Reviewerly/exHarmony huggingface dataset:
Automatic detection of volcanic eruptions in Doppler radar observations using a neural network approach

&amp;lt;p&amp;gt;Immediate detection of volcanic eruptions is essential when trying to mitigate the impact on the health of people living in the vicinity of a volcano or the impact on infrastructure and aviation. Eruption detection is most often done by either visual observation or the analysis of acoustic data. While visual observation is often difficult due to environmental conditions, infrasound data usually provide the onset of an event. Doppler radar data, admittedly not available for a lot of volcanoes, however, provide information on the dynamics of the eruption and the amount of material released. Eruptions can be easily detected in the data by visual analysis and here we present a neural network approach for the automatic detection of eruptions in Doppler radar data. We use data recorded at Colima volcano in Mexico in 2014/2015 and a data set recorded at Turrialba volcano between 2017 and 2019. In a first step we picked eruptions, rain and typical noise in both data sets, which were the used for training two networks (training data set) and testing the performance of the network using a separate test data set. The accuracy for classifying the different type of signals was between 95 and 98% for both data sets, which we consider quite successful. In case of the Turriabla data set eruptions were picked based on observations of OVSICORI data. When classifying the complete data set we have from Turriabla using the trained network, an additional 40 eruptions were found, which were not in the OVSICORI catalogue.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In most cases data from the instruments are transmitted to an observatory by radio, so the amount of data available is an issue. We therefore tested by what amount the data could be reduced to still be able to successfully detect an eruption. We also kept the network as small as possible to ideally run it on a small computer (e.g. a Rasberry Pi architecture) for eruption detection on site, so only the information that an eruption is detected needs to be transmitted.&amp;lt;/p&amp;gt;
Bronze Age bronzes and the Portable Antiquities of the Netherlands (PAN) : A state of affairs

Bronze Age artefacts in PAN: numbers and contexts Amongst the over fifty-thousand finds registered, 113 Bronze Age bronze items have been documented. After weeding out a few cases that were attributed to the Bronze Age erroneously or whose date range was too wide, a total of 93 items is discussed here.
Fast Stream Cipher based Chaos Neural Network for Data Security in CAN Bus

Vehicle systems are controlled by embedded electronic devices called electronic control units (ECUs).These ECUs are connected together with network protocols.The Controller Area Network (CAN) protocol is widely implemented due to its high fault tolerance.However, the CAN is a serial broadcast bus, and it has no protection against security threats.In this paper, we propose a fast stream cipher based on a chaos neural network (CNN) that is able to generate pseudo-random numbers at a high speed, faster than that of the Advanced Encryption Standard, to protect ECUs on the CAN bus by encrypting CAN messages.We discuss the chaotic orbit of the CNN and statistical properties of pseudo-random numbers from the CNN.For a stream cipher, it is very important to share the symmetric key.We designed a symmetric key that is shared with ID-based encryption without the need to use digital certificates.We evaluated our method's performance with embedded boards and showed that the stream cipher is efficient for the embedded software of the ECU.Further, it does not need a hardware security module to accelerate the encryption.
AN ANALYSIS OF FOCUSED METALINGUISTIC WRITTEN FEEDBACK: How would learners react?

Learners of higher education are encouraged to write a scholarly publication in that it helps corroborate them as professionals in their fields of study. Practices in academic writing are thus indispensable to do to achieve a higher level of competency. This study explores learners’ reactions towards focused metalinguistic written feedbacks provided by a lecturer. The lecturer used electronic mail to provide constructive feedback to her students. This mixed-method study involved 22 post-graduate students from an Indonesian university. In analyzing the qualitative data, the researchers employed Miles and Huberman’s qualitative data analysis approach. Meanwhile, the quantitative data, namely the basic analysis of focused metalinguistic written feedbacks were analyzed by employing Cumming’s writing approach. The findings reveal that: (1) learners revised and expanded their draft after getting back their paper; (2) lecturer’s feedbacks through email have reportedly motivated learners because such the feedbacks did not lead learners perplexity compared to the handwritten feedbacks; (3) the majority of participants used revising and responding, consulting a dictionary/grammar book, and referring to the previous composition as the ways to handle lecturer’s input.
Persepsi Masyarakat tentang Tokoh Joker dalam Film Joker (Studi Kasus pada Penonton Film di Studio 21 Makassar Town Square)

