Reviewerly/exHarmony huggingface dataset:
Development of The Interactive Learning Media Based on Augmented Reality 3D on The Petroleum Concept

Chemistry is studied by students at the high school level. However, due to the abstract concept of chemistry, chemistry becomes difficult for some students to learn. One problem with studying chemistry is the petroleum material in online and offline learning systems. This study aimed to determine the feasibility and practicality of interactive learning media based on the 3D augmented reality of petroleum topics. This study aims to describe the making of applications with augmented reality technology as a medium for learning chemistry. The research type was development research that adopted a 4D model (define, design, develop, disseminate) but was limited to the development stage. The population in this study was 192 students of public high school SMAN 7 Mataram, Indonesia. This study's sampling was carried out using a random sampling technique measured through an expert validation sheet with three assessment aspects assessed by three validators. The practicality level of the media was measured through a practicality questionnaire filled out by 22 students in eleventh-grade students at SMAN 7 Mataram. The results showed that the validator assessed media feasibility was 90.16%, which indicated that the media developed was very feasible. Meanwhile, the media practicality test results revealed that students responded positively to all media components with a practicality percentage of 94.39%. In conclusion, the media developed is feasible and practical to use.
Danimer will buy polymer firm Novomer

Danimer Scientific, a maker of the biodegradable polymer polyhydroxyalkanoate (PHA), will buy Novomer in a $152 million transaction. Novomer makes poly(3-hydroxypropionate) from the raw materials carbon monoxide and ethylene oxide. The polymer, a PHA, could cost less to produce than other PHAs because it circumvents fermentation. Novomer operates a pilot plant in Rochester, New York, and has 20 employees. Danimer says it will likely modify its $700 million expansion project, installing fewer fermenters in Bainbridge, Georgia, than originally planned and expanding Novomer’s capacity. Novomer was founded in 2004 based on technology that originated in the lab of Cornell University chemistry professor Geoffrey W. Coates.
DeeProphet: Improving HTTP Adaptive Streaming for Low Latency Live Video by Meticulous Bandwidth Prediction

The performance of HTTP adaptive streaming (HAS) depends heavily on the prediction of end-to-end network bandwidth. The increasingly popular low latency live streaming (LLLS) faces greater challenges since it requires accurate, short-term bandwidth prediction, compared with VOD streaming which needs long-term bandwidth prediction and has good tolerance against prediction error. Part of the challenges comes from the fact that short-term bandwidth experiences both large abrupt changes and uncertain fluctuations. Additionally, it is hard to obtain valid bandwidth measurement samples in LLLS due to its inter-chunk and intra-chunk sending idleness. In this work, we present DeeProphet, a system for accurate bandwidth prediction in LLLS to improve the performance of HAS. DeeProphet overcomes the above challenges by collecting valid measurement samples using fine-grained TCP state information to identify the packet bursting intervals, and by combining the time series model and learning-based model to predict both large change and uncertain fluctuations. Experiment results show that DeeProphet improves the overall QoE by 17.7%-359.2% compared with state-of-the-art LLLS ABR algorithms, and reduces the median bandwidth prediction error to 2.7%.
Enhancing User Personalization in Conversational Recommenders

Conversational recommenders are emerging as a powerful tool to personalize a user’s recommendation experience. Through a back-and-forth dialogue, users can quickly hone in on just the right items. Many approaches to conversational recommendation, however, only partially explore the user preference space and make limiting assumptions about how user feedback can be best incorporated, resulting in long dialogues and poor recommendation performance. In this paper, we propose a novel conversational recommendation framework with two unique features: (i) a greedy NDCG attribute selector, to enhance user personalization in the interactive preference elicitation process by prioritizing attributes that most effectively represent the actual preference space of the user; and (ii) a user representation refiner, to effectively fuse together the user preferences collected from the interactive elicitation process to obtain a more personalized understanding of the user. Through extensive experiments on four frequently used datasets, we find the proposed framework not only outperforms all the state-of-the-art conversational recommenders (in terms of both recommendation performance and conversation efficiency), but also provides a more personalized experience for the user under the proposed multi-groundtruth multi-round conversational recommendation setting.
Penerapan model regresi multilevel untuk data ketepatan waktu lulus mahasiswa

