nils-herrmann/clean_scirepeval huggingface dataset:
What is a Fog Node A Tutorial on Current Concepts towards a Common Definition

Fog computing has emerged as a promising technology that can bring the cloud applications closer to the physical IoT devices at the network edge. While it is widely known what cloud computing is, and how data centers can build the cloud infrastructure and how applications can make use of this infrastructure, there is no common picture on what fog computing and a fog node, as its main building block, really is. One of the first attempts to define a fog node was made by Cisco, qualifying a fog computing system as a “mini-cloud,” located at the edge of the network and implemented through a variety of edge devices, interconnected by a variety, mostly wireless, communication technologies. Thus, a fog node would be the infrastructure implementing the said mini-cloud. Other proposals have their own definition of what a fog node is, usually in relation to a specific edge device, a specific use case or an application. In this paper, we first survey the state of the art in technologies for fog computing nodes as building blocks of fog computing, paying special attention to the contributions that analyze the role edge devices play in the fog node definition. We summarize and compare the concepts, lessons learned from their implementation, and show how a conceptual framework is emerging towards a unifying fog node definition. We focus on core functionalities of a fog node as well as in the accompanying opportunities and challenges towards their practical realization in the near future.
Harvest: A High-Performance Fundamental Frequency Estimator from Speech Signals

A fundamental frequency (F0) estimator named Harvest is described. The unique points of Harvest are that it can obtain a reliable F0 contour and reduce the error that the voiced section is wrongly identified as the unvoiced section. It consists of two steps: estimation of F0 candidates and generation of a reliable F0 contour on the basis of these candidates. In the first step, the algorithm uses fundamental component extraction by many band-pass filters with different center frequencies and obtains the basic F0 candidates from filtered signals. After that, basic F0 candidates are refined and scored by using the instantaneous frequency, and then several F0 candidates in each frame are estimated. Since the frame-by-frame processing based on the fundamental component extraction is not robust against temporally local noise, a connection algorithm using neighboring F0s is used in the second step. The connection takes advantage of the fact that the F0 contour does not precipitously change in a short interval. We carried out an evaluation using two speech databases with electroglottograph (EGG) signals to compare Harvest with several state-of-the-art algorithms. Results showed that Harvest achieved the best performance of all algorithms.
Dynamic classifier selection: Recent advances and perspectives

Multiple Classifier Systems (MCS) have been widely studied as an alternative for increasing accuracy in pattern recognition. One of the most promising MCS approaches is Dynamic Selection (DS), in which the base classifiers are selected on the fly, according to each new sample to be classified. This paper provides a review of the DS techniques proposed in the literature from a theoretical and empirical point of view. We propose an updated taxonomy based on the main characteristics found in a dynamic selection system: (1) The methodology used to define a local region for the estimation of the local competence of the base classifiers; (2) The source of information used to estimate the level of competence of the base classifiers, such as local accuracy, oracle, ranking and probabilistic models, and (3) The selection approach, which determines whether a single or an ensemble of classifiers is selected. We categorize the main dynamic selection techniques in the DS literature based on the proposed taxonomy. We also conduct an extensive experimental analysis, considering a total of 18 state-of-the-art dynamic selection techniques, as well as static ensemble combination and single classification models. To date, this is the first analysis comparing all the key DS techniques under the same experimental protocol. Furthermore, we also present several perspectives and open research questions that can be used as a guide for future works in this domain.
Biomimetic Spider Leg Joints: A Review from Biomechanical Research to Compliant Robotic Actuators

Due to their inherent compliance, soft actuated joints are becoming increasingly important for robotic applications, especially when human-robot-interactions are expected. Several of these flexible actuators are inspired by biological models. One perfect showpiece for biomimetic robots is the spider leg, because it combines lightweight design and graceful movements with powerful and dynamic actuation. Building on this motivation, the review article focuses on compliant robotic joints inspired by the function principle of the spider leg. The mechanism is introduced by an overview of existing biological and biomechanical research. Thereupon a classification of robots that are bio-inspired by spider joints is presented. Based on this, the biomimetic robot applications referring to the spider principle are identified and discussed.
Measurements for the Paranoid: The Effect of Encrypting Files in Cloud Storage

Cloud storage is increasing in popularity and has been for years. People upload personal data in large quantities to the cloud without concerning themselves with safeguarding of that data. This paper focusses on the encryption of data and the synchronization of that data. Different synchronization techniques are researched to gain an answer on how encryption will affect the techniques. Tests are performed with Dropbox, a service that uses many smart synchronization techniques, in a worst case manner. ECB and CBC modes of operation are compared to no encryption in light of the data exchange needed for synchronization. Conclusions are drawn based on the results of these performance measurements.
Behavior-Based Network Access Control: A Proof-of-Concept

