Reviewerly/exHarmony huggingface dataset:
Analysis and Prediction of Breast Cancer using Machine Learning Techniques

Rapid multiplication of cells in the human body leads to cancer. It is the foremost cause of death due to cancer in females, after lung cancer. As the breast cancer is one of the recurrent kinds of cancer, diagnosis of breast cancer recurring is extremelyessential to increase the survival rate of patient suffering from it. Although cancer is avertible and also treatable in primary/early stages yet a vast number of patients are diagnosed with cancer when it is very late. Almost 8% of females are detected with breast cancer. Its characteristics are mutation of genes, constant pain, changes in the size and redness of skin texture of breasts. With the development of technology and machine learning techniques, cancer diagnosis and detection accuracy has greatly improved. This paper presents an outline of evolved machine learning techniques in this medical field by applying machine learning algorithms on breast cancer dataset like Logistic regression, Random Forest, Decision Trees (DT) etc.
Inclusive Development in Environmental Sustainability in Sub-Saharan Africa: Insights from Governance Mechanisms

This research examines the relevance of inclusive development in modulating the role of governance on environmental degradation. The study focuses on forty-four countries in sub-Saharan Africa for the period 2000-2012. The Generalised Method of Moments is employed as the empirical strategy and CO2 emissions per capita is used to measure environmental pollution. Bundled and unbundled governance dynamics are employed, notably: political governance (consisting of political stability/no violence and “voice and accountability”), economic governance (encompassing government effectiveness and regulation quality), institutional governance (entailing corruption-control and the rule of law), and general governance (a composite measure of political governance, economic governance and institutional governance). The following main findings are established. First, the underlying net effect in the moderating role of inclusive development in the governance-CO2 emissions nexus is not significant in regressions pertaining to political governance and economic governance. Second, there are positive net effects from the relevance of inclusive development in modulating the effects of regulation quality, economic governance and general governance on CO2 emissions. The significant and insignificant effects are elucidated. Policy implications are discussed.
On-device neural speech synthesis

Recent advances in text-to-speech (TTS) synthesis, such as Tacotron and WaveRNN, have made it possible to construct a fully neural network based TTS system, by coupling the two components together. Such a system is conceptually simple as it only takes grapheme or phoneme input, uses Mel-spectrogram as an intermediate feature, and directly generates speech samples. The system achieves quality equal or close to natural speech. However, the high computational cost of the system and issues with robustness have limited their usage in real-world speech synthesis applications and products. In this paper, we present key modeling improvements and optimization strategies that enable deploying these models, not only on GPU servers, but also on mobile devices. The proposed system can generate high-quality 24 kHz speech at 5x faster than real time on server and 3x faster than real time on mobile devices.
Requirement Specification to Develop Digital Health Promotion Platform for Millenial and Post-Millenial Generations in Surabaya Indonesia

Background: Surabaya is facing various urban problems that are increasingly complex. Therefore,innovative solutions are needed such as digital health promotion for millenial and post-millenial as thelargest segment of internet users. The aim of this study is to identify the requirement specification to developdigital health promotion platform for millenial and post millenial generations in Surabaya.Methods: This research used qualitative methods with case study approach. This study used purposefulsampling on 24 informants, consists of several related parties who met the criteria. Data collection techniquewas carried out through in-depth interview and documents review.Results: The requirement specifications were classified into 4 domains (functional, technical, interface,performance requirements). Functional requirements consist of procedures, menus, features, content, data,information, control mechanism, and parties involved. Technical requirements include programs, networks,size, and types of digital devices. Interface requirements consist of visual display related to user interfaceand operating system. Performance requirements include several aspects such as speed, accuracy, efficiency,effectiveness, security, etc.Conclusion: Digital health promotion platform requirement specification must be tailored to the needs ofmillenial and post-millenial generations. If these requirements can be met, the target will be easier to acceptand use digital health promotion platform.
ePiC: Employing Proverbs in Context as a Benchmark for Abstract Language Understanding

While large language models have shown exciting progress on several NLP benchmarks, evaluating their ability for complex analogical reasoning remains under-explored.Here, we introduce a high-quality crowdsourced dataset of narratives for employing proverbs in context as a benchmark for abstract language understanding.The dataset provides fine-grained annotation of aligned spans between proverbs and narratives, and contains minimal lexical overlaps between narratives and proverbs, ensuring that models need to go beyond surfacelevel reasoning to succeed.We explore three tasks: (1) proverb recommendation and alignment prediction, (2) narrative generation for a given proverb and topic, and (3) identifying narratives with similar motifs.Our experiments show that neural language models struggle on these tasks compared to humans, and these tasks pose multiple learning challenges.
A short review of relation extraction methods

