Reviewerly/exHarmony huggingface dataset:
Medical Entity Recognition Based on BiLSTM with Knowledge Graph and Attention Mechanism

Considering the characteristics of non-standard expression in Internet text, some information extraction models have employed knowledge graphs of different areas to improve entity recognition performance. However, these existing models merely use background knowledge in a single step, which leads to insufficient use of additional clues in the knowledge graph. Aiming at the above problems, this paper proposes a new entity recognition model based on BiLSTM with knowledge graph and attention mechanism and applies the model to extract medical entities from Internet medical consultation text. The model extracts the conceptual features and candidate knowledge sets from the medical knowledge graph. The encoded concept features are embedded into the input of BiLSTM to enhance the semantic expression of words. Furthermore, the candidate knowledge sets are integrated into the attention mechanism to capture the important information in the knowledge graph and context. To verify the effectiveness of the model, we extracted medical named entities from the consultation text of Haodafu. Experimental results show that the proposed model can effectively improve the performance of entity recognition tasks.
The German Comment Landscape

Online comment sections revolutionised the participatory discourse as enabled by news media, limiting the hurdles to participate and speeding up the process from submission to publication. What was initially meant to strengthen public debates and democracy turned out to suffer from abusive use: Be it insulting journalists, posting misinformation, or pure hate. While many publishers and journalists are eager to create an engaged audience, user-generated content typically does not create direct revenues. However, keeping the abuse at bay is often obligatory from an ethical and legal perspective and can be costly. Germany has been highly affected by abuse in combination with strict regulation, leading to the shutdown of many comment sections. While reports in 2014 indicated closure rates of 50% and more, a structured overview of the situation in 2020/21 is missing. We conducted a structured assessment of 114 German newspaper websites containing all major outlets to account for this. Our analyses indicate that the deteriorating trend regarding the availability of comment sections slowed down in Germany. However, there are still open issues such as a high number of outlets using post-moderation and limited audience participation options. This provides a reference to researchers and practitioners working on (semi-) automated moderation systems regarding the expectable market and problem size.
City Tourism Services with Mobile Geolocation Sharing

Despite the fact that city tourism has become increasingly attractive, still the planning authorities undervalue it when developing city infrastructures, especially when extending Wi-Fi covering or other mobile location services. In this paper, we are discussing some problems that occur in existing solutions for mobile applications for city tourism. We check a hypothesis if sharing a real-time location of city visitors may be beneficial for choosing urban attractions. Further, we examine a new type of tourism application that is based on gamification models and location-sharing tools.
Iterative development and changing requirements

Developing a safety-critical embedded system poses a high risk, since such systems must usually comply with (potentially changing) rigorous standards set by customers and legal authorities. To reduce risk and cope with changing requirements, manufacturers of embedded devices increasingly use iterative development processes and prototyping both for hard- and firmware. However, hard- and firmware development are difficult to align in a common process, because hardware development cycles are typically longer and more expensive. Thus, seamlessly transitioning software to new hardware revisions and reusing old hardware revisions can be problematic. In this paper, we describe an industrial case study for veterinary anesthesia in which we also faced this problem. To solve it, we introduced preprocessor-based variability to create a small configurable system that could flexibly adapt to our needs. We discuss our solution, alternative solutions for hardware evolution, as well as their pros and cons. Our experiences generalize an interesting evolution scenario for systems that are planned and delivered as a single system, but exhibited variability to cope with problems during agile development processes.
Boundary transformation representation of attractor shape deformation

Detecting parameter changes in chaotic systems depends on characterizing the deformation of the strange attractor. Here, we present a new method for comparing the geometry of two attractors by examining their boundaries in 2D via shape context analysis. Poincaré sections for each attractor are sampled along their outer limits, and a boundary transformation is computed that warps one set of points into the other. This boundary transformation is a rich descriptor of the attractor deformation and approximately proportional to a system parameter change in specific regions. Both simulated and experimental data with various levels of noise are used to demonstrate the effectiveness of this method.
Optimal Sequential Multiclass Diagnosis

In multiclass classification, one faces greater uncertainty when the data fall near the decision boundary. To reduce the uncertainty, one can wait and collect more data, but this invariably delays the decision. How can one make an accurate classification as quickly as possible? The solution requires a multiclass generalization of Wald’s sequential hypothesis testing, but the standard formulation is intractable because of the curse of dimensionality in dynamic programming. In “Optimal Sequential Multiclass Diagnosis,” Wang shows that, in a broad class of practical problems, the reachable state space is often restricted on, or near, a set of low-dimensional, time-dependent manifolds. After understanding the key drivers of sparsity, the author develops a new solution framework that uses a low-dimensional statistic to reconstruct the high-dimensional state. This framework circumvents the curse of dimensionality, allowing efficient computation of the optimal or near-optimal policies for quickest classification with large numbers of classes.
Where do they go for advice? Entrepreneurs’ advice-seeking behavior in Africa

