Reviewerly/exHarmony huggingface dataset:
An Empirical study on designing a security framework for legacy system evolution towards SOA

SOA has become popular in current; the majority of legacy systems are still not SOA enabled.Equally with the legacy system evolution, SOA have to improve a safer approach for implementing and maintaining its logical security.They consume numerous famous difficulties e.g., uncompromising, field undefined, and rigid to sustain, then is familiar by software systems.SOA holds of being informal to practice, flexible and recyclable.The growth in the number of facts that concerns must switch has caused in a substantial rise in the effort of the legacy schemes that supply this information.Henceforth energy is rising to progress legacy schemes in the direction of SOA.The progress of Service-Oriented Architecture is preferred as numerous returns with fine recognized groups of exposed values, platform and language independents boundaries, and strong separation of facility interface and execution of loose-coupling between amenities.Several techniques exist for modernizing legacy systems towards SOA.With the advantage, SOA is also endowed with ease for message tampering and unauthorized access.Here, we present the numerous methods to affecting legacy schemes to the SOA atmosphere.Around important growths popular inheritance to SOA development, besides that consumes caused in huge examine overview.The main trainings were originated and assessed by means of a security framework and upgrading approaches extensively used in the software re-engineering field.This paper confers the importance of exploitation SOA and classifies the errors in the present security framework platforms, and the record of present investigate approaches and practices castoff in legacy to Service-Oriented Architecture growth.
Industrial Techno and SID Sound Design in the Commodore 64 Game<i>Slipstream</i>

Slipstream is a rail shooter developed by the group Bauknecht. The soundtrack, composed by Ronny Engmann, accentuates the game’s futuristic space setting and emphasis on speed by invoking the electronic dance music style of industrial techno, which combines the taut grooves of minimal techno with the abrasive sonics of early industrial music to make for an intensification of the sound that made the club scenes of Detroit and Berlin famous. Despite the Commodore 64 being a supposedly obsolete 8-bit platform that had its manufacture stopped decades ago, contemporary composers like Engmann are compelled to use the machine for its SID (Sound Interface Device) chip, which stands apart from the sound chips of other 8-bit home computers and consoles due to its variety of waveform types, filtering capabilities, ring modulation, and oscillator sync effects. The skills that Engmann developed while exploring the affordances of this versatile chip enabled him to compose a video game soundtrack that is informed by his engagement with the Berlin techno scene from the early 1990s onward. Drawing from ethnographic interviews with Engmann and Slipstream’s programmer Stefan Mader, this article demonstrates how techno’s pivotal theme of synergistic existence of humanity with technology resonates with an ecstatic feeling of forward propulsion that characterizes Slipstream. Focusing on the musical dimension of timbre and addressing the practical issues of modern cross-platform development through an analysis of Engmann’s project files for the Commodore 64 music tool GoatTracker, this article shows that Slipstream’s soundtrack is exemplary in effectively harnessing electronic dance music as video game music.
Disentangled Representation Learning for Privacy-Preserving Case-Based Explanations

The lack of interpretability of Deep Learning models hinders their deployment in clinical contexts. Case-based explanations can be used to justify these models’ decisions and improve their trustworthiness. However, providing medical cases as explanations may threaten the privacy of patients. We propose a generative adversarial network to disentangle identity and medical features from images. Using this network, we can alter the identity of an image to anonymize it while preserving relevant explanatory features. As a proof of concept, we apply the proposed model to biometric and medical datasets, demonstrating its capacity to anonymize medical images while preserving explanatory evidence and a reasonable level of intelligibility. Finally, we demonstrate that the model is inherently capable of generating counterfactual explanations.
In-Group Bias in Deep Learning-Based Face Recognition Models Due to Ethnicity and Age

Humans are known to favor other individuals who exist in similar groups as them, exhibiting biased behavior, which is termed as in-group bias. The groups could be formed on the basis of ethnicity, age, or even a favorite sports team. Taking cues from aforementioned observation, we inspect if deep learning networks also mimic this human behavior, and are affected by in-group and out-group biases. In this first of its kind research, the behavior of face recognition models is evaluated to understand if similar to humans, models also encode group-specific features for face recognition, along with where bias is encoded in these models. Analysis has been performed for two use-cases of bias: age and ethnicity in face recognition models. Thorough experimental evaluation leads us to several insights: (i) deep learning models focus on different facial regions for different ethnic groups and age groups, and (ii) large variation in face verification performance is also observed across different sub-groups for both known and our own trained deep networks. Based on the observations, a novel bias index is presented for evaluating a trained model’s level of bias. We believe that a better understanding of how deep learning models work and encode bias, along with the proposed bias index would enable researchers to address the challenge of bias in AI, and develop more robust and fairer algorithms for mitigating bias as well as developing fairer models.
Learning to Play <i>Koi-Koi</i> Hanafuda Card Games With Transformers

