Reviewerly/exHarmony huggingface dataset:
Kennen wir die Vergangenheit? Oder: Verflacht die IT die Beziehungen zwischen HistorikerInnen und ihren Quellen oder vertieft sie sie?

No AccessKennen wir die Vergangenheit? Oder: Verflacht die IT die Beziehungen zwischen HistorikerInnen und ihren Quellen oder vertieft sie sie?Manfred ThallerManfred ThallerSearch for more papers by this authorhttps://doi.org/10.7788/9783412520663.121SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous chapter Next chapter FiguresReferencesRelatedDetails Download book cover 1. AuflageISBN: 978-3-412-52064-9 eISBN: 978-3-412-52066-3HistoryPublished online:November 2020 PDF download
Image-based Deflection Measurement and Error Analysis

Abstract This paper gives an image-based and computerized solution to the deflection measurement of bridges with its corresponding design scheme of measuring devices. A laser transmitter is fixed on one end of the bridge. A dark cartridge with an optical screen and a camera is installed in the middle of the bridge. Light beam projects onto the optical screen, while the camera captures the image on the screen. Bridge deflection is measured by applying image-processing algorithms and then performing the related computation. The systematic error caused by tilting film and geometric distortion is analysed and eliminated. One of the advantages is that, this low-cost measuring scheme obtains direct deflection data and requires simple operations comparing with existing methods. Other advantages and disadvantages are also highlighted.
Document Fraud Detection

In today scenario for data and fund transfer we are mainly depended on the internet. So prevention of fraud, abuse and data alteration through internet has become a major concern of many organizations. Our paper focuses the direction towards the document fraud detection. In this direction we proposed an efficient approach where we send the encrypted data via internet and maintain the log table of the data sends. The log table contains the information about individual word and numeric value along with the position and count. If any attacker attacks the data for updation or any violation again a log table is created based on the word as well as the position of the word and count. Our algorithm check the alteration based on the position and the frequency count. If any mismatch is detected server alerts the client about the document attack and resend the document to the client. The above scenario is explained by the result analysis which shows the effectiveness of the approach.
Organizational Forms and Methods of Overcoming Pedagogical Risks in the Management of Innovative Educational Platform

The article actualizes the problem of planning ways to overcome pedagogical risks. The purpose of the article is to identify methods of overcoming the pedagogical risks of managing innovative platforms in education, to substantiate the classification of such methods. Research methodology and methods. The methodological basis of the study is a systematic approach, which made it possible to consider the interrelationships of the management elements of the innovation platform. To achieve the goal of the study, methods of analysis of the fundamental ideas of pedagogical science and the theory of risk management, modeling of content analysis of projects of existing federal innovation platforms, ranking of methods for minimizing pedagogical risks in the management of an innovative educational platform were used. Research results. As a result of the study, a structural model of forms and methods of overcoming the pedagogical risks of managing an innovative educational platform was created, the prevailing forms and methods of overcoming the pedagogical risks of managing an innovative educational platform were identified.In conclusion, it is concluded that the methods of overcoming pedagogical risks are adequate to the management require the use of mainly pedagogical forms and methods of minimizing such risks, and the phenomena of the organization of activities determine the priority of management methods to neutralize risks. the teaching staff of the innovation platform.
Reframing research access

Purpose The paper describes how Charles Darwin University (CDU) used a three-pronged approach to better serve its researchers: it developed a single interface for improved accessibility and discoverability of its research outputs, consolidated its corresponding policies and procedures and implemented training programs to support the new portal. This in turn made its suite of research outputs more openly accessible and better discoverable. The intention was to make CDU research compliant with the FAIR (Findable, Accessible, Interoperable and Reusable) policy statement, affirming the need to make Australia's research more visible, thereby enabling better access, better collaboration locally and internationally and researchers more accountable to their community. Design/methodology/approach This paper uses case study methodology and a qualitative approach. Findings CDU Library collaborated with the University’s Research Office in undertaking a series of strategies towards reframing access to its research. The partners migrated their research collections into a single, new, integrated interface; developed new policies and consolidated existing ones; and to this end, rolled out a training and educational program for the research community. The intention of the program was to introduce the Pure repository to new researchers and to train all staff to self archive and curate their own research outputs. This new streamlined approach ensured a more comprehensive and timely availability and accessibility of the University's research outputs. Originality/value A single source of truth was established through the migration of iCDU’s research collections, ensuring data quality was maintained. At the start of this project, there were few institutions in Australia using the Pure system, and even fewer using it as their sole repository for displaying research outputs.
DRGCN: Deep Relation GCN for Group Activity Recognition

