Reviewerly/exHarmony huggingface dataset:
Full-Spectrum Out-of-Distribution Detection

Existing out-of-distribution (OOD) detection literature clearly defines semantic shift as a sign of OOD but does not have a consensus over covariate shift. Samples experiencing covariate shift but not semantic shift from the in-distribution (ID) are either excluded from the test set or treated as OOD, which contradicts the primary goal in machine learning—being able to generalize beyond the training distribution. In this paper, we take into account both shift types and introduce full-spectrum OOD (F-OOD) detection, a more realistic problem setting that considers both detecting semantic shift and being tolerant to covariate shift; and design three benchmarks. These new benchmarks have a more fine-grained categorization of distributions (i.elet@tokeneonedot, training ID, covariate-shifted ID, near-OOD, and far-OOD) for the purpose of more comprehensively evaluating the pros and cons of algorithms. To address the F-OOD detection problem, we propose SEM, a simple feature-based semantics score function. SEM is mainly composed of two probability measures: one is based on high-level features containing both semantic and non-semantic information, while the other is based on low-level feature statistics only capturing non-semantic image styles. With a simple combination, the non-semantic part is canceled out, which leaves only semantic information in SEM that can better handle F-OOD detection. Extensive experiments on the three new benchmarks show that SEM significantly outperforms current state-of-the-art methods. Our code and benchmarks are released in https://github.com/Jingkang50/OpenOOD .
Convergence analysis of flow direction algorithm and its improvement

Flow direction algorithm (FDA) is a new physics-based optimization algorithm for solving global optimization problems. Although the FDA has shown effectiveness in many areas, there has been a lack of rigorous theoretical guarantees. This paper first proves that FDA is globally convergent with probability 1 by establishing a Markov process model. Furthermore, to enhance the FDA’s exploration and exploitation abilities, we propose an improved FDA algorithm (IFDA) by introducing random opposition-based learning and an adaptive neighbour generation strategy. Finally, extensive experiments and statistical tests are investigated on the classical benchmark functions, CEC 2019 benchmark function, and wireless sensor network coverage optimization problem with several state-of-the-art algorithms, demonstrating the proposed algorithm’s efficiency and effectiveness.
A Gaze Data-Based Comparative Study to Build a Trustworthy Human-AI Collaboration in Crash Anticipation

Vehicles with a safety function for anticipating crashes in advance can enhance drivers’ ability to avoid crashes. As dashboard cameras have become a low-cost sensor device accessible to almost every vehicle, deep neural networks for crash anticipation from a dashboard camera are receiving growing interest. However, drivers’ trust in the Artificial Intelligence (AI)-enabled safety function is built on the validation of its safety enhancement toward zero deaths. This paper is motivated to establish a method that uses gaze data and corresponding measures to evaluate human drivers’ ability to anticipate crashes. A laboratory experiment is designed and performed, wherein a screen-based eye tracker collects the gaze data of six volunteers while watching 100 driving videos that include both normal and crash scenarios. Statistical analyses of the experimental data show that, on average, drivers can anticipate a crash up to 2.61 s before it occurs in this pilot study. The chance that drivers have successfully anticipated crashes before they occur is 92.8%. A state of the art AI model can anticipate crashes 1.02 s earlier than drivers on average. The study finds that crash-involving traffic agents in the driving videos can vary drivers’ instant attention level, average attention level, and spatial attention distribution. This finding supports the development of a spatial-temporal attention mechanism for AI models to strengthen their ability to anticipate crashes. Results from the comparison also suggest the development of collaborative intelligence that keeps human-in-the-loop of AI models to further enhance the reliability of AI-enabled safety functions.
Digital Shadow and Its Legality Aspects

It can be said that technology has enabled countless new forms of social relations, which has resulted in a reality that poses challenges to the most conservative sectors. It is in the face of this new scenario that the present investigation will address the concept of digital shadow, as well as the legality of its exploitation by suppliers in consumer relations, considering the right to privacy of consumers who use electronic equipment that transmit digital data. For this purpose, the deductive research method will be used. About the method of procedure, in this research the bibliographic method was used, with research in books, scientific journals and specialized websites on the subject. Finally, it was possible to foresee that the exploitation of consumers' digital shadow by suppliers is illegal.
Unitary coined discrete-time quantum walks on directed multigraphs