At the end of 2019 the people of Makassar were presented with a film with a background story and production from the United States with the title "JOKER". The Joker is an antagonist who is usually portrayed in a clown costume or make up with a psychological disorder who is also the enemy of the super hero Batman. The Joker in this film becomes the main character by telling his dark past life to become a Joker. Researchers see this film as very interesting to be examined when the main character in this film has an antagonistic role played in Indonesia, especially the city of Makassar. This study aims to reveal the public's perception of the Joker character in the Joker film and the factors that influence this perception. This study uses a qualitative descriptive method which is analyzed intensively and continues continuously to obtain the desired data. The results of this study explain that the Joker film is still less attractive to the public, especially the city of Makassar, because the contents of this film have more entertainment functions than informative and educational. However, this film also teaches us to always do good to everyone and not occasionally oppress others if we don't want to be hated or hurt. This perception is based on the inhibiting factors of society from watching the Joker film, namely Ecology, Technology, and Social. While the driving factors for the community in watching the Joker film are Attention, Interests and Mood. The research recommendation for film producers is to be even more creative in packaging this Joker film, and to provide more educational moral messages.
Using Text to Teach Image Retrieval

Image retrieval relies heavily on the quality of the data modeling and the distance measurement in the feature space. Building on the concept of image manifold, we first propose to represent the feature space of images, learned via neural networks, as a graph. Neighborhoods in the feature space are now defined by the geodesic distance between images, represented as graph vertices or manifold samples. When limited images are available, this manifold is sparsely sampled, making the geodesic computation and the corresponding retrieval harder. To address this, we augment the manifold samples with geometrically aligned text, thereby using a plethora of sentences to teach us about images. In addition to extensive results on standard datasets illustrating the power of text to help in image retrieval, a new public dataset based on CLEVR is introduced to quantify the semantic similarity between visual data and text data. The experimental results show that the joint embedding manifold is a robust representation, allowing it to be a better basis to perform image retrieval given only an image and a textual instruction on the desired modifications over the image
Distant Teaching and Learning for the Foreseeable Future: Georgian Universities’ Students and Professors Perspective

Like the rest of the world, because of COVID-19 and the new reality, most professors and students suddenly have found themselves forced to use technology while teaching and learning. All of a sudden, every faculty member faced the challenge of delivering education online and accordingly every student receiving education online. On the other hand, it was an overwhelming time for the administration to achieve quality online education at scale. Nevertheless, most universities in Georgia immediately took the challenges of converting to asynchronous learning. The paper presents findings of the survey conducted at the present stage at Georgian private and state universities, comparing students' and professors' satisfaction with e-learning. Surprisingly, it appears that professors feel better motivated with online teaching rather than students. Almost every respondent agrees that what seems like the best-case scenario out of this crisis needs much stronger contribution and elaboration in the years ahead.
The Model of the Innovative Project as the Multivariable Control Object

The formation of a balanced system of parameters (budget; duration of implementation; customers and developers' satisfaction with the results of the project the course of the project) is a critical factor in the successful implementation of the project.Development of the formal models, which described the direct and cross-linking relationship between the input (budget, implementation time) and output (customer and developer satisfaction) parameters of the project creates the conditions for increasing the validity of decisions related to the project organization.The feature of empirical models constructing is the need to share actual (historical) data about budgets and the previously implemented projects duration, and subjective estimates (determined by experience) of consumers and developers.The paper considers the approach for the construction of parametric regression models based on the joint use of measured data and expert estimates.The additive or multiplicative form of interaction of direct and cross-linking relationship is substantiated, this depend on which target group (consumers or developers) are assigned the outputs of a multivariable object.
Mining text on Coronavirus

This document describes the behavior of the production of academic papers and high circulation newspapers, published under the terms "Coronavirus, Covid-19 or SARS-CoV-2". The purpose of this paper is to employ text mining techniques and qualitative data analysis for pattern identification (Dobrzyński, Dziekoński, Krzysztof, & Jurczuk, 2013; Westergaard, Staerfeldt, Tønsberg, Jensen, & Brunak, 2018). The interest of this study arises from the importance of this topic today and its relationship with the economy, business and society. As a result, the production of scientific papers has been led by the United States, China and Russia. In Latin America, Brazil is the country with the highest scientific output on this subject. The analysis of newspaper articles focusing on economic and business issues showed the work between large companies, the government and the community, as well as companies with businesses and the need to adapt to constant change in the face of the crisis of labor and production.
Frequent Patterns Mining

Frequent pattern mining has been an important subject matter in data mining from many years. A remarkable progress in this field has been made and lots of efficient algorithms have been designed to search frequent patterns in a transactional database. One of the most important technique of datamining is the extraction rule in large database. The time required for generating frequent itemsets plays an important role. This paper provides a comparative study of algorithms Eclat, Apriori and FP-Growth. The performance of these algorithms is compared according to the efficiency of the time and memory usage. This study also focuses on each of the algorithm’s strengths and weaknesses for finding patterns among large item sets in database systems.
Human Activity Prediction using Long Short Term Memory

