Reviewerly/exHarmony huggingface dataset:
New extremal binary self-dual codes from block circulant matrices and block quadratic residue circulant matrices

In this paper, we construct self-dual codes from a construction that involves both block circulant matrices and block quadratic residue circulant matrices. We provide conditions when this construction can yield self-dual codes. We construct self-dual codes of various lengths over F2 and F2+uF2. Using extensions, neighbours and sequences of neighbours, we construct many new self-dual codes. In particular, we construct one new self-dual code of length 66 and 51 new self-dual codes of length 68.
Prioritizing Python Code Smells for Efficient Refactoring Using Multi-criteria Decision-Making Approach

Software is subjected to regular modifications for the fulfilment of new specifications of the end user; which might lead to design issues in the software. The cost of maintaining a good quality software tends to be toilsome when design issues such as code smells are involved enormously. The best solution to rectify code smell is to redesign the code through refactoring without compromising its functionality. This research proposes a ranking order of considered code smells for realizing the priority for cost-effective and efficient refactoring process using the Multi-Criteria Decision-Making (MCDM) approach. The considered five Python-based code smells (Cognitive Complexity, Collapsible "IF", Many Parameter, Naming Convention, Unused Variable) have been examined over 10552 classes of 20 Python applications. The Python classes affected with any of the considered code smells have been explored for obtaining the critical software metrics (criterion) through machine learning rule-based classifiers and are further evaluated for weight estimation. The obtained order rankings have been achieved by the application of "VIKOR": an MCDM technique, supported by its compromise solution. The weight estimators, i.e., Shannon's entropy and CRITIC method, were individually applied with this MCDM technique. This research determines the imperative software metrics with an accuracy of approx. 90% alongside the respective weight estimators. The prominent outcomes portray the preference scores obtained from the VIKOR technique to be the order ranking for the considered code smells in Python application. It was observed that Collapsible "IF" smell, being the most critical smell, should be primarily considered for refactoring followed by cognitive complexity smell for improved and sustainable development of Python software systems.
A Study of Bug Manifestion Process for Ensuring Software Quality

The effect of programming bugs on the current framework disappointments is of crucial anxiety. Numerous bugs are identified and expelled through testing, while others don't show up effectively at improvement time and show themselves just as operative disappointments. Other than the significance of understanding the bug highlights from the software engineer point of view. This paper researches the attributes of the bug appearance process. Different types of the errors in different stages and depending on different environment are outlined in the present paper.
Nested Gaussian filters for recursive Bayesian inference and nonlinear tracking in state space models

We introduce a new sequential methodology to calibrate the fixed parameters and track the stochastic dynamical variables of a state-space system. The proposed method is based on the nested hybrid filtering (NHF) framework of [1], that combines two layers of filters, one inside the other, to compute the joint posterior probability distribution of the static parameters and the state variables. In particular, we explore the use of deterministic sampling techniques for Gaussian approximation in the first layer of the algorithm, instead of the Monte Carlo methods employed in the original procedure. The resulting scheme reduces the computational cost and so makes the algorithms potentially better-suited for high-dimensional state and parameter spaces. We describe a specific instance of the new method and then study its performance and efficiency of the resulting algorithms for a stochastic Lorenz 63 model and for a stochastic volatility model with real data.
Distributed optimization for degenerate loss functions arising from over-parameterization

We consider distributed optimization with degenerate loss functions, where the optimal sets of local loss functions have a non-empty intersection. This regime often arises in optimizing large-scale multi-agent AI systems (e.g., deep learning systems), where the number of trainable weights far exceeds the number of training samples, leading to highly degenerate loss surfaces. Under appropriate conditions, we prove that distributed gradient descent in this case converges even when communication is arbitrarily less frequent, which is not the case for non-degenerate loss functions. Moreover, we quantitatively analyze the convergence rate, as well as the communication and computation trade-off, providing insights into designing efficient distributed optimization algorithms. Our theoretical findings are confirmed by both distributed convex optimization and deep learning experiments.
Automation of Disaster Recovery and Security in Cloud Computing

