Reviewerly/exHarmony huggingface dataset:
Designing to Leverage Presence in VR Rhythm Games

Rhythm games are known for their engaging gameplay and have gained renewed popularity with the adoption of virtual reality (VR) technology. While VR rhythm games have achieved commercial success, there is a lack of research on how and why they are engaging, and the connection between that engagement and immersion or presence. This study aims to understand how the design of two popular VR rhythm games, Beat Saber and Ragnarock, leverages presence to immerse players. Through a mixed-methods approach, utilising the Multimodal Presence Scale and a thematic analysis of open-ended questions, we discovered four mentalities which characterise user experiences: action, game, story and musical. We discuss how these mentalities can mediate presence and immersion, suggesting considerations for how designers can leverage this mapping for similar or related games.
Program equivalence in an untyped, call-by-value functional language with uncurried functions

We aim to reason about the correctness of behaviour-preserving transformations of Erlang programs. Behaviour preservation is characterised by semantic equivalence. Based upon our existing formal semantics for Core Erlang, we investigate potential definitions of suitable equivalence relations. In particular we adapt a number of existing approaches of expression equivalence to a simple functional programming language that carries the main features of sequential Core Erlang; we then examine the properties of the equivalence relations and formally establish connections between them. The results presented in this paper, including all theorems and their proofs, have been machine checked using the Coq proof assistant.
Regenerating vital facial keypoints for impostor identification from disguised images using CNN

Technical advancements in the digital era have eased up the time required to complete complex tasks by a decisive rate. These advancements introduce several threats. Hence, there is a compelling need to provide security for systems against these threats. Identity and data thefts are the major threats which require immediate, effective and responsive solutions. Face recognition systems have evolved over the last few years, providing a suitable solution for authenticating an individual. However, the system running on facial recognition can be interfered by disguise. This paper discusses the approaches used in detecting a disguise and analysing the captured face among the other non-disguised faces in the dataset. The parameters used for detection and analysis are the keypoints of the face. These keypoints may be blocked, hidden or disoriented due to the presence of props on the face. The proposed approach aims to avoid this interference by creating an estimate of the face based on fewer available keypoints. The estimated face so created, is then matched with the available set of faces to determine the probabilistic chance of the matched person's presence even though a disguise had been detected. Thus the solution provides a clear distinction if the image obtained proves to be of an impostor or an intruder with obfuscation with a said probability. The Disguised Faces in the Wild (DFW) dataset has been used.
Image Quality Assessment for Gibbs Ringing Reduction

Gibbs ringing is an artefact that is inevitable in any imaging modality where the measurement is Fourier band-limited. It impacts the quality of the image by creating a ringing appearance around discontinuities. Many novel ways of suppressing the artefact have been proposed, including machine learning methods, but the quantitative comparisons of the results have frequently been lacking in rigour. In this paper, we examine image quality assessment metrics on three test images with different complexity. We determine six metrics which show promise for simultaneously assessing severity of Gibbs ringing and of other error such as blurring. We examined applying metrics to a region of interest around discontinuities in the image and use the metrics on the resulting region of interest. We demonstrate that the region of interest approach does not improve the performance of the metrics. Finally, we examine the effect of the error threshold parameter in two metrics. Our results will aid development of best practice in comparison of algorithms for the suppression of Gibbs ringing.
Fast Image Super-Resolution Using Particle Swarm Optimization-Based Convolutional Neural Networks

Image super-resolution based on convolutional neural networks (CNN) is a hot topic in image processing. However, image super-resolution faces significant challenges in practical applications. Improving its performance on lightweight architectures is important for real-time super-resolution. In this paper, a joint algorithm consisting of modified particle swarm optimization (SMCPSO) and fast super-resolution convolutional neural networks (FSRCNN) is proposed. In addition, a mutation mechanism for particle swarm optimization (PSO) was obtained. Specifically, the SMCPSO algorithm was introduced to optimize the weights and bias of the CNNs, and the aggregation degree of the particles was adjusted adaptively by a mutation mechanism to ensure the global searching ability of the particles and the diversity of the population. The results showed that SMCPSO-FSRCNN achieved the most significant improvement, being about 4.84% better than the FSRCNN model, using the BSD100 data set at a scale factor of 2. In addition, a chest X-ray super-resolution images classification test experiment was conducted, and the experimental results demonstrated that the reconstruction ability of this model could improve the classification accuracy by 13.46%; in particular, the precision and recall rate of COVID-19 were improved by 45.3% and 6.92%, respectively.
A hybrid particle-stochastic map filter

