Reviewerly/exHarmony huggingface dataset:
To Be, or Not to Be … Original Under Copyright Law, That Is (One of) the Main Questions Concerning AI-Produced Works

Abstract This paper will examine whether different types of AI-produced works, namely AI-assisted, AI-generated, and AI-genassisted works, are able to satisfy the originality requirement, as established within the EU copyright acquis. It will argue that even assuming that the choices of AIs can be deemed free, they could not be considered creative due to the anthropological footprint of copyright law which traditionally covers only human creativity. This implies that copyright protection can be granted only to outputs that display a sufficient level of original human intervention, which is most likely present in AI-assisted works and, depending on the case, in AI-genassisted works, too. The desirability of stretching copyright further to accommodate artificial creativity is also lessened by the risk that it could saturate the creative market due to an overexpansion of monopoly rights. Thus, the copyright solution will be evaluated together with some existing alternative legal tools, such as neighbouring rights, sui generis rights, patents, or even the absence of protection over AI-generated works to preserve a robust public domain.
Evaluating Patients’ Experiences with Healthcare Services: Extracting Domain and Language-Specific Information from Free-Text Narratives

Evaluating patients’ experience and satisfaction often calls for analyses of free-text data. Language and domain-specific information extraction can reduce costly manual preprocessing and enable the analysis of extensive collections of experience-based narratives. The research aims were to (1) elicit free-text narratives about experiences with health services of international students in Poland, (2) develop domain- and language-specific algorithms for the extraction of information relevant for the evaluation of quality and safety of health services, and (3) test the performance of information extraction algorithms’ on questions about the patients’ experiences with health services. The materials were free-text narratives about health clinic encounters produced by English-speaking foreigners recalling their experiences (n = 104) in healthcare facilities in Poland. A linguistic analysis of the text collection led to constructing a semantic−syntactic lexicon and a set of lexical-syntactic frames. These were further used to develop rule-based information extraction algorithms in the form of Python scripts. The extraction algorithms generated text classifications according to predefined queries. In addition, the narratives were classified by human readers. The algorithm-based and the human readers’ classifications were highly correlated and significant (p < 0.01), indicating an excellent performance of the automatic query algorithms. The study results demonstrate that domain-specific and language-specific information extraction from free-text narratives can be used as an efficient and low-cost method for evaluating patient experiences and satisfaction with health services and built into software solutions for the quality evaluation in health care.
Study on Marine Fishery Law Enforcement Inspection System based on Improved YOLO V5 with UAV

As a part of China’s territory, the wide sea not only represents China’s powerful territorial power, but also contains incomparably rich Marine resources. And the vast area of the sea has led to a lack of inadequate maritime supervision. The abundant fishery resources are one of the important economic sources of China, but the law enforcement of ocean and fishery has long been plagued by the contradictions among the wide sea area, long coastline, large number of fishing boats in fishing ports, difficult management and limited law enforcement. It is difficult to achieve full-time, all-directional and all-coverage control. Relying on the existing Marine and fishery law enforcement personnel, vehicles and vessels, equipment and so on, it is difficult to achieve sufficient and effective law enforcement supervision. At present, the combination of UAV technology and target detection has a wide application prospect, providing a new technical means for Marine and fishery law enforcement. In this paper, the improved YOLO V5 algorithm is configured on the Unmanned Aerial Vehicle (UAV) to further improve the work efficiency of the UAV. Compared with the initial YOLO V5, the paper changed the 3-scale feature extraction of Neck end into 4-scale feature extraction. By integrating more shallow feature information of more scales, the feature expression ability of path aggregation network was enhanced, the detection accuracy of small targets was improved, and the missed detection rate was reduced. At the same time, the improvement of boundary box detection function and non-maximum suppression NMS improves the research of target recognition and target overlapping detection. Finally, the training results show that the improved YOLO V5 can further improve the effectiveness of UAV Marine fishery law enforcement.
A privacy preservation framework for feedforward-designed convolutional neural networks

