nils-herrmann/clean_scirepeval huggingface dataset:
Performance of the IOTA ADNEX model in preoperative discrimination of adnexal masses in a gynecological oncology center.

To evaluate the performance of the International Ovarian Tumor Analysis (IOTA) ADNEX model in the preoperative discrimination between benign ovarian (including tubal and para-ovarian) tumors, borderline ovarian tumors (BOT), Stage I ovarian cancer (OC), Stage II-IV OC and ovarian metastasis in a gynecological oncology center in Brazil.



This was a diagnostic accuracy study including 131 women with an adnexal mass invited to participate between February 2014 and November 2015. Before surgery, pelvic ultrasound examination was performed and serum levels of tumor marker CA 125 were measured in all women. Adnexal masses were classified according to the IOTA ADNEX model. Histopathological diagnosis was the gold standard. Receiver-operating characteristics (ROC) curve analysis was used to determine the diagnostic accuracy of the model to classify tumors into different histological types.



Of 131 women, 63 (48.1%) had a benign ovarian tumor, 16 (12.2%) had a BOT, 17 (13.0%) had Stage I OC, 24 (18.3%) had Stage II-IV OC and 11 (8.4%) had ovarian metastasis. The area under the ROC curve (AUC) was 0.92 (95% CI, 0.88-0.97) for the basic discrimination between benign vs malignant tumors using the IOTA ADNEX model. Performance was high for the discrimination between benign vs Stage II-IV OC, BOT vs Stage II-IV OC and Stage I OC vs Stage II-IV OC, with AUCs of 0.99, 0.97 and 0.94, respectively. Performance was poor for the differentiation between BOT vs Stage I OC and between Stage I OC vs ovarian metastasis with AUCs of 0.64.



The majority of adnexal masses in our study were classified correctly using the IOTA ADNEX model. On the basis of our findings, we would expect the model to aid in the management of women with an adnexal mass presenting to a gynecological oncology center.
Deep learning detecting fraud in credit card transactions

Credit card fraud resulted in the loss of $3 billion to North American financial institutions in 2017. The rise of digital payments systems such as Apple Pay, Android Pay, and Venmo has meant that loss due to fraudulent activity is expected to increase. Deep Learning presents a promising solution to the problem of credit card fraud detection by enabling institutions to make optimal use of their historic customer data as well as real-time transaction details that are recorded at the time of the transaction. In 2017, a study found that a Deep Learning approach provided comparable results to prevailing fraud detection methods such as Gradient Boosted Trees and Logistic Regression. However, Deep Learning encompasses a number of topologies. Additionally, the various parameters used to construct the model (e.g. the number of neurons in the hidden layer of a neural network) also influence its results. In this paper, we evaluate a subsection of Deep Learning topologies — from the general artificial neural network to topologies with built-in time and memory components such as Long Short-term memory — and different parameters with regard to their efficacy in fraud detection on a dataset of nearly 80 million credit card transactions that have been pre-labeled as fraudulent and legitimate. We utilize a high performance, distributed cloud computing environment to navigate past common fraud detection problems such as class imbalance and scalability. Our analysis provides a comprehensive guide to sensitivity analysis of model parameters with regard to performance in fraud detection. We also present a framework for parameter tuning of Deep Learning topologies for credit card fraud detection to enable financial institutions to reduce losses by preventing fraudulent activity.
Controlling Bad Behavior in Online Communities: An Examination of Moderation Work

A wide range of behavior may be seen as destructive to online communities. Yet behavior that is ‘bad’ in one community may be celebrated in another. The work of community maintenance is therefore strongly contextual, involving complex choices due to differing norms, community cross-membership, and the need to invoke fairness. The experienced, “lived in” work of moderators; how they enact norms and make choices about social maintenance, remains poorly understood. Our study addresses this gap, using a negotiated order lens. We employed netnographic techniques, analyzing online interviews with moderators of sub-communities in Reddit, and records of critical incidents. We find that moderators are intuitive prosecutors who draw on a variety of logics to accomplish their work. Controlling bad behavior, articulating and enforcing norms is, therefore, a collective accomplishment through which moderators make choices, create a jurisprudence record, and reconcile nested community norms in the maintenance of social order.
Semi-supervised Outlier Detection using Generative And Adversary Framework

In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks. Outlier detection, generative adversary networks, semi-supervised learning.
Compressive Privacy: From Information\/Estimation Theory to Machine Learning [Lecture Notes]

