Reviewerly/exHarmony huggingface dataset:
USING OF ONLINE MATHEMATICS LEARNING MEDIA IN CLASS VIII SMP NEGERI 1 PASIR PENYU

The COVID-19 pandemic has significantly impacted various sectors, such as health, social, political, economic, and education. Educators must ensure that teaching and learning activities continue, even though students are home. The Central and Regional Governments took the Large-Scale Social Restriction Policy (PSBB) to contain the spread of the Corona-19 virus, which has disrupted the face-to-face learning process. The solution, educators design learning media as innovations by utilizing bold media (online). This is in accordance with the Minister of Education and Culture of the Republic of Indonesia regarding Circular Letter Number 4 of 2020 concerning the Implementation of Education Policies in the Emergency Period for the Spread of Corona Virus Disease (COVID-19). Educators can do learning together simultaneously using groups on social media such as WhatsApp (WA), telegram, Instagram, zoom application, google meet, learning videos via youtube links, and other media as learning media. The method used in writing this article is a qualitative method that describes something based on data and numbers narrated in concluding sentences. Besides that, the writing of this article is library research.
Identifying Problems in Writing among Year 2 Pupils in a Malaysian Primary School

This preliminary study aims to identify the problems in writing faced by Malaysian Yea 2 pupils in a primary school. A document analysis design was employed to gather qualitative data from 75 participants who were purposively selected due to accessibility. The main instruments used in this study are the reports from English School-Based. The pupil’s essays and worksheets were then analyzed. Findings revealed that 58.67% of the students are only at Band 3 in the School-Based Assessment (SBA) for writing skill performance which indicates that they were only at a satisfactory level. Analysis of the pupils’ work revealed a clear problem with writing skills; where 36 out of 72 (50%) pupils were not able to write simple sentences properly. This implies that early intervention is needed to cater to this issue.
Review of the Pattern of Prostate Cancer Aggressiveness Using the Gleason Scoring System among Men Attending Abia State University Teaching Hospital Aba, South Eastern, Nigeria

Prostate cancer is the most common cancer among African -American men and the second most common cause of death in the western world.It is the most common cancer among Nigerian men with increasing incidence, morbidity and mortality.The morbidity and mortality depend on the tumor aggressiveness.The three most important prognostic parameters for this disease include  Tumor pathologic stage  Tumor histologic differentiation (gleason score)  Level of prostate specific antigen (PSA) These factors are needed in stratifying patients into risk groups.The gleason grading system , both in needle biopsy and radical prostatectomy specimens remains one of the most significant factors in the clinical decision making process.Key Challenges of the biopsy based determination of prostate cancer aggressiveness include  Tumor heterogeneity  Biopsy sampling error and variations in biopsy interpretations The resulting uncertainty in risk assessment leads to significant over treatment with associated cost and morbidity.
What's New Online

This study investigates the dual use of filled prescription medications and dietary supplements as well as the factors associated with their use among US military personnel.
On the Selection of English Discourses for Chinese College Entrance Examination in Recent Five Years (2017-2021)

The paper mainly focuses on the selection of English discourses in the National College Entrance Examination papers of China in recent five years. It is found that there are four discourse types of application, narration, exposition and argumentation. The thematic contexts of the discourses in the papers involve the three categories of “man and self”, “man and society”, “man and nature”,which echoes The National English Curriculum Standard (2017 Edition and 2020 Revision). In the papers, Chinese traditional culture is strengthened and the source of material selection is not limited to Britain and the United States, but expanded to the other countries in the world. The general trend in discourse materials reflects that the scope of material selection is more extensive with more attention to Chinese excellent traditional culture and multi-culture of the world for assessing culture awareness of the students.
Metode Bayesian dan Multilayer Percepton dalam Mengklasifikasi Diabetes Mellitus

Diabetes mellitus is a chronic metabolic disorder that causes glucose regulation in the blood. Blood sugar anomalies can be defined as unwanted readings either due to normal causes or reasons unknown to the patient. Machine learning applications have been widely introduced in diabetes research and blood sugar anomaly detection. However, modeling options and strategies for classification in diabetes mellitus are needed. This study aims to classify the data as diabetic or non-diabetic and improve classification accuracy. Classification accuracy is improved by using many of the data sets as training data and test data. Classification accuracy is improved by using multiple of the data set as data. In the test, the C4.5 and RF hybrid methods, as well as the MLP and Net Bayes hybrid classification methods were developed for the classification of diabetes. In the case of C4.5 + RF it provides an accuracy of 79.31% which is higher than the individual models. Similarly, MLP + Net Bayes, provides an 81.89% higher accuracy than the individual models. In the second case the 85-15% ensemble model training and test partitions have an important role for the classification of diabetes data. The proposed MLP + Net Bayes provides 81.89% accuracy as a robust model for data classification. So that the proposed model achieves the highest accuracy of 81.89% with 6 features and reaches the highest sensitivity of 64.10% and the highest specificity of 90.90%.
Philosophy of the Digital Version of the World: From the Foundation to the Practices Book Review: Alekseev А. Р., Alekseeva 1. Yu. The Fate of Intelligence and the Mission of Reason: Philosophy before the Challenges of the Era of Digitalization. Monograph. Moscow, Prospect, 2021

