nils-herrmann/clean_scirepeval huggingface dataset:
Intelligent Products: A survey

This paper presents an overview of the eld of Intelligent Products. As Intelligent Products have many facets, this paper is mainly focused on the concept behind Intelligent Products, the technical foundations, and the achievable practical goals of Intelligent Products. A novel classi cation of Intelligent Products is introduced, which distinguishes between three orthogonal dimensions. Furthermore, the technical foundations in the areas of automatic identi cation and embedded processing, distributed information storage and processing, and agent-based systems are discussed, as well as the achievable practical goals in the contexts of manufacturing, supply chains, asset management, and product life cycle management.
Big Data Analysis: Apache Storm Perspective

the boom in the technology has resulted in emergence of new concepts and challenges. Big data is one of those spoke about terms today. Big data is becoming a synonym for competitive advantages in business rivalries. Despite enormous benefits, big data accompanies some serious challenges and when it comes to analyzing of big data, it requires some serious thought. This study explores Big Data terminology and its analysis concepts using sample from Twitter data with the help of one of the most industry trusted real time processing and fault tolerant tool called Apache Storm.  Big Data, Apache Storm, real-time processing, open Source.
Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem

The class imbalanced problem occurs in various disciplines when one of target classes has a tiny number of instances comparing to other classes. A typical classifier normally ignores or neglects to detect a minority class due to the small number of class instances. SMOTE is one of over-sampling techniques that remedies this situation. It generates minority instances within the overlapping regions. However, SMOTE randomly synthesizes the minority instances along a line joining a minority instance and its selected nearest neighbours, ignoring nearby majority instances. Our technique called SafeLevel-SMOTE carefully samples minority instances along the same line with different weight degree, called safe level. The safe level computes by using nearest neighbour minority instances. By synthesizing the minority instances more around larger safe level, we achieve a better accuracy performance than SMOTE and Borderline-SMOTE.
License Plate Detection and Character Recognition Using Contour Analysis

License Plate Detection and Recognition System is an image processing technique used to identify a vehicle by its license plate. Here we propose an accurate and robust method of license plate detection and recognition from an image using contour analysis. The system is composed of two phases: the detection of the license plate, and the character recognition. The license plate detection is performed for obtaining the candidate region of the vehicle license plate and determined using the edge based text detection technique. In the recognition phase, the contour analysis is used to recognize the characters after segmenting each character. The performance of the proposed system has been tested on various images and provides better results.
Adaptive Binarization of Unconstrained Hand-Held Camera-Captured Document Images

This paper presents a new adaptive binarization technique for degraded hand-held camera-captured document images. The state-of-the-art locally adaptive binarization methods are sensitive to the values of free parameter. This problem is more critical when binarizing degraded camera-captured document images because of distortions like non-uniform illumination, bad shading, blurring, smearing and low resolution. We demonstrate in this paper that local binarization methods are not only sensitive to the selection of free parameters values (either found manually or automatically), but also sensitive to the constant free parameters values for all pixels of a document image. Some range of values of free parameters are better for foreground regions and some other range of values are better for background regions. For overcoming this problem, we present an adaptation of a state-of-the-art local binarization method such that two different set of free parameters values are used for foreground and background regions respectively. We present the use of ridges detection for rough estimation of foreground regions in a document image. This information is then used to calculate appropriate threshold using different set of free parameters values for the foreground and background regions respectively. The evaluation of the method using an OCR-based measure and a pixel-based measure show that our method achieves better performance as compared to state-of-the-art global and local binarization methods.
Analysis and understanding of multi-class invoices

In this paper a system for processing documents that can be grouped into classes is illustrated. We have considered invoices as a case-study. The system is divided into three phases: document analysis, classification, and understanding. We illustrate the analysis and understanding phases. The system is based on knowledge constructed by means of a learning procedure. The experimental results demonstrate the reliability of our document analysis and understanding procedures. They also present evidence that it is possible to use a small learning set of invoices to obtain reliable knowledge for the understanding phase.
Effects of consumer lifestyles on purchasing behavior on the Internet: a conceptual framework and empirical validation

The purpose of this research is (1) to develop a theoretical model for consumer purchasing behavior on the Internet and (2) to test the effects of consumer lifestyles in the form of price-oriented, net-oriented, and timeoriented lifestyles, and perceived benefit and perceived risk on purchasing behavior. Structural equation modeling is applied to test a hypothesized research model using Graphics Visualization and Usability Center (GVUC) online survey data. Results show that a consumer whose lifestyle is more net-oriented will perceive more benefits and fewer risks to online purchasing. Consumers who are more time-oriented will perceive more benefits to buying goods online than less time-oriented ones. Consumers who have more disposable income are also more prone to purchase online.
SGDR: Stochastic Gradient Descent with Warm Restarts

