Reviewerly/exHarmony huggingface dataset:
Nonlinear error compensation for microfiber knot current sensors based on artificial neural network

To obtain a good linear response, the nonlinear error of the current sensor based on the micro knot ring (MKR) is first predicted by a deep belief network (DBN) and further used to compensate for the response curve. According to the structural parameters of the MKR, the current, the output light intensity, and the working process, the DBN was used to forecast the nonlinear intensity errors of the MKR. Thousands of current-intensity error data pairs acquired from 60 MKR current sensors are used for the network training and the error-prediction ability of the DBN is evaluated by the root mean square error (RMSE). The DBN with a single-restricted Boltzmann machine has the best prediction performance. From the statistics of the 20 best prediction results, the RMSE in the current-rising and current-falling processes are 0.0257±0.0072 dB and 0.0212±0.0062 dB, respectively. For both working processes, the average Pearson correlation coefficients of the response curves are over ∼0.9950 after compensation, which are much higher than the ones of the raw data. Compared with the traditional linear fitting method, the nonlinear error of the MNF sensor could be accurately forecasted by the DBN according to the structure of the sensing unit and the response curve can be effectively linearly compensated on time. This may provide an effective and flexible method for promoting performance improvement of the MNF sensors with different sizes in the application.
An evolutionary robust soft measurement technique via enhanced atom search optimization and outlier robust extreme learning machine for wastewater treatment process

Accurate and prompt measurement of certain key variables occurring during wastewater treatment, especially effluent ammonia nitrogen (NH4-N) and biological oxygen demand (BOD), is of great importance. In this study, a soft measurement model combining random forest (RF), enhanced atomic search optimization (EASO) and online sequential outlier robust extreme learning machine (OSORELM) is proposed for wastewater treatment. First, RF is used to select auxiliary variables with high correlation of NH4-N and BOD as model inputs. Second, a dynamic perturbation strategy and a generalized opposition-based learning are added to ASO to improve the algorithm performance. And EASO is used for model hyperparameter optimization. Then, the optimized OSORELM model is used for soft measurements of NH4-N and BOD. Finally, to verify the stability of the model, 5 %, 10 % and 15 % noise were added for further experiments. The results show that the proposed model has better prediction and stronger robustness in soft measurements of NH4-N and BOD.
Adaptive multiple zero-watermarking based on local fast quaternion radial harmonic Fourier moments for color medical images

The copyright protection of medical images is receiving increasing attention. Zero-watermarking schemes are a critical technology for copyright protection of medical images and show better imperceptibility performance than embedded watermarking schemes. However, existing zero-watermarking schemes still have some limitations that prevent them from fully exploiting their potential, including failure against complex desynchronization attacks and high computational complexity. To this end, we propose a novel zero-watermarking scheme based on fast quaternion radial harmonic Fourier moments (FQRHFMs) that is resistant to desynchronization attacks. Specifically, this article formulates multiple local feature regions (LFRs) based on a speeded-up robust feature (SURF) operator and provides a fast computation method for QRHFMs by using fast Fourier transform (FFT). The magnitudes of FQRHFMs are computed in each incomplete overlapping LFR for generating multiple zero-watermarks to resist desynchronization attacks. Experimental results show that the proposed scheme achieves state-of-the-art robustness against various desynchronization attacks (e.g., translation, cropping, and length-width ratio changing), and outperforms existing local zero-watermarking frameworks with regard to accuracy and efficiency.
Black Hole Instead of Black Box?: The Double Opaqueness of Recommender Systems on Gaming Platforms and Its Legal Implications

Abstract Recommender systems that support us in our everyday lives are becoming more precise and accurate in terms of the appropriateness of recommendations to users’ needs – with the result that the user often follows these recommendations. This is mainly due to the filtering methods and various algorithms used. In our paper, we will look specifically at the recommender systems on gaming platforms. These consist of different components: a shopping component, a streaming component and a social media component. The recommender systems of these components, when considered individually, have certain characteristics in terms of the machine learning and filtering methods used, which are mixed by combining them on one platform. As a result, it is unclear which of the information collected about the user at any time is lost and disappears into obscurity, and what information is used to generate recommendations. The frequently discussed “black box” problem exacerbates at this point and becomes a “black hole.” With the interests of platform users, platform operators, and software developers in mind, we examine the legal provisions that have been established to address this opaqueness: transparency obligations. Derived from the Digital Services Act and the Artificial Intelligence Act, we present various legally valid solutions to address the “black hole” problem and also lead them to practical suggestions for implementation.
Recommending Ourselves to Death: Values in the Age of Algorithms

