Reviewerly/exHarmony huggingface dataset:
Plan, Prepare and Respond: A Holistic Cyber Security Risk Management Platform

Cyber attacks pose an escalating threat to organisations, carrying the potential for substantial financial losses, reputational damage, and legal ramifications. To effectively safeguard against such attacks, organisations must adopt a proactive approach that includes comprehensive planning and preparation for potential incidents. Unfortunately, many organisations struggle in this process due to a lack of efficient mechanisms, tools, training, and processes for defending, responding to, and recovering from cyber attacks. Cyber readiness serves as a vital strategy for preparing organisations to combat these threats. However, the existing frameworks and tools available for assisting organisations in handling cyber attacks and managing cybersecurity risks are insufficient. This paper presents a holistic solution aimed at enhancing organisations’ cyber readiness in responding to cyber attack events. The proposed approach involves conducting a thorough assessment of the organisation’s current security posture, identifying vulnerabilities and weaknesses, and offering recommendations for improvement. By implementing this comprehensive strategy, organisations can enhance their ability to withstand and respond effectively to cyber attacks.
CFENet: Leveraging CLIP Text Features for Enhanced Few-Shot Semantic Segmentation

Few-shot semantic segmentation (FSS) confronts the task of constructing class-agnostic models capable of effectively segmenting novel classes with minimal annotations. Our paper introduces a novel approach to FSS by integrating a text feature module, specifically leveraging the CLIP’s text feature module (CFM). Our primary objective is to augment segmentation model performance on novel classes with handful annotated images through the strategic inclusion of textual information. Simultaneously, we propose a multi-scale prior correlation module (MPM) that thoroughly exploits support-query image features at different scales, effectively supplementing and enhancing the prototype features in the images. The experimental results on the PASCAL-5i and COCO-20i dataset demonstrate a substantial performance boost in FSS task. The findings showcase the efficacy of our method, highlighting the excellent performance of the CFM and the MPM. This underscores the robustness and adaptability of our approach, establishing it as a promising solution for addressing the challenges posed by FSS task.
A simple yet efficient unsupervised anomaly detection network for industrial machine

Anomaly detection, aiming to detect items, events, or observations that do not match expected patterns or other items in a data set, is a crucial problem in many fields such as network intrusion detection, fraud detection and so on. Due to the rarity of labeled anomalous datasets, the research aims at detecting anomalies without anomalous samples and only using normal ones which called unsupervised learning. While traditional machine learning devoted to find a hard marginal between abnormal and normal ones, some unavoidable disturbance along with the signal can cause deviation. Moreover, the deep learning research of this subject focus on construct an autoencoder network only using normal data to memory the patterns of the normality. However, considering the generalization ability of neural network, it may occur that abnormal ones can reconstruct well thus leading the mistake of predicting. In this passage, to overcome the above two problem, we design a noise generator to simulate some reasonable noises, utilize and combine the neural network and memory bank to strengthen the ability of the network to memorize the patterns of the normal instances. We experiment and compare our algorithm on the MIMII sound anomaly dataset and the corresponding results shows the effectiveness and efficiency of our approach. It reaches 91.37 % in Accuracy and 93.76 % in F1 score. To our knowledge, it is the state of the art work up to now.
Multi-modal Domain Adaptation for Text Visual Question Answering Tasks

Domain adaptation aims to train a model on the labeled source data and unlabeled target data while improving the performance of the same model on the target domain. Recently, multi-modal domain adaptation is a relatively more challenging task compared with single-feature ones due to diverse forms of feature modalities. This paper aims to achieve transfer learning between two Visual Question Answering tasks: Text Visual Question Answering (Text-VQA) and Scene Text Visual Question Answering (ST-VQA). Compared to typical domain adaptation methods on single-modality analysis, the critical challenge for these tasks is that the input features contain complicated information from visual objects, question words, and optical character recognition (OCR) tokens in different forms. We consider a novel domain adaptation framework to facilitate knowledge transfer under the multiple modalities. Specially, the proposed Text-VQA domain adaptation framework (TDAF) consists of three important modules: (1) A mix-feature module fuses input features from multiple modalities and reduces the feature discrepancy for the cross-domain fused features. (2) A discrepancy discriminator focuses on keeping the source prediction correctly while detecting the ambiguous target samples by maximizing the discrepancy of probability outputs between two domains. (3) An self-supervised learning module is used for reducing information loss during representation learning by computing cosine similarity between collected inputs and decoded outputs. Through three joint modules, we successfully improve the performance of domain adaptation from Text-VQA to ST-VQA by raising 19.6% of accuracy and 18.2% of Average Normalized Levenshtein Similarity (ANLS). In addition, extra experiments were carried out to demonstrate the effectiveness of our proposed method compared with other SOTA approaches.
Deep Semi-supervised Anomaly Detection Using VQ-VAE

