nils-herrmann/clean_scirepeval huggingface dataset:
Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans

Automatic segmentation of an organ and its cystic region is a prerequisite of computer-aided diagnosis. In this paper, we focus on pancreatic cyst segmentation in abdominal CT scan. This task is important and very useful in clinical practice yet challenging due to the low contrast in boundary, the variability in location, shape and the different stages of the pancreatic cancer. Inspired by the high relevance between the location of a pancreas and its cystic region, we introduce extra deep supervision into the segmentation network, so that cyst segmentation can be improved with the help of relatively easier pancreas segmentation. Under a reasonable transformation function, our approach can be factorized into two stages, and each stage can be efficiently optimized via gradient back-propagation throughout the deep networks. We collect a new dataset with 131 pathological samples, which, to the best of our knowledge, is the largest set for pancreatic cyst segmentation. Without human assistance, our approach reports a 63.44% average accuracy, measured by the Dice-Sørensen coefficient (DSC), which is higher than the number (60.46%) without deep supervision.
Identification of novel factors enhancing recombinant protein production in multi-copy Komagataella phaffii based on transcriptomic analysis of overexpression effects

The methylotrophic yeast Komagataella phaffii (Pichia pastoris) has been developed into a highly successful system for heterologous protein expression in both academia and industry. However, overexpression of recombinant protein often leads to severe burden on the physiology of K. phaffii and triggers cellular stress. To elucidate the global effect of protein overexpression, we set out to analyze the differential transcriptome of recombinant strains with 12 copies and a single copy of phospholipase A2 gene (PLA 2) from Streptomyces violaceoruber. Through GO, KEGG and heat map analysis of significantly differentially expressed genes, the results indicated that the 12-copy strain suffered heavy cellular stress. The genes involved in protein processing and stress response were significantly upregulated due to the burden of protein folding and secretion, while the genes in ribosome and DNA replication were significantly downregulated possibly contributing to the reduced cell growth rate under protein overexpression stress. Three most upregulated heat shock response genes (CPR6, FES1, and STI1) were co-overexpressed in K. phaffii and proved their positive effect on the secretion of reporter enzymes (PLA2 and prolyl endopeptidase) by increasing the production up to 1.41-fold, providing novel helper factors for rational engineering of K. phaffii.
Attack Tree Visualization for Cyber Security Situational Awareness

Situational awareness in cyber domain is one of the key features for quick and accurate decision making and anomaly detection. In order to provide situational awareness, certain methods have been introduced so far and attack graph is one of them. Attack graphs help the security analyst to visualize the network topology and understand typical vulnerability and exploit behaviors in cyber domain (e.g., IT asset and the network). They provide more proactive view compared to other reactive views; hence risk management and evaluation can be done in an efficient and interactive fashion. Attack trees can be used for various purposes since they can map network assets, network attacks and possible vulnerabilities which may exist in the IT assets. This study introduces an integrated cyber security capability called, BSGS, which can help analysts to create attack trees, identify vulnerabilities and have effective risk assessment procedures. In this way, the cyber security specialists will have a more efficient and holistic way to assess their environments and take the most effective precautions to minimize cyber risks.
Tensor Decomposition for Signal Processing and Machine Learning

Tensors or <italic>multiway arrays</italic> are functions of three or more indices <inline-formula> <tex-math notation="LaTeX">$(i,j,k,\ldots)$</tex-math></inline-formula>—similar to matrices (two-way arrays), which are functions of two indices <inline-formula><tex-math notation="LaTeX">$(r,c)$</tex-math></inline-formula> for (row, column). Tensors have a rich history, stretching over almost a century, and touching upon numerous disciplines; but they have only recently become ubiquitous in signal and data analytics at the confluence of signal processing, statistics, data mining, and machine learning. This overview article aims to provide a good starting point for researchers and practitioners interested in learning about and working with tensors. As such, it focuses on fundamentals and motivation (using various application examples), aiming to strike an appropriate balance of breadth <italic>and depth</italic> that will enable someone having taken first graduate courses in matrix algebra and probability to get started doing research and/or developing tensor algorithms and software. Some background in applied optimization is useful but not strictly required. The material covered includes tensor rank and rank decomposition; basic tensor factorization models and their relationships and properties (including fairly good coverage of identifiability); broad coverage of algorithms ranging from alternating optimization to stochastic gradient; statistical performance analysis; and applications ranging from source separation to collaborative filtering, mixture and topic modeling, classification, and multilinear subspace learning.
The physiological basis of bird flight.

