Reviewerly/exHarmony huggingface dataset:
BFBE-Net: deep bilateral fusion and bilateral embedded network for real-time semantic segmentation

Real-time semantic segmentation has been challenging, and the fusion of features from different branches remains crucial to improvement. The two-branch structure has shown promising results in real-time semantic segmentation. However, upsampling feature maps from the semantic branch to match the detail branch leads to a loss of object feature information and compromises segmentation accuracy. We propose a deep bilateral fusion and bilateral embedded network (BFBE-Net) based on the encoder–decoder structure for real-time semantic segmentation to address these issues. The BFBE-Net adopts a two-branch design in the encoder, with a top-down fusion module and a bottom-up fusion module designed to integrate multi-scale context information in the channel dimension, and assigns different weights to detailed information and semantic information to enhance information characteristics. In the decoder, a bilateral embedded attention module under the guidance of spatial and channel attention integrates semantic and spatial features, gradually upsampling feature maps to reduce the loss of feature information. In addition, an enhanced aggregation pyramid pooling module is designed to efficiently extract contextual information by combining depth-wise asymmetric convolution. The proposed algorithm is evaluated on two benchmark datasets, Cityscapes and CamVid, achieving 78.5% mean intersection over union (mIoU) at 82 frames per second (FPS) on the Cityscapes test set and 79.2% mIoU at 131 FPS on the CamVid test set. The proposed BFBE-Net not only improves segmentation accuracy but also ensures real-time performance.
Query-Based Extractive Text Summarization Using Sense-Oriented Semantic Relatedness Measure

This paper presents a query-based extractive text summarization approach by using sense-oriented semantic relatedness measure. To find the query relevant sentences, we have to find semantic relatedness measure between query and input text sentences. To find the relatedness score, we need to know the exact sense of the words present in query and input text sentences. Word sense disambiguation (WSD) finds the actual meaning of a word according to its context of the sentence. We have proposed a WSD technique to extract query relevant sentences which is used to find a sense-oriented sentence semantic relatedness score between the query and input text sentence. Here, a feature-based method is presented to find semantic relatedness score between query and input text sentence. Finally the proposed query-based text summary method uses relevant and redundancy-free features to form cluster. There is a high probability that same featured cluster may contain redundant sentences. Therefore, a redundancy removal method is proposed to get redundancy-free sentences. In the end, redundancy-free query relevant sentences are obtained with an information rich summary. We have evaluated our proposed WSD technique with other existing methods by using Senseval and SemEval datasets and proposed Sense-Oriented Sentence Semantic Relatedness Score by using Li et al. dataset. We compare our proposed query-based extractive text summarization method with other methods participated in Document Understanding Conference and as well as with current methods. Evaluation and comparison state that the proposed query-based extractive text summarization method outperforms many existing and recent methods.
Few-Shot Relation Extraction via the Entity Feature Enhancement and Attention-Based Prototypical Network

In order to overcome the problems that the feature representation and classification effect of the existing methods need to be improved in complex contexts, this paper presents a novel few-shot relation extraction approach via the entity feature enhancement and attention-based prototypical network. The proposed model uses the pretrained RoBERTa model as the encoder while using the BiLSTM module for directional feature extraction. We further incorporate the entity feature enhancement module to improve the feature representation ability of the model. At last, the attention-based prototypical network is used to predict relations. The experimental results show that the proposed method not only outperforms the baseline models on the datasets from the bridge inspection and health domains but also achieves competitive results on the FewRel dataset in the general domain.
Security and privacy concerns in assisted living environments

Assisted living environments use interconnected devices to assist people with limitations in performing daily activities. In these environments, human activity recognition is critical for detecting abnormal situations, such as falls or health problems, and providing appropriate assistance to inhabitants. Despite their advantages, assisted living environments raise security and privacy concerns due to the collection and storage of sensitive data about their inhabitants. This paper addresses security and privacy concerns related to intelligent environments designed to assist individuals with limitations. It discusses the weaknesses of IoT devices and domotic technologies and presents research conducted by authors to mitigate these issues. This study shows that, despite hardware constraints, it is possible to design a relatively secure assisted living environment that prevents hacker attacks and data leaks. Additionally, it is essential to comprehend which information should be shared with external entities, such as health care services, and when to share it to ensure the inhabitants’ well-being. Our main goal has been to gather knowledge to improve the privacy and data protection of technology-rich assisted living environments implemented to assist people with limitations and their family members in performing their daily tasks.
Assessment of open-pit captive limestone mining areas using sentinel-2 imagery with spectral indices and machine learning algorithms

