Reviewerly/exHarmony huggingface dataset:
Exploring Transformers for Intruder Detection in Complex Maritime Environment

The vast expanse of the coastline makes it difficult and expensive to deploy resources for monitoring it for safety from intruders or illegal activities. The advent of very sophisticated cameras and the myriad of object detection techniques applied to the surveillance photos and videos, provides new methods for automation of monitoring. In this paper we present a study on evaluating the various state-of-the-art object detection methods for identifying marine vessels for intruder detection. Particularly, a comparison of anchor-based, anchor-free and transformer-based object detection techniques is presented for intruder detection. Analysis on the suitability of transformer-based methods for intruder detection is also presented, with experiments performed on a combined marine vessels dataset. Our experiments show that CenterNet, an anchor-free, one-stage technique is still the fastest detection method and suited for online surveillance. Whereas the high accuracy of Transformer based methods, such as DETR, work best for offline video based surveillance.
The Vision of Students of Their Professional Activities in the Industry 4.0

The active spread of digital technologies and the development of artificial intelligence have led to the transformation of the world of work and to the replacement of not only physical, but also mental work tasks. Requirements for the competencies and psychological characteristics of future workers in helping professions have changed. There is a need to improve the training system in higher educational institutions in order to prepare students for professional activities in the context of technological changes. Students’ vision of the threats and opportunities of Industry 4.0 for professional activities, awareness of technological innovations and attention to the development of digital competencies form their ability to adapt to changing working conditions. The article presents the results of a narrative analysis of the vision of the professional activity of future specialists in the field of psychology and social work, studying at the university of the Russian Federation. The students’ vision about changes in the requirements for professional competencies of a psychologist and a social worker in connection with digitalization and robotization of labor were analyzed. Four groups of respondents were identified with different views on the Industry 4.0 threats to future professional activities. The results of the study show that the majority of students studying helping professions are not sufficiently informed about innovative processes in their professional sphere and have a low level of readiness to develop the necessary competencies, which poses a threat to their well-being in the future workplace.
Unified Definition and Framework for Synthetic Text Detection and Removal

Synthetic Text Instance(STI) is a widely-used secondary-processed text instance in e-commercial and graphic design, which is essential for many tasks like visual question answering(VQA) and document layout analysis(DLA). However, existing text detection methods detect all types of text instances in the image, which cannot specifically detect STI. Moreover, they can also cause misunderstanding and should be removed while keeping the consistency with the background of the image. Therefore, this paper gives a brief definition of STI and proposes a novel special network for synthetic text detection and removal, named STNet. Itâ€™s composed of two architectures, i.e., an encoder-decoder structure for image structure reconstruction and synthetic text instance region prediction, and another GAN framework for finer reconstruction. We propose a series of manual benchmarks based on a well-known text detection dataset for synthetic text detection and removal tasks. Extensive experiments of both functions are conducted on the synthesized datasets, and the results demonstrate the effectiveness of our STNet.
Modelling Organizational Recovery

Organizations today face a significant set of sophisticated information security threats, including rapidly spreading malware that can affect many devices across the organization. The impacts of such attacks are amplified by customers’ rising expectations of high-quality and rapid delivery of products and services, as well as by organizational attempts to increase demand artificially. This leads to the development of defence mechanisms that prioritize availability and integrity for the sake of reducing the overall time of organizational recovery. However, such mechanisms and strategies around recovery must suit the organization that deploys them. Each organization will have different priorities in terms of budget, speed of recovery, and priority of services or devices, and all of these will be impacted by the architecture of the organization and its networks. In this paper, we show how modelling can play a role in helping organizations understand the consequences of the different recovery mechanisms and strategies available to them. We describe a rigorous modelling framework and methodology grounded in mathematical systems modelling and simulation, and present as an example a comparative analysis of recovery strategies and mechanisms on a medium-scale organization.
Data Preservation System using BoCA: Blockchain-of-Custody Application

