Reviewerly/exHarmony huggingface dataset:
BrAgriSpeech: A Corpus of Brazilian-Portuguese Agricultural Reported Speech

Agriculture is one of Brazil’s largest industries. In Brazil, the price of crops such as sugarcane is driven not only by the production levels but also by speculation and rumour. Also, some crop derivatives such as ethanol have their prices regulated by the government. Reported comments from influential speakers such as government ministers and agricultural-business leaders can impact the prices and in some cases the level of production of food products. Currently, there are no corpora in Brazilian-Portuguese that contains agricultural-related speech, the speakers and their employer. BrAgriSpeech is a corpus that uses linguistic rules and pre-trained models to extract reported speech, the speaker and where available the speaker’s employer as well as a discourse connector that connects the speaker with the quote. The resource has 6982 quotes which are in JSONL format. A sample of 50 quotes was manually evaluated and had an accuracy of 0.77 for quote identification, 0.82 for the identification of the speaker and 0.87 for the identification of the discourse connector. The resource is publicly available to encourage further research in the area.
Attributes Functioning in S. T. Coleridge’s Poem “The Rime of the Ancient Mariner”

The paper aims to reveal specificity of the attributes functioning in S. T. Coleridge’s poem “The Rime of the Ancient Mariner”, the earliest example of the English romantic poetry. Scientific originality of the study lies in the fact that relying on quantitative analysis methods, the researcher reveals tendencies in distribution of different types of attributes in the text. The conducted analysis allows identifying hidden principles of attributes distribution. One of the key principles is the principle of compensation when increase in frequency of a particular attribute causes a proportional decrease in frequency of other types of attributes.
EN-DIVINE: An Enhanced Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning

Knowledge Graphs (KGs) are often incomplete and sparse. Knowledge graph reasoning aims at completing the KG by predicting missing paths between entities. The reinforcement learning (RL) based method is one of the state-of-the-art approaches to this work. However, existing RL-based methods have some problems, such as unstable training and poor reward function. Although the DIVINE framework, which a novel plug-and-play framework based on generative adversarial imitation learning, improved existing RL-based algorithms without extra reward engineering, the rate of policy update is slow. This paper proposes the EN-DIVINE framework, using Proximal Policy Optimization algorithms to perform gradient descent when discriminator parameters take policy steps to improve the framework’s training speed. Experimental results show that our work can provide an accessible improvement for the DIVINE framework.
Multi image cross hybrid encryption method based on combined chaotic system

In order to improve the security and encryption efficiency of multi image cross hybrid encryption, a multi image cross hybrid encryption method based on combined chaotic system is proposed. On the basis of chaos theory, the characteristics of Logistic chaotic system and Lorenz chaotic system are analyzed, and Logistic chaotic system and Lorenz chaotic system are combined to form a combined chaotic system. In order to improve the security of multi image encryption, the plaintext image is preprocessed before encryption. The preprocessing process is embedding random number sequence in the plaintext image. Based on the random number embedded image, the combined chaotic system is applied to the multi image cross chaotic encryption method. Experimental results show that the proposed method has high encryption security and high encryption efficiency.
Speakers Talking Foreign Languages in a Multi-lingual TTS System

This paper presents experiments with a multi-lingual multi-speaker TTS synthesis system jointly trained on English, German, Russian, and Czech speech data. The experimental LSTM-based TTS system with a trainable neural vocoder utilizes the International Phonetic Alphabet (IPA) which allows a straight combination of different languages. We analyzed whether the joint model is capable to generalize and mix the information contained in the training data and whether particular voices can be used for the synthesis of different languages, including the language-specific phonemes. The intelligibility of generated speech was assessed by an SUS (Semantically Unpredictable Sentences) listening tests containing Czech sentences spoken by non-Czech speakers. The performance of the joint multi-lingual model was also compared with independent single-voice models where the missing non-native phonemes were mapped to the most similar native phonemes. Besides the Czech sentences, the preference test also contained the English sentences spoken by Czech voices. The multi-lingual model was preferred for all evaluated voices. Although the generated speech did not sound like a native speaker, the phonetic and prosodic features were definitely better.
Detection and extraction of geophysical features associated with carbonatites and alkaline complexes

