Reviewerly/exHarmony huggingface dataset:
Application of Wireless Virtual Reality Perception and Simulation Technology in Film and Television Animation

Wireless virtual reality integrated multidisciplinary technology, combined with related industries and fields, has changed the way of human-computer interaction and opened up a new field of user experience. In recent years, with the rapid improvement of computer technology and hardware conditions, interactive technology has developed rapidly. The existing wireless virtual reality interactive system is too single and cannot be used in multiple environments. The original system requires a large number of sensor equipment, the cost is high, and the traditional perception technology is too restrictive and cannot realize human-computer interaction more naturally. This paper proposes a dual intention perception algorithm based on the fusion of touch (obtained by experimental simulation equipment), hearing, and vision. The algorithm can perceive the user’s operation intention through the user’s natural behavior and can identify the user’s two intentions at the same time. This paper proposes a navigational interactive mode, which provides users with multimodal intelligent navigation through intelligent perception of user intent and experimental progress. We determine the impact model of the interactive system effect evaluation and analyze its effect evaluation strategy in depth and then further quantify the indicators under the four effect dimensions of information perception, artistic reflection, social entertainment, and aesthetic experience. A combination of qualitative and quantitative methods was used to carry out relevant research on effect evaluation, usability test, and questionnaire interview. The experimental results show that this interactive system has better entertainment effects than other forms of film and television animation, but still needs to pay attention to and strengthen the construction and embodiment of film and television animation content, as well as the optimization and perfection of the fault-tolerant mechanism in the design process.
TRIG: Transformer-Based Text Recognizer with Initial Embedding Guidance

Scene text recognition (STR) is an important bridge between images and text, attracting abundant research attention. While convolutional neural networks (CNNS) have achieved remarkable progress in this task, most of the existing works need an extra module (context modeling module) to help CNN to capture global dependencies to solve the inductive bias and strengthen the relationship between text features. Recently, the transformer has been proposed as a promising network for global context modeling by self-attention mechanism, but one of the main short-comings, when applied to recognition, is the efficiency. We propose a 1-D split to address the challenges of complexity and replace the CNN with the transformer encoder to reduce the need for a context modeling module. Furthermore, recent methods use a frozen initial embedding to guide the decoder to decode the features to text, leading to a loss of accuracy. We propose to use a learnable initial embedding learned from the transformer encoder to make it adaptive to different input images. Above all, we introduce a novel architecture for text recognition, named TRansformer-based text recognizer with Initial embedding Guidance (TRIG), composed of three stages (transformation, feature extraction, and prediction). Extensive experiments show that our approach can achieve state-of-the-art on text recognition benchmarks.
A Pilot Study for BERT Language Modelling and Morphological Analysis for Ancient and Medieval Greek

This paper presents a pilot study to automatic linguistic preprocessing of Ancient and Byzantine Greek, and morphological analysis more specifically.To this end, a novel subword-based BERT language model was trained on the basis of a varied corpus of Modern, Ancient and Post-classical Greek texts.Consequently, the obtained BERT embeddings were incorporated to train a fine-grained Part-of-Speech tagger for Ancient and Byzantine Greek.In addition, a corpus of Greek Epigrams was manually annotated and the resulting gold standard was used to evaluate the performance of the morphological analyser on Byzantine Greek.The experimental results show very good perplexity scores (4.9) for the BERT language model and state-of-the-art performance for the fine-grained Part-of-Speech tagger for indomain data (treebanks containing a mixture of Classical and Medieval Greek), as well as for the newly created Byzantine Greek gold standard data set.The language models and associated code are made available for use at https://github.com/pranaydeeps/Ancient-Greek-BERT
A dangerous triangularization of conflicting values in academic publishing: ORCID, fake authors, and risks with the lack of criminalization of the creators of fake elements

The mainstream publishing establishment is under attack from multiple known and unknown forces. This is neither hyperbole nor fantasy. Many academics may believe that the main threat lies with “predatory” journals or publishers, but this is not necessarily the case because such entities are not always easy to distinguish clearly from veritable scholarly journals or publishers. Moreover, there is a gray zone that may involve both predatory and exploitative qualities. Current submission systems are not fail-safe because they allow unscholarly or fraudulent elements to register and abuse them, for example for submitting fake research or falsified peer reports, while author identification tools like ORCID are imperfect and provide a platform for similar-minded individuals to “validate” themselves. This toxic mix of tools aimed at fortifying integrity, while allowing fake authors to breed, currently without many, or any, ethical or legal repercussions will rapidly erode the entire publishing landscape if serious legal action is not taken. The creation of fake papers by fake authors will eventually trickle down into valid literature, by virtue of the fact that cited literature cannot be thoroughly vetted, even in peer review. The integrity of valid scholarly venues is thus at high risk unless suitable, strict and ethically and legally enforceable preventative measures are implemented.
The impact of supply chain practice on green hotel performance through internal, upstream, and downstream integration

