Miracle-dz/UniFAR-FTUs huggingface dataset:
USAGE OF DRONES OR UNMANNED AERIAL VEHICLES (UAVS) FOR EFFECTIVE AERIAL SURVEILLANCE, MAPPING SYSTEM AND INTELLIGENCE GATHERING IN COMBATING INSECURITY IN NIGERIA

: One the major problems threatening the Nigerian state at present is the rising spate of insecurity which has now assumed a formidable dimension. The security problem which has posed dangerous threats to national security and statehood therefore is believed to have now necessitated yearnings for the use of drones for effective aerial surveillance, effective, intelligence gathering and monitoring security activities. The daily thirst for blood of innocent citizens and the preference for violence which has now become a huge task to handle by the Nigerian security agencies is a pointer to the need to adopt the use of drones also known as (Unmanned Aerial Vehicles) for security surveillance and intelligence gathering. Unmanned aerial vehicles (UAVs) known variously as drones or remotely piloted vehicles (RPVs) are small aircraft that can fly without an onboard human operator. They are autonomous machines that can carry cameras, sensors, communication equipment or other payloads. Drones’ usage has been identified as the most effective for intelligence gathering, aerial surveillance and security monitoring in recent times. The study, however, reveals the various instances of insecurity in Nigeria which include: armed banditry, and robbery, herdsmen killings, ritual killings, kidnapping, Boko Haram attacks, Niger/Delta militancy among others. Factors aiding the upsurge of insecurity and violence are exhibited by the study to include: disconnects between the people and government; weak and poorly funded military establishments; interagency rivalry; absence of intelligence gathering on the part of the security agencies; unemployment for the teaming graduate; non-prosecution of perpetrators of violence in Nigeria among others. In this paper, the various trends in usage of drones for effective security purpose vis-à-vis their application in aerial surveillance in the country are addressed. The study therefore reveals and recommends that drones or UAVs are very effective for aerial surveillance, mapping system, intelligence gathering, and manning security - and it is to be adopted by the security agencies in Nigeria.
Real-time QoE estimation method using EEG for video

Video streaming services such as YouTube, Netflix, and Hulu, and remote web confer-encing systems such as Zoom and Cisco WebEx are rapidly gaining popularity, and the number of users of such services continues to increase every year. Research on improving the quality of user experience (QoE) in video streaming delivery (hereafter referred to as video delivery), which is common to these services, has become very important for both users and service providers. In recent years, most video delivery services have adopted HTTP adaptive streaming (HAS) in order to provide video delivery with appropriate service quality for various service usage environments. Dynamic Adaptive Streaming over HTTP (MPEG-DASH) is one of the most popular HAS standards, and there has been a lot of research on developing QoE-based adaptive streaming using MPEG-DASH. QoE is expected to be used as an indicator to reflect user satisfaction. However, QoE depends on various user’s external factors and internal factors. In our previous research, we made a classifier for estimating QoE by using extracting temporal and frequency features from the user’s EEG, and clarified that the estimation accuracy of the method. In the context of the QoE-based video delivery control, it is important to accurately estimate the user’s QoE especially when the video quality changes. Therefore, in this thesis, in addition to our previous method, we investigate a method to obtain the timing for controlling the video bitrate. We propose a delivery control method based on the user QoE, using event-related potentials (ERPs), which are EEGs occurred by a specific event (stimulus) and are endogenous potentials that reflect the subject’s cognitive attitude toward the stimulus. The P300 is an event-related potential and is known to occur in association with the presentation of low-frequency stimuli in stimulus-classification tasks such as the oddball task, but recent studies have reported that it is also evoked when perceiving changes in a
FPGA IMPLEMENTATION OF IMAGE WATERMARKING USING XILINX SYSTEM GENERATOR

