Reviewerly/exHarmony huggingface dataset:
Anomaly Detection Based on Convex Analysis: A Survey

As a crucial technique for identifying irregular samples or outlier patterns, anomaly detection has broad applications in many fields. Convex analysis (CA) is one of the fundamental methods used in anomaly detection, which contributes to the robust approximation of algebra and geometry, efficient computation to a unique global solution, and mathematical optimization for modeling. Despite the essential role and evergrowing research in CA-based anomaly detection algorithms, little work has realized a comprehensive survey of it. To fill this gap, we summarize the CA techniques used in anomaly detection and classify them into four categories of density estimation methods, matrix factorization methods, machine learning methods, and the others. The theoretical background, sub-categories of methods, typical applications as well as strengths and limitations for each category are introduced. This paper sheds light on a succinct and structured framework and provides researchers with new insights into both anomaly detection and CA. With the remarkable progress made in the techniques of big data and machine learning, CA-based anomaly detection holds great promise for more expeditious, accurate and intelligent detection capacities.
U-NO: U-shaped Neural Operators

Neural operators generalize classical neural networks to maps between infinite-dimensional spaces, e.g., function spaces. Prior works on neural operators proposed a series of novel methods to learn such maps and demonstrated unprecedented success in learning solution operators of partial differential equations. Due to their close proximity to fully connected architectures, these models mainly suffer from high memory usage and are generally limited to shallow deep learning models. In this paper, we propose U-shaped Neural Operator (U-NO), a U-shaped memory enhanced architecture that allows for deeper neural operators. U-NOs exploit the problem structures in function predictions and demonstrate fast training, data efficiency, and robustness with respect to hyperparameters choices. We study the performance of U-NO on PDE benchmarks, namely, Darcy's flow law and the Navier-Stokes equations. We show that U-NO results in an average of 26% and 44% prediction improvement on Darcy's flow and turbulent Navier-Stokes equations, respectively, over the state of the art. On Navier-Stokes 3D spatiotemporal operator learning task, we show U-NO provides 37% improvement over the state of art methods.
Reconstructing virtual large slides can improve the accuracy and consistency of tumor bed evaluation for breast cancer after neoadjuvant therapy

To explore whether the "WSI Stitcher", a program we developed for reconstructing virtual large slide through whole slide imaging fragments stitching, can improve the efficiency and consistency of pathologists in evaluating the tumor bed after neoadjuvant treatment of breast cancer compared with the conventional methods through stack splicing of physical slides.This study analyzed the advantages of using software-assisted methods to evaluate the tumor bed after neoadjuvant treatment of breast cancer. This new method is to use "WSI Stitcher" to stitch all the WSI fragments together to reconstruct a virtual large slide and evaluate the tumor bed with the help of the built-in ruler and tumor proportion calculation functions.Compared with the conventional method, the evaluation time of the software-assisted method was shortened by 35%(P < 0.001). In the process of tumor bed assessment after neoadjuvant treatment of breast cancer, the software-assisted method has higher intraclass correlation coefficient when measuring the length (0.994 versus 0.934), width (0.992 versus 0.927), percentage of residual tumor cells (0.947 versus 0.878), percentage of carcinoma in situ (0.983 versus 0.881) and RCB index(0.997 versus 0.772). The software-assisted method has higher kappa values when evaluating tumor staging(0.901 versus 0.687) and RCB grading (0.963 versus 0.857).The "WSI Stitcher" is an effective tool to help pathologists with the assessment of breast cancer after neoadjuvant treatment.
Using Dynamic Pruning Technique for Efficient Depth Estimation for Autonomous Vehicles

Even with the significant progress that has been achieved in monocular depth estimation in recent years, the need for better real-time inference and reduction in computing resources usage associated with the network performance is persistent. In this paper, an enquiry into the efficacy of pruning on depth estimation models is performed. Encoder-decoder model based on the ResNet-50 backbone architecture employing pruning based on channel prioritization is designed to achieve higher performance and prediction speed. This is while attempting to keep a balance in the trade-off between accuracy and performance of the network. The presented approach is trained and evaluated for outdoor scenery on the KITTI dataset to demonstrate the effectiveness and the performance improvement of the presented framework when compared to similar methods. This shows competitive accuracy when compared to state-of-the-art methods and highlights how pruning can speed up inference time by more than 16% and leading to fewer operations compared to the non-pruned model.
Flaming as a speech conflict inciting tool in Internet-discourse

