Miracle-dz/UniFAR-FTUs huggingface dataset:
Radar and optical remote sensing in offshore domain to detect, characterize, and quantify ocean surface oil slicks

Radar and optical sensors are operationally used by authorities or petroleum companies for detecting and characterizing maritime pollution. The interest lies not only in exploration but also in the monitoring of the maritime environment. Occurrence of natural seeps on the sea surface is a key indicator of the presence of mature source rock in the subsurface. These natural seeps, as well as the oil slicks, are commonly detected using radar sensors but the addition of optical imagery can deliver extra information such as the oil real fraction, which is critical for both exploration purposes and efficient cleanup operations. Today state-of-the-art approaches combine multiple data collected by optical and radar sensors embedded on-board different airborne and spaceborne platforms, to ensure wide spatial coverage and high frequency revisit time. Multi-wavelength imaging system may create a breakthrough in remote sensing applications, but it requires adapted processing techniques that need to be developed. To explore performances offered by multi-wavelength radar and optical sensors for oil slick monitoring, remote sensing data have been collected by SETHI, the airborne system developed by ONERA, during an oil spill cleanup exercise carried out in 2015 in the North Sea, Europe. The uniqueness of this data set lies in its high spatial resolution, low noise level and quasi-simultaneous acquisitions of different part of the electromagnetic spectrum. Specific processing techniques have been developed in order to extract meaningful information associated with oil-covered sea surface. Analysis of this unique and rich dataset demonstrates that remote sensing imagery, collected in both optical and microwave domains, allows to estimate slick surface properties such as the spatial abundance of oil and the relative concentration of hydrocarbons on the sea surface.
Edinburgh Research Explorer Unravelling the GSK3-related genotypic interaction network influencing hippocampal volume in recurrent major depressive disorder

Objective Glycogen synthase kinase 3 β (GSK3 β ) has been implicated in mood disorders. We previously reported associations between a GSK3 β polymorphism and hippocampal volume in major depressive disorder (MDD). We then reported similar associations for a subset of GSK3 β -regulated genes. We now investigate an algorithm-derived comprehensive list of genes encoding proteins that directly interact with GSK3 β to identify a genotypic network influencing hippocampal volume in MDD. Participants and methods We used discovery ( N = 141) and replication ( N = 77) recurrent MDD samples. Our gene list was generated from the NetworKIN database. Hippocampal measures were derived using an optimized Freesurfer protocol. We identified interacting single nucleotide polymorphisms using the machine learning algorithm Random Forest and verified interactions using likelihood ratio tests between nested linear regression models. Results The discovery sample showed multiple two-single nucleotide polymorphism interactions with hippocampal volume. The replication sample showed a replicable interaction (likelihood ratio test: P = 0.0088, replication sample; P = 0.017, discovery sample; Stouffer ’ s combined P = 0.0007) between genes associated previously with endoplasmic reticulum stress, calcium regulation and histone modifications. Conclusion Our results provide genetic evidence supporting associations between hippocampal volume and MDD, which may reflect underlying cellular stress responses. Our study provides evidence of biological mechanisms that should be further explored in the search for disease-modifying therapeutic targets for depression.
Cognitive Components of Speech at Different Time Scales

We discuss the cognitive components of speech at different time scales. We investigate cognitive features of speech including phoneme, gender, height, speaker identity. Integration by feature stacking based on short time MFCCs. Our hypothesis is basically ecological: we assume that features that essentially independent in a reasonable ensemble can be efficiently coded using a sparse independent component representation. This means that supervised and unsupervised learning should result in similar representations. We do indeed find that supervised and unsupervised learning of a model based on identical representations have closely corresponding abilities as classifiers.
Proposing a New Model for Quality of Service Acceptance

 Abstract: Quality of service (QoS) has been proofed to be a great significance effect in the utilization of cloud service in higher education institutions (HEI’s), due to having many aspects determine the acceptance level of QoS while providing cloud service, QoS acceptance not given sufficient attention from HEIs and decision-makers. In this research, previous QoS studies are review in this field to identify QoS acceptance variables by review studies for the last 5 years. Thus, with the aim to discover QoS aspects this research intends to develop an integrated model to understand these variables and relationship between QoS acceptance variables, which must be efficiently studied. Hence, the integrated model between TOE (Technological, Organizational, and environmental) and HOT (Human, Organizational, and Technological) theory is presented in this research to be the core contribution. The integrated model helps HEIs to evaluate and explore QoS aspects to increase utilizing of cloud service, improving HEIs efficiency, and help decision-makers to facilitate the acceptance of QoS in HEIs. (cid:10)
Real-time social distance measurement and face mask detection in public transportation systems during the COVID-19 pandemic and post-pandemic Era: Theoretical approach and case study in Italy Transportation Research Interdisciplinary Perspectives

