Reviewerly/exHarmony huggingface dataset:
A Comprehensive Study on Fuzzy Logic System

Fuzzy logic systems have gained popularity due to their ability to handle complex, uncertain, and imprecise data.This journal aims to provide a comprehensive study of fuzzy logic systems by discussing their definition, application, and methodology.It also presents the advantages and disadvantages of using fuzzy logic systems and their future potential.Fuzzy systems are a class of computational models that are based on fuzzy logic, which provides a way of reasoning under uncertainty.In this journal, we explore the concept of fuzzy systems, their methodology, and their various applications.We discuss the basics of fuzzy sets, fuzzy logic, and fuzzy inference, along with their advantages and disadvantages.We also examine the different types of fuzzy systems and their components, such as fuzzy rules, membership functions, and de fuzzification techniques.The study concludes that fuzzy logic systems are an efficient tool for solving problems in various fields, including engineering, medical science, and economics.
The Effect of Social Media on Entrepreneurship in Women in Mental Health Setup: India

Mental Health and Women are both well-defined concepts and genres which had the same experience in the past, both have been undermined and stereotyped.For many years, females have faced a lot of stereotypes when it comes to the corporate world, there are a lot of stigmas attached to being a working woman alone, so, building their own business, even thinking of it is admirable and inspirational, especially when it's related to mental health.Like the status of women, mental health has always been taken for granted and mostly ignored.But Now it's improving as this growing world finally accepts and respects women and mental health.The current study focuses on the effect of social media on Entrepreneurship in Women building Mental Health startups.More women across rural and urban India are using technology tools to do business today.Intuitive technology platforms that inform and enable women to learn, use tools, network, connect and access that many women face today.Thematic Analysis was conducted to examine the impact of digital media on the process of female entrepreneurship.It also analysed what aspire women to get into entrepreneurship.Creating Support Networks to share ideas, gain support and potential collaborations were suggested as well as addressing different cultural and social barriers was recommended in the study.
THE METHODS AND THEIR FORMS AND MEANS GAIN PRIORITY IN THE PROCESS OF FORMING VALUE RELATIONSHIPS IN THE SYSTEM OF PRESCHOOL EDUCATIONAL ORGANIZATIONS

The article contains opinions on the scientific-theoretical issues of forming a valuable attitude to the family among the pupils of the preschool educational organization, taking into account the age characteristics of the pupil, and creating comfortable conditions in the socio-cultural educational environment.
Strength Analysis on Horizontal Axis Wind Turbine Propeller Blade PVC Pipe with Angled Ends

The basic problem of appropriate wind energy technology is how to design wind turbines from materials that are easily available on the market, one solution is PVC pipe as the blade material. For this reason, it is necessary to analyze the working stress that occurs in the blade construction, so that the PVC pipe propeller wind turbine is safe when applied in society. The purpose of this study was to determine the effect of wind speed and tip elbow width on working stress. The simulation test method uses SolidWorks Flow Simulation Software and then the results are exported to Static Simulation to determine the strength of the material. Simulation tests were carried out with wind loads on PVC pipe propellers with wind speeds of 5 m/s, 6 m/s, and 7 m/s and elbow tip widths of 100 mm, 110 mm, and 130 mm. The results of the maximum stress value in the wind turbine simulation test with the addition of an elbow tip were obtained at a wind speed of 7 m/s and an elbow tip width of 130 mm
Design of Energy Education Media For Solar and Wind Power Plants at Bomo Beach, Banyuwangi Regency

Bomo Beach, which is managed by community groups to become a tourist attraction, has the potential for renewable energy in the form of solar and wind energy. This potential can be harnessed and converted into electrical energy. This study aims to design energy education media for solar power plants and wind power plants at Bomo beach, Banyuwangi regency. The design is carried out in accordance with the natural management of tourist beach. The power plant designed is the integration of the solar power plant system (PLTS) and the wind power plant system (PLTB). Solar power plants use solar panels while PLTB uses horizontal shaft wind turbines and vertical shafts. The energy housing is made as a power store with a 12 volts battery. The design results show that the integration design of solar power plants and PLTB can be applied as an energy education media.
Collection Development in Academic Libraries: Challenges and Way Forward

