Miracle-dz/UniFAR-FTUs huggingface dataset:
Selected Nature-Inspired Algorithms in Function of PM Synchronous Motor Cogging Torque Minimisation

Abstract Both permanent magnet brushless DC motors and permanent magnet synchronous motors have attracted wide attention and are increasingly used in industrial high-performance applications in recent years. Those motors are known for their good electrical, magnetic and performance characteristics, but there is one parameter known as cogging torque that has a negative influence on the performance characteristics of the motor. This pulsating torque is generated as a result of the interaction between the stator teeth and the permanent magnets. The minimisation of the ripple of this torque in those permanent magnet motors is of great importance and is generally achieved by a special motor design which in the design process involves a variety of many geometrical motor parameters. In this research work, a novel approach will be introduced where two different nature-inspired algorithms, such as genetic algorithm (GA) and cuckoo search (CS) algorithm are used as an optimisation tool, in which the defined equation for the maximum value of the cogging torque is applied as an objective function. Therefore, a proper mathematical presentation of the maximum value of the cogging torque for the analysed synchronous motor is developed and implemented in the research work. For a detailed analysis of the three different motor models, the initial motor and the two optimised motor models are modelled and analysed using a finite element method approach. The cogging torque is analytically and numerically calculated and the results for all the models are presented.
Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search

Recently, sponsored search has become one of the most lucrative channels for market-ing. As the fundamental basis of sponsored search, relevance modeling has attracted in-creasing attention due to the tremendous prac-tical value. Most existing methods solely rely on the query-keyword pairs. However, keywords are usually short texts with scarce semantic information, which may not precisely reﬂect the underlying advertising intents. In this paper, we investigate the novel problem of advertiser-aware relevance modeling, which leverages the advertisers’ information to bridge the gap between the search intents and advertising purposes. Our motivation lies in incorporating the unsupervised bidding behaviors as the complementary graphs to learn desirable advertiser representations. We further propose a B idding- G raph augmented T riple-based R elevance model BGTR with three towers to deeply fuse the bidding graphs and semantic textual data. Empirically, we evaluate the BGTR model over a large industry dataset, and the experimental results consis-tently demonstrate its superiority.
DialogueTRM: Exploring Multi-Modal Emotional Dynamics in a Conversation

Emotion dynamics formulates principles explaining the emotional ﬂuctuation during conversations. Recent studies explore the emotion dynamics from the self and inter-personal dependencies, however, ignoring the temporal and spatial dependencies in the situation of multi-modal conversations. To address the issue, we extend the concept of emotion dynamics to multi-modal settings and propose a Di-alogue Transformer for simultaneously modeling the intra-modal and inter-modal emotion dynamics. Speciﬁcally, the intra-modal emotion dynamics is to not only capture the temporal dependency but also satisfy the context preference in every single modality. The inter-modal emotional dynamics aims at handling multi-grained spatial dependency across all modalities. Our models outperform the state-of-the-art with a margin of 4%-16% for most of the metrics on three benchmark datasets.
An Uncertainty-Aware Encoder for Aspect Detection

Aspect detection is a fundamental task in opinion mining. Previous works use seed words either as priors of topic models, as anchors to guide the learning of aspects, or as features of aspect classiﬁers. This paper presents a novel weakly-supervised method to exploit seed words for aspect detection based on an encoder architecture. The encoder maps segments and aspects into a low-dimensional embedding space. The goal is approximating similarity between segments and aspects in the embedding space and their ground-truth similarity generated from seed words. An objective function is proposed to capture the uncertainty of ground-truth similarity. Our method outperforms previous works on several benchmarks in various domains.
RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification

The relationships that exist between entities can be a reliable indicator for classifying sensitive information, such as commercially sensitive information. For example, the relation person-IsDirectorOf-company can indicate whether an individual’s salary should be considered as sensitive personal information. Representations of such relations are often learned using a knowledge graph to produce embeddings for relation types, generalised across different entity-pairs. However, a relation type may or may not correspond to a sensitivity depending on the entities that partic-ipate to the relation. Therefore, generalised relation embeddings are typically insufﬁcient for classifying sensitive information. In this work, we propose a novel method for representing entities and relations within a single embedding to better capture the relationship between the entities. Moreover, we show that our proposed entity-relation-entity embedding approach can signiﬁcantly improve (McNemar’s test, p < 0 . 05 ) the effectiveness of sensitivity classiﬁcation, compared to classiﬁcation approaches that leverage relation embedding approaches from the literature (0.426 F 1 vs 0.413 F 1 ).
Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation

