Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Secure Electronic Voting System Using Fingerprint Biometrics

Fingerprint-Based Voting Project is an application that recognizes users based on their fingerprints. Each person has a different finger pattern, so you can easily authenticate voters. This system allows voters to vote using their fingerprints. Fingerprints are used to identify users uniquely. As the fingerprint minutiae features differ for each human being, the fingerprint is utilized to authenticate the voters. Voters can only vote once for a candidate. The system will not allow the voter to vote for a second time. The system allows administrators to add names and candidate photos of candidates nominated for election. The administrator only has the right to add the names and photos of nominated candidates. The admin will verify voters and register voter names. Administrators authenticate users by verifying their identities and then enrolling voters. Users can log in and vote for candidates after receiving a user ID and password from the administrator. The system allows the user to vote for only one candidate for a particular election. The voter and the admin can view the election results using the election ID. Voting results will be updated immediately. This study shows that the proposed web-based voting system is fast, efficient and fraud-free.
Hand Gesture Controlled Presentation using OpenCV and MediaPipe

In today's digital era, presentations play a crucial role in various domains, ranging from education to business. However, traditional manual presentation methods, reliant on input devices such as keyboards or clickers, have inherent limitations in terms of mobility, interactivity, and user experience. To address these limitations, gesture-controlled presentations have emerged as a promising solution, harnessing the power of computer vision techniques to interpret hand gestures and enable natural interaction with presentation content. This paper presents a comprehensive system for hand gesture-controlled presentations using OpenCV and MediaPipe libraries. OpenCV is employed to capture video input from a webcam, while MediaPipe is utilized for hand tracking and landmark extraction. By analyzing finger positions and movements, the system accurately recognizes predefined gestures. Presenters can seamlessly control the slides, hold a pointer, annotate the content, and engage with the audience in a more interactive manner. The responsiveness and real-time performance contribute to an enhanced presentation experience.
Design of a VR Environment Optimized for Cultural Heritage Sites and Objects: The Use Case of Its Kale, Ioannina, Greece

We report on the processes and techniques used to create 3D objects and characters, their composition into scenes and the development of an interactive VR application optimized for cultural heritage sites and objects. To this end, technologies and methods are presented to achieve these objectives and some key decisions for the smoothest possible functionality of the produced applications are analyzed. The process of conversion of characters, objects, and platforms to integrate seamlessly in the context of specific historical eras with distinct cultural heritage characteristics is discussed in detail. A user study is presented, where the final VR platform is evaluated by a group of users via the System Usability Scale (SUS) and the Technology Acceptance Model (TAM). The VR platform is deployed on three devices: HTC VIVE Cosmos Elite, HTC VIVE Focus 3 and Oculus Quest 2.
Associating Geodetic Theory to Digital Pottery Reassembly Practice

Geodesy is a scientific branch of the science of Geography dealing with accuracy and precision in measuring and understanding Earth’s geometry, including its orientation in space. It is mainly based on geometric quantities, such as angles, distances, distance differences, directions, curved lines, elevations, elevation differences, and it primarily solves geometric problems, determining the shape and size of our planet and calculating, coordinates on its surface. Similarly to the study of Earth’s spatial elements, theories of geodesy can also be transferred to investigate smaller-scale spherical/ellipsoidal objects. To this end, since the shape of ancient ceramic pottery is an ellipsoid formed by the rotation of a potter’s wheel, in this paper it is presumed that on a ceramic pottery a similar to Earth’s geodetic network may be hypothesized, researched and used. Therefore, in line with all the above, we describe a methodology based on precise geodetic distances that can be extracted from arcs of ellipses deriving from the traces left by the potter’s fingers on the internal side of sherds. This can be achieved through high precision digital 3D models that we have previously obtained using photogrammetry and macrophotography (close-range photography). By extracting geometric quantities from each digitally modeled sherd, it is possible to gather local geodetic set of distances, which we will then compare with respective sets of candidate conjoined sherds for successfully arranging them their original position.
Face Mask Detection using MobileNetV2 and OpenCV

