Reviewerly/exHarmony huggingface dataset:
Learning Behavior Trees From Planning Experts Using Decision Tree and Logic Factorization

The increased popularity of Behavior Trees (BTs) in different fields of robotics requires efficient methods for learning BTs from data instead of tediously handcrafting them. Recent research in learning from demonstration reported encouraging results that this paper extends, improves and generalizes to arbitrary planning domains. We propose BT-Factor as a new method for learning expert knowledge by representing it in a BT. Execution traces of previously manually designed plans are used to generate a BT employing a combination of decision tree learning and logic factorization techniques originating from circuit design. We test BT-Factor in an industrially-relevant simulation environment from a mining scenario and compare it against a state-of-the-art BT learning method. The results show that our method generates compact BTs easy to interpret, and capable to capture accurately the relations that are implicit in the training data.
ASP and subset minimality: Enumeration, cautious reasoning and MUSes

Answer Set Programming (ASP) is a well-known logic-based formalism that has been used to model and solve a variety of AI problems. For several years, ASP implementations primarily focused on the main computational task: the computation of one answer set of a (logic) program. Nonetheless, several AI problems, that can be conveniently modelled in ASP, require to enumerate solutions characterized by an optimality property that can be expressed in terms of subset-minimality with respect to some objective atoms. In this context, solutions are often either (i) answer sets that are subset-minimal w.r.t. the objective atoms or (ii) atoms that are contained in all subset-minimal answer sets, or (iii) sets of atoms that enforce the absence of answer sets on the ASP program at hand — such sets are referred to as minimal unsatisfiable subsets (MUSes). In all the above-mentioned cases, the corresponding computational task is currently not supported by plain state-of-the-art ASP solvers. In this paper, we study formally these tasks and fill the gap in current implementations by proposing several algorithms to enumerate MUSes and subset-minimal answer sets, as well as perform cautious reasoning on subset-minimal answer sets. We implement our algorithms on top of wasp and perform an experimental analysis on several hard benchmarks showing the good performance of our implementation.
Distraction versus focusing during VR exposure therapy for acrophobia: A randomized controlled trial

The therapeutic mechanisms of exposure therapy are not well understood. Research suggests that focusing on the most feared aspect might not be necessary, and that distraction with a low cognitive load (e.g., conversation) might enhance exposure. We aimed at systematically testing the efficacy of exposure therapy with focusing vs. conversational distraction, hypothesizing that distracted exposure would yield superior effects.Thirty-eight patients with acrophobia (specific phobia of heights; clinician-determined) (free from relevant somatic or other mental disorders) were randomly assigned (1:1) to one virtual reality (VR) session of either focused (n = 20) or distracted exposure (n = 18). This monocentric trial took place at a psychiatric university hospital.Both conditions resulted in a significant reduction of acrophobic fear and avoidance, and a significant increase of self-efficacy (primary outcome variables). However, condition did not have a significant effect on any of these variables. Effects were stable at four-week follow-up. Heart rate and skin conductance level indicated significant arousal, but did not differ between conditions.Eye-tracking was unavailable, nor did we assess emotions other than fear. Power was limited due to sample size.A balanced exposure protocol combining attention to fear cues with conversational distraction, while not being superior, might be as effective as focused exposure for acrophobia, at least during the initial stages of exposure therapy. These results support previous findings. This study demonstrates how VR can be exploited for therapy process research, as VR supports dismantling designs and the incorporation of online process measures.
Chat and Voice Bot Implementation for Cardio and ENT Queries Using NLP

Human to machine conversational UI's, commonly referred to as chatbots, helps users with a new method to talk with computers. Users can ask queries to chatbots just like they would to a human. But chatbots are increasingly often utilized in online conversation services. Artificial intelligence is used by these bots to comprehend human input and react appropriately. The three most important areas to focus on are medicine/health, agriculture, and education. Nowadays, chatbots can be utilized anyplace a person can talk to a computer at any time. The fastest expanding sectors for hiring are customer service, sales, marketing, and branding, as well as human resources. This prediction tasks are now carried out by other chatbots (especially in the medical industry), made possible by developments in AI and data mining methods. The number of patients is rising quickly every day as lifestyles change, just like in the modern world. As a result, both the hospital and the local health care systems are expanding quickly. Patients with hectic schedules must wait a long time for doctors. Some diseases take longer to recover from. Around the world, heart disease and ENT diseases are prevalent issues. 4,444 people, both young and elderly, pass away from heart attacks every day. The main challenges of cardio patients are they do not know how to prevent and cure the diseases and the solutions are not available for their doubts. In order to improve both business and healthcare outcomes, healthcare practitioners will be able to collect data remotely, diagnose patients accurately from a distance, and use AI to evaluate data. Our chatbot performs using Natural language tool kit (NLTK) of python, Natural Language Processing (NLP), and its methods include stemming, tokenization, pattern matching and also sequence to sequence methods accordingly. Every user of chatbot is not educated. So, we need voice based chatbots for illiterate users who can speak but cannot write.
AI-Driven Deep Structured Learning for Cross-Site Scripting Attacks