Precise information of word boundary can alleviate the problem of lexical ambiguity to improve the performance of natural language processing (NLP) tasks. Thus, Chinese word segmentation (CWS) is a fundamental task in NLP. Due to the development of pre-trained language models (PLM), pre-trained knowledge can help neural methods solve the main problems of the CWS in signiﬁcant mea-sure. Existing methods have already achieved high performance on several benchmarks (e.g., Bakeoff-2005). However, recent outstanding studies are limited by the small-scale annotated corpus. To further improve the performance of CWS methods based on ﬁne-tuning the PLMs, we propose a novel neural framework, L B GCN, which incorporates a l exicon-b ased g raph c onvolutional n etwork into the Transformer encoder. Experimental results on ﬁve benchmarks and four cross-domain datasets show the L B GCN successfully captures the information of candidate words and helps to improve performance on the benchmarks (Bakeoff-2005 and CTB6) and the cross-domain datasets (SIGHAN-2010). Further experiments and analyses demonstrate that our proposed framework effectively models the lexicon to enhance the ability of basic neural frameworks and strengthens the robustness in the cross-domain scenario. 1
Accelerated Testing Procedures for Evaluating Separation Layer Performance in Open-Graded Base Courses

An accelerated testing procedure was developed to evaluate pumping and deformation performance of separation layers used between open-graded bases and lime-treated and untreated subgrade soils. A pneumatically driven, electronically controlled repeated loading laboratory apparatus was developed. A low plasticity clay and a silty clay till soil were tested. A nonwoven geotextile and a dense-graded base aggregate were evaluated as separation layers. Tests were conducted at various loading levels and durations to develop the testing procedure. Comparisons of specimen pumping and deflection performance for stabilized, separated specimens were made relative to nonseparated and nonstabilized control specimens. Ultimately, loading at levels upwards of five times that expected in the field, for short durations (20,000 cycles or less), was performed to evaluate separation layer performance. Separation layer performance under accelerated loading correlated well to long-term field loading level performance. The testing method was sensitive to the use of geotextiles for separation between soils and open-graded aggregates, though some pumping of fines still occurred. The dense-graded separation level showed a marked drop-off in performance as moisture contents increased above optimum. A distinct breakpoint in subgrade soil performance occurred at a cone index of 200 (California bearing ratio 4). Soils with strengths less than cone index of 200 performed poorly, whereas soils above that strength pumped and deformed minimally regardless of separation layer type. Based upon performance comparisons with nonseparated test cases, the use of a separation layer between lime-stabilized subgrades and open-graded aggregate bases is recommended.
Discourse-Based Sentence Splitting

Sentence splitting involves the segmentation of a sentence into two or more shorter sentences. It is a key component of sentence simpliﬁcation, has been shown to help human comprehension and is a useful preprocessing step for NLP tasks such as summarisation and relation extraction. While several methods and datasets have been proposed for developing sentence splitting models, little attention has been paid to how sentence splitting interacts with discourse structure. In this work, we focus on cases where the input text contains a discourse connective, which we refer to as discourse-based sentence splitting. We create synthetic and organic datasets for discourse-based splitting and explore different ways of combining these datasets using different model architectures. We show that pipeline models which use discourse structure to mediate sentence splitting outperform end-to-end models in learning the various ways of expressing a discourse relation but generate text that is less grammatical; that large scale synthetic data provides a better basis for learning than smaller scale organic data; and that training on discourse-focused, rather than on general sentence splitting data provides a better basis for discourse splitting.
Low-Cost Millimeter-Wave Interactive Sensing through Origami Reflectors

Millimeter-Wave (mm-wave) sensing provides an increasingly viable sensing solution for smart environments for its compact and solid-state form factor, non-intrusiveness, and low cost. While prior work in this domain has mostly focused on sensing humans – e.g., location, motion, and posture, we propose a new approach that leverage mm-wave sensing to enable tangible ubiquitous controllers such as buttons and switches. By encoding the controller state with the Radar Cross Section (RCS) of origami structures, our component-free controllers cost less than 40 cents per unit and require virtually zero maintenance effort, while achieving long-range wireless sensing with sufficient accuracies.
Extracting Domain Entities from Scientific Papers Leveraging Author Keywords

