Reviewerly/exHarmony huggingface dataset:
MEMER - Multimodal Encoder for Multi-signal Early-stage Recommendations

Millions of content gets created daily on platforms like YouTube, Facebook, TikTok etc. Most of such large scale recommender systems are data demanding, thus taking substantial time for content embedding to mature. This problem is aggravated when there is no behavioral data available for new content. Poor quality recommendation for these items lead to user dissatisfaction and short content shelf-life. In this paper we propose a solution MEMER (Multimodal Encoder for Multi-signal Early-stage Recommendations), that utilises the multimodal semantic information of content and uses it to generate better quality embeddings for early-stage items. We demonstrate the flexibility of the framework by extending it to various explicit and implicit user actions. Using these learnt embeddings, we conduct offline and online experiments to verify its effectiveness. The predicted embeddings show significant gains in online early-stage experiments for both videos and images (videos: 44% relative gain in click through rate, 46% relative gain in explicit engagements, 9% relative gain in successful video play, 20% relative reduction in skips, images: 56% relative gain in explicit engagements). This also compares well against the performance of mature embeddings (83.3% RelaImpr (RI) [18] in Successful Video Play, 97.8% RelaImpr in Clicks).
Improving Netflix Video Quality with Neural Networks

Video downscaling is an important component of adaptive video streaming, which tailors streaming to screen resolutions of different devices and optimizes picture quality under varying network conditions. With video downscaling, a high-resolution input video is downscaled into multiple lower-resolution videos. This is typically done by a conventional resampling filter like Lanczos. In this talk, we describe how we improved Netflix video quality by developing neural networks for video downscaling and deploying them at scale.
An Optimal Control Approach to the Minimum-Time Trajectory Planning of Robotic Manipulators

Trajectory planning is a classic problem in robotics, with different approaches and optimisation objectives documented in the literature. Most of the time, the path is assumed, i.e., pre-defined, and optimisation consists of finding the timing of motion under a number of constraints. The focus of this work is on the minimum-time manoeuvring of robotic manipulators. A nonlinear optimal control approach is proposed that does not require the provision of either a pre-defined path or a pre-defined control structure and allows the inclusion of dynamic constraints. The solution (path and timing of motion) is obtained by transforming the optimal control problem into a nonlinear programming problem. The proposed approach is applied to a two-link manipulator for illustration purposes. The optimisation is carried out both without and with obstacles. The minimum-distance and minimum-time solutions are compared, and some classic results are obtained, including the trapezoidal pattern of the joint velocity and the bang–bang structure of the control torques. The effects of limitations on the jerks of actuators and the rate of change in torque inputs are discussed. The application to a four-link manipulator is also included to show the ‘scalability’ of the approach, together with a comparison with a classic path-and-motion-planning method, to highlight the characteristics and performance of the proposed approach. Finally, the possibility of enforcing a number of via-points along the path is demonstrated. The proposed method allows the computation of the path and motion simultaneously with the computation time, which is 1–30 times the manoeuvre time, on a standard PC with the current implementation.
OntoEval: an Automated Ontology Evaluation System

Developing semantically-aware web services requires comprehensive and accurate ontologies. Evaluating an existing ontology or adapting it is a labor-intensive and complex task for which no automated tools exist. Nevertheless, in this paper we propose a tool that aims at making this vision come true, i.e., we present a tool for the automated evaluation of ontologies that allows one to rapidly assess an ontology’s coverage of a domain and identify specific problems in the ontology’s structure. The tool evaluates the domain coverage and correctness of parent-child relations of a given ontology based on domain information derived from a text corpus representing the domain. The tool provides both overall statistics and detailed analysis of sub-graphs of the ontology. In the demo, we show how these features can be used for the iterative improvement of an ontology.
Linear local tangent space alignment with autoencoder

Abstract Linear local tangent space alignment (LLTSA) is a classical dimensionality reduction method based on manifold. However, LLTSA and all its variants only consider the one-way mapping from high-dimensional space to low-dimensional space. The projected low-dimensional data may not accurately and effectively “represent” the original samples. This paper proposes a novel LLTSA method based on the linear autoencoder called LLTSA-AE (LLTSA with Autoencoder). The proposed LLTSA-AE is divided into two stages. The conventional process of LLTSA is viewed as the encoding stage, and the additional and important decoding stage is used to reconstruct the original data. Thus, LLTSA-AE makes the low-dimensional embedding data “represent” the original data more accurately and effectively. LLTSA-AE gets the recognition rates of 85.10, 67.45, 75.40 and 86.67% on handwritten Alphadigits, FERET, Georgia Tech. and Yale datasets, which are 9.4, 14.03, 7.35 and 12.39% higher than that of the original LLTSA respectively. Compared with some improved methods of LLTSA, it also obtains better performance. For example, on Handwritten Alphadigits dataset, compared with ALLTSA, OLLTSA, PLLTSA and WLLTSA, the recognition rates of LLTSA-AE are improved by 4.77, 3.96, 7.8 and 8.6% respectively. It shows that LLTSA-AE is an effective dimensionality reduction method.
Semidefinite-programming-based optimization of quantum random access codes over noisy channels

