nils-herrmann/clean_scirepeval huggingface dataset:
Big Data Perspective and Challenges in Next Generation Networks

With the development towards the next generation cellular networks, i.e., 5G, the focus has shifted towards meeting the higher data rate requirements, potential of micro cells and millimeter wave spectrum. The goals for next generation networks are very high data rates, low latency and handling of big data. The achievement of these goals definitely require newer architecture designs, upgraded technologies with possible backward support, better security algorithms and intelligent decision making capability. In this survey, we identify the opportunities which can be provided by 5G networks and discuss the underlying challenges towards implementation and realization of the goals of 5G. This survey also provides a discussion on the recent developments made towards standardization, the architectures which may be potential candidates for deployment and the energy concerns in 5G networks. Finally, the paper presents a big data perspective and the potential of machine learning for optimization and decision making in 5G networks.
Automated Feature Selection and Churn Prediction using Deep Learning Models

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract – In this competitive world, mobile telecommunications market tends to reach a saturation state and faces a fierce competition. This situation forces the telecom companies to focus their attention on keeping the customers intact instead of building a large customer base. According to telecom market, the process of subscribers (either prepaid or postpaid) switching from a service provider is called customer churn. Several predictive models have been proposed in the literature for churn prediction. The efficiency of any churn prediction model depends highly on the selection of customer attributes (feature selection) from the dataset for its model construction. These traditional methods have two major problems: 1) With hundreds of customer attributes, existing manual feature engineering process is very tedious and time consuming and often performed by a domain expert: 2) Often it is tailored to specific dataset, hence we need to repeat the feature engineering process for different datasets. Since deep learning algorithms automatically comes up with good features and representation for the input data, we investigated their applications for customer churn prediction problem. We developed three deep neural network architectures and built the corresponding churn prediction model using two telecom dataset. Our experimental results show that deep-learning based models are performing as good as traditional classification models, without even using the hand-picked features.
Road surface crack detection: Improved segmentation with pixel-based refinement

Cracks are among the most commonly found road surface degradations, requiring periodical road surveys for monitoring pavement quality. Images of road pavement surface can be automatically processed, typically employing segmentation algorithms to identify cracks. However, a set of distinct connected components often result, leading to the detection of several independent crack segments, although they may belong to the same pavement surface defect. This is often observed for cracks that exhibit a longer linear development or present several branches. This paper presents a new strategy to identify cracks on images captured during road pavement surveys, even when those cracks appear with a complex shape. The proposed crack segmentation algorithm includes two stages: (i) selection of prominent "crack seeds", adopting an efficient segmentation procedure, after appropriate image smoothing, minimizing the detection of false positives; (ii) iterative binary pixel classification, into the crack or non-crack classes, extending the "seeds" to identify the complete crack shape. The paper also tests the combination of the proposed two stage crack segmentation with three smoothing techniques, to evaluate their suitability for crack detection. As a final step the system classifies the identified cracks as longitudinal, transversal or miscellaneous types. Tests performed with images acquired from different types of sensors (active and non-active), show improved crack segmentation results.
IOT solution for public bicycle system

In this paper, a Microcontroller based IOT Solution for modern city public bicycle system (Bike-sharing system) is presented. The proposed system can not only effectively reduce bicycle theft rate through localization method, but also allow the remote operators to relocate the spares' bike in order to reduce the bicycle station congestion statue. Meanwhile, the on board Bluetooth BLE module can in addition provide more system function allows user received more information on a smart phone from the monitoring center.
Deep learning to classify difference image for image change detection

Image change detection is a process to analyze multi-temproal images of the same scene for identifying the changes that have occurred. In this paper, we propose a novel difference image analysis approach based on deep neural networks for image change detection problems. The deep neural network learning algorithm for classification includes unsupervised feature learning and supervised fine-tuning. Some samples with the labels of high accuracy obtained by a pre-classification are used for fine-tuning. Since a deep neural network can learn complicated functions that can represent high-level abstractions, it can obtain satisfactory results. Theoretical analysis and experiment results on real datasets show that the proposed method outperforms some other methods.
Continuous Influence Maximization: What Discounts Should We Offer to Social Network Users?

