Reviewerly/exHarmony huggingface dataset:
Implementing a PLC-Service bus with IEC 61499

With an increased demand for customization, a better quality of products, and available variability in machines, enforcing machine developers to rethink how PLC-software is structured and developed. It demands the PLC-software to be adaptable, flexible, and modular. Current PLC-software uses global variables for interaction between its program organization units (POUs). Global variables increase tight coupling and reduce adaptability, flexibility, and modularity. A new architectural concept, the PLC-Service bus, was suggested to address this issue. To reduce coupling, POUs and software components of the PLC communicate via a service bus. This concept leads to two problems. There are no standard models to describe these interactions clearly, and there is no implementation of the PLC-Service bus to develop PLC-software based on message interaction patterns (MIPs). This paper uses the capping station from the VDMA R+a OpcUA Demonstrator as a running example. With an Event-driven process chain model (EPC), we describe the interactions between various software components of the capping station to identify MIPs. Based on these MIPs, we implement the PLC-software of the capping station with IEC 61499.
Vehicle Analysis System Based on DeepSORT and YOLOv5

Nowadays, the constantly expanding coverage of expressways leads to an astronomical volume of daily high-speed traffic. In order to address the poor real-time performance of traditional traffic statistics and low accuracy in detection of densely distributed small targets for original algorithms, this paper proposes a vehicle analysis system that is based on DeepSORT and YOLOv5. More specifically, YOLOv5 is adopted to detect the target, Kalman filter is employed for prediction and update, and the Hungarian algorithm is applied for trajectory matching in the cascade matching, so as to realize the multi-target tracking of vehicles. Besides, inverse perspective mapping is performed to obtain the actual distance traveled by a vehicle per unit time, and to calculate the speed of it. The experimental results show that the vehicle analysis system based on DeepSORT and YOLOv5 has an average accuracy of 96% for vehicle targets, a 98.36% accuracy of vehicle speed measurement, and a lower model loss of than 2% on average, which meets the needs of real-time analysis.
Trans-U: Transformer Enhanced U-Net for Medical Image Segmentation

The growth of healthcare systems, particularly illness diagnosis and treatment planning, requires the segmentation of medical images. U-Net became the basic standard for several medical image segmentation tasks, with great success. Due to the intrinsic locality of convolutional processes, U-Net frequently has constraints in explicitly representing long-range dependence. Transformer, which is built for sequence-to-sequence prediction, has emerged as a viable architecture with an intrinsic global self-attention mechanism, however owing to insufficient low-level information, it may have restricted localization capabilities. In this paper, Trans-U is proposed as an powerful option for medical image segmentation in this research combined with Transformer and U-Net. Transformer utilizes tokenized picture patching from convolutional neural network feature maps as input data in order to extract global context. To achieve exact localization, the decoder upsamples the encoded features, which are subsequently integrated with the high-resolution CNN feature maps. We claim that Transformers can be effective encoders for medical picture segmentation tasks, especially when used with U-Net to recover localized spatial information and improve finer details. Trans-U outperforms many rival approaches in a variety of medical applications, such as multi-organ segmentation and cardiac segmentation.
Practices to improve teamwork in software development during the COVID-19 pandemic

Context. Due to the COVID-19 pandemic, software professionals had to abruptly shift to ostensibly temporary home offices, which affected teamwork in several ways. Goal. This study aims to explore how these professionals coped with remote work during the pandemic and to identify practices that supported the team activities. Method. Ethnographic methods, including participant observation and qualitative data analysis, were used. Results. Three practices were created by the observed team to improve their engagement: costume meeting, second-language day, and project happy hour. These practices appear to increase individual involvement, improve team cohesion, reduce monotony, and create opportunities for knowledge acquisition. Conclusions. The three observed practices may help remote software teams cope with adversity. More research is needed to determine if these practices work in other settings, including remote-first and hybrid remote / on-site teams, post-pandemic.
Review on quantum safe algorithms based on Symmetric Key and Asymmetric Key Encryption methods

In the past few years, cloud infrastructure has advanced to handle applications that distribute huge volumes of data across a large number of users. It provides users with massive amounts of data storage and computing power through the Internet. Numerous problems arise as a result of the rapid growth of data. There are several data protection methods and implementations. These algorithms implemented a variety of encryption techniques. Security is a crucial topic in cloud computing. We explained asymmetric encryption security algorithms such as Elliptic Curve, DSA, RSA, and Diffie-Hellman, as well as encryption algorithms (symmetric) such as Blowfish, DES and AES.
Implementation of Machine Learning and KNN Algorithm for Finding Missing Person

