nils-herrmann/clean_scirepeval huggingface dataset:
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent

We study the resilience to Byzantine failures of distributed implementations of Stochastic Gradient Descent (SGD). So far, distributed machine learning frameworks have largely ignored the possibility of failures, especially arbitrary (i.e., Byzantine) ones. Causes of failures include software bugs, network asynchrony, biases in local datasets, as well as attackers trying to compromise the entire system. Assuming a set of n workers, up to f being Byzantine, we ask how resilient can SGD be, without limiting the dimension, nor the size of the parameter space. We first show that no gradient aggregation rule based on a linear combination of the vectors proposed by the workers (i.e, current approaches) tolerates a single Byzantine failure. We then formulate a resilience property of the aggregation rule capturing the basic requirements to guarantee convergence despite f Byzantine workers. We propose Krum, an aggregation rule that satisfies our resilience property, which we argue is the first provably Byzantine-resilient algorithm for distributed SGD. We also report on experimental evaluations of Krum.
7 Key Challenges for Visualization in Cyber Network Defense

What does it take to be a successful visualization in cyber security? This question has been explored for some time, resulting in many potential solutions being developed and offered to the cyber security community. However, when one reflects upon the successful visualizations in this space they are left wondering where all those offerings have gone. Excel and Grep are still the kings of cyber security defense tools; there is a great opportunity to help in this domain, yet many visualizations fall short and are not utilized.
 In this paper we present seven challenges, informed by two user studies, to be considered when developing a visualization for cyber security purposes. Cyber security visualizations must go beyond isolated solutions and "pretty picture" visualizations in order to impact users. We provide an example prototype that addresses the challenges with a description of how they are met. Our aim is to assist in increasing utility and adoption rates for visualization capabilities in cyber security.
Multivariate data analysis of NMR data.

Multivariate methods based on principal components (PCA and PLS) have been used to reduce NMR spectral information, to predict NMR parameters of complicated structures, and to relate shift data sets to dependent descriptors of biological significance. Noise reduction and elimination of instrumental artifacts are easily performed on 2D NMR data. Configurational classification of triterpenes and shift predictions in disubstituted benzenes can be obtained using PCA and PLS analysis. Finally, the shift predictions of tripeptides from descriptors of amino acids open the possibility of automatic analysis of multidimensional data of complex structures.
A Qualitative Account of The Nature and Use of Self-Regulated Learning ( SRL ) Strategies Employed by University Students

Students' conceptions of how they initiate, plan, implement and monitor self-regulated learning (SRL) strategies have practical implications for teaching and learning. This study explores the nature and use of SRL strategies employed by university students as it occurs in naturalistic settings, for example, studying in non-classroom environments. Framed within the social cognitive perspective, it focuses on a group of students from an under-researched population. Focus group interviews were used to elicit information about the nature of SRL strategies and contexts for their use. The findings reveal that students employ a range of SRL strategies, from shallow to cognitively rich and deep processing. Furthermore, the use of SRL strategies alters under different contextual influences such as personal goals, SRL phase specific conditions, semester and academic
Malware capturing and detection in dionaea honeypot

This paper proposes software based malware capturing and detection method. Which implements an efficient malware capturing and detection method in honeypot environment. It includes collecting the logs from the network using honeypot system. It create incident table based on the logs, in the test case we are using metasploit frame work for attacking the honeypot system through LibEmu manually. In the metasploit there are thousands of malicious payloads are available, using the payloads we can exploit the vulnerabilities present in the services on the honeypot.
EAP as Artificial Muscles-Progress and Challenges

During the last decade and a half new polymers have emerged that respond to electrical stimulation with a significant shape or size change. This capability of electroactive polymer (EAP) materials is attracting the attention of engineers and scientists from many different disciplines. Practitioners in biomimetics are particularly excited about these materials since the artificial muscle aspect of EAPs can be applied to mimic the movements of animals and insects. In the foreseeable future, robotic mechanisms actuated by EAP will enable engineers to create devices previously imaginable only in science fiction. Last year, significant accomplishments were reported include the emergence of the first commercial product and the possibility that an arm can be made with EAP actuators having the potential of winning a wrestling match with human. As such major accomplishments continue to be reported it is interesting to review the progress and provide a prospective regarding the development since the first EAPAD conference in 1999. This manuscript covers the progress in the field of EAP and the challenges that are being addressed.
A cloud-assisted design for autonomous driving

