Reviewerly/exHarmony huggingface dataset:
Label prediction based constrained non-negative matrix factorization for semi-supervised multi-view classification

Semi-supervised multi-view classification can improve the performance by leveraging the information from both labeled and unlabeled data. But it is often a challenge to capture the information from the unlabeled multi-view data. By analyzing the relation between labeled and unlabeled data under multi-view scenario, we propose a novel model with the ability of leveraging the latent label information from the unlabeled data. In our model, a label prediction (LP) term is proposed to jointly obtain the predicted labels of unlabeled data from multiple views. The LP term is integrated into a constrained non-negative matrix factorization based multi-view framework. In this way, the LP and the multi-view representation learning are integrated into one joint learning problem, where they boost each other. Particularly, the predicted label vector is formulated to be the one-hot vector, such that the labels can be obtained directly. Moreover, we propose a new lemma about the gradient of the ℓ2,1 norm in the case of 3-factor matrix decomposition and its corollary about multi-factor matrix decomposition. Based on which, we develop an efficient algorithm and prove its convergence. Experimental results verify that our method can obtain state-of-the-art performance.
Chalmers on Virtual Reality: Realism on the Cheap?

You sit in your office and put on the latest pair of virtual reality (VR) goggles. Suddenly, you stand in the middle of the Time Square. A car almost hits you. You move away from the street and find a 7-Eleven. You enter the store, greet the shop owner and buy a Coke. Outside the store, you open the can and start drinking. Episodes like this are likely to become a normal way of spending one’s time in the not too-distant future. VR technology will enable us to visit all kinds of places and do all kinds of things while sitting comfortably in our office chair. But are the things and the events we experience in VR real? In the above scenario: are you really having a Coke at Time Square? Or is that a mere fiction, akin to a kind of video game? In his recent book Reality+. Virtual worlds and the problems of philosophy (Chalmers 2022), David Chalmers advocates for virtual realism: ‘the thesis that virtual reality is genuine reality’ (p. 106). A key step in Chalmers’ case for virtual realism consists in showing that virtual objects and events meet the marks of reality identified by philosophers of the present and the past: they really exist, they have causal powers, they are mind-independent, they are non-illusory and they can sometimes be counted as genuine instances of their kinds. Although interesting in its own right, Chalmers argues that virtual realism also has important upshots for a whole range of moral and epistemological questions concerning VR.
The right tools for the job: towards preference and performance considerations in the design of virtual reality interactions

Virtual reality (VR) users interact with virtual objects using motion-tracked controllers. While many devices utilise abstract button pushes for interactions, some allow for limited finger tracking by estimating finger positions based on sensors. In this study, the Vive Wands and the Valve Index controllers were compared in three tasks: direct interaction with objects (throwing), tool usage (bow) and indirect control of a character (remote-control). Forty-four participants completed each task with both devices and rated the usability of the device after each task. Results showed only differences in preference for the remote-control task. Some participants noted that using the thumbstick of the Index instead of the touchpad of the Wands controller felt more natural in this task. However, performance did not differ between devices in any task. Therefore, future research should not only compare designs of controllers but also consider assets and interactions, as there may be preference and performance differences for certain combinations.
A hybrid recommender system based on description/dialetheic logic and linked data

Abstract In this research, we developed a hybrid recommender system based on description/dialetheic logic, which provides an innovative architecture for the recommendation based on ambiguous reasoning. In addition, our approach allows enriching the knowledge during the reasoning using the linked data paradigm. Thus, the architecture allows the integration of linked data, in order to be exploited by the recommender. On the other hand, the reasoning mechanisms of dialetheic logic allow handling situations of contradiction or inconsistency, to determine the information to offer to users. According to the reviewed literature, our proposal is the first that implements a dialetheic engine in a Recommender System. In general, there are a lot of works about the utilization of linked open data (LOD) to improve the Recommender Systems, for example, to enrich the information to recommend or to solve the cold start problem. However, there are no proposals to deal with the problems of ambiguity, incoherence or incompleteness of the information in these environments extended with LOD. In this way, this paper proposes a hybrid reasoning engine that uses the linked data paradigm for the knowledge extraction, and dialetheic logic for the management of inconsistency/ambiguity information, in order to obtain recommendations. Particularly, the information extracted with Linked Data is processed with the Dialetheic Logic reasoner to solve ambiguous cases. Thus, each recommendation is enriched with related content extracted from the linked data sources, which has been disambiguated. The results are very promising since our hybrid reasoning mechanism allows obtaining more precise recommendations, considering the different classical states of ambiguity in dialetheic logic (contingent statements about the future, failure of a presupposition, vagueness, counterfactual reasoning), according to various metrics of quality used to evaluate the recommendations achieved.
Conductive Fiducial Tangibles for Everyone

