Reviewerly/exHarmony huggingface dataset:
Time Series Temperature Forecasting by using ConvLSTM Approach, Case Study in Jakarta

Climate change has occurred in several countries, especially in tropical countries such as Indonesia. It causes extreme temperature changes in several Indonesian areas, especially Jakarta, one of the world's most populated cities. The population of Jakarta causes the activities carried out by residents to be disturbed by extreme temperature changes. In addition, drastic temperature changes also affect the energy consumption used by residents. Therefore, it is necessary to predict temperature to determine future temperature conditions so that residents can plan their activities. Temperature forecast can be done in several ways, one of which uses a machine learning approach. This research uses a deep learning model called the Convolutional Long Short-Term Memory (ConvLSTM). Moreover, we also compare the model with Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM). We use temperature data taken from the ERA-5 period years 2018 to 2020 located in Kemayoran, Jakarta, Indonesia. This research aims to investigate the accuracy of short-term temperature forecasting by using these three models. The model is built to predict short-term temperatures for 1, 3, and 7 days ahead. The performance of the three methods is measured by calculating the Root Mean Square Error (RMSE), Mean Square Error (MAE), and Coefficient Correlation (CC). The result shows that the LSTM performs better than the other methods to forecast 1, 3, and 7 days, i.e., with the lowest RMSE, MAE, and higher CC. &#x0D;
Use of Excel Software for Graphing for PAUD Luwu Timur Teachers

The training aims to provide insight, abilities, and skills of PAUD Luwu Timur teachers in graphing using excel software. The method used is to conduct lectures, practice, and discussions between teachers and the implementation team. The result of this training is that the teachers are skilled and proficient in using Microsoft excel features in drawing graphs or charts, including Bar charts, Scatter, Line charts, and Pie, the teachers are also pleased and interested in using the features possessed by Microsoft Excel.
A Brief Introduction of Indonesian Culture and Its National Identity to College Students in the University of Nueva Caceres

Culture is the identity of a nation. The cultural diversity of the archipelago will further strengthen the identity of the Indonesian nation. The purpose of this service is to introduce Indonesian culture and its national identity to English-language education students at the University of Nueva Caceres, Naga City, Philippines. This Community service activities are carried out for English language education students at the University of Nueva Caceres, Naga City, Philippines. In this activity, data is gathered through observations, interviews, and studies of the literature. There are three steps to this activity: planning, implementation, and evaluating. The result showed that both English education lecturer and students at the University of Nueva Caceres in Naga City, Philippines, learnt more about Indonesian culture and national identity, and they also acquired more vocabulary in the Indonesian language. This means that the activity had a good effect.&#x0D; Abstrak&#x0D; Kebudayaan merupakan identitas suatu bangsa. Keanekaragaman budaya nusantara akan semakin memperkuat jati diri bangsa Indonesia. Tujuan pengabdian ini adalah untuk memperkenalkan budaya indonesia dan identitas nasionalnya kepada mahasiswa pendidikan bahasa inggris the University of Nueva Caceres , Naga City, Philippines. Kegiatan Pengabdian masyarakat dilakukan pada mahasiswa pendidikan bahasa inggris the University of Nueva Caceres , Naga City, Philippines. Pengumpul data dalam kegiatan ini menggunakan observasi,interview and studi literature. Tahapan dalam kegiatan ini ada tiga yaitu perencanaan , pelaksanaan dan evaluasi. Hasil dari kegiatan adalah bahwa baik dosen maupun mahasiswa pendidikan bahasa inggris the University of Nueva Caceres , Naga City, Philippines, ada mengalami penambahan wawasan dan pengetahuan mengenai budaya indonesia dan identitas nasionalnya serta menambah kosakata dalam bahasa indonesia artinya kegiatan ini memberikan dampak yang positif .
Support Vector Regression Method for Predicting Off-Grid Photovoltaic Output Power in the Short Term