The recent growth in popularity of blockchains has been primarily fueled by their support of cryptocurrencies and smart contracts. However, a blockchain is primarily a distributed, trustworthy ledger and one of its greatest features is its ability to preserve data in a robust and verifiable manner. In this paper we present a general Blockchain-of-Custody Application proof-of-concept. BoCA allows data to be preserved on the blockchain in a transparent and verifiable way. We present our application and discuss the advantages this approach has over the state-of-the-art, as well as discuss different scenarios in which it could be used.
Comparative Study on Data Storage algorithms and solution to improve CIA on Cloud using Dynamic Encryption

Cloud computing has become a topic of great significance in todays fast-growing techworld. Everyone wants to access their data seamlessly across platforms and devices thus, leading to exponential requests in cloud computing technologies and services across the globe. With the increase in demand for cloud computing services, it has become very important for service providers to provide a secure and reliable platform to their users. Upon performing a comparative study on various data storage algorithms and their performance based on 3 important factors namely, Confidentiality, Availability, and Integrity, there has not been much improvement over a long period. Thus, to suffice this need, we have proposed a solution to overcome this issue using the concept called Dynamic Encryption. This solution targets the most crucial factors in data storage and its transfer over the cloud network.
Analysis of the Application Effect of the Micro-Assisted Teaching Mode Based on SPSS

This paper discusses the application effect of micro teaching assistant in the teaching of basic courses of ideology, morality and law. Taking the students majoring in food quality and safety of grade 2018as the control group, the traditional teaching method is adopted, and the students of grade 2020 as the experimental group, the micro teaching assistant teaching mode is adopted. The same teacher in the control group and the experimental group completed the course teaching, test paper proposition and review, and completed the quantitative and qualitative analysis of students' final grades based on SPSS. The analysis results show that there is no significant difference in the overall mean value of students' scores between micro assistant teaching and traditional teaching, but the proportion of students' scores in 70-80 segments is greatly improved compared with the traditional teaching mode, and the passing rate is high. It can be seen that the micro assistant teaching mode does improve some students' learning enthusiasm and examination scores [1-2].
A Method for Endpoint Aware Inspection in a Network Security Solution

Due to the surge in remote work after the outbreak of COVID-19, network security has gained an enormous focus.The issue of erroneous inspection decisions in network security solutions has long been criticised, but the importance of the decision accuracy has never been as important as today.In this paper we provide a solution for improving the inspection decision accuracy by specifying a method for endpoint aware inspection in a network security solution capable of performing deep packet inspection.The method utilises a subset of the protected network to gather hash fingerprints from the endpoint application network traffic patterns.The information gathered from this subset is then utilised for gaining endpoint awareness for the rest of the protected network.We use methods that work on the application layer of the protocol stack.This makes the method applicable not only for local implementations, such as NGFWs and IPSs, but also for SaaS and SASE solutions.The method is, however, easily utilised with lower layer information, such as network and transport layer information, for operating system awareness as well.We also present a proof-of-concept case study where we observe that, of the applicable network connections, 100% could be identified when the operating system and endpoint application were present in the source group.To our knowledge, this is the first method to enhance the inspection process accuracy by leveraging a subset of the protected network to gain endpoint awareness.
Design and braking calculation of traveling device of gantry crane

Gantry crane is an important part of the hydropower plant equipment, undertaking the task of opening and closing the dam gates. At present, the operation of the gantry crane is mainly controlled manually by the ground commanders who rely on visual inspection to direct the driver, and the positioning accuracy and efficiency are low. To address the problems, this paper is based on RFID technology to design the automatic walking positioning system of the gantry crane, analyze the braking process of the hoist carriage walking mechanism, calculate the braking distance and braking time of the carriage walking mechanism under different braking conditions, and obtain the relationship between the braking distance S and the braking time x of the walking mechanism under normal braking conditions, as well as the dynamics of the braking distance S during emergency braking. equation.
Brain tissue segmentation using modified fuzzy C-means clustering

This research proposes a new approach for segmenting and calculating tissue area in MRI images of the brain. Since the boundaries of the brain tissues are usually complex, we used K-means clustering, fuzzy C-means (FCM) and modified version of fuzzy C-means named as MFCM to segment the brain tissues into three different tissue segment namely white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The findings of the modified FCM (MFCM) segmentation are fed into the level set process for further refining. In this paper the work defines an efficient image segmentation approach using fuzzy C-means algorithm integrated with K-means clustering technique and it is followed by noise removal using median filter named a modified fuzzy C-mean (MFCM). We are here fixing the fuzzy parameter in such a manner which is actually giving the better result than the rest. Pixel counting is used to calculate the percentages of WM, GM, and CSF. This method can be used to calculate tissue volume in a variety of diseases and age groups.
Multidisciplinary and Multi-objective Design Optimization for Unmanned Aerial Vehicle System

