Reviewerly/exHarmony huggingface dataset:
Requirements Management Tools for Software Systems: A Systematic Tools Review

This article contains a systematic review of Requirements Management tools (RM tools) for Software Systems. A research question was defined as the following: requirements development process of large-scale software system (with thousands of requirements) and an interaction problem during this process (communication, coordination and control). The problem is caused by the fact that the requirements development process is a cross-disciplinary task and it involves multiple parties — stakeholders, domain experts, and suppliers with own goals and constraints, and thus, the interaction between them seriously slows down the overall requirements development process more than writing the requirements specification itself. The tools were classified into the following classes: Requirements Management Tools, ALM/PLM/SDLC Tools, Model-based System Engineering tools with Requirements support, Natural Language based tools for Requirements Engineering, WYSIWYG-prototyping and Requirements platform, Open Source tools, and tools developed by Russian vendors. Each tool class was described and represented with list of relevant tools — including feature-rich solutions, and a description of key features. A contribution of functionality from each tool into the research question was analyzed and summarized including potential further improvement steps. Advantages and disadvantages of the existing RM tools were represented in this work. To approach the research question, further potential directions were described.
Research on Heterogeneous UAVs Task Assignment Based on Improved Contract Net Algorithm

The traditional contract network algorithms has problems such as unbalanced load, low distribution efficiency, and lack of real-time dynamic planning ability when solving the task assignment of UAVs. In response to the above problems, this paper proposes a heterogeneous UAV cooperative task allocation model based on the improved contract network algorithm. Combined with the actual application scenarios of UAV task allocation, this paper carries out laboratory simulation verification. The simulation results show that, compared with the traditional contract network algorithm, the improved contract network algorithm proposed in this paper can effectively improve the efficiency of task allocation, improve the balance of task allocation, and has dynamic task planning capabilities, which proves the effectiveness of the algorithm.
Optimization of Information Security Sharing Mechanism of Online Education Platform based on Block Chain Technology

Under the ravages of the COVID-19, online education platform business has achieved rapid development, and excessive platform integration and the spread of paid knowledge have also spawned security problems of data sharing between platforms and users. Meanwhile, the development of Internet technology has also increased the difficulty of knowledge safeguarding rights on education platforms and cracking down on piracy. Based on the application characteristics of blockchain technology, this paper proposes a dual authentication mode of node + platform based on hash detection. Through the smart contract provided by Ethereum, data encryption linking and two-way verification between different platforms are realized. After linking to blockchain, data transmitted over the network can be protected from hacking and tampering. The research in this paper has an enlightening effect on promoting the integration development of blockchain technology and emerging industries, and also makes a specific judgment on the next integration development direction.
Preface

Thanks for your participations in 2021 4th International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2021), which was re-scheduled to be held in virtual meeting via Tencent Meeting on September 17, 2021 due to the current severe situation of the epidemic worldwide. AMMS 2021 virtual meeting aims to provide an excellent and valuable opportunity for experts to exchange ideas and results covering Mathematics, Optimization Methods, Computing Methods and Advanced Computing, and strengthen the collaboration among researchers, faculty, and doctors from both academia and industry. With its strong emphasis on innovative approaches, AMMS 2021 Online Meeting offered a chance for scientists and educators working in different areas and help them advance their own research and forge new professional relationships and collaborations. This virtual meeting including Keynote Speech, Oral presentations, Poster presentations, Q &amp; A section started at 09:00 am, Beijing time on September 17, 2021. And our staff set up a test room before the formal meeting to ensure the stability of all participants including video, audio, screen sharing, network connection, etc. List of AMMS 2021 Organization Committees, General Chairs, General Co-Chairs, Publication Chairs, Publication Co-Chairs, International Scientific Committees, Algeria are available in the pdf
Application of RFID Technology in Olusegun Oke Library, Ladoke Akintola University of Technology, Ogbomoso: Challenges and Future Prospects

Radio Frequency Identification (RFID) offers tracking capability to locate equipment, supplies and people in real time, and provides efficient and accurate access to library transactions such as book search, security, inventory, borrowing and book return. The paper studied many aspects of the RFID application such as budgeting, purchase process, advantages and limitation of RFID technology, documentation and financial implication, challenges faced in initial stage and remedies. A structured questionnaire using descriptive survey method was designed to collect data. The study employed the method of quantitative research to gather an in-depth understanding of the nature of usage of RFID system among users of Olusegun Oke Library. The findings of the study revealed that RFID is functional and useful in such areas as speedy circulation, easy book identification, security of library materials and other value added services while the major problem to implementation include technological limitations, interference concerns, prohibitive costs and lack of global standards. The study concluded that orientation programme for all users for the RFID enabled is very much important.
An Asian Games merchandise display system with online and offline modes based on AR technology