This paper presents Carcel, a cloud-assisted system for autonomous driving. Carcel enables the cloud to have access to sensor data from autonomous vehicles as well as the roadside infrastructure. The cloud assists autonomous vehicles that use this system to avoid obstacles such as pedestrians and other vehicles that may not be directly detected by sensors on the vehicle. Further, Carcel enables vehicles to plan efficient paths that account for unexpected events such as road-work or accidents.
 We evaluate a preliminary prototype of Carcel on a state-of-the-art autonomous driving system in an outdoor testbed including an autonomous golf car and six iRobot Create robots. Results show that Carcel reduces the average time vehicles need to detect obstacles such as pedestrians by 4.6x compared to today's systems that do not have access to the cloud.
Handwritten Tamil Character Recognition and Conversion using Neural Network

Hand written Tamil Character recognition refers to the process of conversion of handwritten Tamil character into Unicode Tamil character. The scanned image is segmented into paragraphs using spatial space detection technique, paragraphs into lines using vertical histogram, lines into words using horizontal histogram, and words into character image glyphs using horizontal histogram. The extracted features considered for recognition are given to Support Vector Machine, Self Organizing Map, RCS, Fuzzy Neural Network and Radial Basis Network. Where the characters are classified using supervised learning algorithm. These classes are mapped onto Unicode for recognition. Then the text is reconstructed using Unicode fonts. This character recognition finds applications in document analysis where the handwritten document can be converted to editable printed document. This approach can be extended to recognition and reproduction of hand written documents in South Indian languages. In the training set, a recognition rate of 100% was achieved and in the test set the recognized speed for each character is 0.1sec and accuracy is 97%. Understandably, the training set produced much higher recognition rate than the test set. Structure analysis suggested that the proposed system of RCS with back propagation network is given higher recognition rate.
Disciplina : Blockchain for Education

In this paper we analyze the main issues that arise from storing educational records in blockchain and propose the architecture of the Disciplina platform – a domain-specific blockchain implementation. The platform is designed to act as a decentralized ledger, with special regard for privacy and mechanisms of data disclosure. We present an overview of the main entities, their roles and incentives to support the network. Please note that the project is a work-in-progress and the descriptions provided are subject to change.
Measuring Word Significance using Distributed Representations of Words

Distributed representations of words as real-valued vectors in a relatively lowdimensional space aim at extracting syntactic and semantic features from large text corpora. A recently introduced neural network, named word2vec (Mikolov et al., 2013a; Mikolov et al., 2013b), was shown to encode semantic information in the direction of the word vectors. In this brief report, it is proposed to use the length of the vectors, together with the term frequency, as measure of word significance in a corpus. Experimental evidence using a domain-specific corpus of abstracts is presented to support this proposal. A useful visualization technique for text corpora emerges, where words are mapped onto a two-dimensional plane and automatically ranked by significance.
Steerable Higher Order Mode Dielectric Resonator Antenna With Parasitic Elements for 5G Applications

This paper presents the findings of a steerable higher order mode (TE<inline-formula> <tex-math notation="LaTeX">$^{\mathrm {y}}_{1\delta 3}$ </tex-math></inline-formula>) dielectric resonator antenna with parasitic elements. The beam steering was successfully achieved by switching the termination capacitor on the parasitic element. In this light, all of the dielectric resonator antennas (DRAs) have the same dielectric permittivity similar to that of ten and excited by a <inline-formula> <tex-math notation="LaTeX">$50\Omega $ </tex-math></inline-formula> microstrip with a narrow aperture. The effect of the mutual coupling on the radiation pattern and the reflection coefficient, as well as the array factor, was investigated clearly using MATLAB version 2014b and ANSYS HFSS version 16. As the result, the antenna beam of the proposed DRA array managed to steer from −32° to +32° at 15 GHz. Furthermore, the measured antenna array showed the maximum gain of 9.25 dBi and the reflection coefficients which are less than −10 dB with the bandwidth more than 1.3 GHz, which is viewed as desirable for device-to-device communication in 5G Internet of Things applications.
Pose tracking from natural features on mobile phones

