Reviewerly/exHarmony huggingface dataset:
BanglaLM: Bangla Corpus for Language Model Research

Natural language processing (NLP) is an area of machine learning that has garnered a lot of attention in recent days due to the revolution in artificial intelligence, robotics, and smart devices. NLP focuses on training machines to understand and analyze various languages, extract meaningful information from those, translate from one language to another, correct grammar, predict the next word, complete a sentence, or even generate a completely new sentence from an existing corpus. A major challenge in NLP lies in training the model for obtaining high prediction accuracy since training needs a vast dataset. For widely used languages like English, there are many datasets available that can be used for NLP tasks like training a model and summarization but for languages like Bengali, which is only spoken primarily in South Asia, there is a dearth of big datasets which can be used to build a robust machine learning model. Therefore, NLP researchers who mainly work with the Bengali language will find an extensive, robust dataset incredibly useful for their NLP tasks involving the Bengali language. With this pressing issue in mind, we have prepared a dataset whose content is curated from social media, blogs, newspapers, wiki pages, and other similar resources. The amount of samples in this dataset is 19132010, and the length varies from 3 to 512 words. This dataset can easily be used to build any unsupervised machine learning model with an aim to performing necessary NLP tasks involving the Bengali language. We are also releasing two preprocessed version of this dataset that is especially suited for training both core machine learning-based and statistical-based model. As very few attempts have been made in this domain, keeping Bengali language researchers in mind, we believe our dataset will significantly contribute to the Bengali machine learning and NLP community.
Axial Symmetry Detection Using AF8 Code

Symmetry is a very common geometric feature in natural objects and, highly marked, in artificial objects; particularly, mirror symmetry is a relevant topic in fields such as computer vision and pattern recognition. Previous work on symmetry has shown that there are useful solutions to the problem of mirror symmetry detection; however, there are still important challenges to successfully model symmetry objects and properly detect multiple axes. And there is also the challenge of assigning a level of symmetry to quasi-symmetrical objects. In this paper, we propose an algorithm that detects level and axes of symmetry for quasi-mirror and mirror symmetry in 2D contours represented by AF8 code.
Discrete Time Multi-particle Grover Search

Grover's algorithm is a quantum search algorithm that produces a quadratic speedup compared to its classical analog, a brute-force algorithm, and has a complexity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(\sqrt{N})$</tex> . Past works have shown that Grover's algorithm can asymptotically be improved by generalizing the number of marked sites. I examine Grover's algorithm when the number of searching particles is generalized in discrete time setting, which allows to understand the search algorithm in terms of “interactions” between particles. I provide a novel design of Grover with appended interaction terms to analyze <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}$</tex> as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N\rightarrow\infty$</tex> . I utilize the eigenvalue perturbation theory to deduce the optimal interaction condition and show that the condition results in an asymptotic improvement denendent upon the number of particles.
The Chess Board Independent Domatic Number Of Queen Graph

Abstract The independent domatic queen number of a graph Q n is the maximum number of pairwise disjoint minimum independent queen dominating sets of P n and it is denoted by i d ( Q n ) while maximum independent domatic queen number is denoted by I d ( Q n ). We discuss about in this paper the independent (maximum independent) domatic number of queen graph Q n on n × n chess board.
Material prediction from confocal images of lasered samples

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Adaptive Neuro-Fuzzy Inference system for the Prediction of Solar Power

Solar energy is an intermittent source of energy due to which forecasting of solar energy is very critical task for facility managers and while doimg it lot of uncertainties are faced depending on atmospheric parameters. The proposed work is focussed at forecasting Photovoltaic power, which is very essential for the integration to electricity grid and to predict the solar power Adaptive Neuro-Fuzzy Inference System is used in MATLAB. The proposed method is significantly reliable and accurate in the prediction of solar power
Design and Development of Hybrid Feature Selection Method for Classification

To enhance the capability of the learning model in this research paper we have developed a hybrid feature selection method. To defeat the curse of dimensionality, to speed up the classification process, and to get more accurate results a hybrid feature selection model is developed which is a combination of multiple filter methods and a wrapper method. In this model, we employed two sets of Filter Methods-Basic filter methods, a correlation filter method and two Statistical & Ranking filter methods (ANOVA and ROC-AUC) to generate two different subsets of important features, and a Wrapper method (Recursive Feature Elimination with Cross-Validation) is applied on the combined subset to generate a final subset of important features for better prediction results. Five machine learning algorithms-Logistic Regression (LR), Decision Tree, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbour are used to evaluate the classification accuracy. The proposed hybrid method is applied over four low and four microarray datasets. Outputs are compared to find out which algorithm works best with the proposed model as the results diverge with the machine learning algorithm. Precision, Sensitivity, and Specificity are calculated for each outcome and they demonstrate that the suggested method improved the accuracy of the algorithms.
An extensible framework for video ASIC development and validation at Facebook scale

