nils-herrmann/clean_scirepeval huggingface dataset:
IPv6 Addressing Strategies for IoT

In this paper, we analyze the suitability of different IPv6 addressing strategies for nodes, gateways, and various access network deployment scenarios in the Internet of Things. A vast number of things connected to the Internet need IPv6 addresses, as the IPv4 address space was effectively consumed before the introduction of the Internet of Things. We highlight how the heterogeneity of nodes and network technologies, extreme constraint and miniaturization, renumbering, and multihoming, present serious challenges toward IPv6 address allocation. By considering the topologies of various types of IoT networks, their intended uses as well as the types of IPv6 addresses that need to be deployed, we draw attention to allocation solutions as well as potential pitfalls.
Windows Instant Messaging App Forensics: Facebook and Skype as Case Studies

Instant messaging (IM) has changed the way people communicate with each other. However, the interactive and instant nature of these applications (apps) made them an attractive choice for malicious cyber activities such as phishing. The forensic examination of IM apps for modern Windows 8.1 (or later) has been largely unexplored, as the platform is relatively new. In this paper, we seek to determine the data remnants from the use of two popular Windows Store application software for instant messaging, namely Facebook and Skype on a Windows 8.1 client machine. This research contributes to an in-depth understanding of the types of terrestrial artefacts that are likely to remain after the use of instant messaging services and application software on a contemporary Windows operating system. Potential artefacts detected during the research include data relating to the installation or uninstallation of the instant messaging application software, log-in and log-off information, contact lists, conversations, and transferred files.
Measuring User Credibility in Social Media

People increasingly use social media to get first-hand news and information. During disasters such as Hurricane Sandy and the tsunami in Japan people used social media to report injuries as well as send out their requests. During social movements such as Occupy Wall Street (OWS) and the Arab Spring, people extensively used social media to organize their events and spread the news. As more people rely on social media for political, social, and business events, it is more susceptible to become a place for evildoers to use it to spread misinformation and rumors. Therefore, users have the challenge to discern which piece of information is credible or not. They also need to find ways to assess the credibility of information. This problem becomes more important when the source of the information is not known to the consumer. In this paper we propose a method to measure user credibility in social media. We study the situations in which we cannot assess the credibility of the content or the credibility of the user (source of the information) based on the user’s profile. We propose the CredRank algorithm to measure user credibility in social media. The algorithm analyzes social media users’ online behavior to measure their credibility.
Design of MPPT algorithm under partial shadows

Owing to the complexity of the photovoltaic system structure and their environment, especially under the partial shadows environment, the output characteristics of photovoltaic arrays are greatly affected. Under the partial shadows environment, power-voltage (P-V) characteristics curve is of multi-peak. This makes that it is a difficult task to track the actual maximum power point. In addition, most programs are not able to get the maximum power point under these conditions. In this paper, we study the P-V curves under both uniform illumination and partial shadows environments, and then design an algorithm to track the maximum power point and select the strategy to deal with the MPPT algorithm by DSP chips and DC-DC converters. It is simple and easy to allow solar panels to maintain the best solar energy utilization resulting in increasing output at all times. Meanwhile, in order to track local peak point and improve the tracking speed, the algorithm proposed DC-DC converters operating feed-forward control scheme. Compared with the conventional controller, this controller costs much less time. This paper focuses mainly on specific processes of the algorithm, and being the follow-up basis for implementation of control strategies.
Space-Time Visual Analytics of Eye-Tracking Data for Dynamic Stimuli

We introduce a visual analytics method to analyze eye movement data recorded for dynamic stimuli such as video or animated graphics. The focus lies on the analysis of data of several viewers to identify trends in the general viewing behavior, including time sequences of attentional synchrony and objects with strong attentional focus. By using a space-time cube visualization in combination with clustering, the dynamic stimuli and associated eye gazes can be analyzed in a static 3D representation. Shot-based, spatiotemporal clustering of the data generates potential areas of interest that can be filtered interactively. We also facilitate data drill-down: the gaze points are shown with density-based color mapping and individual scan paths as lines in the space-time cube. The analytical process is supported by multiple coordinated views that allow the user to focus on different aspects of spatial and temporal information in eye gaze data. Common eye-tracking visualization techniques are extended to incorporate the spatiotemporal characteristics of the data. For example, heat maps are extended to motion-compensated heat maps and trajectories of scan paths are included in the space-time visualization. Our visual analytics approach is assessed in a qualitative users study with expert users, which showed the usefulness of the approach and uncovered that the experts applied different analysis strategies supported by the system.
Intrinsic Depth: Improving Depth Transfer with Intrinsic Images

