Reviewerly/exHarmony huggingface dataset:
Non-Parametric Bayesian Subspace Models for Acoustic Unit Discovery

This work investigates subspace non-parametric models for the task of learning a set of acoustic units from unlabeled speech recordings. We constrain the base-measure of a Dirichlet-Process mixture with a phonetic subspace—estimated from other source languages—to build an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">educated prior</i> , thereby forcing the learned acoustic units to resemble phones of known source languages. Two types of models are proposed: (i) the Subspace HMM (SHMM) which assumes that the phonetic subspace is the same for every language, (ii) the Hierarchical-Subspace HMM (H-SHMM) which relaxes this assumption and allows to have a language-specific subspace estimated on the unlabeled target data. These models are applied on 3 languages: English, Yoruba and Mboshi and they are compared with various competitive acoustic units discovery baselines. Experimental results show that both subspace models outperform other systems in terms of clustering quality and segmentation accuracy. Moreover, we observe that the H-SHMM provides results superior to the SHMM supporting the idea that language-specific priors are preferable to language-agnostic priors for acoustic unit discovery.
Multi-strategies Integrated Information Extraction for Scholar Profiling Task

Although the traditional information extraction tasks with labeled data sets are convenient for model design and training, they are also limited by the labeled data sets. In contrast, information extraction directly oriented to web search results is more flexible, practical and challenging. The evaluation task of CCKS-2021 “Aminer Scholar Profiling” requires accurate extraction of character attributes in the limited search range. A group of web information extraction methods based on multi-strategies integration are proposed for the task: (1) give priority to extracting attributes from semi-structured web page tags, otherwise try to mine from unstructured webpage text; (2) transform the unstructured attribute extraction tasks into text classification tasks, and construct training data sets for them respectively; (3) design a special OCR method to recognize the text attributes embedded in the images. Using the above strategies and methods, the accuracy in the validation set and test set reached 75.68 and 74.84 respectively, and finally won the first place in this evaluation task. When deep learning algorithms develop to a relatively mature stage on some specific tasks, taking advantage of the characteristics of the business and pre-processing of the data are more effective than tuning of the model.
Towards Model Based Electronic Institutions and Contract Regulated Services in a Component-Based Robotic Software Ecosystem

In this paper, which is only an opening exploration, we are imagining a multi-vendor component-based robotic software system as a digital business ecosystem. We have explored implementing model-based electronic institutions, “SmartIN” on RobMoSys (EU H2020 project) compliant SmartMDSD toolchain. This extension to SmartMDSD lets software components spontaneously remodel their service behaviour according to the institution they conform to. Electronic institutions in “SmartIN” are envisioned as models that collect several services and encapsulate patterns of their related behaviour in a rule-based multi-agent organisation. The presented idea and associated tooling are functionally adequate as a vessel. The authors lack inputs from multi-disciplinary experts for an accurate and deep understanding of possible models and mechanisms that should be incorporated to make the presented vessel truly beneficial. Hence parts of the work and tools are being shared with the community at an early stage.
Robotic Vehicle Control by Hand Gestures of Authorized Users

We all know how robot technology is advancing and emerging as an integral part of innovation. One such great innovation is the smart robots, not controlled by a remote but by hand gestures using computer vision, deep learning, and face recognition. To implement this, we have collected the photos of authorized persons and the data of different hand gestures with appropriate labels. We have trained a convolutional neural network model for hand gesture recognition and used LBPH face recognizer for face recognition. The person (controller) gets authenticated through face recognition. The detected hand gesture image of the authorized user from the real-time video is passed as test data to our model to predict the gesture the image refers to. The processed gesture acts as a command to the microcontroller that controls the motion of the robot. The potential technique of controlling the robot with hand gestures is explained in detail.
Named Entity Extractors for New Domains by Transfer Learning with Automatically Annotated Data

Named entity recognition (NER) tasks imply token-level labels. Annotating documents can be time-consuming, costly, and prone to human error. In many real-life scenarios, the lack of labeled data has become the biggest bottleneck preventing NER being effectively used in some domains and with some natural languages, with negative impacts on the quality of some tasks. To overcome the barrier of the lack of annotated data for new application domains in some natural languages, we propose a method that uses the output of an ensemble of NER’s to automatically annotate the data needed to train a Bidirectional Encoder Representations from Transformers (BERT) based NER for Portuguese. The performance was assessed using MiniHAREM dataset with promising results. For domain relevant classes such as LOCAL, F1, Precision and Recall above 50% were obtained when training only with automatically annotated data.
Cancer Detection in Automated 3-D Breast Ultrasound Using a Patch Bi-Convlstm Network

