nils-herrmann/clean_scirepeval huggingface dataset:
Securing Docker Containers from Denial of Service (DoS) Attacks

The concept of containerization and virtualization is getting traction in the cloud based IT environments. Docker engine is popular implementation for simplifying and streamlining containerization technology. IT industry realizes numerous automation and acceleration features and facilities through the embracement of the Docker-sponsored containerization paradigm, which is an operating system (OS)-level and lightweight virtualization. However the security issues affect the widespread and confident usage of Docker platform. In this paper, we have discussed important security issues of the Docker containers as well as the related work that is being carried out in this area. Also we have proposed security algorithms and methods to address DoS attacks related issues in the Docker container technology. The preliminary experiments and testing of the security methods are promising.
O Bitcoin Where Art Thou? Insight into Large-Scale Transaction Graphs

Bitcoin is a rising digital currency and exemplifies the growing need for systematically gathering and analyzing public transaction data sets such as the blockchain. However, the blockchain in its raw form is just a large ledger listing transfers of currency units between alphanumeric character strings, without revealing contextually relevant real-world information. In this demo, we present GraphSense, which is a solution that applies a graph-centric perspective on digital currency transactions. It allows users to explore transactions and follow the money flow, facilitates analytics by semantically enriching the transaction graph, supports path and graph pattern search, and guides analysts to anomalous data points. To deal with the growing volume and velocity of transaction data, we implemented our solution on a horizontally scalable data processing and analytics infrastructure. Given the ongoing digital transformation in financial services and technologies, we believe that our approach contributes to development of analytics solutions for digital currency ecosystems, which is relevant in fields such as financial analytics, law enforcement, or scientific research.
Deep-learning-based face detection using iterative bounding-box regression

Multi-view face detection in open environments is a challenging task due to the diverse variations of face appearances and occlusion. In the task of face detection, localization accuracy is one of the key factors. However, many of the existing methods do not pay enough attention to localization. Some of the current methods have applied localization techniques, but they have not fully realized its potential and realized more accurate localization. In this paper, we propose a deep cascaded detection method that iteratively exploits bounding-box regression, a localization technique, to approach the detection of potential faces in images. In addition, we consider the inherent correlation of classification and bounding-box regression and exploit it to further increase overall performance. In particular, our method leverages a cascaded architecture with three stages of carefully designed deep convolutional networks to predict the existence of faces. Extensive experiments demonstrate the efficiency of our algorithm by comparing it with several popular face-detection algorithms on the widely used AFW and FDDB datasets.
Touch, Taste, & Smell User Interfaces: The Future of Multisensory HCI

The senses we call upon when interacting with technology are very restricted. We mostly rely on vision and audition, increasingly harnessing touch, whilst taste and smell remain largely underexploited. In spite of our current knowledge about sensory systems and sensory devices, the biggest stumbling block for progress concerns the need for a deeper understanding of people's multisensory experiences in HCI. It is essential to determine what tactile, gustatory, and olfactory experiences we can design for, and how we can meaningfully stimulate such experiences when interacting with technology. Importantly, we need to determine the contribution of the different senses along with their interactions in order to design more effective and engaging digital multisensory experiences. Finally, it is vital to understand what the limitations are that come into play when users need to monitor more than one sense at a time. The aim of this workshop is to deepen and expand the discussion on touch, taste, and smell within the CHI community and promote the relevance of multisensory experience design and research in HCI.
DeepPore: Fingerprint Pore Extraction Using Deep Convolutional Neural Networks

As technological developments have enabled high-quality fingerprint scanning, sweat pores, one of the Level 3 features of fingerprints, have been successfully used in automatic fingerprint recognition systems (AFRS). Since the pore extraction process is a critical step for AFRS, high accuracy is required. However, it is difficult to extract the pore correctly because the pore shape depends on the person, region, and pore type. To solve the problem, we have presented a pore extraction method using deep convolutional neural networks and pore intensity refinement. The deep networks are used to detect pores in detail using a large area of a fingerprint image. We then refine the pore information by finding local maxima to identify pores with different intensities in the fingerprint image. The experimental results show that our pore extraction method performs better than the state-of-the-art methods.
SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data

