Reviewerly/exHarmony huggingface dataset:
3D Virtual Reality Implementation of Tourist Attractions Based on the Deep Belief Neural Network

In today's society, information technology is widely used, and virtual reality technology, as one of the emerging frontier technologies, has entered a stage of rapid development. Virtual reality is the use of computer technology to simulate the real-life environment into a virtual simulation environment, with the help of special equipment to realize the natural interaction between users and technical environment, in which the tourism industry is the most widely used. In order to realize 3D virtual reality of tourist attractions and improve users' immersive experience in the process of interaction, the deep belief neural network is introduced to realize the target recognition and reconstruction in virtual reality. The results show that the algorithm has excellent performance in target recognition and target reconstruction, and deep belief networks improve the accuracy by 0.57% and 0.81% and the accuracy by 0.21% and 2.06%, respectively, compared with the current optimal algorithm in target recognition of 12 and 20 view regular projection images. Compared with the current optimal algorithm, deep belief networks are reduced by 0.2%, 3.7%, and 0.6%, respectively. The accuracy index was increased by 2%, 0.1%, and 0.1%, respectively. The above results show that the proposed algorithm based on the deep belief neural network can realize 3D virtual reality of complex scenes such as tourist attractions according to its excellent performance.
Role of information security-based tourism management system in the intelligent recommendation of tourism resources

With the rapid development of tourism and the Internet industry, tourism activities have increasingly become a fashion behavior of people. The role of intelligent tourism resources in tourism activities has gradually become prominent. In order to meet the needs of all kinds of users, the tourism management system services are developing in the direction of diversification and individualization, and recommending the tourism resource products that best meet the needs of users to users has become a top priority. This article aims to improve the practical value of the system through the intelligent functions of the tourism management system based on information security in the intelligent recommendation of tourism resources. The tourism management system can display the received information about tourists. Through the experimental research of the accompanying information security algorithm and the analysis of the recommendation of the tourism system, the intelligent functions of the tourism management system based on information security can be captured in the intelligent recommendation of tourism resources. Develop the tourism management system to solve efficiency problems and realize tourism management information. Experimental results show that based on information security, 80% of tourists have become a popular choice for smart recommendation countries, which will bring more convenience to tourists during the game.
Visualizing QUIC and HTTP/3 with qlog and qvis

The new QUIC and HTTP/3 (H3) protocols being finalized by the IETF are powerful but also highly complex. They combine advanced approaches from predecessors such as TCP (e.g., congestion and flow control, reliability) and HTTP/2 (H2) (e.g., stream multiplexing, prioritization), with cutting-edge features (e.g., 0-RTT data, connection migration). As QUIC runs on top of UDP, these intricate systems have to be re-implemented from scratch, often in userspace, which has turned out to be error-prone. QUIC also fully integrates TLS 1.3 and is end-to-end encrypted at the transport layer. This means that, unlike with TCP, elements like packet and acknowledgement numbers are indiscernible to passive observers in encrypted packet traces (e.g., .pcap files analyzed with tools like Wireshark). As such, QUIC requires (ephemeral) TLS keys for even high-level analysis, leading to scalability, privacy and security issues. It is clear that QUIC+H3's overall complexity and heavy security focus make them difficult to implement, debug, observe, analyse, use and teach in practice.
Automatic Generation of Road Trip Summary Video for Reminiscence and Entertainment using Dashcam Video

Vehicle dashboard cameras are becoming an increasingly popular kind of automotive accessory. While it is easy to obtain the high-definition video data recorded by dashcams using Secure Digital memory cards, this data is rarely used except for safety purposes because it takes substantial time and effort to review or edit many hours of such recorded videos. In this paper, we propose a new usage for this data through the automatic video editing system we have developed that can create enjoyable video summaries of road trips utilizing video and other data from the vehicle. We also report the results of comparisons between automatically edited videos created by the proposed system and manually edited videos created by study participants. The prototype developed in this study and the findings from our experiments will contribute to improving the driving experience by providing entertainment for automobile users after road trips, and by memorializing their travels.
Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities

