image is an extremely challenging task. This thesis addresses this task by regression with deep features, combined with surface normal constrained depth refinement. The proposed framework consists of two steps. First, we implement a convolutional neural network (CNN) to learn the mapping from multi-scale image patches to depth on the super-pixel level. In this step, we apply the proposed label encoding method to transfer the contin-
Women and women of color in leadership: complexity, identity, and intersectionality.

This article describes the challenges that women and women of color face in their quest to achieve and perform in leadership roles in work settings. We discuss the barriers that women encounter and specifically address the dimensions of gender and race and their impact on leadership. We identify the factors associated with gender evaluations of leaders and the stereotypes and other challenges faced by White women and women of color. We use ideas concerning identity and the intersection of multiple identities to understand the way in which gender mediates and shapes the experience of women in the workplace. We conclude with suggestions for research and theory development that may more fully capture the complex experience of women who serve as leaders.
The Rational Unified Process Made Easy - A Practitioner's Guide to the RUP

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500+ Times Faster than Deep Learning: (A Case Study Exploring Faster Methods for Text Mining StackOverflow)

Deep learning methods are useful for high-dimensional data and are becoming widely used in many areas of software engineering. Deep learners utilizes extensive computational power and can take a long time to train- making it difficult to widely validate and repeat and improve their results. Further, they are not the best solution in all domains. For example, recent results show that for finding related Stack Overflow posts, a tuned SVM performs similarly to a deep learner, but is significantly faster to train.
 This paper extends that recent result by clustering the dataset, then tuning every learners within each cluster. This approach is over 500 times faster than deep learning (and over 900 times faster if we use all the cores on a standard laptop computer). Significantly, this faster approach generates classifiers nearly as good (within 2% F1 Score) as the much slower deep learning method. Hence we recommend this faster methods since it is much easier to reproduce and utilizes far fewer CPU resources.
 More generally, we recommend that before researchers release research results, that they compare their supposedly sophisticated methods against simpler alternatives (e.g applying simpler learners to build local models).
Energy storage and its use with intermittent renewable energy

A simple probabilistic method has been developed to predict the ability of energy storage to increase the penetration of intermittent embedded renewable generation (ERG) on weak electricity grids and to enhance the value of the electricity generated by time-shifting delivery to the network. This paper focuses on the connection of wind generators at locations where the level of ERG would be limited by the voltage rise. Short-term storage, covering less than 1 h, offers only a small increase in the amount of electricity that can be absorbed by the network. Storage over periods of up to one day delivers greater energy benefits, but is significantly more expensive. Different feasible electricity storage technologies are compared for their operational suitability over different time scales. The value of storage in relation to power rating and energy capacity has been investigated so as to facilitate appropriate sizing.