The company builds communication and collaboration with suppliers and customers to increase competitiveness in the supply chain flow. The organization's ability to involve suppliers and customers in business activities to achieve efficiency and effectiveness is one of the objectives of supply chain practice. The distribution of questionnaires directly to hotel practitioners was 25 respondents, and 65 respondents obtained the distribution of google form links. The results showed that supply chain practices with supplier relationship management and quality information sharing activities could increase internal and upstream integration and not directly impact downstream integration. Internal integration with data integration activities accurately and coordination between functions on an ongoing basis can affect upstream and downstream integration and green hotel performance. Upstream integration and downstream integration with joint decision activities and planning synchronization with external parties can directly impact green hotel performance. They were increasing the market share and image of the hotel with the implementation of caring for the environment. This research contributes to hotel practitioners adopting practical supply chains in building internal and external integration to increase competitiveness and theoretical contribution to developing supply chain theory and green performance.
Discrete Kalman Filter Invariant to Perturbations

Fault detection problems in dynamic objects and their localization are a very critical and rather challenging tasks for many practical applications.The Kalman-filter technology is used for these purposes most often.The correct operation indicator of the specified filter is the innovation process to be represented as a normal uncorrelated stochastic process with zero mean value and a priori calculated covariation matrix, except the specified conditions, are violated in case of unforeseen perturbations.The aim of the presented work is to develop a method allowing to restore the normal performance of the Kalman filter in the presence of uncertain disturbances.This aim is attained by applying a special one-to-one transformation of the output equation of the testing system, as a result of it, the disturbance component is modified by the extrapolation equation of the state vector dynamic system.This feature will be used in the sequel when modified Kalman filter is applied to the transformed system.The properties of the obtained filter concerning the stability of estimation errors, their convergence, and optimality are discussed.The efficiency of the method has been verified by the method of statistical modeling on a test example of a third-order dynamic system.
Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization

Meta-learning algorithms such as MAML, Reptile, and FOMAML have led to improved performance of several neural models.The primary difference between standard gradient descent and these meta-learning approaches is that they contain as a small component the gradient for maximizing dot-product between gradients of batches, leading to improved generalization.Previous work has shown that aligned gradients are related to generalization, and have also used the Reptile algorithm in a single-task setting to improve generalization.Inspired by these approaches for a single task setting, this paper proposes to use the finite differences first-order algorithm to calculate this gradient from dot-product of gradients, allowing explicit control on the weightage of this component relative to standard gradients.We use this gradient as a regularization technique, leading to more aligned gradients between different batches.By using the finite differences approximation, our approach does not suffer from O(n 2 ) memory usage of naively calculating the Hessian and can be easily applied to large models with large batch sizes.Our approach achieves state-of-the-art performance on the Gigaword dataset, and shows performance improvements on several datasets such as SQuAD-v2.0,Quasar-T, NewsQA and all the SuperGLUE datasets, with a range of models such as BERT, RoBERTa and ELEC-TRA.Our method also outperforms previous approaches of Reptile and FOMAML when used as a regularization technique, in both single and multi-task settings.Our method is model agnostic, and introduces no extra trainable weights.
Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation

Although pre-training models have achieved great success in dialogue generation, their performance drops dramatically when the input contains an entity that does not appear in pretraining and fine-tuning datasets (unseen entity).To address this issue, existing methods leverage an external knowledge base to generate appropriate responses.In real-world scenario, the entity may not be included by the knowledge base or suffer from the precision of knowledge retrieval.To deal with this problem, instead of introducing knowledge base as the input, we force the model to learn a better semantic representation by predicting the information in the knowledge base, only based on the input context.Specifically, with the help of a knowledge base, we introduce two auxiliary training objectives: 1) Interpret Masked Word, which conjectures the meaning of the masked entity given the context; 2) Hypernym Generation, which predicts the hypernym of the entity based on the context.Experiment results on two dialogue corpus verify the effectiveness of our methods under both knowledge available and unavailable settings.
Improvement of the model of object recognition in aero photographs using deep convolutional neural networks

