Intelligent fault diagnosis of worm gearbox based on adaptive CNN using amended gorilla troop optimization with quantum gate mutation strategy

The worm gearbox is a high-speed transmission system that plays a vital role in various industries. Therefore it becomes necessary to develop a robust fault diagnosis scheme for worm gearbox. Due to advancements in sensor technology, researchers from academia and industries prefer deep learning models for fault diagnosis purposes. The optimal selection of hyperparameters (HPs) of deep learning models plays a significant role in stable performance. Existing methods mainly focused on manual tunning of these parameters, which is a troublesome process and sometimes leads to inaccurate results. Thus, exploring more sophisticated methods to optimize the HPs automatically is important. In this work, a novel optimization, i.e. amended gorilla troop optimization (AGTO), has been proposed to make the convolutional neural network (CNN) adaptive for extracting the features to identify the worm gearbox defects. Initially, the vibration and acoustic signals are converted into 2D images by the Morlet wavelet function. Then, the initial model of CNN is developed by setting hyperparameters. Further, the search space of each Hp is identified and optimized by the developed AGTO algorithm. The classification accuracy has been evaluated by AGTO-CNN, which is further validated by the confusion matrix. The performance of the developed model has also been compared with other models. The AGTO algorithm is examined on twenty-three classical benchmark functions and the Wilcoxon test which demonstrates the effectiveness and dominance of the developed optimization algorithm. The results obtained suggested that the AGTO-CNN has the highest diagnostic accuracy and is stable and robust while diagnosing the worm gearbox.

An Adaptive feature mode decomposition based on a novel health indicator for bearing fault diagnosis

The vibration analysis of the bearing is very crucial because of its non-stationary nature and low signal-to-noise ratio. Therefore, a novel scheme for detecting bearing defects is put forward based on the extraction of single-valued neutrosophic cross-entropy (SVNCE) to address this issue. Initially, the artificial hummingbird algorithm (AHA) is used to make the feature mode decomposition (FMD) adaptive by optimizing its parameter based on a newly developed health indicator (HI) i.e. sparsity impact measure index (SIMI). This HI ensures full sparsity and impact properties simultaneously. The raw signals are disintegrated into different modes by adaptive FMD at optimal values of its parameters. The energy of these modes is calculated for different health conditions. The energy interval range has been decided based on energy eigen which are then transformed into single-valued neutrosophic sets (SVNSs) for unknown defect conditions. The minimum argument principle employs the least SVNCE values between SVNSs of testing samples (obtained from unknown bearing conditions) and SVNSs of training samples (obtained from known bearing conditions) to recognize the different defects in the bearing. It has been discovered that the suggested methodology is more adept at identifying the various bearing defects.

Can LLMs Compute with Reasons?

Large language models (LLMs) often struggle with complex mathematical tasks, prone to "hallucinating" incorrect answers due to their reliance on statistical patterns. This limitation is further amplified in average Small LangSLMs with limited context and training data. To address this challenge, we propose an "Inductive Learning" approach utilizing a distributed network of SLMs. This network leverages error-based learning and hint incorporation to refine the reasoning capabilities of SLMs. Our goal is to provide a framework that empowers SLMs to approach the level of logic-based applications achieved by high-parameter models, potentially benefiting any language model. Ultimately, this novel concept paves the way for bridging the logical gap between humans and LLMs across various fields.

Deep Generative Attacks and Countermeasures for Data-Driven Offline Signature Verification

While previous studies have explored attacks via random, simple, and skilled forgeries, generative attacks have received limited attention in the data-driven signature verification (DASV) process. Thus, this paper explores the impact of generative attacks on DASV and proposes practical and interpretable countermeasures. We investigate the power of two prominent Deep Generative Models (DGMs), Variational Auto-encoders (VAE) and Conditional Generative Adversarial Networks (CGAN), on their ability to generate signatures that would successfully deceive DASV. Additionally, we evaluate the quality of generated images using the Structural Similarity Index measure (SSIM) and use the same to explain the attack's success. Finally, we propose countermeasures that effectively reduce the impact of deep generative attacks on DASV. We first generated six synthetic datasets from three benchmark offline-signature datasets viz. CEDAR, BHSig260- Bengali, and BHSig260-Hindi using VAE and CGAN. Then, we built baseline DASVs using Xception, ResNet152V2, and DenseNet201. These DASVs achieved average (over the three datasets) False Accept Rates (FARs) of 2.55%, 3.17%, and 1.06%, respectively. Then, we attacked these baselines using the synthetic datasets. The VAE-generated signatures increased average FARs to 10.4%, 10.1%, and 7.5%, while CGAN-generated signatures to 32.5%, 30%, and 26.1%. The variation in the effectiveness of attack for VAE and CGAN was investigated further and explained by a strong (rho = -0.86) negative correlation between FARs and SSIMs. We created another set of synthetic datasets and used the same to retrain the DASVs. The retained baseline showed significant robustness to random, skilled, and generative attacks as the FARs shrank to less than 1% on average. The findings underscore the importance of studying generative attacks and potential countermeasures for DASV.