The article analyzes flaming as a tool for a speech conflict inciting. The author backs up the idea that flaming as a mean of speech aggression appeared on the threshold of a new era due to the development of the mass media and computer-mediated communication in the Internet discourse. It is shown that flaming is actively used in combination with other forms of speech aggression in Internet discourse, such as trolling and cyberbullying. Phenomenon of «hate speech» is assigned a special role in flamewars. This term as the international legal practice shows, denotes the opposition of the subjects described in the text based on certain characteristics (race, language, religion, nationality, or belonging to any group) and inducement to violent actions that are carried out based on them. Using specific examples, the author attempts to classify flaming to reveal their strategic goals and to describe the tactics of discreditation illustrating the intent of the content creator. The work emphasizes the need to reveal the communicating tactics in the framework of forensic linguistics examination as well as deanonymization of the flaming content creators via forensic authorship examination.
Structured Unitary Matrices and Quantum Entanglement

We explore the set of unitary matrices characterized by a given structure in the context of their applications in the field of Quantum Information. In the first part of the Thesis we focus on classification of special classes of unitary matrices and possibility of introducing certain internal parameterizations. Several new results and conjectures are discussed. Second part of the Thesis is devoted to the concept of multipartite quantum entanglement. We present a solution to the problem of absolutely maximally entangled states of four subsystems with six levels each. Finally, we analyze the excess of a matrix and the corresponding Bell inequalities. This combination allows us to draw new conclusions related to quantum nonlocality.
ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer

Using synthetic data for training neural networks that achieve good performance on real-world data is an important task as it can reduce the need for costly data annotation. Yet, synthetic and real world data have a domain gap. Reducing this gap, also known as domain adaptation, has been widely studied in recent years. Closing the domain gap between the source (synthetic) and target (real) data by directly performing the adaptation between the two is challenging. In this work, we propose a novel two-stage framework for improving domain adaptation techniques on image data. In the first stage, we progressively train a multi-scale neural network to perform image translation from the source domain to the target domain. We denote the new transformed data as "Source in Target" (SiT). Then, we insert the generated SiT data as the input to any standard UDA approach. This new data has a reduced domain gap from the desired target domain, which facilitates the applied UDA approach to close the gap further. We emphasize the effectiveness of our method via a comparison to other leading UDA and image-to-image translation techniques when used as SiT generators. Moreover, we demonstrate the improvement of our framework with three state-of-the-art UDA methods for semantic segmentation, HRDA, DAFormer and ProDA, on two UDA tasks, GTA5 to Cityscapes and Synthia to Cityscapes.
MEDeep: A Deep Learning Based Model for Memotion Analysis

A meme is an idea or an expression that becomes a trend and usually spreads within a culture through imitation, carrying a significant meaning representing a specific concept or theme.It represents a particular message by the combination of image and text.An enormous number of memes are spread every day via social media platforms to share sarcastic, humorous, and offensive messages.Therefore, to control the spread of offensive language and propaganda, there is a need for an automated mechanism to classify the meme.Considering this, the proposed study aimed to investigate the impact of using different data modality i.e., text, images, combined (text and images) for memotion analysis using Deep Learning (DL) models.Several pre-trained DL models such as ResNet152V2, VGG19, EfficientNetB7 were used for the images.While, Convolutional Neural Network (CNN) and CNN+ Long short-term memory (LSTM) were implemented for the text classification.Experimental results reveal that the CNN model outperformed for the text, and EfficientNetB7 achieved the best performance on the images.However, for the multimodal analysis, early fusion technique was used and classification was performed using CNN and EfficientNetB7 model.The study found that Glove embedding and CNN model using text produced the highest results among all the experiments conducted.The model achieved an accuracy, F1-macro, precision, and recall of 0.8387, 0.8352, 0.8361, 0.8487, respectively.The results exhibit that the proposed model outperforms other baseline studies.
Learning Dynamics by Reservoir Computing (In Memory of Prof. Pavol Brunovský)

Abstract We study reservoir computing, a machine learning method, from the viewpoint of learning dynamics. We present numerical results of learning the dynamics of the logistic map, one of the typical examples of chaotic dynamical systems, using a 30-node reservoir and a three-node reservoir. When the learning is successful, an attractor that is smoothly conjugate to the logistic map to be learned is observed in the phase space of the reservoir. Inspired by this numerical result, we introduce a degenerate reservoir system and use it to mathematically confirm this observation. We also show that reservoir computing can learn information about dynamics not included in the training data, which we believe is a remarkable feature of reservoir computing compared to other machine learning methods. We discuss this feature in connection with the above observation that there is a smooth conjugacy between the attractor in the reservoir and the dynamics to be learned.
Interaction Design for VR Applications: Understanding Needs for University Curricula

