Reviewerly/exHarmony huggingface dataset:
Loki: A Physical Security Key Compatible IoT Based Lock for Protecting Physical Assets

The twenty first century has witnessed an enormous rise in data produced per person and it has also witnessed newer and advanced forms of digital attacks and instinctively, witnessed a rise in the need for data protection.However, the essential assets are still physical and needs to be protected.Usually vaults, lockers, safes and so on and used for the safe keeping of the physical assets.However, studies have shown they are vulnerable to various attacks.This paper proposes a novel and robust physical lock for safekeeping of physical assets called Loki.A Physical Security key is used to authenticate the lock and it uses a cloud-server architecture.It employs best cloud security practices, proper use of cryptography and trusted computing to mitigate all common risks.The cloud architecture runs a Virtual Machine (VM) to securely authenticate using Fast IDentity Online (FIDO2) specifications.The physical authenticator data is stored in the cloud for security and only accessed when an unlock is requested.The cloud allows web-based physical key management for adding more keys or removing keys.The whole system has been implemented in a Internet of Things (IoT) scenario.
Detection System of Strawberry Ripeness Using K-Means

Strawberry is one type of fruit that is favored by the people of Indonesia. The detection process to identify strawberries can be done by utilizing advances in computer technology, One of them is in the field of digital image processing. In this study, we made a strawberry ripeness detection system using the values of Red, Green and Blue as the reference values, while for identification in determining the type of classification using the K-Means algorithm that uses the Euclidean distance difference as the reference. Based on the results of testing using the K-Means algorithm on 51 strawberry images consisting of ripe, semi ripe and raw fruit yielding an accuracy rate of 82.14%, we also conducted tests other than strawberry images as many as 8 images yielded an accuracy rate of 100%.
E-LEARNING AND MODERN DIGITAL PROFESSIONAL SKILLS FOR MEXICO

The article follows the deductive method, with a hermeneutic paradigm and a qualitative approach. Online learning has allowed students to have academic preparation without having to go to a traditional educational center, where there is already physical displacement and there are no pre-established schedules, in addition, they allow lowering travel and tuition costs. Permanent relearning, asynchronous models and self-learning are managing to train individuals who know how to search for adequate information and solve problems as they arise, increasing their sense of responsibility and achieving permanent updating of their knowledge. In the last decade, modern digital media have generated the appearance of new professional activities, among which they have stood out for generating higher income: Chief Digital Officer (CDO); Chief Technology Officer (CTO); Digital Business Manager. Artificial intelligence allows imitating the functions that humans perform constantly and repetitively, making its application trend grow exponentially in the future.
Adaptation of Population Size in Differential Evolution and Its Effects on Localization of Target Nodes

The differential evolution (DE) is a well known population-based evolutionary algorithm that has shown capabilities for solving real-world problems such as resource allocation, multicast routing, and localization of target nodes. However, the accuracy of the DE, like other evolutionary algorithms, depends on the settings of its control parameters. The localization of target nodes is highly nonlinear and multi-modal, which may trap the DE in a local optimum. A local optimum may be avoided by a proper selection of the control parameters. One of the key control parameters is the population size (PS), which affects directly the localization accuracy and computational complexity. Finding an adequate PS throughout the evolution process is a challenging task. Even if an adequate PS is found it may not be the adequate PS anymore when the scenario of a problem changes. Although several approaches have been proposed for adapting the PS, they have not been evaluated when solving the localization problem. In this paper, a comprehensive comparison in terms of accuracy and computational demand is conducted among the state-of-the-art PS adaptation techniques when employed with the DE for solving the localization problem of target nodes in various scenarios.We also propose three new PS adaptation techniques, namely, exponential, parabolic, and logistic reduction. The results from extensive numerical simulations show that, after setting the initial PS properly, there is no technique that outperforms the others in practically all the scenario of the localization problem. Additionally, the DE with the proposed techniques provides competitive localization accuracy with considerably less computational complexity. Specifically, The proposed approaches reduce the computational demand by approximately 50% over the standard DE in all the scenarios considered here.
Investigating the impact of demographic and device information in the recommendation of mobile applications

The number of people with access to mobile devices, as well as applications to these devices (i.e., apps), has been increasing significantly. Thus, users have to choose among a large number of apps proposing to do the same functions, those that better serve them. A possible solution to this problem is the adoption of recommendation systems. Meanwhile, usually these systems consider only users' preferences to create a profile or request sensitive data (e.g., call and message logs). This work investigates the impact of using demographic and device information on app recommendation by using only easy-to-obtain data to enrich a user profile. We evaluate two approaches: a similarity-based Collaborative Filtering with a limited number of apps and a topic-based approach (i.e., LDA) with wider large-scale data. We also inspected the results under both apps and categories context. The general results reveal that the enriched data provides a better app recommendation with the addition of information about the user's region mean wage achieving up to 210% (or 12 percentage points) of improvement in terms of recall.
DETECTING FRAUDULENT OF FINANCIAL STATEMENTS USING FRAUD S.C.O.R.E MODEL AND FINANCIAL DISTRESS

