nils-herrmann/clean_scirepeval huggingface dataset:
Machine learning based QoE prediction in SDN networks

Our work in this paper presents a prediction of quality of experience based on full reference parametric (SSIM, VQM) and application metrics (resolution, bit rate, frame rate) in SDN networks. First, we used DCR (Degradation Category Rating) as subjective method to build the training model and validation, this method is based on not only the quality of received video but also the original video but all subjective methods are too expensive, don't take place in real time and takes much time for example our method takes three hours to determine the average MOS (Mean Opinion Score). That's why we proposed novel method based on machine learning algorithms to obtain the quality of experience in an objective manner. Previous researches in this field help us to use four algorithms: Decision Tree (DT), Neural Network, K nearest neighbors KNN and Random Forest RF thanks to their efficiency. We have used two metrics recommended by VQEG group to assess the best algorithm: Pearson correlation coefficient r and Root-Mean-Square-Error RMSE. The last part of the paper describes environment based on: Weka to analyze ML algorithms, MSU tool to calculate SSIM and VQM and Mininet for the SDN simulation.
Evaluating the Potential of Texture and Color Descriptors for Remote Sensing Image Retrieval and Classification

Classifying Remote Sensing Images (RSI) is a hard task. There are automatic approaches whose results normally need to be revised. The identification and polygon extraction tasks usually rely on applying classification strategies that exploit visual aspects related to spectral and texture patterns identified in RSI regions. There are a lot of image descriptors proposed in the literature for content-based image retrieval purposes that may be useful for RSI classification. This paper presents a comparative study to evaluate the potential of using successful color and texture image descriptors for remote sensing retrieval and classification. Seven descriptors that encode texture information and twelve color descriptors that can be used to encode spectral information were selected. We perform experiments to evaluate the effectiveness of these descriptors, considering image retrieval and classification tasks. To evaluate descriptors in classification tasks, we also propose a methodology based on KNN classifier. Experiments demonstrate that Joint Auto-Correlogram (JAC), Color Bitmap, Invariant Steerable Pyramid Decomposition (SID) and Quantized Compound Change Histogram (QCCH) yield the
Noise Analysis, Calculation, and Reduction of External Rotor Permanent-Magnet Synchronous Motor

A detailed analysis of electromagnetic noise in external rotor permanent-magnet synchronous motors is presented in this paper. First, the spatial distribution and frequency characteristics of the electromagnetic force acting on the surface of the permanent magnet are discussed. Then, calculation models for electromagnetic force, structural vibration, and acoustic radiation are developed to predict noise by taking an external rotor in-wheel motor as example. The uneven distribution of electromagnetic force on the surface of the permanent magnet is taken into account by means of loading nodal force into the structural model which is verified by modal test. Through the mode superposition method, the vibration on the surface of the outer rotor is calculated, and the acoustic boundary element method is used to predict the acoustic radiation. Noise test is conducted to validate the simulated noise. It is shown in both simulation results and noise test that the electromagnetic force due to slotting effects contributes the most remarkable component to the overall noise. Finally, slot opening width is optimized to reduce the amplitude of magnetic force close to resonance frequencies, and the overall sound pressure level decreases by 6 dB(A) after optimization.
Snow White: Provably Secure Proofs of Stake

Decentralized cryptocurrencies have pushed deployments of distributed consensus to more stringent environments than ever before. Most existing protocols rely on proofs-of-work which require expensive computational puzzles to enforce, imprecisely speaking, “one vote per unit of computation”. The enormous amount of energy wasted by these protocols has been a topic of central debate, and well-known cryptocurrencies have announced it a top priority to alternative paradigms. Among the proposed alternative solutions, proofs-of-stake protocols have been of particular interest, where roughly speaking, the idea is to enforce “one vote per unit of stake”. Although the community have rushed to propose numerous candidates for proofs-of-stake, no existing protocol has offered formal proofs of security, which we believe to be a critical, indispensible ingredient of a distributed consensus protocol, particularly one that is to underly a high-value cryptocurrency system. In this work, we seek to address the following basic questions: • What kind of functionalities and robustness requirements should a consensus candidate offer to be suitable in a proof-of-stake application? • Can we design a provably secure protocol that satisfies these requirements? To the best of our knowledge, we are the first to formally articulate a set of requirements for consensus candidates for proofs-of-stake. We argue that any consensus protocol satisfying these properties can be used for proofs-of-stake, as long as money does not switch hands too quickly. Moreover, we provide the first consensus candidate that provably satisfies the desired robustness properties.
A cooperative perception system for multiple UAVs: Application to automatic detection of forest fires

