Reviewerly/exHarmony huggingface dataset:
Using Siamese BiLSTM Models for Identifying Text Semantic Similarity

The field of Natural Language Processing (NLP) has flourished during the past decades in computer science, and that is largely due to the exponential growth of internet applications, like search engines, social network platforms, chatbots and Internet of Things (IoT). On the other hand, the robotics and human computer interaction fields have been largely connected to NLP development, by exploring ways of human-robot or human-computer communication in natural language. In this work, we deal with the problem of semantic similarity between text passages, which is one of the problems faced in many NLP applications, like human-computer/robot communication through natural language text. More specifically, we developed three deep learning models to face the problem: two variations of the Siamese BiLSTM model and a variation of the Simple BiLST model. We used two different techniques of word embeddings, (a) classic token-to-vec embedding using GloVe, and (b) one implementing the encoder part of the BERT model. Finally, we train and compare each model in terms of performance, through experimental studies on two datasets, MRPC (MSRP) and Quora, and we draw conclusions about the advantages and disadvantages of each one of them. Siamese BERT-BiLSTM model achieves accuracy 83,03% on the Quora dataset, which is comparable to the state of the art.
Taming Detection Transformers for Medical Object Detection

The accurate detection of suspicious regions in medical images is an error-prone and time-consuming process required by many routinely performed diagnostic procedures. To support clinicians during this difficult task, several automated solutions were proposed relying on complex methods with many hyperparameters. In this study, we investigate the feasibility of DEtection TRansformer (DETR) models for volumetric medical object detection. In contrast to previous works, these models directly predict a set of objects without relying on the design of anchors or manual heuristics such as non-maximum-suppression to detect objects. We show by conducting extensive experiments with three models, namely DETR, Conditional DETR, and DINO DETR on four data sets (CADA, RibFrac, KiTS19, and LIDC) that these set prediction models can perform on par with or even better than currently existing methods. DINO DETR, the best-performing model in our experiments demonstrates this by outperforming a strong anchor-based one-stage detector, Retina U-Net, on three out of four data sets.
Extending nnU-Net Is All You Need

Semantic segmentation is one of the most popular research areas in medical image computing. Perhaps surprisingly, despite its conceptualization dating back to 2018, nnU-Net continues to provide competitive out-of-the-box solutions for a broad variety of segmentation problems and is regularly used as a development framework for challenge-winning algorithms. Here, we use nnU-Net to participate in the AMOS-2022 challenge, which was a MICCAI22 challenge and comes with a unique set of tasks: not only is the dataset one of the largest ever created and boasts 15 target structures, but the second task of the competition also requires submitted solutions to handle both MRI and CT scans. Through careful modification of nnU-net's hyperparameters, the addition of residual connections in the encoder and the design of a custom postprocessing strategy, we were able to substantially improve upon the nnU-Net baseline. Advances in GPU memory capacity and processing speed allowed larger models and batch sizes than the default nnU-Net. Our final ensemble achieves Dice scores of 90.13 for Task 1 (CT) and 89.06 for Task 2 (CT+MRI) in a 5-fold cross-validation on the provided training cases.We submitted almost the same solution for both tasks, but adjusted the intensity normalization for task 2 to account for the additional MRI images. For the final testing stage, participants were asked to provide a docker container with their final solution to be applied to an unpublished test set. On both tasks, our nnU-Net extension achieved the first place by a wide margin.
Multi-scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset

In histopathology, scanner-induced domain shifts are known to impede the performance of trained neural networks when tested on unseen data. Multidomain pre-training or dedicated domain-generalization techniques can help to develop domain-agnostic algorithms. For this, multi-scanner datasets with a high variety of slide scanning systems are highly desirable. We present a publicly available multi-scanner dataset of canine cutaneous squamous cell carcinoma histopathology images, composed of 44 samples digitized with five slide scanners. This dataset provides local correspondences between images and thereby isolates the scanner-induced domain shift from other inherent, e.g. morphology-induced domain shifts. To highlight scanner differences, we present a detailed evaluation of color distributions, sharpness, and contrast of the individual scanner subsets. Additionally, to quantify the inherent scanner-induced domain shift, we train a tumor segmentation network on each scanner subset and evaluate the performance both in- and cross-domain. We achieve a class-averaged in-domain intersection over union coefficient of up to 0.86 and observe a cross-domain performance decrease of up to 0.38, which confirms the inherent domain shift of the presented dataset and its negative impact on the performance of deep neural networks.
Contrastive Representations for Unsupervised Anomaly Detection and Localization