Knowledge graphs embeddings (KGE) are lately at the center of many artificial intelligence studies due to their applicability for solving downstream tasks, including link prediction and node classification. However, most Knowledge Graph embedding models encode, into the vector space, only the local graph structure of an entity, i.e., information of the 1-hop neighborhood. Capturing not only local graph structure but global features of entities are crucial for prediction tasks on Knowledge Graphs. This work proposes a novel KGE method named Graph Feature Attentive Neural Network (GFA-NN) that computes graphical features of entities. As a consequence, the resulting embeddings are attentive to two types of global network features. First, nodes’ relative centrality is based on the observation that some of the entities are more “prominent” than the others. Second, the relative position of entities in the graph. GFA-NN computes several centrality values per entity, generates a random set of reference nodes’ entities, and computes a given entity’s shortest path to each entity in the reference set. It then learns this information through optimization of objectives specified on each of these features. We investigate GFA-NN on several link prediction benchmarks in the inductive and transductive setting and show that GFA-NN achieves on-par or better results than state-of-the-art KGE solutions.
Transducer Misalignment in Ultrasound Tongue Imaging

A long-standing problem for ultrasound tongue imaging is thetransducer misalignment during longer data recording sessions.In this paper, we present an initial idea for analyzing such misalignment.The method employs Mean Square Error (MSE) distanceto identify the relative displacement between the chin andthe transducer. We visualize these measures as a function ofthe timestamp of the utterances. Experiments are conductedon various ultrasound tongue datasets (UltraSuite, and recordingsof Hungarian children and adults). The results suggest thatextreme values of MSE indicate corruptions or issues duringthe data recordings, which can either be caused by transducermisalignment, lack of gel, or missing contact between the skinand the transducer. The methods are language independent andmight be applied in phonetic analysis of ultrasound recordings.
Detecting and correcting automatic speech recognition errors with a new model

The purpose of automatic speech recognition (ASR) systems is to recognize speech signals obtained from people and convert them into text so that they can be processed by a computer.Although many ASR applications are versatile and widely used in the real world, they still generate relatively inaccurate results.They tend to generate spelling errors in recognized words, especially in noisy environments, in situations where the vocabulary size is increased, and at times when the input speech is of poor quality.The permanent presence of errors in ASR systems has led to the need to find alternative methods for automatic detection and correction of such errors.In this study, the basic principles of ASR evaluation are first summarized, and then a new approach based on the suggestion of an alternative hypothesis is proposed for the detection and correction of these errors generated by ASR systems.The proposed method involves a series of processes such as identifying incorrect words, selecting the ones that can be corrected, and identifying candidate words to replace these words.As a result of the tests carried out by creating different test environments, significant performance improvements for Turkish were achieved and an average of 4.60 % performance improvement was provided.
The role of automated evaluation techniques in online professional translator training

The rapid technologisation of translation has influenced the translation industry's direction towards machine translation, post-editing, subtitling services and video content translation. Besides, the pandemic situation associated with COVID-19 has rapidly increased the transfer of business and education to the virtual world. This situation has motivated us not only to look for new approaches to online translator training, which requires a different method than learning foreign languages but in particular to look for new approaches to assess translator performance within online educational environments. Translation quality assessment is a key task, as the concept of quality is closely linked to the concept of optimization. Automatic metrics are very good indicators of quality, but they do not provide sufficient and detailed linguistic information about translations or post-edited machine translations. However, using their residuals, we can identify the segments with the largest distances between the post-edited machine translations and machine translations, which allow us to focus on a more detailed textual analysis of suspicious segments. We introduce a unique online teaching and learning system, which is specifically "tailored" for online translators' training and subsequently we focus on a new approach to assess translators' competences using evaluation techniques-the metrics of automatic evaluation and their residuals. We show that the residuals of the metrics of accuracy (BLEU_n) and error rate (PER, WER, TER, CDER, and HTER) for machine translation post-editing are valid for translator assessment. Using the residuals of the metrics of accuracy and error rate, we can identify errors in post-editing (critical, major, and minor) and subsequently utilize them in more detailed linguistic analysis.
U-net-based multiscale feature preserving method for low light image enhancement

Low light image enhancement is a challenging task, and it has been a hot research topic. Inspired by retinex theory and U-Net network, we propose a U-Net-based multiscale feature preserving method for low light image enhancement, which can realize the extraction of low-level features and high-level semantic features. Before feature extraction, we carry out multiscale pre-extraction processing on the image to improve the feature extraction ability of the network. Considering the discontinuity between low-level features and high-level semantic features, we propose a spatial consistency method to maintain the global feature correlation. Finally, we propose a new multiscale structure calculation method, which greatly alleviates the phenomenon of uneven illumination and color deviation after enhancement and makes the enhancement results more consistent with human visual perception. Extensive experiments demonstrate that compared with other advanced enhancement methods, our method has better enhancement effect and can retain more details. The enhanced image not only has good visual perception but also is better than other methods in objective evaluation.
The Contribution of Genre Awareness and Reading Habits Towards Students' Reading Comprehension

