nils-herrmann/clean_scirepeval huggingface dataset:
Efficient Content Authentication in Peer-to-Peer Networks

We study a new model for data authentication over peer-topeer (p2p) storage networks, where data items are stored, queried and authenticated in a totally decentralized fashion. The model captures the security requirements of emerging distributed computing applications. We present an efficient construction of a distributed Merkle tree (DMT), which realizes an authentication tree over a p2p network, thus extending a fundamental cryptographic technique to distributed environments. We show how our DMT can be used to design an authenticated distributed hash table that is secure against replay attacks and consistent with the update history. Our scheme is built on top of a broad class of existing p2p overlay networks and achieves generality by using only the basic functionality of object location. We use this scheme to design the first efficient distributed authenticated dictionary.
Study and Analysis of Data mining Algorithms for Healthcare Decision Support System

Data mining technology provides a user oriented approach to novel and hidden information in the data. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. Data mining in healthcare medicine deals with learning models to predict patients’ disease. Data mining applications can greatly benefit all parties involved in the healthcare industry. For example, data mining can help healthcare insurers detect fraud and abuse, healthcare organizations make customer relationship management decisions, physicians identify effective treatments and best practices, and patients receive better and more affordable healthcare services. The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. The main aim of this survey is, analysis of the uniqueness of medical data mining, overview of Healthcare Decision Support Systems currently used in medicine, identification and selection of the most common data mining algorithms implemented in the modern HDSS, comparison between different algorithms in Data mining.
Managing departure aircraft release for efficient airport surface operations

This paper presents a model for managing departure aircraft at the spot or gate on the airport surface. The model is applied over two time frames: long term (one hour in future) for collaborative decision making, and short term (immediate) for decisions regarding the release of aircraft. The purpose of the model is to provide the controller a schedule of spot or gate release times optimized for runway utilization. This model was tested in nominal and heavy surface traffic scenarios in a simulated environment, and results indicate average throughput improvement of 10% in high traffic scenarios even with up to two minutes of uncertainty in spot arrival times.
Universal Dependencies : A cross-linguistic typology

Revisiting the now de facto standard Stanford dependency representation, we propose an improved taxonomy to capture grammatical relations across languages, including morphologically rich ones. We suggest a two-layered taxonomy: a set of broadly attested universal grammatical relations, to which language-specific relations can be added. We emphasize the lexicalist stance of the Stanford Dependencies, which leads to a particular, partially new treatment of compounding, prepositions, and morphology. We show how existing dependency schemes for several languages map onto the universal taxonomy proposed here and close with consideration of practical implications of dependency representation choices for NLP applications, in particular parsing. This paper is a minor revision of our LREC 2014 paper “Universal Stanford dependencies: A cross-linguistic typology”. The content is largely identical, but the taxonomy of relations has been revised to be consistent with the final Universal Dependencies (UD) taxonomy for version 1.0 described at http://universaldependencies.github.io/docs/. Version of 2015-11-12.
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs

The driving force behind the recent success of LSTMs has been their ability to learn complex and non-linear relationships. Consequently, our inability to describe these relationships has led to LSTMs being characterized as black boxes. To this end, we introduce contextual decomposition (CD), an interpretation algorithm for analysing individual predictions made by standard LSTMs, without any changes to the underlying model. By decomposing the output of a LSTM, CD captures the contributions of combinations of words or variables to the final prediction of an LSTM. On the task of sentiment analysis with the Yelp and SST data sets, we show that CD is able to reliably identify words and phrases of contrasting sentiment, and how they are combined to yield the LSTM’s final prediction. Using the phrase-level labels in SST, we also demonstrate that CD is able to successfully extract positive and negative negations from an LSTM, something which has not previously been done.
Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks

Channel modeling is a critical topic when considering designing, learning, or evaluating the performance of any communications system. Most prior work in designing or learning new modulation schemes has focused on using highly simplified analytic channel models such as additive white Gaussian noise (AWGN), Rayleigh fading channels or similar. Recently, we proposed the usage of a generative adversarial networks (GANs) to jointly approximate a wireless channel response model (e.g. from real black box measurements) and optimize for an efficient modulation scheme over it using machine learning. This approach worked to some degree, but was unable to produce accurate probability distribution functions (PDFs) representing the stochastic channel response. In this paper, we focus specifically on the problem of accurately learning a channel PDF using a variational GAN, introducing an architecture and loss function which can accurately capture stochastic behavior. We illustrate where our prior method failed and share results capturing the performance of such as system over a range of realistic channel distributions.
Joint scheduling of processing and Shuffle phases in MapReduce systems

