Reviewerly/exHarmony huggingface dataset:
Learning Customised Decision Trees for Domain-knowledge Constraints

When applied to critical domains, machine learning models usually need to comply with prior knowledge and domain-specific requirements. For example, one may require that a learned decision tree model should be of limited size and fair, so as to be easily interpretable, trusted, and adopted. However, most state-of-the-art models, even on decision trees, only aim to maximising expected accuracy. In this paper, we propose a framework in which a diverse family of prior and domain knowledge can be formalised and imposed as constraints on decision trees. This framework is built upon a newly introduced tree representation that leads to two generic linear programming formulations of the optimal decision tree problem. The first one targets binary features, while the second one handles continuous features without the need for discretisation. We theoretically show how a diverse family of constraints can be formalised in our framework. We validate the framework with constraints on several applications and perform extensive experiments, demonstrating empirical evidence of comparable performance w.r.t. state-of-the-art tree learners.
Modified Generalized Feistel Network Block Cipher for the Internet of Things

With the advent of the Internet-of-Things (IoT) technologies, millions of low-resource devices are constantly used at the network’s edge. As a result, the large amount of private and sensitive data generated by these devices must be securely transported, stored, and processed, posing a challenge because these resource-constrained IoT devices cannot meet the criteria of conventional encryption ciphers. Due to this limitation on IoT-enabled devices, lightweight cryptography has emerged as a new area of study. Lightweight block ciphers, a subfield of lightweight cryptography, include the substitution–permutation network (SPN) and Feistel-based networks. Feistel networks are further divided into two types: classical Feistel networks and generalized Feistel networks (GFN). While classical Feistel ciphers divide a message into two sub-blocks, GFN divides a message into k sub-blocks for some k &gt; 2 called the partition number. One popular form of GFN is the so-called Type-II. Unfortunately, this type of Feistel structure needs a large number of rounds to obtain a full diffusion property. A full diffusion means all output sub-blocks are affected by all input sub-blocks. Therefore, this paper proposed a new lightweight block cipher by modifying the GFN structure, focusing on providing optimal security to the cipher with a small number of rounds. The algorithm was subjected to a series of statistical and cryptographic randomization analyses in order to investigate the avalanche effect on the ciphertext and the algorithm’s random properties, such as confusion, diffusion, and independence. The avalanche criterion and output randomness results show that this algorithm meets the fundamental security requirement for a lightweight block cipher.
Quality assessment of higher resolution images and videos with remote testing

In many research fields, human-annotated data plays an important role as it is used to accomplish a multitude of tasks. One such example is in the field of multimedia quality assessment where subjective annotations can be used to train or evaluate quality prediction models. Lab-based tests could be one approach to get such quality annotations. They are usually performed in well-defined and controlled environments to ensure high reliability. However, this high reliability comes at a cost of higher time consumption and costs incurred. To mitigate this, crowd or online tests could be used. Usually, online tests cover a wider range of end devices, environmental conditions, or participants, which may have an impact on the ratings. To verify whether such online tests can be used for visual quality assessment, we designed three online tests. These online tests are based on previously conducted lab tests as this enables comparison of the results of both test paradigms. Our focus is on the quality assessment of high-resolution images and videos. The online tests use AVrate Voyager, which is a publicly accessible framework for online tests. To transform the lab tests into online tests, dedicated adaptations in the test methodologies are required. The considered modifications are, for example, a patch-based or centre cropping of the images and videos, or a randomly sub-sampling of the to-be-rated stimuli. Based on the analysis of the test results in terms of correlation and SOS analysis it is shown that online tests can be used as a reliable replacement for lab tests albeit with some limitations. These limitations relate to, e.g., lack of appropriate display devices, limitation of web technologies, and modern browsers considering support for different video codecs and formats.
An Improved Ship Classification Method Based on YOLOv7 Model with Attention Mechanism

Deep learning (DL) is widely used in ship detection, but there are still some problems in the effective classification, such as inaccurate object feature extraction and inconspicuous feature information in deep layers. To address these problems, we propose a YOLOv7-residual convolutional block attention module (YOLOv7-RCBAM) by combining the convolutional attention mechanism and residual connections to the YOLOv7. First, to accelerate the training speed, the parameters in the backbone network of the pretrained model are frozen by using transfer learning, and the model is fine-tuned for training. Second, to enhance the information relevance of channel dimensional features, an attention mechanism with residual connectivity is adopted. Finally, a feature fusion attention mechanism is introduced to improve the effective feature extraction. The effectiveness of the proposed method is fully validated on the SeaShips dataset. The results show that the YOLOv7-RCBAM model achieves better performance with a 97.59% in <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mtext>mAP</mtext> </math> and effectively extracts object feature in deep layers. Meanwhile, the YOLOv7-RCBAM model can accurately locate ship in complex environments with darkness and noise with the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mtext>mAP</mtext> </math> reaching 96.13% to achieve effective ship classification detection.
The Paradoxes of Trust and Transparency of Blockchain Technologies