We present SWELLSHARK, a framework for building biomedical named entity recognition (NER) systems quickly and without hand-labeled data. Our approach views biomedical resources like lexicons as function primitives for autogenerating weak supervision. We then use a generative model to unify and denoise this supervision and construct large-scale, probabilistically labeled datasets for training high-accuracy NER taggers. In three biomedical NER tasks, SWELLSHARK achieves competitive scores with state-of-the-art supervised benchmarks using no hand-labeled training data. In a drug name extraction task using patient medical records, one domain expert using SWELLSHARK achieved within 5.1% of a crowdsourced annotation approach – which originally utilized 20 teams over the course of several weeks – in 24 hours.
Human-Inspired Neurorobotic System for Classifying Surface Textures by Touch

Giving robots the ability to classify surface textures requires appropriate sensors and algorithms. Inspired by the biology of human tactile perception, we implement a neurorobotic texture classifier with a recurrent spiking neural network, using a novel semisupervised approach for classifying dynamic stimuli. Input to the network is supplied by accelerometers mounted on a robotic arm. The sensor data are encoded by a heterogeneous population of neurons, modeled to match the spiking activity of mechanoreceptor cells. This activity is convolved by a hidden layer using bandpass filters to extract nonlinear frequency information from the spike trains. The resulting high-dimensional feature representation is then continuously classified using a neurally implemented support vector machine. We demonstrate that our system classifies 18 metal surface textures scanned in two opposite directions at a constant velocity. We also demonstrate that our approach significantly improves upon a baseline model that does not use the described feature extraction. This method can be performed in real-time using neuromorphic hardware, and can be extended to other applications that process dynamic stimuli online.
Photonic Crystals: Molding the Flow of Light (second Edition)

John D. Joannopoulos, Steven G. Johnson, Joshua N. Winn & Robert D. Meade: Photonic Crystals: Molding the Flow of Light (Second Edition) is published by Princeton University Press and copyrighted,  No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher, except for reading and browsing via the World Wide Web. Users are not permitted to mount this file on any network servers.
Name-Ethnicity Classification and Ethnicity-Sensitive Name Matching

Personal names are important and common information in many data sources, ranging from social networks and news articles to patient records and scientific documents. They are often used as queries for retrieving records and also as key information for linking documents from multiple sources. Matching personal names can be challenging due to variations in spelling and various formatting of names. While many approximated name matching techniques have been proposed, most are generic string-matching algorithms. Unlike other types of proper names, personal names are highly cultural. Many ethnicities have their own unique naming systems and identifiable characteristics. In this paper we explore such relationships between ethnicities and personal names to improve the name matching performance. First, we propose a name-ethnicity classifier based on the multinomial logistic regression. Our model can effectively identify nameethnicity from personal names in Wikipedia, which we use to define name-ethnicity, to within 85% accuracy. Next, we propose a novel alignment-based name matching algorithm, based on Smith–Waterman algorithm and logistic regression. Different name matching models are then trained for different name-ethnicity groups. Our preliminary experimental result on DBLP’s disambiguated author dataset yields a performance of 99% precision and 89% recall. Surprisingly, textual features carry more weight than phonetic ones in nameethnicity classification.
Classification of Arrhythmia Using Conjunction of Machine Learning Algorithms and ECG Diagnostic Criteria

This paper proposes a classification technique using conjunction of Machine Learning Algorithms and ECG Diagnostic Criteria which improves the accuracy of detecting Arrhythmia using Electrocardiogram (ECG) data. ECG is the most widely used first line clinical instrument to record the electrical activities of the heart. The data-set from UC Irvine (UCI) Machine Learning Repository was used to implement a multi-class classification for different types of heart abnormalities. After implementing rigorous data preprocessing and feature selection techniques,different machine learning algorithms such as Neural Networks, Decision trees, Random Forest, Gradient Boosting and Support Vector Machines were used. Maximum experimental accuracy of 84.82% was obtained via the conjunction of SVM and Gradient Boosting. A further improvement in accuracy was obtained by validating the factors which were important for doctors to decide between normal and abnormal heart conditions.The performance of classification is evaluated using measures such as confusion matrix, kappa-score, confidence interval, Area under curve (AUC) and overall-accuracy.  Machine Learning, Arrhythmia Classification, ECG, Neural Networks, SVM, Gradient Boosting
Overview of High-Step-Up Coupled-Inductor Boost Converters

High-step-up, high-efficiency, and cost-effective dc-dc converters, serving as an interfacing cell to boost the low-voltage output of renewable sources to the utility voltage level, are an important part in renewable energy systems. Over the past few years, there has been a substantial amount of studies devoted to high-step-up dc-dc converters. Among them, the category of coupled-inductor boost converters is widely researched and considered to be a promising solution for high-step-up applications. In this paper, these converters are categorized into five groups according to the major topological features. The derivation process, advantages, and disadvantages of these converters are systematically discussed, compared, and scrutinized. This paper aims to provide an introduction, review, and framework for the category of high-step-up coupled-inductor boost converters. General structures for the topologies are proposed to clarify the topological derivation process and to show potential gaps. Furthermore, challenges or directions are presented in this paper for deriving new topologies in this field.
Automated Bangla text summarization by sentence scoring and ranking