Photovoltaic (PV) technology is a renewable technology utilizing conversion of solar power or solar radiation into electrical energy. In the manufacture of Solar Power Generation systems, reference is needed regarding the cost of generation and scheduling of maintenance plans. To obtain this reference, it is necessary to predict the photovoltaic power output which is used to determine the power output of PV in the future. In this study, a system that is used to predict short-term power output in PV is designed. This system uses solar irradiation data and 42 days of power output in off-grid PV mini-grid as the dataset. The dataset obtained from the PV output is processed using the Support Vector Regression method with the Kernel Radial Basis Function (RBF) function. Based on the dataset used, this study succeeded in testing the best kernel, namely the RBF kernel. Evaluation of the prediction model obtained a smaller error value than other kernel tests with a Mean Absolute Percentage Error (MAPE) value of 21.082%, Mean Square Error (MSE) value of 0.122, and Mean Absolute Error (MAE) value of 0.262. The prediction model obtained is used to predict the short-term PV power output for the next 3 days. The results of the prediction model have an error value of 5.785 % for MAPE, 0.005 for MAE and 0.069 for MSE. Therefore, the predictive model can be categorized as very good and feasible to predict short-term power output
Analysis of Public Sentiment Towards Goverment Efforts to Break the Chain of Covid-19 Transmission in Indonesia Using CNN and Bidirectional LSTM

COVID-19 is a new disease that has a negatively impacts in Indonesia, so the government is taking several measures to suppress the spread of COVID-19, such as new normal, social distancing, health protocols fines, and COVID-19 vaccination. The government's handling efforts have reaped a variety of negative to positive responses from the public on social media, so this study aims to determine the effectiveness of the government's efforts by analyzing public sentiment using the Deep Learning method with 1,875 training datasets consisting of four types government efforts and taken from various media social. The use of Deep Learning begins with testing several Deep Learning architectures to determine the best architecture for predicting data. The architectures tested include CNN and Bi-LSTM, where from these tests, Bi-LSTM outperforms CNN with the best performance achieving the accuracy of 97.34% and 97.33% for precision, recall, and F1-score. The results of public sentiment analysis show that social distancing efforts are considered the most effective by obtaining the most positive sentiments by 33.93%, while the effort to health protocol fines is considered lacking because it obtains the most negative sentiment of 35.64%, so the government must continue to enforce social distancing and optimize other efforts that are still considered ineffective.&#x0D;
Sentiment Analysis of Peduli Lindungi Application Using the Naive Bayes Method

Peduli Lindung application as a form of government policy in the context of handling Covid-19. The level of usability in an application is really needed to see the usefulness of the application itself. The analysis is carried out in the form of a fine-grained sentiment analysis based on a five-star review. Models used in conducting the analysis in this study using Naïve Bayes. Data used in get it through Google Play Store until April 2022. Rating 1 has the most number from other ratings, namely as many as 467 reviews and rating 4 has the lowest number, namely 55 reviews. The data is classified as negative as many as 146 data, a lot of data are classified as negative classified as true positive as many as 30 data, and data classified as neutral as many as 30 data, with classification accuracy still at 73%. The results obtained by the community tend to show words that refer to the problems that exist in the application.
User Access Models to Event-Centric Information

Events such as terrorist attacks and Brexit play an important role in the research of social scientists and Digital Humanities researchers. They need innovative user access models to event-centric information which support them throughout their research. Current access models such as recommendation and information retrieval methods often fail to adequately capture essential features of events and provide acceptable access to event-centric information. This PhD research aims to develop efficient and effective user access models to event-centric information by leveraging well-structured information in Knowledge Graphs. The goal is to tackle the challenges researchers encounter during their research in a workflow, from exploratory search to accessing well-defined and complete information collections. This paper presents the specific research questions and presents the approach and preliminary results.
Symbolic State Estimation with Predicates for Contact-Rich Manipulation Tasks

Manipulation tasks often require a robot to adjust its sensorimotor skills based on the state it finds itself in. Taking peg-in-hole as an example: once the peg is aligned with the hole, the robot should push the peg downwards. While high level execution frameworks such as state machines and behavior trees are commonly used to formalize such decision-making problems, these frameworks require a mechanism to detect the high-level symbolic state. Handcrafting heuristics to identify symbolic states can be brittle, and using data-driven methods can produce noisy predictions, particularly when working with limited datasets, as is common in real-world robotic scenarios. This paper proposes a Bayesian state estimation method to predict symbolic states with predicate classifiers. This method requires little training data and allows fusing noisy observations from multiple sensor modalities. We evaluate our framework on a set of real-world peg-in-hole and connector-socket insertion tasks, demonstrating its ability to classify symbolic states and to generalize to unseen tasks, outperforming baseline methods. We also demonstrate the ability of our method to improve the robustness of manipulation policies on a real robot.
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding

Attributed network embedding (ANE) is to learn low-dimensional vectors so that not only the network structure but also node attributes can be preserved in the embedding space. Existing ANE models do not consider the specific combination between graph structure and attributes. While each node has its structural characteristics, such as highly-interconnected neighbors along with their certain patterns of attribute distribution, each node's neighborhood should be not only depicted by multi-hop nodes, but consider certain clusters or social circles. To model such information, in this paper, we propose a novel ANE model, Context Co-occurrence-aware Attributed Network Embedding (CoANE). The basic idea of CoANE is to model the context attributes that each node's involved diverse patterns, and apply the convolutional mechanism to encode positional information by treating each attribute as a channel. The learning of context co-occurrence can capture the latent social circles of each node. To better encode structural and semantic knowledge of nodes, we devise a three-way objective function, consisting of positive graph likelihood, contextual negative sampling, and attribute reconstruction. We conduct experiments on five real datasets in the tasks of link prediction, node label classification, and node clustering. The results exhibit that CoANE can significantly outperform state-of-the-art ANE models.
RF Fingerprinting to Automatic Detection of Wireless Nodes

RF Fingerprinting to Automatic Detection of WirelessNodes
Handling Inflation In The World Of Marketing By Using Communication And Information Technology And Its Impact On Economic Growth

This study examines Inflation, Communication and Information Technology, Marketing and Economic Growth, and the connection among them. This study investigates data from the 2000 to 2020 starting point to generate “autoregressive vectors” that can be used to determine relationships between variables. This model is used to analyze Inflation, Information Communication and Technology, Marketing and Economic Growth in Indonesia using secondary data from the World Bank. We find that Inflation can reduce the quality of economic development in each period from the beginning to the end, but inflation can be overcome with the existence of Information Communication and Technology, not only that Information Communication and Technology also helps in the development of a Marketing in Indonesia which has resulted in increased economic growth in Indonesia.
The Digital Economy and the Importance of Technology in Economic Growth

This study investigates internet users, economic growth and digital. This study investigates data at the start point year of 2000 to 2020 to generate "autoregressive vectors" that can be utilize for determine relationship among the variables. This model is to analyze among growth of the economic, the individual user from internet, also there is digital economic spending at Indonesia using secondary data from the World Bank. We discovered there is a development of digital economy who has a substantial and has influence on the growth of economic, implying that there is digital economy is critical to economic progress, but the growth of the modern economy cannot being separated from the influence of internet users. This study proves that a decrease in internet users will increase economic growth, just as an increase in the economy when it declines will lead to an increase in internet users.
How Riba Affect Economic Performance in Cambodia

This study investigates about interest rate to economy including economic growth, investment, and consumption in Cambodia.This study uses vector analysis method with a time period starting from 2000 to 2020 using secondary data from the World Bank. This study investigates the causal relationship between variables including interest rates, consumption, investment and GDP. We found that Interest rates in Cambodia have a negative return on economic growth, this shows that interest rates suppress economic growth and have a significant impact on economic performance in Cambodia. Interest rates also suppress investment and domestic consumption of course this has a bad impact on economic performance. However, an increase in economic growth actually pushed up interest rates. This will result in greater pressure on the economy in the future.
Cybercrime in international law

Because of the seriousness has been focused on the response of states and international organizations concluded on the international conventions and the regulations and instructions necessary to prevent them and hit them and hold those responsible accountable, the most important convention in Europe ( Budapest ) and the system of data protection, who issued it and the Arab convention for the protection of the data and the convention of the African to protect the data and finally the efforts of the United Nations set up the convention on the design of inclusive of all countries of the world . The research aims to answer the following questions.
A multi-label classification approach via hierarchical multi-label classification

Abstract Multi-label classification (MLC) is a very explored field in recent years. The most common approaches that deal with MLC problems are classified into two groups: (i) problem transformation which aims to adapt the multi-label data, making the use of traditional binary or multiclass classification algorithms feasible, and (ii) algorithm adaptation which focuses on modifying algorithms used into binary or multiclass classification, enabling them to make multi-label predictions. Several approaches have been proposed aiming to explore the relationships among the labels, with some of them through the transformation of a flat multi-label label space into a hierarchical multi-label label space, creating a tree-structured label taxonomy and inducing a hierarchical multi-label classifier to solve the classification problem. This paper presents a novel method in which a label hierarchy structured as a directed acyclic graph (DAG) is created from the multi-label label space, taking into account the label co-occurrences using the notion of closed frequent labelset. With this, it is possible to solve an MLC task as if it was a hierarchical multi-label classification (HMC) task. Global and local HMC approaches were tested with the obtained label hierarchies and compared with the approaches using tree-structured label hierarchies showing very competitive results. The main advantage of the proposed approach is better exploration and representation of the relationships between labels through the use of DAG-structured taxonomies, improving the results. Experimental results over 32 multi-label datasets from different domains showed that the proposed approach is better than related approaches in most of the multi-label evaluation measures. Moreover, we found that both tree and in specially DAG-structured label hierarchies combined with a local hierarchical classifier are more suitable to deal with imbalanced multi-label datasets.
A Deep Learning Algorithm for Special Action Recognition of Football