A substantial danger of attacks from numerous sources, with varying intensity and sophistication, is posed by the worldwide nature of online applications. A code injection attack known as cross-site scripting (XSS) occurs on the client side or via web browsers. Cross-Site Scripting (XSS), a severe attack that may be used to change a user's or an organization's data, is typical in online applications. This paper suggests a hybrid convolutional neural network (CNN) and machine learning (ML) framework for quickly and effectively identifying the XSS attacks to enhance the security. According to the simulation findings, the proposed model is more effective than the existing detection techniques. It generates results for XSS attack categorization that are more than 99.9% correct and is exceptionally resistant to XSS attack.
Framework for Evaluating Ethics in AI

With the growing advancement in the field of technology., it is seen that artificial intelligence is becoming the need of the hour., with the advent of self-driven cars., smart cities., healthcare robots and many more. The probability that AI will soon relieve humans of the daily tasks that humans usually do and as such the trust in this technology must be paramount. As society is diving deeper into understanding the prospects of this technology., the dilemma of whether it is capable to be empathetic enough to replace human intelligence is chanced upon and should be addressed. The various issues such as human sense and empathy are lacking by any AI algorithm., the security of data as well as the misuse of personal information., the opaque nature of the algorithms., and the level of authority between humans and AI have made for the rethinking of the ethical issues. The cost of ethical issues which is being understood by humans may not be fully understood by these artificial brains and therefore it becomes imperative to evaluate their ethics. In this paper., the main focus is on the fundamentals of ethics as well as a framework is proposed for evaluating and ensuring that the technology being used shall adhere to the moral rules and would not be used for illicit purposes. The various reasons for which the study of ethical AI is becoming imperative are also highlighted as well as a solution to the saidissues is given in the paper.
Supervised Learning Methods for Identifying Credit Card Fraud

Credit card fraud has increased due to the growing usage of credit cards for in-store and online transactions. These cards allow the user to make substantial purchases without carrying much money. They have simplified payment for clients and ushered in a new era of cashless commerce. Despite its many advantages, this kind of digital currency transaction is not without its own special risks. The incidence of credit card fraud increases proportionally with the growth of the user base. Therefore, it is essential to create models and solutions that prevent a user or community from falling victim and suffering financial and reputational losses. Typically, these methods are employed alone or in combination with ensemble or conceptual approaches to generate classifiers. This paper gives an overview of the fraud detection system using various supervised Machine Learning approaches i.e., Extreme Gradient Boosting (XGB), Decision Tree (DT), Logistic Regression (LR) and, Random Forest (RF), and compares their performance on factors like accuracy, sensitivity, etc. on a dataset for European cardholders obtained from Kaggle.
Optical convolutional neural network with atomic nonlinearity

Due to their high degree of parallelism, fast processing speeds and low power consumption, analog optical functional elements offer interesting routes for realizing neuro-morphic computer hardware. For instance, convolutional neural networks lend themselves to analog optical implementations by exploiting the Fourier-transform characteristics of suitable designed optical setups. However, the efficient implementation of optical nonlinearities for such neural networks still represents challenges. In this work, we report on the realization and characterization of a three-layer optical convolutional neural network where the linear part is based on a 4f-imaging system and the optical nonlinearity is realized via the absorption profile of a cesium atomic vapor cell. This system classifies the handwritten digital dataset MNIST with 83.96% accuracy, which agrees well with corresponding simulations. Our results thus demonstrate the viability of utilizing atomic nonlinearities in neural network architectures with low power consumption.
An efficient framework for few-shot skeleton-based temporal action segmentation

