Reviewerly/exHarmony huggingface dataset:
Using DNA Algorithm and Steganography to Secure a Compressed Text

Cryptography is a main item to generate confidence and empower services in the digital technology, while Steganography has vastly expanded in digital medias. Although developing these technologies hackers has established new methods to hack confidential information. To overcome these challenges, we proposed algorithm that encrypts a message by using DNA encryption algorithm, then multiply the output by a factor then compress the multiplying message by using GZIP algorithm, the last stage is hiding a compressed message in (LSB) image pixels. By using image measures such that PSNR, MSE, SSIM, NPCR, UACI and the ratio of data compression up to 75% where reduced the image difference, we obtained a good result when we combined these technologies. This work contributes to reducing the risk of information breach when sending and receiving confidential information between parties.
Multihead Self-attention and LSTM for Spacecraft Telemetry Anomaly Detection

In spacecraft operations, a significant amount of resources is invested on constant monitoring of the health parameters of various subsystems. Any deviation from the normal in the telemetry data remains a cause of serious concern and requires immediate attention and appropriate action. In this paper, Multihead Self Attention (MHSA) and Long Short-Term Memory (LSTM) blocks have been applied for detection of spacecraft anomalies using Nominal Behaviour Modelling approach of telemetry for different subsystems. The performance of the algorithms has been compared. The required threshold to classify a particular point as anomaly has been attained by employing Non-Parametric Dynamic Thresholding (NPDT) method.
Research on Block Image Texture Retrieval Method Based on Depth Hash

In order to improve the retrieval ability of super-resolution multi-space block images, a texture retrieval method of block images based on depth hash is proposed. A texture feature analysis model of super-resolution multi-dimensional partitioned images is constructed, which combines texture spatial structure mapping method to realize depth information fusion of partitioned images, adopts edge feature detection and texture sparse feature clustering to realize texture hierarchical structure feature decomposition of super-resolution multi-dimensional partitioned images, and adopts deep image parameter analysis method to construct pixel structure recombination model of multi-dimensional partitioned images. Multi-dimensional texture parameter structure analysis and information clustering are realized for the collected partitioned images in multi-dimensional space. According to the information clustering results, the texture retrieval and extraction of partitioned images are realized by using the deep hash fusion algorithm, and the information detection and feature recognition capabilities of partitioned images in multi-dimensional space are improved. Simulation results show that this method has higher precision and better feature resolution in texture retrieval of partitioned images in multidimensional space, which improves the texture retrieval and recognition ability of partitioned images.
Temporal Convolution Network Based on Attention for Intelligent Anomaly Detection of Wind Turbine Blades

With the concept of “carbon neutral”, wind power industry ushers in a golden period of development. Wind turbine blades are the key components of wind turbine, their normal operation directly affects the operation of the whole wind power generation. The traditional diagnosis method of wind turbine blades needs to disassemble the wind turbine blades. However, due to the potential of long-short-term memory network model and feedforward network based on attention mechanism in anomaly detection and diagnosis, sequence model has become the mainstream of wind turbine blades anomaly detection. Unfortunately, RNN needs to wait for the end of the forward transmission of the previous time step before the forward transmission of the next time step. It can only input all the historical information by default, and can not achieve the fine control of the input information. Therefore, this paper applies TCN-ATT, a temporal convolution network based on attention mechanism, to intelligent anomaly detection of wind turbine blades by combining dilation convolution, causal convolution, the skip connection of residual blocks and attention module. In this method, causal convolution and dilation convolution are used to adapt to the frame of multi-dimensional time series data of wind turbine blades, which provides more flexibility for changing the size of receptive field. At the same time, attention mechanism is added to capture the relevant features in the sequence, so as to improve the classification accuracy of anomaly detection of wind turbine blades. To verify the effectiveness of the system, this paper compares the performance of TCN-ATT with TCN, LSTM, GRU and other methods. The comparative test shows that TCN-ATT has better performance of feature extraction and anomaly detection. In addition, TCN-ATT is applied to multi fault anomaly detection, which also achieves high classification accuracy.
Power Flow Analysis in Urban Distribution Networks with Implementation of Grid-Connected Photovoltaic Systems