Person to person relation is an essential clue for group activity recognition (GAR). And the relation graph and the graph convolution neural network (GCN) have become powerful presentation and processing tools of relationship. The previous methods are difficult to capture the complex relationship between people. We propose an end-to-end framework called Deep Relation GCN (DRGCN) for recognizing group activities by exploring the high-level relations between individuals. In DRGCN, we use a horizontal slicing strategy to layer each individual into smaller individual parts, then apply a deep GCN to learn the relation graph of these individual parts. We perform experiments on two widely used datasets and obtain competitive results that demonstrated the effectiveness of our method.
Mongolian Questions Classification Based on Multi-Head Attention

Question classification is a crucial subtask in question answering system. Mongolian is a kind of few resource language. It lacks public labeled corpus. And the complex morphological structure of Mongolian vocabulary makes the data-sparse problem. This paper proposes a classification model, which combines the Bi-LSTM model with the Multi-Head Attention mechanism. The Multi-Head Attention mechanism extracts relevant information from different dimensions and representation subspace. According to the characteristics of Mongolian word-formation, this paper introduces Mongolian morphemes representation in the embedding layer. Morpheme vector focuses on the semantics of the Mongolian word. In this paper, character vector and morpheme vector are concatenated to get word vector, which sends to the Bi-LSTM getting context representation. Finally, the Multi-Head Attention obtains global information for classification. The model experimented on the Mongolian corpus. Experimental results show that our proposed model significantly outperforms baseline systems.
A File-Level Continuous Data Protection Scheme for Enforcing Security Baseline

Massive data is the basis of machine learning, and continuous data protection is an effective means to ensure data integrity and availability. At present, continuous data protection adopts the same backup strategy in different host security environments, ignoring the potential relationship between host security state and data destruction, resulting in a low utilization rate of backup storage space. To make better use of backup storage space and improve data recovery speed, this paper proposes a file-level continuous data protection scheme for enforcing security baseline (SB-CDP). SB-CDP combines a security baseline with continuous data protection to dynamically adjust the backup strategy based on the host security status provided by the security baseline. The experimental results show that, on the one hand, SB-CDP can effectively utilize the backup storage space, on the other hand, it can effectively improve the data recovery speed and reduce the impact on the system performance.
Subcategorization frame identification for learner English

Abstract As large-scale learner corpora become increasingly available, it is vital that natural language processing (NLP) technology is developed to provide rich linguistic annotations necessary for second language (L2) research. We present a system for automatically analyzing subcategorization frames (SCFs) for learner English. SCFs link lexis with morphosyntax, shedding light on the interplay between lexical and structural information in learner language. Meanwhile, SCFs are crucial to the study of a wide range of phenomena including individual verbs, verb classes and varying syntactic structures. To illustrate the usefulness of our system for learner corpus research and second language acquisition (SLA), we investigate how L2 learners diversify their use of SCFs in text and how this diversity changes with L2 proficiency.
Littlebits Versus Makey Makey with Scratch: An User Perception for Tangible User Interfaces

The main goal of this paper is to compare how different commercial systems with tangible user interfaces (TUIs) [11, 12] impact the user’s experience when targeting the same application. Two commercial systems were compared. The first one uses the littlebits Synth Kit [3] and the second one uses Scratch [18] and Makey Makey [5, 14] together, connected to a computer. The tested application was a MIDI keyboard that was implemented using both systems. The applications were designed with the same functions even with different interfaces provided by both systems. Usability was tested with the intent of assuring that both systems had similar functionalities and, with this aspect in mind, focus primarily on the UX itself. To test the Usability, we used the System Usability Score [2], while the UX was tested using Attrakdiff [8]. As a result of the case study, it was possible to realize that, considering the user’s point of view, the Littlebits Synth Kit is better than the combination of Makey Makey with Scratch. To prove so, the usability was analyzed and, since they were similar, it was possible to compare the UX, which lead to better results by the littlebits than its counterpart, the system with Makey Makey.
Person tracking with non-overlapping multiple cameras