Temporal action segmentation (TAS) aims to classify and locate actions in the long untrimmed action sequence. With the success of deep learning, many deep models for action segmentation have emerged. However, few-shot TAS is still a challenging problem. This study proposes an efficient framework for the few-shot skeleton-based TAS, including a data augmentation method and an improved model. The data augmentation approach based on motion interpolation is presented here to solve the problem of insufficient data, and can increase the number of samples significantly by synthesizing action sequences. Besides, we concatenate a Connectionist Temporal Classification (CTC) layer with a network designed for skeleton-based TAS to obtain an optimized model. Leveraging CTC can enhance the temporal alignment between prediction and ground truth and further improve the segment-wise metrics of segmentation results. Extensive experiments on both public and self-constructed datasets, including two small-scale datasets and one large-scale dataset, show the effectiveness of two proposed methods in improving the performance of the few-shot skeleton-based TAS task.
Uniting Countries and Continents: Dialogue of Professionals at the International Library Forum

The article focuses on the results of the International Video Conference “Library Services on Three Continents” organized by the National Library of Russia. The idea to hold the conference was motivated by the interest in intensifying professional contacts. Capabilities of modern Internet technologies, including the use of remote forms of cooperation, play an invaluable role there. The COVID-19 pandemic, followed by the special military operation, created unfavourable conditions for Russian librarians to visit some foreign countries and for colleagues from these countries to come to Russia. The exchange of professional information becomes difficult. At the same time, maintaining a dialogue between librarians of different states is an objective need. The video conference held on November 15, 2022, provided the comprehensive overview of how libraries in different countries on three continents can contribute to the progress of society, the development of information literacy; it showed their place in the global information exchange, and impact on the development of science, culture and education. 34 expert reports analysed the further introduction of information and communication technologies into the work of libraries at different levels, demonstrated actions to attract new readers in terms of using subscriptions on demand and other ways of providing literature, organizing open access. The conference discussed the projects for children and environmental education programs, presented the interesting practices of working in the pandemic, as well as considered the other issues. Professionals from 21 countries of Eurasia, Africa and South America have presented their experience of working with readers, interacting with the public authorities of their country and their partners all around the world and discussed the system of professional education. The reports showed that libraries are currently solving a wide range of problems, increasing the cultural level of readers and preserving their national heritage.
OpenMix+: Revisiting Data Augmentation for Open Set Recognition

Open set recognition requires models to recognize samples of known classes learned in the training set while reject unknowns not learned. Compared with the structural risk minimization theory for closed-set problems, structural risk in open set tasks remains rarely explored. In this paper, we point out that balancing between structural risk and open space risk is crucial for open set recognition, and re-formalize it as open set structural risk. This brings a new view towards the general relationship between closed set recognition and open set recognition against the common intuition, which argues that a good closed set classifier always benefits for open set recognition. Specifically, we theoretically and experimentally show that recent mix-based data augmentation methods are aggressive closed set regularization methods, which reduce structural risk at cost of sacrificing open space risk. Besides, we show that existing negative data augmentation designed for open space risk reduction also ignore the trade-off problem between structural risk and open space risk, which limits their performance. We propose an efficient negative data augmentation strategy named self-mix and a corresponding method named OpenMix. OpenMix generates high-quality negative samples by mixing samples themselves, which can take care of both risks simultaneously. When combining OpenMix with conservative closed set regularization methods to form OpenMix+, models can achieve lower open set structural risk. Extensive experiments validate the superiority of OpenMix and OpenMix+ in terms of both effectiveness and universality.
A Non-Prehensile Object Transportation Framework With Adaptive Tilting Based on Quadratic Programming

This work proposes an operational space control framework for non-prehensile object transportation using a robot arm. The control actions for the manipulator are computed by solving a quadratic programming problem considering the object's and manipulator's kinematic and dynamic constraints. Given the desired transportation trajectory, the proposed controller generates control commands for the robot to achieve the desired motion whilst preventing object slippage. In particular, the controller minimizes the occurrence of object slippage by adaptively regulating the tray orientation. The proposed approach has been extensively evaluated numerically with a 7-degree-of-freedom manipulator, and it is also verified and validated with a real experimental setup.
Enhanced Text-to-Image Synthesis With Self-Supervision

The task of Text-to-Image synthesis is a difficult challenge, especially when dealing with low-data regimes, where the number of training samples is limited. In order to address this challenge, the Self-Supervision Text-to-Image Generative Adversarial Networks (SS-TiGAN) has been proposed. The method employs a bi-level architecture, which allows for the use of self-supervision to increase the number of training samples by generating rotation variants. This, in turn, maximizes the diversity of the model representation and enables the exploration of high-level object information for more detailed image construction. In addition to the use of self-supervision, SS-TiGAN also investigates various techniques to address the stability issues that arise in Generative Adversarial Networks. By implementing these techniques, the proposed SS-TiGAN has achieved a new state-of-the-art performance on two benchmark datasets, Oxford-102 and CUB. These results demonstrate the effectiveness of the SS-TiGAN method in synthesizing high-quality, realistic images from text descriptions under low-data regimes.
Compositional Learning for Interleaving Parallel Automata

