Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Model Based Testing Tool Architecture for Exascale Computing System

Exascale computing refers to a computing system which is capable to at least one exaflop in next couple of years. Many new programming models, architectures and algorithms have been introduced to attain the objective for exascale computing system. The primary objective is to enhance the system performance. In modern/super computers, GPU is being used to attain the high computing performance. However, it’s the objective of proposed technologies and programming models is almost same to make the GPU more powerful. But these technologies are still facing the number of challenges including parallelism, scale and complexity and also many more that must be fixed to achieve make computing system more powerful and efficient. In this paper, we have present a testing tool architecture for a parallel programming approach using two programming models as CUDA and OpenMP. Both CUDA and OpenMP could be used to program shared memory and GPU cores. The object of this architecture is to identify the static errors in the program that occurred during writing the code and cause absence of parallelism. Our architecture enforces the developers to write the feasible code through we can avoid from the essential errors in the program and run successfully.
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS (IJAE)

Most of the current Intrusion Detection Systems have mainly concentrated on detection of intrusions with no mechanisms incorporated to respond to such intrusions. The major problem in automating IDS responses has mainly been because currently IDS experience high false alarms which if automated would introduce denial of service or related problems. In this paper we propose a mechanism that allows for some level of automation of intrusions response. In particular we emphasize that patterns exclusively associated with intrusions should be used as the basis, thereby separating between the network connections that require further processing to establish as to whether they are anomalous. We base our argument on the Human Immune system immune system and as such some biological overview of the same is presented. Finally, we demonstrate that our proposed approach incorporates most of the desired features that have for long been considered advantageous from studies of the immune system.
DEVELOPMENT OF A LARGE-AREA AND SPHERICAL ARRAY OF POLYMERIC PHOTOVOLTAIC PIXELS FOR ARTIFICIAL VISION

We developed a large-area epiretinal prosthesis based on organic electronic materials embedded into a stretchable matrix. High-density photovoltaic pixels were designed to provide artificial vision up to 45 ° of visual field thanks to a transparent and spherical substrate. The prosthesis can withstand rolling and injection into the eye through a small scleral cut. To manufacture such implants, standard microfabrication techniques were adapted to soft, temperatureand solvent-sensitive materials.
ADVANCES IN CHARACTERISATION AND CALIBRATION OF DIGITAL IMAGING SYSTEMS

Real-Time Photogrammetry is making inroads into various applieations due to its ability for on-line and real-time response. This opens applieations in industrial measurement, roboties, traffie, medicine, ete. The aeeuraey of these RealTime Photogrammetrie Systems is a primary requirement for a large number of tasks. It is thus of utmost importanee to investigate the aeeuraey potential of digital imaging systems. The radiometrie and geometrie properties of all elements involved in image aequisition and information extraetion must be investigated in order to loeate sources of degradations. With respect to photogrammetrie applieations effeets leading to adegradation of the aeeuraey of three-dimensional measurements are of primary interest, whereas purely radiometrie and geometrie deformations whieh have no effeet and/or whieh ean be modelled are not of eoneem. It is thus neeessary to loeate the origins of degrading faetors and to quantify their effeet on the aeeuraey of three-dimensional measurements. This paper deals in a first part with the analysis of the radiometric and geometrie eharaeteristics of a number of eomponents. Their effeets on thc aeeuraey of photogrammetrie are diseussed. A three-dimensional aeeuraey test with a testfield is used to demonstrate the aeeuraey potential of off-theshelf hardware. Some limitations found in the test are pointed out and potential improvements are diseussed.
Realtime Energy Efficient Digital Image Watermarking on Mobile Devices using Android

This paper proposes a real time and energy efficient image watermarking scheme using DCT – DWT hybrid transformation. The proposed method is using a 2 – level of quantization on the Y component of true color image captured in real time and low frequency band coefficients are selected for the dataset prepared of size 256 * 10 using these coefficients, which is supplied to Extreme Learning Machine (ELM) a single layer feed forward network. A normalized column vector of size 256 * 1 is generated by ELM for its usage as key sequence for embedding the watermark. This hybrid transforms provide a better imperceptibility and reduction in the time taken by entire watermarking process i.e. within a second, makes it energy efficient and suitable for the proposed smart phone android app for a real time image watermarking.
Poster: Barometer: Sizing Up Android Applications Through Statistical Evaluation