Breast cancer mortality can be significantly reduced by early detection of its symptoms. The 3-D Automated Breast Ultrasound (ABUS) has been widely used for breast screening due to its high sensitivity and reproducibility. The large number of ABUS slices, and high variation in size and shape of the masses, make the manual evaluation a challenging and time-consuming process. To assist the radiologists, we propose a convolutional BiLSTM network to classify the slices based on the presence of a mass. Because of its patch-based architecture, this model produces the approximate location of masses as a heat map. The prepared dataset consists of 60 volumes. These volumes have 15,360 slices in total, with 10% containing masses. The precision, recall, accuracy, F1-score, and AUC of the proposed model for slice classification were 84%, 84%, 93%, 84%, and 97%, respectively. Based on the FROC analysis, the proposed detector obtained a sensitivity of 82% with two false positives per volume.
Exploration and practice of online and offline mixed teaching mode based on PBL in basic computer courses

The epidemic situation in 2020 has promoted the development of online education. After the epidemic, in order to make full use of various online education resources, we began to explore online and offline hybrid teaching. In order to improve students' learning interest and enthusiasm, PBL is combined with online and offline teaching mode, and the implementation of teaching mode and teaching effect are introduced.
Focus on the 2015 Revised Curriculum of Primary School, Junior High School and High School

This paper illuminates how Korea’s Dokdo education policy is being reinforced in the 2015 revised curriculum. The discussion is focused reviewing the system of the contents of Dokdo education of elementary, middle and high schools in terms of Dokdo education as a way to strengthen territorial education and figuring out facing actual conditions of Korea’s Dokdo education policy. The completion of the Dokdo education content system began with the establishment of a plan to streng then Dokdo education in the revision of the curriculum in 2015. In response to the purpose of adopting Dokdo education as a cross-curricular learning subject in the 2015 revised curriculum, the Ministry of Education is adopting a policy to operat e in an integrated manner throughout education such as curriculum and creative experience activities[chang-uijeog cheheomhwaldong]. Nevertheless, Korea’s Dok do education policy is highly responsive. Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) conducted distorted territorial education in earnest ever since Dokdo as Japanese territorial was stated in the social studies textbook of elementary school in the March, 2010, and in March 2017, elementary and middle schools, and in March 2018, high schools revised the teaching guidelineand explanation of teaching guideline and the revised edition was published in July of that year. After that, Korea’s Ministry of Education presented the reactive policy in order to enhance the quantity and quality of the Dokdo territorial training from the 2009 curriculum to the 2015 curriculum. In other words, Dokdo education can be characterized as follows; reinforcing the territory of Dokdo in the area of education, improving the territory of Dokdo in the creative experience activities[cha ng-uijeog cheheomhwaldong] and strengthening of the territory of Dokdo educatio n in new media space. However, Dokdo education is faced with limitations. First, there are the division and difficulty of classification items in the curriculum of lower and upper grades. Second, the curriculum is not suitable for field education in the middle school. Third, Dokdo education is proceeding formal or one-time activities because of the reality of entrance-oriented education in the high school.
Meta-Transfer Learning Using Wavelet Decomposition for Multi-Horizon Time Series Forecasting

Multi-horizon time series forecasting is a very challenging task in many fields of research. In the field of machine learning, artificial neural networks have been used to carry out these tasks. However, there are still problems that are of general interest to researchers such as: Loss of data in data acquisition and long-term forecast. In this paper, we propose a hybrid Meta-Transfer Learning technique based on transfer-learning, meta-learning and signal detection by means of the discrete wavelet transform to solve the aforementioned problems in multi-horizon time series forecasting. Input-to-state stability analysis and the strong and weak convergence analysis for the proposed method are included. To validate the effectiveness of the method, the long-term prediction of earthquakes magnitude (M>4.5) in Italy is taken as a case of study, using information from Italy and Mexico. Simulations of classic methods for forecasting time series based on neural models are performed. The forecasting performance obtained is the minimum square error (MSE) is 0.091, while for the meta-transfer learning, the MSE is 0.032.
Optimizations of optical chaos in semiconductor lasers based on multiobjective genetic algorithms

We investigated the frequency bandwidth, autocorrelation function, and complexity of chaotic temporal waveforms in unidirectionally coupled semiconductor lasers with time-delayed optical feedback. The effective bandwidth, peak value of autocorrelation function, and maximum Lyapunov exponent were simultaneously optimized by searching several control parameters of the laser systems based on multiobjective genetic algorithms. We found a conflicting relation between the effective bandwidth enhancement and the time-delay signature suppression, and a detailed relationship between the maximum Lyapunov exponent and the peak value of autocorrelation function.
Automated Handwriting Recognition and Speech Synthesizer for Indigenous Language Processing