In the last few years, renewable energies became more socially and economically relevant, and among them, photovoltaic systems stand out. Residential self-consumption of electricity is a field with great potential, and implementation of grid-connected photovoltaic systems (GCPS) is in full rise. The installation of distributed generation systems in residential environments could alter the performance of low-voltage distribution networks, since these are designed for unidirectional power flow and adding these generators means fluctuations in power-flows. For these reasons, a study of the fundamental magnitudes of three low-voltage distribution networks located in Madrid was made for various photovoltaic penetration rates, making use of simulations via the software OpenDSS and subsequent analysis of results. The research concludes that, among other aspects, GCPS produce load flow variations that are dependent on: the penetration rates; the distance from the point of interest and the distribution transformer, increasing the voltage variation between the most productive hours and the night hours with that distance; and on the rate between consumption and generation, so that when it diminishes, the self-sufficiency of the system increases, and with it the voltage of all the buses that tend to the rated voltage. Moreover, there are wide seasonal fluctuations: specifically, in summer months, generation profiles override consumption fluctuations, while in winter months consumption guides voltage and power profiles. Both the code implemented and the results of the analysis were published in an open source website using a free software license.
Malarial Parasite Detection Based on Smartphone Microscopic Imaging Using Deep Learning Approach

Malaria is a dangerous and life-risking disease and sometimes leads to death. Microscopy examination was used for diagnosing malaria-infected cells in the early days. Due to a large number of samples for analysis and complexity of time, it may lead to false detection. More time consumption and false detection resulted in a great need for automated parasite detection systems. The proposed work aims to detect the malaria-infected images from microscopic blood smear images which are acquired by smartphones. Detection of malaria-infected images is done by using a convolutional neural network model called ResNet. In the proposed work, the deep learning approach is used to provide a more reliable diagnosis, specifically in resource-limited areas and it also reduces the cost of diagnosis. As the microscopic blood smear images are acquired by smartphones, it provides cost-effectiveness and is easy for gathering datasets with less time. It can also quickly transfer the blood smear images for early diagnosis. In the proposed work, the images are passed through a convolutional layer consisting of residual units which are defined by ReLu and Batch normalization. Finally, it is proceeded by fully connected layer to give the predicted output either malarial infected or uninfected images. The training and validation accuracy and loss graphs have been plotted and the performance metrics of the model have been evaluated.
Reducing Time Complexity of Fuzzy C Means Algorithm

The Fuzzy C-Means clustering technique is one of the most popular soft clustering algorithms in the field of data segmentation. However, its high time complexity makes it computationally expensive, when implemented on very large datasets. Kolen and Hutcheson [1] proposed a modification of the FCM Algorithm, which dramatically reduces the runtime of their algorithm, making it linear with respect to the number of clusters, as opposed to the original algorithm which was quadratic with respect to the number of clusters. This paper proposes further modification of the algorithm by Kolen et al., by suggesting effective seed initialisation (by Fuzzy C-Means++, proposed by Stetco et al. [2]) before feeding the initial cluster centers to the algorithm. The resultant model converges even faster. Empirical findings are illustrated using two synthetic and two real-world datasets.
A Novel GNN Model for Fraud Detection in Online Trading Activities

The previous graph neural network-based fraud detection techniques were usually realized by clustering the neighbors with different relationships. However, the graph-based datasets face the issues of imbalanced features, classifications, and relationships, which directly decreases the detection performance. In this case, this work proposes a novel real-time GNN model to address this issue. Firstly, the features are measured to find the entities which have the highest similarity to the fraudster. The entities are sampled to identify the fraudsters in training. The fraudsters are far less than the normal nodes in the dataset. We then combine the Under-Sampling algorithm and the long-distance sampling algorithm to find the nodes that are similar to the neighbors. Finally, a reinforcement learning (RL)-based reward and punishment mechanism is proposed for sampling the weight between the relationships. It is effective to the issue of imbalanced relationships in the graph-based dataset. Experiments show that the proposed technique is superior to the comparative models on the real-world fraud dataset.
Impossible Asks: Can the Transparency and Consent Framework Ever Authorise Real-Time Bidding After the Belgian DPA Decision?

Michael Veale, Midas Nouwens and Cristiana Santos, ‘Impossible Asks: Can the Transparency and Consent Framework Ever Authorise Real-Time Bidding After the Belgian DPA Decision?’ (2022) 2022 Technology and Regulation 12.On 2 February 2022, the Belgian Data Protection Authority handed down a decision concerning IAB Europe and its Transparency and Consent Framework (TCF), a system designed to facilitate compliance of real-time bidding (RTB), a widespread online advertising approach, with the GDPR. Here, we summarise and analyse this large, complex case. We argue that by characterising IAB Europe as a joint controller with RTB actors, this important decision gives DPAs an agreed-upon blueprint to deal with a structurally difficult enforcement challenge. Furthermore, under the DPA’s simple-looking remedial orders are deep technical and organisational tensions. We analyse these “impossible asks”, concluding that absent a fundamental change to RTB, IAB Europe will be unable to adapt the TCF to bring RTB into compliance with the decision.
How to Extend CTRT for AES-256 and AES-192

