Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Optimization for Multimodal Heterogeneous Network Orchestration via Hybrid Probability Process

In the era of 5G and beyond, heterogeneous network orchestration has become a tremendous issue. The dilemma facing future systems is how to allocate integrated resources to satisfy multifarious services, which is an imperative but arduous task in forming a systematic mathematical model and quantifying the model with its multi-layer uncertainty characteristics. Aiming at the statistical representation and optimization in multimodal heterogenous networks for 5G and beyond, we propose a novel hybrid probability process (HPP) as a generalized surrogate model and a weighted degenerated upper confidence bound (WDUCB) criterion for Bayesian optimization (BO). We apply the proposed HPP-WDUCB combination to our developed simulation platform and configure several applications of the integration of space information network in next generation communication systems. And we compared the proposed method with other surrogate models and acquisition strategies from a range of perspectives. The experiment results yield significant applicability and excellent performance in multimodal system representation and optimization which provides an effective statistical modeling and orchestration references for network tuning.
A Fast Peak Searching Method for DOA Estimation Based on CTA

The Multiple Signal Classification (MUSIC) algorithm has an excellent performance in the direction of arrival (DOA) estimation. The heavy computational burden of eigenvalue decomposition and peak searching limits the MUSIC algorithm to utilizing in the high real-time processing environment. In order to reduce the computational complexity of peak searching, a novel DOA estimation method based on the Chirp Transform Algorithm (CTA) is proposed. Considering the relationship between the Discrete Fourier Transform (DFT) and the MUSIC algorithm, an accurate estimation of DOA can be quickly obtained by using the CTA to search peaks in a local angle range. It has great calculation efficiency with no need to search angles one by one in the local angle range. With this new method, the computational complexity can be reduced significantly. The theoretical analysis and computer simulation results show that the new method achieves the performance of the MUSIC algorithm with very low computational complexity.
Multiple Controlled Antirandom Testing (MCAT) for High Fault Coverage in a Black Box Environment

Among the black-box approaches to digital circuit testing, Random testing is popular due to its simplicity and cost effectiveness. Unfortunately, available evidences suggest that Random testing is equipped with a number of redundant patterns that increase test length without significantly raising the fault coverage. An extension to Random testing is Antirandom that removes redundancy by introducing a divergent pattern with every subsequent test pattern selection. A divergent pattern is induced by maximizing the Hamming distance and Cartesian distance of every subsequent test pattern from the set of previously applied test patterns. However, an enumeration of input combinations is required for the selection of a divergent pattern. Therefore, selection of a divergent pattern from all input combinations restricts the scalability of an Antirandom test pattern generation. One of the recently considered approaches is the stacking of locally optimized short sequences to generate a complete test sequence. Locally optimized short sequences originate from randomly chosen patterns instead of divergent patterns to avoid enumeration of input space. Seeding of random patterns for short sequences affects global diversity of the generated test sequence and hence, fault coverage is compromised. Therefore, this paper firstly proposes a tree traversal search based selection of divergent patterns that eliminates the search space. Ease in divergent pattern selection is used to generate optimal short sequences for divergent patterns instead of random patterns. Consequently, Multiple Controlled Antirandom Tests (MCATs) are generated that maximize distance between locally optimal short sequences to elevate the fault coverage. Fault simulation results on both ISCAS’85 and ISCAS’89 benchmark circuits prove the scalability and effectiveness of the proposed approach. Moreover, the comparison shows that up to 12% of fault coverage is improved as a result of proposed MCAT test pattern generation.
Plant Disease Classification Using SOFT COMPUTING Supervised Machine Learning

Plants are always concerned about the diseases introduced by pathogens For example infections, microorganisms and parasites in the plant bodies. It is globally recognized that, pathogens tends to cause huge yield misfortunes. Various researchers have explored how to diminish the harmfulness of pathogens in plants. A few analysts have explored some opposition qualities in plants and attempts to improve the obstruction of plants to pathogens. Meanwhile, different analysts have created ID and s coring framework for monitoring and examining the advancement or quality and also by anticipating the infection bolstered leaves. The reason for this Review work is to display the use of AI in the revelation of plant opposition.
Northup: Divide-and-Conquer Programming in Systems with Heterogeneous Memories and Processors

