nils-herrmann/clean_scirepeval huggingface dataset:
User Association for Load Balancing in Vehicular Networks: An Online Reinforcement Learning Approach

Recently, a number of technologies have been developed to promote vehicular networks. When vehicles are associated with the heterogeneous base stations (e.g., macrocells, picocells, and femtocells), one of the most important problems is to make load balancing among these base stations. Different from common mobile networks, data traffic in vehicular networks can be observed having regularities in the spatial–temporal dimension due to the periodicity of urban traffic flow. By taking advantage of this feature, we propose an online reinforcement learning approach, called ORLA. It is a distributed user association algorithm for network load balancing in vehicular networks. Based on the historical association experiences, ORLA can obtain a good association solution through learning from the dynamic vehicular environment continually. In the long run, the real-time feedback and the regular traffic association patterns both help ORLA cope with the dynamics of network well. In experiments, we use QiangSheng taxi movement to evaluate the performance of ORLA. Our experiments verify that ORLA has higher quality load balancing compared with other popular association methods.
Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects

Braincomputer interfaces (BCIs) allow their users to communicate or control external devices using brain signals rather than the brain's normal output pathways of peripheral nerves and muscles. Motivated by the hope of restoring independence to severely disabled individuals and by interest in further extending human control of external systems, researchers from many fields are engaged in this challenging new work. BCI research and development has grown explosively over the past two decades. Efforts have begun recently to provide laboratory-validated BCI systems to severely disabled individuals for real-world applications. In this paper, we discuss the current status and future prospects of BCI technology and its clinical applications. We will define BCI, review the BCI-relevant signals from the human brain, and describe the functional components of BCIs. We will also review current clinical applications of BCI technology and identify potential users and potential applications. Lastly, we will discuss current limitations of BCI technology, impediments to its widespread clinical use, and expectations for the future.
Social marketing: an approach to planned social change.

This article examines the applicability of marketing concepts to social causes and social change. Social marketing is defined as the design, implementation, and control of programs calculated to influence the acceptability of social ideas and involving considerations of product planning, pricing, communication, distribution and marketing research. Wiebe examined four social advertising campaigns and concluded that their effectiveness depended on the presence of adequate force, direction, adequate and compatible social mechanism, and distance (the "cost" of the new attitude as seen by message's message"s recepient). A marketing planning approach is not a guarantee for the achievement of social objectives; yet, it represents a bridging mechanism linking the knowledge of the behavioral scientist with the socially useful implementation of that knowledge.
Application of Machine Learning for Channel based Message Authentication in Mission Critical Machine Type Communication

The design of robust wireless communication systems for industrial applications such as closed loop control processes has been considered manifold recently. Additionally, the ongoing advances in the area of connected mobility have similar or even higher requirements regarding system reliability and availability. Beside unfulfilled reliability requirements, the availability of a system can further be reduced, if it is under attack in the sense of violation of information security goals such as data authenticity or integrity. In order to guarantee the safe operation of an application, a system has at least to be able to detect these attacks. Though there are numerous techniques in the sense of conventional cryptography in order to achieve that goal, these are not always suited for the requirements of the applications mentioned due to resource inefficiency. In the present work, we show how the goal of message authenticity based on physical layer security (PHYSEC) can be achieved. The main idea for such techniques is to exploit user specific characteristics of the wireless channel, especially in spatial domain. Additionally, we show the performance of our machine learning based approach and compare it with other existing approaches.
Transarterial degradation of hyaluronic acid filler by hyaluronidase.

Hyaluronidase (HYAL) has been recommended in the emergency treatment of ischemia caused by accidental intra-arterial injection of hyaluronic acid (HA) dermal fillers. To date, there have been no published studies showing that HYAL can pass through intact arterial wall to hydrolyze HA emboli.



The goal of this study was to study whether or not HYAL could cross intact human facial arterial wall to hydrolyze HA filler.



Short tied-off segments of fresh human cadaver-sourced facial artery specimens, overfilled with a monophasic dermal filler (dermal filler "sausages"), were immersed in either HYAL or normal saline as controls. At 4 and 24 hours, the vessels were removed from the preparations, and one end of each vessel was cut open.



Only the HYAL-immersed specimens showed degradation of filler gel.



