Reviewerly/exHarmony huggingface dataset:
Feature selection considering interaction, redundancy and complementarity for outlier detection in categorical data

Feature selection is usually used as a preprocessing step for outlier detection to obtain significant performance. There is little work on feature selection for outlier detection in categorical data, and many studies do not consider the interaction and complementarity of features simultaneously, or even confuse them. In this paper, we first design criteria to measure the feature complementarity and redundancy based on the feature weight gain, and then propose a new feature selection method for outlier detection in categorical data which takes into account the feature relevance, interaction, redundancy and complementarity. The proposed method can find potential complementary features for the selected feature subset containing as much positive information as possible and remove redundant features without beneficial information. Extension evaluation results on 14 real-world datasets demonstrate that the proposed method acquires an average 77% dimensionality reduction rate and enables three distinct types of outlier detection methods to obtain superior AUC than that on the original dataset. Our method performs best in all outlier detectors on average compared with the other five state-of-the-art feature selection methods.
Formalizing Piecewise Affine Activation Functions of Neural Networks in Coq

Verification of neural networks relies on activation functions being piecewise affine (pwa)—enabling an encoding of the verification problem for theorem provers. In this paper, we present the first formalization of pwa activation functions for an interactive theorem prover tailored to verifying neural networks within Coq using the library Coquelicot for real analysis. As a proof-of-concept, we construct the popular pwa activation function ReLU. We integrate our formalization into a Coq model of neural networks, and devise a verified transformation from a neural network $$\mathcal {N}$$ to a pwa function representing $$\mathcal {N}$$ by composing pwa functions that we construct for each layer. This representation enables encodings for proof automation, e.g. Coq 's tactic lra – a decision procedure for linear real arithmetic. Further, our formalization paves the way for integrating Coq in frameworks of neural network verification as a fallback prover when automated proving fails.
Lifelog Discovery Assistant: Suggesting Prompts and Indexing Event Sequences for FIRST at LSC 2023

AI-assisted tools have become more prevalent than ever in the last few years. However, applying them to build a lifelog retrieval system is still non-trivial due to the disparity in interfaces and interactions. The Lifelog Search Challenge (LSC) aims to provide a testing ground where systems can be benchmarked in a highly competitive setting. In this paper, we present the fourth iteration of our participating system FIRST. For this year, we adopt generative models to equip the system with predictive ability rather than entirely relying on the user to input the query. We also index a sequence of images as an event for improved search speed. Finally, we demonstrate how the additional features can assist users in searching.
The Best of Both Worlds: Lifelog Retrieval with a Desktop-Virtual Reality Hybrid System

Personal lifelog data collections are becoming more common as a memory aid, as well as for analytical tasks, such as health and fitness analysis. Due to the multimodal and personal nature of lifelog data, interactive multimedia retrieval approaches are required to facilitate flexible and iterative query formulation and result exploration for retrieval and analysis. In recent years, novel user interface modalities have emerged, that allow new ways for users to interact with a retrieval system. Virtual reality, one such new modality, provides advantages as well as challenges for interactive multimedia retrieval in comparison to conventional desktop-based interfaces.
MyEachtra: Event-Based Interactive Lifelog Retrieval System for LSC’23

Retrieval is a fundamental challenge within the research community of lifelog and the Lifelog Search Challenge (LSC) has been an important annual benchmarking activity for interactive lifelog retrieval systems since 2018. This paper proposes MyEachtra (/mai-AK-truh/), a system designed for the upcoming LSC'23 workshop. Improved upon MyScéal, which was the top performing system from LSC'20 to LSC'22, MyEachtra includes modifications to address the challenges of non-owner user understanding of lifelog contexts and open-ended lifelog question answering. Specifically, MyEachtra shifts the focus from images to events as retrieval units. Events are segmented using location metadata as well as visual and time differences between successive images. A pilot study on different approaches to aggregate images into events was conducted to test the automatic performance of the system, which showed promising results. For known-item queries, showing only the top 3 events proved to be adequate to find relevant images. However, future evaluation of the performance for ad-hoc and question-answering queries is necessary for a complete analysis of the MyEachtra.
Multi-Mode Clustering for Graph-Based Lifelog Retrieval

