Reviewerly/exHarmony huggingface dataset:
FindICI: Using machine learning to detect linguistic inconsistencies between code and natural language descriptions in infrastructure-as-code

Linguistic anti-patterns are recurring poor practices concerning inconsistencies in the naming, documentation, and implementation of an entity. They impede the readability, understandability, and maintainability of source code. This paper attempts to detect linguistic anti-patterns in Infrastructure-as-Code (IaC) scripts used to provision and manage computing environments. In particular, we consider inconsistencies between the logic/body of IaC code units and their short text names. To this end, we propose FindICI a novel automated approach that employs word embedding and classification algorithms. We build and use the abstract syntax tree of IaC code units to create code embeddings used by machine learning techniques to detect inconsistent IaC code units. We evaluated our approach with two experiments on Ansible tasks systematically extracted from open source repositories for various word embedding models and classification algorithms. Classical machine learning models and novel deep learning models with different word embedding methods showed comparable and satisfactory results in detecting inconsistent Ansible tasks related to the top-10 used Ansible modules.
Reducing the Biases in False Correlations Between Discrete Characters

Abstract The correlation between two characters is often interpreted as evidence that there exists a significant and biologically important relationship between them. However, Maddison and FitzJohn (in The unsolved challenge to phylogenetic correlation tests for categorical characters. Syst. Biol. 2015;64:127–136) recently pointed out that evidence of correlated evolution between two categorical characters is often spurious, particularly, when the dependent relationship stems from a single replicate deep in time. Here we will show that there may, in fact, be a statistical solution to the problem posed by Maddison and FitzJohn naturally embedded within the expanded model space afforded by the hidden Markov model (HMM) framework. We demonstrate that the problem of single unreplicated evolutionary events manifests itself as rate heterogeneity within our models and that this is the source of the false correlation. Therefore, we argue that this problem is better understood as model misspecification rather than a failure of comparative methods to account for phylogenetic pseudoreplication. We utilize HMMs to develop a multirate independent model which, when implemented, drastically reduces support for correlation. The problem itself extends beyond categorical character evolution, but we believe that the practical solution presented here may lend itself to future extensions in other areas of comparative biology. [Macroevolution; model adequacy; phylogenetic comparative methods; rate heterogeneity].
A CNN-Based Neural Network for Tumor Detection Using Cellular Pathological Imaging for Lobular Carcinoma

In neural network-based learning techniques, there are several models of convolutional networks. Whenever the methods are deployed with large datasets only then could their applicability and appropriateness be determined. Clinical and pathological pictures of lobular carcinoma are thought to exhibit a large number of random formations and textures. Working with so pictures is a difficult problem in machine learning. Focusing on wet laboratories and following the outcomes, numerous studies have been published with fresh commentaries in the investigation. In this research, we provide a framework that can operate effectively on raw photos of various resolutions while easing the issues caused by the existence of patterns and texturing. The suggested approach produces very good findings that may be used to make decisions in the diagnosis of cancer.
Language database construction method based on big data and deep learning

This paper first realizes the construction of multi-level language database by using big data technology and deep learning algorithm. Then, through the analysis of neural network algorithm, the extended Kalman filter (EKF) model is established to form a data query system. Finally, simulation experiments verify whether the language database can correctly retrieve language bytes. Experiments show that the database based on big data and deep learning algorithm has high result adaptability. The debugging result of the simulation experiment is the average fitness of each classification after the simulation program runs 20 times continuously. When the number of analysis is more than 15, it is significantly higher than the other two methods, and it is also the smoothest on the convergence curve. The simulation results show that the language database construction method based on the combination of deep learning algorithm and C-type traveling wave has a good effect on the processing of branch point feature data. In addition, it has a good fitting effect on large-scale data, which is conducive to improve the recognition ability of different language systems in the database, and greatly improve the accuracy and efficiency of retrieval.
Learning new physics efficiently with nonparametric methods

Abstract We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. Based on the original proposal by D’Agnolo and Wulzer (Phys Rev D 99(1):015014, 2019, arXiv:1806.02350 [hep-ph]), the model evaluates the compatibility between experimental data and a reference model, by implementing a hypothesis testing procedure based on the likelihood ratio. Model-independence is enforced by avoiding any prior assumption about the presence or shape of new physics components in the measurements. We show that our approach has dramatic advantages compared to neural network implementations in terms of training times and computational resources, while maintaining comparable performances. In particular, we conduct our tests on higher dimensional datasets, a step forward with respect to previous studies.
Nonlinear error full-field compensation method for phase measuring profilometry