In recent years, researchers in handwriting recognition analysis relating to indigenous languages have gained significant internet among research communities.The recent developments of artificial intelligence (AI), natural language processing (NLP), and computational linguistics (CL) find useful in the analysis of regional low resource languages.Automatic lexical task participation might be elaborated to various applications in the NLP.It is apparent from the availability of effective machine recognition models and open access handwritten databases.Arabic language is a commonly spoken Semitic language, and it is written with the cursive Arabic alphabet from right to left.Arabic handwritten Character Recognition (HCR) is a crucial process in optical character recognition.In this view, this paper presents effective Computational linguistics with Deep Learning based Handwriting Recognition and Speech Synthesizer (CLDL-THRSS) for Indigenous Language.The presented CLDL-THRSS model involves two stages of operations namely automated handwriting recognition and speech recognition.Firstly, the automated handwriting recognition procedure involves preprocessing, segmentation, feature extraction, and classification.Also, the Capsule Network (CapsNet) based feature extractor is employed for the recognition of handwritten Arabic characters.For optimal hyperparameter tuning, the cuckoo search (CS) optimization technique was included to tune the parameters of the CapsNet method.Besides, deep neural network with hidden Markov model (DNN-HMM) model is employed for the automatic speech synthesizer.To validate the effective performance of the proposed CLDL-THRSS model, a detailed experimental validation process takes place and investigates the outcomes interms of different measures.The experimental outcomes denoted that the CLDL-THRSS technique has demonstrated the compared methods.
HIVE: Evaluating the Human Interpretability of Visual Explanations

As AI technology is increasingly applied to high-impact, high-risk domains, there have been a number of new methods aimed at making AI models more human interpretable. Despite the recent growth of interpretability work, there is a lack of systematic evaluation of proposed techniques. In this work, we introduce HIVE (Human Interpretability of Visual Explanations), a novel human evaluation framework that assesses the utility of explanations to human users in AI-assisted decision making scenarios, and enables falsifiable hypothesis testing, cross-method comparison, and human-centered evaluation of visual interpretability methods. To the best of our knowledge, this is the first work of its kind. Using HIVE, we conduct IRB-approved human studies with nearly 1000 participants and evaluate four methods that represent the diversity of computer vision interpretability works: GradCAM, BagNet, ProtoPNet, and ProtoTree. Our results suggest that explanations engender human trust, even for incorrect predictions, yet are not distinct enough for users to distinguish between correct and incorrect predictions. We open-source HIVE to enable future studies and encourage more human-centered approaches to interpretability research.
Chinese Knowledge Graph Completion based on Improved BERT Model

The existing knowledge graph completion methods ignore semantic information, and there are relatively little researches on Chinese corpus. To solve the above problems, this paper first proposes an improved BERT model based on mean-pooling strategy to effectively solve the problem of insufficient sequence representation ability of BERT model; Then, the entity description information is introduced as the knowledge supplement of the model input, which solves the problem of insufficient background knowledge of the previous model; Finally, the knowledge completion problem is transformed into a classification problem. The experimental results show that this method improves the mean rank (MR) index by 5.83 compared with the traditional method, and the top 10 hit rate (Hit@10) The index is improved by 1.67%, which verifies the effectiveness of the model in Chinese corpus.
Study of control systems with transistor keys

Problems of control and stability of solutions to systems of differential equations for describing processes defined for linear operations are considered. This makes it possible to identify important properties inherent in all systems of the same class. For a structurally linear system that describes with a sufficient degree of accuracy the behavior of a system with transistor switches, the problem of constructing self-oscillations is considered. A qualitative study of the behavior of the system when choosing a control is carried out and a control is constructed under which self-oscillation occurs in the system.
Practical and Scalable ML-Driven Cloud Performance Debugging With Sage

Cloud applications are increasingly shifting from large monolithic services to complex graphs of loosely coupled microservices. Despite their benefits, microservices are prone to cascading performance issues, and can lead to prolonged periods of degraded performance. We present Sage, a machine-learning-driven root cause analysis system for interactive cloud microservices that is both accurate and practical. We show that Sage correctly identifies the root causes of performance issues across a diverse set of microservices and takes action to address them, leading to more predictable, performant, and efficient cloud systems.
Cross-Domain Sentiment Classification with Attention-Assisted GAN