MapReduce has emerged as an important paradigm for processing data in large data centers. MapReduce is a three phase algorithm comprising of Map, Shuffle and Reduce phases. Due to its widespread deployment, there have been several recent papers outlining practical schemes to improve the performance of MapReduce systems. All these efforts focus on one of the three phases to obtain performance improvement. In this paper, we consider the problem of jointly scheduling all three phases of the MapReduce process with a view of understanding the theoretical complexity of the joint scheduling and working towards practical heuristics for scheduling the tasks. We give guaranteed approximation algorithms and outline several heuristics to solve the joint scheduling problem.
Microcontroller based PMDC motor control for driving 0.5KW scooter

In the recent years, the use of clean energy to reduce pollution and hence to save the environment is an urgent need to keep the communities against the dangers that began to show with huge negative consequences. This paper illustrates with full details how to built DC scooter which can be used with solar cells to charge the DC batteries used to drive the scooter safely, economically and with negligible environmental effects. The old variable speed drives, had various limitations, such as poor efficiencies, larger space, lower speeds, etc., however, the development of power electronic devices such as power MOSFETs, IGBTs beside Micro-Controllers, overturned the scene completely, today we can built a variable speed drive systems with better comparable characteristics such as smaller in size, higher efficiency, more reliable and meeting all demands requirements for modern control applications. The aim of this paper is to drive a scooter, by using a Permanent Magnet DC Motor (PMDC), by using PIC16F877 microcontroller to produce variable duty cycle pulse width modulation PWM through up and down button to increase or decrease the speed with less-losses and with very low price. Also we intend to control the mechanical speed, starting, braking, and 4-Quadrant directions of the used PMDC motor safely and economically to increase the half life of the battery used and to increase the working time compared by other type of traditional controlled scooters. Also we explain the starting of the scooter with and without starting resistors to decrease the losses more and more.
Experiences measuring sleep and physical activity patterns across a large college cohort with fitbits

In the past few years, a wide variety of highly capable and inexpensive wearable health sensors have emerged. One of the interesting aspects of such sensors is the capability for researchers to longitudinally and automatically quantify important health behaviors, such as physical activity and sleep, with little intervention required by the participant. While the accuracy of these devices has been evaluated in laboratory settings, there exists little public data with respect to user compliance and the consistency of the resulting measurements at a large scale. The focus of this paper is to share our experience in distributing five hundred Fitbit Charge HR devices across a group of college freshmen and to introduce the resulting dataset from our study, the NetHealth Study. We find that when users are compliant, they tend to be exceptionally so, having an average compliance of 86%. User non-compliance does play a role, however, reducing the overall average compliance rate to 67%. We discuss various reasons for non-compliance and also briefly highlight preliminary monitored characteristics of physical activity and sleep in our student population.
A 20μW 10MHz relaxation oscillator with adaptive bias and fast self-calibration in 40nm CMOS for micro-aerial robotics application

Efficient actuation control of flapping-wing microrobots requires a low-power frequency reference with good absolute accuracy. To meet this requirement, we designed a fully-integrated 10MHz relaxation oscillator in a 40nm CMOS process. By adaptively biasing the continuous-time comparator, we are able to achieve a power consumption of 20μW, a 68% reduction to the conventional fixed bias design. A built-in self-calibration controller enables fast post-fabrication calibration of the clock frequency. Measurements show a frequency drift of 1.2% as the battery voltage changes from 3V to 4.1V.
Semantically Decomposing the Latent Spaces of Generative Adversarial Networks

We propose a new algorithm for training generative adversarial networks to jointly learn latent codes for both identities (e.g. individual humans) and observations (e.g. specific photographs). In practice, this means that by fixing the identity portion of latent codes, we can generate diverse images of the same subject, and by fixing the observation portion we can traverse the manifold of subjects while maintaining contingent aspects such as lighting and pose. Our algorithm features a pairwise training scheme in which each sample from the generator consists of two images with a common identity code. Corresponding samples from the real dataset consist of two distinct photographs of the same subject. In order to fool the discriminator, the generator must produce images that are both photorealistic, distinct, and appear to depict the same person. We augment both the DCGAN and BEGAN approaches with Siamese discriminators to accommodate pairwise training. Experiments with human judges and an off-the-shelf face verification system demonstrate our algorithm’s ability to generate convincing, identity-matched photographs.
Smart Home Communication Technologies and Applications : Wireless Protocol Assessment for Home Area Network Resources

