Miracle-dz/UniFAR-FTUs huggingface dataset:
Oil spill detection and monitoring in the Cyprus region

Cyprus can be found in a critical location, a geostrategic location among Asia, North Africa, and Europe. This is particularly important, thus, can affect the marine environment with the vessels that pass through the sea. A severe form of pollution can be an oil spill. An oil spill is a liquid, usually petroleum hydrocarbon, released to the sea and can affect the marine ecosystem and humans. Oil spills can be recorded as marine incidents or accidents. In August 2021, one of the essential accidental oil spills came from the northeast coast of Syria. In the present study, we will try to implement a spatial analysis of oil spill detection by combing remote sensing techniques and monitoring, using the Sentinel Application Platform (SNAP) and the consequences that follow this procedure. The implementation was made using two different polarization; vertical polarization (VV) and horizontal comparison (VH). The preliminary results show that the Sentinel-1 SAR data would give effective results and spatial information on oil spill detection to decision-makers.
SAMPLE with a side of MSTAR: extending SAMPLE with outliers and target variants from MSTAR

Deep learning is a technology applied to a host of problems in the decade since its introduction. Of particular interest for both defense and civil applications is the technology of automatic target recognition, which is a subset of visual detection and classification. However, these classification algorithms must be robust to out-of-library confusers and able to generalize across a variety of target types. In this paper, we augment the existing Synthetic and Measured Paired Labeled Experiment dataset of synthetic aperture radar images with the remainder of the public MSTAR dataset and define a set of experiments to encourage the development of traits beyond simple classification accuracy for target recognition algorithms.
Impact of model architecture on robustness and interpretability of multispectral deep learning models

Including information from additional spectral bands (e.g., near-infrared) can improve deep learning model performance for many vision-oriented tasks. There are many possible ways to incorporate this additional information into a deep learning model, but the optimal fusion strategy has not yet been determined and can vary between applications. At one extreme, known as “early fusion,” additional bands are stacked as extra channels to obtain an input image with more than three channels. At the other extreme, known as “late fusion,” RGB and non-RGB bands are passed through separate branches of a deep learning model and merged immediately before a final classification or segmentation layer. In this work, we characterize the performance of a suite of multispectral deep learning models with different fusion approaches, quantify their relative reliance on different input bands and evaluate their robustness to naturalistic image corruptions affecting one or more input channels.
MI-AFR: multiple instance active learning-based approach for fish species recognition in underwater environments

Video surveys are commonly used to monitor the abundance and distribution of managed species to support management. However, considerable effort, time, and cost are required for human review and automated fish species recognition provides an effective solution to remove the bottleneck of post-processing. Implementing fish species detection techniques for underwater imagery is a challenging task. In this work, we present the Multiple Instance Active-learning for Fish-species Recognition (MI-AFR), which is formulated as an object detection-based approach to perform localization and classification of fish species. It can select the most informative fish images from unlabeled sets by estimating the uncertainty of unlabeled images by using adversarial classifiers trained on labeled sets. Moreover, we have analyzed the improved performance of MI-AFR by considering different backbone networks as a trade-off between speed and accuracy. For experiments, we have used the fine-grained and large-scale reef fish dataset obtained from the Gulf of Mexico – the Southeast Area Monitoring and Assessment Program Dataset 2021 (SEAMAPD21). The experimental results illustrate that the superiority of the proposed method can establish a solid foundation for active learning in fish species recognition, especially with a small number of labeled sets.
Lidar tomography for remote sensing

Most lidar approaches configured for long range applications rely on large aperture telescopes or else aperture synthesis to resolve objects. Here we describe a different technique that draws its inspiration from computed tomography (CT) scan technology. This method, called lidar tomography, relies on narrow laser pulses to generate a series of highly-range resolved measurements from different look angles as the lidar platform moves around the object. We compare the convolutional backprojection technique for image reconstruction to a model based iterative algorithm to reconstruct images of various objects in a lab. We show through modelling and simulation that the lidar tomography approach can generate <1-inch resolved imagery from an airborne platform if sufficient angular diversity and appropriate geolocation accuracy requirements can be met.
SATEN: SAR adversarial attack using targets to evaluate neural networks