There are many approaches when it comes to exploring the potential of the random access code (RAC) communication protocol. Given its versatility, the RAC is particularly useful when the communication between parties is restricted. In previous works, it has been proven that ${2}^{(d)}\ensuremath{\rightarrow}1$ quantum random access codes (QRAC), in the absence of noise, outperform their classical counterpart (the ${2}^{(d)}\ensuremath{\rightarrow}1$ CRAC) for any dimension $d$. Here, building upon these works, we showed that noisy channels can significantly decrease the performance of ${2}^{(d)}\ensuremath{\rightarrow}1$ QRAC. In order to mitigate noise-driven efficiency losses, we employed the see-saw optimization algorithm with semidefinite programming (SDP) to find the best quantum encoding and decoding strategies for the QRAC given a known quantum channel.
Universal photonic quantum gate by Cooper-pair-based optical nonlinearity

We propose a compact and highly efficient scheme for solid-state photonic quantum gates. At the core of our scheme is a $SWA{P}^{\ensuremath{\phi}}$ gate based on giant Cooper-pair-based optical nonlinearity we predict in a semiconductor-superconductor structure, selectively introducing a phase to the $|{\mathrm{\ensuremath{\Psi}}}_{+}\ensuremath{\rangle}$ Bell state. We theoretically demonstrate this scheme on a practical device based on a superconducting contact coupled to a GaAs/AlGaAs waveguide structure. We model the Cooper-pair-induced nonlinear change of the refractive index showing strong nonlinearities at energies close to the superconducting gap inside a semiconductor band. We calculate the fidelity of the proposed $SWA{P}^{\ensuremath{\phi}}$ gate, as well as the sensitivity of the gate to device parameters. As short photon wave packets are crucial for efficient higher-order interactions, we investigate the integrated fidelity for short wave packets with different central wavelengths and bandwidths, providing limiting factors as well as possible optimizations. This theoretically demonstrated concept can pave the way toward practical realizations of scalable photonic quantum circuits.
Analysis of the application methods of film and television media and images in the era of big data cloud

Abstract In order to give full play to the application of big data in film and television media and imaging in the cloud era, this study proposes a communication-efficient distributed deep neural network training method based on the DANE algorithm framework. The DANE algorithm is an approximate Newtonian method that has been widely used in communication-efficient distributed machine learning. It has the advantages of fast convergence and no need to calculate the inverse of the Hessian matrix, which can significantly reduce the communication and computational overhead in high-dimensional situations. In order to further improve the computational efficiency, it is necessary to study how to speed up the local optimization of DANE. It is a feasible method to choose to use the most popular adaptive gradient optimization algorithm Adam to replace the commonly used stochastic gradient descent method to solve the local single-machine suboptimization problem of DANE. Experiments show that Adam-based optimization can converge significantly faster than the original SGD-based implementation with little sacrifice in model generalization performance. With the increase of sampling rate, DANE-Adam significantly outperforms the DANE method in terms of convergence speed, and at the same time, the accuracy can be kept almost unchanged, which are 0.96, 0.88 and 0.75, respectively. This shows that Adam-based optimization can converge significantly faster than the original SGD-based implementation with little sacrifice in model generalization performance, with significant potential value.
A study on the influence of the spread of Yangming Studies in Japan on the psychology of the Japanese people based on big data analysis

Abstract The analysis of the psychological impact of the spread of Yangming studies in Japan on the Japanese people is to enable Yangming studies to be better developed in Japan. Based on big data analysis technology, this paper constructs a hybrid data analysis model using the EM algorithm and proposes performance evaluation indexes for the model. Under the EM data analysis model constructed in this paper, the example indicators of the Japanese people’s psychological impact in disseminating Yangming studies by big data analysis are explored, i.e., the psychological acceptability of the dissemination method and the psychological and moral construction impact. Regarding the dissemination method, the Japanese people are more receptive to disseminating Yangming studies in Japan through “learning rules”, with an average percentage of 39.37%. Regarding psychological and moral construction, 90.22% of the Japanese people believe that disseminating Yangming studies can promote self-improvement of value standards and correct self-examination. Based on the big data analysis, we can effectively see from the data the impact of Yangming studies on the audience in the process of dissemination, and improve the scope of Yangming studies dissemination according to the data feedback, so that more people can recognize the idea of unity of knowledge and action.
Innovative reform strategy of artistic practice of university folk dance education based on information fusion technology

