nils-herrmann/clean_scirepeval huggingface dataset:
Introduction to Document Analysis and Recognition

Document Analysis and Recognition (DAR) aims at the automatic extraction of information presented on paper and initially addressed to human comprehension. The desired output of DAR systems is usually in a suitable symbolic representation that can subsequently be processed by computers. Over the centuries, paper documents have been the principal instrument to make permanent the progress of the humankind. Nowadays, most information is still recorded, stored, and distributed in paper format. The widespread use of computers for document editing, with the introduction of PCs and wordprocessors in the late 1980’s, had the effect of increasing, instead of reducing, the amount of information held on paper. Even if current technological trends seem to move towards a paperless world, some studies demonstrated that the use of paper as a media for information exchange is still increasing . Moreover, there are still application domains where the paper persists to be the preferred media . The most widely known applications of DAR are related to the processing of office documents (such as invoices, bank documents, business letters, and checks) and to the automatic mail sorting. With the current availability of inexpensive high-resolution scanning devices, combined with powerful computers, state-of-the-art OCR packages can solve simple recognition tasks for most users. Recent research directions are widening the use of the DAR techniques, significant examples are the processing of ancient/historical documents in digital libraries, the information extraction from “digital born” documents, such as PDF and HTML, and the analysis of natural images (acquired with mobile phones and digital cameras) containing textual information. The development of a DAR system requires the integration of several competences in computer science, among the others: image processing, pattern recognition, natural language processing, artificial intelligence, and database systems. DAR applications are particularly suitable for the incorporation of
Structural Health Monitoring

can be found at: Structural Health Monitoring Additional services and information for http://shm.sagepub.com/cgi/alerts  Email Alerts: http://shm.sagepub.com/subscriptions  Subscriptions: http://www.sagepub.com/journalsReprints.nav Reprints: http://www.sagepub.com/journalsPermissions.nav Permissions: http://shm.sagepub.com/cgi/content/refs/2/3/257 SAGE Journals Online and HighWire Press platforms): (this article cites 14 articles hosted on the Citations
The Application of Data Mining to Build Classification Model for Predicting Graduate Employment

Data mining has been applied in various areas because of its ability to rapidly analyze vast amounts of data. This study is to build the Graduates Employment Model using classification task in data mining, and to compare several of data-mining approaches such as Bayesian method and the Tree method. The Bayesian method includes 5 algorithms, including AODE, BayesNet, HNB, NaviveBayes, WAODE. The Tree method includes 5 algorithms, including BFTree, NBTree, REPTree, ID3, C4.5. The experiment uses a classification task in WEKA, and we compare the results of each algorithm, where several classification models were generated. To validate the generated model, the experiments were conducted using real data collected from graduate profile at the Maejo University in Thailand. The model is intended to be used for predicting whether a graduate was employed, unemployed, or in an undetermined situation.  Classification model; Data mining; Tree method
A Closer Look on Artist Filters for Musical Genre Classification

Musical genre classification is the automatic classification of audio signals into user defined labels describing pieces of music. A problem inherent to genre classification experiments in music information retrieval research is the use of songs from the same artist in both training and test sets. We show that this does not only lead to overoptimistic accuracy results but also selectively favours particular classification approaches. The advantage of using models of songs rather than models of genres vanishes when applying an artist filter. The same holds true for the use of spectral features versus fluctuation patterns for preprocessing of the audio files.
A Fast Sampling Method of Exploring Graphlet Degrees of Large Directed and Undirected Graphs

Exploring small connected and induced subgraph patterns (CIS patterns, or graphlets) has recently attracted considerable attention. Despite recent efforts on computing the number of instances a specific graphlet appears in a large graph (i.e., the total number of CISes isomorphic to the graphlet), little attention has been paid to characterizing a node’s graphlet degree, i.e., the number of CISes isomorphic to the graphlet that include the node, which is an important metric for analyzing complex networks such as social and biological networks. Similar to global graphlet counting, it is challenging to compute node graphlet degrees for a large graph due to the combinatorial nature of the problem. Unfortunately, previous methods of computing global graphlet counts are not suited to solve this problem. In this paper we propose sampling methods to estimate node graphlet degrees for undirected and directed graphs, and analyze the error of our estimates. To the best of our knowledge, we are the first to study this problem and give a fast scalable solution. We conduct experiments on a variety of real-word datasets that demonstrate that our methods accurately and efficiently estimate node graphlet degrees for graphs with millions of edges.
Performance of a low-power wide-area network based on LoRa technology: Doppler robustness, scalability, and coverage