Unsupervised anomaly detection in medical imaging aims to detect and localize arbitrary anomalies without requiring labels during training. Often, this is achieved by learning a data distribution of normal samples and detecting anomalies as regions in the image which deviate from this distribution. In the medical imaging domain, most current state-of-the-art methods use latent variable generative models operating directly on the images. However, generative models have been shown to mostly capture low-level features s.a. pixel-intensities instead of rich semantic features, which also applies to their representations. We circumvent this problem by proposing CRADL whose core idea is to model the distribution of normal samples directly in the low-dimensional representation space of an encoder which has been trained with a contrastive pretext-task. By utilizing the representations of contrastive learning we aim to fix the over-fixation on low-level features and aim to learn more semantic-rich representations. Our experiments on the task of anomaly localization on three distinct datasets show that 1) the contrastive representations are superior to generative latent variable models and 2) the CRADL framework shows competetive or superior performance to state-of-the-art.
Insights of Deep Learning-Based Video Anomaly Detection Approaches

Deep learning is a powerful computing strategy that has changed the landscape of computer vision. It has been used to tackle complicated cognitive tasks such as detecting abnormalities in videos. Anomalies in the video are events or objects in the footage that don’t fit the typical, learned patterns. Using deep learning, it is possible to automatically and in real-time identify unusual actions and objects like fights, riots, traffic rule violations, abrupt rushes, and the presence of weapons in restricted areas or abandoned luggage. Despite the challenges posed by video anomaly detection, this review offers a comprehensive assessment of published deep learning algorithms for the task. Future research can build on this work by understanding the existing methods to create more effective solutions. First, the challenges of video anomaly identification are discussed as the benefits of deep learning in anomaly detection. Furthermore, several types of abnormalities were explored, followed by diverse methodologies for anomaly identification. Furthermore, significant aspects of anomaly detection using deep learning, including learning approaches, were presented. Finally, numerous datasets used in anomaly detection were examined, followed by a discussion of deep learning-based algorithms for spotting video anomalies.
Metaverse 3C: Concept, Components, and Challenges in Travel and Tourism Sector

The metaverse is a global and immersive virtual shared environment formed by the fusion of virtual and physical reality, made possible through the use of augmented reality and virtual reality headsets. The vision of an immersive Internet as a massive, determined, united, and communal realm is vital to metaverse technology. In the context of digital reality, the metaverse was conceptualized as Web 3.0 or 3D Internet. A new open and decentralized virtual reality will emerge as a result of Web 3.0. The metaverse, which has entirely opened up in numerous fields, is made up of three key technical advancements: artificial intelligence, augmented reality, and virtual reality. The traditional tourism industry is having a serious impact because of the COVID-19 pandemic, which has a higher influence on lowering the country's GDP. As a result, it is critical to address this issue through digital technology with enhanced user experiences. This paper focuses on the fundamental concepts, components, and key challenges of metaverse technology in the travel and tourism sectors. This technology is still being developed and will require extensive research before it can be realized to its full potential.
Innovative Online Ticketing Model on an Intelligent Public Blockchains

Online Ticketing systems face the issue of eliminating ticket counterfeit and scalping while ensuring privacy protection and information openness. Another concern is ticketing fraud, when duplicate tickets allow unauthorised entry and cost hosts money. An Ethereum-based ticketing DApp would fix all the issues. Ticket holders may easily sell their tickets using the DApp. Ticket numbers are set, and each ticket is owned by a concert goer. When paying using an Ethereum wallet, the consumer gains ownership of the tickets. The ownership of tickets cannot be modified once changed, thereby preventing ticket fraud. When tickets are purchased, they become the purchasers’ property. This paper proposes a hybrid online ticketing model based on the blockchain to address these problems. It makes need for blockchain technology to make ticketing data transparent and utilises encryption techniques to keep user information private. It also employs the use of digital signature advanced technologies to assure ticket validity and includes a revolutionary ticket approval process to avoid digital piracy. An assessment of the system concludes with a description and analysis of the tests carried out during the installation of the system.
Blockchain-Based Implementation on Electronic Know Your Customer (e-KYC)

