Reviewerly/exHarmony huggingface dataset:
Pengembangan Media Pembelajaran Interaktif Materi Sistem Pernapasan Pada Manusia Untuk Sekolah Dasar

This research and development aims to produce a product in the form of Interactive Learning Media (ILM) on the material of the human respiratory system. Research and development is a research method used to produce products. The product development method used refers to the 4D model that has been adjusted by needs (define, design, development, disseminate). However, in the field application, it is only up to the development stage due to time constraints and it is still in a pandemic condition where face-to-face learning is not recommended. In its application, ILM is a complement to learning material for the respiratory system in humans, media as a complement means that the media is applied to complement the material received by students in the classroom. The results of the product feasibility test that have been tested on media experts, material experts and students get a positive and feasible response. Based on the results that have been obtained, ILM about the respiratory system in humans is suitable for use as a learning medium in the classroom, with the ILM providing convenience in learning, stimulating students to learn, producing unique and attractive products for students.
Accurate Measurement Method of Drilling Depth Based on Multi-Sensor Data Fusion

Based on the advanced detection drilling rigs used in underground coal mines, a real-time method of obtaining the depth of drilling is proposed. Displacement sensors are used to measure the stroke of the drilling rig’s feeding device during drilling, and an equation to calculate the depth of drilling is put forward. The same measurements are made by several sensors, improving measurement accuracy and reliability. The final drilling depth is obtained using a multi-sensor data fusion algorithm, combined with the calculation equation. The necessary derivation and calculation processes of the multi-sensor, adaptive weighted fusion algorithm are given. To optimize the integrated result, the weighting coefficients can be found through the algorithm corresponding to each sensor in an adaptive mode to optimize the fusion result. Three kinds of displacement sensors are installed on the feeding device of the drilling rig, and the drilling process is simulated in a laboratory test. The test proves that, compared with the mean method of three sensors, the data obtained by the multi-sensor and adaptive weighted fusion algorithm have the higher accuracy, and the sensor with the least variance in the fusion process has the most significant weighting coefficient. The drilling depth data that are obtained are more accurate than those obtained through the mean method with measurement data from a single sensor. The weighted coefficient of the measurement data is minimal when the measurement accuracy of the sensor suddenly deteriorates, so it has little effect on the measurement results. An experiment verifies this method’s effectiveness and fault tolerance, showing an improvement in measurement accuracy.
Walking through Algebraic Thinking with Theme-Based (Mobile) Math Trails

Tasks are a key resource in the process of teaching and learning mathematics, which is why task design continues to be one of the main research issues in mathematics education. Different settings can influence the principles underlying the formulation of tasks, and so does the outdoor context. Specifically, a math trail can be a privileged context, known to promote positive attitudes and additional engagement for the learning of mathematics, confronting students with a sequence of real-life tasks, related to a particular mathematical theme. Recently, mobile devices and apps, i.e., MathCityMap, have been recognized as an important resource to facilitate the extension of the classroom to the outdoors. The study reported in this paper intends to identify the principles of design for mobile theme-based math trails (TBT) that result in rich learning experiences in early algebraic thinking. A designed-based research is used, through a qualitative approach, to develop and refine design principles for TBT about Sequences and Patterns. The iterative approach is described by cycles with the intervention of the researchers, pre-service and in-service teachers and students of the targeted school levels. The results are discussed taking into account previous research and data collected along the cycles, conducing to the development of general design principles for TBT tasks.
Target-based computer-assisted orchestration: Complexity and approximation algorithms

Target-based computer-assisted orchestration can be thought of as the process of searching for combinations of orchestral sounds in a database of sound samples to match a given sound (called target) while respecting specific symbolic constraints (such as the musical instruments that we can use). It is modeled as a combinatorial optimization problem, where the feasible solutions are subsets of orchestral sounds, and the function to minimize measures a distance between the chosen subset of orchestral sounds and the target. In this article, we focus on this optimization problem and analyse it through the lens of computational complexity and approximation algorithms. We first study the so-called static case where there is no temporality in the target sound (we want to reproduce a ‘single’ constant sound). We show that the problem is already NP-hard, but is solvable by a pseudo-polynomial-time algorithm. We also provide an approximation algorithm with additive error. We then consider the more general case with temporality, when the target sound can be seen as a sequence of sounds over a time horizon. We show how the previous results in the static case generalize in this temporal case. We conclude our study with some experiments on real target sounds.
And Rijndael?