Limestone mining is a significant economic activity in India, accounting for around 10% of the GDP however, it has certain negative environmental consequences. The objective of this study is to determine the spatial distribution area of captive limestone mines using remote sensing datasets, spectral index, and machine learning algorithms and compare their area estimation with industrial field survey reports for the financial year 2019. The study area includes a limestone resource area of 2226.16 ha with an excavation area of 487.10 ha in 2019. In the present research, we used a high-resolution Sentinel-2A satellite dataset to map and compute the active mining area by implementing the Normalised Vegetation Index (NDVI), Iterative Self-Organizing Data Analysis Technique (ISODATA), K-Nearest Neighbours (KNN), and Random Forest (RF) algorithms in the QGIS 3.18 software tool. The RF classifier estimated a limestone mine area of 379.57 ha with user accuracy (UA) of 97.25% and producer accuracy (PA) of 99.18% with a kappa coefficient value of 0.957. The mine area was estimated at 417.47 ha with a UA of 98.99% and PA of 99.10% and kappa value of 0.947 of the KNN method, The NDVI method estimated 469.92 ha with a UA of 93.63% and PA of 92.04% and kappa value 0.685. This research confirmed that the RF classifier well performed in classification with overall accuracy (OA) of 95.79% to KNN (OA of 94.78%), NDVI (OA of 79.84%) classifiers, and ISODATA poor in classification with OA of 64.16%. This research assists limestone mine owners and environmental engineers in making environmentally sustainable decisions, eco-friendly mine design, and monitoring.
Novel developments of refractive power measurement techniques in the automotive world

Abstract Refractive power measurements serve as the primary quality standard in the automotive glazing industry. In the light of autonomous driving new optical metrics are becoming more and more popular for specifying optical quality requirements for the windshield. Nevertheless, the link between those quantities and the refractive power needs to be established in order to ensure a holistic requirement profile for the windshield. As a consequence, traceable high-resolution refractive power measurements are still required for the glass quality assessment. Standard measurement systems using Moiré patterns for refractive power monitoring in the automotive industry are highly resolution limited, wherefore they are insufficient for evaluating the camera window area. Consequently, there is a need for more sophisticated refractive power measurement systems that provide a higher spatial resolution. In addition, a calibration procedure has to be developed in order to guarantee for the comparability of the measurement results. For increasing the resolution, a measurement setup based on an auto-correlation algorithm is tested in this paper. In order to benchmark the proposed high-resolution refractive power measurement technique, a novel laser-based setup has been realized in the Volkswagen Laser Laboratory as a reference method tuned for high-accuracy measurements. Furthermore, a calibration procedure is established by using a single reference lens with a nominal refractive power of <?CDATA $100\,\mathrm{km}^{-1}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>100</mml:mn> <mml:msup> <mml:mrow> <mml:mi mathvariant="normal">k</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:math> . For the calibration of the entire measurement range of the system, the lens is tilted by an inclination angle orthogonal to the optical axis. The effective refractive power is then given by the Kerkhof model. By adopting the measurement and calibration procedure presented in this paper, glass suppliers in the automotive industry will be able to detect relevant manufacturing defects within the camera window area more accurately paving the way for a holistic quality assurance of the windshield for future advanced driver-assistance system (ADAS) functionalities. Concurrently, the traceability of the measurement results is ensured by establishing a calibration chain based on a single reference lens, which is traced back to international standards.
15. A Few Words in Conclusion

This collection of reflective essays is a treasure trove of advice, reflection and hard-won experience from experts in the field of open and distance education. Each chapter offers tried-and-tested advice for nascent academic writers, delivered with personal, rich, and wonderful stories of the authors’ careers, their process, their research and their writing, and the struggles and triumphs they have encountered in the course of their careers.
Accurate subvoxel location and characterization of edges in 3D images based on the Partial Volume Effect

An accurate estimation of the position, orientation, principal curvatures, and change in intensity of the edges in a 3D image provides highly useful information for many applications. The use of derivative operators to compute the gradient vector and the Hessian matrix in each voxel usually generates inaccurate results. This paper presents a new edge detector which is derived from the Partial Volume Effect (PVE). Instead of assuming continuity in the image values, edge features are extracted from the distribution of intensities within a neighborhood of each edge voxel. First, the influence of the intensities of the voxels in first- and second-order edges is analyzed to demonstrate that these types of edges can be precisely characterized from the intensity distribution. Afterward, this approach is extended to especially demanding situations by considering how adverse conditions can be tackled. This extension includes filtering noisy images, characterizing edges in blurred regions, and using windows with floating limits for close edges. The proposed technique has been tested on synthetic and real images, including some particularly difficult objects, and achieving a highly accurate subvoxel characterization of the edges. An open source implementation of our method is provided.
The applicability of G-BASE stream sediment geochemistry as a combined geological mapping, and prospective exploration tool for As-Co-Cu-Ni mineralisation across Cumbria, UK

