Reviewerly/exHarmony huggingface dataset:
Unifilar Machines and the Adjoint Structure of Bayesian Filtering

We elucidate the mathematical structure of Bayesian filtering, and Bayesian inference more broadly, by applying recent work on category theoretical probability, specifically the concept of a strongly representable Markov category. We show that filtering, along with related concepts such as conjugate priors, arise from an adjunction: the process of taking a hidden Markov process is right adjoint to a forgetful functor. This has an interesting consequence. In practice, filtering is usually implemented using parametrised families of distributions. The Kalman filter is a particularly important example, which uses Gaussians. Rather than calculating a new posterior each time, the implementation only needs to udpate the parameters. This structure arises naturally from our adjunction; the correctness of such a model is witnessed by a map from the model into the system being modelled. Conjugate priors arise from this construction as a special case. In showing this we define a notion of unifilar machine, which has its origins in the literature on epsilon-machines. Unifilar machines are useful as models of the "observable behaviour" of stochastic systems; we show additionally that in the Kleisli category of the distribution monad there is a terminal unifilar machine, and its elements are controlled stochastic processes, mapping sequences of the input alphabet probabilistically to sequences of the output alphabet.
NeRSemble: Multi-view Radiance Field Reconstruction of Human Heads

We focus on reconstructing high-fidelity radiance fields of human heads, capturing their animations over time, and synthesizing re-renderings from novel viewpoints at arbitrary time steps. To this end, we propose a new multi-view capture setup composed of 16 calibrated machine vision cameras that record time-synchronized images at 7.1 MP resolution and 73 frames per second. With our setup, we collect a new dataset of over 4700 high-resolution, high-framerate sequences of more than 220 human heads, from which we introduce a new human head reconstruction benchmark 1 . The recorded sequences cover a wide range of facial dynamics, including head motions, natural expressions, emotions, and spoken language. In order to reconstruct high-fidelity human heads, we propose Dynamic Neural Radiance Fields using Hash Ensembles (NeRSemble). We represent scene dynamics by combining a deformation field and an ensemble of 3D multi-resolution hash encodings. The deformation field allows for precise modeling of simple scene movements, while the ensemble of hash encodings helps to represent complex dynamics. As a result, we obtain radiance field representations of human heads that capture motion over time and facilitate re-rendering of arbitrary novel viewpoints. In a series of experiments, we explore the design choices of our method and demonstrate that our approach outperforms state-of-the-art dynamic radiance field approaches by a significant margin.
Abusive Language Detection in Speech Dataset

Abstract: In the topic, speech recognition there is a rapid increasing in it into area of engineering technology. Speech recognition delivers many different kinds of pros and it's been uses in a multiple field. Having a different type of language placed a restriction of talking between people. From this project we will going to create and develop to supports different systems that allows persons in that place or situation to change of data through interacting with end device users by voice or speech, after developing this project we will destroy the barriers of communication. This project takes that into consideration and makes an attempt to guarantee that it can identify speech and transform audio input into text. The speech is converted into text format. To overcome the offensive content in real-time every social media platform should implement an effectual hate speech detection system. There are many ways from that we can classify hate speech such as Machine Learning, Rule Based, Deep Learning Based and Hybrid
Mathematical Model of Disease Progression and Application of Machine Learning Algorithms for Predicting Disease Stages

Accurately diagnosing a patient's disease stage is critical to deal with the varying symptoms and applying appropriate therapeutics at different stages. Therefore, we used and compared machine learning algorithms on synthetic data and classified disease stages. This study was performed on the Google Colab environment with Python's machine learning libraries (Sklearn). The simulated data resembled one that might be reasonably seen in real patients' data since it contained both temporal variation and various noise levels. Three types of machine learning algorithms were used: Nearest Neighbor, Decision Tree, and Neural Network. These algorithms could classify whether the current stage of disease progression was at its early, mid, or late stages under different noise levels and other time intervals. The neural network algorithm showed the best performance. Although this study used a synthetic data set, it demonstrated how machine learning could be applied in the medical field to enhance patient care.
Comparative Study of Color Image Authentication and Encryption in Cloud

Cloud computing is the provision of computing and storage capacity to users as a service. Cloud storage is a type of networked online storage where data is stored in virtualized storage pools as a subservice of infrastructure as a service (IaaS) in cloud computing. Cloud computing plays a significant role in the efficient use of resources and in the utilization of service. Regardless of the cloud category (e.g., private, public, hybrid or inter-cloud), all service providers rely on domain server data. As a rapid development and deployment of cloud computing and cloud storage, users are increasingly concerned about security and privacy issues involved in these techniques. This paper provides a summary of basic security problems that consist of conventional security issues. It also addresses the additional challenges resulting from the cloud computing paradigm being used by cloud system providers and consumers. In addition, solutions suggested by some researchers are presented with a focus on cryptographic techniques which support secure storage of the cloud.
Autonomous Tagging of Stack Overflow Questions

