Reviewerly/exHarmony huggingface dataset:
METHOD AND ALGORITHM FOR OPERATION PLANNING BASED ON FUZZY FINITE AUTOMATA MODEL

In this paper the planning and scheduling problem as an important optimization problem inmany transportation and robotic applications is discussed. To solve planning problems, the mainapproaches are based on optimization methods, sampling-based methods, and usually such kindsof problems are NP-hard and high dimensional. In this work, the method for planning and schedulingbased on the fuzzy finite state machine model is developed. Fuzzy graph presentation of thescheduling problem and operation planning is given. The paper presents two approaches to theformulation of the planning problem with limited resources and temporal variables: state-oriented(with transitions between states), temporal ordering-oriented (on a time scale). Temporal modelingfor planning problems implies a qualitative approach to managing the distribution of operationsor topological ordering, as well as a quantitative approach to handling imprecise durationsrelationships between operations in multiple parameters. The concepts of fuzzy intervals and fuzzyrelations are introduced for planning operations on a graph. A planning algorithm based on thetheory of automata and temporal modeling under uncertainty has been developed. Using this formalism,a path planning problem is solved by successively altering a state using various operationsuntil a solution is found. The idea of temporal-ordered partial schedule associated with theplanning state of the system is discussed. A model of a finite automaton for a planning system underconditions of uncertainty is proposed. A method and algorithm for scheduling operationsbased on a non-deterministic finite automaton and an enumeration scheme have been developed.The non-deterministic computation for a scheduling problem is a decision tree whose root correspondsto the beginning of the scheduling process, and each branch point in the tree correspondsto a computation point at which the machine has multiple choices. And the finite state machinemodel (automata) for the planning system under uncertainty is suggested.
PEMBUATAN GAME 2D PLATFORM “BOLA” BERBASIS WEBSITE

This game was created using Unity game engine with C# as its programming language. The goal of this game is to complete all the stages with lowest time. This game got 3 characters: red ball, hardened ball and smollie ball. At each stage, player will face obstacles such as spike, spike ball, and hidden spike. Testing is done by blackbox testing, alpha testing by the supervisor, and beta testing by 38 respondents through a questionnaire. The test results show that game Bola can make players enjoy it and encourage them to play again.
Speakers prioritize whole object semantics in scene descriptions

This work investigates the linearization strategies used by speakers when describing real-world scenes to better understand production plans for multi-utterance sequences. Scene meaning predicts visual attention across tasks, but does not predict the order in which objects in scenes are described, contrary to previous work suggesting a tight coupling between the visual attention and language production systems in simple description tasks. In this study, 30 participants described real-world scenes aloud. To investigate which semantic features of scenes predict order of mention, we quantified three features (meaning, graspability, and interactability) using two techniques (whole-object ratings and feature map values). We found that object-level semantic features, broadly defined and affordance-based, predicted order of mention in a scene description task. Our findings provide the first evidence for an object-related semantic feature that guides linguistic ordering decisions and offer theoretical support for the role of object semantics in scene viewing and description.
Advantages of multi-copy nonlocality distillation and its application to minimizing communication complexity

Nonlocal correlations are a central feature of quantum theory, and understanding why quantum theory has a limited amount of nonlocality is a fundamental problem. Since nonlocality also has technological applications, e.g., for device-independent cryptography, it is useful to understand it as a resource and, in particular, whether and how different types of nonlocality can be interconverted. Here we focus on nonlocality distillation which involves using several copies of a nonlocal resource to generate one with more nonlocality. We introduce several distillation schemes which distil an extended part of the set of nonlocal correlations including quantum correlations. Our schemes are based on a natural set of operational procedures known as wirings that can be applied regardless of the underlying theory. Some are sequential algorithms that repeatedly use a two-copy protocol, while others are genuine three-copy distillation protocols. In some regions we prove that genuine three-copy protocols are strictly better than two-copy protocols. By applying our new protocols we also increase the region in which nonlocal correlations are known to give rise to trivial communication complexity. This brings us closer to an understanding of the sets of nonlocal correlations that can be recovered from information-theoretic principles, which, in turn, enhances our understanding of what is special about quantum theory.
METHODICAL TEACHING SYSTEM SOLVING TASKS

In the article of Skvortsovoy Svetlany “Methodical system of studies of untiing of tasks”. The aims of with plots tasks are certain in a course mathematics of primary school, maintenance of task material and programmatic minimum is shortly described certain the basic method of studies of untiing of tasks, as the special systems of educational tasks, organizational forms and facilities of studies are reflected, among which a certain place belongs to the design as text of task so process of its untiing.
Easy Variational Inference for Categorical Models via an Independent Binary Approximation