Monitoring and tracking of any target in a surveillance system is an important task. When these targets are human then this problem comes under person identification and tracking. At present, large scale smart video surveillance system is an essential component for any commercial or public campus. Since field of view (FOV) of a camera is limited; for large area monitoring, multiple cameras are needed at different locations. This paper proposes a novel model for tracking a person under multiple non-overlapping cameras. It builds the reference signature of the person at the beginning of the tracking system to match with the upcoming signatures captured by other cameras within the specified area of observation with the help of trained support vector machine (SVM) between two cameras. For experiments, wide area re-identification dataset (WARD) and a real-time scenario have been used with color, shape and texture features for person's re-identification.
R-MSSIM: image quality assessment while performing object detection

As deep learning continues to mature, researchers have increasingly turned to deep neural network to process object detection in images. While the accuracy of object detection continues to increase, researchers can analyze object features in images better. Image quality assessment is an important research topic in computer vision. When analyzing and processing images, it will lead to more computing overhead if different tasks are performed separately. It can save computing overhead that combining object detection and quality assessment. In order to process quality assessment better while detecting object and to improve the efficiency of image processing, we introduce R-MSSIM, a new system of image quality assessment stronger based on region proposal and structural similarity measure. R-MSSIM can improves the performance of classical algorithms greatly based on object detection. R-MSSIM fits the features of local regions to the final mean opinion score(MOS) while detecting region proposals in an image, which makes the interpretability of image quality assessment stronger. Finally, we prove that our method has a large performance improvement compared with the classical algorithm through a series of experiments, and achieved relatively good performance.
A Robust MP3 Audio Steganography with Improved Capacity

Data transmission on the internet should be protected from severe threats. An effective and efficient method to achieve this is Steganography. Steganography is a type of secret communication in which the secret information is hidden in a carrier such as an image, audio, or a video. Audio files are very often used by the steganographers as the carriers of secret information because of the large amount of space available for information hiding. This paper proposes a robust steganography technique applied to MP3 audio with improved capacity. The proposed method is robust against compression, cropping, filtering, resampling and normalization. The performance of the technique is measured using the metrics - Signal to Noise Ratio (SNR) and Squared Pearson Correlation Coefficient (SPCC).
End-to-end Saliency-Guided Deep Image Retrieval

A challenging issue of content-based image retrieval (CBIR) is to distinguish the target object from cluttered backgrounds, resulting in more discriminative image embeddings, compared to situations where feature extraction is distracted by irrelevant objects. To handle the issue, we propose a saliency-guided model with deep image features. The model is fully based on convolution neural networks (CNNs) and it incorporates a visual saliency detection module, making saliency detection a preceding step of feature extraction. The resulted saliency maps are utilized to refine original inputs and then compatible image features suitable for ranking are extracted from refined inputs. The model suggests a working scheme of involving saliency information into existing CNN-based CBIR systems with minimum impacts on the them. Some work assist image retrieval with other methods like object detection or semantic segmentation, but they are not so fine-grained as saliency detection, meanwhile some of them require additional annotations to train. In contrast, we train the saliency module in weak-supervised end-to-end style and do not need saliency ground truth. Extensive experiments are conducted on standard image retrieval benchmarks and our model shows competitive retrieval results.
Building Label-Balanced Emotion Corpus Based on Active Learning for Text Emotion Classification

In Supervised-learning of emotions from human language, to keep the emotion labels balanced in the training set is a challenging task since emotion labels are highly biased in raw data of human language. In this paper, we propose a novel method based on active learning to partially inhibit the polarization of text samples with more frequently observed emotion labels for constructing the training set, and to encourage the selection of samples with less frequently observed emotion labels. For each batch of unlabeled samples, the selected samples by our approach are given the ground truth emotion labels from human experts before they are merged to the training data. Our experiment of multi-label emotion classification on Chinese Weibo messages suggests that the proposed method is effective in constructing the label-balanced training set for text emotion classification, and the supervised text emotion classification results have been steadily improved with such training set.
Single-Letter Characterizations for Information Erasure under Restriction on the Output Distribution

In order to erase data including confidential information stored in storage devices, an unrelated and random sequence is usually overwritten, which prevents the data from being restored. The problem of minimizing the cost for information erasure when the amount of information leakage of the confidential information should be less than or equal to a constant asymptotically has been introduced by T. Matsuta and T. Uyematsu. Whereas the minimum cost for overwriting has been given for general sources, a single-letter characterization for stationary memoryless sources is not easily derived. In this paper, we give single-letter characterizations for stationary memoryless sources under two types of restrictions: one requires the output distribution of the encoder to be independent and identically distributed (i.i.d.) and the other requires it to be memoryless but not necessarily i.i.d. asymptotically. The characterizations indicate the relation among the amount of information leakage, the minimum cost for information erasure and the rate of the size of uniformly distributed sequences. The obtained results show that the minimum costs are different between these restrictions.
Hierarchical Interactive Matching Network for Multi-turn Response Selection in Retrieval-Based Chatbots