Traditional packaging and new augmented reality technology have a better application in the realization of multi-platform commodity display performance, so that the new technology can produce a better visual effect for packaging. By designing the packaging box image of Asian Games cultural and creative goods with AR technology with high degree of freedom, realize the real goods to the virtual model and complete the rendering display steps, as well as the online and offline multiend integration of virtual fitting. Through the integrated online and offline integrated creative sales platform, the display and trial of cultural and creative goods and internal commodities, which greatly improves the interactive multi-dimensional display effect in consumer purchase experience. AR technology, while retaining all the advantages of traditional packaging, can better promote consumers’ understanding and understanding of commodity content, and can better promote sales.
Eye Movement Feature Set and Predictive Model for Dyslexia

Dyslexia is a learning disorder that can cause difficulties in reading or writing. Dyslexia is not a visual problem but many dyslexics have impaired magnocellular system which causes poor eye control. Eye-trackers are used to track eye movements. This research work proposes a set of significant eye movement features that are used to build a predictive model for dyslexia. Fixation and saccade eye events are detected using the dispersion-threshold and velocity-threshold algorithms. Various machine learning models are experimented. Validation is done on 185 subjects using 10-fold cross-validation. Velocity based features gave high accuracy compared to statistical and dispersion features. Highest accuracy of 96% was achieved using the Hybrid Kernel Support Vector Machine- Particle Swarm Optimization model followed by the Xtreme Gradient Boosting model with an accuracy of 95%. The best set of features are the first fixation start time, average fixation saccade duration, the total number of fixations, total number of saccades and ratio between saccades and fixations.
Immersive 4D Intelligent Interactive Platform Based on Deep Learning

With the advent of the Internet era, the cost of realizing virtual characters has been greatly reduced. The high cost and low efficiency exhibited by traditional virtual technology can't meet social needs. People are seeking fast, convenient and accurate virtual character reproduction technology. Through researching the characteristics of the characters appearance, language habits, voice tone and so on, the research direction of this paper is to simulate and reshape voice and images, construct a cloud platform-based on user-side and client-side, and integrate deep learning, natural language processing, digital twins and other technologies to an immersive 4D intelligent interactive platform. The platform under in the form of application software provides integrated services of intelligent voice interaction and virtual character interaction. In the industrial diagnosis mode, the transition from traditional video retention and voice retention to a new intelligent voice recognition and simulation mode is realized.
Time Sensitive Sweep Coverage with Multiple UAVs

The sweep coverage of UAVs can provide emergency communication support and conduct disaster surveys in emergency disaster relief. These rescue operations are extremely time sensitive. If the UAVs cannot arrive on time, the best rescue opportunity may be missed. When the number and energy of UAVs are limited, it is likely that some Points of Interest (POI) cannot be covered. Thus it’s crucial to design a collaborative sweep coverage scheme for multiple UAVs to achieve the maximum effective coverage rate. In this paper, we first propose a novel problem named MEC-TS. Under the constraints of time sensitivity and the number of UAVs, the objective of the MES-TS problem is to maximize the effective coverage rate in sweep coverage. We prove that the MEC-TS problem is NP-hard. Accordingly, we propose the GCS algorithm for the MEC-TS problem. Finally, we conduct extensive simulations and the experimental results demonstrate GCS has improved the performance by 30% compared with existing algorithms.
Construction of Error Correcting Output Codes for Robust Deep Neural Networks Based on Label Grouping Scheme

Error-Correcting Output Codes (ECOCs) have been proposed to construct multi-class classifiers using simple binary classifiers. Recently, the principle of ECOCs has been employed for improving the robustness of deep classifiers. In this paper, a novel ECOC framework is developed by presenting a novel label grouping and code-construction method. The proposed label grouping is based on linear discriminant analysis (LDA) similarity. Via simulations, it is demonstrated that deep classifiers trained with the proposed ECOC yield better classification performance on pure data and better adversarial robustness than the state-of-the-art deep neural classifiers using ECOCs.
Vehicle Tracking in Campus with the Particle Filter

