nils-herrmann/clean_scirepeval huggingface dataset:
A novel design of hidden web crawler using ontology

Deep Web is content hidden behind HTML forms. Since it represents a large portion of the structured, unstructured and dynamic data on the Web, accessing Deep-Web content has been a long challenge for the database community. This paper describes a crawler for accessing Deep-Web using Ontologies. Performance evaluation of the proposed work showed that this new approach has promising results. Keywordsdeepweb,ontology,hiddenweb,domain,mapping.
Intermodal Freight Transportation

The 21st century will see a renewed focus on intermodal freight transportation driven by the changing requirements of global supply chains. Each of the transportation modes (air, inland water, ocean, pipeline, rail, and road) has gone through technological evolution and has functioned separately under a modally based regulatory structure for most of the 20th century. With the development of containerization in the mid-1900s, the reorientation toward deregulation near the end of the century, and a new focus on logistics and global supply chain requirements, the stage is set for continued intermodal transportation growth. The growth of intermodal freight transportation will be driven and challenged by four factors: (a) measuring, understanding, and responding to the role of intermodalism in the changing customer requirements and hypercompetition of supply chains in a global marketplace; (b) the need to reliably and flexibly respond to changing customer requirements with seamless and integrated coordination of freight and equipment flows through various modes; (c) knowledge of current and future intermodal operational options and alternatives, as well as the potential for improved information and communications technology and the challenges associated with their application; and (d) constraints on and coordination of infrastructure capacity, including policy and regulatory issues, as well as better management of existing infrastructure and broader considerations on future investment in new infrastructure.
The effectiveness of a homemade IMSI catcher build with YateBTS and a BladeRF

An IMSI-catcher, also known as a cell-sitesimulator, is a telephone eavesdropping device mainly used by law enforcement. By simulating a cell-site it can force mobile phones to connect with the fake cell-site and force unencrypted (A5/0) communication. IMSI-catchers used to be expensive and only available to law-enforcement. Nowadays an IMSI catcher can be set up cheaply using a software defined radio and open source software such as OpenBTS. Deloitte would like to find out if this technology can be used for gathering OSINT (Open Source Intelligence) for red-teams.To set up the IMSI catcher the BladeRF x40 was used running YateBTS. Then observations were made on how effective this IMSI catcher actually is in spoofing a modern phone. YateBTS is capable of simulating a 2.5G (GPRS) cell site and it was observed that phones always prefer the faster base station (regardless of signal strength). The general conclusion is that an IMSI catcher isn’t as effective nowadays against modern phones, and require specific conditions along with forcing the use of 2G for the IMSI catcher to work. Acknowledgements—I would like to thank the hacking team of Deloitte for enabling me to work and lending the necessary hardware to conduct the experiments. Also, I would like to thank Cedric van Bockhaven for helping with the initial setup of YateBTS, as well as pointing out the current status of A5/1. IMSI catcher, fake base station, GSM, SDR, MITM, OSINT, BladeRF, YateBTS
Convolutional capsule network for classification of breast cancer histology images

Automatization of the diagnosis of any kind of disease is of great importance and its gaining speed as more and more deep learning solutions are applied to different problems. One of such computer-aided systems could be a decision support tool able to accurately differentiate between different types of breast cancer histological images – normal tissue or carcinoma (benign, in situ or invasive). In this paper authors present a deep learning solution, based on convolutional capsule network, for classification of four types of images of breast tissue biopsy when hematoxylin and eosin staining is applied. The crossvalidation accuracy, averaged over four classes, was achieved to be 87 % with equally high sensitivity.
Terrorism--a (self) love story: redirecting the significance quest can end violence.

Jean-Jacques Rousseau's concepts of self-love (amour propre) and love of self (amour de soi même) are applied to the psychology of terrorism. Self-love is concern with one's image in the eyes of respected others, members of one's group. It denotes one's feeling of personal significance, the sense that one's life has meaning in accordance with the values of one's society. Love of self, in contrast, is individualistic concern with self-preservation, comfort, safety, and the survival of self and loved ones. We suggest that self-love defines a motivational force that when awakened arouses the goal of a significance quest. When a group perceives itself in conflict with dangerous detractors, its ideology may prescribe violence and terrorism against the enemy as a means of significance gain that gratifies self-love concerns. This may involve sacrificing one's self-preservation goals, encapsulated in Rousseau's concept of love of self. The foregoing notions afford the integration of diverse quantitative and qualitative findings on individuals' road to terrorism and back. Understanding the significance quest and the conditions of its constructive fulfillment may be crucial to reversing the current tide of global terrorism.
FACTORS AFFECTING TEACHERS ’ USE OF INFORMATION AND COMMUNICATION TECHNOLOGY