The Republic and Canton of Jura has decided to adopt and integrate the CERTUS digital seal and the KSI blockchain as the central technological components of their e-government portal and of their digital government transformation program. However, this project is taking place in turbulent times. While blockchain suffers from a bad reputation for ecological reasons and while the Swiss Confederation has rejected the selected blockchain solution, this article seeks to answer how to grasp the paradoxes of blockchain technologies acceptance by state citizens in an extreme management situation. Through an action research that aims to solve practical problems and create scientific knowledge, this study highlights the paradox of trust and considers blockchain technologies to enhance trust between citizens in politically stable countries. Furthermore, it investigates the paradox of transparency of blockchain technologies in an extreme management situation and proposes communicating in two levels of abstraction to limit tensions in turbulent times.
An assurance process for Big Data trustworthiness

Modern (industrial) domains are based on large digital ecosystems where huge amounts of data and information need to be collected, shared, and analyzed by multiple actors working within and across organizational boundaries. This data-driven ecosystem poses strong requirements on data management and data analysis, as well as on data protection and system trustworthiness. However, although Big Data has reached its functional maturity and represents a key enabler for enterprises to compete in the global market, the assurance and trustworthiness of Big Data computations (e.g., security, privacy) are still in their infancy. While functionally appealing, Big Data does not provide a transparent environment with clear non-functional properties, impairing the users' ability to evaluate its behavior and clashing with modern data-privacy regulations. In this paper, we present a novel assurance process for Big Data, which evaluates the Big Data pipelines, and the Big Data ecosystem underneath, to provide a comprehensive measure of their trustworthiness. To the best of our knowledge, this approach is the first attempt to address the general problem of Big Data trustworthiness in an holistic way. We experimentally evaluate our solution in a real Big Data Analytics-as-a-Service environment, first presenting a detailed walkthrough evaluation, and then showing its feasibility and negligible performance overhead (i.e., approx 1 min).
Constructions of cyclic codes and extended primitive cyclic codes with their applications

Linear codes with a few weights have many nice applications including combinatorial design, distributed storage system, secret sharing schemes and so on. In this paper, we construct two families of linear codes with a few weights based on special polynomials over finite fields. The first family of linear codes are extended primitive cyclic codes which are affine-invariant. The second family of linear codes are reducible cyclic codes. The parameters of these codes and their duals are determined. As the first application, we prove that these two families of linear codes hold t-designs, where t=2,3. As the second application, the minimum localities of the codes are also determined and optimal locally recoverable codes are derived.
Local multi-scale feature aggregation network for real-time image dehazing

Haze causes visual degradation and obscures image information, which gravely affects the reliability of computer vision tasks in real-time systems. Leveraging an enormous number of learning parameters as the restoration costs, learning-based methods have gained significant success, but they are runtime intensive or memory inefficient. In this paper, we propose a local multi-scale feature aggregation network, called LMFA-Net, which has a lightweight model structure and can be used for real-time dehazing. By learning the local mapping relationship between the clean value of a haze image at a certain point and its surrounding local region, LMFA-Net can directly restore the final haze-free image. In particular, we adopt a novel multi-scale feature extraction sub-network (M-Net) to extract features from different scales. As a lightweight network, LMFA-Net can achieve fast and efficient dehazing. Extensive experiments demonstrate that our proposed LMFA-Net surpasses previous state-of-the-art lightweight dehazing methods in both quantitatively and qualitatively.
Deep and Hybrid Learning Techniques for Diagnosing Microscopic Blood Samples for Early Detection of White Blood Cell Diseases

