Reviewerly/exHarmony huggingface dataset:
The USYD-JD Speech Translation System for IWSLT2021

This paper describes the University of Sydney & JD's joint submission of the IWSLT 2021 low resource speech translation task.We participated in the Swahili→English direction and got the best scareBLEU (25.3) score among all the participants.Our constrained system is based on a pipeline framework, i.e.ASR and NMT.We trained our models with the officially provided ASR and MT datasets.The ASR system is based on the open-sourced tool Kaldi and this work mainly explores how to make the most of the NMT models.To reduce the punctuation errors generated by ASR model, we employ our previous work SlotRefine to train a punctuation correction model.To achieve better translation performance, we explored the most recent effective strategies, including back translation, knowledge distillation, multi-feature reranking and transductive finetuning.For model structure, we tried autoregressive and non-autoregressive models, respectively.In addition, we proposed two novel pre-train approaches, i.e. de-noising training and bidirectional training to fully exploit the data.Extensive experiments show that adding the above techniques consistently improves the BLEU scores, and the final submission system outperforms the baseline (Transformer ensemble model trained with the original parallel data) by approximately 10.8 BLEU score, achieving the SOTA performance.
Labelling the past: data set creation and multi-label classification of Dutch archaeological excavation reports

Abstract The extraction of information from Dutch archaeological grey literature has recently been investigated by the AGNES project. AGNES aims to disclose relevant information by means of a web search engine, to enable researchers to search through excavation reports. In this paper, we focus on the multi-labelling of archaeological excavation reports with time periods and site types, and provide a manually labelled reference set to this end. We propose a series of approaches, pre-processing methods, and various modifications of the training set to address the often low quality of both texts and labels. We find that despite those issues, our proposed methods lead to promising results.
MFVFD: A Multi-Agent Q-Learning Approach to Cooperative and Non-Cooperative Tasks

Value function decomposition (VFD) methods under the popular paradigm of centralized training and decentralized execution (CTDE) have promoted multi-agent reinforcement learning progress. However, existing VFD methods proceed from a group's value function decomposition to only solve cooperative tasks. With the individual value function decomposition, we propose MFVFD, a novel multi-agent Q-learning approach for solving cooperative and non-cooperative tasks based on mean-field theory. Our analysis on the Hawk-Dove and Nonmonotonic Cooperation matrix games evaluate MFVFD's convergent solution. Empirical studies on the challenging mixed cooperative-competitive tasks where hundreds of agents coexist demonstrate that MFVFD significantly outperforms existing baselines.
The Set of Separable States has no Finite Semidefinite Representation Except in Dimension $$3\times 2$$

Abstract Given integers $$n \ge m$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>n</mml:mi><mml:mo>≥</mml:mo><mml:mi>m</mml:mi></mml:mrow></mml:math> , let $$\text {Sep}(n,m)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mtext>Sep</mml:mtext><mml:mo>(</mml:mo><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> be the set of separable states on the Hilbert space $$\mathbb {C}^n \otimes \mathbb {C}^m$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msup><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mi>n</mml:mi></mml:msup><mml:mo>⊗</mml:mo><mml:msup><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mi>m</mml:mi></mml:msup></mml:mrow></mml:math> . It is well-known that for $$(n,m)=(3,2)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:mo>(</mml:mo><mml:mn>3</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:math> the set of separable states has a simple description using semidefinite programming: it is given by the set of states that have a positive partial transpose. In this paper we show that for larger values of n and m the set $$\text {Sep}(n,m)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mtext>Sep</mml:mtext><mml:mo>(</mml:mo><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> has no semidefinite programming description of finite size. As $$\text {Sep}(n,m)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mtext>Sep</mml:mtext><mml:mo>(</mml:mo><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> is a semialgebraic set this provides a new counterexample to the Helton–Nie conjecture, which was recently disproved by Scheiderer in a breakthrough result. Compared to Scheiderer’s approach, our proof is elementary and relies only on basic results about semialgebraic sets and functions.
Digitally Assisted Preservation and Restoration of a Fragmented Mural in a Tang Tomb