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. The best way to prevent and slow down transmission is by staying at least 2 meters apart from others and wearing a properly fitted face mask. The use of face masks is mandatory as per World Health Organization (WHO) guidelines to avert the spread of CORONA virus. Presently the inspection of people with/without masks is being done manually and visually by sentries/guards present at entry/exit points. Guards/Sentries cannot be stationed at every place to keep a check on such people. In this work, efforts have been made in inspecting people with/without masks automatically with the help of Computer vision and Artificial Intelligence. This module detects the face of the individual and identifies whether he/she is wearing a mask or not. The rectangular bounding box is drawn on the face which is displayed on the camera to detect whether the individual is wearing the facemask or not. The face mask detection in the work is performed by using the deep learning model MobileNetV2 and OpenCV and an accuracy of 99% is achieved.
Prediction of Cryptocurrency Price using Time Series Data and Deep Learning Algorithms

One of the most significant and extensively utilized cryptocurrencies is Bitcoin (BTC). It is used in many different financial and business activities. Forecasting cryptocurrency prices are crucial for investors and academics in this industry because of the frequent volatility in the price of this currency. However, because of the nonlinearity of the cryptocurrency market, it is challenging to evaluate the unique character of time-series data, which makes it impossible to provide accurate price forecasts. Predicting cryptocurrency prices has been the subject of several research studies utilizing machine learning (ML) and deep learning (DL) based methods. This research suggests five different DL approaches. To forecast the price of the bitcoin cryptocurrency, recurrent neural networks (RNN), long short-term memories (LSTM), gated recurrent units (GRU), bidirectional long short-term memories (Bi-LSTM), and 1D convolutional neural networks (CONV1D) were used. The experimental findings demonstrate that the LSTM outperformed RNN, GRU, Bi-LSTM, and CONV1D in terms of prediction accuracy using measures such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared score (R2). With RMSE= 1978.68268, MAE=1537.14424, MSE= 3915185.15068, and R2= 0.94383, it may be considered the best method.
Distributed ledger technologies for authentication and access control in networking applications: A comprehensive survey

The accelerated growth of networking technologies highlights the importance of Authentication and Access Control (AAC) as protection against associated attacks. Controlling access to resources, facilitating resource sharing, and managing user mobility are some of the notable capabilities provided by AAC methods. Centralized methods are the most common deployment architectures, that can be threatened by several attacks at their central points. Emerging Distributed Ledger Technology (DLT) has attracted significant interest in the AAA community. The distributed nature of DLT and its immutability can bring unprecedented opportunities to resolve many of the challenges of conventional systems. We survey the state-of-the-art in deploying authentication and access control approaches via DLT for several networking use cases. More precisely, we explore DLT applications in (1) Authentication; (2) Access Control; and (3) Comprehensive AAC solutions. First, we present the challenges of centralized solutions and discuss the capability of DLT for their resolution. Then, we propose a taxonomy to categorize the existing methods. Analysis, comparison, and discussion on the advantages and disadvantages of these methods have been provided regarding different parameters such as DLT types, AAC approaches, security, reliability, scalability, etc. While DLT provides various benefits, several challenges remain for the migration to DLT-based AAC. In light of these general limitations, we propose some future directions, targeting the current lacunae and future needs.
Bodies/Technology on Standby: The Importance of Cooperative Waiting for Digital Work

Based on an ethnographic study of the production of visual effects, this chapter analyses the role of waiting in digital work. I argue that temporal discrepancies between computational processes, computer operating and human interaction are a constant part of cooperation in digital work. In my analysis, the phenomenon of waiting, hitherto conceived almost exclusively as human (in)activity, is made visible as a sociotechnical practice that is fundamental to the accomplishment of cooperation. The observable forms of waiting—as situated practices—in visual effects production provide information on how cooperation in digital work is established between different participants on a daily basis. Both digital forms of waiting presented in this chapter, waiting in a standby pose as well as waiting in rendering queues, contributed to making design processes accountable for those involved in the production of visual effects.
The Irreducibility of Technical Skills: Before and After Science

In classical media theory, the synaesthetic space of human bodies constituted the invariant conditions for the independent and dependent variables of media, namely, the innovations of technical media and their impact on culturally encoded perception and social organisation. But the invariant was depending upon cultural techniques too: on linguistic skills, body techniques and ritual transformations of signs, things and persons. The article proposes to call these three groups of activities “cold techniques”, because they can neither be accumulated without losing the complexity gained by prior incremental learning, nor can they be reduced to a less complex structure than today or, for that matter, in the Upper Paleolithic. Some of the consequences of the irreducibility of these skills are spelled out; and tentatively, the essay explores the possibility that bundling the three techniques for different purposes is “the medium before media”, and within the incessantly creative destruction and self-destruction of new media, the medium before and after science.
IMPLEMENTASI METODE SIMPLE ADDITIVE WEIGHTING PADA PEMILIHAN DESA PENERIMA KEGIATAN TMMD KOREM 042/GAPU