In Natural Language Processing (NLP) the document summarization is an area that is getting interest of modern researchers. Though there are many techniques that have been proposed for English language but a few notable works have been done for Bangla text summarization. This paper deals with the development of an extraction based summarization technique which works on Bangla text documents. The system summarizes a single document at a time. Before creating the summary of a document, it is pre-processed by tokenization, removal of stop words and stemming. In the document summarization process, the countable features like word frequency and sentence positional value are used to make the summary more precise and concrete. Attributes like cue words and skeleton of the document are included in the process, which help to make the summary more relevant to the content of the document. The proposed technique has been compared with summary of documents generated by human professionals. The evaluation shows that 83.57% of summary sentences selected by the system agreed with those made by human.
Changing how and what children learn in school with computer-based technologies.

Schools today face ever-increasing demands in their attempts to ensure that students are well equipped to enter the workforce and navigate a complex world. Research indicates that computer technology can help support learning, and that it is especially useful in developing the higher-order skills of critical thinking, analysis, and scientific inquiry. But the mere presence of computers in the classroom does not ensure their effective use. Some computer applications have been shown to be more successful than others, and many factors influence how well even the most promising applications are implemented. This article explores the various ways computer technology can be used to improve how and what children learn in the classroom. Several examples of computer-based applications are highlighted to illustrate ways technology can enhance how children learn by supporting four fundamental characteristics of learning: (1) active engagement, (2) participation in groups, (3) frequent interaction and feedback, and (4) connections to real-world contexts. Additional examples illustrate ways technology can expand what children learn by helping them to understand core concepts in subjects like math, science, and literacy. Research indicates, however, that the use of technology as an effective learning tool is more likely to take place when embedded in a broader education reform movement that includes improvements in teacher training, curriculum, student assessment, and a school's capacity for change. To help inform decisions about the future role of computers in the classroom, the authors conclude that further research is needed to identify the uses that most effectively support learning and the conditions required for successful implementation.
Outlier Detection using Evolutionary Computing

In this paper, we proposed Harmony Search and Differential Evolution based outlier detection for medium dimensional numerical datasets. The sparsity coefficient is taken as the objective function for finding outliers in the data. The upper limit of the number of dimensions for a dataset is fixed using the threshold suggested by Chebyshev's inequality. A t-test is conducted on the optimal sparsity coefficient for both methods over 30 simulations. At a 1% level of significance, the t-test confirmed that the Harmony Search based method is statistically more significant than Differential Evolution based one for all four datasets. Both methods outperformed the previous approaches.
Comparison of high-performance packet processing frameworks on NUMA

Recent studies have been concentrated on comparison of forwarding ability in multiple dimensions among different high-speed packet processing frameworks, such as PF_RING and DPDK on the whole where transmitting and receiving impact each other, without enough study on frameworks' ability for receiving or transmitting separately. Characteristics of multithreading is another hot spot as multi-core platforms play more important roles. It requires more microscopic insight into comparison of these frameworks' characteristics on multi-core platform, especially on Non-Uniform Memory Access Architecture (NUMA). This paper mainly compared DPDK and PF_RING's processing ability focusing on receiving and multithreading characteristics without transmitting in run-to-completion model and pipeline model, i.e. a kind of load balancing model. Some characteristics about how performance of PF_RING ZC and DPDK in pipeline model specifically on NUMA varies with distribution type of threads among NUMA nodes, were made insight into and concluded. For studying above, a new indicator, ρ, giving the ratio of CPU time consumed on processing per unit packets, was put forward and played an important role in above analysis. By means of ρ, one of this paper's discovery shows that two frameworks' threads' distributions types among NUMA nodes in the pipeline model contribute to different performance of traffic processing.
Pado: A Data Processing Engine for Harnessing Transient Resources in Datacenters