Synthetic Aperture Radar (SAR) imaging provides useful remote sensing capabilities because of its ability to image day-or-night and through clouds by using radar waves. However, understanding SAR vulnerabilities is important in developing data exploitation techniques that are resistant to “spoofing. ” “Spoofing” is a type of attack where a virtual object is created in a SAR image by coherently adding the expected radar returns from a target into radar returns from the background. This research explored the effects of spoofing on Convolutional Neural Network (CNN) models for vehicle classification from the SAMPLE V2 data set. CNN models trained on SAR images with real targets in the scene were not able to accurately generalize to images with virtual targets in the scene; however, a model trained on real data identified spoofed images with an accuracy over 95.0% based on the confidence value outputs and a known proportion of spoofed images. Furthermore, a specialized training methodology enabled a CNN model to classify images as real or spoofed with an accuracy of more than 99.9% and classify the vehicle type with an accuracy of over 99.5%. This research determined the effects of real and spoofed SAR images on CNN models and what methods could be leverage to improve model performance.
Primitive Normal Element and Its Inverse in Finite Fields

Let q be a prime power, n be a positive integer, Fqn denote a finite field with qn elements. We prove that if n≥32, then there exists a primitive element ξ of Fqn satisfying that ξ and ξ-1 are normal elements of Fqn over Fq, i.e., both {ξ,ξq,. ,ξqn-1} and {ξ-1,ξ-q,. ,ξ-qn-1} are primitive normal bases over Fq.
Deep semi-supervised label propagation for SAR image classification

Automatic target recognition with synthetic aperture radar (SAR) data is a challenging problem due to the complexity of the images and the difficulty in acquiring labels. Recent work1 used a convolutional variational autoencoder to extract relevant features prior to constructing a similarity graph in a graph-based active learning framework for SAR data. In this work we present two novel methods for classifying SAR data that use convolutional neural network (CNN) feature extraction together with techniques from graph-based semi-supervised learning in an end-to-end manner that can provide improved classification performance in the small labeled dataset regimes that are common in SAR ATR. First, we introduce Laplace Output Activation Neural Networks (LOAN Networks) as a way of directly optimizing feature embeddings for use with graph-based semi-supervised learning techniques. Next, we introduce Pseudo Label Propagation Neural Networks (PsLaPN Networks) as a inexpensive way to both boost the training signal as well as combat overconfidence and poor model calibration in neural networks. We present a novel derivation of simple formulas for the direct and efficient computation of derivatives of the outputs of graph-based algorithms like label propagation2 for use in the training of our networks. We test the proposed end-to-end networks for active learning on OpenSARShip, a SAR dataset, where both methods surpass the previous state-of-the-art.
Why write address translation OS code yourself when you can synthesize it?

Address translation hardware is at the cornerstone of modern computer systems. It provides a wide range of security-relevant features and abstractions such as memory partitioning, address space isolation, and virtual memory. Hardware designers have developed different memory protection schemes with varying features and means of configuration. Correct configuration is mission-critical for a system's integrity. It is the operating system's task to safely and securely manage and configure the memory hardware of a compute platform - a task that operating systems developers must repeat for every new memory hardware unit. We present a new approach that frees the OS programmer from writing system code to set up and configure translation hardware. We leverage software synthesis to automatically generate correct systems code that interfaces with translation hardware to create or modify memory mappings from a high-level, behavioral specification. By synthesizing correct, low-level systems code from a high-level specification we make it easier to port operating systems and facilitate incorporating accelerators into existing systems. Moreover, we believe that our system can generate actual and simulated hardware components enabling research in new memory translation and protection schemes.
Exploiting Hybrid Index Scheme for RDMA-based Key-Value Stores

RDMA (Remote Direct Memory Access) is widely studied in building key-value stores to achieve ultra-low latency. In RDMA-based key-value stores, the indexing time takes a large fraction of the overall operation latency as RDMA enables fast data access. However, the single index structure used in existing RDMA-based key-value stores, either hash-based or sorted index, fails to support range queries efficiently while achieving high performance for singlepoint operations. In this paper, we explore the adoption of a hybrid index in the key-value stores based on RDMA, especially under the memory disaggregation architecture, to combine the benefits of a hash table and a sorted index. We propose HStore, an RDMA-based key-value store that uses a hash table for single-point lookups and leverages a skiplist for range queries to index the values stored in the memory pool. Guided by previous work on using RDMA for key-value services, HStore dedicatedly chooses different RDMA verbs to optimize the read and write performance. To efficiently keep the index structures within a hybrid index consistent, HStore asynchronously applies the updates to the sorted index by shipping the update log via two-sided verbs. Compared to state-of-the-art Sherman and Clover, HStore improves the throughput by up to 54.5% and 38.5% respectively under the YCSB benchmark.
Iterator Interface Extended LSM-tree-based KVSSD for Range Queries

