Miracle-dz/UniFAR-FTUs huggingface dataset:
INVESTIGATING CONSUMER BEHAVIORAL INTENTION IN SMART TECHNOLOGY CONTEXT

Today, an increasing amount of trades would be improving these new skills of internet and smart technology as the main tool to promote their products and services. Increasing the skill and ability both to attract the new consumers of young and elder in smart business context is an important challenge for trade managers and has described their considerable attention from the academic groups. More recently, driven by advance developments in Information and Communications Technologies (ICTs) and Mixed-Reality (MR) technologies, Virtual-Reality (VR), and Augmented-Reality (AR), embodied devices, sensors, mobile terminal device, microchip design, power efficiency and more broadly the Internet of Things (IoT), the chance to remotely connect to physical and virtual objects, mobile devices and products would have given rise to the appearance of smart technologies and smart services. Therefore, these businesses directors imminently want to realize the connections with consumer motivation, trust, security risk and the behavioral intention of consumer in requirement of user's utilizing smart technologies, in order to capture a crucial chance from now on smart trade proliferation to find those possible users, The primary concern in this article would be to understand the need and inspect these influences of sensing on perceived value, consumer's reputation, trust and to employ the smart technology of user behavioral intention. Next, the questionnaire would be made complete by online investigation proof ($N=92$) collected from those active users who would have attended in using smart technologies. Thus, analyzed results with confirmatory factor analysis, and structural equation modeling would be also used, applying the SmartPLS 2.0, to certify if these questionnaire proofs would get proper to research model. Lastly, these outcomes would apply the evidence that perceived value, consumer's reputation and trust all could affect behavioral intention to employ smart technologies. All these consequences of the article with significances for practice and theory could be more cared enough to keep in mind, too.
BERT-Based Stock Market Sentiment Analysis

This paper explores the performance of natural language processing in financial sentiment classification. We collected people's views on U.S. stocks from the Stocktwits website. The messages on this website reflect investors' views on the stock. These messages are classified into positive or negative sentiments using a BERT-based language model. Investor sentiment can be further analyzed to help more investors, businesses or organizations make effective decisions. The experimental results show that the pre-trained BERT model has been fine-tuned on the labeled sentiment dataset, and can recognize the sentiment of investors with an accuracy of more than 87.3%.
Learning Analytics - New Flavor and Benefits for Educational Environments

Amount of educational data has been constantly increasing for years in all domains and kinds of education (formal or informal) and educational activities (teaching, learning, assessment, use of social media and collaboration and so on). Accordingly, Learning Analytics (LA) become a powerful mechanism for supporting learners, instructors, teachers, learning system designers and developers to better understand educational processes and predict learners’ needs and performances. In this paper, we analyze the important dimensions and objectives of LA, application possibilities and some challenges to the beneficial exploitation of educational data. The required skills and capabilities that make meaningful use of LA techniques and technologies in this domain are considered and identified. Presented findings can act as a valuable guide for setting up LA services in support of educational practice. Also, they can be used as learner guidance, in quality assurance, curriculum development, and in improving learning process effectiveness and efficiency. Finally, this paper proposes the unavoidable constraints that affect LA technologies in education.
Automatic speech recognition system

The Speech recognition systems are diﬃcult and challenging as the same word can be spelled in many ways also change in accent has a huge impact on the accuracy of speech recognition. Most of the ASR systems in use today are designed to recognize speech in English. The major objective of this research is to design an ASR system which recognizes discrete words from Hindi language & controls some action depending on the provided input. The input voce is captured using a microphone which are then preprocessed using several algorithms like Dynamic Time Wrapping (DTW), Hidden Markov Model (HMM) etc. This paper aims at developing a simplified technique for recognition of speech spoken in the Hindi language by first modeling the system on computer-based design and then deploying it on an embedded system.
Study of the toxic action of chemical weapons through the use of simulants

Chemical weapons have been used repeatedly in the history of mankind in a number of wars and terrorist acts. They have caused major damage to human health and taken many lives. Instead of chemical weapons, various simulants compounds with a similar action of these weapons, but without such high toxicity for human health could be used for developing antidotes to specific chemicals, doing research on detecting them, developing sorbents for gas masks, military exercises, etc. The purpose of this report is to examine the possibilities of using chemical weapons through their simulants which resemble their toxic effects.
PLL and Costas loop based carrier recovery circuits for 4QAM: non-linear analysis and simulation