Abstract Recommender systems are increasingly being used for many purposes. This is creating a deeply problematic situation. Recommender systems are likely to be wrong when used for these purposes because there are distorting forces working against them. RS’s are based on past evaluative standards which will often not align with current evaluative standards. RS’s algorithms must reduce everything to computable information – which will often, in these cases, be incorrect and will leave out information that we normally consider to be important for such evaluations. The algorithms powering these RSs also must use proxies for the evaluative ‘good’. These proxies are not equal to the ‘good’ and therefore will often go off track. Finally, these algorithms are opaque. We do not have access to the considerations that lead to a particular recommendation. Without these considerations we are taking the machine’s output on faith. These algorithms also have the potential to modify how we evaluate. YouTube has modified its algorithm explicitly to ‘expand our tastes’. This is an extraordinary amount of power – and one that if my first argument goes through, is likely to take us away from the good. This influences our behavior which feeds back into the algorithms that make recommendations. It is important that we establish some meaningful human control over this process before we lose control over the evaluative.
Red‐Teaming as a Research Validation Method for Systems Engineering Thesis Students

Abstract All research projects need a forward path method for performing the investigation, making findings and reaching conclusions. In addition, project methodology must include methods that test the truth of the knowledge claimed to have been developed through the project. We address the specific issue of validation in thesis projects in systems engineering (SE) programs where the intended outcome is either an application of SE method or an investigation of a topic in SE. We present red‐teaming (RTing) as a validation method for results of SE research. We discuss two case studies of thesis projects which used a RTing method to evaluate a proposed method for doing something. From this we discuss the strengths and weaknesses of the RTing method in thesis projects and provide guidelines for use of RTing as a project outcomes evaluation method. We conclude RTing is a useful method to evaluate a thesis project which generates a design or a method because it uses a method not directly influenced by the student's assumptions in the design of the project. The RTing method is constrained by the challenges of finding willing red‐team (RT) members, project schedule, and the RT member's knowledge of the subject.
A low-cost 3D reconstruction and measurement system based on structure-from-motion (SFM) and multi-view stereo (MVS) for sewer pipelines

Traditional image-based detection methods for pipelines lack quantification information (e.g., depth, area, and perimeter of defects) despite the high accuracy. Furthermore, existing three-dimensional (3D) reconstruction methods based on laser and depth cameras are expensive and not maneuverable. To remedy these problems, a simple and novel measurement system for sewer pipeline potholes based on low-cost 3D reconstruction is proposed. First, a sparse reconstruction method based on structure-from-motion (SFM) is proposed to estimate the camera parameters and reconstruct the sparse point cloud from multi-view images that are easily acquired. Second, a dense reconstruction method based on multi-view stereo (MVS) is proposed to generate the depth maps and dense 3D points that can provide a stereo display of pipelines. Third, an automatic segmentation and measurement method based on cylinder fitting and projection for pipeline potholes is proposed. The measurement information of potholes is obtained by projection, triangulation and boundary search. Furthermore, a metric calibration method is proposed to convert the voxel size to the actual size. Comparison experiments show that the average errors of maximum depths, mean depths, areas and perimeters between the predicted and real values are 9.48 %, 13.16 %, 6.70 % and 14.01 %, respectively. Furthermore, the measurement method is robust to the point density of the potholes. The whole proposed system only requires several overlapping images taken from ordinary cameras, which is a low-cost and accurate way for 3D reconstruction of pipelines and measurement of defects.
Implementation of explainable artificial intelligence in commercial communication systems using micro systems

The developments in the field of artificial intelligence (AI) and decision making systems are identified as virtuous models for banking and finance sector (BFS) applications. Even though AI provides great advantage in application changes it is essential to remodel the system using explainable artificial intelligence (XAI) design system. Also the standard sensing models provides appropriate monitoring values but huge size of sensors is considered as a major drawback. Thus two diverse problems are addressed in this research work where XAI has been integrated with micro electro-mechanical systems (MEMS) for solving the problems related to BFS applications. Further the data security has been enhanced as XAI is implemented with conviction managements and real time experiments are carried out by developing a unique application by integrating new mathematical designs. To validate the effectiveness of BFS application the developed model is tested with five scenarios which includes multiple parametric arrangements with interpretability process. The tested and compared outcomes with existing models indicates that XAI and MEMS provides inordinate improvements in terms of data impairments thus increasing the transparency of the projected system to an average 96%.
Content preserving image translation with texture co-occurrence and spatial self-similarity for texture debiasing and domain adaptation