Multilevel logistic regression is one of the alternatives to solving a problem that has a nested data structure like the student data in Alauddin in 2016. The data indicates that students are nested in each different study program. This condition allows the students in the same study program tend to have similar characteristics. The study aims to gain a student graduating model of punctuality using multilevel regression analysis and recognize factors that have a significant impact on student graduating time. Based on our research, we find the best model that fits the data to be the random intercepts model with a random slope of gender variable. The variables that have significant effects are gender, cumulative achievement index, educational background, and accredited program.&#x0D; Keywords: logistic regression, nested, multilevel logistic regression, graduation of studentMSC2020: 62J05
Dynamics of N Two-level moving atoms under the influence of the non-linear Kerr medium

Abstract Dynamical evolution of global quantum discord (GQD) and von Neumann entropy (VNE) is studied for N two-level atomic system (TLS) (two, three and four TLS). The system interacts with the single mode Fock field and the cavity is filled with the non-linear Kerr medium (NLKM). It is seen that for large values of the Kerr parameter, the GQD and VNE show periodic behavior which leads to the sudden death and birth of the quantum entanglement (QE). It is found that the oscillations of the GQD is suppressed for the initial mixed state. The VNE has non-zero values of quantum interference throughout the dynamics of the system for initial mixed states. It is also found that the multipartite systems show sustained response to the QE during the time evolution. Moreover, the GQD and VNE are notably affected in the presence of the NLKM and affected for the entire range of Kerr values as we increase the number of photons in the cavity. Furthermore, for both initial pure and mixed states, the periodic behavior of the GQD and VNE is almost the same for the case of moving TLS. The sudden death and birth of QE is more prominent for moving TLS case. The atomic motion is favorable to sustain the QE in the atomic systems in a NLKM.
Characterizing the vibrational behavior of steelpans with a reduced number of notes

The skilled artisans who construct lead (tenor) steelpans generally attempt to tune the first three resonances of each note harmonically. When successful, the tuning leads to neighboring notes sharing the same frequency for the first two harmonics of each note on the outer ring of the steelpan. The sympathetic excitation of the neighboring notes is believed to contribute significantly to the unique timbre of the steelpan's characteristic sound. A series of custom steelpans with fewer notes was commissioned to understand better the nature of the coupling between the struck note and sympathetically vibrating notes. Electronic speckle pattern interferometry was used to compare the coupling between notes on standard tenor steelpan with the coupling between notes on the customized steelpan having fewer notes.
Spectral degradations in the TIMIT, QuickSIN, NU-6, and other popular bandlimited speech materials

Many popular speech corpora were recorded decades ago using transducers, recording techniques, or post-processing techniques that resulted in spectral degradations. The use of such materials very likely has consequences for measures of speech perception, but this is not often considered in studies that use these materials. We analyzed several of these popular corpora to ascertain spectral degradations that may be present. Spectral degradations we frequently observed included limited extended high-frequency (EHF) (&amp;gt;8 kHz) content, and, for a few corpora, substantial low-frequency roll-off &amp;lt;500 Hz. Two corpora analyzed here, the TIMIT/PRESTO and NU-6, exhibited both high- and low-frequency degradations. Such spectral degradations will likely have an effect on behavioral measures because they do not match the high-fidelity speech signals listeners encounter in their everyday lives. This should be considered when selecting stimuli for speech perception experiments. [Work supported by NIH Grant R01-DC019745]
Using an online graphing calculator as an aid in teaching the physics of waves

In teaching the physics of wave phenomena, it is frequently helpful to be able to interact with various representations of waves rather than showing static images to students. Through the online graphing calculator platform, Desmos.com, students can interact with animated graphics of different wave behaviors, including traveling waves, standing waves, wave interference, beats, and the Doppler effect. The platform also includes tools for instructors to design interactive activities for students to complete individually as homework or as part of a classroom activity. Instructors can share graphs, interactive simulations, and class activities with other instructors for reuse and modification in other classes. This online tool is an efficient way to bring more interactivity into the classroom for exploring wave behavior.
Wireless intrusion prevention system in the information networks of the internal affairs

The article considers the possibilities of using specially designed services for detecting and preventing intrusions into the wireless network of internal affairs bodies. When choosing an intrusion detection and prevention service, it is necessary to proceed from the goals of its operation (software, architecture type, service package, potential threats). While an attacker needs physical access to a wired network to launch an attack, a wireless network allows anyone within range to passively monitor traffic or even launch an attack. One countermeasure can be the use of wireless intrusion prevention systems.
Multi-Branch Multi Layer Perceptron: A Solution for Precise Regression using Machine Learning

