nils-herrmann/clean_scirepeval huggingface dataset:
Software as a Service: Implications for Investment in Software Development

Software as a service (SaaS) is a rapidly growing model of software licensing. In contrast to traditional software where users buy a perpetual-use license, SaaS users buy a subscription from the publisher. Whereas traditional software publishers typically release new product features as part of new versions of software once in a few years, publishers using SaaS have an incentive to release new features as soon as they are completed. We show that this property of the SaaS licensing model leads to greater investment in product development under most conditions. This increased investment leads to higher software quality in equilibrium under SaaS compared to perpetual licensing. The software publisher earns greater profits under SaaS while social welfare is also higher
Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition

The presence of noise in data is a common problem that produces several negative consequences in classification problems. In multi-class problems, these consequences are aggravated in terms of accuracy, building time, and complexity of the classifiers. In these cases, an interesting approach to reduce the effect of noise is to decompose the problem into several binary subproblems, reducing the complexity and, consequently, dividing the effects caused by noise into each of these subproblems. This paper analyzes the usage of decomposition strategies, and more specifically the One-vs-One scheme, to deal with noisy multi-class datasets. In order to investigate whether the decomposition is able to reduce the effect of noise or not, a large number of datasets are created introducing different levels and types of noise, as suggested in the literature. Several well-known classification algorithms, with or without decomposition, are trained on them in order to check when decomposition is advantageous. The results obtained show that methods using the One-vs-One strategy lead to better performances and more robust classifiers when dealing with noisy data, especially with the most disruptive noise schemes.
Robust Extreme Multi-label Learning

Tail labels in the multi-label learning problem undermine the low-rank assumption. Nevertheless, this problem has rarely been investigated. In addition to using the low-rank structure to depict label correlations, this paper explores and exploits an additional sparse component to handle tail labels behaving as outliers, in order to make the classical low-rank principle in multi-label learning valid. The divide-and-conquer optimization technique is employed to increase the scalability of the proposed algorithm while theoretically guaranteeing its performance. A theoretical analysis of the generalizability of the proposed algorithm suggests that it can be improved by the low-rank and sparse decomposition given tail labels. Experimental results on real-world data demonstrate the significance of investigating tail labels and the effectiveness of the proposed algorithm.
Comic Chat

Comics have a rich visual vocabulary, and people find them appealing. They are also an effective form of communication. We have built a system, called Comic Chat, that represents on-line communications in the form of comics. Comic Chat automates numerous aspects of comics generation, including balloon construction and layout, the placement and orientation of comic characters, the default selection of character gestures and expressions, the incorporation of semantic panel elements, and the choice of zoom factor for the virtual camera. This paper describes the mechanisms that Comic Chat uses to perform this automation, as well as novel aspects of the program’s user interface. Comic Chat is a working program, allowing groups of people to communicate over the Internet. It has several advantages over other graphical chat programs, including the availability of a graphical history, and a dynamic graphical presentation.  I.3.2 [Computer Graphics]: Graphics Systems— distributed /network graphics; K.8.1 [Personal Computing]: Application Packages; D.2.2 [Information Interfaces and Presentation]: Tools and Techniques—user interfaces.
A comparative study between LBP and Haar-like features for Face Detection using OpenCV

Face Detection is an important step in any face recognition systems, for the purpose of localizing and extracting face region from the rest of the images. There are many techniques, which have been proposed from simple edge detection techniques to advance techniques such as utilizing pattern recognition approaches. This paper evaluates two methods of face detection, her features and Local Binary Pattern features based on detection hit rate and detection speed. The algorithms were tested on Microsoft Visual C++ 2010 Express with OpenCV library. The experimental results show that Local Binary Pattern features are most efficient and reliable for the implementation of a real-time face detection system.
Long short-term memory based operation log anomaly detection