In conclusion, cross-linked HA is susceptible to hydrolysis by HYAL when contained within the intact facial artery in a cadaver model, indicating that direct intra-arterial injection of HYAL is likely not necessary to help restore the circulation of ischemic tissues. This bench study provides support for the current recommended treatment of accidental intra-arterial injection with HYAL injection diffusely into ischemic tissues.
ON EDUCATIONAL DATA MINING TECHNIQUES

The educational data mining is emerging field that focuses on analyzing educational data to develop models for improving learning experiences and improving institutional effectiveness. Increasing interest in data mining and educational systems, make educational data mining as a new growing research community. Educational Data Mining means to extract the hidden knowledge from large educational databases with the use of techniques and tools. Educational Data Mining develops new methods to discover knowledge from educational database and it is used for decision making in educational system. In this paper we focus on comparative analysis of various educational data mining techniques with their algorithms. We compare the accuracy of these techniques with their algorithms on weka tool. The compared techniques and algorithms are presented together with some experimental data that give rise to the final conclusion.  Association Rule, Classification, Clustering, Educational Data Mining (EDM), KDED (Knowledge Discovery In Educational Databases), Web Mining, Weka.
Detection of Glaucoma from Retinal Fundus Images by analysing ISNT Measurement and features of Optic Cup and Blood Vessels

Glaucoma is an ocular syndrome caused due to increased fluid pressure called Intraocular Pressure (IOP) in the optic nerve. It blocks the transmission of visual information to the brain which results partial or total vision loss.Glaucoma is one of the 2nd most leading causes of blindness. Glaucoma can be characterized by changes in Optic Nerve Head (ONH) structure&Retinal Nerve FibreLayer (RNFL). Optic Nerve Head can be analysed for its Cup Shape, Notching and Cup to Disc Ratio to detect symptoms of glaucoma. RNFL is the first to be affectedby Glaucoma disease. RNFL thickness can be found from Optical Coherence Tomography (OCT) test but it is still not widely used due to high cost and less availability. Another approach is to analyseOptic Nerve Headatrophy from images taken by normal Ophthalmoscope (Fundus Camera) to extractfeatures of ONH andanalyse morphological changes which are significant signs of glaucoma.Inferior Superior Nasal Temporal (ISNT) measures can be calculated by extracting circular edges of Optic Cup and Optic Disc as one of the important features of ONH.In this paper, we have proposed new algorithm to extract Optic disc and Optic Cup from fundus images which is used to analyse changes in ONH. This will help in classifying the image into Healthy or Glaucomatous.
Model-Free Segmentation and Grasp Selection of Unknown Stacked Objects

We present a novel grasping approach for unknown stacked objects using RGB-D images of highly complex real-world scenes. Specifically, we propose a novel 3D segmentation algorithm to generate an efficient representation of the scene into segmented surfaces (known as surfels) and objects. Based on this representation, we next propose a novel grasp selection algorithm which generates potential grasp hypotheses and automatically selects the most appropriate grasp without requiring any prior information of the objects or the scene. We tested our algorithms in real-world scenarios using live video streams from Kinect and publicly available RGB-D object datasets. Our experimental results show that both our proposed segmentation and grasp selection algorithms consistently perform superior compared to the state-of-the-art methods.
Perceptron learning for reuse prediction

The disparity between last-level cache and memory latencies motivates the search for efficient cache management policies. Recent work in predicting reuse of cache blocks enables optimizations that significantly improve cache performance and efficiency. However, the accuracy of the prediction mechanisms limits the scope of optimization. This paper proposes perceptron learning for reuse prediction. The proposed predictor greatly improves accuracy over previous work. For multi-programmed workloads, the average false positive rate of the proposed predictor is 3.2%, while sampling dead block prediction (SDBP) and signature-based hit prediction (SHiP) yield false positive rates above 7%. The improvement in accuracy translates directly into performance. For single-thread workloads and a 4MB last-level cache, reuse prediction with perceptron learning enables a replacement and bypass optimization to achieve a geometric mean speedup of 6.1%, compared with 3.8% for SHiP and 3.5% for SDBP on the SPEC CPU 2006 benchmarks. On a memory-intensive subset of SPEC, perceptron learning yields 18.3% speedup, versus 10.5% for SHiP and 7.7% for SDBP. For multi-programmed workloads and a 16MB cache, the proposed technique doubles the efficiency of the cache over LRU and yields a geometric mean normalized weighted speedup of 7.4%, compared with 4.4% for SHiP and 4.2% for SDBP.
Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging

Environmental audio tagging aims to predict only the presence or absence of certain acoustic events in the interested acoustic scene. In this paper, we make contributions to audio tagging in two parts, respectively, acoustic modeling and feature learning. We propose to use a shrinking deep neural network DNN framework incorporating unsupervised feature learning to handle the multilabel classification task. For the acoustic modeling, a large set of contextual frames of the chunk are fed into the DNN to perform a multilabel classification for the expected tags, considering that only chunk or utterance level rather than frame-level labels are available. Dropout and background noise aware training are also adopted to improve the generalization capability of the DNNs. For the unsupervised feature learning, we propose to use a symmetric or asymmetric deep denoising auto-encoder syDAE or asyDAE to generate new data-driven features from the logarithmic Mel-filter banks features. The new features, which are smoothed against background noise and more compact with contextual information, can further improve the performance of the DNN baseline. Compared with the standard Gaussian mixture model baseline of the DCASE 2016 audio tagging challenge, our proposed method obtains a significant equal error rate EER reduction from 0.21 to 0.13 on the development set. The proposed asyDAE system can get a relative 6.7% EER reduction compared with the strong DNN baseline on the development set. Finally, the results also show that our approach obtains the state-of-the-art performance with 0.15 EER on the evaluation set of the DCASE 2016 audio tagging task while EER of the first prize of this challenge is 0.17.
Wideband Low-Loss Linear and Circular Polarization Transmit-Arrays in V-Band

Several linearly-polarized and circularly-polarized transmit-arrays are designed and demonstrated in the 60-GHz band. These arrays have a fairly simple structure with three metal layers and are fabricated with a standard printed-circuit board technology. The simulation method is based on an electromagnetic model of the focal source and the unit-cells, associated to an analytical modeling of the full structure. A theoretical analysis is presented for the optimization of the power budget with respect to the F/D ratio. Several prototypes are designed and characterized in V-band. The experimental results are in very good agreement with the simulations and demonstrate very satisfactory characteristics. Power efficiencies of 50-61% are reached with a 1-dB gain bandwidth up to 7%, and low cross-polarization level.
Human Visual Search as a Deep Reinforcement Learning Solution to a POMDP

When people search for a target in a novel image they often make use of eye movements to bring the relatively high acuity fovea to bear on areas of interest. The strategies that control these eye movements for visual search have been of substantial scientific interest. In the current article we report a new computational model that shows how strategies for visual search are an emergent consequence of perceptual/motor constraints and approximately optimal strategies. The model solves a Partially Observable Markov Decision Process (POMDP) using deep Q-learning to acquire strategies that optimise the tradeoff between speed and accuracy. Results are reported for the Distractor-ratio task.
Inverse Reinforcement Learning

Recently researches on imitation learning have shown that Markov Decision Processes (MDPs) are a powerful way to characterize this problem. Inverse reinforcement learning tries to describe observed behavior by ascertaining a reward function (or respectively a cost function) by solving a Markov Decision Problem. This paper shows three different approaches to find an optimal policy which mimics observed behavior. The differences and issues will be pointed out and compared on some applications. The first approach handles different cases in which the policy and states are finite and known, the state size is continuous, and the policy is only known through a finite set of observed trajectories. The second approach LEARCH extends Maximum Margin Planning and is simpler to implement like many other approaches while satisfying constraints on the cost function in a more naturally way. The last approach is based on the principle of maximum entropy and reduces learning to the problem of recovering utility function that closely mimics demonstrated behavior.
Single Stage Solar PV Fed Brushless DC Motor Driven Water Pump

In order to optimize the solar photovoltaic (PV) generated power using a maximum power point tracking technique, a dc–dc conversion stage is usually required in solar PV fed water pumping which is driven by a brushless dc (BLDC) motor. This power conversion stage leads to an increased cost, size, complexity, and reduced efficiency. As a unique solution, this paper addresses a single stage solar PV energy conversion system feeding a BLDC motor-pump, which eliminates the dc–dc conversion stage. A simple control technique capable of operating the solar PV array at its peak power using a common voltage source inverter is proposed for BLDC motor control. The proposed control eliminates the BLDC motor phase current sensors. No supplementary control is associated for the speed control of motor-pump and its soft start. The speed is controlled through the optimum power of solar PV array. The suitability of proposed system is manifested through its performance evaluation using MATLAB/Simulink-based simulated results and experimental validation on a developed prototype, under the practical operating conditions.
Process reengineering in the public sector : learning some private sector lessons

The possible applicability of business process reengineering (BPR) to organisations in the public sector is explored through analysis of the central issues in BPR and the emerging experience of organisations which have recently implemented it. In particular, the paper suggests that success of reengineering may depend critically on the strategic capability of the organisation prior to undertaking the effort. For that reason well-performing organisations are more likely to improve performance by means of BPR than are weak ones. Yet, in the public sector, it tends to be badly performing agencies which are most encouraged to undertake BPR. Knowing and understanding the reasons for success or failure of BPR in private organisations can prepare public sector managers for undertaking the effort, but each reengineering initiative must be tailored to the specific needs and circumstances of the individual agency. Public sector managers should use the widest possible definition of 'value' when analysing value-added in process reengineering and should be especially sensitive to the way in which 'value' in the public sector is differently interpreted by major stakeholders. During this learning process, public sector agencies would be well advised to be conservative in estimating gains from BPR.
LIP ACTIVITY DETECTION FOR TALKING FACES CLASSIFICATION IN TV-CONTENT

