Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Evolutionary Game Model of Bidding Behavior for Electricity Purchase and Sale in Power Market

There are a lot of random interference factors in the power market, which seriously affect the bidding behavior between power generation and power purchasing enterprises. So, in this paper the Gaussian white noise is introduced into the replication dynamic equation to reflect the random interference in the multi-agent bidding process of electricity purchase and sale in the power market, and a random dynamic system is established. The stochastic evolutionary game model of three heterogeneous groups under asymmetric information is constructed, and the model is solved by using stochastic differential equation theory. From the three aspects of enterprise quotation strategy, enterprise income and incentive measures, this paper analyzes the conditions for the gradual stability of bidding behavior in the process of random evolutionary game between power purchasing and power selling groups. The stability of bidding behavior in the process of stochastic evolutionary game between power purchasing and selling groups is analyzed. Finally, taking the bidding behavior of three heterogeneous groups as an example, the dynamic evolution simulation of bidding behavior in power market is carried out. The results show that the established model can more accurately reflect the evolution process of the bidding behavior of each group under the interference of random factors, and is closer to the dynamic change process of the bidding behavior of each power sale and purchase in the actual environment.
The Overview of the Relevance of Virtual Reality and its Applications in Education

Nowadays, we are encased by mixes of improvements and technologies. Virtual Reality commonly known as VR, a magnificent technology which combines two totally different dimensions in a web-Pc generated world with all similar sensations and adventures. Limitation of VR is one's imagination. In this paper an explained layout of enlarged truth is introduced, composing review, favorable circumstances and difficulties, urgent wording and logical examination of VR present and future advancement are recorded. Every single point of convergence and loads are recorded as well. The objective is to have an analysis on the role of virtual laboratories in integrating student's learning ability along with introducing it as an effective tool in lab courses. There was a survey which included questions about perceptions of virtual laboratories and also their role in virtualization of sophisticated instruments. The outcome of the survey suggested that the virtual labs usage enhanced autonomous and guided educational methods.
A Context-Relevant Smart Water Quality Monitoring Testbed for Mining Communities in Ghana

In this paper, we propose a context-relevant and task-scalable real-time water quality monitoring testbed for addressing the impacts of mining-induced water pollution issues in Ghana. The exposure of most water bodies to heavy metals such as mercury and arsenic, which are used during mineral extractions, has resulted in many health challenges in the affected communities. Many households have resorted to sinking domestic boreholes but could not address the challenge since most mine-polluted water bodies in Ghana connect to the underground water table. However, existing IoT-based water quality monitoring systems (WQMS) are founded on expensive, complex, location-restricted, hard-to-access technologies that do not suit this context by informing real-time decisions regarding the quality and usage of our water resources. To address this challenge, this paper proposes a custom-built, real-time and task-scalable IoT-based water quality monitoring testbed that is evaluated via field deployment. This technology is recommended for national policy consideration and adoption by individuals, water suppliers and industries for monitoring spatial and temporal water pollution.
Effectiveness of SVM kernel for YOUTUBE Comment Filtering

The impact of Social networking platform is evident in each generation and almost all fields. It made many tasks more convenient that was not possible earlier. YouTube is one such platform. It can be utilized as a source of entertainment, discussion portal, information sharing, knowledge gathering. Various newborn and popularly known organizations display their advertisements over the platform as short video or links in comment section that help content creators of the video through some financial gains. These advertisements can be greatly helpful to visitors and firm. Also the comment section may contain some cogitative discussions. With all such activities found on the platform some spiteful ones are also lurking over it. Spammers used to suffuse malicious links and data over the platform. In this study, we directed towards the comment section of the platform and executed a comparative analysis of different SVM kernels for the classification of Spam or ham comments over the YouTube application. We also studied the impact of feature extraction incorporating two feature extraction techniques Tf-Idf and BOW.
Chance-constrained Energy Management Strategy for Micro-grids Intra-day Operation

With increasingly high penetration of renewable energy resources (RERs), modern energy system suffers great uncertainties from the perspective of stable intra-day operation. Micro-grid (MG) is an effective solution integrating the RERs to provide clean energy. In this regard, a chance-constrained intra-day energy management strategy (CC-IEMS) is proposed in this study. Kernel density estimation (KDE) is employed to capture distribution features of RERs output by taking advantage of historical data. Assisted by the estimated distribution, chance-constrained electricity balance constraints can be derived with high probability. Then model predictive control (MPC) based strategy is proposed to alleviate negative effects caused by imprecise day-ahead predictions. Moreover, the adjustment cost is also taken into consideration to measure the transfer cost from day-ahead scheduling plan to the rescheduled. Three cases are studied to verify the performance of the proposed strategy. Simulated results show that the strategy doesn't only achieve probabilistic guarantee but also improve the computation effectiveness.
Distributed Adaptive Output Consensus of Unknown Heterogeneous Non-Minimum Phase Multi-Agent Systems