Long short-term memory (LSTM) architecture is an important approach for capturing long-range temporal dependencies in sequences of arbitrary length. Moreover, stacked-LSTM (S-LSTM: formed by adding recurrent LSTM layer to the existing LSTM network in hidden layer) has capability to learn temporal behaviors quickly with sparse representations. To apply this to anomaly detection, we model the operation log samples of normal and anomalous events occurred in 1 minute time interval as time-series with the aim to detect and classify the events as either normal or anomalous. To select an appropriate LSTM network, experiments are conducted for various network parameters and network structures with the dataset provided by Cyber Security Data Mining Competition (CDMC2016). The experiments are run up to 1000 epochs with learning rate in the range [0.01-05]. S-LSTM network architecture has showed its strength by achieving the highest accuracy 0.996 with false positive rate 0.02 on the provided real-world test data by CDMC2016.
5G and the Fog — Survey of related technologies and research directions

The rapid and quick growth of smart mobile devices has caused users to demand pervasive mobile broadband services comparable to the fixed broadband Internet. In this direction, the research initiatives on 5G networks have gained accelerating momentum globally. 5G Networks will act as a nervous system of the digital society, economy, and everyday peoples life and will enable new future Internet of Services paradigms such as Anything as a Service, where devices, terminals, machines, also smart things and robots will become innovative tools that will produce and will use applications, services and data. However, future Internet will exacerbate the need for improved QoS/QoE, supported by services that are orchestrated on-demand and that are capable of adapt at runtime, depending on the contextual conditions, to allow reduced latency, high mobility, high scalability, and real time execution. A new paradigm called Fog Computing, or briefly Fog has emerged to meet these requirements. Fog Computing extends Cloud Computing to the edge of the network, reduces service latency, and improves QoS/QoE, resulting in superior user-experience. This paper provides a survey of 5G and Fog Computing technologies and their research directions, that will lead to Beyond-5G Network in the Fog.
Meta Networks

Neural networks have been successfully applied in applications with a large amount of labeled data. However, the task of rapid generalization on new concepts with small training data while preserving performances on previously learned ones still presents a significant challenge to neural network models. In this work, we introduce a novel meta learning method, Meta Networks (MetaNet), that learns a meta-level knowledge across tasks and shifts its inductive biases via fast parameterization for rapid generalization. When evaluated on Omniglot and Mini-ImageNet benchmarks, our MetaNet models achieve a near human-level performance and outperform the baseline approaches by up to 6% accuracy. We demonstrate several appealing properties of MetaNet relating to generalization and continual learning.
Convolutional neural network-based encoding and decoding of visual object recognition in space and time

Representations learned by deep convolutional neural networks (CNNs) for object recognition are a widely investigated model of the processing hierarchy in the human visual system. Using functional magnetic resonance imaging, CNN representations of visual stimuli have previously been shown to correspond to processing stages in the ventral and dorsal streams of the visual system. Whether this correspondence between models and brain signals also holds for activity acquired at high temporal resolution has been explored less exhaustively. Here, we addressed this question by combining CNN-based encoding models with magnetoencephalography (MEG). Human participants passively viewed 1,000 images of objects while MEG signals were acquired. We modelled their high temporal resolution source-reconstructed cortical activity with CNNs, and observed a feed-forward sweep across the visual hierarchy between 75 and 200 ms after stimulus onset. This spatiotemporal cascade was captured by the network layer representations, where the increasingly abstract stimulus representation in the hierarchical network model was reflected in different parts of the visual cortex, following the visual ventral stream. We further validated the accuracy of our encoding model by decoding stimulus identity in a left-out validation set of viewed objects, achieving state-of-the-art decoding accuracy.
Learning to Resolve Natural Language Ambiguities: A Unified Approach

We analyze a few of the commonly used statistics based and machine learning algorithms for natural language disambiguation tasks and observe that they can bc recast as learning linear separators in the feature space. Each of the methods makes a priori assumptions, which it employs, given the data, when searching for its hypothesis. Nevertheless, as we show, it searches a space that is as rich as the space of all linear separators. We use this to build an argument for a data driven approach which merely searches for a good linear separator in the feature space, without further assumptions on the domain or a specific problem. We present such an approach a sparse network of linear separators, utilizing the Winnow learning aigorlthrn and show how to use it in a variety of ambiguity resolution problems. The learning approach presented is attribute-efficient and, therefore, appropriate for domains having very large number of attributes. In particular, we present an extensive experimental comparison of our approach with other methods on several well studied lexical disambiguation tasks such as context-sensltlve spelling correction, prepositional phrase attachment and part of speech tagging. In all cases we show that our approach either outperforms other methods tried for these tasks or performs comparably to the best.
Building web service ontologies