Restart techniques are common in gradient-free optimization to deal with multimodal functions. Partial warm restarts are also gaining popularity in gradientbased optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions. In this paper, we propose a simple warm restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks. We empirically study its performance on the CIFAR-10 and CIFAR-100 datasets, where we demonstrate new state-of-the-art results at 3.14% and 16.21%, respectively. We also demonstrate its advantages on a dataset of EEG recordings and on a downsampled version of the ImageNet dataset. Our source code is available at https://github.com/loshchil/SGDR
Ceramic Based Small LTE MIMO Handset Antenna

A compact multiband antenna for handset and tablet applications is presented, covering most LTE Bands (698-960 and 1710-2690 MHz). This study utilizes a new ceramic material suitable for the internal antenna design which gives a shorter guided wavelength and wider bandwidth for each resonant mode to achieve antenna size reduction, sufficient space utilization, and very low correlation coefficient as MIMO antennas. The occupied volume of the antenna radiator is only 35 × 11 × 5 mm<sup>3</sup>, equal to 0.082 λ<sub>m</sub> × 0.026λ<sub>m</sub> × 0.012λ<sub>m</sub> (λ<sub>m</sub> is the wavelength at 700 MHz) whose radiation aperture is only λ<sub>m</sub>/11 so the antenna is a good candidate as an internal antenna.
Machine translation evaluation versus quality estimation

Most evaluation metrics for machine translation (MT) require reference translations for each sentence in order to produce a score reflecting certain aspects of its quality. The de facto metrics, BLEU and NIST, are known to have good correlation with human evaluation at the corpus level, but this is not the case at the segment level. As an attempt to overcome these two limitations, we address the problem of evaluating the quality of MT as a prediction task, where reference-independent features are extracted from the input sentences and their translation, and a quality score is obtained based on models produced from training data. We show that this approach yields better correlation with human evaluation as compared to commonly used metrics, even with models trained on different MT systems, language-pairs and text domains.
SAWSDL: Semantic Annotations for WSDL and XML Schema

Web services are important for creating distributed applications on the Web. In fact, they're a key enabler for service-oriented architectures that focus on service reuse and interoperability. The World Wide Web Consortium (W3C) has recently finished work on two important standards for describing Web services the Web Services Description Language (WSDL) 2.0 and Semantic Annotations for WSDL and XML Schema (SAWSDL). Here, the authors discuss the latter, which is the first standard for adding semantics to Web service descriptions.
Self-aware cyber-physical systems and applications in smart buildings and cities

The world is facing several challenges that must be dealt within the coming years such as efficient energy management, need for economic growth, security and quality of life of its habitants. The increasing concentration of the world population into urban areas puts the cities in the center of the preoccupations and makes them important actors for the world's sustainable development strategy. ICT has a substantial potential to help cities to respond to the growing demands of more efficient, sustainable, and increased quality of life in the cities, thus to make them "smarter". Smartness is directly proportional with the "awareness". Cyber-physical systems can extract the awareness information from the physical world and process this information in the cyber-world. Thus, a holistic integrated approach, from the physical to the cyber-world is necessary for a successful and sustainable smart city outcome. This paper introduces important research challenges that we believe will be important in the coming years and provides guidelines and recommendations to achieve self-aware smart city objectives.
Turboiso: towards ultrafast and robust subgraph isomorphism search in large graph databases

Given a query graph q and a data graph g, the subgraph isomorphism search finds all occurrences of q in g and is considered one of the most fundamental query types for many real applications. While this problem belongs to NP-hard, many algorithms have been proposed to solve it in a reasonable time for real datasets. However, a recent study has shown, through an extensive benchmark with various real datasets, that all existing algorithms have serious problems in their matching order selection. Furthermore, all algorithms blindly permutate all possible mappings for query vertices, often leading to useless computations. In this paper, we present an efficient and robust subgraph search solution, called TurboISO, which is turbo-charged with two novel concepts, candidate region exploration and the combine and permute strategy (in short, Comb/Perm). The candidate region exploration identifies on-the-fly candidate subgraphs (i.e, candidate regions), which contain embeddings, and computes a robust matching order for each candidate region explored. The Comb/Perm strategy exploits the novel concept of the neighborhood equivalence class (NEC). Each query vertex in the same NEC has identically matching data vertices. During subgraph isomorphism search, Comb/Perm generates only combinations for each NEC instead of permutating all possible enumerations. Thus, if a chosen combination is determined to not contribute to a complete solution, all possible permutations for that combination will be safely pruned. Extensive experiments with many real datasets show that TurboISO consistently and significantly outperforms all competitors by up to several orders of magnitude.
Zab: High-performance broadcast for primary-backup systems

