nils-herrmann/clean_scirepeval huggingface dataset:
Automated Lip Reading Technique for Password Authentication

As technology is starting to conquer every strata of the society, the war for protecting confidential data from being intercepted is growing intense by the hour. Biometricsecurity stands out as the most secure form of authentication in high security zones such as defense, space missions and research head-quarters. Today, forms of password-protection range from facerecognition to retina -scan. Here, we develop a system for recognizing and converting lip movement of an individual into a recognized pattern which is set as a password for the system using image-processing. This system is also a break-through for providing people with motor-disabilities a robust and easy way of protecting their data. By capturing and tracing the successive movement of lips during speech, the corresponding word can be detected. The captured images are represented as points on a two-dimensional flat manifold that enables us to efficiently define the pronunciation of each word and thereby analyze or synthesize the motion of the lips. The motion of lips helps us track the word syllable-by-syllable. With multiple levels of image processing, it becomes possible to set the matching parameters to a very close value, hence not allowing any bruteforce or other infamous hacking techniques to break into the user’s system. This lip reading technique also serves applications in areas where communication via direct speech is not possible.
Design, fabrication and testing of smart lighting system

Smart lighting is an illumination technology designed for energy efficiency. This may include high efficiency features and automated controls that make adjustments in light intensity based on conditions such as availability of daylight or occupancy. Lighting is an application of light to realize both appeal and visual comfort. Nowadays, modern systems are able to reproduce intensities and pivot on smart sensors and actuators incorporating information and communication technologies. This research work aims at developing a smart lighting system that combines heterogeneous lighting technologies enabling intelligent functions. The light intensities can be shifted according to the visual comfort or control according to the occupant's activity based needs. This is achieved by design, development and testing of smart lighting system through ZigBee according to the availability of natural daylight, occupancy and required lighting level by the occupants.
Commercial DC-DC converters: A survey of isolated low-voltage very-high-current systems for transportation electrification

As the energy demand from low-voltage (LV) DC sources on vehicles keeps increasing, the need for DC-DC converters capable of handling very high current on the LV side is growing. A group of original equipment manufacturers (OEM) have set performance targets for such converters for the years 2015 and 2020. This paper presents an extensive survey of DC-DC converters that are: a) galvanically isolated, b) able to scale 12/24/48 V up to several hundred volts, and c) rated to carry several hundred amperes of current on the LV side. More than eighty converters from thirty manufacturers are analyzed. Characteristics such as voltage ranges, current capacity, power rating, efficiency, operating temperature, cooling types, weight, power density, volume, specific power, compliant codes, and standards are collected and presented in a graphical form. The current insights and trends are gathered and compared with the OEM targets. The results can serve as a technology indicator of this kind of converters in 2015 for researchers and practicing engineers of transportation electrification and others alike.
Easy Handling of Sensors and Actuators over TCP/IP Networks by Open Source Hardware/Software

There are several specific solutions for accessing sensors and actuators present in any process or system through a TCP/IP network, either local or a wide area type like the Internet. The usage of sensors and actuators of different nature and diverse interfaces (SPI, I2C, analogue, etc.) makes access to them from a network in a homogeneous and secure way more complex. A framework, including both software and hardware resources, is necessary to simplify and unify networked access to these devices. In this paper, a set of open-source software tools, specifically designed to cover the different issues concerning the access to sensors and actuators, and two proposed low-cost hardware architectures to operate with the abovementioned software tools are presented. They allow integrated and easy access to local or remote sensors and actuators. The software tools, integrated in the free authoring tool Easy Java and Javascript Simulations (EJS) solve the interaction issues between the subsystem that integrates sensors and actuators into the network, called convergence subsystem in this paper, and the Human Machine Interface (HMI)-this one designed using the intuitive graphical system of EJS-located on the user's computer. The proposed hardware architectures and software tools are described and experimental implementations with the proposed tools are presented.
Trends, Tips, Tolls: A Longitudinal Study of Bitcoin Transaction Fees

The Bitcoin protocol supports optional direct payments from transaction partners to miners. These “fees” are supposed to substitute miners’ minting rewards in the long run. Acknowledging their role for the stability of the system, the right level of transaction fees is a hot topic of normative debates. This paper contributes empirical evidence from a historical analysis of agents’ revealed behavior concerning their payment of transaction fees. We identify several regime shifts, which can be largely explained by changes in the default client software or actions of big intermediaries in the ecosystem. Overall, it seems that rules dominate ratio, a state that is sustainable only if fees remain negligible.
Analytical Tools for Blockchain: Review, Taxonomy and Open Challenges