As the number of Android based devices continues to grow rapidly, malware is quickly permeating the Android ecosystem. Recent efforts at automatically detecting and classifying malware have focused on the static analysis of Android applications [1]. In principle, static analysis covers all paths in the code. However, in practice, it is thwarted by obfuscation, native and dynamically-loaded code, or modification of objects at runtime. In contrast, dynamic analysis involves running and observing the resulting behaviors and circumvents these shortcomings. It has been recently shown that dynamic analysis can be successfully used to classify Android malware [2]. Although this is useful for characterising family of threats, it is essential to design a framework that also identifies and precisely classifies zero-day Android malware—variants of existing samples or new, previously-unseen families. The difficulty in dealing with zero-day samples is that it may not be possible to train on similar samples. Interestingly, however, this impediment can be turned into an advantage using statistical evaluation and used towards understanding whether the sample is a zero-day threat. In [3], a statistical assessment of a classification decision was used to understand how well a test sample fits into the training classes. Identification of zero-day threats is a related problem; it is necessary to establish whether the malware does not belong to any of the established classes that the classifier was trained on. In this work we show how statistical assessment of decisions made by a trained classifer can be used to identify previously unseen malware samples from unknown families. We present a framework which is based on statistical evaluation of Android samples to reliably classify zero-day malware. We describe the statistical overview of our approach in §I and discuss how it can be applied to detecting and classifying zero-day malware in §II.
17-07 Biased Policy Professionals

A large literature focuses on the biases of individuals and consumers, as well as “nudges” and other policies that can address those biases. Although policy decisions are often more consequential than those of individual consumers, there is a dearth of studies on the biases of policy professionals: those who prepare and implement policy on behalf of elected politicians. Experiments conducted on a novel subject pool of development policy professionals (public servants of the World Bank and the Department for International Development in the UK) show that policy professionals are indeed subject to decision making traps, including sunk cost bias, the framing of losses and gains, frame-dependent risk-aversion, and, most strikingly, confirmation bias correlated with ideological priors, despite having an explicit mission to promote evidenceinformed and impartial decision making. These findings should worry policy professionals and their principals in governments and large organizations, as well as citizens themselves. A further experiment, in which policy professionals engage in discussion, shows that deliberation may be able to mitigate the effects of some of these biases. JEL classification codes
Nonlinear Analysis of the Effect of Horizontal Construction Joint on the Flexural Behavior of Reinforced Concrete Slabs

The Study of the behavior of slabs under the influence of horizontal construction joints has become necessity in the case of large thickness slabs such that raft and some special cases of flat slabs which were casted in two or more layers. The first layer is usually casted up to cover the reinforcement steel mesh and the other layers were casting to get the actual thickness of slabs. The purpose of this research is to examine the effect of horizontal construction joints on the behavior of simply supported slabs. A commercial non linear finite element program, such as "ANSYS version 12" [1] was used to study the previous slabs. The results which obtained from theortical analysis wre comared with that obtained from expermintal work [2]. The main parametric study used in this search was the case of interface layer between the two layers of the reinforced concrete slabs. A comparison was made between the slab which was casted as one layer and to that slabs which were casted in multy layers with different interface properties the results showed that the results from theortical modeling and expermintal work were nearly approximate as well as the studding of horizontal construction joints on the flexural behavior of slabs is very important.
Arabic Dialect Identification of Twitter Text Using PPM Compression

This paper explores the use of the Prediction by Partial Matching (PPM) compression scheme for Arabic dialect identification of Twitter text. The PPMD variant of the compression scheme with different orders was used to perform the categorisation. We present experimental results identifying single tweet and multiple author tweets from five major Arabic dialect regions: Gulf; Egyptian; Levantine; Maghrebi; and Iraqi; in addition to Modern Standard Arabic (MSA) and Classical Arabic (CA). We used the Bangor Twitter Arabic corpus (BTAC) which we built for dialect research. We also applied different machine learning algorithms such as Multinomial Naïve Bayes (MNB), K-Nearest Neighbours (KNN), and an implementation of Support Vector Machine (LIBSVM) using several N-grams features. PPMD shows significantly better results in comparison to the other machine learning algorithms achieving 74.1% and 87.1% accuracy for single and multiple tweets dialect identification respectively.
Empirical Design Guidelines for Enhanced Incorporation of Task Management in Web Browsing

Task management features have become a necessity in web browsing, especially with the high proliferation of pages and information in the web. This paper presents a novel approach called TaskBar which helps manage pending tasks during web browsing. It works as a to-do list in the web browser and provides various task management features such as reminders and priorities to help decide which tasks should be dealt with first. A two-session controlled experiment was carried out to evaluate TaskBar and compare users' performance with and without task management features. The obtained data were analyzed in terms of task accomplishment time, rate of completion, and users' satisfaction. The results showed that incorporating task management features in web browsing, particularly TaskBar, significantly improved users’ performance in terms of task completion time, completion rate, and satisfaction. These results were interpreted into a set of design guidelines for the employment of task management features
Classification of Hyperlink Networks by Multivariate Analysis of Network Indicators: The Case of Japanese Public Libraries

