Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial pattern of motor vehicle thefts in Ciudad Juárez, Mexico: an analysis using geographically weighted Poisson regression

Abstract Most Latin American cities have been experiencing a rise in criminality and Ciudad Juarez is not the exception. The aim of this paper is to examine the spatial heterogeneity of factors influencing motor vehicle theft (MVT) in Ciudad Juarez, Mexico. Starting in 2007, the city experienced a rise in crimes, such as motor vehicle theft, which peaked at 9,109 in 2011 and has since decreased to 2,826 in 2014. The data used for this analysis includes criminal warrants of motor vehicle theft provided by Chihuahua’s State Police (2014) and the socioeconomic and demographic variables were provided by the Housing and Population Census (2010), all of them at census tract level. The methodology used Generalized Linear Model (GLM) Poisson and an extension of it called geographically weighted Poisson regression (GWPR). The paper identifies some variables that behave different to the predictions of environmental criminology theories in developed countries.
Land use dynamics and base and peak flow responses in the Choke mountain range, Upper Blue Nile Basin, Ethiopia

ABSTRACT Land use dynamic has become a major driving force of environmental problems that affects hydrological processes. This study aimed to detect the patterns of land use dynamics and its causative factors in the Muga and Suha watersheds of the Choke mountain range, Ethiopia. Decadal temporal land use mappings were generated using remote sensing integrated with field data and spatial analysis tools, which were conducted for the years 1985, 1995, 2005 and 2015. The land use classification results showed that between 1985 and 2015, grass and shrub land area coverages were decreased by 18.1% and 11.9%, respectively. While agriculture and urban area coverages were increased by 29.6% and 0.53%, respectively. In the Muga–Suha watershed, the impacts of land use dynamics to the stream base flow or the low flow in the dry season and the peak flow in the wet season were also assessed using the gauged stream flow data between 1985 and 2004. Base separation technique result indicated the base flow (the low flow) in the dry season decreased by 0.73 m3/year in Suha and by 0.37 m3/year in Muga watersheds. The land use dynamics impact on the base flow showed a consistent decline trend owing to the reduced recharge in the dry season. Similarly, the peak flow in the wet season increased as high as 94% in Suha and 50% in Muga watersheds in the considered period.
A Microwave Scattering Model for Simulating the C-Band SAR Backscatter of Wheat Canopy

Accurate simulation of microwave scattering characteristics of wheat canopy can provide valuable insights into the scattering mechanisms of wheat crops. In this study, a wheat canopy scattering model (WCSM) was developed on a basis of first-order microwave radiative transfer equation. Several WCSM inputs, including wheat canopy and soil parameters, were measured in situ at the time (or near the time) of the satellite observation. The backscattering coefficients of wheat fields were then simulated at various incident angles and polarization modes. Four C-band quad-polarized (Radarsat-2 and Gaofen-3) SAR data were used to evaluate the WCSM performance in four key growth stages of winter wheat from stem elongation to ripening in 2017. Results showed that the WCSM simulated backscattering coefficients of wheat fields with error lower than 1.8 dB. This study demonstrates that the proposed WCSM is effective in characterizing the C-band backscatter features of wheat crops for various growth phases. It also indicated that the operational potential of C-band satellite SAR systems such as the Radarsat-2 and the China Gaofen-3 SAR in monitoring wheat growth for food safety in important agricultural regions.
Internet of Things security and privacy-preserving method through nodes differentiation, concrete cluster centers, multi-signature, and blockchain

Now, the security and privacy-preserving of Internet of Things are receiving more attention. This article proposes a new method of Internet of Things security and privacy preserving by combining differentiated nodes, precision clustering, RSA, multi-signature, and blockchain. To calculate the node rank value in Internet of Things, the number of nodes’ links is taken as the weight of nodes based on PageRank. This method increases the node rank values’ difference between nodes. Accurate clustering determines the initial central nodes of k-means based on the differentiated node rank values, which effectively differentiates the active and inactive nodes in Internet of Things and achieve different levels of protection. To ensure data’s reusability, we adopt the multi-signature. And considering node’s incoming nodes have certain decision on whether data can be transmitted, we filter out two nodes with the highest node rank value from active node’s incoming nodes to do 2/3 multi-signature which saves resources. We encrypt and sign the transmitted data with RSA, helping receivers to verify data. Data are transmitted safely through the blockchain. Successful validation indicates successful transmission. Experiments show that the proposed method can effectively distinguish between active and inactive nodes, which increases the difficulty of attackers’ attack and effectively protects the security and privacy of Internet of Things nodes and data.
Parameter Inversions of Multi-Layer Media of Mars Polar Region with Validation of SHARAD Data

