Reviewerly/exHarmony huggingface dataset:
Law

This chapter outlines how legal arguments can be constructed whereby a nonhuman legal person – an AI – could be capable of corporate immortality and how such radical positions challenge the human-centred design of intellectual property (IP). The chapter begins by noting the global movement to harmonise IP law and establishes the centrality of music copyright to the music industry’s economy. The historical development of fundamental legal theories is presented so that essential concepts in music copyright such as creativity and originality can be accessed concerning AI. To comprehend the legal perplexities of music generated by unsupervised machine learning, DeepMind’s WaveNet AI is considered. Supporting legal challenges, including the potential of AI being granted legal personhood for AI, are noted, and the chapter concludes that the protection offered by music copyright law is fragile when its case law has its roots in the precomputerised age. In a chapter addendum, David Hughes, Chief Technology Officer at the Recording Industry Association of America (RIAA) 2006–2021, provides high-level music industry reflection on chapter themes.
Automatic liver tumor segmentation and identification using fully connected convolutional neural network from <scp>CT</scp> images

Summary In recent years, one of the largest causes of death in human beings is liver tumor and cancer. In the current scenario, identifying the cancer tumor manually is very difficult and takes a lot of time as the world is battling with COVID‐19. The doctors and physicians are busy in serving and curing them. To predict the stage of liver tumor and plan the treatment, the segmentation is done from CT scanned images. In this research, two‐stage automatic liver segmentation and tumor identification framework using a fully connected convolutional neural network (FC‐CNN) model is proposed. During the first stage, the liver region is segmented using level set method from the preprocessed (OTSU thresholding) CT images. The features are extracted and utilized to detect the liver tumor in the second stage. The developed FC‐CNN is trained and tested using the extracted second order statistical textural features to classify the tumor affected and normal image. The proposed FC‐CNN model is trained and tested with 3D‐IRCADb‐01 and Kaggle datasets. The tested results prove that the proposed FC‐CNN model outperforms other reported methods. From the performance analysis, it is observed that it achieves a good accuracy and sensitivity rate of 99.11% and 98.10%, respectively.
Investigation of Quadrate Fresnel Zone Plates Fabricated by Femtosecond Laser Direct Writing

The circular Fresnel zone plate (FZP) has been extensively used in micro-optics due to its outstanding focusing performance. Despite that, the curved edge of the circular zone has drawbacks limiting its use in terahertz imaging, array generator, and micro/nano-manufacturing. Therefore, a circular structure is not very practical to use. In this paper, Quadrate FZPs were proposed, and diffraction properties of the linear FZP (L-FZP), orthogonal FZP (O-FZP), and square FZP (S-FZP) were studied. Theoretically, the relationship between half side length of S-FZP and the radius of circular FZP is established, and the focal length formula of the S-FZP is derived. The linear and crossline focusing properties of quadrate FZPs were studied experimentally along with simulations. With the addition of blockers and phase shifting, the square and rectangular diffraction patterns were successfully obtained.
Robustness Enhancement of Neural Networks via Architecture Search with Multi-Objective Evolutionary Optimization

Along with the wide use of deep learning technology, its security issues have drawn much attention over the years. Adversarial examples expose the inherent vulnerability of deep learning models and make it a challenging task to improve their robustness. Model robustness is related not only to its parameters but also to its architecture. This paper proposes a novel robustness enhanced approach for neural networks based on a neural architecture search. First, we randomly sample multiple neural networks to construct the initial population. Second, we utilize the individual networks in the population to fit and update the surrogate models. Third, the population of neural networks is evolved through a multi-objective evolutionary algorithm, where the surrogate models accelerate the performance evaluation of networks. Finally, the second and third steps are performed alternately until a network architecture with high accuracy and robustness is achieved. Experimental results show that the proposed method outperforms some classical artificially designed neural networks and other architecture search algorithms in terms of robustness.
Creating Formal Models from Informal Design Artefacts

The use of robust software engineering processes is essential in the design and development of interactive systems.This ensures that software is both functionally correct and also usable in the required context.There are a variety of software engineering techniques that can be used to consider different aspects of a system under construction, and at different stages in the development process.There is, however, a natural tension between formal approaches (typically used in the domain of safety-critical systems to consider functional correctness) and informal design approaches, which focus on users and user requirements in a manner which is accessible by stakeholders.In this paper we present two new approaches which enable a tighter coupling of informal design requirements with formal models.We present examples of these two approaches and discuss the benefits that transformations between the informal and formal provide.We also discuss the current limitations of such work along with recommendations about how these might be addressed.
An Improved Machine Translation Model and its Application in Japanese Multi-Context Translation