In recent years we have seen rapid development in both frontiers of emerging memory technologies and accelerator architectures. Future memory systems are becoming deeper and more heterogeneous. Adopting NVM and die-stacked DRAM on each HPC node is a new trend of development. On the other hand, GPUs and many-core processors have been widely deployed in today's supercomputers. However, software for programming and managing a system that consists of heterogeneous memories and processors is still in its very early stage of development. How to exploit such deep memory hierarchy and heterogeneous processors with minimal programming effort is an important issue to address. In this paper, we propose Northup, a programming and runtime framework, using a divide-and-conquer approach to map an application efficiently to heterogeneous systems. The proposed solution presents a portable layer that abstracts the system architecture, providing flexibility to support easy integration of new memories and processor nodes. We show that Northup out-of-core execution with SSD is only an average of 17% slower than in-memory processing for the evaluated applications.
A Study on the Future of DNA Biosensors as Disease Detectors

This paper deals with the study of the different kinds of DNA biosensors that play a key role in the field of medical sciences specifically in the field of disease recognition. Deoxy-ribonucleic acid (DNA) carries the genetic material of an organism and it is a proven fact the analysis of DNA has formed the pivot for the detection and diagnosis of inherited genetic defects as well as the identification of other disease-causing pathogens. Utilizing the structurally unique nature of each individual’s genome, DNA hybridization and profiling are some techniques that help in disease diagnosis. DNA detection is done both clinically and by employing miniaturizing techniques. Clinical methods like Comet Assays and Polymerase Chain Reactions stand out among other potential strategies because of their increased efficiency. CMOS (Complementary Metal Oxide Semiconductor) biosensors are the key technological advancement toward the sensing of the damaged DNA fragments that will enhance our insight into the deadly diseases faced by the human population.
Pre-processing of SAR Images through Compressed Sensing

Any remote sensing image regardless of the source (satellite, aircraft or any sensors and platforms) from which it is acquired undergoes distortion. This generally happens because of the various aspects concerning the optics, scanning mechanism; or motion, velocity, altitude and attitude associated with platforms or may be due rotations and curvature of the earth. Synthetic Aperture Radar (SAR) imaging is one of the emerging technology of remote sensing which has grabbed the attention of most of the researchers. Also, in recent years Compressed Sensing (CS) theory has emerged in both signal processing and image processing domains. Compressed sensing promises image reconstruction at a lower sampling rate than traditional Nyquist sampling and hence CS can also be used for image retrieval. Not only this but CS also provides a faster and efficient way to reproduce the signals. This paper demonstrates the application of CS theory for pre-processing on high-resolution images for SAR image reconstruction.
Analysing Social Presence in Online Discussions Through Network and Text Analytics

This paper presents an approach to studying relationships between students' social presence and course topics from transcripts of asynchronous discussions in online learning environments. Specifically, the paper uses topic modeling and epistemic network analysis to investigate how students' social presence is expressed across different course topics. Finally, we show how this method can be adopted to examine how students' social presence changed due to an instructional intervention. The results of this study and its implications are further discussed.
Book Genre Classification Based on Titles with Comparative Machine Learning Algorithms

This paper presents algorithmic comparisons for producing a book’s genre based on its title. While some titles are easy to interpret, some are irrelevant to the genre that they belong to. Henceforth, we seek to determine the optimal and most accurate method for accomplishing the task. Several data preprocessing steps were implemented, in which word embeddings were created to make the titles operable by the computer. Five different machine learning models were tested throughout the experiment. Each different algorithm was fine-tuned for attaining the best parameter values, while no modifications were conducted on the dataset. The results indicate that the Long Short-Term Memory (LSTM) with a dropout is the top performing architecture among the algorithms, with an accuracy of 65.58%. To the authors’ knowledge, no prior study has been done about book genre classification by title, therefore the present study is the current best in the field.
Performance Evaluation of Moving Sink based Pollution Data Collection for Different Sink Movements in Wireless Sensor Network

Pollution data needs to be collected in urban areas for monitoring purposes. Among various approaches moving sink/ data mule based approach is more effective from energy consumption perspective. In this approach mobile sink gathers information from the static sensors of the network and transfer it to uploading server or base station. The Sink Trail protocol is one such protocol which handles this task in an efficient way. The logical coordinate system of Sink Trail protocol ease routing and forwarding without using GPS.The impact of different moving patterns of sink can affect the performance of packet received and energy consumption. In this paper various sink movements such as straight line, linear and zigzag pattern at constant speed is considered for protocol evaluation. The size of the network is also varied for different movements. The simulation results demonstrate substantial differences for different sink movements.
Colour based Image Segmentation for Automated Detection of Leukaemia: A comparison between CIELAB and CMYK colour spaces