In the problem setting of cross-domain sentiment classification, two different domains are introduced, and we refer to them as source and target domains respectively. In the source domain, sentiment labels to instances are available, while labels to instances in the target domain are not available. This problem is critical and practical, as in the real world, data in some domains (source) are abundant, while those in other domains (target) may become scarce. In this paper, we propose a cross-domain sentiment classification framework based on Generative Adversarial Networks (GANs) with the assistance of the attention mechanism, which aims to leverage the information available from the source domain to the target domain. Existing state-of-the-art methods mainly use multi-task learning to minimize the distance between the source and the target instances in a latent feature space. However, the projections may suffer as the deep model always tries to overfit the cross-domain adaptation task. We introduce a framework with multiple tasks, including adversarial example generation, cycle reconstruction, and cross-domain classification. Empirical evaluation and analysis on real-world datasets are being performed to validate the effectiveness of our proposed algorithm compared to state-of-the-art techniques.
Improving Quality of Counterexamples in Model Checking via Automated Planning

There is a wide agreement that model checking and automated planning (planning for short) are closely related fields. Planning is the task of finding a sequence of appropriate moves that achieves a goal. Model checking aims to prove or disprove a system model that satisfies a given property which is often specified by temporal logics. In this paper we investigate the application of advanced planning techniques to model checking. To this end, a system model is expressed by means of a planning model, and temporal logic property can be treated as a special form of planning goal, i.e., Temporally Extended Goal (TEG). Therefore, the model checking task can be reduced into a planning scheme what we call planning with TEG. In order to utilize the state-of-the-art planners, we further propose two novel compilation methods to translate a planning with TEG problem into a classical planning problem and a non-deterministic planning one respectively. The obtained valid plans in planning just correspond to the counterexamples in model checking. We provide detailed evaluations of our approach on a series of benchmarks. The experimental results are encouraging, showing that existing planners can provide significant improvements in the quality of the counterexamples compared with the model checkers.
FID Civil Engineering, Architecture and Urbanism digital - A platform for science (BAUdigital)

University Library Braunschweig (UB Braunschweig), University and State Library Darmstadt (ULB Darmstadt), TIB – Leibniz Information Centre for Technology and Natural Sciences and the Fraunhofer Information Centre for Planning and Building (Fraunhofer IRB) are jointly establishing a specialised information service (FID, "Fachinformationsdienst") for the disciplines of civil engineering, architecture and urbanism. The FID BAUdigital, which is funded by the German Research Foundation (DFG, "Deutsche Forschungsgemeinschaft"), will provide researchers working on digital design, planning and production methods in construction engineering with a joint information, networking and data exchange platform and support them with innovative services for documentation, archiving and publication in their data-based research.
The role of independent work in the research activity of students

This article analysis the role of independent work in the research activities of students. Independent work contributes to the conscious perception of information, which forms skills, abilities and competencies not only in educational and scientific activities, but also professional activities in the future, also develops responsibility, the ability to independently solve a problem, find constructive solutions and a way out of a crisis situation.
Node Selection Model for Block Transmission in a Blockchain Network

In a permissionless blockchain network, the random selection of nodes to transmit blocks results in a low transmission rate. To solve the problem, an optimization model of trusted node selection is proposed for block transmission. The model analyzes node distance and connectivity in a block synchronization time, and introduces node trust in terms of whether they transmit or validate blocks. Experimental results show that the proposed method has more advantages in block synchronization time, block transmission rate, and block transmission success rate than its peers.
Keynote 6

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Ultrasound reporting plays an important role in diagnosis as images produced during an ultrasound examination do not give the whole view of the medical conditions. However, in practice there are many issues that are inherent to ultrasound reporting and the most important was identified to be the lack of standardisation when producing these reports. There is a resistance to change from some radiologists preferring the free writing style, making any attempt to computerise the processing of these reports difficult. This research explores the possibility of using Rhetorical Structure Theory (RST) together with a domain ontology to transform free-form ultrasound reports into a structured form. It discusses a new approach in segmenting and identifying rhetorical relations that are more applicable to ultrasound reports from classical RST relations. The approach was evaluated on a sample ultrasound reports where the system's parsing was compared to the manual parsing performed by experts. The results show that discourse parsing using RST in ultrasound reports can be performed effectively using the support of a domain ontology. The results also demonstrate that the transformation of free-form ultrasound reports into a structured form can be performed with the support of RST relations identified and the domain ontology.
A Supervised Approach to Monitoring Social Distancing From CCTV

This paper employs a deep learning algorithm and statistical technique to identify correct social distancing for COVID-19. As our community slowly recovers from the COVID-19, new concerns about the workplace and school safety have arisen. WHO (World Health Organization) encourages individuals to keep at least 6 feet away from others and use face masks. By analyzing a video stream (CCTV Footage), we’ve built a detecting tool that can inform individuals when they’re not keeping a safe distance from one another.