As virtual reality (VR) is emerging in the tech sector, developers and designers are under pressure to create immersive experiences for their products. However, the current curricula from top institutions focus primarily on technical considerations for building VR applications, missing out on concerns and usability problems specific to VR interaction design. To better understand current needs, we examined the status quo of existing university pedagogies by carrying out a content analysis of undergraduate and graduate courses about VR and related areas offered in the major citadels of learning and conducting interviews with 7 industry experts. Our analysis reveals that the current teaching practices underemphasize design thinking, prototyping, and evaluation skills, while focusing on technical implementation. We recommend VR curricula should emphasize design principles and guidelines, offer training in prototyping and ideation, prioritize practical design exercises while providing industry insights, and encourage students to solve VR design problems beyond the classroom.
Quantum Prudent Contracts with Applications to Bitcoin

Smart contracts are cryptographic protocols that are enforced without a judiciary. Smart contracts are used occasionally in Bitcoin and are prevalent in Ethereum. Public quantum money improves upon cash we use today, yet the current constructions do not enable smart contracts. In this work, we define and introduce quantum payment schemes, and show how to implement prudent contracts -- a non-trivial subset of the functionality that a network such as Ethereum provides. Examples discussed include: multi-signature wallets in which funds can be spent by any 2-out-of-3 owners; restricted accounts that can send funds only to designated destinations; and "colored coins" that can represent stocks that can be freely traded, and their owner would receive dividends. Our approach is not as universal as the one used in Ethereum since we do not reach a consensus regarding the state of a ledger. We call our proposal prudent contracts to reflect this. The main building block is either quantum tokens for digital signatures (Ben-David and Sattath QCrypt'17, Coladangelo et al. Crypto'21), semi-quantum tokens for digital signatures (Shmueli'22) or one-shot signatures (Amos et al. STOC'20). The solution has all the benefits of public quantum money: no mining is necessary, and the security model is standard (e.g., it is not susceptible to 51\% attacks, as in Bitcoin). Our one-shot signature construction can be used to upgrade the Bitcoin network to a quantum payment scheme. Notable advantages of this approach are: transactions are locally verifiable and without latency, the throughput is unbounded, and most importantly, it would remove the need for Bitcoin mining. Our approach requires a universal large-scale quantum computer and long-term quantum memory; hence we do not expect it to be implementable in the next few years.
Social Networks as a Phenomenon of Virtual Learning in the Information Society

The paper presents in a brief form the main strategies for the development of education in the information society, gives a reflexive review of literature and practice to assess the impact of information and communication technologies on the education system. An analysis of the normative legal acts governing this still very young sphere of education demonstrates active institutional participation in integrating new technologies into all spheres of society. The paper describes the conceptual and methodological foundations of the culture of the information society, proposed for the interpretation and comprehensive assessment of learning processes, particularly the formation of digital competencies through the technology of social networks. The author noted that the sustainable development of modern society is impossible without the active involvement of the educational resource of social networks. At the same time, the humanistic significance of the information society is noted, its democratic potential in the process of socialization of the personality of each person and citizen is noted. The author concluded that, despite all the problems and difficulties, further work is needed in this important and promising direction in the context of a dynamically developing information society.
Standing out among the daily spam: How to catch website owners’ attention by means of vulnerability notifications

Running a business without having a website is nearly impossible nowadays. Most business owners use content managements systems to manage their websites. Yet, those can pose security risks and provide vulnerabilities for manipulations. With vulnerability notifications, website owners are notified about security risks. To identify common themes with respect to vulnerability notifications and provide deeper insight into the motivations of website owners to react to those notifications, we conducted 25 semi-structured interviews. In compliance with previous research, we could confirm that distrust in unexpected notifications is high and, in contrast to previous research, we suggest that verification possibilities are the most important factors to establish trust in notifications. We also endorse the findings that raising awareness for the severity and the complexity of the problems is crucial to increase remediation rates.
Understanding the Characteristics of Visual Contents in Open Source Issue Discussions: A Case Study of Jupyter Notebook

