Reviewerly/exHarmony huggingface dataset:
Adaptive Fuzzy Fault-Tolerant Control against Time-Varying Faults via a New Sliding Mode Observer Method

In this study, the problem of observer-based adaptive sliding mode control is discussed for nonlinear systems with sensor and actuator faults. The time-varying actuator degradation factor and external disturbance are considered in the system simultaneously. In this study, the original system is described as a new normal system by combining the state vector, sensor faults, and external disturbance into a new state vector. For the augmented system, a new sliding mode observer is designed, where a discontinuous term is introduced such that the effects of sensor and actuator faults and external disturbance will be eliminated. In addition, based on a tricky design of the observer, the time-varying actuator degradation factor term is developed in the error system. On the basis of the state estimation, an integral-type adaptive fuzzy sliding mode controller is constructed to ensure the stability of the closed-loop system. Finally, the effectiveness of the proposed control methods can be illustrated with a numerical example.
Disfluency Detection with Unlabeled Data and Small BERT Models

Disfluency detection models now approach high accuracy on English text. However, little exploration has been done in improving the size and inference time of the model. At the same time, automatic speech recognition (ASR) models are moving from server-side inference to local, on-device inference. Supporting models in the transcription pipeline (like disfluency detection) must follow suit. In this work we concentrate on the disfluency detection task, focusing on small, fast, on-device models based on the BERT architecture. We demonstrate it is possible to train disfluency detection models as small as 1.3 MiB, while retaining high performance. We build on previous work that showed the benefit of data augmentation approaches such as self-training. Then, we evaluate the effect of domain mismatch between conversational and written text on model performance. We find that domain adaptation and data augmentation strategies have a more pronounced effect on these smaller models, as compared to conventional BERT models.
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems

As the labeling cost for different modules in task-oriented dialog (ToD) systems is expensive, a major challenge is to train different modules with the least amount of labeled data.Recently, large-scale pre-trained language models, have shown promising results for few-shot learning in ToD.In this paper, we devise a selftraining approach to utilize the abundant unlabeled dialog data to further improve state-ofthe-art pre-trained models in few-shot learning scenarios for ToD systems.Specifically, we propose a self-training approach that iteratively labels the most confident unlabeled data to train a stronger Student model.Moreover, a new text augmentation technique (GradAug) is proposed to better train the Student by replacing non-crucial tokens using a masked language model.We conduct extensive experiments and present analyses on four downstream tasks in ToD, including intent classification, dialog state tracking, dialog act prediction, and response selection.Empirical results demonstrate that the proposed self-training approach consistently improves state-of-the-art pre-trained models (BERT, ToD-BERT) when only a small number of labeled data are available.
Integrating Deep Learning and Machine Translation for Understanding Unrefined Languages

In the field of natural language processing (NLP), the advancement of neural machine translation has paved the way for cross-lingual research. Yet, most studies in NLP have evaluated the proposed language models on well-refined datasets. We investigate whether a machine translation approach is suitable for multilingual analysis of unrefined datasets, particularly, chat messages in Twitch. In order to address it, we collected the dataset, which included 7,066,854 and 3,365,569 chat messages from English and Korean streams, respectively. We employed several machine learning classifiers and neural networks with two different types of embedding: word-sequence embedding and the final layer of a pre-trained language model. The results of the employed models indicate that the accuracy difference between English, and English to Korean was relatively high, ranging from 3% to 12%. For Korean data (Korean, and Korean to English), it ranged from 0% to 2%. Therefore, the results imply that translation from a low-resource language (e.g., Korean) into a high-resource language (e.g., English) shows higher performance, in contrast to vice versa. Several implications and limitations of the presented results are also discussed. For instance, we suggest the feasibility of translation from resource-poor languages for using the tools of resource-rich languages in further analysis.
Generalized MSFA Engineering With Structural and Adaptive Nonlocal Demosaicing