We formulate the estimation of dense depth maps from video sequences as a problem of intrinsic image estimation. Our approach synergistically integrates the estimation of multiple intrinsic images including depth, albedo, shading, optical flow, and surface contours. We build upon an example-based framework for depth estimation that uses label transfer from a database of RGB and depth pairs. We combine this with a method that extracts consistent albedo and shading from video. In contrast to raw RGB values, albedo and shading provide a richer, more physical, foundation for depth transfer. Additionally we train a new contour detector to predict surface boundaries from albedo, shading, and pixel values and use this to improve the estimation of depth boundaries. We also integrate sparse structure from motion with our method to improve the metric accuracy of the estimated depth maps. We evaluate our Intrinsic Depth method quantitatively by estimating depth from videos in the NYU RGB-D and SUN3D datasets. We find that combining the estimation of multiple intrinsic images improves depth estimation relative to the baseline method.
Byzantine Consensus from Moderately-Hard Puzzles : A Model for Bitcoin

We present a formal model of synchronous processes without distinct identifiers (i.e., anonymous processes) that communicate using one-way public broadcasts. Our main contribution is a proof that the Bitcoin protocol achieves consensus in this model, except for a negligible probability, when Byzantine faults make up less than half the network. The protocol is scalable, since the running time and message complexity are all independent of the size of the network, instead depending only on the relative computing power of the faulty processes. We also introduce a requirement that the protocol must tolerate an arbitrary number of passive clients that receive broadcasts but can not send. This leads to a tight 2f + 1 resilience bound.
Less is More: Zero-Shot Learning from Online Textual Documents with Noise Suppression

Classifying a visual concept merely from its associated online textual source, such as a Wikipedia article, is an attractive research topic in zero-shot learning because it alleviates the burden of manually collecting semantic attributes. Recent work has pursued this approach by exploring various ways of connecting the visual and text domains. In this paper, we revisit this idea by going further to consider one important factor: the textual representation is usually too noisy for the zero-shot learning application. This observation motivates us to design a simple yet effective zero-shot learning method that is capable of suppressing noise in the text. Specifically, we propose an l2,1-norm based objective function which can simultaneously suppress the noisy signal in the text and learn a function to match the text document and visual features. We also develop an optimization algorithm to efficiently solve the resulting problem. By conducting experiments on two large datasets, we demonstrate that the proposed method significantly outperforms those competing methods which rely on online information sources but with no explicit noise suppression. Furthermore, we make an in-depth analysis of the proposed method and provide insight as to what kind of information in documents is useful for zero-shot learning.
A flat car-roof antenna module for phone and GPS applications

A flat car-roof antenna module for phone and GPS applications is proposed. The module consists of a planar inverted-F antenna (PIFA) for the phone operation and a planar loop antenna for the GPS one. The two antennas are located in a same plane parallel to car roof and put close to each other so that the dimension of the module is only 50 × 80 mm2 in plane. Gap between the antenna plane and the car roof is only 19 mm. This low profile makes the module almost invisible when it is installed on a car roof. Even with this narrow gap, the two antennas keep high performance in their operation frequencies.
Online Information Privacy: Measuring the Cost-Benefit Trade-Off

Concern over information privacy is widespread and rising. However, prior research is silent about the value of information privacy and the benefit of privacy protection. We conducted a conjoint analysis to explore individuals trade-offs between the benefits and costs of providing personal information to Websites. We find that economic incentives (monetary reward and future convenience) do affect individuals preferences over Websites with differing privacy policies. For instance, the disallowance of secondary use of personal information is worth between $39.83 and $49.78. Surprisingly, we find that cost-benefit trade-offs did not vary with personal characteristics including gender, contextual knowledge, individualism, and trust propensity.
Delving deeper into the whorl of flower segmentation

We describe an algorithm for automatically segmenting flowers in colour photographs. This is a challenging problem because of the sheer variety of flower classes, the variability within a class and within a particular flower, and the variability of the imaging conditions – lighting, pose, foreshortening, etc. The method couples two models – a colour model for foreground and background, and a light generic shape model for the petal structure. This shape model is tolerant to viewpoint changes and petal deformations, and applicable across many different flower classes. The segmentations are produced using a MRF cost function optimized using graph cuts. We show how the components of the algorithm can be tuned to overcome common segmentation errors, and how performance can be optimized by learning parameters on a training set. The algorithm is evaluated on 13 flower classes and more than 750 examples. Performance is assessed against ground truth trimap segmentations. The algorithms is also compared to several previous approaches for flower segmentation.
Quad-Active-Bridge DC–DC Converter as Cross-Link for Medium-Voltage Modular Inverters

