Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of hunting regimes on three regeneration in lowland beech (Fagus sylvaticaL.) forests

AndreAs Fichtner1, Knut sturm2, JeAnine WAgner3, AiKo hucKAuF1 and hermAnn ellenberg4 1institute for nature and resource conservation, christian-Albrechts-university Kiel, olshausenstr. 75, 24118 Kiel, germany 2stadtwald lübeck, Kronsforder hauptstraße 80, 23560 lübeck, germany 3bavarian state institute of Forestry, hans-carl-von-carlowitz-platz 1, 85354 Freising, germany 4deceased 06.11. 2009. Formerly: institute for silviculture, Federal research institute of rural Areas, Forestry and Fisheries (vti), leuschnerstr. 91, 21031 hamburg, germany forstarchiv 82, 75-81 (2011)
Class Break Point Determination Using CK Metrics Thresholds

The design and development of any project has got a well-defined project development cycle. But once the project or the product has been developed, it is subject to change due a lot a policy changes on the part of the organization or the government. These changes are implemented on the code but most of the time these changes are not reflected on the design document. This leads to inconsistencies in terms of design and code thereby causing depreciation in terms of quality. In this work we propose to use the object oriented metrics which uses the parameters mentioned in the CK metrics suite to assess the quality of the software at the class level. The proposed tool namely \"Class Break Point\" which could be used to determine the class design validity. This tool can be used to check if the class is adhering to the OO design specifications. The tool is useful in predicting the decomposition point of the class.
Indonesian-English code mixing in Raditya Dika’s Manusia Setengah Salmon

This study was aimed at investigating Indonesian-English code mixing in Raditya Dika’s Manusia Setengah Salmon , particularly to find out the word classes of the code mixing, the meaning of the code mixing, the dominant use of the code mixing, and the reasons for using the code mixing. Content analysis in qualitative approach was applied in this study. To collect the data, it was through data collection, data reduction, data display, and conclusion drawing. The results showed that there are 65 sentences or utterances consisted English nouns as code mixing. They were categorized into countable nouns (87.69%), uncountable nouns (3.08%), abstract nouns (6.15%), concrete nouns (1.54%), and plural nouns (1.54%). Among them, countable nouns are more dominant than others. Also, three kinds of translating process found are: borrowing consisted of 25 sentences, literal consisted of 14 sentences, and adaptation consisted of 28 sentences.
ROBUST 2D AUGMENTED REALITY BASED ON HOMOGRAPHY REFINEMENT AND TEMPORAL COHERENCE

To augment real planes with virtual objects, the camera pose relative to the planes must be estimated, which can be done by plane-based camera tracking. In plane-based camera tracking, robustness and real-time capability are two critical factors but they are conflicting. To resolve the problem, we first employ a homography refinement method. Then, we use the temporal coherence of consecutive frames to eliminate the computational overhead caused by iterative refinements. It means that the camera pose estimated in the preceding frame is used as an initial camera pose of the current frame. Use of temporal coherence allows us to use more efficient feature tracking methods and thus we can achieve additional reduction in processing time. Experimental results demonstrate that 50% of the processing time can be saved while maintaining the robustness.
Recent Contributions of Theory to Our Understanding of the Atlantic Meridional Overturning Circulation

Revolutionary observational arrays, together with a new generation of ocean and climate models, have provided new and intriguing insights into the Atlantic Meridional Overturning Circulation (AMOC) over the last two decades. Theoretical models have also changed our view of the AMOC, providing a dynamical framework for understanding the new observations and the results of complex models. In this paper we review recent advances in conceptual understanding of the processes maintaining the AMOC. We discuss recent theoretical models that address issues such as the interplay between surface buoyancy and wind forcing, the extent to which the AMOC is adiabatic, the importance of mesoscale eddies, the interaction between the middepth North Atlantic Deep Water cell and the abyssal Antarctic Bottom Water cell, the role of basin geometry and bathymetry, and the importance of a three‐dimensional multiple‐basin perspective. We review new paradigms for deep water formation in the high‐latitude North Atlantic and the impact of diapycnal mixing on vertical motion in the ocean interior. And we discuss advances in our understanding of the AMOC's stability and its scaling with large‐scale meridional density gradients. Along with reviewing theories for the mean AMOC, we consider models of AMOC variability and discuss what we have learned from theory about the detection and meridional propagation of AMOC anomalies. Simple theoretical models remain a vital and powerful tool for articulating our understanding of the AMOC and identifying the processes that are most critical to represent accurately in the next generation of numerical ocean and climate models.
Simple Online Multiple Human Tracking based on LK Feature Tracker and Detection for Embedded Surveillance