The paper discusses Home Area Networks (HAN) communication technologies for smart home and domestic application integration. The work is initiated by identifying the application areas that can benefit from this integration. A broad and inclusive home communication interface is analysed utilizing as a key piece a Gateway based on machine-to-machine (M2M) communications that interacts with the surrounding environment. Then, the main wireless networks are thoroughly assessed, and later, their suitability to the requirements of HAN considering the application area is analysed. Finally, a qualitative analysis is portrayed.
A 16.6μW 32.8MHz monolithic CMOS relaxation oscillator

This paper presents a 32.8MHz low power, supply insensitive monolithic CMOS relaxation oscillator. Instead of using voltage-mode comparators for cycle-to-cycle capacitor voltage swing (CVS) threshold voltage comparison, the CVS is regulated by a low-power closed-loop control which consists of a current-controlled delay cell (CCDC), a Gm-C error integrator and a comparator-free switch logic block. The CCDC and switching logics are powered by a logic supply regulator for reducing switching-losses and line sensitivity. The oscillator consumes 16.6μW from a 1.5V supply voltage at room temperature, achieving a FOM of 0.51μW/MHz. The measured output frequency variation is <;±0.13%/V @ 32.8MHz, for a supply range of 1.5V to 3.6V It occupies 0.013mm2 in a 0.18μm CMOS process.
Clothoid-based model predictive control for autonomous driving

This paper presents a novel linear time-varying model predictive controller (LTV-MPC) using a sparse clothoid-based path description: a LTV-MPCC. Clothoids are used world-wide in road design since they allow smooth driving associated with low jerk values. The formulation of the MPC controller is based on the fact that the path of a vehicle traveling at low speeds defines a segment of clothoids if the steering angle is chosen to vary piecewise linearly. Therefore, we can compute the vehicle motion as clothoid parameters and translate them to vehicle inputs. We present simulation results that demonstrate the ability of the controller to produce a very comfortable and smooth driving while maintaining a tracking accuracy comparable to that of a regular LTV-MPC. While the regular MPC controllers use path descriptions where waypoints are close to each other, our LTV-MPCC has the ability of using paths described by very sparse waypoints. In this case, each pair of waypoints describes a clothoid segment and the cost function minimization is performed in a more efficient way allowing larger prediction distances to be used. This paper also presents a novel algorithm that addresses the problem of path sparsification using a reduced number of clothoid segments. The path sparsification enables a path description using few waypoints with almost no loss of detail. The detail of the reconstruction is an adjustable parameter of the algorithm. The higher the required detail, the more clothoid segments are used.
Capturing Hands in Action Using Discriminative Salient Points and Physics Simulation

Hand motion capture is a popular research field, recently gaining more attention due to the ubiquity of RGB-D sensors. However, even most recent approaches focus on the case of a single isolated hand. In this work, we focus on hands that interact with other hands or objects and present a framework that successfully captures motion in such interaction scenarios for both rigid and articulated objects. Our framework combines a generative model with discriminatively trained salient points to achieve a low tracking error and with collision detection and physics simulation to achieve physically plausible estimates even in case of occlusions and missing visual data. Since all components are unified in a single objective function which is almost everywhere differentiable, it can be optimized with standard optimization techniques. Our approach works for monocular RGB-D sequences as well as setups with multiple synchronized RGB cameras. For a qualitative and quantitative evaluation, we captured 29 sequences with a large variety of interactions and up to 150 degrees of freedom.
Hostile Control of Ships via False GPS Signals : Demonstration and Detection

An attacker’s ability to control a maritime surface vessel by broadcasting counterfeit civil Global Positioning System (GPS) signals is analyzed and demonstrated. The aim of this work is to explore civil maritime transportation’s vulnerability to deceptive GPS signals and to develop a detection technique that is compatible with sensors commonly available on modern ships. It is shown that despite access to a variety of high-quality navigation and surveillance sensors, modern maritime navigation depends crucially on satellite navigation, and that a deception attack can be disguised as the effects of slowly-changing ocean currents. An innovations-based detection framework that optimally chooses the measurement sampling interval to minimize the probability of a ship exceeding its alert limits without detection is developed and analyzed. A field experiment confirms the vulnerability analysis by demonstrating hostile control of a 65-m yacht in the Mediterranean Sea.
Web page clustering using a self-organizing map of user navigation patterns