Current methods of domain entity extraction of scientific texts rely heavily on manually annotation corpus and thus have poor generalization ability. In this paper, we proposed a two-stage methodology that can make good use of existed author keywords of the given domain to solve this problem. Firstly, the author keyword set was used to mark the boundary of candidate entities, and then their features are integrated to classify their entity type. In the experiment on artificial intelligence (AI) documents from WOS, our approach obtains an F1 value of 0.753 without manual annotation, which is slightly lower than the BERT-BiLSTM-CRF baseline model (F1=0.772) trained on manual annotation corpus, showing the usability of our approach in practice.
Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts

Short text nowadays has become a more fashionable form of text data, e.g., Twitter posts, news titles, and product reviews. Extracting semantic topics from short texts plays a signiﬁcant role in a wide spectrum of NLP applications, and neural topic modeling is now a major tool to achieve it. Motivated by learning more coherent and semantic topics, in this paper we develop a novel neural topic model named Dual Word Graph Topic Model (DWG TM ), which extracts topics from simultaneous word co-occurrence and semantic correlation graphs. To be speciﬁc, we learn word features from the global word co-occurrence graph, so as to ingest rich word co-occurrence information; we then generate text features with word features, and feed them into an encoder network to get topic proportions per-text; ﬁnally, we reconstruct texts and word co-occurrence graph with topical distri-butions and word features, respectively. Besides, to capture semantics of words, we also apply word features to reconstruct a word semantic correlation graph computed by pre-trained word embeddings. Upon those ideas, we formulate DWG TM in an auto-encoding paradigm and efﬁciently train it with the spirit of neural variational inference. Empirical results validate that DWG TM can generate more semantically coherent topics than baseline topic models.
HOTTER: Hierarchical Optimal Topic Transport with Explanatory Context Representations

Natural language processing (NLP) is often the backbone of today’s systems for user interactions, information retrieval and others. Many of such NLP applications rely on specialized learned representations (e.g. neural word embeddings, topic models) that improve the ability to reason about the relationships between documents of a corpus. Paired with the progress in learned representations, the similarity metrics used to compare representations of documents are also evolving, with numerous proposals differing in computation time or interpretability. In this paper we propose an extension to a specific emerging hybrid document distance metric which combines topic models and word embeddings: the Hierarchical Optimal Topic Transport (HOTT). In specific, we extend HOTT by using contextenhanced word representations. We provide a validation of our approach on public datasets, using the language model BERT for a document categorization task. Results indicate competitive performance of the extended HOTT metric. We furthermore apply the HOTT metric and its extension to support educational media research, with a retrieval task of matching topics in German curricula to educational textbooks passages, along with offering an auxiliary explanatory document representing the dominant topic of the retrieved document. In a user study, our explanation method is preferred over regular topic keywords.
UMR-Writer: A Web Application for Annotating Uniform Meaning Representations

We present UMR-Writer, a web-based application for annotating Uniform Meaning Representations (UMR), a graph-based, cross-linguistically applicable semantic representation developed recently to support the development of interpretable natural language applications that require deep semantic analysis of texts. We present the functionalities of UMR-Writer and discuss the challenges in developing such a tool and how they are addressed.
Recent use of deep learning techniques in clinical applications based on gait: a survey

Gait analysis has been studied for a long time and applied to fields such as security, sport, and medicine. In particular, clinical gait analysis has played a significant role in improving the quality of healthcare. With the growth of machine learning technology in recent years, deep learning-based approaches to gait analysis have become popular. However, a large number of samples are required for training models when using deep learning, where the amount of available gait-related data may be limited for several reasons. This paper discusses certain techniques that can be applied to enable the use of deep learning for gait analysis in case of limited availability of data. Recent studies on the clinical applications of deep learning for gait analysis are also reviewed, and the compatibility between these applications and sensing modalities is determined. This article also provides a broad overview of publicly available gait databases for different sensing modalities.
GiBERT: Enhancing BERT with Linguistic Information using a Lightweight Gated Injection Method

Large pre-trained language models such as BERT have been the driving force behind recent improvements across many NLP tasks. However, BERT is only trained to predict missing words – either through masking or next sentence prediction – and has no knowledge of lexical, syntactic or semantic information beyond what it picks up through un-supervised pre-training. We propose a novel method to explicitly inject linguistic information in the form of word embeddings into any layer of a pre-trained BERT. When injecting counter-ﬁtted and dependency-based embeddings, the performance improvements on multiple semantic similarity datasets indicate that such information is beneﬁcial and cur-rently missing from the original model. Our qualitative analysis shows that counter-ﬁtted embedding injection is particularly beneﬁcial, with notable improvements on examples that require synonym resolution.
Fine-grained Typing of Emerging Entities in Microblogs