In this paper, we propose a simple online multiple object (human) tracking method, LKDeep (Lucas-Kanade feature and Detection based Simple Online Multiple Object Tracker), which can run in fast online enough on CPU core only with acceptable tracking performance for embedded surveillance purpose. The proposed LKDeep is a pragmatic hybrid approach which tracks multiple objects (humans) mainly based on LK features but is compensated by detection on periodic times or on necessity times. Compared to other state-of-the-art multiple object tracking methods based on ‘Tracking-By-Detection (TBD)’ approach, the proposed LKDeep is faster since it does not have to detect object on every frame and it utilizes simple association rule, but it shows a good object tracking performance. Through experiments in comparison with other multiple object tracking (MOT) methods using the public DPM detector among online state-of-the-art MOT methods reported in MOT challenge [1], it is shown that the proposed simple online MOT method, LKDeep runs faster but with good tracking performance for surveillance purpose. It is further observed through single object tracking (SOT) visual tracker benchmark experiment [2] that LKDeep with an optimized deep learning detector can run in online fast with comparable tracking performance to other state-of-the-art SOT methods.
Situational crime prevention for automotive cybersecurity

The increase in number and types of various stakeholders interacting with self-driving vehicles expands the relevant automotive cybersecurity attack vectors that can be compromised. Furthermore, given the prominent role that human behavior plays in the lifetime of a vehicle, social and human-based factors must be considered in tandem with the technical factors when addressing cybersecurity. A focus on informing and enabling stakeholders and their corresponding actions promotes security of the vehicle through a human-focused and technology-enabled approach. Example stakeholders include the consumer operating the vehicle, the technicians working on the car, and the engineers designing the software. Strategies can be applied in both a social and technical manner to increase preventative security measures for autonomous vehicles by leveraging theoretical foundations from the criminology domain. In this work we harness a criminology theory approach to crime prevention, where we synergistically combine cybercrime theory, human factors, and technical solutions to develop a cybercrime prevention framework that accounts for a range of stakeholders relevant to an autonomous vehicle domain.
Profiles of software utilization by university mathematics faculty

Tools and methods used to maximize mathematics learning are a primary concern for mathematics education. According to the literature, software applications are becoming increasing more prevalent in mathematics. While there exists a consensus establishing technology as a valuable tool in mathematics, and consequently ample recognition that it is important to mathematics education, arbitrary application of technology is both inefficient and ineffective. The purpose of this study is to describe software utilization by academic mathematicians. Survey research was conducted using an electronic questionnaire distributed to members of 47 AMS Group I university mathematics departments (n=2857). Data were used to describe, differentiate, and explain practices regarding the nature and extent of utilizing mathematical software to facilitate mathematics. Further, beliefs about mathematics education were explored. In addition to increasing awareness in the education community, profiles based on participants’ responses (n=422) were created to aid in identifying core practices of mathematicians with respect to technology. The results indicate that software is increasingly important to mathematics, especially to communicating mathematics. However, participants expressed less favorable views when relating these technologies to learning and understanding mathematics. MATLAB, Mathematica, and Maple were the three primary software applications utilized. Selection of these software depended on the branch of mathematics but ii not necessarily on mathematical activities. In addition, results show that computer programming is considered an important aspect of mathematizing. In conclusion, there are appropriate choices and uses of mathematical software for mathematics and thus for mathematics education. LaTeX should be used for communication, C++ or Fortran for programming, and MATLAB for applied mathematics. Mathematics education researchers should play an important role by monitoring mathematical activities and software use in order to develop new strategies for teaching and learning.
How to better utilize code graphs in semantic code search?

Semantic code search greatly facilitates software reuse, which enables users to find code snippets highly matching user-specified natural language queries. Due to the rich expressive power of code graphs (e.g., control-flow graph and program dependency graph), both of the two mainstream research works (i.e., multi-modal models and pre-trained models) have attempted to incorporate code graphs for code modelling. However, they still have some limitations: First, there is still much room for improvement in terms of search effectiveness. Second, they have not fully considered the unique features of code graphs. In this paper, we propose a Graph-to-Sequence Converter, namely G2SC. Through converting the code graphs into lossless sequences, G2SC enables to address the problem of small graph learning using sequence feature learning and capture both the edges and nodes attribute information of code graphs. Thus, the effectiveness of code search can be greatly improved. In particular, G2SC first converts the code graph into a unique corresponding node sequence by a specific graph traversal strategy. Then, it gets a statement sequence by replacing each node with its corresponding statement. A set of carefully designed graph traversal strategies guarantee that the process is one-to-one and reversible. G2SC enables capturing rich semantic relationships (i.e., control flow, data flow, node/relationship properties) and provides learning model-friendly data transformation. It can be flexibly integrated with existing models to better utilize the code graphs. As a proof-of-concept application, we present two G2SC enabled models: GSMM (G2SC enabled multi-modal model) and GSCodeBERT (G2SC enabled CodeBERT model). Extensive experiment results on two real large-scale datasets demonstrate that GSMM and GSCodeBERT can greatly improve the state-of-the-art models MMAN and GraphCodeBERT by 92% and 22% on R@1, and 63% and 11.5% on MRR, respectively.
DynaPyt: a dynamic analysis framework for Python

