Reviewerly/exHarmony huggingface dataset:
On Generalizing Permutation-Based Representations for Approximate Search

In the domain of approximate metric search, the Permutation-based Indexing (PBI) approaches have been proved to be particularly suitable for dealing with large data collections. These methods employ a permutation-based representation of the data, which can be efficiently indexed using data structures such as inverted files. In the literature, the definition of the permutation of a metric object was derived by reordering the distances of the object to a set of pivots. In this paper, we aim at generalizing this definition in order to enlarge the class of permutations that can be used by PBI approaches. As a practical outcome, we defined a new type of permutation that is calculated using distances from pairs of pivots. The proposed technique permits us to produce longer permutations than traditional ones for the same number of object-pivot distance calculations. The advantage is that the use of inverted files built on permutation prefixes leads to greater efficiency in the search phase when longer permutations are used.
Optimalisasi Umur Pemakaian AC Melalui Sistem Informasi Reminder Perawatan

There is no periodic scheduling of Air Conditioner (AC) maintenance, which stems from the lack of recording on the maintenance card and maintenance book, resulting in a lot of AC damage that propagates from one component to component until finally the AC cannot function completely. A system is needed to provide information that will remind the time of maintenance as the first step in optimizing the life of the air conditioner. The purpose of this study is to build an information system of maintenance-reminder, so that the air conditioner used is maintained according to a predetermined schedule. The method used to build this system is a waterfall with five steps, namely: analysis, design, implementation, and testing. To Test the system that has been made, in this study using black box-testing by checking every part of each system. Our findings are to produce a standardized maintenance reminder system. The test results show that all components in the system are functioning properly, so it is easier for users to know which air conditioners need maintenance. With this system, it can maximize the maintenance function to optimize service life.
On multi-class aerial image classification using learning machines

Chapter Contents: Abstract 15.1 Introduction 15.2 Learning approaches 15.2.1 Deep learning networks 15.2.1.1 Recursive neural network 15.2.1.2 Recurrent neural network 15.2.1.3 Convolutional neural network 15.2.1.4 Deep generative networks 15.2.2 Feature learning 15.2.3 Challenges for deep learning 15.2.4 Challenges related to aerial video classification 15.2.5 Applications 15.3 Learning architecture and classification 15.3.1 Supervised learning architectures 15.3.2 Unsupervised learning 15.3.3 Deep learning for planning and situational awareness 15.3.4 Deep learning for motion control 15.3.5 Object detection 15.3.6 Classification 15.3.6.1 Binary classification 15.3.6.2 Multi-class classification 15.4 Training 15.4.1 Weight initialization 15.4.2 Convolutional methods 15.4.3 Activation functions 15.4.4 Subsampling or pooling layer 15.4.5 Optimization techniques 15.4.6 Benchmark datasets 15.5 Energy efficiency in learning approaches 15.6 Performance metrics 15.7 Development kits and frameworks 15.8 Discussions and future directions References
Using explainable deep learning in da Vinci Xi robot for tumor detection

Abstract Deep learning has proved successful in computer-aided detection in interpreting ultrasound images, COVID infections, identifying tumors from computed tomography (CT) scans for humans and animals. This paper proposes applications of deep learning in detecting cancerous cells inside patients via laparoscopic camera on da Vinci Xi surgical robots. The paper presents method for detecting tumor via object detection and classification/localizing using GRAD-CAM. Localization means heat map is drawn on the image highlighting the classified class. Analyzing images collected from publicly available partial robotic nephrectomy videos, for object detection, the final mAP was 0.974 and for classification the accuracy was 0.84.
Embracing Technological Change in Higher Education

Access to information has never been easier, thanks to the rapid development of the internet and communication technologies, and the ubiquity of smartphones and other internet-enabled devices. In traditional classroom learning, teachers provide students with various sources of information that are known to be reliable. Nowadays, especially in a post-pandemic era, students increasingly rely on a host of resources available on the internet. Exposure to vast amounts of scattered information could adversely affect students’ learning process. Meanwhile, pedagogical approaches, classroom learning practices, and student learning activities have evolved significantly to cope with contemporary challenges. This study reviews the current learning practices and the technological interventions in a rapidly evolving higher education landscape. In particular, the challenges when integrating technology into higher education are considered in detail and ways put forward for doing so in that context.
Modalized normality in pictorial narratives

An analysis of pictorial free perception using universal quantiﬁcation over worlds that are consistent with the embedded picture is incorrect, because it entails that depicted characters pick up all of the information in a scene they look at. Human agents typically do not. This is corrected by replacing universal quantiﬁcation over epistemic alternatives with existential quantiﬁcation, and by introducing a normality condition.
Short Paper: Organizational Security: Implementing a Risk-Reduction-Based Incentivization Model for MFA Adoption