Filtering in nonlinear state-space models is known to be a challenging task due to the posterior distribution being either intractable or expressed in a complex form. One of the most successful methods, particle filtering (PF), although generally outperforming traditional filters, suffers from sample degeneracy. Drawing from optimal transport theory, the stochastic map filter (SMF) accommodates a solution to this problem, but its performance is influenced by the limited flexibility of nonlinear map parameterisation. To alleviate these drawbacks, we propose a hybrid filter which combines the PF and SMF, and hence call it PSMF. Specifically, the PSMF splits the likelihood into two parts, which are then updated by PF and SMF, respectively. The proposed approach adopts systematic resampling and smoothing to break the particle degeneracy caused by the PF. To investigate the influence of the nonlinearity of transport maps, we introduce two variants of the proposed filter, the PSMF-L and PSMF-NL, which are based on linear and nonlinear maps, respectively. The PSMF is tested on various nonlinear state-space models and a nonlinear non-Gaussian target tracking model. The proposed linear PSMF-L outperforms all the reference models for medium-to-large numbers of particles, whilst the PSMF-NL shows better resilience to parameter changes.
Enhanced Movie Recommendation and Sentiment Analysis Model Achieved by Similarity Method through Cosine and Jaccard Similarity algorithms

Nowadays, with technology at the fore front of almost every business, there is an abundance of data and information that is difficult to sort through and understand. Consequently, a recommendation system is beneficial in dealing with such a big amount of information and filtering out the valuable material that is rapid also, pertinent to the client’s choice. In order to suggest films that are similar to the one the user has chosen, this paper describes a method for a movie recommendation system that uses different similarity approaches. In order to improve the user experience, this system performs sentiment analyses the reviews of the movie that was selected using optimization techniques. To increase accuracy and efficiency, a novel recommends-based sentiment classification technique that combines the Support Vector Machine Classifier with the Harris Hawks optimization method is utilised. The proposed approach is capable of handling high-dimensional data, according to empirical findings on the dataset (Movie). In terms of classification accuracy, we found that the suggested method performs better than the other recommendation systems strategies examined in this study. The trial findings revealed that the suggested strategy had a success rate of 97% accuracy.
Confluence of Artificial Intelligence and Blockchain Powered Smart Contract in Finance System

Artificial intelligence and Blockchain are two of the most important forces driving innovation today. At the point when Blockchain and AI join their assets, this gives a more significant investigation of the viability of the details of the agreement, and the work processes it manages. Consequently, the requirement for human investigation, intercession and check, is enormously diminished. Man-made intelligence alludes to the capacity of machines to grasp, think, and learn likewise to people, demonstrating the chance of utilizing PCs to mimic human knowledge. A smart contract is computer code running on a blockchain that contains a set of norms by which the smart contract’s parties’ consent to communication between one another. Examining AI integration with smart contracts that are enabled by blockchain in the enhancing finance system operations is the main objective of this endeavor. AI is added to well-established smart contracts, their efficiency increases exponentially. This article presumes that AI and blockchain enabled smart contract will have an enormous effect in future for Finance industry and Digital trading.
Hindi Document Extractive Summarization: Neural Method on A New Data Set

Extractive summarization is one of the vital tasks in text analysis and natural language processing. Although Hindi is one of the world's highly speaking languages and produces thousands of online documents daily, most existing text summarization works focus on the English language. A Neural network-based summarizer is popular for abstractive summarization but has not been explored for extractive one except in a few recent studies. The present work uses a neural extractive summarizing model to develop a Hindi language extractive summarizer. The main contribution of the paper is two-fold. First, we generated a new Hindi-based text summarization data set from a popular Hindi news channel AajTak. The code to generate the data set is available at https://tinyurl.com/sonaa-hindi-text. Then we use this data set to train a Neural Extractive Summarization model. The model also learns the word embeddings while learning itself. The ROUGE-2-Fl and ROUGE-1-Fl results on test data show promising output with a score of 20.02 and 39.81, respectively.
A Study to Improve Image Steganography Using Linear Feedback Shift Register

