Textual and structural approaches to detecting figure plagiarism in scientific publications

The figures play important role in disseminating important ideas and findings which enable the readers to understand the details of the work. The part of figures in understanding the details of the documents increase more use of them, which have led to a serious problem of taking other peoples’ figures without giving credit to the source. Although significant efforts have been made in developing methods for estimating pairwise diagram figure similarity, there are little attentions found in the research community to detect any of the instances of figure plagiarism such as manipulating figures by changing the structure of the figure, inserting, deleting and substituting the components or when the text content is manipulated. To address this gap, this project compares theeffectiveness of the textual and structural representations of techniques to support the figure plagiarism detection. In addition to these two representations, the textual comparison method is designed to match the figure contents based on a word-gram representation using the Jaccard similarity measure, while the structural comparison method is designed to compare the text within the components as well as the relationship between the components of the figures using graph edit distance measure. These techniques are experimentally evaluated across the seven instances of figure plagiarism, in terms of their similarity values and the precision and recall metrics. The experimental results show that the structural representation of figures slightly outperformed the textual representation in detecting all the instances of the figure plagiarism

TWINLATIN: Twinning European and Latin-American river basins for research enabling sustainable water resources management. Combined Report D3.1 Hydrological modelling report and D3.2 Evaluation report

Water use has almost tripled over the past 50 years and in some regions the water demand already exceeds supply (Vorosmarty et al., 2000). The world is facing a “global water crisis”; in many countries, current levels of water use are unsustainable, with systems vulnerable to collapse from even small changes in water availability. The need for a scientifically-based assessment of the potential impacts on water resources of future changes, as a basis for society to adapt to such changes, is strong for most parts of the world. Although the focus of such assessments has tended to be climate change, socio-economic changes can have as significant an impact on water availability across the four main use sectors i.e. domestic, agricultural, industrial (including energy) and environmental. Withdrawal and consumption of water is expected to continue to grow substantially over the next 20-50 years (Cosgrove & Rijsberman, 2002), and consequent changes in availability may drastically affect society and economies. One of the most needed improvements in Latin American river basin management is a higher level of detail in hydrological modelling and erosion risk assessment, as a basis for identification and analysis of mitigation actions, as well as for analysis of global change scenarios. Flow measurements are too costly to be realised at more than a few locations, which means that modelled data are required for the rest of the basin. Hence, TWINLATIN Work Package 3 “Hydrological modelling and extremes” was formulated to provide methods and tools to be used by other WPs, in particular WP6 on “Pollution pressure and impact analysis” and WP8 on “Change effects and vulnerability assessment”. With an emphasis on high and low flows and their impacts, WP3 was originally called “Hydrological modelling, flooding, erosion, water scarcity and water abstraction”. However, at the TWINLATIN kick-off meeting it was agreed that some of these issues resided more appropriately in WP6 and WP8, and so WP3 was renamed to focus on hydrological modelling and hydrological extremes. The specific objectives of WP3 as set out in the Description of Work are

On the use of smartphones as novel photogrammetric water gauging instruments: Developing tools for crowdsourcing water levels

