Reviewerly/exHarmony huggingface dataset:
Exploring mathematical connections in the context of proof and mathematical argumentation: A new proposal of networking of theories

Extended theory of mathematical connections (ETC) and theory of mathematical argumentation (TMA) based on Toulmin’s (1984) model were articulated for the study of mathematical connections activated in the argumentation process. For this purpose, a “networking of theories” was made to obtain the complementarities between both theories. Then, a class episode was selected that dealt with the demonstration of the continuity theorem of functions of real variable “&lt;i&gt;if a function is derivable at a point then it is continuous at that point&lt;/i&gt;”, made by an in-service mathematics teacher of differential calculus, who participated in a non-participant observation, where his classes were videotaped. The arguments of this episode were analyzed through with Toulmin’s (1984) model, after with thematic analysis method to identify mathematical connections, and, finally, the connections in the proof and mathematical argumentation were analyzed. The main result of the research reveals that the mathematical connections play a fundamental role in the argumentation process of the episode, given that, connection is important for the establishment and identification the argument and the warrant that supports it. In addition, complementarities were found between both theories, which makes this networking a useful tool for a better analysis of mathematical argumentation processes.
Active learning for deep object detection by fully exploiting unlabeled data

Object detection is a challenging task that requires a large amount of labeled data to train high-performance models. However, labeling huge amounts of data is expensive, making it difficult to train a good detector with limited labeled data. Existing approaches mitigate this issue via active learning or semi-supervised learning, but there is still room for improvement. In this paper, we propose a novel active learning method for deep object detection that fully exploits unlabeled data by combining the benefits of active learning and semi-supervised learning. Our method first trains an initial model using limited labeled data, then uses self-training and data augmentation strategies to train a semi-supervised model using labeled and unlabeled data. We then select query samples based on informativeness and representativeness from the unlabeled data to further improve the model through semi-supervised training. Experimental results on commonly used object detection datasets demonstrate the effectiveness of our approach, outperforming state-of-the-art methods.
Task weighting based on particle filter in deep multi-task learning with a view to uncertainty and performance

Recently multi-task learning (MTL) has been widely used in different applications to build more robust models by sharing knowledge across several related tasks. However, one challenge that arises is the variability in the learning pace of different tasks causing the inefficiency of naively training all tasks. Therefore, it is of great importance to consider some coefficients to balance tasks in the process of learning, but, due to the large search space and the significance of setting them properly, conventional search methods such as grid or random search are no longer effective. In this paper, we propose a learning mechanism for these coefficients based on the high efficiency of the particle filter (PF) algorithm to deal with nonlinear search problems. PF considers each state of the tasks' coefficients as a particle and recursively converges coefficients to an optimum point. While in most previous works coefficients were evaluated to only increase performance, to address the recent concerns related to applying AI in real-world applications, we also incorporate uncertainty alongside our method to prevent learning coefficients leading to unstable outcomes. This mechanism is independent of the models main learning process and can be easily added to every learning system without changing its training algorithm. Extensive experiments on real-world data sets demonstrate the superiority of the proposed method over the state-of-the-art methods on both performance and uncertainty. We also proved the acceptable performance of the method using Cramer Rao lower bound theory.
Memento: a prototype search engine for LSC 2021

Abstract In this extended paper, we describe our lifelog retrieval system called Memento which participated in the 2021 Lifelog Search Challenge in detail. Memento leverages semantic representations of images and textual queries projected into a common latent space to facilitate effective retrieval, aiming to bridge the existing semantic gap between complex visual scenes/events and user information needs expressed as textual and faceted queries. Our system also has a minimalist user interface which includes functionalities such as visual data filtering and temporal search. Finally, we include a comparative analysis of Memento’s performance at LSC 2021 and suggest improvements for future iterations of the system.
Recognition of Plasma-Treated Rice Based on 3D Deep Residual Network with Attention Mechanism

Low-temperature plasma is a new agricultural green technology, which can improve the yield and quality of rice. How to identify the harvest rice grown by plasma seed treatment plays an important role in the popularization and application of low-temperature plasma in agriculture. This study collected hyperspectral data of harvest rice, including plasma seed treated rice, and constructed a recognition model based on the hyperspectral image (HSI) by 3D ResNet (HSI-3DResNet), which extracts spatial spectral features of HSI data cubes through 3D convolution. In addition, a spectral channels 3D attention module (C3DAM) is proposed, which can extract key features of spectra. Experiments showed that the proposed C3DAM can improve the recognition accuracy of the model to 4.2%, while the size and parameters of the model only increase by 4.1% and 3.8%, respectively. The HSI-3DResNet proposed in this study is superior to other methods with the overall accuracy of 97.47%. At the same time, the algorithm proposed in this paper was also verified on a public dataset.
Impact of code smells on software development environments: a study based on ENTROPY-CODAS method

