Reviewerly/exHarmony huggingface dataset:
Smart Device Design Education Contents for Non-art Design Majors

<p class="a" style="line-height:130%;layout-grid-mode:char"><span lang="EN-US" style="font-size:12.0pt;line-height:130%;font-family:&quot;Times New Roman&quot;,&quot;serif&quot;;mso-fareast-font-family:&quot;맑은 고딕&quot;">Smart devices are succeeding in commercialization while harmonizing the innovative technology and the external design for realizing device functions. Aesthetics is important factor that stimulates emotion when purchasing a wearable computer product that can satisfy aesthetic sensibility. </span><span lang="EN-US" style="font-size:12.0pt;line-height:130%;font-family:&quot;Times New Roman&quot;,&quot;serif&quot;;mso-fareast-font-family:&quot;맑은 고딕&quot;;color:black;mso-themecolor:text1">Smart device design education contentfor non-art majors is needed. </span><span lang="EN-US" style="font-size:12.0pt;line-height:130%;font-family:&quot;Times New Roman&quot;,&quot;serif&quot;;mso-fareast-font-family:함초롬바탕">The purpose of this thesis is to develop wearable computer educational contents for non-art design major students. </span><span lang="EN-US" style="font-size:12.0pt;line-height:130%;font-family:&quot;Times New Roman&quot;,&quot;serif&quot;;mso-fareast-font-family:&quot;맑은 고딕&quot;">The smart device design education content developed in this study is a case study that derives the research results by applying it to the class in the spring semester of 2021. A 15-week class had been conducted with 10 non-art design major. Through this</span><span lang="EN-US" style="font-size:12.0pt;line-height:130%;font-family:&quot;Times New Roman&quot;,&quot;serif&quot;">&nbsp;study on smart device education contents, it is expected that the aesthetic aspect of the wearable computer design planned for non-art students can be considered and reflected in the wearable computer design to nurturing talents with convergence knowledge.<o:p></o:p></span>
EllipseIoU: A General Metric for Aerial Object Detection

Existing works in object detection usually adopt different Intersection over Union (IoU) metrics to define the similarity between predicted box and target box, which greatly boost the detection performance. For the task of aerial object detection, since the predicted and target boxes are with different sizes, aspect ratios and orientations, existing IoU metrics can not measure their similarity well. In this work, we propose a new and generic metric for robust aerial object detection, called EllipseIoU, which incorporates the ellipse distance between the predicted box and the target box. In particular, we find the inner ellipse from the box, and then calculate the ellipse distance between predicted and target boxes based on the distances of focal points. We prove that the proposed ellipse distance well measures the relations of sizes, aspect ratios and orientations between predicted and target boxes. Moreover, we also incorporate the relation of short axis length between two boxes to measure the relation of sizes. Based on the ellipse distance, we design the EllipseIoU metric by combining with the IoU metric and integrate it into detectors for performance boosting of aerial object detection. Experimental results on public datasets show that the detector with EllipseIoU significantly improve the baseline without impact on the efficiency and achieves state-of-the-art performance.
High-end image classification on the dogs vs. cats dataset using convolutional neural network

The enormous benefits and applications of Image classification and recognition are umpteen. Machine learning algorithms and Deep Neural Networks are like windfall to fathom the objective proficiently in streamlined manner. The prevalent improvement in this technology is that these networks do not call for any prior blueprints in terms of algorithms as prerequisites. The presented paper is an attempt to create a Convolutional Neural Network from scratch to classify the images from the well-known dataset – Cats and Dogs into their relevant baskets. Manifold open source accessible approaches to amplify the efficiency of the network are no more onerous. Further, data augmentation technique boosts the efficiency tremendously by extending the dataset with reoriented features from the same images. To untangle the same problem, Transfer Learning is also a compelling technique in which all the layers, neurons in each layer, weights of each neuron and all other parameters are predefined and we can amend the output layer as per the classes in the respective problem statement. In the present paper, we have tried to obtain a comparable efficiency with a significant reduction in parameters.
Hotel Recommendation System with Content-Based Filtering Approach (Case Study: Hotel in Yogyakarta on Nusatrip Website)