Educational resources like question-and-answer websites like Stack Exchange and Quora are growing in popularity online. A large number of these gatherings depend on labeling, which includes a part marking a post with a suitable assortment of subjects that depict the post and make it more straightforward to find and sort. We give a multi-name order framework that naturally distinguishes clients' requests to upgrade the client experience. A straight SVM and a carefully selected portion of the researched highlight set are used to create a one-versus-rest classifier for a Stack Overflow dataset. By utilizing a subsample of the initial data that is restricted to 100 labels and at least 500 events of each label throughout the data, our characterization framework achieves an ideal F1 score of 62.35 percent.
Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema

Task-oriented dialog systems empower users to accom-plish their goals by facilitating intuitive and expres-sive natural language interactions. State-of-the-art ap-proaches in task-oriented dialog systems formulate theproblem as a conditional sequence generation task andfine-tune pre-trained causal language models in the su-pervised setting. This requires labeled training datafor each new domain or task, and acquiring such datais prohibitively laborious and expensive, thus makingit a bottleneck for scaling systems to a wide rangeof domains. To overcome this challenge, we intro-duce a novel Zero-Shot generalizable end-to-end Task-oriented Dialog system, ZS-ToD, that leverages domainschemas to allow for robust generalization to unseen do-mains and exploits effective summarization of the dia-log history. We employ GPT-2 as a backbone model andintroduce a two-step training process where the goal ofthe first step is to learn the general structure of the dialogdata and the second step optimizes the response gen-eration as well as intermediate outputs, such as dialogstate and system actions. As opposed to state-of-the-artsystems that are trained to fulfill certain intents in thegiven domains and memorize task-specific conversa-tional patterns, ZS-ToD learns generic task-completionskills by comprehending domain semantics via domainschemas and generalizing to unseen domains seam-lessly. We conduct an extensive experimental evaluationon SGD and SGD-X datasets that span up to 20 uniquedomains and ZS-ToD outperforms state-of-the-art sys-tems on key metrics, with an improvement of +17% onjoint goal accuracy and +5 on inform. Additionally,we present a detailed ablation study to demonstrate theeffectiveness of the proposed components and trainingmechanism.
A survey: Breast Cancer Classification by Using Machine Learning Techniques

Breast cancer in general is a common and deadly disease. Early detection can significantly reduce the chances of death. Using automated feature extraction and classification algorithms, physicians' experience in diagnosing and detecting breast cancer can be aided. This paper focuses on various statistical and machine learning studies of mammography datasets for enhancing the accuracy of breast cancer diagnosis and classification based on various variables. The Naïve Bayes, the K-nearest neighbors (KNN), the Support Vector Machine (SVM), the Random Forest, the Logistic Regression, Multilayer Perceptron (MLP), fuzzy classifier, and Convolutional Neural Network (CNN) classifiers, are the most widely used technologies in this field. In this study, we provide an overview of the existing CAD systems based on artificial intelligence classification techniques and many types of medical image modalities. Potential research initiatives to build more efficient and accurate CAD systems have been investigated.
Morphophono-Logics of Interrogatives in Fulfulde Noun Classes

Fulfulde language is classified under African languages, as a family member of Niger-Congo, under West Atlantic group. It has some family members such as Wolof, Serer, and Joola. (Greenberg 1963). It has six dialects across West Africa, which are: Fuuta Tooro, Fuuta Jaaloo, Masina, Sokoto and Western Niger, Central Northern Nigeria and Eastern Northern Nigeria, and Adamawa (Arnott 1972 p. 8). In Fulfulde language, nominal stems must be accompanied by a nominal class suffix (McIntosh 1984 p. 43) and agreement in noun affects both words and sentences in the language (Ahmed 2011 p. 27). The noun classes peculiar to Adamawa dialect of Fulfulde attach an interrogative –yee suffix to form an interrogative word. In order to answer the questions formed by attaching the –yee suffix, a modification of -yee to -yaa occurs in the process. The formations and the answers of the interrogatives follow some logical ordering from simplex to complex, which are discussed in this paper, and supported with examples. This paper is a descriptive linguistics research with a native intuition of the researcher, whom is a native to the Adamawa dialect, as a primary source of data. In conclusion, the paper found out that in Fulfulde dialect of Adamawa, all the noun classes form an interrogative word by adding -yee affix, and form an answer through ablaut of -yee to –yaa morphemes. Phonological processes such as assimilation, deletion and metathesis occur in the formation of the interrogatives.
Choreographic Choice