Card games are regarded as an idealized model for many real-world problems for their rich hidden information and strategic decision-making process. It provides a fertile environment for artificial intelligence (AI), especially reinforcement learning (RL) algorithms. With the boom of deep neural networks, increasing breakthroughs have been made in this challenging domain. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Koi-Koi</i> is a traditional two-player imperfect-information playing card game. However, due to its unique deck and complex rules, related researches are mostly based on handcrafted features and the custom network architecture. In this article, we design a more general AI framework, relying a transformer encoder as the network backbone with tokenized card state input, which is trained by the Monte-Carlo RL with phased round reward. Experimental results show that our AI achieves a winning rate of 53% and +2.02 average difference point versus experienced human players in multiround <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Koi-Koi</i> games. Moreover, with the aid of attention mechanism, we provide a novel view for analyzing the playing strategy. Such framework design can be applied to various card games.
Optimal Linear MMSE Estimation Under Correlation Uncertainty in Restricted Bayesian Framework

A restricted Bayes approach is proposed for linear estimation of a scalar random parameter based on a scalar observation under uncertainty regarding the correlation between the parameter and the observation. In particular, the optimal linear estimator that minimizes the average mean-squared error (MSE) is derived under a constraint on the worst-case MSE by considering possible values of the correlation coefficient and its probability distribution. A closed-form expression is derived for the optimal linear estimator in the proposed restricted Bayesian framework by considering a generic statistical characterization of the correlation coefficient. Performance of the proposed estimator is evaluated via numerical examples and its benefits are illustrated in various scenarios. The proposed framework is also extended to the case of vector-valued observation and the properties of the optimal linear estimator are characterized.
Applying Disentanglement in the Medical Domain: An Introduction for the MAD Workshop

For medical applications, trustworthiness, interpretability, and robustness are necessary properties of (deep) neural networks. For generative models, one approach towards this could be analyzing and structuring the latent space representation. In this context, the term disentanglement is often used, but still not uniquely defined. In 2022, we organized the first workshop about Medical Applications with Disentanglements (MAD) at the MICCAI conference in Singapore ( https://mad.ikim.nrw/ ). The workshop had a general call for disentanglement papers in the medical field and the submitted papers are published in a Springer challenge proceedings. The aim of the introduction paper of this proceeding is to present the necessary background information for them. Thus, we give a short overview of this field and how challenges for deep learning in healthcare could be addressed with the help of disentanglement.
Subspace Clustering via Adaptive Non-Negative Representation Learning and Its Application to Image Segmentation

Self-representation subspace clustering based on graphs has the merits of capability and efficiency. However, the graph built by the self-representation methods has two issues: (i) usually lacking conciseness and informativeness due to the negative representation coefficients. (ii) no guarantee of an overall optimal solution due to the separation of representation learning and graph construction. To alleviate these issues, we propose a novel subspace clustering via learning non-negative representation with an adaptive graph. Specifically, we explicitly impose the non-negative constraint on the self-representation learning, ensuring that each data point is approximated from a group of homogeneous samples and enhancing the distinguishability of data representation. Meanwhile, an adaptive graph is developed so that both representation and the geometric structure of data are simultaneously learned in a unified procedure. Moreover, the learned representation is less sensitive to data noise imposed by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> -norm, so the adaptive graph will be further improved. An efficient optimization procedure is developed to find the optimal solution. Extensive experiments on subspace clustering and the extension application to image segmentation validate the advantages of our method against state-of-the-art methods.
A-BEBLID: A Hybrid Image Registration Method for Lithium-Ion Battery Cover Screen Printing