The increasing of Covid-19 pandemic has hampered people's activities, often causing stress if they are only stay at home continuously. This has led to an increasing trend of staycations or holiday activities in the city itself by renting a hotel. Hotel rental technology has begun to be transferred with the existence of OTA (Online Travel Agent). The existence of various kinds of hotels with various kinds of facilities that makes people feel confused in choosing which hotel to occupy. To help overcome this, the researchers tried to create a recommendation system to help prospective hotel residents choose the hotel according to their choice. In addition, it can also assist companies in increasing hotel room reservations through its website. In this study, researchers will build a hotel recommendation system in Yogyakarta at one of the OTAs in Indonesia using Content-Based Filtering Methods, weighting text data using Term Frequency-Inverse Document Frequency (TF-IDF) Methods and measuring document similarity using Cosine Similarity Methods. Based on the results of the Good Karma Yogyakarta hotel recommendations as a test example, 10 similar hotels were obtained, namely Happy Buddha Yogyakarta – Hostel, Nextdoor Homestay, Hotel Puspita, OYO 426 Hotel Gading Resto, Omah Jegog Homestay, Prawirotaman Homestay, RedDoorz near Prawirotaman, Ayodhya Garden Hostel Yogyakarta by HOM, Bringin House Yogyakarta, and House 24 Yogyakarta with cosine similarity values 0.956666513, 0.946570717, 0.917459394, 0.912534877, 0.886439718, 0.88221982, 0.881775275, 0.875845794, 0.872030219, and 0.871514859.
Machine Learning Algorithm for Abnormality Detection and Classification of Kidney Stones in Ultrasound Images

For feature extraction and classification, a novel Discrete Tchebichef Moment-based Machine Learning (DTM) technique is suggested. The echo patterns seen inside the kidney and its sub-regions are used by experts to make diagnoses. Hyperechoic, hypoechoic, isoechoic, and anechoic echos are classified as hyperechoic, hypoechoic, isoechoic, and anechoic, respectively, and need a high level of knowledge to understand. Various renal disorders with closely connected echo patterns necessitate the system's quantitative quantification of echoes. When considering the entire complete kidney area for feature extraction, this fixed ROI strategy may eliminate the size uncertainty. The classification was carried out with the help of a multiclass support vector machine classifier with fivefold cross validation. The suggested DTM is evaluated using quantitative criteria including accuracy, precision, recall, and F -Score. The suggested work's promising results show the potential for using CAD in ultrasound images to classify renal disorders.
2D Fingertip Localization on Depth Videos Using Paired Video-to-Video Translation

We propose a two-stage pipeline and formulate 2D hand keypoint localization as a problem of conditional video generation. The goal is to learn a mapping function from an input depth video in the source domain to an output depth video along with 5 color marks on each fingertip by enforcing temporal consistency constraints. Next, by applying color segmentation techniques in HSV domain, we extract the center of each segmented part as 2D coordinates of fingertips on the translated video. To the best of our knowledge, this is the first work on fingertip localization on depth videos through domain adaptation. Our comparative experimental results with the state-of-the-art single-frame hand pose estimation on the challenging NYU dataset demonstrates that by exploiting temporal information, our model manifests better hand appearance consistency in video-to-video synthesis stage which leads to accurate estimations of 2D hand poses under motion blur by fast hand motion.
Key Rotation with Always Encrypted

Key rotation is the process of refreshing your encryption keys. It’s generally seen as good practice to do this periodically or if you have a serious breach and think they could have been accessed or stolen by an unauthorized individual. When you’re planning an implementation of Always Encrypted, you should make sure you understand this process and how your organization is going to manage it over time. In this chapter we go over the methods you can use to rotate both your CMKs and CEKs.
CAR: Community Aware Graph Reordering for Efficient Cache Utilization in Graph Analytics

Graph workloads exhibit highly irregular memory access patterns, resulting in poor cache utilization. By modifying the layout of the stored graph prior to processing, cache utilization can be enhanced. Two factors need to be considered for modifying the layout of the graph. First, the nature of computation in vertex-centric algorithms suggests that vertex neighbours are visited in succession throughout processing. Second, the degree distribution of vertices in real-world networks exhibits power-law distribution, implying that a few vertices are responsible for the majority of the connections. As a result, such nodes can be clustered together to improve temporal and spatial locality. In this paper, we propose Community Aware Graph Reordering (CAR), which leverages both these aspects to enhance the performance of graph applications when compared to existing reordering strategies. While previous state-of-the-art reordering techniques with comparable reordering overheads, such as Hub Cluster, DBG, and Sorder, deliver speedup of 9%, 11%, and 17%, respectively, CAR provides a speedup of 20%.
Reducing Intrusion Alert Trees to Aid Visualization

Cyber defense tools, such as intrusion detection systems, often produce huge amounts of alerts which must be parsed for defensive purposes, particularly cyber triage. In this paper, we utilize the notion of alert trees to represent the collection of routes that may have been used by a cyber attacker to compromise a set of computers. Although alert trees can be visualized to aid analysis, their usefulness in practice is often discounted by the fact that they can become unmanageable in size. This makes it difficult for cyber defenders to identify patterns or pinpoint network hotspots in order to prioritize defensive maneuvers, raising the need to reduce strain on defenders by minimizing the presence of non-critical information. To address this problem, we propose several methods, as well as a novel data structure, for modifying alert trees in order to reduce visual strain on defenders. We evaluate our methods using a real-world dataset, which demonstrates that our methods are effective at reducing redundancy while limiting collateral information loss.
Language transmission coming to a halt after a 500-year survival story: the case of Arberesh in Italy