In this paper, co-hardware simulation of Image Watermarking has been performed using Xilinx System Generator and VIRTEX 6 target device on ML605 FPGA Board. In Watermarking, valuable information can be hidden in the set of digital media by modifying the graphic contents. Here, Watermarking is performed in DWT domain after scaling of watermark data. Watermarking is implemented in MATLAB Simulink with Xilinx System Generator toolbox. Then, Co-hardware simulation is performed using VIRTEX 6 device on ML605 Board. The visual results of both simulation and Co-hardware simulation models are achieved and it is observed that the Co-hardware simulation results are better. The device utilization during the implementation of Co-hardware simulation of watermarking process on ML605 Board is compared with the implementation on LX240 Board. The ML605 implementation shows a requirement of smaller number of components as compared to other researchers work.
AN EXPERIMENTAL STUDY ON UTILIZATION OF ICT TOOLS FOR IMPROVING THE PERFORMANCE OF LEARNERS THROUGH TASK BASED PEDAGOGY IN TODAY’S TECHNOLOGICAL CONTEXT

: The language classroom occupies a major role in learning a language through information and communication tools (ICT). The learning atmosphere is very important for a learner. While performing a task inside the classroom, each and every student has his/her own stimulus in carrying out the given activities. In such situation, Task-Based Language Teaching plays a vital aspect in teaching and acquiring the language. With this new methodology, the teachers can have authentic task designs and make the students to involve themselves in communicating through various phases of activities in the acquisition process. This paper has focused on the outcome of the tasks that were tested on students based on TBLT. It is an experimental study where the attention is on all respects of Task Based Language Teaching specifically with respect to design and detailing the real execution of activities in language classroom in comparison with traditional mode of teaching and learning process.
Application of Technology Acceptance Model and Trust in Determining Purchase Intention: A Case of Facebook Influence

: The extensive acceptance of Facebook to communicate and connect has given rise to understand the user behavior and is elementary in developing and implantation of new technologies. The approach that is empirically used is Technology Acceptance Model. TAM is a common method to explain the acceptance behavior of various information and communication system. TAM predicts the intention and acceptance of technology. This study explores the determination of purchase intention influenced by Facebook. The influence of Facebook applying perceived usefulness, perceived ease to use and trust is observed with primary data set of 275 users of Facebook assembled from a web-based questionnaire survey. The results exhibited by the TAM model proposed for the study rejects all the null hypothesis. Based on existing literature, explains the influence of Facebook on purchase intention. This paper can be a base for policy makers and marketers to gain better understanding of user’s behavior.
Energy-Saving Design and Control of a Methyl Methacrylate Separation Process

An alternative design for the separation of a mixture including methyl methacrylate, methanol, and water is investigated as compared to a previous design using the same process units. In this alternative design, top and bottom products of the distillation column are designed to be at unstable and stable nodes, respectively. Thus, significant savings in the steam cost (16.3%) can be realized as opposed to the previous design of placing the bottom product at a saddle point. Another benefit is that the loss of methyl methacrylate product through the water outlet stream is also less than that of the previous design. This represents another 9.6% savings of the operating cost for this alternative design. Furthermore, an overall control structure for this alternative design is also devised based on a novel way of using open-loop and closed-loop sensitivity tests. By the control of a temperature difference at two trays of the distillation column and another single-tray temperature at the stripper, both MMA and wa.
Logit and Feature Dual-level Alignment for Visible-Infrared Person Re-Identiﬁcation

Person re-identiﬁcation (Re-ID) is an important task in video surveillance which automatically searches and identiﬁes people across different cameras. Despite the extensive Re-ID progress in Visible cameras, few works have studied the Re-ID between infrared and Visible images, which is essentially a cross-modality problem and widely encountered in real-world scenarios. The key challenge lies in two folds, i.e., the lack of discriminative information to re-identify the same person between Visible and infrared modalities, and the dif-ﬁculty to learn a robust metric for such a large-scale cross-modality retrieval. In this paper, we tackle the above two challenges by proposing a novel Dual-level Alignment Network (DANet). To handle the lack of insufﬁcient discriminative information, we design a cutting-edge metric learning objective function to learn discriminative feature representation from different modalities. To handle the issue of modality discrepancy, we integrate a mutual learning manner, which minimize modality discrepancy by close the distribution of two modalities. The entire DANet can be trained in an end-to-end manner by using a standard deep neural network framework. We have quantized the performance of our work in the newly-released RegDB Visible-Infrared Re-ID benchmark, and have reported superior performance
Image Classification using Deep Neural Networks for Malaria Disease Detection

