Reviewerly/exHarmony huggingface dataset:
Situation calculus for controller synthesis in manufacturing systems with first-order state representation

Manufacturing is transitioning from a mass production model to a service model in which facilities ‘bid’ to produce products. To decide whether to bid for a complex, previously unseen product, a facility must be able to synthesize, on the fly, a process plan controller that delegates abstract manufacturing tasks in a supplied process recipe to the available manufacturing resources. Often manufacturing processes depend on the data and objects (parts) they produce and consume. To formalize this aspect we need to adopt a first-order representation of the state of the processes. First-order representations of the state are commonly considered in reasoning about action in AI, and here we show that we can leverage the wide literature on the Situation Calculus and ConGolog programs to formalize this kind of manufacturing. With such a formalization available, we investigate how to synthesize process plan controllers in this first-order state setting. We also identify two important decidable cases—finite domains and bounded action theories—for which we provide techniques to actually synthesize the controller.
Fast Stabiliser Simulation with Quadratic Form Expansions

This paper builds on the idea of simulating stabiliser circuits through transformations of quadratic form expansions. This is a representation of a quantum state which specifies a formula for the expansion in the standard basis, describing real and imaginary relative phases using a degree-2 polynomial over the integers. We show how, with deft management of the quadratic form expansion representation, we may simulate individual stabiliser operations in $O(n^2)$ time matching the overall complexity of other simulation techniques [arXiv:quant-ph/0406196, arXiv:quant-ph/0504117, arXiv:1808.00128]. Our techniques provide economies of scale in the time to simulate simultaneous measurements of all (or nearly all) qubits in the standard basis. Our techniques also allow single-qubit measurements with deterministic outcomes to be simulated in constant time. We also describe throughout how these bounds may be tightened when the expansion of the state in the standard basis has relatively few terms (has low 'rank'), or can be specified by sparse matrices. Specifically, this allows us to simulate a 'local' stabiliser syndrome measurement in time $O(n)$, for a stabiliser code subject to Pauli noise -- matching what is possible using techniques developed by Gidney [arXiv:2103.02202] without the need to store which operations have thus far been simulated.
Emergent Interfaces: Vague, Complex, Bespoke and Embodied Interaction between Humans and Computers

Most Human–Computer Interfaces are built on the paradigm of manipulating abstract representations. This can be limiting when computers are used in artistic performance or as mediators of social connection, where we rely on qualities of embodied thinking: intuition, context, resonance, ambiguity and fluidity. We explore an alternative approach to designing interaction that we call the emergent interface: interaction leveraging unsupervised machine learning to replace designed abstractions with contextually derived emergent representations. The approach offers opportunities to create interfaces bespoke to a single individual, to continually evolve and adapt the interface in line with that individual’s needs and affordances, and to bridge more deeply with the complex and imprecise interaction that defines much of our non-digital communication. We explore this approach through artistic research rooted in music, dance and AI with the partially emergent system Sonified Body. The system maps the moving body into sound using an emergent representation of the body derived from a corpus of improvised movement from the first author. We explore this system in a residency with three dancers. We reflect on the broader implications and challenges of this alternative way of thinking about interaction, and how far it may help users avoid being limited by the assumptions of a system’s designer.
Explainable Recommendation via Neural Rating Regression and Fine-Grained Sentiment Perception

Compared with traditional recommendation systems, explainable recommendation systems have certain advantages in terms of system transparency, result credibility, and user satisfaction. However, the existing text explanation generation methods are often limited by pre-defined templates which limit the ability of text expression. The free-style text generation methods have stronger expressive ability, but are less controllable and ignore fine-grained sentiment perception from user comments. In this paper, a Dual Learning-based Explainable Recommendation Model (called DLER) is proposed, which uses a dual learning mechanism to perform rating prediction and explanation generation respectively. The parameters can be adjusted iteratively via the collaborative promotion between the two phases. A rating prediction algorithm based on neural rating regression and an explanation generation algorithm based on fine-grained sentiment perception are respectively proposed. On the one hand, the ratings are predicted via MLP. On the other hand, users' fine-grained sentiments are perceived by analyzing comments, which will be used for GRU-based explanation generation. The experiments demonstrate the effectiveness and the efficiency of our proposed method in comparison with traditional methods.
Leveraging VVC partitioning for low-latency video decoder output