We introduce choreographic choice, another construct for choosing between alternative choreographies. Differently from conditionals, choreographic choices are not necessarily resolved by a single process but might require performing interactions instead.
9 Simulation Tools for Data Fabrication

Big data platform components like Hadoop, data lakes, and NoSQL have made big data architectures more logical, enabling businesses to pursue insightdriven competitive advantage. Moving corporate data to these platforms, especially when dealing with distributed data across data centers, is hampered by security issues, complicated data structures, issues with moving historical data, big volumes, latency issues, and variable speed of ingestion. In contrast to a unified platform for insights, we discovered that the majority of enterprises are developing various repositories and platforms. One of the techniques that can assist in identifying probable in the social and medical sciences is a statistical tool. Using statistical techniques, it is possible to identify data creation a mixture of data sets from legitimate and false. Fabrication of data calls into question the scientific method’s quest for knowledge, undermines the reliability of results that have been published, and makes it harder to detect. Over the past 10 years, automated tools have been employed as screening methods to find image alteration and plagiarism in articles that have been submitted or accepted quantitative and founded on behavioral observations, surveys, (cognitive) tests, and so on. At any point in the research process, incorrect data might be found using computerized systems that automatically discover and screen articles for statistical anomalies. These screening procedures appear to be useful and effective in identifying the various types of research misconduct, according to numerous case studies. The picture detection technologies, for instance, are essentially useless in the disciplines of social media and medical science because the data are mostly quantitative.
"IMPLEMENTING DOCUMENT CLASSIFICATION IN PYTHON AND EVALUATING RESULTS"

"In our article, we will cover general information about machine learning, its main types, as well as the most important libraries for machine learning in Python. Machine learning is one of the main methods for demonstrating data science in general. In machine learning, the computational and algorithmic capabilities of data science are combined with approaches, and the result is a set of data mining approaches, mainly related to the efficiency and theory of computation. Document classification plays an important role in the archiving department, they classify pre-defined documents and store them in a digital archive. Relevance of the topic In digital archives, document classification is an important process for many document preservation organizations. In the course of the study, a technology for testing the use of deep learning algorithms is being created. Deep learning, i.e. deep structured learning, is part of a broad group of machine learning methods based on artificial neural networks. Learning cannot be controlled, partially controlled or controlled. Keywords: Machine learning, Artificial neural networks, Python, Scikit-learn, Keras, TensorFlow."
Future SI

This study is devoted to the construction of a fractional perturbation theory which represents the basic technique of approximation in quantum mechanics. Our analysis is based on the fractional actionlike variational approach (FALVA) implementation of fractionality. After discussing the basic properties of fractional harmonic oscillator through the fractional Feynman's path integral approach, we have discussed the Rabi oscillations within the context of two-level quantum systems for simulated optical transition. In particular the case α>1 is correlated to the emergence of negative damping and negative probabilities which have been obtained in several quantum out-of-equilibrium systems. The fractional Fermi Golden rule and the fractional Heisenberg's uncertainty principle have been also obtained and their main properties and features have been discussed accordingly. We have discussed optical transitions in bulk semiconductors. It was observed that the fundamental properties of transitions rates are recovered if the charged particle moving inside the semiconductor is variable with time.
DESIGNING A FRAMEWORK FOR CYBER PROTECTION BASED ON UNIQUE IDENTIFICATION TO IMPROVE THE SECURITY OF ETHIOPIAN SOCIAL MEDIA USERS OVER FACEBOOK

The social media like Facebook is one of the prominent having 2.6 billion monthly active users. However, Facebook users pose different security threats by impersonation and spreading untrue information. Nowadays, there are numerous threats to social media such as Facebook in Ethiopia. Due to this problem, it is significant to design remedial solutions for such problems. This research study aims to establish a Unique-ID-based Cyber Defense Framework to enhance the security of Facebook users and inhibit the creation of fake profiles and impersonation by spammers. The researcher followed an exploratory with a constructive research design using surveys, and in-depth interview-based primary data analytics. The study concluded with a Unique-ID-based Cyber Defense Framework consisting of application and security layers with various critical components. In the proposed system, the Digital Residential ID issued for Addis Ababa residents is used to cross-check the user-provided data with the resident DB to check the authenticity of the user based on the country code. After that, a framework and its prototype are developed. The result of user acceptance test shows an optimistic answer i.e., 87% of respondents accepted the research outcomes as a positive contribution. On the other hand, 13% still have a dilemma to accept a new and innovative contribution for an enhanced Unique-ID-based Cyber defense system to improve the security of Facebook users. The study concluded by noticing every time a user attempts to create a Facebook account, the user will be asked to confirm his/her uniqueness. Also, they could be prohibited from establishing fake or multiple accounts. Keywords: Country code, Cyber defense Facebook, Fake profile, Impersonation, Security Threat, Unique Id
Determining the effect of money supply as a ratio of GDP on economic growth in Kenya from 1985-2020