Nowadays, finding a missing person is a difficult task despite the fact that we are all kept up to date by social media. It requires a great deal of paper work and is a time-consuming process additionally, even after all of this hard labour there are minimal aspects of obtaining a satisfactory result. This project provides a mechanism that benefits both the police and the normal public by speeding up the procedure. Here we used face recognition as a search process. If a person goes missing, the individual&#x0027;s guardian can upload the photograph which will then be stored in the system. The facial recognition model in our system will then search the database for a match of that individual. If there is a match is found, the police and the guardian will be alerted.
Comparison of Various Proposed Techniques for Processing of Different Images

In this paper, the various image processing techniques are discussed as images are processed to improve the quality of the pictures and extract information about various features of them and for this purpose various methods are proposed. some useful techniques are discussed that can be used according to type of the image and how that image needs to be improved. Various filters are used to improve brightness and contrast enhancement. CNN is used for solving image problems and sparse transformation method is best for denoising the images as well as for underwater images color balancing and fusion methods are there.
Detailed Investigation of Influence of Machine learning (ML) and Big Data on Digital Transformation in Marketing

The study explores with Machine learning (ML), which is a type of neural network (AI) that empowers software programmers to start increasing prediction without being done with full to do so. Because data is so valuable, improving strategies for intelligently having to manage the now-ubiquitous content infrastructures is a necessary part of the process toward completely autonomous agents. Computer vision and computer vision have improved a wide range of industries, including medical diagnoses, data display and procedures, science and research, and so. Just as preparing for a sport may be risky for individuals who are prone to injury, learning from contaminated or erroneous data can be costly. As described in the article Approaching Data Science, incorrectly trained algorithms result in expenses for a corporation rather than savings. Because incorrectly labeled, missing, or irrelevant data might impair the accuracy of any algorithm, organizations must be able to vouch for the quality and integrity of any data sets, along with their sources.
An Empirical Analysis of Artificial Intelligence (AI) as a Growth Engine for the Healthcare Sector

The present research shed light on AI technology and its impact on the healthcare industry. Based on the surveys made by the researcher it is understood that without AI technology rapid development in the healthcare industry could not become possible for them. As for this, the adoption of AI technology is important for all health care firms to save their time and efforts for specific work. The research has been done with the aim of measuring whether there is a huge role of AI technology in the UK healthcare sector or not.
Designing a Framework of an Integrated Network and Security Operation Center: A Convergence Approach

Cyber-security incidents have grown significantly in modern networks, far more diverse and highly destructive and disruptive. According to the 2021 Cyber Security Statistics Report [1], cybercrime is up 600% during this COVID pandemic, the top attacks are but are not confined to (a) sophisticated phishing emails, (b) account and DNS hijacking, (c) targeted attacks using stealth and air gap malware, (d) distributed denial of services (DDoS), (e) SQL injection. Additionally, 95% of cyber-security breaches result from human error, according to Cybint Report [2]. The average time to identify a breach is 207 days as per Ponemon Institute and IBM, 2022 Cost of Data Breach Report [3]. However, various preventative controls based on cyber-security risk estimation and awareness results decrease most incidents, but not all. Further, any incident detection delay and passive actions to cyber-security incidents put the organizational assets at risk. Therefore, the cyber-security incident management system has become a vital part of the organizational strategy. Thus, the authors propose a framework to converge a "Security Operation Center" (SOC) and a "Network Operations Center" (NOC) in an "Integrated Network Security Operation Center" (INSOC), to overcome cyber-threat detection and mitigation inefficiencies in the near-real-time scenario. We applied the People, Process, Technology, Governance and Compliance (PPTGC) approach to develop the INSOC conceptual framework, according to the requirements we formulated for its operation [4], [5]. The article briefly describes the INSOC conceptual framework and its usefulness, including the central area of the PPTGC approach while designing the framework.
Cultural Heritage Copyright Protection: A blind and robust watermarking technique for heritage images