Emerging video applications, such as cloud gaming and cloud rendering, rely on time critical decoding operations so that user interactions are reflected on the user's screen as soon as possible. In these applications content is encoded, transmitted and decoded before finally being displayed on the user's screen with each of these steps adding latency. Furthermore, often between the output of a decoded frame and its display additional operations may be required, such as up/downscaling, UI overlays or color space conversions. Current decoders only release the decoded samples once a full frame has been decoded, although under such low delay requirements, it would be beneficial if applications could start to process the decoded samples prior to the complete decoding of a coded picture. This would allow for parallel processing of a frame in the decoder and throughout the display pipeline, which could ultimately allow for lower latencies and eventually pave the way for more enhancement operations without inducing latency. We modified a decoder for the newly published Versatile Video Coding (VVC) codec to demonstrate that it is possible to output partial frames. The changes in the decoder to enable such partial output are straightforward and allow for the new partitioning schemes to be leveraged for partial output. The demonstrator will show how the progressive output can then be used by elements further on in the decoding/display pipeline.
Effective fusion of deep multitasking representations for robust visual tracking

Visual object tracking remains an active research field in computer vision due to persisting challenges with various problem-specific factors in real-world scenes. Many existing tracking methods based on discriminative correlation filters (DCFs) employ feature extraction networks (FENs) to model the target appearance during the learning process. However, using deep feature maps extracted from FENs based on different residual neural networks (ResNets) has not previously been investigated. This paper aims to evaluate the performance of 12 state-of-the-art ResNet-based FENs in a DCF-based framework to determine the best for visual tracking purposes. First, it ranks their best feature maps and explores the generalized adoption of the best ResNet-based FEN into another DCF-based method. Then, the proposed method extracts deep semantic information from a fully convolutional FEN and fuses it with the best ResNet-based feature maps to strengthen the target representation in the learning process of continuous convolution filters. Finally, it introduces a new and efficient semantic weighting method (using semantic segmentation feature maps on each video frame) to reduce the drift problem. Extensive experimental results on the well-known OTB-2013, OTB-2015, TC-128, UAV-123 and VOT-2018 visual tracking datasets demonstrate that the proposed method effectively outperforms state-of-the-art methods in terms of precision and robustness of visual tracking.
Attention Network with GMM Based Feature for ASV Spoofing Detection

Automatic Speaker Verification (ASV) is widely used for its convenience, but is vulnerable to spoofing attack. The 2-class Gaussian Mixture Model classifier for genuine and spoofed speech is usually used as the baseline in ASVspoof challenge. The GMM accumulates the scores on all frames in a speech independently, and does not consider its context. We propose the self-attention network spoofing detection model whose input is the log-probabilities of the speech frames on the GMM components. The model relies on the self-attention mechanism which directly draws the global dependencies of the inputs. The model considers not only the score distribution on GMM components, but also the relationship of frames. And the pooling layer is used to capture long-term characteristics for detection. We also proposed the two-path attention network, which is based on two GMMs trained on genuine and spoofed speech respectively. Experiments on the ASVspoof 2019 challenge logical and physical access scenarios show that the proposed models can improve performance greatly compared with the baseline systems. LFCC feature is more suitable for our models than CQCC in experiments.
Language Adaptation for Speaker Recognition Systems Using Contrastive Learning

In this article we propose to study several approaches to adapt a system between two languages. To train the state of the art x-vector Speaker Verification system, we need a huge amount of labeled speech data. If this constraint is satisfied in English (due to Voxceleb), it is not in our target domain: French. We use a supervised Contrastive Learning to transfer knowledge between source and target domain. Among the two other proposed adaptation approaches (Multilingual Learning and Transfert Learning) we show that the one based on Contrastive Learning gives the best performance: about 30% relative gain in term of Equal Error Rate with respect to the baseline system. We also show the robustness of the Contrastive Learning with respect to the duration (from very short to short) as well as to distortion presence (noise, reverberation).
Tear the Image Into Strips for Style Transfer

Recently, Deep Convolutional Neural Networks (DCNNs) have achieved remarkable progress in computer vision community, including in style transfer tasks. Normally, most methods feed the full image to the DCNN. Although high-quality results can be achieved in this manner, several underlying problems arise. For one, with the increase in image resolution, the memory footprint will increase dramatically, leading to high latency and massive power consumption. Furthermore, these methods are usually unable to integrate with the commercial image signal processor (ISP), which processes the image in a line-sequential manner. To solve the above problems, we propose a novel ISP-friendly deep learning-based style transfer algorithm: SequentialStyle. A brand new line-sequential processing mode is proposed, where the image is torn into strips, and each strip is sequentially processed, contributing to less memory demand. We further propose a Spatial-Temporal Synergistic (STS) mechanism that decouples the previously simplex 2-D image style transfer into spatial feature processing (in-strip) and temporal correlation transmission (in-between strips). Compared with the SOTA style transfer algorithms, experimental results show that our SequentialStyle is competitive. Besides, SequentialStyle has less demand for memory consumption, even for the images whose resolutions are 4 k or higher.
Multi-object Multi-sensor Tracking Simulation Using Poisson Multi-Bernoulli Mixture Filter