Research studies in the past decade have shown that computer technology is an effective means for widening educational opportunities, but most teachers neither use technology as an instructional delivery system nor integrate technology into their curriculum. Studies reveal a number of factors influencing teachers’ decisions to use ICT in the classroom: non-manipulative and manipulative school and teacher factors. These factors are interrelated. The success of the implementation of ICT is not dependent on the availability or absence of one individual factor, but is determined through a dynamic process involving a set of interrelated factors. It is suggested that ongoing professional development must be provided for teachers to model the new pedagogies and tools for learning with the aim of enhancing the teaching-learning process. However, it is important for teacher trainers and policy makers to understand the factors affecting effectiveness and cost-effectiveness of different approaches to ICT use in teacher training so training strategies can be appropriately explored to make such changes viable to all.
ACS Surgery: Principles and Practice

The procedures used to diagnose, stage, and treat breast disease are rapidly becoming less invasive and more cosmetically satisfying while remaining oncologically sound. In particular, percutaneous core biopsy has largely replaced excisional breast biopsy for both palpable and nonpalpable breast lesions and has proved to be an equally accurate, less invasive, and less costly means of pathologic  Moreover, in clinically appropriate patients, sentinel lymph node biopsy (SLNB) has proved to be an accurate method of staging the axilla that reduces the incidence of many of the complications associated with traditional axillary node dissection.2 Furthermore, breast conservation has largely supplanted mastectomy for definitive surgical treatment of breast cancer; randomized trials continue to demonstrate equivalent survival rates for the two  Even in those cases where mastectomy is either required or preferred, advances in reconstructive techniques have been made that yield significantly improved outcomes after breast reconstruction.4 Finally, in an effort to eliminate the need for open surgical treatment of breast cancer, various percutaneous extirpative and ablative local therapies have been developed and are being evaluated for potential use in managing breast cancer in carefully selected  A more minimally invasive approach to breast disease will depend to a substantial extent on the availability of accurate and efficient imaging modalities. Adeptness with such modalities is rapidly becoming an essential part of the general surgeon’s skill set. In this chapter, we describe selected standard, novel, and investigational procedures employed in the diagnosis and management of breast disease.The application of these procedures is a dynamic process that is shaped both by technological advances and by physicians’ evolving understanding of the biology of breast diseases.
AI as Evaluator: Search Driven Playtesting of Modern Board Games

This paper presents a demonstration of how AI can be useful in the game design and development process of a modern board game. By using an artificial intelligence algorithm to play a substantial amount of matches of the Ticket to Ride board game and collecting data, we can analyze several features of the gameplay as well as of the game board. Results revealed loopholes in the game’s rules and pointed towards trends in how the game is played. We are then led to the conclusion that large scale simulation utilizing artificial intelligence can offer valuable information regarding modern board games and their designs that would ordinarily be prohibitively expensive or time-consuming to discover manually.
Data-efficient Deep Learning for RGB-D Object Perception in Cluttered Bin Picking

Deep learning methods often require large annotated data sets to estimate their high numbers of parameters, which is not practical for many robotic domains. One way to migitate this issue is to transfer features learned on large datasets to related tasks. In this work, we describe the perception system developed for the entry of team NimbRo Picking into the Amazon Picking Challenge 2016. Object detection and semantic Segmentation methods are adapted to the domain, including incorporation of depth measurements. To avoid the need for large training datasets, we make use of pretrained models whenever possible, e.g. CNNs pretrained on ImageNet, and the whole DenseCap captioning pipeline pretrained on the Visual Genome Dataset. Our system performed well at the APC 2016 and reached second and third places for the stow and pick tasks, respectively.
Machine Learning Techniques for Automatically Extracting Contextual Information from Scientific Publications