The term global climate change is omnipresent since the beginning of the last decade. Changes in the global climate are associated with an increase in heavy rainfalls that can cause nearly unpredictable flash floods. Consequently, spatio-temporally high-resolution monitoring of rivers becomes increasingly important. Water gauging stations continuously and precisely measure water levels. However, they are rather expensive in purchase and maintenance and are preferably installed at water bodies relevant for water management. Small-scale catchments remain often ungauged. In order to increase the data density of hydrometric monitoring networks and thus to improve the prediction quality of flood events, new, flexible and cost-effective water level measurement technologies are required. They should be oriented towards the accuracy requirements of conventional measurement systems and facilitate the observation of water levels at virtually any time, even at the smallest rivers. A possible solution is the development of a photogrammetric smartphone application (app) for crowdsourcing water levels, which merely requires voluntary users to take pictures of a river section to determine the water level. Today’s smartphones integrate high-resolution cameras, a variety of sensors, powerful processors, and mass storage. However, they are designed for the mass market and use low-cost hardware that cannot comply with the quality of geodetic measurement technology. In order to investigate the potential for mobile measurement applications, research was conducted on the smartphone as a photogrammetric measurement instrument as part of the doctoral project. The studies deal with the geometric stability of smartphone cameras regarding device-internal temperature changes and with the accuracy potential of rotation parameters measured with smartphone sensors. The results show a high, temperature-related variability of the interior orientation parameters, which is why the calibration of the camera should be carried out during the immediate measurement. The results of the sensor investigations show considerable inaccuracies when measuring rotation parameters, especially the compass angle (errors up to 90° were observed). The same applies to position parameters measured by global navigation satellite system (GNSS) receivers built into smartphones. According to the literature, positional accuracies of about 5 m are possible in best conditions. Otherwise, errors of several 10 m are to be expected. As a result, direct georeferencing of image measurements using current smartphone technology should be discouraged. In consideration of the results, the water gauging app Open Water Levels (OWL) was developed, whose methodological development and implementation constituted the core of the thesis project. OWL enables the flexible measurement of water levels via crowdsourcing without requiring additional equipment or being limited to specific river sections. Data acquisition and processing take place directly in the field, so that the water level information is immediately available. In practice, the user captures a short time-lapse sequence of a river bank with OWL, which is used to calculate a spatio-temporal texture that enables the detection of the water line. In order to translate the image measurement into 3D object space, a synthetic, photo-realistic image of the situation is created from existing 3D data of the river section to be investigated. Necessary approximations of the image orientation parameters are measured by smartphone sensors and GNSS. The assignment of camera image and synthetic image allows for the determination of the interior and exterior orientation parameters by means of space resection and finally the transfer of the image-measured 2D water line into the 3D object space to derive the prevalent water level in the reference system of the 3D data. In comparison with conventionally measured water levels, OWL reveals an accuracy potential of 2 cm on average, provided that synthetic image and camera image exhibit consistent image contents and that the water line can be reliably detected. In the present dissertation, related geometric and radiometric problems are comprehensively discussed. Furthermore, possible solutions, based on advancing developments in smartphone technology and image processing as well as the increasing availability of 3D reference data, are presented in the synthesis of the work. The app Open Water Levels, which is currently available as a beta version and has been tested on selected devices, provides a basis, which, with continuous further development, aims to achieve a final release for crowdsourcing water levels towards the establishment of new and the expansion of existing monitoring networks.Der Begriff des globalen Klimawandels ist seit Beginn des letzten Jahrzehnts allgegenwärtig. Die Veränderung des Weltklimas ist mit einer Zunahme