Multi-object tracking and multi-sensor fusion algorithms are used in many areas of science and technology. For example, in an advanced field such as autonomous vehicles, multi-object tracking and multi-sensor fusion are indispensable technologies. The purpose of these technologies is to processing noisy measurements from different sensors to determine the number and state of objects with high accuracy. Some of the algorithms frequently used in multi-object tracking applications are Global Nearest Neighbor Filter (GNN), Joint Probabilistic Data Association Filter (JPDA), Multiple Hypothesis Tracking (MHT), Probability Hypothesis Density Filter (PHD), and other algorithms. These algorithms are very effective for estimating the state of objects and can combine measurements from multiple sensors to improve estimation accuracy [1,2]. But an even more powerful tool for multi-object tracking is the Poisson multi-Bernoulli mixture filter (PMBM Filter) [1]. In this paper, we will introduce the PMBM filter and simulate an algorithm using a PMBM filter that combines measurements from multiple automotive sensors to estimates object’s states. At the same time, we also compare the performance of the aforementioned algorithms in some specific simulation scenarios.
Variance Stabilised Optimisation of Neural Networks: A Case Study in Additive Manufacturing

A new framework is presented for training neural networks that is based on the characterisation and stabilisation of measurement variations. The framework results in a number of useful properties that maximises the use of data as well as aiding in the interpretation of results in a principled manner. This is achieved via variance stabilisation and a subsequent standardisation step. The method is a general approach that may be used in any context where repeatability data is available. Standardisation in this manner allows goodness of fit to be quantified and measurement data to be interpreted from a statistical perspective. We demonstrate the utility of this framework in the analysis of advanced manufacturing data.
Collaborative filtering recommendation algorithm integrating user similarity and trust

Abstract The rapid development of the Internet has brought convenience to people and has also produced the problem of “information overload”. In view of the traditional collaborative filtering algorithm facing some bottlenecks to be solved, this study proposes a collaborative filtering algorithm that combines similarity and trust. First of all, in view of the large deviation of traditional similarity calculation and prediction of user ratings, this study proposes an optimized Pearson correlation coefficient calculation method; secondly, the trust relationship is established based on the user’s rating of the common project, and the trust relationship between users who do not have a direct trust relationship is established through the transfer of trust; then find the nearest neighbor set of the target user through the fusion of user similarity and trust; finally, the item is scored and predicted to generate a recommendation list. Experimental results show that the algorithm proposed in this study can effectively improve the accuracy of recommendation.
Feature Enhanced Capsule Networks for Robust Automatic Essay Scoring

Automatic Essay Scoring (AES) Engines have gained popularity amongst a multitude of institutions for scoring test-taker's responses and therefore witnessed rising demand in recent times. However, several studies have demonstrated that the adversarial attacks severely hamper existing state-of-the-art AES Engines' performance. As a result, we propose a robust architecture for AES systems that leverages Capsule Neural Networks, contextual BERT-based text representation, and key textually extracted features. This end-to-end pipeline captures semantics, coherence, and organizational structure along with fundamental rule-based features such as grammatical and spelling errors. The proposed method is validated by extensive experimentation and comparison with the state-of-the-art baseline models. Our results demonstrate that this approach performs significantly better on 6 out of 8 prompts on the Automated Student Assessment Prize (ASAP) dataset. In addition, it shows an overall best performance with a Quadratic Weighted Kappa (QWK) metric of 81%. Moreover, we empirically demonstrate that it is successful in identifying adversarial responses and scoring them lower.
Design of Virtual Reality System for Organic Chemistry