Video consumption across social platforms has increased at a rapid pace. Video processing is a compute-heavy workload, and domain-specific accelerators (ASICs) allow more efficient scaling than general purpose CPUs. One of the challenges for video ASIC adoption is that videos ingested in datacenters are user-generated content and have a long-tail distribution of uncommon features. Software stack can handle the outliers gracefully, but these uncommon features may pose a challenge for the ASIC with undesirable effects for the unsupported/unhandled end cases. To avoid undesirable effects in the production, it is critical to proof our system against the long-tail conditions early in the product cycle of the ASIC development. Similarly, critical signals like BD-rate quality and outlier detection are needed from production traffic early in the product cycle. To address these needs, we propose an extensible framework that allows a continuous development strategy using production traffic, through progressive evaluation in various product phases of the video ASIC development cycle. A similar framework would benefit other ASIC accelerator programs in reducing time to deploy on large-scale platforms.
Fine-grained Emotion Classification: Class Imbalance Effects on Classifier Performance

We explore a set of machine learning experiments in fine-grained emotion classification to test different proportion of positive and negative samples in the training data with the goal to examine if class imbalance affects classifier performance. The class distribution in a tweet corpus (EmoTweet-28) labelled with 28 emotion categories varies significantly with the largest category (happiness) occurring 11.5% and the smallest category occurring only 0.2%. For each emotion category, there are far more negative examples than positive examples. Unlike conventional wisdom, downsampling the data in our skewed corpus did not improve classifier performance. However, we found that increasing the negative examples in the training data leads to lower recall but higher precision. Demonstrating how the ratio of positive and negative examples in the training data affect the performance of emotion classifiers is the main contribution of this study.
DESAIN PENGELOLAAN LINGKUNGAN BERMAIN DALAM MENINGKATKAN KEMAMPUAN BERBAHASA ANAK

A safe and comfortable children's learning environment can make children more enthusiastic about learning. So it is necessary to have a conducive environment in every learning for children. Playing is one of the fun things that can foster children's enthusiasm for learning. The purpose of writing this article is to examine the concept of play environment design management in improving children's language skills. The method used in this research is a literature study with descriptive analysis techniques. The data used are qualitative. This study examines relevant literature and references. This research method uses literature with appropriate theories and sources and existing problems. As for the data analysis used, among others, reduce data, present data, and conclude. The results of the study concluded that the design of a play environment that is conducive and safe and comfortable can make children develop their language skills with the motivation given according to their needs.
The Idea of Landing Sites and Its Inheritance through Tactileology

From the perspective of sustainability, empowering people to live positively without being dominated by death is an important issue. One thing we can do in this vein is to expand one’s own physical sensation, which is the basis for us to live. From this point of view, Shusaku Arakawa and Madeline Gins’ idea of “landing sites” is very important. Landing sites are physical experiences that result from person–environment collaboration. In order to make as many people as possible aware of their physical sensations through landing sites, Arakawa and Gins created artificial environments such as “Site of Reversible Destiny Yoro” where people could gain new physical sensations. They wanted people to build new ethics and move toward social reformation based on their new physical sensations. However, at present, these artificial environments have some problems. It is the time to seriously consider how we can pass on the experience of landing sites to future generations. The aim of this paper is to provide an answer to the question by Yasuhiro Suzuki’s scientific research on tactile sense, called tactileology. I first introduce Arakawa and Gin’s text about the idea of “landing sites” and make clear its importance. Next, I point out that, now, “landing sites” present certain difficulties. I then confirm that tactileology inherits the idea of “landing sites”.
Analysis of Benchmark Development Times in the Transaction Processing Performance Council and Ideas on How to Reduce It with a Domain Independent Benchmark Evolution Model