Multi-factor authentication (MFA) is a useful measure for strengthening authentication. Despite its security effectiveness, the adoption of MFA tools remains low. To create more human-centric authentication solutions, we designed and evaluated the efficacy of a risk-reduction-based incentivization model and implemented our proposed model in a large-scale organization with more than 92, 025 employees, and collected survey data from 287 participants and interviewed 41 participants. We observed negative perceptions and degraded understandings of MFA technology due to the absence of proper risk and benefit communication in the control group. Meanwhile, the experimental group employees showed positive perceptions of MFA use for their work and personal accounts. Our analysis and implementation strategy are critical for reducing users’ risks, creating positive security tool usage experiences, and motivating users to enhance their security practices.
Semantic and Pragmatic Potential of Emojis in Context of a COVID-19 Pandemic

In this paper, an attempt is made to analyze the semantic and pragmatic potential of emoji signs that explicate the “covid” meaning. The Internet thesaurus emoji with a common semantic component “medicine / coronavirus pandemic ”, circulating in the space of the Russian-speaking and English-speaking segments of the Internet serve as the material to be investigated. The authors of the article pose and analyze a number of significant issues to understand the role of emoji within the framework of pandemic discourse: the terminological variability of the terms smile, emoticon and emoji in the context of the described problems is clarified and specified; the structural and content potential of the emoji sign is characterized; the possibilities of the sign in the process of its use by of mass Internet culture carriers are determined; groups of emoji signs in the English-speaking and Russian-speaking segments of the Internet are distinguished and characterized. The authors use a field approach with the allocation of the core and periphery in the context of the description of the thematic thesaurus of emoji signs. In addition, attention is paid to the structural and semantic features of the emoji sign, which, ultimately, cause the appearance of a simple or complex pictogram. It is concluded that emoji signs are included in many social spheres of the Internet space within the general information context of the COVID-19 epidemic.
Voice biometrics: perspective from the industry

Chapter Contents: 8.1 Automated password reset: an example of a commercial application using voice biometrics 8.1.1 Overview 8.1.2 Introduction 8.1.3 System architecture 8.1.4 Voice biometric system 8.1.5 Summary 8.2 Testing of commercial voice biometric systems 8.2.1 Introduction 8.2.1.1 Biometric testing 8.2.2 User analysis 8.2.3 Summary 8.3 Forensic speaker recognition 8.3.1 Introduction 8.3.2 Forensic speaker recognition and the strength of evidence 8.3.3 The forensic experts workflow 8.3.4 Technical challenges 8.3.4.1 Improving interpretability of scores 8.3.4.2 Score normalization 8.3.4.3 Score calibration 8.3.4.4 Condition adaptation 8.3.4.5 Dealing with multi-speaker recordings 8.3.5 Training—communication between system developers and end-users 8.3.6 Conclusions References
Exploring the Potential of Push Notification in ESL : A Qualitative Inquiry

In higher education institutions in Hong Kong and beyond, there is great emphasis upon efforts to maximize opportunities for student learning-and to optimise the resources with which learners may engage in their studies.As learners are becoming increasingly comfortable with accessing teaching and learning resources from their mobile devices (Huang et al., 2019), educators are considering how best to Lucas Kohnke et al.
Weighted Decoding for the Competence Reliability Problem of ECOC Multiclass Classification

Error-Correcting Output Codes has become a well-known, established technique for multiclass classification due to its simplicity and efficiency. Each binary split contains different original classes. A noncompetent classifier emerges when it classifies an instance whose real class does not belong to the metasubclasses which is used to learn the classifier. How to reduce the error caused by the noncompetent classifiers under diversity big enough is urgent for ECOC classification. The weighted decoding strategy can be used to reduce the error caused by the noncompetence contradiction through relearning the weight coefficient matrix. To this end, a new weighted decoding strategy taking the classifier competence reliability into consideration is presented in this paper, which is suitable for any coding matrix. Support Vector Data Description is applied to compute the distance from an instance to the metasubclasses. The distance reflects the competence reliability and is fused as the weight in the base classifier combination. In so doing, the effect of the competent classifiers on classification is reinforced, while the bias induced by the noncompetent ones is decreased. Reflecting the competence reliability, the weights of classifiers for each instance change dynamically, which accords with the classification practice. The statistical simulations based on benchmark datasets indicate that our proposed algorithm outperforms other methods and provides new thought for solving the noncompetence problem.
Waste-to-Energy Potential Using Municipal Solid Waste as One Implementation of Jakarta Smart City