In this paper we present two techniques for natural feature tracking in real-time on mobile phones. We achieve interactive frame rates of up to 20 Hz for natural feature tracking from textured planar targets on current-generation phones. We use an approach based on heavily modified state-of-the-art feature descriptors, namely SIFT and Ferns. While SIFT is known to be a strong, but computationally expensive feature descriptor, Ferns classification is fast, but requires large amounts of memory. This renders both original designs unsuitable for mobile phones. We give detailed descriptions on how we modified both approaches to make them suitable for mobile phones. We present evaluations on robustness and performance on various devices and finally discuss their appropriateness for augmented reality applications.
Design and optimization of concentric saddle shaped coils for permanent magnet transverse flux machine in segmented construction

Design of multi-turns concentric saddle windings are required for flux concentrated permanent magnet transverse flux machine, FCPM-TFM in a sector wise configuration. In the segmented construction of FCPM-TFM, saddle coils or concentric type of windings are used to replace the ring coils, which are normally utilised in the conventional layered-phase structures for generating fields' perpendicular to the axis of cylindrical shell to which the coils are confined. This paper investigates the optimum design of saddle phase windings for this special construction of TFM that is constructed as an outer-rotor for in-wheel motor applications. Meshed and non-meshed coils are designed and their parameters effects are studied via finite element method (FEM). Throughout the study, optimisation of two stator structures, i.e., laminated and soft magnetic composite, SMC-pressed, are shown.
A Modular Framework for Versatile Conversational Agent Building

This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible.
Mobile Big Data: The Fuel for Data-Driven Wireless

In the past decade, the smart phone evolution has accelerated the proliferation of the mobile Internet and spurred a new wave of mobile applications, leading to an unprecedented mobile data volume generated from the mobile devices, content servers, and network operators, which are mainly nonstructured. In this big data era, such nonstructured data fragments are pieced together such that, drastically differing from the traditional practice where services determine and define the data, data is becoming a proactive entity that may drive and even create new services. Compared with the so-termed 5V characteristics of generic big data, namely volume, variety, velocity, veracity, and value, mobile big data is distinct in its unique multidimensional, personalized, multisensory, and real-time features. In this survey, we provide in-depth and comprehensive coverage on the features, sources and applications of mobile big data, as well as the current state-of-the-art, challenges and opportunities for research and development in this field, with an emphasis on the user modeling, infrastructure supporting, data management, and knowledge discovery aspects.
Measuring #GamerGate: A Tale of Hate, Sexism, and Bullying

2009-2014 PhD in Computer Science, University of California Santa Barbara, Santa Barbara, CA. Dissertation title: “Stepping Up the Cybersecurity Game: Protecting Online Services from Malicious Activity” 2014 MSc in Computer Science, University of California Santa Barbara, Santa Barbara, CA. 2006-2009 Laurea Specialistica in Computer Engineering (MSc equivalent), Università degli Studi di Genova, Genova, Italy. Thesis title: “A Distributed System for Intrusion Prevention” 2003-2006 Laurea Triennale in Computer Engineering (BSc equivalent), Università degli Studi di Genova, Genova, Italy. Thesis title: “Computer Security in a Linux System” (in Italian) 1998-2003 Liceo Classico A. D’Oria, High School, Genova, Italy. Focus on humanities
Integrating DTN and MANET routing

Mobile Ad-hoc Network (MANET) routing protocols aim at establishing end-to-end paths between communicating nodes and thus support end-to-end semantics of existing transports and applications. In contrast, DTN-based communication schemes imply asynchronous communication (and thus often require new applications) but achieve better reachability, particularly in sparsely populated environments. In this paper, we suggest a hybrid scheme that combines AODV and DTN-based routing and allows keeping the AODV advantage of maintaining end-to-end semantics whenever possible while, at the same time, also offering DTN-based communication options whenever available---leaving the choice to the application. We present our protocol and system design, particularly including the interaction of AODV and DTN, demonstrate achievable performance gains based upon measurements, and report on initial experiments with our implementation in an emulation environment.
Statistical physics of vaccination