Anomaly detection is a fundamental and challenging task in computer vision, which determines whether an image contains anomaly or not. Prior works using autoencoders for anomaly detection are based on pixel-wise learning in the continuous latent space, which is inefficient since images contain a lot of redundant information. Meanwhile, for most of the anomaly detection methods, the training set only contains normal data due to the unavailability or paucity of labeled anomalous data. However, an exposure to a fraction of labeled anomalous images, even infinitesimal in size in comparison to the amount of normal data, can significantly improve the anomaly detection performance while slightly increasing labeling costs. In this paper, we propose a Semi-Supervised Vector Quantized Variational Autoencoder (ss-VQ-VAE) for anomaly detection. Our ss-VQ-VAE leverages discretized latent space embeddings of VQ-VAE [1] to reduce noise and redundancies for better reconstruction of normal data in comparison with anomalous data. At the core of ss-VQ-VAE, we introduce a new loss to incorporate a few anomalous images available to train the model. In addition, based on the VQ-VAE architecture, we further propose an anomaly score that compares the encoded features of the input with the dictionary embeddings in VQ-VAE to make more accurate predictions. Experimental results on two datasets, MVTec and the corrosion dataset, show the significance of the novelties in our method. The code is available online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/RenukaSharma/ss-vq-vae
Defense Guild: A Privacy-preserving Intrusion Detection System for Healthcare Data in Cloud

The economic benefits of cloud computing continue to motivate organizations across private and public sectors to transition their information technology to the cloud. Healthcare organizations however, find it challenging to achieve the desired cost reduction without compromising on security, privacy, or compliance. Machine learning is increasingly used to identify and detect attack behavior based on more versatile patterns. However, aggregation of all network data for training machine learning models negatively impacts privacy. Federated learning algorithms have recently gained attention as a means to ensure data privacy by enabling training to be distributed across individual clients. In this work, we propose Defense Guild, an intrusion detection system that relies on federated learning to detect attacks within partitioned cloud healthcare record systems. We apply federated learning techniques to maintain privacy boundaries between healthcare providers in the cloud. Experimental results show that the proposed Defense Guild system delivers attack detection rates and false positive rates comparable to traditional systems while enhancing privacy.
Zero Trust Model Implementation Considerations in Financial Institutions: A Proposed Framework

The finance industry faces an evolving threat landscape and increasing regulatory obligations, necessitating a comprehensive security framework. This paper proposes implementing the Zero Trust model in financial institutions, focusing on data protection, Identity and Access Management (IAM), and device and network security. The framework is evaluated through the development of a demo bank app, and its effectiveness in addressing security challenges is discussed. The IAM component demonstrates robust authentication and authorization processes, while device and network security measures protect against internal and external threats. Data protection mechanisms ensure the confidentiality and integrity of sensitive information. The implementation highlights strengths in comprehensive coverage and effective integration of security measures. Challenges include integration with legacy systems and managing the user experience. Insights and recommendations are provided. This framework enables financial institutions to establish a robust security framework, mitigating cyber threats and enhancing consumer trust.
Research on Pin Hole Measurement Based on Machine Vision

In order to meet the requirements of real-time measurement of pin holes in parts production, this paper uses a bilateral filter to denoise the image, image enhancement technology based on gamma transform, extracting edge features of pin holes using least squares fitting method, extracting horizontal and vertical structured light centerlines using improved directional template method. Improved algorithm of subpixel edge detection using Zernike orthogonal moments was proposed for circular edge detection, and conduct experiments on an online inspection system for pin hole machining quality based on machine vision technology. The experimental results indicate that the improved algorithm not only meets the accuracy requirements of pin hole machining, but also improves the calculation speed, and can be applied to online real-time measurement in production and manufacturing.
Sentence Pair Semantic Enhanced Matching Network for Text Information Retrieval