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsDetection and extraction of geophysical features associated with carbonatites and alkaline complexesAuthors: Malcolm AranhaAlok PorwalManikandan SundaralingamIgnacio González-ÁlvarezMalcolm AranhaIndian Institute of Technology BombaySearch for more papers by this author, Alok PorwalIndian Institute of Technology BombaySearch for more papers by this author, Manikandan SundaralingamDatacodeSearch for more papers by this author, and Ignacio González-ÁlvarezCommonwealth Scientific and Industrial Research Organisation (CSIRO)Search for more papers by this authorhttps://doi.org/10.1190/segam2021-3582301.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractEconomically important mineral-rich carbonatites and alkaline complexes often occur along deep crustal faults and have a typical circular signature. This study attempts to detect and extract these features from geophysical and topographic data using edge and circular feature detection methods; to locate prospective areas for carbonatites and alkaline complexes. Lineaments and circular features persisting at multiple depth levels were extracted and used in conjunction to delineate and prioritise prospective regions for the occurrence of carbonatites and alkaline complexes.Keywords: predictive analytics, edge detection, crustal structurePermalink: https://doi.org/10.1190/segam2021-3582301.1FiguresReferencesRelatedDetails First International Meeting for Applied Geoscience & Energy Expanded AbstractsISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2021 Pages: 3561 publication data© 2021 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 01 Sep 2021 CITATION INFORMATION Malcolm Aranha, Alok Porwal, Manikandan Sundaralingam, and Ignacio González-Álvarez, (2021), "Detection and extraction of geophysical features associated with carbonatites and alkaline complexes," SEG Technical Program Expanded Abstracts : 894-899. https://doi.org/10.1190/segam2021-3582301.1 Plain-Language Summary Keywordspredictive analyticsedge detectioncrustal structurePDF DownloadLoading ...
Evaluation and Enhancement of the Actionability of Publicly Available Cyber Threat Information in Digital Forensics

Cyber threat information can be utilized to investigate incidents by leveraging threat-related knowledge from prior incidents with digital forensic techniques and tools. However, the actionability of cyber threat information in digital forensics has not yet been evaluated. Such evaluation is important to ascertain that cyber threat information is as actionable as it can be and to reveal areas of improvement. In this study, a dataset of cyber threat information products was created from well-known cyber threat information sources and its actionability in digital forensics was evaluated. The evaluation results showed a high level of cyber threat information actionability that still needs enhancements in supporting some widely present types of attacks. To further enhance the provision of actionable cyber threat information, the development of the new TREVItoSTIX Autopsy module is presented. TREVItoSTIX allows the expression of the findings of an incident investigation in the structured threat information expression format in order to be easily shared and reused in future digital forensics investigations.
Corpus Analysis for Literary Studies: Application and Relevance

The use of corpus tools for the teaching-learning and research of literary texts is not widespread. The present study attempts to explore the possibility of the use of corpus tools in literature classrooms. It involves the creation of a corpus of representative poetry of major English poets belonging to the Modern period of English literature, W. B. Yeats, T. S. Eliot, and W. H. Auden. Using concordance software AntConc, a corpus of Word Types and Word Tokens was prepared. The corpus data showed the most frequent words, collocations, and concordances used in the selected poetry of the period. An attempt at juxtaposing the results of the study with the common thematic, narrative, and stylistic interpretations of the poems is made. The results also indicate the potential of enhancing the reliability of the corpus-based analysis of literary texts, especially when corpora were extensive. A significant implication of the study was found to be that the corpus-tools could play a crucial role in promoting learner autonomy in a traditionally teacher-led literature classroom.
Visualizing Copyright-Protected Video Archive Content Through Similarity Search

Providing access to protected media archives can be difficult due to licensing restrictions. In this paper, an alternative way to examine video content without violating terms of use is proposed. For this purpose, keyframes of the original, archived videos are replaced with images from publicly available sources using person recognition and visual similarity search for scenes and locations.
A Fast and Accurate Tube Diameter Visualization Method for Digestive System Medical Images

Although the tube diameter and stenosis rate are important observations in the diagnosis using medical images of digestive organ, such as digestive systems, the diagnosis is based on the physician's subjective evaluation. On the other hand, automatic measurement of tube diameters by image processing has been used for blood vessels, which are tubular tissues similar to digestive systems, and it is also possible to visualize the vessel diameter by assigning pseudo-colors according to the length of the vessel. However, it is difficult to apply the existing diameter visualization methods directly to digestive system medical images due to the processing time and accuracy problems because the tubes are large and tortuous, unlike blood vessels. In this paper, we propose a method to measure the tube diameter quickly by dynamically calculating the next featured pixel using the history of the midpoints of the tube diameter line. Furthermore, we derive an approximate curve corresponding to the centerline of the tube from the history of the featured pixels and measure the tube diameter with high accuracy. We applied the proposed method to various types of digestive system medical images and confirmed that the proposed method could accurately measure and visualize the tube diameter along the shape of the tube and reduce the processing time by up to 99%.
The Special Theory of Relativity Presented in Physics Textbooks is Incorrect