The electronic Know Your Customer (e-KYC) is basically a system for all the banking enterprises or for the identity providers to establish and provide a customer identity such as data verification process between agreeing parties. Nowadays, most of the banks are implementing e-KYC on cloud due to its efficient resource consumption and high degree of accessibility and availability of cloud computing. KYC is also a procedure by which banks gather information about their customers' identities and addresses. It is a regulated practice of completing due diligence on clients to verify their identity. This procedure aids in ensuring that clients are genuine and the bank's services are not being abused. The banks are in charge of completing the KYC process. In this paper, we have discussed the demerits of the process for establishing accounts and KYC may be a time-consuming, manual process that is duplicated across institutions. Financial institutions would be able to use blockchain to share KYC information, to improve compliance outcomes, efficiency, and customer service, and to avoid redundancy by gaining experience.
Some Results for the Redundancy Bound of the Optimal Ternary AIFV Codes

Ternary AIFV codes are almost instantaneous fixed-to-variable length codes, and are constructed based on two code trees. It is known that the redundancy of ternary AIFV codes is no more than one. In this paper, we provide a tighter upper bound on the redundancy of the ternary Huffman codes when the greatest probability of the source <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p_{max}$ </tex-math></inline-formula> is known. As a result, the redundancy of the optimal ternary AIFV codes is bounded by Huffman codes, since the ternary Huffman codes can be seen as the special AIFV codes. To achieve lower redundancy than Huffman codes, we also propose a method to construct a class of ternary AIFV codes with time complexity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n)$ </tex-math></inline-formula> for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> source symbols. In addition, the redundancy of the proposed AIFV codes is analyzed and compared with Huffman codes. Analyzing the ternary AIFV codes constructed by the algorithm, we derive a tighter redundancy upper bounds under some conditions, which are superior to Huffman codes.
A Sparse Sensor Placement Strategy Based on Information Entropy and Data Reconstruction for Ocean Monitoring

Sparse sensor placement strategies are applied to reconstruct a region’s full-state data conditioned to a limited number of sensors; particularly, crucial to ocean monitoring systems. In maritime systems, existing sparse sensor placement methods mainly consider the reconstruction error of data or rely on specific requirements. Considering how sensors acquire essential information for monitoring systems, the utilization of entropy from information theory becomes quite interesting. In this article, we show that entropy measurements on different quantities of information are sensitive to indicate the border areas, thus requiring a balance between the number of sensors needed and the amount of information collected by them in coastal areas. Due to such, we propose: 1) a novel sparse sensor placement strategy based on entropy, where the entropy measurements in temporal dimension are utilized for sample selection, so portions of samples selected are utilized for training data, significantly improving the training efficiency without sacrificing accuracy of subsequent data reconstruction. In the proposed strategy, 2) we use orthogonal triangle decomposition from linear algebra where a low-cost sensor is employed as pivot and in terms of spatial dimension, the entropy of each location is adopted as entropy weight to reconstruct full state data. Additionally, 3) the strategy employs a greedy algorithm of weighted column pivoting for the orthogonal triangle decomposition, which is designed to suit yet effectively seek additional information and minimal reconstruction error in each iteration processing step. Experimental results using sea surface temperature (SST) data show that the proposed strategy outperforms existing methods, acquiring more information, ensuring higher efficiency, and reducing costs while minimizing reconstruction errors.
Continuous Vector Fields for Precise Cable-Guided Landing of Tethered UAVs

This letter presents a method for the precise cable-guided landing of tethered drones. The proposed approach is based on a continuous velocity vector field that relies on the tether variables only. We assume that the vehicle is attached to a landing platform through a tensioned and free-of-collision cable whose length and angles are measured by a tether management system. We do not assume that the cable is taut. The method is suitable for highly degraded environments, where traditional landing strategies based on GNSS and vision are not feasible, such as dark underground mines and caves. This letter shows that, besides being easy to implement and adjust, the proposed vector field is proven to correctly guide the vehicle to its desired landing position. Experiments with a quad-rotor vehicle landing on static and moving platforms illustrate the precision, robustness, and efficacy of the method.
SUBDIVISION OF THE OBJECT INTO SOLID POLYGONS AND ITS APPLIED AND ECONOMIC ASPECTS