Finding optimal related-key differential characteristics for a given cipher is a problem that hardly scales. For the first time, we study this problem against the 25 instances of the block cipher Rijndael, which are the little brothers of the AES. To achieve this, we adapt and improve an existing approach for the AES which is based on Constraint Programming. The attacks presented here overpass all the previous cryptanalytic results of Rijndael. Among all our results, we obtain a 12-round (out of 13 rounds) related-key differential attack for Rijndael with a block size equal to 128 bits and a key size equal to 224 bits. We also obtain an 11-round related-key differential characteristic distinguisher for Rijndael with a block size equal to 160 bits and a key size equal to 256 bits leading to an attack on 12 rounds (out of 14 rounds).
Object Detection using YOLO

Abstract: A technical assessment is always conducted prior to the installation of traffic signs at crosswalks, safety lanes, or even expanding areas. In these cases, using cameras to gather local photos and then analysing them is a valuable method. This paper describes a technique for detecting and tracking four types of vehicles: automobiles, buses, trucks, and motorcyclists, by analysing video footage of road crossings. The first findings of vehicle route accounting reveal that the technique is highly promising, with good outcomes in a number of scenarios, but much more study is needed to make these systems resistant against occlusions and other unforeseen events. Keywords: Deep Learning, Tracking, Multiple Objects Tracking, CNNs, YOLO, DeepSORT.
Half circular modified burr−III distribution, application with different estimation methods

The data related to many medical, environmental and ecological variables are often measured in terms of angles wherein its range is defined in [0, π ). This type of data is referred to as axial or half circular data. Modeling based on half circular data has not received its due share of attention in statistical literature. In this paper, we introduce a new half circular distribution based on inverse stereographic projection technique on modified Burr−III distribution, called the half circular modified Burr−III (hcMB−III) distribution. The basic properties of the proposed distribution are derived. It is common observation that while estimating the parameters of a model, one usually adopts maximum likelihood estimation method as the starting point. In this paper, we consider seven frequentist methods of estimation, besides using maximum likelihood method for estimating the parameters of the hcMB−III distribution. Monte Carlo simulations are performed for investigating the performances of the considered methods in terms of their biases and mean square errors using small, medium and large sample sizes. Finally, one data set related to posterior corneal curvature of the eyes of 23 patients, is analyzed to check potentiality of the newly proposed model.
Quantitative 3D shape descriptors of gold grains from Quaternary sediments: application to mineral exploration in glaciated terrain, Amaruq orogenic gold deposit (Nunavut, Canada)

Native gold is malleable and deforms during glacial, fluvial and eolian sedimentary processes. Quantification of its morphology is used in mineral exploration to estimate a distance of transport from its primary source. However, this quantification depends on textural aspects, or on partial 2D measurements of gold grain shapes. In the glacial domain, the classification of the shape remains qualitative and subjective. Microtomography and photogrammetry using scanning electron microscope images are two methods used to reconstruct the gold grain shape in 3D and to quantify accurately grain shape parameters. Reconstructions are applied on gold grains ranging in size from 35 μm to 415 μm for the long axis and 2716 μm3 to 760,713 μm3 for the volume. Shape parameters such as particle length, volume, and surface area are estimated in order to compute 3D shape descriptors of roundness, sphericity, and convexity. This study integrates two new shape descriptors (the ellipticity shape descriptor and inscribed sphericity) in order to estimate the ellipticity and sphericity of gold particles. These descriptors can be used to evaluate a morphological change with the distance of transport in glacial sediments such as in the Amaruq gold deposit area.
Desktop Voice Assistant

Abstract: The primary goal of trending technology artificial intelligence (AI) is to realize natural human-machine dialogue. Various IT-based companies also utilized dialogue networks technology to create various types of Virtual Personal Assistants focused on their products and areas for expanding human-machine contact, such as Alexa, Cortana, Google's Assistant, Siri and so more. Just like the Microsoft voice assistant named 'Cortana', we designed our virtual assistant which performs basic tasks based on the instruction provided to it on the Windows platform using Python. Here, Python is used as a scripting language as it has a large library that is used to perform instructions. Using Python packages, a personalized virtual assistant recognizes and processes the user's voice. Voice assistants are a fantastic advancement in the sector of Artificial Intelligence that can transform people's lives in a variety of ways. The voice-based assistant was initially given on cellphones and quickly gained popularity. It was widely acknowledged by all. Previously, voice assistants were largely found in smartphones and laptops, but they are now increasingly available in various home automation setups and smart speakers. Many technologies seem to become wiser in their very own way, allowing them to converse with humans in a simple language. Desktop voice assistants are programme that can identify people's speech and answer through an integrated speech system. This paper will outline how different voice assistants work, as well as their primary challenges and limitations. The way of developing a voice-based assistant without requiring cloud services is discussed in this paper, which would promote the future growth of such devices. Keywords: Voice Assistant, Speech Recognition, Python, Smtplib, Automation.
Diagnostic test of bioimpedance-based neural network algorithm in early cervical cancer

