Reviewerly/exHarmony huggingface dataset:
An Analysis of Students’ Problem in Writing a Personal Letter at 11th Grade of MAN 4 Agam

There were some students’ problems that the writer found at MAN 4 AGAM. First, some students could not develop their ideas when writing. Second, some students were confused about using tenses. Next, some students had limited vocabulary. The last, some students were wrong in diction. This research aimed to discover the students’ problems in writing personal letters and the causes of students’ problems in writing personal letters. In this research, the researcher used mix method by using descriptive design. The mixed-method was the combination of quantitative and qualitative research. The informants of this research were the students of the XI IS-1 class, which consisted of 19 students. The data were obtained through documentation and interview. This research found five aspects of students’ problems in writing a personal letter—the problems which percentage 18,2 % was content. The second was organization, with a percentage of 15,4 %. Next, the student's problem in grammar was 25,3 %. After that, the problem of vocabulary was 23,2 %. The last, mechanics’ problem was 17,7 %. Therefore, from the percentage, the most common of students’ problems was grammar. There were some causes why students got a problem in writing a personal letter. There was the psychological, linguistic, and cognitive problem. Thus, based on an interview with some students, the most cause of the problem was linguistic problems.
Scribble-Supervised Semantic Segmentation by Uncertainty Reduction on Neural Representation and Self-Supervision on Neural Eigenspace

Scribble-supervised semantic segmentation has gained much attention recently for its promising performance without high-quality annotations. Due to the lack of supervision, confident and consistent predictions are usually hard to obtain. Typically, people handle these problems to either adopt an auxiliary task with the well-labeled dataset or incorporate the graphical model with additional requirements on scribble annotations. Instead, this work aims to achieve semantic segmentation by scribble annotations directly without extra information and other limitations. Specifically, we propose holistic operations, including minimizing entropy and a network embedded random walk on neural representation to reduce uncertainty. Given the probabilistic transition matrix of a random walk, we further train the network with self-supervision on its neural eigenspace to impose consistency on predictions between related images. Comprehensive experiments and ablation studies verify the proposed approach, which demonstrates superiority over others; it is even comparable to some full-label supervised ones and works well when scribbles are randomly shrunk or dropped.
Sermo Vulgaris: una multimedia para la enseñanza-aprendizaje del Latín Vulgar

The teaching-learning of Latin in the Cuban university corresponds to the Classical Letters Discipline. Between the multiple needs derived from the lack of bibliography of the elective / elective course of Latin IV and the complexity of this subject, the teaching process is affected. One of the tasks carried out to minimize the negative effects has been the search for information on the Internet, the digitization of the few copies that exist on the subject in the library and the production of media as the final activity of the subject. This article aims to make a multimedia Sermo Vulgaris for the teaching of Latin IV to students of the Literature career at the Universidad de Oriente.
Analysis and Research on Radar Gesture Micro-motion Feature Extraction Method

Abstract Radar gesture recognition technology is widely used in daily life, and the micro motion feature is the key information to distinguish the feature quantity of different gestures. The extraction of micro motion feature is the key step of radar gesture recognition. Joint time-frequency analysis is a common method for radar signal micro motion feature extraction. Firstly, this paper makes a theoretical analysis of the common joint time-frequency analysis methods; Then radar gesture model is completed, and the performance of different JTFA is compared by extracting the time-frequency features of the motion gesture echo; Finally, the measured data are collected under the condition of microwave anechoic chamber. After analysis, the theoretical simulation results are consistent with the measured data, which verifies the reliability of the simulation.
Physical Unclonable Functions using speckle patterns of perfect optical vortices

Encryption techniques demonstrate a great deal of security when implemented in an optical system (such as holography) due to the inherent physical properties of light and the precision it demands. However, such systems have shown to be vulnerable during digital implementations under various crypt-analysis attacks. One of the primary reasons for this is the predictable nature of the security keys (i.e., simulated random keys) used in the encryption process. To alleviate, in this work, we are presenting a Physically Unclonable Functions (PUFs) for producing a robust security key for digital encryption systems. To note, a correlation function of the scattered perfect optical vortex (POV) beams is utilized to generate the encryption keys. To the best of our knowledge, this is the first report on properly utilizing the scattered POV in optical encryption system. To validate the generated key, one of the standard optical encryption systems i.e., Double Random Phase Encoding, is opted. Experimental and simulation results validate that the proposed key generation method is an effective alternative to the digital keys.
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect

