nils-herrmann/clean_scirepeval huggingface dataset:
Attributed Social Network Embedding

Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing methods focused only on leveraging network structure. For social networks, besides the network structure, there also exists rich information about social actors, such as user profiles of friendship networks and textual content of citation networks. These rich attribute information of social actors reveal the homophily effect, exerting huge impacts on the formation of social networks. In this paper, we explore the rich evidence source of attributes in social networks to improve network embedding. We propose a generic Attributed Social Network Embedding framework (ASNE), which learns representations for social actors (i.e., nodes) by preserving both the structural proximity and attribute proximity. While the structural proximity captures the global network structure, the attribute proximity accounts for the homophily effect. To justify our proposal, we conduct extensive experiments on four real-world social networks. Compared to the state-of-the-art network embedding approaches, ASNE can learn more informative representations, achieving substantial gains on the tasks of link prediction and node classification. Specifically, ASNE significantly outperforms  node2vec with an 8.2 percent relative improvement on the link prediction task, and a 12.7 percent gain on the node classification task.
Strong Security for Network-Attached Storage

We have developed a scheme to secure networkattached storage systems against many types of attacks. Our system uses strong cryptography to hide data from unauthorized users; someone gaining complete access to a disk cannot obtain any useful data from the system, and backups can be done without allowing the super-user access to cleartext. While insider denial-of-service attacks cannot be prevented (an insider can physically destroy the storage devices), our system detects attempts to forge data. The system was developed using a raw disk, and can be integrated into common file systems. All of this security can be achieved with little penalty to performance. Our experiments show that, using a relatively inexpensive commodity CPU attached to a disk, our system can store and retrieve data with virtually no penalty for random disk requests and only a 15–20% performance loss over raw transfer rates for sequential disk requests. With such a minor performance penalty, there is no longer any reason not to include strong encryption and authentication in network file systems.
An Indoor Bluetooth-Based Positioning System: Concept, Implementation and Experimental Evaluation

This paper presents the experimental evaluation of a Bluetooth-based positioning system. The method has been implemented in a Bluetooth-capable handheld device. Empirical tests of the developed considered positioning system have been realized in different indoor scenarios. The range estimation of the positioning system is based on an approximation of the relation between the RSSI (Radio Signal Strength Indicator) and the associated distance between sender and receiver. The actual location estimation is carried out by using the triangulation method. The implementation of the positioning system in a PDA (Personal Digital Assistant) has been realized by using the Software Microsoft eMbedded Visual C++ Version 3.0.
A Knowledge-Driven Approach to Activity Recognition in Smart Homes

This paper introduces a knowledge-driven approach to real-time, continuous activity recognition based on multisensor data streams in smart homes. The approach goes beyond the traditional data-centric methods for activity recognition in three ways. First, it makes extensive use of domain knowledge in the life cycle of activity recognition. Second, it uses ontologies for explicit context and activity modeling and representation. Third and finally, it exploits semantic reasoning and classification for activity inferencing, thus enabling both coarse-grained and fine-grained activity recognition. In this paper, we analyze the characteristics of smart homes and Activities of Daily Living (ADL) upon which we built both context and ADL ontologies. We present a generic system architecture for the proposed knowledge-driven approach and describe the underlying ontology-based recognition process. Special emphasis is placed on semantic subsumption reasoning algorithms for activity recognition. The proposed approach has been implemented in a function-rich software system, which was deployed in a smart home research laboratory. We evaluated the proposed approach and the developed system through extensive experiments involving a number of various ADL use scenarios. An average activity recognition rate of 94.44 percent was achieved and the average recognition runtime per recognition operation was measured as 2.5 seconds.
Successfully Attacking Masked AES Hardware Implementations

During the last years, several masking schemes for AES have been proposed to secure hardware implementations against DPA attacks. In order to investigate the effectiveness of these countermeasures in practice, we have designed and manufactured an ASIC. The chip features an unmasked and two masked AES-128 encryption engines that can be attacked independently. In addition to conventional DPA attacks on the output of registers, we have also mounted attacks on the output of logic gates. Based on simulations and physical measurements we show that the unmasked and masked implementations leak side-channel information due to glitches at the output of logic gates. It turns out that masking the AES S-Boxes does not prevent DPA attacks, if glitches occur in the circuit.
Dynamic Data-Driven Estimation of Non-Parametric Choice Models