Background: Colposcopy is a critical component of cervical cancer screening services, but the accuracy of colposcopy varies greatly due to the lack of standardized training for colposcopists and pathologists.Thus, to improve the accuracy of colposcopy in the detection of cervical lesions intelligently is urgent.Here, we explored the sensitivity and specificity of a bioimpedance-based neural network algorithm in distinguishing normal and precancerous cervical tissues.Methods: Bioimpedance data were collected using a bioimpedance analyzer (Mscan1.0B,Sealand Technology, Chengdu, China) from the cervices of 102 female patients with abnormal cervical cytology (≥atypical squamous cells of undetermined significance) who required further colposcopy.Finally, the data of 106 samples from 37 patients were included, among which 85were used as the training set and 21 as the validation set.Using the biopsy pathology at each locus as the gold standard, the sensitivity, specificity, predictive value, likelihood ratio, and false positive and false negative rates of the bioimpedance-based neural network in identifying the normal and precancerous cervical tissues were calculated. Results:The bioimpedance method had a sensitivity of 0.90 [95% confidence interval (CI): 0.54 to 0.99], specificity of 0.82 (95% CI: 0.48 to 0.97), positive predictive value of 0.82 (95% CI: 0.48 to 0.97), and a negative predictive value of 0.90 (95% CI: 0.54 to 0.99) in distinguishing normal and precancerous cervical tissues.The Kappa value was 0.72. Conclusions:The bioimpedance method was an intelligent method with relative good sensitivity and specificity in distinguishing benign cervical tissue and precancerous lesions and can therefore be used as an adjunctive test to colposcopy to improve the detection of cervical lesions.
Design of Mobile Robot Path Planning Algorithm Based on Improved Whale Optimization Algorithm

In this paper, an improved whale optimization algorithm (IWOA) is proposed to solve the problem of independent path planning for mobile robots, which makes mobile robots move along the optimal path. By combining inverse initial coding optimization with Levy flight, The classic whale optimization algorithm (WOA) was improved, the optimization ability of WOA and the solving ability of optimal path point are improved, then the local optimal solutions are maximally avoided. Finally, by comparing the IWOA with the path planning effect diagram of the classical WOA through simulation, the feasibility and efficiency of the IWOA in path planning are verified.
A Composite Particle Swarm Optimization Algorithm for Hospital Equipment Management Risk Control Optimization and Prediction

Aiming at the problem that particles cannot realize multidimensional analysis and poor global search ability, a composite particle swarm optimization algorithm is proposed, improving the accuracy of particle swarm optimization. Firstly, k-clustering is used to cluster risk management particle swarm optimization. The advantages of particle swarm optimization have to be given full play, and the risk of hospital equipment management from various aspects has to be controlled. Then, the multidimensional particle swarm is segmented to obtain an ordered multidimensional risk particle swarm set, which provides a basis for later risk prediction. Finally, through the fusion function of multidimensional risk particle swarm, the risk particle swarm set based on the clustering degree is constructed, and the optimal extreme value is obtained, so as to improve the accuracy of management risk calculation results. Through MATLAB simulation analysis, it can be seen that the composite particle swarm optimization algorithm is better than particle swarm optimization algorithm in global search accuracy and search time. Moreover, the calculation time and accuracy are better. Therefore, the composite particle swarm optimization algorithm can be used to analyze the risk of hospital equipment and effectively control the risk of hospital equipment management.
Zoom-DF: A Dataset for Video Conferencing Deepfake

With the rapid growth of deep learning methods, AI technologies for generating deepfake videos also have been significantly advanced. Nowadays, the manipulated videos such as deepfakes are so sophisticated that one cannot easily differentiate between real and fake, and one can create such videos with little effort. However, such technologies can be likely to be abused by people with malicious intents. To address this issue, approaches and efforts to detect deepfakes have been researched significantly. However, the performances of the detectors in general depends on the quantity and quality of the training data. In this paper, we introduce a new deepfake dataset, Zoom-DF, which can be injected during the remote meeting and video conferencing, to create a sequence of fake participant images. While most deepfake datasets focus on the face area, our dataset primarily targets for the remote meeting, and manipulates movements of the participants. We evaluate existing deepfake detectors on our new Zoom-DF dataset and present the performance results.
Local Explanation-Based Method for Healthcare Risk Stratification

Decision support tools in healthcare require a strong confidence in the developed Machine Learning (ML) models both in terms of performances and in their ability to provide users a deeper understanding of the underlying situation. This study presents a novel method to construct a risk stratification based on ML and local explanations. An open-source dataset was used to demonstrate the efficiency of this method that well identified the main subgroups of patients. Therefore, this method could help practitioners adjust and build protocols to improve care deliveries that would better reflect patient's risk level and profile.
Validation of Multiple Path Translation for SNOMED CT Localisation