Our objective is to index people in a TV-Content. In this context, because of multi-face shots and non-speaking face shots, it is difficult to determine which face is speaking. There is no guaranteed synchronization between sequences of a person’s appearance and sequences of his or her speech. In this work, we want to separate talking and non-talking faces by detecting lip motion. We propose a method to detect the lip motion by measuring the degree of disorder of pixel directions around the lip. Results of experiments on a TV-Show database show that a high correct classification rate can be achieved by the proposed method.
An Improvement of AdaBoost to Avoid Overfitting

Recent work has shown that combining multiple versions of weak classiiers such as decision trees or neural networks results in reduced test set error. To study this in greater detail, we analyze the asymptotic behavior of AdaBoost. The theoretical analysis establishes the relation between the distribution of margins of the training examples and the generated voting classiication rule. The paper shows asymptotic experimental results with RBF networks for the binary classi-cation case underlining the theoretical ndings. Our experiments show that AdaBoost does overrt, indeed. In order to avoid this and to get better generalization performance, we propose a regularized improved version of AdaBoost, which is called AdaBoostreg. We show the usefulness of this improvement in numerical simulations.
PersuAIDE ! An Adaptive Persuasive Text Generation System for Fashion Domain

Persuasiveness is a creative art which aims at inducing certain set of beliefs in the target audience. In an e-commerce seing, for a newly launched product, persuasive descriptions are oen composed to motivate an online buyer towards a successful purchase. Such descriptions can be catchy taglines, product-summaries, style-tips etc.. In this paper, we present PersuAIDE! a persuasive system based on linguistic creativity to generate various forms of persuasive sentences from the input product specication. To demonstrate the eectiveness of the proposed system, we have applied the technology to fashion domain, where, for a given fashion product like ”red collar shirt” we were able to generate descriptive sentences that not only explain the item but also garner positive aention, making it persuasive. PersuAIDE! identies fashion related keywords from input specications and intelligently expands the keywords to creative phrases. Once such compatible phrases are obtained, persuasive descriptions are synthesized from the set of phrases and input keywords with the help of a neural language model trained on a large domainspecic fashion corpus. We evaluate the system on a large fashion corpus collected from dierent sources using (a) automatic text generation metrics used for Machine Translation and Automatic Summarization evaluation and Readability measurement, and (b) human judgment scores evaluating the persuasiveness and uency of the generated text. Experimental results and qualitative analysis show that an unsupervised system like ours can produce more creative and beer constructed persuasive output than supervised generative counterparts based on neural sequence-to-sequence models and statistical machine translation.
Driver Identification: a Time Series Classification Approach

In the last years, researchers and auto vehicle developers are focusing their attention on the topic of driver identification encouraged by an increasing number of always more sophisticated vehicle sensors able to extract information about the driver. Indeed, driver identification can be very useful to customize and improve driver experience, increase the safety on the road and reduce the global environmental problems. In this work, we propose and explore a set of features (extracted from a car monitoring system) identifying the driver basing on his/her driving behavior. The proposed behavioral features are exploited using a time series classification approach and a multi-layer perceptron (MLP) network is used to evaluate the ability of the proposed features to identify the vehicle driver. The proposed features are tested on a real data set composed of totally 66 observations (each observation consists of a given person driving a given car on a predefined path). The obtained results show that the proposed features have effective driver identification ability.
Gated Residual Networks With Dilated Convolutions for Monaural Speech Enhancement

For supervised speech enhancement, contextual information is important for accurate mask estimation or spectral mapping. However, commonly used deep neural networks DNNs are limited in capturing temporal contexts. To leverage long-term contexts for tracking a target speaker, we treat speech enhancement as a sequence-to-sequence mapping, and present a novel convolutional neural network CNN architecture for monaural speech enhancement. The key idea is to systematically aggregate contexts through dilated convolutions, which significantly expand receptive fields. The CNN model additionally incorporates gating mechanisms and residual learning. Our experimental results suggest that the proposed model generalizes well to untrained noises and untrained speakers. It consistently outperforms a DNN, a unidirectional long short-term memory LSTM model, and a bidirectional LSTM model in terms of objective speech intelligibility and quality metrics. Moreover, the proposed model has far fewer parameters than DNN and LSTM models.