Cloud computing has become a vital part of our everyday life. A decade ago, proposing an idea on the improvement of cloud seemed like a reach. Just like with any other technology, cloud has its own drawbacks too. The way our life is moving at a fast pace, we can't begin to think of checking our backups every other day. It exhausts most people to even give it a thought. Ransom ware attacks have become so much common which eventually gave rise to the creation of multiple software. Retrieving lost files and data will become much easier if it were to be stored elsewhere prior to an attack. The subject cloud in itself has been overlooked and the fact that the same is the base for further alleviation hasn't been paid enough attention too. Improving an already existing technology almost always starts with going through its previous version's capabilities. This paper will mainly focus on how certain techniques are favoured over the other due to it meeting the standards for both personal and official usage. In simpler words, how those said techniques help small businesses develop themselves with the help of dependable backup with low cloud cost. The combination of machine learning and artificial intelligence in automation of disaster recovery testing is the future of cloud computing. When all of this checks the box, security would still be an issue. In this paper, I propose a combined approach to the access of cloud with two major algorithms: Seed block and Rivest Shamir Adleman.
Context-Adaptive Secondary Transform For Video Coding

It is well-known that non-separable transforms can efficiently decorrelate arbitrarily directed textures that are often present in image and video content. Due to the computational complexity involved, it is usually applied as a secondary transform operating on low frequency primary transform coefficients. In order to represent a variety of arbitrary directional textures in natural images /videos, it is ideal to have sufficient coverage of secondary transform kernels for the codec to choose from. However, this may lead to increased signaling cost and encoder complexity. This paper proposes a context-adaptive secondary transform (CAST) kernel selection approach to enable the usage of more secondary transform kernels with no signaling cost increase and minimal encoder and decoder complexity increase. The proposed approach uses the variance of the top row and left column of reconstructed pixels adjacent to the transform block, if available, as a context for selecting the set of transform kernels. Experimental results show that, compared to libaom, the proposed algorithm achieves a luma BD-rate reduction of 2.17% and 3.11% for All Intra coding using PSNR and SSIM quality metrics, respectively.
PCs Take a Page From Xbox With Pluton

Microsoft’s Pluton is dedicated silicon that aims to strengthen the security and integrity of PCs; it is derived from the Xbox One video game console SoC which was designed in partnership between Microsoft and AMD. Pluton implements and extends the functionality of the Trusted Computing Group’s (TCG) Trusted Platform Module 2.0, protects keys as they move around the SoC using dedicated silicon referred to as Secure Hardware Cryptography Key, and implements the TCG’s Device Identifier Composition Engine and Microsoft’s Robust Internet of Things specifications to perform strong device attestation with a hardware root of trust. While Microsoft is following Apple and Google’s lead by integrating special-purpose silicon for secure cryptographic operations, it is taking a different approach by leveraging existing open standards and technologies. Microsoft’s Windows 11 is designed for a world where all PCs feature Pluton.
Enhancing Multi-Step Action Prediction for Active Object Detection

Active vision for robots is one promising solution to open world visual detection problems. A fundamental issue is view planning, i.e., predicting next best views to capture images of interest to reduce uncertainty. While multi-step action in a reinforcement learning (RL) setup can boost the efficiency of view planning, existing methods suffer from unstable detection outcome when the Q-values of multiple branches of action advantages (i.e., action range and action type) are combined naively. To tackle this issue, we propose a novel mechanism to disentangle action range from action type through a two-stage training strategy on a deep Q-network. It combines well-crafted loss functions with respect to action range and action type to enforce separated training of these two branches. We evaluate our method on two public datasets and show that it facilitates substantial gain in view planning efficiency, while enhancing detection accuracy.
Iterative Reweighted Local Cross Correlation Method for Nonlinear Registration of Multiphase Liver CT Images

It is of great importance for proper radiological interpretations to register multiphase liver scans, but much challenging due to different breathing-holding levels and physiological motions while imaging. In this paper, we propose a novel nonlinear registration method for multiphase liver scans upon iterative reweighted local cross correlation (IRLCC) measure, which employs the metric of local cross correlation (LCC) to evaluate image similarity. In addition, the introduced method explores a new volume-preserving prior of liver to boost registration accuracy. A multilevel iterative fixed-point optimization algorithm is proposed to compute pixel-wise displacement efficiently with a moderate numerical load. We validate our approach over a LSHCC dataset acquired from the radiology department in hospital and a LSPIG public dataset. The results over LSHCC demonstrate that the volume-preserving prior well improved the registration accuracy. The results over LSPIG reveal that our method outperforms several state-of-the-art methods while keeping efficiency in time.
PANDA: Perceptually Aware Neural Detection of Anomalies

