Reviewerly/exHarmony huggingface dataset:
D2-Net: Weakly-Supervised Action Localization via Discriminative Embeddings and Denoised Activations

This work proposes a weakly-supervised temporal action localization framework, called D2-Net, which strives to temporally localize actions using video-level supervision. Our main contribution is the introduction of a novel loss formulation, which jointly enhances the discriminability of latent embeddings and robustness of the output temporal class activations with respect to foreground-background noise caused by weak supervision. The proposed formulation comprises a discriminative and a denoising loss term for enhancing temporal action localization. The discriminative term incorporates a classification loss and utilizes a top-down attention mechanism to enhance the separability of latent foreground-background embeddings. The denoising loss term explicitly addresses the foreground-background noise in class activations by simultaneously maximizing intra-video and inter-video mutual information using a bottom-up attention mechanism. As a result, activations in the foreground regions are emphasized whereas those in the background regions are suppressed, thereby leading to more robust predictions. Comprehensive experiments are performed on two benchmarks: THUMOS14 and ActivityNet1.2. Our D2-Net performs favorably in comparison to the existing methods on both datasets, achieving gains as high as 3.6% in terms of mean average precision on THUMOS14.
Aspek Motivasi dalam Pembentukan Perilaku

The theory of motivation and its relationship to human behavior has attracted the interest of scholars since Maslow introduced the Pyramid of Needs theory in the 1940s. Since then, many studies have tried to examine the motivational aspect in the formation of human behavior. Related to this topic, this study aims to explore the developing theories about motivation and human behavior. The methodology used is to conduct a literature review to find the relationship among studies and getting a conclusion. The results showed that the motivation of human behavior has many aspects and is formed from a combination of personal interest drives and social relationships. It can appear in the form of conscious motives that encourage the emergence of discursive behavior or unconscious motives that lead to practical behavior.Therefore, the motivation of human behavior cannot be generalized and must be placed in the context faced by the individual when he performs the action.Keywords: motivation;human behavior;Maslow’s pyramide
Factorizing Perception and Policy for Interactive Instruction Following

Performing simple household tasks based on language directives is very natural to humans, yet it remains an open challenge for AI agents. The 'interactive instruction following' task attempts to make progress towards building agents that jointly navigate, interact, and reason in the environment at every step. To address the multifaceted problem, we propose a model that factorizes the task into interactive perception and action policy streams with enhanced components and name it as MOCA, a Modular Object-Centric Approach. We empirically validate that MOCA outperforms prior arts by significant margins on the ALFRED benchmark with improved generalization.
IoT based Real-Time Monitoring System for Industrial Automation with Big Data Analytics using Machine Learning Approach

Since the last decade, voluminous volumes have been created as Internet of Things (IoT) devices have been more miniaturized. However, without analytical power such data are not meaningful. Many huge data solutions, IoT and analytics have enabled individuals to gain significant information on enormous data created by IoT devices. These solutions, however, are still in its infancy and a full survey is lacking in the area. A solution for efficient monitoring of the production process may be seen in the current techniques such as the IoT (internet of things)-based sensors. This paper proposes a real-time monitoring system which uses IoT-based sensors, large-scale data processing and the paradigm of machine learning. This article mixes the concept of industrial workstation Raspberry Pi with IoT industrial automation. The programming is in python language and uses the Raspberry Pi as controller and server. Apache Kafka, Apache Storm, and the MongoDB are used to store sensor data from the production procedure in real-time as a messaging queue on the suggested big data processing platform. All sensor data is gathered via raspberry pi. All data is used by the Internet of thing platform on a distant basis. This gives an industrial example of the blade ageing system of the cutter-tool and monitors current on the site using raspberry pi as server. The solution presented should support management through better decision making and assist reduce unexpected losses caused by defects during production processes.
Olisipo: A Probabilistic Approach to the Adaptable Execution of Deterministic Temporal Plans.

