nils-herrmann/clean_scirepeval huggingface dataset:
Link layer behavior of body area networks at 2.4 GHz

Body Area Networks (BANs) can perform the task of continuous, remote monitoring of a patient's physiological signals in diverse environments. Apart from providing healthcare professionals with extensive logs of a patient's physiological history, BANs can be used to identify and react to emergency situations. We identify three important factors that afflict wireless communication in BANs: impermeability of the human body to radio waves at frequencies commonly used in BANs, efficient operation in mobile and time-varying environments, and mission-critical requirements for quick response to emergencies. An understanding of the link layer behavior of wireless sensor nodes placed on the body is crucial to address these and other challenges such as reducing energy consumption and increasing network lifetime.
 In this paper, we investigate link layer behavior by placing nodes on the body and directly measuring metrics of interest to engineers such as packet delivery ratio (PDR) and RSSI. Emulating a possible real-life BAN operating at the 2.4 GHz band with 12 sensor nodes, we collect over 80 hours of data from 14 volunteers in $3$ different environments that BANs are expected to operate in. We analyze the data to reveal several link layer characteristics to provide insight and guidelines for the designing of BANs. We also evaluate the performance of common routing metrics on our data.
 Our analysis helps us make the following conclusions. Link PDR is highly affected by the environment and not significantly by the volunteer for the experiment. Routing between nodes on the same side of the body is preferred to routing between nodes on the opposite sides. For links with the same source, failure of packet transmission to a certain node, in some cases, implies the increased probability of reception for other nodes. Most errors occur in bursts of length 1, but a small fraction occur in longer periods ($40$ packets or more).
An Adaptive-Gain Complementary Filter for Real-Time Human Motion Tracking With MARG Sensors in Free-Living Environments

High-resolution, real-time data obtained by human motion tracking systems can be used for gait analysis, which helps better understanding the cause of many diseases for more effective treatments, such as rehabilitation for outpatients or recovery from lost motor functions after a stroke. In order to achieve real-time ambulatory human motion tracking with low-cost MARG (magnetic, angular rate, and gravity) sensors, a computationally efficient and robust algorithm for orientation estimation is critical. This paper presents an analytically derived method for an adaptive-gain complementary filter based on the convergence rate from the Gauss-Newton optimization algorithm (GNA) and the divergence rate from the gyroscope, which is referred as adaptive-gain orientation filter (AGOF) in this paper. The AGOF has the advantages of one iteration calculation to reduce the computing load and accurate estimation of gyroscope measurement error. Moreover, for handling magnetic distortions especially in indoor environments and movements with excessive acceleration, adaptive measurement vectors and a reference vector for earth's magnetic field selection schemes are introduced to help the GNA find more accurate direction of gyroscope error. The features of this approach include the accurate estimation of the gyroscope bias to correct the instantaneous gyroscope measurements and robust estimation in conditions of fast motions and magnetic distortions. Experimental results are presented to verify the performance of the proposed method, which shows better accuracy of orientation estimation than several well-known methods.
CoolSpots: reducing the power consumption of wireless mobile devices with multiple radio interfaces

CoolSpots enable a wireless mobile device to automatically switch between multiple radio interfaces, such as WiFi and Bluetooth, in order to increase battery lifetime. The main contribution of this work is an exploration of the policies that enable a system to switch among these interfaces, each with diverse radio characteristics and different ranges, in order to save power - supported by detailed quantitative measurements. The system and policies do not require any changes to the mobile applications themselves, and changes required to existing infrastructure are minimal. Results are reported for a suite of commonly used applications, such as file transfer, web browsing, and streaming media, across a range of operating conditions. Experimental validation of the CoolSpot system on a mobile research platform shows substantial energy savings: more than a 50% reduction in energy consumption of the wireless subsystem is possible, with an associated increase in the effective battery lifetime.
VANET security surveys

Vehicular ad hoc networks (VANETs), a subset of Mobile Ad hoc NETworks (MANETs), refer to a set of smart vehicles used on the road. These vehicles provide communication services among one another or with Road Side Infrastructure (RSU) based on wireless Local Area Network (LAN) technologies. The main benefits of VANETs are that they enhance road safety and vehicle security while protecting drivers’ privacy from attacks perpetrated by adversaries. Security is one of the most critical issues related to VANETs since the information transmitted is distributed in an open access environment. VANETs face many challenges. This paper presents a survey of the security issues and the challenges they generate. The various categories of applications in VANETs are introduced, as well as some security requirements, threats and certain architectures are proposed to solve the security problem. Finally, global security architecture for VANETs is proposed.
Hybrid music recommender using content-based and social information

