Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Ant Colony Algorithm Based on Evacuation Wayfinding Optimization Model in Building Fire

Comprehensively considering human behavior rules in building fire,equivalent length of evacuation route is defined on the base of mobility coefficient in fire smoke circumstances,as well as the difficulty coefficient of evacuation route.As a result,an adaptive ant colony algorithm model is established to realize the evacuation wayfinding optimization in building fire(AACABF). Then the AACABF model is applied in a public building fire.The optimal result illustrates that AACABF can be well applied to the wayfinding optimization field in building fire.Compared with the traditional Max-Min Ant Colony System,AACABF can adaptively adjust the route selection strategy and the information updating strategy,and can obtain a good balance the convergence rate,precocity and stagnant phenomenon.The global optimal searching efficiency is greatly improved as a result.
Particle swarm optimization for dynamically changing environments with particular focus on scalability and switching cost

Change is an inescapable aspect of natural and artificial systems, and adaptation is central to their resilience. Optimization problems are no exception to this maxim. Indeed, viability of businesses depends heavily on their effectiveness in responding to a change in the myriad of optimization problems they entail. Changes in optimization problems usually are result of change in the objective function and/or number of variables and/or constraints. Such optimization problems are denoted as dynamic optimization problems (DOPs) in the literature. Despite the large body of literature on DOPs and algorithms in this domain, there are still noticeable gaps between real-world DOPs and academic research. The first objective of this thesis is investigating DOPs to identify any class of DOPs or any DOPs' characteristics that are common in practical situation but have not been studied by the researchers. In this thesis, two important gaps are identified, namely considering switching cost in DOPs and large-scale DOPs. Both are common in many real-world dynamic problem but a few research investigated them in the past. In an attempt to bridge these gaps, this thesis makes the following contributions: First, this thesis considers the impact of cost for changing solutions after environmental changes. In fact, changing solutions in real-world problems is costly. Furthermore, larger changes have higher cost and need more resources such as time, human resources and energy. Thus, lack of switching cost consideration in most previous algorithms makes them unsuitable for many of real-world DOPs. In this thesis, different scenarios of DOPs with switching cost are investigated, their challenges are identified, and the performance of the state-of-the-art methods are investigated for solving them. Contributions include developing a novel robust optimization over time (ROOT) framework, a novel adaptive method for maximizing efficiency by changing or keeping solutions after environmental changes, and a novel multi-objective and time-linkage based method for minimizing switching cost. Second, this thesis investigates large-scale DOPs. Up to now, little attention has been given to the scalability of DOPs. Indeed, the dimension of typical DOPs studied in the literature hardly exceeds twenty. In this thesis, the challenges of large-scale DOPs are studied, then the efficiency of the current methods are investigated for solving them. Moreover, this thesis proposes a novel cooperative coevolution algorithm based on a multi-population approach which benefits from a new resource allocation method for DOPs with high-dimensional search space. All the proposed methods in this thesis use particle swarm optimization as the core optimizer embedded in a multi-population framework. The performance of the proposed methods are compared with state-of-the-art methods on a wide range of problem instances generated by the state-of-the-art and the proposed DOP benchmarks. The comparison results indicate the superiority of the proposed methods.
A Summary and Analysis of Low Complexity Methods for Massive MIMO Systems

The massive multiple-input multiple-output (MIMO) systems help increase the wireless channel capacity and spectral efficiency manifold, and the coverage as well as power consumption. In MIMO systems, the linear minimum mean-square error (MMSE) can achieve the near-performance. However, along with the huge number of the BS-to-user antennas at the BS station, the computational complexity becomes a serious problem due to computing matrix inversion. Many methods were proposed to improve complexity, for instance, Neumann series expansion method, Jacobi methods and conjugate gradient (CG) method, but it is still costly. And recently many other methods on account of the above methods were proposed, respectively. Such as Gauss-Seidel method, Symmetric Successive Over Relaxation (SSOR) method, further improved Jacobi method based on soft-output massive MIMO detection scheme, pre-conditioner conjugate gradient (PCG) based on SSOR, split pre-conditioned conjugate gradient (SPCG) method, etc, which all show the reduced complexity, improved convergence and BER performance.
The Navigation Design of Mobile Robot in the Man-Machine Environment for Service Assist