In modern wars, unmanned aerial vehicle (UAV) swarm has been developed for cooperative engagements. To further improve the UAV swarm combat effectiveness, it is necessary to apply multidisciplinary design optimization (MDO) for UAV system design considering the mission scenario. Based on the characteristics of UAVs, the UAV multidisciplinary analysis models are constructed including flight range, penetration, search, strike, and cost disciplines. The MDO problem is then formulated to maximize the effectiveness indexes (i.e., mission completion probability and flight range) and minimize the expenditure indexes (i.e., take-off weight and cost) simultaneously. Additionally, a surrogate-based multi-objective optimization method is utilized to solve the studied UAV MDO problem efficiently. Compared with the initial design, the optimized solution can save the cost and increase the flight range simultaneously, which demonstrates the effectiveness and practicality of the UAV multidisciplinary optimization work in this paper.
Construction of Online System for Comprehensive Experiments of Waterborne Materials Based on Information Technology

We will develop online education and distance education, promote the open sharing of all kinds of high-quality educational resources, provide services to the whole society, and fully demonstrate the supporting role of educational informatization in educational modernization. Accelerate the exploration of digital education resource service provision models and effectively enhance the level and capacity of digital education resource services. In order to improve the teaching effect of the "Comprehensive Experiment of Water-based Materials" course, the teaching method reflects advanced and interactive, vigorously promote the deep integration of modern information technology and teaching, and actively guide students to conduct inquiry-based and personalized learning. "In this paper, we study the construction and application of online resources for the first-class courses, design the technical framework and core modules of the system, and solve the key technical problems of the system development.
Design and construction of multi-functional intelligent and integrated auxiliary AIDS for the elderly

Based on the World Health Organization report that about 150,000 elderly people die from falls every year worldwide. For the elderly, fall has been the primary cause of fatal and non-fatal injuries. Among all kinds of fall protection supplies, it is more necessary to let the elderly take the initiative to prevent falls. In this design, the multi-functional intelligent auxiliary AIDS for the elderly is composed of personalized wearable alarm insoles equipped with ankle protective straps, belt pop-up airbag, elastic protective chair, and combined with rehabilitation. Through Bluetooth, APP, big data terminal and other integrated connection, forming a multi-functional intelligent help line integrated auxiliary tool.
Modeling and Verification of CKB Consensus Protocol in Coq

Blockchain has been prospering for the last decade. Despite the tremendous success of block chain, it is still vulnerable due to the complexity of distributed execution environment. Malicious attacks that exploit these vulnerabilities can lead to serious losses. Common Knowledge Base (CKB) is a public permissionless blockchain and the base layer of N ervos Network, which is gaining popularity in recent years. It adopts a novel con-sensus protocol to overcome two shortcomings of Bitcoin: the low transaction processing throughput and the vulnerability to selfish mining attacks. Considering the high-security requirements in CKB application scenarios, it is essential to provide formalization and verification of the safety and security properties of the CKB blockchain. In this paper, we provide a formal model of CKB consensus protocol in the theorem prover Coq. Fundamental components of the protocol are implemented, including the block structure, the two-step transaction confirmation mechanism, and the peer-to-peer asynchronous network. We also use Coq to establish the quiescent consistency property of the consensus protocol, which is a kind of eventual consensus that all the participants agree on the same ledger when there are no inflight messages.
The Application Path of Artificial Intelligence Technologies in Precision Teaching of College English

The development of artificial intelligence has made education step into the intelligence age. AI technologies are not only applied to language practice but also pedagogically contribute to English learning. U sing precision teaching as theoretical framework, this paper discusses how to apply big-data modeling, human-computer interaction, smart push system, etc. in the four teaching stages: students profiling, teaching resource customization, teaching methods optimization and assessment of student's performance in college English. It is concluded that with the assistance of AI technologies, the individualized English education can be achieved.
Photon Sources for Quantum Technologies

