Reviewerly/exHarmony huggingface dataset:
Effectively computing transition patterns with privacy-preserved trajectory datasets

Recent advances in positioning techniques, along with the widespread use of mobile devices, make it easier to monitor and collect user trajectory information during their daily activities. An ever-growing abundance of data about trajectories of individual users paves the way for various applications that utilize user mobility information. One of the most common analysis tasks in these new applications is to extract the sequential transition patterns between two consecutive timestamps from a collection of trajectories. Such patterns have been widely exploited in diverse applications to predict and recommend next user locations based on the current position. Thus, in this paper, we explore the computation of the transition patterns, especially with a trajectory dataset collected using differential privacy, which is a de facto standard for privacy-preserving data collection and processing. Specifically, the proposed scheme relies on geo-indistinguishability, which is a variant of the well-known differential privacy, to collect trajectory data from users in a privacy-preserving manner, and exploits the functionality of the expectation-maximization algorithm to precisely estimate hidden transition patterns based on perturbed trajectory datasets collected under geo-indistinguishability. Experimental results using real trajectory datasets confirm that a good estimation of transition pattern can be achieved with the proposed method.
[Patient isolation during Sars-CoV-2 pandemic: the caregivers' experience in a neuro-rehabilitation unit].

. Patient isolation during Sars-CoV-2 pandemic: the caregivers' experience in a neuro-rehabilitation unit.During the Covid-19 pandemic, caregiver visits were suspended in many health care facilities and, even today, limitations remain. These restrictions have impacted not only the patients but also caregivers.To explore the life experience of caregivers of hospitalized patients, during the pandemic and the impact of visit limitations in a rehabilitation unit.An exploratory qualitative study was conducted, following the COREQ guidelines. Caregivers of inpatients were purposefully sampled and interviewed, until data saturation. A qualitative thematic analysis with an inductive approach was performed.Fifteen caregivers were interviewed. The themes emerged were: 1. perception of the illness as a sudden change; 2. concerns for their loved one (i.e. the loneliness or the suffering); 3. critical (difficult to organize and perceived too short) and positive aspects of the visits; 4. barriers to communication (for example, masks when in presence; or the difficult use of technologies by elders); 5. patient's protection and support (thinking that the relative's presence may improve the rehabilitation); 6. emotions (from discouragement when visits were not allowed, to joy when meeting their loved one).Caregivers experienced uncertainty, worry, anxiety, fear, and lack of understanding of their loved one's clinical situation. Alternatives to the visits or the visits with physical barriers were helpful but not always effective.
ACADEMIC TEXT CLUSTERING USING NATURAL LANGUAGE PROCESSING

Accessing data is very easy nowadays. However, to use these data in an efficient way, it is necessary to get the right information from them. Categorizing these data in order to reach the needed information in a short time provides great convenience. All the more, while doing research in the academic field, text-based data such as articles, papers, or thesis studies are generally used. Natural language processing and machine learning methods are used to get the right information we need from these text-based data. In this study, abstracts of academic papers are clustered. Text data from academic paper abstracts are preprocessed using natural language processing techniques. A vectorized word representation extracted from preprocessed data with Word2Vec and BERT word embeddings and representations are clustered with four clustering algorithms.
A Study of Real-time Semantic Segmentation Performance Improvement in Unstructured Outdoor Environment

Semantic segmentation in autonomous driving for unstructured environments is challenging due to the presence of uneven terrains, unstructured class boundaries, irregular features and strong textures. Current off-road datasets exhibit difficulties like class imbalance and understanding of varying environmental topography. To overcome these issues, we propose a deep learning framework for semantic segmentation that involves a pooled class semantic segmentation with five classes. The evaluation of the framework is carried out on two off-road driving datasets, RUGD and TAS500. The results show that our proposed method achieves high accuracy and real-time performance.
Dynamics of Entrepreneurship Education and Small and Medium Enterprises (SMEs) Development in North Central Nigeria