The robust execution of a temporal plan in a perturbed environment is a problem that remains to be solved. Perturbed environments, such as the real world, are non-deterministic and filled with uncertainty. Hence, the execution of a temporal plan presents several challenges and the employed solution often consists of replanning when the execution fails. In this paper, we propose a novel algorithm, named Olisipo, which aims to maximise the probability of a successful execution of a temporal plan in perturbed environments. To achieve this, a probabilistic model is used in the execution of the plan, instead of in the building of the plan. This approach enables Olisipo to dynamically adapt the plan to changes in the environment. In addition to this, the execution of the plan is also adapted to the probability of successfully executing each action. Olisipo was compared to a simple dispatcher and it was shown that it consistently had a higher probability of successfully reaching a goal state in uncertain environments, performed fewer replans and also executed fewer actions. Hence, Olisipo offers a substantial improvement in performance for disturbed environments.
Clifton Fowler Library: Computer Science, Networking, & Cyber Security: Articles

Collection of resources for Computer Science, Networking and Cyber Security (articles, books, and websites)
CLASS ENGAGEMENT USING SOCIAL MEDIA

Many adaptations had to be made to classes worldwide amid the COVID-19 pandemic and keeping students engaged and interested in class became a priority for educators. This pandemic has posed a challenge since it forced schools to rapidly migrate the teaching-learning process to an online platform. Particularly, Gen-Z class engagement has been an issue for most teachers, because students tend to rapidly drift away from class; this, alongside the anxiety and stress over the future and the health of their loved ones, has made class engagement an even greater challenge. Using social media as a tool in class may seem unorthodox but given the main features that distinguish Gen-Z, it can be a useful tool to approach and engage them. In order to do so, three different phases of educational innovations were implemented, coronabunker, teaching with memes, and open thread. Each implementation built on the learnings from the previous one, to develop strategies targeted for the participants. The results indicated that students perceive these implementations as fun, when developing the posts from the thread activity, students stated that it also allowed them to integrate curricular content. This generation shares unique language and cultural references that may provide educators a rare glimpse to approach and start to build a bridge that would eventually diminish the gap between generations. Keywords: medical education, digital education, distance learning, educational innovation, student engagement
The Language Function in the Discourse Produced by Traditional Medicine Sellers

AbstractThe purpose of this research is to investigate the language function from the discourse produced by traditional medicine sellers. The method used in this research is descriptive method. The data of this research were obtained from the discourse produced by five medicine sellers in different area. The data were analyzed by using Geoffery Leech (1974) theory. The finding divided into three types, they were informative, directive, and phatic function. In informative function also found four type of informative, they were giving opinion, advice, making announcement, and admonish. In directive function was found two types, they were command and request. Last was phatic function. Most of conversations had similar types of language function. However, findings showed that there were some types of language function were not found in each section of conversation produced by traditional medicine sellers. The language functions used by traditional medicine sellers were in line with the purpose of marketing, they were to inform, persuade, and remind the costumer about the product that sold.
Learning Theorem Proving Components

Saturation-style automated theorem provers (ATPs) based on the given clause procedure are today the strongest general reasoners for classical first-order logic. The clause selection heuristics in such systems are, however, often evaluating clauses in isolation, ignoring other clauses. This has changed recently by equipping the E/ENIGMA system with a graph neural network (GNN) that chooses the next given clause based on its evaluation in the context of previously selected clauses. In this work, we describe several algorithms and experiments with ENIGMA, advancing the idea of contextual evaluation based on learning important components of the graph of clauses.
PERANCANGAN SISTEM INFORMASI PEMINJAMAN DAN PENGEMBALIAN BERKAS REKAM MEDIS RAWAT JALAN DI RUMAH SAKIT KHUSUS GIGI DAN MULUT KOTA BANDUNG

This research aimed to determine designing information system of borrowing and returning outpatients at rumah sakit gigi dan mulut Kota Bandung. In this study, the method used is qualitative method with descriptive approach. Data collection techniques used is by observations, interviews, library research which had relation to the subject. Software development method used is waterfall and using microsoft visual studio 2010. Problems from the results of research conducted namely: processing of borrowing and returning medical record for outpatients were still manually recording in expedition book and there were some delays in returning outpatients medical records. As for suggestions given were is: 1) designing new information system which help jobs properly, effectively and efficiently, and 2) needing for resocialization of the maximum due date of outpatients medical records borrowed
Correction to: Analysis of Algorithms in Medical Image Processing

In the original version of the book, the following belated corrections have been incorporated: The author name “Mamata Mittal” has been changed to “Mamta Mittal” in the Frontmatter, Backmatter and in Chapter “Analysis of Algorithms in Medical Image Processing”.
Agir professoral

This contribution proposes to look back at the planning and organisation of my didactic sequences of FLE during the six weeks of distance teaching imposed by the confinement. After a presentation of the teaching context in which I find myself, I will present the digital tools mobilised to carry out the sequences and achieve the learning objectives. I will then discuss the phases that structured this period, namely a revision phase, a creative phase followed by a phase of discovery of new notions. After describing these three phases, I will give my personal reflections on how I experienced these different periods, and in particular on the place and role I gave to digital technology in the teaching scenario.
Removal of Noises in IOT Based Imaging for Optimized Face Recognition