The background of this research is the escalation of DKI Jakarta’s residence which lead to the other two impacting factors, the use of electricity that increase to 5.8% in 2019 and the municipal waste escalation. The objective of this research is to find potential electrical energy that can be produced from waste-to-energy (WTE) technology of IWMS (Integrated Waste Management Site) Bantargebang (Bekasi), where most of the waste come from the people of DKI Jakarta. Furthermore, with the application of WTE, there is also a potential from the smart city implementation in DKI Jakarta. By using the mathematical model and secondary data gathering from the government of DKI Jakarta, we found that the potential of electrical energy that can be produced can reach 8.6 GWh/day, which contributed up to 9% to from the energy distributed to DKI Jakarta. Moreover, by applying WTE, there is also a potential of implementing other indicators of smart city that has been propagated by the government of DKI Jakarta.
EDUCATIONAL PROJECT AS A MEANS OF FORMING OF FUTURE SPECIALISTS’ PROFESSIONAL COMPETENCE: A PSYCHOLOGICAL ASPECT

The article highlights the actual problem of the dependence of the psychological component of the professional competence of future specialists on the use of an educational project in the educational process. The purpose of the article is to study the features of the influence of the use of an educational project in the educational activities of a higher educational institution on the psychological component of the professional competence of future specialists. The content and structure of the psychological component of the professional competence of future specialists have been determined, which is considered as a complex of characteristics of future specialists, contains psychological knowledge, skills, motivational, emotional processes, personal properties that help to carry out professional activities, self-improvement, and consists of motivational, emotional, activity, personal, cognitive components. The experience of the formation of the psychological component of the professional competence of future specialists with the help of an educational project introduced into the educational process of the Institute of Philology of Borys Grinchenko Kyiv University is characterized. Among the research tools were used: diagnostic methods for studying the components, indicators of the psychological component of the professional competence of future specialists, the experimental method, the comparison method, and correlation analysis. The relevance of the topic is confirmed by the results of a survey of future specialists who underwent diagnostics on the problems of studying the level of the psychological component of professional competence for use in the educational process of an educational project and after. The experiment involved 227 future specialists in the specialty «035 Philology». Through experimental activities, the features of the application of the educational project in the educational process of the institution have been determined. It was revealed that the level of the psychological component of professional competence before the implementation of the educational project was lower than the level of the studied phenomenon after the application of the project in the educational process. The results of the analysis showed that the process of creating and implementing an educational project by future specialists affects the formation of the psychological component of students’ professional competence.
Pacta sunt servanda: legal contracts in Stipula.

There is a growing interest in running legal contracts on digital systems, at the same time, it is important to understand to what extent software contracts may capture legal content. We then undertake a foundational study of legal contracts and we distill four main features: agreement, permissions, violations and obligations. We therefore design Stipula, a domain specific language that assists lawyers in programming legal contracts through specific patterns. The language is based on a small set of abstractions that correspond to common patterns in legal contracts, and that are amenable to be executed either on centralized or on distributed systems. Stipula comes with a formal semantics and an observational equivalence, that provide for a clear account of the contracts' behaviour. The expressive power of the language is illustrated by a set of examples that correspond to template contracts that are often used in practice.
Micro hydropower plant potential study based on Landsat 8 operational land imager satellite data

Remote sensing technology has been widely applied in various fields, including oil, gas, and mineral exploration, spatial planning, and environmental monitoring. This paper describes the application of remote sensing technology for the potential study of a renewable micro hydropower plant (MHP) using Landsat 8 satellite data. The Sukaati Watershed, West Java, Indonesia, was selected as the case study area. Landsat 8 satellite data, acquired on August 21, 2020, was applied to extract information on land use, geology, and potential landslides. Drainage patterns, watershed boundaries, and head height were obtained from topographic map data. Drainage patterns, watershed boundaries, and land use are used to calculate flow rates. Geological map and landslide are the basis of layout of MHP components, such as water intake, dam, waterway, settling tank, penstock, and powerhouse. A field survey to acquire actual flow rate and head height was conducted to validate the results of the remote sensing data interpretation. Two potential sites of MHP were selected with a hydropower design of 129 kW and 5.18 MW. This study showed that remote sensing technology is beneficial for studying the potential of MHP because fieldwork can be done more quickly and efficiently.
Studi Literatur Penggunaan Sensor untuk Sistem Deteksi Gempa