Abstract Digital technology is being increasingly designed and employed to support the preservation and restoration of ancient murals. The contributions of digital technologies to this study were planned from the very beginning, as the complexity of this restoration necessitated the design of innovative procedures to manage the restoration and reconstruction processes and to record the details and deformation of the mural. This paper takes the digital restoration of the "maid holding a fan" mural in the Tang Zhaoling Duan Jianbi tomb as an example. In view of the large number and dislocation of mural fragments, the application of digital restoration technology and its achievements in the restoration of this ancient mural are preliminarily explored in combination with high-precision digital information collection and recording. Technical suggestions and details regarding the digital restoration of the mural are put forward to serve as guidance for the preservation and restoration of ancient murals.
Level Measurement in Grain Silos with Extreme Learning Machine Algorithm

In this study, a new method based on a machine learning technique to detect the amount of grain in silos with radar is proposed. In order to carry out the measurements in the laboratory environment, a model silo corresponding to 8% of the actual commercial silo was produced by a 3D printer and the radar measurements were performed in the frequency band of 18 - 40 GHz. In the proposed method, 2000 back scattering information belonging to different levels of grain were collected and they were used as input data to the extreme learning machine (ELM). The accuracy of the algorithm was obtained by K-fold cross validation technique. As a result, the ELM algorithm was determined the grain amount with an accuracy rate of 84%. The results show that the measurement system based on machine learning is more practical than traditional methods.
PoseGTAC: Graph Transformer Encoder-Decoder with Atrous Convolution for 3D Human Pose Estimation

Graph neural networks (GNNs) have been widely used in the 3D human pose estimation task, since the pose representation of a human body can be naturally modeled by the graph structure. Generally, most of the existing GNN-based models utilize the restricted receptive ﬁelds of ﬁlters and single-scale information, while neglecting the valuable multi-scale contextual information. To tackle this issue, we propose a novel Graph Transformer Encoder-Decoder with Atrous Convolution, named PoseGTAC, to effectively extract multi-scale context and long-range information. In our proposed PoseGTAC model, Graph Atrous Convolution (GAC) and Graph Transformer Layer (GTL), respectively for the extraction of local multi-scale and global long-range information, are combined and stacked in an encoder-decoder structure, where graph pooling and unpooling are adopted for the interaction of multi-scale information from local to global (e.g., part-scale and body-scale). Extensive experiments on the Human3.6M and MPI-INF-3DHP datasets demonstrate that the proposed PoseGTAC model exceeds all previous methods and achieves state-of-the-art performance.
Normalisation and subformula property for a system of classical logic with Tarski’s rule

Abstract This paper considers a formalisation of classical logic using general introduction rules and general elimination rules. It proposes a definition of ‘maximal formula’, ‘segment’ and ‘maximal segment’ suitable to the system, and gives reduction procedures for them. It is then shown that deductions in the system convert into normal form, i.e. deductions that contain neither maximal formulas nor maximal segments, and that deductions in normal form satisfy the subformula property. Tarski’s Rule is treated as a general introduction rule for implication. The general introduction rule for negation has a similar form. Maximal formulas with implication or negation as main operator require reduction procedures of a more intricate kind not present in normalisation for intuitionist logic.
Novelty Detection via Contrastive Learning with Negative Data Augmentation

Novelty detection is the process of determining whether a query example differs from the learned training distribution. Previous generative adversarial networks based methods and self-supervised approaches suffer from instability training, mode dropping, and low discriminative ability. We overcome such problems by introducing a novel decoder-encoder framework. Firstly, a generative network (decoder) learns the representation by mapping the initialized latent vector to an image. In particular, this vector is initialized by considering the entire distribution of training data to avoid the problem of mode-dropping. Secondly, a contrastive network (encoder) aims to ``learn to compare'' through mutual information estimation, which directly helps the generative network to obtain a more discriminative representation by using a negative data augmentation strategy. Extensive experiments show that our model has significant superiority over cutting-edge novelty detectors and achieves new state-of-the-art results on various novelty detection benchmarks, e.g. CIFAR10 and DCASE. Moreover, our model is more stable for training in a non-adversarial manner, compared to other adversarial based novelty detection methods.
Automatic Mixed-Precision Quantization Search of BERT