The emerging multispectral-filter-array (MSFA) cameras require generalized demosaicing for MSFA engineering. The existing interpolation, compressive sensing and deep learning based methods suffer from either limited reconstruction accuracy or poor generalization. In this work, we report a generalized demosaicing method with structural and adaptive nonlocal optimization, enabling boosted reconstruction accuracy for different MSFAs. The advantages lie in the following three aspects. First, the nonlocal low-rank optimization is applied and extended to the multiple spatial-spectral-temporal dimensions to exploit more crucial details. Second, the block matching accuracy is promoted by employing a novel structural similarity metric instead of the conventional Euclidean distance. Third, the running efficiency is boosted by an adaptive iteration strategy. We built a prototype system to capture raw mosaic images under different MSFAs, and used the technique as an off-the-shelf tool to demonstrate MSFA engineering. The experiments show that the binary tree (BT) based filter array produces higher accuracy than the random and regular ones for different number of channels.
Characterization of Sb scaling and fluids in saline geothermal power plants: A case study for Germencik Region (Büyük Menderes Graben, Turkey)

Turkey is located on the seismically active Alpine-Himalayan belt. Although tectonic activity causes seismicity in the Anatolian plate, it also constitutes an important geothermal energy resource. Today, geothermal energy production is heavily concentrated in Turkey's Western Anatolia region. Graben systems in this region are very suitable for geothermal resources. The B\"uy\"uk Menderes Graben (BMG) is an area of complex geology with active tectonics and high geothermal potential power. Germencik (Aydin) is located in the BMG, where the geothermal waters include mainly Na-Cl-HCO3 water types. This study examined the stibnite scaling formed in the preheater system of the Germencik Geothermal Field (GGF). The formation of the stibnite scaling on the preheater system dramatically reduces the energy harvesting of the GGF. Considering the stibnite scaling in the surface equipment, the optimum reinjection temperature was determined as 95 $^\circ$C to prevent stibnite scaling in the GGF.
A puncturable attribute-based data sharing scheme for the Internet of Medical Robotic Things

Purpose In line with the fast development of information technology, the Internet of Medical Robotic Things (IoMRT) is gaining more ground in health care. Sharing patients' information effectively and securely can improve sensing data usage and confidentiality. Nevertheless, current IoMRT data sharing schemes are lacking in terms of supporting efficient forward secrecy; when secret key for a robotic nurse as a data requester is compromised, all the historically shared data with this robotic nurse will be leaked. Design/methodology/approach The presented paper suggests an efficient puncturable attribute-based data sharing scheme enabling guaranteed firm security and versatile access control over health sensing data in IoMRT. This scheme integrates attribute-based and puncturable encryption to avail a shared secret key for data sharing that can be encrypted by an access structure over the Data Requester (DR) attributes. Additionally, the establishment of the shared key and the mutual authentication is simultaneously done between the cloud servers and DRs. Findings The proposed scheme can achieve forward secrecy by adopting the bloom filter technique that efficiently helps the updating of a private key with no need for the key distributor to reissue the key. The security proof illustrates that this scheme adheres to the security model. Besides, the performance evaluation expresses the feasibility of the suggested scheme. Originality/value The main goal of designing a puncture algorithm is to devise an updated key from the ciphertext and a secret key, allowing the decryption of all ciphertexts except the one that has been punctured on. This research illustrates the first effort to develop a puncturable attribute-based encryption scheme to achieve efficient finegrained data sharing in IoMRT.
Chinese Judicial Summarising Based on Short Sentence Extraction and GPT-2

This paper studies the compilation of judicial case summarisation in China. Judicial case summaries are made through the abridgement, generalisation, and summarisation of court verdicts. It is a time-consuming, inefficient manual process done by legal professionals. The automatic generation of such summaries could save much time of legal professionals. Court verdicts are generally lengthy, exceeding the maximum word limit for inputs into pre-trained models. Through the observation and analysis of existing data sets, this paper conducts further treatment of these datasets. The dataset of one court verdict is split into five via phrase extraction to obtain the extracts of five key components of a court verdict and the corresponding manual summaries. In this way, we convert one text summarisation problem into five text compression and integration problems for sentences of five different categories. We adopt the GPT-2 pre-trained model, which excels in text generation, to conduct text compression and integration. From that, key points for compression of various parts of the verdict are obtained, which are eventually put together to obtain the summary of the court verdict. This paper divides datasets using extractive algorithms and compresses and integrates them using abstractive algorithms. Our experiments show that our approach proposed by this paper performs well.
Prediction of Photovoltaic Panel Power Outputs using Time Series and Artificial Neural Network Methods