Using time-lagged primary data from entrepreneurs in two developing countries (Ghana, N = 261 and Ethiopia, N = 166), we examine how institutional voids drive entrepreneurs’ advice seeking behavior. Our findings show that higher levels of institutional voids in these developing countries are negatively related to internal advice-seeking but positively associated with external advice-seeking. The results also show that a heightened perception of environmental dynamism negatively moderates the relationship between institutional voids and internal advice-seeking but positively moderates the effect of institutional voids and external advice-seeking. Furthermore, we find that the negative relationship between institutional voids and internal advice-seeking is positive when entrepreneurs develop ties with politicians. The positive relationship between institutional voids and external advice-seeking is amplified when political network connections are stronger. Implications and direction for future research are discussed.
An Analysis on Machine Learning Approaches for Sentiment Analysis

Social media is a wide source of sharing user’s opinions on different areas. These opinions are known as sentiments. Social media is an application of research for sentiment analysis. Sentiment research gives a direction to the organization about user’s views on their products and services. Many approaches exist for sentiment analysis, and machine learning is one of them. This paper has selected research articles from the year 2013–2019 and studies these to find out the key insights on the most efficient ML techniques used in sentiment analysis. The analysis of the study concludes that the SVM and NB approaches of machine learning are more operationally efficient as compared to others.
COAA* — An Optimized Obstacle Avoidance and Navigational Algorithm for UAVs Operating in Partially Observable 2D Environments

Obstacle avoidance and navigation (OAN) algorithms typically employ offline or online methods. The former is fast but requires knowledge of a global map, while the latter is usually more computationally heavy in explicit solution methods, or is lacking in configurability in the form of artificial intelligence (AI) enabled agents. In order for OAN algorithms to be brought to mass produced robots, more specifically for multirotor unmanned aerial vehicles (UAVs), the computational requirement of these algorithms must be brought low enough such that its computation can be done entirely onboard a companion computer, while being flexible enough to function without a prior map, as is the case of most real life scenarios. In this paper, a highly configurable algorithm, dubbed Closest Obstacle Avoidance and A* (COAA*), that is lightweight enough to run on the companion computer of the UAV is proposed. This algorithm frees up from the conventional drawbacks of offline and online OAN algorithms, while having guaranteed convergence to a global minimum. The algorithms have been successfully implemented on the Heavy Lift Experimental (HLX) UAV of the Autonomous Robots Research Cluster in Taylor’s University, and the simulated results match the real results sufficiently to show that the algorithm has potential for widespread implementation.
A Novel Named Entity Recognition Scheme for Steel E-Commerce Platforms Using a Lite BERT

In the era of big data, E-commerce plays an increasingly important role, and steel E-commerce certainly occupies a positive position. However, it is very difficult to choose satisfactory steel raw materials from diverse steel commodities online on steel E-commerce platforms in the purchase of staffs. In order to improve the efficiency of purchasers searching for commodities on the steel E-commerce platforms, we propose a novel deep learning-based loss function for named entity recognition (NER). Considering the impacts of small sample and imbalanced data, in our NER scheme, the focal loss, the label smoothing, and the cross entropy are incorporated into a lite bidirectional encoder representations from transformers (BERT) model to avoid the over-fitting. Moreover, through the analysis of different classic annotation techniques used to tag data, an ideal one is chosen for the training model in our proposed scheme. Experiments are conducted on Chinese steel E-commerce datasets. The experimental results show that the training time of a lite BERT (ALBERT)-based method is much shorter than that of BERT-based models, while achieving the similar computational performance in terms of metrics precision, recall, and F1 with BERT-based models. Meanwhile, our proposed approach performs much better than that of combining Word2Vec, bidirectional long short-term memory (Bi-LSTM), and conditional random field (CRF) models, in consideration of training time and F1.
Smart Irrigation: case study for Hybrid-SIX methodology