The problem of fraud in financial statements is a global problem that has crossed borders of time and country. This research was created to prevent fraud by detecting financial statements listed on the Indonesia Stock Exchange (IDX). The fraud detection tools used in this research are F-S.C.O.R.E and Financial Distress. S.C.O.R.E stands for Stimulus, Capability, Opportunity, Rationalization, and Ego, which are supporting variables in detecting fraud. The research results show that Stimulus, Capability, and Financial Distress effectively detect fraud. Fraud detection in the research concluded that companies that have strong indications of committing fraud are companies categorized in industrials in the IDX Industrial Classification.
PrePARE: Predictive Proprioception for Agile Failure Event Detection in Robotic Exploration of Extreme Terrains

Legged robots can traverse a wide variety of terrains, some of which may be challenging for wheeled robots, such as stairs or highly uneven surfaces. However, quadruped robots face stability challenges on slippery surfaces. This can be resolved by adjusting the robot's locomotion by switching to more conservative and stable locomotion modes, such as crawl mode (where three feet are in contact with the ground always) or amble mode (where one foot touches down at a time) to prevent potential falls. To tackle these challenges, we propose an approach to learn a model from past robot experience for predictive detection of potential failures. Accordingly, we trigger gait switching merely based on proprioceptive sensory information. To learn this predictive model, we propose a semi-supervised process for detecting and annotating ground truth slip events in two stages: We first detect abnormal occurrences in the time series sequences of the gait data using an unsupervised anomaly detector, and then, the anomalies are verified with expert human knowledge in a replay simulation to assert the event of a slip. These annotated slip events are then used as ground truth examples to train an ensemble decision learner for predicting slip probabilities across terrains for traversability. We analyze our model on data recorded by a legged robot on multiple sites with slippery terrain. We demonstrate that a potential slip event can be predicted up to 720 ms ahead of a potential fall with an average precision greater than 0.95 and an average F-score of 0.82. Finally, we validate our approach in real-time by deploying it on a legged robot and switching its gait mode based on slip event detection.
Fuzzy PID Control for Multi-joint Robotic Arm

This paper studies the experimental control of multi-joint robotic arm (Geomagi Touch haptic device) via a fuzzy PID controller. First, the system of Geomagi Touch haptic device is introduced, and the hardware-in-loop control scheme is established through Matlab Simulink. Then, fuzzy logic is utilized to update PID controller gains, where the parameters can be updated adaptively such that the performance of multiply joints can be optimized. Finally the effectiveness of the presented fuzzy controller is validated through real experiment.
ASR - VLSP 2021: Automatic Speech Recognition with Blank Label Re-weighting

End-to-end models have significant potential in most languages and recently proved the robustness in ASR tasks. Many robust architectures are proposed, and among many techniques, Recurrent Neural Network - Transducer (RNN-T) shows remarkable success. However, with background noise or reverb in spontaneous speech, this architecture generally suffers from high deletion error problems. For this reason, we propose the blank label re-weighting technique to improve the state-of-the-art Conformer transducer model. Our proposed system adopts the Stochastic Weight Averaging approach, stabilizing the training process. Our work achieved the first rank with a 4.17% of word error rate in Task 2 of the VLSP 2021 Competition.&#x0D;
Exploring the Best Parameters of Deep Learning for Breast Cancer Classification System

Breast cancer is one of the deadliest cancers in the world. It is essential to detect the signs of cancer as early as possible, to make the survival rate higher. However, detecting the signs of breast cancer using the machine or deep learning algorithms from the diagnostic imaging results is not trivial. Slight changes in the illumination of the scanned area can significantly affect the automatic breast cancer classification process. Hence, the research aims to propose an automatic classifier for breast cancer from digital medical imaging (e.g., Positron Emission Tomography or PET, X-Ray of Mammogram, and Magnetic Resonance Imaging (MRI) images). The research proposes modified deep learning architecture with five different settings to model automatic breast cancer classifiers. In addition, five machine learning algorithms are also explored to model the classifiers. The dataset used in the research is the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM). A total of 2,676 mammogram images are used in the research and are split into 80%:20% (2,141:535) for training and testing datasets. The results demonstrate that the model trained with eight layers of Convolutional Neural Networks (CNN) (SET-8) achieves the best accuracy score of 94.89% and 93.75% in the training and validation dataset, respectively.
Experience in building an ontology for a subject area based on the content of a thematic journal