The publication presents a review of the monograph by Alekseyev Alexander Petrovich and Alekseeva Irina Yurievna “The Fate of intelligence and the mission of reason: philosophy facing the challenges of the era of digitalization”. The authors systematically and deeply analyze the theoretical and cognitive, socio-philosophical, philosophical and anthropological problems that arise during the dynamic digitalization of all spheres of society. The author's representation of society as an intellectual system of a high degree of complexity makes it possible to comprehend the concepts of reason and intelligence in connection with the development and implementation of artificial intelligence systems. Of particular interest is the disclosure by the authors of the potential of philosophical tools in countering manipulative technologies in the digital environment. The monograph is recommended to readers interested in achieving an understanding of the essence of digitalization processes, philosophical foundations and prospects for the development of technical intelligent systems.
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning

Fine-tuning large pre-trained models on downstream tasks has been adopted in a variety of domains recently. However, it is costly to update the entire parameter set of large pre-trained models. Although recently proposed parameter-efficient transfer learning (PETL) techniques allow updating a small subset of parameters (e.g. only using 2% of parameters) inside a pre-trained backbone network for a new task, they only reduce the training memory requirement by up to 30%. This is because the gradient computation for the trainable parameters still requires backpropagation through the large pre-trained backbone model. To address this, we propose Ladder Side-Tuning (LST), a new PETL technique that can reduce training memory requirements by more substantial amounts. Unlike existing parameter-efficient methods that insert additional parameters inside backbone networks, we train a ladder side network, a small and separate network that takes intermediate activations as input via shortcut connections (called ladders) from backbone networks and makes predictions. LST has significantly lower memory requirements than previous methods, because it does not require backpropagation through the backbone network, but instead only through the side network and ladder connections. We evaluate our method with various models (T5 and CLIP-T5) on both NLP (GLUE) and vision-and-language (VQA, GQA, NLVR2 , MSCOCO) tasks. LST saves 69% of the memory costs to fine-tune the whole network, while other methods only save 26% of that in similar parameter usages (hence, 2.7x more memory savings). Moreover, LST achieves higher accuracy than Adapter and LoRA in a low-memory regime. To further show the advantage of this better memory efficiency, we also apply LST to larger T5 models, attaining better GLUE performance than full fine-tuning and other PETL methods. The accuracy-efficiency trade-off also holds on VL tasks.
Heuristic Analysis of the Indonesian Covid-19 Official Website Interface

The Government of Indonesia's official Covid-19 website is the most trusted information link between relevant agencies and the wider community. Usability is an important factor in determining how easily and successfully users can obtain information from a website. Heuristic studies were conducted to obtain findings on the discrepancies in the previously released website design. The website's homepage has a usability percentage of 78.74 percent and a standard deviation of 8.84 percent. The Covid-19 distribution page is 70.36 percent usable, with a standard deviation of 9.84 percent. The evaluation results are based on the United States' Research-Based Web Design and Usability Guidelines. The Department of Health and Human Services (HHS) discovered that the home page's links were poorly identified, that important information was not relevant, and that the use of icons was inconsistent. The distribution page discovered difficult-to-identify links, scrollbar redundancy in the frameset, and content complexity that required the user to be able to read diagrams well.
Search Engine

Search engines play a crucial role in the success of the Internet; they assist any Internet user in quickly finding relevant information. However, the semantic Web was born out of the unaddressed challenges of contemporary search engines.
Replacement of Bitumen with Waste Thermoplastic and Crumb Rubber

Plastic replace the other material easily due to this product cost is less. It is more economical than any other material but its large drawback is its disposal problem. Waste plastic affects the environment as well as human life in large extent. Thermo-plastic has properties due to this it can be used in the construction of road, because of this the construction cost is less. In this paper some amount of bitumen is replaced with thermoplastic and crumb rubber together to achieve more strength and durability in pavement construction.
A NOVEL APPROACH USING FLUTTER FOR VOICE CONTROLLED ASSISTANT