Analyzing microblogs where we post what we experience enables us to perform various applications such as social-trend analysis and entity recommendation. To track emerging trends in a variety of areas, we want to categorize information on emerging entities ( e.g., Avatar 2) in microblog posts according to their types ( e.g., Film). We thus introduce a new entity typing task that assigns a ﬁne-grained type to each emerging entity when a burst of posts containing that entity is ﬁrst observed in a microblog. The challenge is to perform typing from noisy microblog posts without relying on prior knowledge of the target entity. To tackle this task, we build large-scale Twitter datasets for English and Japanese using time-sensitive distant supervision. We then propose a modular neural typing model that encodes not only the entity and its contexts but also meta information in multiple posts. To type ‘homo-graphic’ emerging entities ( e.g., ‘Go’ means an emerging programming language and a classic board game), which contexts are noisy, we devise a context selector that ﬁnds related contexts of the target entity. Experiments on the Twitter datasets conﬁrm the effectiveness of our typing model and the context selector.
Combining Curriculum Learning and Knowledge Distillation for Dialogue Generation

Curriculum learning, a machine training strategy that feeds training instances to the model from easy to hard, has been proven to facili-tate the dialogue generation task. Meanwhile, knowledge distillation, a knowledge transformation methodology among teachers and students networks can yield signiﬁcant performance boost for student models. Hence, in this paper, we introduce a combination of curriculum learning and knowledge distillation for efﬁcient dialogue generation models, where curriculum learning can help knowledge distillation from data and model aspects. To start with, from the data aspect, we cluster the training cases according to their complexity, which is calculated by various types of features such as sentence length and coherence between dialog pairs. Furthermore, we employ an adversarial training strategy to identify the complexity of cases from model level. The intuition is that, if a discriminator can tell the generated response is from the teacher or the student, then the case is difﬁcult that the student model has not adapted to yet. Finally, we use self-paced learning, which is an extension to curriculum learning to assign weights for distillation. In conclusion, we arrange a hierarchical curriculum based on the above two aspects for the student model under the guidance from the teacher model. Experimental results demon-strate that our methods achieve improvements compared with competitive baselines.
EDTC: A Corpus for Discourse-Level Topic Chain Parsing

Discourse analysis has long been known to be fundamental in natural language processing. In this research, we present our insight on discourse-level topic chain (DTC) parsing which aims at discovering new topics and in-vestigating how these topics evolve over time within an article. To address the lack of data, we contribute a new discourse corpus with DTC-style dependency graphs annotated upon news articles. In particular, we ensure the high reliability of the corpus by utilizing a two-step annotation strategy to build the data and ﬁltering out the annotations with low conﬁdence scores. Based on the annotated corpus, we introduce a simple yet robust system for automatic discourse-level topic chain parsing.
Sequence-to-Lattice Models for Fast Translation

Non-autoregressive machine translation (NAT) approaches enable fast generation by utilizing parallelizable generative processes. The re-maining bottleneck in these models is their decoder layers; unfortunately unlike in autoregressive models (Kasai et al., 2020), remov-ing decoder layers from NAT models signif-icantly degrades accuracy. This work proposes a sequence-to-lattice model that replaces the decoder with a search lattice. Our approach ﬁrst constructs a candidate lattice using efﬁcient lookup operations, generates lattice scores from a deep encoder, and ﬁnally ﬁnds the best path using dynamic program-ming. Experiments on three machine translation datasets show that our method is faster than past non-autoregressive generation approaches, and more accurate than naively reducing the number of decoder layers.
Syntactically Diverse Adversarial Network for Knowledge-Grounded Conversation Generation

Generative conversation systems tend to pro-duce meaningless and generic responses, which signiﬁcantly reduce the user experience. In order to generate informative and diverse responses, recent studies proposed to fuse knowledge to improve informativeness and adopt latent variables to enhance the diversity. How-ever, utilizing latent variables will lead to the inaccuracy of knowledge in the responses, and the dissemination of wrong knowledge will mislead the communicators. To address this problem, we propose a Syntactically Diverse Adversarial Network (SDAN) for knowledge-grounded conversation model. SDAN contains an adversarial hierarchical semantic network to keep the semantic coherence, a knowledge-aware network to attend more related knowledge for improving the informativeness and a syntactic latent variable network to generate syntactically diverse responses. Addition-ally, in order to increase the controllability of syntax, we adopt adversarial learning to decouple semantic and syntactic representations. Experimental results show that our model can not only generate syntactically diverse and knowledge-accurate responses but also signif-icantly achieve the balance between improving the syntactic diversity and maintaining the knowledge accuracy.