Reviewerly/exHarmony huggingface dataset:
Compressed-Coding Particle Swarm Optimization for Large-Scale Feature Selection

Particle swarm optimization (PSO) is a popular method for feature selection. However, when dealing with large-scale features, PSO faces the challenges of poor search performance and long running time. In addition, a suitable representation for particles to deal with the discrete binary optimization problem like the feature selection is still in great need. This paper proposes a PSO algorithm for large-scale feature selection problems named compressed-coding PSO (CCPSO). It uses the N-base encoding method for the representation of particles and designs a particle update mechanism based on the Hamming distance, which can be performed in the discrete space. It also proposes a local search strategy to dynamically shorten the length of particles, thus reducing the search space. The experimental results show that CCPSO performs well for large-scale feature selection problems. The solutions obtained by CCPSO contain small feature subsets and have an excellent performance in classification problems.
Dual Adversarial Federated Learning on Non-IID Data

Federated Learning (FL) enables multiple distributed local clients to coordinate with a central server to train a global model without sharing their private data. However, the data owned by different clients, even with the same label, may induce conflicts in the latent feature maps, especially under the non-IID FL scenarios. This would fatally impair the performance of the global model. To this end, we propose a novel approach, DAFL, for Dual Adversarial Federated Learning, to mitigate the divergence on latent feature maps among different clients on non-IID data. In particular, a local dual adversarial training is designed to identify the origins of latent feature maps, and then transforms the conflicting latent feature maps to reach a consensus between global and local models in each client. Besides, the latent feature maps of the two models become closer to each other adaptively by reducing their Kullback Leibler divergence. Extensive experiments on benchmark datasets validate the effectiveness of DAFL and also demonstrate that DAFL outperforms the state-of-the-art approaches in terms of test accuracy under different non-IID settings.
Synthesis of Target Angle Tracking System Combining Kalman Filter Algorithm with Fuzzy Logic Controller

The article proposes a method to synthesize the target angle tracking system combining the Kalman filter algorithm with a fuzzy logic controller, to improve the quality of the target phase coordinate filter. The tracking system is capable of adapting to the varied maneuvering present in the reality of the target as the evaluation process progresses the most suitable model. As a result, the evaluation quality of the target phase coordinates is advanced, the tracking error is significantly reduced. Because the tracking system does not use directly the signal balance direction as the target coordinate evaluation signal, but uses a separate line of sight coordinate evaluation filter. At the same time, the related states are evaluated by the Kalman filter algorithm in the tracking loop. The structure of the filter is simple, the evaluation error is small.
Tafl-ES: Exploring Evolution Strategies for Asymmetrical Board Games

NeuroEvolution Strategies (NES) are a subclass of Evolution Strategies (ES). While their application to games and board games have been studied in the past [11], current state of the art in most of the games is still held by classic RL models, such as AlphaGo Zero [16]. This is despite recent work showing their desirable properties [12]. In this paper we use NES applied to the board game Hnefatafl, a known hard environment given its asymmetrical nature. In the experiment we set up we coevolve two populations of intelligent agents. With results collected thus far we show the validity of this approach and useful techniques to overcome its large computation resource and time requirements.
A Lightweight Target Detection Algorithm Based on Improved MobileNetv3-YOLOv3

To solve the problems of complex model structure, large number of parameters, and high resource consumption that make it difficult to meet the real-time requirements of embedded target detection tasks, this paper proposed a lightweight target detection algorithm based on improved MobileNetv3-YOLOv3. This algorithm uses MobileNetv3 network to replace the backbone of the original YOLOv3 network, and the reduction of network parameters greatly improves the detection speed of the algorithm; the loss function is modified to CIoU to improve the accuracy and detection speed of the network. The experimental results showed that the improved lightweight detection algorithm on the VOC07 + 12 dataset has a 1.55% improvement in mAP and a 2.47 times improvement in FPS on CPU compared to the original YOLOv3 algorithm. This improved algorithm ensures the detection accuracy based on a significant increase in detection speed, which reflects the theoretical and application value of the research.
GAN-Based Fusion Adversarial Training

In the field of artificial intelligence security, adversarial machine learning has made breakthroughs. However, it is still vulnerable to attacks under a wide variety of adversarial samples, and adversarial training is a very effective method against a wide variety of adversarial sample attacks. However, adversarial training tends to improve the accuracy of the adversarial samples while reducing the accuracy of the original samples. Thus, the robustness of adversarial training is greatly reduced. In order to improve the robustness of adversarial training, this paper proposes a fusion adversarial training model based on Generate adversarial network (GAN), which is applied to the adversarial training process, and this unsupervised learning framework can better learn the distribution of samples to generate high-quality samples. By controlling the proportion of different training losses, we improve the classification accuracy of the adversarial training model while maintaining the relatively high accuracy of the model for the original samples, thus greatly improving the robustness of the adversarial training model. In this paper, we conduct experiments based on the Fashion-MNIST and CIFAR10 datasets, and the results show that our model has high accuracy on both the original and adversarial samples for both grayscale and color maps.
MEOD: A Robust Multi-stage Ensemble Model Based on Rank Aggregation and Stacking for Outlier Detection