The article provides an analysis and reports experimental validation of the various performance metrics of the LoRa low-power wide-area network technology. The LoRa modulation is based on chirp spread spectrum, which enables use of low-quality oscillators in the end device, and to make the synchronization faster and more reliable. Moreover, LoRa technology provides over 150 dB link budget, providing good coverage. Therefore, LoRa seems to be quite a promising option for implementing communication in many diverse Internet of Things applications. In this article, we first briefly overview the specifics of the LoRa technology and analyze the scalability of the LoRa wide-area network. Then, we introduce setups of the performance measurements. The results show that using the transmit power of 14 dBm and the highest spreading factor of 12, more than 60% of the packets are received from the distance of 30 km on water. With the same configuration, we measured the performance of LoRa communication in mobile scenarios. The presented results reveal that at around 40 km/h, the communication performance gets worse, because duration of the LoRa-modulated symbol exceeds coherence time. However, it is expected that communication link is more reliable when lower spreading factors are used.
Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks

We propose novel cooperative spectrum sensing (CSS) algorithms for cognitive radio (CR) networks based on machine learning techniques which are used for pattern classification. In this regard, unsupervised (e.g., K-means clustering and Gaussian mixture model (GMM)) and supervised (e.g., support vector machine (SVM) and weighted K-nearest-neighbor (KNN)) learning-based classification techniques are implemented for CSS. For a radio channel, the vector of the energy levels estimated at CR devices is treated as a feature vector and fed into a classifier to decide whether the channel is available or not. The classifier categorizes each feature vector into either of the two classes, namely, the "channel available class" and the "channel unavailable class". Prior to the online classification, the classifier needs to go through a training phase. For classification, the K-means clustering algorithm partitions the training feature vectors into K clusters, where each cluster corresponds to a combined state of primary users (PUs) and then the classifier determines the class the test energy vector belongs to. The GMM obtains a mixture of Gaussian density functions that well describes the training feature vectors. In the case of the SVM, the support vectors (i.e., a subset of training vectors which fully specify the decision function) are obtained by maximizing the margin between the separating hyperplane and the training feature vectors. Furthermore, the weighted KNN classification technique is proposed for CSS for which the weight of each feature vector is calculated by evaluating the area under the receiver operating characteristic (ROC) curve of that feature vector. The performance of each classification technique is quantified in terms of the average training time, the sample classification delay, and the ROC curve. Our comparative results clearly reveal that the proposed algorithms outperform the existing state-of-the-art CSS techniques.
Generalized Consensus and Paxos

Theoretician’s Abstract Consensus has been regarded as the fundamental problem that must be solved to implement a fault-tolerant distributed system. However, only a weaker problem than traditional consensus need be solved. We generalize the consensus problem to include both traditional consensus and this weaker version. A straightforward generalization of the Paxos consensus algorithm implements general consensus. The generalizations of consensus and of the Paxos algorithm require a mathematical detour de force into a type of object called a command-structure set.
FPGA-optimised high-quality uniform random number generators

This paper introduces a method of constructing random numbergenerators from four of the basic primitives provided by FPGAs: Flip-Flips, Lookup-Tables, Shift Registers, and RAMs. The construction methodis designed to ensure maximum clock rates, while using the minimum of resources, and providing statistical quality at the level of the best software generators. In all platforms tested, the generators are limited in speed only by the clock distribution network or the maximum clockspeed of the underlying RAM primitives, using a platform independent VHDL description with no placement or other hints. The area utilisation is also very low, with a Virtex-5 generator requiring just one Block-RAMand 41 slices to produce 48Gb/s at 550MHz: over 14 times faster than the commonly used Mersenne-Twister RNG on an Opteron at 2.2GHz, while providing the same level of quality
Psychological flexibility as a fundamental aspect of health.