Bitcoin has introduced a new concept that could feasibly revolutionise the entire Internet as it exists, and positively impact on many types of industries including, but not limited to, banking, public sector and supply chain. This innovation is grounded on pseudo-anonymity and strives on its innovative decentralised architecture based on the blockchain technology. Blockchain is pushing forward a race of transaction-based applications with trust establishment without the need for a centralised authority, promoting accountability and transparency within the business process. However, a blockchain ledger (e.g., Bitcoin) tend to become very complex and specialised tools, collectively called “Blockchain Analytics”, are required to allow individuals, law enforcement agencies and service providers to search, explore and visualise it. Over the last years, several analytical tools have been developed with capabilities that allow, e.g., to map relationships, examine flow of transactions and filter crime instances as a way to enhance forensic investigations. This paper discusses the current state of blockchain analytical tools and presents a thematic taxonomy model based on their applications. It also examines open challenges for future development and research.
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection

To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection. However, its security against well-crafted attacks has not only been recently questioned, but it has been shown that machine learning exhibits inherent vulnerabilities that can be exploited to evade detection at test time. In other words, machine learning itself can be the weakest link in a security system. In this paper, we rely upon a previously-proposed attack framework to categorize potential attack scenarios against learning-based malware detection tools, by modeling attackers with different skills and capabilities. We then define and implement a set of corresponding evasion attacks to thoroughly assess the security of Drebin, an Android malware detector. The main contribution of this work is the proposal of a simple and scalable secure-learning paradigm that mitigates the impact of evasion attacks, while only slightly worsening the detection rate in the absence of attack. We finally argue that our secure-learning approach can also be readily applied to other malware detection tasks.
Cardiorespiratory fitness estimation in free-living using wearable sensors

In this paper we propose artificial intelligence methods to estimate cardiorespiratory fitness (CRF) in free-living using wearable sensor data.



Our methods rely on a computational framework able to contextualize heart rate (HR) in free-living, and use context-specific HR as predictor of CRF without need for laboratory tests. In particular, we propose three estimation steps. Initially, we recognize activity primitives using accelerometer and location data. Using topic models, we group activity primitives and derive activities composites. We subsequently rank activity composites, and analyze the relation between ranked activity composites and CRF across individuals. Finally, HR data in specific activity primitives and composites is used as predictor in a hierarchical Bayesian regression model to estimate CRF level from the participant's habitual behavior in free-living.



We show that by combining activity primitives and activity composites the proposed framework can adapt to the user and context, and outperforms other CRF estimation models, reducing estimation error between 10.3% and 22.6% on a study population of 46 participants.



Our investigation showed that HR can be contextualized in free-living using activity primitives and activity composites and robust CRF estimation in free-living is feasible.
Evolution, neural networks, games, and intelligence

Intelligence pertains to the ability to make appropriate decisions in light of specific goals and to adapt behavior to meet those goals in a range of environments. Mathematical games provide a framework for studying intelligent behavior in models of real-world settings or restricted domains. The behavior of alternative strategies in these games is defined by each individual’s stimulus-response mapping. Limiting these behaviors to linear functions of the environmental conditions renders the results to be little more than a façade: Effective decision making in any complex environment almost always requires nonlinear stimulus-response mappings. The obstacle then comes in choosing the appropriate representation and learning algorithm. Neural networks and evolutionary algorithms provide useful means for addressing these issues. This paper describes efforts to hybridize neural and evolutionary computation to learn appropriate strategies in zeroand nonzero-sum games, including the iterated prisoner’s dilemma, tic-tac-toe, and checkers. With respect to checkers, the evolutionary algorithm was able to discover a neural network that can be used to play at a near-expert level without injecting expert knowledge about how to play the game. The implications of evolutionary learning with respect to machine intelligence are also discussed. It is argued that evolution provides the framework for explaining naturally occurring intelligent entities and can be used to design machines that are also capable of intelligent behavior.
BP-STDP: Approximating backpropagation using spike timing dependent plasticity