In ensemble-based unsupervised outlier detection, the lack of ground truth makes the combination of basic outlier detectors a challenging task. The existing outlier ensembles usually use certain fusion rules (majority voting, averaging) to aggregate base detectors, which results in relatively low model accuracy and robustness. To overcome this problem, in this research, we propose a robust Multi-stage Ensemble model based on rank aggregation and stacking for Outlier Detection (MEOD). The proposed model uses multiple unsupervised outlier detection algorithms to form a base detector pool. Such a pool can be utilized for extracting useful representations from the train set and integrating base detector results using a ranking aggregation-based approach. To further optimize the proposed model, a stacking-based dynamic classifier selection ensemble model is also proposed, and the best-behaved classifier is adaptively selected as the base learner in the stacking stage on different datasets. Some extensive experiments are also committed to prove that MEOD outperforms the other seven state-of-the-art benchmarks in most cases.
CapSense: A Real-Time Capacitive Sensor Simulation Framework for Physical Human-Robot Interaction

This letter presents CapSense, a real-time open-source capacitive sensor simulation framework for robotic applications. CapSense provides raw data of capacitive proximity sensors based on a fast and efficient 3D finite-element method (FEM) implementation. The proposed framework is interfaced to off-the-shelf robot and physics simulation environments to couple dynamic interaction of the environment with an electrostatic solver for capacitance computation in real-time. The FEM method proposed in this letter relies on a static tetrahedral mesh of the sensor surrounding without a-posteriori re-meshing and achieves high update rates by an adaptive update step. CapSense is flexible due to various configuration parameters (i.e. number, size, shape and location of electrodes) and serves as a platform for investigation of capacitive sensors in robotic applications. By using the proposed framework, researchers can simulate capacitive sensors in different scenarios and investigate these sensors and their configuration prior to installation and fabrication of real hardware. The proposed framework opens new research opportunities via sim-to-real transfer of capacitive sensing. The simulation approach is validated by comparing real-world results of different scenarios with simulation results. In order to showcase the benefits of CapSense in physical Human-Robot Interaction (pHRI), the framework is evaluated in a robotic healthcare scenario.
MCSN: Multi-graph Collaborative Semantic Network for Chinese NER

Named Entity Recognition (NER) is not only one of the most important directions in Natural Language Processing (NLP), but also plays an essential pre-processing role in many downstream NLP tasks. In recent years, most of the existing methods solve Chinese NER tasks by leveraging word lexicon, which have been empirically proven to be effective. However, these methods that depend on lexical knowledge too much tend to be confused by lexicon words, which leads to recognizing false entities. In addition, the lexicon is just a method to augment performance for the NER models, but cannot provide the dependency information of every Chinese word in a sentence, which causes relatively poor results in complex text. In order to solve these issues, this paper proposes a Multi-graph Collaborative Semantic Network (MCSN) fusing the dependency information of Chinese words. We build the dependency relationships of Chinese words by leveraging Graph Attention Network. With the dependency relationships of Chinese words, MCSN not only overcomes the shortages of lexicon, but also better captures the semantic information of Chinese words. Experimental results on some Chinese benchmarking datasets show that our methods are not only effective, but also outperform the state-of-the-art (SOTA) results. Especially in the Weibo-NM dataset, our methods can outperform it more than 9.34% in F1 score, in contrast with the SOTA models.
Combining Knowledge Graphs with Semantic Similarity Metrics for Sentiment Analysis

The paper proposes a new semantic similarity method with an asymmetry coefficient. The motivation behind this idea is that in some cases it is justified to break the symmetry while comparing certain entities. Such semantic similarity in some cases might be desirable from the psychological point of view. It allows us to enrich embedding methods with knowledge graphs by adding additional information about the specificity of a concept. For the evaluation of the proposed solution, the method has been used as a component for determining a sentiment of reviews. The values of the asymmetry coefficient for the selected set of chosen pairs of words were computed and compared. We present the results of series of experiments comparing the accuracy of different methods in the context of sentiment analysis in various configurations.
Towards a Similarity Algorithm for Controlled Vocabularies Within the Digital Humanities

