Reviewerly/exHarmony huggingface dataset:
The Impact of Variable Selection and Transformation on the Interpretability and Accuracy of Fuzzy Models

Data transformation is an important step in Machine Learning pipelines which can strongly improve their performance. For instance, min-max normalization is often used to make all variables lie in the same range, while log-transformation is used to map data that is scattered across several orders of magnitude to a logarithmic space. Such transformations can be beneficial when the machine learning approach measures distance in a metric space, such as cluster-based approaches. These two transformation approaches can be combined to reveal hidden patterns in the data in the case of log-normally distributed data points, which commonly occur in biological and medical data. In this work we introduce a novel evolutionary approach designed to automatically determine the optimal log-transformation and selection of variables. Our approach is built around an interpretable AI system (created by pyFUME), so that all transformations are followed by inverse transformations to map back the values into the original universe of discourse, and preserve the interpretability of the results. We test our approach on two synthetic datasets, designed to reproduce a condition in which some variables are normally distributed, some variables are log-normally distributed, and some variables are just noise in the dataset. Our results show that our approach yields better performing models compared to conventional methods, and that the resulting model is also characterised by a better interpretability, making such approach particularly useful to study biomedical datasets.
Attribute-Guided Fashion Image Retrieval by Iterative Similarity Learning

Image retrieval methods in the fashion field mainly take advantage of query images that reflect user needs, without considering additional keywords that users can provide to specify the attributes in their interests. To achieve the fine-grained fashion retrieval, we propose an iterative similarity learning network (ISLN) for attribute-guided image retrieval, which takes a query image and a specified attribute as input, and outputs other images with the same or similar attribute values. The core of the network is the iterative similarity learning module, which leverages the aggressive learning ability of the deep neural network (DNN) to focus on the area of interest and extract a more accurate feature embedding during the learning process of image and text semantic mapping. Extensive experiments on FashionAI and DARN (+8.33% and +10.73% in mAP) datasets show that ISLN performs better than the state-of-the-art methods in fine-grained similarity retrieval tasks.
C<sup>3</sup>Net: A Cross-Channel Cross-Scale and Cross-Stage Network for Single Image Super-Resolution

In this paper, we propose a cross-channel, cross-scale, and cross-stage network (C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net) for single image super-resolution, which effectively shares the features learned from multiple channels, multiple scales, and multiple stages. Multi-scale spatial features are extracted in each stage in an encoder-decoder fashion. The channel attention is performed after each encoder to exploit the inter-channel dependencies. After that, we design a cross-stage and cross-scale feature sharing module to accelerate the feature sharing across different scales and different stages. The whole network is optimized by multiple similar stages to reduce the number of parameters. Finally, super-resolution images of multiple resolutions are reconstructed simultaneously. We evaluate the proposed network on four benchmark datasets by comparing it with eleven state-of-the-art methods. Comprehensive experiments show the proposed network outperforms state-of-the-art methods by fewer parameters. The source code is available at https://github.com/thinkerww/SR_Version.
Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels

CubeSat requirements in terms of size, weight, and power restrict the possibility of having redundant systems. Consequently, telemetry data are the primary way to verify the status of the satellites in operation. The monitoring and interpretation of telemetry parameters relies on the operator’s experience. Therefore, telemetry data analysis is less reliable, considering the data’s complexity. This paper presents a Machine Learning (ML) approach to detecting anomalies in solar panel systems. The main challenge inherited from CubeSat is its capability to perform onboard inference of the ML model. Nowadays, several simple yet powerful ML algorithms for performing anomaly detection are available. This study investigates five ML algorithm candidates, considering classification score, execution time, model size, and power consumption in a constrained computational environment. The pre-processing stage introduces the windowed averaging technique besides standardization and principal component analysis. Furthermore, the paper features the background, bus system, and initial operational data of BIRDS-4, a constellation made of three 1U CubeSats released from the International Space Station in March 2021, with a ML model proposal for future satellite missions.
DWT-SVD Based Watermarking for High-Resolution Medical Holographic Images