HF (high frequency) radar sounder technology has been developed for several missions of Mars surface/subsurface exploration. This paper presents a model of rough surface and stratified sub-surfaces to describe the multi-layer structure of Mars polar deposits. Based on numerical simulation of radar echoes from rough surface/stratified interfaces, an inversion approach is developed to obtain the parameters of Polar Layered Deposits, i.e. layers thickness and dielectric constants. As a validation example, the SHARAD radar sounder data of the Promethei Lingula of Mars South Polar region is adopted for parameters inversion. The result of stratification is also analyzed and compared with the optical photo of the deep cliff of Chasma Australe canyon. Dielectric inversions show that the deposit media are not uniform, and the dielectric constants of the Promethei Lingula surfaces are large, and become reduced around the depth of 20 m - 30 m, below where most of the deposits are nearly pure ice, except a few thin layers with a lot of dust.
Automated program repair: a perspective from metamorphic testing

Test suite based automated program repair (which is abbreviated as test suite based APR, or simply APR) refers to a broad category of techniques that use input test suites to automatically fix faulty programs. Test suite based APR techniques have been receiving increasing attention in recent years, and several novel repair methodologies have been developed that have yielded very promising results. However, APR still faces a number of challenges, and there is a pressing need to both extend its scope of applicability, and enhance its repair effectiveness. This thesis reports on the application of metamorphic testing (MT) concepts to address some challenging APR problems. A series of formalisations of different aspects of test suite based APR are introduced, and later used to support the systematic measurements and evaluations in the thesis. In order to extend the scope of applicability of test suite based APR techniques, an integration of APR and MT, called APR-MT, is proposed. A key advantage of APR-MT compared with conventional APR techniques is that it does not rely on the availability and feasible use of a test oracle, and thus can be applied to repair a broader range of programs. The problem of how to improve APR repair effectiveness is also addressed, from two perspectives. Firstly, motivated by the observation that a better input test suite helps APR techniques to deliver a higher repair effectiveness, a novel input test suite generation approach is proposed. This approach uses specific information related to program properties and failures, generating test suites that contain richer information than those constructed by other approaches, such as random and coverage based. Secondly, an innovative APR approach drawing on the strengths of MT, MTRepair, is proposed. MTRepair has several novel aspects, including the use of a metamorphic relation instead of a test suite as input, a quality based program validation procedure, and an incremental repair process. The proposed approaches were implemented into a set of prototype tools, and have been evaluated using several subject programs from benchmark suites commonly used by the APR community. These approaches are demonstrated to be not only feasible, but also
Web-Based Metacognitive Scaffolding for Internet Search

This study investigates the effects of web-based scaffolding in comparison with teacher scaffolding (TS) and no scaffolding (NS) on students’ metacognitive skill development in web-search process. The study utilized a static-group pretest–posttest quasi-experimental design. The first experimental group received web-based Internet search scaffolding (WISS) tool treatment; the second experimental group received TS; and the control group had NS. Receiving WISS during an Internet search had a significant effect on the improvement of metacognitive skills when compared with NS; however, it was not significant when compared with TS. While WISS group’s scores in all subscales improved significantly compared with those of NS, TS’s strategy generation scores were significantly higher than those of NS group. Moreover, WISS group’s control of attention scores were significantly higher than those of TS group.
A Medical ChatBot

Normally Users are not aware about all the treatment or symptoms regarding the particular disease. For small problem user have to go personally to the hospital for check-up which is more time consuming. Also handling the telephonic calls for the complaints is quite hectic. Such a problem can be solved by using medical ChatBot by giving proper guidance regarding healthy living. The medical chat-bots functioning depends on Natural language processing that helps users to submit their problem about the health. The User can ask any personal query related to health care through the chat-Bot without physically available to the hospital. By Using Google API for voice-text and text-voice conversion. Query is sent to ChatBot and gets related answer and display answer on android app. The System’s major concern behind developing this web based platform is analysing customer’s sentiments
Short-term acidification detection in soils along the northern Dráva floodplain (SW Hungary) by means of Acid Neutralising Capacity (ANC) analysis

Various methods can be used for soil acidification monitoring, which can be useful towards remediation or preventing environmental degradation. It has been demonstrated that acidification can be made evident over the span of a few years, with proper monitoring. However, a reliance on pH as a main indicator can lead to detection inadequacies, especially where soils are relatively well buffered against acidity and acid deposition is negligible. A technique employing acid-neutralising capacity (ANC) derivation was applied to cultivated and uncultivated Alluvial Meadow soils to find out whether ANC data could prove effective in determining the occurrence and degree of acidification. Sampling and lab work were carried out between 2009 and 2010 on 33 sites under various land uses. Unlike pH, ANC, soil organic matter (SOM), and cation exchange capacity (CEC) decreased significantly. ANC analysis appears to be effective in detecting acidification trends over short periods and, in contrast to previous studies, under ostensibly unremarkable conditions.
Cost effective nitrogen removal – Novel control strategies