Detection and recognition of objects in images is the main problem to be solved by computer vision systems. As part of solving this problem, the model of object recognition in aerial photographs taken from unmanned aerial vehicles has been improved. A study of object recognition in aerial photographs using deep convolutional neural networks has been carried out. Analysis of possible implementations showed that the AlexNet 2012 model (Canada) trained on the ImageNet image set (China) is most suitable for this problem solution. This model was used as a basic one. The object recognition error for this model with the use of the ImageNet test set of images amounted to 15 %. To solve the problem of improving the effectiveness of object recognition in aerial photographs for 10 classes of images, the final fully connected layer was modified by rejection from 1,000 to 10 neurons and additional two-stage training of the resulting model. Additional training was carried out with a set of images prepared from aerial photographs at stage 1 and with a set of VisDrone 2021 (China) images at stage 2. Optimal training parameters were selected: speed (step) (0.0001), number of epochs (100). As a result, a new model under the proposed name of AlexVisDrone was obtained. The effectiveness of the proposed model was checked with a test set of 100 images for each class (the total number of classes was 10). Accuracy and sensitivity were chosen as the main indicators of the model effectiveness. As a result, an increase in recognition accuracy from 7 % (for images from aerial photographs) to 9 % (for the VisDrone 2021 set) was obtained which has indicated that the choice of neural network architecture and training parameters was correct. The use of the proposed model makes it possible to automate the process of object recognition in aerial photographs. In the future, it is advisable to use this model at ground stations of unmanned aerial vehicle complex control when processing aerial photographs taken from unmanned aerial vehicles, in robotic systems, in video surveillance complexes and when designing unmanned vehicle systems
A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring

Conditions monitoring of industrial robot gears has the potential to increase the productivity of highly automated production systems. The huge amount of health indicators needed to monitor multiple gears of multiple robots requires an automated system for anomaly and trend detection. In this publication, such a system is presented and suitable anomaly detection and trend detection methods for the system are selected based on synthetic and real world industrial application data. A statistical test, namely the Cox-Stuart test, appears to be the most suitable approach for trend detection and the local outlier factor algorithm or the long short-term neural network performs best for anomaly detection in the application of industrial robot gear condition monitoring in the presented experiments.
A Study on the Intelligent Translation Model for English Incorporating Neural Network Migration Learning

Under the current artificial intelligence boom, machine translation is a research direction of natural language processing, which has important scientific research value and practical value. In practical applications, the variability of language, the limited capability of representing semantic information, and the scarcity of parallel corpus resources all constrain machine translation towards practicality and popularization. In this paper, we conduct deep mining of source language text data to express complex, high-level, and abstract semantic information using an appropriate text data representation model; then, for machine translation tasks with a large amount of parallel corpus, I use the capability of annotated datasets to build a more effective migration learning-based end-to-end neural network machine translation model on a supervised algorithm; then, for machine translation tasks with parallel corpus data resource-poor language machine translation tasks, migration learning techniques are used to prevent the overfitting problem of neural networks during training and to improve the generalization ability of end-to-end neural network machine translation models under low-resource conditions. Finally, for language translation tasks where the parallel corpus is extremely scarce but monolingual corpus is sufficient, the research focuses on unsupervised machine translation techniques, which will be a future research trend.
Implementation of Usability Testing Methods to Measure the Usability Aspect of Management Information System Mobile Application (Case Study Sukamiskin Correctional Institution)

The aim of this study is to measure the usability aspects of Management Information System mobile application in terms of effectiveness, efficiensy, and user satisfaction by using performance measurement techniques, usability questionnaires and retrospective think aloud.Management Information System application is an internal mobile application that has functions to exchange information, fill daily absences, apply for leave and fill in the daily work journal of the Ministry of Justice and Human Rights employees.Until today, the mobile application of Management Information System has not been evaluated yet using the level of usability.This research was conducted by measuring the level of effectiveness, efficiency, satisfaction and retrospective think aloud.Due to the limitations of this study, researcher only gains 20 respondents who were all employees of Sukamiskin Correctional Institution within questionnaire.In addition, respondents also filled out the questionnaire given.Based on the results obtained the effectiveness level reached 100% and all respondents expressed satisfaction with mobile application of Management Information System.But in terms of efficiency needs to be more improved.And the results of the retrospective think aloud are still some improvements from the features and reduction of icons that appear on the screen.The results showed that using performance measurements and retrospective think aloud techniques had a significant effect on user satisfaction in sukamiskin correctional institution.With these techniques also shows the level of employee ability to complete tasks in the application does not fail.
Blockchain Technology as A Media for Sharing Information that Generates User Access Rights and Incentives