The core of the library is the collection. Library, being an information bank, its pivotal role is enshrined in the basic mandate of stocking information materials to meet the needs of users. The quality of the collection remains the litmus test of service delivery effectiveness in the academic world. It is the functional domain of collection development to peg collection quality high. Well-conceived collection development encompasses a range of activities such as selection, acquisition, user studies, stock evaluation, weeding and interlibrary cooperation. None of these activities is inconsequential in the bid to achieve high quality collection as they all work in synergy. The collection development which composed of the information resources, constitutes the basic instrument of service delivery in the library. Thus, librarians involved in collection development roles must now reach as far as possible into the larger world of prints and web scholarly content to add, organize, promote and make quality material discoverable and accessible. Librarians need to expose the research materials housed in repositories and on the open web, even as these materials are often hidden from scholars due to lack of indexing, inadequate crawling by major search engines, existence of poor quality metadata, or a lack of librarian effort at marketing of material that might maximize usage and accessibility. Therefore, Collection development librarians and subject specialists are best poised to know which public domain materials might best be needed and made more accessible to institutional scholars and to local communities.
REANALYSIS SYSTEM - An Exam’s Result Analysis System

Abstract: The REANALYSIS SYSTEM is an innovative tool that addresses the challenges associated with manual analysis of exam results. This desktop-based application is designed to automate the analysis process, making it more efficient and less prone to errors. With a modern user interface and Telegram bot integration, the REANALYSIS SYSTEM is user-friendly and easy to use. One of the key advantages of the REANALYSIS SYSTEM is that it can complete the analysis process in just 20-30 seconds for a class of 100 students. This is a significant improvement over the 2-3 hours it would take to do the same task manually. This feature makes it a time-saving tool that allows teachers to focus on other important tasks. The REANALYSIS SYSTEM is also designed to reduce the occurrence of errors that commonly arise when performing mathematical calculations and statistical analysis manually. This is especially important in large classes, where the number of calculations involved can be overwhelming. The automation process of the system ensures that every calculation is accurate and error-free. Another notable feature of the REANALYSIS SYSTEM is its ability to generate password-protected reports. This is particularly useful when it comes to maintaining the confidentiality of exam result’s report. Teachers can rest assured that only authorized individuals will have access to the reports. The REANALYSIS SYSTEM is an efficient and reliable tool that simplifies the analysis process of exam results. Its modern user interface, Telegram bot integration, and password-protected reports make it a user-friendly and secure tool. It saves time, reduces errors, and ensures accuracy, making it an essential tool for teachers and educators.
Text to Image Synthesis using Generative Adversarial Networks

Abstract: Image generation has been a significant field of research in computer vision and machine learning for several years. It involves generating new images that resemble real-world images based on a given input or set of inputs. This process has a wide range of applications, including video games, computer graphics, and image editing. With the advancements in deep learning, the development of generative models has revolutionized the field of image generation. Generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) have demonstrated remarkable success in generating high-quality images from input data. The focus of this paper is to propose a new technique for generating highquality images from text descriptions using Stack Generative Adversarial Networks (StackGAN). Through a sketch-refinement process, the problem is also divided into smaller manageable problems. The proposed StackGAN model comprises two stages, Stage-I and Stage-II. Stage-I GAN generates low-resolution images by sketching the primitive shape and colors of the object based on the provided textual description. Stage-II GAN generates high-resolution photo-realistic images with refined details by taking the Stage-I results and textual descriptions as inputs, along with detecting defects and adding details.
Faster Prefix-Sorting Algorithms for Deterministic Finite Automata

Sorting is a fundamental algorithmic pre-processing technique which often allows to represent data more compactly and, at the same time, speeds up search queries on it. In this paper, we focus on the well-studied problem of sorting and indexing string sets. Since the introduction of suffix trees in 1973, dozens of suffix sorting algorithms have been described in the literature. In 2017, these techniques were extended to sets of strings described by means of finite automata: the theory of Wheeler graphs [Gagie et al., TCS'17] introduced automata whose states can be totally-sorted according to the co-lexicographic (co-lex in the following) order of the prefixes of words accepted by the automaton. More recently, in [Cotumaccio, Prezza, SODA'21] it was shown how to extend these ideas to arbitrary automata by means of partial co-lex orders. This work showed that a co-lex order of minimum width (thus optimizing search query times) on deterministic finite automata (DFAs) can be computed in $O(m^2 + n^{5/2})$ time, $m$ being the number of transitions and $n$ the number of states of the input DFA. In this paper, we exhibit new combinatorial properties of the minimum-width co-lex order of DFAs and exploit them to design faster prefix sorting algorithms. In particular, we describe two algorithms sorting arbitrary DFAs in $O(mn)$ and $O(n^2\log n)$ time, respectively, and an algorithm sorting acyclic DFAs in $O(m\log n)$ time. Within these running times, all algorithms compute also a smallest chain partition of the partial order (required to index the DFA). We present an experiment result to show that an optimized implementation of the $O(n^2\log n)$-time algorithm exhibits a nearly-linear behaviour on large deterministic pan-genomic graphs and is thus also of practical interest.
Gradient Derivation for Learnable Parameters in Graph Attention Networks