The problem of training spiking neural networks (SNNs) is a necessary precondition to understanding computations within the brain, a field still in its infancy. Previous work has shown that supervised learning in multi-layer SNNs enables bio-inspired networks to recognize patterns of stimuli through hierarchical feature acquisition. Although gradient descent has shown impressive performance in multi-layer (and deep) SNNs, it is generally not considered biologically plausible and is also computationally expensive. This paper proposes a novel supervised learning approach based on an event-based spike-timing-dependent plasticity (STDP) rule embedded in a network of integrate-and-fire (IF) neurons. The proposed temporally local learning rule follows the backpropagation weight change updates applied at each time step. This approach enjoys benefits of both accurate gradient descent and temporally local, efficient STDP. Thus, this method is able to address some open questions regarding accurate and efficient computations that occur in the brain. The experimental results on the XOR problem, the Iris data, and the MNIST dataset demonstrate that the proposed SNN performs as successfully as the traditional NNs. Our approach also compares favorably with the state-of-the-art multi-layer SNNs.
Hidden Voice Commands

Voice interfaces are becoming more ubiquitous and are now the primary input method for many devices. We explore in this paper how they can be attacked with hidden voice commands that are unintelligible to human listeners but which are interpreted as commands by devices. We evaluate these attacks under two different threat models. In the black-box model, an attacker uses the speech recognition system as an opaque oracle. We show that the adversary can produce difficult to understand commands that are effective against existing systems in the black-box model. Under the white-box model, the attacker has full knowledge of the internals of the speech recognition system and uses it to create attack commands that we demonstrate through user testing are not understandable by humans. We then evaluate several defenses, including notifying the user when a voice command is accepted; a verbal challenge-response protocol; and a machine learning approach that can detect our attacks with 99.8% accuracy.
Character usage in Chinese short message service (SMS): a real-world study in Mainland China

Short Message Service (SMS) is an important component of modern mobile services. Given unique characteristics of Chinese language, it is imperative to conduct study to understand characteristic of language usage patterns in Chinese SMS so that important facts like why and how people in China use SMS can be discovered. In this paper, we report an analysis of Chinese SMS logs from three different provinces in China. A computational approach was applied to extract n-grams from logs of SMS. The language usage patterns reported in this paper consist of two aspects: 1) most popular n-grams that represent what types of information were transmitted via SMS;
Exploring the Placement and Design of Word-Scale Visualizations

We present an exploration and a design space that characterize the usage and placement of word-scale visualizations within text documents. Word-scale visualizations are a more general version of sparklines-small, word-sized data graphics that allow meta-information to be visually presented in-line with document text. In accordance with Edward Tufte's definition, sparklines are traditionally placed directly before or after words in the text. We describe alternative placements that permit a wider range of word-scale graphics and more flexible integration with text layouts. These alternative placements include positioning visualizations between lines, within additional vertical and horizontal space in the document, and as interactive overlays on top of the text. Each strategy changes the dimensions of the space available to display the visualizations, as well as the degree to which the text must be adjusted or reflowed to accommodate them. We provide an illustrated design space of placement options for word-scale visualizations and identify six important variables that control the placement of the graphics and the level of disruption of the source text. We also contribute a quantitative analysis that highlights the effect of different placements on readability and text disruption. Finally, we use this analysis to propose guidelines to support the design and placement of word-scale visualizations.
Meta-Learning with Hessian Free Approach in Deep Neural Nets Training

Meta-learning is a promising method to achieve efficient training method towards deep neural net and has been attracting increases interests in recent years. But most of the current methods are still not capable to train complex neuron net model with long-time training process. In this paper, a novel second-order meta-optimizer, named Meta-learning with Hessian-Free(MLHF) approach, is proposed based on the Hessian-Free approach. Two recurrent neural networks are established to generate the damping and the precondition matrix of this Hessian-Free framework. A series of techniques to meta-train the MLHF towards stable and reinforce the meta-training of this optimizer, including the gradient calculation of H . Numerical experiments on deep convolution neural nets, including CUDA-convnet and ResNet18(v2), with datasets of CIFAR10 and ILSVRC2012, indicate that the MLHF shows good and continuous training performance during the whole long-time training process, i.e., both the rapiddecreasing early stage and the steadily-deceasing later stage, and so is a promising meta-learning framework towards elevating the training efficiency in real-world deep neural nets.
Analysis and Prediction of Question Topic Popularity in Community Q&A Sites: A Case Study of Quora