Design of stable carrier recovery circuits are used in many applications: wireless digital communication, optical communication, microwave devices and other applications. Quadrature Phase-Shift Keying (QPSK, 4-QAM) is used as modulation technique in many of these applications, since QPSK provides double the data rate of classic Binary PSK (BPSK) modulation. Analysis of Costas loop is a hard task because of its non-linearity. In this work we consider two well-known modifications of 4QAM Costas loop circuits and discuss a new circuit proposed by J. Ladvanszky. MATLAB Simulink models of the circuits are provided and the noise analysis is performed.
Service composition optimization approach based on affection ant colony algorithm

In the service computing mode,affection behaviour was employed to improve the efficiency of service composition.Firstly,an affection space was built to meet behaviour demands,and cognition was defined to reason state change of affection.In the change processing,mapping was done between affection and cognition,and emotion decay and emotion update were defined to maintain the stability of affective change.Secondly,affective mechanism was put into ant colony algorithm,which formed an affection ant colony algorithm,and the algorithm was applied to Web Service Modeling Ontology(WSMO) service composition.Finally,the paper adopted a Virtual Travel Agency(VTA) example under WSMO to show this approach was effective and feasible.
Prioritization method of link protection with convolutional neural networks

In order to provide high quality services to users, communication networks need to keep high reliability. Therefore, they are operated so as to have tolerance to node and link failures. In this paper, we propose a prioritization method of link protection using convolutional neural networks, which are applied to a lot of research fields owing to its dramatic performance. To decide which links should be protected, we calculate the network availability. However, in general, the calculation time of the network availability exponentially increases as the number of links increases. The proposed method gives the priority of link to be protected in a short time by convolutional neural networks, where weighted adjacency matrices of nodes are used as input data. Through numerical experiments with CNN, we show the fundamental efficiency of our prioritization method.
Online frame-to-model pipeline to 3D reconstruction with depth cameras using RGB-D information

This work presents an online pipeline for incremental 3D reconstruction and 6-DoF camera pose estimation based on colored point clouds captured by consumer RGB-D cameras. The proposed approach combines geometric matching provided by the point cloud with photometric matching provided by the color sensor through an adaptive weighting scheme that avoids eventual misalignment errors between RGB and depth data. Our experimental results indicate that the 3D reconstructions achieved by the proposed scheme are visually better or similar than a competitive approach.
Hole filling algorithm based on contours information

A hole filling algorithm based on contours information is presented,and it conquers the limitation of the conventional ones to recognize the hole region automatically.First both the inside contours and outside ones are traced to get the information about their geo-metric positions,and the hole edges are identified,then the scan line filling method is applied to fill the hole regions.In contrast with the flood filling algorithm and the scan line filling algorithm,the new algorithm needs no complicated computation about the whole region,and the intricacy is reduced because the less repeated computations are needed,and it is easy to program.The experimental result shows the algorithm is fast and effective for many arbitrary shapes.
Chaotic mapping multi population Quantum-behaved Particle Swarm Optimization algorithm

In order to solve the premature convergence problem of Quantum-behaved Particle Swarm Optimization(QPSO),a logistics Chaotic Mutation Quantum-behaved Particle Swarm Optimization(CMQPSO)is presented.Particles in population are first initialized using segmental Logistics chaotic mapping,and then particles are divided into two sub population-top population and bottom population based on their fitness values.Particles in top population are scattered with Gaussian disturbance when particles accumulate to a certain degree.Particles in bottom population are chosen by mutation probability and mutated with Logistics chaotic mapping,which in return,improve diversity of particles.Algorithm's local and global search performance are well balanced with the introduction of mutation mapping and division of population.Results on Benchmark functions show that the proposed algorithm shows better search and convergence performance than standard QPSO and other algorithms.Effects of stagnation limit Cσ and proportion coefficient S on algorithm's performance are analyzed in detail.And rational scope of the parameters is determined.
Arithmetic predictive control on neural network

A neural network predictive control simulation is made, aimed at a diesel oil blending process which is a strong non-linearity, multi-perturbation, MIMO system, a linear programming is applied on optimal calculation. Conquering the traditional control idea model predictive control (MPC) establish object model through the input-output characteristic. Simulation results show that the predictive control strategy can takes on the good performances of both set-point tracking and anti-perturbations.
A Kind of Hierarchical Ring Interconnection Networks-on-Chip