The problem of simple regression using Multi Layer Perceptron (MLP) has its limitations. The main problem with MLP is that it is difficult to find a perfect architecture to fit all the data. The more complex and multi-dimensional the data we use, the deeper the network has to be, which increases training time as well as optimization and tuning of the network. The solution to fit the data more precisely could be to split the data into groups based on the input variable and use a different model to train and predict data for each of these groups. In most cases, datasets contain a large number of non-linear input features, which makes the method for finding thresholds for each group very difficult. The proposed technique tackles this problem using multiple branches consisting of shallow MLP, one of which acts as a selector (classifier) for the output. The selector’s main goal is to generate a weight for the other prediction branches which in other words means, results in one branch being more proficient in predicting certain parts of the feature space while the other branches will be better at predicting completely different parts of the feature space.
A Tasty Cup of Truth

Symbolic InteractionEarly View Book Review A Tasty Cup of Truth David Wästerfors, David Wästerfors [email protected] orcid.org/0000-0001-6271-5659 Lund University, Lund, SwedenSearch for more papers by this author David Wästerfors, David Wästerfors [email protected] orcid.org/0000-0001-6271-5659 Lund University, Lund, SwedenSearch for more papers by this author First published: 27 April 2023 https://doi.org/10.1002/symb.647Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Integrated Recognition Assistant Framework Based on Deep Learning for Autonomous Driving: Human-Like Restoring Damaged Road Sign Information

Unpredictable situations frequently occur in real driving environments, and it is often difficult to recognize road signs. In this case, autonomous vehicles (AVs) have a limited ability to predict areas that cannot be detected, making it difficult to judge objects accurately when some information is lost. Therefore, we propose a framework that helps AVs infer proper information under limited conditions. The entire process consists of three steps. First, the missing part of the road sign is restored using the image generative pre-trained transformer model. Next, the sample image with the highest classification accuracy and restored quality is selected among several sample images. Finally, the selected image is provided to users through the designed user interface. The proposed framework improved recognition accuracy compared with unrestored accuracy, indicating the possibility of application as a driving assistance system, and is meaningful in that it is a system that mimics human reasoning ability.
Optimized 2D ground area coverage through unmanned aerial vehicle using metaheuristic approach

2D ground area coverage is essential for various applications like rescue operation, ground target detection, agricultural inspection, etc. In order to successfully implement the above-mentioned applications, researchers have used unmanned aerial vehicles (UAVs) for the ground area coverage. UAVs equipped with visual sensors are extensively used in various coverage applications. The performance of the coverage applications is dependent on maximum target area inspection, which leads to an optimization problem. For any optimization problem, researchers have used various metaheuristic approaches. For the proposed work, authors have considered the maximization of a 2D target area coverage using a single UAV system through the optimization approaches as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Cuckoo Optimization Algorithm (COA). The effectiveness of the proposed work is measured by the proportion of both interior and exterior space that is covered.
Factors that affect guitar timbre

This is part one of two presentations on the same project. Guitars have been used for centuries, but their versatility due to technical developments have made them very popular in most genres of music. Our research has investigated how various physical properties of the guitar affect its timbre. We have investigated the effects of (a) string excitement location, (b) string type and gauge, (c) pick versus pluck excitement, and (e) the placement of a capo on an acoustic guitar. The main goal is to investigate how each of these factors play a role in certain genres of music where guitars are one of the main instruments. Measurements were taken at the LU acoustics lab and the anechoic chamber at Northwestern. This talk will present theoretical basis for our measurements and discuss the effects of the pick versus pluck and placing a capo on the neck of the guitar. The following talk will discuss the remaining factors and how our study contributes to the capability of the player to create different moods.
Extended Kalman Filter State Estimation for Autonomous Competition Robots

Autonomous mobile robot competitions judge based on a robot’s ability to quickly and accurately navigate the game field. This means accurate localization is crucial for creating an autonomous competition. Two common localization methods are odometry and computer vision landmark detection. Odometry provides frequent velocity measurements, while landmark detection provides infrequent position measurements. The state can also be predicted with a physics model. These three types of localization can be “fused” to create a more accurate state estimate using an Extended Kalman Filter (EKF). The EKF is a nonlinear full-state estimator that approximates the state estimate with the lowest covariance error when given the sensor measurements, the model prediction, and their variances. In this paper, we demonstrate the effectiveness of the EKF by implementing it on a 4-wheel mecanum-drive robot simulation. The position and velocity accuracy of fusing together various combinations of these three data sources are compared. We also discuss the assumptions and limitations of an EKF.
Didactic Principles of Modeling Virtual Learning Environments: the Analysis of Theoretical and Empirical Value