Most issue tracking systems for open source software (OSS) development include features for community members to embed visual contents, such as images and videos, to enhance the discussion. Although playing an important role, there is little knowledge on the characteristics of the visual contents to support their use. To address this gap, we conducted an empirical study on the Jupyter Notebook project. We found that more than a quarter of the issues in the Jupyter Notebook project included visual contents. Additionally, issues that had visual contents in the comments but not in the issue posts tended to have a longer discussion and to involve a larger number of discussants. In our qualitative analysis, we identified eight types of visual contents in our sample, with about 60% including screenshots or mockups of the main product. We also found that visual contents served diverse purposes, touching both problem and solution spaces of the issues. Our effort serves as an important step towards a comprehensive understanding of the characteristics of visual contents in OSS issue discussions. Our results provided several design implications for issue tracking systems to better facilitate the use of visual contents.
Barriers and Enablers for Adopting Virtual Reality and Augmented Reality in Apparel Retailing: Insights From Generation Z and Millennials

The global pandemic has accelerated the adoption of augmented reality and virtual reality in digital retailing. The purpose of this study is to explore the barriers and enablers that influence the consumer adoption of virtual try-on (AR) and 3D virtual stores (VR) using empirical data from Generation Z and Millennials. To this end, we developed two mock e-commerce websites for a fictitious apparel brand to embed the AR and VR technology. Two websites were identical except the type of technology used (AR vs. VR). A total of 194 female students participated in the lab experiment. Participants were assigned one of the two conditions (AR or VR), explored the website, and completed the survey that included open-ended questions asking the specific features of the assigned technology that they liked/disliked and the reasons for liking/disliking. Four key enablers and barriers for each technology (i.e., consumption vision, interactivity, enjoyment, novelty) emerged.&nbsp
Doctor-Bot: AI Powered Conversational Chatbot for Delivering E-Health

Abstract: Nowadays, making time for even the smallest of things has become quite difficult as everyone wants to save their time. Health suffersthe most due to this. Due to the shortage of time people have developed a habit of seeing a doctor and having a proper checkup only when it's extremely important and there's no way around it could be postponed. And sometimes people are just way too nervous to visit their nearest medical clinic, especially, in the times of COVID, when there was a massive scarcity of any medical assistance, something that could give you information about your specific medical condition, and recommend you solutions and medication, without the need of you going out, could be a great deal. This project takes the same issue in consideration and aims to provide an easy and accessible solution to help people in dealing withtheir specific medical condition with the help of this AI modules and machine learning powered Health Care Bot. Having a service that can give you the solutions without participating in the activity of reaching out to a doctor for yourminor issues not only just saves your time but also gives you the freedom and flexibility to choose any suitable time to take advantage of the mentioned services. The aim for creating this is to help people to have their minor medical problems sorted from the comfort of their home.And in case of something extremely serious that needs an expert assistance it will show the nearby medical facilities that the patient can reach out to with appropriate information. All the information is scraped using trustable sources and even after that refining the process by introducing AI modules to get the best possible solution. Keywords: AI chatbot, Conversational bot, Digital health, Machine Learning, Natural Language Processing
Paraconsistent Belief Revision: An Algebraic Investigation

Abstract This paper offers a logico-algebraic investigation of AGM belief revision based on the logic of paradox ( $$\mathrm {LP}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>LP</mml:mi> </mml:math> ). First, we define a concrete belief revision operator for $$\mathrm {LP}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>LP</mml:mi> </mml:math> , proving that it satisfies a generalised version of the traditional AGM postulates. Moreover, we investigate to what extent the Levi and Harper identities, in their classical formulation, can be applied to a paraconsistent account of revision. We show that a generalised Levi-type identity still yields paraconsistent-based revisions that are fully compatible with the AGM postulates. The main outcome is that, once the classical AGM framework is lifted up to an appropriate level of generality, it still appears as a regulative ideal for treating of paraconsistent-based epistemic operators.
Electronic Document Flow Monitoring And Control System Using Document Structure Analysis

We reside in the community that produce enormous amount of information. The magnitude of digitation in document is about transforming its movement into paperless form. The electronic document is secure with digital signature that bind with approval accepted by authorities and members of the organization. The evolving digitization use the document structure algorithm to add value tailored its services to develop as solution that improve that document flow of one university in Manila. The electronic document has the structure that can be partitioned in an order level and hierarchy pf physical elements and use the indexing to manage the document information. Documents are the most important source of knowledge and experience for any institution. Today, universities produce and use many documents in several types. These documents are paper or digital trails generated to coordinate and cooperate. Producing, sharing, copying, and archiving documents in a specific and strict document format. These documents are also needed for future decision-making based on analytics over historic data. Document Management System is a process or activity that starts from the making, receiving, collecting, organizing, controlling, maintaining, and storing. With this research, the documents from the creation, distribution, approval, modification, accessibility, recovery, storage, the printing of copies are controlled in one place. The system was developed to manage and create a centralized repository of documents. These documents can be retrieved and can be tracked online at anytime and anywhere.