The interconnection performance of the 2-D Networks-on-Chip is so poor in the large and very large scales that the multi-dimensional interconnections become one of the choices to achieve better interconnection performanceThis paper gives a design of a kind of hierarchical ring Networks-on-Chip based on the regional partition and analyses the static characteristics of the hierarchical rings.Then it proposes a kind of the hierarchical ring router and the routing algorithm based on the Karnaugh map coding.It tests the characteristics with different sets of the buffers in the hierarchical router nodes under the uniform random communication pattern and gives a detailed analysis of the dynamic characteristics of the hierarchical rings with the buffers in the hierarchical router nodes set in accordance with the geometric sequence under the global uniform random communication pattern.Finally,it points out the cases when the hierarchical rings work well according to the comparison of the characteristics of the hierarchical ring interconnections and some of the 2-D Network-on-Chip interconnections such as Mesh.The results show that,although working poor in small scales,the hierarchical rings can interconnect the large and very large scales Networks-on-Chip with lower cost and higher performance.Comparatively,the single-hierarchical ring interconnection works better under lower loads and the double-hierarchical ring interconnection has larger load capability and works better under higher loads.
Placing Rotational Inertia in Power Grids

A major transition in the operation of electric power grids is the replacement of bulk generation based on synchronous machines with rotational inertia by distributed generation connected via power electronic converters. The accompanying “loss of inertia” and fluctuations by renewable sources jeopardize the system stability, as testified by the ever-growing number of frequency incidents. As a remedy, numerous studies demonstrate how virtual inertia can be emulated through various devices, but few of them address the question of “where” to place this inertia. A recent case study demonstrates that the placement of virtual inertia hugely impacts system efficiency. In this article, we carry out a comprehensive analysis in an attempt to address the optimal inertia placement problem. We consider a linear network-reduced power system model along with an ℋ2 performance metric accounting for the network coherency. The optimal inertia placement problem turns out to be highly non-convex, yet we provide a set of closed-form global optimality results for particular problem instances as well as a computational approach resulting in locally optimal solutions, that outperform placement heuristics.
Fast Adaboost Training Algorithm by Dynamic Weight Trimming

This paper presents a novel fast Adaboost training algorithm by dynamic weight trimming,which increases the training speed greatly when dealing with large datasets.At each iteration,the algorithm discards most of the samples with small weight and keeps only the samples with large weight to train the weak classifier.Then it checks the performance of the weak classifier on all the samples,if the weighted error is above 0.5,it will increase the number of training samples and retrain the weak classifier.During training,only a small portion of the samples are used to train the weak classifier,so the speed is increased greatly.
Facial feature extraction techniques for face detection: A review

The researches in the area of face detection have made significant progress in the past few decades. The main challenge in this stage of face detection is to find a suitable effective method for finding facial features. Sub-areas under feature extraction methods are skin color and texture based segmentation, deformable template matching, snake models, feature searching and constellation analysis. In this paper we represented a review on some important contribution in the field of feature extraction for face detection.
Hardware Assisted Protection for Data Validation at Run-Time on Embedded Processors

The security of embedded systems has attracted much attention as they are being used in more and more fields in people’s life. Traditional technologies to protect the normal program execution are often software-based and they mainly focus on the protection of application code. In this paper, a hardware assisted framework is proposed to protect the program data validation at run-time. It divides the data into static data and dynamic data according to whether its values and spaces are constant or not. We extract the static data previously and get its validation information before the program runs. Since the information of static data stay constant during the execution, any deviation from it in the real-time data validation can be detected dynamically. Attacks to the integrity of dynamic data are defended by checking the data validation according to the store and load instructions at run-time. CRC algorithm is used in this proposal to compromise the security checking and resource consumption. The experimental result shows our proposed technique can protect program data validation with an acceptable resource overhead.
Systematic Methods of Fingerprint Ridge Orientation Estimation and Image Segmentation

A scheme is proposed for systematically estimating fingerprint ridge orientation and seg-menting fingerprint image by means of evaluating the correctness of the ridge orientation on the basis of neural network.The neural network was used to learn the correctness of the estimated orientation by gradient-based method.The trained network was able to distinguish correct and incorrect ridge orientations,and as a consequence,the falsely estimated ridge orientation of a local image block could be corrected by using the around blocks of which orientations were correctly estimated.A coarse segmentation could also be done on the basis of trained neural network by taking the blocks of correctly estimated orientation as foreground and the blocks of incorrectly estimated orientation as background. In addition,following the steps of estimating ridge orientation correctness,a secondary segmentation method is proposed to segment the remaining ridges which are the afterimage of the previously scanned fingers.The proposed scheme served for minutiae detection and was compared with VeriFinger 4.2 published by Neurotechnologija Ltd.in 2004,and the comparison result shows that the proposed scheme leads to an improved accuracy of minutiae detection.
The Effectiveness Evaluation of Emergency Plan Based on Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation

Effectiveness of emergency plan is the key to guarantee the plan to achieve the ideal disposal.The factors that cause emergencies are uncertain or difficult to quantify,so emergency plan effectiveness evaluation index system is built on the integrated analytic hierarchy process and fuzzy comprehensive evaluation method to evaluate the emergency plan effectively.New ideas are put forward to evaluate the effectiveness emergency plan comprehensively,accurately and scientifically,which has important guiding significance for us to formulate scientific and effective emergency plan.
A privacy-preserving Internet of Things device management scheme based on blockchain

Blockchain as a new technique has attracted attentions from industry and academics for sharing data across organizations. Many blockchain-based data sharing applications, such as Internet of Things devices management, need privacy-preserving access services over encrypted data with dual capabilities. On one hand, they need to keep the sensitive data private such that others cannot trace and infer sensitive data stored in the block. On the other hand, they need to support fine-grained access control both from time and users’ attributes. However, to the best of our knowledge, no blockchain systems can support time-bound and attributes-based access with high efficiency. In this article, we propose a privacy-preserving Internet of Things devices management scheme based on blockchain, which provides efficient time-bound and attribute-based access and supports key automatic revocation. The analysis and experiments show that our scheme is quite efficient and deployable.
Theory of knowledge-based image analysis with applications to SAR data of agriculture

The requirements for a theory of image analysis imply predictability of RS image measurements. RS images are predicted from a combined model of objects in 3 dimensions with samples taken at short time intervals. Image analysis is the inverse of image synthesis or image prediction. Inversion of the model of image synthesis requires additional knowledge about objects, processes and sensing. The role of knowledge is mainly to constrain the search effort in a problem space of hypotheses and parameters. The method of image analysis as reported here is a hypothesis driven method, in contrast to data driven methods of image interpretation, image processing or data fusion. In a reaction to a failing search for suitable GIS theories and structures, an alternative is reported for the classical integration of 2.5 dimensional GIS and RS with data driven image processing. The required theories and structures are taken from the domain of physical modelling. Knowledge about 3 dim objects and about processes is represented in physical models which may have a probabilistic component. Given a model for sensors, the atmosphere, radiation with matter interaction, and a set of hypotheses and parameters about objects and their state, hypotheses are evaluated and parameters are estimated. Hypothesis based analysis means comparison of hypotheses in the model domain with evidence coming from the RS measurement domain or feature domain. A specific problem addressed here is that of the estimation of geometric parameters of objects in microwave images. The treatment of prior probabilities appears to be critical. The relationship between statistics of the radiometric and geometric parameter estimators was investigated and results are reported. After the introduction of basic concepts of geometric and radiometric parameter estimation, a case of agricultural landuse classification is given. The case introduces the problem of converting classical vector data to parameterised geometric decision functions.
Design and Analysis Dynamic Auto Scaling Algorithm (DASA) for 5G Mobile Networks.

Network Function Virtualization (NFV) enables mobile operators to virtualize their network entities as Virtualized Network Functions (VNFs), offering fine-grained on-demand network capabilities. VNFs can be dynamically scale-in/out to meet the performance desire and other dynamic behaviors. However, designing the auto-scaling algorithm for desired characteristics with low operation cost and low latency, while considering the existing capacity of legacy network equipment, is not a trivial task. In this paper, we propose a VNF Dynamic Auto Scaling Algorithm (DASA) considering the tradeoff between performance and operation cost. We develop an analytical model to quantify the tradeoff and validate the analysis through extensive simulations. The NFV enabled Evolved Packet Core (EPC) is modeled as queueing model while legacy network equipment are considered as reserved a block of servers; and VNF instances are powered on and off according to the number of job requests present. The results show that the DASA can significantly reduce operation cost given the latency upper-bound. Moreover, the models provide a quick way to evaluate the cost-performance tradeoff and system design without wide deployment, which can save cost and time. Index Terms Auto Scaling Algorithm, Modeling and Analysis, Network Function Virtualization, 5G, Cloud Networks, Virtualized EPC