Flapping flight is energetically more costly than running, although it is less costly to fly a given body mass a given distance per unit time than it is for a similar mass to run the same distance per unit time. This is mainly because birds can fly faster than they can run. Oxygen transfer and transport are enhanced in migrating birds compared with those in non-migrators: at the gas-exchange regions of the lungs the effective area is greater and the diffusion distance smaller. Also, migrating birds have larger hearts and haemoglobin concentrations in the blood, and capillary density in the flight muscles tends to be higher. Species like bar-headed geese migrate at high altitudes, where the availability of oxygen is reduced and the energy cost of flapping flight increased compared with those at sea level. Physiological adaptations to these conditions include haemoglobin with a higher affinity for oxygen than that in lowland birds, a greater effective ventilation of the gas-exchange surface of the lungs and a greater capillary-to-muscle fibre ratio. Migrating birds use fatty acids as their source of energy, so they have to be transported at a sufficient rate to meet the high demand. Since fatty acids are insoluble in water, birds maintain high concentrations of fatty acid-binding proteins to transport fatty acids across the cell membrane and within the cytoplasm. The concentrations of these proteins, together with that of a key enzyme in the β-oxidation of fatty acids, increase before migration.This article is part of the themed issue 'Moving in a moving medium: new perspectives on flight'.
Detecting and Preventing Cyber Insider Threats: A Survey

Information communications technology systems are facing an increasing number of cyber security threats, the majority of which are originated by insiders. As insiders reside behind the enterprise-level security defence mechanisms and often have privileged access to the network, detecting and preventing insider threats is a complex and challenging problem. In fact, many schemes and systems have been proposed to address insider threats from different perspectives, such as intent, type of threat, or available audit data source. This survey attempts to line up these works together with only three most common types of insider namely traitor, masquerader, and unintentional perpetrator, while reviewing the countermeasures from a data analytics perspective. Uniquely, this survey takes into account the early stage threats which may lead to a malicious insider rising up. When direct and indirect threats are put on the same page, all the relevant works can be categorised as host, network, or contextual data-based according to audit data source and each work is reviewed for its capability against insider threats, how the information is extracted from the engaged data sources, and what the decision-making algorithm is. The works are also compared and contrasted. Finally, some issues are raised based on the observations from the reviewed works and new research gaps and challenges identified.
Total ozone from the ozone monitoring instrument (OMI) using the DOAS technique

This paper describes the algorithm for deriving the total column ozone from spectral radiances and irradiances measured by the Ozone Monitoring Instrument (OMI) on the Earth Observing System Aura satellite. The algorithm is based on the differential optical absorption spectroscopy technique. The main characteristics of the algorithm as well as an error analysis are described. The algorithm has been successfully applied to the first available OMI data. First comparisons with ground-based instruments are very encouraging and clearly show the potential of the method.
Investigating socio-cultural behavior of users reflected in different social channels on K-pop

In this paper we investigated the socio-cultural behavior of users reflected in the two different social media channels, YouTube and Twitter. We conducted the comparative analysis of the networks generated from the two channels. The relationship we set for each network is the relatedness on YouTube and the co-links on Twitter. From the results, we revealed that the social media influenced the distinct socio-cultural behaviors of their users. Specifically, Twitter network better showed the actual consumption of contents in the field of the k-pop culture than YouTube. From this study, we contributed to offer a novel approach for exploring the socio-cultural behavior of users on the social media.
FontCode: Embedding Information in Text Documents Using Glyph Perturbation

We introduce FontCode, an information embedding technique for text documents. Provided a text document with specific fonts, our method embeds user-specified information in the text by perturbing the glyphs of text characters while preserving the text content. We devise an algorithm to choose unobtrusive yet machine-recognizable glyph perturbations, leveraging a recently developed generative model that alters the glyphs of each character continuously on a font manifold. We then introduce an algorithm that embeds a user-provided message in the text document and produces an encoded document whose appearance is minimally perturbed from the original document. We also present a glyph recognition method that recovers the embedded information from an encoded document stored as a vector graphic or pixel image, or even on a printed paper. In addition, we introduce a new error-correction coding scheme that rectifies a certain number of recognition errors. Lastly, we demonstrate that our technique enables a wide array of applications, using it as a text document metadata holder, an unobtrusive optical barcode, a cryptographic message embedding scheme, and a text document signature.
Eye-tracking verification of the strategy used to analyse algorithms expressed in a flowchart and pseudocode