As part of the 6th Lifelog Search Challenge, this paper presents an approach to arrange Lifelog data in a multi-modal knowledge graph based on cluster hierarchies. We use multiple sequence clustering approaches to address the multi-modal nature of Lifelogs in relation to temporal, spatial, and visual factors. The resulting clusters, along with semantic metadata captions and augmentations based on OpenCLIP, provide for the semantic structure of a graph including all Lifelogs as entries. Textual queries on this hierarchical graph can be expressed to retrieve individual Lifelogs, as well as clusters of Lifelogs.
Collaborative Extraction of Intervariable Coupling Relationships and Dynamics for Prediction of Silicon Content in Blast Furnaces

Soft sensor of silicon content in hot metal is challenging due to the nonlinearity, dynamics, and non-Euclidean coupling relationship between process variables. The inter-variable coupling relationship widely exists in the ironmaking process. Nevertheless, they are generally overlooked in modeling. To solve this issue, this paper proposes a novel temporal graph convolutional network with supervised graphs (TGCN-S). Different from traditional soft sensor methods, TGCN-S explicitly models the inherent non-Euclidean and irregular coupling relationships in the ironmaking process. First, a graph structure learning module is designed, which can adaptively learn potential inter-variable relationships from data. This module is embedded in TGCN-S to learn the supervised graph structures in an end-to-end manner. Second, a novel methodological framework is proposed based on the supervised graph structures, which can collaboratively extract the inter-variable coupling relationships and intra-variable dynamics. In this structure, information between process variables is selectively aggregated while taking into account the dynamics within variables. Finally, experiments based on the real ironmaking process demonstrate the effectiveness of the proposed method.
Fast Decoding of Images With Cryptcodes for Burst Channels

The concept of cryptcoding arises from the need to obtain secure and accurate transmission. This has led to an intensive development of coding theory and cryptography as scientific fields dealing with these problems. To ensure efficient and secure data transmission at the same time, the concept of cryptcoding is being developed, in which the coding and encryption processes are merged into one process. Cryptcodes provide correction of a certain number of errors in the transmitted message and data confidentiality, using only one algorithm. In this paper, we consider cryptcodes based on quasigroups, proposed elsewhere. Also, Burst-Cut-Decoding, Burst-4-Sets-Cut-Decoding, FastB-Cut-Decoding and FastB-4-Sets-Cut-Decoding algorithms for transmission of messages through burst channels have been defined and investigated elsewhere. Here, we investigate performances of these algorithms for transmission of images through burst channels. We made experiments for different channel parameters and compared the results obtained with different decoding algorithms of these cryptcodes. In all experiments, we considered the bit-error probability and the differences between the transmitted and decoded images. From the results presented in this paper we can conclude that Fast algorithms improve the performances of these cryptcodes for transmission of images over a burst channel. The best results are obtained by FastB-4-Sets-Cut-Decoding algorithm. In addition, to enhance the quality of the decoded images, we examined the application of a filter for visual correction of unsuccessfully decoded pixels using the surrounding pixels. The considered cryptcodes and presented results can be useful for application in satellite digital video broadcasting (DVB-S) coding and encryption schemes.
Multi-FusNet of Cross Channel Network for Image Super-Resolution