: Since the 19 th century, Malaria has become a terrifying life-threating disease in most of the countries. Its been identified that five countries namely Nigeria with 25%, Congo with a ratio of 11%, Mozambique with ratio of 5%, India with ratio of 4% and Uganda with ratio of 4%. World Health Organization stated that above 90% of malaria death cases were recorded every year. Most of the Indian states like Odisha, Madhya Pradesh, Maharastra, northern countries, Chhattisgarh got affected by Malaria. India spotted death cases of malaria from millions to thousands that have reduced in recent years. Directorate of National vector Bore disease control program has started malaria control strategies using early case detection and treatments, vector control, protective measures against mosquito bites and management of Environment. The major challenge was to identify the disease at early stage. The key contributions avoid malaria disease is to provide antimalaria drugs, using indoor spray with residual insecticides, mosquito nets. For the treatment, medical technologies, deep learning architectures related to Convolutional Neural Networks to train and test performing different combinations for image classification using ResNet34 which helps patient go through prior examination for microscopic diagnosis. For patients examination, this paper considers Malaria Cell Images dataset with Parasitized and uninfected images. Thus, this clearly shows that one can easily identify person’s condition whether he is infected or uninfected by enabling open-source Artificial Intelligence. It shows the start-of-the-art accuracy by checking individual details.
A Low Dissipative and Stable Cell-Centered Finite Volume Method with the Simultaneous Approximation Term for Compressible Turbulent Flows

A simultaneous-approximation term is a non-reflecting boundary condition that is usually accompanied by summation-by-parts schemes for provable time stability. While a high-order convective flux based on reconstruction is often employed in a finite-volume method for compressible turbulent flow, finite-volume methods with the summation-by-parts property involve either equally weighted averaging or the second-order central flux for convective fluxes. In the present study, a cell-centered finite-volume method for compressible Naiver–Stokes equations was developed by combining a simultaneous-approximation term based on extrapolation and a low-dissipative discretization method without the summation-by-parts property. Direct numerical simulations and a large eddy simulation show that the resultant combination leads to comparable non-reflecting performance to that of the summation-by-parts scheme combined with the simultaneous-approximation term reported in the literature. Furthermore, a characteristic boundary condition was implemented for the present method, and its performance was compared with that of the simultaneous-approximation term for a direct numerical simulation and a large eddy simulation to show that the simultaneous-approximation term better maintained the average target pressure at the compressible flow outlet, which is useful for turbomachinery and aerodynamic applications, while the characteristic boundary condition better preserved the flow field near the outlet.
A Follow Up to Our Qualitative Inquiry on the Perspective of Tax Practitioners on Corporate Social Responsibility and Taxation

Researchers continue to interpret the conclusions of prior research on the important relationship between Corporate Social Responsibility (“CSR”) and taxation differently. The current literature on the relationship between CSR and taxation is limited. In addition, the results and conclusions of prior research remain inconclusive and conflicting. The purpose of this paper is to explore the perspective of tax practitioners on the link between taxation and CSR. We conducted a qualitative study by interviewing tax practitioners about their perspectives, experiences, and understanding on the relationship between taxation and CSR. From these interviews, we found that tax practitioners believed that: (1) compliance and CSR were equivalent; (2) their role was to merely comply with the law; and (3) altruism motivated clients to distribute wealth more than taxation does. These findings are beneficial to the fields of CSR and taxation. We hope that this study elevates and adds to the discussion on CSR and taxation, and provides further insight on the relationship between CSR and taxation.
Nanoparticle Molybdenum Dioxide: A New Alternative Catalytic Material for Hydrogen Production via Partial Oxidation of Jet-A Fuels