Current NAC technologies implement a pre-connect phase whe re t status of a device is checked against a set of policies before being granted access to a network, an d a post-connect phase that examines whether the device complies with the policies that correspond to its rol e in the network. In order to enhance current NAC technologies, we propose a new architecture based on behaviorsrather thanrolesor identity, where the policies are automatically learned and updated over time by the membe rs of the network in order to adapt to behavioral changes of the devices. Behavior profiles may be presented as identity cards that can change over time. By incorporating an Anomaly Detector (AD) to the NAC server or t each of the hosts, their behavior profile is modeled and used to determine the type of behaviors that shou ld be accepted within the network. These models constitute behavior-based policies. In our enhanced NAC ar chitecture, global decisions are made using a group voting process. Each host’s behavior profile is used to compu te a partial decision for or against the acceptance of a new profile or traffic. The aggregation of these partial vote s amounts to the model-group decision. This voting process makes the architecture more resilient to attacks. E ven after accepting a certain percentage of malicious devices, the enhanced NAC is able to compute an adequate deci sion. We provide proof-of-concept experiments of our architecture using web traffic from our department netwo rk. Our results show that the model-group decision approach based on behavior profiles has a 99% detection rate o f nomalous traffic with a false positive rate of only 0.005%. Furthermore, the architecture achieves short latencies for both the preand post-connect phases.
A 3.6mW 2.4-GHz multi-channel super-regenerative receiver in 130nm CMOS

Super-regeneration is re-examined for its simplicity and power efficiency for low-power, short-range communication. Although previous approaches rely on a high quality off-chip LC-tuned circuit, this paper describes a fully integrated 2.4-GHz ISM band super-regenerative receiver implemented in 130 nm CMOS. Several new design features, that take advantage of digital processing, are proposed. A synthesizer scheme tunes the circuit for multi-channel operation. Frequency selectivity is improved through Q-enhancement. The entire receiver occupies less than 1 mm2, and consumes 3 mA from a 1.2 V supply, with a data rate of up to 500 Kbps, an energy per received bit of 7.2 nJ/bit, a channel spacing of 10 MHz, and a sensitivity of -80 dBm
Systems and Experiment Paper: Construction of Panoramic Image Mosaics with Global and Local Alignment

This paper presents a complete system for constructing panoramic image mosaics from sequences of images. Our mosaic representation associates a transformation matrix with each input image, rather than explicitly projecting all of the images onto a common surface (e.g., a cylinder). In particular, to construct a full view panorama, we introduce a rotational mosaic representation that associates a rotation matrix (and optionally a focal length) with each input image. A patch-based alignment algorithm is developed to quickly align two images given motion models. Techniques for estimating and refining camera focal lengths are also presented. In order to reduce accumulated registration errors, we apply global alignment (block adjustment) to the whole sequence of images, which results in an optimally registered image mosaic. To compensate for small amounts of motion parallax introduced by translations of the camera and other unmodeled distortions, we use a local alignment (deghosting) technique which warps each image based on the results of pairwise local image registrations. By combining both global and local alignment, we significantly improve the quality of our image mosaics, thereby enabling the creation of full view panoramic mosaics with hand-held cameras. We also present an inverse texture mapping algorithm for efficiently extracting environment maps from our panoramic image mosaics. By mapping the mosaic onto an arbitrary texture-mapped polyhedron surrounding the origin, we can explore the virtual environment using standard 3D graphics viewers and hardware without requiring special-purpose players.
Transferring Subspaces Between Subjects in Brain--Computer Interfacing

Compensating changes between a subjects' training and testing session in brain-computer interfacing (BCI) is challenging but of great importance for a robust BCI operation. We show that such changes are very similar between subjects, and thus can be reliably estimated using data from other users and utilized to construct an invariant feature space. This novel approach to learning from other subjects aims to reduce the adverse effects of common nonstationarities, but does not transfer discriminative information. This is an important conceptual difference to standard multi-subject methods that, e.g., improve the covariance matrix estimation by shrinking it toward the average of other users or construct a global feature space. These methods do not reduces the shift between training and test data and may produce poor results when subjects have very different signal characteristics. In this paper, we compare our approach to two state-of-the-art multi-subject methods on toy data and two datasets of EEG recordings from subjects performing motor imagery. We show that it can not only achieve a significant increase in performance, but also that the extracted change patterns allow for a neurophysiologically meaningful interpretation.
Tool Demonstration: FSolidM for Designing Secure Ethereum Smart Contracts