Internet resources available today, including songs, albums, playlists or podcasts, that a user cannot discover if there is not a tool to filter the items that the user might consider relevant. Several recommendation techniques have been developed since the Internet explosion to achieve this filtering task. In an attempt to recommend relevant songs to users, we propose an hybrid recommender that considers real-world users information and high-level representation for audio data. We use a deep learning technique, convolutional deep neural networks, to represent an audio segment in a n-dimensional vector, whose dimensions define the probability of the segment to belong to a specific music genre. To capture the listening behavior of a user, we investigate a state-of-the-art technique, estimation of distribution algorithms. The designed hybrid music recommender outperforms the predictions compared with a traditional content-based recommender.
Automated exam question generator using genetic algorithm

Manual preparation of exam questions is a challenging task for educators especially within a short time frame. It requires a lot of time and efforts in order to meet the standard quality of exam questions. This research introduces an automated exam question generator to resolve this issue in preparation of multiple choice exam questions. The generator can auto generate new exam questions set using Genetic Algorithm and covers six levels of Bloom's Taxonomy to produce high quality exam questions that can evaluate different level of learners based on Bloom's cognitive domains and the selection of chapters made by educators. The prototype with 500 sample questions has been run 50 times with different number of chapters selected for each test case. It manages to achieve 90% for the highest exam questions weightage while the average value of exam questions weightage percentage generated is 70%. The lowest exam questions weightage percentage generated is 40%. The result is affected by the smaller number of questions for each Bloom's taxonomy level in questions bank. The automated exam generator can extends to be used for any type of exam questions and it can be used for preparation of quiz or test questions.
Analysis of Flux Leakage in a Segmented Core Brushless Permanent Magnet Motor

This paper presents an analytical model to analyze the leakage flux associated with a brushless permanent magnet motor that utilizes segmented stator core. Due to the presence of a segmented stator structure, the leakage of flux through the tooth tips becomes worthy of analysis and consideration in such a motor. A leakage parameter alpha is developed to show the effects of various design parameters on tooth-tip flux leakage that is essential in accurately predicting the air gap flux density and other output parameters in the motor. Finite-element analysis and experimental verifications are also done to validate the analytical model.
Common mode EMI reduction technique for interleaved MHz critical mode PFC converter with coupled inductor

Coupled inductor has been widely adopted in VR applications for many years because of its benefits such as reducing current ripple and improving transient performance. In this presentation, coupled inductor concept is applied to interleaved MHz totem-pole CRM PFC converter with GaN devices. The coupled inductor in CRM PFC converter can reduce switching frequency variation, help achieving ZVS and reduce circulating energy. Therefore the coupled inductor can improve the efficiency of PFC converter. In addition, balance technique is applied to help minimize CM noise. This paper will introduce how to achieve balance with coupled inductor. The novel PCB winding inductor design will be provided. The PCB winding coupled inductor will have similar loss with litz wire inductor but much less labor in manufacture.
Gaussian process uncertainty in age estimation as a measure of brain abnormality

Multivariate regression models for age estimation are a powerful tool for assessing abnormal brain morphology associated to neuropathology. Age prediction models are built on cohorts of healthy subjects and are built to reflect normal aging patterns. The application of these multivariate models to diseased subjects usually results in high prediction errors, under the hypothesis that neuropathology presents a similar degenerative pattern as that of accelerated aging. In this work, we propose an alternative to the idea that pathology follows a similar trajectory than normal aging. Instead, we propose the use of metrics which measure deviations from the mean aging trajectory. We propose to measure these deviations using two different metrics: uncertainty in a Gaussian process regression model and a newly proposed age weighted uncertainty measure. Consequently, our approach assumes that pathologic brain patterns are different to those of normal aging. We present results for subjects with autism, mild cognitive impairment and Alzheimer's disease to highlight the versatility of the approach to different diseases and age ranges. We evaluate volume, thickness, and VBM features for quantifying brain morphology. Our evaluations are performed on a large number of images obtained from a variety of publicly available neuroimaging databases. Across all features, our uncertainty based measurements yield a better separation between diseased subjects and healthy individuals than the prediction error. Finally, we illustrate differences in the disease pattern to normal aging, supporting the application of uncertainty as a measure of neuropathology.
SecSIFT: Secure Image SIFT Feature Extraction in Cloud Computing