On the basis of reviewing the history of relationship extraction, this paper divides the research into three categories: traditional machine learning based relation extraction method, deep learning-based relationship extraction method, and open domain relationship extraction method. Then, each method is divided into several different sub methods according to the characteristics of the processing. Based on the analysis of the relevant literature of each method, the relationship and difference between the various methods are briefly described.
TV Ad Detection Using the Base64 Encoding Technique

Automatic TV ad detection is a challenging task in computer vision. Manual ad detection is considered a tedious job. Detecting advertisements automatically saves time and human effort. In this paper, a method is proposed for detecting repeated video segments automatically, since generally, ads appear in TV transmissions frequently. At first, the user is allowed to browse the advertisements needed to be detected, and the video in which they are to be detected. The videos are then converted into a text file using the Base64 encodings. In the third step, the advertisements are detected using string comparison methods. In the end, a report, with the names of the advertisements is shown against the total time and the number of times these advertisements appeared in the stream. The implementation was carried out in python.
A Buffer Management Algorithm Based on Dynamic Marking Threshold to Restrain MicroBurst in Data Center Network

The data center has become the infrastructure of most Internet services, and its network carries different types of business flow, such as query, data backup, control information, etc. At the same time, the throughput-sensitive large flows occupy a lot of bandwidth, resulting in the small flow’s longer completion time, finally affecting the performance of the applications. Recent proposals consider only dynamically adjusting the ECN threshold or reversing the ECN packet priority. This paper combines these two improvements and presents the HDCQ method for coordinating data center queuing, separating large and small flows, and scheduling in order to ensure flow completion time. It uses the ECN mechanism to design load-adaptive marking threshold update algorithms for small flows to prevent micro-bursts from occurring. At the same time, packets marked with ECN or ACK are raised in priority, prompting these packets to be fed back to the sender as soon as possible, effectively reducing the TCP control loop delay. Extensive experimental analysis on the network simulator (NS-2) shows that the HDCQ algorithm has better performance in the face of micro-burst traffic, reducing the average flow completion time by up to 24% compared with the PIAS.
Graph Based Gaussian Processes on Restricted Domains

In nonparametric regression, it is common for the inputs to fall in a restricted subset of Euclidean space. Typical kernel-based methods that do not take into account the intrinsic geometry of the domain across which observations are collected may produce sub-optimal results. In this article, we focus on solving this problem in the context of Gaussian process (GP) models, proposing a new class of Graph Laplacian based GPs (GL-GPs), which learn a covariance that respects the geometry of the input domain. As the heat kernel is intractable computationally, we approximate the covariance using finitely-many eigenpairs of the Graph Laplacian (GL). The GL is constructed from a kernel which depends only on the Euclidean coordinates of the inputs. Hence, we can benefit from the full knowledge about the kernel to extend the covariance structure to newly arriving samples by a Nystr\"{o}m type extension. We provide substantial theoretical support for the GL-GP methodology, and illustrate performance gains in various applications.
On the regularized risk of distributionally robust learning over deep neural networks

In this paper, we explore the relation between distributionally robust learning and different forms of regularization to enforce robustness of deep neural networks. In particular, starting from a concrete min-max distributionally robust problem, and using tools from optimal transport theory, we derive first-order and second-order approximations to the distributionally robust problem in terms of appropriate regularized risk minimization problems. In the context of deep ResNet models, we identify the structure of the resulting regularization problems as mean-field optimal control problems where the number and dimension of state variables are within a dimension-free factor of the dimension of the original unrobust problem. Using the Pontryagin maximum principles associated with these problems, we motivate a family of scalable algorithms for the training of robust neural networks. Our analysis recovers some results and algorithms known in the literature (in settings explained throughout the paper) and provides many other theoretical and algorithmic insights that to our knowledge are novel. In our analysis, we employ tools that we deem useful for a future analysis of more general adversarial learning problems.
Features of formation of motivation for online learning of students of pedagogical specialties