Eye-tracking verification of the strategy used to analyse algorithms expressed in a flowchart and pseudocode Magdalena Andrzejewska, Anna Stolińska, Władysław Błasiak, Paweł Pęczkowski, Roman Rosiek, Bożena Rożek, Mirosława Sajka & Dariusz Wcisło To cite this article: Magdalena Andrzejewska, Anna Stolińska, Władysław Błasiak, Paweł Pęczkowski, Roman Rosiek, Bożena Rożek, Mirosława Sajka & Dariusz Wcisło (2015): Eyetracking verification of the strategy used to analyse algorithms expressed in a flowchart and pseudocode, Interactive Learning Environments,  To link to this article: http://dx.doi.org/10.1080/10494820.2015.1073746
Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks

A major drawback of backpropagation through time (BPTT) is the difficulty of learning long-term dependencies, coming from having to propagate credit information backwards through every single step of the forward computation. This makes BPTT both computationally impractical and biologically implausible. For this reason, full backpropagation through time is rarely used on long sequences, and truncated backpropagation through time is used as a heuristic. However, this usually leads to biased estimates of the gradient in which longer term dependencies are ignored. Addressing this issue, we propose an alternative algorithm, Sparse Attentive Backtracking, which might also be related to principles used by brains to learn long-term dependencies. Sparse Attentive Backtracking learns an attention mechanism over the hidden states of the past and selectively backpropagates through paths with high attention weights. This allows the model to learn long term dependencies while only backtracking for a small number of time steps, not just from the recent past but also from attended relevant past states.
A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis

Opinion mining from customer reviews has become pervasive in recent years. Sentences in reviews, however, are usually classified independently, even though they form part of a review’s argumentative structure. Intuitively, sentences in a review build and elaborate upon each other; knowledge of the review structure and sentential context should thus inform the classification of each sentence. We demonstrate this hypothesis for the task of aspect-based sentiment analysis by modeling the interdependencies of sentences in a review with a hierarchical bidirectional LSTM. We show that the hierarchical model outperforms two non-hierarchical baselines, obtains results competitive with the state-of-the-art, and outperforms the state-of-the-art on five multilingual, multi-domain datasets without any handengineered features or external resources.
AppScale: Open-Source Platform-as-a-Service

In this paper we overview AppScale, an open source cloud platform. AppScale is a distributed and scalable cloud runtime system that executes cloud applications in public, private, and hybrid cloud settings. AppScale implements a set of APIs in support of common and popular cloud services and functionality. This set includes those defined by the Google App Engine public cloud as well as those for large-scale data analytics and high-performance computing. Our goal with AppScale is to enable research and experimentation into cloud computing and to facilitate a ``write once, run anywhere'' programming model for the cloud, i.e., to expedite portable application development and deployment across disparate cloud fabrics. In this work, we describe the current AppScale APIs and the ways in which users can deploy AppScale clouds and applications in public and private settings. In addition, we describe the internals of the system to give insight into how developers can investigate and extend AppScale as part of research and development on next-generation cloud software and services.
Analytical Model for Design of High-Speed Slotless Brushless Machines with SMC Stators

This paper presents a general analytical model for the design of high-speed slotless permanent magnet brushless motors equipped with surface-mounted magnets and using soft magnetic composite material (SMC) in the stator. The electromagnetic model is formulated in polar coordinates and based on the prediction of the two-dimensional magnetic field distribution in the magnets/air-gap/windings/stator core regions, produced by the magnets and the stator currents windings. It takes into account the effects of the stator core eddy currents induced in the massive homogeneous SMC material. Different machine performances like the instantaneous torque under brushless operation and the total magnetic losses are evaluated. The analytical design model is validated by FE analysis in terms of magnetic field distribution and total stator core loss. The influence of the stator yoke thickness and the SMC material conductivity on the motor performances is investigated. Experimental results are given for the stator magnetic losses.
BugCache for inspections: hit or miss?

Inspection is a highly effective but costly technique for quality control. Most companies do not have the resources to inspect all the code; thus accurate defect prediction can help focus available inspection resources. BugCache is a simple, elegant, award-winning prediction scheme that "caches" files that are likely to contain defects . In this paper, we evaluate the utility of BugCache as a tool for focusing inspection, we examine the assumptions underlying BugCache with the aim of improving it, and finally we compare it with a simple, standard bug-prediction technique. We find that BugCache is, in fact, useful for focusing inspection effort; but surprisingly, we find that its performance, when used for inspections, is not much better than a naive prediction model -- viz., a model that orders files in the system by their count of closed bugs and chooses enough files to capture 20% of the lines in the system.
Automatic License Plate Recognition System Based on Color Image Processing