In order to further improve the application of machine translation model in Japanese translation, analytic analysis method is adopted to optimize the original machine translation model. The improved machine translation model is used to analyze and describe Japanese translation. Finally, the optimized machine translation model is used to analyze Japanese multicontext. The relevant indexes and parameters were extracted and verified, and finally the model was verified by relevant experiments. The results show that the vector variation graph with different parameters can be divided into slow decline stage, stable change stage, and fast decline stage according to the increase of iteration number and the influence of corresponding change trend. In addition, it can be seen from the value of PE curve that the influence of parameter pe is the least, while the influence of corresponding re parameter is the greatest. The multicontext index of Japanese has the greatest influence on Japanese fluency and the least influence on Japanese keywords, and the trend of influence is parabolic. The application curve of the optimized machine translation model to Japanese in multiple contexts shows that different parameters have different effects on Japanese, which should be represented by the positive parameter V. Finally, the accuracy of the model is verified by experimental data. The above research can provide support for the application of machine learning in different fields and also provide research ideas for the multicontext translation of Japanese.
Threshold Segmentation and Length Measurement Algorithms for Irregular Curves in Complex Backgrounds

It is an urgent problem to know how to quickly and accurately measure the length of irregular curves in complex background images. To solve the problem, we first proposed a quasi-bimodal threshold segmentation (QBTS) algorithm, which transforms the multimodal histogram into a quasi-bimodal histogram to achieve a faster and more accurate segmentation of the target curve. Then, we proposed a single-pixel skeleton length measurement (SPSLM) algorithm based on the 8-neighborhood model, which used the 8-neighborhood feature to measure the length for the first time, and achieved a more accurate measurement of the curve length. Finally, the two algorithms were tested and analyzed in terms of accuracy and speed on the two original datasets of this paper. The experimental results show that the algorithms proposed in this paper can quickly and accurately segment the target curve from the neon design rendering with complex background interference and measure its length.
Automatic analysis system for abnormal red blood cells in peripheral blood smears

The type and ratio of abnormal red blood cells (RBCs) in blood can be identified through peripheral blood smear test. Accurate classification is important because the accompanying diseases indicated by abnormal RBCs vary. In clinical practice, this task is time-consuming because the RBCs are manually classified. In addition, because the classification depends on the subjective criteria of pathologists, objective classification is difficult to achieve. In this paper, an automatic classification method that is solely based on images of RBCs captured under a microscope and processed using machine learning (ML) is proposed. The size and hemoglobin abnormalities of RBCs were classified by optimizing the criteria used in clinical practice. For morphologically abnormal RBCs classification, used seven geometric features information (major axis, minor axis, ratio of major and minor axis, perimeter, circularity, number of convex hulls, difference between area and convex area) and five types of multiple classifiers (Support Vector Machine, Decision Tree, K-Nearest Neighbor, Random Forest, and Adaboost models). Among was categorized using SVM, highly accurate results (99.9%) were obtained. The classification is performed simultaneously, and results are provided to the user through a graphical user interface (GUI).
Theoretical Explanations of the Development of Youth Hacking

Hacking, particularly among youth, is a relatively new form of deviance and its etiology is not well understood. Moreover, there is a lack of developmental approaches to understanding youth hacking, and the majority of studies on predictors of hacking have been cross-sectional. In light of this, we draw on prospective longitudinal data on a sample of Korean youth to explore theoretical predictors of youth hacking through a developmental lens. Cross-lagged dynamic panel models are employed to examine time-variant and time-invariant theoretical predictors of hacking. Results show that well-known criminological predictors are significantly associated with youth hacking, which implies the applicability of traditional criminological theories in online deviance. Limitations, suggestions for future research, and implications for practice are discussed.
Benchmarking Data Science: 12 Ways to Lie With Statistics and Performance on Parallel Computers

We humorously discuss 12 fallacies when focusing on compute performance that we have frequently observed in practice. We follow each with a recommendation to mitigate the danger and hope to contribute to good benchmarking etiquette for data science.
Closing the Security Agility Gap

Cybercriminals are agile when it comes to new technology. They find weak links before vulnerabilities are secured. This article discusses ways companies and average users can combat cybercriminal agility.
Privacy-Preserving Computation: Doomed to Succeed