We pursue tractable Bayesian analysis of generalized linear models (GLMs) for categorical data. Thus far, GLMs are difficult to scale to more than a few dozen categories due to non-conjugacy or strong posterior dependencies when using conjugate auxiliary variable methods. We define a new class of GLMs for categorical data called categorical-from-binary (CB) models. Each CB model has a likelihood that is bounded by the product of binary likelihoods, suggesting a natural posterior approximation. This approximation makes inference straightforward and fast; using well-known auxiliary variables for probit or logistic regression, the product of binary models admits conjugate closed-form variational inference that is embarrassingly parallel across categories and invariant to category ordering. Moreover, an independent binary model simultaneously approximates multiple CB models. Bayesian model averaging over these can improve the quality of the approximation for any given dataset. We show that our approach scales to thousands of categories, outperforming posterior estimation competitors like Automatic Differentiation Variational Inference (ADVI) and No U-Turn Sampling (NUTS) in the time required to achieve fixed prediction quality.
Desafíos tecnológicos y pedagógicos que enfrentaron los docentes del idioma inglés durante la pandemia por la COVID-19

The pandemic caused by SARS CoV 2 has modified the way we learn and teach in the last 18 months. In Mexico, where this study took place, as in any other countries, and as a response to the pandemic, teachers were forced to change their teaching methodology, and face to face classes turned into remote teaching. Some teachers were not prepared to face this new modality, and some challenges emerged. This is a qualitative study that reports the experience of three ELT teachers. Results reveal that these teachers faced two main challenges, technological and pedagogical.
Information retrieving competence of the students during COVID-19

With the advancement of computer science and information technology, enormous generation of data and information in particular has been observed in every field of knowledge over the past decades. The ultimate purpose of information creation is to serve the need of information seekers. Due to complex nature of information, it needs to be organised and retrieved effectively so that it can be put to use. Information Retrieval can be defined as the process of representing, storing, organising and accessing of information, generally documents that are suitable to an information need at the user end. The present study attempts to make an analysis of information retrieving competence of the students of the University of Jammu during COVID-19. In order to meet the purpose of this study, survey method was adopted and a questionnaire was distributed online to the students. The outcomes of the study reveal if information retrieval was difficult during the period, various reasons for information seeking and the effect on information retrieving ability of the students. It also brings into light the capability of students to evaluate information for further use and the things they have learnt while retrieving information during the pandemic.
Volumetric Disentanglement for 3D Scene Manipulation

Recently, advances in differential volumetric rendering enabled significant breakthroughs in the photo-realistic and fine-detailed reconstruction of complex 3D scenes, which is key for many virtual reality applications. However, in the context of augmented reality, one may also wish to effect semantic manipulations or augmentations of objects within a scene. To this end, we propose a volumetric framework for (i) disentangling or separating, the volumetric representation of a given foreground object from the background, and (ii) semantically manipulating the foreground object, as well as the background. Our framework takes as input a set of 2D masks specifying the desired foreground object for training views, together with the associated 2D views and poses, and produces a foreground-background disentanglement that respects the surrounding illumination, reflections, and partial occlusions, which can be applied to both training and novel views. Our method enables the separate control of pixel color and depth as well as 3D similarity transformations of both the foreground and background objects. We subsequently demonstrate the applicability of our framework on a number of downstream manipulation tasks including object camouflage, non-negative 3D object inpainting, 3D object translation, 3D object inpainting, and 3D text-based object manipulation. Full results are given in our project webpage at https://sagiebenaim.github.io/volumetric-disentanglement/
KLASIFIKASI JENIS PENYAKIT PADA DAUN TOMAT DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

Tomato is one of the farming commodities in Indonesia, easy to plant but easy to get sick. Analizing the disease in plain view still not yet achieve high accuracy result, so we use the help of Convolutional Neural Network (CNN) algorithm. This research is quantitative, with image of a single tomato leaf that is infected as the input. The constructed model gains an accuracy of 58.33% with 12.716 image consisting of Bacterial Spot, Early Blight, Late Blight, Leaf Mold, Target Spot, Spider Mites, Mosaic Virus, Yellow Leaf Curl Virus, Septoria Leaf Spot and healthy leaf. The conclusion from this research is that classification of Tomato leaf disease using CNN can help achieve a higher accuracy but using LeNet-5 as the model architecture is not very effective.
Early Prediction of Ventilator-Associated Pneumonia in ICU Patients Using An Interpretable Machine Learning Algorithm