Village selection for the Manunggal Army Building a Village (TMMD) activity at Korem 042/GAPU is still using the manual method, making it difficult to select and consider villages that are entitled to receive assistance. The method used is Fuzzy Attribute Decision Making (FMADM) to determine the results of the selection of each alternative and the calculation in this study uses Simple Additive Weighting (SAW), then calculations are carried out on each predetermined criterion. The results of this research are web-based. The data input into this system is in the form of population, village name, village address, data on the criteria for the most prioritized infrastructure facilities, data on budget funds criteria, and data on criteria for mileage from the village to the city. Then carry out the data input process and produce output in the form of recommendations for which villages are entitled to receive assistance from TMMD activities. By using this system, it is hoped that the activity organizers can determine which villages are entitled to receive assistance appropriately and efficiently.
Prediction of axillary lymph node metastasis in early breast cancer patients with ultrasonic videos based deep learning

To develop a deep learning (DL) model for predicting axillary lymph node (ALN) metastasis using dynamic ultrasound (US) videos in breast cancer patients.A total of 271 US videos from 271 early breast cancer patients collected from Xiang'an Hospital of Xiamen University andShantou Central Hospitabetween September 2019 and June 2021 were used as the training, validation, and internal testing set (testing set A). Additionally, an independent dataset of 49 US videos from 49 patients with breast cancer, collected from Shanghai 10th Hospital of Tongji University from July 2021 to May 2022, was used as an external testing set (testing set B). All ALN metastases were confirmed using pathological examination. Three different convolutional neural networks (CNNs) with R2 + 1D, TIN, and ResNet-3D architectures were used to build the models. The performance of the US video DL models was compared with that of US static image DL models and axillary US examination performed by ultra-sonographers. The performances of the DL models and ultra-sonographers were evaluated based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Additionally, gradient class activation mapping (Grad-CAM) technology was also used to enhance the interpretability of the models.Among the three US video DL models, TIN showed the best performance, achieving an AUC of 0.914 (95% CI: 0.843-0.985) in predicting ALN metastasis in testing set A. The model achieved an accuracy of 85.25% (52/61), with a sensitivity of 76.19% (16/21) and a specificity of 90.00% (36/40). The AUC of the US video DL model was superior to that of the US static image DL model (0.856, 95% CI: 0.753-0.959, P<0.05). The Grad-CAM technology confirmed the heatmap of the model, which highlighted important subregions of the keyframe for ultra-sonographers' review.A feasible and improved DL model to predict ALN metastasis from breast cancer US video images was developed. The DL model in this study with reliable interpretability would provide an early diagnostic strategy for the appropriate management of axillary in the early breast cancer patients.
Peningkatan Minat Belajar Siswa Melalui Pendekatan Problem Based Learning Pada Mata Pelajaran Matematika Kelas II SDN Bawuran

The low interest in learning of grade II students at SDN Bawuran encouraged researchers to conduct class action research at SDN Bawuran. This study aims to (1) know the implementation of learning using the Problem Based Learning approach as an effort to increase students' learning interest in grade II mathematics subjects at SDN Bawuran, and (2) increase students' learning interest in grade II mathematics subjects at SDN Bawuran using the Problem Based Learning approach. The research method used is class action research, Kemmis &amp;Taggart model. The subjects of this study were 23 grade II students of SD N Bawuran. The research was conducted in two cycles. Each cycle consists of two meetings. Research in an effort to increase students' learning interest through PBL in grade II mathematics subjects sdn Bawuran has been conducted with the results can increase students' learning interest, it is shown the initial condition of the average learning interest score of 61 (less interested) with a percentage of the number of students at least less interested 56%; Cycle 1 is 66 (quite interested) with a percentage of 65%; Cycle 2 is 73 (quite interested) with a percentage of 82%.
Learning from the Past: Fast NAS for Tasks and Datasets