Recently, using sensors for campus positioning has been prevalent. Cameras with distance and angle measurement are ubiquitous especially in campus or special districts, where all kinds of sensors connect to the control center by different transmission technologies. This paper proposes a particle filter based scheme for tracking unknown cars in campus. The algorithm approximates the location of an unknown car in the campus which dynamic model is assumed. Sensors located at load sides and corners send detect a vehicle and receive its location messages. The control center hence can collect all the information and performs the proposed algorithm to estimate and track the locations of the unknown vehicle. We conduct simulations and experiments to show the performance of the proposed algorithm. Simulation results show that our scheme can quickly converge in the first four steps and the relative root-mean-square errors (RMSE) are less than 0.1% with 50000 experiments using 1000 particles.
Deep Cross-Modal Supervised Hashing Based on Joint Semantic Matrix

Cross-modal hashing is to project sample data of different modalities into a common binary space, where the correlation between different modalities can be effectively measured. The purpose of cross-modal retrieval is to realize flexible search between different modalities. This paper proposes a novel cross-modal retrieval model, called the Deep Cross-modal Supervised Hashing based on Joint Semantic Matrix (DCSJM). We utilize the joint semantic matrix as the uniform similarity metrics, which meticulously integrates the original domain information of different modalities and can capture the potential inherent semantic similarity between different modalities. Furthermore, by combing the label information and the joint semantic similarity information as the supervision information, we can get more comprehensive and accurate search results. A large number of experiments show that the proposed DCSJM model achieves significant improvements in various cross-modal retrieval tasks.
Study of the Spectrum of Side Electromagnetic Radiations of Video Interface DVI

This paper presents the results of experimental studies of the spectra of side electromagnetic radiation of the DVI video interface, depending on the screen resolution and the monitor frame refresh rate.
Security Amalgamation using Cloud Computing

The Huge measure of substance data available should be composed along these lines keeping up a vital separation from the misuse of storage space, which can be expert by equivalent join framework. In this paper, we propose, near join strategy, which is an in light of Map Reduce structure used is uses strings for tantamount join and support set based and character based functionalities. Key regard sets are made using marks. Association based count can be used execute, which unite broad number of noteworthy worth key sets which aide decreases the transmission cost. Here we are using "light-weight" channel units to grow the execution, which generally trims huge number of various regard key sets with less transmission cost.
Underwater Unmanned Vehicle Search Path Optimization Arithmetic

The characteristic of UUV(Underwater Unmanned Vehicle) search path is concluded. The target distribution model and posterior probability distribution model is proposed. The optimal detection point computing method is raised based on maximum posterior detection probability criterion based on the two models above. The paper carries out the simulation calculation according to the proposed method. Compared with the traditional model, the proposed model greatly improves the search probability of UUV.
Prediction &amp; Anticipation Features based Intellectual Assistant in Location Based Services

Intellectual assistant in a location based service (LBS) is considered here as assistive technology in healthcare system for helping users with sensory, physical or intellectual diminished capabilities. The proposed system has the speculative computation module which helps in orientation for predicting the users’ error and alerting them for ensuring a true traveling direction well in advance for the handicapped people of mild to moderate condition of disability in earlier phases of cognitive disabilities. A person with cognitive impairments has the Trajectory (T) associated with them which is a sequenced set of past global positioning system (GPS) points. The implementation and evaluation of the trajectory data mining method have been done for the purpose of achieving the travel route to the user preferences. For the simulation of proposed system, a client server queuing model has been used. The results show the minimization of the overhead with better precision as compared to earlier existing path guiding systems.
Construction of Distributed Sensing System for Peristaltic Continuous Mixing Conveyor Simulating Intestines

To mix and convey solid-liquid mixtures continuously, a peristaltic continuous mixing conveyor has been developed previously. This device imitates the movement of intestines by inflating a rubber tube using air pressure. We estimated the mixing degree of the contents via machine learning using data from pneumatic sensors, however, the accuracy was low. Therefore, in this study, to improve the sensing system, we installed an accelerometer that could measure the behavior of the rubber tube without any contact with the contents. The sensor angle was obtained from the accelerometer values for each input. We obtained different sensor angles with the rigid bodies and the powder-liquid mixtures. The results suggested that type of the contents can be determined by using the accelerometer.
Discriminative Sequence Labelling

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an online resource, this textbook is an invaluable tool for the upper undergraduate and graduate student.
A Topologically Consistent Color Digital Image Representation by a Single Tree

