Miracle-dz/UniFAR-FTUs huggingface dataset:
Clustering Structure-Induced Robust Multi-View Graph Recovery

Graph based classification methods have been widely applied in the fields of computer vision and machine learning. The quality of the graph highly affects the performance of these methods. The same object is commonly represented by different features, i.e., multi-view features, which leads to multiple graphs corresponding to different features in multi-view learning. However, what kind of graph is important for the task is unknown in advance. Moreover, existing multi-view learning methods become weak in dealing with noisy graphs when the data is corrupted by the noise. In this paper, we address this problem by observing that the noise of each graph has specific structure. Then, based on this observation we propose a robust multi-view graph recovery (RMGR) method in which the specific structure is used to clean the multiple input noisy graphs and these cleaned graphs are simultaneously aggregated into a consensus graph by adaptively assigning great weighted coefficients for important graphs. To make the consensus graph suit classification, the clustering structure is introduced to restrain the rank of Laplacian matrix of the consensus graph such that the number of its connected components is equal to that of clustering. In doing so, the graph is adaptively adjusted during optimization to more accurately partition data. The optimization problem is solved by proposed the iterative update algorithm. Extensive experiments on synthetic and several benchmark data sets show the effectiveness of the proposed method.
Multi-Sensor UAV Tracking of Individual Seedlings and Seedling Communities at Millimetre Accuracy

The increasing spatial and temporal scales of ecological recovery projects demand more rapid and accurate methods of predicting restoration trajectory. Unmanned aerial vehicles (UAVs) offer greatly improved rapidity and efficiency compared to traditional biodiversity monitoring surveys and are increasingly employed in the monitoring of ecological restoration. However, the applicability of UAV-based remote sensing in the identification of small features of interest from captured imagery (e.g., small individual plants, <100 cm2) remains untested and the potential of UAVs to track the performance of individual plants or the development of seedlings remains unexplored. This study utilised low-altitude UAV imagery from multi-sensor flights (Red-Green-Blue and multispectral sensors) and an automated object-based image analysis software to detect target seedlings from among a matrix of non-target grasses in order to track the performance of individual target seedlings and the seedling community over a 14-week period. Object-based Image Analysis (OBIA) classification effectively and accurately discriminated among target and non-target seedling objects and these groups exhibited distinct spectral signatures (six different visible-spectrum and multispectral indices) that responded differently over a 24-day drying period. OBIA classification from captured imagery also allowed for the accurate tracking of individual target seedling objects through time, clearly illustrating the capacity of UAV-based monitoring to undertake plant performance monitoring of individual plants at very fine spatial scales.
Implementation and optimisation of pulse compression algorithm on open CL‐based FPGA

As Moore's law meets bottlenecks, the demand for heterogeneous parallel processing systems is increasing. Field-programmable gate arrays (FPGAs) are becoming more efficient acceleration devices due to their powerful processing performance, and the CPU + FPGA architecture under the OpenCL framework has become the trend of heterogeneous parallel processing systems. This study focuses on the optimisation of pulse compression algorithm in FPGA based on OpenCL, which plays an important role in modern radar signal processing systems. By using double cache for ping–pang storage of data between matched filter and inverse fast Fourier transform (IFFT), an optimised processing method is proposed by using a pipeline and verify the method by using Arria 10 GX1150 FPGA with two groups of 2 GB DDR3; the results show that the proposed method can achieve 2.89× performance improvement over the conventional implementation.
gMig: Efficient vGPU Live Migration with Overlapped Software-Based Dirty Page Verification

This paper introduces gMig, an open-source and practical vGPU live migration solution for full virtualization. Taking the advantage of the dirty pattern of GPU workloads, gMig presents the One-Shot Pre-Copy mechanism combined with the hashing based Software Dirty Page technique to achieve efficient vGPU live migration. Particularly, we propose three core techniques for gMig: 1) Dynamic Graphics Address Remapping, which parses and manipulates GPU commands to adjust the address mapping and adapt to a different environment after migration, 2) Software Dirty Page, which utilizes a hashing based approach with sampling pre-filtering to detect page modification, overcomes the commodity GPU's hardware limitation, and speeds up the migration by only sending the dirtied pages, 3) Overlapped Migration Process, which significantly compresses the hanging overhead by overlapping the dirty page verification and transmission concurrently. Our evaluation shows that gMig achieves GPU live migration with an average downtime of 302 ms on Windows and 119 ms on Linux. With the help of Software Dirty Page, the number of GPU pages transferred during the downtime is effectively reduced by up to 80.0 percent . The design of sampling filter and overlapped processing can bring about further 30.0 and 10.0 percent improvements in page processing.
Environmental Control Programs the Emergence of Distinct Product Ensembles from Unconstrained Chemical Reaction Networks