The work is devoted to the problem of constructing an ontology of an applied subject area as part of a semantic library. The initial data are presented by publications of a scientific thematic journal, an array of archival and current versions. The main stages of ontology formation are considered in accordance with the definition and requirements of Web Ontology Language. Examples of the structure of thesaurus articles are given.
A SECURITY MODEL FOR PREVENTING E-COMMERCE RELATED CRIMES

The major challenge being faced by the financial related institutions, such as e-Commerce has been insecurity. Therefore, there is urgent need to develop a scheme to protect transmitted financial information or messages from getting to the third party, intruder and/or unauthorized person(s). Such scheme will be based on Advanced Encryption Standard (AES) and Neural Data Security (NDS) Model. Based on this background, an AES using Time-based Dynamic Key Generation coupled with NDS model will be used to develop security model for preventing e-commerce related crimes. While AES will secure users’ details in the database server and ensures login authentications, NDS model will fragment or partition sensitive data into High and Low levels of confidentiality. The sensitivity of the data will determine, which category of confidentiality the data will fall into. The fragmented data are saved into two different databases, on two different servers and on the same datacenter. In addition, an exploratory survey was carried out using different performance metrics with different classifications of algorithms. Out of the four algorithms considered, Naive Bayes performs better as it shows, out of a total of 105 instances that were observed, 85.71% were correctly classified while 14.29% were misclassified.
The Correlation Between Intensity and Objectives of Using The Internet Towards Students Learning Achievement Using Apriori Algorithm

Internet is a basic need for everyone, including students. The internet can be used as a means to improve a student's learning achievement or vice versa, if the use of the internet is excessive. So that 3 categories were taken, including intensity, purpose of internet use and student GPA. This study aims to see the correlation between the intensity and purpose of internet use and student learning achievement which is implemented in a web-based system. The method used in this research is apriori method, which is a method for finding patterns or correlations between items. Data taken with a total of 100 data. The results of this study resulted in a combination of the goal item set, intensity and GPA along with the association rules. The highest item obtained was social media with a value of 64 and for the correlations of the correlation between intensity, goal and student GPA, the 3 highest association rules were obtained as follows: 'Online Game, Rare Intensity → Low GPA' with a confidence value 100, 'Very Frequent Intensity, Search Engine →GPA Medium' with a confidence value 85.71 and 'social media, Frequent Intensity→High GPA 'with a confidence value 41.38.
The state of COVID-19 contact tracing following relaxation of the national lockdown: A qualitative study among surveillance response teams in Kampala, Uganda

Introduction: The surge in COVID-19 cases triggered a need for surveillance systems to implement new strategies that augment and accelerate manual contact tracing. In Uganda, COVID-19 cases increased exponentially following the relaxation of the national lockdown. However, little is known about how the surveillance system mitigated COVID-19 transmission during this period. We sought to understand how contact tracing was being conducted in Kampala district following relaxation of the national lockdown.
Data Balancing Methods on Radiographic Image Classification on Unbalance Dataset

Covid-19 is a disease caused by a corona virus infection that infects the victim's respiratory tract. Covid-19 disease has a high infectious ability and if treated too late can result in death. Covid-19 has been a problem faced by everyone in the world since the end of 2019. Fast and accurate detection can save many lives. This study aims to develop a predictive model of COVID-19 detection based on radiographic images using a machine learning model from 4 categories of health status, namely positive covid, normal, lung opacity sufferers, and viral pneumonia sufferers. Deep learning is based on ResNet50 and MobileNetV2 and trials of undersampling and oversampling data balancing methods, and uses a confusion matrix for the evaluation process of model results. The model with the highest performance achieves 95.58% accuracy in the multi-class classification. Also based on the findings, we provide results from using a different data balancing approach or not using one at all.
Research on the role of modern information technology in promoting the leadership of informationization of higher education

The construction of information-based leadership is of great practical significance in driving the development of higher education. By giving full play to the application advantages of modern information technology, an educational system covering all levels of higher education is constructed, we will comprehensively improve the level of informationization and intellectualization of higher education, and help to realize high-quality development of higher education. This paper analyzes the role of modern information technology in improving the leadership of information-based education in colleges and universities, and puts forward some relevant strategies to improve the leadership of information-based education in colleges and universities by using modern information technology.
Document Image Rectification in Complex Scene Using Stacked Siamese Networks