Early symptoms of dementia is one of the causes decrease in quality of life. Human activity recognition (HAR) system is proposed to recognize the daily routines which has an important role in detecting early symptoms of dementia. Long Short Term Memory (LSTM) is very useful for sequence analysis that can find the pattern of activities that carried out in daily routines. However, the LSTM model is slow to achieving convergence and take a long time during training. In this paper, we investigated the sequence of actions recorded in smart home sensors data using LSTM model, then the model will be optimized using several optimization methods. The optimization methods were Stochastic Gradient Descent (SGD), Adagrad, Adadelta, RMSProp, and Adam. The results showed that using Adam to optimized LSTM is better than other optimization methods.
User authentication based on the three-stage keyboarding model

Objective . The article offers a three-stage model that allows increasing the effectiveness of authentication in the implementation of distance learning systems, not only in automatic proctoring systems but also in complex information systems of critical objects. Methods . Increasing the effectiveness of user authentication is achieved by increasing the accuracy of authentication using keyboarding. Results . The proposed model will help to implement the process of access differentiation for fraudsters and legal users, by adapting to the slightest changes in the keyboarding parameters, which allows increasing the authentication accuracy. During testing, one of the tested users was authenticated using a deviation comparison, while the other three users were authenticated using the χ 2 criterion. The remaining users were not able to complete the authentication procedure at all stages of the system. Conclusion . The results of an experimental study showed the high ability of the proposed model of access control for legitimate users and attackers with some minor changes in the parameters of the keystrokes dynamics, improving the accuracy of user authentication. The user authentication reliability in practice was 97.5%.
Matching Theory and Data with Personal-ITY: What a Corpus of Italian YouTube Comments Reveals About Personality

As a contribution to personality detection in languages other than English, we rely on distant supervision to create Personal-ITY, a novel corpus of YouTube comments in Italian, where authors are labelled with personality traits. The traits are derived from one of the mainstream personality theories in psychology research, named MBTI. Using personality prediction experiments, we (i) study the task of personality prediction in itself on our corpus as well as on TwiSty, a Twitter dataset also annotated with MBTI labels; (ii) carry out an extensive, in-depth analysis of the features used by the classifier, and view them specifically under the light of the original theory that we used to create the corpus in the first place. We observe that no single model is best at personality detection, and that while some traits are easier than others to detect, and also to match back to theory, for other, less frequent traits the picture is much more blurred.
A Support System to Encourage Idea Generation During Videoconference Brainstorming

Search Websites' Images using Search Suggestions (SWISS) is an idea generation support system for smart-phones that searches for images based on spoken words. However, during videoconferencing sessions, when participants brainstorm using their smartphones, it is difficult for them to see each other's faces because the screens of their devices are filled with the images retrieved by SWISS. Therefore, in order that users may be able to maintain visual contact with other users during a videoconference while using SWISS, this paper introduced a system that estimates who is about to speak by using OpenPose.
TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search

Recent breakthroughs of Neural Architecture Search (NAS) extend the field's research scope towards a broader range of vision tasks and more diversified search spaces. While existing NAS methods mostly design architectures on a single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks. Many of them leverage transfer learning and seek to preserve, reuse, and refine network design knowledge to achieve higher efficiency in future tasks. However, the enormous computational cost and experiment complexity of cross-task NAS are imposing barriers for valuable research in this direction. Existing NAS benchmarks all focus on one type of vision task, i.e., classification. In this work, we propose TransNAS-Bench-101, a benchmark dataset containing network performance across seven tasks, covering classification, regression, pixel-level prediction, and self-supervised tasks. This diversity provides opportunities to transfer NAS methods among tasks and allows for more complex transfer schemes to evolve. We explore two fundamentally different types of search space: cell-level search space and macro-level search space. With 7,352 backbones evaluated on seven tasks, 51,464 trained models with detailed training information are provided. With TransNAS-Bench-101, we hope to encourage the advent of exceptional NAS algorithms that raise cross-task search efficiency and generalizability to the next level. Our dataset file will be available at Mindspore, VEGA.
Knowledge Authoring and Question Answering with KALM