Due to its remarkable learning ability and benefits in several areas of real-life, deep learning-based applications have recovered to be a research topic of great importance in the last few years. This article presents a method devoted to guaranteeing safety conditions in public transportation systems (PTS) during the COVID-19 pandemic and post-pandemic era. The paper describes a viable real-time model based on deep learning for monitoring social distance between users and detecting face masks in stop areas and inside vehicles of public transportation systems. Detections are made using the deep learning approach and YOLOv3 algorithm. The safety rule violations are represented by red bounding boxes and red circles in a bird ’ s eye view as output of the video surveillance analysis. The datasets used to train the neural network are the “ Caltech Pedestrian Dataset ” and the “ COVID-19 Medical Face Mask Detection Dataset ” . Metrics, such Loss Accuracy, and Precision, obtained in the testing process of the neural network were used to evaluate the performance of the model in detecting users and face masks. The proposed method was recently tested in the Public Transportation System of the Municipality of Piazza Armerina (Italy). The results show a significant reliability of the method in detecting real-time interactions between users of the PTS in terms of over-time variations in their mutual distancing, as well as in recognising cases of violation of the imposed social distancing and FFP2 face mask use.
Ordovizische und silurische Holothurien (Echinodermata)

Ordovician and Silurian holethurians (Echinodermata). A synopsis of Ordovician and Silurian helothurians is given and the arguments for the limited knowledge of this echinoderm class are discussed in detail. New findings of holothurian sclerites and pieces of the calcareous ring from Ordovician glacial erratic boulders (geschiebe) and the Silurian of Gotland are presented and figured. The oldest known holothurian sclerites come from Llanvirnian strata, and the oldest pieces of the calcareous ring are of Ludlovian age (Gotland, Sweden) .
Review of Robust Document Image BINARIZATION Technique for Degraded Document Images

Segmentation of badly degraded document images is done for discriminating a text from background images but it is a very challenging task. So, to make a robust document images, till now many binarization techniques are used. But in existing binarization techniques thresholding and filtering is unsolved problem. In the existing method, an Adaptive contrast map is first constructed then binarized and combined with cannny edge map to identify text stroke edge pixels, the documented is further segmented by local threshold . So the existing methods are divided into four main steps out of which last two steps used two different algorithms. In the proposed method, we can modify algorithms and test degraded document images then compare the result that come from previous paper results.
A Survey on Active Deep Learning: From Model-driven to Data-driven

Which samples should be labelled in a large data set is one of the most important problems for trainingof deep learning. So far, a variety of active sample selection strategies related to deep learning havebeen proposed in many literatures. We defined them as Active Deep Learning (ADL) only if theirpredictor is deep model, where the basic learner is called as predictor and the labeling schemes iscalled selector. In this survey, three fundamental factors in selector designation were summarized. Wecategory ADL into model-driven ADL and data-driven ADL, by whether its selector is model-drivenor data-driven. The different characteristics of the two major type of ADL were addressed in indetail respectively. Furthermore, different sub-classes of data-driven and model-driven ADL are alsosummarized and discussed emphatically. The advantages and disadvantages between data-driven ADLand model-driven ADL are thoroughly analyzed. We pointed out that, with the development of deeplearning, the selector in ADL also is experiencing the stage from model-driven to data-driven. Finally,we make discussion on ADL about its uncertainty, explanatory, foundations of cognitive science etc.and survey on the trend of ADL from model-driven to data-driven.
Technological Innovations in Adaptive and Dependable Systems : Advancing Models and Concepts

Often the systems our societies depend upon are built in such a way as to result too inflexible and intolerant to changes. The deployment of such systems in environments where change is the rule rather than the exception leads to situations where quality-of-service and quality-of-experience are often strongly and negatively affected. As a result, there is an urgent need to investigate structuring techniques, architectures, algorithms, tools, and paradigms for the expression and the management of adaptive-and-dependable systems, i.e., software, devices, and services that are built so as to sustain an agreed-upon quality-of-service and quality-of-experience despite the occurrence of potentially significant and sudden changes or failures in their infrastructure and surrounding environments. This need is the primary motivation for this book as well as the core business of several research lines worldwide – including the one I am responsible for in the Performance Analysis of Telecommunication Systems group at the University of Antwerp, Belgium. Research in the above investigation domains is usually carried out through design, development, and evaluation of techniques and models to structure computer systems as adaptive systems – systems that is that are able to constantly re-optimize in view of changes, both exogenous (i.e. either environmentor user-specific) and endogenous (that is, pertaining to internal assets.)
Ivy: Bringing a Weighted-Mesh Representation to Bear on Generative Architectural Design Applications

