Reviewerly/exHarmony huggingface dataset:
Automatic Recognition of Continuous Malayalam Speech using Pretrained Multilingual Transformers

This article presents our work on the development of automatic speech recognition system for Malayalam language. Multilingual transformer models trained on self supervised learning technique has been fine-tuned for Malayalam language using publicly available annotated speech datasets. The datasets are diverse, which vary in the number of speakers, domain of speech and recording environments. We compare the results obtained by fine-tuning the transformer based models with traditional speech recognition approaches, in which the acoustic and language models are trained independently and then combined. The resulting word error rates vary widely with the test dataset under consideration. Our results point to the importance of considering pre-trained speech representations when developing ASR systems for languages with limited training resources. We make the model openly available on HuggingFace for reproducibility and adaptations to similar languages.
Parametrization of Optical Flow Kymograms

Laryngeal high-speed video endoscopy is used to visualize the vocal folds pattern of vibration for appropriate diagnosis and evaluation of voice disorders. However, it is tedious to examine LHSV frames merely through visual perception by explicating underlying vibration patterns, time dynamics and spatial dynamics. As a result, its crucial to employ a visualization approach that examines the vocal folds vibratory characteristics. To investigate and assess the vibratory patterns of vocal folds, image processing techniques can be employed to interpret motion, and to provide facilitative playbacks, and quantitative characteristics. Additionally, optical flow approaches can be utilized to investigate the local and global dynamics of the vocal fold vibrations. In this paper, optical flow kymograms and glottal optical flow waveforms are developed applying two distinct motion estimation method, namely Lucas-Kanade and Horn-Schunck. The vocal fold characteristics are analyzed both visually and quantitatively from OFKG images and GOFW waveforms. OFKG enables visualization of light artifacts. GOFW peak value was estimated using facilitative playback and the mathematical model was used to compute physical parameters like opening speed and closing speed. The correlation of these parameters has been derived from GOFW peak values of varying upper and lower margin amplitudes. The linear proportionality between the GOFW peak value and opening speed is evident in these quantifiable characteristics.
An Improved Numerical Method for Calculating the Fundamental Construction of Digital Technology

Quantum mechanics is the field of physics that studies what happens inside atoms. This is not for the faint hearted. Because quantum theory says that everything, we know about the physical world that we can perceive with our senses is wrong. In the quantum world, i.e., inside atoms, everything looks strange. Atomic particles can be in two places at the same time. This is called ‘Superposition’. In this paper, an improved numerical method has proposed for calculating the fundamental construction of digital technology. Apart from that, pairs of atomic particles have another peculiar property. No matter how far away one of the pair is, the other is well aware of it. If one is disturbed, the other will be stimulated at the same time. This is called ‘quantum entanglement’. Quantum physics is the physics of all matter as subatomic particles are the basic building blocks of all matter.
An Intelligent Fault Identification Model for Complex Wiring Connections in House Hold Heating Radiator Systems

Panel radiators are made of sheet steel. The surface is degreased, phosphate and primed with cathodes varnishing using a cataphoretic immersion method. (ETL) and powder coated (EPS). The output is a smooth glossy finish. The paint job is not only beautiful but also eco-friendly. In the standard version, the devices are offered in white from plate by optional order you can choose from any color side strips and top decorative grill, mounting kit with air vent valve and installed plugs. Models with a height of 300–900 mm additionally include a kit with a separating plug and mounting accessories. They are hung on the wall with the help of straps on the back of the radiator. Horizontal and vertical adjustment is possible when assembled. 200 mm high models are supplied without mounting plates and accessories and without insert. Low radiators can be installed on the floor or mounted on the wall. For this, special brackets are provided, which are ordered separately. There are four branch pipes on the sides of each radiator to connect to the heating system. Radiators are delivered already assembled in a cardboard box packed with protective film, which does not need to be removed during installation.
ASSESSMENT OF NEUROIMAGING DATA AND IDENTIFICATION OF ALZHEIMER'S DISEASE USING EXTREME LEARNING MACHINES

Alzheimer's disease (AD), one of the most common forms of dementia, is a cognitive disorder that is progressive in nature and causes a dynamic deterioration of the mental state of an individual. It severely damages the brain cells, neurotransmitters, and nerves, leading to irreparable damage to the brain, which is one of the major causes of dementia. Early identification, assessment, and timely diagnosis are of paramount importance to slow down the progression of the disease, which calls for the design and development of algorithms and technology-aided tools for accurate detection, diagnosis, and prediction of the severity of Alzheimer's disease. To provide a solution to this, we propose an extreme learning machine (ELM) algorithm that is trained on neuroimaging data from longitudinal MRI scans obtained from the OASIS database. We adopt an extensive feature engineering pipeline to choose the most significant features for early identification of the onset of dementia. We obtain an overall accuracy of 98.3&amp;#37;, sensitivity of 0.956, specificity of 0.962, and F1 score of 0.972. We also show that our proposed ELM algorithm outperforms several other contemporary classifiers based on a range of evaluation metrics. The paper also provides a feasibility analysis of the proposed model for real-time clinical deployment.
Discriminative Identity-Feature Exploring and Differential Aware Learning for Unsupervised Person Re-Identification