This study sought to determine entrepreneurship education and small and medium enterprises development in North Central, Nigeria. The nonchalant entrepreneurial attitude and intention of youths and individuals in the society has posed a lot of challenges leading to slow development of small and medium enterprises, with the resultant effects of poor economic growth and development. The specific objectives of the study were to: (i) Ascertain the extent to which entrepreneurship vocational training affects products development of small and medium enterprises in North Central, Nigeria; (ii) Determine the extent to which entrepreneurial skill acquisition enhances productivity of small and medium enterprises in North central, Nigeria; (iii) Determine the extent to which entrepreneurial networking affects market expansion of SMEs in North Central, Nigerian. On the conduct of the study, the survey design was used. The target population of the study was 17,973 consisting of all staff of private enterprises (including owner-founder) that are registered with the various state ministries of Trade, Commerce, Industry, and Tourism in North Central, Nigeria. A sample size of 377 was drawn from the population using Krejcie and Morgan sample size determination. Bowley’s proportional statistical technique was used to determine the proportional allocation of questionnaire to the number of enterprise and staff in each state. The instruments used for data collection was the mixed method: which is quantitative (structured questionnaire) and qualitative (interview) method. Face and content validity, a panel of management experts in the department was used to critiques the validity of the instrument. The instrument was checked for reliability using Test-retest method and the results computation was carried out using Spearman Ranking Correlation Coefficient of test-retest results and a value of 0.98 indicated a high degree of item consistency. The hypotheses were tested using Pearson product moment correlation technique for hypotheses one and two; simple linear regression for hypothesis three. The findings revealed that: To a large extent entrepreneurship vocational training enhances products development of small and medium enterprises in North Central, Nigeria (r=.979, p&lt;0.05): Entrepreneurial skill acquisition enhances productivity of small and medium enterprises in North Central, Nigeria (r=.949, p&lt;0.05). Based on the findings of the study, it was recommended that there should be more government and private sector involvement in the concept of entrepreneurship education; more so, policy makers and implementers should see entrepreneurship as a pivot engine that steers the economic hub of any nation to a virile and greater height. More importantly taking a turn from the “Asian tigers” economy, that has strategic turnaround was from entrepreneurship development.
Web-Based Academic Information System Design Plan for Kindergarten

Website is one of the services that can be used by computer users connected to the internet . The website makes it easy for computer users to interact with other internet users and browse information on the internet network . Kindergarten is one of the institutions engaged in the field of education for early childhood. The purpose of making this thesis is to create an academic information website for parents of students in kindergarten. To create a kindergarten website , several methods are needed. In this thesis report the methods used include literature study, observation and interviews. This website was developed using the PHP programming language and database used is a MySQL database. In this thesis report, a system has been created that has facilities such as student data, teacher data, class data, child achievement data, child grade data, child report cards data, announcement data, and activities in kindergarten.
Study of Encrypted Transmission of Private Data During Network Communication: Performance Comparison of Advanced Encryption Standard and Data Encryption Standard Algorithms

The involvement of the Internet in the production of daily life has increased the demand for the security of private data on the Internet. This paper briefly introduced the principles of advanced encryption standard (AES) and data encryption standard (DES) algorithms and then conducted simulation experiments on the two encryption algorithms in a laboratory server. The results showed that both algorithms had excellent sensitivity to plaintext keys, and the sensitivity of the AES algorithm was higher; the encryption and decryption time of both algorithms increased as the file got larger, and the encryption and decryption time of the same algorithm was not much different; the encryption and decryption time of the AES algorithm was less than that of the DES algorithm for the same file, and the time taken to crack the AES-encrypted data by brute force was also much longer; during the transmission of encrypted data, as the data increased, the integrity of the ciphertext decryption by the third-party decreased, and the integrity of the AES algorithm-encrypted file was significantly smaller than that of the DES algorithm-encrypted file when it was decrypted.
A Combination of BB84 Quantum Key Distribution and An Improved Scheme of NTRU Post-Quantum Cryptosystem

The BB84 quantum key distribution (QKD) protocol is based on the no-cloning quantum physic property, so if an attacker measures a photon state, he disturbs that state. This protocol uses two channels: (1) A quantum channel for sending the quantum information (photons polarized). (2) And a classical channel for exchanging the polarization and the measurement information (base sets or filters). The BB84 supposes that the classical channel is secure, but it is not always right, because it depends on the methods used during the communication over this channel. If an eavesdropper gets the sender or the receiver filters or both of them, he can leak some or all bits of the constructed key. In this context, we contribute by creating a protocol that combines the BB84 protocol with an improved scheme of NTRU post-quantum cryptosystem, which will secure the transmitted information over the classical channel. NTRU is a structured lattice scheme, and it is based on the hardness to solve lattice problems in Rn. Actually, it is one of the most important candidates for the NIST post-quantum standardization project.
Peningkatan Kualitas Pembelajaran Dan Digitalisasi Sekolah Berbasis Learning Management System Di Madrasah Aliyah Al-Imam Sawoo

