Miracle-dz/UniFAR-FTUs huggingface dataset:
A hybrid solver based on IEM and vector FEM for 3D CSEM modeling

The integral equation method (IEM) and differential equation methods have been widely applied to provide numerical solutions of the electromagnetic (EM) fields caused by the inhomogeneity for the controlled source electromagnetic method (CSEM). IEM has a bounded computational domain and been well known for its efficiency while the differential equation methods are commonly used for complex geological models. To utilize the advantages of the two types of approaches, a hybrid method is developed based on the combination of IEM and the edge-based finite element method (vector FEM). In the hybrid scheme, Maxwell’s differential equations of the secondary electric fields in the frequency domain are derived for a volume with boundary placed slightly away from the inhomogeneity. The vector FEM is applied to solve the Maxwell’s differential equations and a system of linear equations for the secondary electric fields can be derived by the minimum theorem. The secondary electric fields on the boundary are represente.
Service Selection Algorithm Based on Dynamic Assessment for Web of Things

Due to WoT(Web of Things) is composed of numbers of resources-limited devices,it is a challenging work that the optimal web service is selected from a similar functionality service group based on the non-functional properties(QoS) without heavy interactive query process in SOA(Service Oriented Architecture). This work focus on the dynamic update process of QoS attributes vector and the optimal service selection algorithm based on the client′s feedback in WoT.The service time is estimated by the REM(Random Exponential Marking) which use the concurrent clients number as the price index,and the exponentially weighted average of the current service expense and the previous record.The service availability is estimated by the online probability with the service state detection.The Empirical results based on NS-3.13 simulation show that the accuracy of optimal web service selection based on our presented approaches is significantly better than the simple QoS attributes.
A framework for detection of malicious software in Android handheld systems using machine learning techniques

Nowadays the Android platform is the fastest growing handheld operating system. As such, it has become the most coveted and viable target of malicious applications. The present study aims at designing and developing new approaches to detect malicious applications in Android-based devices. More precisely, MaLDroide (Machine Learning–based Detector for Android malware), a framework for detection of Android malware based on Machine Learning techniques, is introduced here. It is devised to identify malicious applications. To start with, features of real–world known-benign and known–malicious applications are extracted, gathering 299 features grouped into 8 categories: stats, requested permissions, used permission, API calls, Intents, risks, system calls, and content access. MaLDroide uses static analysis in order to automatically extract these features. As an essential part of the work, a mechanism for dimensionality reduction, using Principal Component Analysis (PCA), was applied, the original 299 features are reduced to 27, retaining 90% of the variance, and dramatically reducing time and space consumption. Once determined the appropriate data set, a training process of the seven most widely used Machine Learning algorithms (Naı̈ve Bayes, Decision Tree, Random Forest, k-Nearest Neighbor, Support Vector Machine, Multi-Layer Perceptron, and AdaBoost) is undertaken. After an evaluation process with 436 normal applications and 2295 malware samples, and on the basis of the evaluation results, one can conclude that Support Vector Machine classifier produces the most accurate predictions, and identifies malware with an accuracy of 99.8%. The evaluation also shows that our framework constitutes a valuable tool which is effective in detecting malware for Android devices.
Power Pylon Reconstruction Based on Abstract Template Structures Using Airborne LiDAR Data

As an important power facility for transmission corridors, automatic three-dimensional (3D) reconstruction of the pylon plays an important role in the development of smart grid. In this study, a novel three-dimensional reconstruction method using airborne LiDAR (Light Detection And Ranging) point cloud is developed and tested. First, a principal component analysis (PCA) algorithm is performed for pylon redirection based on the structural feature of the upper part of a pylon. Then, based on the structural similarity of a pylon, a pylon is divided into three parts that are inverted triangular pyramid lower structures, quadrangular frustum pyramid middle structures, and complex upper or lateral structures. The reconstruction of the inverted triangular pyramid structures and quadrangular frustum pyramid structures is based on prior knowledge and a data-driven strategy, where the 2D alpha shape algorithm is used to obtain contour points and 2D linear fitting is carried out based on the random sample consensus (RANSAC) method. Complex structures’ reconstruction is based on the priori abstract template structure and a data-driven strategy, where the abstract template structure is used to determine the topological relationship among corner points and the image processing method is used to extract corner points of the abstract template structure. The main advantages in the proposed method include: (1) Improving the accuracy of the pylon decomposition method through introducing a new feature to identify segmentation positions; (2) performing the internal structure of quadrangular frustum pyramids reconstruction; (3) establishing the abstract template structure and using image processing methods to improve computational efficiency of pylon reconstruction. Eight types of pylons are tested in this study, and the average error of pylon reconstruction is 0.32 m and the average of computational time is 0.8 s. These results provide evidence that the pylon reconstruction method developed in this study has high accuracy, efficiency, and applicability.
A regional population viability approach for threatened and endangered species management on Army installations