While tangibles enrich the interaction with touchscreens, with projected capacitive screens being mainstream, the recognition possibilities of tangibles are nearly lost. Deep learning approaches to improve the recognition of conductive triangles require collecting huge amounts of data and domain-specific knowledge for hyperparameter tuning. To overcome this drawback, we present a toolkit that allows everyone to train a deep learning tangible recognizer based on simulated data. Our toolkit uses a pre-trained Generative Adversarial Network to simulate the imprint of fiducial tangibles, which we then use to train a deployable recognizer based on our pre-defined neuronal network architecture. Our evaluation shows that our approach can recognize fiducial tangibles such as AprilTags with an average accuracy of 99.3% and an average rotation error of only 4.9°. Thus, our toolkit is a plug-and-play solution requiring no domain knowledge and no data collection but allows designers to use deep learning approaches in their design process.
A Study into the Effect of Mobile Device Configurations on Co-Located Collaboration using AR

The increasing availability of portable handheld mobile Augmented Reality technology is revolutionising the way digital information is embedded into the real world. As this data is embedded, it enables new forms of cross-device collaborative work. However, despite the widespread availability of handheld AR, little is known about the role that device configurations and size play on collaboration. This paper presents a study that examines how completing tasks using a simple mobile AR interface on different device sizes and configurations impacts key factors of collaboration such as collaboration strategy, behaviour, and efficacy. Our results show subtle differences between device size and configurations that have a direct influence on the way people approach tasks and interact with virtual models. We highlight key observations and strategies that people employ across different device sizes and configurations.
Prediction Method of Government Economic Situation based on Big Data Analysis

In order to improve the forecasting accuracy of economic situation, a government economic situation forecasting method based on big data analysis is proposed. According to the hardware structure of the system, STC12C5608AD is used as the data acquisition terminal chip to simplify the circuit. The proposed forecasting method can give real-time early warning to the government's economic situation. The software part screens the influencing factors of government economic development, constructs a government economic development index system, collects government economic index data, cleans, clusters, classifies, and standardizes the government economic index data, and extracts the preprocessed government economic index data from the preprocessed government economic index data through data mining. The economic development features are extracted and then input into the neural network. After training and learning, the predicted value of the economic situation is output, and the economic situation level is classified. The experimental results show that the proposed method reduces the error rate of economic situation forecast, shortens the forecast time, improves the forecast accuracy and efficiency, with the peak error ratio not exceeding 15%.
A Comprehensive Assessment of Cultivation Environment of Top Innovative High-Level Talents Based on Deep Learning Algorithm

The quality of talent has increased across all fields due to the constant growth of different industries and the growing job saturation. Real-time job information on recruitment platforms can, therefore, accurately reflect the demand for talent from businesses, serving as a basis for the creation of training policies in schools. In international competition, the development of talents, especially top-level talents, will become more and more crucial. Growing in importance is China's economy and social development. The evaluation of higher vocational and technical talents, however, should also be assessed from a variety of angles, given the diversification of talent training objectives and teaching methods, as well as the expansion of teaching functions. An emerging machine learning technology called deep learning (DL) has been developed to bring machine learning closer to the goals of artificial intelligence. This essay offers a thorough evaluation of the depth of deep learning as it relates to the development of innovative talent in schools. The entire school must be strengthened. It is demonstrated that the average execution time is slashed by 0.0024 s, and the learning sample size error of the DL model is reduced by 0.05276 when compared to the Apriori method. As a result, implementing and researching the DL model can significantly improve both the overall teaching quality of schools and their capacity for innovation.
Uncommon Commons? Creative Commons Licencing in Horizon 2020 Data Management Plans