Key-Value SSD (KVSSD) has shown great potential for several important classes of emerging data stores due to its high throughput and low latency. When designing a key-value store with range queries, an LSM-tree is considered a better choice than a hash table due to its key ordering. However, the design space for range queries in LSM-tree-based KVSSDs has yet to be explored, despite range queries being one of the most demanding features. In this paper, we investigate the design constraints in LSM-tree-based KVSSDs from the perspective of range queries and propose three design principles. Based on these principles, we present IterKVSSD, an Iterator interface extended LSM-tree-based KVSSD for range queries. We implement IterKVSSD on OpenSSD Cosmos+, and our evaluation shows that it increases range query throughput by up to 4.13× and 7.22× for random and sequential key distributions, respectively, compared to existing KVSSDs.
Next-Generation Security Entity Linkage: Harnessing the Power of Knowledge Graphs and Large Language

With the continuous increase in reported Common Vulnerabilities and Exposures (CVEs), security teams are overwhelmed by vast amounts of data, which are often analyzed manually, leading to a slow and inefficient process. To address cybersecurity threats effectively, it is essential to establish connections across multiple security entity databases, including CVEs, Common Weakness Enumeration (CWEs), and Common Attack Pattern Enumeration and Classification (CAPECs). In this study, we introduce a new approach that leverages the RotatE [4] knowledge graph embedding model, initialized with embeddings from Ada language model developed by OpenAI [3]. Additionally, we extend this approach by initializing the embeddings for the relations.
TurboHash: A Hash Table for Key-value Store on Persistent Memory

Major efforts on the design of persistent hash table on a non-volatile byte-addressable memory focus on efficient support of crash consistency with fence/flush primitives as well on non-disruptive table rehashing operations. When a data entry in a hash bucket cannot be updated with one atomic write, out-of-place update, instead of in-place update, is required to avoid data corruption after a failure. This often causes extra fences/flushes. Meanwhile, when open addressing techniques, such as linear probing, are adopted for high load factor, the scope of search for a key can be large. Excessive use of fence/flush and extended key search paths are two major sources of performance degradation with hash tables in persistent memory. To address the issues, we design a persistent hash table, named TurboHash, for building high-performance key-value store. Turbo-Hash has a number of much desired features all in one design. (1) It supports out-of-place update with a cost equivalent to that of an in-place write to provide lock-free reads. (2) Long-distance linear probing is minimized (only when necessary). (3) It conducts only shard resizing for expansion and avoids expensive directory-level rehashing; And (4) it exploits hardware features for high I/O and computation efficiency, including Intel's Optane DC's performance characteristics and Intel AVX instructions. We have implemented TurboHash on the Optane persistent memory and conducted extensive evaluations. Experiment results show that TurboHash improves state-of-the-arts by 2-8 times in terms of throughput and latency.
Profiling Cultural Tourists by Using User Generated Big Data from Online Travel Agencies

Cultural tourism, as one of the most popular forms of tourism, has recently witnessed a remarkable development. However, rapid development of cultural tourism has brought fierce competition. In order to increase the attractiveness of scenic spots, it is very necessary for tourism enterprises to accurately understand cultural tourists‘ preference. This paper proposes a systematic method for profiling cultural tourists based on user generated big data. In this method, topic model, sentiment analysis and clustering algorithms are combined to cluster tourists, and then multinomial logistic regression model are applied to match tourists’ basic attributes. Calculation results show that cultural tourists are mainly divided into four groups with different characteristics. According to the characteristics of each group, suggestions for improving the management of scenic spots are put forward.
A Closed-loop Detection Algorithm for Online Updating of Bag-Of-Words Model

In indoor scenes, VSLAM-based mobile robots face the challenges of poor closed-loop detection and low localization accuracy. Based on a monocular camera, we propose a closed-loop detection algorithm based on an improved real-time updating bag-of-words model. By extracting feature descriptors of online images and fusing them with loaded offline words, a fused bag of words related to the mobile robot application scenario is generated, which changes with the robot application scenario. In this paper, the improved bag-of-words and the original bag-of-words are combined with ORB-SLAM3 for closed-loop detection experiments, respectively. The experimental results show that the error between the predicted trajectory and the real trajectory of the ORB-SLAM3 system combined with the improved bag-of-words model is significantly reduced, and the robustness of the system is also improved, resulting in a certain improvement in the closed-loop detection capability of the small mobile robot.
Observable Surface Anomalies Preceding Simulated Isolated Convective Initiation

AbstractIdealized ensemble simulations of isolated convective initiation (CI) are analyzed to identify storm-scale features in surface weather fields that precede initiation in a variety of background environments and the observations that would be needed to resolve these features. Precipitating storms are identified with an object-based method and composites of surface anomalies are generated for the variables of interest surrounding times and locations of initiation. Correlation length scales and anomaly magnitudes throughout the CI process are examined in detail with the latter comparing favorably to anomaly estimates obtained from previous observational and modeling studies. Negative temperature anomalies due to cloud shadowing are found to be the most prominent storm-scale feature prior to initiation. Significant spatial correlations are shown to extend from the surface throughout the boundary layer and even into the cloud-bearing layer once deep convective clouds become established. The findings are.
Study of unsteady thrust characteristics of pulse detonation engines