With a growing amount and increasing complexity of data and metadata in the Digital Humanities, the use of semantic tools such as controlled vocabularies and taxonomies becomes more and more important to gain new research insights. Their use enables new research possibilities by introducing machine readable semantic links and standardised data and metadata. A validation and recommender system that ensures a quick development of high quality vocabularies is essential in such a scientific workflow. The base of this system is a similarity algorithm. State of the art algorithms and editors for controlled vocabularies do not meet the special requirements of the Digital Humanities domain. Therefore, this work proposes to fill the research gap in the Digital Humanities domain with a similarity algorithm and a recommender and validation system for controlled vocabularies. The methodology and evaluation for achieving this goal as well as preliminary results are presented in this contribution.
Consistency Regularization Helps Mitigate Robust Overfitting in Adversarial Training

Adversarial training (AT) has been shown to be one of the most effective ways to protect deep neural networks (DNNs) from adversarial attacks . However, the phenomenon of robust overfitting, that is, the robustness will drop sharply at a certain stage, always exists in the AT process. In order to obtain a robust model, it is important to reduce this robust generalization gap. In this paper, we delve into robust overfitting from a new perspective. We observe that consistency regularization, a popular technique in semi-supervised learning, has similar goals to AT and can help mitigate robust overfitting. We empirically verify this observation and find that most previous solutions are implicitly linked to consistency regularization. Inspired by this, we introduce a new AT solution that integrates consistency regularization and mean teacher (MT) strategy into AT. Specifically, we introduce a teacher model derived from the average weights of the student models in the training step. We then design a consistency loss function to make the predicted distribution of the student model on adversarial samples consistent with the predicted distribution of the teacher model on clean samples. Experiments show that our proposed method can effectively mitigate robust overfitting and improve the robustness of DNN models against common adversarial attacks.
CLINER: Clinical Interrogation Named Entity Recognition

The automatic generation of electronic medical record (EMR) data aims to create EMRs from raw medical text (e.g., doctor-patient interrogation dialog text) without human efforts. A critical problem is how to accurately locate the medical entities mentioned in the doctor-patient interrogation text, as well as identify the state of each clinical entity (e.g., whether a patient genuinely suffers from the mentioned disease). Such precisely extracted medical entities and their states can facilitate clinicians to trace the whole interrogation process for medical decision-making. In this work, we annotate and release an online clinical dialog NER dataset that contains 72 types of clinical items and 3 types of states. Existing conventional named entity recognition (NER) methods only take a candidate entity’s surrounding context information into consideration. However, identifying the state of a clinical entity mentioned in a doctor-patient dialog turn requires the information across the whole dialog rather than only the current turn. To bridge the gap, we further propose CLINER, a CLinical Interrogation NER model, which exploits both fine-grained and coarse-grained information for each dialog turn to facilitate the extraction of entities and their corresponding states. Extensive experiments on the medical dialog information extraction (MIE) task and clinical interrogation named entity recognition task show that our approach shows significant performance improvement (3.72 on NER F1 and 6.12 on MIE F1) over the state-of-art on both tasks.
Matrix Representation of Arbitrarily Controlled Quantum Gates

Controlled operations allow for the entanglement of quantum registers. In particular, a controlled-U gate allows an operation,U, to be applied to the target register and entangle the results to certain values in the control register. This can be generalised by making use of the classical notion of conditional statements, where if a value (or state) satisfies some condition then a sequence of operations can be performed. A method is introduced to represent these generalised controlled operations that are based on classical conditional statements. Throughout examples are given to highlight the use of introduced gates.
Spelling-Aware Word-Based End-to-End ASR

We propose a new end-to-end architecture for automatic speech recognition that expands the &#x201C;listen, attend and spell&#x201D; (LAS) paradigm. While the main word-predicting network is trained to predict words, the secondary, speller network, is optimized to predict word spellings from inner representations of the main network (e.g. word embeddings or context vectors from the attention module). We show that this joint training improves the word error rate of a word-based system and enables solving additional tasks, such as out-of-vocabulary word detection and recovery. The tests are conducted on LibriSpeech dataset consisting of 1000&#x00A0;h of read speech.
Lippmann Photography: A Signal Processing Perspective

Lippmann (or interferential) photography is the first and only analog photography method that can capture the full color spectrum of a scene in a single take. This technique, invented more than a hundred years ago, records the colors by creating interference patterns inside the photosensitive plate. Lippmann photography provides a great opportunity to demonstrate several fundamental concepts in signal processing. Conversely, a signal processing perspective enables us to shed new light on the technique. In our previous work, we analyzed the spectra of historical Lippmann plates using our own mathematical model. In this paper, we provide the derivation of this model and validate it experimentally. We highlight new behaviors whose explanations were ignored by physicists to date. In particular, we show that the spectra generated by Lippmann plates are in fact distorted versions of the original spectra. We also show that these distortions are influenced by the thickness of the plate and the reflection coefficient of the reflective medium used in the capture of the photographs. We verify our model with extensive experiments on our own Lippmann photographs.
Extending AgreementMakerLight to Perform Holistic Ontology Matching