Models trained on datasets with texture bias usually perform poorly on out-of-distribution samples since biased representations are embedded into the model. Recently, various image translation and debiasing methods have attempted to disentangle texture biased representations for downstream tasks, but accurately discarding biased features without altering other relevant information is still challenging. In this paper, we propose a novel framework that leverages image translation to generate additional training images using the content of a source image and the texture of a target image with a different bias property to explicitly mitigate texture bias when training a model on a target task. Our model ensures texture similarity between the target and generated images via a texture co-occurrence loss while preserving content details from source images with a spatial self-similarity loss. Both the generated and original training images are combined to train improved classification or segmentation models robust to inconsistent texture bias. Evaluation on five classification- and two segmentation-datasets with known texture biases demonstrates the utility of our method, and reports significant improvements over recent state-of-the-art methods in all cases.
BamnetTL: Bidirectional Attention Memory Network with Transfer Learning for Question Answering Matching

In KBQA (knowledge base question answering), questions are processed using NLP (natural language processing), and knowledge base technology is used to generate the corresponding answers. KBQA is one of the most challenging tasks in the field of NLP. Q&amp;A (question and answer) matching is an important part of knowledge base QA (question answering), in which the correct answer is selected from candidate answers. At present, Q&amp;A matching task faces the problem of lacking training data in new fields, which leads to poor performance and low efficiency of the question answering system. The paper puts forward a KBQA Q&amp;A matching model for deep feature transfer based on a bidirectional attention memory network, BamnetTL. It uses biattention to collect information from the knowledge base and question sentences in both directions in order to improve the accuracy of Q&amp;A matching and transfers knowledge from different fields through a deep dynamic adaptation network. BamnetTL improves the accuracy of Q&amp;A matching in the target domain by transferring the knowledge in the source domain with more training resources to the target domain with fewer training resources. The experimental results show that the proposed method is effective.
P‐2.27: Application of 3D reconstruction technology in VR industry

VR content is a key link in building a VR ecosystem, but the extreme lack of high-quality content has become the core shortcoming restricting the development of the VR industry, so in the medium and long term, the VR industry will shift from hardware technology upgrades to high-quality content-oriented, and is expected to usher in a new round of growth driven by business model innovation and content explosion. With 3D reconstruction, users can experience virtual scenes visually and audibly. The development of 3D reconstruction technology will bring great changes to existing players, and also greatly promote the rapid development of metaverse content Through continuous algorithm improvement, 3D reconstruction continues to be applied to all aspects of life.
Digital transformation in banking industry

Digital technology silently has changed society and the corporate environment. Accelerated digitalization has forced banks to rethink their strategies and respond swiftly and profitably to customer requests while providing secure and direct services. Technology has transformed banking beyond the brick-and-mortar branch banking of a decade ago. Over the previous several decades, computerization has altered the banking environment, with all financial activity carried to our wallets and us holding and managing our banks. In recent years, various scholarly papers have fueled studies on business transformation triggered by digital technologies.
Neural Networks: From the Perceptron to Deep Nets

Spin Glass Theory and Far Beyond, pp. 477-497 (2023) No AccessChapter 24: Neural Networks: From the Perceptron to Deep NetsMarylou Gabrié, Surya Ganguli, Carlo Lucibello, and Riccardo ZecchinaMarylou GabriéÉcole Polytechnique, Paris, France, Surya GanguliDepartment of Applied Physics, Stanford University, USA, Carlo LucibelloArtificial Intelligence Lab, Bocconi University, Milan, Italy, and Riccardo ZecchinaArtificial Intelligence Lab, Bocconi University, Milan, Italyhttps://doi.org/10.1142/9789811273926_0024Cited by:0 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: Artificial networks have been studied through the prism of statistical mechanics as disordered systems since the 1980s, starting from the simple models of Hopfield’s associative memory and the single-neuron perceptron classifier. Assuming a data is generated by a teacher model, asymptotic generalisation predictions were originally derived using the replica method and the online learning dynamics has been described in the large system limit. In this chapter, we review the key original ideas of this literature along with their heritage in the ongoing quest to understand the efficiency of modern deep learning algorithms. One goal of current and future research is to characterize the bias of the learning algorithms toward well generalising minima in a complex overparametrized loss landscapes with many solutions perfectly interpolating the training data. Works on perceptrons, two-layer committee machines and kernel-like learning machines shed light on the benefits of overparametrizations. Another goal is to understand the advantage of depth while models now commonly feature tens or hundreds of layers. If replica computations apparently fall short in describing general deep neural networks learning, studies of simplified linear or untrained models, as well as the derivation of scaling laws provide first elements of answers. FiguresReferencesRelatedDetails Recommended Spin Glass Theory and Far BeyondMetrics History PDF download
Model-Free Guidance Method for Drones in Complex Environments Using Direct Policy Exploration and Optimization

