Miracle-dz/UniFAR-FTUs huggingface dataset:
A methodology for designing countermeasures against current and future code injection attacks

This paper proposes a methodology to develop countermeasures against code injection attacks, and validates the methodology by working out a specific countermeasure. This methodology is based on modeling the execution environment of a program. Such a model is then used to build countermeasures. The paper justifies the need for a more structured approach to protect programs against code injection attacks: we examine advanced techniques for injecting code into C and C++ programs and we discuss state-of-the-art (often ad hoc) approaches that typically protect singular memory locations. We validate our methodology by building countermeasures that prevent attacks by protecting a broad variety of memory locations that may be used by attackers to perform code injections. The paper evaluates our approach and discusses ongoing and future work.
Role-based Interpretation of ISs

In this paper we present a role based approach to the analysis of computer-based information systems (ISs). The concept of work role is a good tool for representing work and division of labour, and the computer supported tasks make no exception of this. Relationships between actors, roles, tasks and computer systems are discussed from the act-oriented perspective. Role based analysis of an activity brings the problems of competence, responsibility and co-operation with the ISs into the surface.
Understanding Composition, Formation, and Aging of Organic Aerosols in Wildfire Emissions via Combined Mountain Top and Airborne Measurements

Department of Environmental Toxicology, University of California, Davis, CA 95616, USA School of Science and Technology, University of Washington, Bothell, WA 98011, USA Aerodyne Research Inc., Billerica, MA 01821, USA 4 Pacific Northwest National Laboratory, Richland, WA 99352, USA Brookhaven National Laboratory, Upton, NY 11973, USA * Correspondence to Qi Zhang, 4251A Meyer Hall, 1 Shields Ave, Davis, CA 95616; dkwzhang@ucdavis.edu; 530-752-5779
Building acceptable classification models for financial engineering applications: thesis summary

Classification is a popular data mining task, where the value of a discrete (dependent) variable is predicted, based on the values of several independent variables. In this research, we investigate how predictive classification models can be inferred from the available data. The classification models are required to make good predictions, and be comprehensible and intuitive. The aspect of humanly understandable and intuitive models is of crucial importance in any domain where the model needs to be validated before it can be implemented, such as in the medical diagnosis and credit scoring domain. A classification model that is accurate, comprehensible and intuitive is defined in this thesis as acceptable for implementation. Building such acceptable models is the goal of this text. We examine how rule based classifiers can be built that satisfy these requirements. In a first approach, we use rule extraction from Support Vector Machines (SVMs) to extract rules that are accurate, comprehensible, and mimic the SVM model as much as possible. Next, the use of artificial ant colonies for classification is studied, attempting to induce acceptable classification models from data. In a final part, we discuss the application of the investigated algorithms for real-life case studies, such as the prediction of defaults, going concern opinions, software faults, and business/ICT alignment.
Efficient Video Object Segmentation via Modulated Cross-Attention Memory

Recently, transformer-based approaches have shown promising results for semi-supervised video object segmentation. However, these approaches typically struggle on long videos due to increased GPU memory demands, as they frequently expand the memory bank every few frames. We propose a transformer-based approach, named MAVOS, that introduces an optimized and dynamic long-term modulated cross-attention (MCA) memory to model temporal smoothness without requiring frequent memory expansion. The proposed MCA effectively encodes both local and global features at various levels of granularity while efficiently maintaining consistent speed regardless of the video length. Extensive experiments on multiple benchmarks, LVOS, Long-Time Video, and DAVIS 2017, demonstrate the effectiveness of our proposed contributions leading to real-time inference and markedly reduced memory demands without any degradation in segmentation accuracy on long videos. Compared to the best existing transformer-based approach, our MAVOS increases the speed by 7.6x, while significantly reducing the GPU memory by 87% with comparable segmentation performance on short and long video datasets. Notably on the LVOS dataset, our MAVOS achieves a J&F score of 63.3% while operating at 37 frames per second (FPS) on a single V100 GPU. Our code and models will be publicly available at: https://github.com/Amshaker/MAVOS.
DeepMIF: Deep Monotonic Implicit Fields for Large-Scale LiDAR 3D Mapping