This article addresses the leader-following output consensus problem of heterogeneous linear multi-agent systems with unknown agent parameters under directed graphs. The dynamics of followers are allowed to be non-minimum phase with unknown arbitrary individual relative degrees. This is contrary to many existing works on distributed adaptive control schemes where agent dynamics are required to be minimum phase and often of the same relative degree. A distributed adaptive pole placement control scheme is developed, which consists of a distributed observer and an adaptive pole placement control law. It is shown that under the proposed distributed adaptive control scheme, all signals in the closed-loop system are bounded and the outputs of all the followers track the output of the leader asymptotically. The effectiveness of the proposed scheme is demonstrated by one practical example and one numerical example.
High Accuracy Silent Speech BCI Using Compact Deep Learning Model for Edge Computing

Studies on silent speech brain-computer interface (BCI) systems are underway to facilitate communication with care recipients who have lost their language faculty or suffer from aphasia. This technology hypothesizes what a care recipient is attempting to communicate through electroencephalographic analysis when attempting to speak ‘‘without actually speaking This study shows a method of performing electroencephalographic analysis when a person is imagining a vowel sound to realize a silent speech BCI nursing care support system. To construct a system to reduce the burden on users, constructing a model that can achieve a high accuracy of brain activity with less electrode channels and a compact classifier size that can be implemented in edge devices is essential. Experiments were performed by attaching eight electrodes to the left temporal lobes of the subjects, measuring brain waves when each subject was imagining 5 sounds and a mute, i.e., a/i/u/e/0 and mute and performing analysis using four methods: support vector machine, decision tree, linear discriminant analysis, which is a commonly used machine-learning algorithm, and long short-term memory (LSTM) and EEGNet which are extensively investigated deep learning models. The performance of the system was evaluated with four healthy male subjects (ages 23-24). Consequently, the average classification accuracies of 68.8% and 80.9% for four subjects obtained using LSTM and EEGNet.
A Review of An Idea For Secure Resource Usage And Load Balancing Using Integrated Cloud Computing Strategies

In this study, numerous cloud computing load balancing algorithms are examined, and important difficulties that must be considered while building new load balancing algorithms are also explored. Various measurement criteria, such as performance, scalability, throughput, resource usage, fault tolerance, reaction time, and others, are studied in the literature to compare current static and dynamic load balancing methods. Cloud computation offloading and work schedules have been the subject of some research. Degrading cloud computing performance is a major concern for cloud enterprises of the huge amount of data and varied sources inside this cloud that must be managed. To maximize system performance and reduce carbon emissions, load balancing aims to use resources in the most efficient way possible while minimizing the number of resources used. This paper focuses on cloud computing load balancing strategies. Several qualitative factors, including throughput, reliability, energy-saving features, performance, scalability, and associated overhead, are considered to examine, and compare the present techniques of load balancing.
Faster & Smoother Detection of Forest Fire Using YOLO v3

The precise detection of fire in a virtual system, such as photos, movies, and live stream videos, is crucial for a variety of applications that use various methods. As the name suggests, fire detection can detect the spreading of the fire in the forest by any means, more precisely. To overcome the difficulties of such natural disasters as the Amazon fire or the great Australian bush fire, such scenarios were very devastating. We proposed a solution utilizing Machine Learning with the help of YOLO [4] which could get us out of this hefty situation by detecting fire as well as reporting in a generalized manner to spread the news of fire to the higher authorities or localities.
Using BERT and Word Definitions for Implicit Sentiment Analysis

Implicit sentiment frequently appears in texts. To accurately classify the implicit sentiments of sentences, this paper uses search engines to retrieve definitions of words and incorporates knowledge into language models for implicit sentiment analysis. We propose a model using BERT and word definitions for implicit sentiment analysis (BDISA). Specifically, we use BERT and Word2Vec to represent tokens and word definitions, respectively. Then, by using a fusing layer, we fuse these two representations. Finally, the model outputs the predicted sentiment labels. The experimental results show that the macro-F1 value of the BDISA model is 9.56% and 0.46% higher than KG-MPOA (random and BERT) on the SMP2019-ECISA dataset. The ablation experiments illustrate that word definitions and the fusion layer are critical for the model.
Fine-grained Cloud Edge Collaborative Dynamic Task Scheduling Based on DNN Layer-Partitioning