The agile approach, the idea of dividing a project into a set of increments and carrying them out iteratively, comes to solve traditional approach problems. This approach which brings together a set of methods such as Scrum, eXtreme Programming (XP), etc., on the one hand, is becoming too important in the software development industries. On the other hand, the emergence of the Internet of Things poses an obstacle to agile development because the IoT project contains two parts: software, and hardware (i.e., its specificity at the level of project implementation). All this leads the management teams to produce and propose solutions to deal with this type of project. Among these methods, they are scalable methods based on the agile approach, especially Scrum. However, the IoT company teams find them complex, and they offer their own methods later called Ignite | IoT methodology and IoT methodology. These methods are monolithic, and they are based on the traditional approach. Hence, the proposal of a method that meets all requirements of the project and the team remains a major need in the labor market. In this paper, we will propose a hybridization of agile methods and IoT method named Hybrid SIX (Scrum, Ignite, XP). Then we will validate the method Hybrid SIX in a case study "Smart Irrigation", an intelligent system which facilitates irrigation task, that is one of the important cases in Smart Agriculture.
Uniform Detectability-Aided Boundedness Analysis of Error Covariances of Kalman Filter for Time-Varying Systems

Some new results about the uniform lower and upper bounds of the predicted and filtered error covariances of the Kalman filter for linear time-varying systems from the perspective of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">uniform detectability</i> are provided. First, a novel and general extendedly uniform detectability (EUD) is proposed, and the appropriate conditions to guarantee the existence of the uniform upper and lower bounds of the predicted and filtered error covariances are presented, respectively. Next, the explicit expressions of both upper and lower bounds are obtained with less conservatism. Moreover, the relations between those bounds, and their connections to estimation errors are revealed. Finally, an illustrative example is given to validate the effectiveness of the proposed theoretical developments in deriving the uniform lower and upper bounds, and a comparison with the existing results is made.
Human-in-the-Loop Model Explanation via Verbatim Boundary Identification in Generated Neighborhoods

The black-box nature of machine learning models limits their use in case-critical applications, raising faithful and ethical concerns that lead to trust crises. One possible way to mitigate this issue is to understand how a (mispredicted) decision is carved out from the decision boundary. This paper presents a human-in-the-loop approach to explain machine learning models using verbatim neighborhood manifestation. Contrary to most of the current eXplainable Artificial Intelligence (XAI) systems, which provide hit-or-miss approximate explanations, our approach generates the local decision boundary of the given instance and enables human intelligence to conclude the model behavior. Our method can be divided into three stages: 1) a neighborhood generation stage, which generates instances based on the given sample; 2) a classification stage, which yields classifications on the generated instances to carve out the local decision boundary and delineate the model behavior; and 3) a human-in-the-loop stage, which involves human to refine and explore the neighborhood of interest. In the generation stage, a generative model is used to generate the plausible synthetic neighbors around the given instance. After the classification stage, the classified neighbor instances provide a multifaceted understanding of the model behavior. Three intervention points are provided in the human-in-the-loop stage, enabling humans to leverage their own intelligence to interpret the model behavior. Several experiments on two datasets are conducted, and the experimental results demonstrate the potential of our proposed approach for boosting human understanding of the complex machine learning model.
ParCzech 3.0: A Large Czech Speech Corpus with Rich Metadata

We present ParCzech 3.0, a speech corpus of the Czech parliamentary speeches from The Czech Chamber of Deputies which took place from 25th November 2013 to 1st April 2021.Different from previous speech corpora of Czech, we preserve not just orthography but also all the available metadata (speaker identities, gender, web pages links, affiliations committees, political groups, etc.) and complement this with automatic morphological and syntactic annotation, and named entities recognition. The corpus is encoded in the TEI format which allows for a straightforward and versatile exploitation.The rather rich metadata and annotation make the corpus relevant for a wide audience of researchers ranging from engineers in the speech community to theoretical linguists studying rhetorical patterns at scale.
Positioning the technologies curriculum: a snapshot of Australian initial teacher education programs

Globally, technology is now a vital element of the school curriculum. Technology has changed the way children learn, and when teachers integrate technology into pedagogical practices, resources, and assessment it expands the way teachers teach. This paper explores how initial teacher education (ITE) programs across Australia position the Technologies Curriculum. It uses data provided at a symposium of teacher educators who deliver ITE in the use of technologies. The paper maps data from 32 universities, including primary, secondary, undergraduate and postgraduate programs. It also investigates the naming conventions of courses in these programs and exemplifies the student experience by providing three vignettes from three programs in three different states. The findings suggest that Technologies education in Australia is offered in many ways to pre-service teachers, thus the landscape of this teaching area is diverse. This paper contributes to the field in being the first research to explore how Australian universities teach Technologies within their ITE programs. It offers a snapshot of how technologies are positioned in Australian ITE programs.
An Evaluation of RGB-Thermal Image Segmentation for Snowy Road Environment