As policies, good practices and mandates on research data management evolve, more emphasis has been put on the licencing of data, which allows potential re-users to quickly identify what they can do with the data in question. In this paper I analyse a pre-existing collection of 840 Horizon 2020 public data management plans (DMPs) to determine which ones mention creative commons licences and among those who do, which licences are being used. &#x0D; I find that 36% of DMPs mention creative commons and among those a number of different approaches towards licencing exist (overall policy per project, licencing decisions per dataset, licencing decisions per partner, licensing decision per data format, licensing decision per perceived stakeholder interest), often clad in rather vague language with CC licences being â€œrecommendedâ€ or â€œsuggestedâ€. Some DMPs also â€œkick the can further down the roadâ€ by mentioning that â€œaâ€ CC licence will be used, but not which one. However, among those DMPs that do mention specific CC licences, a clear favourite emerges: the CC-BY licence, which accounts for half of the total mentioning of a specific licence. &#x0D; The fact that 64% of DMPs did not mention creative commons at all is an indication for the need for further training and awareness raising on data management in general and licencing in particular in Horizon Europe. For those DMPs that do mention specific licences, 60% would be compliant with Horizon Europe requirements (CC-BY or CC0). However, it should be carefully monitored whether content similar to the 40% that is currently licenced with non- Horizon Europe compliant licences will in the future move to CC-BY or CC0 or whether such content will simply be kept fully closed by projects (by invoking the â€œas open as possible, as close as necessaryâ€ principle), which would be an unintended and potentially damaging consequence of the policy.
An Effective COVID-19 CT Image Denoising Method Based on a Deep Convolutional Neural Network

Background: Faced with the global threat posed by SARS-CoV-2 (COVID-19), lowdose computed tomography (LDCT), as the primary diagnostic tool, is often accompanied by high levels of noise. This can easily interfere with the radiologist's assessment. Convolutional neural networks (CNN), as a method of deep learning, have been shown to have excellent effects in image denoising. Objective: The objective of the study was to use modified convolutional neural network algorithm to train the denoising model. The purpose was to make the model extract the highlighted features of the lesion region better and ensure its effectiveness in removing noise from COVID-19 lung CT images, preserving more important detail information of the images and reducing the adverse effects of denoising. Methods: We propose a CNN-based deformable convolutional denoising neural network (DCDNet). By combining deformable convolution methods with residual learning on the basis of CNN structure, more image detail features are retained in CT image denoising. Result: According to the noise reduction evaluation index of PSNR, SSIM and RMSE, DCDNet shows excellent denoising performance for COVID-19 CT images. From the visual effect of denoising, DCDNet can effectively remove image noise and preserve more detailed features of lung lesions. Conclusion: The experimental results indicate that the DCDNet-trained model is more suitable for image denoising of COVID-19 than traditional image denoising algorithms under the same training set.
Privacy and contact tracing efficacy

As the COVID-19 pandemic emerged, public health authorities and software designers considered the possibility that smartphones could be used for contact tracing to control disease spread. Smartphone-based contact tracing was attractive in part because it promised to allow the tracing of contacts that might not be reported using traditional contact tracing methods. Comprehensive contact tracing raises distinctive privacy concerns, however, that have not been previously explored. Contacts outside of an individual's ordinary social network are more likely to be privacy-sensitive, making fear that such contacts will be disclosed a potential disincentive to adoption of smartphone contact tracing. Here, we modify the standard SEIR infectious disease transmission model to incorporate contact tracing and perform a series of simulations aimed at studying the importance of tracing socially distant (and potentially privacy-sensitive) contacts. We find that, for a simple model network, ensuring that distant contacts are traced is surprisingly unimportant as long as contact tracing adoption is sufficiently high. These results suggest that policy-makers designing contact tracing systems should be willing to trade off comprehensiveness for more widespread adoption.
Audio–visual language instruction understanding for robotic sorting

For robot in human environment, it has always been expected that the robot can execute specified tasks following language instructions. Most current methods only rely on visual perception to understand the language instruction, while it may be not sufficient to fully interpret some language instructions when visually identical objects exist. In this paper, we propose a task of audio–visual language instruction understanding for robotic sorting, in which the robot is able to use both the visual and audio information to fully understand and execute the given instruction. To solve the proposed task, an audio–visual fusion framework is developed, which combines the visual localization and audio recognition models together for the robotic sorting task following language instruction. We have also collected a multimodal dataset for evaluation, and extensive experiments are conducted within the dataset and generalized to new scenarios in physical world demonstrating the effectiveness of the proposed framework.
Lightweight noncommutative key exchange protocol for IoT environments