The article aims to analyze a range of didactic principles that have to be included in methodological substantiation when creating a model of Virtual Learning Environment (VLE) or other models of distance learning. To systematize and range the didactic principles in order of their pedagogical importance, several quantitative and qualitative research methods were used, including content analysis, questionary-based survey, and statistical data processing. The main aim of the study was to compare the didactic principles’ theoretical impact, which is based on the content analysis results, and their empirical value, indicated by students’ responses to the survey. The initial findings indicate the substantial difference between the didactic principles that are most frequently referenced in theoretical studies and the principles that are practically required to address students’ needs. The main outcome of the research is the assignment of the didactic principles into three categories: those having low theoretical and empirical value; those having low theoretical but high empirical value; and those having both high theoretical and empirical value. As such, the results of the study allow for the development of VLEs in various disciplines using relevant didactic principles that have both theoretical and empirical value.
Workshops at the Web Conference 2023

This paper introduces the 20 workshops that were organized at The Web Conference 2023.
Path planning based on improved A* algorithm

Autonomous driving technology, mobile robot technology and AGV vehicle technology in warehousing and logistics have been a hot topic of research in recent years, path planning plays a pivotal role in these technologies. A good path can improve the operation efficiency of the vehicle robot, reduce its energy consumption and improve its endurance. In this paper, the A-star algorithm is optimized and improved from its planning speed, the number of search points and turning points. Finally using matlab simulation verification, the final proof that the average planning speed increased by more than 20%, the number of search points reduced by more than 25%, the number of turning points reduced by more than 30%.
Research on event trigger word extraction based on semantic enhancement

With the rapid development of Internet information technology, more and more information pours into the Internet. Information extraction technology came into being, in which event extraction technology is the focus and difficulty. Event trigger word extraction is the first important stage of event extraction. Aiming at the problem of multiple triggers in an event mention, an event trigger extraction model based on semantic enhancement is proposed. In the pre-processing stage, by optimizing the MLM mask mechanism and integrating external knowledge such as element embedding information and Chinese military event terminology, the model can obtain more reliable Chinese military language representation. Then BiLSTM is used to mine the context features of Chinese military news long texts. Finally, CRF is used to assist label correction to achieve event trigger word extraction. Based on the data set constructed in this paper, the effectiveness of the event trigger word extraction model is verified. This method will provide some reference for domain event trigger word extraction.
Design and Implementation of Power-Efficient Cryptography Scheme Using a Novel Multiplication Technique

The threat application and data hacking systems rule today’s digital world. Hence, securing digital information is the most needed to keep personal detail confidential. Several advanced crypto techniques were implemented in the past, resulting in the finest securing outcome. However, those models required additional features and maintenance based on the utilized applications. Considering these difficulties, Data-encryption-standard has been implemented, and the security functions were enriched by performing the Bernoulli mathematical model. So, the presented technique is named a novel Bernoulli Data-encryption-standard (B-DES). Here, the Bernoulli function has been performed after the XOR process, and then during the decryption process, the function of the novel B-DES has been reversed. Moreover, the designed encryption approach is checked with different attacks like brute force attack, Known plain-text Analysis, Chosen-Plaintext Analysis, Man-in-the-Middle Attack, and Ciphertext analysis. Here, the gained power usage by the designed scheme is 193 mW, compared to other models; it has diminished the power usage by 7%. Moreover, the earned memory utilization is 0.6 kB; compared to other models, it has reduced memory utilization by 3%. The delay rate recorded by the designed model is 3.43 ns; compared to other models, it has minimized the delay score by 3%.
Designing Globally Time-Optimal Entangling Gates Using Geometric Space Curves

High-fidelity entangling gates are essential for quantum computation. Currently, most approaches to designing such gates are based either on simple, analytical pulse waveforms or on ones obtained from numerical optimization techniques. In both cases, it is typically not possible to obtain a global understanding of the space of waveforms that generate a target gate operation, which can make it challenging to identify globally time-optimal waveforms that respect amplitude and bandwidth constraints. Here, we show that in the case of weakly coupled qubits, it is possible to find all pulses that implement a target entangling gate in near-minimal time. We do this by mapping quantum evolution onto geometric space curves. We derive the minimal conditions these curves must satisfy in order to guarantee a gate with a desired entangling power is implemented. Pulse waveforms are extracted from the curvatures of these curves. We illustrate our method by designing fast, cnot-equivalent entangling gates for silicon quantum dot spin qubits with fidelities exceeding 99 %. We show that fidelities can be further improved while maintaining low bandwidth requirements by using geometrically derived pulses as initial guesses in numerical optimization routines.