One of the main challenges of the solid-state transformer (SST) lies in the implementation of the dc–dc stage. In this paper, a quadruple-active-bridge (QAB) dc–dc converter is investigated to be used as a basic module of a modular three-stage SST. Besides the feature of high power density and soft-switching operation (also found in others converters), the QAB converter provides a solution with reduced number of high-frequency transformers, since more bridges are connected to the same multiwinding transformer. To ensure soft switching for the entire operation range of the QAB converter, the triangular current-mode modulation strategy, previously adopted for the dual-active-bridge converter, is extended to the QAB converter. The theoretical analysis is developed considering balanced (equal power processed by the medium-voltage (MV) cells) and unbalanced (unequal power processed by the MV cells) conditions. In order to validate the theoretical analysis developed in the paper, a 2-kW prototype is built and experimented.
ERONTO: a tool for extracting ontologies from extended E/R diagrams

Realization of Semantic Web requires structuring of web data using domain ontologies. Most data intensive websites are powered by relational databases whose design process involves developing conceptual model using E/R or Extended E/R diagrams. This paper discusses the implementation details of a tool that builds domain ontologies in OWL (Ontology Web Language) from Extended E/R diagrams. Ontology development being a knowledge intensive task, our tool would be helpful in reducing the developmental efforts by automating the process. We bring out the differences and the similarities between the expressive capabilities of the two conceptual modeling methods, namely OWL and Extended E/R diagrams.
Dimensions of Leader Behavior in Sports : Deveiopment of a Leadership

Three different samples (total N = 485) participated in the development and refinement ofthe Leadership Scale for Sports (LSS). A five-factor solution with 40 items describing the most salient dimensions of coaching behavior was selected as the most meaningful. These factors were named Training and Instruction, Democratic Behavior, Autocratic Behavior, Social Support, and Positive Feedback. Internal consistency estimates ranged from .45 to .93 and the test-retest reliability coefficients ranged from .71 to .82. The relative stability of the factor structure across the different samples confirmed the factorial validity ofthe scale. The interpretation ofthe factors established the content validity of the scale. Finally, possible uses of the LSS were pointed out.
A Multiresolution 3D Morphable Face Model and Fitting Framework

3D Morphable Face Models are a powerful tool in computer vision. They consist of a PCA model of face shape and colour information and allow to reconstruct a 3D face from a single 2D image. 3D Morphable Face Models are used for 3D head pose estimation, face analysis, face recognition, and, more recently, facial landmark detection and tracking. However, they are not as widely used as 2D methods the process of building and using a 3D model is much more involved. In this paper, we present the Surrey Face Model, a multi-resolution 3D Morphable Model that we make available to the public for non-commercial purposes. The model contains different mesh resolution levels and landmark point annotations as well as metadata for texture remapping. Accompanying the model is a lightweight open-source C++ library designed with simplicity and ease of integration as its foremost goals. In addition to basic functionality, it contains pose estimation and face frontalisation algorithms. With the tools presented in this paper, we aim to close two gaps. First, by offering different model resolution levels and fast fitting functionality, we enable the use of a 3D Morphable Model in time-critical applications like tracking. Second, the software library makes it easy for the community to adopt the 3D Morphable Face Model in their research, and it offers a public place for collaboration.
Zero-Shot Super-Resolution Using Deep Internal Learning

Deep Learning has led to a dramatic leap in SuperResolution (SR) performance in the past few years. However, being supervised, these SR methods are restricted to specific training data, where the acquisition of the low-resolution (LR) images from their high-resolution (HR) counterparts is predetermined (e.g., bicubic downscaling), without any distracting artifacts (e.g., sensor noise, image compression, non-ideal PSF, etc). Real LR images, however, rarely obey these restrictions, resulting in poor SR results by SotA (State of the Art) methods. In this paper we introduce "Zero-Shot" SR, which exploits the power of Deep Learning, but does not rely on prior training. We exploit the internal recurrence of information inside a single image, and train a small image-specific CNN at test time, on examples extracted solely from the input image itself. As such, it can adapt itself to different settings per image. This allows to perform SR of real old photos, noisy images, biological data, and other images where the acquisition process is unknown or non-ideal. On such images, our method outperforms SotA CNN-based SR methods, as well as previous unsupervised SR methods. To the best of our knowledge, this is the first unsupervised CNN-based SR method.
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Convolutional neural networks (CNNs) have greatly improved state-of-the-art performances in a number of fields, notably computer vision and natural language processing. In this work, we are interested in generalizing the formulation of CNNs from low-dimensional regular Euclidean domains, where images (2D), videos (3D) and audios (1D) are represented, to high-dimensional irregular domains such as social networks or biological networks represented by graphs. This paper introduces a formulation of CNNs on graphs in the context of spectral graph theory. We borrow the fundamental tools from the emerging field of signal processing on graphs, which provides the necessary mathematical background and efficient numerical schemes to design localized graph filters efficient to learn and evaluate. As a matter of fact, we introduce the first technique that offers the same computational complexity than standard CNNs, while being universal to any graph structure. Numerical experiments on MNIST and 20NEWS demonstrate the ability of this novel deep learning system to learn local, stationary, and compositional features on graphs, as long as the graph is well-constructed.
Web-scale information extraction with vertex