Purpose The relationship between the various existing smell taxonomies and the smell impacting factors has been established. The ideology is to identify the most critical smell influencing factors in the vicinity of various software development environments. Design/methodology/approach To fulfill the said task, the utilization of the amalgamation of two multicriteria decision-making techniques, namely, Entropy method and CODAS method, is presented. Findings Through this article, the most critical smell impacting criteria with respect to the smell taxonomies is identified. Furthermore, the behaviour of 4 software development principles was then analysed, and their working state has been successfully assessed. Originality/value The ideology to study design-related smells in the software system has been studied by a lot of researchers. Some of them have worked upon their detection and the corresponding refactoration process with the help of several algorithms like machine learning and artificial intelligence. But how and to what extent these design-related smells impact the software development environment has remained out of the limelight till now. Through this article, this research gap has been identified, and an attempt to fill it has been made.
Parentheses used as pragmatic strategies in Chinese online socialization

Abstract This paper details the use of parentheses as pragmatic strategies in Chinese network socialization. The data were collected from Weibo, Wechat, QQ and other online social platforms, and analyzed from the perspective of Cyberpragmatics. We consider the pragmatic features and functions of parentheses as an indivisible part of the whole unit in relation to emojis or graphicons. As pragmatic strategies, parentheses aid speakers in indirectly performing various pragmatic intentions, like adding supplementary information to the interaction, isolating different topics, and contrasting content between the text outside and within parentheses. Further pragmatic functions realized by parentheses include indirectly conveying intention, relieving communicative awkwardness and adjusting interlocutors expectations. We conclude that using parentheses as pragmatic strategies in network socialization is the result of the evolution of the Internet language, and varies from the use of other emojis, which directly convey intentions and meaning.
Politeness and impoliteness in social network service communication in Korea

With the technological benefits and challenges computer-mediated communication provides, interactants in social network service (SNS) communication are driven to use language creatively, overcoming the disadvantages and exploiting advantages. This creative language use leads to innovative language change that often extends beyond SNS environments. In this regard, the medium is not merely a restrictive but also a facilitative factor. Communicative acts are fundamentally bound by the interactants’ desire to express politeness, especially in face-threatening acts, well articulated in Brown and Levinson’s (1987) model. In recent research, however, the issues of the norms of politeness and impoliteness as well as those of appropriateness have been highlighted (Locher Watts 2005, Locher Bousfield 2008). Interactants employ not only mitigating strategies to alleviate face-threatening but also use impoliteness strategies, which are often disguised politeness. Drawing upon the data from a 26-million-word corpus of synchronous SNS communication, involving two or more participants, in 3,836 instances, developed by the National Institute of the Korean Language, this paper addresses how SNS interactants make use of diverse elements of language to show their polite and impolite stances in interpersonal negotiation. For instance, interactants use fragments, interjections, letter-based ideophones and emoticons, exaggerated punctuations for emotiveness, omission of regular punctuation marks, intentional violation of orthographic rules, prolific slang expressions, deviated spelling to create cuteness or intimacy, among numerous others. All these creative strategies lead to language change at lexical, grammatical and discourse levels.
Key Technologies for Networked Virtual Environments

Abstract Thanks to the improvements experienced in technology during the last few years, most especially in virtual reality systems, the number and potential of networked virtual environments or NVEs and their users have been increasing. NVEs aim to give distributed users a feeling of immersion in a virtual world and the possibility of interacting with other users or with virtual objects inside it, just like when they interact in the real world. Being able to provide that feeling and natural interactions when the users are geographically separated is one of the goals of these systems. Nevertheless, this goal is especially sensitive to different issues, such as different connections with heterogeneous throughput or particular network latencies, which can lead to consistency and synchronization problems and, thus, to a worsening of the users’ quality of experience or QoE. With the purpose of solving these issues, researchers have proposed and evaluated numerous technical solutions, in fields like network architectures, data distribution and filtering, resource balancing, computing models, predictive modeling and synchronization in NVEs. This paper gathers and classifies them, summarizing their advantages and disadvantages, using a new way of classification. With the current increase in the number of NVEs and the multiple solutions proposed so far, this paper aims to become a useful tool and a starting point not only for future researchers in this field but also for those who are new to NVEs development, in which guaranteeing a good users’ QoE is essential.
Ernie-Gram BiGRU Attention: An Improved Multi-Intention Recognition Model for Air Traffic Control