In the past few years, Quora a community-driven social platform for question and answering, has grown exponentially from a small community of users into one of the largest and reliable source of Q&A on the Internet. Quora has a built-in social structure integrated to its backbone; users can follow each other, follow question, topics etc. Apart from the social connections that Quora provides, it has developed a knowledge base nicely organized via hierarchy and relatedness of topics. In this paper, we consider a massive dataset of more than four years and analyze the dynamics of topical growth over time; how various factors affect the popularity of a topic or its acceptance in Q&A community. We also propose a regression model to predict the popularity of the topics and discuss the important discriminating features. We achieve a high prediction accuracy (correlation coefficient ⇠0.773) with low root mean square error (⇠1.065). We further categorize the topics into a few broad classes by implementing a simple Latent Dirichlet Allocation (LDA) model on the question texts associated with the topics. In comparison to the data sample with no categorization, this stratification of the topics enhances the prediction accuracies for several categories. However, for certain categories there seems to a slight decrease in the accuracy values and we present an in-depth discussion analyzing the cause for the same pointing out potential ways for improvement. We believe that this thorough measurement study will have a direct application to a service like recommending trending topics in Quora.
Design and Modeling of a Textile Pressure Sensor for Sitting Posture Classification

A textile pressure sensor has been designed for measuring pressure distribution on the human body. Electrodes built with conductive textiles are arranged on both sides of a compressible spacer, forming a variable capacitor. The use of textiles makes unobtrusive, comfortable, lightweight, and washable sensors possible. This simplifies the goal to integrate such sensors into clothing in the future, to be simple, and fast to mount just as getting dressed. Hysteresis induced by the spacer of the sensor has been modeled with the Preisach model to reduce the measurement error from 24% to 5% on average and the maximal error from above 50% to below 10%. Standard textiles that are not specially optimized for low hysteresis can be used for designing the sensor due to the modeling. The model may also be used for other pressure or even strain sensors to reduce their hysteresis. The modeling enhances the accuracy of the textile sensor to those of commercial, nontextile pressure sensing mats. Furthermore, we used the sensor system for the classification of sitting postures in a chair. The data of nine subjects have been classified with Naive Bayes classifier, achieving an average recognition rate of 82%. We show that the textile sensor performs similarly to a commercial, nontextile pressure sensing mat for this application.
Adolescent thriving: the role of sparks, relationships, and empowerment.

Although most social science research on adolescence emphasizes risks and challenges, an emergent field of study focuses on adolescent thriving. The current study extends this line of inquiry by examining the additive power of identifying and nurturing young people's "sparks," giving them "voice," and providing the relationships and opportunities that reinforce and nourish thriving. A national sample of 1,817 adolescents, all age 15 (49% female), and including 56% white, 17% Hispanic/Latino, and 17% African-American adolescents, completed an online survey that investigated their deep passions or interests (their "sparks"), the opportunities and relationships they have to support pursuing those sparks, and how empowered they feel to make civic contributions (their "voice"). Results consistently supported the hypothesis that linking one's spark with a sense of voice and supportive opportunities and relationships strengthens concurrent outcomes, particularly those reflecting prosociality, during a key developmental transition period. The three developmental strengths also predicted most outcomes to a greater degree than did demographics. However, less than 10 percent of 15-year-olds reported experiencing high levels of all three strengths. The results demonstrate the value of focusing on thriving in adolescence, both to reframe our understanding of this age group and to highlight the urgency of providing adolescents the opportunities and relationships they need to thrive.
A GRAPH-THEORETIC DEFINITION OF A SOCIOMETRIC CLIQUE

The intent of this paper is to provide a definition of a sociometric clique in the language of graph theory. This problem is viewed from two perspectives: maintaining fidelity to the intuitive notion of a clique; and providing adequate computational mechanics for large bodies of data. Luce's (1950) concept of an K-clique is used, but further qualifications are added. Two statistics or measures with associated probability distributions are defined for testing the adequacy of a subgraph which qualifies according to the definition.
A GENE-EXPRESSION SIGNATURE AS A PREDICTOR OF SURVIVAL IN BREAST CANCER

M ARC J. VAN DE V IJVER , M.D., P H .D., Y UDONG D. H E , P H .D., L AURA J. VAN ’ T V EER , P H .D., H ONGYUE D AI , P H .D., A UGUSTINUS A.M. H ART , M.S C ., D ORIEN W. V OSKUIL , P H .D., G EORGE J. S CHREIBER , M.S C ., J OHANNES L. P ETERSE , M.D., C HRIS R OBERTS , P H .D., M ATTHEW J. M ARTON , P H .D., M ARK P ARRISH , D OUWE A TSMA , A NKE W ITTEVEEN , A NNUSKA G LAS , P H .D., L EONIE D ELAHAYE , T ONY VAN DER V ELDE , H ARRY B ARTELINK , M.D., P H .D., S JOERD R ODENHUIS , M.D., P H .D., E MIEL T. R UTGERS , M.D., P H .D., S TEPHEN H. F RIEND , M.D., P H .D.,