Watermarking is one of the most common techniques used to protect data’s authenticity, integrity, and security. The obfuscation in the frequency domain used in the watermarking method makes the watermarking stronger than the obfuscation in the spatial domain. It occupies an important place in watermarking works in imperceptibility, capacity, and robustness. Finding the optimal location to hide the watermarking is one of the most challenging tasks in these methods and affects the method’s performance. In this article, sample identification information is processed with the method of watermaking on the hiding environment created by using a chaos-based random number generator on biomedical data to provide solutions to problems such as visual attack, identity theft, and information confusion. In order to obtain biomedical data, a lensless digital in-line holographic microscopy (DIHM) setup was designed, and holographic data of human blood and cancer cell lines, which are widely used in the laboratory environment, were obtained. The standard USAF 1951 target was used to evaluate the resolution of our imaging setup. Various QR codes were generated for medical sample identification, and the captured medical data were processed by watermarking it with chaos-based random number generators. A new method using chaos-based discrete wavelet transform (DWT) and singular value decomposition (SVD) has been developed and applied to high-resolution data to eliminate the problem of encrypted data being directly targeted by third-party attacks. The performance of the proposed new watermarking method has been demonstrated by various robustness and invisibility tests. Experimental results showed that the proposed scheme reached an average PSNR value of 564588 dB and SSIM value of 0.9972 against several geometric and destructive attacks, which means that the proposed method does not affect the image quality and also ensures the security of the watermarking information. The results of the proposed method have shown that it can be used efficiently in various fields.
Computational Linguistics with Deep-Learning-Based Intent Detection for Natural Language Understanding

Computational linguistics explores how human language is interpreted automatically and then processed. Research in this area takes the logical and mathematical features of natural language and advances methods and statistical procedures for automated language processing. Slot filling and intent detection are significant modules in task-based dialogue systems. Intent detection is a critical task in any natural language understanding (NLU) system and constitutes the base of a task-based dialogue system. In order to build high-quality, real-time conversational solutions for edge gadgets, there is a demand for deploying intent-detection methods on devices. This mandates an accurate, lightweight, and fast method that effectively operates in a resource-limited environment. Earlier works have explored the usage of several machine-learning (ML) techniques for detecting intent in user queries. In this article, we propose Computational Linguistics with Deep-Learning-Based Intent Detection and Classification (CL-DLBIDC) for natural language understanding. The presented CL-DLBIDC technique receives word embedding as input and learned meaningful features to determine the probable intention of the user query. In addition, the presented CL-DLBIDC technique uses the GloVe approach. In addition, the CL-DLBIDC technique makes use of the deep learning modified neural network (DLMNN) model for intent detection and classification. For the hyperparameter tuning process, the mayfly optimization (MFO) algorithm was used in this study. The experimental analysis of the CL-DLBIDC method took place under a set of simulations, and the results were scrutinized for distinct aspects. The simulation outcomes demonstrate the significant performance of the CL-DLBIDC algorithm over other DL models.
Quantum memory and coherence dynamics of two dipole-coupled qubits interacting with two cavity fields under decoherence effect

This paper investigates the intrinsic decoherence effect on quantum memory and coherence dynamics of two dipole-coupled qubits in a non-degenerate bimodel cavity. Entropic uncertainty, entropy, and concurrence are utilized to examine the dynamics of quantum-memory-assisted entropic uncertainty relations, mixedness, and entanglement. The results indicate that the nonlinear interactions between the two qubits and the cavity generate quantum-memory-assisted entropic uncertainty, mixedness, and entanglement, which are reliant not only on the qubit–qubit interaction but also on the intrinsic decoherence and coherence intensity of the initial two-mode cavity states. For specific values of the dipole–dipole interaction, non-classical correlations can be enhanced By reducing the initial intensity coherence, the stability of the quantum memory-assisted entropic uncertainty relations, as well as the two-qubit mixedness of the intrinsic decoherence, are accelerated.
Critical Phenomena in Light–Matter Systems with Collective Matter Interactions

We study the quantum phase diagram and the onset of quantum critical phenomena in a generalized Dicke model that includes collective qubit-qubit interactions. By employing semiclassical techniques, we analyze the corresponding classical energy surfaces, fixed points, and the smooth Density of States as a function of the Hamiltonian parameters to determine quantum phase transitions in either the ground (QPT) or excited states (ESQPT). We unveil a rich phase diagram, the presence of new phases, and new transitions that result from varying the strength of the qubits interactions in independent canonical directions. We also find a correspondence between the phases emerging due to qubit interactions and those in their absence but with varying the strength of the non-resonant terms in the light-matter coupling. We expect our work to pave the way and stimulate the exploration of quantum criticality in systems combining matter-matter and light-matter interactions.
The Improved Framework for Traffic Sign Recognition Using Guided Image Filtering

