Reviewerly/exHarmony huggingface dataset:
Novel Pruning Techniques in Convolutional Neural Networks

Deep Learning allows us to build powerful models to solve problems like image classification, time series prediction, natural language processing, etc. This is achieved at the cost of huge amounts of storage and processing requirements which are sometimes not possible in machines with limited resources. In this paper, we compare different methods which tackle this problem with network pruning. Selected few pruning methodologies from the deep learning literature were implemented to display their results. Modern neural architectures have a combination of different layers like convolutional layers, pooling layers, dense layers, etc. We compare pruning techniques for dense layers (such as unit/neuron pruning, and weight Pruning), and convolutional layers as well (using L1 norm, taylor expansion of loss to determine importance of convolutional filters, and Variable Importance in Projection using Partial Least Squares) for the image classification task. This study aims to ease the overhead in terms of optimization of the model for academic, as well as commercial, use of deep neural networks.
Detection of Suspicious Timestamps in NTFS using Volume Shadow Copies

When a computer gets involved in a crime, it is the mission of the digital forensic experts to extract the left binary artifacts on that device. Among those artifacts, there may be some volume shadow copy files left on the Windows operating system. Those files are snapshots of the volume recorded by the system in case of a needed restore to a specific past date. Before this study, we did not know if the valuable forensic information hold within those snapshot files can be exploited to locate suspicious timestamps in an NTFS formatted partition. This study provides the reader with an inter-snapshot time analysis for detecting file system timestamp manipulation. In other words, we will leverage the presence of the time information within multiples volume shadow copies to detect any suspicious tampering of the file system timestamps. A detection algorithm of the suspicious timestamps is contributed. Its main role is to assist the digital investigator to spot the manipulation if it has occurred. In addition, a virtual environment has been set up to validate the use of the proposed algorithm for the detection.
E-LAS (Letter of Active Status) Pada Biro Administrasi Akademik

The Academic Administration division (BAA) plays a role in the process of issuing student certificates at the National University. Although the data has been computerized, the process of issuing the letter is still manual so it is less effective and efficient. Students must come to the BAA unit to apply for the issuance of a student certificate, including for data correction purposes if needed. This will require more effort for students in terms of time, energy and material. This research is to design a Web-Based Student Certificate Making System that can simplify the service process and submission of a certificate for students at the National University. This system is integrated and responsive and is accessed online. The application was developed with the waterfall model and utilizes the Unified Modeling Language (UML) tool and uses the PHP programming language. The result of this study is the existence of a web-based certificate application design that can facilitate students in obtaining student certificates at the National University. The results of testing the application using the white-box and black-box methods, indicate that the logic and functionality of the application is running well and can be used by the BAA unit of the National University.
Analysis of Flood Disaster Mitigation in West Sumatra

Watershed development based on sustainable development requires open management that ensures the continuity of the coordination process between related institutions. This development is characterized by cross-sectoral and multidisciplinary. This research problem answers about 1) What is the cause of flooding in West Sumatra?; 2) What are the alternative mitigations for the flood disaster management?; 3) What are the impacts of the flood?; and 4) What are the strategic steps that can be taken?. The research study uses the library method, which is based on data obtained from the Agency of Regional Disaster Management (BPBD) West Sumatra Province in 2019. The results of this study indicate that the principles of disaster mitigation in West Sumatra have not been fully implemented, such as before a disaster, during a disaster, and after a disaster.
Enhancing Middle School Pupils’ Motivation Toward Writing Through Edmodo Website

The study aimed at investigating the impact of using Edmodo on enhancing motivation of the middle school pupils. To fulfill the purpose of the study,  an instrument was constructed (a motivation scale). The instrument was administered before and after the experiment to measure the students' motivation toward writing. The validity and reliability of the study instrument were established before its use in the study. The quasi-experimental design had been adopted in the study. The participants of the study consisted of 28 pupils of eighth graders at Adwaa Alhedaya International Schools, in the kingdom of Saudi Arabia, where 14 pupils represented the experimental group and 14 represented the control group. Edmodo was implemented with the experimental group, whereas the control group received the regular teaching. Results of the study revealed that practicing EFL writing through Edmodo led to significant improvement in students  motivation. Therefore, it was concluded that using Edmodo as a mean of communicating and practicing the English language was highly effective in improving the participants' performance. Key words: motivation, Edmodo, Egypt
Hope for the future: An analysis of HOPE/HÅP(E) across genres and languages