A dynamical model of a pulse detonation engine was developed to study the effects of dynamic excitation and pulse-to-pulse interaction on the unsteady thrust generated. Inertial loads arise due to cyclic acceleration when the system is excited dynamically in a repetitive manner and interfere with the generated thrust. In addition, influence of system dynamics and pulse-to-pulse interaction results in the convolution of generated thrust and structural response. The convolution of structural response and the interference of inertial loads have to be accounted in order to accurately predict the actual thrust. A general approach incorporating the study of system dynamics to facilitate deconvolution procedure followed by an acceleration compensation method to account for added inertial loads was used to recover actual thrust generated. The study of system dynamics involved the establishment of system transfer function to reconstruct the thrust using known input. The acceleration compensation method required the determination of effective mass and calculation of induced acceleration of the thrust stand. The product of two quantities, effective mass and measured acceleration, yielded inertial load which was deducted from the reconstructed thrust to estimate actual thrust generated. The calculated and compensated thrust values were expressed in the form of impulse for comparison. The results confirmed the effectiveness of the approach as the inertial loads were effectively reduced and obtained results were in good agreement with published information.
Anomaly Detection on IBM Z Mainframes: Performance Analysis and More

Anomalous events can signal a variety of problems in any system. As such, robust, fast detection of anomalies is important so issues can be fixed before they cascade to create larger problems. In this paper we focus on IBM Z mainframes, although most of the problems addressed and techniques used are broadly applicable. For example, anomalies can signal issues such as disk malfunctions, slow or unresponsive modules, crashes and latent bugs, lock contention, excessive retries, the need to allocate more resources to reduce contention, etc. Although there are specific techniques for addressing individual issues, anomaly detection is useful in its broad spectrum utility, and its ability to identify combinations of problems for which there may not be a specific approach implemented. In addition, anomaly detection serves as a backstop: truly anomalous events suggest that normal mechanisms did not work. Our input for detecting anomalies is low-level, summarized information available in time series to the zOS operating system. Although such information lacks some high-level context, it does provide an operating system awareness that benefits from universal applicability to any zOS system and any code running on such a system. The data is also quite rich with 100 - 100,000 metrics per sample depending on how "metric" is defined. As might be expected the data contains metrics such as CPU utilization, execution priorities, internal locking behavior, bytes read and written by an executing process, etc. It also contains higher-level information such as the executing process names or transactional identifiers from online transactional processing facilities. Names are useful not only in detecting anomalies, but in conveying context to users trying to isolate and fix problems. Our techniques build on KL divergence [21] and learn continuously without supervision and with low overhead. Continuous learning is important. The first instance of an aberrant behavior is an anomaly. The 10th instance probably is not. This point also illustrates the utility of anomaly detection in pinpointing root cause: early detection is essential and broad-spectrum anomaly detection provides excellent capability to do just that. This paper outlines these techniques and demonstrates their efficacy in detecting and resolving key problems.
Software-Defined CPU Modes

Our CPUs contain a compute instruction set, which regular applications use. But they also feature an intricate underworld of different CPU modes, combined with trap and exception handling to transition between these modes. These mechanisms are manifold and complex, yet the layering and functionality offered by the CPU modes is fixed. We have to take what CPU vendors provide, including potential security problems from unneeded modes. This paper explores the question, whether CPU modes could instead be defined entirely by software. We show how such a design would function and explore the advantages it enables. We believe that pushing all existing modes under a common design umbrella would enforce a cleaner structure and more control over exposed functionality. At the same time, the flexibility of software-defined modes enables interesting new use cases.
Automatic Kernel Offload Using BPF

BPF support in Linux has made kernel extensions easier. Recent efforts have shown that using BPF to offload portions of server applications, e.g., memcached and service proxies, can improve application performance and efficiency. However, thus far, the community has not looked at the question of what parts of an application should be offloaded? This paper first shows that blindly offloading application functionality to the kernel is neither beneficial nor desirable, and care must be taken when deciding what to offload. Furthermore, when deciding what to offload, developers must consider not just the application, but also the workload being handled, and the kernel being targetted, Therefore, we advocate automating this decision process in a compiler, that can analyze application code, and produce two executables, a kernel offload and a userspace program, that jointly implement the application's functionality. This paper discusses the challenges that must be addressed to build such a compiler, and why they can be feasibly addressed.