Phase measuring profilometry (PMP) has the highest measuring accuracy among structured light projection-based three-dimensional (3D) sensing methods. Due to their low-cost and high-resolution features, commercial projectors are extensively used in PMP, but they are all designed with a gamma effect purpose that considers the characteristics of human vision. Affected by the gamma effect, a set of phase-shifting sinusoidal deformed patterns captured in PMP may contain high-order harmonics which lead to nonlinear phase errors. Then, a novel nonlinear error full-field compensation method is proposed. First, the unwrapped phases modulated by the reference plane are measured several times, and their average phase is taken as the measured phase modulated by the reference plane to eliminate random errors as much as possible. Second, an expected phase plane is fitted from this average phase with the least-squares method. Third, the nonlinear phase error can be detected by subtracting the fitted expected phase from this average phase. Finally, the full-field look-up table (LUT) can be established between the nonlinear phase error and the measured phase. When an object is measured, the unwrapped phase modulated by the object is taken as the measured phase of the LUT, so the corresponding nonlinear phase error can be directly searched in the LUT. In this way, the full-field nonlinear phase error can be efficiently compensated. Experimental results show the feasibility and validity of the proposed method. The mean absolute error (MAE) can be improved from 0.48 mm to 0.06 mm, and the root mean square error (RMSE) can be improved from 0.55 mm to 0.07 mm.
Effects of dipolar coupling on an entanglement storage device

Abstract Quantum computation requires efficient long-term storage devices to preserve quantum states. An attractive candidate for such storage devices is qubits connected to a common dissipative environment. The common environment gives rise to persistent entanglements in these qubit systems. Hence these systems act efficiently as a storage device of entanglement. However, the existence of a common environment often requires the physical proximity of the qubits and hence results in direct dipolar coupling between the qubits. In this work, we investigate effects of the secular and the nonsecular part of the dipolar coupling on the environment-induced entanglement using a recently-proposed fluctuation-regulated quantum master equation (Chakrabarti and Bhattacharyya 2018 Phys. Rev. A 97 063837). We show that nonsecular part of the dipolar coupling results in reduced entanglement and hence less efficiency of the storage devices. We also discuss the properties of efficient storage that mitigates the detrimental effects of the dipolar coupling on the stored entanglement.
Study on Detection and Recognition of Traffic Lights Based on Improved YOLOv4

To resolve the issues of a deep backbone network, a large model, slow reasoning speed on a mobile terminal, low detection accuracy for small targets and difficulties detecting and recognizing traffic lights in real time and accurately with YOLOv4, a traffic lights recognition method based on improved YOLOv4 is proposed. The lightweight ShuffleNetv2 network is utilized to replace the CSPDarkNet53 network of YOLOv4 to satisfy the requirements of a mobile terminal. The reformed k-means clustering algorithm is applied to generate anchor boxes for avoiding the sensitivity issue of outliers and initial values. A novel attention mechanism named CS2A is added to enhance the extraction capability of effective features. Multiple data augmentation methods are combined to improve the generalization ability of the model. Ultimately, the detection and recognition of traffic lights can be realized. The S2TLD dataset is selected for training and testing, and it can be proved that the recognition accuracy and model size are greatly optimized. Meanwhile, a self-made dataset is selected for training and testing. Compared with the conventional YOLOv4, the recognition accuracy of the proposed algorithm for traffic lights' state information increases by 1.79%, and the model size decreases by 81.97%. Appropriate scenes are selected for real-vehicle testing and the results demonstrate that the detection speed of the presented algorithm increases by 16%, and the recognition effect for small targets increases by 37% in comparison with conventional YOLOv4.
Cryptanalysis and Improved Image Encryption Scheme Using Elliptic Curve and Affine Hill Cipher

In the present era of digital communication, secure data transfer is a challenging task in the case of open networks. Low-key-strength encryption techniques incur enormous security threats. Therefore, efficient cryptosystems are highly necessary for the fast and secure transmission of multimedia data. In this article, cryptanalysis is performed on an existing encryption scheme designed using elliptic curve cryptography (ECC) and a Hill cipher. The work shows that the scheme is vulnerable to brute force attacks and lacks both Shannon’s primitive operations of cryptography and Kerckchoff’s principle. To circumvent these limitations, an efficient modification to the existing scheme is proposed using an affine Hill cipher in combination with ECC and a 3D chaotic map. The efficiency of the modified scheme is demonstrated through experimental results and numerical simulations.
Digital twin- and extended reality-based telepresence for collaborative robot programming in the 6G perspective