Abstract Active automata learning has been a successful technique to learn the behaviour of state-based systems by interacting with them through queries. In this paper, we develop a compositional algorithm for active automata learning in which systems comprising interleaving parallel components are learned compositionally. Our algorithm automatically learns the structure of systems while learning the behaviour of the components. We prove that our approach is sound and that it learns a maximal set of interleaving parallel components. We empirically evaluate the effectiveness of our approach and show that our approach requires significantly fewer numbers of input symbols and resets while learning systems. Our empirical evaluation is based on a large number of subject systems obtained from a case study in the automotive domain.
Biometrics Authentication Techniques in E-Learning Assessment

The rate at which the internet is used globally has helped to improve the growth of the educational sector. E-assessment is now one of the main approaches for undergoing examination in most academic establishments. Due to this development, problems of impersonation and examination malpractices have become an issue of concern in most educational sectors. The use of a traditional technique such as a user login ID and password can no longer control this act of misconduct. The biometrics technique is still one of the approaches used to either reduce or stop this act. Biometrics, which can be either physiological (face recognition, palm recognition, iris, fingerprint) or behavioral (mouse dynamics, keystroke dynamics, handwriting signature), is proposed as a more reliable approach for human identification and authentication. This paper discusses the advantages of E-assessment over the traditional pen-paper method. The different biometric techniques and their application regarding E-assessments were also discussed. The results in this paper indicate that there is still a need for improvement in the E-assessment security approach, especially in the area of bimodal biometrics.
Creation of a Mobile App for Engineering Students' Online Lab

This app helps you access your study materials on your mobile phone. It is an application that allows you to easily communicate with the instructor. Instructors can provide reading materials such as text, images, videos, and links to other sources such as YouTube. It can also be an online video session between instructor and student. Furthermore, this app is a miniature/similar app that adds a laboratory simulation to the university's current online education system (UZEM SYSTEM). This is because the app is aimed at engineering students, and most engineering courses require practicality, so lab internships are required. This allows students to perform laboratory simulations with the help of instructors. The student will need to connect into the app in order to access the contents. Both the teacher and the student will need to select a course from a selection of courses after logging into the site. The chosen course will offer a list of alternatives for learning/teaching techniques, including face-to-face instruction, lab simulations, presentations on a screen, messaging, etc.
Change Management: Library Discourse on Implementation of State Cultural Policy in the Current Conditions

On November 16—17, 2022, the Annual Meeting of the Heads of Federal and Central Regional Libraries of Russia was held at the National Library of Russia (St. Petersburg). The organizers of the meeting are the Ministry of Culture of the Russian Federation, the National Library of Russia and the Russian State Library. About 300 specialists attended the event in full-time mode from 76 regions of the Russian Federation, including the Donetsk Peopleʼs Republic and the Luhansk Peopleʼs Republic. During the two days of work, the participants presented 35 reports. The 2022 main topic is “Managing Changes in the Implementation of the ‘Strategy for the Development of Librarianship in the Russian Federation for the Period up to 2030ʼ”. The participants relied in their speeches on the “Fundamentals of State Policy for the Preservation and Strengthening of Traditional Russian Spiritual and Moral Values”, approved by the No. 809 Executive Order of the President of the Russian Federation of November 9, 2022. The event became the discussion site of the following issues: the library and the formation of socially responsible individual; the formation of strategies and programs for the development of libraries; the central library of the subject of the Russian Federation as the object of modernization; the formation and preservation of library collections. Among the presented draft documents developed as part of the implementation of the “Strategy for the Development of Librarianship in the Russian Federation for the Period up to 2030” are “Quality standard for the modernization of a public municipal library”, “Unified concept of scientific and methodological support for libraries” and “Indicative regulations on the organization of research activities in libraries in Russia”. Special attention is paid to the issues of interaction with the Donetsk Republican Universal Scientific Library named after N.K. Krupskaya and Lugansk Republican Universal Scientific Library named after M. Gorky. There were summed up the results of the All-Russian competition “Librarian of the Year — 2022” and the All-Russian competition “Library Analytics”. Within the framework of the meeting, the exhibition “Library Context — 2022” was held. The Meeting of the Board of the Russian Library Association took place as a parallel event.