Recently, significant progress has been achieved in sensing real large-scale outdoor 3D environments, particularly by using modern acquisition equipment such as LiDAR sensors. Unfortunately, they are fundamentally limited in their ability to produce dense, complete 3D scenes. To address this issue, recent learning-based methods integrate neural implicit representations and optimizable feature grids to approximate surfaces of 3D scenes. However, naively fitting samples along raw LiDAR rays leads to noisy 3D mapping results due to the nature of sparse, conflicting LiDAR measurements. Instead, in this work we depart from fitting LiDAR data exactly, instead letting the network optimize a non-metric monotonic implicit field defined in 3D space. To fit our field, we design a learning system integrating a monotonicity loss that enables optimizing neural monotonic fields and leverages recent progress in large-scale 3D mapping. Our algorithm achieves high-quality dense 3D mapping performance as captured by multiple quantitative and perceptual measures and visual results obtained for Mai City, Newer College, and KITTI benchmarks. The code of our approach will be made publicly available.
The Brain Chondroitin Sulfate Proteoglycan Brevican Associates with Astrocytes Ensheathing Cerebellar Glomeruli and Inhibits Neurite Outgrowth from Granule Neurons

Brevican is a nervous system-specific chondroitin sulfate proteoglycan that belongs to the aggrecan family and is one of the most abundant chondroitin sulfate proteoglycans in adult brain. To gain insights into the role of brevican in brain development, we investigated its spatiotemporal expression, cell surface binding, and effects on neurite outgrowth, using rat cerebellar cortex as a model system. Immunoreactivity of brevican occurs predominantly in the protoplasmic islet in the internal granular layer after the third postnatal week. Immunoelectron microscopy revealed that brevican is localized in close association with the surface of astrocytes that form neuroglial sheaths of cerebellar glomeruli where incoming mossy fibers interact with dendrites and axons from resident neurons. In situ hybridization showed that brevican is synthesized by these astrocytes themselves. In primary cultures of cerebellar astrocytes, brevican is detected on the surface of these cells. Binding assays with exogenously added brevican revealed that primary astrocytes and several immortalized neural cell lines have cell surface binding sites for brevican core protein. These cell surface brevican binding sites recognize the C-terminal portion of the core protein and are independent of cell surface hyaluronan. These results indicate that brevican is synthesized by astrocytes and retained on their surface by an interaction involving its core protein. Purified brevican inhibits neurite outgrowth from cerebellar granule neurons in vitro, an activity that requires chondroitin sulfate chains. We suggest that brevican presented on the surface of neuroglial sheaths may be controlling the infiltration of axons and dendrites into maturing glomeruli.
On the properties of distance covariance for categorical data: Robustness, sure screening, and approximate null distributions

Pearson's Chi-squared test, though widely used for detecting association between categorical variables, exhibits low statistical power in large sparse contingency tables. To address this limitation, two novel permutation tests have been recently developed: the distance covariance permutation test and the U-statistic permutation test. Both leverage the distance covariance functional but employ different estimators. In this work, we explore key statistical properties of the distance covariance for categorical variables. Firstly, we show that unlike Chi-squared, the distance covariance functional is B-robust for any number of categories (fixed or diverging). Second, we establish the strong consistency of distance covariance screening under mild conditions, and simulations confirm its advantage over Chi-squared screening, especially for large sparse tables. Finally, we derive an approximate null distribution for a bias-corrected distance correlation estimate, demonstrating its effectiveness through simulations.
SPLICE: A Singleton-Enhanced PipeLIne for Coreference REsolution

Singleton mentions, i.e.~entities mentioned only once in a text, are important to how humans understand discourse from a theoretical perspective. However previous attempts to incorporate their detection in end-to-end neural coreference resolution for English have been hampered by the lack of singleton mention spans in the OntoNotes benchmark. This paper addresses this limitation by combining predicted mentions from existing nested NER systems and features derived from OntoNotes syntax trees. With this approach, we create a near approximation of the OntoNotes dataset with all singleton mentions, achieving ~94% recall on a sample of gold singletons. We then propose a two-step neural mention and coreference resolution system, named SPLICE, and compare its performance to the end-to-end approach in two scenarios: the OntoNotes test set and the out-of-domain (OOD) OntoGUM corpus. Results indicate that reconstructed singleton training yields results comparable to end-to-end systems for OntoNotes, while improving OOD stability (+1.1 avg. F1). We conduct error analysis for mention detection and delve into its impact on coreference clustering, revealing that precision improvements deliver more substantial benefits than increases in recall for resolving coreference chains.
Machine Learning on Blockchain Data: A Systematic Mapping Study

