Reviewerly/exHarmony huggingface dataset:
Benchmark on Real-Time Long-Range Aircraft Detection for Safe RPAS Operations

The growing market in Remotely Piloted Aircraft Systems (RPAS) and the need for cost-effective “Detect and Avoid (DAA)” systems are critical issues up to date towards enabling safe beyond visual line of sight (BVLOS) operations. In hopes of promoting earlier threat detection on DAA systems, we benchmark several object detection algorithms on multiple graphical processing units for the concrete DAA use case. Two state-of-the-art “real-time object detection” and “object detection” model sets are trained using our CENTINELA dataset, and their performances are compared for a wide range of configurations. Results demonstrate that one-stage architecture YOLO variants outperform ViT on all tested hardware in terms of mean average precision and inference speed despite their architecture complexity gap. Additional resources are available to the reader at https://github.com/fada-catec/detection-for-safe-rpas-operation .
Design, Development and Validation of a Ground Control Station for Aerial Manipulators

In the last years, aerial manipulators, UAVs (Unmanned Aerial Vehicles) with robotic manipulators, have been used for several applications such as inspection tasks, systems monitoring or transportation of loads. In order to supervise the missions, a GCS (Ground Control Station) is used. However, the GCS of the commercial autopilots are just designed to monitor the state of the aerial platform, forcing the use of other software to monitor the state of the robotic manipulator. This paper presents the design, development and validation of a Ground Control Station for aerial manipulators. The GCS developed in this work allows the integration of the UAV state and manipulator state in the same GUI (Graphical User Interface). In order to plan missions, it also allows the definition of waypoints in a simple way. In addition, the GCS integrates a slave mode for teleoperation of the aerial manipulator, using a ground manipulator and a video streaming to facilitate the task of the ground operator. All the functionalities of the GCS are validated through experiments. This GCS will be useful to perform aerial manipulation mission, allowing the monitoring of the complete aerial manipulator in only one software.
Control and Stability of an Aerial Manipulator Depending on the Inertial Parameters

Control and stabilization of aerial manipulators is a challenging problem due to the effects of the relative movements of the arm and aerial platform on the dynamics. In many cases, conventional multirotor controllers and autopilots are based on the cascaded structure of underactuated mechanical systems, and consider these effects as perturbations. This control scheme, which controls the arm and the aerial platform as two separated systems, is known as decoupled control scheme. In practice, the success of this approach will depend on the design and movements of the arm. This paper presents the analysis of the control performance and stability of an aerial manipulator with a decoupled controller for different speeds of movement and inertia moments of the manipulator arm. The aerial manipulator consists of a hexarotor and a 2-DoF manipulator arm with variable length links. The arm was built in this way to change its inertia, so it may be representative of a widely class of arms used in aerial manipulation. The proposed aerial manipulator has been analysed in simulation tests for two representative tasks. For both cases, the stability limits of the system, represented as stability maps, are identified as a function of the inertial design and the typical velocities of the arm movements. These stability maps are validated through experiments. This analysis will be useful to guide the inertial design of the arm and to define the envelope of arm movements for safe operation.
Pengembangan E-Book berbasis Kearifan Lokal pada Pembuatan Jamu Tradisional sebagai Literasi Kimia dengan Pendekatan Etno-STEM

The low chemical literacy of students and chemistry teachers of SMA Negeri 1 Insana is due to not developing an electronic-based learning media. This study aims to develop an E-Book based on local wisdom on the manufacture of traditional herbal medicine as chemical literacy with an Ethno-STEM approach. The research method is 3D model R and D. Data collection techniques through interviews, validation and questionnaires. The instruments used in this study were validation sheets and questionnaire sheets. The E-Book was validated by two experts, namely material experts and media experts and the subjects in this study were ten students of class XI science at SMA Negeri 1 Insana. The results of the validity that have been analyzed obtained a material validation value of 84 percent and a media validation value of 92 percent so that the average percentage of expert validators on the product developed was 88 percent with very valid criteria, while the response of students on a small scale obtained a value of 75 percent with good criteria and large scale score of 90.66 percent with very good criteria. Based on the results of the study, it can be concluded that the developed E-Book was very valid and very good to use.
Reconciling Shannon and Scott with a Lattice of Computable Information