von Starkregenereignissen verbunden, die nahezu unvorhersehbare Sturzfluten verursachen können. Folglich gewinnt die raumzeitlich hochaufgelöste Überwachung von Fließgewässern zunehmend an Bedeutung. Pegelmessstationen erfassen kontinuierlich und präzise Wasserstände, sind jedoch in Anschaffung und Wartung sehr teuer und werden vorzugsweise an wasserwirtschaftlich-relevanten Gewässern installiert. Kleinere Gewässer bleiben häufig unbeobachtet. Um die Datendichte hydrometrischer Messnetze zu erhöhen und somit die Vorhersagequalität von Hochwasserereignissen zu verbessern, sind neue, kostengünstige und flexibel einsetzbare Wasserstandsmesstechnologien erforderlich. Diese sollten sich an den Genauigkeitsanforderungen konventioneller Messsysteme orientieren und die Beobachtung von Wasserständen zu praktisch jedem Zeitpunkt, selbst an den kleinsten Flüssen, ermöglichen. Ein Lösungsvorschlag ist die Entwicklung einer photogrammetrischen Smartphone-Anwendung (App) zum Crowdsourcing von Wasserständen mit welcher freiwillige Nutzer lediglich Bilder eines Flussabschnitts aufnehmen müssen, um daraus den Wasserstand zu bestimmen. Heutige Smartphones integrieren hochauflösende Kameras, eine Vielzahl von Sensoren, leistungsfähige Prozessoren und Massenspeicher. Sie sind jedoch für den Massenmarkt konzipiert und verwenden kostengünstige Hardware, die nicht der Qualität geodätischer Messtechnik entsprechen kann. Um das Einsatzpotential in mobilen Messanwendungen zu eruieren, sind Untersuchungen zum Smartphone als photogrammetrisches Messinstrument im Rahmen des Promotionsprojekts durchgeführt worden. Die Studien befassen sich mit der geometrischen Stabilität von Smartphone-Kameras bezüglich geräteinterner Temperaturänderungen und mit dem Genauigkeitspotential von mit Smartphone-Sensoren gemessenen Rotationsparametern. Die Ergebnisse zeigen eine starke, temperaturbedingte Variabilität der inneren Orientierungsparameter, weshalb die Kalibrierung der Kamera zum unmittelbaren Messzeitpunkt erfolgen sollte. Die Ergebnisse der Sensoruntersuchungen zeigen große Ungenauigkeiten bei der Messung der Rotationsparameter, insbesondere des Kompasswinkels (Fehler von bis zu 90° festgestellt). Selbiges gilt auch für Positionsparameter, gemessen durch in Smartphones eingebaute Empfänger für Signale globaler Navigationssatellitensysteme (GNSS). Wie aus der Literatur zu entnehmen ist, lassen sich unter besten Bedingungen Lagegenauigkeiten von etwa 5 m erreichen. Abseits davon sind Fehler von mehreren 10 m zu erwarten. Infolgedessen ist von einer direkten Georeferenzierung von Bildmessungen mittels aktueller Smartphone-Technologie abzusehen. Unter Berücksichtigung der gewonnenen Erkenntnisse wurde die Pegel-App Open Water Levels (OWL) entwickelt, deren methodische Entwicklung und Implementierung den Kern der Arbeit bildete. OWL ermöglicht die flexible Messung von Wasserständen via Crowdsourcing, ohne dabei zusätzliche Ausrüstung zu verlangen oder auf spezifische Flussabschnitte beschränkt zu sein. Datenaufnahme und Verarbeitung erfolgen direkt im Feld, so dass die Pegelinformationen sofort verfügbar sind. Praktisch nimmt der Anwender mit OWL eine kurze Zeitraffersequenz eines Flussufers auf, die zur Berechnung einer Raum-Zeit-Textur dient und die Erkennung der Wasserlinie ermöglicht. Zur Übersetzung der Bildmessung in den 3D-Objektraum wird aus vorhandenen 3D-Daten des zu untersuchenden Flussabschnittes ein synthetisches, photorealistisches Abbild der Aufnahmesituation erstellt. Erforderliche Näherungen der Bildorientierungsparameter werden von Smartphone-Sensoren und GNSS gemessen. Die Zuordnung von Kamerabild und synthetischem Bild erlaubt die Bestimmung der inneren und äußeren Orientierungsparameter mittels räumlichen Rückwärtsschnitt. Nach Rekonstruktion der Aufnahmesituation lässt sich die im Bild gemessene 2D-Wasserlinie in den 3D-Objektraum projizieren und der vorherrschende Wasserstand im Referenzsystem der 3D-Daten ableiten. Im Soll-Ist-Vergleich mit konventionell gemessenen Pegeldaten zeigt OWL ein erreichbares Genauigkeitspotential von durchschnittlich 2 cm, insofern synthetisches und reales Kamerabild einen möglichst konsistenten Bildinhalt aufweisen und die Wasserlinie zuverlässig detektiert werden kann. In der vorliegenden Dissertation werden damit verbundene geometrische und radiometrische Probleme ausführlich diskutiert sowie Lösungsansätze, auf der Basis fortschreitender Entwicklungen von Smartphone-Technologie und Bildverarbeitung sowie der zunehmenden Verfügbarkeit von 3D-Referenzdaten, in der Synthese der Arbeit vorgestellt. Mit der gegenwärtig als Betaversion vorliegenden und auf ausgewählten Geräten getesteten App Open Water Levels wurde eine Basis geschaffen, die mit kontinuierlicher Weiterentwicklung eine finale Freigabe für das Crowdsourcing von Wasserständen und damit den Aufbau neuer und die Erweiterung bestehender Monitoring-Netzwerke anstrebt

