nils-herrmann/clean_scirepeval huggingface dataset:
NMF-SVM Based CAD Tool Applied to Functional Brain Images for the Diagnosis of Alzheimer's Disease

This paper presents a novel computer-aided diagnosis (CAD) technique for the early diagnosis of the Alzheimer's disease (AD) based on nonnegative matrix factorization (NMF) and support vector machines (SVM) with bounds of confidence. The CAD tool is designed for the study and classification of functional brain images. For this purpose, two different brain image databases are selected: a single photon emission computed tomography (SPECT) database and positron emission tomography (PET) images, both of them containing data for both Alzheimer's disease (AD) patients and healthy controls as a reference. These databases are analyzed by applying the Fisher discriminant ratio (FDR) and nonnegative matrix factorization (NMF) for feature selection and extraction of the most relevant features. The resulting NMF-transformed sets of data, which contain a reduced number of features, are classified by means of a SVM-based classifier with bounds of confidence for decision. The proposed NMF-SVM method yields up to 91% classification accuracy with high sensitivity and specificity rates (upper than 90%). This NMF-SVM CAD tool becomes an accurate method for SPECT and PET AD image classification.
The Pleasure of the Playable Text: Towards an Aesthetic Theory of Computer Games

This paper argues that the dominant study of the form and structure of games – their poetics – should be complemented by the analysis of their aesthetics (as understood by modern cultural theory): how gamers use their games, what aspects they enjoy and what kinds of pleasures they experience by playing them. The paper outlines a possible aesthetic theory of games based on different aspects of pleasure: the psychoanalytical, the social and the physical form of pleasure.
Large Scale Image Annotations on Amazon Mechanical Turk

We describe our experience with collecting roughly 250, 000 image annotations on Amazon Mechanical Turk (AMT). The annotations we collected range from location of keypoints and figure ground masks of various object categories, 3D pose estimates of head and torsos of people in images and attributes like gender, race, type of hair, etc. We describe the setup and strategies we adopted to automatically approve and reject the annotations, which becomes important for large scale annotations. These annotations were used to train algorithms for detection, segmentation, pose estimation, action recognition and attribute recognition of people in images.
Harnessing the Power of Text Mining for the Detection of Abusive Content in Social Media

The issues of cyberbullying and online harassment have gained considerable coverage in the last number of years. Social media providers need to be able to detect abusive content both accurately and efficiently in order to protect their users. Our aim is to investigate the application of core text mining techniques for the automatic detection of abusive content across a range of social media sources include blogs, forums, media-sharing, Q&A and chat using datasets from Twitter, YouTube, MySpace, Kongregate, Formspring and Slashdot. Using supervised machine learning, we compare alternative text representations and dimension reduction approaches, including feature selection and feature enhancement, demonstrating the impact of these techniques on detection accuracies. In addition, we investigate the need for sampling on imbalanced datasets. Our conclusions are: (1) Dataset balancing boosts accuracies significantly for social media abusive content detection; (2) Feature reduction, important for large feature sets that are typical of social media datasets, improves efficiency whilst maintaining detection accuracies; (3) The use of generic structural features common across all our datasets proved to be of limited use in the automatic detection of abusive content. Our findings can support practitioners in selecting appropriate text mining strategies in this area.
Data-driven modelling: some past experiences and new approaches

Dimitri P. Solomatine (corresponding author) UNESCO-IHE Institute for Water Education, PO Box 3015, 2601 DA, Delft, The Netherlands E-mail: d.solomatine@unesco-ihe.org Avi Ostfeld Faculty of Civil and Environmental Engineering, Technion-Israel Institute of Technology, Haifa, 32000, Israel Physically based (process) models based on mathematical descriptions of water motion are widely used in river basin management. During the last decade the so-called data-driven models are becoming more and more common. These models rely upon the methods of computational intelligence and machine learning, and thus assume the presence of a considerable amount of data describing the modelled system’s physics (i.e. hydraulic and/or hydrologic phenomena). This paper is a preface to the special issue on Data Driven Modelling and Evolutionary Optimization for River Basin Management, and presents a brief overview of the most popular techniques and some of the experiences of the authors in data-driven modelling relevant to river basin management. It also identifies the current trends and common pitfalls, provides some examples of successful applications and mentions the research challenges.
Network analysis of the Íslendinga sögur - the Sagas of Icelanders