This paper presents a cooperative perception system for multiple heterogeneous UAVs. It considers different kind of sensors: infrared and visual cameras and fire detectors. The system is based on a set of multipurpose low-level image-processing functions including segmentation, stabilization of sequences of images and geo-referencing, and it also involves data fusion algorithms for cooperative perception. It has been tested in field experiments that pursued autonomous multi-UAV cooperative detection, monitoring and measurement of forest fires. This paper presents the overall architecture of the perception system, describes some of the implemented cooperative perception techniques and shows experimental results on automatic forest fire detection and localization with cooperating UAVs.
From Vulnerability to Growth : Positive and Negative Effects of Stressful Life Events

Severely stressful life events can have a substantial impact on those who experience them. For some, experience with a traumatic life event can leave them confused, withdrawn, depressed, and increasingly vulnerable to the next stressful situation that arises. The clinical literature, for example, has found various stressful life events to be risk factors for the development of depression, anxiety, and in extreme cases, posttraumatic stress disorder (PTSD). For other individuals, a traumatic experience can serve as a catalyst for positive change, a chance to reexamine life priorities or develop strong ties with friends and family. Recent research has explored the immediate and long-term positive effects of similarly severe life events, such as cancer, bereavement, and HIV-infection, to identify the factors and processes that appear to contribute to resilience and growth. These two lines of research, however, have developed largely independent of each other and a number of questions remain to be explored in their integration. For example, do the roots of these apparently divergent patterns lie in the events themselves or in the people who experience them? Do some experiences typically lead to negative outcomes, whereas others contribute to the development of positive changes? What psychological factors appear to moderate these outcomes? How do positive outcomes, such as perceptions of stress-related growth and benefit, relate to measures of negative adjustment? To address these questions, we begin with a review of positive outcomes that have been reported in response to stressful life events, such as the perceptions of stressrelated growth and benefit and theories that help to explain these changes. We then
A unifying view on dataset shift in classification

The field of dataset shift has received a growing amount of interest in the last few years. The fact that most real-world applications have to cope with some form of shift makes its study highly relevant. The literature on the topic is mostly scattered, and different authors use different names to refer to the same concepts, or use the same name for different concepts. With this work, we attempt to present a unifying framework through the review and comparison of some of the most important works in the
Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt

In this study, maximum likelihood supervised classification and post-classification change detection techniques were applied to Landsat images acquired in 1987 and 2001, respectively, to map land cover changes in the Northwestern coast of Egypt. A supervised classification was carried out on the six reflective bands for the two images individually with the aid of ground truth data. Ground truth information collected during six field trips conducted between 1998 and 2002 and land cover map of 1987 were used to assess the accuracy of the classification results. Using ancillary data, visual interpretation and expert knowledge of the area through GIS further refined the classification results. Post-classification change detection technique was used to produce change image through cross-tabulation. Changes among different land cover classes were assessed. During the study period, a very severe land cover change has Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.
Radio signal propagation and attenuation measurements for modern residential buildings

This paper outlines problems that cellular network operators will face as energy-efficient housing becomes more popular. We report measurement results from houses made of modern construction materials that are required to achieve sufficient level of energy-efficiency, but that impact heavily also on radio signal propagation. Energy-efficiency is especially important in northern countries, where houses need to be properly isolated as heating generates a big share of the total energy consumption of households. However, the energy-efficiency trend will also reach rest of the Europe and other warmer countries as the tightening energy-efficiency requirements concern also cooling the houses. The measurement results indicate severe problems originating from radio signal attenuation as it increases up to 35 dB for individual construction materials for cellular frequencies around 2 GHz. From the perspective of actual building penetration losses in modern, energy-efficient houses, average attenuation values even up to 30 dB have been measured. Additional attenuation is very sensitive to buildings materials, but could jeopardize cellular coverage in the future.
A General Framework for Temporal Calibration of Multiple Proprioceptive and Exteroceptive Sensors