Unitary coined discrete-time quantum walks (UCDTQW) constitute a universal model of computation, meaning that any computation done by a general purpose quantum computer can either be done using the UCDTQW framework. In the last decades, great progress has been made in this field by developing quantum walk-based algorithms that can outperform classical ones. However, current quantum computers work based on the quantum circuit model of computation, and the general mapping from one model to the other is still an open problem. In this work, we provide a matrix analysis of the unitary evolution operator of UCDTQW, which is composed at a time of a shift and a coin operators. We conceive the shift operator of the system as the unitary matrix form of the adjacency matrix associated with the graph on which the UCDTQW takes place, and provide a set of equations to transform the latter into the former and vice versa. However, this mapping modifies the structure of the original graph into a directed multigraph, by splitting single edges and arcs of the original graph into multiple arcs. Thus, the fact that any unitary operator has a quantum circuit representation means that any adjacency matrix that complies with the transformation equations will be automatically associated with a quantum circuit, and any quantum circuit acting on a bipartite system will be always associated with a directed multigraph. Finally, we extend the definition of the coin operator to a superposition of coins in such a way that each coin acts on different vertices of the directed multigraph on which the UCDTQW takes place, and provide a description of how this can be implemented in circuit form.
Spam review detection with Metapath-aggregated graph convolution network

The large flux of online products in today’s world makes business reviews a valuable source for consumers for making sound decisions before making online purchases. Reviews are useful for readers in learning more about the product and gauge its quality. Fake reviews and reviewers form the bulk of the review corpus, making review spamming an open research challenge. These spam reviews require detection to nullify their contribution to product recommendations. In the past, researchers and communities have taken spam detection problems as a matter of serious concern. Yet, for all that, there is space for the performance of exploration on large-scale complex datasets. The work contributes towards robust feature selection with derived features that provide more details on malicious reviews and spammers. Ensemble and other standard machine learning techniques are trained and evaluated over optimal feature sets. In addition, the Metapath-based Graph Convolution Network (M-GCN) framework is proposed, which is an implicit knowledge extraction method to automatically capture the complex semantic meaning of reviews from the heterogeneous network. It makes analysis of triplet (users, reviews, and products) relationships in e-commerce sites through examination of Top-n feature sets in a mutually reinforcing manner. The proposed model is demonstrated on Yelp and Amazon benchmark datasets for evaluation of efficacy and it is shown outperforming state-of-the-art techniques with and without graph-utilization, providing an accuracy of 96% in the prediction task.
Meta-Machine-Learning-Based Quantum Scar Detector

A remarkable phenomenon in contemporary physics is quantum scarring in systems whose classical dynamics are chaotic, where certain wave functions tend to concentrate on classical periodic orbits of low periods. Quantum scarring has been studied for more than four decades, but detecting quantum scars still mostly relies on human visualization of the wave-function patterns. The widespread and successful applications of machine learning in many branches of physics suggest the possibility of using artificial neural networks for automated detection of quantum scars. Conventional machine learning often requires substantial training data, but, for quantum scars, this poses a significant challenge: in typical systems the available distinct quantum scarring states are rare. We develop a meta machine-learning approach to accurately detect quantum scars in a fully automated and highly efficient fashion. In particular, taking advantage of some standard large datasets such as Omniglot from the field of image classification, we train a ``preliminary'' version of the neural network that has the ability to distinguish different classes of noisy images. We then perform few-shot classification to further train the neural network but with a small number of quantum scars. We demonstrate that the meta-learning scheme can find the correct quantum scars from thousands of images of wave functions without any human intervention, regardless of the symmetry of the underlying system. From a general applied point of view, our success opens the door to exploiting meta learning for solving challenging image detection and classification problems in other fields of science and engineering. For example, in microlasing systems, identifying scarring states is critical as these states are desired for directional emission. The task is also important for quantum-dot devices where the scarring states can lead to resonances in the conductance.
An Intelligent Recommendation Platform that Utilizes Artificial Intelligence to Drive People to Make Better Food Decisions