Most of our daily activities are now moving online in the big data era, with more than 25 billion devices already connected to the Internet, to possibly over a trillion in a decade. However, big data also bears a connotation of &ldquo;big brother&rdquo; when personal information (such as sales transactions) is being ubiquitously collected, stored, and circulated around the Internet, often without the data owner's knowledge. Consequently, a new paradigm known as online privacy or Internet privacy is becoming a major concern regarding the privacy of personal and sensitive data.
FingerNet: An unified deep network for fingerprint minutiae extraction

Minutiae extraction is of critical importance in automated fingerprint recognition. Previous works on rolled/slap fingerprints failed on latent fingerprints due to noisy ridge patterns and complex background noises. In this paper, we propose a new way to design deep convolutional network combining domain knowledge and the representation ability of deep learning. In terms of orientation estimation, segmentation, enhancement and minutiae extraction, several typical traditional methods performed well on rolled/slap fingerprints are transformed into convolutional manners and integrated as an unified plain network. We demonstrate that this pipeline is equivalent to a shallow network with fixed weights. The network is then expanded to enhance its representation ability and the weights are released to learn complex background variance from data, while preserving end-to-end differentiability. Experimental results on NIST SD27 latent database and FVC 2004 slap database demonstrate that the proposed algorithm outperforms the state-of-the-art minutiae extraction algorithms. Code is made publicly available at: https://github.com/felixTY/FingerNet.
EDGE DETECTION TECHNIQUES FOR IMAGE SEGMENTATION

Interpretation of image contents is one of the objectives in computer vision specifically in image processing. In this era it has received much awareness of researchers. In image interpretation the partition of the image into object and background is a severe step. Segmentation separates an image into its component regions or objects. Image segmentation t needs to segment the object from the background to read the image properly and identify the content of the image carefully. In this context, edge detection is a fundamental tool for image segmentation. In this paper an attempt is made to study the performance of most commonly used edge detection techniques for image segmentation and also the comparison of these techniques is carried out with an experiment by using MATLAB software.
Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services

The Internet of Things (IoT) has made it possible for devices around the world to acquire information and store it, in order to be able to use it at a later stage. However, this potential opportunity is often not exploited because of the excessively big interval between the data collection and the capability to process and analyse it. In this paper, we review the current IoT technologies, approaches and models in order to discover what challenges need to be met to make more sense of data. The main goal of this paper is to review the surveys related to IoT in order to provide well integrated and context aware intelligent services for IoT. Moreover, we present a state-of-the-art of IoT from the context aware perspective that allows the integration of IoT and social networks in the emerging Social Internet of Things (SIoT) term.
Empathy in Clinical Practice: How Individual Dispositions, Gender, and Experience Moderate Empathic Concern, Burnout, and Emotional Distress in Physicians

To better understand clinical empathy and what factors can undermine its experience and outcome in care-giving settings, a large-scale study was conducted with 7,584 board certified practicing physicians. Online validated instruments assessing different aspects of empathy, distress, burnout, altruistic behavior, emotional awareness, and well-being were used. Compassion satisfaction was strongly associated with empathic concern, perspective taking and altruism, while compassion fatigue (burnout and secondary traumatic stress) was more closely related to personal distress and alexithymia. Gender had a highly selective effect on empathic concern, with women displaying higher values, which led to a wide array of negative and devalued feelings. Years of experience did not influence dispositional measures per se after controlling for the effect of age and gender. Participants who experienced compassion fatigue with little to no compassion satisfaction showed the highest scores on personal distress and alexithymia as well as the strongest indicators of compassion fatigue. Physicians who have difficulty regulating their negative arousal and describing and identifying emotions seem to be more prone to emotional exhaustion, detachment, and a low sense of accomplishment. On the contrary, the ability to engage in self-other awareness and regulate one's emotions and the tendency to help others, seem to contribute to the sense of compassion that comes from assisting patients in clinical practice.
Implicature, Relevance and Default Pragmatic Inference