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination–one of the most important preventive measures of modern times–is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.
Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection

Raising audience awareness over the creation and evolution of a cultural participatory digital platform is a critical point for its acceptance. The proposed platform adopts user involvement in the content collection level through the implementation of a mobile application easily downloadable to the user’s smartphone and the use of a web portal application. Complementary web portal permits the management of the collected content in a trustworthy manner adopting an extended role-based access control model for authorization purposes. Users can formulate private groups to contribute and share content. Platform guarantees the soundness of contributed content through an auditing procedure requested by the contributors and conducted by experts selected randomly. In order to stress the applicability of our platform to various cultural environments, we present a number of usage scenarios targeting various stakeholders from specialists and museum curators to students, teachers and simple enthusiasts aiming in the devel‐ opment of coherent narrations.
Random Forests and Decision Trees

In this paper, we have compared the classification results of two models i.e. Random Forest and the J48 for classifying twenty versatile datasets. We took 20 data sets available from UCI repository [1] containing instances varying from 148 to 20000. We compared the classification results obtained from methods i.e. Random Forest and Decision Tree (J48). The classification parameters consist of correctly classified instances, incorrectly classified instances, F-Measure, Precision, Accuracy and Recall. We discussed the pros and cons of using these models for large and small data sets. The classification results show that Random Forest gives better results for the same number of attributes and large data sets i.e. with greater number of instances, while J48 is handy with small data sets (less number of instances). The results from breast cancer data set depicts that when the number of instances increased from 286 to 699, the percentage of correctly classified instances increased from 69.23% to 96.13% for Random Forest i.e. for dataset with same number of attributes but having more instances, the Random Forest accuracy increased.
Passing a Hide-and-Seek Third-Person Turing Test

Hiding and seeking are cognitive abilities frequently demonstrated by humans in both real life and video games. To determine to which extent these abilities can be replicated with AI, we introduce a specialized version of the Turing test for hiding and seeking. We then develop a computer agent that passes the test by appearing indistinguishable from human behavior to a panel of human judges. We analyze the AI techniques that enable the agent to imitate human hide-and-seek behavior and their relative contribution to the agent's performance.
Can machine learning explain human learning?

Learning Analytics (LA) has a major interest in exploring and understanding the learning process of humans and, for this purpose, benefits from both Cognitive Science, which studies how humans learn, and Machine Learning, which studies how algorithms learn from data. Usually, Machine Learning is exploited as a tool for analyzing data coming from experimental studies, but it has been recently applied to humans as if they were algorithms that learn from data. One example is the application of Rademacher Complexity, which measures the capacity of a learning machine, to human learning, which led to the formulation of Human Rademacher Complexity (HRC). In this line of research, we propose here a more powerful measure of complexity, the Human Algorithmic Stability (HAS), as a tool to better understand the learning process of humans. The experimental results from three different empirical studies, on more than 600 engineering students from the University of Genoa, showed that HAS (i) can be measured without the assumptions required by HRC, (ii) depends not only on the knowledge domain, as HRC, but also on the complexity of the problem, and (iii) can be exploited for better understanding of the human learning process. & 2016 Elsevier B.V. All rights reserved.
An approach to automotive ECG measurement validation using a car-integrated test framework

Development and integration of physiological sensors into automotive applications is gaining importance. Assistance systems which possess knowledge about the driver's cognitive state could increase road safety. In this paper we present a flexible framework that enables the development, evaluation and verification of sensors and algorithms for automotive applications using physiological signals under realistic driving conditions. We have integrated a custom capacitive ECG measurement system into a test car and validated its performance in real world driving tests. During first test runs, the capacitive system achieved a sensitivity of up to 95.5% and a precision rate of up to 92.6%. Our system also records synchronized vehicle dynamics. We discuss the road test measurements which suggest that the driving situation highly impacts the quality of ECG signal. Therefore, information on driving dynamics could be used to improve the precision rate of future capacitive ECG measurement.