Traditionally, positive emotions and thoughts, strengths, and the satisfaction of basic psychological needs for belonging, competence, and autonomy have been seen as the cornerstones of psychological health. Without disputing their importance, these foci fail to capture many of the fluctuating, conflicting forces that are readily apparent when people navigate the environment and social world. In this paper, we review literature to offer evidence for the prominence of psychological flexibility in understanding psychological health. Thus far, the importance of psychological flexibility has been obscured by the isolation and disconnection of research conducted on this topic. Psychological flexibility spans a wide range of human abilities to: recognize and adapt to various situational demands; shift mindsets or behavioral repertoires when these strategies compromise personal or social functioning; maintain balance among important life domains; and be aware, open, and committed to behaviors that are congruent with deeply held values. In many forms of psychopathology, these flexibility processes are absent. In hopes of creating a more coherent understanding, we synthesize work in emotion regulation, mindfulness and acceptance, social and personality psychology, and neuropsychology. Basic research findings provide insight into the nature, correlates, and consequences of psychological flexibility and applied research provides details on promising interventions. Throughout, we emphasize dynamic approaches that might capture this fluid construct in the real-world.
An Analysis of Visual Question Answering Algorithms

In visual question answering (VQA), an algorithm must answer text-based questions about images. While multiple datasets for VQA have been created since late 2014, they all have flaws in both their content and the way algorithms are evaluated on them. As a result, evaluation scores are inflated and predominantly determined by answering easier questions, making it difficult to compare different methods. In this paper, we analyze existing VQA algorithms using a new dataset called the Task Driven Image Understanding Challenge (TDIUC), which has over 1.6 million questions organized into 12 different categories. We also introduce questions that are meaningless for a given image to force a VQA system to reason about image content. We propose new evaluation schemes that compensate for over-represented question-types and make it easier to study the strengths and weaknesses of algorithms. We analyze the performance of both baseline and state-of-the-art VQA models, including multi-modal compact bilinear pooling (MCB), neural module networks, and recurrent answering units. Our experiments establish how attention helps certain categories more than others, determine which models work better than others, and explain how simple models (e.g. MLP) can surpass more complex models (MCB) by simply learning to answer large, easy question categories.
Interpretation of the modality of touch on an artificial arm covered with an EIT-based sensitive skin

During social interaction humans extract important information from tactile stimuli that can improve their understanding of the interaction. The development of a similar capability in a robot will contribute to the future success of intuitive human–robot interaction. This paper presents a thin, flexible and stretchable artificial skin for robotics based on the principle of electrical impedance tomography. This skin, which can be used to extract information such as location, duration and intensity of touch, was used to cover the forearm and upper arm of a full-size mannequin. A classifier based on the ‘LogitBoost’ algorithm was used to classify the modality of eight different types of touch applied by humans to the mannequin arm. Experiments showed that the modality of touch was correctly classified in approximately 71% of the trials. This was shown to be comparable to the accuracy of humans when identifying touch. The classification accuracies obtained represent significant improvements over previous classification algorithms applied to artificial sensitive skins. It is shown that features based on touch duration and intensity are sufficient to provide a good classification of touch modality. Gender and cultural background were examined and found to have no statistically significant effect on the classification results.
Low vs. high-fidelity prototyping debate

An ongoing controversy exists in the prototyping community about how closely in form and function a user-interface prototype should represent the final product. This dispute is referred to as the " Low-versus High-Fidelity Prototyping Debate.'' In this article, we discuss arguments for and against low-and high-fidelity prototypes , guidelines for the use of rapid user-interface proto-typing, and the implications for user-interface designers.
Design of LTCC Wideband Patch Antenna for LMDS Band Applications

This letter presents a wideband patch antenna on a low-temperature cofired ceramic substrate for Local Multipoint Distribution Service band applications. Conventional rectangular patch antennas have a narrow bandwidth. The proposed via-wall structure enhances the electric field coupling between the stacked patches to achieve wideband characteristics. We designed same-side and opposite-side feeding configurations and report on the fabrication of an experimental 28-GHz antenna used to validate the design concept. Measurements correlate well with the simulation results, achieving a 10-dB impedance bandwidth of 25.4% (23.4-30.2 GHz).
Classification of streaming time series under more realistic assumptions