Abstract With the development of history, some folk dances are annihilated and lost in the inheritance. In order to protect and inherit national intangible cultural heritage and promote the construction of advanced socialist culture, this paper uses information fusion technology, combines national aesthetic personality, and refers to multi-factor theory, construction theory, and distillation theory to design and reform the art practice model of folk dance education. The data of dancers’ skeletal joint points are collected by using multiple Kinect sensors, and the Kalman filter algorithm is selected to establish the human skeletal motion model and fuse the data information of each joint point. Taking the hand_right and elbow_right joints as examples, it was found that the mean value of the error between the testers’ joint trajectory and the template movement was reduced from 10mm to 4mm, and the mean value of the error between the joint angle and the template movement was reduced from 90° to 35°. Comparing the dance movement training effects and scores of the experimental group and the control group, it was found that the experimental group kicked the front and side legs an average of 70 times/minute, the middle jump height was improved by 10cm, and the average score was 3.33 points higher. This indicates that the integration of information fusion technology in the art practice of dance education can realize the practical innovation of college folk dance education and improve the comprehensive level of folk dance.
Strategies for improving Chinese language proficiency based on artificial intelligence technology

Abstract In recent years, the development of artificial intelligence technology and theory has been rapid, and the application in language science has been gradually comprehensive and diversified, especially the accuracy rate of artificial intelligence for Chinese language is up to 90%. In the era of artificial intelligence, the effect of different structures and parameters of arithmetic models on Chinese language recognition varies greatly. Language science is an important research area for realizing machine-human communication, and accurate comprehension of the meaning of linguistic expressions is the key to realize communication. In this paper, we construct a speech system that is different from the traditional stable time series for the irreplaceable characteristics of artificial intelligence technology to improve Chinese language ability. A dynamic Bayesian network (DBN) is used for modeling and analysis, and a DBN construction method is investigated to import a hidden Markov model in a speech recognition system to reveal the interactions between nodes within multiple time slices. The accuracy of dynamic Bayesian networks in Chinese dialect inference algorithms is demonstrated using Matlab simulations to characterize the reliability of speech features using a speech spectrogram. It proves that artificial intelligence technology and Chinese language science are complementary and mutually reinforcing, showing a good and rapid development trend.
Training Evaluation Model of Outdoor Cross-Country Sports Tourism Project Based on Lagrangian Differential Equation Calculation

Abstract This paper uses Lagrangian differential equations to study the effect of road roughness on the average speed of outdoor trail running. The purpose is to analyze the dynamic model of off-road sports. In this paper, the longitudinal and cross-sectional data of the road are collected and pre-processed, mainly on gravel pavement. Then this paper proposes an approximate formula for the relationship between running speed and road surface roughness. Furthermore, this paper uses the Lagrangian differential equation to solve the road non-uniformity and running speed. Finally, this paper presents an off-road speed prediction method on random uneven road conditions. Finally, the simulation experiments demonstrate that the model based on the Lagrangian differential equation can comprehensively reflect various factors affecting the average outdoor off-road speed. The power spectral density of each sampling point of the model, load input, etc., are all meaningful. This model meets the needs of simulation experiments.
Digital Film Music Creation Model Based on Inhomogeneous First Order Constant Coefficient Linear Differential Equation

Abstract Computer music production is an essential branch of computer technology in artistic creation. This is an interdisciplinary subject with information science and art. This paper proposes a creation model for digital music with first-order inhomogeneous first-order constant coefficient linear differential equations. This mode includes acoustic modes for waveform envelopes and bands. It has a straight-line mapping of phonetic patterns and rhythmic patterns. Then this paper uses the signal reconstruction principle and iterative extrapolation method to restore the signal in the frequency domain to obtain the simulated music. The experimental results show that this digital music creation model of inhomogeneous first-order constant coefficient linear differentiation can save a lot of labor costs. And this way can improve the audio quality and output effect. Compared with the undetermined factor method and the Laplace transform method in conventional advanced mathematics, the inhomogeneous first-order constant coefficient linear differentiation is more convenient in music composition.
Disclosure of Multiple "Patient Characteristics" Format Statistics leaks Quasi-identifier Linkage