The anammox process, used to remove nitrogen from wastewaters is conside red a promising approach due to its advantages over traditional processes. The current study emphasizes on the cost effective nitrogen removal from the sidestream effl uent of anaerobic digester with partial nitration (PN) and anaerobic ammonium oxidation (anammox) process for the municipal wastewater treatment plant. The main objective of this study was to model a cost effective strategy for setting up a lab-scale sequencing batch reactor (SBR) by using activated sludge model (ASM) process equations with applying novel control strategies (NCS) for improving nitrogen-removal effi ciency (NRE). An average rate of removal 80% was obtained during the period of its operation. NCS (intermittent aeration, alteration in the cycle length, etc) were introduced to optimize the operating cost. The overall system contributes to lowering in the greenhouse gas emissions by minimizing the use of energy (60–65%) and hence supporting the WHO mission of achieving sustainable development goals. Results further indicate the future possibility of escalating the lab-scale to full-scale applications.
Embedded neural network design on the ZYBO FPGA for vision based object localization

During recent years we have been witnessing great advancements in the field of computer vision due to the utilization of machine learning. Those achievements are attributed to the evolution of Convolutional Neural Networks (CNNs) as nowadays they are the basic building block of every state-of-the-art object detector and classifier. There is an abundance of possible applications for CNNs and especially in cyber-physical systems, but the utilization of a CNN comes with a very high computation and memory requirements. Therefore the objective of this work is to study the suitability of machine learning and more specifically of a CNN in a visual servoing application. The application itself consists of two robots where one has the role of prey and the other of the predator. The CNN is defined as a classifier and trained on a generated training dataset in order to avoid manual annotation. Then it is integrated into an object detection pipeline in order to extract the precise location of the prey robot. Furthermore, a pre-trained neural network is utilized so as to improve the overall performance of the object detection pipeline. Finally, an accelerator is developed in order to port the object detector into the ZYBO FPGA board and to meet the real-time constraint. Robust performance from both the object tracking algorithm and the accelerator is reported. Although the generated training dataset is the limiting factor of this work, the methodology overall offers a lot of potential for future improvements.
From mapped faults to fault-length earthquake magnitude (FLEM): a test on Italy with methodological implications

Abstract. Empirical scaling relationships between fault or slip dimensions and earthquake magnitudes are often used to assess the maximum possible earthquake magnitude of a territory. Upon the assumption of the reactivability of any fault, the earthquake magnitudes derived from the surface fault length (FLEM) are compared at the national scale in Italy against catalogued magnitudes. FLEMs are obtained by considering a comprehensive fault dataset regardless of fault age, stress field orientation, strain rate, etc. In particular, (1) a comprehensive catalogue of all known faults is compiled by merging the most complete databases available; (2) FLEM is then derived from fault length; and (3) the resulting FLEMs are compared (i.e. the mathematical difference) with catalogued earthquake magnitudes. Results show that the largest FLEMs as well as the largest differences between FLEMs and catalogued magnitudes are observed for poorly constrained faults, mainly inferred from subsurface data. It is suggested that these areas have to be further characterized to better estimate fault dimension and segmentation and hence properly assess the FLEM. Where, in contrast, the knowledge of faults is geologically well constrained, the calculated FLEM is often consistent with the catalogued seismicity, with the 2σ value of the distribution of differences being 1.47 and reducing to 0.53 when considering only the Mw≥6.5 earthquakes. Our work highlights areas, in Italy, where further detailed studies on faults are required.
Burial and Origin of Permafrost Derived Carbon in the Nearshore Zone of the Southern Canadian Beaufort Sea