The Íslendinga sögur – or Sagas of Icelanders – constitute a collection of medieval literature set in Iceland around the late 9th to early 11th centuries, the so-called Saga Age. They purport to describe events during the period around the settlement of Iceland and the generations immediately following and constitute an important element of world literature thanks to their unique narrative style. Although their historicity is a matter of scholarly debate, the narratives contain interwoven and overlapping plots involving thousands of characters and interactions between them. Here we perform a network analysis of the Íslendinga sögur in an attempt to gather quantitative information on interrelationships between characters and to compare saga society to other social networks.
Error-Driven Incremental Learning in Deep Convolutional Neural Network for Large-Scale Image Classification

Supervised learning using deep convolutional neural network has shown its promise in large-scale image classification task. As a building block, it is now well positioned to be part of a larger system that tackles real-life multimedia tasks. An unresolved issue is that such model is trained on a static snapshot of data. Instead, this paper positions the training as a continuous learning process as new classes of data arrive. A system with such capability is useful in practical scenarios, as it gradually expands its capacity to predict increasing number of new classes. It is also our attempt to address the more fundamental issue: a good learning system must deal with new knowledge that it is exposed to, much as how human do.
 We developed a training algorithm that grows a network not only incrementally but also hierarchically. Classes are grouped according to similarities, and self-organized into levels. The newly added capacities are divided into component models that predict coarse-grained superclasses and those return final prediction within a superclass. Importantly, all models are cloned from existing ones and can be trained in parallel. These models inherit features from existing ones and thus further speed up the learning. Our experiment points out advantages of this approach, and also yields a few important open questions.
Skin lesion image segmentation using Delaunay Triangulation for melanoma detection

Developing automatic diagnostic tools for the early detection of skin cancer lesions in dermoscopic images can help to reduce melanoma-induced mortality. Image segmentation is a key step in the automated skin lesion diagnosis pipeline. In this paper, a fast and fully-automatic algorithm for skin lesion segmentation in dermoscopic images is presented. Delaunay Triangulation is used to extract a binary mask of the lesion region, without the need of any training stage. A quantitative experimental evaluation has been conducted on a publicly available database, by taking into account six well-known state-of-the-art segmentation methods for comparison. The results of the experimental analysis demonstrate that the proposed approach is highly accurate when dealing with benign lesions, while the segmentation accuracy significantly decreases when melanoma images are processed. This behavior led us to consider geometrical and color features extracted from the binary masks generated by our algorithm for classification, achieving promising results for melanoma detection.
Toward 3D-Printed Movable Tactile Pictures for Children with Visual Impairments

Many children's books contain movable pictures with elements that can be physically opened, closed, pushed, pulled, spun, flipped, or swung. But these tangible, interactive reading experiences are inaccessible to children with visual impairments. This paper presents a set of 3D-printable models designed as building blocks for creating movable tactile pictures that can be touched, moved, and understood by children with visual impairments. Examples of these models are canvases, connectors, hinges, spinners, sliders, lifts, walls, and cutouts. They can be used to compose movable tactile pictures to convey a range of spatial concepts, such as in/out, up/down, and high/low. The design and development of these models were informed by three formative studies including 1) a survey on popular moving mechanisms in children's books and 3D-printed parts to implement them, 2) two workshops on the process creating movable tactile pictures by hand (e.g., Lego, Play-Doh), and 3) creation of wood-based prototypes and an informal testing on sighted preschoolers. Also, we propose a design language based on XML and CSS for specifying the content and structure of a movable tactile picture. Given a specification, our system can generate a 3D-printable model. We evaluate our approach by 1) transcribing six children's books, and 2) conducting six interviews on domain experts including four teachers for the visually impaired, one blind adult, two publishers at the National Braille Press, a renowned tactile artist, and a librarian.
Security threats in cloud computing

Cloud computing is set of resources and services offered through the Internet. Cloud services are delivered from data centers located throughout the world. Cloud computing facilitates its consumers by providing virtual resources via internet. General example of cloud services is Google apps, provided by Google and Microsoft SharePoint. The rapid growth in field of “cloud computing” also increases severe security concerns. Security has remained a constant issue for Open Systems and internet, when we are talking about security cloud really suffers. Lack of security is the only hurdle in wide adoption of cloud computing. Cloud computing is surrounded by many security issues like securing data, and examining the utilization of cloud by the cloud computing vendors. The wide acceptance www has raised security risks along with the uncountable benefits, so is the case with cloud computing. The boom in cloud computing has brought lots of security challenges for the consumers and service providers. How the end users of cloud computing know that their information is not having any availability and security issues? Every one poses, Is their information secure? This study aims to identify the most vulnerable security threats in cloud computing, which will enable both end users and vendors to know about the key security threats associated with cloud computing. Our work will enable researchers and security professionals to know about users and vendors concerns and critical analysis about the different security models and tools proposed.
Obstacles in Moving to Agile Software Development methods; at a Glance