It is shown that the entire practice of using Ohm’s law in the theory of linear AC circuits is a theoretical and experimental evidence of physical reality of imaginary numbers. And therefore, the version of the special theory of relativity (STR) set forth in all physics textbooks, denying physical reality of imaginary numbers through the use of the principle of light speed non-exceedance, is incorrect.
SOCIAL MEDIA: AN INSTRUMENT TO STRENGTHEN EPERSONALITY IN THE ORGANIZATIONAL CITIZENSHIP BEHAVIOUR TO IMPROVE QUALITY OF WORK LIFE BALANCE

The purpose of current study is to analyse different dimensions of the organizational citizenship behaviour and quality of work life balance. Meanly social media has formed virtual all time connections and communication involving formal and informal elements which plays important role in personal involvement and attachment which adds value to the organization and employee also. Organizational citizenship behaviour closely related to Quality work life balance is emerging era to implement and develop new models which can have significant impact on organizational outcome. Earlier study focuses on internal usage of social media but not external reliability, dependability as part of strength of communication. Day by day, the challenges of employee work environment are drastically changing and increasing. Different dimensions due to demographical characters, task variety, team variety and advancement in standards, structures, availability in resources and technology demands thorough study on unique identification of employees for development in their organizational citizenship behaviour. Social media provides participative platform to open up collective voice and to harmonize democratic environment improving organizational citizenship behaviour. It builds e personality of the employee with cardinality which contributes to both work and life quality and in business and project organisations it differs. The researcher aims to elaborate and study the same.
ANALISIS SENSOR LANDSAT 7 ETM + PANJANG GELOMBANG 1,55-1,75 µM DAN 8-14 µM UNTUK IDENTIFIKASI LENGAS TANAH

Remote sensing data is basically a form of information on the characteristics of objects when interacting with electromagnetic waves. The remote sensing system is based on the energy reflected by objects, while the thermal system is based on the energy emitted by objects. Analysis was conducted to determine differences in soil characteristics due to their interaction with electromagnetic waves in the form of reflections and rays to determine soil moisture. Based on the results of image processing and analysis carried out, it is obtained that the thermal system is more suitable to be used for soil moisture identification because it provides better gray value information than the reflection system where the study results show that the study location has a soil moisture value of 1 - 10%. Keywords : Remote Sensing, Moisture
Sustainable principles in designing Lombok’s Beachside Area: a Gading Beach case study

Abstract Recognized as a global best halal tourism, Lombok in West Nusa Tenggara (NTB) is anticipating significant tourism growth. As a response to this, the local government are trying to accelerate the supporting facilities for tourism development, including the Gading Beach area. On contrary, however, buildings are known among the world’s biggest energy consumption. Therefore, the design proposal of these new buildings in the Gading Beach area ought to consider a holistic sustainable approach to create buildings that will encourage environmental, social and economic benefits. Five key sustainable design principles were utilized for the development of the Gading Beach area. Those principles are including site and ecology, community and culture, health, materials and energy. The implementation of these design strategies in the Gading Beach area is expected to exhibit an effective application of a sustainable approach in beachside buildings.
Improved Extreme Learning Machine Method for Wind Turbine Clutter Mitigation

Due to its rapid learning capacity and well generalization performance, the Extreme Learning Machine (ELM) is creatively introduced into wind turbine clutter (WTC) mitigation for weather radar. Aiming at the difficulty of setting the number of hidden layer nodes in ELM algorithm, an improved algorithm‐‐Incremental Extreme Learning Machine (I-ELM) is proposed. First, the training samples are constructed by using the radial velocity and spectral width of the weather signal from the neighboring range bins. Then through the training of samples, the model parameters are searched and optimized according to the least square criterion. Finally, the optimized I-ELM model is utilized to recover the weather signal of the contaminated range bin. Theoretical analysis and simulation results show that the proposed algorithm can effectively suppress WTC and significantly reduce the deviation of radial velocity estimation and spectral width estimation caused by WTC contamination.
Swipe Gesture Robotic System Based on Image Processing and Servo Compensation