We propose another blockchain client displaying stage that can be utilized to share data without letting completely go and possession and apply it to the field of movement booking. Our new stage gives answers for different central points of interest: extraordinary information insurance and client the board, and shared motivating forces. Track the functioning techniques, objects, time, strategies and reasons for UN associations in an unquestionable way. Gather client profile data and empower clients to trade data with elective travel suppliers to get endowments dependent on protection inclinations determined in great agreements. Client information dependent on the storehouse will be changed over into an open information design and communicated by means of blockchain transmission. This permits the information to be effectively handled and utilized by different hubs. The keen agreement confirms and executes the concurred information. Terms of utilization and token exchange become free help to clients. Savvy agreements can give an assortment of stores to guarantee that all members act with honesty. This record additionally assesses the presentation of the new stage dependent on the investigation of holding up time and memory utilization by characterizing three test situations. The outcomes show that the hub can rapidly react to every one of our issues by utilizing the exchange port nonconcurrently.
Geometric quantification of multiparty entanglement through orthogonality of vectors

The wedge product of vectors has been shown to yield the generalised entanglement measure I-concurrence, wherein the separability of the multiparty qubit system arises from the parallelism of vectors in the underlying Hilbert space of the subsystems. Here, we demonstrate the geometrical conditions of the post-measurement vectors which maximise the entanglement corresponding to the bi-partitions and can yield non-identical set of maximally entangled states. The Bell states for the two-qubit case, GHZ and GHZ like states with superposition of four constituents for three qubits, naturally arise as the maximally entangled states. The geometric conditions for maximally entangled two-qudit systems are derived, leading to the generalised Bell states, where the reduced density matrices are maximally mixed. We further show that the reduced density matrix for an arbitrary finite dimensional subsystem of a general qudit state can be constructed from the overlap of the post-measurement vectors. Using this approach, we discuss the trade-off between the local properties, namely predictability and coherence with the global property, entanglement for the non-maximally entangled two-qubit state.
Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification

With the advent of machine learning in applications of critical infrastructure such as healthcare and energy, privacy is a growing concern in the minds of stakeholders. It is pivotal to ensure that neither the model nor the data can be used to extract sensitive information used by attackers against individuals or to harm whole societies through the exploitation of critical infrastructure. The applicability of machine learning in these domains is mostly limited due to a lack of trust regarding the transparency and the privacy constraints. Various safety-critical use cases (mostly relying on time-series data) are currently underrepresented in privacy-related considerations. By evaluating several privacy-preserving methods regarding their applicability on time-series data, we validated the inefficacy of encryption for deep learning, the strong dataset dependence of differential privacy, and the broad applicability of federated methods.
Multilingual Sequence Labeling Approach to solve Lexical Normalization

The task of converting a nonstandard text to a standard and readable text is known as lexical normalization. Almost all the Natural Language Processing (NLP) applications require the text data in normalized form to build quality task-specific models. Hence, lexical normalization has been proven to improve the performance of numerous natural language processing tasks on social media. This study aims to solve the problem of Lexical Normalization by formulating the Lexical Normalization task as a Sequence Labeling problem. This paper proposes a sequence labeling approach to solve the problem of Lexical Normalization in combination with the word-alignment technique. The goal is to use a single model to normalize text in various languages namely Croatian, Danish, Dutch, English, Indonesian-English, German, Italian, Serbian, Slovenian, Spanish, Turkish, and Turkish-German. This is a shared task in “2021 The 7th Workshop on Noisy User-generated Text (W-NUT)” in which the participants are expected to create a system/model that performs lexical normalization, which is the translation of non-canonical texts into their canonical equivalents, comprising data from over 12 languages. The proposed single multilingual model achieves an overall ERR score of 43.75 on intrinsic evaluation and an overall Labeled Attachment Score (LAS) score of 63.12 on extrinsic evaluation. Further, the proposed method achieves the highest Error Reduction Rate (ERR) score of 61.33 among the participants in the shared task. This study highlights the effects of using additional training data to get better results as well as using a pre-trained Language model trained on multiple languages rather than only on one language.