(1) Background: Ventilator-associated pneumonia (VAP) causes high mortality among patients with respiratory disease and imposes major burdens on healthcare infrastructure. Models that use electronic health record data to predict the onset of VAP may spur earlier treatment and improve patient outcomes. We developed and studied the performance of interpretable machine learning (ML) models that predict the onset of VAP from electronic health records (EHRs); (2) Methods: We trained Logistic Regression (LR), full feature Explainable Boosting Machine (fEBM), and eXtreme Gradient Boosting (XGBoost) ML models on data from the MIMIC- III (v1.3) database. Model performance was measured by area under the receiver operating characteristic curves (AUCs). We trained a minimal-feature EBM model (mEBM) with features derived from white blood cell (WBC) counts, duration of ventilation, and Glasgow Coma Scale (GCS). Finally, model robustness was evaluated on randomly sparsified EHR datasets; (3) Results: The fEBM model outperformed the XGBoost and LR models at 24 hours post-intubation. The mEBM model maintained an AUC of 0.893. The fEBM model performance remained robust on sparsified datasets; (4) Conclusions: Our novel interpretable ML algorithm reliably predicts the onset of VAP in intubated patients. Integration of this EBM-based model into clinical practice may enable clinicians to better anticipate and prevent VAP.
THE USE OF ANIMATED FILMS IN THE FORMATION OF NATURAL SCIENCE COMPETENCE OF CHILDREN OF PRIMARY SCHOOL AGE

The article describes the essence of the concepts of «natural science competence», «formation of natural science competence of children of primary school age», «animated films»; analyzed the potential for the use of animated films; the criteria for the selection of animated films from the point of view of their suitability for use as a means of forming the natural science competence of children of primary school age have been determined. Research methods: theoretical (analysis of literature on research problems), empirical (pedagogical observations, creation of special diagnostic situations). It was found out that animated films perform outstanding educational, cognitive, developmental and educational functions. The criteria for the selection of cartoons from the point of view of their suitability for use in the formation of natural science competence of children of primary school age have been determined. Children's animations that should be used in working with children are highlighted. The system of thematic planning of viewing of animated films for children of primary school age is proposed, which provides for three stages of work: preparatory, revision, reflective. The levels of natural science competence of children of primary school age were clarified, which made it possible to distinguish low, medium and high. The expediency of using animated films as a means of forming the natural science competence of children of primary school age is experimentally substantiated, which makes it possible to supplement classes with various information and encourage children to learn the surrounding reality.
PLANER OBJECT DETECTION USING SURF AND SIFT METHOD

Object Detection refers to the capability of computer and software to locate objects in an image/scene and identify each object. Object detection is a computer vision technique works to identify and locate objects within an image or video. In this study, we compare and analyze Scale-invariant feature transform (SIFT) and speeded up robust features (SURF) and propose a various geometric transformation. To increase the accuracy, the proposed system firstly performs the separation of the image by reducing the pixel size, using the Scale-invariant feature transform (SIFT). Then the key points are picked around feature description regions. After that we perform one more geometric transformation which is rotation, and is used to improve visual appearance of image. By using this, we perform Speeded Up Robust Features (SURF) feature which highlights the high pixel value of the image. After that we compare two different images and by comparing all features of that object from image, the desired object detected in a scene.
Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling

Sentence scoring aims at measuring the likelihood score of a sentence and is widely used in many natural language processing scenarios, like reranking, which is to select the best sentence from multiple candidates. Previous works on sentence scoring mainly adopted either causal language modeling (CLM) like GPT or masked language modeling (MLM) like BERT, which have some limitations: 1) CLM only utilizes unidirectional information for the probability estimation of a sentence without considering bidirectional context, which affects the scoring quality; 2) MLM can only estimate the probability of partial tokens at a time and thus requires multiple forward passes to estimate the probability of the whole sentence, which incurs large computation and time cost. In this paper, we propose \textit{Transcormer} -- a Transformer model with a novel \textit{sliding language modeling} (SLM) for sentence scoring. Specifically, our SLM adopts a triple-stream self-attention mechanism to estimate the probability of all tokens in a sentence with bidirectional context and only requires a single forward pass. SLM can avoid the limitations of CLM (only unidirectional context) and MLM (multiple forward passes) and inherit their advantages, and thus achieve high effectiveness and efficiency in scoring. Experimental results on multiple tasks demonstrate that our method achieves better performance than other language modelings.
Reversives: The case of un-prefixation in verbs

This paper investigates the group of un-prefixed verbs or "reversives" with respect to their aspectual features. The aim is to see whether or not these verbs form a homogeneous group. With the new stream of words and the high "productivity" of the prefix un-, new terms are coined according to the needs and conceptualizations of language users. Examples of this sort include uninstall, unlike, unfriend, etc. These newly-coined terms do not easily fall into the conventional categories defined for the group of reversive verbs and show diversity with respect to their aspectual features. Therefore, the group of un-prefixed verbs cannot be defined by a set of simplistic word-formation rules.
Towards A Proactive ML Approach for Detecting Backdoor Poison Samples