Solar energy is one of the renewable energy sources that has been in high demand in the last decades. With the increasing penetration of photovoltaic (PV) systems in around the world, accurate estimation of the power output of PV systems has become an important issue. Since PV systems directly convert sunlight into electrical energy, PV power output varies depending on environmental conditions. In order to deal with the periodic and non-stationary problems of PV output power, modelling methods are widely use for forecasting. The main purpose of this study is to lead an assessment of forecasting of the PV power outputs in short-time. For this purpose, data are obtained from experimental activities carried out on a real 250 kWp PV stystem, which is located in T.C Tekirdağ Namık Kemal University, Süleymanpaşa district of Tekirdağ province. All parametres are measured hourly with three times according to inclination of the panel setups (0˚, 30˚,60˚). In this sense, this study differs from the previously studies in literature, as it expands the forecasting model with considering of different panel angle. In the first stage, the significant variables for predicting PV power output are identified based on both correlation analysis and stepwise regression analysis. The findings are shown that solar radiation and angle of inclination of the panel are significant predictors of the generation of PV power. In the second stage, three different model are proposed based on Time Series Analysis (TSA) and Artificial Neural Network (ANN) approaches in order to predict power production of PV system. Furthermore, the accuracies of the models are analyzed in order to better understand the intrinsic errors caused and to evaluate its potential in energy forecasting applications. All models are compared in terms of the correlation coefficient (R), coefficient of determination (R2), mean absolute percentage error (MAPE). The results of analyses show that the ANN models have higher accuracy than the TSA model for forecasting PV power.
Zero-Trust Principles for Legacy Components

Abstract In this paper we briefly outline as set of rules for integration of legacy devices into a modern industrial control system. These rules are fairly simple, and are mostly derived from “Zero Trust” principles. These rules aim to be pragmatic, and cost-effectiveness trumps completeness.
Kalman Filtering With Delayed Measurements in Non-Gaussian Environments

Traditionally, Kalman filter (KF) is designed with the assumptions of non-delayed measurements and additive white Gaussian noises.However, practical problems often fail to satisfy these assumptions and the conventional Kalman filter suffers from poor estimation accuracy.This paper proposes a modified Kalman filter to address both the problems of delayed measurements and non-Gaussian noises.The proposed filter is updated using correntropy maximization criterion, which is suitable for non-Gaussian noise environments.It falls short of a closed-form solution due to analytically complex equations that appear during the filtering.We use fixed-point iterative method to find an approximate solution.The delayed measurement problem is addressed by implementing a likelihood-based approach to identify the delay.Based on the identified delay information, the measurement is used to update the desired state in the subsequent past instant.To perform real-time filtering, the estimated state is further updated up to the current time instant using the process dynamics.The performance analysis validates the improved accuracy of the proposed method compared to the ordinary Kalman filter and its existing extensions.
Transferable Persona-Grounded Dialogues via Grounded Minimal Edits

Grounded dialogue models generate responses that are grounded on certain concepts.Limited by the distribution of grounded dialogue data, models trained on such data face the transferability challenges in terms of the data distribution and the type of grounded concepts.To address the challenges, we propose the grounded minimal editing framework, which minimally edits existing responses to be grounded on the given concept.Focusing on personas, we propose Grounded Minimal Editor (GME), which learns to edit by disentangling and recombining persona-related and persona-agnostic parts of the response.To evaluate persona-grounded minimal editing, we present the PERSONAMI-NEDIT dataset, and experimental results show that GME outperforms competitive baselines by a large margin.To evaluate the transferability, we experiment on the test set of BLEND-EDSKILLTALK and show that GME can edit dialogue models' responses to largely improve their persona consistency while preserving the use of knowledge and empathy. 1
eBook Technology Facilitating University Education During COVID-19: Japanese Experience

UNESCO reported that 90% of students are affected in some way by COVID-19 pandemic. Like many countries, Japan too imposed emergency remote teaching and learning at both school and university level. In this study, we focus on a national university in Japan, and investigate how teaching and learning were facilitated during this pandemic period using an ebook platform, BookRoll, which was linked as an external tool to the university’s learning management system. Such an endeavor also reinforced the Japanese national thrust regarding explorations of e-book-based technologies and using Artificial Intelligence in education. Teachers could upload reading materials for instance their course notes and associate an audio of their lecture. While students who registered in their course accessed the learning materials, the system collected their interaction logs in a learning record store. Across the spring semesters from April - July 2020, BookRoll system collected nearly 1.5 million reading interaction logs from more than 6300 students across 243 courses in 6 domains. The analysis highlighted that during emergency remote teaching and learning BookRoll maintained a weekly average traffic above 1,900 learners creating more than 78,000 reading logs and teachers perceived it as useful for orchestrating their course.
UniMS: A Unified Framework for Multimodal Summarization with Knowledge Distillation