People are often given options on restaurants to eat at and are also given the ratings of those restaurants. However, the ratings can sometimes be rather similar and hard to choose from, and it can also be hard to find a restaurant that suits a person's special needs, and people often eat at a singular place once they find a good restaurant; we want to change that by trying to encourage people to try new restaurants. While our idea isn't original, we still decided to add it to the list of probably hundreds of sites out there that do the same thing. We made a restaurant recommendation with one purpose in mind, to gather data from restaurants and share that data with everyone. We used many methods to get that data, create a user interface, and add that data to the site so everyone can use it. This project had originated from another idea for a Roblox sniping site which I was told was a bit too advanced and was suggested something similar in design. A restaurant recommender and a Roblox sniping site are similar in the way they both use web scraping. Web scraping is the ability of a website to get the code from other sites. Our restaurant recommender gets data from Yelp to add to our database in the way that a Roblox sniping site can get information from the Roblox catalog to display to people to see when there's a good bargain. The restaurant recommender uses the data it gets to give recommendations for a better restaurant and it gives the 3 worst reviews on the restaurant, which are to highlight some of the potential flaws of the input restaurant. The main pieces of data the recommender gets are the restaurant genre for people to see what kind of restaurant it is, the restaurant region to see what type of food the restaurant serves, the restaurant type to see if its a bar or restaurant or what type it is, the restaurant's overall rating to see how good the restaurant is, and the Yelp page of the restaurant, for a deeper look into the restaurant itself. We then use the data to get a better restaurant. We use the restaurant type, region, and genre to find a similar restaurant, and we use the rating to find a restaurant with a better rating. We used many libraries and coding languages to build our site. We used HTML and CSS to build the user interface, we used Python to run the server we were using and build the web scraper, and then we used csv for the database containing all the data. We used beautiful soup to organize the data, and we used requests to get user input. We used pandas for the data analysis and we used sklearn to build the predictor for a better restaurant.
Forest Kindergarten in Germany and Korea - An Empirical Comparative Study on the Educational Field of Aesthetics

This article presents a comparative international and cross-cultural study about aesthetics in in early childhood education in forest kindergarten education in Germany and Korea.It deals with differences in the understanding of education and the (educational) goals of the forest kindergarten pedagogy.In addition, the educational area of aesthetics in forest kindergartens in both countries is examined.This article presents the current state of research on aesthetic education in early childhood education in forest kindergartens.This is the first study that focuses on forest kindergartens and aesthetic education in an international and cross-cultural context.A written survey with mostly quantitative questions and some qualitative parts is conducted, in which both the parents (314) and the education experts (84) in Germany and South Korea were interviewed.The frequencies of the different statements were determined and presented graphically with Word clouds.The main result of this study is that the forest kindergarten pedagogy in Germany and Korea, despite different educational understandings and different cultures, has a lot in common, because the Koreans have taken over many aspects and attitudes from Germany.The educational field of aesthetics is explicitly more relevant in Germany, but the Korean experts are presenting many projects that have a close connection to the aesthetic education sector, without assigning them to this field.This includes especially traditional offers such as masquerade and calligraphy.The concluding consideration critically discusses the results and places them in the discourse of forest kindergarten pedagogy.
Node-Weighted Centrality Ranking for Unsupervised Long Document Summarization

Supervised methods have demonstrated superior performance to unsupervised methods in text summarization. However, supervised methods heavily rely on human-generated summaries, which can be costly and difficult to obtain in large quantities. They also face challenges in summarizing long documents due to input length restrictions. Graph-based methods are frequently employed in unsupervised text summarization owing to their capacity to examine interrelationships between. However, these methods usually depend on unique node weights, resulting in limited mapping capabilities and weak performance on long documents. To address these difficulties, this study proposes an unsupervised method that employs a graph model with augmented node weights with a novel centrality ranking algorithm. Comprehensive experiments on standard datasets demonstrate the effectiveness of the proposed method, which outperforms both unsupervised and supervised techniques when evaluated using the ROUGE metric.
Knowledge Graph Representation Learning via Generated Descriptions

Knowledge graph representation learning (KGRL) aims to project the entities and relations into a continuous low-dimensional knowledge graph space to be used for knowledge graph completion and detecting new triples. Using textual descriptions for entity representation learning has been a key topic. However, the current work has two major constraints: (1) some entities do not have any associated descriptions; (2) the associated descriptions are usually phrases, and they do not contain enough information. This paper presents a novel KGRL method for learning effective embeddings by generating meaningful descriptive sentences from entities’ connections. The experiments using four public datasets and a new proposed dataset show that the New Description-Embodied Knowledge Graph Embedding (NDKGE for short) approach introduced in this paper outperforms most of the existing work in the task of link prediction. The code and datasets of this paper can be obtained from GitHub ( https://github.com/MiaoHu-Pro/NDKGE .)
Text to Image Synthesis Using Bridge Generative Adversarial Network and Char CNN Model