This paper proposes a reconciliation of two different theories of information. The first, originally proposed in a lesser-known work by Claude Shannon (some five years after the publication of his celebrated quantitative theory of communication), describes how the information content of channels can be described qualitatively , but still abstractly, in terms of information elements , where information elements can be viewed as equivalence relations over the data source domain. Shannon showed that these elements have a partial ordering, expressing when one information element is more informative than another, and that these partially ordered information elements form a complete lattice. In the context of security and information flow this structure has been independently rediscovered several times, and used as a foundation for understanding and reasoning about information flow. The second theory of information is Dana Scott’s domain theory, a mathematical framework for giving meaning to programs as continuous functions over a particular topology. Scott’s partial ordering also represents when one element is more informative than another, but in the sense of computational progress – i.e. when one element is a more defined or evolved version of another. To give a satisfactory account of information flow in computer programs it is necessary to consider both theories together, in order to understand not only what information is conveyed by a program (viewed as a channel, à la Shannon) but also how the precision with which that information can be observed is determined by the definedness of its encoding (à la Scott). To this end we show how these theories can be fruitfully combined, by defining the Lattice of Computable Information (LoCI), a lattice of preorders rather than equivalence relations. LoCI retains the rich lattice structure of Shannon’s theory, filters out elements that do not make computational sense, and refines the remaining information elements to reflect how Scott’s ordering captures possible varieties in the way that information is presented. We show how the new theory facilitates the first general definition of termination-insensitive information flow properties, a weakened form of information flow property commonly targeted by static program analyses.
Linguistic Test of Chinese and American National Spirit based on Speech Recognition and Artificial Intelligence

In this paper, the subband analysis method is introduced, and a new multi-feature subband hybrid model is proposed. This paper first proposes a method to use the user-confirmed text in the process of human-computer interaction to help improve the accuracy of self-adaptive data machine labeling; the paper selects the typical American "Social Learningʺ of Western national spiritual education and my country's "People's Education Editionʺ. The content of national spiritual education in grades 3-6 in Morality and Society was tested in linguistics, and some useful enlightenments for the current national spiritual education in our country were put forward. Then an acoustic confidence method based on the confirmation model is proposed.
The Future Direction of Korean Mission Studies: With a Focus on the Korean Society of Mission Studies

In celebration of the Korean Society of Mission Studies’ 30th anniversary, the study examines the history of the organization, analyzes the changing research topic in the past ten years and suggests the future direction of the society. Along with the burst of growth in Korean mission work, the KSOMS has benefited great success over the last thirty years. The Korean Society of Mission Studies was the main platform for Korean mission scholars to interact and share their work. Immense effort among scholars generated unique and Korean-centric research after 2010. Along with entering the 30th anniversary, Korean mission scholars are now facing a new landscape that has been affected by the Covid-19 pandemic. The paper suggests five different ways that the Korean Society of Mission Studies must engage in this new landscape: (1) escape from the uncritical acceptance of Western scholarship model; (2) develop Korean-centric mission studies model; (3) increase cooperation among Korean missiologists; (4) strengthen the bond between Asian and African missiologists; and (5) encourage deeper theologiziation of Korean mission work by encouraging young scholars to publish and share with the global church.
The IoT-Based Estimation of a Human Heart Condition for the Smart Healthcare System

In today’s era, smart healthcare monitoring and diagnostic system is day by day becoming of great concern and is need of the hour. With the development in the sector of IoT. The healthcare sector is being integrated with wireless technology. The advent of these technologies will promote the development of a smart healthcare system. In current prevalent scenario all patients are in tough situation facing the problems of unforeseen demise due to various problems but specifically large number of heart problems because of their daily life style and lack of physical activity which leads to sudden blockage, severe attacks resulting to death. This is all due to lack of portable smart healthcare system which could be a life savior option at the needed time. It is very essential to monitor the regular health parameters for the old age patients. These regular monitoring of body vitals will help in reducing the probabilistic chances of severe health issue. Even with the help of IoT environment the data can be sent to doctors also. The doctor will go through it accordingly and he will advise to the patient at same instant. This chapter proposes an Artificial Intelligence based smart healthcare system which will record the initial vitals and basic health parameters of the body. The data will record in the system in real time at the room condition. It will record the data specifically required for the heart related issue such as blood pressure, weight, ECG and PCG. After obtaining the data from the real time system it will be matched through the healthy and unhealthy datasets which will be pre-stored in the system through AI techniques. On the basis of it a pre mature report can be obtained which will help the doctor to take immediate steps to start the diagnosis immediately. Even this strategy can also be adopted in case of telemedicine.
VdistCox: Vertically distributed Cox proportional hazards model with hyperparameter optimization

Vertically partitioned data is distributed data in which information about a patient is distributed across multiple sites. In this study, we propose a novel algorithm (referred to as VdistCox) for the Cox proportional hazards model (Cox model), which is a widely-used survival model, in a vertically distributed setting without data sharing. VdistCox with a single hidden layer feedforward neural network through extreme learning machine can build an efficient vertically distributed Cox model. VdistCox can tune hyperparameters, including the number of hidden nodes, activation function, and regularization parameter, with one communication between the master site, which is the site set to act as the server in this study, and other sites. In addition, we explored the randomness of hidden layer input weights and biases by generating multiple random weights and biases. The experimental results indicate that VdistCox is an efficient distributed Cox model that reflects the characteristics of true centralized vertically partitioned data in the model and enables hyperparameter tuning without sharing information about a patient and additional communication between sites.
Energy Considerations

