Reviewerly/exHarmony huggingface dataset:
The Elastic Lottery Ticket Hypothesis

Lottery Ticket Hypothesis (LTH) raises keen attention to identifying sparse trainable subnetworks, or winning tickets, which can be trained in isolation to achieve similar or even better performance compared to the full models. Despite many efforts being made, the most effective method to identify such winning tickets is still Iterative Magnitude-based Pruning (IMP), which is computationally expensive and has to be run thoroughly for every different network. A natural question that comes in is: can we "transform" the winning ticket found in one network to another with a different architecture, yielding a winning ticket for the latter at the beginning, without re-doing the expensive IMP? Answering this question is not only practically relevant for efficient "once-for-all" winning ticket finding, but also theoretically appealing for uncovering inherently scalable sparse patterns in networks. We conduct extensive experiments on CIFAR-10 and ImageNet, and propose a variety of strategies to tweak the winning tickets found from different networks of the same model family (e.g., ResNets). Based on these results, we articulate the Elastic Lottery Ticket Hypothesis (E-LTH): by mindfully replicating (or dropping) and re-ordering layers for one network, its corresponding winning ticket could be stretched (or squeezed) into a subnetwork for another deeper (or shallower) network from the same family, whose performance is nearly the same competitive as the latter's winning ticket directly found by IMP. We have also extensively compared E-LTH with pruning-at-initialization and dynamic sparse training methods, as well as discussed the generalizability of E-LTH to different model families, layer types, and across datasets. Code is available at https://github.com/VITA-Group/ElasticLTH.
Sexism Prediction in Spanish and English Tweets Using Monolingual and Multilingual BERT and Ensemble Models.

The popularity of social media has created problems such as hate speech and sexism. The identification and classification of sexism in social media are very relevant tasks, as they would allow building a healthier social environment. Nevertheless, these tasks are considerably challenging. This work proposes a system to use multilingual and monolingual BERT and data points translation and ensemble strategies for sexism identification and classification in English and Spanish. It was conducted in the context of the sEXism Identification in Social neTworks shared 2021 (EXIST 2021) task, proposed by the Iberian Languages Evaluation Forum (IberLEF). The proposed system and its main components are described, and an in-depth hyperparameters analysis is conducted. The main results observed were: (i) the system obtained better results than the baseline model (multilingual BERT); (ii) ensemble models obtained better results than monolingual models; and (iii) an ensemble model considering all individual models and the best standardized values obtained the best accuracies and F1-scores for both tasks. This work obtained first place in both tasks at EXIST, with the highest accuracies (0.780 for task 1 and 0.658 for task 2) and F1-scores (F1-binary of 0.780 for task 1 and F1-macro of 0.579 for task 2).
Specialize and Fuse: Pyramidal Output Representation for Semantic Segmentation

We present a novel pyramidal output representation to ensure parsimony with our "specialize and fuse" process for semantic segmentation. A pyramidal "output" representation consists of coarse-to-fine levels, where each level is "specialize" in a different class distribution (e.g., more stuff than things classes at coarser levels). Two types of pyramidal outputs (i.e., unity and semantic pyramid) are "fused" into the final semantic output, where the unity pyramid indicates unity-cells (i.e., all pixels in such cell share the same semantic label). The process ensures parsimony by predicting a relatively small number of labels for unity-cells (e.g., a large cell of grass) to build the final semantic output. In addition to the "output" representation, we design a coarse-to-fine contextual module to aggregate the "features" representation from different levels. We validate the effectiveness of each key module in our method through comprehensive ablation studies. Finally, our approach achieves state-of-the-art performance on three widely-used semantic segmentation datasets -- ADE20K, COCO-Stuff, and Pascal-Context.
On the Complexity of Anonymous Communication Through Public Networks.

Onion routing is the most widely used approach to anonymous communication online. The idea is that Alice wraps her message to Bob in layers of encryption to form an onion, and routes it through a series of intermediaries. Each intermediary's job is to decrypt (peel) the onion it receives to obtain instructions for where to send it next, and what to send. The intuition is that, by the time it gets to Bob, the onion will have mixed with so many other onions, that its origin will be hard to trace even for an adversary that observes the entire network and controls a fraction of the participants, possibly including Bob. In spite of its widespread use in practice, until now no onion routing protocol was known that simultaneously achieved, in the presence of an active adversary that observes all network traffic and controls a constant fraction of the participants, (a) fault-tolerance, where even if a few of the onions are dropped, the protocol still delivers the rest; (b) reasonable communication and computational complexity as a function of the security parameter and the number of participants; and (c) anonymity. In this paper, we give the first onion routing protocol that meets these goals: our protocol (a) tolerates a polylogarithmic (in the security parameter) number of dropped onions and still delivers the rest; (b) requires a polylogarithmic number of rounds and a polylogarithmic number of onions sent per participant per round; and (c) achieves anonymity. We also show that to achieve anonymity in a fault-tolerant fashion via onion routing, this number of onions and rounds is necessary. Of independent interest, our analysis introduces two new security properties of onion routing -- mixing and equalizing -- and we show that together they imply anonymity.
Performance analysis of CPU scheduling algorithms – A problem solving approach