Virtual reality (VR) is an advanced technology widely used in many fields.Education is essential for human resources development, and the use of technology in education can enhance teaching and learning methods.This study aims to present new methods and tools for visual and interactive education in organic chemistry.The experimental design and chemical equipment used in this research are based on the basic theory of organic chemistry, and the related materials are simulated as three-dimensional (3D) models to perform the experiments in a VR system.Chemical reactions are simulated by mixing the chemicals, and the students can observe the chemical reactions that occur.In addition, we measured the impact of using virtual organic chemistry experiments.The system was evaluated by students of the organic chemistry department, and the effectiveness of the experimental apparatus was evaluated in discussions and surveys.The results indicate that VR technologies may be considered suitable for adoption as an accessible educational tool, and that simulating laboratory circumstances through a VR system can improve students' learning outcomes in experimental chemistry.
Using Artificial Intelligence-based Online Translation Website to improve the Health Education in International Students

The purpose of this study is to test the feasibility of using AI-based online translation tools in health management education to determine its effect on improving health management education. Several of the current mainstream online translation tools for translating English literature related to health management education have been tested. The knowledge of health management translated by the online translation tool is translated into the mother tongue for the evaluation of the students’ cognitive ability of the relevant knowledge. The online questionnaire was used to provide feedback on students’ understanding and mastery of health management knowledge translated into their native language. The mainstream online translation tools already allow students to translate basic health education materials into their native language. The mainstream translation tools with the assistance of artificial intelligence can achieve high accuracy in the translation of short sentences in health education materials. The results show that the online translation of health education materials from non-native language to native language can basically enable students to obtain the correct understanding of health education materials. Of course, converting long sentences in health education materials into short sentences to improve the accuracy of intelligent translation tools can effectively improve the cognitive effect. Future research could further validate the AI-assisted learning of health education materials in non-native languages.
Blockchain and the Internet of Things: Opportunities and Challenges

Since the emergence of Bitcoin, the cryptocurrency, Blockchain technology has received substantial attention from researchers, businesses, and governments. Due to its distinctive feature in which a Blockchain allows for the creation of a trustworthy ledger of transactions that is shared by all entities in the network; eliminating the need for a third party to manage the transactions. The blockchain utilizes cryptographic mechanisms to protect tampering with the transactions stored in the ledger. With such features, blockchain technology shows a great potential to be applied to domains other than finance. It can be utilized in healthcare, transportation and smart cities in which data generated from great many devices (i.e., sensors, IoTs) needs to be stored and managed in a secure way. Throughout this paper, we will discuss how Blockchain could provide security services in environments that encompass IoT devices. We will further explore the possible integration of these two technologies and challenges.
Training Classifiers that are Universally Robust to All Label Noise Levels

For classification tasks, deep neural networks are prone to overfitting in the presence of label noise. Although existing methods are able to alleviate this problem at low noise levels, they encounter significant performance reduction at high noise levels, or even at medium noise levels when the label noise is asymmetric. To train classifiers that are universally robust to all noise levels, and that are not sensitive to any variation in the noise model, we propose a distillation-based framework that incorporates a new subcategory of Positive-Unlabeled learning. In particular, we shall assume that a small subset of any given noisy dataset is known to have correct labels, which we treat as “positive”, while the remaining noisy subset is treated as “unlabeled”. Our framework consists of the following two components: (1) We shall generate, via iterative updates, an augmented clean subset with additional reliable “positive” samples filtered from “unlabeled” samples; (2) We shall train a teacher model on this larger augmented clean set. With the guidance of the teacher model, we then train a student model on the whole dataset. Experiments were conducted on the CIFAR-10 dataset with synthetic label noise at multiple noise levels for both symmetric and asymmetric noise. The results show that our framework generally outperforms at medium to high noise levels. We also evaluated our framework on Clothing1M, a real-world noisy dataset, and we achieved 2.94% improvement in accuracy over existing state-of-the-art methods.
A Preliminary Investigation on a Multimodal Controller and Freehand Based Interaction in Virtual Reality

In the last years, the synergy between VR and HCI has dramatically increased the user's feeling of immersion within virtual scenes, improving VR applications' user experience and usability. Two main aspects have emerged with the evolution of these technologies concerning immersion in the 3D scene: locomotion within the 3D scene and interaction with the components of the 3D scene. Locomotion with classical freehand approaches based on hand tracking can be stressful for the user due to the need to keep the hand still in a specific position for a long time to activate the locomotion gesture. Likewise, using a classic Head Mounted Display (HMD) controller for the interaction with the 3D scene components could be unnatural for the user, using the hand to pinch and grab the 3D objects. This paper proposes a multimodal approach, mixing the Leap Motion and 6-DOF controller to navigate and interact with the 3D scene to reduce the locomotion gesture stress based on the hand tracking and increase the immersion and interaction feeling using freehand to interact with the 3D scene objects.