Voice assistants are programs on digital devices that listen and respond to verbal commands. These are software agents that can interpret human speech and respond via synthesized voices. Initially voice assistant is used by some of the search engines like google and amazon but due to advancement of technology voice assistant usage increased in these days. The aim of this project is to develop a voice assistant for the college which helps students and parents to know more about the college. Also, faculty can use this application to find out any updates of research related to their field occurred in the college. Instead of manual typing we can use voice feature to search for query. This application provides voice feature that doesn’t exist in existing applications of our college. Keywords : Voice assistant, Verbal commands, Synthesized voices , Digital devices.
Survey on Indian Sign Language Recognition Systems

Sign Language is a way of communication used by hearing-impaired people. As many normal people don’t know the Sign Language it is difficult to understand by them to understand thus, increasing the communication gap between normal and hearing-impaired people. To overcome this problem many systems,using Machine Learning(ML), Artificial Intelligence(AI), Deep Learning(DL), computer vision methods, were developed and being developed. These systems are used to translate Indian Sign Language(ISL) to understandable text, facilitating the communication between normal and hearing-impaired people. This study briefly explains various type of Indian Sign Language(ISL) recognition systems.
Spatialized video communication platforms: Applications in design education and conferencing

During the pandemic, the impact of video communication platforms drastically increased.But in front of a screen, flexible and serendipitous interactions known from real-life settings were reduced to a minimum.Zoom fatigue occurred very quickly and early on developments were started to provide more flexible alternatives to videofocused platforms.Spatialized Video Communication Platforms (SVCP) reintroduce more flexibility, serendipity and improved dynamic group forming by providing gamelike environments.After a brief review on SVCPs, we are focusing here on the highlycustomizable Gather.townplatform.We developed an Open-Source pipeline to create 3D-based environments that meet the demand for visual requirements from designengineering students as well as visualization professionals.Based on three surveys, we evaluated the potential of SVCPs in the context of virtualizing learning, teaching, exhibiting as well as conferencing.Along the way we tested several new features designed for increased user engagement and creating a sense of ownership.
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training

Data clipping is crucial in reducing noise in quantization operations and improving the achievable accuracy of quantization-aware training (QAT). Current practices rely on heuristics to set clipping threshold scalars and cannot be shown to be optimal. We propose Optimally Clipped Tensors And Vectors (OCTAV), a recursive algorithm to determine MSE-optimal clipping scalars. Derived from the fast Newton-Raphson method, OCTAV finds optimal clipping scalars on the fly, for every tensor, at every iteration of the QAT routine. Thus, the QAT algorithm is formulated with provably minimum quantization noise at each step. In addition, we reveal limitations in common gradient estimation techniques in QAT and propose magnitude-aware differentiation as a remedy to further improve accuracy. Experimentally, OCTAV-enabled QAT achieves state-of-the-art accuracy on multiple tasks. These include training-from-scratch and retraining ResNets and MobileNets on ImageNet, and Squad fine-tuning using BERT models, where OCTAV-enabled QAT consistently preserves accuracy at low precision (4-to-6-bits). Our results require no modifications to the baseline training recipe, except for the insertion of quantization operations where appropriate.
An Abstract View on Optimizations in Propositional Frameworks

Search-optimization problems are plentiful in scientific and engineering domains. Artificial intelligence has long contributed to the development of search algorithms and declarative programming languages geared toward solving and modeling search-optimization problems. Automated reasoning and knowledge representation are the subfields of AI that are particularly vested in these developments. Many popular automated reasoning paradigms provide users with languages supporting optimization statements: answer set programming or MaxSAT on minone, to name a few. These paradigms vary significantly in their languages and in the ways they express quality conditions on computed solutions. Here we propose a unifying framework of so-called weight systems that eliminates syntactic distinctions between paradigms and allows us to see essential similarities and differences between optimization statements provided by paradigms. This unifying outlook has significant simplifying and explanatory potential in the studies of optimization and modularity in automated reasoning and knowledge representation. It also supplies researchers with a convenient tool for proving the formal properties of distinct frameworks; bridging these frameworks; and facilitating the development of translational solvers.
Slimmable Domain Adaptation