We study multi-turn response selection in open domain dialogue systems, where the best-matched response is selected according to a conversation context. The widely used sequential matching models match a response candidate with each utterance in the conversation context through a representation-interaction-aggregation framework, but do not pay enough attention to the inter-utterance dependencies at the representation stage and global information guidance at the interaction stage. They may lead to the result that the matching features of utterance-response pairs may be one-sided or even noisy. In this paper, we propose a hierarchical interactive matching network (HIMN) to model both aspects in a unified framework. In HIMN, we model the dependencies between adjacency utterances in the context with multi-level attention mechanism. Then a two-level hierarchical interactive matching is exploited to introduce the global context information to assist in distilling important matching features of each utterance-response pair at the interaction stage. Finally, the two-level matching features are merged through gate mechanism. Empirical results on both Douban Corpus and Ecommerce Corpus show that HIMN can significantly outperform the competitive baseline models for multi-turn response selection.
Weight Aware Feature Enriched Biomedical Lexical Answer Type Prediction

Lexical Answer Type (LAT) prediction is an essential part of question classification. It aims to assign certain lexical answer type to the questions to narrow down the search space and improve the classifier's performance. LAT prediction is a challenge in the biomedical domain since it is more of a multi-label classification question, which means each question has more than one label. In this paper, we employ the Label Powerset method to transform multi-label classification problems into multi-classification problems. Afterwards we introduced a random forest based mechanism to partition the features into used (important) and unused (unimportant) sets with corresponding weights. Furthermore, by assuming that the unimportant features are not useless, we employ principal components analysis to get the information from the unused feature set. By combing these two types of features, the experimental study on the BioMedLAT dataset has demonstrated our method's potential.
Graph Based Subjective Matching of Trusted Strings and Blockchain Based Filtering for Connected Vehicles

Advances in technology have lead to the creation of a connected world. Due the increase in the number of smart and autonomous cars, the safety and associated comfort level of driving has led to attempts to adopt conventional vehicular access network to the world of connected vehicles. Consolidating the cooperative safety and collected mobility management from different distributed devices are of the utmost importance. However, the prime objective of connected vehicles is not only to impose security and trust measures for individual vehicles, but the strategy of connected vehicle should also concentrate on the cooperative and collective environment on fleets of vehicles. Therefore, keeping simple authentication and access control may not be efficient to evaluate trust and assurance for all the distributed stakeholders. Trust being an important entity for this entire system, the strategy for trust evaluation becomes also, crucial. In this paper we propose a broader content matching model of trusted strings and block chain based filtering for connected Vehicles. Where a content and subject headings are first matched and then the outcome of that is consolidated by a distributed block chain consensus voting mechanism for any decision taken with respect to trust evaluation .
A Smart Interface HUD Optimized for VR HMD and Leap Motion

Abstract Virtual reality head-mounted displays (VR HMDs) can provide three-dimensional content to increase user immersion that is relatively acceptable. However, user interactions with VR HMDs often do not satisfy a virtual environment. In this article, the authors propose a smart human‐computer interaction technique that runs on VR HMDs and Leap Motion. In this study, they focused on improving interaction methods for VR content and providing interfaces and information necessary for the operation of such content in a user-friendly manner, which should reduce fatigue and dizziness. To this end, they propose and validate a smart head-up display that is suitable for VR content. Specifically, a virtual head-up display (HUD) was designed and optimized for use with VR HMD and Leap Motion technologies. Additionally, they present comparative performance tests to validate the proposed system.
An Adaptive RK-UKF Projectile Impact Point Prediction Method Based on Strong Tracking Filter

This paper proposes an adaptive RK-UKF projectile impact point prediction (IPP) method based on strong tracking filter. This method processes the trajectory model by unscented Kalman filter (UKF) to avoid linearization error, and by Runge- Kutta integral to avoid discretization error, then combines the strong tracking filter to adaptively adjust the state prediction covariance matrix and gain matrix to compensate for the deviation of the trajectory model. Simulation proves that this method can improve the convergence performance of the filter and the accuracy of impact point prediction.