Unsupervised person re-identification (Re-ID) aims to learn discriminative representations for person retrieval from unlabeled data. Currently, state-of-the-art techniques accomplish this task by using instance contrastive learning, which contrasts the similarities of the instances in different views. However, existing contrastive methods only focus on the positive effects of inter-instance relationships, while neglecting the negative effects of intra-instance redundancy information. This redundancy information can generate invalid or spurious intra-class relationships during the instance contrasting process, which enlarges the intra-class gaps and increases the noisy pseudo-labels. To address this issue, we propose a discriminative identity-feature exploring and differential aware learning (DiDAL) framework to learn more discriminative intra-identity representations. Specifically, the DiDAL extracts intra-instance salient features by synthetic complementary attention, and further explores the discriminative identity features by modeling the relationship among these salient features based on graph neural networks. This strategy aims to reduce the intra-instance redundancy information. Moreover, DiDAL explores hard instances by leveraging the extracted intra-instance salient features, and matches an anchor with multiple hard positive instances to enhance the robustness of the model to noisy pseudo-labels. Extensive experiment results on two widely used person re-identification datasets and a vehicle re-identification dataset demonstrate the superiority of the proposed method compared with existing state-of-the-art methods.
HARPS: An Online POMDP Framework for Human-Assisted Robotic Planning and Sensing

The ability of autonomous robots to model, communicate, and act on semantic “soft data” remains challenging. The human-assisted robotic planning and sensing (HARPS) framework is presented for active semantic sensing and planning in human–robot teams to address these gaps by formally combining the benefits of online sampling-based partially observable Markov decision process policies, multimodal human–robot interaction, and Bayesian data fusion. HARPS lets humans impose model structure and extend the range of soft data by sketching and labeling new semantic features in uncertain environments. Dynamic model updating lets robotic agents actively query humans for novel and relevant semantic data, thereby improving model and state beliefs for improved online planning. Simulations of a unmanned aerial vehicle-enabled target search in a large-scale partially structured environment show significant improvements in time and beliefs required for interception versus conventional planning with robot-only sensing. A human subject study in the same environment shows an average doubling in dynamic target capture rate compared to the lone robot case and highlights the robustness of HARPS over a range of user characteristics and interaction modalities.
A Secure Key Expansion Algorithm for Present

The efficiency of a cryptographic algorithm in terms of security depends on the resistance against cryptanalytic attacks. Besides the complexity of the encryption algorithm, the key plays an essential role in the security against cryptanalytic attacks. The strength and complexity of the encryption algorithm do not suffice and serve the fundamental purpose of security if the key is compromised at any stage. Thus, apart from the cryptanalytically robust encryption algorithm, a strong key schedule is also essential to thwart possible attacks against a particular algorithm. PRESENT, a lightweight encryption technique with a simple design, is resistant to linear and differential attacks but has a weak key schedule and is susceptible to cryptanalytic attacks. This article proposes an efficient key scheduling algorithm for PRESENT lightweight encryption technique resistant against cryptanalytic attacks. Statistical tests examine the proposed key schedule’s cryptographic strength on the properties of subkeys produced in the key generation mechanism. The tests prove the efficiency of the proposed key schedule in terms of cryptanalytic attacks. Implementation and comparison of the efficacy of the proposed key schedule in terms of security and implementation costs are illustrated with the PRESENT-80 key schedule and the PRESENT-128 key schedule. The key scheduling algorithm is implemented on the advanced RISC machine (ARM) Cortex M3-based NXP LPC 1857 and 1768 hardware development platforms. The results indicate the efficiency of the proposed algorithm in terms of security, performance, and power consumption across the two hardware platforms.
Two-Stage Deep Single Image Super-Resolution with Multiple Blur Kernels for the Internet of Things