Scheduling algorithms plays a significant role in optimizing the CPU in operating system. Each scheduling algorithms [8] schedules the processes in the ready queue with its own algorithm design and its properties. In this paper, the performance analysis of First come First serve scheduling, non-pre-emptive scheduling, Pre-emptive scheduling, Shortest Job scheduling First (SJF) and Round Robin algorithm has been discussed with an example and the results has been analysed with the performance parameters such as minimum waiting time, minimum turnaround time and Response time. This will help the young researchers to analyse algorithms to develop a new optimized algorithm for CPU optimization.
CÓMO DETECTAR SARS-CoV-2 EMPLEANDO PRUEBAS DE LABORATORIO

The SARS-CoV-2 virus is an emerging coronavirus identified in 2019 and is responsible for the disease called COVID-19, which was declared a global pandemic, in 2020 by the World Health Organization (WHO). At the beginning of May 2020, more than 4 million infected people have been confirmed in the world and more than 278 thousand deaths, estimating an economic impact of more than 3 billion dollars. In Mexico, a total of 36,327 positive cases have been confirmed. The impact of this disease is mainly associated with the rapid spread among the human population and the severity of the clinical signs, which, if not treated, can lead to death. Currently, there is no vaccine available to prevent the disease and its control is based on the clinical diagnosis, which must be carried out by a specialist. This diagnosis must be confirmed by laboratory tests, which are of two types, mainly: for the detection of the etiological agent (virological and molecular) and serological (detection of antibodies), both tests must be carried out by institutions approved by the InDRE. The objective of molecular diagnosis (which is the most widely used worldwide) is to identify a fragment of the viral genome, which would indicate that the individual has had exposure and has been infected with SARS-CoV-2. In the case of serological tests, the diagnosis is based on identifying the presence of specific antibodies in serum samples from patients in the convalescent phase, with this test the degree of protection in a population can be evaluated. Both types of tests can be performed in patients with or without symptoms and are of utmost importance for the establishment of prevention and control measures for this pandemic.
The Conflict-Free Replicated Datatype Algorithm for Detecting E-Commerce Phishing Website

By posing as a trustworthy company through electronic communication, a fraud effort is made to get sensitive and personal information such as passwords, usernames, and financial data such as credit/debit card numbers. The phishing website will seem just like the real website and will send the visitor to a page on the malicious website where they will be asked to submit personal information. Machine learning techniques may be used to increase the accuracy of phishing website prediction. The suggested technique predicts URL-based phishing websites using W3C-defined characteristics and provides the highest level of accuracy. This technique makes advantage of URL characteristics. Phishing site urls have certain characteristics, which we discovered. We are all experiencing a worldwide epidemic, which has resulted in a significant shift in people's lifestyles. People were completely unprepared for such an event. As a result, everyone has had a tough time dealing with the circumstance. However, as we all know, life moves on, and individuals have changed their lifestyles and adapted to the present circumstances. As a result, the internet and online apps have become one of the most essential aspects of people's working environments. People's jobs are becoming increasingly reliant on internet applications. As a result, the main concept is to alleviate the challenges that programmers face in the IT industry.
Instalasi Perpipaan Air Bersih Masjid Al-Ikhwan Rancagong Legok Tangerang

Water is essential for life, but not all people can access tap water. In fact, many people use ground water, which is not measured its purity, to supply household, places of worship, public facilities, private offices, government offices, and etc. This problem was also observed on Al-Ikhwan Mosque building Jl. Masjid RT 01 RW 08 Rancagong Village, Legok District, Tangerang Regency, Banten Province. In addition, in this place the water installation was not available. Therefore, it was feasible to carry out community service activities in that location regarding to clean water supply and installation. The method of this project were (1) drilling to make wells as clean water supply, (2) Calculating the need for clean water as the basis in determining the capacity and the type of water pump, (3) building water storage (equipped with a floating sensor as a control media turn the pump on or off automatically), and (4) piping to support distribution system of clean water from well to tap. This method was expected to meet Indonesian National Standard of clean water installation system. This project was supported by lecturers / instructors as their responsibility to practice Tridharma of higher education. Cadets/students was also involved in this project so they can demonstrate their knowledge about standard of building facilities in society.
Engaging Learners in Online Video Lectures with Dynamically Scaffolding Conversational Agents