Detailed organic geochemical and carbon isotopic (δC and ΔC) analyses are performed on permafrost deposits affected by coastal erosion (Herschel Island, Canadian Beaufort Sea) and adjacent marine sediments (Herschel Basin) to understand the fate of organic carbon in Arctic nearshore environments. We use an end‐member model based on the carbon isotopic composition of bulk organic matter to identify sources of organic carbon. Monte Carlo simulations are applied to quantify the contribution of coastal permafrost erosion to the sedimentary carbon budget. The models suggest that ~40% of all carbon released by local coastal permafrost erosion is efficiently trapped and sequestered in the nearshore zone. This highlights the importance of sedimentary traps in environments such as basins, lagoons, troughs, and canyons for the carbon sequestration in previously poorly investigated, nearshore areas. Plain Language Summary Increasing air and sea surface temperatures at high latitudes leads to accelerated thaw, destabilization, and erosion of perennially frozen soils (i.e., permafrost), which are often rich in organic carbon. Coastal erosion leads to an increased mobilization of organic carbon into the Arctic Ocean, which there can be converted into greenhouse gases and may therefore contribute to further warming. Carbon decomposition can be limited if organic matter is efficiently deposited on the seafloor, buried in marine sediments, and thus removed from the short‐term carbon cycle. Basins, canyons, and troughs near the coastline can serve as sediment traps and potentially accommodate large quantities of organic carbon along the Arctic coast. Here we use biomarkers (source‐specific molecules), stable carbon isotopes, and radiocarbon to identify the sources of organic carbon in the nearshore zone of the southern Canadian Beaufort Sea near Herschel Island. We quantify the contribution of coastal permafrost erosion to the sedimentary carbon budget of the area and estimate that more than a third of all carbon released by local permafrost erosion is efficiently trapped in marine sediments. This highlights the importance of regional sediment traps for carbon sequestration.
Optical Interconnects for Cloud Computing Data Centers: Recent Advances and Future Challenges

It is widely argued that optical communication and networking technologies will play a significant role in future data centers. Although the optical technologies have made a significant advancements over the last few years towards providing a very high data transmission rate as well as increased flexibility and efficiency, an additional effort is needed to investigate suitable architectures and technologies for optical network within (intra) and outside (inter) data centers. This paper presents a brief overview on optical networks for data centers. Furthermore, the paper provides a qualitative categorization of the proposed schemes based on the type of optical switches. In the end, future research direction and opportunities of optical interconnect for data centers are discussed.
Elasticity Debt Analytics Exploitation for Green Mobile Cloud Computing: An Equilibrium Model

Mobile cloud computing is the model to ubiquitously access a shared pool of cloud computing resources, data, and services on-demand. This paper introduces the elasticity debt analytics paradigm as a solution concept for the resource provisioning problem in mobile cloud computing environments, guaranteeing the quality of service requirements. A novel green-centric, game theoretic approach to minimizing the elasticity debt on mobile cloud-based service level is proposed, investigating the mobile cloud offloading case. The decision to offload a mobile device user’s task on cloud affects the level of elasticity debt minimization for the provided services. The modeling for the computation of the processing time, energy, and overhead in mobile opportunistic offloading is presented. A utility-driven elasticity debt and profit quantification approach is also examined for maximization of resource utilization, exploiting the hidden Markov model. The problem is formulated as an elasticity debt quantification game, elaborating on an incentive mechanism to predict elasticity debt, mitigate the risk of service over-utilization, achieve scalability, and optimize cloud resource provisioning. The experimental results prove the effectiveness of the equilibrium model, which allocates the mobile device user requests to high elasticity debt-level services and facilitates the elasticity debt minimization for green mobile cloud computing environments.
Tutorial: tomographic Bayesian uncertainty estimation

Abstract Whether we build a subsurface parameter model or deliver a subsurface image, our industry has been sadly lacking in attempting to assign ‘error bars’ to any of the products created. It transpires that this is an extremely difficult task to undertake in a quantitative manner. Assuming that we have an acceptable migration algorithm that honours the physics of the problem, then there are certain minimum acceptance criteria, which tell us that at least the derived model explains the observed data to within some acceptance threshold. Namely: image gathers that are ‘flat’ following migration with the obtained model, and which also match available well data — but these criteria do not tell us how accurate or precise the model or image is. Bayesian analysis of tomographic model error offers one approach to quantifying image positioning uncertainty, and here we give an overview of the elements involved in this procedure.
GNSS Radio Occultation Advances the Monitoring of Volcanic Clouds: The Case of the 2008 Kasatochi Eruption

The products of explosive volcanic eruptions, in particular, volcanic ash, can pose a severe hazard to, for example, international aviation. Detecting volcanic clouds and monitoring their dispersal is hence, the subject of intensive current research. However, the discrepancies between the different available methods lead to detected cloud altitude with significant uncertainties. Here we show the results of an algorithm developed explicitly for high vertical resolution detection of volcanic cloud altitude by using the Global Navigation Satellite System radio occultation (RO) observations. Analyzing the energetic Kasatochi eruption of August 2008 in a case study, we find the volcanic cloud altitudes detected with RO in good agreement (within ~1 km) with cloud altitude estimations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar backscatter images in the 4 h range between RO and CALIOP acquisitions. The tracking by combined RO and imaging of the volcanic cloud evolution during the weeks after the eruption indicates a promising potential for operational global cloud altitude monitoring.