Single image super-resolution (SISR) aims to reconstruct a high-resolution image from a single low-resolution (LR) image. Although convolutional neural network (CNN)-based SISR methods greatly enhance image restoration, they face critical challenges. First, SISR models using CNNs process image patches uniformly regardless of importance, causing spatial inefficiency in computation and representation. However, due to resource constraints, edge devices in the Internet of Things (IoT) cannot bear heavy computations or large memory storage. Second, most of the existing SISR methods are designed only for the widely adopted bicubic degradation and cannot handle LR images with arbitrary blur kernels, resulting in poor recovery performance. To address these issues, in this paper we propose a two-stage semantic and spatial deep super-resolution (SSDSR) model suitable for the IoT environment. The proposed SSDSR model is capable of handling a variety of blur kernels (e.g., isotropic Gaussian, motion and disk blur) by effectively using their prior information. Moreover, the Semantic Feature Extraction (SFE) module enables the proposed model to focus on key areas of LR images rather than treating all pixels equally, which significantly reduces the computational load. The semantic information from the SFE module and the spatial information from the Spatial Attention module are fused adaptively, allowing the proposed model to extract key information in LR images, thereby increasing the representation capacity of the CNN and improving image recovery. Compared with state-of-the-art SISR methods on benchmark datasets, the proposed SSDSR model demonstrates superior performance. When run in real time on an IoT edge device, our model exhibits high computational efficiency and excellent image quality.
Data-Augmentation-Enabled Continuous User Authentication via Passive Vibration Response

Continuous identity authentication is critical for privacy protection throughout an entire user login session. In this article, we propose a continuous user authentication mechanism, namely, HandPass, which employs the vibration responses from hand biometrics and is passively activated by natural user-device interaction. Hand vibration responses are embedded in the mechanical vibration of a force-bearing body consisting of one mobile device and one user hand. A built-in accelerometer of the device can capture hand-dependent vibration signals. Considering the concealment of vibration generation and the nonreplicability of hand structure, it is difficult for attackers to counterfeit user identity. Moreover, for ensuring the robustness of authentication performance to tapping behavior interference, we construct a data augmentation module jointly leveraging a signal processing and learning-based pipeline. It can generate enough vibration responses representing hand structure biometrics under various behaviors, thereby making HandPass comprehensively understand vibration response variation. We prototype HandPass on smartphones, and extensive experiments demonstrate that HandPass can achieve satisfactory authentication accuracy.
A Survey on Privacy-Preserving Electronic Toll Collection Schemes for Intelligent Transportation Systems

As part of Intelligent Transportation Systems (ITS), Electronic toll collection (ETC) is a type of toll collection system (TCS) which is getting more and more popular as it can not only help to finance the government’s road infrastructure but also it can play a crucial role in pollution reduction and congestion management. As most of the traditional ETC schemes (ETCS) require identifying their users, they enable location tracking. This violates user privacy and poses challenges regarding the compliance of such systems with privacy regulations such as the EU General Data Protection Regulation (GDPR). So far, several privacy-preserving ETC schemes have been proposed. To the best of our knowledge, this is the first survey that systematically reviews and compares various characteristics of these schemes, including components, technologies, security properties, privacy properties, and attacks on ETCS. This survey first categorizes the ETCS based on two technologies, GNSS and DSRC. Then under these categories, the schemes are classified based on whether they provide formal proof of security and support security analysis. We also demonstrate which schemes specifically are/are not resistant to collusion and physical attacks. Then, based on these classifications, several limitations and shortcomings in privacy-preserving ETCS are revealed. Finally, we identify several directions for future research.
NMTF-LTM: Towards an Alignment of Semantics for Lifelong Topic Modeling

Aiming at mining high quality topics by accumulating and utilizing semantic knowledge for a stream of documents, lifelong topic modeling (LTM) has attracted more and more attentions recently. However, the permutation of topics may change over time, resulting in a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">semantic misalignment</i> between the topic representations of document chunks across the stream. Such a misalignment deteriorates the model performances of various downstream tasks, while it has been overlooked by the existing lifelong topic models. Towards addressing the misalignment of semantics, we formulate LTM as a problem of non-negative matrix tri-factorization (NMTF) and propose a consolidation framework (i.e., NMTF-LTM) to enforce an alignment in a mapped topic space. In addition, a distributed parallel algorithm, namely PNMTF-LTM, is developed to meet the real-time requirement for large-scale stream processing. Empirical results show that our method can not only obtain a superior alignment of semantics without loss of topic quality, but also achieve effective speedup when deployed to a high performance computing cluster.
Library Intelligent Book Return Robot Design