I was perplexed by “Blockchain’s Carbon and Environmental Footprints”5 in the “Computing’s Economics” column in the August 2022 issue of Computer. The article considered the comparative energy costs of mining Bitcoin against the energy costs of existing banking technology but did not consider the other side—comparative benefits.
Toward extreme face super-resolution in the wild: A self-supervised learning approach

Extreme face super-resolution (FSR), that is, improving the resolution of face images by an extreme scaling factor (often greater than ×8) has remained underexplored in the literature of low-level vision. Extreme FSR in the wild must address the challenges of both unpaired training data and unknown degradation factors. Inspired by the latest advances in image super-resolution (SR) and self-supervised learning (SSL), we propose a novel two-step approach to FSR by introducing a mid-resolution (MR) image as the stepping stone. In the first step, we leverage ideas from SSL-based SR reconstruction of medical images (e.g., MRI and ultrasound) to modeling the realistic degradation process of face images in the real world; in the second step, we extract the latent codes from MR images and interpolate them in a self-supervised manner to facilitate artifact-suppressed image reconstruction. Our two-step extreme FSR can be interpreted as the combination of existing self-supervised CycleGAN (step 1) and StyleGAN (step 2) that overcomes the barrier of critical resolution in face recognition. Extensive experimental results have shown that our two-step approach can significantly outperform existing state-of-the-art FSR techniques, including FSRGAN, Bulat's method, and PULSE, especially for large scaling factors such as 64.
Inversion Formula for the Wavelet Transform on Abelian Group

In this paper a reconstruction and inversion formula of the continuous wavelet transform on abelian group for band-limited function is defined. This formula possesses a more explicit expression than the well-known result. Also, Parseval and other interesting results on abelian group are obtained.
The Effects of International Students" Stress on Travel Intention During the COVID-19 Pandemic: The Moderating Effect of Gender

As the inbound tourism market stagnated due to the COVID-19 pandemic, international students have become important potential tourists in the domestic tourism market. The purpose of this study is to examine the effects of international students
Pilot study on physicochemical and biological remediation of nitrochlorobenzene in soil

Based on the technical advantages and mechanical characteristics of zero-valent iron (ZVI) reduction and microbial degradation process for the treatment of chlorine-containing nitroaromatic hydrocarbon pollutants, this pilot remediation project takes o-nitrochlorobenzene and p-nitrochlorobenzene in the contaminated soil on the contaminated land to be treated in the typical relocated chemical site as the research object, and adopts the in situ ectopic remediation method to repair 100 m3 of contaminated soil, the physicochemical biological combined remediation technology coupled with zero-valent iron and microorganism is adopted. After 18 weeks of pilot remediation, the total amount of pollutants concerned is less than 5%, which is far lower than the goal of safe utilization.
COCM: Co-Occurrence-Based Consistency Matching in Domain-Adaptive Segmentation

This paper focuses on domain adaptation in a semantic segmentation task. Traditional methods regard the source domain and the target domain as a whole, and the image matching is determined by random seeds, leading to a low degree of consistency matching between domains and interfering with the reduction in the domain gap. Therefore, we designed a two-step, three-level cascaded domain consistency matching strategy—co-occurrence-based consistency matching (COCM)—in which the two steps are: Step 1, in which we design a matching strategy from the perspective of category existence and filter the sub-image set with the highest degree of matching from the image of the whole source domain, and Step 2, in which, from the perspective of spatial existence, we propose a method of measuring the PIOU score to quantitatively evaluate the spatial matching of co-occurring categories in the sub-image set and select the best-matching source image. The three levels mean that in order to improve the importance of low-frequency categories in the matching process, we divide the categories into three levels according to the frequency of co-occurrences between domains; these three levels are the head, middle, and tail levels, and priority is given to matching tail categories. The proposed COCM maximizes the category-level consistency between the domains and has been proven to be effective in reducing the domain gap while being lightweight. The experimental results on general datasets can be compared with those of state-of-the-art (SOTA) methods.
PROJECT-BASED EDUCATION IN PRIMARY SCHOOL WITH THE USE OF VIRTUAL AND AUGMENTED REALITY