We study non-parametric estimation of consumer choice models. Non-parametric approaches were introduced to alleviate unreasonable assumptions and issues of suboptimal model fit/selection present in traditional parametric approaches, and are prevalent in several application areas. We present two convex optimization-based frameworks, a primal approach and a dual approach, to efficiently learn a non-parametric choice model from data that is close to the best-fitting one. As opposed to the existing literature, both approaches enjoy provable convergence guarantees and extend naturally to the dynamic observation setting. Our computational study on the dynamic setting reveals the true impact of how much data are needed and at what rate to achieve the best trade-off in terms of estimation accuracy and model simplicity. In the static setup, we also compare our non-parametric approach with existing parametric approaches.
A low-voltage 5.1-5.8-GHz image-reject receiver with wide dynamic range

A monolithic 5-6-GHz band receiver, consisting of a differential preamplifier, dual doubly balanced mixers, cascaded injection-locked frequency doublers, and a quadrature local oscillator generator and prescaler, realizes over 45 dB of image-rejection in a mature 25-GHz silicon bipolar technology. The measured single sideband (50 /spl Omega/) noise figure is 5.1 dB with an IIP3 of -4.5 dBm and 17-dB conversion gain at 5.3 GHz. The 1.9/spl times/1.2 mm/sup 2/ IC is packaged in a standard 32-pin ceramic quad flatpack and consumes less than 50 mW from a 2.2-V supply.
Annotating Named Entities in Twitter Data with Crowdsourcing

We describe our experience using both Amazon Mechanical Turk (MTurk) and CrowdFlower to collect simple named entity annotations for Twitter status updates. Unlike most genres that have traditionally been the focus of named entity experiments, Twitter is far more informal and abbreviated. The collected annotations and annotation techniques will provide a first step towards the full study of named entity recognition in domains like Facebook and Twitter. We also briefly describe how to use MTurk to collect judgements on the quality of “word clouds.”
Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.
Analysis and design of OFDM/OQAM systems based on filterbank theory

A discrete-time analysis of the orthogonal frequency division multiplex/offset QAM (OFDM/OQAM) multicarrier modulation technique, leading to a modulated transmultiplexer, is presented. The conditions of discrete orthogonality are established with respect to the polyphase components of the OFDM/OQAM prototype filter, which is assumed to be symmetrical and with arbitrary length. Fast implementation schemes of the OFDM/OQAM modulator and demodulator are provided, which are based on the inverse fast Fourier transform. Non-orthogonal prototypes create intersymbol and interchannel interferences (ISI and ICI) that, in the case of a distortion-free transmission, are expressed by a closed-form expression. A large set of design examples is presented for OFDM/OQAM systems with a number of subcarriers going from four up to 2048, which also allows a comparison between different approaches to get well-localized prototypes.
Improving Automated Controversy Detection on the Web

Automatically detecting controversy on the Web is a useful capability for a search engine to help users review web content with a more balanced and critical view. The current state-of-the art approach is to find K-Nearest-Neighbors in Wikipedia to the document query, and to aggregate their controversy scores that are automatically computed from the Wikipedia edit-history features. In this paper, we discover two major weakness in the prior work and propose modifications. First, the generated single query from document to find KNN Wikipages easily becomes ambiguous. Thus, we propose to generate multiple queries from smaller but more topically coherent paragraph of the document. Second, the automatically computed controversy scores of Wikipedia articles that depend on "edit war" features have a drawback that without an edit history, there can be no edit wars. To infer more reliable controversy scores for articles with little edit history, we smooth the original score from the scores of the neighbors with more established edit history. We show that the modified framework is improved by up to 5% for binary controversy classification in a publicly available dataset.
A Transient-Enhanced 20μA-Quiescent 200mA-Load Low-Dropout Regulator With Buffer Impedance Attenuation