Harnessing and then controlling combinatorial explosions in uncontrolled condensation reactions of simple building blocks is a key problem to many hypotheses on the origin of life. Much has been achieved in understanding how the building blocks of biopolymers may be formed, and in understanding how macromolecules may produce functional and increasingly life-like systems. How these steps can be joined, and how defined populations of macromolecules can form from mixtures of simple building blocks, instead of an undifferentiated mess, remain open questions. Herein, we show how unconstrained condensation reactions of both amino acids, and prebiotic soup mixtures produced by spark discharge, can be steered by changes in the reaction environment, such as order of reactant addition (mixing history), and addition of salts or minerals. Using techniques akin to untargeted metabolomics to survey product distributions we demonstrate that while these reactions do produce a large range of species, there are distinct, significant, and reproducible differences between the product ensembles. Furthermore, we observe that differences in composition are demonstrated through clearly different structural and functional properties. Using this approach, we demonstrate for the first time that simple variations in environmental parameters can mediate the differentiation of distinct ensembles from both amino acid mixtures and a classic primordial soup model (products of a ‘Miller Urey’ type spark discharge reaction). This shows that the synthetic complexity produced by such unconstrained reactions is not as intractable as often suggested, when viewed through a chemically agnostic lens. An open approach to complexity can generate compositional, structural, and functional diversity from fixed sets of simple starting materials, suggesting that differentiation of product mixtures can occur in the wider environment without the need for biological machinery.
An onboard-camera pose estimation method based on 3D digital surface model

Accurate onboard-camera pose estimation is one of the challenges of satellite systems. Improving remote sensing camera pose accuracy never ceases for various applications, including autonomous navigation, 3D reconstruction and continuous city modeling. 3D products of very high spatial accuracy can be created with 3m@SE90 (3 meters error with SE90, which is the abbreviation for Spherical Error 90%) with leading companies, for example, Vricon company in USA. Aiming at the problem of the accuracy of pose estimation, a new method from captured images with the reference 3D products is presented in this paper. Distinguished from the existing methods, our method employs the 3D model to calibrate the pose of the remote sensing camera. Firstly, the high-precision 3D digital surface model is projected onto image space using a virtual calibrated camera. Then, the camera motion parameters of the neighboring moment are estimated by the information of the adjacent frames. This process consists of three steps: i) feature extraction; ii) similarity measurement, and feature matching; iii) camera pose estimation and verification. Finally, the camera pose of the captured image can be determined. Experiment results were compared with the initial exterior orientation parameters used to achieve perspective transformation of the captured images. Furthermore, the method proposed in this study is tested by hardware experiment which simulates remote sensors and platform. Results showed that acceptable accuracy of camera pose can be achievable by using the proposed approach.
Land cover classification and change detection analysis of multispectral satellite images using machine learning

Land cover classification and change detection analysis based on remote sensing images using machine learning algorithm has become one of the important factors for environmental management and urban planning. We select Yangon as the study area because the government faces many problems in urban planning sectors due to the population growth and urban sprawl. Therefore, the proposed method aims to perform the land cover classification in Yangon using Random Forest (RF) classifier in Google Earth Engine (GEE) and post-classification change detection between 1987 and 2017 with 5 years interval periods are evaluated. Despite land cover classifications using satellite imagery have been executed in the past decades, the classification of remotely sensed data integrating with multiple spectral, temporal and textural features and processing time for classification using time series data still have limitations. To overcome these limitations, features extracted from Sentinel-2, Landsat-8, Landsat-7, Landsat-5 and Open Street Map (OSM) are executed for classification and cloud-based GEE platform is used to reduce the processing time. Some spectral indexes such as NDVI, NDBI and slope from SRTM are calculated to achieve better classification. Land cover classification is performed by using the RF classifier with the different bands’ combination. Land cover classification map with 7 classes (Shrub Land, Bare Land, Forest, Vegetation, Urban Area, Lake and River) is obtained with the overall accuracy of 96.73% and kappa statistic of 0.95 for 2017. Finally, change detection analysis over 30 years is performed and the significant changes in build-up, bare land, and agriculture have been resulted.
Predicting technology integration based on a conceptual framework for ICT use in education