It is only less than a decade that agile SD methods were introduced and got popular steadily. The defined values in these methods and their outcomes have motivated many software producers to use these methods. Since migration from traditional software development methods to agile methods is growing highly, managers of the companies should be aware of problems, hindrances and challenges they may face with during the agile transformation process. This study focused on challenges which companies may face with and it is necessary that managers think about solving them. Classifying them into four main categories; organization and management, people, process and tools are the areas that challenges have been seen in recent studies.
Multilevel Inverter For Grid-Connected PV System Employing Digital PI Controller

This paper presents a single-phase five-level photovoltaic (PV) inverter topology for grid-connected PV systems with a novel pulsewidth-modulated (PWM) control scheme. Two reference signals identical to each other with an offset equivalent to the amplitude of the triangular carrier signal were used to generate PWM signals for the switches. A digital proportional-integral current control algorithm is implemented in DSP TMS320F2812 to keep the current injected into the grid sinusoidal and to have high dynamic performance with rapidly changing atmospheric conditions. The inverter offers much less total harmonic distortion and can operate at near-unity power factor. The proposed system is verified through simulation and is implemented in a prototype, and the experimental results are compared with that with the conventional single-phase three-level grid-connected PWM inverter.
Design and simulation of a fuzzy controller for a busy intersection

Traffic in large cities is one of the biggest problems that can lead to excess utilization of fuel by motor vehicles, accidents, and the waste of time of citizens. To have an effective and efficient city management system, it is necessary to intelligently control all the traffic light signals. For this reason, many researchers have tried to present optimal algorithms for traffic signal control. Some common methods exist for the control of traffic light signal, including the preset cycle time controller and vehicle-actuated controller. Results obtained from previous works indicate that these traffic light signal controllers do not exhibit an effective performance at moments of traffic peak. So to resolve this dilemma at such moments, traffic cops are employed. The application of fuzzy logic in traffic signal controllers has been seriously considered for several decades and many research works have been carried out in this regard. The fuzzy signal controllers perform the optimization task by minimizing the waiting time of the vehicles and maximizing the traffic capacity. A new fuzzy logic based algorithm is proposed in this article, which not only can reduce the waiting time and the number of vehicles behind a traffic light and at an intersection, but can consider the traffic situations at adjacent intersections as well. Finally, a comparison is made between the designed fuzzy controller and the preset cycle time controller.
The use of asymmetric numeral systems as an accurate replacement for Huffman coding

Entropy coding is an integral part of most data compression systems. Huffman coding (HC) and arithmetic coding (AC) are two of the most widely used coding methods. HC can process a large symbol alphabet at each step allowing for fast encoding and decoding. However, HC typically provides suboptimal data rates due to its inherent approximation of symbol probabilities to powers of 1 over 2. In contrast, AC uses nearly accurate symbol probabilities, hence generally providing better compression ratios. However, AC relies on relatively slow arithmetic operations making the implementation computationally demanding. In this paper we discuss asymmetric numeral systems (ANS) as a new approach to entropy coding. While maintaining theoretical connections with AC, the proposed ANS-based coding can be implemented with much less computational complexity. While AC operates on a state defined by two numbers specifying a range, an ANS-based coder operates on a state defined by a single natural number such that the x ∈ ℕ state contains ≈ log2(x) bits of information. This property allows to have the entire behavior for a large alphabet summarized in the form of a relatively small table (e.g. a few kilobytes for a 256 size alphabet). The proposed approach can be interpreted as an equivalent to adding fractional bits to a Huffman coder to combine the speed of HC and the accuracy offered by AC. Additionally, ANS can simultaneously encrypt a message encoded this way. Experimental results demonstrate effectiveness of the proposed entropy coder.
Campaign extraction from social media

In this manuscript, we study the problem of detecting coordinated free text campaigns in large-scale social media. These campaigns—ranging from coordinated spam messages to promotional and advertising campaigns to political astro-turfing—are growing in significance and reach with the commensurate rise in massive-scale social systems. Specifically, we propose and evaluate a content-driven framework for effectively linking free text posts with common “talking points” and extracting campaigns from large-scale social media. Three of the salient features of the campaign extraction framework are: (i) first, we investigate graph mining techniques for isolating coherent campaigns from large message-based graphs; (ii) second, we conduct a comprehensive comparative study of text-based message correlation in message and user levels; and (iii) finally, we analyze temporal behaviors of various campaign types. Through an experimental study over millions of Twitter messages we identify five major types of campaigns—namely Spam, Promotion, Template, News, and Celebrity campaigns—and we show how these campaigns may be extracted with high precision and recall.
Primal least squares twin support vector regression