The global novel coronavirus COVID-19 has spread far beyond any previous cases in the world, due to its high infect ability, robots are in high demand all over the world, which can help hospital services, protect the health and safety of front-line medical workers. This paper investigates the use of an autonomous service robot in an indoor complex environment, such as a hospital ward or a retirement home. The research is based on the method of fusing lidar and image information to identify, locate, and track the human body in the indoor environment. Use lidar SLAM to obtain the pose of pedestrians in the environment, and incorporate the predicted pedestrian trajectory into the planning constraints of the navigation algorithm to ensure that human behavior intentions are respected, and verify the humanized navigation of the mobile robot in the human-machine inclusive indoor environment through online simulation and experimental platform. The main advantages of the design of this mobile robot are that it can improve the friendliness of human-machine integration in order to do better serves in public environments such as medical institutions.
Recognition using Cyber bullying in view of Semantic-Enhanced Minimized Auto-Encoder

As a reaction of progressively famous online networking, cyber bullying has risen as a significant issue distressing kids, teenagers and youthful grown-ups. Machine learning strategies make programmed identification of harassing messages in online networking conceivable, and this could develop a solid and safe web-based social networking environment. In this significant research zone, one basic issue is strong and discriminative numerical representation learning of instant messages. In this paper, we propose another representation learning strategy to handle this issue. Our strategy named Semantic-Enhanced Marginalized Demising Auto-Encoder (smSDA) is produced by means of semantic expansion of the famous profound learning model stacked demising auto-encoder. The semantic expansion comprises of semantic dropout clamor and sparsely limitations, where the semantic dropout commotion is planned in view of area learning and the word installing procedure. Our proposed strategy can abuse the concealed element structure of tormenting data and take in a powerful and discriminative representation of content. Exhaustive tests on two open cyber bullying corpora (Twitter and MySpace) are led, and the outcomes demonstrate that our proposed approaches beat other gauge content representation learning techniques.
A Method for System Error Correction and Precision Analysis in Radar Network System

System errors influence data fusion precision,so the error crrection method based on ECEF in radar network is put forward,the method to evaluate the estimate precision of system errors is defined,CRLB is adopted to evaluate the estimate precision of system errors.At last,the simulation validated that the error crrection method was viable and the evaluation method for estimate precision was effective.
Wireless Network Sensor Node Design Based on CC2530 and ZigBee Protocol Stack

According to the actual needs of intelligent household,environmental monitoring etc,this paper designed a wireless sensor node of long-distance communication system.This system used the second SoC CC2530 set in RF and controller chips as the core module and externally connected with CC2591 RF front-end power amplifier module.Based on ZigBee2006 in software agreement stack,it realized each application layer function based on ZStack.It also introduced wireless data acquisition networks based on the ZigBee agreement construction,and has given the hardware design schematic diagram and the software flow chart of sensor node,synchronizer node.The experiment proved that the node is good in performance and the communication is reliable.The communication distance has increased obviously compared with the first generation TI product.
Determining factors of perceived elf-efficacy in information seeking practices through Facebook

The paper reports on factors that drive students’ perceived self-efficacy in information seeking practices through Facebook. The quantitative study employed random sampling of postgraduate students in a higher learning institution. A total of 354 responses were gathered through a self-reported survey. The findings showed four factors, namely, past performance, other’s experience, verbal persuasion and psychological state, have statistically significant correlations with students’ perceived self-efficacy in information seeking practices through Facebook. Past performance was found to be the most influential factor followed by other’s experience. Verbal persuasion and psychological states were the least influential factor. The outcome of the study contributes to the existing literature particularly on the self-efficacy standpoint in social networking sites (SNS) platform. In addition, the findings are relevant to the field of information seeking behaviour especially in providing evidence that people are searching information using Facebook
Performance Analysis Based on Packet Arrival Rate for the IEEE 802.11 DCF

A performance analytic model based on packet arrival rate is proposed for IEEE 802.11 DCF (distributed coordination function)in infrastructure-based WLAN(wireless local area network). The model does not only take into account the factors of number of terminals,traffic loads,the binary exponential back-off mechanism, but also analyzes the impact of the finite queue system at the MAC(medium access control)layer.Each terminal is modeled as an M/M/1/K queue,and the virtual time slot is introduced to discretize the state of the queue system. Based on this,a three dimensional discrete time Markov chain is used to model the system.By using the model, normalized throughput,packet delay and packet losing ratio are obtained.Simulation results show that the proposed model can validly predict the performance of the DCF under various packet arrival rates.
Remote Sensing Image Classification Based on BPNN Supported by ERDAS