AONT (All-or-Nothing Transform) is a kind of (n, n)-threshold secret sharing scheme that distributes a message m into a set of n shares such that the message m can be reconstructed if and only if n shares are collected. At CRYPTO 2000, Desai proposed a simple and faster AONT based on the CTR mode of encryption (called CTRT) and proved its security in the ideal cipher model. Though AES-128, whose key length k = 128 and block length l = 128, can be used in CTRT as a block cipher, AES-256 and AES-192 cannot be used due to its intrinsic restriction of k ≤ l. In this paper, we propose an extended CTRT (for short, XCTRT) suitable for AES-256. By thoroughly evaluating all the tricky cases, we prove that XCTRT is secure in the ideal cipher model under the same CTRT security definition. Also, we discuss the security result of XCTRT in concrete parameter settings. For more flexibility of key length, we propose a variant of XCTRT dealing with l<k ≤ 2l by slightly modifying the construction of the last block. After showing implementation details and performance evaluation of CTRT, XCTRT, and the variant, we can say that our XCTRT and its variant have high-speed encoding and decoding performance and are quite practical enough to be deployed in real-world applications.
Two-in-One Secret Image Sharing Scheme with Higher Visual Quality of the Previewed Image

Secret image sharing (SIS) scheme is a secret image encoding and decoding scheme that reconstructs the secret image only if the number of participants is sufficient. In contrast, inadequate participants gain no information about the secret image. Two-in-one secret image sharing (TiOSIS) scheme is a kind of SIS scheme with two decoding options, quick preview without computation and accurate recovery with computer. For higher decoding speed, Li et al. proposed an improved two-in-one secret image sharing scheme, utilizing Boolean operation for less computational complexity, where the visual quality of the previewed image is deteriorated. In this paper, we use q-bit gray visual cryptography to build a mathematical model for better visual quality of the previewed image based on Li et al.’s TiOSIS scheme. The black sub-pixels of shadows corresponding to a black secret pixel are replaced by a q-bit grayscale value rather than an 8-bit grayscale value where q is a positive integer smaller than 8. The theoretical analysis and experiments are exhibited to guarantee feasibility and effectiveness of the proposed scheme.
Automating the Repair of Faulty Logical Theories

Abstract This thesis aims to develop a domain-independent system for repairing faulty Datalog-like theories by combining three existing techniques: abduction, belief revision, and conceptual change. Accordingly, the proposed system is named the ABC repair system (ABC). Given an observed assertion and a current theory, abduction adds axioms, which explain that observation by making the corresponding assertion derivable from the expanded theory. Belief revision incorporates a new piece of information which conflicts with the input theory by deleting old axioms. Conceptual change uses the reformation algorithm for blocking unwanted proofs or unblocking wanted proofs. The former two techniques change an axiom as a whole, while reformation changes the language in which the theory is written. These three techniques are complementary. But they have not previously been combined into one system. We are working on aligning these three techniques in ABC, which is capable of repairing logical theories with better result than each individual technique alone. In addition, ABC extends abduction and belief revision to operate on preconditions: the former deletes preconditions from rules, and the latter adds preconditions to rules. Datalog is used as the underlying logic of theories in this thesis, but the proposed system has the potential to be adapted to theories in other logics. Abstract prepared by Xue Li by taking directly from the thesis. E-mail : xuerr.lee@gmail.com
3 A linguistic sketch of Kanashi

This chapter presents a grammar sketch of Kanashi, covering its main features and contrasting it with its closely related and better described sister language Kinnauri.
An Improved Unordered Pair Bat Algorithm for Solving the Symmetrical Traveling Salesman Problem

Abstract Bat algorithm is an effective swarm intelligence optimization algorithm which is widely used to solve continuous optimization problems. But it still has some limitations in search process and can’t solve discrete optimization problems directly. Therefore, this paper introduces an unordered pair and proposes an unordered pair bat algorithm (UPBA) to make it more suitable for solving symmetric discrete traveling salesman problems. To verify the effectiveness of this method, the algorithm has been tested on 23 symmetric benchmarks and compared its performance with other algorithms. The results have shown that the proposed UPBA outperforms all the other alternatives significantly in most cases.
A Mirror Environment to Produce Artificial Intelligence Training Data