With the rapid increase of multimedia data, a large body of literature has emerged to work on multimodal summarization, the majority of which target at refining salient information from textual and image modalities to output a pictorial summary with the most relevant images. Existing methods mostly focus on either extractive or abstractive summarization and rely on the presence and quality of image captions to build image references. We are the first to propose a Unified framework for Multimodal Summarization grounding on BART, UniMS, that integrates extractive and abstractive objectives, as well as selecting the image output. Specially, we adopt knowledge distillation from a vision-language pretrained model to improve image selection, which avoids any requirement on the existence and quality of image captions. Besides, we introduce a visual guided decoder to better integrate textual and visual modalities in guiding abstractive text generation. Results show that our best model achieves a new state-of-the-art result on a large-scale benchmark dataset. The newly involved extractive objective as well as the knowledge distillation technique are proven to bring a noticeable improvement to the multimodal summarization task.
Development of a New Negative Obstacle Sensor for Augmented Electric Wheelchair

Due to pathologies or age-related problems, in some disabled people, motor impairment is associated with cognitive and/or visual impairments. This combination of limitations unfortunately leads to an inability to move around independently. Indeed, their situation does not allow them to use a conventional electric wheelchair, for safety reasons, and for the moment there is no other technological solution providing safe movement capacity. This lack of access to an autonomous travel solution has the consequence of weakening the intellectual, personal, social, cultural and moral development, as well as the life expectancy, of the people concerned. In this context, our team is working on the development of an optoelectronic system that secures the displacement of electric wheelchairs. This is a large project that requires the development of several functionalities such as: the anti-collision of the wheelchair with its environment, the prevention of falls from the wheelchair on uneven levels, and the adaptation of the system mechanically and electronically to the majority of commercially available electric wheelchair models, among others. In this article, we introduce our solution for detecting dangerous height differences, also called "negative obstacles", through the creation of a dedicated sensor. This sensor works by optical triangulation and can embed several laser beams in order to extend its detection zone. It has the particularity of being robust in direct sunlight and rain and has a sufficiently high measurement rate to be suitable for the displacement of electric wheelchairs. We develop an adapted algorithm, and point out compromises, in particular between the orientation of the laser beams and the maximal speed of the wheelchair.
Log-Domain Decoding of Quantum LDPC Codes Over Binary Finite Fields

A quantum stabilizer code over <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$\GF(q)$</tex></formula> corresponds to a classical additive code over <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$\GF(q^2)$</tex></formula> that is self-orthogonal with respect to a symplectic inner product. We study the decoding of quantum low-density parity-check (LDPC) codes over binary finite fields <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$\GF(q=2^l)$</tex></formula> by the sum-product algorithm, also known as belief propagation (BP). Conventionally, a message in a nonbinary BP for quantum codes over <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$\GF(2^l)$</tex></formula> represents a probability vector over <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$\GF(2^{2l})$</tex></formula> , inducing high decoding complexity. In this paper, we explore the property of the symplectic inner product and show that scalar messages suffice for BP decoding of nonbinary quantum codes, rather than vector messages necessary for the conventional BP. Consequently, we propose a BP decoding algorithm for quantum codes over <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>${\GF(2^l)}$</tex></formula> by passing scalar messages so that it has low computation complexity. The algorithm is specified in log domain by using log-likelihood ratios (LLRs) of the channel statistics to have a low implementation cost. Moreover, techniques such as message normalization or offset can be naturally applied in this algorithm to mitigate the effects of short cycles to improve BP performance. This is important for nonbinary quantum codes since they may have more short cycles compared to binary quantum codes. Several computer simulations are provided to demonstrate these advantages. The scalar-based strategy can also be used to improve the BP decoding of classical linear codes over <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$\GF(2^l)$</tex></formula> with many short cycles.