Leukaemia is an abnormal proliferation of White Blood Cells in the bone marrow and peripheral blood. The diagnosis of leukaemia is done by the pathologists and haematologists by observing a peripheral blood smear and bone marrow smear under a microscope. Advanced cytochemistry tests, immunophenotyping, flow cytometry, chromosome analysis etc. are used to confirm the incidence of leukaemia. Since these tests are time intense and exorbitant, image analysis of the blood and bone marrow smear can be used to assist the pathologists and haematologists for a quick diagnosis of leukaemia. A simple method to detect the incidence of leukaemia automatically by image processing techniques is proposed in this paper. The method uses colour based image segmentation using k-means clustering and image analysis by Hausdorff Dimension to detect leukaemia. The image in the RGB colour model is converted to the CIELAB as well as CMYK colour spaces and a comparison between the two in detecting leukaemia is also presented.
AHTDT- Automatic Hate Text Detection Techniques in Social Media

Online social media provides a friendly platform to internet users. Hence they express their thoughts and opinions freely. Due to internet's non-restrictive nature, users may mistreat the medium to endorse offensive and hateful language. Existing technique of manual identification for such offensive content is time-consuming and may not detect all offensive content. Hence automatic detection of offensive content is needed to identify and to analyze the abundance of unpleasant text in online social media. This paper discusses automatic hate text detection techniques and highlights the parametric comparison of those techniques.
Energy Efficient Detection-Removal Algorithm for Selective Forwarding Attack In Wireless Sensor Networks

Wireless sensor networks (WSNs) propose the promise of a flexible, low cost solution for monitoring critical infrastructure. Sensor networks have been recommended for applications such as traffic monitoring, military and battlefield surveillance. Wireless sensor networks are more prone to security attacks due to their broadcasting nature of the transmission medium and unattended deployment of nodes in hostile and unfriendly areas where they are not protected as compared to wired networks. Attackers can deploy various types of security attacks to obstruct the security of WSNs. Network layer attacks are more severe since if the routing information is disregarded, disturbances may bring about routing loops, changing of routes etc. Selective forwarding attack is a type of active attack affecting network layers that selectively drops or refuses to forward the data packets. This paper discusses about an energy efficient detection-removal algorithm for effective detection of selective forwarding attack in a clustered WSN scenario. The impact of the malicious node in network parameters like packet delivery ratio, throughput, residual energy of network and end to end delay are analyzed.
Dryland Crop Recognition Based on Multi-temporal Polarization SAR Data

Dryland crops in China have a large planting area and wide spatial distribution, which contributes a lot to grain production. Accurate and timely acquisition of information on dryland crop types, planting areas and spatial distribution in dryland can provide an important basis for agricultural production and management, as well as the formulation of national food policy and economic plan, therefore, it is of great significance for promoting structural reform of agricultural supply side and national food security. The key stage of crop growth in the north of China is affected by cloud and rain weather, which makes it difficult to obtain sufficient effective optical data, and the current recognition accuracy of dryland crops based on polarized SAR data is low. In order to solve these problems, this study selected Jizhou City, Hebei Province as the research area, using the full-polarization RADARSAT -2 data of July 17, August 7 and September 24, 2018, and then selected three polarization decomposition methods (Cloude-Pottier decomposition, Freeman decomposition and Yamaguchi decomposition) and two classification methods (maximum likelihood and random forest) to construct 18 classification combinations. The identification of corn and cotton in study area was studied by using various schemes. Finally, the accuracy of dry land crop identification under various combination schemes was compared quantitatively with the ground survey data. The results showed that, Yamaguchi decomposition combined with maximum likelihood classification method was used on August 7 (flowering and boll period of cotton), and the classification accuracy was the highest (production accuracy was 78.98%). For corn, Yamaguchi decomposition combined with random forest classification method was used on September 24 (mature period of corn), and the classification accuracy was the highest (production accuracy was over 90%). I For the decomposition method, Yamaguchi decomposition has the highest classification accuracy among the three decomposition methods, followed by Freeman decomposition, Cloude-Pottier decomposition has the lowest classification accuracy; as far as the classification method is concerned, the maximum likelihood classification method has the highest recognition accuracy for cotton, but the random forest classification has the highest recognition accuracy for corn; in terms of the best identification phase, the flowering and boll period is the best recognition period for cotton, and the maturity period is the best recognition time for corn. This study will help to improve the recognition accuracy of corn and cotton in fully polarized SAR data, and provide reference for the identification of multi-temporal dryland crops under complex planting structures.
A flowchart to reduce mutual reinforcement effect on web page ranking based on web strucuture mining