There has been significant progress in the field of autonomous vehicles in recent years. Many successful attempts to realize self-driving in urban areas have been possible due to the advancement in sensor technology and accelerated computing. However, several challenges exist to achieve autonomous driving in challenging scenarios such as in harsh weather. Inclement weather conditions such as rain, fog, or snow can severely hamper visibility and lead to accidents on the road. Particularly snowy road conditions are challenging due to the slippery road surfaces and hidden lane markings because of snow cover. Such conditions are challenging for autonomous vehicles because of the inability to track distinct visual features in such weather conditions. Most existing image segmentation methods that perform well in clear weather conditions fail in snowy environments. Due to the low gradient of color pixels, the snow-covered objects become a challenge to recognize. This work evaluates some of the state-of-the-art semantic segmentation methods for classifying snow road surfaces using RGB images. We present an entirely new dataset for feature classification in different light conditions (day and night). We tested several existing publicly available deep learning methods and evaluated their efficiency for feature detection in snow conditions. Notably, this work utilizes multiple inputs semantic segmentation techniques to classify snowy road conditions for snow removal machines. Human classification in snow cover is crucial for the safety during the operation of such machines. Therefore we utilize thermal maps and camera images to improve image segmentation efficiency in human detection during snow conditions. The results show that using a thermal map can improve human segmentation efficiency in a snowy environment, especially during the nighttime.
Efficient Nearest Neighbor Search by Removing Anti-hub

The central research question of the nearest neighbor search is how to reduce the memory cost while maintaining its accuracy. Instead of compressing each vector as is done in the existing methods, we propose a way to subsample unnecessary vectors to save memory. We empirically found that such unnecessary vectors have low hubness scores and thus can be easily identified beforehand. Such points are called anti-hubs in the data mining community. By removing anti-hubs, we achieved a memory-efficient search while preserving accuracy. In million-scale experiments, we showed that any vector compression method improves search accuracy by partial replacement with anti-hub removal under the same memory usage. A billion-scale benchmark showed that our data reduction combined with the best search method achieves higher accuracy under the assumption of fixed memory consumption. For example, our method had a much higher [email protected] (0.53) compared with the existing method (0.23) for the same memory consumption (6GB).
Waste management in smart cities using blockchaining technology

Population increment and urbanization are responsible for producing higher amount of waste. Waste management is one of the major challenges to every country because that affects the environment in a serious manner. Government and nongovernmental organizations are working toward solutions for better waste management. Unorganized management of waste such as domestic waste, e-waste, industrial waste, and plastic waste creates many human problems such as pollution, diseases, and also affects the hygiene of living being. When waste generation rate increases lead to issue of finding new disposal places, proper waste disposal, collection, and management methods are needed. Management of waste includes proper collection, segregation, and recycling of discarded wastes in an environment friendly manner. In order to overcome all these problems, we propose the idea of modern waste management system in smart cities which manage waste in order to clean environment without interaction of human by blockchaining technology. Blockchaining is a super secure digital decentralized ledger, where transactions records are kept chronologically and publicly. Blockchaining waste management platform can provide the stakeholders with traceable and transparent way to regulate waste management activities and also achieve sustainable sanitation in waste management. Waste tracking is always difficult but with blockchaining, it must be easier. In this method, also connecting internet of things sensors with the decentralized network can help to generate the data and offer real-time tracking facility to garbage trucks throughout their journey. By implementing blockchaining methodology in waste management system also helpful in charging the penalties or financial rewards to the waste management service providers if they fail to provide better time-based services. Blockchaining can provide the public and the waste collection service with real-time information on trash bags so that they can always know whether they are full or empty.
Automatic Detection and Recognition Method of Digital Instrument Representation

In order to automatically identify and monitor the display data of the existing digital meters, and improve the automation level of industrial meters, an automatic identification method for the number of digital meters is proposed. This method first preprocesses the picture, uses the text detection algorithm CRAFT to locate the digital display area on the position of the digital display screen, and cuts and divides the individual characters, and finally uses the improved threading method to recognize and read the single digital characters. , Get the specific indication of the meter. Experiments show that the algorithm is easy to implement, has high accuracy, and can basically meet the identification requirements of various meter numbers, and has certain guiding significance for the existing digital meter identification.