A content to picture production approach seeks to produce photorealistic images that are semantically coherent with the provided descriptions from text descriptions. Applications for creating photorealistic visuals from text includes photo editing and more. Strong neural network topologies, such as GANs (Generative Adversarial Networks) have been shown to produce effective outcomes in recent years. Two very significant factors, visual reality and content consistency, must be taken into consideration when creating images from text descriptions. Recent substantial advancements in GAN have made it possible to produce images with a high level of visual realism. However, generating images from text ensuring high content consistency between the text and the generated image is still ambitious. To address the above two issues, a Bridge GAN model is proposed, where the bridge is a transitional space containing meaningful representations of the given text description. The proposed systems incorporate Bridge GAN and char CNN – RNN model to generate the image in high content consistency and the results shows that the proposed system outperformed the existing systems.
Leveraging Small-BERT and Bio-BERT for Abbreviation Identification in Scientific Text

Abbreviations are short forms of phrases that aid in the communication of long sentences in texts and are an essential part of the writing process. Abbreviations save a lot of time and space in writing scientific documents such as research articles, papers, clinical notes, etc. However, it is challenging to identify or map abbreviations to the complete form in scientific documents due to the vast and dynamic range of rules for forming an abbreviation. On the other hand, a massive increase in scientific papers over the Web has raised the need for an automatic abbreviation identification system by many folds. Thus, this paper proposes an LSTM-based deep learning system that encodes the target word and its context sentence using two different forms of pre-trained BERT embeddings (Small BERT and Bio BERT). The proposed system classifies whether the target word is an abbreviation or not. We experimented with two scientific datasets, viz., MeDal and SciAI, for the abbreviation detection task. We built abbreviation detection systems with two different settings, 1. having a lowercase module and 2. no explicit lowercase module. We observe that retaining the actual case of the letters in the abbreviation is crucial for abbreviation detection. Our system results in an F1-score of $$90.04\%$$ on the SciAI dataset and $$85.68\%$$ on the MeDal dataset for the abbreviation detection task. To observe the domain-specific behavior of the abbreviations, we also performed cross-domain evaluation (trained on MeDal, tested on SciAI, and vice versa). We obtained an F1-score of $$76.50\%$$ on SciAI data and $$62.72\%$$ on MeDal data in the cross-domain settings. We compared our system with six other statistical systems for the abbreviation detection task. Results show that our system is able to outperform other models by a significant margin.
SP-BERT: A Language Model for Political Text in Scandinavian Languages

Language models are at the core of modern Natural Language Processing. We present a new BERT-style language model dedicated to political texts in Scandinavian languages. Concretely, we introduce SP-BERT, a model trained with parliamentary speeches in Norwegian, Swedish, Danish, and Icelandic. To show its utility, we evaluate its ability to predict the speakers' party affiliation and explore language shifts of politicians transitioning between Cabinet and Opposition.
Improving Context-Awareness on Multi-Turn Dialogue Modeling with Extractive Summarization Techniques

The study of context-awareness in multi-turn generation-based dialogue modeling is an important but relatively underexplored topic. Prior research has employed hierarchical structures to enhance the context-awareness of dialogue models. This paper aims to address this issue by utilizing two extractive summarization techniques, namely the PMI topic model and the ORACLE algorithm, to filter out unimportant utterances within a given context. Our proposed approach is assessed on both non-hierarchical and hierarchical models using the distracting test, which evaluates the level of attention given to each utterance. Our proposed methods gain significant improvement over the baselines in the distracting test.
Abstractive Summarization Based Question-Answer System For Structural Information

Query-based answer summarization is the process by which the answer from structured information like books is extracted in a concise fashion based on the given query, with minimal loss of information. Whether an abstractive or extractive answer summarization technique gets executed automatically depends on the information that is being extracted with respect to the question raised. The lexical relationship between sentence-to-sentence and query-to-sentence reduces the conflict between answer summary and user query and also improves the quality of the answer summary. For the fastest content retrieval from the structured information, book, our system finds the semantic and syntactic similarity between the subtitles of each chapter and the query. This work uses techniques of the Natural Language Processing paradigm along with some of the advanced Deep Learning techniques such as attention mechanism and Transfer Learning. The implementation results demonstrate the accuracy and efficiency of the extracted answer summary for the respective query, without compromising the quality of content.
Estimation by Stable Motions and its Applications

We propose a family of confidence intervals for nonparametric moment estimators if the observations have large or infinite variances.The theoretical underpinnings which guarantee the soundness of the method are demonstrated.Extensive numerical simulations show its superiority over bootstrap and normal approximation and its wide applicability.Finally, a confidence interval to estimate the coupling strength in neuronal networks is proposed.