Dictionary Attack on IMU-based Gait Authentication

We present a novel adversarial model for authentication systems that use gait patterns recorded by the inertial measurement unit (IMU) built into smartphones. The attack idea is inspired by and named after the concept of a dictionary attack on knowledge (PIN or password) based authentication systems. In particular, this work investigates whether it is possible to build a dictionary of IMUGait patterns and use it to launch an attack or find an imitator who can actively reproduce IMUGait patterns that match the target's IMUGait pattern. Nine physically and demographically diverse individuals walked at various levels of four predefined controllable and adaptable gait factors (speed, step length, step width, and thigh-lift), producing 178 unique IMUGait patterns. Each pattern attacked a wide variety of user authentication models. The deeper analysis of error rates (before and after the attack) challenges the belief that authentication systems based on IMUGait patterns are the most difficult to spoof; further research is needed on adversarial models and associated countermeasures.

Synthesizing Human Gaze Feedback for Improved NLP Performance

Integrating human feedback in models can improve the performance of natural language processing (NLP) models. Feedback can be either explicit (e.g. ranking used in training language models) or implicit (e.g. using human cognitive signals in the form of eyetracking). Prior eye tracking and NLP research reveal that cognitive processes, such as human scanpaths, gleaned from human gaze patterns aid in the understanding and performance of NLP models. However, the collection of real eyetracking data for NLP tasks is challenging due to the requirement of expensive and precise equipment coupled with privacy invasion issues. To address this challenge, we propose ScanTextGAN, a novel model for generating human scanpaths over text. We show that ScanTextGAN-generated scanpaths can approximate meaningful cognitive signals in human gaze patterns. We include synthetically generated scanpaths in four popular NLP tasks spanning six different datasets as proof of concept and show that the models augmented with generated scanpaths improve the performance of all downstream NLP tasks.

GANTouch: An Attack-Resilient Framework for Touch-based Continuous Authentication System

Previous studies have shown that commonly studied (vanilla) implementations of touch-based continuous authentication systems (V-TCAS) are susceptible to active adversarial attempts. This study presents a novel Generative Adversarial Network assisted TCAS (G-TCAS) framework and compares it to the V-TCAS under three active adversarial environments viz. Zero-effort, Population, and Random-vector. The Zero-effort environment was implemented in two variations viz. Zero-effort (same-dataset) and Zero-effort (cross-dataset). The first involved a Zero-effort attack from the same dataset, while the second used three different datasets. G-TCAS showed more resilience than V-TCAS under the Population and Random-vector, the more damaging adversarial scenarios than the Zero-effort. On average, the increase in the false accept rates (FARs) for V-TCAS was much higher (27.5% and 21.5%) than for G-TCAS (14% and 12.5%) for Population and Random-vector attacks, respectively. Moreover, we performed a fairness analysis of TCAS for different genders and found TCAS to be fair across genders. The findings suggest that we should evaluate TCAS under active adversarial environments and affirm the usefulness of GANs in the TCAS pipeline.

iCTGAN--An Attack Mitigation Technique for Random-vector Attack on Accelerometer-based Gait Authentication Systems

A recent study showed that commonly (vanilla) studied implementations of accelerometer-based gait authentication systems ($v$ABGait) are susceptible to random-vector attack. The same study proposed a beta noise-assisted implementation ($\beta$ABGait) to mitigate the attack. In this paper, we assess the effectiveness of the random-vector attack on both $v$ABGait and $\beta$ABGait using three accelerometer-based gait datasets. In addition, we propose $i$ABGait, an alternative implementation of ABGait, which uses a Conditional Tabular Generative Adversarial Network. Then we evaluate $i$ABGait's resilience against the traditional zero-effort and random-vector attacks. The results show that $i$ABGait mitigates the impact of the random-vector attack to a reasonable extent and outperforms $\beta$ABGait in most experimental settings.

Sparse Image based Navigation Architecture to Mitigate the need of precise Localization in Mobile Robots

Traditional simultaneous localization and mapping (SLAM) methods focus on improvement in the robot's localization under environment and sensor uncertainty. This paper, however, focuses on mitigating the need for exact localization of a mobile robot to pursue autonomous navigation using a sparse set of images. The proposed method consists of a model architecture - RoomNet, for unsupervised learning resulting in a coarse identification of the environment and a separate local navigation policy for local identification and navigation. The former learns and predicts the scene based on the short term image sequences seen by the robot along with the transition image scenarios using long term image sequences. The latter uses sparse image matching to characterise the similarity of frames achieved vis-a-vis the frames viewed by the robot during the mapping and training stage. A sparse graph of the image sequence is created which is then used to carry out robust navigation purely on the basis of visual goals. The proposed approach is evaluated on two robots in a test environment and demonstrates the ability to navigate in dynamic environments where landmarks are obscured and classical localization methods fail.

Estimation and Prediction of Deterministic Human Intent Signal to augment Haptic Glove aided Control of Robotic Hand

The paper focuses on Haptic Glove (HG) based control of a Robotic Hand (RH) executing in-hand manipulation. A control algorithm is presented to allow the RH relocate the object held to a goal pose. The motion signals for both the HG and the RH are high dimensional. The RH kinematics is usually different from the HG kinematics. The variability of kinematics of the two devices, added with the incomplete information about the human hand kinematics result in difficulty in direct mapping of the high dimensional motion signal of the HG to the RH. Hence, a method is proposed to estimate the human intent from the high dimensional HG motion signal and reconstruct the signal at the RH to ensure object relocation. It is also shown that the lag in synthesis of the motion signal of the human hand added with the control latency of the RH leads to a requirement of the prediction of the human intent signal. Then, a recurrent neural network (RNN) is proposed to predict the human intent signal ahead of time.