The MTP (multiple translation paths) approach supports human translators in clinical terminology localization. It exploits the results of web-based machine translation tools and generates, for a chosen target language, a scored output of translation candidates for each input terminology code. We present first results of a validation, using four SNOMED CT benchmarks and three translation engines. For German as target language, there was a significant advantage of MTP as a generator of plausible translation candidate lists, and a moderate advantage of the top-ranked MTP translation candidate over single best performing direct-translation approaches.
VORI: A framework for testing voice user interface interactability

The ability of Voice User Interface (VUI) to understand how users will express their commands naturally and intuitively is an essential component of user experience, especially when the user is interacting with the VUI for the first time. Designing an automated method for testing the usability of VUI is a challenge for two reasons. First, there are many different ways for a user to express the same intention, e.g. “play some music”, ””put some music on”, etc., that is difficult to determine in advance. Second, many VUI apps today typically rely on the platform service provider (e.g. Amazon, Google, etc.) to perform many of the speech recognition and natural language processing tasks, and these services are provided as a blackbox. Consequently, it is difficult for the app developer to obtain information about errors and user feedback. In this paper, we propose a framework, VORI, to systematically evaluate the interactability of VUI, as well as a new metric for quantifying the interactability of a VUI. We use VORI to analyze 127 applications on Alexa by sending over 82,931 commands. Our analysis results highlight that 41.7% of apps only accept strict input that has to exactly match the developer’s predefined sample commands with an interactability score of 20% or less. This suggests developers should consider a better interactability strategy in the design of VUIs, and more research is needed to further explore the design space to improve the interactability.
No PANE, No Gain

Given a graph G where each node is associated with a set of attributes, attributed network embedding (ANE) maps each node v 2 G to a compact vector Xv, which can be used in downstream machine learning tasks in a variety of applications. Existing ANE solutions do not scale to massive graphs due to prohibitive computation costs or generation of low-quality embeddings. This paper proposes PANE, an effective and scalable approach to ANE computation for massive graphs in a single server that achieves state-of-the-art result quality on multiple benchmark datasets for two common prediction tasks: link prediction and node classification. Under the hood, PANE takes inspiration from well-established data management techniques to scale up ANE in a single server. Specifically, it exploits a carefully formulated problem based on a novel random walk model, a highly efficient solver, and non-trivial parallelization by utilizing modern multi-core CPUs. Extensive experiments demonstrate that PANE consistently outperforms all existing methods in terms of result quality, while being orders of magnitude faster.
Dynamic path planning for traversing autonomous vehicle in off-road environment using MAVS

Navigating through an unknown environment is one of the key capabilities of the autonomous ground vehicle (AGV). It is relatively easier to traverse through an on-road environment, but moving through an off-road environment is challenging because of the inadequate driving path, obstacles, surface roughness, slope, dense vegetation, poor soil conditions, lack of road signs, etc. This complexity requires simulation of the AGV through many environments before facing an unknown situation in the real world. This paper proposes a dynamic path planning technique for AGV navigation in an off-road environment. First, a brief discussion on the traversability model and the factors mentioned in state of art such as vegetation density, soil condition, surface roughness, and slope individually. Secondly, we have proposed some modifications in the traversability model by introducing weights and exponents with each factor. Then, the A* algorithm has been analyzed by penalizing the weight and exponent values to get an optimal path. We used the Mississippi State University Autonomous Vehicular Simulator (MAVS) for simulation by creating an off-road scenario and using an AGV. We have generated a cost map based on the traversability score. The higher the score, the better the result. The optimal path is selected considering the traversability score. The novelty of this work is that we are exploring the linearity and non-linearity of the traversability model and applying the A* algorithm for path planning.
On the Robustness of Digital Modulation Recognition for Cooperative Relaying Networks under Imperfect CSI

Cognitive radio (CR) technology offers the ability to adapt the transmission or reception parameters, thus efficiently improving the wireless spectrum resources utilization. As one of the important components of CR, automatic modulation recognition (AMR) aims to improve the overall communication performance besides promoting efficient and secure transmissions. In this paper, we tackle the semi blind modulation recognition problem in a distributed space-time block coding (D-STBC) scheme for a cooperative relaying network of three nodes, employing the Amplify-and-Forward (AaF) protocol. As classifier, we design a multi-layer artificial neural network (ANN) and train it using higher-order statistics (HOS) as features of the received signals. We evaluate two D- STBC configurations identifying the modulation with and without channel state information (CSI) by exploiting both the temporal and spatial signal dimensions. Experimental results demonstrate that the proposed identifier ensures a good probability for achieving correct modulation identification in acceptable signal-to-noise ratio (SNR) ranges.