The training algorithm of classical twin support vector regression (TSVR) can be attributed to the solution of a pair of quadratic programming problems (QPPs) with inequality constraints in the dual space. However, this solution is affected by time and memory constraints when dealing with large datasets. In this paper, we present a least squares version for TSVR in the primal space, termed primal least squares TSVR (PLSTSVR). By introducing the least squares method, the inequality constraints of TSVR are transformed into equality constraints. Furthermore, we attempt to directly solve the two QPPs with equality constraints in the primal space instead of the dual space; thus, we need only to solve two systems of linear equations instead of two QPPs. Experimental results on artificial and benchmark datasets show that PLSTSVR has comparable accuracy to TSVR but with considerably less computational time. We further investigate its validity in predicting the opening price of stock.
Automated model-based Android GUI testing using multi-level GUI comparison criteria

Automated Graphical User Interface (GUI) testing is one of the most widely used techniques to detect faults in mobile applications (apps) and to test functionality and usability. GUI testing exercises behaviors of an application under test (AUT) by executing events on GUIs and checking whether the app behaves correctly. In particular, because Android leads in market share of mobile OS platforms, a lot of research on automated Android GUI testing techniques has been performed. Among various techniques, we focus on model-based Android GUI testing that utilizes a GUI model for systematic test generation and effective debugging support. Since test inputs are generated based on the underlying model, accurate GUI modeling of an AUT is the most crucial factor in order to generate effective test inputs. However, most modern Android apps contain a number of dynamically constructed GUIs that make accurate behavior modeling more challenging. To address this problem, we propose a set of multi-level GUI Comparison Criteria (GUICC) that provides the selection of multiple abstraction levels for GUI model generation. By using multilevel GUICC, we conducted empirical experiments to identify the influence of GUICC on testing effectiveness. Results show that our approach, which performs model-based testing with multi-level GUICC, achieved higher effectiveness than activity-based GUI model generation. We also found that multi-level GUICC can alleviate the inherent state explosion problems of existing a single-level GUICC for behavior modeling of real-world Android apps by flexibly manipulating GUICC.
Production, use, and fate of all plastics ever made

Plastics have outgrown most man-made materials and have long been under environmental scrutiny. However, robust global information, particularly about their end-of-life fate, is lacking. By identifying and synthesizing dispersed data on production, use, and end-of-life management of polymer resins, synthetic fibers, and additives, we present the first global analysis of all mass-produced plastics ever manufactured. We estimate that 8300 million metric tons (Mt) as of virgin plastics have been produced to date. As of 2015, approximately 6300 Mt of plastic waste had been generated, around 9% of which had been recycled, 12% was incinerated, and 79% was accumulated in landfills or the natural environment. If current production and waste management trends continue, roughly 12,000 Mt of plastic waste will be in landfills or in the natural environment by 2050.
MEKA: A Multi-label/Multi-target Extension to WEKA

Multi-label classification has rapidly attracted interest in the machine learning literature, and there are now a large number and considerable variety of methods for this type of learning. We present Meka: an open-source Java framework based on the well-known Weka library. Meka provides interfaces to facilitate practical application, and a wealth of multi-label classifiers, evaluation metrics, and tools for multi-label experiments and development. It supports multi-label and multi-target data, including in incremental and semi-supervised contexts.
THE FABLE OF THE KEYS *

The economic literature on standards has focused recently on the possibility of market failure with respect to the choice of a standard. In its strongest form, the argument is essentially this: an established standard can persist over a challenger, even where all users prefer a world dominated by the challenger, if users are unable to coordinate their choices. For example, each of us might prefer to have Beta-format videocassette recorders as long as prerecorded Beta tapes continue to be produced, but individually we do not buy Beta machines because we don't think enough others will buy Beta machines to sustain the prerecorded tape supply. I don't buy a Beta format machine because I think that you won't you don't buy one because you think that I won't. In the end, we both turn out to be correct, but we are both worse off than we might have been. This, of course, is a catch-22 that we might suppose to be common in the economy. There will be no cars until there are gas stations there will be no gas stations until there are cars. Without some way out of this conundrum, joyriding can never become a favorite activity of
COnGRATS: Realistic simulation of traffic sequences for autonomous driving

For evaluating or training different kinds of vision algorithms, a large amount of precise and reliable data is needed. In this paper we present a system to create extended synthetic sequences of traffic environment scenarios, associated with several types of ground truth data. By integrating vehicle dynamics in a configuration tool, and by using path-tracing in an external rendering engine to render the scenes, a system is created that allows ongoing and flexible creation of highly realistic traffic images. For all images, ground truth data is provided for depth, optical flow, surface normals and semantic scene labeling. Sequences that are produced with this system are more varied and closer to natural images than other synthetic datasets before.