Concealing data during communicating is often a security requirement. Image steganography is one way to do that by hiding data in the cover image. Least Significant Bit (LSB) insertion is one of the spatial domain methods, wherein, a message is concealed in the least significant bits of the pixels composing an image. However, such technique is vulnerable to various attacks. Furthermore, low-cost hardware is essential to employ steganography in lightweight security applications. This paper, thus, proposes a way to improve LSB based image Steganography by integrating a Linear-Feedback Shift Register (LFSR), as a low cost hardware, in the hiding process in such a way that the message is hidden in a pseudorandom fashion. Thereafter, the efficiency of the improved algorithm is analyzed and compared to the original algorithm. Experimental results demonstrate the effectiveness of our proposed LSB based image Steganography which successfully hides the message with a linear computational complexity.
College Student Employment Management Recommendation System Based on Decision Tree Algorithm

With the continuous expansion of colleges and universities, the number of graduates continues to grow, and both students and the society will face huge employment pressure. The purpose of this paper is to design a recommendation system for college student employment management based on decision tree algorithm. A simple employment recommendation system is developed under Windows using Django architecture. Determine the research significance of employment recommendation for college graduates, then introduce the current development of recommendation systems and the research status of employment recommendation, and finally introduce mature application examples of user portraits. This paper introduces the application and implementation of ID3 algorithm in the employment analysis system of colleges and universities, including the process of building a decision tree, and then uses the generated decision tree to generate corresponding classification rules, obtains an employment prediction model, and uses the information of fresh students in the School of Computer Science and Engineering to predict employment. The model is tested, thereby realizing the prediction of the employment level of fresh graduates. There are 45 graduates whose employment status is in large-scale enterprises. The student employment recommendation system has been implemented, and the employment recommendation algorithm has been successfully applied to the employment recommendation work of the school's students.
Application Optimization of NLP System under Deep Learning Technology in Text Semantics and Text Classification

How to use advanced deep learning technology to build an effective and powerful text classification model, extract text semantic attributes and achieve good classification results on large-scale test datasets has always been the main problem and severe challenge of text classification tasks. The purpose of this paper is to study the application optimization of NLP system in text semantics and text classification based on deep learning technology. A WC-GCN classification algorithm combining GCN and Attention mechanism is proposed. The related tasks of text classification are discussed, including three typical text classification tasks, including sentiment analysis, topic analysis, and news classification. The algorithm model proposed in this paper is verified from the experimental point of view. Compared with other deep learning-based text classification methods, the proposed algorithm model performs better on the public datasets of three text classification tasks. The news text also achieved a high accuracy rate.
Zero-shot Object Detection Through Vision-Language Embedding Alignment

Recent approaches have shown that training deep neural networks directly on large-scale image-text pair collections enables zero-shot transfer on various recognition tasks. One central issue is how this can be generalized to object detection, which involves the non-semantic task of localization as well as semantic task of classification. To solve this problem, we introduce a vision-language embedding alignment method that transfers the generalization capabilities of a pretrained model such as CLIP to an object detector like YOLOv5. We formulate a loss function that allows us to align the image and text embeddings from the pretrained model CLIP with the modified semantic prediction head from the detector. With this method, we are able to train an object detector that achieves state-of-the-art performance on the COCO, ILSVRC, and Visual Genome zero-shot detection benchmarks. During inference, our model can be adapted to detect any number of object classes without additional training. We also find that standard object detection scaling can transfer well to our method and find consistent improvements across various scales of YOLOv5 models and the YOLOv3 model. Lastly, we develop a self-labeling method that provides a significant score improvement without needing extra images nor labels. The code is available at https://github.com/Johnathan-Xie/ZSD-YOLO.
Visual inspection intelligent robot technology for large infusion industry

Abstract The application of intelligent technology has realized the transformation of people’s production and lifestyle, and it has also promoted the development of the field of medicine. At present, the intensity of intelligence in the field of medicine is increasing. By using its cash methods and techniques combined with the mechanical field, this article proposes to use visual inspection technology to understand the fusion of the medical field and the mechanical field. It is helpful to analyze and solve objective problems such as low efficiency in current infusion and insufficient rigidity of large infusion plastic bottles. Drawing on the principles and laws of deep learning algorithms and neural networks, the technical research of intelligent robots for visual inspection is carried out to realize the intelligence of infusion robots. In the research accuracy of detection, the detection rate of standard particles higher than 85 µM has reached almost 100%, and the rate of 50 µM standard particles is lower and unstable. The detection effect of the control light bulb control was different, and the detection rate was between 50 and 80%, which was obviously worse than the detection robot effect. Therefore, the current research on the technology of intelligent robots is very important.
Improving Your Graph Neural Networks: A High-Frequency Booster