Stream sediment geochemistry is a useful tool to analyse the geochemistry of the local geology within the source catchment area. This has significant applicability within the field of mineral exploration where understanding regional lithological geochemistry and how this is reflected in stream sediment geochemistry is needed, facilitating the identification of critical metal deposits. Successful identification of these deposits is essential to help tackle the deficit of these metals supply chains, especially for cobalt. This is in order to meet future carbon-neutral technological demand as part of global initiatives towards a more environmentally sustainable society. We make use of the UK Geochemical Baseline Survey of the Environment (G-BASE) dataset to demonstrate this stream sediment geochemical data has the potential to be used as a useful tool for isolating potential critical metals in host rocks across the UK Lake District. We reduced the dimensionality of the G-BASE stream sediment data, creating geochemical maps that identify a combination of volcanic, sedimentary, and plutonic lithologies lining up geological boundaries from established 50 k scale geological maps of the area. This was conducted through a combined statistical and mapping approach within QGIS and ioGAS. Furthermore, we derived average ore metal concentrations (Ag, As, Bi, Co, Cu, Mo, Ni, Sn, Zn) for the Skiddaw Group and the Borrowdale Volcanic Group, two established host lithologies for As-Co-Cu-Ni mineralisation. Average concentrations of Co in the Skiddaw have been modelled to be ~63 ppm, and ~ 28 ppm in the Borrowdale volcanics. These values, combined with As, Cu, and Ni modelled concentrations, and other available exploration-related data (structural maps, underlying batholith topography, mining history etc.) have allowed us to identify 10 prospective areas of interest for possible As-Co-Cu-Ni mineralisation. This workflow has strong applicability within critical metal exploration in the UK and other, prospective regions across the globe.
Design of recognition algorithm for multiclass digital display instrument based on convolution neural network

Digital display instrument identification is a crucial approach for automating the collection of digital display data. In this study, we propose a digital display area detection CTPNpro algorithm to address the problem of recognizing multiclass digital display instruments. We developed a multiclass digital display instrument recognition algorithm by combining the character recognition network constructed using a convolutional neural network and bidirectional variable-length long short-term memory (LSTM). First, the digital display region detection CTPNpro network framework was designed based on the CTPN network architecture by introducing feature fusion and residual structure. Next, the digital display instrument identification network was constructed based on a convolutional neural network using two-way LSTM and Connectionist temporal classification (CTC) of indefinite length. Finally, an automatic calibration system for digital display instruments was built, and a multiclass digital display instrument dataset was constructed by sampling in the system. We compared the performance of the CTPNpro algorithm with other methods using this dataset to validate the effectiveness and robustness of the proposed algorithm.
Multiple sparse graphs condensation

The high complexity of graph neural networks (GNNs) on large-scale networks hinders their industrial application. Graph condensation (GCond) was recently proposed to condense the original large-scale graph into a small-scale one to address the problem. The goal is to make GNNs trained on the condensed graph perform similarly to those trained on the original graph. GCond achieves satisfactory performance on some datasets. However, GCond uses a single fully connected graph to model the condensed graph, which limits the diversity of embeddings obtained, especially when there are few synthetic nodes. We propose Multiple Sparse Graphs Condensation (MSGC), which condenses the original large-scale graph into multiple small-scale sparse graphs. MSGC takes standard neighborhood patterns as the essential substructures and can construct various connection schemes. Correspondingly, GNNs can obtain numerous sets of embeddings, which significantly enriches the diversity of embeddings. Experiments show that, compared with GCond and other baselines, MSGC has significant advantages at the same condensed graph scale. MSGC can retain nearly 100% performance on Flickr and Citeseer datasets while reducing their graph scale by over 99.0%.
A robust and real-time DNN-based multi-baseline stereo accelerator in FPGAs