This work provides a comprehensive derivation of the parameter gradients for GATv2 [4], a widely used implementation of Graph Attention Networks (GATs). GATs have proven to be powerful frameworks for processing graph-structured data and, hence, have been used in a range of applications. However, the achieved performance by these attempts has been found to be inconsistent across different datasets and the reasons for this remains an open research question. As the gradient flow provides valuable insights into the training dynamics of statistically learning models, this work obtains the gradients for the trainable model parameters of GATv2. The gradient derivations supplement the efforts of [2], where potential pitfalls of GATv2 are investigated.
A Multi-Fidelity Bayesian Approach to Safe Controller Design

Safely controlling unknown dynamical systems is one of the biggest challenges in the field of control. Oftentimes, an approximate model of a system's dynamics exists which provides beneficial information for the selection of controls. However, differences between the approximate and true systems present challenges as well as safety concerns. We propose an algorithm called SAFE-SLOPE to safely evaluate points from a Gaussian process model of a function when its Lipschitz constant is unknown. We establish theoretical guarantees for the performance of SAFE-SLOPE and quantify how multi-fidelity modeling improves the algorithm's performance. Finally, we demonstrate how SAFE-SLOPE achieves lower cumulative regret than a naive sampling method by applying it to find the control gains of a linear time-invariant system.
Think Before You Act: Unified Policy for Interleaving Language Reasoning with Actions

The success of transformer models trained with a language modeling objective brings a promising opportunity to the reinforcement learning framework. Decision Transformer is a step towards this direction, showing how to train transformers with a similar next-step prediction objective on offline data. Another important development in this area is the recent emergence of large-scale datasets collected from the internet, such as the ones composed of tutorial videos with captions where people talk about what they are doing. To take advantage of this language component, we propose a novel method for unifying language reasoning with actions in a single policy. Specifically, we augment a transformer policy with word outputs, so it can generate textual captions interleaved with actions. When tested on the most challenging task in BabyAI, with captions describing next subgoals, our reasoning policy consistently outperforms the caption-free baseline.
Approximate Shielding of Atari Agents for Safe Exploration

Balancing exploration and conservatism in the constrained setting is an important problem if we are to use reinforcement learning for meaningful tasks in the real world. In this paper, we propose a principled algorithm for safe exploration based on the concept of shielding. Previous approaches to shielding assume access to a safety-relevant abstraction of the environment or a high-fidelity simulator. Instead, our work is based on latent shielding - another approach that leverages world models to verify policy roll-outs in the latent space of a learned dynamics model. Our novel algorithm builds on this previous work, using safety critics and other additional features to improve the stability and farsightedness of the algorithm. We demonstrate the effectiveness of our approach by running experiments on a small set of Atari games with state dependent safety labels. We present preliminary results that show our approximate shielding algorithm effectively reduces the rate of safety violations, and in some cases improves the speed of convergence and quality of the final agent.
Training Automated Defense Strategies Using Graph-based Cyber Attack Simulations

We implemented and evaluated an automated cyber defense agent. The agent takes security alerts as input and uses reinforcement learning to learn a policy for executing predefined defensive measures. The defender policies were trained in an environment intended to simulate a cyber attack. In the simulation, an attacking agent attempts to capture targets in the environment, while the defender attempts to protect them by enabling defenses. The environment was modeled using attack graphs based on the Meta Attack Language language. We assumed that defensive measures have downtime costs, meaning that the defender agent was penalized for using them. We also assumed that the environment was equipped with an imperfect intrusion detection system that occasionally produces erroneous alerts based on the environment state. To evaluate the setup, we trained the defensive agent with different volumes of intrusion detection system noise. We also trained agents with different attacker strategies and graph sizes. In experiments, the defensive agent using policies trained with reinforcement learning outperformed agents using heuristic policies. Experiments also demonstrated that the policies could generalize across different attacker strategies. However, the performance of the learned policies decreased as the attack graphs increased in size.
HeRo: RoBERTa and Longformer Hebrew Language Models

In this paper, we fill in an existing gap in resources available to the Hebrew NLP community by providing it with the largest so far pre-train dataset HeDC4, a state-of-the-art pre-trained language model HeRo for standard length inputs and an efficient transformer LongHeRo for long input sequences. The HeRo model was evaluated on the sentiment analysis, the named entity recognition, and the question answering tasks while the LongHeRo model was evaluated on the document classification task with a dataset composed of long documents. Both HeRo and LongHeRo presented state-of-the-art performance. The dataset and model checkpoints used in this work are publicly available.
Joint Repetition Suppression and Content Moderation of Large Language Models