ABSTRACT The purpose of this study is twofold: (1) to propose a conceptual framework for classifying ICT use in education and (2) to develop and test a structural model including both teacher- and school-related factors, to explain technology integration. A conceptual framework has been developed in order to classify use of ICT (information and communications technologies) in education with an instrumentalist perspective. Indicators of technology integration have been clearly identified and associated with the constructs in the framework. Access, technical support, administrative support and professional development support are examined under school-related variables, while teachers’ beliefs and ICT competencies are investigated under teacher variables. A model based on these variables has been proposed and tested through structural equation modelling. Data was collected from 403 teachers working in primary and secondary schools. Results have indicated that factors in the model explained 70% of the variance in the technology integration that showed that the substantial amount of variance was predicted.
Determination of the detection threshold of human observers in acoustic drone detection

Background: Nowadays, small drones are inexpensive and can be purchased and used very easily. Unfortunately, they are also relatively easy to convert to weapons. As they become more widespread, these drones may become a serious security risk. One possible way to address this threat could be the early detection of small drones by using acoustic cameras. However, the question arises as to how good the detection performance of such cameras is, compared to that of a human observer. The goal of this project was to determine the acoustic detection-threshold of human observers for drones in the presence of ambient noise. Methods: Nineteen subjects volunteered to take part in the study. The constancy method was used to determine the detection threshold. During the test, the study participants were presented with a recording of a DJI Phantom2 Vision+ drone that varied in level in steps of 1dB over a range of 27dB around the estimated threshold value. The signals were superimposed by three different kinds of ambient noise which were presented in three successive test-runs. The subjects wore headphones over which they heard the ongoing ambient noise while they were presented with the drone sound at random intervals and levels. The test signal was on for 2 seconds during which the trial subject had to confirm the detection of the drone sound by pressing an assigned key on a notebook. Results: We’ve found detection thresholds for white noise, water or highway noise at -17dB, -18dB and -17dB respectively, expressed as level differences between test signal and noise. Comparison of our results with the detection performance of human observers in a simulated drone detection scenario, reproduced by loudspeakers in an anechoic chamber, showed good agreement. Further, it seems possible to assess the detection performance of an acoustic camera using our results.
IBFET: Index‐based features extraction technique for scalable code clone detection at file level granularity

Many techniques have been developed over the years to detect code clones in different software systems to maintain security measures. These techniques often require the source code to compare the subject system against a very large data set of big code. This paper presents index‐based features extraction technique (IBFET) to detect code clones at a very large‐scale level to billions of LOC at file level granularity. We performed preprocessing, indexing, and clone detection for more than 324 billion of LOC using a Hadoop distributed environment, which is quite faster and more efficient as compared to existing distributed indexing and clone detection techniques; meanwhile, it detects all three types of clones efficiently. The MapReduce rule of divide and conquer is used for a count and retrieve the similar features between different systems. We evaluated the execution time, scalability, precision, and recall of IBFET by using a well‐known clone detection data set IJaDataset and BigCloneBench; furthermore, we compared the results with other state‐of‐the‐art tools. Our approach is faster, flexible, scalable, and provides accurate results with high authenticity and can be implemented at a large‐scale level.
A Deep Fully Convolutional Network for Distal Radius and Ulna Semantic Segmentation

Semantic segmentation is an essential step to do further image analysis and scene understanding tasks. In medical imaging analysis applications, it is even more challenging to do automatic segmentation due to tissues’ complicated boundaries. In this paper, a fully convolutional network (FCN) based model is constructed to segment distal radius and ulna (DRU) areas from hand X-ray images. We evaluated the proposed network on a clinical DRU dataset with different network configurations. The proposed network can achieve 98% accuracy and 96% mean Intersection over Union (IoU).
Solving a location-allocation problem by a fuzzy self-adaptive NSGA-II

This paper proposes a modified non-dominated sorting genetic algorithm (NSGA-II) for a bi-objective location-allocation model. The purpose is to define the best places and capacity of the distribution centers as well as to allocate consumers, in such a way that uncertain consumers demands are satisfied. The objectives of the mixed-integer non-linear programming (MINLP) model are to (1) minimize the total cost of the network and (2) maximize the utilization of distribution centers. To solve the problem, a fuzzy modified NSGA-II with local search is proposed. To illustrate the results, computational experiments are generated and solved. The experimental results demonstrate that the performance metrics of the fuzzy modified NSGA-II is better than the original NSGA-II.
Guide de la première prescription des benzodiazépines dans les troubles anxieux et l'insomnie : une revue systématique de la littérature