The Cultural Heritage (CH) media, which include photographs, audio, and videos, are the valuable asset of a region, state, or even a nation. This not only provides them with information about their ancestors, but also portrays their way of life, artistic approach, and a variety of other historical and archaeological principles. The protection of the heritage media is one of the main concerns in today’s world where cyber-crime is on the rise and unauthorized users can easily access the precious heritage media. For copyright protection of the CH images, this paper presents a robust blind watermarking algorithm for color images based on Integer Wavelet Transform (IWT). The watermark is embedded in the Cb component of the YCbCr color space. The IWT divides the Cb component into four sub-bands, among those four sub-bands, the LL sub-band being highly robust is selected for embedding the watermark by employing inter pixel-differencing procedure. Arnold transform has also been used to increase the security of the watermark. From the experimental results, it is evident that the proposed scheme is highly robust to various types of singular as well as hybrid attacks. The proposed scheme offers the mean Peak-Signal-to-Noise-Ratio (PSNR) of 46.12dB for 100 cultural images. Also, Normalized Correlation (NC) values are close to 1 which not only shows better robustness of the scheme but also guarantees its imperceptibility.
Speech Recognition using Weighted Finite-State Transducers

Speech recognition has always been a prominent field of research in NLP, due to its numerous applications such as speech-to-text conversion, voice assistants, enabling smart homes, etc. Computer algorithms are used by speech recognition systems to process, interpret, and transform spoken words into textual content. Modern statistically-based speech recognition systems use a variety of algorithms such as Dynamic Time Warping(DTW), Neural Networks, and end-to-end automatic system. In this paper, we will perform speech recognition using a type of Finite Automata called Weighted Finite-State Transducer.A Finite Transducer is a machine that has no final state. It only takes in some input and produces the appropriate output. Finite State Transducers can contain weights, in which case they are called Weighted Finite-State Transducers (WFST), where each transition is labeled with a weight along with their input and output labels. Weights can represent penalties, probabilities, durations, or any other type of value that gets added along the paths for the computation of the overall weight of mapping an input sequence to an output sequence. This property of weighted transducers allows it to be an essential choice for representation of the probabilistic finite-state models that are widely used in speech processing. Thus, weighted finite-state transducers elucidate a common framework for the formulation and use of models in speech recognition, with shared algorithms that provide significant algorithmic and software engineering benefits.
Walk This Way!

RDF2vec is a knowledge graph embedding mechanism which first extracts sequences from knowledge graphs by performing random walks, then feeds those into the word embedding algorithm word2vec for computing vector representations for entities. In this poster, we introduce two new flavors of walk extraction coined e-walks and p-walks, which put an emphasis on the structure or the neighborhood of an entity respectively, and thereby allow for creating embeddings which focus on similarity or relatedness. By combining the walk strategies with order-aware and classic RDF2vec, as well as CBOW and skip-gram word2vec embeddings, we conduct a preliminary evaluation with a total of 12 RDF2vec variants.
3-D Prostate MR and TRUS Images Detection and Segmentation for Puncture Biopsy

Prostate cancer is one of the male cancers with high mortality rate in the world. The mainstream diagnostic method is puncture biopsy. Accurate detection and segmentation of the prostate in MR (Magnetic Resonance) and TRUS (Transrectal Ultrasonography) images is an important prerequisite for image-guided puncture biopsy. Manual or semi-automatic detection and segmentation methods and traditional automatic detection and segmentation methods are difficult to achieve real-time and accurate detection and segmentation under the conditions of poor imaging effect or less labeled data. In this paper, a real-time and accurate automatic detection and segmentation algorithm DRC U-Net for 3D MR and TRUS images of prostate is proposed for prostate puncture biopsy. A novel convolution module that integrates residual connections, dense connections, and deep separable convolutions is proposed for multi-scale feature extraction and fusion. At the same time, deep supervision mechanism and a variety of attention mechanisms are integrated in the network to improve training efficiency and ensure segmentation effect. The experimental results on the public MR dataset Promise12 and the private TRUS dataset show that the real-time performance and accuracy of this method are better than the existing 3D medical image detection and segmentation methods, such as 3D U-Net and V-Net, and also have some advantages compared with the most advanced 3D medical image detection and segmentation methods, which proves that the proposed network structure has important clinical significance and practical value for image-guided puncture biopsy.
Safe Reinforcement Learning Using Black-Box Reachability Analysis

Reinforcement learning (RL) is capable of sophisticated motion planning and control for robots in uncertain environments. However, state-of-the-art deep RL approaches typically lack safety guarantees, especially when the robot and environment models are unknown. To justify widespread deployment, robots must respect safety constraints without sacrificing performance. Thus, we propose a Black-box Reachability-based Safety Layer (BRSL) with three main components: (1) data-driven reachability analysis for a black-box robot model, (2) a trajectory rollout planner that predicts future actions and observations using an ensemble of neural networks trained online, and (3) a differentiable polytope collision check between the reachable set and obstacles that enables correcting unsafe actions. In simulation, BRSL outperforms other state-of-the-art safe RL methods on a Turtlebot 3, a quadrotor, a trajectory-tracking point mass, and a hexarotor in wind with an unsafe set adjacent to the area of highest reward.
Human Detection in Drone Images Using YOLO for Search-and-Rescue Operations