This article reports on a contrastive study of the cognate nouns and verbs hope and håp(e) that investigates their lexico-grammatical conditions of use in English vs. Norwegian fiction texts and football match reports. The complex dataset consists of material from a parallel corpus of fiction texts and a comparable corpus of football match reports. An interesting finding is that the verb use outnumbers the noun use in the fiction texts, whereas the noun use outnumbers the verb use in the match reports in both languages. Moreover, the analysis of the lemmas suggests that they have similar potential of use but with slightly different preferences, both across the genres and languages. It is also suggested that the English lemmas are more consistently used in negative contexts than the Norwegian ones. Finally, the method of combining data from two different types of contrastive corpora proved fruitful, as the results become more robust.
Acquiring the tradition

The initial ontological, semantical and philosophical challenges presented by the emergence of Bitcoin are introduced. Blockchain is described. A distinction is drawn between the concepts enabling the technology and specific iterations, use contexts, products or general functions they may give rise to. The notion of enabling concepts is expressed through the term cryptographic consensus technology or CCTech. Reflecting previous technical approaches to the technology, three core components of CCTech are discussed, namely, cryptography, consensus and decentralization. The surge of interest in cryptoassets offered via initial coin offerings, from both the retail public and regulators, is introduced. The primary cause of the present regulatory problem is identified as understanding how CCTech interacts with a legacy system of laws, regulations and commercial practices. The general position that it is appropriate to apply existing capital markets and financial services regulation to cryptoassets is defined as the standard narrative, which the book will explore.
Topic Classification of Islamic Question and Answer Using Naïve Bayes and TF-IDF Method

Information spread through the internet is widely used by people to find anything. One of the most searched information on the internet is information related to Islamic religious knowledge. However, the large amount of information available from various sources makes it difficult for people to find the correct information. Previous researchers have researched this topic, but the dataset used only comes from one source. Therefore, in this study, a classification system for Islamic question and answer topics was built using the Naïve Bayes and TF-IDF methods. This study using 1000 question and answer article data taken from Islamic consultation websites, namely rumahfiqih.com and islamqa.info. The multi-class classification uses five categories which are manually labeled using the category classes on the website. From several test scenarios in this study, the Naïve Bayes classification method using TF-IDF (n-gram level) with a maximum feature of 1000 at a data separation ratio of 70:30 produces the highest accuracy of 81%. The 81% accuracy value was also generated by the SVM classification method, but the difference was in the SVM the highest accuracy value using TF-IDF (word level). It is expected that in the subsequent research will be used more website sources and the use of other classification and feature extraction methods with more optimal value than previous research.
KEBAKARAN LAHAN BASAH DAN FAKTOR MANUSIA SEBAGAI PENYEBABNYA

Currently, forest and land fires have become a regular disaster in Indonesia. The cause of the fires was mostly due to human factors. Ogan Ilir is an area with forest and land fires every year because its dominated by wetlands. The study aim was to analyze human factors as the cause of land fires in knowledge factors, cultural factors, social factors, economic factors, and policy roles. This study is a descriptive analytic study with a qualitative approach. Data was obtained through interviews with key informants who had been sentenced to be fire perpetrators and regular informants from Regional Disaster Management Agency of Regency and Police in Ogan Ilir. The cause of the community burning the land was due to the low level of community understanding regarding the impact of burning the land with the intention of clearing the land and the presence of sonor culture which the community practiced almost every year on the residual harvest. In addition, there are social conflicts between the community and the surrounding land owners, cost savings in land clearing, and the community continues to burn land even though regulations related to land burning already exist. The main factors that cause people to burn are social and economic factors. It is necessary to enforce laws regarding idle land whose ownership is unclear and the local government can provide borrowing heavy equipment at rental prices that are affordable to the community.
Topic Modeling in the News Document on Sustainable Development Goals