To address the problem of miss- and false detection during quality inspection of lithium-ion battery cover screen printing (LBCSP), we propose a hybrid image registration method using a point-based feature extraction algorithm and nonlinear-scale space construction. Our proposed method integrates the accelerated-KAZE algorithm with the boosted efficient binary local image descriptor (BEBLID), and is named A-BEBLID. Facing the challenge of the inevitable offset caused by machine vibration during production, we combine a nonlinear diffusion filter with a local image descriptor to extract features from images, and then use the grid-based motion statistics algorithm to remove the incorrect matching pairs. We tested the method on a custom dataset created using images taken from actual lithium-ion battery production lines, named LBCSP. We also evaluated the method on the public HPatches dataset. The average precision achieved by A-BEBLID on the LBCSP dataset is 89% (threshold: 2 pixels), with a localization error of 1.11 pixels, while on the HPatches dataset, the average precision is 73% (threshold: 2 pixels), with a localization error of 1.52 pixels. Comprehensive experimental results also showed that the proposed A-BEBLID can outperform other approaches found in the literature. The method can be further applied to other industry scenarios with similar image registration requirements.
Instance-Specific Augmentation of Brain MRIs with Variational Autoencoders

Spatial data augmentation is a standard technique for regularizing deep segmentation networks that are tasked with localizing medical abnormalities. However, a typical spatial augmentation scheme is built upon ad hoc selections of spatial transformation parameters which are not determined by the data set and therefore may not capture spatial variations in the data. For segmentation networks trained in the low-data regime, these ad hoc transformation techniques often fail to encourage better generalization. To address this problem, we propose a variational autoencoder framework for spatial data augmentation. We show how this framework provides a natural, data-driven approach to probabilistic, instance-specific spatial augmentation. Further, we observe that U-Nets trained on data augmented using this framework compare favorably with U-Nets trained using standard spatial augmentation methods.
Leroy and Blazy Were Right: Their Memory Model Soundness Proof is Automatable

Xavier Leroy and Sandrine Blazy in 2007 conducted a formal verification, using the Coq proof assistant, of a memory model for low-level imperative languages such as C. Considering their formalization was performed essentially in first-order logic, one question left open by the authors was whether their proofs could be automated using a verification framework for first-order logic. We took the challenge and automated their formalization using Why3, significantly reducing the proof effort. We systematically followed the Coq proofs and realized that in many cases at around one third of the way Why3 was able to discharge all VCs. Furthermore, the proofs still requiring interactions (e.g. induction, witnesses for existential proofs, assertions) were factorized isolating auxiliary results that we stated explicitly. In this way, we achieved an almost-automatic soundness and safety proof of the memory model. Nonetheless, our development allows an extraction of a correct-by-construction concrete memory model, going thus further than the preliminary Why version of Leroy and Blazy.
A teacher-student based attention network for fine-grained image recognition

Fine-grained image recognition (FGIR) task is dedicated to distinguishing similar sub-categories that belong to the same super-category, such as bird species and car types. In order to highlight visual differences, existing FGIR works often follow two steps: discriminative sub-region localization and local feature representation. However, these works pay less attention on global context information. They neglect a fact that the subtle visual difference in challenging scenarios can be highlighted through exploiting the spatial relationship among different sub-regions from a global view point. Therefore, in this paper, we consider both global and local information for FGIR, and propose a collaborative teacher-student strategy to reinforce and unity the two types of information. Our framework is implemented mainly by convolutional neural network, referred to Teacher-Student Based Attention Convolutional Neural Network (T-S-ACNN). For fine-grained local information, we choose the classic multi-attention network (MA-Net) as our baseline, and propose a type of boundary constraint to further reduce background noises in the local attention maps. In this way, the discriminative sub-regions tend to appear in the area occupied by fine-grained objects, leading to more accurate sub-region localization. For fine-grained global information, we design a graph convolution based global attention network (GA-Net), which can combine extracted local attention maps from MA-Net with non-local techniques to explore spatial relationship among sub-regions. At last, we develop a collaborative teacher-student strategy to adaptively determine the attended roles and optimization modes, so as to the cooperative reinforcement of MA-Net and GA-Net. Extensive experiments on CUB-200-2011, Stanford Cars and FGVC Aircraft datasets illustrate the promising performance of our framework. © 2022 Published by Elsevier Ltd.
Twofold dynamic attention guided deep network and noise-aware mechanism for image denoising