Pre-trained language models such as BERT have shown remarkable effectiveness in various natural language processing tasks. However, these models usually contain millions of parameters, which prevents them from practical deployment on resource-constrained devices. Knowledge distillation, Weight pruning, and Quantization are known to be the main directions in model compression. However, compact models obtained through knowledge distillation may suffer from significant accuracy drop even for a relatively small compression ratio. On the other hand, there are only a few quantization attempts that are specifically designed for natural language processing tasks. They suffer from a small compression ratio or a large error rate since manual setting on hyper-parameters is required and fine-grained subgroup-wise quantization is not supported. In this paper, we proposed an automatic mixed-precision quantization framework designed for BERT that can simultaneously conduct quantization and pruning in a subgroup-wise level. Specifically, our proposed method leverages Differentiable Neural Architecture Search to assign scale and precision for parameters in each sub-group automatically, and at the same time pruning out redundant groups of parameters. Extensive evaluations on BERT downstream tasks reveal that our proposed method outperforms baselines by providing the same performance with much smaller model size. We also show the feasibility of obtaining the extremely light-weight model by combining our solution with orthogonal methods such as DistilBERT.
Specialize and Fuse: Pyramidal Output Representation for Semantic Segmentation

We present a novel pyramidal output representation to ensure parsimony with our "specialize and fuse" process for semantic segmentation. A pyramidal "output" representation consists of coarse-to-fine levels, where each level is "specialize" in a different class distribution (e.g., more stuff than things classes at coarser levels). Two types of pyramidal outputs (i.e., unity and semantic pyramid) are "fused" into the final semantic output, where the unity pyramid indicates unity-cells (i.e., all pixels in such cell share the same semantic label). The process ensures parsimony by predicting a relatively small number of labels for unity-cells (e.g., a large cell of grass) to build the final semantic output. In addition to the "output" representation, we design a coarse-to-fine contextual module to aggregate the "features" representation from different levels. We validate the effectiveness of each key module in our method through comprehensive ablation studies. Finally, our approach achieves state-of-the-art performance on three widely-used semantic segmentation datasets—ADE20K, COCO-Stuff, and Pascal-Context.
A Fine-grained Emotional Analysis of E-commerce Product Review Data based on Dictionaries

The development of Web 2.0 era has driven the online information sharing behavior of consumers. A large amount of evaluation information not only provides reference advice for other consumers, but also provides many valuable information for merchants. In this paper, Python was used to crawling Logitech M110 mouse comment on Jingdong platform, and Latent Dirichlet Allocation theme model analysis and word frequency statistics were carried out. It was found that focus of users on this mouse mainly included customer service, logistics, packaging, appearance, performance, price, brand and after-sales service. Then, the manually labeled text was classified with multiple labels, and the accuracy of the results reached more than 81.82%, indicating the feasibility of classification. Finally, based on the HowNet emotion dictionary, the author calculates the emotion score of the comments in each aspect and puts forward corresponding suggestions to the merchants according to the score results of each aspect.
Analysis of Presumed IQOS Influencer Marketing on Instagram in the Czech Republic in 2018–2019

BACKGROUND: Heated tobacco products (HTP) are novel electronic devices that produce an aerosol by heating modified tobacco. In July 2017, Philip Morris launched a heated tobacco product, IQOS, on the Czech market. The release of IQOS was promoted by a massive marketing campaign using various marketing channels. AIM: This paper presents an analysis of the influencers’ posts promoting a heated tobacco product (HTP), IQOS, produced by Philip Morris, in the Czech Republic. METHODS: Critical discourse analysis (CDA) was used to uncover the hidden power relationships in both textual and visual representations of IQOS in Instagram posts. We analysed the posts of 22 Czech influencers identified with the hashtags #IQOSambassador, #IQOSambasabor, #IQOSlounge, #IQOSveVarech, and #mujIQOS, together with associated pictures and videos on Instagram. RESULTS: The hashtag #iqosambassador was used internationally in 940 posts (as of May 16, 2019). Our findings show subtle forms of persuasion that associate the IQOS product with an aspirational, exclusive lifestyle, healthy living, and a relaxed atmosphere within a community of friends. Preliminary results also show that influencers promoted IQOS to any and all Instagram users (including children and non-smokers). Covert advertising was indicated indirectly by the use of hashtags (#notriskfree, #onlyforadults, and #iqosambassador), which might be evidence that the influencers were paid indirectly by a digital marketing or PR agency. CONCLUSIONS: Czech celebrities and influencers have been actively presenting IQOS in their posts and videos since 2018 on Instagram. They present IQOS as a gateway to an aspirational, healthy, attractive and celebrity lifestyle. The preliminary results are being published as a part of a larger interdisciplinary research project by Charles University, Prague.
Plinius: Secure and Persistent Machine Learning Model Training