In his paper ‘Logic and conversation’ Grice (1989: 37) introduced a distinction between generalized and particularized conversational implicatures. His notion of a generalized conversational implicature (GCI) has been developed in two competing directions, by neo-Griceans such as Horn (1989) and Levinson (1983, 1987b, 1995, 2000) on the one hand, and relevance theorists such as Sperber & Wilson (1986) and Carston (1988, 1993, 1995, 1997, 1998a,b) on the other. Levinson defends the claim that GCIs are inferred on the basis of a set of default heuristics that are triggered by the presence of certain sorts of lexical items. These default inferences will be drawn unless something unusual in the context blocks them. Carston reconceives GCIs as contents that a speaker directly communicates, rather than as contents that are merely conversationally implicated. GCIs are treated as pragmatic developments of semantically underspecified logical forms. They are not the products of default inferences, since what is communicated depends heavily on the specific context, and not merely on the presence or absence of certain lexical items. We introduce two processing models, the Default Model and the Underspecified Model, that are inspired by these rival theoretical views. This paper describes an eye monitoring experiment that is intended to test the predictions of these two models. Our primary concern is to make a case for the claim that it is fruitful to apply an eye tracking methodology to an area of pragmatic research that has not previously been explored from a processing perspective.
CUTTING DOWN TRAINING MEMORY

Deep Neutral Networks(DNNs) require huge GPU memory when training on modern image/video databases. Unfortunately, the GPU memory as a hardware resource is always finite, which limits the image resolution, batch size, and learning rate that could be used for better DNN performance. In this paper, we propose a novel training approach, called Re-forwarding, that substantially reduces memory usage in training. Our approach automatically finds a subset of layers in DNNs, and stores tensors only at these layers during the first forward. During backward, extra local forwards (called the Re-forwarding process) are conducted to compute the missing tensors between the subset of layers. The total memory cost becomes the sum of (1) the memory cost at the subset of layers and (2) the maximum memory cost among local re-forwards. Re-forwarding trades training time for memory and does not compromise any performance in testing. We propose theories and algorithms that achieve the optimal memory solutions for DNNs with either linear or arbitrary computation graphs. Experiments show that Re-forwarding cuts down up-to 80% of training memory on popular DNNs such as Alexnet, VGG, ResNet, Densenet and Inception net.
Visualizing Bitcoin Flows of Ransomware: WannaCry One Week Later

Because of its pseudo-anonimity and decentralisation characteristics, bitcoin payments are often a tool utilised by ransomware: this kind of malware infects a victim computer by encrypting some/all its data and/or denying the access to it. Then, the victim has to pay a given amount of bitcoins to see all the blocked functionalities restored. The goal of this paper is to visualise these bitcoin transactions, and in particular we focus on the effects of one of such ransomware, i.e., WannaCry, one/two weeks after its diffusion. We exploit BlockChainVis, a tool for visualising flows of bitcoins through the use of Visual Analytics.
CRISPR: new horizons in phage resistance and strain identification.

Bacteria have been widely used as starter cultures in the food industry, notably for the fermentation of milk into dairy products such as cheese and yogurt. Lactic acid bacteria used in food manufacturing, such as lactobacilli, lactococci, streptococci, Leuconostoc, pediococci, and bifidobacteria, are selectively formulated based on functional characteristics that provide idiosyncratic flavor and texture attributes, as well as their ability to withstand processing and manufacturing conditions. Unfortunately, given frequent viral exposure in industrial environments, starter culture selection and development rely on defense systems that provide resistance against bacteriophage predation, including restriction-modification, abortive infection, and recently discovered CRISPRs (clustered regularly interspaced short palindromic repeats). CRISPRs, together with CRISPR-associated genes (cas), form the CRISPR/Cas immune system, which provides adaptive immunity against phages and invasive genetic elements. The immunization process is based on the incorporation of short DNA sequences from virulent phages into the CRISPR locus. Subsequently, CRISPR transcripts are processed into small interfering RNAs that guide a multifunctional protein complex to recognize and cleave matching foreign DNA. Hypervariable CRISPR loci provide insights into the phage and host population dynamics, and new avenues for enhanced phage resistance and genetic typing and tagging of industrial strains.
Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey

Computer vision has evolved in the last decade as a key technology for numerous applications replacing human supervision. In this paper, we present a survey on relevant visual surveillance related researches for anomaly detection in public places, focusing primarily on roads. Firstly, we revisit the surveys done in the last 10 years in this field. Since the underlying building block of a typical anomaly detection is learning, we emphasize more on learning methods applied on video scenes. We then summarize the important contributions made during last six years on anomaly detection primarily focusing on features, underlying techniques, applied scenarios and types of anomalies using single static camera. Finally, we discuss the challenges in the computer vision related anomaly detection techniques and some of the important future possibilities.
Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks

Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning, and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations, which are challenging for abdominal images, but alternative methods without registration have not yet achieved higher accuracy for most abdominal organs. We present a registration-free deep-learning-based segmentation algorithm for eight organs that are relevant for navigation in endoscopic pancreatic and biliary procedures, including the pancreas, the gastrointestinal tract (esophagus, stomach, and duodenum) and surrounding organs (liver, spleen, left kidney, and gallbladder). We directly compared the segmentation accuracy of the proposed method to the existing deep learning and MALF methods in a cross-validation on a multi-centre data set with 90 subjects. The proposed method yielded significantly higher Dice scores for all organs and lower mean absolute distances for most organs, including Dice scores of 0.78 versus 0.71, 0.74, and 0.74 for the pancreas, 0.90 versus 0.85, 0.87, and 0.83 for the stomach, and 0.76 versus 0.68, 0.69, and 0.66 for the esophagus. We conclude that the deep-learning-based segmentation represents a registration-free method for multi-organ abdominal CT segmentation whose accuracy can surpass current methods, potentially supporting image-guided navigation in gastrointestinal endoscopy procedures.
StegExpose - A Tool for Detecting LSB Steganography

Steganalysis tools play an important part in saving time and providing new angles of attack for forensic analysts. StegExpose is a solution designed for use in the real world, and is able to analyse images for LSB steganography in bulk using proven attacks in a time efficient manner. When steganalytic methods are combined intelligently, they are able generate even more accurate results. This is the prime focus of StegExpose.
Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline

Using a random walk model of text generation, Arora et al. (2017) proposed a strong baseline for computing sentence embeddings: take a weighted average of word embeddings and modify with SVD. This simple method even outperforms far more complex approaches such as LSTMs on textual similarity tasks. In this paper, we first show that word vector length has a confounding effect on the probability of a sentence being generated in Arora et al.’s model. We propose a random walk model that is robust to this confound, where the probability of word generation is inversely related to the angular distance between the word and sentence embeddings. Our approach beats Arora et al.’s by up to 44.4% on textual similarity tasks and is competitive with state-of-the-art methods. Unlike Arora et al.’s method, ours requires no hyperparameter tuning, which means it can be used when there is no labelled data.
Security Threats and Countermeasures for Intra-vehicle Networks

Controller Area Network (CAN) is the leading serial bus system for embedded control. More than two billion CAN nodes have been sold since the protocol's development in the early 1980s. CAN is a mainstream network and was internationally standardized (ISO 11898–1) in 1993. This paper describes an approach to implementing security services on top of a higher level Controller Area Network (CAN) protocol, in particular, CANopen. Since the CAN network is an open, unsecured network, every node has access to all data on the bus. A system which produces and consumes sensitive data is not well suited for this environment. Therefore, a general-purpose security solution is needed which will allow secure nodes access to the basic security services such as authentication, integrity, and confidentiality.
A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition

Plant Diseases and Pests are a major challenge in the agriculture sector. An accurate and a faster detection of diseases and pests in plants could help to develop an early treatment technique while substantially reducing economic losses. Recent developments in Deep Neural Networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. In this paper, we present a deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions. Our goal is to find the more suitable deep-learning architecture for our task. Therefore, we consider three main families of detectors: Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Fully Convolutional Network (R-FCN), and Single Shot Multibox Detector (SSD), which for the purpose of this work are called "deep learning meta-architectures". We combine each of these meta-architectures with "deep feature extractors" such as VGG net and Residual Network (ResNet). We demonstrate the performance of deep meta-architectures and feature extractors, and additionally propose a method for local and global class annotation and data augmentation to increase the accuracy and reduce the number of false positives during training. We train and test our systems end-to-end on our large Tomato Diseases and Pests Dataset, which contains challenging images with diseases and pests, including several inter- and extra-class variations, such as infection status and location in the plant. Experimental results show that our proposed system can effectively recognize nine different types of diseases and pests, with the ability to deal with complex scenarios from a plant's surrounding area.
Malware traffic classification using convolutional neural network for representation learning

Traffic classification is the first step for network anomaly detection or network based intrusion detection system and plays an important role in network security domain. In this paper we first presented a new taxonomy of traffic classification from an artificial intelligence perspective, and then proposed a malware traffic classification method using convolutional neural network by taking traffic data as images. This method needed no hand-designed features but directly took raw traffic as input data of classifier. To the best of our knowledge this interesting attempt is the first time of applying representation learning approach to malware traffic classification using raw traffic data. We determined that the best type of traffic representation is session with all layers through eight experiments. The method is validated in two scenarios including three types of classifiers and the experiment results show that our proposed method can satisfy the accuracy requirement of practical application.