The image and multimedia data produced by individuals and enterprises is increasing every day. Motivated by the advances in cloud computing, there is a growing need to outsource such computational intensive image feature detection tasks to cloud for its economic computing resources and on-demand ubiquitous access. However, the concerns over the effective protection of private image and multimedia data when outsourcing it to cloud platform become the major barrier that impedes the further implementation of cloud computing techniques over massive amount of image and multimedia data. To address this fundamental challenge, we study the state-of-the-art image feature detection algorithms and focus on Scalar Invariant Feature Transform (SIFT), which is one of the most important local feature detection algorithms and has been broadly employed in different areas, including object recognition, image matching, robotic mapping, and so on. We analyze and model the privacy requirements in outsourcing SIFT computation and propose Secure Scalar Invariant Feature Transform (SecSIFT), a high-performance privacy-preserving SIFT feature detection system. In contrast to previous works, the proposed design is not restricted by the efficiency limitations of current homomorphic encryption scheme. In our design, we decompose and distribute the computation procedures of the original SIFT algorithm to a set of independent, co-operative cloud servers and keep the outsourced computation procedures as simple as possible to avoid utilizing a computationally expensive homomorphic encryption scheme. The proposed SecSIFT enables implementation with practical computation and communication complexity. Extensive experimental results demonstrate that SecSIFT performs comparably to original SIFT on image benchmarks while capable of preserving the privacy in an efficient way.
Feature Learning for Detection and Prediction of Freezing of Gait in Parkinson's Disease

Freezing of gait (FoG) is a common gait impairment among patients with advanced Parkinson’s disease. FoG is associated with falls and negatively impact the patient’s quality of life. Wearable systems that detect FoG have been developed to help patients resume walking by means of auditory cueing. However, current methods for automated detection are not yet ideal. In this paper, we first compare feature learning approaches based on time-domain and statistical features to unsupervised ones based on principal components analysis. The latter systematically outperforms the former and also the standard in the field – Freezing Index by up to 8.1% in terms of F1-measure for FoG detection. We go a step further by analyzing FoG prediction, i.e., identification of patterns (pre-FoG) occurring before FoG episodes, based only on motion data. Until now this was only attempted using electroencephalography. With respect to the three-class problem (FoG vs. pre-FoG vs. normal locomotion), we show that FoG prediction performance is highly patient-dependent, reaching an F1-measure of 56% in the pre-FoG class for patients who exhibit enough gait degradation before FoG.
Attendance checker for students of Mapúa University

Attendance checking can be tedious as there are hundreds or even thousands of students to be checked every day whether they attend class or not and instances where checking attendances manually just makes it worse. This design aims to create a method of checking the attendances of students without the use of the traditional pen and paper method. The design made use of the RFID tags within the school ID of the students, Raspberry Pi as the processing unit of the system, and an android application that operates the entire system. The results include successes in the functionalities of the design including registration of students and checking of the attendance itself. It can be concluded that the design's effectiveness enables the professor to check the attendance of the students without the use of the pen and paper method.
Two-stage pooling of deep convolutional features for image retrieval

Convolutional Neural Network (CNN) based image representations have achieved high performance in image retrieval tasks. However, traditional CNN based global representations either provide high-dimensional features, which incurs large memory consumption and computing cost, or inadequately capture discriminative information in images, which degenerates the functionality of CNN features. To address those issues, we propose a two-stage partial mean pooling (PMP) approach to construct compact and discriminative global feature representations. The proposed PMP is meant to tackle the limits of traditional max pooling and mean (or average) pooling. By injecting the PMP pooling strategy into the CNN based patch-level mid-level feature extraction and representation, we have significantly improved the state-of-the-art retrieval performance over several common benchmark datasets.
Mining long-term search history to improve search accuracy

Long-term search history contains rich information about a user's search preferences, which can be used as search context to improve retrieval performance. In this paper, we study statistical language modeling based methods to mine contextual information from long-term search history and exploit it for a more accurate estimate of the query language model. Experiments on real web search data show that the algorithms are effective in improving search accuracy for both fresh and recurring queries. The best performance is achieved when using clickthrough data of past searches that are related to the current query.
Remote control laboratory via Internet using Matlab and Simulink

This article describes the general architecture and application of a remote laboratory for teaching control theory based in Matlab/Simulink. The proposed system allows solving the time and spatial limitations of laboratories that rely on real physical systems used in control courses. In this way, control lab assignments with various physical processes present in the remote laboratories can be performed. Also, some examples that show the validity and applicability of the presented architecture are introduced. 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 18: 694 702, 2010; View this article online at wileyonlinelibrary.com; DOI 10.1002/cae.20274
Dual methods for nonconvex spectrum optimization of multicarrier systems