Vanilla unsupervised domain adaptation methods tend to optimize the model with fixed neural architecture, which is not very practical in real-world scenarios since the target data is usually processed by different resource-limited devices. It is therefore of great necessity to facilitate architecture adaptation across various devices. In this paper, we introduce a simple framework, Slimmable Domain Adaptation, to improve cross-domain generalization with a weight-sharing model bank, from which models of different capacities can be sampled to accommodate different accuracy-efficiency trade-offs. The main challenge in this framework lies in simultaneously boosting the adaptation performance of numerous models in the model bank. To tackle this problem, we develop a Stochastic EnsEmble Distillation method to fully exploit the complementary knowledge in the model bank for inter-model interaction. Nevertheless, considering the optimization conflict between inter-model interaction and intra-model adaptation, we augment the existing bi-classifier domain confusion architecture into an Optimization-Separated Tri-Classifier counterpart. After optimizing the model bank, architecture adaptation is leveraged via our proposed Unsupervised Performance Evaluation Metric. Under various resource constraints, our framework surpasses other competing approaches by a very large margin on multiple benchmarks. It is also worth emphasizing that our framework can preserve the performance improvement against the source-only model even when the computing complexity is reduced to $1/64$. Code will be available at https://github.com/hikvision-research/SlimDA.
Visual Radial Basis Q-Network

While reinforcement learning (RL) from raw images has been largely investigated in the last decade, existing approaches still suffer from a number of constraints. The high input dimension is often handled using either expert knowledge to extract handcrafted features or environment encoding through convolutional networks. Both solutions require numerous parameters to be optimized. In contrast, we propose a generic method to extract sparse features from raw images with few trainable parameters. We achieved this using a Radial Basis Function Network (RBFN) directly on raw image. We evaluate the performance of the proposed approach for visual extraction in Q-learning tasks in the Vizdoom environment. Then, we compare our results with two Deep Q-Network, one trained directly on images and another one trained on feature extracted by a pretrained auto-encoder. We show that the proposed approach provides similar or, in some cases, even better performances with fewer trainable parameters while being conceptually simpler.
Supervised Dictionary Learning with Auxiliary Covariates

Supervised dictionary learning (SDL) is a classical machine learning method that simultaneously seeks feature extraction and classification tasks, which are not necessarily a priori aligned objectives. The goal of SDL is to learn a class-discriminative dictionary, which is a set of latent feature vectors that can well-explain both the features as well as labels of observed data. In this paper, we provide a systematic study of SDL, including the theory, algorithm, and applications of SDL. First, we provide a novel framework that `lifts' SDL as a convex problem in a combined factor space and propose a low-rank projected gradient descent algorithm that converges exponentially to the global minimizer of the objective. We also formulate generative models of SDL and provide global estimation guarantees of the true parameters depending on the hyperparameter regime. Second, viewed as a nonconvex constrained optimization problem, we provided an efficient block coordinate descent algorithm for SDL that is guaranteed to find an $\varepsilon$-stationary point of the objective in $O(\varepsilon^{-1}(\log \varepsilon^{-1})^{2})$ iterations. For the corresponding generative model, we establish a novel non-asymptotic local consistency result for constrained and regularized maximum likelihood estimation problems, which may be of independent interest. Third, we apply SDL for imbalanced document classification by supervised topic modeling and also for pneumonia detection from chest X-ray images. We also provide simulation studies to demonstrate that SDL becomes more effective when there is a discrepancy between the best reconstructive and the best discriminative dictionaries.
Modelling Guidance in Software Engineering: A Systematic Literature Review

Despite potential benefits in Software Engineering (SE), adoption of software modelling in industry is low. Technical issues such as tool support have gained significant research before, but individual guidance and training have received little attention. As a first step towards providing the necessary guidance in modelling, we conduct a systematic literature review (SLR) to explore the current state of the art. We searched academic literature for modelling guidance, and selected 25 papers for full-text screening through three rounds of selection. We find research on modelling guidance to be fragmented, with inconsistent usage of terminology, and a lack of empirical validation or supporting evidence. We outline the different dimensions commonly used to provide guidance on software modelling. Additionally, we provide definitions of the three terms modelling method, style, and guideline as current literature lacks a well-defined distinction between them. These definitions can help distinguishing between important concepts and provide precise modelling guidance.
Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception

Data-driven models (DDM) based on machine learning and other AI techniques play an important role in the perception of increasingly autonomous systems. Due to the merely implicit definition of their behavior mainly based on the data used for training, DDM outputs are subject to uncertainty. This poses a challenge with respect to the realization of safety-critical perception tasks by means of DDMs. A promising approach to tackling this challenge is to estimate the uncertainty in the current situation during operation and adapt the system behavior accordingly. In previous work, we focused on runtime estimation of uncertainty and discussed approaches for handling uncertainty estimations. In this paper, we present additional architectural patterns for handling uncertainty. Furthermore, we evaluate the four patterns qualitatively and quantitatively with respect to safety and performance gains. For the quantitative evaluation, we consider a distance controller for vehicle platooning where performance gains are measured by considering how much the distance can be reduced in different operational situations. We conclude that the consideration of context information of the driving situation makes it possible to accept more or less uncertainty depending on the inherent risk of the situation, which results in performance gains.