Abstract In the lighting conditions such as hazing, raining, and weak lighting condition, the accuracy of traffic sign recognition is not very high due to missed detection or incorrect positioning. In this article, we propose a traffic sign recognition (TSR) algorithm based on Faster R-CNN and YOLOv5. The road signs were detected from the driver’s point of view and the view was assisted by satellite images. First, we conduct image preprocessing by using guided image filtering for the input image to remove noises. Second, the processed image is input into the proposed networks for model training and testing. Three datasets are employed to verify the effectiveness of the proposed method finally. The outcomes of the traffic sign recognition are promising.
Face detection for rail transit passengers based on single shot detector and active learning

COVID-19 spreads rapidly among people, so that more and more people are wearing masks in rail transit stations. However, the current face detection algorithms cannot distinguish between a face wearing a mask and a face not wearing a mask. This paper proposes a face detection algorithm based on single shot detector and active learning in rail transit surveillance, effectively detecting faces and faces wearing masks. Firstly, we propose a real-time face detection algorithm based on single shot detector, which improves the accuracy by optimizing backbone network, feature pyramid network, spatial attention module, and loss function. Subsequently, this paper proposes a semi-supervised active learning method to select valuable samples from video surveillance of rail transit to retrain the face detection algorithm, which improves the generalization of the algorithm in rail transit and reduces the time to label samples. Extensive experimental results demonstrate that the proposed method achieves significant performance over the state-of-the-art algorithms on rail transit dataset. The proposed algorithm has a wide range of applications in rail transit stations, including passenger flow statistics, epidemiological analysis, and reminders of passenger who do not wear masks. Simultaneously, our algorithm does not collect and store face information of passengers, which effectively protects the privacy of passengers.
Deep learning approaches for vehicle type classification with 3-D magnetic sensor

In the Intelligent Transportation Systems , it is crucial to determine the type of vehicles to improve traffic management, increase human comfort, and enable future development of transport infrastructures. This paper presents a deep learning-based vehicle type classification approach for intermediate road traffic. Specifically, a low-cost, easy-to-install, battery-operated 3-D traffic sensor is designed and developed. In addition, a total of 376 vehicle samples are collected, and the vehicles are identified into three different classes according to their structures: light, medium, and heavy. Firstly, an oversampling method is applied to increase the number of samples in the training set. Then, the signals are converted into time series for LSTM and GRU and 2-D images for transfer learning models. Finally, soft voting is proposed using the LSTM, GRU, and VGG16, which is the best transfer learning method for vehicle type classification. The developed system is portable, power-limited, battery-operated, and reliable. Comparative performance results show that the soft voting ensemble method using a deep learning classifier improves the accuracy and f-measure performances by 92.92% and 93.42%, respectively. Additionally, the battery lifetime of the developed magnetic sensor node can work for up to 2 years.
Lorentz chaotic trigonometric function pedigree based arithmetic optimization algorithm

With the increasing complexity and difficulty of numerical optimization problems in the real world, many efficient meta-heuristic optimization methods have been proposed to solve these problems. The arithmetic optimization algorithm (AOA) design is inspired by the distribution behavior of the main arithmetic operators in mathematics, including multiplication (M), division (D), subtraction (S) and addition (A). In order to improve the global search ability and local development ability of the AOA, the Lorentz triangle search variable step coefficient was proposed based on the broad-spectrum trigonometric functions combined with the Lorentz chaotic mapping strategy, which include a total of 24 search functions in four categories, such as regular trigonometric functions, inverse trigonometric functions, hyperbolic trigonometric functions, and inverse hyperbolic trigonometric functions. The position update was used to improve the convergence speed and accuracy of the algorithm. Through test experiments on benchmark functions and comparison with other well-known meta-heuristic algorithms, the superiority of the proposed improved AOA was proved.
An Efficient Method for Deidentifying Protected Health Information in Chinese Electronic Health Records: Algorithm Development and Validation