Creating rich knowledge graphs that allow the representation of data encompassing multiple domains requires the integration of different ontologies. However, the challenge of matching multiple ontologies is not properly addressed by the current pairwise strategy espoused by state-of-the-art ontology alignment systems. We have extended the ontology alignment system AgreementMakerLight (AML) to address this particular challenge through a scalable cluster-based incremental matching strategy. We make use of AML’s fast and precise matching algorithms to determine the semantic affinity between the ontologies and cluster them, then apply AML’s full ontology matching pipeline incrementally, within each cluster, by matching and then merging ontologies pairwise. The strategy was applied to the integration of 28 biomedical ontologies and achieved a runtime reduction of almost 50%. This poster expands on the extensions applied to the AML system as the technical contribution that accompanies our In-Use Technology accepted submission “Matching Multiple Ontologies to Build a Knowledge Graph for Personalized Medicine”.
A Novel Spectral Ensemble Clustering Algorithm Based on Social Group Migratory Behavior and Emotional Preference

Clustering is an unsupervised machine learning technique for data mining to find objects with similar characteristics in a group. However, due to the lack of relevant prior information on the data, numerous single models or methods cannot identify the shape and size of the clusters. Therefore, an ensemble of multiple weak models is required to further mine the implicit information of the data and improve the clustering accuracy. LSMC-EPMC is an evolutionary clustering algorithm that consists of three parts, the emotional preference and migration behavior clustering (EPMC) model, the Laplacian spectral clustering model, and the Monte Carlo statistical data simulation model. This paper mainly integrates the spectral clustering model and the Monte Carlo statistical data simulation method into the EPMC algorithm by mapping the individual in EPMC and the optimized center point in the other two methods. Through numerous experiments, LSMC-EPMC shows a significantly increased performance to EPMC and is highly competitive with the other seven clustering algorithms on several standard datasets.
Understanding Expert Knowledge for Chinese Essay Grading

Essay grading is an important issue in natural language processing. There are two challenges for Chinese essay grading, namely the subjectivity of expert grading standards and the lack of fine-grained labeled data. In this paper, we propose an automatic Chinese essay grading method based on multi-aspect expert knowledge. We introduce essay grading expert rules to turn the existing standards into indexes, such as ‘The Essay Grading Standards for College Entrance Examination’ and ‘The Chinese Curriculum Standards for Compulsory Education’. Based on the expert rules, we propose different encoders to learn multiple essay features in three aspects, namely the topic consistency, structure rationality and linguistics proficiency. An essay is graded by unifying the three grades in different aspects. Experimental results on two real datasets show the effectiveness of our method. We also analysis the influence of each aspect on the essay grading results. The experiment on the material essay grading dataset shows the practicability of our model in general exam scenarios.
A Rating Prediction Model Based on Knowledge Modeling

Traditional recommendation algorithms are prone to the problem of data sparsity and do not take full use of the semantic information about users and items. In order to alleviate these problems, a rating prediction model based on knowledge modelling (RPMKG) is proposed. The user knowledge model and the item knowledge model are built from user knowledge and item knowledge respectively. A knowledge model graph is constructed by linking the user knowledge model and the item knowledge model with rating relationships while maintaining the original knowledge links between the items. And an efficient algorithm is proposed to embed all knowledge into a unified vector space for knowledge representation learning. Then the rating prediction is performed directly based on the knowledge representation of the knowledge model. The experiments show that the knowledge model graph and learning algorithms proposed in this paper are effective in the field of recommendation systems.
System Level Recommender System for Academic Venue Personalization: Multi vs. Linked Domain

Standard academic venue recommender systems have been emerged aiming to help computer science researchers to find a suitable academic venue, in which they may publish their works. Therefore, using all the available user data provided from various domains is helpful to guide users, in their decision making process, to choose a new suitable academic venue that can be the right shape for their preferences in terms of academic venue type (conference/journal), publisher, ranking and/or location. In this context, to enhance the quality of delivered recommendation results, we propose a system level recommender system for academic venue personalization, based on multi domain vs. linked domain, comparative analysis. We investigate not only authors past publications, and authors, from the reference list, past publications from the DBLP dataset, but also from the IEEE dataset using system level multi and linked domain recommendation methods. Experimental results demonstrate the efficiency of our new system level multi domain recommender system.