Context: Blockchain technology has drawn growing attention in the literature and in practice. Blockchain technology generates considerable amounts of data and has thus been a topic of interest for Machine Learning (ML). Objective: The objective of this paper is to provide a comprehensive review of the state of the art on machine learning applied to blockchain data. This work aims to systematically identify, analyze, and classify the literature on ML applied to blockchain data. This will allow us to discover the fields where more effort should be placed in future research. Method: A systematic mapping study has been conducted to identify the relevant literature. Ultimately, 159 articles were selected and classified according to various dimensions, specifically, the domain use case, the blockchain, the data, and the machine learning models. Results: The majority of the papers (49.7%) fall within the Anomaly use case. Bitcoin (47.2%) was the blockchain that drew the most attention. A dataset consisting of more than 1.000. 000 data points was used by 31.4% of the papers. And Classification (46.5%) was the ML task most applied to blockchain data. Conclusion: The results confirm that ML applied to blockchain data is a relevant and a growing topic of interest both in the literature and in practice. Nevertheless, some open challenges and gaps remain, which can lead to future research directions. Specifically, we identify novel machine learning algorithms, the lack of a standardization framework, blockchain scalability issues and cross-chain interactions as areas worth exploring in the future.
Dr.Hair: Reconstructing Scalp-Connected Hair Strands without Pre-training via Differentiable Rendering of Line Segments

In the film and gaming industries, achieving a realistic hair appearance typically involves the use of strands originating from the scalp. However, reconstructing these strands from observed surface images of hair presents significant challenges. The difficulty in acquiring Ground Truth (GT) data has led state-of-the-art learning-based methods to rely on pre-training with manually prepared synthetic CG data. This process is not only labor-intensive and costly but also introduces complications due to the domain gap when compared to real-world data. In this study, we propose an optimization-based approach that eliminates the need for pre-training. Our method represents hair strands as line segments growing from the scalp and optimizes them using a novel differentiable rendering algorithm. To robustly optimize a substantial number of slender explicit geometries, we introduce 3D orientation estimation utilizing global optimization, strand initialization based on Laplace's equation, and reparameterization that leverages geometric connectivity and spatial proximity. Unlike existing optimization-based methods, our method is capable of reconstructing internal hair flow in an absolute direction. Our method exhibits robust and accurate inverse rendering, surpassing the quality of existing methods and significantly improving processing speed.
Open Source Software And Open Content As Models For eBusiness

The search for viable eBusiness models continues. But important information is being overlooked. Discussions of open source software all too often focus on the limited context of Microsoft appearing to feel threatened by what it portrays as unbusinesslike competition from Linux and OpenOffice.org; whereas that debate is merely one small facet of the whole. Moreover, in addition to software, a great deal of text, image and sound content is readily available, rather than being constrained by tight copyright clauses. Because discussions have been too superficial, too little of the business world has grasped how open models are working in those organisations that have adopted them. Their experiences draw attention to several key assumptions that are inherent in conventional economic models, but that are not applicable in these new markets. Rather than being merely unworldy and communitarian, open source and open content herald a new wave of business activity that transcends naive economic rationalism, and embody implications for business models that deserve serious study by eBusiness leaders.
Trampling vs. Retouch in a Lithic Assemblage: A Case Study from a Middle Stone Age Site, Steenbokfontein 9KR (Limpopo, South Africa)

ABSTRACT At the site of Steenbokfontein 9KR, virtually 80% of the lithic blanks presented retouch—an extreme proportion for Middle Stone Age assemblages. The high percentage of putative retouch led us to investigate whether post-depositional processes played a role in the preservation of the lithic assemblage. We designed and performed an experiment that reproduced all the archaeological conditions inferred from the site formation analyses to determine if the archaeological assemblage was trampled or retouched. We defined several qualitative attributes and studied both trampled and retouched experimental samples, and compared them to the archaeological material. We subsequently performed different statistical analyses and a correspondence analysis with all the macro qualitative attributes that we defined. We were then able to discern which attributes were the most eloquent variables and assess the usefulness of a multivariate analysis in discriminating between trampling and retouch.
Chart Symbol Recognition Based on Computer Natural Language Processing

ABSTRACT Zhang, X. , 2018. Chart symbol recognition based on computer natural language processing. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 724–728. Coconut Creek (Florida), ISSN 0749-0208. The precise identification of chart symbol is of great significance for ocean sailing. The current recognition methods for chart symbol have obvious shortcomings in recognition efficiency and control accuracy. To address this problem, it is necessary to discuss the construction and implementation of the grammatical rule of chart symbol language from two aspects of theory and practice. In this paper, the grammatical rule and the essential features of chart symbol language are discussed. The transformation of computer language service mode of chart symbol is achieved. Firstly, the description of various chart symbols is carried out by the computer natural language, and the recognition and processing of chart pattern information are realized. Then based on the data mining technology in computer language, a recognition system for chart symbol is constructed. Finally, based on the computer natural language and the BP neural network information processing method, the precise identification of complex chart symbol is achieved, which better guarantees the safety of ship navigation and provides powerful support for space information service for various marine development and production activities. Simulation results show that the chart symbols recognition method based on computer natural language can effectively realize the noise processing of target symbols, optimize the convergence performance of data processing, and improve the accuracy of chart symbol recognition.
AI Consciousness is Inevitable: A Theoretical Computer Science Perspective