Imagine we are introducing a new product through a social network, where we know for each user in the network the purchase probability curve with respect to discount. Then, what discount should we offer to those social network users so that the adoption of the product is maximized in expectation under a predefined budget? Although influence maximization has been extensively explored, surprisingly, this appealing practical problem still cannot be answered by the existing influence maximization methods. In this paper, we tackle the problem systematically. We formulate the general continuous influence maximization problem, investigate the essential properties, and develop a general coordinate descent algorithm as well as the engineering techniques for practical implementation. Our investigation does not assume any specific influence model and thus is general and principled. At the same time, using the most popularly adopted independent influence model as a concrete example, we demonstrate that more efficient methods are feasible under specific influence models. Our extensive empirical study on four benchmark real world networks with synthesized purchase probability curves clearly illustrates that continuous influence maximization can improve influence spread significantly with very moderate extra running time comparing to the classical influence maximization methods.
The Ensembl gene annotation system

The Ensembl gene annotation system has been used to annotate over 70 different vertebrate species across a wide range of genome projects. Furthermore, it generates the automatic alignment-based annotation for the human and mouse GENCODE gene sets. The system is based on the alignment of biological sequences, including cDNAs, proteins and RNA-seq reads, to the target genome in order to construct candidate transcript models. Careful assessment and filtering of these candidate transcripts ultimately leads to the final gene set, which is made available on the Ensembl website. Here, we describe the annotation process in detail.Database URL: http://www.ensembl.org/index.html.
A causal test of the motor theory of speech perception: a case of impaired speech production and spared speech perception.

The debate about the causal role of the motor system in speech perception has been reignited by demonstrations that motor processes are engaged during the processing of speech sounds. Here, we evaluate which aspects of auditory speech processing are affected, and which are not, in a stroke patient with dysfunction of the speech motor system. We found that the patient showed a normal phonemic categorical boundary when discriminating two non-words that differ by a minimal pair (e.g., ADA-AGA). However, using the same stimuli, the patient was unable to identify or label the non-word stimuli (using a button-press response). A control task showed that he could identify speech sounds by speaker gender, ruling out a general labelling impairment. These data suggest that while the motor system is not causally involved in perception of the speech signal, it may be used when other cues (e.g., meaning, context) are not available.
High-speed asynchronous Optical Camera Communication using LED and rolling shutter camera

This paper introduces the asynchronous Optical Camera Communication (asynchronous OCC) using LED and rolling shutter camera as a pair of wireless communication. Aiming to enhance the capacity of unidirectional wireless link, the response of camera is measured, and then the operating frequency range, system architecture, decoding procedure and algorithm are proposed. The frame and super-frame structure are also considered especially for OCC using rolling shutter camera. The performance of implemented system is evaluated and discussed.
Efficient Restoration Method for Images Corrupted with Impulse Noise

This paper proposes a two-stage adaptive method for restoration of images corrupted with impulse noise. In the first stage, the pixels which are most likely contaminated by noise are detected based on their intensity values. In the second stage, an efficient average filtering algorithm is used to remove those noisy pixels from the image. Only pixels which are determined to be noisy in the first stage are processed in the second stage. The remaining pixels of the first stage are not processed further and are just copied to their corresponding locations in the restored image. The experimental results for the proposed method demonstrate that it is faster and simpler than even median filtering, and it is very efficient for images corrupted with a wide range of impulse noise densities varying from 10% to 90%. Because of its simplicity, high speed, and low computational complexity, the proposed method can be used in realtime digital image applications, e.g., in consumer electronic products such as digital televisions and cameras.
Listening Comprehension over Argumentative Content

This paper presents a task for machine listening comprehension in the argumentation domain and a corresponding dataset in English. We recorded 200 spontaneous speeches arguing for or against 50 controversial topics. For each speech, we formulated a question, aimed at confirming or rejecting the occurrence of potential arguments in the speech. Labels were collected by listening to the speech and marking which arguments were mentioned by the speaker. We applied baseline methods addressing the task, to be used as a benchmark for future work over this dataset. All data used in this work is freely available for research.
Radiology of eating disorders: a pictorial review.