The remote sensing image classification system is based on improved BPNN. At first, the Classifier module in ERDAS Imagine was used to carry out the non-monitoring classification and contrast of original remote sensing images; and the class identification numbers of classification were ensured. Then, the training samples and testing samples of varied classes were gathered in remote images; the BP neural network classifier was trained through inputting training samples. At last, the complete remote sensing image was inputted into trained classifier to get classification images. The test proved that the precision was higher than traditional statistical classification method.
Privacy Enrichment Model for IoT Based Healthcare System

The tremendous progression in hardware and network technology in juxtaposition with Internet-of-Things (IoTs) have offered feasible competency for easier progression of human life. Indeed of some smart environments such as smart agriculture, smart city, smart homes, IoT have been showing its reliability in e-healthcare systems for medical consultancy and real time diagnosis. To improve IoT competency in healthcare systems, edge based layers have been used and it reflects its worth by offering low latency and response time. Moreover, these developments provide diverse challenges in preserving user privacy which has to handle privacy and security issues to certain extent. Commencing from infant stage, these technologies has consistently works with less privacy controls. Henceforth, it is extremely about e-healthcare monitoring framework that handles Electronic Medical Records (EMRs) should be maintained with more privacy concern. However, the anticipated model is experimented in MATLAB environment to deal with delay and response time. This model is compared with some recent works and shows better trade off in contrast to other approaches. The anticipated model is effectual in offering privacy with standard IoT parameters.
Blind Multi-user detection algorithm in DS-CDMA system

Blind Multi-User Detection(BMUD) is mainly applied to downlink mobile user detection for the case of only knowing the spreading sequence of target user itself.In this paper,the performances of Bit Error Rate(BER) and Signal to Interference Ratio(SIR) are compared and discussed through simulating some linear and non-linear BMUD methods like constant modulus algorithm(CMA),minor output energy(MOE),recursive least-square(RLS),Kalman filter and independent component analysis(ICA). The experimental results show that the performance of ICA-based BMUD method is superior to those of the others.In addition,the computational complexity of these algorithms is also analyzed for engineering consideration.
Big data‐driven machine learning‐enabled traffic flow prediction

Real‐time effective traffic flow big data prediction network has important application significance. Over the past few years, traffic flow data have been exploding and we have entered the big data era. The key challenge of traffic flow prediction network is how to construct an adaptive model relying on historical data. Existing big data‐driven traffic flow prediction networking approaches mainly use shallow learning, and there are unsatisfying for many realistic applications, which inspire us to rethink the traffic flow big data prediction problem with deep learning. In this paper, we propose a novel prediction approach based on machine learning. In addition to the minimum prediction error as the goal, we present the long short‐term memory model, which is a typical machine learning algorithm with deep learning network. This method is applied into the real‐world traffic big data from performance measurement system. Experimental results show that the proposed machine learning algorithm has more applicability and higher performance, compared with shallow machine learning prediction network.
Intrusion detection system model based on multi detection technology

In view of the weakness of current intrusion detection system and intrusion analysis method,a distributed fusing multi detection technology intrusion detection system model based on multi-Agent is brought forward.Apart from traditional analysis technology,file integrity analysis method based on mobile agent is added to the analysis Agent too.The model realizes the dist-ribution of intrusion detection,owns good scalability,improves the accuracy of intrusion detection,enhances the intrusion detection system's capability by using multi detection technology.So it can meet extensive security demand of the distributed network envi-ronment.
Developing a Prototype to Translate Text and Speech to Pakistan Sign Language With Bilingual Subtitles: A Framework