The immune system is one of the most critical systems in humans that resists all diseases and protects the body from viruses, bacteria, etc. White blood cells (WBCs) play an essential role in the immune system. To diagnose blood diseases, doctors analyze blood samples to characterize the features of WBCs. The characteristics of WBCs are determined based on the chromatic, geometric, and textural characteristics of the WBC nucleus. Manual diagnosis is subject to many errors and differing opinions of experts and takes a long time; however, artificial intelligence techniques can help to solve all these challenges. Determining the type of WBC using automatic diagnosis helps hematologists to identify different types of blood diseases. This work aims to overcome manual diagnosis by developing automated systems for classifying microscopic blood sample datasets for the early detection of diseases in WBCs. Several proposed systems were used: first, neural network algorithms, such as artificial neural networks (ANNs) and feed-forward neural networks (FFNNs), were applied to diagnose the dataset based on the features extracted using the hybrid method between two algorithms, the local binary pattern (LBP) and gray-level co-occurrence matrix (GLCM). All algorithms attained superior accuracy for WBC diagnosis. Second, the pre-trained convolutional neural network (CNN) models AlexNet, ResNet-50, GoogLeNet, and ResNet-18 were applied for the early detection of WBC diseases. All models attained exceptional results in the early detection of WBC diseases. Third, the hybrid technique was applied, consisting of a pair of blocks: the CNN models block for extracting deep features and the SVM algorithm block for the classification of deep features with superior accuracy and efficiency. These hybrid techniques are named AlexNet with SVM, ResNet-50 with SVM, GoogLeNet with SVM, and ResNet-18 with SVM. All techniques achieved promising results when diagnosing the dataset for the early detection of WBC diseases. The ResNet-50 model achieved an accuracy of 99.3%, a precision of 99.5%, a sensitivity of 99.25%, a specificity of 99.75%, and an AUC of 99.99%.
How we use automatic speaker comparison in forensic practice

Automatic methods are rapidly gaining ground in forensic speaker comparison, next to the existing auditory-acoustic methodology, performed by human experts with an academic background in phonetics. In this article we set out the steps that were taken before we could introduce the automatic method and start combining the two methods (software and human) in casework. We further provide a comprehensive explanation of the automatic method (originally written for readers of forensic reports) in the appendix. We discuss the legal reception of the combined approach, based on a court ruling in an appeal case in which the reliability of the speaker comparison was challenged by the defence. We also address the important issue of how conflicting results from the two methods may be dealt with in practice.
Deep Learning Approaches to Osteosarcoma Diagnosis and Classification: A Comparative Methodological Approach

Osteosarcoma is the most common primary malignancy of the bone, being most prevalent in childhood and adolescence. Despite recent progress in diagnostic methods, histopathology remains the gold standard for disease staging and therapy decisions. Machine learning and deep learning methods have shown potential for evaluating and classifying histopathological cross-sections.This study used publicly available images of osteosarcoma cross-sections to analyze and compare the performance of state-of-the-art deep neural networks for histopathological evaluation of osteosarcomas.The classification performance did not necessarily improve when using larger networks on our dataset. In fact, the smallest network combined with the smallest image input size achieved the best overall performance. When trained using 5-fold cross-validation, the MobileNetV2 network achieved 91% overall accuracy.The present study highlights the importance of careful selection of network and input image size. Our results indicate that a larger number of parameters is not always better, and the best results can be achieved on smaller and more efficient networks. The identification of an optimal network and training configuration could greatly improve the accuracy of osteosarcoma diagnoses and ultimately lead to better disease outcomes for patients.
Application of Scenario Forecasting Methods and Fuzzy Multi-Criteria Modeling in Substantiation of Urban Area Development Strategies

The existing approaches to supporting the tasks of managing the urban areas development are aimed at choosing an alternative from a set of ready-made solutions. Little attention is paid to the procedure for the formation and analysis of acceptable options for the use of territories. The study's purpose is to understand how various factors affect the efficiency of using the city’s territory. In addition, we are trying to use this understanding to assess the possible consequences of the implementation of management decisions on the territory transformation. We use the method of structuring knowledge about the study area, taking into account the influence of the external environment. This method implements the significant factors list formation and assessment of their impact on development. Fuzzy cognitive modeling was used to build scenarios for identifying contradictions in achieving sustainable development goals. The scenario modeling results are necessary for the formation of the alternative. Alternatives are evaluated on the basis of fuzzy multi-criteria optimization. The integration of methods makes it possible to increase the objectivity of the analysis of strategies for urban areas development. The Belman-Zadeh method is used to analyze the selected options based on criteria that determine the feasibility and effectiveness of each project.
A novel adaptive marker segmentation graph convolutional network for aspect-level sentiment analysis

Aspect-level sentiment analysis is a fine-grained sentiment classification task that aims to identify the sentiment polarity of specific aspects in online reviews. Attention mechanisms and graph convolutional networks have recently been widely used to model associations between aspects and opinion words. However, these methods face challenges in accurately modeling the alignment of aspects and exploiting multiaspect sentiment dependencies due to the limitations of dependency trees and the complexities of online reviews. In this paper, we propose a novel adaptive marker segmentation graph convolutional network (AMS-GCN) for aspect-level sentiment analysis. Specifically, the proposed AMS-GCN model enhances the information capacity of words by merging marker information from two datasets and uses an adaptive marker segmentation module to divide different marker information into separate modules. Furthermore, the model employs bi-syntax-aware and semantic auxiliary modules to obtain syntactic and semantic information. The bi-syntax-aware module combines component and dependency trees to capture comprehensive syntactic information. In contrast, the semantic auxiliary module uses an attention score matrix to capture the semantic association information of each word. Moreover, the aspect-related graph is devised to aggregate information about the sentiment of different aspects. Experiments on several benchmark datasets demonstrate that the proposed model achieves state-of-the-art results.
A Wearable Gesture Recognition System With Ultrahigh Accuracy and Robustness Enabled by the Synergy of Multiple Fabric Sensing Devices