Blockchain-based distributed computing platforms enable the trusted execution of computation—defined in the form of smart contracts—without trusted agents. Smart contracts are envisioned to have a variety of applications, ranging from financial to IoT asset tracking. Unfortunately, the development of smart contracts has proven to be extremely error prone. In practice, contracts are riddled with security vulnerabilities comprising a critical issue since bugs are by design nonfixable and contracts may handle financial assets of significant value. To facilitate the development of secure smart contracts, we have created the FSolidM framework, which allows developers to define contracts as finite state machines (FSMs) with rigorous and clear semantics. FSolidM provides an easy-to-use graphical editor for specifying FSMs, a code generator for creating Ethereum smart contracts, and a set of plugins that developers may add to their FSMs to enhance security and functionality.
Face Recognition-Based Mobile Automatic Classroom Attendance Management System

Classroom attendance check is a contributing factor to student participation and the final success in the courses. Taking attendance by calling out names or passing around an attendance sheet are both time-consuming, and especially the latter is open to easy fraud. As an alternative, RFID, wireless, fingerprint, and iris and face recognition-based methods have been tested and developed for this purpose. Although these methods have some pros, high system installation costs are the main disadvantage. The present paper aims to propose a face recognition-based mobile automatic classroom attendance management system needing no extra equipment. To this end, a filtering system based on Euclidean distances calculated by three face recognition techniques, namely Eigenfaces, Fisherfaces and Local Binary Pattern, has been developed for face recognition. The proposed system includes three different mobile applications for teachers, students, and parents to be installed on their smart phones to manage and perform the real-time attendance-taking process. The proposed system was tested among students at Ankara University, and the results obtained were very satisfactory.
Advanced Local Binary Patterns for Remote Sensing Image Retrieval

The standard Local Binary Pattern (LBP) is considered among the most computationally efficient remote sensing (RS) image descriptors in the framework of large-scale content based RS image retrieval (CBIR). However, it has limited discrimination capability for characterizing high dimensional RS images with complex semantic content. There are several LBP variants introduced in computer vision that can be extended to RS CBIR to efficiently overcome the above-mentioned problem. To this end, this paper presents a comparative study in order to analyze and compare advanced LBP variants in RS CBIR domain. We initially introduce a categorization of the LBP variants based on the specific CBIR problems in RS, and analyze the most recent methodological developments associated to each category. All the considered LBP variants are introduced for the first time in the framework of RS image retrieval problems, and have been experimentally compared in terms of their: 1) discrimination capability to model high-level semantic information present in RS images (and thus the retrieval performance); and 2) computational complexities associated to retrieval and feature extraction time.
Relighting humans: occlusion-aware inverse rendering for full-body human images

Relighting of human images has various applications in image synthesis. For relighting, we must infer albedo, shape, and illumination from a human portrait. Previous techniques rely on human faces for this inference, based on spherical harmonics (SH) lighting. However, because they often ignore light occlusion, inferred shapes are biased and relit images are unnaturally bright particularly at hollowed regions such as armpits, crotches, or garment wrinkles. This paper introduces the first attempt to infer light occlusion in the SH formulation directly. Based on supervised learning using convolutional neural networks (CNNs), we infer not only an albedo map, illumination but also a light transport map that encodes occlusion as nine SH coefficients per pixel. The main difficulty in this inference is the lack of training datasets compared to unlimited variations of human portraits. Surprisingly, geometric information including occlusion can be inferred plausibly even with a small dataset of synthesized human figures, by carefully preparing the dataset so that the CNNs can exploit the data coherency. Our method accomplishes more realistic relighting than the occlusion-ignored formulation.
Service Interaction: Patterns, Formalization, and Analysis

As systems become more service oriented and processes increasingly cross organizational boundaries, interaction becomes more important. New technologies support the development of such systems. However, the paradigm shift towards service orientation, requires a fundamentally different way of looking at processes. This survey aims to provide some foundational notions related to service interaction. A set of service interaction patterns is given to illustrate the challenges in this domain. Moreover, key results are given for three of these challenges: (1) How to expose a service?, (2) How to replace and refine services?, and (3) How to generate service adapters? These challenges will be addressed in a Petri net setting. However, the results extend to other languages used in this domain.
Prospects and challenges for social media data in conservation science

*Correspondence: Enrico Di Minin, Finnish Centre of Excellence in Metapopulation Biology, Department of Biosciences, Biocenter 3, University of Helsinki, PO Box 65 (Viikinkaari 1), 00014 Helsinki, Finland; School of Life Sciences, Westville Campus, University of KwaZulu-Natal, PO Box 54001 (University Road), Durban 4000, South Africa enrico.di.minin@helsinki.fi; Tuuli Toivonen, Finnish Centre of Excellence in Metapopulation Biology, Department of Biosciences, Biocenter 3, University of Helsinki, PO Box 65 (Viikinkaari 1), 00014 Helsinki, Finland; Department of Geosciences and Geography, University of Helsinki, PO Box 64 (Gustaf Hällströminkatu 2a), 00014 Helsinki, Finland tuuli.toivonen@helsinki.fi These authors have contributed equally to this work.
Framework for Analyzing Android I/O Stack Behavior: From Generating theWorkload to Analyzing the Trace