As a country located among three major tectonic plate confluences, namely the Eurasian Plate, the Indo-Australian Plate, and the Pacific Plate, Indonesia is an earthquake-prone area. Information about the earthquake occurrences is distributed by the BMKG through social media, websites and television. However, it is undeniable that there are still people who do not have televisions or smartphones. Therefore, earthquake warning systems began to be widely developed. Furthermore, sensors for earthquake vibration detection have an important role in earthquake warning systems. So in this article, the author has conducted a literature study and analysis of the sensors commonly used in earthquake detection systems. This study aimed to present sensor recommendations to be used as earthquake sensors. The result of the literature study, that the author had done, is the selection of sensors is customized to the needs and budget of the project. A description of the sensor recommendations for each project has also been included in this article.
DIBR-Synthesized Image Quality Assessment With Texture and Depth Information

Accurately predicting the quality of depth-image-based-rendering (DIBR) synthesized images is of great significance in promoting DIBR techniques. Recently, many DIBR-synthesized image quality assessment (IQA) algorithms have been proposed to quantify the distortion that existed in texture images. However, these methods ignore the damage of DIBR algorithms on the depth structure of DIBR-synthesized images and thus fail to accurately evaluate the visual quality of DIBR-synthesized images. To this end, this paper presents a DIBR-synthesized image quality assessment metric with Texture and Depth Information, dubbed as TDI. TDI predicts the quality of DIBR-synthesized images by jointly measuring the synthesized image's colorfulness, texture structure, and depth structure. The design principle of our TDI includes two points: (1) DIBR technologies bring color deviation to DIBR-synthesized images, and so measuring colorfulness can effectively predict the quality of DIBR-synthesized images. (2) In the hole-filling process, DIBR technologies introduce the local geometric distortion, which destroys the texture structure of DIBR-synthesized images and affects the relationship between the foreground and background of DIBR-synthesized images. Thus, we can accurately evaluate DIBR-synthesized image quality through a joint representation of texture and depth structures. Experiments show that our TDI outperforms the competing state-of-the-art algorithms in predicting the visual quality of DIBR-synthesized images.
A Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on CancerA Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on Cancer

BACKGROUND: Cancer is a major health problem not only in Indonesia but also throughout the world. Cancer is the growth and spread of abnormal cells that have distinctive characteristics, that if can no longer be controlled will usually cause death. The number of deaths due to cancer is generally caused by late diagnosis and inappropriate treatment. To reduce mortality from cancer, it is necessary to strive for early detection and monitoring of cancer in patients undergoing therapy. Convolutional neural networks (CNNs) as one of machine learning methods are designed to produce or process data from two dimensions that have a network tier and many applications carried out in the image. Moreover, support vector machines (SVMs) as a hypothetical space in the form of linear functions feature have high dimensions and trained algorithm based on optimization theory. AIM: In connection with the above, this paper discusses the role of the machine learning technique named a hybrid CNN-SVM. METHODS: The proposed method is used in the detection and monitoring of cancers by determining the classification of cancers in X-ray computed tomography (CT) patients’ images. Several types of cancer that used for determination in detection and monitoring of cancers diagnosis are also discussed in this paper, such as lung, liver, and breast cancer. RESULTS: From the discussion, the results show that the combining model of hybrid CNN-SVM has the best performance with 99.17% accuracy value. CONCLUSION: Therefore, it can be concluded that machine learning plays a very important role in the detection and management of cancer treatment through the determination of classification of cancers in X-ray CT patients’ images. As the proposed method can detect cancer cells with an effective mechanism of action so can has the potential to inhibit in the future studies with more extensive data materials and various diseases.
Implementasi Pembelajaran Bahasa Inggris Berbasis Tematik Terpadu Bagi Siswa Sekolah Dasar

This study aims to determine how integrated thematic-based English learning is for elementary school students. The method used is a qualitative research method with descriptive analysis techniques with library research by examining the implementation of integrated thematic-based English learning for elementary school students. Data collection is done by reducing documents in the form of reports, news and research articles both nationally and internationally related to the topic of this research article. The results showed that the integration between student experience and previous knowledge possessed by children with English learning presented in a theme, will help children to gain knowledge, skills, and attitudes holistically. The theme developed is of course taking into account the children's knowledge, interests, and environment. Each skill in English is also not taught separately but is learned in an integrated manner with other skills. Therefore, the concept of learning English as a foreign language in accordance with the concept of an integrated thematic approach will be able to provide the value of knowledge, skills, and attitudes as a whole whose process is in line with the characteristics of children at the elementary school level.
Augmented Reality Guidance based on 6D Positioning and HD Lanes

It has felt like Covid-19 had a stranglehold on us. But we haven‘t allowed ourselves to be defeated. On the contrary, we are taking advantage of the opportunities that arise as a result. Not only the long-overdue push towards digitalization, for example, but also the time gained by making fewer journeys. Those who show strength now and position themselves for the future will win. And that is exactly the reason why we have been preparing ELIV 2021 with such a lot of enthusiasm.