In recent years, the emergence of large-scale pre-trained language models has made transfer learning possible in natural language processing, which overturns the traditional model architecture based on recurrent neural networks (RNN). In this study, we constructed a multi-intention recognition model, Ernie-Gram_Bidirectional Gate Recurrent Unit (BiGRU)_Attention (EBA), for air traffic control (ATC). Firstly, the Ernie-Gram pre-training model is used as the bottom layer of the overall architecture to implement the encoding of text information. The BiGRU module that follows is used for further feature extraction of the encoded information. Secondly, as keyword information is very important in Chinese radiotelephony communications, the attention layer after the BiGRU module is added to realize the extraction of keyword information. Finally, two fully connected layers (FC) are used for feature vector fusion and outputting intention classification vector, respectively. We experimentally compare the effects of two different tokenizer tools, the BERT tokenizer tool and Jieba tokenizer tool, on the final performance of the Bert model. The experimental results reveal that although the Jieba tokenizer tool has considered word information, the effect of the Jieba tokenizer tool is not as good as that of the BERT tokenizer tool. The final model’s accuracy is 98.2% in the intention recognition dataset of the ATC instructions, which is 2.7% higher than the Bert benchmark model and 0.7–3.1% higher than other improved models based on BERT.
Electrical signal reconstruction method of target-simulated minesweeper based on small unmanned boat platform

In recent years, with the continuous development of military unmanned boat technology, there has been a significant increase in interest in small unmanned boat-based minesweeper hunting technology. In this paper, an electrical signal reconstruction method based on time–frequency characteristics and used for small minesweeping gear is proposed. Compared with the existing methods, the method does not require measurement to obtain the complete electrical signal of the distribution and is applicable to both surface ships and submarines for signal reconstruction, which can complete the reconstruction of the original signal with high accuracy in the case of incomplete signal acquisition and can be applied to the signal reconstruction under the harsh marine acquisition environment or strong electromagnetic interference. In this paper, the mathematical model between the maximum sampling interval and the captain is established by extracting the time–frequency features of large ships, and the optimal size of the minesweeper and the optimal parameters of the point charge model are obtained based on the point charge model, and the performance of the method under low signal-to-noise ratio conditions is evaluated by depth permutation. Under the challenging signal-to-noise ratio conditions, the proposed method can accomplish the high-precision reconstruction of the electrical signal distribution of a large ship with the passage data collected by only one measurement node. From the results of simulation and ship model experiments, it can be seen that the electric signal can be simulated with a series unmanned boat array of one-tenth the size of a large ship, and the amplitude error of the reconstructed signal is less than 0.2 mV, the inversion error of EF(Electrostatic Field) is less than 10%, and the inversion error of SEF(shaft-frequency electric field) is less than 12%.
A design strategy for Geo AR mobile game sustainable success emphasizing game completeness

Current geolocation-based augmented reality mobile games lost more than 90% of their active users compared to the peak volume, threatening their sustainable success. Calls for making this type of game “complete” have been made in the literature. This paper uses the grounded theory approach to investigate a unique game, Pokémon GO, which attains its sustainable success to find a general pattern. First, key game design changes of Pokémon GO are identified in the nearly five-year history of this game. Second, relationships between various system updates, design components in the game, and the active user level change are identified. Third, a theory of sustainable success is proposed based on the relationships identified. Finally, the “complete game strategy” has been proposed and detailed for game designers’ consideration.
Textual tag recommendation with multi-tag topical attention

Tagging can be regarded as the action of connecting relevant user-defined keywords to an item, indirectly improving the quality of the information retrieval services that rely on tags as data sources. Tag recommendation dramatically enhances the quality of tags by assisting users in tagging. Although there exist many studies on tag recommendation for textual content, few of them consider two characteristics in real applications, i.e., the long-tail distribution of tags and the topic-tag correlation. In this paper, we propose a Topic-Guided Tag Recommendation (TGTR) model to recommend tags by jointly incorporating dynamic neural topic. Specifically, TGTR first generates dynamic neural topic that would indicate the tags by a neural topic generator. Then, a sequence encoder is used to distill indicative features from the post. To effectively leverage the topic and alleviate the data imbalance, we design a multi-tag topical attention mechanism to get a tag-specific post representation for each tag with the help of dynamic neural topic. These three modules are seamlessly joined together via an end-to-end multi-task learning model, which is helpful for the three parts to enhance each other and balance the effects of topics and tags. Extensive experiments have been conducted on four real-world datasets and demonstrate that our model outperforms the state-of-the-art approaches by a large margin, especially on tail-tags. The code, data and hyper-parameter settings are publicly released for reproducibility.
A Path to Real-World Evidence in Critical Care Using Open-Source Data Harmonization Tools