Recently, numerous wearable devices for gesture recognition have been reported; however, most of them are fabricated by assembling the separately designed sensing layers with the substrate through linking agents, which will lead to the shifting or detaching of the sensing layers after long-duration use. Subsequently, over-reliance on multiple filtering algorithms to smooth the collected datasets becomes very popular, unavoidably resulting in the loss of data features and overfitting. In our point of view, it is much more crucial and effective for gesture recognition performance to reinforce the stability of the sensing layers. Therefore, in this work, we designed a type of integrated MXene/polyurethane (I-M/PU) sensor based on textile fabrics and further prepared a data glove, a smart wristband, and a smart elbow pad. A wearable system for gesture recognition with ultrahigh sensitivity to strain, as well as excellent stability and durability, is successfully realized by the synergy of those three devices. The collected original data by the integrated sensors were steady with specific features, making the simplification of the classification model feasible. Finally, 19 static gestures (including nine complex gestures) with a recognition accuracy of 100% in 6998 data samples have been impressively achieved by the simple multilayer perceptron (MLP) algorithm, which is the state-of-art best result as we know in the gesture recognition reports without any assistant computer vision algorithms, demonstrating our solution with a promising prospect in virtual reality (VR) and human–machine interaction (HMI) fields.
An Elliptical Modeling Supported System for Human Action Deep Recognition Over Aerial Surveillance

The advancement of computer vision technology has led to the development of sophisticated algorithms capable of accurately recognizing human actions from red-green-blue videos recorded by drone cameras.Hence, possessing an exceptional potential, human action recognition also faces many challenges including, tendency of humans to perform the same action in different ways, limited camera angles, and field of view.In this research article, a system has been proposed to tackle the forementioned challenges by using red-green-blue videos as input while the videos were recorded by drone cameras.First of all, the video was split into its constituent frames and then gamma correction was applied on each frame to obtain an optimized version of the image.Then the Felzenszwalb's algorithm performed the segmentation to segment out human from the input image and human silhouette was generated.Utilizing the silhouette, skeleton was extracted to spot thirteen body key points.The key points were then used to perform elliptical modeling to estimate the individual boundaries of the body parts while the elliptical modeling was governed by the Gaussian mixture model-expectation maximization algorithm.The elliptical models of the body parts were utilized to spot fiducial points that if tracked, could provide very useful information about the performed action.Some other features that were extracted for this study include, the 3d point cloud feature vector, relative distance and velocity of the key-points, and their mutual angles.The features were then forwarded for optimization under a quadratic discriminant analysis and finally, a convolutional neural network was trained to perform the action classification.Three benchmark datasets including, the Drone-Action dataset, the UAV-Human dataset, and the Okutama-Action dataset were used for a comprehensive experimentation.The system outperformed the state-of-the-art approaches by securing accuracies of 80.03%, 48.60%, and 78.01%over the Drone-Action dataset, the UAV-Human dataset, and the Okutama-Action dataset respectively.
An RGB Color Image Steganography Scheme by Binary Lower Triangular Matrix

Steganography is an area where researchers focus on how to develop novel hiding techniques that are aided by the mathematical or computational model. In order to conceal the secret bits in a cover image, the imperceptible property and embedding payload capacity must be maintained. In most of the developed methods, some selective least significant bits (LSBs) of each pixel of the cover image is modified to hide the data which may lead to substantial distortion in the stego-image especially when the payload size is large. Therefore, to attain the trade-off between payload capacity and image quality, the authors have devised a binary lower triangular matrix-based secret message embedding process in which data can be hidden in the color pixel. In this secret message embedding process, the secret data is embedded aided by the binary lower triangular matrix. The formulated mathematical model for the secret message embedding process has ensured a secure embedding technique with reduced aberration on the stego-image. The proposed steganography scheme is preserving significant visual quality while emphasizing the security aspect by adopting an indirect data-hiding mechanism in contrast to the conventional mechanism. Furthermore, for the betterment of the visual properties of the stego-image, we have generically extended the formulation and tested it to check the visual quality. The empirical results of the proposed steganography scheme depict outcomes satisfactorily for the RGB color images. Researchers can explore and apply this novel data-hiding scheme in different data communication applications.