Regional partnering for threatened and endangered species (TES) management can be an effective strategy, allowing the Department of Defense to achieve conservation goals while minimizing potential conflict with its training and testing missions. However, the potential benefits of regional partnering are determined by where TES habitat occurs within a landscape, how populations interact with one another demographically, and the proportion of TES habitat managed by various agencies and potential partners. To assess the opportunities for and potential value of regional TES conservation partnering, we evaluated the relative conservation values of habitat networks for 84 TES known to occur on or near 54 Army and Army National Guard installations. The highest relative network conservation values were estimated for mammals and birds at Fort Huachuca. High relative network conservation values are associated with large amounts of public land. On average, 52.3% of identified habitat networks occurred on public lands compared to 3.8% on private conservation lands. Assessment of habitat networks provides an efficient framework for guiding regional partnering efforts, and multispecies regional conservation partnerships will be critical in addressing the combined threats of encroachment and climate change. Prioritization of regional conservation partnerships will maximize the benefits of limited conservation funding. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents. DESTROY THIS REPORT WHEN NO LONGER NEEDED. DO NOT RETURN IT TO THE ORIGINATOR. ERDC/CERL TR-19-11 iii
Changes in Fire Activity in Africa from 2002 to 2016 and Their Potential Drivers

While several studies have reported a recent decline in area burned in Africa, the causes of this decline are still not well understood. In this study, we found that from 2002 to 2016 burned area in Africa declined by 18.5%, with the strongest decline (80% of the area) in the Northern Hemisphere. One third of the reduction in burned area occurred in croplands, suggesting that changes in agricultural practices (including cropland expansion) are not the predominant factor behind recent changes in fire extent. Linear models that considered interannual variability in climate factors directly related to biomass productivity and aridity explained about 70% of the decline in burned area in natural land cover. Our results provide evidence that despite the fact that most fires are human‐caused in Africa, increased terrestrial moisture during 2002–2016 facilitated declines in fire activity in Africa.
BIOS Rootkit Detection Technology Based on Cooperation Feature

To research and analyze the theory of framework for modeling trojans and trojans model about cooperative concealment,this paper gives BIOS Rootkit’s formal model about cooperative and presents a BIOS Rootkit detection technology based on the character of cooperation in Windows environment.Current detection technology of BIOS Rootkit can detect but lacks the function of resuming,so the method uses characteristic of codes searching and dynamic resuming to solving it.Experimental results show that this is a detection method of higher reliability,efficiency and integrity.
Improving Land-Use Based Estimation of Traffic-Related Air Pollution by Extending Models over Space and Time

Land-use regression (LUR) modeling is used to predict traffic-related air pollution (TRAP) at the urban-scale; which makes it a common tool for estimating exposure in health studies. However, these models are developed for a specific time and geographic area. This can lead to inaccurate exposure assessments when TRAP estimations are required for past or future years, or in cities beyond the model’s geographic area. This research used mobile sampled data to identify ways to improve the accuracy of model estimations when predicting exposure across time and space. Mobile measurements were separated into three resolved concentrations (local, neighbourhoodand regionalbackground) based on the spline of minimums method, a time-series approach. Associations between concentrations and land-use data (proximity to highways, industries, parks) were stronger for resolved than unresolved concentrations. To more accurately model observed concentration patterns across a wider geographic area, LUR models were developed from these resolved concentrations. For most air pollutants (ultrafine particles, nitrogen dioxide and nitric oxide), the resolved models were better able to assess exposure than unresolved models (those developed with total ambient concentrations) when they were spatially extended to areas bordering the model’s geographic area. Furthermore, these improvements in the resolved models’
Modelling biodiversity trends from occurrence records