Nowadays, with the advancement of technology, many retail companies require in-house data scientist teams to build machine learning tasks, such as user segmentation and item price prediction. These teams typically use a trial-and-error process to obtain a good model for a given dataset and machine learning task, which is time-consuming and requires expertise. However, the team may have built models for other tasks on different datasets. This article proposes a framework to obtain a model architecture using the previous solved machine learning tasks and datasets. By analyzing real datasets with over 70,000 images from 11 online retail e-commerce websites, it is demonstrated that the performance of a model is related to the similarity among datasets, models, and machine learning tasks. A framework is hence proposed to obtain the model using the similarities among them. It was proven that the model was 26.6% better in accuracy, and using only 20% of the runtime while comparing to an auto network architecture search library, Auto-Keras, in predicting the attributes of fashion images. To the best of our knowledge, this is the first article to obtain the best model based on the similarity among machine learning tasks, models, and datasets.
DBO: Fairness for Cloud-Hosted Financial Exchanges

We consider the problem of hosting financial exchanges in the cloud. Exchanges necessitate strong fairness guarantees for competing participants, particularly for use cases such as "high frequency trading". Today, exchanges achieve such guarantees by providing equal latency across all market participants in their on-premise deployments. However, ensuring equal latency for fairness is notably challenging in current multi-tenant cloud deployments, mainly due to factors such as network congestion and non-equidistant network paths.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN AYAM BROILER SEHAT BERBASIS WEB

Putra West Sumatra is a family company engaged in the delivery of goods using land transportation. CV. With the increasing number of shipments of goods, problems often occur, this is because the company is still using a data system for shipping goods that has not been computerized. The purpose of this study is to design an information system for freight forwarding services at CV. Putra West Sumatra and modify it into a new system. The need for a new system input is recipient data, sender data, goods type data, delivery schedule data, and transaction data, the process needs to process sender data and process data for recipients who receive goods and payment reports to be sent according to the sender's schedule, the output needs namely the recipient data report, the sender data report, the goods data report and the payment data report. The result of this research is that the data processing of the delivery service can help effectively and reduce the risk of errors in data input and the collision of the delivery schedule.
Intelligent Unsupervised Network Traffic Classification Method Using Adversarial Training and Deep Clustering for Secure Internet of Things

Network traffic classification (NTC) has attracted great attention in many applications such as secure communications, intrusion detection systems. The existing NTC methods based on supervised learning rely on sufficient labeled datasets in the training phase, but for most traffic datasets, it is difficult to obtain label information in practical applications. Although unsupervised learning does not rely on labels, its classification accuracy is not high, and the number of data classes is difficult to determine. This paper proposes an unsupervised NTC method based on adversarial training and deep clustering with improved network traffic classification (NTC) and lower computational complexity in comparison with the traditional clustering algorithms. Here, the training process does not require data labels, which greatly reduce the computational complexity of the network traffic classification through pretraining. In the pretraining stage, an autoencoder (AE) is used to reduce the dimension of features and reduce the complexity of the initial high-dimensional network traffic data features. Moreover, we employ the adversarial training model and a deep clustering structure to further optimize the extracted features. The experimental results show that our proposed method has robust performance, with a multiclassification accuracy of 92.2%, which is suitable for classification with a large number of unlabeled data in actual application scenarios. This paper only focuses on breakthroughs in the algorithm stage, and future work can be focused on the deployment and adaptation in practical environments.
Masking Corruption Packet Losses in Datacenter Networks with Link-local Retransmission

Packet loss due to link corruption is a major problem in large warehouse-scale datacenters. The current state-of-the-art approach of disabling corrupting links is not adequate because, in practice, all the corrupting links cannot be disabled due to capacity constraints. In this paper, we show that, it is feasible to implement link-local retransmission at sub-RTT timescales to completely mask corruption packet losses from the transport endpoints. Our system, LinkGuardian, employs a range of techniques to (i) keep the packet buffer requirement low, (ii) recover from tail packet losses without employing timeouts, and (iii) preserve packet ordering. We implement LinkGuardian on the Intel Tofino switch and show that for a 100G link with a loss rate of 10−3, LinkGuardian can reduce the loss rate by up to 6 orders of magnitude while incurring only 8% reduction in effective link speed. By eliminating tail packet losses, LinkGuardian improves the 99.9th percentile flow completion time (FCT) for TCP and RDMA by 51x and 66x respectively. Finally, we also show that in the context of datacenter networks, simple out-of-order retransmission is often sufficient to significantly mitigate the impact of corruption packet loss for short TCP flows.