Online education creates new opportunities for learners, which has led to sharply increasing enrollment in the last few years. Despite these benefits, past research shows that the lack of individual interaction with educators creates low learner engagement that leads to high attrition rates, which remains a major challenge in the field. Dynamically scaffolding conversational agents built into online video lectures promise to address this problem by individually interacting with learners, similar to educators’ scaffolding behavior. These agents are equipped with recent natural language processing capabilities, creating human-like conversations that help learners to be more engaged in the learning process. To test our hypothesis, we built a dynamically scaffolding conversational agent named Sara and compared it with an often-implemented static conversational agent built into two online video lectures. We deployed a lab experiment with 182 learners. The neurophysiological measurements revealed that Sara significantly improved learner engagement partly explained by differences in learners’ perceptions in the way they experienced the interaction. This study connects to already existing conversational agent studies in online education and highlights the importance of including dynamically scaffolding conversational agents in online video lectures to address the problem of low learner engagement in online education.
Data hiding technique using image pixel value and spatial encryption technique

In this paper, we proposed a technique for hiding the double-encrypted confidential data in the image using the pixel value of the image and the spatial encryption technique. The proposed technique inserts encrypted confidential data into the LSB of an image pixel in order to maintain high image quality. The stego-image generated by hiding the encrypted confidential data has very good quality and is visually indistinguishable from the original cover image, so that it is impossible to recognize whether the confidential data is hidden in the stego-image. It is possible to extract the original confidential data from the stego-image without loss. By conducting an experiment on the proposed technique, it was confirmed that the proposed technique is an effective technique for the practical application of data hiding. The proposed technique can be used in applications such as military and intellectual property protection that require high security.
Readiness Of Mathematics Teachers To Develop Students’ Functional Literacy

The article emphasises the problem of mathematics teachers’ readiness to form a relatively new educational result for Russian school, i.e. functional literacy of students. In order to identify the degree of teachers’ readiness to form functional literacy of students in the process of teaching mathematics the authors conducted the research among the teachers of Krasnoyarsk and Krasnoyarsk Krai. Following observation of the educational process at mathematics lessons, interviewing and surveying teachers, carrying out diagnostic work the authors found discrepancy between teachers’ self-assessment of their competencies in the field of formation of functional literacy of students and actual educational practices. Less than half of the respondents have an adequate understanding of the essence of functional literacy and methods of its formation in the process of teaching mathematics. Teachers recognize the relevance of this educational task and are ready to improve their qualification in this area. The data and the results of the study have stressed a set of professional deficits of mathematics teachers in the region in terms of subject and methodological knowledge, which is a major contributor to the formation of functional literacy of students. To address the identified deficits in the future it is planned to organize targeted personalized assistance to teachers on professional development courses, training and consulting support for the development of professional competencies in the field of achieving new educational outcomes, as well as improve the subject and methodological university education of future mathematics teachers.
Application of Artificial Neural Networks for Catalysis

Catalyst, as an important material, plays a crucial role in the development of chemical industry. By improving the performance of the catalyst, the economic benefit can be greatly improved. Artificial neural network (ANN), as one of the most popular machine learning algorithms, relies on its good ability of nonlinear transformation, parallel processing, self-learning, self-adaptation and good associative memory, has been widely applied to various areas. Through the optimization of catalyst by ANN, the consumption of time and resources can be greatly reduced and greater economic benefits can be obtained. In this review, we show how this powerful technique helps people address the highly complicated problems and accelerate the progress of the catalysis community.
INFORMATION EDUCATIONAL TECHNOLOGIES AND INNOVATIONS IN THE DEVELOPMENT OF THE PEDAGOGICAL EDUCATIONAL PROCESS

The article presents information about the informatization of education and the technology of pedagogical educational processes, as well as about new innovations that play an important role in improving the effectiveness of student learning.The existing problems in the development and implementation of educational technologies are analyzed, scientific conclusions are made about the use of distance learning in the classroom as a modern learning technology andthe effectiveness of the use of information technologies. It also shows the characteristics of the existing achievements in the field of education in developed countries related to informatization
Making of Intelligent Simulation Experiment of Sound Velocity Measurement