The continuous growth in the size and use of the Internet is creating difficulties in the search for information. A sophisticated method to organize the layout of the information and assist user navigation is therefore particularly important. In this paper, we evaluate the feasibility of using a self-organizing map (SOM) to mine web log data and provide a visual tool to assist user navigation. We have developed LOGSOM, a system that utilizes Kohonen’s self-organizing map to organize web pages into a two-dimensional map. The organization of the web pages is based solely on the users’ navigation behavior, rather than the content of the web pages. The resulting map not only provides a meaningful navigation tool (for web users) that is easily incorporated with web browsers, but also serves as a visual analysis tool for webmasters to better understand the characteristics and navigation behaviors of web users visiting their pages. D 2002 Elsevier Science B.V. All rights reserved.
Making graphene visible

Microfabrication of graphene devices used in many experimental studies currently relies on the fact that graphene crystallites can be visualized using optical microscopy if prepared on top of Si wafers with a certain thickness of SiO2. The authors study graphene’s visibility and show that it depends strongly on both thickness of SiO2 and light wavelength. They have found that by using monochromatic illumination, graphene can be isolated for any SiO2 thickness, albeit 300 nm the current standard and, especially, 100 nm are most suitable for its visual detection. By using a Fresnel-law-based model, they quantitatively describe the experimental data.
Once More with Feeling: Supportive Responses to Social Sharing on Facebook

Life is more than cat pictures. There are tough days, heartbreak, and hugs. Under what contexts do people share these feelings online, and how do their friends respond? Using millions of de-identified Facebook status updates with poster-annotated feelings (e.g., “feeling thankful” or “feeling worried”), we examine the magnitude and circumstances in which people share positive or negative feelings and characterize the nature of the responses they receive. We find that people share greater proportions of both positive and negative emotions when their friend networks are smaller and denser. Consistent with social sharing theory, hearing about a friendâs troubles on Facebook causes friends to reply with more emotional and supportive comments. Friendsâ comments are also more numerous and longer. Posts with positive feelings, on the other hand, receive more likes, and their comments have more positive language. Feelings that relate to the posterâs self worth, such as “feeling defeated,” “feeling unloved,” or “feeling accomplished” amplify these effects.
NLP based sentiment analysis on Twitter data using ensemble classifiers

Most sentiment analysis systems use bag-of-words approach for mining sentiments from the online reviews and social media data. Rather considering the whole sentence/ paragraph for analysis, the bag-of-words approach considers only individual words and their count as the feature vectors. This may mislead the classification algorithm especially when used for problems like sentiment classification. Traditional machine learning algorithms like Naive Bayes, Maximum Entropy, SVM etc. are widely used to solve the classification problems. These machine learning algorithms often suffer from biasness towards a particular class. In this paper, we propose Natural Language (NLP) based approach to enhance the sentiment classification by adding semantics in feature vectors and thereby using ensemble methods for classification. Adding semantically similar words and context-sense identities to the feature vectors will increase the accuracy of prediction. Experiments conducted demonstrate that the semantics based feature vector with ensemble classifier outperforms the traditional bag-of-words approach with single machine learning classifier by 3-5%.
EXTRACTION OF RESPIRATORY RATE FROM PPG SIGNALS USING PCA AND EMD

Photoplethysmography is a non-invasive electro-optic method developed by Hertzman, which provides information on the blood volume flowing at a particular test site on the body close to the skin. PPG waveform contains two components; one, attributable to the pulsatile component in the vessels, i.e. the arterial pulse, which is caused by the heartbeat, and gives a rapidly alternating signal (AC component). The second one is due to the blood volume and its change in the skin which gives a steady signal that changes very slowly (DC component). PPG signal consists of not only the heart-beat information but also a respiratory signal. Estimation of respiration rates from Photoplethysmographic (PPG) signals would be an alternative approach for obtaining respiration related information.. There have been several efforts on PPG Derived Respiration (PDR), these methods are based on different signal processing techniques like filtering, wavelets and other statistical methods, which work by extraction of respiratory trend embedded into various physiological signals. PCA identifies patterns in data, and expresses the data in such a way as to highlight their similarities and differences. Since patterns in data can be hard to find in data of high dimension, where the luxury of graphical representation is not available, PCA is a powerful tool for analyzing such data. Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes. The basis functions, called Intrinsic Mode Functions (IMFs) represent a complete set of locally orthogonal basis functions whose amplitude and frequency may vary over time. The contribution reviews the technique of EMD and related algorithms and discusses illustrative applications. Test results on PPG signals of the well known MIMIC database from Physiobank archive reveal that the proposed EMD method has efficiently extracted respiratory information from PPG signals. The evaluated similarity parameters in both time and frequency domains for original and estimated respiratory rates have shown the superiority of the method.