In the past few decades, drones have become lighter, with longer hang times, and exhibit more agile performance. To maximize their capabilities during flights in complex environments, researchers have proposed various model-based perception, planning, and control methods aimed at decomposing the problem into modules and collaboratively accomplishing the task in a sequential manner. However, in practical environments, it is extremely difficult to model both the drones and their environments, with very few existing model-based methods. In this study, we propose a novel model-free reinforcement-learning-based method that can learn the optimal planning and control policy from experienced flight data. During the training phase, the policy considers the complete state of the drones and environmental information as inputs. It then self-optimizes based on a predefined reward function. In practical implementations, the policy takes inputs from onboard and external sensors and outputs optimal control commands to low-level velocity controllers in an end-to-end manner. By capitalizing on this property, the planning and control policy can be improved without the need for an accurate system model and can drive drones to traverse complex environments at high speeds. The policy was trained and tested in a simulator, as well as in real-world flight experiments, demonstrating its practical applicability. The results show that this model-free method can learn to fly effectively and that it holds great potential to handle different tasks and environments.
A Deep Learning-Based Multi-Signal Radio Spectrum Monitoring Method for UAV Communication

Unmanned aerial vehicles (UAVs), relying on wireless communication, are inevitably influenced by the complex electromagnetic environment, attributed to the development of wireless communication technology. The modulation information of signals can assist in identifying device information and interference in the environment, which is significant for UAV communication environment monitoring. Therefore, in scenarios involving the communication of UAVs, it is necessary to find out how to perform the spectrum monitoring method to obtain the modulation information. Most existing methods are unsuitable for scenarios where multiple signals appear in the same spectrum sequence or do not use an end-to-end structure. Firstly, we established a spectrum dataset to simulate the UAV communication environment and developed a label method. Then, detection networks were employed to extract the presence and location information of signals in the spectrum. Finally, decision-level fusion was used to combine the output results of multiple nodes. Five modulation types, including ASK, FSK, 16QAM, DSB-SC, and SSB, were used to simulate different signal sources in the communication environment. Accuracy, recall, and F1 score were used as evaluation metrics. The networks were tested at different signal-to-noise ratios (SNRs). Among the different modulation types, FSK exhibits the most stable recognition performance across different models. The proposed method is of great significance for wireless radio spectrum monitoring in complex electromagnetic environments and is adaptable to scenarios where multiple receivers are used in vast terrains, providing a deep learning-based approach to radio monitoring solutions for UAV communication.
Construction of Asynchronous Relaxed Difference Set of <i>Q</i>-Ary Maximum-Length Channel-Hopping Sequences for Cognitive Radio Wireless Networks

In this letter, a new family of Relaxed Difference Set of Maximum-Length Channel-Hopping (RDSML-CH) sequences is constructed using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -ary m-sequences in an extension finite field F( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p^{2s}$ </tex-math></inline-formula> ), where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q=p^{s}&gt;3$ </tex-math></inline-formula> for any prime <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p \ge 2$ </tex-math></inline-formula> and integer <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s \ge 1$ </tex-math></inline-formula> . They have more ways of construction and flexible choices of period than the original RDSML-CH sequences. More importantly, by applying “light-weight” RDSs with fewer consecutive elements and improved channel assignments in the matrix, the maximum-time-to-rendezvous is reduced to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4q+3$ </tex-math></inline-formula> , which is much shorter than the period, a notable improvement over the original RDSML-CH and similar constructions.
A View on Model Misspecification in Uncertainty Quantification

Estimating uncertainty of machine learning models is essential to assess the quality of the predictions that these models provide. However, there are several factors that influence the quality of uncertainty estimates, one of which is the amount of model misspecification. Model misspecification always exists as models are mere simplifications or approximations to reality. The question arises whether the estimated uncertainty under model misspecification is reliable or not. In this paper, we argue that model misspecification should receive more attention, by providing thought experiments and contextualizing these with relevant literature.