Eating disorders are a major challenge for health professionals, with many patients receiving ineffective care due to underdiagnosis or poor compliance with treatment. The incidence of eating disorders is increasing worldwide, producing an increasing burden on healthcare systems, and they most often affect young patients, with significant long-term complications. The effects of long-term malnutrition manifest in almost every organ system, and many can be detected radiologically, even without overt clinical findings. Musculoskeletal complications including osteoporosis result in a high incidence of insufficiency fractures, with long-term implications for bone health and growth, while respiratory complications are often recognized late due to disordered physiologic responses to infection. Gastrointestinal complications are numerous and in extreme cases may result in fatal outcomes after acute gastric dilatation and rupture subsequent to binge eating. In patients with severely disordered eating, in particular anorexia nervosa, marked derangement of electrolyte levels may result in refeeding syndrome, which requires emergent management. Recognition of such complications is critical to effective patient care and requires radiologists to be aware of the spectrum of imaging abnormalities that may be seen. Since many patients are reluctant to disclose their underlying condition, radiologists also play a critical role in identifying previously undiagnosed eating disorders.
Accelerometer based wireless air mouse using Arduino micro-controller board

With the day to day advancements in technology, the interaction between the human and the digital world is diminishing. Lot of improvement has taken place in the mobile technology from button keypads to touch screens. However the current PCs still need a pad to operate its mouse and are wired most of the time. The idea is to develop a wireless mouse which works on the hand gestures of the user without the need of a pad making an effortless interaction between the human and the computer. The implementation is done using a sensor named accelerometer to sense the hand gestures. Accelerometer is a motion sensor which sense changes in motion in any of the three axes. In here the accelerometer will be at the users side, attached to the hand to sense the movement and gives the output to a micro-controller to process it. Necessary modifications are done by the microcontroller to these values and are transmitted through a RF module to the PC. At the receiving end a mouse control program which contains functions to control the mouse reads these values and performs the necessary action.
CONTEXTUAL CLASSIFICATION OF POINT CLOUD DATA BY EXPLOITING INDIVIDUAL 3D NEIGBOURHOODS

The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification.
An agile process model for product derivation in software product line engineering

Software Product Lines (SPL) and Agile practices have emerged as new paradigms for developing software. Both approaches share common goals; such as improving productivity, reducing time to market, decreasing development costs and increasing customer satisfaction. These common goals provide the motivation for this research. We believe that integrating Agile practices into SPL can bring a balance between agility and formalism. However, there has been little research on such integration. We have been researching the potential of integrating Agile approaches in one of the key SPL process areas, product derivation. In this paper we present an outline of our Agile process model for product derivation that was developed through industry based case study research.
Differential privacy via t-closeness in data publishing

k-Anonymity and e-differential privacy are two main privacy models proposed within the computer science community. Whereas the former was proposed for privacy-preserving data publishing, i.e. data set anonymization, the latter initially arose in the context of interactive databases and was later extended to data publishing. We show here that t-closeness, one of the extensions of k-anonymity, can actually yieldε-differential privacy in data publishing when t =exp(ε). We detail a construction based on bucketization that realizes the previous implication; hence, as an ancillary result, we provide a new computational procedure to achieve t-closeness and ε-differential privacy in data publishing.
A simple genetic algorithm for music generation by means of algorithmic information theory

Recent large scale experiments have shown that the normalized information distance, an algorithmic information measure, is among the best similarity metrics for melody classification. This paper proposes the use of this distance as a fitness function which may be used by genetic algorithms to automatically generate music in a given pre-defined style. The minimization of this distance of the generated music to a set of musical guides makes it possible to obtain computer-generated music which recalls the style of a certain human author. The recombination operator plays an important role in this problem and thus several variations are tested to fine tune the genetic algorithm for this application. The superiority of the relative pitch envelope over other music parameters, such as the lengths of the notes, brought us to develop a simplified algorithm that nevertheless obtains interesting results.
Convergent multi-view geometric error correction with pseudo-inverse projection homography