In this dynamic web it is very difficult task to find related and productive information. Bunch of pages with lots of information newly added and deleted from the web as it is dynamic in nature. Basically, Web mining divided into three categories web content mining, web structure mining and web usage mining. One of the important factor of web mining is Link analysis. Web structure mining plays a major role in link analysis. The main objective of web structure mining is to find significant search results. The google search engine uses PageRank algorithm which is based on web structure mining. Everyone knows that PageRank algorithm works on links, so web users creates links between web pages’ weather that are related or not. The reason behind this link creation is to get higher page rank then other web pages. This problem is known as mutual reinforcement. In this paper author proposed flowchart which reduce the effect of mutual reinforcement. To reduce effect of mutual reinforcement author used different parameters like number of different domain inlinks, outlinks and reachability. We also compare the result of proposed flowchart with PageRank and WPR algorithm.
Counter Example Analysis of Robot Action Design for Self-localization Based on Model Checking Using Probability Removed Model

Cyber Physical Systems (CPSs) are growing and play important role in our society. CPSs integrate various techniques such as computation and observation of physical environment. Model checking is one technique of formal methods which has been successfully applied to many systems for ensuring these systems satisfy properties. We studied self-localization algorithm in probabilistic robotics. In the study, model checking has used to validate convergence of robot's position. One remaining problem is that some non-convergence cases are analyzed by hand. In this study, we adopt counter example analysis by converting probabilistic model to non-probabilistic model. We show such analyses can be applied to design motions of a robot.
Stochastic Dual Dynamic integer Programming for a multi-echelon lot-sizing problem with remanufacturing and lost sales

We consider an uncapacitated multi-echelon lot-sizing problem within a remanufacturing system involving three production echelons: disassembly, refurbishing and reassembly. We seek to plan the production activities on this system over a multi-period horizon. We assume a stochastic environment, in which the input data of the optimization problem are subject to uncertainty. We consider a multi-stage stochastic integer programming approach relying on scenario trees to represent the uncertain information structure and propose a solution method based on an extension of the stochastic dual dynamic programming algorithm. Our results show that this approach can provide good quality solutions for large-size instances in a reasonable time and significantly outperforms the use of a stand-alone mathematical solver.
Effective Tensile Strength Estimation of Natural Fibers through Micromechanical Models: The Case of Henequen Fiber Reinforced-PP Composites

The performance of henequen fibers and polypropylene composites obtained by injection molding with and without coupling agent was evaluated. Henequen fibers are natural non-wood fibers mainly used in textile sector or in thermosetting matrix composites. In this work, henequen fibers have been used as a possible substitute reinforcement material for sized glass fibers. The surface charge density of the materials used was evaluated, as well as the morphology of the fibers inside the material. A significant reduction in the length of the fibers was observed as a consequence of the processing. The use of a 4% coupling agent based on fiber content was found to be effective in achieving significant improvements in the tensile strength of the composites in the reinforcement range studied. The influence of the aspect ratio on the coupling factor was determined, as well as the evaluation of the interface quality. The results obtained demonstrate the great potential of henequen fibers as reinforcement of composite materials, giving rise to strong interfaces with coupling. Finally, the comparison of henequen fiber composites with sized glass fiber composites showed that it is possible to substitute polypropylene composites with 20 wt. % glass fiber for 50 wt. % henequen fibers.
Chapter 7 Methods of Measuring Carbon in Forests

Accurate measurement of carbon stocks and flux in forests is one of the most important scientific bases for successful climate and carbon policy implementation. A measurement framework for monitoring carbon storage and emissions from forests should provide the core tool to qualify country and project level commitments under the United Nations Framework Convention on Climate Change, and to monitor the implementation of the Kyoto Protocol. Currently, there are several methods for estimating forest carbon stocks and flux, ranging from the relatively simple forest biomass inventory to complex, sophisticated experiments and models. Advanced carbon estimation methodologies such as LiDAR and eddy covariance carbon flux experiments may provide reliable, accurate and transparent data and serve as a basis for market tools and international policymaking such as carbon trading, carbon taxes, and credits for reducing emissions from deforestation and forest degradation in developing countries (REDD, REDD+). Nevertheless, developing countries, which have limited capacity for data collection and management, need low-cost methodologies with acceptable spatial and temporal resolution and appropriate sampling intensity. If a standardized verification system across projects, countries, and regions is to ever be attained, policymakers should be aware that there are different basic approaches to measuring forest carbon, which have advantages and disadvantages, and varying degrees of accuracy and precision. We review the four categories of methods for measuring forest biomass and estimating carbon which are currently in use: i) forest inventory (biomass); ii) remote sensing (relationship between biomass and land cover); iii) eddy covariance (direct * Yale PhD Candidate