The concept of "motivation" and modern theories of learning motivation are theoretically substantiated and described: expectancy-value theory, attribution theory, social-cognitive theory, goal orientation theory, self-determination theory. Teaching strategies and pedagogical conditions that motivate students to educational and cognitive activities in the online environment, taking into account the theory of self-determination, are determined. In particular, in order to succeed in mastering something new, a student must feel competent (acquire the appropriate skills to perform practical tasks), related (feel belonging and attachment to groups or individuals), autonomous (feel that he/she controls his/her behavior and is responsible for its goals, capable of self-learning).&#x0D; The pedagogical conditions for the formation of positive motivation and involvement of students in learning online are substantiated and determined, namely: giving a student the opportunity to be an accomplice in the learning process; structuring educational materials in accordance with learning objectives; promoting student initiative and creativity, ensuring group activities and a multidimensional assessment system.&#x0D; The key to fulfilling the pedagogical conditions is the teacher's motivation to implement online learning at the level of the course author, mentor and tutor for higher education students. External and internal factors of motivation of teachers for introduction of elements of distance (online) and mixed (alternation of online and offline) training by teachers in the educational process of establishments of higher education are defined.&#x0D; Experimentally confirmed: positive impact on the motivation of the implemented online learning strategies and pedagogical conditions; revealed a significant direct relationship between the student's ability to self-development and self-learning and his/her motivation for online learning
Generating Concurrent Programs From Sequential Data Structure Knowledge Using Answer Set Programming

We tackle the problem of automatically designing concurrent data structure operations given a sequential data structure specification and knowledge about concurrent behavior.Designing concurrent code is a non-trivial task even in simplest of cases.Humans often design concurrent data structure operations by transforming sequential versions into their respective concurrent versions.This requires an understanding of the data structure, its sequential behavior, thread interactions during concurrent execution and shared memory synchronization primitives.We mechanize this design process using automated commonsense reasoning.We assume that the data structure description is provided as axioms alongside the sequential code of its algebraic operations.This information is used to automatically derive concurrent code for that data structure, such as dictionary operations for linked lists and binary search trees.Knowledge in our case is expressed using Answer Set Programming (ASP), and we employ deduction and abduction-just as humans do-in the reasoning involved.ASP allows for succinct modeling of first order theories of pointer data structures, run-time thread interactions and shared memory synchronization.Our reasoner can systematically make the same judgments as a human reasoner, while constructing provably safe concurrent code.We present several reasoning challenges involved in transforming the sequential data structure into its equivalent concurrent version.All the reasoning tasks are encoded in ASP and our reasoner can make sound judgments to transform sequential code into concurrent code.To the best of our knowledge, our work is the first one to use commonsense reasoning to automatically transform sequential programs into concurrent code.We also have developed a tool that we describe that relies on state-of-the-art ASP solvers and performs the reasoning tasks involved to generate concurrent code.
Orders from Uninorms on Bounded Lattices: Some Perspectives

Recently many works have proposed different ways of obtaining orders from associative fuzzy logic operations.While the order investigated by Karaçal and Kesicioglu [10] was modified in [6] to obtain orders on uninorms, this order relation was based on the sub-domains of the arguments.Recently in [9], a property called Quasi-Projectivity (QP) was shown to be important to obtain an order from the relation investigated in [10] and showed that all t-norms, t-conorms and nullnorms satisfy (QP) giving rise to posets, when the underlying domain is [0, 1], while not all classes of uninorms satisfied (QP).Many constructions of uninorms U exist on bounded lattices, which unlike [0, 1], may neither be total nor complete.In this work, we investigate the satisfaction of (QP) for these constructions.Our study shows that interestingly, almost all existing constructions satisfy (QP) and hence give rise to posets.Further, the orders obtained based on and differ majorly, with the relation consistently giving rise to richer order-theoretic structures.This study further merits attention since it offers an alternate perspectivethat a uninorm U on a lattice (L, ≤) can be made to be seen as a t-norm on the U-poset obtained (L, U ).
Applying Instructional Design Methods to Improve the Effectiveness of Blended-Learning

The article describes the application of instructional design techniques to form and increase the effectiveness of training courses. A key emphasis is placed on blended learning techniques, involving the separation of study time between learning using online technology and learning with a more traditional form of instruction involving a teacher.The basic principles of the organization of the educational process using instructional design are outlined; the nuances of this approach are considered taking into account the specifics of blended learning. The classification and descriptions of the main methods of blended learning are given. The nuances and features of each of the methods are considered.The process is described, and the basic principles of creating curricula are described taking into account the specifics of the mixed form of training. The main approaches and methods for the formation of the training course are determined; the use of this technique in practice is described as an experiment
AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method