With the increasing popularity of cloud based machine learning (ML) techniques there comes a need for privacy and integrity guarantees for ML data. In addition, the significant scalability challenges faced by DRAM coupled with the high access-times of secondary storage represent a huge performance bottleneck for ML systems. While solutions exist to tackle the security aspect, performance remains an issue. Persistent memory (PM) is resilient to power loss (unlike DRAM), provides fast and fine-granular access to memory (unlike disk storage) and has latency and bandwidth close to DRAM (in the order of ns and GB/s, respectively). We present PLINIUS, a ML framework using Intel SGX enclaves for secure training of ML models and PM for fault tolerance guarantees. P LINIUS uses a novel mirroring mechanism to create and maintain (i) encrypted mirror copies of ML models on PM, and (ii) encrypted training data in byte-addressable PM, for near-instantaneous data recovery after a system failure. Compared to disk-based checkpointing systems,PLINIUS is 3.2x and 3.7x faster respectively for saving and restoring models on real PM hardware, achieving robust and secure ML model training in SGX enclaves.
Autonomic Computing: Models, Applications, and Brokerage

Autonomic computing is not a core, rather a convergence of numerous concepts and supporting technologies. It is the junction that integrates salient computing domains and subdomains to create a self-driven, self-healing, and self-manageable computing environment. The possible integration, exploration, and hybridization toward the development of the new models and applications are new knowledge contributions. Modeling is a conceptualization of autonomic computing in general and contextualization in specific applications. In the context of cloud-based autonomic computing, a model presents the fact that the operations of the autonomic computing systems are goal-oriented and driven by certain activities and follow certain policies and behavioral aspects, with the existing features. Currently, client organizations or individual clients prefer to use packaged computing products and services over the cloud or distributed systems. This chapter covers how autonomic computing models hold the promising features for simplification, and the ease of computing system management over clouds such as process management, autonomic client migration for load balancing, monitoring, energy efficiency(green), automatic updating of software tools/drivers, predictive warning before failure, error detection and correction, backups, and recovery from sudden disasters. This chapter also covers the possible applications and related issues encountered during adaption and adoption at salient scales and types of organizations. The summarized feature-based comparative analysis of the existing computing and emerging autonomic models are also incorporated. A cloud-based green broker model for cloud service selection is designed and incorporated for the autonomic brokerage of green cloud services. Furthermore, the applications of the autonomic process management architecture to salient applications such as governance, commerce, management, industrial automation, etc. are included.
Lifting Symmetry Breaking Constraints with Inductive Logic Programming

Efficient omission of symmetric solution candidates is essential for combinatorial problem solving. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced problem encodings might be problematic. Moreover, the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances. To overcome these limitations, we introduce a new model-oriented approach for Answer Set Programming that lifts the SBCs of small problem instances into a set of interpretable first-order constraints using the Inductive Logic Programming paradigm. Experiments demonstrate the ability of our framework to learn general constraints from instance-specific SBCs for a collection of combinatorial problems. The obtained results indicate that our approach significantly outperforms a state-of-the-art instance-specific method as well as the direct application of a solver.
Digital Media Image of Business University Professor

Digitalization of business and society inevitably affects almost all spheres, and education is not an exception. New high-tech tools and solutions are rapidly coming into this industry, without which further development and implementation of the educational process is no longer possible. Today target audience of any educational organizations is informationally advanced and prefers to source useful information from social networks, often making business decisions based on it. Such conditions put forward new requirements for educational organizations to increase their activity in social networks and, above all, to form a digital image. Professors are the face of any educational organization. Inasmuch as they are the subjects who directly interact with students, their personal digital image plays an important role in shaping corporate image of the university. Relevance of this article is in the research of the digital image process formation by professors of business universities, which are the flagships and market-oriented subjects of local higher education. The article aims to identify the current state of forming the digital image of teachers of entrepreneurial universities in social networks. To achieve the goal, the article discusses the features of an entrepreneurial university and the characteristics of its corporate image, shows the need to form a teacher’s digital image and identifies strategies for such formation in social networks. The study of the activity of Russian entrepreneurial universities’ teachers in social networks was carried out and the comparison of the results with the similar activity of foreign universities’ teachers was made.