Text semantic matching is a core problem in Natural Language Processing (NLP), such as information retrieval and question answering, which is significant in intelligent human-computer interaction. However, most deep neural matching models are driven by external knowledge, which lacks fine-grained feature extraction from the sentences, leading to limited performance improvement. Therefore, this paper focuses on generating sentence semantic representations without external knowledge for sentence pair matching. We propose a Gated Attentive Convolutional Recurrent Neural Network (GACRNN), which incorporates a Gated Convolutional Neural Network (GCNN), Multi-scale Cross-Channel Attention Block (MC2AB), and bidirectional gate recurrent units (BiGRUs). First, a gate mechanism is introduced in the convolutional neural network to control the information interaction to extract multi-scale features from the sentence. Then, a multi-scale cross-channel attention mechanism is utilized to capture the feature dependencies at different scales in the channel dimension to generate expressive sentence representation. Finally, an extensive evaluation is conducted on two open-domain and two restricted-domain datasets. The experiment results show that the proposed model outperforms other baselines in terms of sentence pair semantic matching accuracy.
JVAP: A Joint Video Acceleration Processing Architecture for Online Edge Systems

In visual intelligent scenarios, large amounts of real-time video data are generated at the end. During the optimization process for different video tasks, frequent data copying between devices and hosts can be limited by data bandwidth, resulting in high system latency. We investigate computing bottlenecks in online video processing to reduce processing latency and improve efficiency. In this paper, we propose a joint video acceleration processing (JVAP) architecture for online edge systems. First, video-compressed streams are transmitted to the GPU for decoding and conversion of data content. Second, we design data pre-processing and post-processing modules to achieve specific functional operators and separately complete operator combinations and stitching. Different computing tasks can reuse the implemented operator library. Third, we modify the data interface of the inference task model to maintain the consistent flow of data in the GPU. We conduct experiments using videos of different qualities and model frameworks of varying scales. The results indicate that the proposed method enhances the average processing efficiency over 11% with respect to existing representative acceleration frameworks and extends the potential application of online intelligent inference algorithms.
Output Recurrent Fuzzy Neural LSTM-BLS Controller for Nonlinear Digital Time-Delay Dynamic Systems

In this paper, a novel control architecture is presented by integrating an Output Recurrent Fuzzy Neural Long Short-Term Memory (ORFNLSTM) and a Broad Learning System (BLS) for a class of single-input-single-output (SISO) nonlinear dynamic systems. This new controller, abbreviated as the ORFNLSTM-BLS controller, is especially proposed by combining the techniques of deep learning and broad learning method to establish an adaptive intelligent controller with an online deepest gradient descent learning algorithm to online update its weights. The ORFNLSTM-BLS controller aims to improve the performance of the ORFBLS controller by incorporating the memory of LSTM to handle time-series data more effectively. A sufficient condition of the proposed controller is established to accomplish its uniformly asymptotical stability. The effectiveness and superiority of the ORFNLSTM-BLS controller are well exemplified by carrying out one comparative simulation in comparison with a fixed-gain proportional-integral-derivative (PID) controller, an adaptive predictive PID controller augmented with ORFBLS (ORFBLS-APPID), and an existing ORFBLS controller in terms of two types of control performance indexes: the overall performance indexes and transient state performance indexes. The results show that the proposed ORFNLSTM-BLS controller outperforms the three existing control methods. The developed techniques would provide useful references for professionals working in the fields of process and servomechanism control.
Refining Nonparametric Mixture Models with Explainability for Smart Building Applications

Nonparametric mixture models are a powerful and flexible approach to data clustering; they account for uncertainty by using Bayesian inference to obtain a posterior distribution over the model's parameters and a better fit to the data. Additionally, nonparametric mixture models can adaptively adjust the number of components to fit the data. Incorporating asymmetric generalized Gaussian distribution (AGGD) within the mixture framework extends the capabilities of the widely used Gaussian mixture model (GMM) by adding parameters that control the shape and the skewness of the per-component distribution, which enables the model to capture diverse and complex data patterns. Furthermore, we incorporate explainability within our proposed infinite asymmetric generalized Gaussian mixture model (IAGGMM) to provide interpretable insights into the clustering results, enhancing the model's practicality and transparency. This integration facilitates a deeper understanding of the underlying data structures and the rationale behind the model's decisions, fostering trust and promoting the adoption of our approach in various real-world scenarios. In this study, we explore the application of occupancy estimation for optimizing energy efficiency and facility management in smart buildings. Our approach demonstrates superior performance in modelling complex and asymmetric data distributions, resulting in improved accuracy and adaptability for occupancy level estimation. Therefore, we achieve the optimal trade-off between model complexity and accuracy.
Meta Pattern Concern Score: A Novel Evaluation Measure with Human Values for Multi-classifiers