Datacenters are under-utilized, primarily due to unused resources on over-provisioned nodes of latency-critical jobs. Such idle resources can be used to run batch data analytic jobs to increase datacenter utilization, but these transient resources must be evicted whenever latency-critical jobs require them again. Resource evictions often lead to cascading recomputations, which is usually handled by checkpointing intermediate results on stable storages of eviction-free reserved resources. However, checkpointing has major shortcomings in its substantial overhead of transferring data back and forth. In this work, we step away from such approaches and focus on observing the job structure and the relationships between computations of the job. We carefully mark the computations that are most likely to cause a large number of recomputations upon evictions, to run them reliably using reserved resources. This lets us retain corresponding intermediate results effortlessly without any additional checkpointing. We design Pado, a general data processing engine, which carries out our idea with several optimizations that minimize the number of additional reserved nodes. Evaluation results show that Pado outperforms Spark 2.0.0 by up to 5.1×, and checkpoint-enabled Spark by up to 3.8×.
Bitcoin Mining Pools: A Cooperative Game Theoretic Analysis

Bitcoin is an innovative decentralized cryptocurrency whose core security relies on a “proof of work” procedure, which requires network participants to repeatedly compute hashes on inputs from a large search space. Finding one of the rare inputs that generates an extremely low hash value is considered a successful attempt, allowing miners to approve new transactions and, in return, to collect rewards in bitcoins. This reward allocation, which provides the incentive for miners to participate, is a random process with a large variance. Miners who desire a steady income thus often participate in mining pools that divide among their members the earned rewards, and reduce this variance. Mining pools are slightly better at coordinating participants due to lowerlatency communication, a fact which implies that they manage to collect slightly higher rewards. We examine dynamics of pooled mining and the rewards that pools manage to collect, and use cooperative game theoretic tools to analyze how pool members may share these rewards. We show that for some network parameters, especially under high transaction loads, it is difficult or even impossible to distribute rewards in a stable way: some participants are always incentivized to switch between pools.
Design and analysis of a 6-DOF force/torque sensor for human gait analysis

This paper describes the development of a compact and robust 6-DOF force/torque sensor for human gait analysis. The forces and torques exerted on human foot are important information for the gait analysis. Multi-axis force/torque sensor can be installed under human foot the sense the forces and torques. The sensor should have large force range without reducing the accuracy to support the human weight and small size not to disturb the motion of the subject. In this paper, we present a 6-DOF force/torque sensor which has good accuracy, large measuring range, and new compact structure. And it is much cheaper than a commercial one. The proposed design is simulated by FEM software (ANSYS), and it shows good results being applied large forces and moments. This new sensor can be used to sense the center of pressure (CoP) and zero moment point (ZMP) of the human foot for gait analysis.
Water content of latent fingerprints - Dispelling the myth.

Changing procedures in the handling of rare and precious documents in museums and elsewhere, based on assumptions about constituents of latent fingerprints, have led the author to an examination of available data. These changes appear to have been triggered by one paper using general biological data regarding eccrine sweat production to infer that deposited fingerprints are mostly water. Searching the fingerprint literature has revealed a number of reference works similarly quoting figures for average water content of deposited fingerprints of 98% or more. Whilst accurate estimation is difficult there is no evidence that the residue on fingers could be anything like 98% water, even if there were no contamination from sebaceous glands. Consideration of published analytical data of real fingerprints, and several theoretical considerations regarding evaporation and replenishment rates, indicates a probable initial average water content of a fingerprint, soon after deposition, of 20% or less.
Teaching CS unplugged in the high school (with limited success)

CS Unplugged is a set of active learning activities designed to introduce fundamental computer science principles without the use of computers. The program has gained significant momentum in recent years, with proponents citing deep engagement and enjoyment benefits. With these benefits in mind, we initiated a one-year outreach program involving a local high school, using the CS Unplugged program as the foundation. To our disappointment, the results were at odds with our enthusiasm --- significantly. In this paper, we describe our approach to adapting the CS Unplugged materials for use at the high school level, present our experiences teaching it, and summarize the results of our evaluation.
Wavelet-based statistical signal processing using hidden Markov models

Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly Gaussian. These models are unrealistic for many real-world signals. In this paper, we develop a new framework for statistical signal processing based on wavelet-domain hidden Markov models (HMM’s) that concisely models the statistical dependencies and non-Gaussian statistics encountered in real-world signals. Wavelet-domain HMM’s are designed with the intrinsic properties of the wavelet transform in mind and provide powerful, yet tractable, probabilistic signal models. Efficient expectation maximization algorithms are developed for fitting the HMM’s to observational signal data. The new framework is suitable for a wide range of applications, including signal estimation, detection, classification, prediction, and even synthesis. To demonstrate the utility of wavelet-domain HMM’s, we develop novel algorithms for signal denoising, classification, and detection.