A battery-extender fuel cell auxiliary power unit (BE-APU) is the first step in a far-reaching and long-term vision that Boeing has for utilizing fuel cells as APUs on board commercial airplanes for greater energy efficiency and improved air quality at airports. A critical requirement for developing a commercially viable BE-APU is a catalyst that can reform and electrochemically oxidize fossil fuel-based aviation fuels. This paper reviews the catalytic performance of nanoparticle MoO 2 , which can effectively reform Jet-A fuels to produce hydrogen and carbon monoxide (synthesis gas) under partial oxidation conditions. Nanoparticle MoO 2 was synthesized by the reduction of molybdenum trioxide (MoO 3 ) powder in a 1:3 volume ratio of ethylene glycol to distilled water at 180 °C for 12 h. The resulting nanoparticle MoO 2 was characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and BET surface area measurements. For the partial oxidation of Jet-A fuel at 850 °C, oxygen-to-carbon (O 2 /C) ratio of 0.55 and weight-hourly-space-velocity (WHSV) of 1.5 h ―1 , the synthesized nanoparticle MoO 2 displayed a fuel conversion of ∼99% and a hydrogen yield of ∼60% after 9 h on-stream. Under similar operating conditions, a reference Ni catalyst was deactivated due to coking after less than 4 h of operation. Based on these findings, nanoparticle MoO 2 appears to display a coking resistance higher than that of the reference Ni catalyst during the partial oxidation of Jet-A fuels.
Preliminary Analyses on the Importance of Iodine Chemistry Reactions in the Gas Phase

Due to high affinity of a human thyroid and relatively long half-life nature, potential radiological impact of radioactive iodine is significant during a severe accident. Under conditions of the severe accident, such as radiation field with high temperature and humidity in the containment, the radioactive iodine has multiplicity of the oxidation states, which leads to have various chemical forms, e.g., aerosol, organic, inorganic, and etc. Considering that effective dose coefficients are various depending on the chemical form [1], chemical behavior of the iodine is an area of particular relevance in the source term studies of the nuclear accident. In order to analyze the chemical behaviors of the radioactive iodine, computer codes on the iodine chemistry have been developed based on the combination of separate experimental results on the various phenomena related with iodine [2, 3]. Therefore, it is difficult to find the dominant and important phenomena on the source term of radioactive iodine during severe accidents. In this paper, based on Latin hypercube sampling on the reaction coefficients in the iodine chemistry code, we will perform importance analyses on the iodine chemistry model in the gas phase. From the analyses we will find dominant and important phenomena for the source term on the iodine, which will provide direction on the evaluation of source term in regulation and the source term research in terms of experiment and computation.
An Integrated MCDM Model based on Pythagorean Fuzzy Sets for Green Supplier Development Program

Green supplier development is becoming vital for many industrial firms for effective green supply chain management. Most of the suppliers are willing to invest in many green supplier programs that developed in their firms’ performance. The evaluation and selection of an adequate green supplier development program is too complex and challenging as it has multiple criteria and alternatives to be chosen. These criteria involve both qualitative and quantitative information. To select the best alternative of the green supplier development program, it is necessary to settle these problems using multi-criteria decision-making (MCDM) method. This paper proposes the integration of Pythagorean fuzzy AHP and Pythagorean fuzzy VIKOR approach to resolve the green supplier development program selection. The main goal of this study is to present Wan Mohd, W. R. & et al. a useful and reliable method to identify the most important criteria and alternatives using Pythagorean fuzzy AHP and Pythagorean fuzzy VIKOR. The first innovation is finding the weight for each criteria using Pythagorean fuzzy AHP. In order to do so, the crisp value evaluated by the decision makers (DMs) are presented in the pair-wise comparison matrix and converted to Pythagorean fuzzy number. The VIKOR is used to rank the alternatives of the green supplier development programs and suggest which program is the best program. Then, the obtained results are compared with the existing VIKOR method in the same case study. The results found the supplier training is the best alternative to select in the green supplier development programs. It is noted that the integration of Pythagorean fuzzy AHP and Pythagorean fuzzy VIKOR is a holistic approach to the MCDM problem.
Freshness-Aware Information Update and Computation Offloading in Mobile-Edge Computing