In the context of Industry 4.0, a paradigm shift from traditional industrial manipulators to Collaborative Robots (CRs) is ongoing, with the latter serving ever more closely humans as auxiliary tools in many production processes. In this scenario, continuous technological advancements offer new opportunities for further innovating robotics and other areas of next-generation industry. For example, 6G could play a prominent role due to its human-centric view of the industrial domains. In particular, its expected dependability features will pave the way for new applications exploiting highly effective Digital Twin (DT)- and eXtended Reality (XR)-based telepresence. In this work, a novel application for the above technologies allowing two distant users to collaborate in the programming of a CR is proposed. The approach encompasses demanding data flows (e.g., point cloud-based streaming of collaborating users and robotic environment), with network latency and bandwidth constraints. Results obtained by analyzing this approach from the viewpoint of network requirements in a setup designed to emulate 6G connectivity indicate that the expected performance of forthcoming mobile networks will make it fully feasible in principle.
Federated learning for preserving data privacy in collaborative healthcare research

Generalizability, external validity, and reproducibility are high priorities for artificial intelligence applications in healthcare. Traditional approaches to addressing these elements involve sharing patient data between institutions or practice settings, which can compromise data privacy (individuals’ right to prevent the sharing and disclosure of information about themselves) and data security (simultaneously preserving confidentiality, accuracy, fidelity, and availability of data). This article describes insights from real-world implementation of federated learning techniques that offer opportunities to maintain both data privacy and availability via collaborative machine learning that shares knowledge, not data. Local models are trained separately on local data. As they train, they send local model updates (e.g. coefficients or gradients) for consolidation into a global model. In some use cases, global models outperform local models on new, previously unseen local datasets, suggesting that collaborative learning from a greater number of examples, including a greater number of rare cases, may improve predictive performance. Even when sharing model updates rather than data, privacy leakage can occur when adversaries perform property or membership inference attacks which can be used to ascertain information about the training set. Emerging techniques mitigate risk from adversarial attacks, allowing investigators to maintain both data privacy and availability in collaborative healthcare research. When data heterogeneity between participating centers is high, personalized algorithms may offer greater generalizability by improving performance on data from centers with proportionately smaller training sample sizes. Properly applied, federated learning has the potential to optimize the reproducibility and performance of collaborative learning while preserving data security and privacy.
Blockchain Technology in Agriculture for Indian Farmers: A Systematic Literature Review, Challenges, and Solutions

Blockchain technology swiftly rose to prominence in a wide range of applications within the smart agriculture field. The necessity to construct smart peer-to-peer systems capable of validating, securing, monitoring, and analyzing agricultural data has prompted discussions regarding the development of blockchain-based Internet of Things (IoT) systems in smart agriculture. Blockchain technology plays a critical role in the transformation of traditional means of storing, sorting, and exchanging agricultural data into a more trustworthy, immutable, transparent, and decentralized method of sharing data. Smart farming will benefit from the integration of the IoT and the blockchain, which will take us from having merely smart farms to having an Internet of smart farms as well as provide better control over supply chain networks in general. As a consequence of this combination, smart agriculture will be managed more autonomously and intelligently, resulting in greater efficiency and optimization of operations.
Decoherence factor as a convolution: an interplay between a Gaussian and an exponential coherence loss

Abstract We identify and investigate the origin and nature of the transition between Gaussian and exponential forms of decoherence: the decoherence factor (that controls the time dependence of the off-diagonal terms of the density matrix expressed in the pointer basis representation) is the convolution of the Fourier transforms of the spectral density and of the overlap (between the eigenstates the environment with and without couplings to the system). Spectral density alone tends to lead to the (approximately) Gaussian decay of coherence while the overlap alone results in a (largely) exponential decay. We show that these two contributions combine as a convolution, their relative importance controlled by the strength of the system-environment coupling. The resulting decoherence factor in the strong and weak coupling limits leads to predominantly Gaussian or exponential decay, respectively, as is demonstrated with two paradigmatic examples of decoherence—a spin-bath model and the quantum Brownian motion.
Towards Creative Version Control

Version control systems are powerful tools for managing history information and shaping personal and collaborative processes. While many complex tools exist for software engineering, and basic functionality for capturing versions is often found in collaborative applications such as text editors and design layout tools, these systems are not attuned to the needs and behaviors of creative practitioners within those domains, and fail to support creative practitioners in many others. Through 18 semi-structured interviews across diverse domains of creativity, we investigate how creative practitioners use version histories in their process. With the familiar paradigms and features of software version control as an organizing structure, we discuss how these creative practitioners embrace, challenge, and complicate uses of version histories in four ways: using versions as a palette of materials, providing confidence and freedom to explore, leveraging low-fidelity version capture, and reflecting on and reusing versions across long time scales. We discuss how the themes present across this wide range of mediums and domains can provide insight into future designs and uses of version control systems to support creative process.
Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation

Abstract Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer from low reliability and robustness. Uncertainty estimation is an efficient solution to this problem, as it provides a measure of confidence in the segmentation results. The current uncertainty estimation methods based on quantile regression, Bayesian neural network, ensemble, and Monte Carlo dropout are limited by their high computational cost and inconsistency. In order to overcome these challenges, Evidential Deep Learning (EDL) was developed in recent work but primarily for natural image classification and showed inferior segmentation results. In this paper, we proposed a region-based EDL segmentation framework that can generate reliable uncertainty maps and accurate segmentation results, which is robust to noise and image corruption. We used the Theory of Evidence to interpret the output of a neural network as evidence values gathered from input features. Following Subjective Logic, evidence was parameterized as a Dirichlet distribution, and predicted probabilities were treated as subjective opinions. To evaluate the performance of our model on segmentation and uncertainty estimation, we conducted quantitative and qualitative experiments on the BraTS 2020 dataset. The results demonstrated the top performance of the proposed method in quantifying segmentation uncertainty and robustly segmenting tumors. Furthermore, our proposed new framework maintained the advantages of low computational cost and easy implementation and showed the potential for clinical application.
Complete Frequency-Bin Bell Basis Synthesizer

We report the experimental generation of all four frequency-bin Bell states in a single versatile setup via successive pumping of spontaneous parametric downconversion with single and dual spectral lines. Our scheme utilizes intensity modulation to control the pump configuration and offers turn-key generation of any desired Bell state using only off-the-shelf telecommunication equipment. We employ Bayesian inference to reconstruct the density matrices of the generated Bell states, finding fidelities $\geq$ 97% for all cases. Additionally, we demonstrate the sensitivity of the frequency-bin Bell states to common-mode and differential-mode temporal delays traversed by the photons comprising the state$\unicode{x2013}$presenting the potential for either enhanced resolution or nonlocal sensing enabled by our complete Bell basis synthesizer.
Online AutoML: an adaptive AutoML framework for online learning

Automated Machine Learning (AutoML) has been used successfully in settings where the learning task is assumed to be static. In many real-world scenarios, however, the data distribution will evolve over time, and it is yet to be shown whether AutoML techniques can effectively design online pipelines in dynamic environments. This study aims to automate pipeline design for online learning while continuously adapting to data drift. For this purpose, we design an adaptive Online Automated Machine Learning (OAML) system, searching the complete pipeline configuration space of online learners, including preprocessing algorithms and ensembling techniques. This system combines the inherent adaptation capabilities of online learners with fast automated pipeline (re)optimization. Focusing on optimization techniques that can adapt to evolving objectives, we evaluate asynchronous genetic programming and asynchronous successive halving to optimize these pipelines continually. We experiment on real and artificial data streams with varying types of concept drift to test the performance and adaptation capabilities of the proposed system. The results confirm the utility of OAML over popular online learning algorithms and underscore the benefits of continuous pipeline redesign in the presence of data drift.
Efficient Anonymous Authentication Based on Physically Unclonable Function in Industrial Internet of Things

Owing to the open Industrial Internet of Things (IIoT) environment, information interacting between devices and servers is transmitted over the public channel, which may lead to privacy breach of the device identity. Furthermore, communication entities are not fully trusted, and they may maliciously disclose the device identity information. Therefore, the anonymity of devices must be guaranteed. In addition, IIoT is resource-constrained, and complex algorithms are unsuitable for the IIoT system. Several researchers have attempted to design anonymous authentication schemes. The one-authentication-multiple-access approach allows devices to access server resources multiple times after a single authentication, and its authentication overhead is independent of the number of accesses. This can reduce the computational burden for devices that need to access the server frequently. However, existing anonymous authentication schemes do not support multiple accesses after one authentication, and still suffer from privacy issues and low efficiency for devices that need frequent access to the server. To address these issues, we propose a new anonymous authentication scheme that uses group signature technology to ensure device anonymity and uses Merkle hash tree technology to achieve multiple accesses after one authentication, thereby greatly reducing the authentication overhead of IIoT devices. Then, we validate the security of the scheme using the random oracle model and the BAN logic. Finally, compared with other related schemes, the experimental results show that our proposed scheme is more efficient and practical for resource-constrained IIoTs than other schemes.