Vertex is a Wrapper Induction system developed at Yahoo! for extracting structured records from template-based Web pages. To operate at Web scale, Vertex employs a host of novel algorithms for (1) Grouping similar structured pages in a Web site, (2) Picking the appropriate sample pages for wrapper inference, (3) Learning XPath-based extraction rules that are robust to variations in site structure, (4) Detecting site changes by monitoring sample pages, and (5) Optimizing editorial costs by reusing rules, etc. The system is deployed in production and currently extracts more than 250 million records from more than 200 Web sites. To the best of our knowledge, Vertex is the first system to do high-precision information extraction at Web scale.
Development of Usability Questionnaires for Electronic Mobile Products and Decision Making Methods

As the growth of rapid prototyping techniques shortens the development life cycle of software and electronic products, usability inquiry methods can play a more significant role during the development life cycle, diagnosing usability problems and providing metrics for making comparative decisions. A need has been realized for questionnaires tailored to the evaluation of electronic mobile products, wherein usability is dependent on both hardware and software as well as the emotional appeal and aesthetic integrity of the design. This research followed a systematic approach to develop a new questionnaire tailored to measure the usability of electronic mobile products. The Mobile Phone Usability Questionnaire (MPUQ) developed throughout this series of studies evaluates the usability of mobile phones for the purpose of making decisions among competing variations in the end-user market, alternatives of prototypes during the development process, and evolving versions during an iterative design process. In addition, the questionnaire can serve as a tool for identifying diagnostic information to improve specific usability dimensions and related interface elements. Employing the refined MPUQ, decision making models were developed using Analytic Hierarchy Process (AHP) and linear regression analysis. Next, a new group of representative mobile users was employed to develop a hierarchical model representing the usability dimensions incorporated in the questionnaire and to assign priorities to each node in the hierarchy. Employing the AHP and regression models, important usability dimensions and questionnaire items for mobile products were identified. Finally, a case study of comparative usability evaluations was performed to validate the MPUQ and models. A computerized support tool was developed to perform redundancy and relevancy analyses for the selection of appropriate questionnaire items. The weighted geometric mean was used to combine multiple numbers of matrices from pairwise comparison based on decision makers’ consistency ratio values for AHP. The AHP and regression models provided important usability dimensions so that mobile device usability practitioners can simply focus on the interface elements related to the decisive usability dimensions in order to improve the usability
Flow-based Brute-force Attack Detection

Brute-force attacks are a prevalent phenomenon that is getting harder to successfully detect on a network level due to increasing volume and encryption of network traffic and growing ubiquity of high-speed networks. Although the research in this field advanced considerably, there still remain classes of attacks that are undetectable. In this chapter, we present several methods for the detection of brute-force attacks based on the analysis of network flows. We discuss their strengths and shortcomings as well as shortcomings of flow-based methods in general. We also demonstrate the fragility of some methods by introducing detection evasion techniques.
A Review of Commercial Energy Harvesters for Autonomous Sensors

Current commercial autonomous sensors are mainly powered by primary batteries. Batteries need to be replaced and hence can become the largest and most expensive part of the system. On the other hand, our environment is full of waste and unused energy such as that coming from the sun or mechanical vibrations. As a result, commercial energy harvesters are increasingly available to power autonomous sensors. This work presents and analyses commercial energy harvesters currently available. First, environmental energy sources are classified and described. Then, energy harvesting principles are described and some guidelines are given to calculate the maximum power consumption allowed and the energy storage capacity required for the autonomous sensor. Finally, commercial energy harvesters are evaluated to determine their capability to power a commercial autonomous sensor in some given circumstances.