Knowledge representation and reasoning (KRR) is one of the key areas in artificial intelligence (AI) field.It is intended to represent the world knowledge in formal languages (e.g., Prolog, SPARQL) and then enhance the expert systems to perform querying and inference tasks.Currently, constructing large scale knowledge bases (KBs) with high quality is prohibited by the fact that the construction process requires many qualified knowledge engineers who not only understand the domainspecific knowledge but also have sufficient skills in knowledge representation.Unfortunately, qualified knowledge engineers are in short supply.Therefore, it would be very useful to build a tool that allows the user to construct and query the KB simply via text.Although there is a number of systems developed for knowledge extraction and question answering, they mainly fail in that these system don't achieve high enough accuracy whereas KRR is highly sensitive to erroneous data.In this thesis proposal, I will present Knowledge Authoring Logic Machine (KALM), a rule-based system which allows the user to author knowledge and query the KB in text.The experimental results show that KALM achieved superior accuracy in knowledge authoring and question answering as compared to the state-of-the-art systems.
OnionBot: A System for Collaborative Computational Cooking

An unsolved challenge in cooking automation is designing for shared kitchen workspaces. In particular, robots struggle with dexterity in the unstructured and dynamic kitchen environment. We propose that human-machine collaboration can be achieved without robotic manipulation. We describe a novel system design using computer vision to inform intelligent cooking interventions. This human-centered approach does not require actuators and promotes dynamic, natural collaboration. We show that automation that assists user-led actions can offer meaningful cooking assistance and can generate the image databases needed for fully autonomous robotic systems of the future. We provide an open source implementation of our work and encourage the research community to build upon it.
Introducing Sage: Cyberinfrastructure for Sensing at the Edge.

&amp;lt;p&amp;gt;There are many networks of sensors for earth system science. Most networks are local or regional in scale (eg mesonets). National weather services maintain networks for meeting stakeholder needs and responsibilities to the WMO Global Observing System. These systems are comprised of single task rigid sensors generally attached to logger systems. Sage [1] is a project which will deliver a cyberinfrastructure network allowing multi-tenant, multi-tasked sensor packages. In addition to traditional meteorological instrumentation and advanced static and pan-tilt-zoom cameras Sage nodes have powerful compute infrastructure allowing machine learning based phenomenology detection at the edge. This allows science question-based reconfiguration of sensor operation. A well described Application Programming Interface (API) will allow new algorithms to be pushed to the edge and new sensor packages to be added including those that have complex configuration spaces like LIDAR and Radar. This presentation will introduce Sage and present early example results such as using cameras for cloud classification, inundation caused by heavy rainfall and early wildfire ignition detection.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[1]&amp;amp;#160; https://www.research.northwestern.edu/world-watchers/&amp;lt;/p&amp;gt;
Text Information Aggregation with Centrality Attention

A lot of natural language processing problems need to encode the text sequence as a fix-length vector, which usually involves aggregation process of combining the representations of all the words, such as pooling or self-attention. However, these widely used aggregation approaches did not take higher-order relationship among the words into consideration. Hence we propose a new way of obtaining aggregation weights, called eigen-centrality self-attention. More specifically, we build a fully-connected graph for all the words in a sentence, then compute the eigen-centrality as the attention score of each word. The explicit modeling of relationships as a graph is able to capture some higher-order dependency among words, which helps us achieve better results in 5 text classification tasks and one SNLI task than baseline models such as pooling, self-attention and dynamic routing. Besides, in order to compute the dominant eigenvector of the graph, we adopt power method algorithm to get the eigen-centrality measure. Moreover, we also derive an iterative approach to get the gradient for the power method process to reduce both memory consumption and computation requirement.}
Learn2Sing: Target Speaker Singing Voice Synthesis by learning from a Singing Teacher

Singing voice synthesis has been paid rising attention with the rapid development of speech synthesis area. In general, a studio-level singing corpus is usually necessary to produce a natural singing voice from lyrics and music-related transcription. However, such a corpus is difficult to collect since it's hard for many of us to sing like a professional singer. In this paper, we propose an approach -- Learn2Sing that only needs a singing teacher to generate the target speakers' singing voice without their singing voice data. In our approach, a teacher's singing corpus and speech from multiple target speakers are trained in a frame-level auto-regressive acoustic model where singing and speaking share the common speaker embedding and style tag embedding. Meanwhile, since there is no music-related transcription for the target speaker, we use log-scale fundamental frequency (LF0) as an auxiliary feature as the inputs of the acoustic model for building a unified input representation. In order to enable the target speaker to sing without singing reference audio in the inference stage, a duration model and an LF0 prediction model are also trained. Particularly, we employ domain adversarial training (DAT) in the acoustic model, which aims to enhance the singing performance of target speakers by disentangling style from acoustic features of singing and speaking data. Our experiments indicate that the proposed approach is capable of synthesizing singing voice for target speaker given only their speech samples.