We propose a computationally-friendly adaptive learning rate schedule, ``AdaLoss", which directly uses the information of the loss function to adjust the stepsize in gradient descent methods. We prove that this schedule enjoys linear convergence in linear regression. Moreover, we extend the to the non-convex regime, in the context of two-layer over-parameterized neural networks. If the width is sufficiently large (polynomially), then AdaLoss converges robustly to the global minimum in polynomial time. We numerically verify the theoretical results and extend the scope of the numerical experiments by considering applications in LSTM models for text clarification and policy gradients for control problems.
Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative.

Pre-training, where models are trained on an auxiliary objective with abundant data before being fine-tuned on data from the downstream task, is now the dominant paradigm in NLP. In general, the pre-training step relies on little to no direct knowledge of the task on which the model will be fine-tuned, even when the end-task is known in advance. Our work challenges this status-quo of end-task agnostic pre-training. First, on three different low-resource NLP tasks from two domains, we demonstrate that multi-tasking the end-task and auxiliary objectives results in significantly better downstream task performance than the widely-used task-agnostic continued pre-training paradigm of Gururangan et al. (2020). We next introduce an online meta-learning algorithm that learns a set of multi-task weights to better balance among our multiple auxiliary objectives, achieving further improvements on end task performance and data efficiency.
Digital Technology Implementation Practice during Conversion from Fundamental University Educational System into Online Format

The paper discusses the effectiveness of digital technologies in the educational process. The authors consider the use of digital services in the university educational process in the context of the need to full transfer basic education into online format. A comparative analysis of public and corporate digital services is carried out. Their advantages and disadvantages are considered the application effectiveness in the educational process. The article examines the following digital services: Zoom, Jitsi, MS Teams, Google Documents, Google Classroom, LMS HSE, Smart LMS. The authors propose the methods of organizing the educational process, involving the integration of these services. The results of the students' survey revealed the following features that affect the interaction of teachers and students: the Internet speed and availability, preferences in the ways of course materials obtaining, the desired forms of interaction between students and the teacher. The paper describes the effective methods of conducting online classes in various conditions: group discussion of the pre-voiced and cloud-uploaded presentations in chat, conducting classes in the form of a video-chat, using pre-prepared dynamic graphic illustrations, using gamification elements, laboratory work remote running on a virtual equipment, splitting the lecture into thematic sections and frequent changes in the students' activity. The authors offer various online forms and methods of learning outcomes intermediate control for the disciplines of the university curriculum. This study offers solutions to the problems of organizing online classes, including in conditions of limited Internet traffic.
Research on Network Layer Recursive Reduction Model Compression for Image Recognition

ResNet has been widely used in the field of machine learning since it was proposed. This network model is successful in extracting features from input data by superimposing multiple layers of neural networks and thus achieves high accuracy in many applications. However, the superposition of multilayer neural networks increases their computational cost. For this reason, we propose a network model compression technique that removes multiple neural network layers from ResNet without decreasing the accuracy rate. The key idea is to provide a priority term to identify the importance of each neural network layer, and then select the unimportant layers to be removed during the training process based on the priority of the neural network layers. In addition, this paper also retrains the network model to avoid the accuracy degradation caused by the deletion of network layers. Experiments demonstrate that the network size can be reduced by 24.00%–42.86% of the number of layers without reducing the classification accuracy when classification is performed on CIFAR-10/100 and ImageNet.
Optimizing Few-Shot Learning based on Variational Autoencoders

Despite the importance of few-shot learning, the lack of labeled training data in the real world, makes it extremely challenging for existing machine learning methods as this limited data set does not represent the data variance well. In this research, we suggest employing a generative approach using variational autoencoders (VAEs), which can be used specifically to optimize few-shot learning tasks by generating new samples with more intra-class variations. The purpose of our research is to increase the size of the training data set using various methods to improve the accuracy and robustness of the few-shot face recognition. Specifically, we employ the VAE generator to increase the size of the training data set, including the basic and the novel sets while utilizing transfer learning as the backend. Based on extensive experimental research, we analyze various data augmentation methods to observe how each method affects the accuracy of face recognition. We conclude that the face generation method we proposed can effectively improve the recognition accuracy rate to 96.47% using both the base and the novel sets.