Reviewerly/exHarmony huggingface dataset:
A Murder and Protests, the Capitol Riot, and the Chauvin Trial: Estimating Disparate News Media Stance

In this paper, we analyze the responses of three major US cable news networks to three seminal policing events in the US spanning a thirteen month period--the murder of George Floyd by police officer Derek Chauvin, the Capitol riot, Chauvin's conviction, and his sentencing. We cast the problem of aggregate stance mining as a natural language inference task and construct an active learning pipeline for robust textual entailment prediction. Via a substantial corpus of 34,710 news transcripts, our analyses reveal that the partisan divide in viewership of these three outlets reflects on the network's news coverage of these momentous events. In addition, we release a sentence-level, domain-specific text entailment data set on policing consisting of 2,276 annotated instances.
Adversarial Bi-Regressor Network for Domain Adaptive Regression

Domain adaptation (DA) aims to transfer the knowledge of a well-labeled source domain to facilitate unlabeled target learning. When turning to specific tasks such as indoor (Wi-Fi) localization, it is essential to learn a cross-domain regressor to mitigate the domain shift. This paper proposes a novel method Adversarial Bi-Regressor Network (ABRNet) to seek more effective cross-domain regression model. Specifically, a discrepant bi-regressor architecture is developed to maximize the difference of bi-regressor to discover uncertain target instances far from the source distribution, and then an adversarial training mechanism is adopted between feature extractor and dual regressors to produce domain-invariant representations. To further bridge the large domain gap, a domain-specific augmentation module is designed to synthesize two source-similar and target-similar intermediate domains to gradually eliminate the original domain mismatch. The empirical studies on two cross-domain regressive benchmarks illustrate the power of our method on solving the domain adaptive regression (DAR) problem.
Enhancing Entity Representations with Prompt Learning for Biomedical Entity Linking

Biomedical entity linking aims to map mentions in biomedical text to standardized concepts or entities in a curated knowledge base (KB) such as Unified Medical Language System (UMLS). The latest research tends to solve this problem in a unified framework solely based on surface form matching between mentions and entities. Specifically, these methods focus on addressing the variety challenge of the heterogeneous naming of biomedical concepts. Yet, the ambiguity challenge that the same word under different contexts may refer to distinct entities is usually ignored. To address this challenge, we propose a two-stage linking algorithm to enhance the entity representations based on prompt learning. The first stage includes a coarser-grained retrieval from a representation space defined by a bi-encoder that independently embeds the mention and entity’s surface forms. Unlike previous one-model-fits-all systems, each candidate is then re-ranked with a finer-grained encoder based on prompt-tuning that utilizes the contextual information. Extensive experiments show that our model achieves promising performance improvements compared with several state-of-the-art techniques on the largest biomedical public dataset MedMentions and the NCBI disease corpus. We also observe by cases that the proposed prompt-tuning strategy is effective in solving both the variety and ambiguity challenges in the linking task.
RoSA: A Robust Self-Aligned Framework for Node-Node Graph Contrastive Learning

Graph contrastive learning has gained significant progress recently. However, existing works have rarely explored non-aligned node-node contrasting. In this paper, we propose a novel graph contrastive learning method named RoSA that focuses on utilizing non-aligned augmented views for node-level representation learning. First, we leverage the earth mover's distance to model the minimum effort to transform the distribution of one view to the other as our contrastive objective, which does not require alignment between views. Then we introduce adversarial training as an auxiliary method to increase sampling diversity and enhance the robustness of our model. Experimental results show that RoSA outperforms a series of graph contrastive learning frameworks on homophilous, non-homophilous and dynamic graphs, which validates the effectiveness of our work. To the best of our awareness, RoSA is the first work focuses on the non-aligned node-node graph contrastive learning problem. Our codes are available at: https://github.com/ZhuYun97/RoSA
Evaluation of pedagogical methods of teaching the saxophone in China

The article deals with the pedagogical problem of the effectiveness of various methods in teaching the saxophone in the People’s Republic of China. The relevance of the research topic is due to the fact that in China the saxophone is gaining popularity every year, so it is necessary to more carefully select teaching methods for saxophonists in music universities. Evaluation results of pedagogical observation in two groups of 1st year students of the music university were used as research materials. The general sample of students was 150 people. The representative sample was 50 people selected by mechanical sampling. Two groups of 15 students, participated in the experimental sessions, and one control group (20 students) were selected. The material was collected in several stages, conditioned by the research logic and objectives. The research methodology is based on a systematic approach and includes the methods of the general scientific group (analysis, synthesis, induction, deduction); as well as special methods: content analysis of scientific literature, the method of included pedagogical observation, statistical analysis using the advanced analytics program Neural Designer. The method of analysis proposed by us made it possible to convert the qualitative results of pedagogical observation into quantitative data. Based on the data obtained, the author came to the following conclusion: the most effective in the development of the performing art of saxophonists in China is demonstrated by the techniques of jazz playing and the technique with elements of the Chinese folk musical tradition.
A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks

© 2019, Springer Nature Switzerland AG. Image classification is a difficult machine learning task, where Convolutional Neural Networks (CNNs) have been applied for over 20 years in order to solve the problem. In recent years, instead of the traditional way of only connecting the current layer with its next layer, shortcut connections have been proposed to connect the current layer with its forward layers apart from its next layer, which has been proved to be able to facilitate the training process of deep CNNs. However, there are various ways to build the shortcut connections, it is hard to manually design the best shortcut connections when solving a particular problem, especially given the design of the network architecture is already very challenging. In this paper, a hybrid evolutionary computation (EC) method is proposed to automatically evolve both the architecture of deep CNNs and the shortcut connections. Three major contributions of this work are: Firstly, a new encoding strategy is proposed to encode a CNN, where the architecture and the shortcut connections are encoded separately; Secondly, a hybrid two-level EC method, which combines particle swarm optimisation and genetic algorithms, is developed to search for the optimal CNNs; Lastly, an adjustable learning rate is introduced for the fitness evaluations, which provides a better learning rate for the training process given a fixed number of epochs. The proposed algorithm is evaluated on three widely used benchmark datasets of image classification and compared with 12 peer Non-EC based competitors and one EC based competitor. The experimental results demonstrate that the proposed method outperforms all of the peer competitors in terms of classification accuracy.
A survey of visual analytics in urban area

Abstract Nowadays, the population has been overgrowing due to urbanization, yielding many severe problems in the urban area, including traffic congestion, unbalanced distribution of urban hotspots, air pollution and so on. Due to the uncertainty of the urban environment, it always needs to integrate experts' domain knowledge into solving these issues. In recent years, the visual analytics method has been widely used to assist domain experts in solving urban problems with its intuitiveness, interactivity and interpretability. In this survey, we first introduce the background of urban computing, present the motivation of visual analytics in the urban area and point out the characteristics of visual analytics methods. Second, we introduce the most frequently used urban data, analyse the main properties and provide an overview on how to use these data. Thereafter, we propose our taxonomy for visual analytics in the urban area and illustrate the taxonomy. The taxonomy provides four levels for visual analytics on urban data from a new perspective based on the four stages in data mining. Four levels from our taxonomy include: descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics. Finally, we conclude this survey by discussing the limitations of the existing related works and the challenges to visual analytics in the urban area.
Graph-based linear genetic programming

Linear genetic programming (LGP) has been successfully applied to various problems such as classification, symbolic regression and hyper-heuristics for automatic heuristic design. In contrast with the traditional tree-based genetic programming (TGP), LGP uses a sequence of instructions to represent an individual (program), and the data is carried by registers. A common issue of LGP is that LGP is susceptible to introns (i.e., instructions with no effect to the program output), which limits the effectiveness of traditional genetic operators. To address these issues, we propose a new graph-based LGP system. Specifically, graph-based LGP uses graph-based crossover and graph-based mutation to produce offspring. The graph-based crossover operator firstly converts each LGP parent to a directed acyclic graph (DAG), and then swaps the sub-graphs between the DAGs. The graph-based mutation selectively modify the connections in DAGs based on the height of sub graphs. To verify the effectiveness of the new graph-based genetic operators, we take the dynamic job shop scheduling as a case study, which has shown to be a challenging problem for LGP. The experimental results show that the LGP with the new graph-based genetic operators can obtain better scheduling heuristics than the LGP with the traditional operators and TGP.
Image Super-Resolution

With the advent of high-resolution image capturing agents, the information captured in images is huge. Technology has moved from ultra HD to 4K and 8K resolutions. Movies are using high-resolution frames these days; however, there are also situations when they need to enhance a low-resolution image to a high-resolution one. Imagine a scene where the protagonist of a movie is trying to determine the license plate captured from a picture of a speeding car. Super-resolution can now help us zoom into an image to a high degree without distorting it. A few interesting advancements have happened in the industry and we are going to discuss those with some examples.
Scanning strategy for surface defects evaluation of large fine optical components