Currently, educational institutions are required to have an innovation that can support educational and teaching activities, such as combining the education system with an informatics system. However, there are still many educational institutions that do not know much about informatics technology, such as Madrasah Aliyah Al-Imam Sawoo, so this community service activity aims to provide innovation and improve the quality of education by implementing an internet-based education system or learning management system. This is attempted so that the aims and objectives of education can be carried out as well as possible by adjusting to the times, especially since many students at Madrasah Aliyah Al-Imam Sawoo already have electronic devices in the form of cell phones and laptops. This PKM activity uses socialization, training, and mentoring methods. In this PKM activity, it provides a new innovation in helping education and teaching activities, namely a special digital platform for MA Al-Imam Sawoo. The platform uses Moodle, which is a special digital platform that can be accessed both via the Web and mobile. Thus, it can be a solution for teaching and learning that is effective and in accordance with the times. and become new solutions and innovations that MA Al-Imam Sawoo uses in their daily learning process in the hope that this method will improve the quality of learning and provide new colors to students in their teaching and learning activities at school.
Multi-factor Authentication

Multi-factor authentication (MFA) is critical in securing account access and guarding against account takeover. In this article, we explain the core concepts that define MFA, explore the characteristics of different MFA types, and discuss the various threats mitigated by using MFA.
Hierarchical model simulation based on EKF for mobile robots

Abstract With the technological advance grows the need for optimization in the tasks performed by mobile robots, these must be endowed with autonomy and other decisions, this is because the operating environments become more complex and with obstacles and objectives that change position, adding to this the interferences of the workspace such as disturbances in the environment and other system noises. To address this problem, the use of navigation algorithms, route creation, position estimation, path tracking, object recognition and others are used. In the present study, a spiral methodology was used with which a mathematical model based on the extended Kalman filter (EKF) was developed for trajectory tracking and position estimation of a differential robot using its kinematics. For the validation of the mathematical model Matlab was used together with CoppeliaSim where the different test scenarios were carried out, later the results of EKF are compared with those of Kevin Passino’s algorithm, finding superior in most aspects to the EKF except in the time when Kevin Passino’s algorithm achieves a shorter simulation time.
Analisis Penerapan Metode Association Rule Mining Untuk Transaksi Penjualan di Toko Bangunan Dengan Algoritma Apriori

In improving the quality of service to customers, UD. Lasmi Jaya store is asked to be able to handle problems that often arise, among others, lack or absence of (out of stock) stock of building goods that are very popular, less strategic layouts, assist in decision making to develop marketing strategies and promote better products and more. Therefore, in this problem, there must be utilization of sales transaction data for processing using a priori algorithms so that it can provide new knowledge that can be utilized by shop owners. From the results of research that has been carried out from 300 sales transaction data from January 1, 2022 - June 30, 2022 with a comparison of a minimum transaction of 15 or a minimum of 5% support and a minimum of 40% confidence with a minimum of 12 transactions or a minimum of 4% support and a minimum of 30% confidence, associations the final results found were more at least 12 transactions or minimum support 4% and minimum confidence 30% because if the minimum support value and minimum confidence value were lower then the association value found would be more, so for determining stock of goods or layout of goods or as promotion and others so that it will make it easier for the owner to manage their sales so that they can grow and provide satisfaction
Klasifikasi Penyakit Infeksi Pada Ayam Berdasarkan Gambar Feses Menggunakan Convolutional Neural Network

Convolutional Neural Network (CNN) is one of the Deep Learning methods that is able to carry out an independent learning process that is popular and appropriate in classifying. The development of technology in the field of Deep Learning, this study aims to assist farmers in identifying the types of infectious diseases that attack chickens based on faecal images using Convolutional Neural Network (CNN) so as to increase production yields. Several infectious diseases that attack chickens can be identified through their feces, including newcastle disease caused by a virus, pullorum caused by bacteria, and coccidiosis caused by parasites. To identify, it is necessary to classify the types of diseases that attack by using images of chicken feces. With deep learning using Keras/TensorFlow, 95.40% of chicken feces images are predicted to be infected with coccidiosis, 94.97% chicken feces images are predicted to be healthy, 90.21% chicken feces images are predicted to be infected with tetelo disease, and 96.50% chicken feces images are predicted to be infected with pullorum disease
Analisis Sentimen Pada Pembelajaran Daring Di Indonesia Melalui Twitter Menggunakan Naïve Bayes Classifier

Education is one of the areas most affected by the covid-19 pandemic. Education during the pandemic must continue. To reduce the spread of covid-19 and learning activities can run as usual, the government, in this case the Ministry of Education and Culture, has implemented a distance education system in Indonesia. In addition, the response from the community is very important for an evaluation of the applied online learning. With the implementation of the policy regarding online learning in Indonesia, it is necessary to conduct a sentiment analysis to find out how the responses, opinions, or comments from the public and online learning actors related to online learning are currently being implemented. So the author conducted a research entitled Sentiment Analysis on Online Learning in Indonesia Through Twitter Using the Naïve Bayes Classifier Method to measure student responses regarding online learning during the covid -19 pandemic in Indonesia. The results of the accuracy of this study is 99.8% and the classification error is 0.12%. Of the total data entered, 83 tweets or 20% were included in the positive class, the negative class was 317 tweets or 80%.
Pengembangan Sistem Rekomendasi Melalui Pendekatan Web Semantik dan Simple Additive Weighting (SAW)