Much of the vast literature on time series classification makes several assumptions about data and the algorithm’s eventual deployment that are almost certainly unwarranted. For example, many research efforts assume that the beginning and ending points of the pattern of interest can be correctly identified, during both the training phase and later deployment. Another example is the common assumption that queries will be made at a constant rate that is known ahead of time, thus computational resources can be exactly budgeted. In this work, we argue that these assumptions are unjustified, and this has in many cases led to unwarranted optimism about the performance of the proposed algorithms. As we shall show, the task of correctly extracting individual gait cycles, heartbeats, gestures, behaviors, etc., is generally much more difficult than the task of actually classifying those patterns. Likewise, gesture classification systems deployed on a device such as Google Glass may issue queries at frequencies that range over an order of magnitude, making it difficult to plan computational resources. We propose to mitigate these problems by introducing an alignment-free time series classification framework. The framework requires only very weakly annotated data, such as “in this ten minutes of data, we see mostly normal heartbeats $$\ldots $$ … ,” and by generalizing the classic machine learning idea of data editing to streaming/continuous data, allows us to build robust, fast and accurate anytime classifiers. We demonstrate on several diverse real-world problems that beyond removing unwarranted assumptions and requiring essentially no human intervention, our framework is both extremely fast and significantly more accurate than current state-of-the-art approaches.
Optimal Integration of Distributed Energy Storage Devices in Smart Grids

Energy storage is traditionally well established in the form of large scale pumped-hydro systems, but nowadays is finding increased attraction in medium and smaller scale systems. Such expansion is entirely complementary to the forecasted wider integration of intermittent renewable resources in future electrical distribution systems (Smart Grids).  The need to quantify benefits of both the Smart Grid where the energy storage devices are included and the external interconnected grid is explored. Numerical applications to a Medium Voltage test Smart Grid show the advantages of using storage systems related to different options in terms of incentives and services to be provided.
Analysis of Requirement Engineering Processes , Tools / Techniques and Methodologies

Requirement engineering is an integral part of the software development lifecycle since the basis for developing successful software depends on comprehending its requirements in the first place. Requirement engineering involves a number of processes for gathering requirements in accordance with the needs and demands of users and stakeholders of the software product. In this paper, we have reviewed the prominent processes, tools and technologies used in the requirement gathering phase. The study is useful to perceive the current state of the affairs pertaining to the requirement engineering research and to understand the strengths and limitations of the existing requirement engineering techniques. The study also summarizes the best practices and how to use a blend of the requirement engineering techniques as an effective methodology to successfully conduct the requirement engineering task. The study also highlights the importance of security requirements as though they are part of the nonfunctional requirement, yet are naturally considered fundamental to secure software development.
Horizon mapping: shadows for bump-mapped surfaces

Bump mapping produces realistic shading by perturbing normal vectors to a surface, but does not show the shadows that the bumps cast on nearby parts of the same surface. In this paper, these shadows are found from precomputed tables of horizon angles, listing, for each position entry, the elevation of the horizon in a sampled collection of directions. These tables are made for bumps on a standard flat surface, and then a transformation is developed so that the same tables can be used for an arbitrary curved parametrized surface patch. This necessitates a new method for scaling the bump size to the patch size. Incremental calculations can be used in a scan line algorithm for polygonal surface approximations. The errors in the bump shadows are discussed, as well as their anti-aliasing.
Security in Wireless Sensor Networks

Wireless sensor networks (WSNs) have attracted a lot of interest over the last decade in wireless and mobile computing research community. Applications of WSNs are numerous and growing, which range from indoor deployment scenarios in the home and office to outdoor deployment in adversary’s territory in a tactical battleground. However, due to distributed nature and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their performance. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes is also very likely in real-life deployment scenarios. Due to resource constraints in the sensor nodes, traditional security mechanisms with large overhead of computation and communication are infeasible in WSNs. Design and implementation of secure WSNs is, therefore, a particularly challenging task. This chapter provides a comprehensive discussion on the state of the art in security technologies for WSNs. It identifies various possible attacks at different layers of the communication protocol stack in a typical WSN and presents their possible countermeasures. A brief discussion on the future direction of research in WSN security is also included.
Characteristics comparison of a conventional and novel stator structure of axial flux permanent magnet motor for cooling fan drive system

This paper compares to investigate output characteristics according to a conventional and novel stator structure of axial flux permanent magnet (AFPM) motor for cooling fan drive system. Segmented core of stator has advantages such as easy winding and fast manufacture speed. However, a unit cost increase due to cutting off tooth tip to constant slot width. To solve the problem, this paper proposes a novel stator structure with three-step segmented core. The characteristics of AFPM were analyzed by time-stepping three dimensional finite element analysis (3D FEA) in two stator models, when stator cores are cutting off tooth tips from rectangular core and three step segmented core. Prototype motors were manufactured based on analysis results, and were tested as a motor.