A novel, flexible (non-unique) and topologically consistent representation called CRIT (Contour-Region incidence Tree) for a color 2D digital image I is defined here. The CRIT is a tree containing all the inter and intra connectivity information of the constant-color regions. Considering I as an abstract cell complex (ACC), its topological information can be packed as a smaller (in terms of cells) ACC, whose 2-cells are the different constant-color regions of I. This modus operandi overcomes the classical connectivity paradoxes of digital images by working with lower-dimensional cells such as 0-cells, 1-cells, and 2-cells. The CRIT structure allows to describe this smaller ACC in a non-redundant way. The proposed technique is based on the previous construction of the Homological Spanning Forest (HSF) structures for encoding homological information of the ACCs canonically associated to I, in terms of rooted trees connecting digital object elements without redundancy.
The display and dissemination of Qiantang River poetry road culture based on Virtual Reality and Human-computer Interaction

Qiantang River Poetry Road has rich literary aesthetics and profound cultural connotations. The display and dissemination of its culture is of great significance for shaping the city’s characteristics, publicizing the city’s image, and promoting Chinese culture. However, many foreign tourists come to Zhejiang to travel due to lack of tour guides and other reasons. They can only watch the natural and humanistic scenery in a fancy way, but it is difficult to appreciate the spiritual connotation of the poetry road culture. From the perspective of virtual reality (VR), this paper develops a display and display that combines visual, auditory, and tactile senses through three-dimensional modeling, simulation algorithms, graphics rendering, virtual humans, multi-modal interaction, mini-map navigation and other VR technologies. The VR system to spread the culture of Qiantang River Poetry Road: The poetry soul is the companion, and you can enjoy the Qianjiang River. The system gives full play to the advantages of VR’s high immersion and high interaction, and integrates more than a hundred ancient poems into various scenes of Qiantang Poetry Road. In VR, users can freely roam the ancient and modern Qiantang River basin in the first person, interacting with the virtual person “Soul of Poetry” as a virtual tour guide, the virtual poet who left the poem, and the local virtual people. After the user’s test, the system can provide a relaxing, realistic and interesting roaming experience, which provides the possibility of digital display and dissemination for the promotion of the city’s image.
Introduction

We now live in a world where we can obtain current information about a global pandemic from our smartphones and Internet of Things (IoT) devices.1 The recent novel coronavirus (COVID-19) outbreak is not just a public health emergency. The pandemic has forced us to further evaluate the extent to which privacy should give way to public health threats and resulting technological innovations.2 It directly raises questions about whether legal frameworks governing our privacy should be relaxed to address public health concerns, and if any such relaxation will continue post pandemic to permanently undermine our privacy.3
Empirical Cloud Security

The standard joke in the IT industry is that ‘cloud’ just means ‘someone else's computer’. This stops being funny as soon as you start thinking of the security and privacy implications. Offloading data and processing to the cloud doesn't simplify the security picture – it can, in fact, make it considerably more complex.
Randomized tomography of on-chip biphoton frequency combs

We perform full state tomography of frequency-bin entangled qudits generated from a Si 3 N 4 microring, up to record-high frequency-bin dimension of d = 8. Our method combines randomized electro-optic modulation and pulse shaping with Bayesian inference.
The End of Exodus?

William Melvin Kelley exposed the predatory nature of many race leaders who would cloak themselves in Moses’s mantle. In <italic>A Different Drummer</italic>(1962), Kelley suggests that, paradoxically, those best suited to fulfill a Mosaic role are least interested in doing so. The novel juxtaposes Rev. Bennett Bradshaw, an embittered, ambitious Mosaic leader, with Tucker Caliban, an accidental Moses who embodies Henry David Thoreau’s ideal of self-reliance. Instead of organizing his fellow sharecroppers and wage laborers by condemning Southern society as Egyptian tyranny and depicting the North as Canaan, Tucker privately decides to leave his home in a fictitious Southern state. Whereas Bradshaw’s ego perverts his Mosaic aspirations, Tucker’s radical individualism inspires a mass exodus of African Americans—not through charismatic moral/political suasion but through quiet, even eccentric, acts whose motivation is obscure. Kelley’s vision of revolution as a chain reaction of individual choices, however, threatens traditional notions of racial solidarity.
A Summary of Researches on Mandarin Tone Acquisition

Tone acquisition has always been a difficulty and focus of second language acquisition. Many studies have used the way of tone production or perception to reveal the influence of mother tongue experience on tone acquisition. Based on the review, this article discusses some experiments on tone production and perception, reveals the significance of these studies and explores possible future research directions.