Stereo vision is essential to many robotic applications such as obstacle avoidance and 3D mapping in autonomous-driving cars, requiring substantial design efforts to achieve an appropriate balance between robustness and speed. Currently, most existing solutions for stereo vision accelerators are designed under two-camera stereo setups using handcrafted features for stereo estimation. This setup generally suffers from several drawbacks, including limited sensing range and high error rates. To resolve the above issues, we present a deep neural network based multi-baseline stereo accelerator for resource-constrained FPGAs, called Ultra-Stereo, that provides a robust and real-time solution for depth sensing. In Ultra-Stereo, binary neural network (BNN) is leveraged to obtain discriminative feature descriptors while a dynamic weighted cost fusion strategy helps to suppress the local minima in the matching cost function which effectively avoids incorrect stereo estimates. In addition, we provide a set of optimized hardware modules for Ultra-Stereo to estimate the depth map from multi-cameras in real-time. To process the data streams from multi-cameras efficiently, we first propose specific multi-baseline fusion hardware architectures against shorter and larger baseline stereo pairs. Then a time-sharing BNN is used to bridge the gap between processing speed and resource efficiency. Evaluation results demonstrate that Ultra-Stereo achieves higher matching accuracy while guaranteeing real-time responsiveness when compared with existing solutions.
Efficient realization of quantum balanced ternary reversible multiplier building blocks: A great step towards sustainable computing

Nowadays, quantum computing plays a significant role in reducing the execution time in complicated computations. There are different reversible algorithms for quantum computation and many quantum circuits theoretically designed until now. These designs are only built from reversible gates because reversibility is a necessary logic in quantum circuits. Additionally, ternary representation can allow designing a circuit with fewer inputs and outputs, leading to a considerable decline in size or number of qutrits. Although other papers in quantum ternary reversible logic are in unbalanced representation, this paper, for the first time, considers balanced ternary advantages to achieve a more efficient design for quantum multipliers as the main component in arithmetic blocks. Six criteria are introduced to show how balanced representation increases the overall performance. In this work, two general balanced ternary adder blocks (full-adder and half-adder) and two balanced ternary partial product generators (BTPPGs) are proposed, and also a 2 × 2 and a 3 × 3 balanced ternary multipliers built from these components. The quantum costs of proposed balanced ternary multipliers enhanced 17% for a 2 × 2 multiplier and 34% for a 3 × 3 multiplier.
Frequency-tunable microwave quantum light source based on superconducting quantum circuits

A nonclassical light source is essential for implementing a wide range of quantum information processing protocols, including quantum computing, networking, communication, and metrology. In the microwave regime, propagating photonic qubits that transfer quantum information between multiple superconducting quantum chips serve as building blocks of large-scale quantum computers. In this context, spectral control of propagating single photons is crucial for interfacing different quantum nodes with varied frequencies and bandwidth. Here we demonstrate a deterministic microwave quantum light source based on superconducting quantum circuits that can generate propagating single photons, time-bin encoded photonic qubits and qudits. In particular, the frequency of the emitted photons can be tuned in situ as large as 200 MHz. Even though the internal quantum efficiency of the light source is sensitive to the working frequency, we show that the fidelity of the propagating photonic qubit can be well preserved with the time-bin encoding scheme. Our work thus demonstrates a versatile approach to realizing a practical quantum light source for future distributed quantum computing.
ZTWeb: Cross site scripting detection based on zero trust

Policy defense technology is the mainstream XSS defense technology. However, defense mechanisms with fixed policies may hardly cover the attack surface persistently in dynamic environments. Moreover, the undifferentiated policy makes the malicious code and developer code have the same resource authorization, which leads to the game between the security and usability of the page. To tackle this problem, we propose a zero trust-based defense model - ZTWeb, which constructs differentiated and dynamic policies to balance the security and usability of the website. Specifically, ZTWeb micro-segments the protect surface code into the trust domain, executing different authorization policies based on the trust level of the code subject. The key of ZTWeb is to break the control risk of static policy authorization and create dynamic trust by continuously evaluating the behavior of untrusted domains. Trust evaluation takes the call sequence of sensitive resources as the judgment element. We associate the parent-child relationship between domains and divide the behavior branches within the domain to construct a complete, accurate, context-containing behavior sequence. Furthermore, the extracted sequence is regarded as a piece of text, and the TextCNN model is introduced to identify XSS attacks. We evaluate ZTWeb using real datasets collected from GitHub. The experimental results show that the model can achieve an accuracy of 99.7%, the overall performance overhead is low, and strong security is maintained without destroying the website's usability.
Bitcoin transaction fees and the decentralization of Bitcoin mining pools

In this paper, we propose a novel mechanism for decentralizing Bitcoin mining pools: the uncertain mining reward resulting from Bitcoin transaction fees. We present a simple model to demonstrate that risk-averse Bitcoin miners are more likely to allocate their computational power across multiple mining pools when transaction fees make up a large fraction of the mining reward. Our empirical study shows a negative correlation between the proportion of transaction fees and the decentralization of Bitcoin mining pools. Our study suggests that a more active Bitcoin market promotes decentralization and the mining industry will become more decentralized in the long run.