Fusion of data from multiple sensors can enable robust navigation in varied environments. However, for optimal performance, the sensors must calibrated relative to one another. Full sensor-to-sensor calibration is a spatiotemporal problem: we require an accurate estimate of the relative timing of measurements for each pair of sensors, in addition to the 6-DOF sensor-to-sensor transform. In this paper, we examine the problem of determining the time delays between multiple proprioceptive and exteroceptive sensor data streams. The primary difficultly is that the correspondences between measurements from different sensors are unknown, and hence the delays cannot be computed directly. We instead formulate temporal calibration as a registration task. Our algorithm operates by aligning curves in a three-dimensional orientation space, and, as such, can be considered as a variant of Iterative Closest Point (ICP). We present results from simulation studies and from experiments with a PR2 robot, which demonstrate accurate calibration of the time delays between measurements from multiple, heterogeneous sensors.
A broadband CPW-fed bow-tie slot antenna

A broadband coplanar waveguide (CPW) fed bow-tie slot antenna is proposed. By using a linear tapered transition, a 37% impedance bandwidth at -10 dB return loss is achieved. The antenna structure is very simple and the radiation patterns of the antenna in the whole bandwidth remain stable; moreover, the cross-polarization level is lower. An antenna model is fabricated on a high dielectric constant substrate. Experiments show that the simulated results agree well with the measured ones.
On the Capacity of Radio Communication Systems with Diversity in a Rayleigh Fading Environment

In this paper, we study the fundamental limits on the data rate of multiple antenna systems in a Rayleigh fading environment. With M transmit and M receive antennas, up to M independent channels can be established in the same bandwidth. We study the distribution of the maximum data rate at a given error rate in the channels between up to M transmit antennas and M receive antennas and determine the outage probability for systems that use various signal processing techniques. We analyze the performance of the optimum linear and nonlinear receiver processor and the optimum linear transmitter/ receiver processor pair, and the capacity of these channels. Results show that with optimum linear processing at the receiver, up to M / 2 channels can be established with approximately the same maximum data rate as a single channel. With either nonlinear processing at the receiver or optimum linear transmitterheceiver processing, up to M channels can be established with approximately the same maximum data rate as a single channel. Results show the potential for large capacity in systems with limited bandwidth.
ATHENA: An Ontology-Driven System for Natural Language Querying over Relational Data Stores

In this paper, we present ATHENA, an ontology-driven system for natural language querying of complex relational databases. Natural language interfaces to databases enable users easy access to data, without the need to learn a complex query language, such as SQL. ATHENA uses domain specific ontologies, which describe the semantic entities, and their relationships in a domain. We propose a unique two-stage approach, where the input natural language query (NLQ) is first translated into an intermediate query language over the ontology, called OQL, and subsequently translated into SQL. Our two-stage approach allows us to decouple the physical layout of the data in the relational store from the semantics of the query, providing physical independence. Moreover, ontologies provide richer semantic information, such as inheritance and membership relations, that are lost in a relational schema. By reasoning over the ontologies, our NLQ engine is able to accurately capture the user intent. We study the effectiveness of our approach using three different workloads on top of geographical (GEO), academic (MAS) and financial (FIN) data. ATHENA achieves 100% precision on the GEO and MAS workloads, and 99% precision on the FIN workload which operates on a complex financial ontology. Moreover, ATHENA attains 87.2%, 88.3%, and 88.9% recall on the GEO, MAS, and FIN workloads, respectively.
Perception of short tactile pulses generated by a vibration motor in a mobile phone

This paper describes an experimental setup and results of user tests focusing on the perception of temporal characteristics of vibration of a mobile device. The experiment consisted of six vibration stimuli of different length. We asked the subjects to score the subjective perception level in a five point Lickert scale. The results suggest that the optimal duration of the control signal should be between 50 and 200 ms in this specific case. Longer durations were perceived as being irritating.
What Recommenders Recommend - An Analysis of Accuracy, Popularity, and Sales Diversity Effects

In academic studies, the evaluation of recommender system (RS) algorithms is often limited to offline experimental designs based on historical data sets and metrics from the fields of Machine Learning or Information Retrieval. In real-world settings, however, other businessoriented metrics such as click-through-rates, customer retention or effects on the sales spectrum might be the true evaluation criteria for RS effectiveness. In this paper, we compare different RS algorithms with respect to their tendency of focusing on certain parts of the product spectrum. Our first analysis on different data sets shows that some algorithms – while able to generate highly accurate predictions – concentrate their top 10 recommendations on a very small fraction of the product catalog or have a strong bias to recommending only relatively popular items than others. We see our work as a further step toward multiple-metric offline evaluation and to help service providers make better-informed decisions when looking for a recommendation strategy that is in line with the overall goals of the recommendation service.
A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor

Accurate estimation of heart rates from photoplethysmogram (PPG) signals during intense physical activity is a very challenging problem. This is because strenuous and high intensity exercise can result in severe motion artifacts in PPG signals, making accurate heart rate (HR) estimation difficult. In this study we investigated a novel technique to accurately reconstruct motion-corrupted PPG signals and HR based on time-varying spectral analysis. The algorithm is called Spectral filter algorithm for Motion Artifacts and heart rate reconstruction (SpaMA). The idea is to calculate the power spectral density of both PPG and accelerometer signals for each time shift of a windowed data segment. By comparing time-varying spectra of PPG and accelerometer data, those frequency peaks resulting from motion artifacts can be distinguished from the PPG spectrum. The SpaMA approach was applied to three different datasets and four types of activities: (1) training datasets from the 2015  Database recorded from 12 subjects while performing treadmill exercise from 1 km/h to 15 km/h; (2) test datasets from the 2015  Database recorded from 11 subjects while performing forearm and upper arm exercise. (3) Chon Lab dataset including 10 min recordings from 10 subjects during treadmill exercise. The ECG signals from all three datasets provided the reference HRs which were used to determine the accuracy of our SpaMA algorithm. The performance of the SpaMA approach was calculated by computing the mean absolute error between the estimated HR from the PPG and the reference HR from the ECG. The average estimation errors using our method on the first, second and third datasets are 0.89, 1.93 and 1.38 beats/min respectively, while the overall error on all 33 subjects is 1.86 beats/min and the performance on only treadmill experiment datasets (22 subjects) is 1.11 beats/min. Moreover, it was found that dynamics of heart rate variability can be accurately captured using the algorithm where the mean Pearson's correlation coefficient between the power spectral densities of the reference and the reconstructed heart rate time series was found to be 0.98. These results show that the SpaMA method has a potential for PPG-based HR monitoring in wearable devices for fitness tracking and health monitoring during intense physical activities.
Remote heart rate measurement using low-cost RGB face video: a technical literature review

Remote photoplethysmography (rPPG) allows remote measurement of the heart rate using low-cost RGB imaging equipment. In this study, we review the development of the field of rPPG since its emergence in 2008. We also classify existing rPPG approaches and derive a framework that provides an overview of modular steps. Based on this framework, practitioners can use our classification to design algorithms for an rPPG approach that suits their specific needs. Researchers can use the reviewed and classified algorithms as a starting point to improve particular features of an rPPG algorithm.
Deep Cascaded Bi-Network for Face Hallucination

We present a novel framework for hallucinating faces of unconstrained poses and with very low resolution (face size as small as 5pxIOD). In contrast to existing studies that mostly ignore or assume pre-aligned face spatial configuration (e.g. facial landmarks localization or dense correspondence field), we alternatingly optimize two complementary tasks, namely face hallucination and dense correspondence field estimation, in a unified framework. In addition, we propose a new gated deep bi-network that contains two functionality-specialized branches to recover different levels of texture details. Extensive experiments demonstrate that such formulation allows exceptional hallucination quality on in-the-wild low-res faces with significant pose and illumination variations.
Leveraging the Wisdom of the Crowd for Fine-Grained Recognition

Fine-grained recognition concerns categorization at sub-ordinate levels, where the distinction between object classes is highly local. Compared to basic level recognition, fine-grained categorization can be more challenging as there are in general less data and fewer discriminative features. This necessitates the use of a stronger prior for feature selection. In this work, we include humans in the loop to help computers select discriminative features. We introduce a novel online game called “Bubbles” that reveals discriminative features humans use. The player's goal is to identify the category of a heavily blurred image. During the game, the player can choose to reveal full details of circular regions (“bubbles”), with a certain penalty. With proper setup the game generates discriminative bubbles with assured quality. We next propose the “BubbleBank” representation that uses the human selected bubbles to improve machine recognition performance. Finally, we demonstrate how to extend BubbleBank to a view-invariant 3D representation. Experiments demonstrate that our approach yields large improvements over the previous state of the art on challenging benchmarks.