Adversaries can embed backdoors in deep learning models by introducing backdoor poison samples into training datasets. In this work, we investigate how to detect such poison samples to mitigate the threat of backdoor attacks. First, we uncover a post-hoc workflow underlying most prior work, where defenders passively allow the attack to proceed and then leverage the characteristics of the post-attacked model to uncover poison samples. We reveal that this workflow does not fully exploit defenders' capabilities, and defense pipelines built on it are prone to failure or performance degradation in many scenarios. Second, we suggest a paradigm shift by promoting a proactive mindset in which defenders engage proactively with the entire model training and poison detection pipeline, directly enforcing and magnifying distinctive characteristics of the post-attacked model to facilitate poison detection. Based on this, we formulate a unified framework and provide practical insights on designing detection pipelines that are more robust and generalizable. Third, we introduce the technique of Confusion Training (CT) as a concrete instantiation of our framework. CT applies an additional poisoning attack to the already poisoned dataset, actively decoupling benign correlation while exposing backdoor patterns to detection. Empirical evaluations on 4 datasets and 14 types of attacks validate the superiority of CT over 14 baseline defenses.
Transfer without Forgetting

This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL). In particular, we shed light on the widespread application of network pretraining, highlighting that it is itself subject to catastrophic forgetting. Unfortunately, this issue leads to the under-exploitation of knowledge transfer during later tasks. On this ground, we propose Transfer without Forgetting (TwF), a hybrid approach building upon a fixed pretrained sibling network, which continuously propagates the knowledge inherent in the source domain through a layer-wise loss term. Our experiments indicate that TwF steadily outperforms other CL methods across a variety of settings, averaging a 4.81% gain in Class-Incremental accuracy over a variety of datasets and different buffer sizes.
An Open Relation Extraction System for Web Text Information

Web texts typically undergo the open-ended growth of new relations. Traditional relation extraction methods lack automatic annotation and perform poorly on new relation extraction tasks. We propose an open-domain relation extraction system (ORES) based on distant supervision and few-shot learning to solve this problem. More specifically, we utilize tBERT to design instance selector 1, implementing automatic labeling in the data mining component. Meanwhile, we design example selector 2 based on K-BERT in the new relation extraction component. The real-time data management component outputs new relational data. Experiments show that ORES can filter out higher quality and diverse instances for better new relation learning. It achieves significant improvement compared to Neural Snowball with fewer seed sentences.
An Ontological Model for a National Cyber-attack Response in South Africa

South Africa is increasingly targeted by cyber criminals and is often ranked under the top five countries suffering the most cyber-attacks. In an initiative to counter these attacks, the South African government has initiated various measures such as a National Cybersecurity Policy Framework Policy (NCPF) and a Cybercrimes Act. However, the structures and policies that follow from these measures have not been fully implemented yet. Although the government published the NCPF in 2015 and enacted the Cybercrimes Act in May 2021, there is still a gap in terms of interoperability and shared understanding within the environment. In addition, numerous new structures have been established and others are still being planned. One example of a new structure is the Cybersecurity Hub, the national CSIRT, which is mandated to co-ordinate attack information and provide support for cyber incidents. In addition, the Hub must also implement a national Cybersecurity Awareness program.&#x0D; This paper presents a model for the Cybersecurity Hub in the event of a cyber incident in South Africa. The model is based on different attack scenarios and depicts the complex interoperability problem of the various roles, responsibilities, and interactions of role players when there is a cyber incident. One of the scenarios is an attack on critical infrastructure. The model is a prototype of a semantic knowledge base (an ontology) that will help with planning and decision making. Core queries that should be answered concern the critical role players during and after a cyber event; the communication activities that have to take place; and the response actions and the skills required to handle the event.
Task Agnostic Restoration of Natural Video Dynamics

In many video restoration/translation tasks, image processing operations are na\"ively extended to the video domain by processing each frame independently, disregarding the temporal connection of the video frames. This disregard for the temporal connection often leads to severe temporal inconsistencies. State-Of-The-Art (SOTA) techniques that address these inconsistencies rely on the availability of unprocessed videos to implicitly siphon and utilize consistent video dynamics to restore the temporal consistency of frame-wise processed videos which often jeopardizes the translation effect. We propose a general framework for this task that learns to infer and utilize consistent motion dynamics from inconsistent videos to mitigate the temporal flicker while preserving the perceptual quality for both the temporally neighboring and relatively distant frames without requiring the raw videos at test time. The proposed framework produces SOTA results on two benchmark datasets, DAVIS and videvo.net, processed by numerous image processing applications. The code and the trained models are available at \url{https://github.com/MKashifAli/TARONVD}.