In recent years, Artificial Intelligence (AI) applications and statistical analysis using big data have become popular. Therefore, regulations for handling personal information have become an issue. However, published statistical information is generally not regulated and is commonly collected and analyzed in many research. In the medical field, "patient characteristics'', a statistical format of the patients who were the subject of medical research, is included in medical papers. For a single patient characteristics, it is pointed out that its disclosure can lead to anonymity violation, and it is also known that its vulnerability can be quantitatively measured by the Patient Family Detect on Overall Category (PFDOC) entropy. However, the impact of disclosing multiple patient characteristics on anonymity is unknown, particularly in a pandemic where large amounts of statistical information are published for humanitarian purposes. In this paper, we model a pandemic situation and propose a new attack for determining the quasi-identifier (QI) linkage from multiple patient characteristics, which is the premise of a known re-identification attack called the linkage attack. We also introduce a risk indicator called "N-table contents range confidentiality'' to quantify the confidentiality of the QI linkage. Through theoretical and experimental analysis, we show that to protect the N-table contents range confidentiality, medical institutions should restrict the disclosure of attributes with a population ratio of less than 25% or more than 75%.
Separation of spherically and translationally covariant finite quantum spaces within the XXX model

A method of separation of subspaces with definite values of the total spin, its z-projection and quasimomentum, within the state space of the XXX model, i.e. the N-th tensor power of a single qubit, is presented. We construct a complete system of orthogonal projectors onto such subspaces by use of the basis of wavelets and chosen measuring operators based on the total spin. We also demonstrate, on examples of N=6 and 8 magnetic spin rings, that the constructed projectors can be further decomposed into the orthogonal sum of density matrices of eigenstates of the XXX model in the corresponding subspaces. This decomposition exhibits a Galois symmetry, stemming from roots of indecomposable factors of characteristic polynomial of the Heisenberg Hamiltonian. In this way, we propose an alternative for Bethe Ansatz for moderate lengths of spin chains, by a return to the original spectral problem, avoiding the highly nonlinear system of Bethe Ansatz equations. Such results are easy to adapt in quantum information processing.
Image to Image Search Engine

In this research, we propose a concept for a search engine that enables users to input a picture from their local database and obtain data from the internet about it. With the exception of the fact that an image is submitted here as a query rather than text-based keywords, this is very similar to the conventional keyword search utilised by the majority of search engines. Because the image is the search query, finding the information corresponding to the uploaded image requires analysis and matching the image's content. This complicates the search process. This is most apt for searching information about images of dogs or any animal or thing that is identifiable.
Unsupervised classification applications in enhancing lithological mapping and geological understanding: a case study from Northern Ireland

Pattern classification algorithms can help us to recognize and predict patterns in large and complex multivariate datasets. Self-organizing maps (SOMs), which use competitive learning, are unsupervised classification tools that are considered to be very useful in pattern classification and recognition. This technique is based on the principles of vector quantification of similarities and clustering in a high-dimensional space and the method can handle the analysis and visualization of high-dimensional data. This tool is ideal for analysing a complex combination of categorical and continuous spatial variables, with particular applications to geological features. We used SOMs to predict geological features based on airborne geophysical data acquired through the Tellus Project in Northern Ireland. The SOMs were applied through 20 experiments (iterations), incorporating radiometric data in combination with geological features, including elevation, slope angle, terrain ruggedness and geochronology. The SOMs were able to differentiate contrasting bedrock geology, such as acidic v. mafic igneous rocks, although data clustering over intermediate rocks was less clear. The presence of a thick cover of glacial deposits in most of the study area presented a challenge for data clustering, particularly over the intermediate igneous and sedimentary bedrock types. Supplementary material: Twenty iterations were carried out during this research. Only six are included in the article. The rest of the iterations are available at https://doi.org/10.6084/m9.figshare.c.6603098
Government Fund Allocation Tracking System Over Blockchain

The state government working involves a large number of trans- actions activities towards various operations throughout the state. This includes new actions, initiatives, projects, granting contracts, farmer schemes, and so on. One of the most challenging factors that top governments face is low- level corruption which at times is hard to follow and denies the state progress. As a result of the current system, tracking is very problematic and this provides needy people with a service that is sometimes difficult to track, which deprives them. In this case, we use blockchain which enables cryptography and transaction security at every stage while maintaining transparency so that every transaction is backed up with proof of its authenticity. Hence, we present a framework that uses blockchain innovation and a full proof fund transfer system. Blockchain contains growing list of records called blocks. Crypto- graphically hashed data, a timestamp, and recent transactions are included in each block