With the popularity of digital cameras and smart-phones, capturing document images of physical documents for electronic storage has become popular, but the captured document images suffer various deformations. Document image rectification has been studied intensively, but existing methods do not perform sufficiently for document images captured in complex scenes due to the various environmental factors. In this paper, we propose an end-to-end rectification model by stacking 3D and 2D Siamese networks. Three regularization terms are used to enforce 3D reconstruction consistency and 2D texture consistency, respectively. Experimental results on real world datasets demonstrate that the three regularization terms with Siamese networks can significantly improve the rectification performance, and our method performs superiorly compared to state-of-the-art methods.
Less Than Few: Self-shot Video Instance Segmentation

The goal of this paper is to bypass the need for labelled examples in few-shot video understanding at run time. While proven effective, in many practical video settings even labelling a few examples appears unrealistic. This is especially true as the level of details in spatio-temporal video understanding and with it, the complexity of annotations continues to increase. Rather than performing few-shot learning with a human oracle to provide a few densely labelled support videos, we propose to automatically learn to find appropriate support videos given a query. We call this self-shot learning and we outline a simple self-supervised learning method to generate an embedding space well-suited for unsupervised retrieval of relevant samples. To showcase this novel setting, we tackle, for the first time, video instance segmentation in a self-shot (and few-shot) setting, where the goal is to segment instances at the pixel-level across the spatial and temporal domains. We provide strong baseline performances that utilize a novel transformer-based model and show that self-shot learning can even surpass few-shot and can be positively combined for further performance gains. Experiments on new benchmarks show that our approach achieves strong performance, is competitive to oracle support in some settings, scales to large unlabelled video collections, and can be combined in a semi-supervised setting. Code: https://github.com/PengWan-Yang/self-shot
An optional splitting extraction based gain-AUPRC balanced strategy in federated XGBoost for mitigating imbalanced credit card fraud detection

Credit card defaults cost the economy tens of billions of dollars every year. However, financial institutions rarely collaborate to build more comprehensive models due to legal regulations and competition. Federated XGBoost is an emerging paradigm that enables several companies to build a classification model cooperatively without transferring local data to others. The conventional Federated XGBoost suffers from the inverse inference according to splitting nodes selection and the class imbalance problem severely. Utilising the characteristic of splitting points' selection, we propose an optional splitting extraction model to reduce the leakage risk of raw data statistics. Moreover, an adjusted AUPRC (the area under the precision-recall curve) is introduced into the gain function to alleviate the class imbalance problem. Our experimental results show recall and AUPRC increased by 7%-10% and 4%-8%, respectively, without sacrificing other estimations compared to the existing state-of-the-art. Furthermore, communication iterations also decreased significantly in our proposed method.
Pengembangan Buku Ajar Bahasa Inggris untuk Peserta Didik Lamban Belajar

This research aims to develop English teaching books for students who are slow to learn grade VII SMP Negeri 29 Surabaya. This development research refers to the development design measures developed by Dick &amp; Carey. The development design is grouped into four development procedures, namely data analysis stage, product design stage, validation and evaluation stage and final production stage. Product trials conducted by material experts and design experts, as well as field tests conducted on 8 students slow to learn grade VII. Data collection using interview guidelines, observations, questionnaires for material experts, questionnaires for design experts, student response questionnaires, and test script instruments. The results of the study, from data analysis, the validation results of content experts obtained a value of 98%, from design experts obtained a value of 80%, and field tests got a score of 92%. In conclusion, the development of English teaching books for students who are slow to learn meets the criteria is very feasible to be used in learning activities, and can improve the learning outcomes of learners sluggish learning, this can be seen from the results of the training and the attitude of students to English teaching materials.&#x0D; Keywords: Textbooks, English, Sluggish learning, Development&#x0D;
The Effect of Pin Shape on the Friction Stir Welding Quality of Aluminum AA1100 Series

Aluminum is a material that is soft, lightweight, resistant to corrosion, and has good electrical and thermal conductivity. Aluminum has a specific gravity of about 2.65-2.8 kg/dm³, a melting point of 658ºC, and tensile strength of 90 MPa. Aluminum 1100 series is an aluminum alloy mixed with other materials, such as copper, iron, chrome, manganese, and zinc, with an aluminum content of 99,0%. Welding is the joining of two metals in a liquid state with or without filler. FSW (friction stir welding) is friction welding; the process does not require filler material. The heat used to melt the metal is obtained from the friction between the indenter and the workpiece. In the research, the shape of the indenter pin used is, ovale shape, cone shape, and changing spiral form with a tool rotation of 2000 rpm and a welding rate of 16 mm/minute. The tests carried out are Rockwell hardness testing and tensile tests. From the results of research conducted, it was found that the shape of the indenter pin greatly affects the quality of the weld. The indenter pin changing spiral form results in better weld quality compared to the other two pin shapes.