COVID-19 highlighted the need for use of real-world data (RWD) in critical care as a near real-time resource for clinical, research, and policy efforts. Analysis of RWD is gaining momentum and can generate important evidence for policy makers and regulators. Extracting high quality RWD from electronic health records (EHRs) requires sophisticated infrastructure and dedicated resources. We sought to customize freely available public tools, supporting all phases of data harmonization, from data quality assessments to de-identification procedures, and generation of robust, data science ready RWD from EHRs. These data are made available to clinicians and researchers through CURE ID, a free platform which facilitates access to case reports of challenging clinical cases and repurposed treatments hosted by the National Center for Advancing Translational Sciences/National Institutes of Health in partnership with the Food and Drug Administration. This commentary describes the partnership, rationale, process, use case, impact in critical care, and future directions for this collaborative effort.
CMC Users' Positive and Negative Emotions

The relationship between social media and users' emotions is a prevalent research topic in computer-mediated communication. This study aims to explore the features of social media platforms that affect users' emotional self-expression and the strategies employed by users to express their emotions in text-based communication. Semi-structured interviews with ten regular users of major social media platforms, namely Facebook and Zalo, were conducted to collect data. Results showed that online network density and size, visual properties, and content display were three features that influenced users' emotional expressions. To convey their emotions on social media platforms, users employed both verbal strategies (e.g., using affect terms and verbosity) and non-verbal strategies (e.g., using punctuations and emoticons). Some participants used punctuation marks, especially question marks, and emoticons to express positive emotions, while others used verbosity to express negative feelings. Implications for using social media platforms are discussed.
Robot Pilot: A New Autonomous System Toward Flying Manned Aerial Vehicles

The robot pilot is a new concept of a robot system that pilots a manned aircraft, thereby forming a new type of unmanned aircraft system (UAS) that makes full use of the platform maturity, load capacity, and airworthiness of existing manned aircraft while greatly expanding the operation and application fields of UASs. In this research, the implementation and advantages of the robot pilot concept are discussed in detail, and a helicopter robot pilot is proposed to fly manned helicopters. The robot manipulators are designed according to the handling characteristics of the helicopter-controlling mechanism. Based on a kinematic analysis of the robot manipulators, a direct-driving method is established for the robot flight controller to reduce the time delay and control error of the robot servo process. A supporting ground station is built to realize different flight modes and the functional integration of the robot pilot. Finally, a prototype of the helicopter robot pilot is processed and installed in a helicopter to carry out flight tests. The test results show that the robot pilot can independently fly the helicopter to realize forward flight, backward flight, side flight, and turning flight, which verifies the effectiveness of the helicopter robot pilot.
Angular phase-shifting interferometry for surface shapes measurement using vortex beam

We proposed a novel optical surface shapes measurement method based on conformal mapping-angular phase-shifting interferometry (APSI). This method rotates a Q-plate in the reference light path to introduce a 90-degree phase shift. The mathematical tool of conformal mapping is used for surface shape demodulation. After removing the helical phase in the compound phase, high-precision surface shape measurement results are obtained, and the measurement surface shape deviations are 3.33 nm (PV) and 0.26 nm (RMS). This work paves the way for surface shape measurement and compound phase demodulation using vortex beams.
A Sampling-Based Distributed Exploration Method for UAV Cluster in Unknown Environments

Rapidly completing the exploration and construction of unknown environments is an important task of a UAV cluster. However, the formulation of an online autonomous exploration strategy based on a real-time detection map is still a problem that needs to be discussed and optimized. In this paper, we propose a distributed unknown environment exploration framework for a UAV cluster that comprehensively considers the path and terminal state gain, which is called the Distributed Next-Best-Path and Terminal (DNBPT) method. This method calculates the gain by comprehensively calculating the new exploration grid brought by the exploration path and the guidance of the terminal state to the unexplored area to guide the UAV’s next decision. We propose a suitable multistep selective sampling method and an improved Discrete Binary Particle Swarm Optimization algorithm for path optimization. The simulation results show that the DNBPT can realize rapid exploration under high coverage conditions in multiple scenes.