Edge computing provides an opportunity to improve the quality of service (QoS) of Artificial Intelligence (AI) apps for the Internet of Things (IoTs) scenarios. It is an important way to improve the QoS of intelligent apps by deploying Deep Neural Network (DNN) models on edge nodes. Though the DNN execution time affects the QoS of apps significantly. Due to the limited and dynamic edge resources, and sudden load to edge nodes, it is hard to guarantee the DNN execution efficiency. In this paper, we conduct fine-grained decomposition of DNN tasks and propose a Cloud Edge Collaborative Dynamic Task Scheduling mechanism based on DNN layer-partitioning technique. The approach can realize the collaborative computing of DNN models between cloud and edge, and improve the execution efficiency of DNN models, which guarantees the QoS of AI apps. Through simulation experiments, compared with the existing task scheduling mechanism and AI app deployment mode, we show that the proposed cloud edge collaborative dynamic task scheduling mechanism can effectively reduce the average service response time in the edge intelligent system, so as to improve the apps' overall QoS of the system. Meanwhile, the task scheduling mechanism designed in this paper makes it possible for more complex intelligent models to run in a resource-constrained edge environment.
Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid)

Aquaculture provides food security to many developing countries and enhances the socio-economic conditions of the fishermen. To enhance the productivity of the aquaculture, it is necessary to maintain stress free controlled eco-system for the fishes. For recognising the stress in fishes, behaviour analysis of fishes via tracking is imperative. Early detection of stress in fish facilitates fishermen to take precautionary measures promptly. Computer vision-based fish behaviour analysis of economically important fish species is challenging due to the lack of datasets, occlusions, rapid changes in swim directions etc. The present study proposes a multiple fish video dataset of an economically important species in a controlled environment, namely Sillago Sihama-Vid with accurate annotations. The study emulates the natural environment of Sillago Sihama in a large aquarium. This work proposes a novel fish tracking algorithm that incorporates swim direction information in addition to temporal, appearance, and spatial information. The inclusion of swim direction information reduces the number of identity switches. Comparative performance analysis of the proposed tracking algorithm with the conventional methods on the developed dataset highlights the performance efficiency. The proposed method has a clear performance improvement in MOTA, MOTP, IDSW and MT with respect to the other compared methods. The study also presents a novel unsupervised continual behaviour modelling strategy to model the evolving behaviours of the fishes. Further, interpretation of fish behaviour from the proposed behaviour modelling is performed to highlight the reliability of the proposed method. The significance of the proposed method is that, it is independent of training and labelled data. In addition, the method represents an innovative alternative to capture all the non observable behaviours of the fishes. The proposed tracking and behaviour modelling strategy act as a benchmark for developing algorithms to study fish behaviour via tracking. Finally, the dataset provides an opportunity for developing computer vision-based models to analyse the different behaviours of fish Sillago Sihama.
Newborn Identification using footprints: Add another line of security

People are getting more interested in biometric identification for a variety of reasons. Identification of new born is still a great problem even in this time. Solving this problem could safeguard children from identity theft and fraud, assist in the reunification of lost children with their parents, improve border control systems in the fight against child trafficking, and help electronic document management systems The research presented in this article focuses on biometric strong authentication systems that leverage foot geometry factors. A footprint has three distinct traits that are adequate to identify a person. Different forms of qualities include shape, smoothness, and detail. We analysed the most often used geometrical features of the foot, comprising length, breadth, area, and circumference, to unique information about a person. The axes, major and minor. The categories of features include spatial, smoothness, and delicacy. To uniquely identify persons, we assessed the most often utilised morphological properties of either the foot utilising height, width, region, main pole, and equivalent diameter. Weights are assigned to each feature, highlighting its importance, and several versions of these traits are computed. We chose the best foot summaries and discovered that the province is by far the most important aspect of determining a person's foot. To improve reliability, foot contour characteristics are coupled with foot descriptions. A Gray scale image founder grids relying on Haralick properties are constructed for texturing using Classifier Model as the discriminator. In situations where people remove their shoes, such as holy sites, security checks, private pools, and leisure facilities, foot biometrics can be employed as an extra covert authentication method.
A theory of causal inference based on additive noise model and its application to radiological security projects

Security is particularly important in the process of radioactive material transportation. In order to solve the impact of the uncertain dynamic characteristics of the actual system and the complex uncertain noise containing in the target tracking and positioning of BDS(BeiDou Navigation Satellite System), an algorithm of causal inference is constructed to explain and deal with the state estimation method of the complex uncertain noise system. The measurement of noise variance is performed by using the covariance matching principle and real-time updated noise data with additive measurement noise of the system. an adaptive process detection model is proposed for the modeling problem of the Kalman filter. In comparing and testing the models, The adaptive process measurement model can give ideal estimation results regardless of the initial value of the measurement noise variance in the results. The paper demonstrates that the adaptive process measurement model has good robustness and is highly effective in dealing with state estimation problems for systems with noise which is complex and uncertain.
Riesz potential versus fractional Laplacian