This research in 2022 was preceded by debates regarding the endangerment of Arberesh. Knowledge and evidence from research have been scarce and far from comprehensive, and language endangerment is not an official issue on the agenda for the politicians, local official and the academia related to Arberesh studies; nor have been any efforts for developing language policies. The data from our field trip in June 2022 confirm an almost complete language shift within the present community and the worrying intragenerational transmission. The quantitative data about the language use have been co-evaluated with the qualitative information provided in interviews about the set of the present language endangerment factors in view of an estimate of Arberesh in 1-2 generations ’ time. This shift in transmission levels and patterns has had an impact in many directions, particularly onto the absolute number of the minority population and the proportion of real speakers. The question is whether the younger generation has been irreversibly impacted and the language has, for certain, stopped being transmitted to the younger generation in the main core of Arberesh communes.
Introduction

In recent years, the recognition and admission of electronic evidence in litigations have been sharply increasing according to the development of information technology and the Internet. To ensure the efficiency and effectiveness of using electronic evidence in civil and commercial dispute resolution, it is necessary to explore the legal framework for electronic evidence and the use of electronic evidence. This book aims to provide deep knowledge on electronic evidence and the use of electronic evidence in civil and commercial dispute resolution under the legal provisions developed by UNCITRAL and the civil procedure legislation of the European Union, Germany, and Vietnam. The first chapter starts by explaining briefly the research problems and the objectives of the book. Following the introduction of the research problems and objectives, this chapter provides the scope and structure of the book to clarify a clear overview of the research to readers.
Study on Improvement of traversability of Articulated Crawler Robot with Active Sub-arm

The purpose of this study is to improve the traversability of the combined articulated crawler robot. We propose a new mechanism that enables the sub-arm to move in a position/force-tracking using a planetary gear mechanism. The output shaft torque enables Reduction of installed motors, Position/Force-tracking of a Sub-Arm, and Active movement of the Sub-Arm depending on the output shaft torque relationship. The purpose of this study is to improve the traversability of the combined articulated crawler robot. We propose a new mechanism that enables the sub-arm to move in a position/force-tracking using a planetary gear mechanism. The output shaft torque enables Reduction of installed motors, Position/Force-tracking of a Sub-Arm, and Active movement of the Sub-Arm depending on the output shaft torque relationship.
Pengembangan Aplikasi Rekomendasi Pet Shop Berbasis Android Menggunakan Arsitektur MVVM (Model-View-Viewmodel)

At this time, keeping pets such as cats has become a lifestyle for pet lovers, sometimes many consider these pets as their own family. Therefore, pet owners always provide the best care for their beloved animals. Sometimes people are confused about which place is the best for their pet, which one is cheaper, which one is suitable for the type of pet, the benefits, and the location is not too far away. This system uses one of the methods in the Decision Support System. By using the Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods in a Decision Support System, it is hoped that it can help solve problems in semi-structured areas such as the problems above. This study aims to assist users in determining which pet shop they want to visit.
Log-MelSpectrum and Excitation features based Speaker Identification using Deep Learning

In the recent years, deep learning techniques are being used to construct the Speaker Identification systems. Currently, Salient Feature Extraction plays prominent role in the Speaker Identification task. We propose the fusion of Log-MelSpectrum and Excitation Features to develop the text-independent system using simple Dilated Convolution Neural Network(CNN). To hold the huge useful information, the dilated convolution is being used by increasing the receptive area. The proposed system exhibits the overall accuracy of 91.19% under complex speech data. The performance of this end to end system with the Libri speech and 250 samples of each speaker is compared against existing baseline systems. The experiments conveys that the accuracy reaches only 80.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> using existing method and it improves significantly using the proposed framework.
Potential Analysis of Renewable Energy in Nduga District Papua Mountain Province

The new renewable energy potential in Papua province is 26,529 MW and the installed capacity in 2018 is 20 MW. In 2025 primary energy is targeted to reach 23% to 31%. The river to the airport is 1.5 km from the airport and the Nogoliah river is at an altitude that has enormous potential and is 24 km from the center of Kenyam. With solargis , topographic, measurement methods, and the data 137,450 - 139,500 East Longitude and 4,000 - 4,500 South Latitude produces data for solar Pvout = 1270,7 kWh/Wp = 3,481kWh/Wp, GHI = 1607,4 kWh/m2, DNI = 890,2 kWh/m2, DIF = 929,9 kWh/m2, GTI = 1594,5 kWh/m2 =5740,1 MJ/m2. Data Wind 2019 capacity 1kW, Hub.height 80 m, vestas V90 2000 model turbine, highest daily average in October 0.02% and low MCF in December 1% -highest in February 4.50%. From the topographic map data obtained the slope of the Kenyam river level.
FaaSPipe: Fast Serverless Workflows on Distributed Shared Memory