With the popularization of electronic health records in China, the utilization of digitalized data has great potential for the development of real-world medical research. However, the data usually contains a great deal of protected health information and the direct usage of this data may cause privacy issues. The task of deidentifying protected health information in electronic health records can be regarded as a named entity recognition problem. Existing rule-based, machine learning-based, or deep learning-based methods have been proposed to solve this problem. However, these methods still face the difficulties of insufficient Chinese electronic health record data and the complex features of the Chinese language.This paper proposes a method to overcome the difficulties of overfitting and a lack of training data for deep neural networks to enable Chinese protected health information deidentification.We propose a new model that merges TinyBERT (bidirectional encoder representations from transformers) as a text feature extraction module and the conditional random field method as a prediction module for deidentifying protected health information in Chinese medical electronic health records. In addition, a hybrid data augmentation method that integrates a sentence generation strategy and a mention-replacement strategy is proposed for overcoming insufficient Chinese electronic health records.We compare our method with 5 baseline methods that utilize different BERT models as their feature extraction modules. Experimental results on the Chinese electronic health records that we collected demonstrate that our method had better performance (microprecision: 98.7%, microrecall: 99.13%, and micro-F1 score: 98.91%) and higher efficiency (40% faster) than all the BERT-based baseline methods.Compared to baseline methods, the efficiency advantage of TinyBERT on our proposed augmented data set was kept while the performance improved for the task of Chinese protected health information deidentification.
An Enhanced Technique to Improve the Performance of Multi-image Compression Technique

In the field of multi-image compression technique, for n number of channels of more than images a single common code vector is formed by adjusting some tuning parameters. The size of the common code vector is very much less compared to the total size of generated code vector of n number of channels of multiple images that reduces amount of memory require to store it, but at the same time a huge amount of data loss occurs which degrade the visual quality of the images. In this work, a new method is proposed that reduces the amount of data loss occur and improve the visual quality of the decompressed images. This proposed method generates two common code vectors instead of one. Those code vectors of n number of channels of multiple images that play vital role in the formation of decompressed images are put as it is in first common code vector pool, whereas the code vectors that are less important are put in the second code vector pool by adjusting some tuning parameters. In these processes, very less amount of data loss occurs compared to the multi-image compression technique found in literature. This proposed method is applied on many standard images found in literature and images from UCID v.2 database. The experimental results are analyzed in terms of SSIM, PSNR, and the compression ratio achieved. From the experimental results, it is analyzed that the visual quality of the decompress images is improved significantly in terms of PSNR and SSIM by scarifying some amount of compression ratio.
Assessing the Quality of Covid-19 Open Data Portals

Open data portals can be used for several purposes, including transparency leverage, access to information, and decision-making support. Recently, the pandemic broke out in the new coronavirus, causing the whole world to suffer its effects. As a result, several countries, institutions, and research teams have worked to understand the behavior of this new virus. Since then, several open data portals have emerged that have set out to disseminate regional, national, and even international information about Covid-19 information. Therefore, it is known that open data resources, together with data-based methodologies, provide many opportunities to improve the response of different administrations, especially to the virus. We identified the variables and methodologies necessary to analyze the fundamental aspects of the quality of Covid-19’s open data portals and described the current aspects such as qualities, limitations, and difficulties found in these open data portals.
SoK: Combating threats in the digital supply chain

Supply chain attacks have been a security concern for many years, and their number and severity are expected to continue to grow in the years to come. In the ICT domain, ensuring the integrity of the supply chain is becoming an essential concern, as components are often manufactured, owned, and operated by different entities across the globe; thus, the cascading effects from a single attack may have a widely propagated impact. This is even more so when components are used in industrial control systems in critical infrastructures. It is therefore important to understand such attacks and attack vectors, the security challenges thereof, and measures to mitigate these; it is also important to educate cybersecurity professionals on these issues. This paper systematizes the security threats and challenges in digital supply chains, as well as relevant cybersecurity measures, discusses the necessary knowledge and skills that cybersecurity professionals should possess to ensure efficient management of the cyber risks in the digital supply chain, and systematizes areas where further research is needed.
The Construction of Shared Wisdom Teaching Practice through IoT Based on the Perspective of Industry-Education Integration

In this paper, to solve the difficulties of overseeing battling reasonings in cordial projects, we use a training-based insight perspective. Based on previous research, managing the tension between friendly government assistance and business is clearly a vital task for social money managers. Accomplishment. Though the social government helps reasoning and its associated characteristics and practices structure the underpinnings of social endeavours, social finance managers similarly need to ensure that their associations are financially sensible, making it essential to attract business reasoning. To this end, we cultivate an instructive program structure taking into account social practice intelligence to assist understudies in getting reasonable data and capacities, organize social business with venturing targets, and consolidate battling reasonings in inventive and legitimate ways.