Image Super-resolution (SR) has gained considerable attention in artificial intelligence (AI) research and image-based applications.Recent deep learning-based SR models have demonstrated remarkable accuracy and perceptual quality in the resulting images.However, the computational cost and model parameters are the most challenging limitations in real-world applications.Additionally, designing an efficient and lightweight SR algorithm to improve the perceptual quality of the SR images is a critical issue.According to these considerations, we propose a Multi-FusNet of Cross Channel Network (MFCC) network by modeling a multipath residual network, named multi-RG, with cross-filtering fusion.Additionally, a pixel shuffling fusion technique is used to fuse low-level features into the up-sampled features of the multi-RG.The experimental results show the comparison of the proposed MFCC to the state-of-the-art SR models.The proposed method significantly reduces the number of network parameters (8.4 times compared to RCAN) while preserving the visual quality of the result and achieving the best PSNR value compared to the other state-of-the-art methods.
Multi-strategy Remora Optimization Algorithm for solving multi-extremum problems

Abstract A metaheuristic algorithm that simulates the foraging behavior of remora has been proposed in recent years, called ROA. ROA mainly simulates host parasitism and host switching in the foraging behavior of remora. However, in the experiment, it was found that there is still room for improvement in the performance of ROA. When dealing with complex optimization problems, ROA often falls into local optimal solutions, and there is also the problem of too-slow convergence. Inspired by the natural rule of “Survival of the fittest”, this paper proposes a random restart strategy to improve the ability of ROA to jump out of the local optimal solution. Secondly, inspired by the foraging behavior of remora, this paper adds an information entropy evaluation strategy and visual perception strategy based on ROA. With the blessing of three strategies, a multi-strategy Remora Optimization Algorithm (MSROA) is proposed. Through 23 benchmark functions and IEEE CEC2017 test functions, MSROA is comprehensively tested, and the experimental results show that MSROA has strong optimization capabilities. In order to further verify the application of MSROA in practice, this paper tests MSROA through five practical engineering problems, which proves that MSROA has strong competitiveness in solving practical optimization problems.
Binary Inscription Character Inpainting Based on Improved Context Encoders

Due to the limitations of textual datasets, there are currently few studies on character inpainting. Second, unlike image inpainting, character inpainting cannot replicate the results of image inpainting, and some methods are even inaccessible. Finally, the extraction of character features in the existing model is insufficient, resulting in the model being unable to reconstruct characters based on complete and accurate character features. In this paper, we first threshold the obtained inscription dataset to obtain a visually better-binarized inscription dataset. Second, we improve the Context encoders to design BCEs(Binary Context Encoders) and add dilated convolution to learn the structural features of the character. It has been experimentally proven that BCEs is not only slightly better than methods in the same field, but can also restore real-life inscription characters with missing strokes.
goldenAGER: A Personalized Feature Fusion Activity Recognition Model for Elderly

Physical and mental health are impacted as a person grows old.A Human Activity Recognition (HAR) system, which tracks a person's activity patterns and intervenes in case of an abnormal activity, could help elderly individuals to live independently.However, because of the strong intra class correlation between different activities, it is a challenging task to recognise such activities.Therefore, we proposed a personalized feature fusion algorithm, goldenAGER, which can be used to build as a model for abnormal activity recognition.In the initial stage, it extracts handcrafted HOG features and self-learned VGG-16 features to provide a rich description about the internal information of images.Then, the extracted features are provided as two different inputs to the deep neural network which are finally concatenated to classify the action type.The dataset is collected from the elderly volunteers over the age of 60 in a homogeneous environment consisting 10 classes of activities.The fusion of the features has resulted in 95% accuracy on primary dataset.The performance of the proposed model has also been tested on Microsoft Research (MSR) Action dataset giving accuracy of 93.08%.A comparison of our proposed model with the other existing models is also performed which shows that our model outperformed the existing models.
A Federated Learning Framework for Enforcing Traceability in Manufacturing Processes