Author(s): Steinfeld, K | Abstract: Mesh segmentation has become an important and well-researched topic in computational geometry in recent years (Agathos et al 2008) . As a result, a number of new approaches have been developed that have led to innovations in a diverse set of problems in computer graphics (CG) (Shamir 2008). Specifically, a range of effective methods for the division of a mesh have recently been proposed, including by K-means (Shlafman et al. 2002), graph cuts (Golovinskiy and Funkhouser 2008; Katz and Tal 2003 ), hierarchical clustering (Garland et al 2001; Gelfand and Guibas 2004 ; Golovinskiy and Funkhouser 2008 ), primitive fitting (Athene et al 2006), random walks(Lai et al), core extraction(Katz et al), tubular multi-scale analysis(Mortara et al. 2004), spectral clustering(Liu and Zhang 2004), and critical point analysis(Lin et al 2007), all of which depend upon a weighted graph representation, typically the dual of the given mesh (Shamir 2008). While these approaches have been proven effective within the narrowly-defined domains of application for which they have been developed (Chen 2009), they have not been brought to bear on wider classes of problems in fields outside of CG, specifically on problems relevant to generative architectural design (GAD). Given the widespread use of meshes and the utility of segmentation in GAD, by surveying the relevant and recently matured approaches to mesh segmentation in CG that share a common representation of the mesh dual, this paper identifies and takes steps to address a heretofore unrealized transfer of technology that would resolve a missed opportunity for both subject areas. Meshes are often employed by architectural designers for purposes that are distinct from and present a unique set of requirements in relation to similar applications that have enjoyed more focused study in computer science. This paper presents a survey of similar applications, including thin-sheet fabrication(Mitani and Suzuki 2004), rendering optimization(Garland et al 2001), 3d mesh compression(Taubin et al 1998), morphing (Shapira et al 2008) and mesh simplification(Kalvin and Taylor 1996), and distinguish the requirements of these applications from those presented by GAD, including non-refinement in advance of fabrication (such that the mesh geometry remain unaltered), the constraining of mesh geometry to planar-quad faces, and the ability to address a diversity of mesh features (such as creased edges or patterns) that may or may not be preserved. Following this survey of existing approaches and unmet needs, the authors assert that if a generalized framework for working with graph representations of meshes is developed, allowing for the interactive adjustment of edge weights, then the recent developments in mesh segmentation may be better brought to bear on GAD problems. This paper presents recent work toward the development of just such a framework, implemented as a plug-in for the visual programming environment Grasshopper.
Optimization of CyberSecurity Investment Strategies in a Smart Grid Using Game-Theory

Power transformers are usually operated within their nameplate MVA rating, as identified by the transformer manufacturer. There are, however, conditions under which a transformer may experience loading beyond which it was originally intended, a situation that drives consideration of alternative responses of either making capital investments, employing operational constraints, or operating the unit beyond its rating. This project aims to define guidelines and criteria to support such decision-making, identifying power transformer loadings that achieve good balance among economics, reliability, and transformer life while considering the influence of increased renewable penetration in the network and the influence of geomagnetic disturbances on transformer performance. In order to address the project objectives, a system called the “Transformer Management Tool (TMT),” is under development; this system will be described. The current focus of the project is on addressing issues raised by Cedar Falls Utilities (CFU); CFU project objectives are presented. Also, a proposed procedure for processing dissolved gas analysis test results is provided.
Decadal Modulation of Precipitation Patterns over Eastern China by Sea Surface Temperature Anomalies

AbstractAnnual precipitation anomalies over eastern China are characterized by a north–south dipole pattern, referred to as the “southern flooding and northern drought” pattern (SF/ND), fluctuating on decadal time scales. Previous research has suggested possible links with oceanic forcing, but the underlying physical mechanisms by which sea surface temperature (SST) variability impacts the dipole pattern remains unclear. Idealized atmospheric general circulation model experiments conducted by the U.S. CLIVAR Drought Working Group are used to investigate the role of historical SST anomalies associated with Pacific El Nino–Southern Oscillation (ENSO)-like and the Atlantic multidecadal oscillation (AMO) patterns in this dipole pattern. The results show that the Pacific SST pattern plays a dominant role in driving the decadal variability of this dipole pattern and the associated atmospheric circulation anomalies, whereas the Atlantic SST pattern contributes to a much lesser degree. The direct atmospheric resp.
The RWTH System Combination System for WMT 2011