The paper presents a geometric error correction method for convergent multi-view images, reducing the vertical parallax and non-uniform horizontal disparities over the set of input camera views. The global optimization method using inter-camera geometric relations on an arc is described. Camera orientations and optical centers are adjusted towards a uniform, circular distribution closest to the actual camera setup. Experimental validation shows up to 20% improvements over state-of-the-art w.r.t. reducing geometric errors and reaching better scalability/robustness to large rotation angles between adjacent camera views. The corrected images can be readily used for visualization on autostereoscopic 3D displays, and/or for further processing in an advanced 3D-TV imaging pipeline.
The Balanced Accuracy and Its Posterior Distribution

Evaluating the performance of a classification algorithm critically requires a measure of the degree to which unseen examples have been identified with their correct class labels. In practice, generalizability is frequently estimated by averaging the accuracies obtained on individual cross-validation folds. This procedure, however, is problematic in two ways. First, it does not allow for the derivation of meaningful confidence intervals. Second, it leads to an optimistic estimate when a biased classifier is tested on an imbalanced dataset. We show that both problems can be overcome by replacing the conventional point estimate of accuracy by an estimate of the posterior distribution of the balanced accuracy.
Process scheduling under uncertainty: Review and challenges

Uncertainty is a very important concern in production scheduling since it can cause infeasibilities and production disturbances. Thus scheduling nder uncertainty has received a lot of attention in the open literature in recent years from chemical engineering and operations research communities. he purpose of this paper is to review the main methodologies that have been developed to address the problem of uncertainty in production cheduling as well as to identify the main challenges in this area. The uncertainties in process scheduling are first analyzed, and the different athematical approaches that exist to describe process uncertainties are classified. Based on the different descriptions for the uncertainties, lternative scheduling approaches and relevant optimization models are reviewed and discussed. Further research challenges in the field of process cheduling under uncertainty are identified and some new ideas are discussed.
The See-Through System: From implementation to test-drive

Cooperative awareness in vehicular networks is probably the killer application for vehicle-to-vehicle (V2V) communications that cannot be matched by infrastructure-based alternatives even when disregarding communication costs. New and improved driver assistance systems can be introduced by extending their reach to sensors residing in neighboring vehicles, such as windshield-installed cameras. In previous work, we defined theoretical foundations for a driver assistance system that leverages on V2V communication and windshield-installed cameras to transform vision-obstructing vehicles into transparent tubular objects. We now present an implementation of the actual See-Through System (STS), where we combine the communication aspects with the control and augmented reality components of the system. We present a validation methodology and test the system with multiple vehicles on a closed road segment. This evaluation shows that the STS is able to increase the visibility of drivers intending to overtake, thus increasing the safety of such critical maneuvers. It also shows that Dedicated Short Range Communication (DSRC) provides the required latency for this delay-critical inter-vehicle communication, which could hardly be guaranteed with infrastructure-based communication technologies.
How Small Labels Create Big Improvements

It is widely believed that identifying communities in an ad hoc mobile communications system, such as a pocket switched network, can reduce the amount of traffic created when forwarding messages, but there has not been any empirical evidence available to support this assumption to date. In this paper, we show through use of real experimental human mobility data, how using a small label, identifying users according to their affiliation, can bring a large improvement in forwarding performance, in term of both delivery ratio and cost.
Variable structure control: a survey

A tutorial account of variable structure control with sliding mode is presented in this paper. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of this powerful control system design approach. This approach is particularly attractive for the control of nonlinear systems. Prominent characteristics such as invariance, robustness, order reduction, and control chattering are discussed in detail. Methods for coping with chattering are presented. Both linear and nonlinear systems are considered. Future research areas are suggested and an extensive list of references is included. CONVENTIONS FOR SYMBOLS Capital, italic letters represent matrices, e.g., A and K. Boldface, roman lower-case letters represent vectors, brief discussions about its historical development are presented. A. The Basic Notion of VSC second-order system [431, [ 1541 similar to the following The basic idea of VSC was originally illustrated by a X = y y = 2 y -x + U