This thesis project requires the design of a library intelligent book return robot, which is mainly used to replace manual retrieval of books, so that it can return to its original position. Therefore, the robot is required to have a walking mechanism, a lifting mechanism, a grappling mechanism, and to be able to determine placement position in accordance with the book's specifications. Due to the characteristics of bookshelves in the library, the robot needs to move freely in a small space and complete the work of placing books, so the robot should have the characteristics of flexibility and stability. The research contents of this paper include the overall structure design of the library intelligent book recycling robot and the control scheme of the grasping system structure. The basic characteristics of the returning robot are analyzed, and the working principle and working characteristics of the returning robot are studied. And accordingly, the problems are raised in all aspects of the target, and the solutions are found by in-depth study of the problems faced in all aspects, and the overall framework is improved to complete the overall design.
STS: A Novel Deep Learning Method for Zero-inflated Crime Prediction

Crime prediction is of vital importance to the allocation of police resources and the maintenance of urban safety. Existing methods typically use deep learning to capture the intra-dependencies in spatial and temporal dimensions. However, numerous key challenges remain unsolved, for instance, sparse zero-inflated data due to crimes occurring with low frequency (which can lead to poor performance on real-world datasets), and both intra- and inter-dependencies of criminal patterns across spatial, temporal, and semantic (i.e., categories of crimes) dimensions. In this paper, we propose STS to jointly capture the intra- and inter-dependencies between the crime patterns and the influential factors in three dimensions. Further, we use a multi-task prediction module with a customized loss function to solve the zero-inflated issue. Experiments on two real-world datasets demonstrate the superiority of STS, which outperforms state-of-the-art methods by 26.02% and 18.95% in the mean absolute error and the root mean square error, respectively.
Site Safety Target Detection With Improved YOLOv5

YOLO is an object detection to redefine object detection as a regression problem, which uses Mosaic, Random affine and MixUp to enhance the data. However, the performance of the model trained on small datasets is poor, the detection accuracy is low, and practical application is difficult. On the foundation of the initial YOLOv5 model, this article incorporates enhanced Mosaic, HSV image enhancement, and Cutout data augmentation techniques and upgrades the original CIoU loss function to SIoU loss function. For target recognition applications on construction sites, a neural network for detecting helmets, falls and fires is trained based on this improved YOLOv5 model. This paper uses a self-built small dataset to verify the improvement of the training effect by improving the data augmentation and loss function. According to the experimental findings, the enhanced YOLOv5 algorithm beats the basic method on a small dataset by 4.7%.
Underwater Image Enhancement Based on Color Different Attenuation

Underwater images play a very important role in projects related to the underwater environment. But in the underwater environment, the R channel attenuation of the RGB image and other reasons make the quality of the image significantly different from that on land in terms of color and clarity. In order to correct the chromatic aberration of the underwater image and improve the clarity, we propose a DAC method based on the compensation of difference. First, we perform contrast stretching and image denoising on the image. Then extract the details layer and the base layer of the RGB channel of the image respectively, and use the detail layer of the G, B channels with more information to compensate for the detail layer of the R channel. Finally, we optimize the details of the picture through the grayscale world algorithm to obtain the true color of the picture. We compared with various methods in the public underwater dataset UIEB, and it turns out that our method can well solve the problems of chromatic aberration and blur.
Study on the Application of Tobacco Mechanical Optical Measuring

Optical measuring technology is widely used in the tobacco industry, not only for quality inspection of tobacco processing, but also for detecting defects in key mechanical components of tobacco processing equipment. Tobacco production requires the use of optical inspection technology to ensure quality. Optical precision measuring technology will therefore remain a research hotspot and focus in the future. In this paper, we first introduce the history of development and the characteristics of optical precision measurement technology. We then review the principle of measurement, key issues, and technical improvements over recent years in terms of structured light, binocular stereo vision, and digital speckle, as well as their applications in engineering. The calibration algorithm and measurement algorithm of structured light measurement technology need to be improved, and the optimization of the calibration technology and stereo matching technology of binocular stereo vision measurement needs to be improved urgently. How does the digital speckle correlation method adapt to a stronger measurement environment? Finally, we suggest a direction for further research that is worthy of further investigation based on the existing research.
Path Planning for Teams of Mobile Robots

Multi-robot path planning is required in more and more scenarios. Since it can be quite expensive using existing methods, more efficient methods can save a great amount of money and energy. Therefore, in this paper, three algorithms are analyzed, the subdimensional expansion, reinforcement learning, and improved gravitational search algorithm. The three algorithms all take the centralized approach and are all topology-based. They attempt to find the optimal path for a multi-robot system without collision with obstacles. By examining their approaches and results of simulations, their efficiency in performance can be validated. They are all proven to perform more efficiently in solving path planning problems for multi-robot systems in a static environment than existing algorithms. Thus, the centralized approach is promising in solving more complex multi-robot path planning problems. However, their efficiency is only proven in a static environment. More research needs to be done for these algorithms to be applied in a dynamic or even in a real-world environment.