Scholarly publishing increasingly requires automated systems that semantically enrich documents in order to support management and quality assessment of scientific output. However, contextual information, such as the authors’ affiliations, references, and funding agencies, is typically hidden within PDF files. To access this information we have developed a processing pipeline that analyses the structure of a PDF document incorporating a diverse set of machine learning techniques. First, unsupervised learning is used to extract contiguous text blocks from the raw character stream as the basic logical units of the article. Next, supervised learning is employed to classify blocks into different meta-data categories, including authors and affiliations. Then, a set of heuristics are applied to detect the reference section at the end of the paper and segment it into individual reference strings. Sequence classification is then utilised to categorise the tokens of individual references to obtain information such as the journal and the year of the reference. Finally, we make use of named entity recognition techniques to extract references to research grants, funding agencies, and EU projects. Our system is modular in nature. Some parts rely on models learnt on training data, and the overall performance scales with the quality of these data sets.
DOM tree based approach for Web content extraction

The World Wide Web plays an important role while searching for information in the data network. Users are constantly exposed to an ever-growing flood of information. Our approach will help in searching for the exact user relevant content from multiple search engines thus, making the search more efficient and reliable. Our framework will extract the relevant result records based on two approaches i.e. Stored URL list and Run time Generated URL list. Finally, the unique set of records is displayed in a common framework's search result page. The extraction is performed using the concepts of Document Object Model (DOM) tree. The paper comprises of a concept of threshold and data filters to detect and remove irrelevant & redundant data from the web page. The data filters will also be used to further improve the similarity check of data records. Our system will be able to extract 75%-80% user relevant content by eliminating noisy content from the different structured web pages like blogs, forums, articles etc. in the dynamic environment. Our approach shows significant advantages in both precision and recall.
Learnability and the Vapnik-Chervonenkis dimension

Valiant's learnability model is extended to learning classes of concepts defined by regions in Euclidean space En. The methods in this paper lead to a unified treatment of some of Valiant's results, along with previous results on distribution-free convergence of certain pattern recognition algorithms. It is shown that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned. Using this parameter, the complexity and closure properties of learnable classes are analyzed, and the necessary and sufficient conditions are provided for feasible learnability.
On Pre-trained Image Features and Synthetic Images for Deep Learning

Deep Learning methods usually require huge amounts of training data to perform at their full potential, and often require expensive manual labeling. Using synthetic images is therefore very attractive to train object detectors, as the labeling comes for free, and several approaches have been proposed to combine synthetic and real images for training. In this paper, we show that a simple trick is sufficient to train very effectively modern object detectors with synthetic images only: We ’freeze’ the layers responsible for feature extraction to generic layers pre-trained on real images, and train only the remaining layers with plain OpenGL rendering. Our experiments with very recent deep architectures for object recognition (Faster-RCNN, R-FCN, MaskRCNN) and image feature extractors (InceptionResnet and Resnet) show this simple approach performs surprisingly well.
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks

Many recent papers address reading comprehension, where examples consist of (question, passage, answer) tuples. Presumably, a model must combine information from both questions and passages to predict corresponding answers. However, despite intense interest in the topic, with hundreds of published papers vying for leaderboard dominance, basic questions about the difficulty of many popular benchmarks remain unanswered. In this paper, we establish sensible baselines for the bAbI, SQuAD, CBT, CNN, and Whodid-What datasets, finding that questionand passage-only models often perform surprisingly well. On 14 out of 20 bAbI tasks, passage-only models achieve greater than 50% accuracy, sometimes matching the full model. Interestingly, while CBT provides 20-sentence passages, only the last is needed for comparably accurate prediction. By comparison, SQuAD and CNN appear better-constructed.
A rule-based recommendation system for selection of software development life cycle models

The selection of a software development life cycle (SDLC) model for a software project is highly dependent upon the characteristics of the software product to be developed. We classified software products according to characteristics that matter for SDLC selection. We surveyed literature to elicit recommendations for SDLC selection. We formalized our findings to present a rule based recommendation system that can be helpful to software developers in selecting the most appropriate SDLC model to be used for the development of a software product. We conducted an initial evaluation of our system. We believe our SDLC recommendation system provides useful hints for selecting an SDLC, and provides a base for validating and refining SDLC recommendation rules.
Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach

This paper presents a new solution for choosing the K parameter in the k-nearest neighbor (KNN) algorithm, the solution depending on the idea of ensemble learning, in which a weak KNN classifier is used each time with a different K, starting from one to the square root of the size of the training set. The results of the weak classifiers are combined using the weighted sum rule. The proposed solution was tested and compared to other solutions using a group of experiments in real life problems. The experimental results show that the proposed classifier outperforms the traditional KNN classifier that uses a different number of neighbors, is competitive with other classifiers, and is a promising classifier with strong potential for a wide range of applications. KeywordsKNN; supervised learning; machine learning; ensemble learning; nearest neighbor;
Research in management accounting innovations : an overview of its development

This paper reviews prior research in management accounting innovations covering the period 1926-2008. Management accounting innovations refer to the adoption of “newer” or modern forms of management accounting systems such as activity-based costing, activity-based management, time-driven activity-based costing, target costing, and balanced scorecards. Although some prior reviews, covering the period until 2000, place emphasis on modern management accounting techniques, however, we believe that the time gap between 2000 and 2008 could entail many new or innovative accounting issues. We find that research in management accounting innovations has intensified during the period 2000-2008, with the main focus has been on explaining various factors associated with the implementation and the outcome of an innovation. In addition, research in management accounting innovations indicates the dominant use of sociological-based theories and increasing use of field studies. We suggest some directions for future research pertaining to management accounting innovations.
Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability

We explore the concept of co-design in the context of neural network verification. Specifically, we aim to train deep neural networks that not only are robust to adversarial perturbations but also whose robustness can be verified more easily. To this end, we identify two properties of network models – weight sparsity and so-called ReLU stability – that turn out to significantly impact the complexity of the corresponding verification task. We demonstrate that improving weight sparsity alone already enables us to turn computationally intractable verification problems into tractable ones. Then, improving ReLU stability leads to an additional 4–13x speedup in verification times. An important feature of our methodology is its “universality,” in the sense that it can be used with a broad range of training procedures and verification approaches.
The contributions of hepatitis B virus and hepatitis C virus infections to cirrhosis and primary liver cancer worldwide.

End-stage liver disease accounts for one in forty deaths worldwide. Chronic infections with hepatitis B virus (HBV) and hepatitis C virus (HCV) are well-recognized risk factors for cirrhosis and liver cancer, but estimates of their contributions to worldwide disease burden have been lacking.



The prevalence of serologic markers of HBV and HCV infections among patients diagnosed with cirrhosis or hepatocellular carcinoma (HCC) was obtained from representative samples of published reports. Attributable fractions of cirrhosis and HCC due to these infections were estimated for 11 WHO-based regions.



Globally, 57% of cirrhosis was attributable to either HBV (30%) or HCV (27%) and 78% of HCC was attributable to HBV (53%) or HCV (25%). Regionally, these infections usually accounted for >50% of HCC and cirrhosis. Applied to 2002 worldwide mortality estimates, these fractions represent 929,000 deaths due to chronic HBV and HCV infections, including 446,000 cirrhosis deaths (HBV: n=235,000; HCV: n=211,000) and 483,000 liver cancer deaths (HBV: n=328,000; HCV: n=155,000).



HBV and HCV infections account for the majority of cirrhosis and primary liver cancer throughout most of the world, highlighting the need for programs to prevent new infections and provide medical management and treatment for those already infected.
Vitamin D and Depression: A Systematic Review and Meta-Analysis Comparing Studies with and without Biological Flaws

Efficacy of Vitamin D supplements in depression is controversial, awaiting further literature analysis. Biological flaws in primary studies is a possible reason meta-analyses of Vitamin D have failed to demonstrate efficacy. This systematic review and meta-analysis of Vitamin D and depression compared studies with and without biological flaws. The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The literature search was undertaken through four databases for randomized controlled trials (RCTs). Studies were critically appraised for methodological quality and biological flaws, in relation to the hypothesis and study design. Meta-analyses were performed for studies according to the presence of biological flaws. The 15 RCTs identified provide a more comprehensive evidence-base than previous systematic reviews; methodological quality of studies was generally good and methodology was diverse. A meta-analysis of all studies without flaws demonstrated a statistically significant improvement in depression with Vitamin D supplements (+0.78 CI +0.24, +1.27). Studies with biological flaws were mainly inconclusive, with the meta-analysis demonstrating a statistically significant worsening in depression by taking Vitamin D supplements (-1.1 CI -0.7, -1.5). Vitamin D supplementation (≥800 I.U. daily) was somewhat favorable in the management of depression in studies that demonstrate a change in vitamin levels, and the effect size was comparable to that of anti-depressant medication.