Python is a widely used programming language that powers important application domains such as machine learning, data analysis, and web applications. For many programs in these domains it is consequential to analyze aspects like security and performance, and with Python’s dynamic nature, it is crucial to be able to dynamically analyze Python programs. However, existing tools and frameworks do not provide the means to implement dynamic analyses easily and practitioners resort to implementing an ad-hoc dynamic analysis for their own use case. This work presents DynaPyt, the first general-purpose framework for heavy-weight dynamic analysis of Python programs. Compared to existing tools for other programming languages, our framework provides a wider range of analysis hooks arranged in a hierarchical structure, which allows developers to concisely implement analyses. DynaPyt features selective instrumentation and execution modification as well. We evaluate our framework on test suites of 9 popular open-source Python projects, 1,268,545 lines of code in total, and show that it, by and large, preserves the semantics of the original execution. The running time of DynaPyt is between 1.2x and 16x times the original execution time, which is in line with similar frameworks designed for other languages, and 5.6%–88.6% faster than analyses using a built-in tracing API offered by Python. We also implement multiple analyses, show the simplicity of implementing them and some potential use cases of DynaPyt. Among the analyses implemented are: an analysis to detect a memory blow up in Pytorch programs, a taint analysis to detect SQL injections, and an analysis to warn about a runtime performance anti-pattern.
Analysis of student’s data using rapid miner

Data mining offers a new advance to data analysis using techniques based on machine learning, together with the conventional methods collectively known as educational data mining (EDM). Educational Data Mining has turned up as an interesting and useful research area for finding methods to improve quality of education and to identify various patterns in educational settings. It is useful in extracting information of students, teachers, courses, administrators from educational institutes such as schools/ colleges/universities and helps to suggest interesting learning experiences to various stakeholders. This paper focuses on the applications of data mining in the field of education and implementation of three widely used data mining techniques using Rapid Miner on the data collected through a survey.
Low Temperature Co-fired Ceramic for System-in-Package Applications at 122 GHz

This dissertation presents two fully-integrated 122 GHz radar System-inPackages (SiPs) in Low Temperature Co-fired Ceramic (LTCC) technology. In both SiPs, an active radar chip is integrated with LTCC-based passive components including antennas, signal transitions and a low frequency package base. The millimeter-wave interconnects between the radar chip and the LTCC-based circuitry are realized through standard half-wavelength wirebonds and the SiPs are encapsulated with an epoxy molding compound, namely, Polytec TC 430-T, which has a well-defined relative permittivity and a relatively low loss tangent in the frequency range of 110 to 170 GHz. The LTCC SiPs are surface-mounted on baseband printed circuit boards, thus building two fully-functional 122 GHz radar sensors, whose functionalities are successfully tested by measuring the distance of a standard target. The performance of one of these radar sensors shows an improvement over a commercial 122 GHz radar sensor. In addition, the LTCC SiPs shown in this work are compact, low-cost and require only standard package assembly steps. In Chapter 1, the primary factors, which lay the foundation for a fullyintegrated millimeter-wave system are defined. Thereafter, the target and workflow of this dissertation are specified. In Chapter 2, the standard LTCC manufacturing process as well as state-of-the-art LTCC manufacturing techniques are presented. The manufacturing defects, commonly observed in an LTCC module, e.g., shrinkage, layer misalignment and warping, are discussed. A suitable LTCC substrate is selected for this work and two LTCC-based SiP concepts are proposed after taking the LTCC capabilities
4D-STEM Characterization of Molecular Ordering in Organic Semiconductors