Zab is a crash-recovery atomic broadcast algorithm we designed for the ZooKeeper coordination service. ZooKeeper implements a primary-backup scheme in which a primary process executes clients operations and uses Zab to propagate the corresponding incremental state changes to backup processes1. Due the dependence of an incremental state change on the sequence of changes previously generated, Zab must guarantee that if it delivers a given state change, then all other changes it depends upon must be delivered first. Since primaries may crash, Zab must satisfy this requirement despite crashes of primaries.
Creating False Memories : Remembering Words Not Presented in Lists

Two experiments (modeled after J. Deese's 1959 study) revealed remarkable levels of false recall and false recognition in a list learning paradigm. In Experiment 1, subjects studied lists of 12 words (e.g., bed, rest, awake); each list was composed of associates of 1 nonpresented word (e.g., sleep). On immediate free recall tests, the nonpresented associates were recalled 40% of the time and were later recognized with high confidence. In Experiment 2, a false recall rate of 55% was obtained with an expanded set of lists, and on a later recognition test, subjects produced false alarms to these items at a rate comparable to the hit rate. The act of recall enhanced later remembering of both studied and nonstudied material. The results reveal a powerful illusion of memory: People remember events that never happened.
Named Entity Recognition Using a Character-based Probabilistic Approach

We present a named entity recognition and classification system that uses only probabilistic character-level features. Classifications by multiple orthographic tries are combined in a hidden Markov model framework to incorporate both internal and contextual evidence. As part of the system, we perform a preprocessing stage in which capitalisation is restored to sentence-initial and all-caps words with high accuracy. We report f-values of 86.65 and 79.78 for English, and 50.62 and 54.43 for the German datasets.
ANASTASIA: ANdroid mAlware detection using STatic analySIs of Applications

The number of malware applications targeting the Android operating system has significantly increased in recent years. Malicious applications pose a significant threat to Android platform security. We propose ANASTASIA, a system to detect malicious Android applications through statically analyzing applications' behaviors. ANASTASIA provides a more complete coverage of security behaviors when compared to state-of-the-art solutions. We utilize a large number of statically extracted features from various security behavioral characteristics of an application. We built a Machine Learning-based detection framework with high performance detection and acceptable false positive rate. The significance of our work is to develop a lightweight malware detection system for Android-powered smartphones that leverages robust, effective, and efficient features. Besides, in order to assess our solution, we used a reliable, large-scale, and updated malware data-set in terms of diversity and number of malware applications. We evaluated the performance of our proposal on large-scale malware data-set (including 18,677 malware and 11,187 benign apps). Our experimental results show a true positive rate of 97.3% and a false negative rate of 2.7%. These results are better than what are reported by state-of-the-art Android malware detection methods.
Remodeling Grounded Theory

This paper outlines my concerns with Qualitative Data Analysis’ (QDA) numerous remodelings of Grounded Theory (GT) and the subsequent eroding impact. I cite several examples of the erosion and summarize essential elements of classic GT methodology. It is hoped that the article will clarify my concerns with the continuing enthusiasm but misunderstood embrace of GT by QDA methodologists and serve as a preliminary guide to novice researchers who wish to explore the fundamental principles of GT.
Chinese Handwriting Recognition Competition

This paper describes the Chinese handwriting recognition competition held at the 12th International Conference on Document Analysis and Recognition (ICDAR 2013). This third competition in the series again used the CASIAHWDB/OLHWDB databases as the training set, and all the submitted systems were evaluated on closed datasets to report character-level correct rates. This year, 10 groups submitted 27 systems for five tasks: classification on extracted features, online/offline isolated character recognition, online/offline handwritten text recognition. The best results (correct rates) are 93.89% for classification on extracted features, 94.77% for offline character recognition, 97.39% for online character recognition, 88.76% for offline text recognition, and 95.03% for online text recognition, respectively. In addition to the test results, we also provide short descriptions of the recognition methods and brief discussions on the results. Chinese handwriting recognition competition; isolated character recongition; handwritten text recognition; offline; online; CASIA-HWDB/OLHWDB database.
ScentBar: A Query Suggestion Interface Visualizing the Amount of Missed Relevant Information for Intrinsically Diverse Search