Indonesia is a developing country and supports the program of the Sustainable Development Goals (SDGs) which consist of 17 goals. SDGs is not only the government’s duty, but a shared duty from any elements. Online media has a crucial role in implementing goals of Indonesia’s SDG. Information published in online news related to the SDGs is an important consideration for the government, society, and all elements. Categorizing news manually to find out news topics is very time-consuming and done by the ability of news editors. News presented by online media on the news site can be used as topic modeling, where hidden topics can be found in the news on online media. Topic modeling will classify data based on a particular topic and determine the relationship between text. Latent Dirichlet allocation (LDA) is one of the methods on topic modeling to find out the trend of topics of SDGs news. Based on the result of this research, the implementation of LDA is the right choice for finding topics in a document. The result of topic modeling with k = 17 obtained the highest coherence score of 0.5405 on topic 8. Topic 8 discussed news related to the eighth SDGs goals, namely decent work and economic growth. This categorization was based on words formed after the LDA process. Then, topic 5 discussed the news on the 17th SDGs goals, namely partnerships for the goals. Topic 6 discussed the news of the first SDGs, namely no poverty.
Reading and Writing Performance of Senior High School Students

The study was conducted to determine the performance and attitudes towards reading and writing of Grade 12 students in public schools of Cabagan, Isabela. There were 244 students involved in the study. Stratified random sampling was used in selecting the participants of the study. A quantitative research method was used in the study to determine performance and attitudes towards reading and writing. Frequency counts, percentage scores, the arithmetic mean, and Kendall’s Rank Correlation Tau was used to describe and analyze the data in the study. The findings of the study revealed that students' reading and writing performance were poor. However, their attitudes toward reading and writing were positive. Hence, the study demonstrated that there is a significant correlation between students’ attitudes and their reading and writing performance. Thus, the study supports DepEd’s "SulongEdukalidad" in creating programs to enhance learners' reading mastery and sustain its basic goal of producing quality learners enhanced by quality education. Language teachers should also be engaged and exposed to training to handle weak foundations of learners in basic academic (reading and writing) skills.
Academic Revolution Impact On The Development Of Higher Education Institutions In Russia

The article considers both the consequences of the impact of the world and Russian academic revolution on the development of the mass education system in higher educational institutions and possible prospects and methods for transformation in the national higher education system. Right now, there are issues developed concerning the universities, their management not just within the framework of routine administration, but in the governance paradigm of the university development, taking into account the formation of a fundamentally new system of higher education in Russia with an emphasis on the academic revolution. The efficiency parameters of the activities and actions of a university are presented in this article, along with the possibilities of a modern developing university are considered, the threats that hinder the development, as well as the methods of taking them into account in planning, are examined. There given analysis for the weaknesses of the leading universities in Russia. Universities are required to identify themselves to which of the categories they will be assigned, what status it will have, what educational standards to base on, develop a strategy for working with organizational units, and principles of work in a competitive environment between universities, to cope with the imitation of research activities on the basis of the university. The institutional policy of the state has made serious adjustments to the development program of each educational institute by regulating the network of non-state universities, which entails the need to optimize and redistribute human and material resources.
Salient Object Ranking with Position-Preserved Attention

Instance segmentation can detect where the objects are in an image, but hard to understand the relationship between them. We pay attention to a typical relationship, relative saliency. A closely related task, salient object detection, predicts a binary map highlighting a visually salient region while hard to distinguish multiple objects. Directly combining two tasks by post-processing also leads to poor performance. There is a lack of research on relative saliency at present, limiting the practical applications such as content-aware image cropping, video summary, and image labeling. In this paper, we study the Salient Object Ranking (SOR) task, which manages to assign a ranking order of each detected object according to its visual saliency. We propose the first end-to-end framework of the SOR task and solve it in a multi-task learning fashion. The framework handles instance segmentation and salient object ranking simultaneously. In this framework, the SOR branch is independent and flexible to cooperate with different detection methods, so that easy to use as a plugin. We also introduce a Position-Preserved Attention (PPA) module tailored for the SOR branch. It consists of the position embedding stage and feature interaction stage. Considering the importance of position in saliency comparison, we preserve absolute coordinates of objects in ROI pooling operation and then fuse positional information with semantic features in the first stage. In the feature interaction stage, we apply the attention mechanism to obtain proposals' contextualized representations to predict their relative ranking orders. Extensive experiments have been conducted on the ASR dataset. Without bells and whistles, our proposed method outperforms the former state-of-the-art method significantly. The code will be released publicly available.
Neuro-Fuzzy for Traffic Volume Forecasting of National Road III Jember Banyuwangi KM 252