La consommation des psychotropes en France, et en particulier des benzodiazepines, est elevee : un Francais sur cinq utilise chaque annee une benzodiazepine ou une apparentee hypnotique. L'importance de la consommation de ces medicaments et les risques qui lui sont lies rendent necessaire la mise en place de mesures favorisant leur bon usage. L'objectif principal de ce travail etait d'analyser les caracteristiques de la primo-prescription des benzodiazepines dans les troubles anxieux et l'insomnie, grâce a une revue systematique de la litterature. 58 articles ont ete retenus et analyses. La synthese des donnees a permis la realisation de deux guides pratiques, destines aux medecins generalistes. Ces guides rappellent les indications de ces medicaments dans les troubles anxieux et du sommeil, conseillent pour le choix du medicament le plus indique ainsi que les conditions d'utilisation. Ils indiquent egalement les informations pratiques a donner au patient, ainsi que le suivi a effectuer.
Trigger Identification Using Difference-Amplified Controllability and Dynamic Transition Probability for Hardware Trojan Detection

To remain dormant in the validation and manufacturing test, Trojans tend to have at least one trigger signal at the gate-level netlist with a very low transition probability. Our paper exploits this stealthy nature of trigger signals to detect Trojans using static and dynamic transition probabilities. The proposed trigger identification is a reference-free scheme, and no prior knowledge of a Trojan-free design is required. First, we reveal the relation between combinational 0/1-controllability and 0/1-probability and propose a static transition probability analysis based on our proposed difference-amplified controllability, which can be easily obtained by the Sandia Controllability/Observability Analysis Program. The k-means clustering method is adopted for potential trigger classification to extend the scalability and adaptability to different circuit sizes. Second, we propose to utilize the transition probability of a dynamic simulation for correction of the results. Experiments show that the proposed detection scheme can obtain a 0% false negative rate and a maximum 11.7% false positive rate on Trust-HUB benchmarks.
An end‐user‐centric test generation methodology for performance evaluation of mobile networked applications

We propose a model‐based test generation methodology to evaluate the impact of the interaction of the wireless network and application configurations on the performance of mobile networked applications. We consider waiting time delay to model wireless network quality. We classify mobile applications into two groups. Group I represents applications where end‐user experience is mainly affected by waiting time delay during service consumption, while group II represents applications where end‐user experience is affected by waiting time delay before service consumption. Test generation is formulated as an inversion problem. However, for group I applications, solving the inversion problem is expensive. Therefore, we utilize metamorphic testing to mitigate the cost of test oracles. We formulate metamorphic test generation as maximization of the distance between seed and follow‐up test cases. Two test coverage criteria are proposed: user experience and user‐experience‐and‐input interaction. Network models are developed for a mobile device that has network access through a WiFi hot spot and uses either transmission control protocol or user datagram protocol. Two mobile applications are used to demonstrate the methodology: multimedia streaming and web browsing. Application of the methodology when user actions are taken into consideration is also addressed. The effectiveness of the methodology is evaluated using two metrics: the incurred time cost and redundancy in the generated test suite. The obtained results show the advantage of casting test generation as an inversion problem, compared with random testing. For apps with intensive performance models, combining metamorphic testing with the methodology has tremendously reduced the cost of test oracles.
An investigation of the form of HIV/AIDS and reproductive health education (RHE) in South African secondary schools for the Deaf and the factors influencing teacher implementation thereof.

The HIV/AIDS pandemic is having a devastating effect on the South African population. Most affected are young people between 15 and 24. HIV/AIDS research has paid little attention to youth in the D/deaf population. Schools play an important role in the reproductive health of youth as they reach youth at a formative time in their development. Aim: To investigate the form of HIV/AIDS and RHE in South African secondary schools for the D/deaf, together with factors associated with teacher implementation thereof. Methodology: The sample was made up of 33 Life Orientation teachers from 16 secondary schools for the Deaf in 6 South African provinces. Quantitative methodology was used to obtain descriptive data and to determine any associations between demographic/ contextual variables and the study's theoretical framework (Theory of Planned Behaviour); qualitative data also aided in answering of the research question. Results: HIV/AIDS and RH education is being implemented at South African secondary schools for the D/deaf and LO teachers recognise the importance of HIV/AIDS and RHE for their D/deaf learners. Despite high coverage levels, a number of obstacles are hindering the optimum implementation of HIV/AIDS and RHE: 1) lack of learner assessment in the HIV/AIDS and RHE portion of the LO curriculum 2) unclear policy mandates regarding the weighting of HIV/AIDS and RHE in the LO curriculum 3) inadequate teacher proficiency in SASL 4) the use of a mainstream LO curriculum that was not specifically developed for Deaf learners 5) the moralistic viewpoints of certain teachers 6) problems with teacher access to suitable HIV/AIDS and RHE training. A number of significant associations between TPB constructs and demographic/contextual variables were also found. Recommendations for future interventions and research are delineated and limitations of the study are discussed. Investigation of HIV/AIDS and RHE in SA secondary schools for the Deaf and factors influencing teacher implementation 3