Soccer (football) is a popular form of exercise on the planet. There are a lot of individuals who tune into football matches in real time on television or the Internet. A game of American football lasts 90 minutes, but to save time, spectators may simply want to see a few highlights. As far as we know, no such tool exists that can be used to extract intelligent highlights from a football match. In this research, we present a technique for clever editing of live football matches. Our technology allows for the automatic extraction of key players’ goals, shots, corner kicks, red and yellow cards, and the presence of key players from a football match’s live stream. During the 2018 FIFA World Cup, our solution was integrated into live streaming platforms and it functioned admirably.
University campuses and types of crime: A case study of the university of KwaZulu-Natal/Howard campus in the city of Durban-South Africa

University campuses are consumed by vast numbers of criminal activities, which affect both the student and the staff. This study aimed at ascertaining the types of crimes that happen at University campuses by focusing on the Howard College Campus of the University of KwaZulu-Natal Durban South Africa. The research used a qualitative approach to gather data from participants, through in-depth semi-structured interviews. The findings of the study indicated that the types of campus crimes include sexual and verbal assaults and housebreaking to steal laptops, cell phones, clothes, and other accessories. Vehicle theft was also reported and considered a major concern. Forced entry and alcohol and drug abuse were also identified. However, the issue with alcohol and drugs use was that it is hard to detect as students are usually not searched when they enter the campus premises with such substances. The findings did not reveal whether criminal cases are handed over to the police after reporting to the supervisor who then decides what happens next.
An Efficient Searchable Encryption Framework with Weighted Keywords for the Internet of Medical Things (IoMT)

&lt;p&gt; With the increasing popularity of smart terminals, the Internet of Things (IoT) vastly expands the influence of information technology and creates great values to our society. The Internet of Medical Things (IoMT) is a typical application of the IoT specifically for medical and healthcare related purposes. In IoMT, personal health records (PHRs) generated by intelligent equipment are usually stored in a cloud sever managed by a semi-trusted cloud provider. However, there is still a possibility of the exposure of private information to unauthorized parties. In order to protect a patient’s private information, and ensure that doctors can search a patient’s PHRs without decrypting all ciphertexts, an efficient searchable encryption framework with weighted keywords is proposed in this paper. In this framework, we implement a weighted keyword model to raise the accuracy of search results, and reduce the computational and storage overhead. Besides, compared with the existing schemes, the framework is supportable of multi-search mechanism, which is adaptable to more application scenarios. Finally, the efficiency and security of this framework is proved through security and performance analysis in this paper. &lt;/p&gt;
Root Cause Analysis for Autonomous Optical Network Security Management

&lt;p&gt;The ongoing evolution of optical networks towards autonomous systems supporting high-performance services beyond 5G requires advanced functionalities for automated security management. To cope with evolving threat landscape, security diagnostic approaches should be able to detect and identify the nature not only of existing attack techniques, but also those hitherto unknown or insufficiently represented. Machine Learning (ML)-based algorithms perform well when identifying known attack types, but cannot guarantee precise identification of unknown attacks. This makes Root Cause Analysis (RCA) crucial for enabling timely attack response when human intervention is unavoidable. We address these challenges by establishing an ML-based framework for security assessment and analyzing RCA alternatives for physical-layer attacks. We first scrutinize different Network Management System (NMS) architectures and the corresponding security assessment capabilities. We then investigate the applicability of supervised and unsupervised learning (SL and UL) approaches for RCA and propose a novel UL-based RCA algorithm called Distance-Based Root Cause Analysis (DB-RCA). The framework’s applicability and performance for autonomous optical network security management is validated on an experimental physical-layer security dataset, assessing the benefits and drawbacks of the SL- and UL-based RCA. Besides confirming that SL-based approaches can provide precise RCA output for known attack types upon training, we show that the proposed UL-based RCA approach offers meaningful insight into the anomalies caused by novel attack types, thus supporting the human security officers in advancing the physical-layer security diagnostics. &lt;/p&gt;