A feedforward-designed convolutional neural network (FF-CNN) is an interpretable neural network with low training complexity. Unlike a neural network trained using backpropagation (BP) algorithms and optimizers (e.g., stochastic gradient descent (SGD) and Adam), a FF-CNN obtains the model parameters in one feed-forward calculation based on two methods of data statistics: subspace approximation with adjusted bias and least squares regression. Currently, models based on FF-CNN training methods have achieved outstanding performance in the fields of image classification and point cloud data processing. In this study, we analyze and verify that there is a risk of user privacy leakage during the training process of FF-CNN and existing privacy-preserving methods for model gradients or loss functions do not apply to FF-CNN models. Therefore, we propose a securely forward-designed convolutional neural network algorithm (SFF-CNN) to protect the privacy and security of data providers for the FF-CNN model. Firstly, we propose the DPSaab algorithm to add the corresponding noise to the one-stage Saab transform in the FF-CNN design for improved protection performance. Secondly, because noise addition brings the risk of model over-fitting and further increases the possibility of privacy leakage, we propose the SJS algorithm to filter the input features of the fully connected model layer. Finally, we theoretically prove that the proposed algorithm satisfies differential privacy and experimentally demonstrate that the proposed algorithm has strong privacy protection. The proposed algorithm outperforms the compared deep learning privacy-preserving algorithms in terms of utility and robustness.
ABNN <sup>2</sup>

Data privacy and security issues are preventing a lot of potential on-cloud machine learning as services from happening. In the recent past, secure multi-party computation (MPC) has been used to achieve the secure neural network predictions, guaranteeing the privacy of data. However, the cost of the existing two-party solutions is expensive and they are impractical in real-world setting.
Flag-based Human Supervised Robot Guidance Using Locality Preserving Projections

With the deployment of collaborative robotics in industrial and domestic applications, intelligent robots are anticipated to work with humans in dynamically challenging daily life environments. Robots must deal with several photometric and spatio-temporal complexity in the workspace to achieve successful human-robot collaboration. To overcome these issues, especially in the case of vision-sensor-based systems, several robustness and adaptability measures need to be incorporated. One of such could be the adaptation of a concise and distinctive feature extraction tool for information-rich, high-dimensional data such as locality preserving projections (LPP). The locality information of the original higher-dimensional data is encoded and preserved in that weight parameter unlike principal component analysis (PCA) and linear discriminant analysis (LDA). This can be very much useful in real-life vision-sensor-based systems where the intrinsic locality structure of the images carries significant information. This paper proposes an LPP-based solution for a real-life human-supervised robot navigation guidance system. The proposed approach outperforms contemporary feature extraction techniques like PCA.
An Introduction to Machine Learning: a perspective from Statistical Physics

The recent progresses in Machine Learning opened the door to actual applications of learning algorithms but also to new research directions both in the field of Machine Learning directly and, at the edges with other disciplines. The case that interests us is the interface with physics, and more specifically Statistical Physics. In this short lecture, I will try to present first a brief introduction to Machine Learning from the angle of neural networks. After explaining quickly some fundamental models and global aspects of the training procedure, I will discuss into more detail two examples illustrate what can be done from the Statistical Physics perspective.
Privacy-aware multi-institutional time-to-event studies

Clinical time-to-event studies are dependent on large sample sizes, often not available at a single institution. However, this is countered by the fact that, particularly in the medical field, individual institutions are often legally unable to share their data, as medical data is subject to strong privacy protection due to its particular sensitivity. But the collection, and especially aggregation into centralized datasets, is also fraught with substantial legal risks and often outright unlawful. Existing solutions using federated learning have already demonstrated considerable potential as an alternative for central data collection. Unfortunately, current approaches are incomplete or not easily applicable in clinical studies owing to the complexity of federated infrastructures. This work presents privacy-aware and federated implementations of the most used time-to-event algorithms (survival curve, cumulative hazard rate, log-rank test, and Cox proportional hazards model) in clinical trials, based on a hybrid approach of federated learning, additive secret sharing, and differential privacy. On several benchmark datasets, we show that all algorithms produce highly similar, or in some cases, even identical results compared to traditional centralized time-to-event algorithms. Furthermore, we were able to reproduce the results of a previous clinical time-to-event study in various federated scenarios. All algorithms are accessible through the intuitive web-app Partea ( https://partea.zbh.uni-hamburg.de ), offering a graphical user interface for clinicians and non-computational researchers without programming knowledge. Partea removes the high infrastructural hurdles derived from existing federated learning approaches and removes the complexity of execution. Therefore, it is an easy-to-use alternative to central data collection, reducing bureaucratic efforts but also the legal risks associated with the processing of personal data to a minimum.
Automatic Segmentation of Target Structures for Total Marrow and Lymphoid Irradiation in Bone Marrow Transplantation