ions. The experts often redistributed the extracted domain concepts according to their domain view. For example, two subclasses identified for Pr tein belong to different domains, molecular biology and bioinformatics, and have to be placed in the corresponding hierarchies accordingly. Such abstractions need to be still manually created according to the ontology engineers view on the domain. However, the abstraction step is considerably supported if the expert has an overview of relevant domain concepts. Support. The curators considered the extracted ontologies as a useful start for deriving a domain ontology. Several complex structures could be directly included in a final ontology (e.g., theSitehierarchy in Figure 7.4), or provided helpful hints on how certain concepts interrelate. The most appreciated contribution was that the learned ontologies even suggested new additions for the manually built ontologies.
A Deep Representation for Volumetric Shape Modeling

3D shape is a crucial but heavily underutilized cue in today’s computer vision system, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape model in the loop. Apart from object recognition on 2.5D depth maps, recovering these incomplete 3D shapes to full 3D is critical for analyzing shape variations. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses. It naturally supports joint object recognition and shape reconstruction from 2.5D depth maps, and further, as an additional application it allows active object recognition through view planning. We construct a largescale 3D CAD model dataset to train our model, and conduct extensive experiments to study our new representation.
A novel stair-climbing wheelchair with transformable wheeled four-bar linkages

This paper describes the development of a novel multi-wheel stair-climbing wheelchair. The necessity for mobility aid technology for elderly and handicapped people that has “minimal invasiveness for use in an historical environment” is described. With this goal in mind, a prototype of a novel wheelchair having a stair-climbing function resulting from transformable wheeled four-bar linkages is proposed. The mechanical design, principle of operation and statics of the proposed mechanism are thoroughly illustrated. The basic performance of the proposed mechanism has been confirmed through experimental results.
The Online Disinhibition Effect

While online, some people self-disclose or act out more frequently or intensely than they would in person. This article explores six factors that interact with each other in creating this online disinhibition effect: dissociative anonymity, invisibility, asynchronicity, solipsistic introjection, dissociative imagination, and minimization of authority. Personality variables also will influence the extent of this disinhibition. Rather than thinking of disinhibition as the revealing of an underlying "true self," we can conceptualize it as a shift to a constellation within self-structure, involving clusters of affect and cognition that differ from the in-person constellation.
Detection of closed sharp edges in point clouds using normal estimation and graph theory

The reconstruction of a surface model from a point cloud is an important task in the reverse engineering of industrial parts. We aim at constructing a curve network on the point cloud that will define the border of the various surface patches. In this paper, we present an algorithm to extract closed sharp feature lines, which is necessary to create such a closed curve network. We use a first order segmentation to extract candidate feature points and process them as a graph to recover the sharp feature lines. To this end, a minimum spanning tree is constructed and afterwards a reconnection procedure closes the lines. The algorithm is fast and gives good results for real-world point sets from industrial applications.
Implementing the Real-Time Publisher/Subscriber Model on the Controller Area Network (CAN)

Designing distributed real-time systems as being composed of communicating objects offers many advantages with respect to modularity and extensibility of these systems. However, distributed real-time applications exhibit communication patterns that significantly differ from the traditional object invocation style. The publisher/subscriber model for inter-object communication matches well with these patterns. Any implementation of that model must address the problems of binding subscribers to publishers, of routing and filtering of messages, as well as reliability, efficiency and latency of message delivery. In the context of real-time applications, all these issues must be subject to a rigid inspection with respect to meeting real-time requirements. We argue that for embedded control systems built around smart microcontroller-powered devices these requirements can only be met when exploiting the properties of the underlying network. The CAN-Bus (CAN: Controller Area Network) which is an emerging standard in the field of real-time embedded systems is particularly suited to implement a publisher/subscriber model of communication. In this paper, we present an implementation of the real-time publisher/subscriber model that exploits the underlying facilities of the CANBus. In particular, we introduce a novel addressing scheme for publisher/subscriber communication that makes efficient use of the CAN-Bus addressing method. We provide a detailed design and implementation details along with some preliminary performance estimations.
Associative Long Short-Term Memory