RWTH participated in the System Combination task of the Sixth Workshop on Statistical Machine Translation (WMT 2011). For three language pairs, we combined 6 to 14 systems into a single consensus translation. A three-level meta-combination scheme combining six different system combination setups with three different engines was applied on the French--English language pair. Depending on the language pair, improvements versus the best single system are in the range of +1.9% and +2.5% abs. on BLEU, and between −1.8% and −2.4% abs. on TER. Novel techniques compared with RWTH's submission to WMT 2010 include two additional system combination engines, an additional word alignment technique, meta combination, and additional optimization techniques.
Supervisory Control of Probabilistic Discrete Event Systems with Recovery

We discuss supervisory control of probabilistic discrete event systems in this paper. Supervisors for such systems will take probabilities of event occurrences into consideration in making control decisions. The essential idea is that, when some undesirable behaviors have small probabilities of occurring, the supervisor will not try to avoid such behaviors in order to allow more desirable behaviors. If the undesirable behaviors do occur, the supervisor will issue reset commands to re-initialize the system as soon as possible. We introduce a new concept called \-containment of a given language" and subsequently study its algebraic properties. We discuss standard supervisor (with only enablement/disablement) synthesis problems and reset supervisor (with recovery mechanism) synthesis problems for both fully observed and partially observed systems. We also provide an example to illustrate the results.
Aalborg Universitet Modeling Supermarket Refrigeration Systems for Demand-Side Management

Modeling of supermarket refrigeration systems for supervisory control in the smart grid is presented in this paper. A modular modeling approach is proposed in which each module is modeled and identified separately. The focus of the work is on estimating the power consumption of the system while estimating the cold reservoir temperatures as well. The models developed for each module as well as for the overall integrated system are validated by real data collected from a supermarket in Denmark. The results show that the model is able to estimate the actual electrical power consumption with a high fidelity. Moreover a simulation benchmark is introduced based on the produced model for demand-side management in smart grid. Finally, a potential application of the proposed benchmark in direct control of the power/energy consumption is presented by a simple simulation example.
Florida International University-University of Miami TRECVID 2021 DSDI Track

This paper presents the framework and results from the team “Florida International University-University of Miami (FIU-UM)” in the TRECVID 2021 Disaster Scene Description and Indexing (DSDI) task. This year our team submitted a total of seven runs in which four achieved the highest score among all participants, regardless of the training datasets utilized. The difference among the runs lies mainly on the method to fuse the feature scores summarized as follows. LADI-based Submission Runs: • run1: fully automated feature score fusion through differential evolution; • run2: mean aggregation of the predicted scores from the best performing models in the ensemble; • run3: fully automated feature score fusion with z-score normalization and averaged z-scores. LADI + Others Submission Runs: • run1: fully automated feature score fusion through differential evolution; • run2: further enhancement of the feature score fusion by the removal of the less relevant feature scores as determined by the differential evolution; • run3: fully automated feature score fusion with z-score normalization and averaged z-scores; • run4: applied the team’s best performing model used to rank videos in TRECVID2020-DSDI. The following processing steps are included in our framework: (1) pre-processing imagery from the provided LADI (Low Altitude Disaster Imagery) dataset; (2) generating soft labels for imagery in the LADI dataset through the advanced fusion of the annotations obtained from both human and machine annotators; and (3) categorizing the frames in the LADI imagery by the pre-trained Convolutional Neural Network (CNN) models, each focused on a different aspect of the target features. We use a variety of training strategies to improve the performance of the CNN models, including using a Confident Learning approach to denoise the training set and fusing the information from multiple models pre-trained on the well-known public dataset benchmarks. The final score is produced by (1) determining which characteristics from multiple models are semantically relevant to the target features in DSDI and (2) searching for the optimum approach to combine the predicted feature scores from multiple pre-trained models using a Differential Evolution optimization technique. The test video clips are then ranked according to their final feature scores that determine their relevance to a certain target feature. The FIU-UM team took the first position in four of the submitted runs this year, regardless of the training dataset used. The submission details are as follows. • Training type: LADI-based (L) and LADI + Others (O) • Team ID: FIU-UM (Florida International University University of Miami) • Year: 2021
“Automatic Deployment of Specification-based Intrusion Detection in the BACnet Protocol”

Speciﬁcation-based intrusion detection has high detection rates and low false positives rates. Its main drawback is that the creation of spec-iﬁcations (rules) often require human intervention. We present a data-mining approach that reads documents and automatically extracts rules from them. Speciﬁcally, we work in the ﬁeld of Building Automation Systems (BAS) using the BACnet protocol (ISO 16484-5). The input documents are provided by manufacturers of BACnet devices. These documents state the capabilities of every device, therefore the extracted rules represent the expected behavior of the devices. In our experiments, the proposed algorithm creates rules with a 94.5% of concordance with the documents, on average. We tested our automatically generated rules in a real BACnet network. Non-standard undocumented capabilities were detected for three kinds of devices during the evaluation period.