Today, unmanned aerial vehicles, more commonly known as drones, can be equipped with high-resolution cameras and embedded GPUs powerful enough to provide effective and efficient aid to Search-and-Rescue (SAR) operations in remote and hostile environments. Locating victims, who may be unconscious or injured, as quickly as possible is critical to improving their chance of survival. Therefore, using drones as flying machines for computer vision can increase the detection rate while reducing rescue time. In this paper, we present the results of an experimental evaluation in which we used the latest, lightweight version of the YOLO detection algorithm, namely YOLOv5, to detect humans in danger using two new benchmark datasets specifically designed for SAR with drones. The results obtained are encouraging, as they are competitive with respect to the state-of-the-art in terms of detection accuracy, but with much faster detection time.
On binary LCD BCH codes of length $ \frac{{{2^m} + 1}}{3} $

&lt;p style='text-indent:20px;'&gt;BCH codes are a special subclass of cyclic codes and have many important applications in data storage and communication systems. In this paper, we investigate the structure of binary linear complementary dual (LCD) BCH codes with length &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ n = \frac{{{2^m} + 1}}{3} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, where &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ m \geq 7 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; is an odd integer. By exploring cyclotomic cosets modulo &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ n $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, we determine the dimension of LCD BCH codes for designed distance &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ \delta $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; in the range &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ 2 \le \delta \le{2^{\frac{{m + 1}}{2}}} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. Furthermore, we compute the first five largest coset leaders modulo &lt;inline-formula&gt;&lt;tex-math id="M7"&gt;\begin{document}$ n $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and construct some binary LCD BCH codes. We also present two families of optimal binary linear codes from LCD BCH codes.&lt;/p&gt;
An FM*-Based Comprehensive Path Planning System for Robotic Floating Garbage Cleaning

A heuristic fast marching (FM*)-based comprehensive path planning system involving task allocation, initial planning, and replanning is presented for the robotic floating garbage cleaning mission. There are three primary contributions in this paper. First, to tackle the invalidation of the Euclidean distance metric in the obstacle environment, the task allocation is modeled as a travelling salesman problem (TSP) employing the FM*-based distance metric in order to obtain an optimal travel sequence. Second, to meet the maneuverability constraint from the surface robot and avoid the collision, a Gaussian filter is employed to adjust the curvature radius of the generated path. Third, for an efficient replanning, a neural network-based replanning point generator with the input of garbage movement vector is provided to strike a compromise for the distance cost and the computational burden. Moreover, a case study and a virtual obstacle experiment in the laboratory water tank demonstrate the feasibility of the proposed comprehensive path planning system. This work lays a firm foundation for the development of intelligent equipment for aquatic environment protection.
Informative Path Planning to Estimate Quantiles for Environmental Analysis

Scientists interested in studying natural phenomena often take physical specimens from locations in the environment for later analysis. These analysis locations are typically specified by expert heuristics. Instead, we propose to choose locations for scientific analysis by using a robot to perform an informative path planning survey. The survey results in a list of locations that correspond to the quantile values of the phenomenon of interest. We develop a robot planner using novel objective functions to improve the estimates of the quantile values over time and an approach to find locations which correspond to the quantile values. We test our approach in four different environments using previously collected aquatic data and validate it in a field trial. Our proposed approach to estimate quantiles has a 10.2% mean reduction in median error when compared to a baseline approach which attempts to maximize spatial coverage. Additionally, when localizing these values in the environment, we see a 15.7% mean reduction in median error when using cross-entropy with our loss function compared to a baseline.
An Efficient Way of Introducing Gender Into Evolutionary Algorithms

Evolutionary algorithms have been extensively used for numerous optimization problems with great success in the past years. They mimic nature&#x2019;s process of evolution to find solutions to a large range of mathematical problems. In this work a new strategy is suggested to introduce gender, defined by a characteristic of an individual, that is easy to implement in evolutionary algorithms, as long as they are based on evaluating a fitness function. The new method outperforms comparable evolutionary approaches without gender for all standard test problems considered. The present study shows that with increasing problem complexity the performance of this gender variant increases to more than double the success rates while keeping the computational effort at about the same level and still being clearly better for easy problems. Additionally, in the mean, the new method results in better fitness values for all presented cases.