Graph neural networks (GNNs) hold the promise of learning efficient representations of graph-structured data, and one of its most important applications is semi-supervised node classification. However, in this application, GNN frameworks tend to fail due to the following issues: over-smoothing and heterophily. The most popular GNNs are known to be focused on the message- passing framework, and recent research shows that these G NN s are often bounded by low-pass filters from a signal processing perspective. We thus incorporate high-frequency information into GNNs to alleviate this genetic problem. In this paper, we argue that the complement of the original graph incorporates a high- pass filter and propose Complement Laplacian Regularization (CLAR) for an efficient enhancement of high-frequency compo- nents. The experimental results demonstrate that CLAR helps G NN s tackle over-smoothing, improving the expressiveness of heterophilic graphs, which adds up to 3.6% improvement over popular baselines and ensures topological robustness.
Ethical governance model for the data economy ecosystems

Purpose Data economy is a recent phenomenon, raised by digital transformation and platformisation, which has enabled the concentration of data that can be used in economic purposes. However, there is a lack of clear procedures and ethical rules on how data economy ecosystems are governed. As a response to the current situation, there has been criticism and demands for the governance of data use to prevent unethical consequences that have already manifested. Thus, ethical governance of the data economy ecosystems is needed. The purpose of this paper is to introduce a new ethical governance model for data economy ecosystems. The proposed model offers a more balanced solution for the current situation where a few global large-scale enterprises dominate the data market and may use oligopolistic power over other stakeholders. Design/methodology/approach This is a conceptual article that covers theory-based discourse ethical reflection of data economy ecosystems governance. The study is based on the premise of the discourse ethics where inclusion of all stakeholders is needed for creating a transparent and ethical data economy. Findings This article offers self-regulation tool for data economy ecosystems by discourse ethical approach which is designed in the governance model. The model aims to balance data “markets” by offering more transparent, democratic and equal system than currently. Originality/value By offering a new ethically justified governance model, we may create a trust structure where rules are visible and all stakeholders are treated fairly.
Machine Learning Approaches for Sustainable Cities Using Internet of Things

As these tech-based cities continue to emerge, so do the experts dive deeper into the research on the various Internet of Things methods, and the most plausible machine learning techniques. What is Machine Learning? What role does it play in IoT-based platforms? How does it contribute to the continuous advancement of smart cities? What is IoT? How has it changed the lives of urban households residing in smart cities? How does IoT interact with machine learning to ensure more efficient cities? This research paper delves into the various existing literature in a bid to respond to these questions as effectively as possible. It looks the research that have handled issues of IoT and machine learning. The search criteria are set out in the methodology section that has inclusion and exclusion criteria for the articles considered in this paper. There is systematic evidence showing that IOT and machine learning can be used for the management of the future cities. The results suggest that IoT and machine learning are at the core of the development, maintenance, and sustainability of smart cities. Further studies are required on how IOT can be securely used by analysis some of the security vulnerabilities faced by these systems.
Deep Learning Based Anomaly Detection on Natural Gas Pipeline Operational Data

In oil and gas industry, production monitoring is a crucial yet challenging problem. Small changes or issues in the production process should be detected and taken care of swiftly to avoid unwanted consequences. Some machine learning tasks can be applied to potentially helping monitoring process. One of these tasks is anomaly detection. However, existing studies have not conducted anomaly detection properly in the field of oil and gas industry due to tendency of using plain techniques or unsuitable paradigms. To handle this, deep learning model of autoencoder type is implemented to detect anomalies on gas pipeline operational data. Specifically, the data used is 2 years long of hourly time series data which contains 17 features and 17520 datapoints. The autoencoder model will be trained to reconstruct the data with a minimum error. Once trained, the reconstructed data can be compared to the original data to detect any potential anomaly. This paper considers 6 different cases of model architecture while comparing the results using mean squared error to obtain the optimal model setup in the reconstruction process. To evaluate the model, we compute Euclidean distance between the reconstructed and original data as the anomaly score. By determining a threshold value of the score, we can identify the anomalies in the data. The resulting identifications are analyzed qualitatively and show good prediction of the presence of abnormalities in data.