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 2975-2983ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.0739Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
PENINGKATAN KETERAMPILAN MEMBACA MENULIS PERMULAAN MENGGUNAKAN MODEL VISUAL, AUDITORY, READ WRITE, KINESTHETIC DI KELAS II SD N 82/II DUSUN PANJANG

This research is a classroom action research, which consists of two cycles. Each cycle consists of planning activities, implementing actions, observing and reflecting. Data collection techniques in this study were observation, tests and documentation. The research subjects were 1 educator and 22 students. The results of this study showed the teaching learning process, the first meeting of the first cycle was 77%, the second meeting of the first cycle was 80%, the first meeting of the second cycle was 90%, the second meeting of the second cycle was 95.45%. Observation of students during the learning process in the first cycle of the first cycle of good category is still 0%, the second meeting of the cycle increased to 54%. Furthermore, the first meeting in the second cycle in the category increased to 77% and the second meeting in the second cycle in the good category increased to 91% which was said to be successful. The results of the initial reading and writing skills assessment of students in cycle I were in the good category of 12 people or 54%. In cycle II the good category increased to 20 people or 91% and was declared successful. Based on the results of this study, it can be concluded that the use of Visual, Auditory, Read Write, Kinesthetic models can improve students' initial reading and writing skills in class II.
PENINGKATAN KEAKTIFAN DAN PRESTASI BELAJAR MATEMATIKA MELALUI GOOGLE MEET BERBANTUAN JAMBOARD DAN PENTABLET

This Best Practice is motivated by the lack of activeness and learning achievements of students in online learning activities during the COVID-19 pandemic. This study is focused on efforts to enhance students' activeness and learning achievement in mathematics through Google Meet assisted by jamboard and pentablet applications. The subjects of this study were students of class VIII D of SMP Negeri 41 Semarang with a total of 32 people. Based on an analysis of the observations results during the study, student learning achievement shows an improvement, as evidenced by an increase in student activity scores compared to the results before using Google Meet assisted by Jamboard and Pentablet applications. Students learning activeness increased to 81.250%. In addition, the achievement of the minimum criteria of mastery learning percentage has increased, before this study by 56.25%, increasing to 84.375%. It can be concluded that online learning through Google Meet assisted by Jamboard and Pentablet applications can enhance student activeness and learning achievement.
Spontaneous, controlled acts of reference between friends and strangers

Speakers enjoy considerable flexibility in how they refer to a given referent––referring expressions can vary in their form (e.g., “she” vs. “the cat”), their length (e.g., “the (big) (orange) cat”), and more. What factors drive a speaker’s decisions about how to refer, and how do these decisions shape a comprehender’s ability to resolve the intended referent? Answering either question presents a methodological challenge; researchers must strike a balance between experimental control and ecological validity. In this paper, we introduce the SCARFS (Spontaneous, Controlled Acts of Reference between Friends and Strangers) Database: a corpus of approximately 20,000 English nominal referring expressions (NREs), produced in the context of a communication game. For each NRE, the corpus lists the concept the speaker was trying to convey (from a set of 471 possible target concepts), formal properties of the NRE (e.g., its length), the relationship between the interlocutors (i.e., friend vs. stranger), and the communicative outcome (i.e., whether the expression was successfully resolved). Researchers from diverse disciplines may use this resource to answer questions about how speakers refer and how comprehenders resolve their intended referent––as well as other fundamental questions about dialogic speech, such as whether and how speakers tailor their utterances to the identity of their interlocutor, how second-degree associations are generated, and the predictors of communicative success.
Individual discrete logarithm with sublattice reduction

The Number Field Sieve and its numerous variants is the best algorithm to compute discrete logarithms in medium and large characteristic finite fields. When the extension degree n is composite and the characteristic p is of medium size, the Tower variant (TNFS) is asymptotically the most efficient one. Our work deals with the last main step, namely the individual logarithm step, that computes a smooth decomposition of a given target T in the finite field thanks to two distinct phases: an initial splitting step, and a descent tree. In this article, we improve on the current state-of-the-art Guillevic's algorithm dedicated to the initial splitting step for composite n. While still exploiting the proper subfields of the target finite field, we modify the lattice reduction subroutine that creates a lift in a number field of the target T. Our algorithm returns lifted elements with lower degrees and coefficients, resulting in lower norms in the number field. The lifted elements are not only much likely to be smooth because they have smaller norms, but it permits to set a smaller smoothness bound for the descent tree. Asymptotically, our algorithm is faster and works for a larger area of finite fields than Guillevic's algorithm, being now relevant even when the medium characteristic p is such that Lpn (1/3) $\le$ p < Lpn (1/2). In practice, we conduct experiments on 500-bit and 700-bit composite finite fields: Our method becomes more efficient as the largest non trivial divisor of n grows, being thus particularly adapted to even extension degrees.