For intrinsically diverse tasks, in which collecting extensive information from different aspects of a topic is required, searchers often have difficulty formulating queries to explore diverse aspects and deciding when to stop searching. With the goal of helping searchers discover unexplored aspects and find the appropriate timing for search stopping in intrinsically diverse tasks, we propose ScentBar, a query suggestion interface visualizing the amount of important information that a user potentially misses collecting from the search results of individual queries. We define the amount of missed information for a query as the additional gain that can be obtained from unclicked search results of the query, where gain is formalized as a set-wise metric based on aspect importance, aspect novelty, and per-aspect document relevance and is estimated by using a state-of-the-art algorithm for subtopic mining and search result diversification. Results of a user study involving 24 participants showed that the proposed interface had the following advantages when the gain estimation algorithm worked reasonably: (1) ScentBar users stopped examining search results after collecting a greater amount of relevant information; (2) they issued queries whose search results contained more missed information; (3) they obtained higher gain, particularly at the late stage of their sessions; and (4) they obtained higher gain per unit time. These results suggest that the simple query visualization helps make the search process of intrinsically diverse tasks more efficient, unless inaccurate estimates of missed information are visualized.
Design, simulation and manufacturing of a Tracked Surveillance unmanned ground vehicle

This paper describes design, Simulation and manufacturing procedures of HIRAD - a teleoperated Tracked Surveillance UGV for military, Rescue and other civilian missions in various hazardous environments. A Double Stabilizer Flipper mechanism mounted on front pulleys enables the Robot to have good performance in travelling over uneven terrains and climbing stairs. Using this Stabilizer flipper mechanism reduces energy consumption while climbing the stairs or crossing over obstacles. The locomotion system mechanical design is also described in detail. The CAD geometry 3D-model has been produced by CATIA software. To analyze the system mobility, a virtual model was developed with ADAMS Software. This simulation included different mobility maneuvers such as stair climbing, gap crossing and travelling over steep slopes. The simulations enabled us to define motor torque requirements. We performed many experiments with manufactured prototype under various terrain conditions Such as stair climbing, gap crossing and slope elevation. In experiments, HIRAD shows good overcoming ability for the tested terrain conditions.
Spring parameters design to increase the loading capability of a hydraulic quadruped robot

A quadruped robot “Baby Elephant” with parallel legs has been developed. It is about 1 m tall, 1.2 m long and 0.5m wide. It weighs about 130 kg. Driven by a new type of hydraulic actuation system, the Baby Elephant is designed to work as a mechanical carrier. It can carry a payload more than 50 kg. In this study, the structure of the legs is introduced first. Then the effect of the springs for increasing the loading capability is explained and discovered. The design problem of the spring parameters is also discussed. Finally, simulations and experiments are carried out to confirm the effect.
Investigations on Dual Slant Polarized Cavity-Backed Massive MIMO Antenna Panel With Beamforming

A cavity-backed dual slant polarized and low mutual coupling antenna array panel with frequency band from 4.9 to 6 GHz is analyzed and realized for the MIMO antenna 5G applications. The beamforming capability of this array is also explored. The printed cross dipoles fed with balun and enclosed in a cavity are used as radiating elements. The two cross dipoles are placed at an angle of 45° and 135° giving slant polarizations. A <inline-formula> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> subarray of dimension <inline-formula> <tex-math notation="LaTeX">$2.8\lambda \times 2.8\lambda \times 0.26\lambda $ </tex-math></inline-formula> where <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> is free space wavelength at 6 GHz is designed, fabricated, and experimentally verified. It shows good impedance matching, port isolation, envelope correlation coefficient, and radiation characteristics which are desired for MIMO applications. Beamforming capability in the digital domain is verified using the Keysight SystemVue simulation tool for both <inline-formula> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$16\times 16$ </tex-math></inline-formula> panel arrays which employ measured 3-D embedded element radiation pattern data of the fabricated <inline-formula> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> subarray. Four simultaneous beams using digital beamforming approach are also presented for the <inline-formula> <tex-math notation="LaTeX">$16 \times 16$ </tex-math></inline-formula> array for multiuser environment base station antenna applications.
Memory efficient fingerprint verification

Fingerprint recognition and verification are often based on local fingerprint features, usually ridge endings or terminations, also called minutiae. By exploiting the structural uniqueness of the image region around a minutia, the fingerprint recognition performance can be significantly enhanced. However, for most fingerprint images the number of minutia image regions (MIR’s) becomes dramatically large, which imposes especially for embedded systems an enormous memory requirement. Therefore, we are investigating different algorithms for compression of minutia regions. The requirement for these algorithms is to achieve a high compression rate (about 20) with minimum loss in the matching performance of minutia image region matching. In this paper we investigate the matching performance for compression algorithms based on the Principal Component and the wavelet transformation. The matching results are presented in form of normalized ROC curves and interpreted in terms of compression rates and the MIR dimension.