The nanoscale ordering amongst the constituent molecules plays a crucial role in determining the electronic, optical, and photovoltaic properties of organic semiconductors [1]. Understanding of the molecular ordering requires characterization methods that can precisely reveal the details of the ordering. In some cases, such as in TFT-LCD, where the degree of ordering is extremely high and the molecules are almost uniformly semi-crystalline, a large area diffraction technique (e.g. X-ray diffraction) can provide fairly good information about the ordering. However, in many cases, such as in organic photovoltaic (OPV) thin films, the ordering is much less in degree and more subtle in detail, which makes it much more difficult to characterize using any conventional diffraction or imaging methods. This “gap in the characterization” and the consequent challenges in understanding the important structure-property relationships have been the major obstacles in the development of new-generation OPV materials and devices for energy harvesting applications.
FlakeRepro: automated and efficient reproduction of concurrency-related flaky tests

Flaky tests, which can non-deterministically pass or fail on the same code, impose significant burden on developers by providing misleading signals during regression testing. Microsoft developers consider flaky tests as one of the top two reasons for slowing down software development. In order to debug the root-cause of a flaky behavior, a developer often needs to first reliably reproduce a failed execution. Unfortunately, this is non-trivial. For example, most of the flakiness in unit tests are caused by concurrency, and reproducing their failures requires specific thread interleaving. To address this challenge, we introduce FlakeRepro that helps developers reproduce a failed execution of a flaky test caused by concurrency. FlakeRepro combines static and dynamic analysis to quickly identify an interleaving that makes a flaky test fail with the same original error message. FlakeRepro is efficient: it can reproduce a failed execution after exploring few tens of interleavings. FlakeRepro integrates well with existing systems: it automatically instruments test binaries that can run on existing and unmodified test pipelines. We have implemented FlakeRepro for . NET and used it in Microsoft. In an experiment with 22 Microsoft projects, FlakeRepro could reproduce 26 of total 31 concurrent-related flaky tests, after exploring only <7 interleavings and within 6 minutes on average.
Participatory agent-based modelling in healthcare: a domain-specific modelling language approach

Management of infectious diseases is a critical issue in hospital emergency departments (ED). Participatory agent-based modelling (ABM) is an effective method of capturing and evaluating potential infection control interventions without incurring real-world costs. However, typical participatory modelling approaches use general-purpose programming languages or technically complex agent-oriented DSMLs during implementation. These languages are often inaccessible to ED staff, making communication difficult during domain analysis and reducing the traceability of model design. We argue that high-abstraction domain-specific modelling languages (DSMLs) can help improve the accessibility of ABM development to ED stakeholders, allowing them to engage more effectively in model design and experimentation. Rather than developing a DSML at the platform-level (i.e. using ABM concepts) we present a DSML that uses a highly-abstract domain-level syntax, specific to the ED domain. We present the current iteration of this DSML, and the vision for a participatory modelling methodology to be used during model and DSML development. We discuss ongoing modelling case studies in collaboration with St Thomas' Hospital in London and present an initial DSML empirical evaluation.
Analysis of synchronisation patterns in active objects based on behavioural types

The active object concept is a powerful computational model for defining distributed and concurrent systems. This model has recently gained prominence, largely thanks to its simplicity and its abstraction level. In this work we study an active object model with no explicit future type and wait-by-necessity synchronisations, a lightweight technique that synchronises invocations when the corresponding values are strictly needed. Although high concurrency combined with a high level of transparency leads to good performances, they also make the system more prone to problems such as deadlocks. This is the reason that led us to study deadlock analysis in this active objects model.The development of our deadlock analysis is divided in two main works. In the first work we focus on the implicit synchronisation on the availability of some value. This way we are able to analyse the data-flow synchronisation inherent to languages that feature wait-by-necessity. In the second work we present a static analysis technique based on effects and behavioural types for deriving synchronisation patterns of stateful active objects and verifying the absence of deadlocks in this context. Our effect system traces the access to object fields, thus allowing us to compute behavioural types that express synchronisation patterns in a precise way. As a consequence we can automatically verify the absence of deadlocks in active object based programs with wait-by-necessity synchronisations and stateful active objects.
Change-aware mutation testing for evolving systems

Although the strongest test criteria, traditional mutation testing has shown to not scale with modern incremental development practices. In this work, we describe our proposal of commit-aware mutation testing and introduce the concept of commit-relevant mutants suitable to evaluate the system's behaviour after being affected by regression changes. We show that commit-relevant mutants represent a small but effective set that assesses the delta of behaviours between two consecutive software versions. Commit-aware mutation testing provides the guidance for developers to quantify to which extent they have tested error-prone locations impacted by program changes. In this paper, we portray our efforts to make mutation criteria change-aware as we study characteristics of commit-relevant mutants striving to bring mutation testing closer to being worthwhile for evolving systems.