Defects, such as scratches and digs etc., play an important role in quality control of fine optical components. Dark field microscopic imaging has become one of the most commonly means for surface defects detection. However, the imaging field of view is very limited compared with the increasing aperture of the test parts and sub-aperture scanning is usually employed to increase the detection aperture while ensuring the system resolution. The problems that classic “S” Shape Sub-Aperture (SSSA) strategy faces are the high positioning accuracy requirement for the translation stage and the time-consuming processing of huge number of sub-apertures. Herein, a Sub-Region and Sub-Aperture (SRSA) scanning strategy is proposed. It divides the aperture of test part into sub-regions. Within each sub-region, classic scanning strategies and stitching methods can be employed the same time when next sub-region is being scanned. The whole aperture of the test part can be obtained when all the sub-regions are stitched together. Thus the positioning accuracy requirement of translation stage is reduced from the whole aperture to a sub-region while most of the sub-aperture stitching and processing time is covered within the scanning process. The relationship between the positioning accuracy of translation stage and the sizes of sub-aperture/sub-region is discussed in detail. Comparison experiments show the superiority of the proposed SRSA scanning strategy with which large-aperture, high precision scanning detection of large-aperture optical components can be accomplished efficiently and economically.
Speech acts in the Dutch COVID-19 Press Conferences

Abstract An open source corpus of all Dutch COVID-19 Press Conferences with sentences annotated on the basis of John Searle’s Speech Act taxonomy was created. It contains all 58 press conferences held between March 6 2020 and April 20 2021 and has 9.441 manually annotated sentences. Speech acts were annotated in a consistent manner, with a Krippendorff’s alpha of .71. The corpus is easy to use and rich in metadata, with lexical, syntactic, discourse (speaker, question or answer) features and information on the type of regulations being present. We analyse the press conferences in terms of speech act usage, giving insight into the use of speech acts over time, the relation of speech act usage to real world phenomena, the general structure of the press conferences and the division of roles between speakers. Relations were found between speech act usage and the type of press conference (i.e. easing, tightening or neutral) as well as the number of hospital admissions. Speech act classes showed preferred locations within the press conferences, indicating a general structure. Distinct roles between speakers were identified. We also investigate the use of our set of labelled sentences for training a speech act classifier and achieve a reasonable accuracy of .73 and a mean reciprocal rank of .74 with the state of the art transformer RoBERTa model.
The Cube Surface Light Field for Interactive Free-Viewpoint Rendering

Free-viewpoint rendering has always been one of the key motivations of image-based rendering and has broad application prospects in the field of virtual reality and augmented reality (VR/AR). The existing methods mainly adopt the traditional image-based rendering or learning-based frameworks, which have limited viewpoint freedom and poor time performance. In this paper, the cube surface light field is utilized to encode the scenes implicitly, and an interactive free-viewpoint rendering method is proposed to solve the above two problems simultaneously. The core of this method is a pure light ray-based representation using the cube surface light field. Using a fast single-layer ray casting algorithm to compute the light ray’s parameters, the rendering is achieved by a GPU-based three-dimensional (3D) compressed texture mapping that converts the corresponding light rays to the desired image. Experimental results show that the proposed method can real-time render the novel views at arbitrary viewpoints outside the cube surface, and the rendering results preserve high image quality. This research provides a valid experimental basis for the potential application value of content generation in VR/AR.
Modifying the quantum-assisted genetic algorithm

Based on the quantum-assisted genetic algorithm (QAGA) [11] and related approaches we introduce several modifications of QAGA to search for more promising solvers on (at least) graph coloring problems, knapsack problems, Boolean satisfiability problems, and an equal combination of these three. We empirically test the efficiency of these algorithmic changes on a purely classical version of the algorithm (simulated-annealing-assisted genetic algorithm, SAGA) and verify the benefit of selected modifications when using quantum annealing hardware. Our results point towards an inherent benefit of a simpler and more flexible algorithm design.
Blockchain Technology and Its Untapped Potentials in the Hospitality Industry

Blockchain technology has recently emerged as a contentious issue in academics and industry. Financial institutions have responded immediately to the adoption of blockchain technology. The remarkable features of this technology, such as high security, dependability, and efficiency, have made it extremely popular in the financial sector. However, the hospitality and tourism industries are blissfully unaware of this new blockchain technology and its exceptional services. Furthermore, the notion of blockchain is still foggy and poorly understood, and information on the level of knowledge and application of blockchain in the hospitality industry is very few. This research thus aims to highlight the blockchain's standard features, advantages, and applications in the hospitality industry. This study has been carried out based on secondary data sources. Specifically, journal articles, book chapters, organisational publications, website materials, and other sources have been used for data and information generation. To better operational efficiency and revenue generation and improve privacy and security, the study examines the consequences and several advantages of blockchain technology using secondary sources. The study contributes to a comprehensive understanding of blockchain technology and the benefits and diverse uses of blockchain technology in the hospitality industry.
OPTICS-Based Outlier Detection with Newton Classification