Convolutional neural networks are given extensive attention towards noise removal due to their good performance over traditional denoising algorithms. With shallow conventional neural networks, the feature extraction ability is not profound. While employing deeper networks, network performance improves with the cost of additional computational requirements. In this paper, we propose an attention-guided twofold denoising network to remove the noise present in the image. The proposed network incorporates dilation convolution to enlarge the receptive fields and improves the feature extraction ability. Also, the presence of attention mechanism strengthens the extracted features and restores the image details during the noise removal. To demonstrate the superiority of the twofold structure, the proposed network is compared with the state-of-the-art denoising models. The experimental results prove that the proposed deep network achieves good peak signal to noise ratio and structural similarity index for different noise levels.
Fuzzy Approach to Object-Detection-Based Image Retrieval

In the paper, a method for image search is proposed. It allows for finding images starting from a text query that contains names of object classes and their expected spatial relations. The input query containing the description of desired objects and their spatial relations is used to select and score relevant images. Next, the score is used to sort the output images putting more relevant ones on the top of the list. The proposed approach is based on object detection methods and a fuzzy approach describing the position of detected objects in relation to other ones. The method may be used to retrieve images from databases containing images with metadata produced by object detectors.
Adaptive Binarization of Metal Nameplate Images Using the Pixel Voting Approach

In the paper, an application of the recently proposed approach to hybrid image binarization based on pixel voting is considered for industrial images. Since such images typically contain the text embossed or engraved in metal nameplates, often non-uniformly illuminated, a proper binarization of such images is usually much harder than for scanned document images, or even for the photos of text documents. Assuming that no single method would be the best solution for such images, a hybrid solution, based on the combination of multiple algorithms using pixel voting, has been recently proposed for document images. The obtained experimental results for the dataset of “industrial” images confirm the usefulness of this approach and the proposed combinations of previously developed algorithms outperform the other methods, making it possible to increase the OCR accuracy also for demanding images containing light reflections and shadows.
On the Influence of Image Features on the Performance of Deep Learning Models in Human-Object Interaction Detection

In recent years Human Object Interaction (HOI) detection has experienced rapid performance growth mainly due to the development of various deep learning-based methods and algorithms. One of the most popular approaches to this task is three-stream architecture consisting of three processing paths: human-stream, object-stream, and relation-stream, gathering information on the scene using features extracted from particular image elements. The total amount of features depends on the detailed processing schemes and model parameters. The increase in the number of features does not necessarily increase the final efficiency of the HOI detection method. This paper focuses on the relation between the quantity of features used to detect HOI and its predictive efficiency. In our study, we investigate the influence of the number of features on the final detection results. Several experiments were conducted to validate various model configurations of different features considered.
On Multi-stream Classification of Two Person Interactions in Video with Skeleton-Based Features

A method of human skeleton-tracking and -refinement, and feature extraction for two-person interaction recognition in video is proposed. Its purpose is to properly reassign the same person-representing skeletons, approximate the missing joints and extract meaningful relational features. In addition, based on the created feature streams, two different multi-stream deep neural networks are designed to perform data transformation and interaction classification. They provide different relations between model complexity and performance quality. The first one is an ensemble of “weak” pose-based action classifiers, which are trained on different time-phases of an interaction. At the same time, the overall classification result is a time-driven aggregation of weighted combinations of their results. In the second approach, three input feature streams were created, which fed a triple-stream LSTM network. Both network models were trained and tested on the interaction subset of the NTU RGB+D data set, showing comparable performance with the best reported CNN- and Graphic CNN-based classifiers.
Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation

Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate both in real-time and with minimal energy consumption (e.g., for wearable devices or autonomous vehicles, edge Internet of Things (IoT), sensor networks), various network optimisation techniques are used, e.g., quantisation, pruning, or dedicated lightweight architectures. Due to the logarithmic distribution of weights in neural network layers, a method providing high performance with significant reduction in computational precision (for 4-bit weights and less) is the Power-of-Two (PoT) quantisation (and therefore also with a logarithmic distribution). This method introduces additional possibilities of replacing the typical for neural networks Multiply and ACcumulate (MAC—performing, e.g., convolution operations) units, with more energy-efficient Bitshift and ACcumulate (BAC). In this paper, we show that a hardware neural network accelerator with PoT weights implemented on the Zynq UltraScale + MPSoC ZCU104 SoC FPGA can be at least 1.4x more energy efficient than the uniform quantisation version. To further reduce the actual power requirement by omitting part of the computation for zero weights, we also propose a new pruning method adapted to logarithmic quantisation.