Mobile-edge computing is a promising computing paradigm with the advantages of reduced delay and relieved outsourcing traffic to the core network. In mobile-edge computing, reducing the computation offloading cost of mobile users and maintaining fresh information at edge nodes are two critical while conflicted objectives, as both consume the limited wireless bandwidth of edge nodes. Although extensive efforts have been devoted to optimizing computation offloading decisions and some works have investigated freshness-aware channel allocation issues recently, no prior works have considered the above conflict. This article is the first work to jointly optimize the channel allocation and computation offloading decisions, aiming at reducing the computation offloading cost within freshness requirements of sensors. We analyze the recursiveness of Age of Information (AoI) in analogy to the evolvement of a queue and formulate the problem as a nonlinear integer dynamic optimization problem. To overcome the challenges of AoI-computation cost tradeoff, AoI time dependency and high complexity caused by the heterogeneity of users, we propose an algorithm to solve the problem with reduced computation complexity. Specifically, we first transform the original problem into a static optimization problem in each time slot (which is NP-hard) based on Lyapunov optimization techniques. To reduce the computation complexity, we exploit the finite improvement property of potential games and further enforce centralized control to reduce the number of improvement iterations. Simulations have been conducted and the results demonstrate that the proposed algorithm shows good effectiveness and scalability.
A Survey of Software Log Instrumentation

Log messages have been used widely in many software systems for a variety of purposes during software development and field operation. There are two phases in software logging: log instrumentation and log management. Log instrumentation refers to the practice that developers insert logging code into source code to record runtime information. Log management refers to the practice that operators collect the generated log messages and conduct data analysis techniques to provide valuable insights of runtime behavior. There are many open source and commercial log management tools available. However, their effectiveness highly depends on the quality of the instrumented logging code, as log messages generated by high-quality logging code can greatly ease the process of various log analysis tasks (e.g., monitoring, failure diagnosis, and auditing). Hence, in this article, we conducted a systematic survey on state-of-the-art research on log instrumentation by studying 69 papers between 1997 and 2019. In particular, we have focused on the challenges and proposed solutions used in the three steps of log instrumentation: (1) logging approach; (2) logging utility integration; and (3) logging code composition. This survey will be useful to DevOps practitioners and researchers who are interested in software logging.
Springer MRW: [AU:0, IDX:0]

Salientia is a superorder of lissamphibians, which includes frogs and toads (order Anura) and the Early Triassic Proanura Triadobatrachus and Czatkobatrachus (Benton 2015). This is the major extant order of the class Amphibia, encompassing more than 7000 species (Frost 2019) with a high morphological diversity. They are distributed all around the globe, except for the poles and most oceanic islands. Salientia synapomorphies (characteristics that are shared by the group, derived from a common ancestral) include: lack of a tail in the adult stage; five to nine presacral vertebrae; postsacral vertebrae (posterior to the pelvis) fused into a bony coccyx; and hind limbs elongated, modified for jumping. Other characteristics of Salientia include: fertilization, often external; eggs laid in water or humid places; aquatic larval stage present in most species; males usually with vocal cords, vocal sac, and a voice (Hickman et al. 2014; Kardong 2015). In this section, we synthesize the main knowledge about larval and adult morphologies, and also include changes during metamorphosis, adult sexual dimorphism, and evolution.