The contemporary world faces shortage of human resources in face-to-face working environment. Collaborative robots are promising for that environment because these robots enable people to work keeping a distance. In the operative tasks, the robot required to precisely follow human moving so as not to cause uncomfortable feelings of the operator. As a solution to this problem, we consider a swipe gesture robotic system for operating a robot. The proposed robotic system captures human gestures in real time by image processing, and commands a mobile robot with servo compensation.
A Dynamic and Distributed Simulation Method for Web-Based Games

This paper proposes a simulation method for web-based games to implement our tank battles with user programming. Our previous implementation had adopted a server-side simulation. The server simulated tank battles and sent its result to a root web browser through WebSocket. The browser forwarded them to the others by using a tree-structured WebRTC network. However, the server’s load and the root browser’s load were high and limited the scalability of the number of users. We focus on a game property that can parallelize the calculations on the simulation if a group of tanks is located far from other groups due to the gun range. This paper shows an algorithm for dynamic grouping tanks, an implementation of a distributed simulation using this algorithm, and evaluations.
Blockchain Empowered Federated Learning for Medical Data Sharing Model

In medical fields, data sharing for patients can improve the collaborative diagnosis and the complexity of traditional medical treatment process. Under the condition of data supervision, federated learning breaks the restrictions between medical institutions and realizes the sharing of medical data. However, there are still some issues. For example, lack of trust among medical institutions leads to the inability to establish safe and reliable cooperation mechanisms. For another example, malicious medical institutions destroy model aggregation by sharing false parameters. In this paper, we propose a new federated learning scheme based on blockchain architecture for medical data sharing. Moreover, we propose an intelligent contract to verify the identity of participants and detect malicious participants in federated learning. The experimental results show that the proposed data sharing scheme provides a credible participation mechanism for medical data sharing based on federal learning, and provides both higher efficiency and lower energy consumption as well.
A Camera-Aware Three-Stage Method for Fully Unsupervised Person Re-identification

Most of existing unsupervised person re-identification methods focus on cross-domain adaptation. In order to further relieve the dependence on manual labels, we propose a camera-aware three-stage method for fully unsupervised person re-identification which only requires the unlabeled target dataset. We exploit camera labels and divide the learning process into three relatively easy sub-tasks: initialization by instance discrimination, intra-camera learning and inter-camera learning. The first stage regards each person image as an instance and tries to distinguish each image. The second stage performs intra-camera clustering while the last stage performs clustering and training on the whole dataset. These three stages share the backbone network. Finally, our method substantially boosts the performance stage by stage without any manual ID annotation. We conduct extensive experiments on three large-scale image-based datasets, including Market-1501, DukeMTMC-reID and MSMT17. The results demonstrate that our method achieves the state-of-the-art performance.
Dual Arm Coordination with Coordination Diagram based on Teleoperation Demonstration

Efficient and collision-free coordination of two robot arms is increasingly needed in various service-oriented robotic applications. This paper proposes a dual-arm coordination algorithm to improve the efficiency of coordination by considering both robot's actions and operating sequences for tasks that require multiple operations with both arms. Teleoperation demonstration is performed to obtain the robot's human-like motion trajectories, so as to reduce the probability of collisions of the two robotic arms. The coordination diagram in time domain is designed to more clearly represent the situations of trajectory collisions and find the collision-free coordination action law. Simultaneously, a coordination pair generator (CPG) is designed to reorganize the operating sequences according to the characteristics of input trajectories in addition to the action coordination. The effectiveness and efficiency of our method are verified on the experiment of the drug sorting task in nursing homes on the ABB YuMi robot model.
Visualizing the Finer Cluster Structure of Large-Scale and High-Dimensional Data

Dimension reduction and visualization of high-dimensional data have become very important research topics because of the rapid growth of large databases with high dimensions in data science. A successful dimension reduction and visualization method seeks to produce a low-dimensional representation of high-dimensional data that preserves both the global and local structure of the data. In this paper, we propose using a generalized sigmoid function to model the distance similarity in both high- and low-dimensional space. In particular, a single parameter v is introduced to the generalized sigmoid function in low-dimensional space, so that we can adjust the slope and the heaviness of the function tail by changing the value of the parameter easily. Using real-world data sets with different sample sizes and dimensions, we show that our proposed method can generate visualization results that are competitive with those of the state-of-the-art methods, such as uniform manifold approximation and projection (UMAP), t-distributed stochastic neighbor embedding (t-SNE), and related methods. In addition, by adjusting the value of v, our proposed method can preserve more of both the global and finer cluster structure of the data. Furthermore, like UMAP, our proposed method can easily scale to massive high-dimensional data. Finally, we use domain knowledge to demonstrate that the finer subclusters that are revealed with small values of v are meaningful.