Recent empirical advances show that training deep models with large learning rate often improves generalization performance. However, theoretical justifications on the benefits of large learning rate are highly limited, due to challenges in analysis. In this paper, we consider using Gradient Descent (GD) with a large learning rate on a homogeneous matrix factorization problem, i.e., $\min_{X, Y} \|A - XY^\top\|_{\sf F}^2$. We prove a convergence theory for constant large learning rates well beyond $2/L$, where $L$ is the largest eigenvalue of Hessian at the initialization. Moreover, we rigorously establish an implicit bias of GD induced by such a large learning rate, termed 'balancing', meaning that magnitudes of $X$ and $Y$ at the limit of GD iterations will be close even if their initialization is significantly unbalanced. Numerical experiments are provided to support our theory.
Walking BookFairs—Independent Bookstore, Free Library Traveling Bookstore &amp; Library, India

Initiated in January 2014 by Satabdi Mishra and Akshaya Bahibala, Walking BookFairs (WBF) is an independent bookstore, free library and bookmobile based out of Bhubaneswar, Orissa, India. The reason behind starting WBF is exactly this—the lack of books, libraries and bookstores in our society. WBF was established in January 2014 in Koraput district of Odisha. WBF is an independent bookstore and free library. For us, spreading the love of books comes before profit. Books, bookstores and libraries are sadly not recognised as essential services in our country at this time and have been closed down, but we think they are absolutely essential, especially in these uncertain times. Books, reading and writing have always been an upper class-caste marker along with a good education in Indian society. The education system focuses more on rote learning and textbooks rather than storybooks or reading for pleasure.
Developing Professional Identity as a Mediator in relationship Transformational Leadership on Higher Education Innovation

Objective: The present research aimed to investigate the effect of the transformational leadership on higher education innovation with mediate of the professional identity development of faculty members of Sistan and Baluchestan University. Methods: The research method was correlational which was performed on a sample of 166 faculty members in Sistan and Baluchestan University by stratified random sampling. The research instrument was three questionnaires of transformational leadership, higher education innovation and professional identity development. Confirmatory validity and composite reliability and Cronbach's alpha were reported as suitable instruments. The analysis method was performed with PLS2 software as a model of the structural equation. Results: The results showed that the Transformational leadership has directly and indirectly affected on higher education innovation and professional identity development. Professional identity development has directly and indirectly affected the process and product innovation of higher education. also, Process innovation was effective on product innovation. Conclusion: It can be said that leadership and professional identity are Constructive in higher education innovation. In other words, soft skills are of particular importance to the adoption of process and product innovation. Keywords: transformational leadership, higher education innovation, professional identity development
Boosting vector quantization technique using fast directional shrinking search optimization (DSSO) algorithm

A new fast vector quantization along with a novel fast heuristic optimization algorithm has been proposed in this paper that efficiently boosts the process of finding the global optimum region in the search region. Stochastic variables are used in this algorithm to excel the speed of exploration and exploitation and at the same time making the whole process of vector quantization (VQ) computationally efficient. The search domain gradually shrinks to make algorithm effective and efficient. On comparing the simulation results of VQ based on particle swarm optimization (PSO), quantum particle swarm optimization (QPSO) and firefly algorithm (FFA) using some well- known benchmark functions has validated the efficiency of the algorithm. In finding the global optimum point, the proposed algorithm decreases the computing time 44 % relative to others. Vector quantization based image compression is a NP-hard problem has been successfully and efficiently solved in this proposed work.
SIGN LANGUAGE RECOGNITION SYSTEM : REVIEW STUDY

Communication is the process of meaningful interactions among human beings. Natural language channels such as speech, writing, and body language (gestures) such as hand movements, head gestures, face expression, lip motion, and so on are methods for humans to connect. Understanding sign language is just as important as understanding normal language since sign language is the best way to communicate with the deaf and blind people. Voluntary movements of hands and fingers are used in sign language to express clear action. There is no universal sign language, just as there is no universal spoken language, because each nation has its own dialect of sign language. As a result, Indian sign language is utilised in India. Because sign language is so distinct from natural language, interpreters of sign language are necessary to convert common language into sign language and vice versa. As such specialists are rare in India in comparison to deaf and deaf individuals, a sign language recognition system is necessary to bridge the communication gap between the hearing-impaired cum speech-impaired population and the rest of society by translating Indian sign language into regular text. There are several approaches to sign language recognition systems that have been developed during the last decade, but there are still certain issues that need to be addressed. In existing systems, various machine learning methods are utilised.
Selective Feature Compression for Efficient Activity Recognition Inference