This paper demonstrates a 200mA low-dropout regulator (LDO) for portable applications. Buffer impedance attenuation is developed to realize a single-pole loop response through shunt feedback. The LDO is thus unconditionally stable without using an ESR zero and output undershoots and overshoots are minimized during load transients. Implemented in 0.35μm twin-well CMOS, the LDO only dissipates 20μA quiescent current. With a 1μF output capacitor, the maximum transient-output variation is 54mV with full-load step change of 200mA.
Where Are the Blobs: Counting by Localization with Point Supervision

Object counting is an important task in computer vision due to its growing demand in applications such as surveillance, traffic monitoring, and counting everyday objects. State-of-the-art methods use regression-based optimization where they explicitly learn to count the objects of interest. These often perform better than detection-based methods that need to learn the more difficult task of predicting the location, size, and shape of each object. However, we propose a detectionbased method that does not need to estimate the size and shape of the objects and that outperforms regression-based methods. Our contributions are three-fold: (1) we propose a novel loss function that encourages the network to output a single blob per object instance using pointlevel annotations only; (2) we design two methods for splitting large predicted blobs between object instances; and (3) we show that our method achieves new state-of-the-art results on several challenging datasets including the Pascal VOC and the Penguins dataset. Our method even outperforms those that use stronger supervision such as depth features, multi-point annotations, and bounding-box labels.
Pleated pneumatic artificial muscles: compliant robotic actuators

Pleated pneumatic artificial muscles (PPAMs), which have recently been developed at the Vrije Universiteit Brussel, Department of Mechanical Engineering are brought forward as robotic actuators in this paper. Their distinguishing feature is their pleated design, as a result of which their contraction forces and maximum displacement are very high compared to other pneumatic artificial muscles. The PPAM design, operation and characteristics are presented. To show how well they are suited for robotics, a rotative joint actuator, made of two antagonistically coupled PPAMs, is discussed. It has several properties that are similar to those of skeletal joint actuators. Positioning tasks are seen to be performed very accurately using simple PI control. Furthermore, the antagonistic actuator can easily be made to have a soft or careful touch, contributing greatly to a safe robot operation. In view of all characteristics PPAMs are very well suited for automation and robotic applications.
A New approach to Requirements Elicitation Using Paper Prototype

Requirement elicitation is a critical activity in the requirement development process and it explores the requirements of stakeholders. The common challenges that analysts face during elicitation process are to ensure effective communication between analyst and the users. Mostly errors in the systems are due to poor communication between user and analyst. This paper proposes an improved approach for requirements elicitation using paper prototype. The paper progresses through an assessment of the new approach using student projects developed for various organizations. A case study project is explained in the paper.
Robust Power Allocation Designs for Multiuser and Multiantenna Downlink Communication Systems through Convex Optimization

In this paper, we study the design of the transmitter in the downlink of a multiuser and multiantenna wireless communications system, considering the realistic scenario where only an imperfect estimate of the actual channel is available at both communication ends. Precisely, the actual channel is assumed to be inside an uncertainty region around the channel estimate, which models the imperfections of the channel knowledge that may arise from, e.g., estimation Gaussian errors, quantization effects, or combinations of both sources of errors. In this context, our objective is to design a robust power allocation among the information symbols that are to be sent to the users such that the total transmitted power is minimized, while maintaining the necessary quality of service to obtain reliable communication links between the base station and the users for any possible realization of the actual channel inside the uncertainty region. This robust power allocation is obtained as the solution to a convex optimization problem, which, in general, can be numerically solved in a very efficient way, and even for a particular case of the uncertainty region, a quasi-closed form solution can be found. Finally, the goodness of the robust proposed transmission scheme is presented through numerical results. Robust designs, imperfect CSI, multiantenna systems, broadcast channel, convex optimization.
Injecting Comments to Detect JavaScript Code Injection Attacks