The study sought to determine the effect of money supply as a ratio of GDP on economic growth in Kenya from 1985-2020. Data on GDP was used as a proxy for economic growth. The study was based on financial intermediation theory backed up by other theories related to financial deepening. The study adopted a historical research design. A bivariate Autoregressive model (bVAR) was used in the study. Data analysis employed both descriptive statistics and inferential statistics. Economic growth is important, and one of the drivers of economic growth is financial deepening. Kenya has been developing its financial sector in order to achieve economic growth. However, there are controversies on the relationship between economic growth and financial deepening; there are no universal conclusions on the nature of the relationship between financial deepening and economic growth. The study established that money supply has a negative effect on economic growth in the long run in Kenya. In this regard, the study recommends that the government tighten the monetary policy regarding money supply.
Linguistic Analysis of Generic-Generic Drug Name Pairs Prone to Wrong-Drug Errors for which Tall-Man Lettering is Recommended

Abstract Objective The Institute for Safe Medication Practices (ISMP) and the United States Food and Drug Administration (FDA) disseminated widely used lists of drug name pairs involved in wrong-drug errors, for which they recommended tall-man lettering (TML). Linguistic similarity is believed responsible for confusion of these drugs. This study aims to quantify linguistic similarity and other linguistic properties of these generic-generic name pairs. Methods The FDA’s Phonetic and Orthographic Computer Analysis (POCA) software was used to generate numerical similarity scores for the generic-generic name pairs on these lists and to identify conflicts between these names and the names of other marketed products. Within each pair, differences in name length and the number of identical prefix (initial) letters and suffix (final) letters were determined. Results The selected pairs shared a mean of 2.5 (± 1.8) identical prefix letters and 3.2 (± 2.9) identical suffix letters. The mean POCA score 69.5 (± 9.7), indicated moderate-to-high similarity. POCA scores for individual pairs ranged from 90 (most similar) to 46 (least similar). Individual names averaged 11.2 (± 9.1) high-similarity conflicts with names of other marketed drugs. Conclusions POCA analysis could be a valuable tool in determining whether linguistic similarity contributes to specific wrong-drug errors. The finding of 11.2 (± 9.1) high-similarity conflicts with names of other marketed drugs is more than for candidate names USAN accepts and suggests the names on the FDA and ISMP lists are linguistically problematic.
Attribute-guided and attribute-manipulated similarity learning network for fashion image retrieval

Learning the similarity between fashion items is essential for many fashion-related tasks. Most methods based on global or local image similarity cannot meet the fine-grained retrieval requirements related to attributes. We are the first to clearly distinguish the concepts of attribute name and their values and divide fashion retrieval tasks that combine images and text into: attribute-guided retrieval and attribute-manipulated retrieval. We propose a hierarchical attribute-aware embedding network (HAEN) that takes images and attributes as input, learns multiple attribute-specific embedding spaces, and measures fine-grained similarity in the corresponding spaces. It can accurately map different attributes to the corresponding areas of the image, thereby facilitating the feature fusion of two different modalities of text and image, including enhancement and replacement. Then on this basis, we propose three attribute-manipulated similarity learning methods, HAEN_Avg, HAEN_Rec, and HAEN_Cmb. With comprehensive validation on two real-world fashion datasets, we demonstrate that our methods can effectively leverage semantic knowledge to improve image retrieval performance, including attribute-guided and attribute-manipulated retrieval tasks.
ANALISI KESALAHAN BERBAHASA PADA TUGAS TEKS EKSPOSISI DAN TEKS IKLAN, SLOGAN, POSTER PESERTA DIDIK KELAS VIII SMP MUHAMMADIYAH BANTUL

Indonesian is a language that is easy to learn, language is also the identity of a nation in the world. This study aims to describe the forms of language errors which include letters, punctuation and standard language errors at the junior high school level. To collect research data, the method used was the observation method with the basic technique of tapping and the advanced technique of the listening technique without engaging proficiently. The results of this study were subjected to the VIII grade students of SMP Muhammadiyah Bantul as the source of the analyzed data. The object of research is language errors in the spelling field in the exposition text material and ad text.