This paper starts by introducing the Grünwald–Letnikov derivative, the Riesz potential and the problem of generalizing the Laplacian. Based on these ideas, the generalizations of the Laplacian for 1D and 2D cases are studied. It is presented as a fractional version of the Cauchy–Riemann conditions and, finally, it is discussed with the n-dimensional Laplacian.
3DS: An Efficient DPDK-based Data Distribution Service for Distributed Real-time Applications

Data Distribution Service (DDS) is a widely-used middleware for data transmission in distributed real-time applications, such as autonomous vehicles and robotics. However, the communication of existing DDS middlewares generally relies on the network stacks of the operating system, which brings long latency to the real-time applications. In this paper, we propose a DPDK-based DDS, dubbed 3DS, to improve the performance of communication of DDS middlewares. First, 3DS reduces the latency of communication between distributed real-time applications by avoiding the latency of kernel. Second, 3DS reduces the overhead of dynamic memory allocation and deallocation by a reserved memory pool. Finally, 3DS adopts a thread-exclusive CPU model to reduce the resource contention between threads. We implement and evaluate 3DS on real systems running Linux. The experimental results show that 3DS reduces 44% of the communication latency over FastDDS, which is the state-of-the-art DDS middleware used by ROS2. Moreover, the throughput of data transfer in 3DS can be 1.51 times that of FastDDS.
Two Phase Methodology for the Detection of Malicious Vehicles from Vehicular Ad hoc Network

The vehicular ad hoc network is the decentralized form of network in which malevolent user can join the network which can trigger a variety of active and passive attacks. The denial of service is an active type of intrusion that is triggered by the malicious nodes present in the network. This research work introduces a new two-phase methodology for detecting and isolating the attack. In the first phase of the methodology tentative nodes will be analyzed which can be the malevolent nodes. The second phase confirms the nodes as spiteful nodes through the promiscuous mode. NS2 is applied to stimulate proposed technique and certain metrics are considered to compute the results.
Numerical Solution of Fractional Partial Differential Equations by Discrete Adomian Decomposition Method

In this paper we find the solution of linear as well as nonlinear fractional partial differential equations using discrete Adomian decomposition method. Here we develop the discrete Adomian decomposition method to find the solution of fractional discrete diffusion equation, nonlinear fractional discrete Schrodinger equation, fractional discrete Ablowitz-Ladik equation and nonlinear fractional discrete Burger’s equation. The obtained solution is verified by comparison with exact solution when α=1. AMS subject classifications: 35R11
Adaptive Exponential Convergence Law Based Sliding Mode Control for Photovoltaic Maximum Power Tracking Control

Maximum power point tracking (MPPT) is one of the key control techniques for photovoltaic (PV) systems. Based on the analysis of PV cell output characteristics, a novel voltage-oriented PV MPPT strategy is proposed in this paper for a stand-alone PV system. The strategy is based on traditional Perturb and Observe (P&O) cascade adaptive exponential reaching law based sliding mode control (SMC), which is divided into inner-loop and outer-loop control. The outer loop uses a voltage P&O to generate the reference voltage at the maximum power point, and the inner loop uses adaptive exponential convergence law based SMC to provide a duty cycle signal for the pulse width modulation (PWM) signal of the power switch, so that the PV voltage is enforced to track the reference voltage of outer loop output. Stability of SMC is proved by Lyapunov's law. The simulation results show that the proposed control strategy has outstanding steady-state and dynamic performance when the solar irradiation changes rapidly compared with the conventional P&O and P&O cascaded PI controller.
ConeSSD: A Novel Policy to Optimize the Performance of HDFS Heterogeneous Storage

HDFS(Hadoop distributed file system) is the core storage service of Hadoop, which stores and processes large datasets efficiently. Therefore, the performance of storage services significantly impacts big data processing applications. Using SSD to replace HDD devices can obtain better performance, but due to the high unit price, it is too expensive to replace all HDD with SSD in HDFS. Therefore, heterogeneous storage has been widely used in HDFS. However, the existing hierarchical strategy of HDFS is simple and cannot obtain better cost performance. This paper presents a novel heterogeneous storage policy named Cone_SSD, which fully uses SSD media on each node to get the IO performance to near SSD. Experimental results show that the proposed policy improves read and write performance by 48.11 % and 47.45%, respectively, and the fluctuation of reading performance decreases by 54.92 % compared with other policies under the same price-performance ratio.