In this study, we classified 47 in-prefecture hyperlink networks formed by the Web pages of Japanese public libraries by applying multivariate analysis to network indicators, and clarified the characteristics of the networks. Our method provides a new way of classifying networks and examining the services of public libraries. We selected nine network indicators representing five aspects of networks: subgroups, position of each node, distance, clustering and degree distribution. We carried out two multivariate analyses of these indicators: principal component analysis and cluster analysis. The results indicate that the position of each node and degree distribution are the aspects that affect the classification most strongly. The in-prefecture networks can be classified into three types: (1) those that have only weak connections; (2) those that have many libraries and only one central library; and (3) those that have many links which are centralized in not one but some libraries. This indicates that in some in-prefecture networks municipal libraries as well as prefectural libraries play a central role.
A Novel Multiple License Plate Extraction Technique for Complex Background in Indian Traffic Conditions

License plate recognition (LPR) is one of the most important applications of applying computer techniques towards intelligent transportation systems (ITS). In order to recognize a license plate efficiently, location and extraction of the license plate is the key step. Hence finding the position of a license plate in a vehicle image is considered to be the most crucial step of an LPR system, and this in turn greatly affects the recognition rate and overall speed of the whole system. This paper mainly deals with the detecting license plate location issues in Indian traffic conditions. The vehicles in India sometimes bare extra textual regions, such as owner’s name, symbols, popular sayings and advertisement boards in addition to license plate. Situation insists for accurate discrimination of text class and fine aspect ratio analysis. In addition to this additional care taken up in this paper is to extract license plate of motorcycle (size of plate is small and double row plate), car (single as well as double row type), transport system such as bus, truck, (dirty plates) as well as multiple license plates present in an image frame under consideration. Disparity of aspect ratios is a typical feature of Indian traffic. Proposed method aims at identifying region of interest by performing a sequence of directional segmentation and morphological processing. Always the first step is of contrast enhancement, which is accomplished by using sigmoid function. In the subsequent steps, connected component analysis followed by different filtering techniques like aspect ratio analysis and plate compatible filter technique is used to find exact license plate. The proposed method is tested on large database consisting of 750 images taken in different conditions. The algorithm could detect the license plate in 742 images with success rate of 99.2%.
Smart Proximity: Annotating the Proximity of Entities In A Smart City Ontology

The smart city concept contributes a new research area that will continue to be the focus of research for a long time. Different works have modelled and presented ontologies for smart cities, especially for data integration processes. In this context, obtaining a model in which the full functionalities of a DL reasoner are employed to generate new knowledge that would be available to the different devices in a smart city. This information can represent a useful picture of the environment around transports, hubs and people, enabling the smart devices in a city to make decisions according to this environment. We present a model of a smart city ontology with different axioms for generating new knowledge from available knowledge using a DL reasoner. This model considers the location and state of proximity between two entities in the environment. To implement our approach, we develop a tool referred to as smart proximity for generating and querying our smart city ontology. We expect the generated knowledge to be useful to many single working devices, especially devices that are available to transportation, and improve several functionalities such as motion, stop, waiting time and connections between two different means of transport.
Optimal window opening based on natural ventilation measurements

From the energy point of view, buildings should be as tight as possible. But lack of ventilation will result in high level of indoor pollutants, which is harmful for occupants. Numerous studies find that lack of ventilation could cause symptoms for occupants, which are characterized by World Health Organization as Sick Building Syndrome. There are lots of real-time ventilation data and rational ventilation standards in the world. However, these kinds of measurements lacks in China, so more data is needed. In China, most public schools have neither air condition nor mechanical ventilation, so the main form of ventilation is natural ventilation. Thus, it is crucial to do research in measuring the natural ventilation rate in order to improve the indoor environment. Natural ventilation is mainly driven by heat buoyancy and wind pressure. The lowest air change rate happens when the wind speeds and temperature differences are low. If the quantity of fresh air can satisfy the ventilation standard under these circumstances, the natural ventilation can satisfy most conditions. According to climate data, the days in which wind speed is below 0.5m/s accounts for about 26%. Most of the school buildings aren’t located in open areas. Furthermore, in consideration of the emergency evacuation for the children, the school buildings are built only with 1-3 floors. So, the disturbances of the surrounding buildings contribute to low wind speeds around the school building. The time period between cooling and heating seasons usually comes with low indoor and outdoor temperature difference, where most classrooms have open windows for ventilation. It is therefore important to find out whether the ventilation rate is sufficient during this period of time. The measuring method is based on the release of a stable rate of the tracer gas CO2 given off by solid CO2 (dry ice) in an insulated box. In theory, the dry ice will sublime at a constant rate as long as there is sufficient dry ice in the box. Thus, the dry ice sublimation rate should remain constant at any time once the steady state heat transfer condition has been reached. The results of the measurement can enrich the database of the Chinese current ventilation situation. The early-design-stage predictions of natural ventilation performance in renovated buildings provide reference to the design of natural ventilation systems.
Streamflow Reconstruction and Variation Characteristic Analysis of the Ganjiang River in China for the Past 515 Years