Indonesia is one of the countries that have the largest growth after China and India in the use of mobile phones. eMarkerter published that the number of mobile phone users in Indonesia increased by 37.1% from 2016 to 2019 and in 2019 it reached 92 million users. This study aims to develop a mobile phone recommendation system with a semantic web approach using the Simple Additive Weighting Method (SAW). The prototype system available online at http://mobile.oss.web.id that has 3 main features, namely searching, browsing, and recommendation features. The process of searching, browsing, and recommending the system using information that has been stored in Ontology. Meanwhile, the recommendation mechanism uses the SAW method as a method of calculating the weight of each cellphone and the weight of the criteria. Evaluation of functional requirements system using Black-Box testing approach, while evaluation of non-functional requirement systems using Technology Acceptance Model (TAM) approach by involving 32 respondents. The final result of the evaluation shows that the system functionality is running well as expected. Moreover, the evaluation of the non-functionality of the system showed that on average the respondents involved in the study agreed that the system developed was useful and easy to use
Densely Connected dan Residual Convolutional Neural Network Untuk Estimasi Jumlah Keluarga Tingkat Desa Dengan Citra Satelit

Indonesia conducts a population census every ten years to collect population data. Variables such as family count are collected to provide general population information for policy making and sampling frames. Indonesia as an archipelagic country with an area of 8.3 million km2 will require a lot of resources to collect such data. In the age of big data, satellite imagery has become more available and inexpensive. In this study, we used West Java as a case study for applying deep learning to estimate family counts at the village level. Sentinel-2 and SPOT-67 data are used to model family counts. Using xgboost, we regress the family count with the softmax probability, resulting from family density classification using deep learning (densenet121 and resnet50 ) as the input. With an R2 of 0.93 and a MAPE of 19%, the regression model provides a good prediction of the number of families in the census. Regarding the input data, Sentinel-2 is sufficient to accomplish this task as there is no significant difference from the modeling results with higher resolution images (SPOT 6-7). The input level in the form of a segment of the estimation area and using structured auxiliary variables also deliver better predictions
Iterative Eigenvalue Decomposition of Hankel Matrix: An EMD Like Tool

&lt;p&gt;The decomposition of a multicomponent non-stationary signal is helpful in obtaining its time-frequency distribution (TFD). In this paper, a novel empirical mode decomposition (EMD) like eigenvalue decomposition of Hankel matrix (EVDHM) technique is proposed, which extracts the mono-component signal iteratively. In each iteration, EVDHM is performed on the residue signal to obtain elementary components (ECs). Later, the most dominant mono-component is extracted from the obtained ECs using a novel parameter proposed namely, envelope of the normalized instantaneous amplitude modulated bandwidth. The separability conditions for the two sinusoidal multicomponent signal are studied for the proposed decomposition technique using the average correlation measure. Finally, the proposed EMD-like EVDHM's performance for signal decomposition is compared with iterative EVDHM and EMD technique for synthetic and real-world speech signal. Signal to reconstruction error (SRE) ratio is used as a performance measure and the proposed technique is found to be giving highest SRE value for all the considered synthetic signals when compared with the baselines. Further, the proposed technique is also found to be providing highest-resolution TFD for speech signal among all the techniques studied in this paper.&lt;/p&gt;
IR-VIO: Illumination-Robust Visual-Inertial Odometry Based on Adaptive Weighting Algorithm With Two-Layer Confidence Maximization

&lt;p&gt;Illumination change, image blur, and fast motion dramatically decrease the performance of visual-inertial navigation systems (VINS). This paper presents a new illumination-robust visual-inertial odometry (IR-VIO) based on adaptive weighting algorithm with two-layer confidence maximization. First, to prevent the VIO performance degradation caused by poor image quality in complex scenes and ignoring the confidence differences of feature points, we develop a novel adaptive weighting algorithm on the multi-sensor layer and visual feature layer to better fuse multi-sensor information and maximize the overall confidence of VIO. Second, to solve the problems of image feature tracking difficulty and excessive image noise in illumination-changing scenes, an image enhancement algorithm is introduced to enhance consecutive images to the same brightness level, while a block noise removal algorithm with constraint protection mechanism is proposed to dynamically remove feature points in noise areas. Finally, experimental results on the public dataset and real-world environments demonstrate that IR-VIO has superior performance in terms of accuracy and robustness compared with the state-of-the-art methods. Supplementary video is available at https://youtu.be/163Sk7twEMY.&lt;/p&gt;