With the increasing maturity of artificial intelligence (AI) technology, business automation technology has also become a trend. Particularly, network operation and maintenance (O<monospace>&#x0026;</monospace>M) is expected to soon become automated and more efficient. However, the automation of O<monospace>&#x0026;</monospace>M is hindered by the lack of network failure data and the cost of collecting data. We thus propose an approach to build a low-cost environment that can produce the same data as the actual production environment and use tools such as chaos engineering to generate training models for fault data. This paper attempts to build the underlying physical network layer using a low-cost single-board computer Raspberry Pi instead of an expensive PC server, while keeping the virtual network layer the same and performing fault simulation, data collection, and AI model training on the constructed virtual network layer. A comparison of the accuracy of the trained AI models verifies the feasibility of replacing the traditional PC server with an inexpensive Raspberry Pi device while keeping the structure and services of the virtual network layer unchanged. Also, a brief comparison with existing techniques is discussed. Our proposed approach solves the problem of insufficient data for AI training while reducing cost and risk.
CREATION OF INFORMATION AND BIBLIOGRAPHIC RESOURCE "OUTSTANDING EDUCATORS OF UKRAINE AND THE WORLD" AND ITS INFLUENCE ON THE DEVELOPMENT OF DIGITAL HUMANITIES

The article reveals the specifics of creation, content, functions and value of the electronic information and bibliographic resource "Outstanding Educators of Ukraine and the World" of V. O. Sukhomlynskyi State Scientific and Pedagogical Library of the National Academy of Educational Sciences of Ukraine in a comparative dimension with similar Ukrainian and foreign electronic biographical resources. The goals of the development of the electronic biographical resource have been formulated; the specifics of formation of its content and structure have been characterized; the importance of electronic biographical resources in the domestic and foreign educational and scientific space as a component of digital humanities has been substantiated. The influence of the use of electronic biographical resources on public opinion and on a person in the educational dimension has been investigated. It has been proven that in the context of the digital transformation of the scientific sphere and the integration of the Ukrainian education into the European educational and scientific space, the development of electronic information and specialized bibliographic resources, dedicated to well-known personalities in the sphere of education, history and culture, got significant repercussions. We have substantiated why the electronic resource "Outstanding Educators of Ukraine and the World", containing information about Ukrainian and foreign educators and public figures, is a unique specialist resource without analogues. It is a significant contribution to the development of digital pedagogical biographics and it makes it possible, through the prism of scientific biographies, to trace systematically the development of the Ukrainian and foreign education, pedagogical thought in both macrohistorical and microhistorical dimensions and to contribute to the restoration of the national pedagogical memory. The representation via the electronic resource of the generalized knowledge about the humanistic ideas of the Ukrainian and foreign educators and public figures of the past is of great importance for education in general, in particular for pedagogical education in Ukraine, namely in the system of professional training; it is a source of formation of a pedagogical worldview in students who will be teachers in the future, increasing their spiritual culture in the face of global civilizational challenges. We have revealed that the information and bibliographic resource requires further updating, taking into account the new achievements of the Ukrainian and foreign historical, educational and biographical studies and the latest information technologies. The priority tasks for improving the electronic resource have been identified, in particular: the prospects for the content and the ways of updating information and making qualitative changes in the functionality of the electronic educational resource.
Face Detection in Unconstrained Environments Using Modified Multitask Cascade Convolutional Neural Network

Due to occlusion and variation of poses, facial detection is a challenging task to accomplish. In facial detection, occlusion has always been a standing challenge. Facial occlusion like sunglasses, scarf, and mask and pose variation are crucial factors that affect the performance of face detection. Undesirably, in the real-world scenario, occlusions are a very common situation that arises in the face detection and recognition problem. To deal with this problem, we put forward the modified multitask cascade convolutional neural network (M-MTCNN) with a slight modification. MTCNN is a trainable unit which may be included in present CNN architectures. With end-to-end training supervised by only the private identity labels, Mask Net learns a correct way of adaptively generating different feature map masks for various occluded face images. This paper deals with an efficient method for the detection of numerous occluded and pose variation faces. In addition to the marking of the face with a square box, there are five landmarks drawn (the two eyes, one in the nose, and two identifying the lips). Also, using the landmark points of the eyes, we have tried to mark the eyes using the eye landmarks as the center point. Using the landmarks of the lips, we also have drawn a straight line marking the edge of the lips. The presented method has been tasted on WIDER database and obtained efficient detection of multiple occluded faces.