River flow reconstruction under the background of long-term climate change is of great significance for understanding the regional response to future drought and flood disasters, and the sustainable development of water resources. Investigating the basic characteristics and changing trends of the streamflow of the Ganjiang River is scientifically important to mitigate drought and flood disasters in the future. This study reconstructed drought and flood grade series of five regional stations of the Ganjiang River based on spatially explicit and well-dated local chronicle materials and used a linear regression model of modern drought/flood grades and precipitation to reconstruct historical precipitation for the past 515 years. The relationships between the modern precipitation of five regional stations and streamflow of Waizhou Station, which is the last hydrological station of the Ganjiang River were analyzed through principal component regression. The adjusted R2 is 0.909, with a low relative bias of −1.82%. The variation of streamflow from AD 1500 to AD 2014 was reconstructed using the proposed model. Result shows that high flows occur in nine periods and low flows occur in 11 periods. Extremely low stream flow in 515 years appears during the middle and late 17th century. Cumulative anomaly and Mann-Kendall mutation test results reveal that a transition point from predominantly low to high flows occur in AD 1720. Redfit power spectrum analysis result shows that the variation periods of streamflow are 2–5, 7–8 years, and approximately 32 years, where the most significant period is 2–3 years. Continuous wavelet transform indicates that the corresponding relation occurs between streamflow and El Nino/Southern Oscillation for eight years. Streamflow is affected by temperature and East Asian monsoon that is controlled by solar activities. The flood may be related to strong solar activity, monsoon failure, and vice versa. Hydrological frequency curve analysis shows that the streamflow of the Ganjiang River once in a hundred years may reach up to 1031 × 108 m3 for flood or 485 × 108 m3 for drought and the standard of once in a millennium runoff may reach up to 1188 × 108 m3 for flood or 450 × 108 m3 for drought. These results may provide basic hydrological data for the sustainable development of society and serve as a reference for mitigating the impact of drought and flood disasters in the future.
Training Larger Models on TensorFlow without Additional GPU

Across different applications, we have seen examples that models with larger capacity achieve better performance. However, the model capacity is ultimately restricted by the limited and expensive GPU memory. In this paper, we present a number of techniques that leverage cheap host memory to reduce GPU memory consumption during training in TensorFlow. Our techniques require no modifications to TensorFlow application programs and enable training models with 3.8× more (up to 2.5 Billion parameters) parameters on a single GPU.
Farm-Scale Estimation of Total Nitrogen Content in Surface Paddy Soils by Extraction with Commercially Available Hydrogen Peroxide

We recently proposed a simple method for estimating total nitrogen content in paddy soil. In this method, soil is extracted with a commercial 3% hydrogen peroxide (H2O2) solution at 25 °C for 40 h, and electrical conductivity (EC (H2O2)) of the extract is measured. This study aimed to further evaluate the method’s applicability to soil samples collected at the farm scale by using the original and six additional H2O2 solutions that are locally and commercially available. The results obtained with the original solution indicated that the determination coefficients between EC (H2O2) and total N were statistically significant at all farms examined: Moka, 0.78 (n = 13); Kyoto, 0.50 (n = 16); Kizu, 0.43 (n = 89); and Kawatabi, 0.25 (n = 18). The EC of the tested H2O2 solutions varied from less than 0.05 to 1.4 mS cm−1 because of the addition of different stabilizers. EC (H2O2) values obtained with the less stabilized H2O2 solutions (one from Japan, one from USA, and the analytical grade 6% solution) agreed well with those obtained with the original solution. Thus, the proposed method can be useful for estimating the farm-scale variation in soil total N, provided a H2O2 solution with a low EC (<0.2 mS cm−1) is used for the extraction.