In today's time, Wireless Sensor Networks (WSNs) and Internet of things (IoTs) have attracted a lot of interest from scientific and businesses communities. They are made up of limited-resource sensors that collect data for various applications (medical, manufacturing, militarily, etc.). However, data collected by sensors are susceptible to have outliers, which need to be detected and classified into errors and events using outlier detection and classification methods. In this paper, we propose a centralized outlier detection and classification approach for WSN. Our solution can distinguish between errors due to a faulty sensor and those due to an event. We also consider the spatial-temporal correlation between sensors' data values and neighbouring sensor nodes. Our approach, titled O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DNC for OPTICS-Based Outlier Detection with Newton Classification, combines the benefits of the OPTICS algorithm with a new method for outlier detection based on computing the variance and the average of the reachability distances. Furthermore, O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DNC uses a new approach based on the Newton interpolation and the K-Nearest Neighbours (KNN) algorithms to classify the outliers. For evaluation, we conduct a comparison study between our approach and two works from the literature and thus for the multivariate data case. Simulation results with both synthetic and real-life datasets show that the O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DNC outperforms the studied techniques in terms of several metrics like Detection Rate (DR), False Alarm Rate (FAR) and Receiver Operating Characteristic (ROC) curve.
Applications of Ensuring Security and Privacy Using Block Chain with IoT for Health Record

The healthcare system has key security and privacy requirements when considered like an enterprise, such as safeguarding patients' medical records from unwanted access, protected drug tracking, secure connection with transportation such as ambulances, and secure and smart e-health surveillance. With suitable security measures, block chain has brought novel concepts in security and safety of medical data, and it may reconcile the discrepancy among sharing data and confidentiality. We combine the strengths of both block chain and cloud computing in this research to provide a confidentiality method for block chain and IoT. This strategy incorporates IoT and delivers IoT services to block chain nodes; in the meantime, it gathers, examines, operates, and preserves in the identity validation for health information. Interaction and addresses the inadequate computing capabilities of some block chain nodes in order to confirm data validity and feasibility. The proposed approach is efficient, as demonstrated by the simulation experiment. It can preserve and verify the integrity of medical data while also addressing issues such as high computer complexity, data exchange, and privacy protection.
On the Intersection of Explainable and Reliable AI for Physical Fatigue Prediction

In the era of Industry 4.0, the use of Artificial Intelligence (AI) is widespread in occupational settings. Since dealing with human safety, explainability and trustworthiness of AI are even more important than achieving high accuracy. eXplainable AI (XAI) is investigated in this paper to detect physical fatigue during manual material handling task simulation. Besides comparing global rule-based XAI models (LLM and DT) to black-box models (NN, SVM, XGBoost) in terms of performance, we also compare global models with local ones (LIME over XGBoost). Surprisingly, global and local approaches achieve similar conclusions, in terms of feature importance. Moreover, an expansion from local rules to global rules is designed for Anchors, by posing an appropriate optimization method (Anchors coverage is enlarged from an original low value, 11%, up to 43%). As far as trustworthiness is concerned, rule sensitivity analysis drives the identification of optimized regions in the feature space, where physical fatigue is predicted with zero statistical error. The discovery of such &#x201C;non-fatigue regions&#x201D; helps certifying the organizational and clinical decision making.
Specification Aware Multi-Agent Reinforcement Learning

Engineering intelligent industrial systems is challenging due to high complexity and uncertainty with respect to domain dynamics and multiple agents. If industrial systems act autonomously, their choices and results must be within specified bounds to satisfy these requirements. Reinforcement learning (RL) is promising to find solutions that outperform known or handcrafted heuristics. However in industrial scenarios, it also is crucial to prevent RL from inducing potentially undesired or even dangerous behavior. This paper considers specification alignment in industrial scenarios with multi-agent reinforcement learning (MARL). We propose to embed functional and non-functional requirements into the reward function, enabling the agents to learn to align with the specification. We evaluate our approach in a smart factory simulation representing an industrial lot-size-one production facility, where we train up to eight agents using DQN, VDN, and QMIX. Our results show that the proposed approach enables agents to satisfy a given set of requirements.