In this article, we have discussed the state of the art in both probabilistic as well as deterministic (on-demand) single photon sources. We have highlighted areas in which further improvement is necessary in different architectures vis-a-vis desirable properties of a single photon source. We have also provided an assessment and overview of the relative merits and demerits of different photon source methodologies towards their applications in quantum science and technologies.
Comparison of Multi-Criteria Decision-Making Techniques for Cloud Services Selection

Cloud Computing is the latest model that supports the idea of computing as a utility. Fundamentally this is not the latest idea, but for users (industries and researchers), it's now a viable truth to pay for the cloud services according to their usage, with no upfront cost and with effectually unlimited adaptability. Although the future of cloud services looks very promising, there are still some challenging problems out of which the selection of cloud services is one of them. This problem has fascinated huge attention both from industry experts and academicians. To help the users to make wise decisions, researchers have developed various techniques by conducting different studies. Considering various criteria during the cloud services selection process, Multi-Criteria Decision-Making (MCDM) techniques are widely adopted to assist the decision-makers to make acute choices. In this paper, a comparison of MCDM-based approaches for cloud service selection has been performed. This paper has identified the challenges and highlighted the limitations of MCDM-based cloud services selection techniques after contrasting and compiling the reviewed approaches. This study is anticipated to bring profits to both academia and industry professionals.
Bayesian Optimization for Sparse Artificial Neural Networks: Application to Change Detection in Remote Sensing

Artificial neural networks (ANNs) are today the most popular machine learning algorithms. ANNs are widely applied in various fields such as medical imaging and remote sensing. One of the main challenges related to the use of ANNs is the inherent optimization problem to be solved during the training phase. This optimization step is generally performed using a gradient-based approach with a backpropagation strategy. For the sake of efficiency, regularization is generally used. When non-smooth regularizers are used to promote sparse networks, this optimization becomes challenging. Classical gradient-based optimizers cannot be used due to differentiability issues. In this paper, we propose an efficient optimization scheme formulated in a Bayesian framework. Hamiltonian dynamics are used to design an efficient sampling scheme. Promising results show the usefulness of the proposed method to allow ANNs with low complexity levels reaching high accuracy rates while performing faster that with other optimizers.
Remote sensing images object detection based on channel aware attention networks

Due to remote sensing images have the characteristics of the large variations in object scale, complex background, and noise, object detection on remote sensing images has always been difficult problem in the field of computer vision. In order to make full use of the correlation of each channel of the feature map, our paper proposes a channel aware network based on FPN network and attention mechanism. The network adaptively assigns weights to the characteristics of each channel to achieve the purpose of suppressing background information and enhancing object information. In addition, our paper also combines the channel aware attention networks with FCOS network, and uses the GIOU Loss function and Focal loss function for training. Comparative experiments were conducted on the DOTA data set. The experimental results show that the FCOS algorithm based on the channel aware attention network has higher detection accuracy and detection speed, which is better than the existing object detection methods.
Vision Transformer Modules for Compositional Reasoning in Visual Question Answering Tasks

Visual Question and Answering is seen as the natural progression from traditional image recognition methods in the path to achieving Artificial General Intelligence. The ability of a machine to query, rationalise and deduce information from an image, rather than merely pattern match, is akin to that of human perceiving and making decision based on their visual surroundings. The Visual Question and Answering field has seen an influx of novel architectures in recent years, with high performance metrics being achieved by numerous studies on each of the core benchmark datasets. Despite this, traditional neural network approaches have demonstrated weaknesses when challenged with compositional reasoning queries, i.e. queries which require logical deduction. Modern techniques attempt to resolve this problem by embedding symbolic reasoning elements within traditional neural network architectures, often through the use of Neural Module Networks. The aims of this paper are two-fold: firstly, to present a new near state-of-the-art variation of the Neural Module Network architecture which harnesses the global image comprehension offered by Vision Transformers; known as Vision Transformer Modules, our system is able to achieve a 98.0% accuracy on a core Visual Question and Answering dataset. Secondly, to investigate the behaviour and suitability of Vision Transformers within a Visual Question and Answering setting.