We are on the quest for mainstream adoption of privacy-preserving computation solutions. As present challenges are solved, homomorphic encryption and related privacy-preserving techniques will change the face of information technology, security, privacy, and policy.
Reversible Circuit Synthesis Method Using Sub-graphs of Shared Functional Decision Diagrams

Abstract Reversible circuit synthesis methods based on decision diagrams achieve low quantum costs but do not account for quantum bit (qubit) limits for the application of reversible logic in quantum computing. Here, a synthesis method using sub-graphs of shared functional decision diagrams (SFDDs) is proposed for reducing the number of lines when synthesizing reversible circuits. An SFDD is partitioned into sub-graphs by exploiting the longest dominant-active paths, and the sub-graphs are mapped to reversible gate cascades. To further reduce the number of lines, template root matching is presented for reusing circuit lines. Experimental results indicate that the proposed method achieves the known minimum number of lines in many cases and has good scalability. Although the proposed method increases the quantum cost over a prior method based on functional decision diagrams, it significantly reduces the number of lines in most cases. Compared with the one-pass method using quantum multiple-valued decision diagrams, the proposed method reduces the quantum cost without increasing the number of lines in many cases. When compared with the lookup table-based method using a direct mapping flow, the method reduces the number of lines in a few cases. Thus, the method aids in the physical realization of a quantum circuit.
REDA: A New Methodology to Validate Sensor Systems for Person Detection under Variable Environmental Conditions

Perception of the environment by sensor systems in variable environmental conditions is very complex due to the interference influences. In the field of autonomous machines or autonomous vehicles, environmental conditions play a decisive role in safe person detection. A uniform test and validation method can support the manufacturers of sensor systems during development and simultaneously provide proof of functionality. The authors have developed a concept of a novel test method, "REDA", for this purpose. In this article, the concept is applied and measurement data are presented. The results show the versatile potential of this test method, through the manifold interpretation options of the measurement data. Using this method, the strengths and weaknesses of sensor systems have been identified with an unprecedented level of detail, flexibility, and variance to test and compare the detection capability of sensor systems. The comparison was possible regardless of the measuring principle of the sensor system used. Sensor systems have been tested and compared with each other with regard to the influence of environmental conditions themselves. The first results presented highlight the potential of the new test method. For future applications, the test method offers possibilities to test and compare manifold sensing principles, sensor system parameters, or evaluation algorithms, including, e.g., artificial intelligence.
A Deep-DrpXML and IAG-GWO based CHST fostered blockchain technology for secured dynamic optimal routing for wireless sensor networks

In wireless sensor network (WSN), routing is one of the substantial maneuvers for distributing data packets to the base station. But malevolent node outbreaks will happen during routing process, which exaggerate the wireless sensor network operations. Therefore, a secure routing protocol is required, which safeguards the routing fortification and the wireless sensor network effectiveness. The existing routing protocol is dynamically volatile during real time instances, and it is very hard to recognize the unsecured routing node performances. In this manuscript, a Deep Dropout extreme Machine learning optimized Improved Alpha-Guided Grey Wolf based Crypto Hash Signature Token fostered Blockchain Technology is proposed for secure dynamic optimal routing in Wireless Sensor Networks (SDOR-DEML-IAgGWO-CHS-BWSN). In this, Crypto Hash signature (CHS) token are generated for flow accesses with a secret key owned by each routing sensor node and it also offers an optimal path for data transmission. Then the secured dynamic optimal routing information is delivered through the proposed Blockchain based wireless sensor network platform with the help of Deep Dropout Extreme Machine learning optimized Improved Alpha-Guided Grey Wolf routing algorithm. Then the proposed method is simulated using the NS-2 (Network Simulator) tool. The simulation performance of the proposed SDOR-DEML-IAgGWO-CHS-BWSN method provide 76.26%, 65.57%, 60.85%, 48.99% and 42.9% lower delay during 30% malicious routing environment, 73.06%, 63.82%, 59.25%, 44.79% and 38.84% lower delay during 60% malicious routing environment is compared with the existing methods.
Possibilistic c-means clustering based on the nearest-neighbour isolation similarity