Semi-supervised methods of anomaly detection have seen substantial advancement in recent years. Of particular interest are applications of such methods to diverse, real-world anomaly detection problems where anomalous variations can vary from the visually obvious to the very subtle. In this work, we propose a novel fine-grained VAE-GAN architecture trained in a semi-supervised manner in order to detect both visually distinct and subtle anomalies. With the use of a residually connected dual-feature extractor, a fine-grained discriminator and a perceptual loss function, we are able to detect subtle, low inter-class (anomaly vs. normal) variant anomalies with greater detection capability and smaller margins of deviation in AUC value during inference compared to prior work whilst also remaining time-efficient during inference. We achieve state-of-the-art anomaly detection results when compared extensively with prior semi-supervised approaches across a multitude of anomaly detection benchmark tasks including trivial leave-one-out tasks (CIFAR-10 - <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{AUPRC}_{avg}$</tex> : 0.91; MNIST - <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{AUPRC}_{avg}$</tex> : 0.90) in addition to challenging real-world anomaly detection tasks (plant leaf disease - AVC: 0.776; threat item X-ray - AVC: 0.51), video frame-level anomaly detection (VCSDPedl - AVC: 0.95) and high frequency texture with object anomalous defect detection (MVTEC - <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{AUC}_{avg}$</tex> : 0.83).
Audio Encryption With Computational Chaotic System Error

This paper proposes a novel method of chaos-based encryption for audio. Our method outperformed the minimal requirement of speed for real time audio transfers while maintaining its high security features. The paper exploits finite errors derived from the computation of chaotic systems. The cipher is built on the lower bound error, which is computed by means of two interval extensions of a chaotic system. It was found that the method was effective, and required little computational power in order to be completed, proving to be faster and still reliable compared to other works.
Effects of screen-responsive visualization on data comprehension

Visualization interfaces designed for heterogeneous devices such as wall displays and mobile screens must be responsive to varying display dimensions, resolution, and interaction capabilities. In this paper, we report on two user studies of visual representations for large versus small displays. The goal of our experiments was to investigate differences between a large vertical display and a mobile hand-held display in terms of the data comprehension and the quality of resulting insights. To this end, we developed a visual interface with a coordinated multiple view layout for the large display and two alternative designs of the same interface – a space-saving boundary visualization layout and an overview layout – for the mobile condition. The first experiment was a controlled laboratory study designed to evaluate the effect of display size on the perception of changes in a visual representation, and yielded significant correctness differences even while completion time remained similar. The second evaluation was a qualitative study in a practical setting and showed that participants were able to easily associate and work with the responsive visualizations. Based on the results, we conclude the paper by providing new guidelines for screen-responsive visualization interfaces.
Application Research of Blockchain Technology in Logistics Supply Chain

With the development of social economy, the rapid development of logistics makes the content of supply chain(SC) increasingly large. The loss and delay of information in the supply chain aggravate the bullwhip effect. In addition, the unreliable information storage leads to the difficulty of traceability and accountability, and makes the management of logistics supply chain more difficult. The blockchain has the characteristics of multi-node distributed networking, node information synchronization, digital encryption, information traceability, and block content cannot be tampered with. It is very suitable for use in supply chain scenarios and can provide solutions for the supply chain. The purpose of this article is to study the application of blockchain technology (BT) in the logistics supply chain. This article briefly describes the blockchain, and then analyzes the application of blockchain technology in the logistics supply chain in detail, including financing, cargo security, credit evaluation, EDI business and anti-express fraud. Based on this theory, this paper designs a supply chain information platform based on blockchain, including the physical structure, operating mode and logical structure of the system. This paper tests the performance of the system and finds that when the sending rate reaches the system peak, the maximum CPU usage will exceed 70%, but the average value of the system in this article is still below 50%.
Limited Memory Bundle Method for Clusterwise Linear Regression

A clusterwise linear regression problem consists of finding a number of linear functions each approximating a subset of the given data. In this paper, the limited memory bundle method is modified and combined with the incremental approach to solve this problem using its nonsmooth optimization formulation. The main contribution of the proposed method is to obtain a fast solution time for large-scale clusterwise linear regression problems. The proposed algorithm is tested on small and large real-world data sets and compared with other algorithms for clusterwise linear regression. Numerical results demonstrate that the proposed algorithm is especially efficient in data sets with large numbers of data points and input variables.
Overcomplete Representations Against Adversarial Videos