The use of total marrow and lymphoid irradiation (TMLI) as part of conditioning regimens for bone marrow transplantation is trending due to its advantages in disease control and low toxicity. Accurate contouring of target structures such as bone and lymph nodes plays an important role in irradiation planning. However, this process is often time-consuming and prone to inter-observer variation. Recently, deep learning methods such as convolutional neural networks (CNNs) and vision transformers have achieved tremendous success in medical image segmentation, therefore enabling fast semiautomatic radiotherapy planning. In this paper, we propose a dual-encoder U-shaped model named DE-Net, to automatically segment the target structures for TMLI. To enhance the learned features, the encoder of DE-Net is composed of parallel CNNs and vision transformers, which can model both local and global contexts. The multi-level features from the two branches are progressively fused by intermediate modules, therefore effectively preserving low-level details. Our experiments demonstrate that the proposed method achieves state-of-the-art results and a significant improvement in lymph node segmentation compared with existing methods.
Insulator and Burst Fault Detection Using an Improved Yolov3 Algorithm

Insulators play an important role in the operation of outdoor high-voltage transmission lines. However, insulators are installed in outdoor environments for long periods and thus failures are inevitable. It is necessary to conduct timely insulator inspection and maintenance. In this paper, an improved Yolov3 target detection network (Yolov3-CK) is proposed in order to achieve higher detection accuracy and speed. First, Yolov3-CK uses the CIOU loss function instead of the mean square error loss function from Yolov3. Second, the Yolov3-CK model uses cluster analysis of the priori box via the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> -means++ algorithm to obtain a priori box size that is more suitable for the detection of insulators and their burst faults. Finally, we use a dataset obtained by performing data enhancement on the China power line insulator dataset to train and test the data-enhanced Yolov3-CK model. The mean precision of Yolov3-CK reaches 91.67% with 47.9 frames processed per second. Yolov3-CK provides better detection accuracy and a higher processing rate than Faster RCNN, SSD, and Yolov3. Therefore, the Yolov3-CK model is more suitable for the detection of insulators and their burst faults.
The Journal “Zodchiy” as a Source on the History of Russian Modern in the late XIX – early XX centuries

The relevance of this study is associated with the formation and spread of the "Russian style" in the architecture of the Russian Empire at the end of the 19th -beginning of the 20th century -a significant trend in the culture of the era under consideration.The professional journal on architecture and construction "Zodchiy" (1872-1924) played an important role in this process, since it was on its pages that the studies of architects and art historians of ancient Russian antiquity were published, justified the need to form a national version of Modern, based on local traditions, projects of temple and secular buildings in the "Russian style" were published.The publications of this historical source were reviewed using qualitative and quantitative content analysis methods.The study recorded the dynamics of publications about the "Russian style" in architecture, in which there is a rise and fall of interest in original projects, made it possible to identify the traditions that architects relied on when designing modern buildings, and the characteristic features of national Modern in architecture, such as: interest in arts and crafts and folk ornamentation, the processing of elements of ancient Russian temple architecture in secular architecture, fabulous facades, etc.
Augmented reality with algorithm animation and their effect on students’ emotions

Abstract Algorithm animations are a resource that assists in learning algorithms by visually displaying the behavior of an algorithm at a higher level of abstraction than source code. On the other hand, augmented reality is a technology that allows extending visible reality in a mobile device, which can result in greater emotional well-being for the student. However, it is not clear how to integrate algorithm animations with augmented reality. The article makes two contributions to this concern. On the one hand, we describe an architecture that allows generating interactive algorithm animations, integrating them appropriately in the context of immersive augmented reality. This way the user can watch the source code of the algorithm, augmented with textual explanations, visualizations and animations of its behavior. We illustrate the use of the architecture by instantiating it to the well-known Dijkstra’s algorithm, resulting in an augmented reality tool that generates text, 2D and 3D visualizations. On the other hand, the influence of the tool on the user’s emotions has been studied by conducting an experience with face-to-face and online students. The results show that, with the joint use of augmented reality and visualizations, the students: experienced significantly more positive than negative emotions, experienced more agitation and stimulation than inactivity or calm, enjoyed as much as they expected, and their feeling of boredom decreased during the experience. However, students felt anxiety from the beginning and it increased with the use of augmented reality. The study also found that the face-to-face or online learning model influences emotions and learning outcomes with augmented reality.
Exploiting compressed sensing and polynomial-based progressive secret image sharing for visually secure image selection encryption with authentication