Clustering is widely used in data mining and machine learning. The possibilistic c-means clustering (PCM) method loosens the constraint of the fuzzy c-means clustering (FCM) method to solve the problem of noise sensitivity of FCM. But there is also a new problem: overlapping cluster centers are not suitable for clustering non-cluster distribution data. We propose a novel possibilistic c-means clustering method based on the nearest-neighbour isolation similarity in this paper. All samples are taken as the initial cluster centers in the proposed approach to obtain k sub-clusters iteratively. Then the first b samples farthest from the center of each sub-cluster are chosen to represent the sub-cluster. Afterward, sub-clusters are mapped to the distinguishable space by using these selected samples to calculate the nearest-neighbour isolation similarity of the sub-clusters. Then, adjacent sub-clusters can be merged according to the presented connecting strategy, and finally, C clusters are obtained. Our method proposed in this paper has been tested on 15 UCI benchmark datasets and a synthetic dataset. Experimental results show that our proposed method is suitable for clustering non-cluster distribution data, and the clustering results are better than those of the comparison methods with solid robustness.
Entropy analysis of organizations' knowledge systems on the example of project management standards

The problems of managing complex project management systems areassociated with numerous parameters that characterize their state. Most modern methods of forecasting project activity are based on the use of statistical modeling of individual processes and tools, for example, a work schedule, which requires both the justification of the adopted laws for the distribution of random work durations and the planning of a certain organizational and technological sequence of work. The collection and processing of data on all parameters is a complex and expensive procedure, anda complete justification of all project characteristics can lead to the infor-mation complexity of the system under consideration. However, it is impossible to refuse this due to the need to obtain relevant and reliable data for the adoption and implementation of management decisions. Thus, it is necessary to look for ways and means to re-duce the number of controlled parameters, create algorithms that allow predicting the presence of undesirable processes in a con-trolled system, and develop recommendations for a more detailed analysis of individual project management subsystems. To over-come the informational complexity of predictive models, it is proposed to use a phenomenological approach that is associated with the definition of entropy, which allows usinga minimum of information about the planned and updated course of the project. The concept of entropy is one of the key concepts of thermodynamics and information theory, and also finds its application in a number of other sciences, the subject of researchand study of which are complex stochastic systems. The possibilities of using entropy and entropy modeling are currently being actively explored in the theory of project management. The paper considers an entropy ap-proach to modeling project management systems, in which, on the basis of mathematical procedures arising from K. Shannon's in-formation theory, information phenomenological models are created. The IPMA ICB 4.0 standard is considered as an example. The developed tool creates the prerequisites for the effective use of the entropy approach to assessing complex economic and social sys-tems.
Deep Learning based Vehicle AR Manual for Improving User Experience

This paper implements an AR manual for a vehicle that can be used even in the vehicle interior space where it is difficult to apply the augmentation method of AR content, which is mainly used, and applies a deep learning model to improve the augmentation matching between real space and virtual objects.Through deep learning, the logo of the steering wheel is recognized regardless of the position, angle, and inclination, and 3D interior space coordinates are generated based on this, and the virtual button is precisely augmented on the actual vehicle parts.Based on the same learning model, the function to recognize the main warning light symbols of the vehicle is also implemented to increase the functionality and usability as an AR manual for vehicles.
Vehicle Video Stabilization Combined with Vestibular Reflex Effect

In the closed-cabin driving environment, the camera video image shakes violently due to bumpy road conditions. The method of combining vestibular reflex effect with electronic image stabilization is adopted to ensure that the driver can obtain stable images of external road conditions. Firstly, the improved Harris corner response function and increased distance constraint are used to complete the corner detection of the image, and then complete the electronic image stabilization. Secondly, the vehicle vestibular reflex model was established to predict the position of eyeballs by measuring the driver’s head position. Finally, the position information of the eyeball is combined with the image stabilization, so that the image is always displayed in the screen area facing the eyeball after image stabilization. Experimental results show that the error matching rate of the improved Harris corner detection algorithm is reduced by 10.1% compared with common algorithms under the premise of ensuring the efficiency of the algorithm. After image stabilization, the signal to noise ratio of the image increases by 6.525dB, and the processing time of single frame is 22.479ms. Simulation results of vehicle vestibular reflex model show that the simulated Angle of eye movement conforms to physiological vestibular reflex effect, and the average tracking time of single frame image is 30.9ms.
Cyber Threats in the Healthcare Sector and Countermeasures

Healthcare is one of the most targeted industries by cybercriminals. The healthcare sector is far behind in cybersecurity as compared to other organizations. The vulnerabilities in the system open the door for cybercriminals to exploit it and get unauthorized access into the system or network to do a malicious activity. Healthcare should have to take cyber threats seriously and follow a security framework that detects and mitigates cyber threats. This chapter presents all cyber threat actors that exist in the healthcare sector, common cyber-attacks that can be launched against all actors, and real incidents that took place during the past years. Based on these, the authors propose in a tabular form a set of recommendations that can be used as countermeasures against any type of attack.