In this article, we have studied an efficient algorithm by which we can efficiently divide a geometric object into convex components.Algorithms for dividing an object into convex polygons are among the main tools in computer graphics, robotics, and machine vision. These algorithms divide complex objects into simpler subsets consisting of convex polygons, which makes it possible to simplify their processing, analysis and visualization.Convex polygons have many advantages over more complex objects. First, they are easily described mathematically and can easily be stored in computer memory. Second, they have simple two-dimensional properties, which allows us to quickly determine their position in space. Third, convex polygons are easily sorted and processed using standard computer graphics algorithms.The described algorithm moves along all the internal angles of the object and checks whether that internal angle is greater than 180°, if so, divides the object by the continuation of the side forming that vertex. The advantage of our developed algorithm is the optimal partitioning of the object by dividing it into as few convex polygons as possible. And the shortcoming of the algorithm is its complexity.
Bipolar Trapezoidal Fuzzy ARAS Method to Identify the Tuberculosis Comorbidities

Tuberculosis causes a high mortality rate worldwide. The World Health Organization (WHO) report shows that TB afflicts over 10.4 million people. It is found that TB comorbidities disease causes a rapid increase in disease level. Therefore, this study aims to analyze the communicable and non-communicable diseases that impact TB levels in patients. This process includes the bipolar trapezoidal fuzzy set to analyze the vagueness or ambiguity in positive and negative perceptions. The entropy technique is used for weighting the influencing criteria in the bipolar view. The top 10 TB-affected Indian states are chosen as criteria. The fuzzy additive ratio assessment (ARAS) technique is applied to determine the influencing comorbidities disease in TB. The obtained result is verified through the extant fuzzy evaluation based on distance from average solution (EDAS) method. Therefore, the proposed and comparative analysis provides the same result, which proves that the proposed method is very effective in the bipolar view.
Enhancing the Protection of Information in Digital Voting Using Application of Block Chain Technology

Electronic voting has evolved as a substitute for paper-based balloting in order to reduce redundancies and inconsistencies. In recent years, it has been found that paper-based balloting fails owing to security and privacy concerns, and it has been recommended that electronic balloting be used instead. To guarantee the security of the data, we devised and implemented an efficient hashing utilising SHA-256. The use of the block sealing idea aids in the adjustment of the block chain. The consortium block chain idea is utilised so that the block chain may only be accessed by authorised users/candidates and is maintained by the election commission. The architecture described in this article may give reliable polling technique results. The hashing technique (SHA-256), block generation, information collection, and final result declaration were the approaches used in this study. The block chain technique will be used to carry out all of the operations. The use of block chain in voting systems may improve information security and make maintaining sensitive data easier in the electronic voting process.
Research on Housing Engineering Cost Based on Improved Neural Network

In view of the low accuracy of the current housing engineering cost prediction, a engineering cost prediction model using PSO to optimize the parameters of BP neural network is proposed. The simulation results show that the prediction accuracy of BP neural network optimized by PSO exceeds 95%, and the prediction error is controlled within 5%. The prediction accuracy meets the requirement of accuracy in engineering cost research, which verifies the feasibility and application value of the prediction system proposed in this study.
Research on Music Intelligent Recommendation System Based on Spark Framework and Hybrid Algorithm

In view of the intelligent demand of music education model, this paper proposes to apply the hybrid recommendation algorithm in music intelligent recommendation to improve the accuracy of music recommendation. Among them, the advantages of collaborative filtering algorithm and tag recommendation algorithm are combined. The results show that when the time weight factor is 0.7, the accuracy, recall and F1 index value of the hybrid recommendation algorithm are stable. Compared with the algorithm before improvement and the label clustering recommendation algorithm, the hybrid recommendation algorithm has the best effect and superiority. At the same time, when the algorithm is applied to the system recommendation, it also obtains good recommendation effect. Therefore, the constructed hybrid recommendation algorithm and system based on big data and artificial intelligence can meet the needs of intelligent upgrading of music education mode.
A SlowFast-Based Violence Recognition Method

With the massive installation of cameras in cities, the requirement of equipment in an urban safety system has been basically satisfied. As a result, an available intelligent video-based safety system is very important. Video-based violence recognition method, which plays an absolutely important role in urban safety, seems to be a useful component of this system. However, a large part of existing violence recognition methods encounter the problems of low efficiency and inaccuracy owing to enormous challenges in accurately identifying the various violence events. In this paper, we propose a violence recognition algorithm based on the SlowFast model. In order to obtain a good performance, we modify the SlowFast model by both improving the convolutional structure and inserting plug-and-play modules. These two schemes can improve the speed and accuracy of violence recognition. The improved model achieves a high accuracy on several publicly available violence datasets surpassing some previous works and can be applied to violence detection in real-world scenarios, which is beneficial to fight crime and maintain social security.