The random change of brightness or colour information in pictures is referred to as image noise. Errors in picture acquisition, transmission, and processing result in noise. The true intensities of the real scene are not reflected in the noise-corrupted picture. The ultimate goal of picture restoration is to improve an image to a certain level of quality. Face detection is a computer technique that recognises human faces in digital pictures and is used in a number of applications. When a picture is in excellent quality and free of random noise, face detectors function well. The objective of this study is to see how adding extra training data to a convolution neural network-based face recognition system affects its performance. The face detector is built with the Caffe deep learning model, and the Alex Net model is utilised. When more training information is provided, the operating frequency increases.
Identification of a Zebra Based On Its Stripes through Pattern Recognition

Visual animal biometrics are commonly used because they provide a non-invasive and cost-effective means of wildlife tracking. Cameras that are motion triggered capture nearby images of wild animals in their natural environments. This collection of chaotic photos taken in the field was used to create a biometric database of individual animals that can be distinguished from their coat markings. The SIFT Descriptors and FLANN parameters were used in this paper to simplify the dataset by extracting only the patterned parts of the body that can be used for resemblance detection. It was tested using two factors: colour match and feature match.
Improve English Speaking Skill for Students through cooperative activities at Thai Nguyen University of Economics and Business Administration.

Students at Thai Nguyen University of Economics & Business Administration are expected to get B1 level in English according to CEFR as the requirement for graduates. Recently, the percentage of students graduating on due date has been decreased as their English proficiency could not meet the standard. Seeking for appropriate teaching methods to improve the quality of students seems to be a challenging task for lecturers at TUEBA. Based on that condition, this study is aimed knowing how cooperative activities improve the students’ speaking skill. The subject of this study was 45 sophomores in Financing and Banking at TUEBA. The research method used was an action research. This study conducted two meetings for the first cycle. The intruments used in this study were speaking tests, observation check list, and questionnaires.The result of the study showed that the students’ speaking skill was improved as they enjoyed the activities in class by having a discussion, sharing, and cooperating well. Their improvement was proved by their speaking test results which increased from test to test. Some certain cooperatives activities were believed to be highly effective when applying to students at the medium and higher level of English.
Determinan Mutu Belajar Mahasiswa Pendidikan Akuntansi Akibat Covid-19

This study aims to partially test the effect of e-learning and motivation on the learning quality of UNIPMA Accounting Education students due to Covid-19. This type of research is quantitative research with data collection techniques using questionnaires. This research was conducted on students of Class 2017, 2018, and 2019 with a population of 78 respondents with a saturated sample of 78 student respondents. The results prove that the learning quality of UNIPMA Accounting Education students as a result of Covid-19 is positively and significantly affected by e-learning, namely with the opportunity to work together, through the availability of software so as to facilitate the learning process simultaneously without any constraints on distance. In addition, motivation also affects the quality of student learning, namely with the ambition and desire to succeed students in improving their competencies.
Extracurricular Management In Improving Student Non-Academic Achievement

This study aims to determine how the management of extracurricular activities in improving students' non-academic achievement at MAN 1 Malang and MA Al-Khoirot Malang, with sub-focuses including: (1) planning extracurricular activities, (2) implementing extracurricular activities, (3) evaluating activities extracurricular, (4) non-academic achievements produced. This study uses a qualitative approach by designing a multi-site study. The data collection is done by interview, observation and documentation. Sources of research data include school principals, vice principals, and coordinator of extracurricular activities. The results showed that: The management of extracurricular activities at MAN 1  and MA Al-Khoirot Malang has been carried out well. This can be seen from the indicators of the management of extracurricular activities carried out and from the results,  the existence of extracurricular activity planning, implementation and evaluation, as well as the achievements he achieved in one year showed an increase. This is due to the well-planned and well-implemented management of extracurricular activities, as well as the gradual evaluation according to the program so that students experience an increase in their non-academic achievement.
The Analysis of the Co-Authorship in Journals of Educational Sciences of Authors from European Post-Socialist Countries