Global Properties of Natural Scenes Shape Local Properties of Human Edge Detectors

Visual cortex analyzes images by first extracting relevant details (e.g., edges) via a large array of specialized detectors. The resulting edge map is then relayed to a processing pipeline, the final goal of which is to attribute meaning to the scene. As this process unfolds, does the global interpretation of the image affect how local feature detectors operate? We characterized the local properties of human edge detectors while we manipulated the extent to which the statistical properties of the surrounding image conformed to those encountered in natural vision. Although some aspects of local processing were unaffected by contextual manipulations, we observed significant alterations in the operating characteristics of the detector which were solely attributable to a higher-level semantic interpretation of the scene, unrelated to lower-level aspects of image statistics. Our results suggest that it may be inaccurate to regard early feature detectors as operating outside the domain of higher-level vision; although there is validity in this approach, a full understanding of their properties requires the inclusion of knowledge-based effects specific to the statistical regularities found in the natural environment

Characterisation of flow regimes of the East Yorkshire Chalk Aquifer

The Cretaceous Chalk is a fractured aquifer with high permeability and low storage, that supplies about 60% of potable groundwater in the UK. Since the 1970s, groundwater quality in the Chalk aquifer in East Yorkshire focusing on the Kilham catchment area has been threatened by nitrate and pesticide contamination. This project sets out to better characterize the groundwater flow velocities, patterns, and regimes in the East Yorkshire Chalk aquifer to facilitate catchment and source protection zone delineation for aquifer protection. The aims were pursued via: conducting of single open well ambient flow dilution testing and reinterpretation of previous data including geophysical logs, well-to-well tracer tests; well hydraulic head and stream discharge hydrographs; conducting of time-series monitoring of springs and wells for temperature, SEC and major ion composition; re-analyses of archived pumping test data to investigate the hydraulic properties of faults and consequent development of conceptual model of aquifer recharge, throughput and outflow. Discrete flow horizons were detected at all depths of investigation (up to 70 mgbl), but with the majority of flow features at shallow depth in promixity to the water table. Ambient flow pattern in wells is location specific, with strong upwards flows of up to 4 m/min in valley boreholes while boreholes at high elevation typically show downflow, and those at intermediate elevation show crossflows. Previous well-to-well and well-to-spring tracer tests are interpreted as indicating karstic flow via solutionally enlarged bedding features connected by vertical fractures, with groundwater velocities ranging between 40 – 480 m/d, connecting wells and springs 4.2 km apart. Novel approaches for estimating local groundwater velocities from single open-well dilution tests were further developed in this work and applied to the single-well data; these gave velocities in broad agreement with those from well-to-well tracer tests, validating the single well method. Monitoring data showed that wells and spring temperatures fell in a narrow range (9.2 – 10.1 °C) reflective of average annual (10.3 °C) temperature suggesting sufficient residence time to enable groundwater temperature to equilibrate with that of the aquifer. Groundwater and spring waters are dominated by Ca2+ and HCO3-, with evidence of nitrate contamination (51 – 74 mg/L NO3-). Well water SEC is non-varying, whereas that for springs showed variation reflective of both degree of access of soil-derived CO2 and seasonal variations in soil biological activity and hence soil pCO2. Ca2+ and HCO3- concentration variation in the springs may also indicate seasonal switching between open and closed carbonate systems. Well hydraulic head and groundwater fed stream discharge hydrographs showed seasonal fluctuation, with well hydraulic head variations increasing towards recharge areas. The effects of faults on aquifer transmissivity could not be ascertained via re-analyses of archived pumping tests. The findings from this work were used to develop both physical and chemical hydrogeological conceptual models for the catchment. The aquifer functions as a hydrogeologic continuum, limiting the utility of the individual source protection zones currently in use by the Environment Agency of England and Wales (EA), but supports the EA’s decision to demarcate the entire Chalk outcrop as nitrate vulnerable. Knowledge of ambient well flow velocities, and flow patterns are required for effective planning of future geochemical sampling, contaminant tracking, remediation activity and pumping tests campaigns. Such knowledge allows both depth specific sampling and vertical hydraulic characterisation of depth intervals in wells, which are vital to develop more accurate simulations of contaminant transport. Further hydraulic characterisation of fault zones is also recommended