Large-scale studies of biodiversity change are limited in their taxonomic coverage: a lot is known about the trends of groups such as birds, butterflies and mammals, but little is known about invertebrates and plants. This is due to the lack of abundance data for these groups. However, alternative data such as occurrence records are available for a vast range of species and advances in methodologies are enabling their robust analysis. Here, I analyse UK occurrence records using Bayesian occupancy modelling techniques to determine the status, patterns and drivers of change in less well-studied taxa. First, variations of an occupancy modelling framework were tested to determine whether methods could be improved. This resulted in a “random walk” model that could analyse sparse occurrence records while producing results with increased precision and reduced bias when compared to other variants. This enabled the application of the model to data for over 12,000 species from 32 taxa including vascular plants and numerous invertebrate groups. The resulting estimates of annual occupancy present new information on large-scale change from fine-grained data. Exploration of these estimates showed that trends are not consistent with those of well-studied taxa. Occupancy estimates were then used to assess change in three forms of biodiversity trend: local alpha diversity, regional alpha diversity and regional beta diversity. This revealed the multifaceted nature of biodiversity change and the need to monitor across metrics and scales. Finally, a Bayesian, multispecies, dynamic occupancy model was used to determine the effect of water quality on the population dynamics of freshwater invertebrates. This work highlights the potential of occurrence records to investigate biodiversity change and brings to light new information on many species that have not previously been studied at a national scale.
Twitter Sentiment Analysis (Almost) from Scratch

A popular application in Natural Language Processing (NLP) is the Sentiment Analysis (SA), i.e., the task of extracting contextual polarity from a given text. The social network Twitter provides an immense amount of text (called tweets) generated by users with a maximum number of 140 characters. In this project, we plan to learn a tweet representation from publicly provided data from Tweets in order to infer sentiment from them. One challenge on this task is the fact that tweets are generated from very different users, making the data very heterogeneous (different from regular data which is written in proper English). Another challenge is, clearly, the large scale of the problem. We propose a deep learning sentence representation (called tweet representation) from user generated data to infer sentiment from tweets. This representation is learned from scratch (directly from the words in tweet) over a large unlabeled corpus of tweets. We demonstrate that we achieve state-of-the-art results for SA on tweets.
An Overview of Intrusion Tolerance Techniques

Web and cloud based systems are expected to be highly available and trustworthy, i.e., they are accessible any time a user wants, they always provide correct services, and they never reveal confidential information to an unauthorized party. To meet such high expectation, the system must be carefully designed and implemented, and rigorously tested (for intrusion prevention). However, considering the intense pressure for short development cycles and the widespread use of commercial-off-the-shelf software components, it is not surprising that software systems are notoriously imperfect. The vulnerabilities due to insufficient design and poor implementation are often exploited by adversaries to cause a variety of damages, e.g., crashing of the system, leaking of confidential information, modifying or deleting of critical data, or injecting of erroneous information into a system. This observation prompted the research on intrusion tolerance techniques (Castro & Liskov, 2002; Deswarte et al., 1991; Verissimo et al., 2003, Yin et al., 2003). Such techniques can tolerate intrusion attacks in two respects: (1) a system continues providing correct services (may be with reduced performance) and (2) no confidential information is revealed to an adversary. The former can be achieved by using the replication techniques, as long as the adversary can only compromise a small number of replicas. The later is often built on top of secrete sharing and threshold cryptography techniques. Plain replication is often perceived to reduce the confidentiality of a system, because there are more identical copies available for penetration. However, if replication is integrated properly with secrete sharing and threshold cryptography, both availability and confidentiality can be enhanced. BACKGROUND
The Error Analysis Approach for the Assessment of Automatic Translation

The Error Analysis Approach has been widely used to improve the learning of foreign languages and to evaluate human translation, but Error Analysis can also be applied to automatic translation, as shown by different scholars (see M. Koponen 2010). This article reports on a European project, called Organic.Lingua, which tries to demonstrate the potential of a multilingual web portal for Sustainable Agricultural & Environmental Education by using a machine translator in order to make available all materials in different languages. The selected texts are taken from the corpus compiled for this project. The source texts are translated into various target languages using computer translation tools. We have chosen some of these texts in English and their translations into Spanish, and have applied a classification of human errors to check whether the errors generated by machines could be avoided if we can ‘teach’ the machines or to what extent they are specific to computer tools or human beings. Some metric systems, such as BLEU (Bilingual Evaluation Understudy), are being employed to evaluate the quality of the target translation. However, our role as philologers is to concentrate on more specific linguistic features. By defining the different types of errors and by trying to establish a scale of gravity, we intend to determine the quality of the automatic translations. After analysing the data, we will propose linguistic measures for improvement that would be implemented by computer experts working on the project.
Provenance and palaeogeographic implications of detrital zircons from the lower Carboniferous Riwanchaka Formation of the central Tibetan Plateau