In this paper the aspect of collaboration on articles (n = 3017) in scientific journals (n = 384) was analysed, in the field of educational sciences of authors from 15 post- -socialist countries in the period from 1996 to 2019. The quantitative data was derived from the base Scopus, and they were complemented with structural interviews conducted with 10 scientists. The purpose of the research is to describe the co-authorship of the addressed groups of scientists and to determine the repercussions of the published articles in the scientific community. The results warn about insufficient representation of international co-authorships and about the low representation of authors from post-socialist countries in internationally relevant journals, which reflects on their low citation and visibility in a wider scientific community.
An Ensemble Approach for Argument Mining on Medical Reviews

Argument mining is the automated identification and extraction of opinions and reasoning from natural language text to understand the author’s intent. It is useful in the medical domain, especially for pharmacists and analysts in tracking drug behavior. In this chapter, we present an ensemble approach towards extract argumentative relationships between drug review sentences, to build an application that provides a deeper insight into people’s opinions on medicines. The system identifies argumentative content with the presence of discourse indicators. After preprocessing and feature extraction, vector representations of review sentence pairs are given to classifiers for predicting support/attack relations. We consider hybrid feature sets using structural features and TF-IDF scores. Three base classification algorithms are evaluated, and ensemble methods of voting and stacking are then used to improve performance. Finally, we deploy our system as a web application that accepts medical reviews and gives a detailed analysis via argument graphs and reports.
Magnetism Organizes into a Wave: Meaning for Standard and a Fundamental Model

After 10 experiments, (1)nine inspired by the (2)double slit experiment and (3)one merely measuring what is coming out of a magnet with no man-made interference like a barrier with slits. This one experiment could itself alone prove what took nine experiments to prove for the paper "Magnetism: Insights from the Thomas Young Experiment." The tenth experiment was merely a control experiment to understand basic quantum behaviour of magnetism.
CONSTITUENTS OF LINGUISTIC SELF-EDUCATION

The paper characterizes the components of linguistic self-education, which allow to reveal its essence. We have ascertained that in modern world there is an increase in attention to self-education, which is gradually taking priority positions in the development of a student’s personality as a subject of educational activity. In this regard, purposeful formation of his/her preparedness for linguistic self-education, which we consider as a priority task of getting ready for the postgraduate stage of foreign language self-instruction, should be carried out while studying at higher educational establishments. Linguistic self-education is both the process and result of an individual’s own conscious work, responsible attitude and significant efforts of self-creation through self-development and self-improvement, including all constituents of «self». To gain a deeper insight into the essence of linguistic self-education, as well as to define its role and place in the theory and practice of future specialists’ training, we have analyzed a number of definitions, united by a single root «self», which are structural components of linguistic self-education. Self-awareness is an internal mechanism, through which a person is able to feel his/her capabilities, independently set goals and choose the best ways to achieve them. Self-knowledge determines an individual’s aspiration for constant personal and professional growth and self-improvement. We have singled out and characterized the spheres of personality that should be covered by self-knowledge and which undergo qualitative changes within the process of linguistic self-education, namely: personality sphere, value-motivational sphere, emotional-volitional sphere, sphere of abilities and capabilities, cognitive sphere, sphere of activity, prognostic sphere. Self-comprehension is important for understanding the tasks and process of one’s own linguistic self-education. Among the most common ways of self- comprehension it is relevant to single out the following ones: self-observation, self-analysis, comparison of oneself with a certain standard. We have concluded that linguistic self-education has undeniable advantages in the context of modernizing the existing system of students’ professional foreign language training and is an effective instrument for personal and professional life-long self-improvement.
Some remarks on unfocused hatred: Identity of the hated one and criteria of adequacy

Thomas Szanto has recently argued that hatred could not be a fitting emotion because of its blurred focus. It thus cannot trace the properties of its intentional object. Although I agree with the core of Szanto?s account, I would like to discuss two connected issues that might be of importance. First, I want to address whether the unfittingness of hatred has anything to do with the possibility that the hated person does not identify with what they are hated for. I conclude that if the focus of hatred is blurred, hatred does not trace the identification of the hated person or group. Next, I propose a possibility that (certain) criteria of adequacy of hatred (why someone is treated by members of society as hateworthy) are embedded in the cultural and social framework in such a way that they are not necessarily intelligibly justified by their relation to the focus and import it has. Under such circumstances, with hatred still being unfitting, these criteria create quasi-correctness of hatred (actually, they trace properties of someone being hateworthy). If this is correct, it will enable us to keep the thesis that hatred cannot be fitting. At the same time, we could use political vocabulary to tackle hatred that is common in cases when a group will not give up their commitment to hatred and argue that some people or group of people is not to be hated under the hating group?s own criteria.