Schema-Driven Information Extraction from Heterogeneous Tables

In this paper, we explore the question of whether large language models can support cost-efficient information extraction from tables. We introduce schema-driven information extraction, a new task that transforms tabular data into structured records following a human-authored schema. To assess various LLM's capabilities on this task, we develop a benchmark composed of tables from four diverse domains: machine learning papers, chemistry literature, material science journals, and webpages. Alongside the benchmark, we present an extraction method based on instruction-tuned LLMs. Our approach shows competitive performance without task-specific labels, achieving F1 scores ranging from 74.2 to 96.1, while maintaining great cost efficiency. Moreover, we validate the possibility of distilling compact table-extraction models to reduce API reliance, as well as extraction from image tables using multi-modal models. By developing a benchmark and demonstrating the feasibility of this task using proprietary models, we aim to support future work on open-source schema-driven IE models

Steganography A Data Hiding Technique

Steganography implements an encryption technique in which communication takes place by hiding information. A hidden message is the combination of a secret message with the carrier message. This technique can be used to hide the message in an image, a video file, an audio file or in a file system. There are large variety of steganography techniques that will be used for hiding secret information in images. The final output image is called as a stego-image which consists of a secret message or information. Imperceptibility, payload, and robustness are three most important parameters for audio steganography. For a more secure approach, encryption can be used, which will encrypt the secret message using a secret key and then sent to the receiver. The receiver after receiving the message then decrypts the secret message to obtain the original one. In this paper, compared steganography with cryptography, which is an encrypting technique and explained how steganography provides better security in terms of hiding the secret message. In this paper, the various techniques are illustrated, which are used in steganography and studying the implementation of those techniques. Also, demonstrated the implementation process of one of the steganography techniques. A comparative analysis is performed between various steganographic tools by using the sample test images and test data. The quality metrics such as PSNR and SSIM are calculated for the final output images which are used for rating the tools. This paper also discusses about the Steganalysis which is known as the process of identifying the use of steganography

Complex adaptive systems based data integration : theory and applications

Data Definition Languages (DDLs) have been created and used to represent data in programming languages and in database dictionaries. This representation includes descriptions in the form of data fields and relations in the form of a hierarchy, with the common exception of relational databases where relations are flat. Network computing created an environment that enables relatively easy and inexpensive exchange of data. What followed was the creation of new DDLs claiming better support for automatic data integration. It is uncertain from the literature if any real progress has been made toward achieving an ideal state or limit condition of automatic data integration. This research asserts that difficulties in accomplishing integration are indicative of socio-cultural systems in general and are caused by some measurable attributes common in DDLs. This research’s main contributions are: (1) a theory of data integration requirements to fully support automatic data integration from autonomous heterogeneous data sources; (2) the identification of measurable related abstract attributes (Variety, Tension, and Entropy); (3) the development of tools to measure them. The research uses a multi-theoretic lens to define and articulate these attributes and their measurements. The proposed theory is founded on the Law of Requisite Variety, Information Theory, Complex Adaptive Systems (CAS) theory, Sowa’s Meaning Preservation framework and Zipf distributions of words and meanings. Using the theory, the attributes, and their measures, this research proposes a framework for objectively evaluating the suitability of any data definition language with respect to degrees of automatic data integration. This research uses thirteen data structures constructed with various DDLs from the 1960\u27s to date. No DDL examined (and therefore no DDL similar to those examined) is designed to satisfy the law of requisite variety. No DDL examined is designed to support CAS evolutionary processes that could result in fully automated integration of heterogeneous data sources. There is no significant difference in measures of Variety, Tension, and Entropy among DDLs investigated in this research. A direction to overcome the common limitations discovered in this research is suggested and tested by proposing GlossoMote, a theoretical mathematically sound description language that satisfies the data integration theory requirements. The DDL, named GlossoMote, is not merely a new syntax, it is a drastic departure from existing DDL constructs. The feasibility of the approach is demonstrated with a small scale experiment and evaluated using the proposed assessment framework and other means. The promising results require additional research to evaluate GlossoMote’s approach commercial use potential