Adversarial robustness of deep neural networks is an extensively studied problem in the literature and various methods have been proposed to defend against adversarial images. However, only a handful of defense methods have been developed for defending against attacked videos. In this paper, we propose a novel Over-and-Under complete restoration network for Defending against adversarial videos (OUDefend). Most restoration networks adopt an encoder-decoder architecture that first shrinks spatial dimension then expands it back. This approach learns undercomplete representations, which have large receptive fields to collect global information but overlooks local details. On the other hand, overcomplete representations have opposite properties. Hence, OUDefend is designed to balance local and global features by learning those two representations. We attach OUDefend to target video recognition models as a feature restoration block and train the entire network end-to-end. Experimental results show that the defenses focusing on images may be ineffective to videos, while OUDefend enhances robustness against different types of adversarial videos, ranging from additive attacks, multiplicative attacks to physically realizable attacks. Code: https://git;hub.com/shaoyuanlo/OUDefend
Syntax for Semantics: Krull’s Maximal Ideal Theorem

Abstract Krull’s Maximal Ideal Theorem (MIT) is one of the most prominent incarnations of the Axiom of Choice (AC) in ring theory. For many a consequence of AC, constructive counterparts are well within reach, provided attention is turned to the syntactical underpinning of the problem at hand. This is one of the viewpoints of the revised Hilbert Programme in commutative algebra, which will here be carried out for MIT and several related classical principles.
Robust Decision-Based Black-Box Adversarial Attack via Coarse-To-Fine Random Search

Many studies on reducing the adversarial vulnerability of deep neural networks have been published in the field of machine learning. To evaluate the actual robustness of networks, various adversarial attacks have been proposed. Most previous works have focused on white-box settings which assume that the adversary can have full access to the target models. Since they are not practical in real-world situations, recent studies on black-box attacks have received a lot of attention. However, existing black-box attacks have critical limitations, such as yielding a low attack success rate or relying too much on gradient estimation and decision boundaries. Those attacks are ineffective against weak defenses using gradient obfuscation. In this paper, we propose a novel gradient-free decision-based black-box attack using random search optimization. The proposed method only needs a hard-label (decision-based) and is effective against defenses using gradient obfuscation. Experimental results validate its query-efficiency and improved L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> distance.
Preserving User Privacy in Personalized Networks

The advances in efficient computation and data processing technologies have ushered in an entirely new era in which network connectivity applications are being revolutionized by Artificial Intelligence (AI). One of the emerging AI applications in wireless networks is network personalization. Personalized wireless networks are designed to track user satisfaction levels using Machine Learning (ML) models that are trained and updated using context data collected from users. Nonetheless, user data collection and transmission make sensitive user information prone to various privacy attacks. Therefore, in this letter, we propose a privacy-preserving framework for personalized wireless networks. The proposed framework is enabled by Differential Privacy (DP), which is an influential privacy technology that offers a rigorous and provable privacy guarantee. Besides, the proposed framework is supported by a multi-stage prediction process, which adds a second layer of privacy protection to personal user data. Lastly, we investigate the impact of privacy budget and data size on the quality of user satisfaction predictors in personalized wireless networks.
Towards an Understanding of Program Writing as a Cognitive Process: Analysis of Keystroke Logs

Program writing is difficult to teach, learn, and assess. One challenge is a lack of theory or understanding about what program writing is. My dissertation will address this challenge by applying theories from natural language (NL) writing to try and understand program writing as a cognitive process. By analyzing keystroke logs collected during program writing, I plan to identify similarities with NL writing, potential diagnostic information, and how program writing changes as students become more proficient.
TSCDNet+: A highly robust substation anomaly detection method

In the process of substation anomaly detection, abnormal samples of substation equipment are scarce. Anomaly detection methods such as object-based detection are difficult to effectively train models for all equipment and are unable to deal with unknown anomalies, resulting in poor robustness. To solve this problem, a weakly supervised learning method is used to detect anomalies through the distance between the image to be measured and the image of normal equipment, so that it can be applied to all devices. Substation anomaly detection data set is constructed, and image pairs are used as training samples to expand the number of negative samples. A lightweight substation change detection network (TSCDNet+) is proposed, which uses a Siamese network structure to add an attention module to reduce the interference of the background environment. The full connection layer was used to calculate the distance of feature vectors to enlarge the feature gap between positive and negative samples. The multi-scale information fusion structure was added, and the feature map was used to replace the feature vector to calculate the distance. The experimental results show that TSCDNET+ has good robustness under different thresholds, and the F1 score of the model with the best effect is 0.945. As it does not rely on the category characteristic information of equipment and anomaly, this method has a good universality, can meet the detection of an unknown anomaly, and still has a good detection effect for new equipment that has not been trained.