We investigate a new method to augment recurrent neural networks with extra memory without increasing the number of network parameters. The system has an associative memory based on complex-valued vectors and is closely related to Holographic Reduced Representations and Long Short-Term Memory networks. Holographic Reduced Representations have limited capacity: as they store more information, each retrieval becomes noisier due to interference. Our system in contrast creates redundant copies of stored information, which enables retrieval with reduced noise. Experiments demonstrate faster learning on multiple memorization tasks.
Automatically characterizing large scale program behavior

Understanding program behavior is at the foundation of computer architecture and program optimization. Many programs have wildly different behavior on even the very largest of scales (over the complete execution of the program). This realization has ramifications for many architectural and compiler techniques, from thread scheduling, to feedback directed optimizations, to the way programs are simulated. However, in order to take advantage of time-varying behavior, we must first develop the analytical tools necessary to automatically and efficiently analyze program behavior over large sections of execution.Our goal is to develop automatic techniques that are capable of finding and exploiting the Large Scale Behavior of programs (behavior seen over billions of instructions). The first step towards this goal is the development of a hardware independent metric that can concisely summarize the behavior of an arbitrary section of execution in a program. To this end we examine the use of Basic Block Vectors. We quantify the effectiveness of Basic Block Vectors in capturing program behavior across several different architectural metrics, explore the large scale behavior of several programs, and develop a set of algorithms based on clustering capable of analyzing this behavior. We then demonstrate an application of this technology to automatically determine where to simulate for a program to help guide computer architecture research.
Cooperative Control of Distributed Multi-Agent Systems

Will reading habit influence your life? Many say yes. Reading cooperative control of distributed multi agent systems is a good habit; you can develop this habit to be such interesting way. Yeah, reading habit will not only make you have any favourite activity. It will be one of guidance of your life. When reading has become a habit, you will not make it as disturbing activities or as boring activity. You can gain many benefits and importances of reading.
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification

This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy.
StreamScope: Continuous Reliable Distributed Processing of Big Data Streams

is a reliable distributed stream computation engine that has been deployed in shared 20,000-server production clusters at Microsoft. STREAMS provides a continuous temporal stream model that allows users to express complex stream processing logic naturally and declaratively. STREAMS supports business-critical streaming applications that can process tens of billions (or tens of terabytes) of input events per day continuously with complex logic involving tens of temporal joins, aggregations, and sophisticated userdefined functions, while maintaining tens of terabytes inmemory computation states on thousands of machines. STREAMS introduces two abstractions, rVertex and rStream, to manage the complexity in distributed stream computation systems. The abstractions allow efficient and flexible distributed execution and failure recovery, make it easy to reason about correctness even with failures, and facilitate the development, debugging, and deployment of complex multi-stage streaming applications.
Biomechanics and muscle coordination of human walking: part II: lessons from dynamical simulations and clinical implications.

Principles of muscle coordination in gait have been based largely on analyses of body motion, ground reaction force and EMG measurements. However, data from dynamical simulations provide a cause-effect framework for analyzing these measurements; for example, Part I (Gait Posture, in press) of this two-part review described how force generation in a muscle affects the acceleration and energy flow among the segments. This Part II reviews the mechanical and coordination concepts arising from analyses of simulations of walking. Simple models have elucidated the basic multisegmented ballistic and passive mechanics of walking. Dynamical models driven by net joint moments have provided clues about coordination in healthy and pathological gait. Simulations driven by muscle excitations have highlighted the partial stability afforded by muscles with their viscoelastic-like properties and the predictability of walking performance when minimization of metabolic energy per unit distance is assumed. When combined with neural control models for exciting motoneuronal pools, simulations have shown how the integrative properties of the neuro-musculo-skeletal systems maintain a stable gait. Other analyses of walking simulations have revealed how individual muscles contribute to trunk support and progression. Finally, we discuss how biomechanical models and simulations may enhance our understanding of the mechanics and muscle function of walking in individuals with gait impairments.