Natural language generation (NLG) is one of the most impactful fields in NLP, and recent years have witnessed its evolution brought about by large language models (LLMs). As the key instrument for writing assistance applications, they are generally prone to replicating or extending offensive content provided in the input. In low-resource data regime, they can also lead to repetitive outputs. Usually, offensive content and repetitions are mitigated with post-hoc methods, including n-gram level blocklists, top-k and nucleus sampling. In this paper, we apply non-exact repetition suppression using token and sequence level unlikelihood loss, and further explore the framework of unlikelihood training objective in order to jointly endow the model with abilities to avoid generating offensive words and phrases from the beginning. Finally, with comprehensive experiments, we demonstrate that our proposed methods work exceptionally in controlling the repetition and content quality of LLM outputs.
Halcyon -- A Pathology Imaging and Feature analysis and Management System

Halcyon is a new pathology imaging analysis and feature management system based on W3C linked-data open standards and is designed to scale to support the needs for the voluminous production of features from deep-learning feature pipelines. Halcyon can support multiple users with a web-based UX with access to all user data over a standards-based web API allowing for integration with other processes and software systems. Identity management and data security is also provided.
A Comparative Neural Radiance Field (NeRF) 3D Analysis of Camera Poses from HoloLens Trajectories and Structure from Motion

Neural Radiance Fields (NeRFs) are trained using a set of camera poses and associated images as input to estimate density and color values for each position. The position-dependent density learning is of particular interest for photogrammetry, enabling 3D reconstruction by querying and filtering the NeRF coordinate system based on the object density. While traditional methods like Structure from Motion are commonly used for camera pose calculation in pre-processing for NeRFs, the HoloLens offers an interesting interface for extracting the required input data directly. We present a workflow for high-resolution 3D reconstructions almost directly from HoloLens data using NeRFs. Thereby, different investigations are considered: Internal camera poses from the HoloLens trajectory via a server application, and external camera poses from Structure from Motion, both with an enhanced variant applied through pose refinement. Results show that the internal camera poses lead to NeRF convergence with a PSNR of 25\,dB with a simple rotation around the x-axis and enable a 3D reconstruction. Pose refinement enables comparable quality compared to external camera poses, resulting in improved training process with a PSNR of 27\,dB and a better 3D reconstruction. Overall, NeRF reconstructions outperform the conventional photogrammetric dense reconstruction using Multi-View Stereo in terms of completeness and level of detail.
Word Sense Induction with Knowledge Distillation from BERT

Pre-trained contextual language models are ubiquitously employed for language understanding tasks, but are unsuitable for resource-constrained systems. Noncontextual word embeddings are an efficient alternative in these settings. Such methods typically use one vector to encode multiple different meanings of a word, and incur errors due to polysemy. This paper proposes a two-stage method to distill multiple word senses from a pre-trained language model (BERT) by using attention over the senses of a word in a context and transferring this sense information to fit multi-sense embeddings in a skip-gram-like framework. We demonstrate an effective approach to training the sense disambiguation mechanism in our model with a distribution over word senses extracted from the output layer embeddings of BERT. Experiments on the contextual word similarity and sense induction tasks show that this method is superior to or competitive with state-of-the-art multi-sense embeddings on multiple benchmark data sets, and experiments with an embedding-based topic model (ETM) demonstrates the benefits of using this multi-sense embedding in a downstream application.
Revival of the Silk Road using the applications of AR/VR and its role on cultural tourism

This research project seeks to investigate the incorporation of augmented reality (AR) and virtual reality (VR) technology with human-computer interaction (HCI) in order to revitalize the Silk Road - specifically in Kermanshah, Iran - and its effect on cultural tourism. Kermanshah has underexplored the rich historical significance of the Silk Road, despite the presence of 24 UNESCO World Heritage sites. From the 2nd century BCE to the 18th century CE, the Silk Road was a vital trade route connecting the West and the East and had enormous cultural, economic, religious, and political effects. The purpose of this study is to examine the application of AR/VR technologies in HCI for the preservation, interpretation, and promotion of the Silk Road's tangible and intangible cultural heritage in Kermanshah, as well as their impact on cultural tourism development. The study also investigates how these innovative technologies can enhance visitors' experiences through immersive and interactive approaches, promote sustainable tourism practices, and contribute to the region's broader socioeconomic benefits. The research will analyze the challenges and opportunities of implementing AR/VR technology in HCI within the context of cultural heritage and tourism in Kermanshah and the Silk Road region more broadly. By combining HCI, AR/VR, and cultural tourism, this research seeks to provide valuable insights into the development of user-centered, immersive experiences that promote a deeper understanding and appreciation of the Silk Road's distinctive cultural heritage.