The existing I/O workload generators and trace capturing tools are not adaptable to generating nor capturing the I/O requests of Android apps. The Android platform needs proper tools to capture and replay real world workload in the Android platform to verify the result of benchmark tools. This paper introduces Android Storage Performance Analysis Tool, AndroStep, which is specifically designed for characterizing and analyzing the behavior of the I/O subsystem in Android based devices. The AndroStep consists of Mobibench (workload generator), MOST (Mobile Storage Analyzer), and Mobigen (workload replayer). Mobibench is an Android app that generates a filesystem as well as SQLite database operations. Mobibench can also vary the number of concurrent threads to examining the filesystem scalability to support concurrency, e.g., metadata updates, journal file creation/deletion. MOST captures the trace and extracts key filesystem access characteristics such as access pattern with respect to file types, ratio between random vs. sequential access, ratio between buffered vs. synchronous I/O, fraction of metadata accesses, etc. MOST implements reverse mapping feature (finding an inode for a given block) and retrospective reverse mapping (finding an inode for a deleted file). Mobigen is a trace capturing and replaying tool that is specifically designed to perform the user experiment without actual human intervention. Mobigen records the system calls generated from the user behavior and sanitizes the trace for replayable form. Mobigen can replay this trace on different Android platforms or with different I/O stack configurations. As an ∗Primitive version of this paper has been presented at the 1st European Workshop on Mobile Engineering (ME13) . Future Internet 2013, 5 592 example of using AndroStep, we analyzed the performances of twelve Android smartphones and the SQLite performances on five different filesystems. AndroStep makes otherwise time consuming I/O stack analysis extremely versatile. AndroStep makes a significant contribution in terms of shedding light on internal behavior of the Android I/O stack.
Type 1 segmental Darier's disease: report of two cases with favourable response to topical retinoids

Darier's disease is a rare late-onset genetic disorder of keratinisation. Mosaic forms of the disease characterised by localised and unilateral keratotic papules carrying post-zygotic ATP2A2 mutation in affected areas have been documented. Segmental forms of Darier's disease are classified into two clinical subtypes: type 1 manifesting with distinct lesions on a background of normal appearing skin and type 2 with well-defined areas of Darier's disease occurring on a background of less severe non-mosaic phenotype. Herein we describe two cases of type 1 segmental Darier's disease with favourable response to topical retinoids.
Inconsistencies and Controversies Surrounding the Amyloid Hypothesis of Alzheimer's Disease

The amyloid hypothesis has driven drug development strategies for Alzheimer's disease for over 20 years. We review why accumulation of amyloid-beta (Aβ) oligomers is generally considered causal for synaptic loss and neurodegeneration in AD. We elaborate on and update arguments for and against the amyloid hypothesis with new data and interpretations, and consider why the amyloid hypothesis may be failing therapeutically. We note several unresolved issues in the field including the presence of Aβ deposition in cognitively normal individuals, the weak correlation between plaque load and cognition, questions regarding the biochemical nature, presence and role of Aβ oligomeric assemblies in vivo, the bias of pre-clinical AD models toward the amyloid hypothesis and the poorly explained pathological heterogeneity and comorbidities associated with AD. We also illustrate how extensive data cited in support of the amyloid hypothesis, including genetic links to disease, can be interpreted independently of a role for Aβ in AD. We conclude it is essential to expand our view of pathogenesis beyond Aβ and tau pathology and suggest several future directions for AD research, which we argue will be critical to understanding AD pathogenesis.
The theory of error-correcting codes

s R esum es Canadian Undergraduate Mathematics Conference 1998 | Part 3 The Brachistochrone Problem Nils Johnson The University of British Columbia The brachistochrone problem is to nd the curve between two points down which a bead will slide in the shortest amount of time, neglecting friction and assuming conservation of energy. To solve the problem, an integral is derived that computes the amount of time it would take a bead to slide down a given curve y(x). This integral is minimized over all possible curves and yields the di erential equation y(1 + (y)) = k as a constraint for the minimizing function y(x). Solving this di erential equation shows that a cycloid (the path traced out by a point on the rim of a rolling wheel) is the solution to the brachistochrone problem. First proposed in 1696 by Johann Bernoulli, this problem is credited with having led to the development of the calculus of variations. The solution presented assumes knowledge of one-dimensional calculus and elementary di erential equations. The Theory of Error-Correcting Codes Dennis Hill University of Ottawa Coding theory is concerned with the transfer of data. There are two issues of fundamental importance. First, the data must be transferred accurately. But equally important is that the transfer be done in an e cient manner. It is the interplay of these two issues which is the core of the theory of error-correcting codes. Typically, the data is represented as a string of zeros and ones. Then a code consists of a set of such strings, each of the same length. The most fruitful approach to the subject is to consider the set f0; 1g as a two-element eld. We will then only