Ultrasound is used as the wave source for the sound velocity measurement experiment. Ultrasonic wave is used to form standing wave in the reflection interval, the position of two adjacent wave anti nodes is used to determine the half wavelength, and the sound velocity in the experimental state is measured. The specific experimental apparatus applies a spiral structure. The hand-wheel rotates through one cycle and the ultrasound emitter moves 1mm accordingly. Therefore, the circumference of the drum-wheel driven by the hand-wheel is equally divided into 100 scales, each scale is for one grid, and a spin of one grid is one percent of 1mm, thus the accuracy can be 0.001mm. But there usually exists half pitch deviation because of the clockwise and counterclockwise rotation of the drum wheel clockwise and counterclockwise usually produces half pitch. For the sound velocity measurement experiment it is unacceptable to rotate the drum-wheel at different directions back and forth. In order to avoid this nuisance, we propose a computer-based simulation experiment system and this system can help to obtain a higher precision which is up to four significant digits when compared to the practical experiment. This simulation system can effectively avoid operational mistakes by the experimenter. The experimental data henceforth will be of ideal precision. This system is also capable of repetitive operation which can make up for the deficiency of practical experiments.
Storytelling training to improve English literacy mastery for teachers of SD Negeri Sukoharjo, Semarang Regency

A person's literacy level in a certain matter of how high his ability or skill in their spesific field. However, the problem faced by partners is the low level of English literacy in supporting learning. The priority of the program to be implemented is to improve (1) English literacy skills, (2) mastery of storytelling techniques, and (3) public speaking skills. Participatory methods, training, mentoring for partners, and evaluative were applied in the implementation of this activity. The output of this community service program is an increase in mastery of English literacy and speaking English in public, as well as knowledge of storytelling techniques.
Data Mining using K-means method for feasibility selection of Non-cash food Assistance recipients in the Era of Covid-19

All countries in the world are currently experiencing a severe economic crisis following the outbreak of the COVID-19 outbreak. In Indonesia, the Large-Scale Social Restriction (PSBB) policy is reported to have increased the number of poor people. Social assistance is a government program to improve the social welfare of the lower economic community. In carrying out the program, the central government and local governments coordinate with each other so that the program is right on target without any element of fraud. In the neighbourhood of Rukun Warga 001, Kelapa Dua Village, there are still obstacles in selecting the eligibility for social assistance recipients, namely Non-Cash Food Aid. The data on the poor are not in accordance with the actual conditions. In this study, to implementing data mining with the K-Means Algorithm. The K-Means Clustering algorithm is used to classify people who are classified as eligible to receive social assistance and those who are not entitled to receive social assistance. The data sample used is the data of Rukun Warga 001, Kelapa Dua Village. The results of this study indicate that cluster 1 with the appropriate category of receiving social assistance according to government programs in the Rukun Warga 001 neighbourhood of Kelapa Dua sub-district amounted to 13 families. And cluster 2 in the category of not eligible to receive social assistance amounted to 97 heads of families out of a total of 110 heads of families in RW 001.
SELF-AWARE ADAPTABLE SOFTWARE ARCHITECTURAL PATTERN BASED ON ASSOCIATION RULE MINING

Software architecture is one of the evergreen research areas, as the technical advancement relies on the software architecture. Due to the technical advancements, most of the users possess different terminals for accessing Internet. Irrespective of the diversity in operating system and user terminal, the web service must be interoperable and compatible. Based on the kind of user terminal, the web service has to be provided without any time complexity. The performance of the architecture is claimed better, only when the appropriate web service is provided to the user without any time delay. In order to map appropriate web services for varying user terminal, this article presents a self-aware adaptable software architectural pattern that is based on association rule mining. The proposed approach does not require any prior knowledge about the system and can work on the go. The performance of this approach is analysed by varying the count of user requests and time with respect to accuracy, precision, recall, F-measure and throughput. The proposed approach proves nominal results with better dynamic mapping capability.
Autonomous Aerial Delivery Vehicles, a Survey of Techniques on how Aerial Package Delivery is Achieved

Autonomous aerial delivery vehicles have gained significant interest in the last decade. This has been enabled by technological advancements in aerial manipulators and novel grippers with enhanced force to weight ratios. Furthermore, improved control schemes and vehicle dynamics are better able to model the payload and improved perception algorithms to detect key features within the unmanned aerial vehicle's (UAV) environment. In this survey, a systematic review of the technological advancements and open research problems of autonomous aerial delivery vehicles is conducted. First, various types of manipulators and grippers are discussed in detail, along with dynamic modelling and control methods. Then, landing on static and dynamic platforms is discussed. Subsequently, risks such as weather conditions, state estimation and collision avoidance to ensure safe transit is considered. Finally, delivery UAV routing is investigated which categorises the topic into two areas: drone operations and drone-truck collaborative operations.