Most action recognition solutions rely on dense sampling to precisely cover the informative temporal clip. Extensively searching temporal region is expensive for a real-world application. In this work, we focus on improving the inference efficiency of current action recognition backbones on trimmed videos, and illustrate that one action model can also cover then informative region by dropping non-informative features. We present Selective Feature Compression (SFC), an action recognition inference strategy that greatly increase model inference efficiency without any accuracy compromise. Differently from previous works that compress kernel sizes and decrease the channel dimension, we propose to compress feature flow at spatio-temporal dimension without changing any backbone parameters. Our experiments on Kinetics-400, UCF101 and ActivityNet show that SFC is able to reduce inference speed by 6-7x and memory usage by 5-6x compared with the commonly used 30 crops dense sampling procedure, while also slightly improving Top1 Accuracy. We thoroughly quantitatively and qualitatively evaluate SFC and all its components and show how does SFC learn to attend to important video regions and to drop temporal features that are uninformative for the task of action recognition.
Social Fabric: Tubelet Compositions for Video Relation Detection

This paper strives to classify and detect the relationship between object tubelets appearing within a video as a <subject-predicate-object> triplet. Where existing works treat object proposals or tubelets as single entities and model their relations a posteriori, we propose to classify and detect predicates for pairs of object tubelets a priori. We also propose Social Fabric: an encoding that represents a pair of object tubelets as a composition of interaction primitives. These primitives are learned over all relations, resulting in a compact representation able to localize and classify relations from the pool of co-occurring object tubelets across all timespans in a video. The encoding enables our two-stage network. In the first stage, we train Social Fabric to suggest proposals that are likely interacting. We use the Social Fabric in the second stage to simultaneously fine-tune and predict predicate labels for the tubelets. Experiments demonstrate the benefit of early video relation modeling, our encoding and the two-stage architecture, leading to a new state-of-the-art on two benchmarks. We also show how the encoding enables query-by-primitive-example to search for spatio-temporal video relations. Code: https://github.com/shanshuo/Social-Fabric.
Enhance Images as You Like with Unpaired Learning

Low-light image enhancement exhibits an ill-posed nature, as a given image may have many enhanced versions, yet recent studies focus on building a deterministic mapping from input to an enhanced version. In contrast, we propose a lightweight one-path conditional generative adversarial network (cGAN) to learn a one-to-many relation from low-light to normal-light image space, given only sets of low- and normal-light training images without any correspondence. By formulating this ill-posed problem as a modulation code learning task, our network learns to generate a collection of enhanced images from a given input conditioned on various reference images. Therefore our inference model easily adapts to various user preferences, provided with a few favorable photos from each user. Our model achieves competitive visual and quantitative results on par with fully supervised methods on both noisy and clean datasets, while being 6 to 10 times lighter than state-of-the-art generative adversarial networks (GANs) approaches.
Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation

Recent studies imply that deep neural networks are vulnerable to adversarial examples -- inputs with a slight but intentional perturbation are incorrectly classified by the network. Such vulnerability makes it risky for some security-related applications (e.g., semantic segmentation in autonomous cars) and triggers tremendous concerns on the model reliability. For the first time, we comprehensively evaluate the robustness of existing UDA methods and propose a robust UDA approach. It is rooted in two observations: (i) the robustness of UDA methods in semantic segmentation remains unexplored, which pose a security concern in this field; and (ii) although commonly used self-supervision (e.g., rotation and jigsaw) benefits image tasks such as classification and recognition, they fail to provide the critical supervision signals that could learn discriminative representation for segmentation tasks. These observations motivate us to propose adversarial self-supervision UDA (or ASSUDA) that maximizes the agreement between clean images and their adversarial examples by a contrastive loss in the output space. Extensive empirical studies on commonly used benchmarks demonstrate that ASSUDA is resistant to adversarial attacks.
Inter-domain Multi-relational Link Prediction