The design and optimization of multicarrier communications systems often involve a maximization of the total throughput subject to system resource constraints. The optimization problem is numerically difficult to solve when the problem does not have a convexity structure. This paper makes progress toward solving optimization problems of this type by showing that under a certain condition called the time-sharing condition, the duality gap of the optimization problem is always zero, regardless of the convexity of the objective function. Further, we show that the time-sharing condition is satisfied for practical multiuser spectrum optimization problems in multicarrier systems in the limit as the number of carriers goes to infinity. This result leads to efficient numerical algorithms that solve the nonconvex problem in the dual domain. We show that the recently proposed optimal spectrum balancing algorithm for digital subscriber lines can be interpreted as a dual algorithm. This new interpretation gives rise to more efficient dual update methods. It also suggests ways in which the dual objective may be evaluated approximately, further improving the numerical efficiency of the algorithm. We propose a low-complexity iterative spectrum balancing algorithm based on these ideas, and show that the new algorithm achieves near-optimal performance in many practical situations
Complementary Cooperation Algorithm Based on DEKF Combined With Pattern Recognition for SOC/Capacity Estimation and SOH Prediction

Differences in electrochemical characteristics among Li-ion batteries result in erroneous state-of-charge (SOC)/capacity estimation and state-of-health (SOH) prediction when using the existing dual extended Kalman filter (DEKF) algorithm. This paper presents a complementary cooperation algorithm based on DEKF combined with pattern recognition as an application Hamming neural network to the identification of suitable battery model parameters for improved SOC/capacity estimation and SOH prediction. Two kinds of pattern such as discharging/charging voltage pattern (DCVP) and capacity pattern (CP) were measured, together with the battery parameters, as representative patterns. Through statistical analysis, the Hamming network is applied for identification of the representative DCVP and CP that most closely matche that of the arbitrary battery to be measured. The model parameters of the representative battery are then applied for SOC/capacity estimation and SOH prediction of the arbitrary battery using the DEKF. This avoids the need for repeated parameter measurement.
Submodular video hashing: a unified framework towards video pooling and indexing

This paper develops a novel framework for efficient large-scale video retrieval. We aim to find video according to higher level similarities, which is beyond the scope of traditional near duplicate search. Following the popular hashing technique we employ compact binary codes to facilitate nearest neighbor search. Unlike the previous methods which capitalize on only one type of hash code for retrieval, this paper combines heterogeneous hash codes to effectively describe the diverse and multi-scale visual contents in videos. Our method integrates feature pooling and hashing in a single framework. In the pooling stage, we cast video frames into a set of pre-specified components, which capture a variety of semantics of video contents. In the hashing stage, we represent each video component as a compact hash code, and combine multiple hash codes into hash tables for effective search. To speed up the retrieval while retaining most informative codes, we propose a graph-based influence maximization method to bridge the pooling and hashing stages. We show that the influence maximization problem is submodular, which allows a greedy optimization method to achieve a nearly optimal solution. Our method works very efficiently, retrieving thousands of video clips from TRECVID dataset in about 0.001 second. For a larger scale synthetic dataset with 1M samples, it uses less than 1 second in response to 100 queries. Our method is extensively evaluated in both unsupervised and supervised scenarios, and the results on TRECVID Multimedia Event Detection and Columbia Consumer Video datasets demonstrate the success of our proposed technique.
MULTIPLE-ATTRIBUTE TEXT REWRITING

The dominant approach to unsupervised “style transfer” in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its “style”. In this paper, we show that this condition is not necessary and is not always met in practice, even with domain adversarial training that explicitly aims at learning such disentangled representations. We thus propose a new model that controls several factors of variation in textual data where this condition on disentanglement is replaced with a simpler mechanism based on back-translation. Our method allows control over multiple attributes, like gender, sentiment, product type, etc., and a more fine-grained control on the trade-off between content preservation and change of style with a pooling operator in the latent space. Our experiments demonstrate that the fully entangled model produces better generations, even when tested on new and more challenging benchmarks comprising reviews with multiple sentences and multiple attributes.
A Dynamic Load Balance Algorithm for the S4 Parallel Stream Processing Engine

Large streams of data can be analyzed in realtimeby Parallel Stream Processing Engines (PSPEs) which arebased on a graph paradigm where vertices represent processingelements (PEs) and edges represent flows of data among PEs. Inthis work, we propose a new elastic strategy for the S4 PSPE toadjust the overall load of PEs in accordance with the utilizationlevels and data traffic at each PE. Our approach exploits aproducer/consumer model to achieve load balance where newworkers pull events from a buffer queue in order to release theamount of traffic in an overloaded PE. Results show that theproposed strategy prevents saturation of PEs and improves theoverall throughput of the system by up to 470%.