While advanced classifiers have been increasingly used in real-world safety-critical applications, how to properly evaluate the black-box models given specific human values remains a concern in the community. Such human values include punishing error cases of different severity in varying degrees and making compromises in general performance to reduce specific dangerous cases. In this paper, we propose a novel evaluation measure named Meta Pattern Concern Score based on the abstract representation of probabilistic prediction and the adjustable threshold for the concession in prediction confidence, to introduce the human values into multi-classifiers. Technically, we learn from the advantages and disadvantages of two kinds of common metrics, namely the confusion matrix-based evaluation measures and the loss values, so that our measure is effective as them even under general tasks, and the cross entropy loss becomes a special case of our measure in the limit. Besides, our measure can also be used to refine the model training by dynamically adjusting the learning rate. The experiments on four kinds of models and six datasets confirm the effectiveness and efficiency of our measure. And a case study shows it can not only find the ideal model reducing 0.53% of dangerous cases by only sacrificing 0.04% of training accuracy, but also refine the learning rate to train a new model averagely outperforming the original one with a 1.62% lower value of itself and 0.36% fewer number of dangerous cases.
ViSPer: A Multilingual TTS Approach Based on VITS Using Deep Feature Loss

In the field of multilingual Text-to-Speech (TTS), current methods, particularly those using Grapheme-to-Phoneme (G2P) conversions or character-level vocabs, often face challenges such as increased computational demands or higher error rates. To address these, we propose a VITS-based model, ViSPer, utilizing Byte-Pair Encoding (BPE) to create subword-level vocabs, simplifying text processing while enhancing pronunciation accuracy. To improve the expressiveness of synthesized speech, we further introduce a deep feature loss with features extracted by a multilingual ASR model, Whisper. Experimental results show that our approach outperforms the baseline system in both voice quality and naturalness of the synthesized speech in terms of subjective and objective evaluation metrics. Ablation studies demonstrate that each design is effective.
Path Planning of Mobile Robot Based on Deep Reinforcement Learning and Improved Artificial Potential Field

The reinforcement learning methods can adapt the state-action functions to the ever-changing environment, which helps robots find their ideal behavior when finishing tasks in complicated surroundings. In this article, we design a path planning method. Firstly, the reward function is designed by using the improved APF, then the successive state and action space are established, and finally, we train the path sizing model based on DDPG and APF algorithms in a randomly generated obstacle environment. We do simulation experiments and analyze the results, compared with traditional methods in path planning tasks, the new algorithm we proposed gives certain guidelines to the model during training and increases the success rate of training, which can greatly shorten the training time and get the optimized model faster.
Analysis and Prediction of Heart Stroke Using Lstm Deep Learning Approach

Combining big data and deep learning is a world-shattering technology that can significantly impact any objective if appropriately used. With a large volume of available healthcare datasets and progressions in deep learning techniques, systems are now well-equipped to predict the future trend of any health problems. From the literature survey, we found that the SVM was used to predict the heart failure rate without relating objective factors. Utilizing the intensity of important historical information in electronic health records (EHR), we have built a smart and predictive model utilizing long short-term memory (LSTM) and incorporating correlation between clinical variables. Hence, the fundamental commitment of this work is to predict the failure of the heart using an LSTM based on the patient’s electronic medicinal information. We have analyzed a dataset containing the medical records of 299 heart failure patients collected at the Faisalabad Institute of Cardiology and the Allied Hospital in Faisalabad (Punjab, Pakistan). We have found an increasing trend in our analysis which will contribute to advancing the knowledge in the field of heart stroke prediction.
Hidden Priors for Bayesian Bidirectional Backpropagation

Non-uniform prior probabilities between hidden layers improved deep neural classifiers trained with bidirectional backpropagation. The resulting Bayesian bidirectional backpropagation algorithm jointly maximizes the forward and backward network likelihoods along with the weight priors. The backward direction exploits a hidden regression that ordinary unidirectional backpropagation ignores. Simulations compared Laplacian, Gaussian, Cauchy, and the new sinc-squared hidden priors on the CIFAR-10 and CIFAR-100 balanced image data sets. These hidden priors improved the classification accuracy of deep neural classifiers compared with default uniform priors and default unidirectional backpropagation. They did so at little extra computational cost. Sinc-squared and Cauchy multivariate priors often had the best classification accuracy. Cauchy hidden priors gave sparse hidden weights similar to the Laplacian priors associated with sparse lasso regression.