Abstract The Longmu Co–Shuanghu suture zone, which divides the Qiangtang terrane into the northern and southern Qiangtang blocks, is regarded as a key locality in reconstructing the evolutionary history of the Palaeo-Tethys Ocean and the break-up of Gondwana. However, although low-temperature – high-pressure metamorphic rocks and ophiolites have been documented within the Longmu Co–Shuanghu suture zone, it remains unclear whether it is an in situ suture zone and represents the relic of the main Palaeo-Tethys Ocean. The uncertainty stems mainly from the limited systematic studies of the provenance, palaeontological evidence and depositional settings of strata on either side of the Longmu Co–Shuanghu suture zone (i.e. northern and southern Qiangtang blocks). Here we report new detrital zircon U–Pb ages and palaeontological data from Lower Carboniferous strata (Riwanchaka Formation) of the northern Qiangtang block, central Tibet. The Riwanchaka Formation contains warm-climate biota with Cathaysian affinities. Provenance analysis reveals that the formation has detrital zircon spectra similar to those from strata of the Yangtze Plate, and it contains a large proportion of zircons with ages (~360 Ma) similar to the timing of synsedimentary magmatic arc activity, implying an active continental margin setting associated with northward subduction of the Palaeo-Tethyan oceanic lithosphere. Conversely, the Carboniferous–Permian strata from the southern Qiangtang block contain cool-water faunas of Gondwanan affinity and exhibit minimum zircon crystallization ages that are markedly older than their depositional ages, suggesting a passive continental margin setting. The differences in provenance, palaeontological assemblages and depositional settings of the Carboniferous to Permian strata either side of the Longmu Co–Shuanghu suture zone indicate the existence of an ancient ocean between the northern and southern Qiangtang blocks. Combining the new findings with previous studies on high-pressure metamorphic rocks, arc magmatism and ophiolites, we support the interpretation that the Longmu Co–Shuanghu suture zone is an in situ suture zone that represents the main suture of the Palaeo-Tethys Ocean.
A New Method to Predict the Software Fault Using Improved Genetic Algorithm

Maintaining and repairing the engineering machinery will be optimal when identification of defect or troubleshooting process, error analysis and fix it and in other word repair process, perform in highest precise, low time and cost. However speed and precise in troubleshooting process, defect analysis and repairing cause to decrease costs. So, the repair and troubleshooting role in a repair and maintenance system is so significant. While today’s, the software has a main role to perform systems tasks, so they have a high importance in systems reliabilities. Therefore, to increase reliability, it is necessary to design systems against error if tolerable. With regard to increasingly development and applying software in different domains, software reliability has an important role during software life time. One of the most important solutions to increase software reliability is predicting software errors that cause to decrease in software maintenance cost in the future. There are many ways to predict software errors. Due to intelligence algorithms such as genetics algorithms, have a high ability to predict, so we can use them to predict software future condition or predict software errors. In this paper, we present a method to predict software error via genetics algorithm. The aim of this method is to predict software error with a higher precise and speed, and also to present structure that be implementation and expansion easily. The attained results show a desired performance of this method from time period for predicting error and output rate or recognition. The results show the recognition rates of suggested method more that 95 percent in best condition.
ADAPTING THE GRAPHIC NOVEL FORMAT FOR UNDERGRADUATE LEVEL TEXTBOOKS

ion Analogy Divide and conquer Draw a picture or graph Hypothesis testing Lateral thinking Look for patterns Proof Reduction Root cause analysis Developing Digital Educational Graphic Narratives Aspect Ratios Currently, converting comic books to a digital platform has meant simply slapping the pages on the screen in situ even though the viewing space is smaller than the print version. The aspect ratio (the ratio of width-to-height) of print comic books directly
Kernel based Collaborative Topic Regression for Tag Recommendation

Tag recommendation is very helpful for users to organize or categorize online resources like music, photos and articles. In recent years, some models, such as collaborative topic regression (CTR) and its variants, have demonstrated promising performance for tag recommendation. In this paper, we propose a novel Bayesian model, called Kernel based CTR (KCTR) to combine kernel based probabilistic matrix factorization which exploits social networks with topic modeling. In contrast to CTR and its existing variants, KCTR model is capable of keeping the real correlation among items rather than ideally assuming the relations among items are mutually independent, which is hardly satisfied in real world. Experimental results on two real datasets show that our method outperforms the state-of-the-art approaches.
Publish-Subscribe Broker for the Constrained Application Protocol (CoAP)

The Constrained Application Protocol (CoAP), and related extensions are intended to support machine-to-machine communication in systems where one or more nodes are resource constrained, in particular for low power wireless sensor networks. This document defines a publish- subscribe broker for CoAP that extends the capabilities of CoAP for supporting nodes with long breaks in connectivity and/or up-time.