The plethora of available data in various manufacturing facilities has boosted the adoption of various data analytics methods, which are tailored to a wide range of operations and tasks.However, fragmentation of data, in the sense that chunks of data could possibly be distributed in geographically sparse areas, hampers the generation of better and more accurate intelligent models that would otherwise benefit from the larger quantities of available data which are derived from various operations taking place at different locations of a manufacturing process.Moreover, in regulated industrial sectors, such as in the medical and the pharmaceutical fields, sector-specific legislation imposes strict criteria and rules for the privacy, maintenance and long-term storage of data.Process reproducibility is often an essential requirement in these regulated industrial sectors, and this issue could be supported by AI models which can be applied to enforce traceability, auditability and integrity of every initial, intermediate and final piece of data used during the AI model training process.In this respect, blockchain technologies could be potentially also useful for enabling and enforcing such requirements.In this paper, we present a multi-blockchain-based platform integrated with federated learning functionalities to train global AI (deep learning) models.The proposed platform maintains an audit trail of all information pertaining the training process using a set of blockchains in order to ensure the training process's immutability.The applicability of the proposed framework has been validated on three tasks by applying three state-of-the-art federated learning algorithms on an industrial pharmaceutical dataset based on two manufacturing lines, achieving promising in terms of both generalizability and convergence time.
The application of text mining in accounting

By facilitating the derivation of knowledge and qualitative measures from textual data, text mining techniques have come into vogue in various domains and industries. Namely in accounting, text mining outputs can elucidate, complement, and validate the customary quantitative data. This study creates an up-to-date view of text mining applications in accounting practice. Through a critical review of text mining literature, insight is given into the stages of a typical text mining process, contemporary text mining techniques that have been named valuable in an accounting context, and the information that can be obtained by applying these techniques.
AutoSegEdge: Searching for the edge device real-time semantic segmentation based on multi-task learning

Real-time semantic segmentation is a challenging task for resource-constrained edge devices. We propose AutoSegEdge, based on Neural Architecture Search (NAS), a semantic segmentation approach that runs on edge devices in real-time. Besides accuracy, we employ FLOPs and latency on the target edge devices as search constraints. Our work is probably one of the first attempts to translate multi-objectives NAS into Multi-Task Learning. Be inspired by Multi-Task Learning, we regard the sub-objective in multi-objective NAS as a learning task in Multi-Task Learning. The total loss function of the multi-objective NAS is deconstructed into the weighted sum of the sub-objective loss function. However, the conflict among the sub-objective will cause the searched networks to "architecture collapse." To avoid the multi-objectives NAS falls into "architecture collapse." Based on uncertainty, this paper proposes a method to learn the weights of sub-objective loss functions automatically. AutoSegEdge was discovered from an efficient cell-level search space that integrates multi-resolution branches. Additionally, AutoSegEdge employs knowledge distillation to further boost accuracy. Finally, we accelerated AutoSegEdge using NVIDIA's TensorRT and deployed it on the Nvidia Jetson NX. Experiments demonstrate that multi-objectives NAS only requires 1.5 GPU days to obtain the best result on a single Nvidia Tesla V100 GPU. On the Cityscapes dataset, AutoSegEdge achieved an mIoU of 70.3% with 16.6 FPS on the Nvidia Jetson NX (and 194.54 FPS on an Nvidia Tesla V100 GPU) at the original resolution (1024 × 2048) using TensorRT. Our method is 2–3 × faster than existing state-of-the-art real-time methods while maintaining competitive accuracy. We also conducted robustness experiments to analyze our method and modules. The code is available: https://github.com/douziwenhit/AutoSeg_edge.git.
Least-squares phase-shifting algorithm of coherence scanning interferometry with windowed B-spline fitting, resampled and subdivided phase points for 3D topography metrology