Serverless workflows consist of multiple chained functions that pass intermediate data through function invocations. Existing platforms implement data passing via remote storage services that incur significant performance and maintenance overhead. Some recent works optimize data passing with local shared memory but require specialized function scheduling support and are available only in constrained settings. In this paper, we observed that a simplified, peer-to-peer form of distributed shared memory (DSM) is sufficient and efficient to pass data in serverless workflows. With the observation, we propose FaaSPipe, a serverless workflow runtime built on the simplified DSM. FaaSPipe provides PipeFunc, a user-friendly shared memory-based serverless workflow programming model. To support PipeFunc and take full advantage of the simplified DSM, FaaSPipe designs the intra-workflow memory sharing scheme for address space coordination and builds full-duplex memory transfer channels to enable fast, non-blocking peer-to-peer data passing. Evaluation results on real-world workflow applications show that FaaSPipe reduces workflow latency by up to 61% and consumes up to 2.07 $$\times $$ less network traffic compared to state-of-art serverless platforms.
Amodal Layout Completion in Complex Outdoor Scenes

A layout is a group of bounding boxes with labels annotating objects in complex scenes. However, manually labelled layouts often annotate only visible parts of objects (modal layout) instead of the whole body including both visible and invisible parts (amodal layout). Modal layouts are caused by occlusion in scenes, while amodal layouts contain more accurate information of objects’ relative positions and sizes. In this paper, we investigate the influence of modal layout on the layout-to-image generation. Specifically, to recover an amodal layout from a modal layout and improve the generation quality, we propose Amodal Layout Completion Network (ALCN) regressing amodal bounding boxes from potential occluded boxes. Following a divide-and-conquer strategy, we divide the modal layout of a scene into occlusion groups of bounding boxes, which are processed by ALCN individually. Furthermore, we propose four challenging IoU variants to measure completion performances for different completion conditions. Experiment results show the ALCN achieves state-of-the-art layout completion performances in most cases and improves the layout-to-image generation performance.
Web-Based Online Sales Information System Design

In the current era of globalization, the advancement of information systems is now very much needed because it can facilitate performance in a company, one of which is in the online sales business. PT Safra Asia Gemilang is a large company engaged in the sale of cosmetics and nutrition, in selling its products it still uses a manual system. So we need a system that is useful for simplifying and accelerating sales of the company's products. Therefore, the purpose of this research is to design a system, namely the Design of a Web-Based Online Sales Information System at PT Safra Asia Gemilang. The system development method used in this study is the Waterfall Method. The data collection stage used was literature study, observation, and interviews. PHP is a programming language in making the system, and MySQL as the database. The results of system testing from this study using blackbox testing by providing input to the program indicate that the features in the system have no errors and are running according to function very well.
Underwater multi-frame target images mosaic method based on adaptive image enhancement

The severe attenuation and scattering of light in the water reduces the effective field of view of the underwater camera, making the scene information contained in a single image limited, and it is difficult to meet the application requirements of the large-scale underwater scenes. To solve this problem, an underwater multi-frame target images mosaic method based on adaptive image enhancement is proposed in this paper. Firstly, the image blur prior is used to achieve the adaptive enhancement of underwater images to suppress the blur and colour distortion of underwater images. Then, the feature point matching method based on the improved SURF realizes the extraction and matching of the feature points of underwater multi-frame target images. Finally, combining with the fusion strategy of gradual in and out, the seamless splicing and fusion of underwater multi-frame target images is realized. The pool and shallow sea tests were carried out respectively, and the results show that the method proposed in this paper increases the number of effective feature point matching and improves the splicing effect.
Klasifikasi Serangan Distributed Denial of Service (DDoS) Menggunakan Random Forest Dengan CFS

Distributed Denial of Service (DDoS) attacks can have serious impacts on your organization and can cause enormous losses. This attack works by sending a computer or server an amount of requests that exceeds the capabilities of that computer. When classifying DDoS attacks in this study, feature selection is performed using correlation-based feature selection (CFS). The dataset used by the author in this study is CSE-CIC-IDS 2018. Feature selection on a dataset using CFS gets the results in the form of features related to the dataset. That is, a total of 31 features with a relationship score greater than 0.1. The average precision generated by the system using the random forest method and CFS function selection is 99.784%. Accuracy is the result of using the number of trees parameter with a value of 10. For a random forest model with no feature selection, the highest accuracy is 49.501%. This indicates that changing the random forest model parameters and selecting the CFS feature will affect high accuracy.