Image encryption is an effective method to protect private images by converting them into meaningless ones, but many current image encryption algorithms still have security and efficiency problems. This paper combines compressed sensing (CS) and secret image sharing (SIS) to propose a certifiable visually secure image selection encryption. Firstly, a selective encryption based on multi-task convolutional neural network (SE-MTCNN) is presented to distinguish and encrypt the sensitive and non-sensitive information of plain images. Subsequently, the SIS algorithm is used to decompose the resulting plain image into multiple shadow images to disperse the risk of cryptographic image loss and resist hacking attacks. Herein, a pseudo-convolutional sliding scrambling based on chaotic sequences driven by plaintext (PCSS-CSDP) is proposed to eliminate the correlation between adjacent image pixels. Finally, the IWT-M-embedding algorithm is used to embed shadow images into carrier images to get multiple visually meaningful cipher images. Additionally, a new authentication scheme based on bit-level cyclic shifts and dynamic combination (AS-BCSDC) is introduced, and the obtained authentication information is fused into cipher images to prevent the deception of illegal users and malicious tampering of cloud platform. Experiments demonstrate the effectiveness of our algorithm, and it may be applied for image secure communication.
Toward a Secure Edge-Enabled and Artificially Intelligent Internet of Flying Things Using Blockchain

Unmanned aerial vehicles (UAVs) with artificial intelligence (AI) have ushered in a new era of mobile edge computing (MEC). Traditional AI models based on the aggregation of UAV sensing data that mostly contain private and sensitive user data, may raise serious privacy and data misuse problems. Federated learning (FL), as a potential distributed AI paradigm, has allowed UAVs to jointly train a global model without exposing their local sensing data. In the general context, flying things communicate only local model updates. The training process ends when the global model reaches a certain threshold. The centralized curator aggregation model can be subject to diverse threats such as DDoS and single point of failure. To improve the security of FL implementation and create a privacy-preserving model, we consider a new blockchain empowered AI paradigm using both wireless miners and edge computing at flying things for security requiring heterogeneous vehicular systems. In our proposal, blockchain is the backbone allowing secure and safe data exchange between them, providing decentralized FL training without the need for a central server. The proposed architecture is apt to cope with FL privacy leakage, insider/outsider attacks, malicious opponents, inference, and poisoning. It transforms intelligent MEC systems into decentralized, secure, and privacy enhanced networks.
Multi-View Design Patterns and Responsive Visualization for Genomics Data

A series of recent studies has focused on designing cross-resolution and cross-device visualizations, i.e., responsive visualization, a concept adopted from responsive web design. However, these studies mainly focused on visualizations with a single view to a small number of views, and there are still unresolved questions about how to design responsive multi-view visualizations. In this paper, we present a reusable and generalizable framework for designing responsive multi-view visualizations focused on genomics data. To gain a better understanding of existing design challenges, we review web-based genomics visualization tools in the wild. By characterizing tools based on a taxonomy of responsive designs, we find that responsiveness is rarely supported in existing tools. To distill insights from the survey results in a systematic way, we classify typical view composition patterns, such as “vertically long,” “horizontally wide,” “circular,” and “cross-shaped” compositions. We then identify their usability issues in different resolutions that stem from the composition patterns, as well as discussing approaches to address the issues and to make genomics visualizations responsive. By extending the Gosling visualization grammar to support responsive constructs, we show how these approaches can be supported. A valuable follow-up study would be taking different input modalities into account, such as mouse and touch interactions, which was not considered in our study.
A block cipher algorithm identification scheme based on hybrid k-nearest neighbor and random forest algorithm

Cryptographic algorithm identification, which refers to analyzing and identifying the encryption algorithm used in cryptographic system, is of great significance to cryptanalysis. In order to improve the accuracy of identification work, this article proposes a new ensemble learning-based model named hybrid k-nearest neighbor and random forest (HKNNRF), and constructs a block cipher algorithm identification scheme. In the ciphertext-only scenario, we use NIST randomness test methods to extract ciphertext features, and carry out binary-classification and five-classification experiments on the block cipher algorithms using proposed scheme. Experiments show that when the ciphertext size and other experimental conditions are the same, compared with the baselines, the HKNNRF model has higher classification accuracy. Specifically, the average binary-classification identification accuracy of HKNNRF is 69.5%, which is 13%, 12.5%, and 10% higher than the single-layer support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) respectively. The five-classification identification accuracy can reach 34%, which is higher than the 21% accuracy of KNN, the 22% accuracy of RF and the 23% accuracy of SVM respectively under the same experimental conditions.