The Transaction Processing Performance Council (TPC) has a very successful history of disseminating objective and verifiable performance data to the industry. However, it lacks the ability to create new benchmarks in a timely fashion. In its initial years, the TPC defined benchmarks in about two years on average while recently this number increased to about eight years. Although TPC benchmarks measure fundamental performance characteristics of transaction systems, which do not change quickly over time, new feature development, e.g. SQL language cannot be ignored. This paper analyses the reasons for this increased development time and proposes ideas on how to improve benchmark development time by using a domain independent benchmark evolution model.
Allocation of public resources to the public universities in Bangladesh: Development initiatives by University grants commission

The paper explores how much development undertakings including conducting research venture have been taken by the apex authority of regulationof public universities in Bangladesh for instance; University Grants Commission. The study will be capitulated with exploratory research design along with secondary data to come out the fact ofdevelopment perspective of public universities in Bangladesh. The study reveals that no hard and fast rules and regulations available to allocate public resources to the public universities. Allocating public resources determines on the basis of previous distributed public resources data to redistributed public resources for the next year. Generally, the commission raises public resources from the four sources that comprises of grants from the government; loans from the government; donation and endowments and receipts of the commission from such other sources as may be approved by the government. Budget towards public universities generally consists of two forms for example; Development budget and Non development budget. Non development budget comprises of paid salary and allowance to the staff and teachers along with administrative cost and other expenses that are prescribed by the assigned authority and development budget composes of physical development of the institutions namely; building infrastructure of those public universities. Availability ofpoor governance, failure of assurance of quality education and adopted fragile accountability mechanismare the leading challenges of the public universities in Bangladesh.
Controlling a formation of a MARV in backward movements with an UAV for inspection tasks

This work aims to show an application in inspection tasks in which an unmanned aerial vehicle (UAV) navigates while in a formation with a multi-articulated robotic vehicle (MARV), this last one executing backward movements. This application illustrates a situation in which the MARV needs a top view of the environment, in order to avoid contact with any obstacle or a risky situation for the load, which in this case is the trailer or trailers being pushed (the MARV corresponds to a tractor pushing one or two trailers). Besides taking care of the control of the MARV-UAV formation, the proposed system also controls the backward movement of the MARV to avoid jackknifing, the shock between the trailers or between the trailer and the tractor, while the multi articulated robot follows the path. Successful experiments show navigation with the multi articulated robot pushing one and two trailers. An inspection task demonstration shows the ability of the UAV to detect obstacles for the MARV, since the ground vehicle does not have sensors to accomplish that.
Analysis of User Behavior of Libraries at Home and Abroad Based on Citespace

This article mainly uses Citespace analysis software to visually analyze the journal articles on library user behavior analysis included in the web of science and CNKI. The research is visualized in these aspects: keyword co-occurrence analysis, keyword cluster analysis, hot topics analysis, topic hot spot analysis, and finally it is concluded that there are four aspects of library user behavior trend analysis: digital reading, user needs, precision and personalization, and data-driven.
INDF: Efficient Transaction Publishing in Blockchain

Blockchain is a distributed ledger technology based on the underlying peer-to-peer network. In this paper, we focus on improving the chances of a transaction being packaged into a valid block so as to be recorded on the main chain. Blockchain nodes typically broadcast transactions they receive to the whole network. Hence, for recording transactions on the blockchain more efficiently, it becomes essential to determine influential nodes to publish transactions, where influential nodes are more actively involved in mining, recording, or broadcasting transactions in the network. To that aim, we propose an Influential Node Determination Framework (INDF) using a series of significant factors, such as hash rate, latency, active time, and degree of a node. Specifically, INDF consists of two parallel schemes: the first scheme figures out influential pools according to their hash rates where a truth-telling mechanism design is employed to encourage the pool nodes to report their true hash rate values; the second one determines influential individual nodes based on an improved L-H index algorithm. Remarkably, the proposed truth-telling mechanism is proved to be incentive-compatible. Our improved L-H index algorithm is comparatively studied to reflect the impacts of different node parameters on the node’s ranking. Extensive experiments are conducted to demonstrate the effectiveness of our proposed framework.
Augmented Reality Application of Schizocosa ocreata: A Tool for Reducing Fear of Arachnids Through Public Outreach