A License plate recognition (LPR) system can be divided into the following steps: preprocessing, plate region extraction, plate region thresholding, character segmentation, character recognition and post-processing. For step 2, a combination of color and shape information of plate is used and a satisfactory extraction result is achieved. For step 3, first channel is selected, then threshold is computed and finally the region is thresholded. For step 4, the character is segmented along vertical, horizontal direction and some tentative optimizations are applied. For step 5, minimum Euclidean distance based template matching is used. And for those confusing characters such as '8' & 'B' and '0' & 'D', a special processing is necessary. And for the final step, validity is checked by machine and manual. The experiment performed by program based on aforementioned algorithms indicates that our LPR system based on color image processing is quite quick and accurate.
FlexiSec: A Configurable Link Layer Security Architecture for Wireless Sensor Networks

Ensuring communications security in Wireless Sensor Networks (WSNs) indeed is critical; due to the criticality of the resources in the sensor nodes as well as due to their ubiquitous and pervasive deployment, with varying attributes and degrees of security required. The proliferation of the next generation sensor nodes, has not solved this problem, because of the greater emphasis on low-cost deployment. In addition, the WSNs use data-centric multi-hop communication that in turn, necessitates the security support to be devised at the link layer (increasing the cost of security related operations), instead of being at the application layer, as in general networks. Therefore, an energy-efficient link layer security framework is necessitated. There do exists a number of link layer security architectures that offer some combinations of the security attributes desired by different WSN applications. However, as we show in this paper, none of them is responsive to the actual security demands of the applications. Therefore, we believe that there is a need for investigating the feasibility of a configurable software-based link layer security architecture wherein an application can be compiled flexibly, with respect to its actual security demands. In this paper, we analyze, propose and experiment with the basic design of such configurable link layer security architecture for WSNs. We also experimentally evaluate various aspects related to our scheme viz. configurable block ciphers, configurable block cipher modes of operations, configurable MAC sizes and configurable replay protection. The architecture proposed is aimed to offer the optimal level of security at the minimal overhead, thus saving the precious resources in the WSNs.
Assessment of gelatinases (MMP-2 and MMP-9) by gelatin zymography.

Gelatin zymography is a simple yet powerful method to detect proteolytic enzymes capable of degrading gelatin from various biological sources. It is particularly useful for the assessment of two key members of the matrix metalloproteinase family, MMP-2 (gelatinase A) and MMP-9 (gelatinase B), due to their potent gelatin-degrading activity. This polyacrylamide gel electrophoresis-based method can provide a reliable assessment of the type of gelatinase, relative amount, and activation status (latent, compared with active enzyme forms) in cultured cells, tissues, and biological fluids. The method can be used to investigate factors that regulate gelatinase expression and modulate zymogen activation in experimental systems. The system provides information on the pattern of gelatinase expression and activation in human cancer tissues and how this relates to cancer progression. Interpretation of the data obtained in gelatin zymography requires a thorough understanding of the principles and pitfalls of the technique; this is particularly important when evaluating enzyme levels and the presence of active gelatinase species. If properly used, gelatin zymography is an excellent tool for the study of gelatinases in biological systems.
Network text sentiment analysis method combining LDA text representation and GRU-CNN

In order to improve the performance of internet public sentiment analysis, a text sentiment analysis method combining Latent Dirichlet Allocation (LDA) text representation and convolutional neural network (CNN) is proposed. First, the review texts are collected from the network for preprocessing. Then, using the LDA topic model to train the latent semantic space representation (topic distribution) of the short text, and the short text feature vector representation based on the topic distribution is constructed. Finally, the CNN with gated recurrent unit (GRU) is used as a classifier. According to the input feature matrix, the GRU-CNN strengthens the relationship between words and words, text and text, so as to achieve high accurate text classification. The simulation results show that this method can effectively improve the accuracy of text sentiment classification.
Strategies for Vietnamese keyword search

We propose strategies for a state-of-the-art Vietnamese keyword search (KWS) system developed at the Institute for Infocomm Research (I2R). The KWS system exploits acoustic features characterizing creaky voice quality peculiar to lexical tones in Vietnamese, a minimal-resource transliteration framework to alleviate out-of-vocabulary issues from foreign loan words, and a proposed system combination scheme FusionX. We show that the proposed creaky voice quality features complement pitch-related features, reaching fusion gains of 17.7% relative (6.9% absolute). To the best of our knowledge, the proposed transliteration framework is the first reported rule-based system for Vietnamese; it outperforms statistical-approach baselines up to 14.93-36.73% relative on foreign loan word search tasks. Using FusionX to combine 3 sub-systems, the actual term-weighted value (ATWV) reaches 0.4742, exceeding the ATWV=0.3 benchmark for IARPA Babel participants in the NIST OpenKWSB Evaluation.