We look at consciousness through the lens of Theoretical Computer Science, a branch of mathematics that studies computation under resource limitations. From this perspective, we develop a formal machine model for consciousness. The model is inspired by Alan Turing's simple yet powerful model of computation and Bernard Baars' theater model of consciousness. Though extremely simple, the model aligns at a high level with many of the major scientific theories of human and animal consciousness, supporting our claim that machine consciousness is inevitable.
High-Resolution Image Translation Model Based on Grayscale Redefinition

Image-to-image translation is a technique that focuses on transferring images from one domain to another while maintaining the essential content representations. In recent years, image-to-image translation has gained significant attention and achieved remarkable advancements due to its diverse applications in computer vision and image processing tasks. In this work, we propose an innovative method for image translation between different domains. For high-resolution image translation tasks, we use a grayscale adjustment method to achieve pixel-level translation. For other tasks, we utilize the Pix2PixHD model with a coarse-to-fine generator, multi-scale discriminator, and improved loss to enhance the image translation performance. On the other hand, to tackle the issue of sparse training data, we adopt model weight initialization from other task to optimize the performance of the current task.
Equipping Sketch Patches with Context-Aware Positional Encoding for Graphic Sketch Representation

The drawing order of a sketch records how it is created stroke-by-stroke by a human being. For graphic sketch representation learning, recent studies have injected sketch drawing orders into graph edge construction by linking each patch to another in accordance to a temporal-based nearest neighboring strategy. However, such constructed graph edges may be unreliable, since a sketch could have variants of drawings. In this paper, we propose a variant-drawing-protected method by equipping sketch patches with context-aware positional encoding (PE) to make better use of drawing orders for learning graphic sketch representation. Instead of injecting sketch drawings into graph edges, we embed these sequential information into graph nodes only. More specifically, each patch embedding is equipped with a sinusoidal absolute PE to highlight the sequential position in the drawing order. And its neighboring patches, ranked by the values of self-attention scores between patch embeddings, are equipped with learnable relative PEs to restore the contextual positions within a neighborhood. During message aggregation via graph convolutional networks, a node receives both semantic contents from patch embeddings and contextual patterns from PEs by its neighbors, arriving at drawing-order-enhanced sketch representations. Experimental results indicate that our method significantly improves sketch healing and controllable sketch synthesis.
Sedimentation history as an indicator of rift initiation and development: The Late Bajocian-Bathonian evolution of the Oseberg-Brage area, northern North Sea

The Tarbert Formation in the Oseberg-Brage area consists of shoreline sandstones and lower delta-plain heterolithics which basinward interdigitate with offshore sediments of the lower Heather Formation and landward with fluvio-deltaic deposits of the upper Ness Formation. The Late Bajocian-Tarbert and lower Heather Formations form three wedge-shaped, regressive-transgressive sequences which constitute offset, landward-stepping shoreline prisms. Initial gentle rotational extensional faulting occurred during the deposition of the uppermost Ness Formation and resulted in basinfloor subsidence and flooding across the Brent delta. Subsequent extensional faulting exerted the major control on the drainage development, basin physiography, the large-scale stacking pattem, i.e. the progradational-to-backstepping nature of the sequences, as well as on the contained facies tracts and higher-order stacking pattem in the regressive and transgressive segments. Progradation occurred during repetitive tectonic dormant stages, whereas the successive transgressive segments are coupled against intervening periods with higher rates of rotational faulting and overall basinal subsidence. Axial drainage dominated during the successive tectonic dormant stages. Transverse drainage increased in influence during the intermittent rotational tilt stages, but only as small, local (fault block) hanging-wall and footwall sedimentary lobes. Rotational extensional faulting was accompanied by footwall uplift which resulted in erosion of older Brent Group deposits on the footwall highs. Several unconformity strands, which are coupled against intervening periods of rotational faulting or stepwise rotation, are mapped in the footwall of the Oseberg and Brage Faults. The individual unconformity strands merge into a composite unconformity in an updip direction on the fault blocks. The uppermost Ness, Tarbert and lower Heather Formations represent the initial to an early phase of the Middle Jurassic rifting. The intermediate-scale syn-rift sedimentary architecture of a single rift phase in such a mixed non-marine-marine rift basin is a threefold sandstone-mudstone-sandstone lithology motif. This motif also applies to shorter-term rotational tilt events superimposed on longer duration rifting events such as the Late Bajocian-Early Bathonian rift phase.