This study aims to create a mobile application for public interaction around the subject of wolf spiders, specifically the brush-legged wolf spider. The hope is that the public will have a reduced level of fear towards arachnids when given a chance to view arachnids in a digital setting. To assist this, the application employs augmented reality animation, which has been shown to have a positive impact on the viewer’s interest and learning. With the opportunity to view a digital spider interacting with surfaces in a repeatable and informative manner, viewers may find that their fears are more understood and can be controlled for both their benefit and that of the local arachnids. In order to accommodate multiple audiences, the application was set up to have a more cartoonish and simpler text as well as a realistic and advanced text. The area of information covered the brush-legged wolf spider’s anatomy, mating behaviour, and general safety practices for handling and avoiding wolf spiders. The modelling and animation were created using Zbrush and Blender, respectively. The programming and application creations used were Unity with Android and AR plugins.
Development of a Mobile Application for Lending Free Books and Class Notes

COVID-19 is the reason why all schools are forced to implement online classes. A lot of students are having a hard time in this new normal learning style and there are many distractions to consider while students are staying at home. Faculty members and administration are also having a difficulty in presenting an effective lesson strategy to their students. The Book Stack was made to focus on helping students, faculty members and administration in enhancing learning experience by lending textbooks and class notes that can be used as educational materials. This application does not require subscription fees and offers free access to numerous academic notes and acts as a medium for lending textbooks to aid the school community in consideration of the current financial situation of the students because of the pandemic right now.
Psychological determinants of manifestation of a person’s creativity at the stage of higher education

Despite the significance and effectiveness of numerous studies, the role and relationship of internal (psychological) and external (environmental) factors in the development of creativity at the stage of studying at a university are not clearly determined. The conducted study aims to analyze the psychological prerequisites for the development of a person’s creativity at student age. The study tests the hypothesis that students demonstrating a higher level of creativity have several individual psychological features including genetically predetermined ones. The deployed psychodiagnostic methods include the “creativity” scale of the Self-actualization diagnostic questionnaire “SAMOAL” (A.V. Lazukina, as adapted by N.F. Kalin), “Questionnaire for diagnostics of the dominating instinct” (V.I. Garbuzov); the Eysenck Personality Inventory (EPI), and “The method of diagnostics of the learning style parameters” (A. Soloman, R. Felder). The obtained results provide for a more precise description of the psychological portrait of a student with high creativity level as well as indicate the presence of genetically predetermined factors of predisposition to the development of creativity as a creative attitude towards life.
Analysis and research on hidden danger of coal mine safety based on improved FP-growth algorithm

An improved FP-growth algorithm is proposed to improve the management level of modern coal mining industry and to prevent and reduce the occurrence of some potential accidents. In order to reduce the time complexity of the original algorithm and improve the efficiency of constructing and mining FP tree, this algorithm deeply analyzes some hidden trouble data, mining strong association rules among frequent items to limit the hidden risk factors in association rules, in order to effectively avoid the occurrence of some accidents or reduce their losses. The results showed that the improved algorithm is more efficient than the traditional Apriori algorithm and FP-growth algorithm in the construction and mining of FP tree when the database changes, which shows the practicability of the algorithm, and for the coal industry to eliminate hidden danger work to provide better development support.
Soil phosphorus content prediction based on improved PSO-LSTM algorithm

Available P is an important index affecting crop growth. Accurate detection of available P content in soil can provide a guarantee for accurate fertilization. Compared with the traditional detection methods, a particle swarm optimization long - short - term memory artificial neural network (PSO-LSTM) algorithm was proposed to predict the soil phosphorus content. In this model, particle swarm optimization algorithm is introduced to find the optimal parameters iteratively, instead of adjusting parameters based on personal experience. The PSO-LSTM model was built under the KERAS framework, and various parameters detected in the planting base of sugar beet in Hulan, Heilongjiang Province were used as input to predict the soil phosphorus content. The experimental results show that the absolute square error, absolute 100% ratio error and R2 of the improved PSO-LSTM model are 0.115, 0.394 and 0.945, respectively. Compared with other neural network models such as LSTM, it has higher prediction accuracy.
Three Dimensional Optimal Path Generation and Tracking for Quadrotors in Dynamic Environment

This paper presents a collision avoidance algorithm for quadrotors in a three-dimensional (3D) cluttered environment filled with dynamic obstacles, including a path re-planning algorithm to rejoin with its original trajectory in optimal time. The algorithms are based on a geometrical approach, accomplished by changing the heading and the pitch angles of the quadrotor simultaneously while keeping its speed constant. The novelty of the proposed work is that the algorithm ensures a smooth path generation for both the collision avoidance and re-planning maneuvers for the quadrotor with very low tracking error, including optimality in path length. To validate the proposed algorithm, the generated path is tracked using a dynamic model of a quadrotor using a Lyapunov-based backstepping controller in the presence of external disturbances.