3D topography metrology of optical micro-structured surfaces is critical for characterizing surface texture and form errors. The coherence scanning interferometry (CSI) faces many challenges for improving the accuracy of 3D topography metrology. In this paper, the least-squares (L-S) phase-shifting algorithm of coherence scanning interferometry with windowed B-spline fitting, resampled and subdivided phase points is proposed, which provides high accuracy of 3D surface topography. The proposed algorithm avoids the introduction of complex judgment criterion of fringe orders, and tedious phase unwrapping. Comparison with generalized phase-shifting algorithm, the wrong textures do not appear on the reconstructed 3D surfaces and 2D profiles for optical grating and Micro-lens array (MLA) surfaces using the proposed algorithm. Experimental results show that a VLSI step surface has high repeated measurement accuracy with a standard deviation of 1.6 nm, the periodic error of the proposed algorithm for PMMA MLA sub-aperture units is about 20 nm, and the mean value of PMMA MLA four random sub-aperture units are 6.66 nm (Sq), 77.63 nm (Sp), 36.38 nm (Sv), 114.18 nm (Sz) and 4.90 nm (Sa), respectively.
Rational software agents with the BDI reasoning model for Cyber–Physical Systems

Cyber–Physical Systems (CPSs) are complex and heterogeneous systems. Interacting with the physical world makes CPS unpredictable because of unanticipated events. Therefore, a CPS needs to reason these events autonomously. Henceforward, suitable reasoning mechanisms, such as rational agents with deliberative capabilities, should be selected and integrated into the CPSs. In this way, the integrated multi-agents can reason on environmental changes to find a plan that sustains the system's operation for CPS. In addition, a layered architecture can pave the way to integrate the rational agents on embedded hardware and control the CPS. To this end, this paper presents an architecture, discusses a reference implementation and elaborates on the high-level integration of agents and CPS. Moreover, a complex and heterogeneous case study is provided to validate the effectiveness of rational agents in conducted experiments. Firstly, the rational agents utilising the belief–desire–intention (BDI) model required approximately three times less development time than simple-reflex agents. Secondly, the proposed approach resulted in up to 3 times less description complexity in language expressiveness. Lastly, the product quality is improved up to 66% by the rational agents and BDI model. As a result, our approach is beneficial to designing multi-agent CPS where it is aimed to use low-level control and high-level reasoning in a single platform.
Understanding expectations from and capabilities of climate-economy models for measuring the impact of crises on sustainability

The multi-faceted emergencies of our time (recession, pandemics, and international conflict) can disrupt progress towards sustainable development goals (SDGs). While climate-economy models can show pathways out of these disruptions, numerous stakeholder-informed enhancements are required to navigate through the interconnected SDG landscape. We introduce a novel Fuzzy Cognitive Maps (FCMs) approach that integrates Monte Carlo simulations, aiming to capture and understand the differences among modellers' and experts' perceptions of the impacts of such crises on broader sustainability, considering models' existing/planned capacity and uncertainty over perceived magnitude and propagation of impacts across SDGs. Our application shows that expert stakeholders appeared more concerned over the implications of these crises for sustainable development than modellers, but expectations were considerably constrained by model capabilities. SDGs 8 (growth), 7 (energy-sector sustainability), and 13 (climate action) were robustly found as most prone to examined shocks. Our proposed Monte Carlo Fuzzy Cognitive Mapping (MCFCM) approach also highlighted potentially important propagation effects as well as gaps in integrated assessment model capabilities to holistically study the SDG spectrum.
A certificate-based proxy re-encryption plus scheme for secure medical data sharing

The sharing of medical data is crucial for advancing medical research, but the risk of sharing medical data with malicious users poses a significant problem for smooth sharing among medical institutions. To address this issue, we propose a certificate-based proxy re-encryption plus (CBPRE+) scheme based on certificate-based proxy re-encryption (CBPRE) and message-level fine-grained controllable and non-transferable proxy re-encryption plus (PRE+). This scheme allows medical organizations to securely distribute their encrypted data to recipients through public cloud storage. The proposed scheme maintains the benefits of CBPRE, while additionally enabling fine-grained message-level sharing and non-transferability. This ensures that user data is only available to authorized health services, thereby preventing malicious disclosure. We validate and analyze the correctness, safety, and performance of our scheme, and our results show that the scheme has chosen-ciphertext security for the original ciphertext and chosen-plaintext security for the re-encrypted ciphertext. Our proposed scheme offers more advantages than the existing CBPRE scheme.