Network communications are expanding rapidly in many fields, including telecommunications, the Internet of Things, space, consumer electronics, and the military, with different privacy and security issues at stake in each of these areas. The Internet of Things (IoT) has drawn increased attention from academic and industrial researchers over the last few decades. In this environment, keys are routinely exchanged through a public protocol to support the highly secure IoT domain and thwart security threats from unauthorized parties. The environment for IoT devices is subject to numerous limitations, including those related to processing, memory, and energy. These devices need to pass through a gateway or sink to connect to the network. Additionally, the environment must enable secure communication between gateways and IoT devices, even when the devices are disconnected from the rest of the network. In this paper, a lightweight key exchange protocol for IoT environments is presented, allowing the gateway and the IoT device to communicate over an open channel. Our proposed protocol improves security by utilizing noncommutative structures and polynomials over noncommutative rings. The underlying idea is to use the generalized decomposition problem associated with noncommutative rings. Furthermore, how the suggested protocol can achieve key certification and perfect onward secrecy is addressed. Results show this protocol is a strong candidate for key sharing and secure communication between IoT devices. We put our methodology into practice and the results of our experiments demonstrate enhancement of security levels. Finally, the performance analysis of the suggested protocol is compared with some other protocols, in terms of security, communication, and computing overhead.
Application of Virtual Reality Technology and 3D Technology in Game Animation Production

With the increasing pressure of modern young people, they begin to look for various ways to relax. Among them, game animation makes them immersive and has a low experience cost, which is one of the best ways to relax. Therefore, animation games have been welcomed by more and more young people. However, virtual reality technology and 3D technology have played a huge role in the production of game animation. Therefore, this paper aimed to study how virtual reality technology and 3D technology were applied in game animation production and put forward their advantages. This paper also proposed a method based on virtual reality technology and 3D technology and analyzed the production of game animation. The experimental results of this paper showed that in the survey results of the popularity of the A game, only 56 experiencers thought its effect was good, accounting for 18%. There were 145 experiencers who disliked the A game because they thought the A game was very boring and the game scene was very simple. In the survey results of the popularity of the B game, 222 experiencers liked this game, accounting for 71.6%, and 43 experiencers did not like the B game because they thought the 3D effect of the B game would make them dizzy and uncomfortable. It can be seen that the integration of virtual reality technology and 3D technology into game animation production is more popular, so the research in this paper is meaningful.
Fast Control for Backlight Power-Saving Algorithm Using Motion Vectors from the Decoded Video Stream

Backlight power-saving algorithms can reduce the power consumption of the display by adjusting the frame pixels with optimal clipping points under some tradeoff criteria. However, the computation for the selected clipping points can be complex. In this paper, a novel algorithm is created to reduce the computation time of the state-of-the-art backlight power-saving algorithms. If the current frame is similar to the previous frame, it is unnecessary to execute the backlight power-saving algorithm for the optimal clipping points, and the derived clipping point from the previous frame can be used for the current frame automatically. In this paper, the motion vector information was used as the measurement of the similarity between adjacent frames, where the generation of the motion vector information requires no extra complexity since it is generated to reconstruct the decoded frame pixels before the display. The experiments showed that the proposed work can reduce the running time of the state-of-the-art methods by 25.21% to 64.22%, while the performances are maintained; the differences with the state-of-the-art methods in PSNR are only 0.02~1.91 dB, and those in power are only -0.001~0.008 W.
Audio Spoofing Detection Using Constant-Q Spectral Sketches and Parallel-Attention SE-ResNet

Automatic speaker verification (ASV) system is widely used in many voice-based applications, which are very vulnerable to spoofing attacks like Text-to-Speech synthesis and converted voice signals. Effectively detecting the spoofed audio is the main solution to protect ASV systems. However, new types of spoofing technologies are emerging rapidly, and existing researches have exposed poor generalization and low robustness to unknown attacks. In this paper, an audio spoofing detection is proposed based on Constant-Q Spectral Sketches (CQSS) and parallel-attention SE-ResNet. Specially, CQSS features are first extracted using the constant-Q transform, characterized by matrix and spectrogram respectively fed into different detection model. Then, a new deep neural network architecture is proposed based on SE-ResNet, and parallel attention is designed to improve generalization ability and training efficiency. Finally, the yielding scores by different model are fused using an average strategy. The experimental results show that the proposed fusion method achieves the tandem decision cost function and equal error rate scores as 0.0307 and 0.96%, respectively, for unknown attacks, which has better verification performance compared with state-of-art methods.