Most web programs are vulnerable to cross site scripting (XSS) that can be exploited by injecting JavaScript code. Unfortunately, injected JavaScript code is difficult to distinguish from the legitimate code at the client side. Given that, server side detection of injected JavaScript code can be a layer of defense. Existing server side approaches rely on identifying legitimate script code, and an attacker can circumvent the technique by injecting legitimate JavaScript code. Moreover, these approaches assume that no JavaScript code is downloaded from third party websites. To address these limitations, we develop a server side approach that distinguishes injected JavaScript code from legitimate JavaScript code. Our approach is based on the concept of injecting comment statements containing random tokens and features of legitimate JavaScript code. When a response page is generated, JavaScript code without or incorrect comment is considered as injected code. Moreover, the valid comments are checked for duplicity. Any presence of duplicate comments or a mismatch between expected code features and actually observed features represents JavaScript code as injected. We implement a prototype tool that automatically injects JavaScript comments and deploy injected JavaScript code detector as a server side filter. We evaluate our approach with three JSP programs. The evaluation results indicate that our approach detects a wide range of code injection attacks.
An Efficient Frequency and Phase Estimation Algorithm With CRB Performance for FMCW Radar Applications

In this paper, a chirp z-transform (CZT)-based algorithm for frequency-modulated continuous wave (FMCW) radar applications is presented. The proposed algorithm is optimized for real-time implementation in field-programmable gate arrays. To achieve a very high accuracy, the FMCW radar uses an additional phase evaluation. Therefore, a phase calculation based on the CZT algorithm is derived and compared with a correlation based algorithm. For a better classification of the algorithm, the respective Cramér-Rao bounds are calculated. The performance of the algorithm is shown by the evaluation of different radar measurements with a K-band radar. In the measurements, an accuracy of 5 μm with a mean standard deviation of 774 nm is achieved, which nearly matches the theoretically predicted mean standard deviation of 160 nm.
Perceived 'usefulness' of online consumer reviews: An exploratory investigation across three services categories

Online reviews, a form of online word-of-mouth (eWOM), have recently become one of the most important sources of information for modern consumers. Recent scholarship involving eWOM often focuses on the transmission and impact of online reviews but sheds less light on the underlying processes that drive consumers’ receptions of them. Similarly, few studies have explored the recipients’ perspectives in the context of various services. This study addresses the aforementioned gaps in extant literature. The research model in this study is built upon the rich stream of literature related to how people are influenced by information and is tested on reviews collected from Yelp.com, a popular online advisory website dedicated to services businesses throughout the United States. The results of the study show that a combination of both reviewer and review characteristics are significantly correlated with the perceived usefulness of reviews. The study also finds several results that are anomalous to established knowledge related to consumers’ information consumption, both offline and online. The authors present the results of the study and discuss their significance for research and practice.
On dominant characteristics of residential broadband internet traffic

While residential broadband Internet access is popular in many parts of the world, only a few studies have examined the characteristics of such traffic. In this paper we describe observations from monitoring the network activity for more than 20,000 residential DSL customers in an urban area. To ensure privacy, all data is immediately anonymized. We augment the anonymized packet traces with information about DSL-level sessions, IP (re-)assignments, and DSL link bandwidth.
 Our analysis reveals a number of surprises in terms of the mental models we developed from the measurement literature. For example, we find that HTTP - not peer-to-peer - traffic dominates by a significant margin; that more often than not the home user's immediate ISP connectivity contributes more to the round-trip times the user experiences than the WAN portion of the path; and that the DSL lines are frequently not the bottleneck in bulk-transfer performance.
Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition

We have recently shown that deep Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as acoustic models for speech recognition. More recently, we have shown that the performance of sequence trained context dependent (CD) hidden Markov model (HMM) acoustic models using such LSTM RNNs can be equaled by sequence trained phone models initialized with connectionist temporal classification (CTC). In this paper, we present techniques that further improve performance of LSTM RNN acoustic models for large vocabulary speech recognition. We show that frame stacking and reduced frame rate lead to more accurate models and faster decoding. CD phone modeling leads to further improvements. We also present initial results for LSTM RNN models outputting words directly.