The purpose of the study is to provide a literature review of the work done on sign language (SL) around the world and in Pakistan and to develop a translation tool of speech and text to Pakistan Sign Language (PSL) with bilingual subtitles. Information and communication technology and tools development for teaching and learning purposes improve the learning process and facilitate both teachers and students. In Pakistan, unimpaired people face a lot of problems to communicate with deaf people due to the lack of SL understanding, learning resources, and interpreters. This problem is faced by the teachers who communicate with deaf students in the classrooms. The communication gap is filled with the development of a translation tool as Haseeb and Illyas concluded in their study that using this kind of tool, deaf people will have more opportunities to communicate with other members of society at every level. Different components of technology such as Python programming language, Natural Language Tool Kit, prerecorded PSL videos, Linux-based server, and databases are used to develop the prototype of PSL translation tool. This study provides a literature review to highlight the existing technological work done around the world and in Pakistan and also provides an architectural framework of the PSL translation tool which is developed by the researchers to facilitate the people who face difficulty to communicate with deaf people.
The influence of geometric factors and data processing algorithms on the error of GNSS position in the GFO "MIHNEVO"

The results of the analysis of the navigation task solution in the conditions of registration of the signals of the global navigation satellite systems GPS and GLONASS in the Mikhnevo GFO (the geophysical observatory) are presented. It is shown that in the classical algorithm, the choice of satellites from the observed constellation should be made not according to the criterion of elevation, but by the number of conditionality of the navigation task matrix. The use of adaptive iterative algorithms almost completely compensates geophysical perturbations while maintaining the twofrequency regime.
RFID pseudorandom number generator based on clock-controlled non-linear sequence

Focused on the study of Pseudo-Random Number Generator (PRNG) in RFID encryption system,a design of complex clock-controlled PRNG was proposed,which is constituted by the linear feedback shift register (LFSR). Some characteristic parameters of the design were analyzed,such as sequence cycle,linear-complexity,uniformity,power consumption etc,through the Matlab and Quartus Ⅱ sofeware. Hardware circuit used the low-cost and low-power Cyclone II of FPGA products. This method maintains these properties of long cycle and high linear complexity of the clock-contrlloed non-linear sequence,but also improves the uniformity of output sequence. In addition,this design of circuit structure is simple and can output 16 bit parallel data. Therefore,it can satisfy the requirements of RFID encryption system.
Dynamic data process algorithm of accelerometer based on modified adaptive Kalman filter

Accelerometer is apt to be influenced by the environment where it is used. In order to implement dynamic test of accelerometer, a method of dynamic-data-process is discussed here. For the time-variation and random of practical situation, the noise statistic characteristic may be unknown and time-variant, and the prior data may be invalidation as the situation changes. At the same time the rough errors in the test have severe influence on the test accuracy. The modified adaptive kalman filter is selected to process the dynamic data. The result shows that the rough errors, the procedure disturbance and environment noises is effectively picked off.
The Detection and Classifying Technology Research of the SAR Image

Several statistics of the image gray-level co-matrix and gray-level co-matrix were first theoretically analyzed. Then a novel C-mean clustering algorithm, which is based on the gray-level co-matrix and can be used for synthetic aperture radar (SAR) image classification, was proposed. The characteristics of different statistics were obtained from experiments. The analysis shows that the entropy, variance, the contrast and mean error will perform better. It is shown that when the statistics, which will maximize the between-class scatter and minimize the within-class scatter, is adopted for classification, a much higher performance is achieved.
Qualitative Data Analysis Challenges in Co-Designing Educational Technology Systems for Refugee Children

There is a growing interest in the potential for technology to facilitate emergency education of refugee children. But designing in this space requires knowledge of the displaced population and the contextual dynamics surrounding it. Design should therefore be informed by both existing research across relevant disciplines, and from those who are on the ground facing the problem in real life. This paper describes a process that is based on literature from emergency education, student engagement and motivation, educational technology, and participatory design. We describe how this process was implemented leading to the design of a digital learning space for children living in a refugee camp in Greece. The challenge of data analysis is critical, as the qualitative data in the process is elicited from activities of various natures and thus moving from qualitative data to designs is a critical challenge that we are looking to cover for our process to be complete and applicable. We discuss some of the challenges that can be expected in such context.
Distributed Integrating Method for Enterprise Information Based on P2P Network

Issues existed in the sharing and transmitting for integration of enterprise information are analyzed,such as server overload, integration difficulty due to decentralization of information collection points. A kind of distributed integrating method for enterprise information based on P2P network is put forward. The key technologies are elaborated, including its components of integrated frame, the running method, and the definition, transmission, control and security of information. The implementation is discussed and is successfully completed in the lab.