Traffic congestion is one of the prevalent phenomena in Indonesia, especially in big cities. There are a variety of factors causing congestion in the city, including the expansion of existing infrastructure, the large number of residents in the area, and the disparity between the growth of motor vehicles and highways. By careful transport planning, this can be fixed. The estimation of traffic volume is one strategy to see how much road growth is to offset vehicle volume growth to prevent congestion. This study tries to estimate the traffic volume of National Road III Jember Banyuwangi KM 252 motorized 2-wheeled vehicles passing by. The data used were obtained from 24 hours observation for 7 days. Using the Neuro-Fuzzy method which applies 1 day out of 7 days as testing data, and obtaining the average error is 13.2747%. This average error shows that the error rate that occurs is 5% of all errors. Neuro-Fuzzy can forecast the traffic volume of 2-wheeled motorized vehicles reasonably well.
PEMANFAATAN FILM ANIMASI SEBAGAI MEDIA PEMBELAJARAN ANAK BERBASIS FLASHMX

Entertainment-based education can be done by teachers and parents to design learning materials about soft skills in children such as helping children. The usefulness of this animated film, has a function as an entertainment tool and as a learning medium for children so that children do not feel bored and are able to provide freshness because of the entertainment element. Of the many animated films that are shown, not many teach about something that contains the meaning of goodness, for example the attitude of helping each other. After making this film, it shows that this film has an interesting storyline and is worthy to be watched by children aged 2-8 years and contains educational values both morally and religiously and can entertain children. Children always want to help people who are having difficulties, namely by helping everyone so that they can help ease the burden of suffering from others and get a reward from the creator. The making of this animation using Macromedia Flash MX software by adding a mixture of visual effects in each scene to make it more interesting and the picture clearer
GLOBAL CLASSROOM: INTERNATIONALIZATION FOR UNDERGRADUATE STUDENTS AMID THE PANDEMIC

The international exchange for undergraduate students is a life-changing experience, an opportunity to interact with others and develop intercultural skills while learning. Nevertheless, the deficit in student participation in internationalization activities demands new formats that offer this academic practice in a virtual environment. Adjacent to this scenario, the greatest impact of the COVID-19 pandemic on higher education was face-to-face exchanges. Therefore, academic programs as Global Classroom was the only way for an international exchange amid the pandemic. The objective of this study was to assess the perceptions of the students about an implementation of a virtual international exchange shared with students from Mexico and Ecuador, focused on this contribution as an educational innovation to promote learning in multicultural and interdisciplinary skills. The study considered a quantitative approach to analyze a student's perception of the experience, and the design was descriptive and cross-sectional. The sample were 44 students enrolled in the experience in the August-December semester in 2020, 22 from Mexico and 22 from Ecuador. The instrument was made of 5 items in which the participants shared their experience regarding the different tools and activities, and their contribution to the educational objectives. Results show that the students valued the intercultural experience positively and the contribution of the different free-access educational technology tools to develop synchronous and asynchronous activities. Other disciplines and institutions can learn from this implementation to design remote educational environments that offer this internationalization experience for learners even after the pandemic. Keywords: higher education, educational innovation, interdisciplinary education, intercultural education, pandemic adaptations, global exchange.
Sentiment Analysis For Customer service

NLP can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. By utilizing Natural Language Processing the customer experience will improve . E-mail is still the most commonly used digital customer service channel 54% of customers have used E-mail customer service channel. The proposal of this paper is to develop an algorithm where customer E-mails are scanned, analyze the sentiment from the body of the message and automate customer e-mail categorization and prioritization for the banking sector. The main goals are to collect bank query related E-mail data, ranging from general information, escalations and request, Develop a machine learning algorithm that can perform text mining and sentimental analysis, Provide priority based categorized information for management to prioritize and improve customer service.
Gravity-Aware Monocular 3D Human-Object Reconstruction

This paper proposes GraviCap, i.e., a new approach for joint markerless 3D human motion capture and object trajectory estimation from monocular RGB videos. We focus on scenes with objects partially observed during a free flight. In contrast to existing monocular methods, we can recover scale, object trajectories as well as human bone lengths in meters and the ground plane's orientation, thanks to the awareness of the gravity constraining object motions. Our objective function is parametrised by the object's initial velocity and position, gravity direction and focal length, and jointly optimised for one or several free flight episodes. The proposed human-object interaction constraints ensure geometric consistency of the 3D reconstructions and improved physical plausibility of human poses compared to the unconstrained case. We evaluate GraviCap on a new dataset with ground-truth annotations for persons and different objects undergoing free flights. In the experiments, our approach achieves state-of-the-art accuracy in 3D human motion capture on various metrics. We urge the reader to watch our supplementary video. Both the source code and the dataset are released; see this http URL.