Previous studies revealed that genre awareness and reading habits partially correlate with reading comprehension. This article reported the results of a study aiming to find out the contribution of genre awareness and reading habits towards the development of students’ comprehension of reading English texts. The participants of this study were 68 students of the Department of English of a state-owned university in East Java, Indonesia, in the 2020/2021 academic year. These students took the three reading courses (Intensive Course, Basic, and Intermediate Reading courses) and were assumed to have been familiar with genres and have developed some reading habits. This research employed a correlational design, involving two predicting variables (genre awareness and reading habits), and one response variable (comprehension). The data were gathered using three instruments: an objective test to measure students’ genre awareness, an online questionnaire to identify students’ reading habits, and an objective test to measure students’ reading comprehension. The data were analyzed using multiple linear regression analysis aided by SPSS/PASW Program. The results showed that the data are normally distributed so that conclusions can be drawn accurately that genre awareness and reading habits partially and significantly contribute to reading comprehension. The results also indicated that genre awareness and reading habits simultaneously and significantly contribute to reading comprehension, as shown by the results of the analysis that genre awareness and reading habits contributed 0.203 or 20.3% to reading comprehension. The rest (79.7%) was attributed to other variables outside the variables under study. This study concludes that good genre awareness and good reading habits help students develop good reading comprehension. The results provided in this paper can be used by English teachers as an alternative to help their students improve their genre awareness and cultivate good reading habits.
Hyper-Personalization of Mobile Applications for Cloud Kitchen Operations

The initial design of cloud kitchen is to deliver the food based on the order received online with the assistance online food ordering system. Also, the geo-tracking facility acts as a bridge to providing the ordered food at their doorstep. Hyper-personalization of food (HPF) is a cutting-edge marvel in a contemporary situation around the globe with the influence of application program interface (API) in mobile applications, the customers of cloud kitchen could create their meal by choosing the ingredients and quantities using an online food delivery app according to their preferences for a nutritious and balanced diet, this builds a stronger relationship between the customer and connected cloud kitchens, and in future, this will contribute for regular business. This study aims to recognize the customer preferences on ordering food online with their feedback. It also focuses on the cloud kitchen’s performance with the customer’s anticipation in the present trend. The outcome of this study proves that the online food ordering apps must customize their options that permit the customers to make modifications according to their health requirements.
KNN Loss and Deep KNN

The k Nearest Neighbor (KNN) algorithm has been widely applied in various supervised learning tasks due to its simplicity and effectiveness. However, the quality of KNN decision making is directly affected by the quality of the neighborhoods in the modeling space. Efforts have been made to map data to a better feature space either implicitly with kernel functions, or explicitly through learning linear or nonlinear transformations. However, all these methods use pre-determined distance or similarity functions, which may limit their learning capacity. In this paper, we present two loss functions, namely KNN Loss and Fuzzy KNN Loss, to quantify the quality of neighborhoods formed by KNN with respect to supervised learning, such that minimizing the loss function on the training data leads to maximizing KNN decision accuracy on the training data. We further present a deep learning strategy that is able to learn, by minimizing KNN loss, pairwise similarities of data that implicitly maps data to a feature space where the quality of KNN neighborhoods is optimized. Experimental results show that this deep learning strategy (denoted as Deep KNN) outperforms state-of-the-art supervised learning methods on multiple benchmark data sets.
Systematic Generalization on gSCAN: What is Nearly Solved and What is Next?

We analyze the grounded SCAN (gSCAN) benchmark, which was recently proposed to study systematic generalization for grounded language understanding.First, we study which aspects of the original benchmark can be solved by commonly used methods in multimodal research.We find that a generalpurpose Transformer-based model with crossmodal attention achieves strong performance on a majority of the gSCAN splits, surprisingly outperforming more specialized approaches from prior work.Furthermore, our analysis suggests that many of the remaining errors reveal the same fundamental challenge in systematic generalization of linguistic constructs regardless of visual context.Second, inspired by this finding, we propose challenging new tasks for gSCAN by generating data to incorporate relations between objects in the visual environment.Finally, we find that current models are surprisingly data inefficient given the narrow scope of commands in gSCAN, suggesting another challenge for future work.
RETRACTED ARTICLE: Vocational education reform based on improved convolutional neural network and speech recognition