Multi-relational graph is a ubiquitous and important data structure, allowing flexible representation of multiple types of interactions and relations between entities. Similar to other graph-structured data, link prediction is one of the most important tasks on multi-relational graphs and is often used for knowledge completion. When related graphs coexist, it is of great benefit to build a larger graph via integrating the smaller ones. The integration requires predicting hidden relational connections between entities belonged to different graphs (inter-domain link prediction). However, this poses a real challenge to existing methods that are exclusively designed for link prediction between entities of the same graph only (intra-domain link prediction). In this study, we propose a new approach to tackle the inter-domain link prediction problem by softly aligning the entity distributions between different domains with optimal transport and maximum mean discrepancy regularizers. Experiments on real-world datasets show that optimal transport regularizer is beneficial and considerably improves the performance of baseline methods.
The Impact of Language Barriers and Discrimination Issues on Work Productivity of Foreign Workers

This study aims to examine the impact of language barriers and discrimination issues on the work productivity of foreign workers.The study was conducted at Sheraton Hotel, Petaling Jaya.A total of 76 questionnaires were completed using an online survey of Google Form.The literature review was conducted to examine the information regarding independent and dependent variables.The findings indicated that language barriers and discrimination issues could affect the work productivity of foreign workers.This study showed that language barriers, discrimination issues, and miscommunication were significantly correlated with the work productivity of foreign workers.All in all, the researchers can conclude that both independent variables can affect the work productivity of foreign workers.
Μετάβαση σε αφηρημένες αλγεβρικές δομές οριοθετώντας τα αντίστοιχα επίπεδα γνώσης

In this dissertation we delimit the levels of knowledge we encounter during the transition from simple arithmetic to abstract algebraic structures. We do this through the generalization of arithmetic operations by approaching the operation as a concept and thus determining the required level of knowledge. We associate abstract algebraic structures with various domains of everyday life through their scientific applications. Questions such as what knowledge is, how we acquire it, what enables us to learn, are analyzed. Our brain plays a key role in trying to capture and process information. Neuroscience has pointed to the brain regions that are responsible for the processing (data, numerical data, relationships) and performing of the arithmetic operations. Our ability for abstract thinking allows us to approach the concepts of generalization and subtraction as mental functions applied to Mathematics. After examining the terms algebra and abstract algebra, and the relationship between them, we focus on the concepts of structure and especially on algebraic structures (that consist of sets equipped with operations). A few of the abstract algebraic structures we address are groups, vector spaces, rings, isomorphisms of functions, matrices and vector spaces, operators, tensors. Particular reference is made to a rather novel algebraic structure of hyperstructures.Through the extant literature we present examples of applications of algebraic structures in the sciences, thus emphasizing the decisive role that Mathematics has in all aspects of our lives.The main conclusion is that the recognition and delimitation of the levels of knowledge is a crucial component in the teaching of Mathematics and in optimising the new Curricula for all educational levels.
LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation

Cross-resolution image alignment is a key problem in multiscale gigapixel photography, which requires to estimate homography matrix using images with large resolution gap. Existing deep homography methods concatenate the input images or features, neglecting the explicit formulation of correspondences between them, which leads to degraded accuracy in cross-resolution challenges. In this paper, we consider the cross-resolution homography estimation as a multimodal problem, and propose a local transformer network embedded within a multiscale structure to explicitly learn correspondences between the multimodal inputs, namely, input images with different resolutions. The proposed local transformer adopts a local attention map specifically for each position in the feature. By combining the local transformer with the multiscale structure, the network is able to capture long-short range correspondences efficiently and accurately. Experiments on both the MS-COCO dataset and the real-captured cross-resolution dataset show that the proposed network outperforms existing state-of-the-art feature-based and deep-learning-based homography estimation methods, and is able to accurately align images under $10\times$ resolution gap.
Software as a service and security

Nowadays, Cloud Computing is an important hi-tech research area in the most recent innovation. These advancements give clients a few benefits: cost-adequacy, effective asset usage, cooperation, catastrophe recuperation, and elite. Cloud offers administrations for processing on a solitary mouse click. The overview was completed in the type of an online survey. This examination paper exhibits that SaaS is safer than other web applications. SaaS is superior to different applications and SaaS is savvy. This Research paper investigates the significance of SaaS for productive distributed computing for associations and its suggestions. This examination paper additionally talks about the significance of SaaS application engineering, usefulness, proficiency, benefits, and weaknesses.