Speech recognition has become a necessary feature of high-quality service industry products. Therefore, the accuracy and efficiency of speech recognition are the key to product applications. At the same time, this article also designs various modular functions for speech recognition. In order to solve the problem of poor recognition performance when the existing convolutional neural network recognizes continuous speech data, we provide an improved convolutional neural network algorithm and backpropagation algorithm to reduce the weight range. In a real speech recognition system, due to the large amount of training data and model parameter training efficiency of the convolutional neural network is very low. Vocational education reform is an important way to realize modern education, and it is also an effective way to improve students’ comprehensive quality and promote personal development. According to the analysis of the current teaching situation in higher vocational colleges, the effect of vocational education reform has not reached the expected standard. This has led to a decrease in the teaching efficiency of higher vocational colleges, coupled with the increasingly fierce competition in modern society, the reform of higher vocational education has become the top priority of the education department and the school. In order to improve the scientific nature of vocational education reform research, it is necessary to strengthen the research on current and future development trends. The research scope of vocational education reform needs to be coordinated, integrated, and expanded. To strengthen research on the integration of industry and academia, it is necessary to establish a team of experts. The cross-border characteristics of vocational education are reflected in the integration of production and education to a large extent. This article will explore how to realize the reform of vocational education in higher vocational colleges based on the improved convolutional neural network and speech recognition.
Sentiment Mining and Analysis over Text Corpora via Complex Deep Learning Naural Architectures

We analyze and compare five deep-learning neural architectures to manage the problem of irony and sarcasm detection for the Italian language. We briefly analyze the model architectures to choose the best compromise between performances and complexity. The obtained results show the effectiveness of such systems to handle the problem by achieving 93\% of F1-Score in the best case. As a case study, we also illustrate a possible embedding of the neural systems in a cloud computing infrastructure to exploit the computational advantage of using such an approach in tackling big data.
TransReID: Transformer-Based Object Re-Identification

Extracting robust feature representation is one of the key challenges in object re-identification (ReID). Although convolution neural network (CNN)-based methods have achieved great success, they only process one local neighborhood at a time and suffer from information loss on details caused by convolution and downsampling operators (e.g. pooling and strided convolution). To overcome these limitations, we propose a pure transformer-based object ReID framework named TransReID. Specifically, we first encode an image as a sequence of patches and build a transformer-based strong baseline with a few critical improvements, which achieves competitive results on several ReID benchmarks with CNN-based methods. To further enhance the robust feature learning in the context of transformers, two novel modules are carefully designed. (i) The jigsaw patch module (JPM) is proposed to rearrange the patch embeddings via shift and patch shuffle operations which generates robust features with improved discrimination ability and more diversified coverage. (ii) The side information embeddings (SIE) is introduced to mitigate feature bias towards camera/view variations by plugging in learnable embeddings to incorporate these non-visual clues. To the best of our knowledge, this is the first work to adopt a pure transformer for ReID research. Experimental results of TransReID are superior promising, which achieve state-of-the-art performance on both person and vehicle ReID benchmarks.
From Libra to Diem. The Pursuit of a Global Private Currency

Abstract The official launch of the Libra project in 2019, and the subsequent troubles experienced by the project, stimulated a vigorous debate, from different perspectives, on the pros and cons of a private currency with global ambitions. This paper describes the main characteristics of Libra and of its heir, Diem, locating both in a partial taxonomy of the increasingly crowded field of so-called ‘digital currencies’. In the light of the distinguishing features and risks of such an ambitious project, the paper also aims to assess the potential impact on a crucial issue of the present international monetary system: the power to create money.
Underwater fuel assembly deformation measurement with multisensor dual-line structured light

The fuel assembly is the core component of the nuclear energy system, and its excessive deformation will affect the normal insertion of the fuel rod and endanger safe operation of the reactor. In this paper, we present an underwater fuel assembly deformation measurement system based on 10 sets of visual measurement units. Benefitting from the waterproof design, the shielding-radiation design, the reflective structure design, the dual-optical line laser triangulation principle, and the underwater multilayer refractive geometry, the measurement system has shorter measurement time, higher measurement accuracy, and better environmental adaptability. Through the underwater field measurement and verification of the standard rod and fuel assembly, the bow deformation measurement accuracy of the measurement system is better than 0.3 mm, and the twist deformation measurement accuracy of the system is better than 0.15°. The proposed method allows transient and high-precision measurement in a certain irradiation dose and certain depth water, which provides a strong guarantee for measurement of fuel assembly deformation parameters in deep water and high radiation.