Reviewerly/exHarmony huggingface dataset:
Combined analysis of polarimetric SAR data and optical imagery for rapid landslide mapping in vegetated areas

&amp;lt;p&amp;gt;Rapid mapping of the extent of the affected area as well as type and grade of damage after a landslide event is crucial to enable fast crisis response, i.e., to support rescue and humanitarian operations. Change detection between pre- and post-event very high resolution (VHR) optical imagery is the state-of-the-art in operational rapid mapping of landslides. However, the suitability of optical data relies on clear sky conditions, which is not often the case after landslides events, as heavy rain is one of the most frequent triggers of landslides. In contrast to this, the acquisition of synthetic aperture radar (SAR) imagery is independent of atmospheric conditions. SAR data-based landslide detection approaches reported in the literature use change detection techniques, requiring VHR SAR imagery acquired shortly before the landslide event, which is commonly not available. Modern VHR SAR missions, e.g., Radarsat-2, TerraSAR-X, or COSMO-SkyMed, do not systematically cover the entire world, due to limitations in onboard disk space and downlink transmission rates. Here, we present a fast and transferable procedure for mapping of landslides in vegetated areas, based on change detection between pre-event optical imagery and the polarimetric entropy derived from post-event VHR polarimetric SAR data. Pre-event information is derived from high resolution optical imagery of Landsat-8 or Sentinel-2, which are freely available and systematically acquired over the entire Earth&amp;amp;#8217;s landmass. The landslide mapping is refined by slope information from a digital elevation model generated from bi-static TanDEM-X imagery. The methodology was successfully applied to two landslide events of different characteristics: A rotational slide near Charleston, West Virginia, USA and a mining waste earthflow near Bolshaya Talda, Russia.&amp;lt;/p&amp;gt;
Vector-Vector-Matrix Architecture: A Novel Hardware-Aware Framework for Low-Latency Inference in NLP Applications

Deep neural networks have become the standard approach to building reliable Natural Language Processing (NLP) applications, ranging from Neural Machine Translation (NMT) to dialogue systems. However, improving accuracy by increasing the model size requires a large number of hardware computations, which can slow down NLP applications significantly at inference time. To address this issue, we propose a novel vector-vector-matrix architecture (VVMA), which greatly reduces the latency at inference time for NMT. This architecture takes advantage of specialized hardware that has low-latency vector-vector operations and higher-latency vector-matrix operations. It also reduces the number of parameters and FLOPs for virtually all models that rely on efficient matrix multipliers without significantly impacting accuracy. We present empirical results suggesting that our framework can reduce the latency of sequence-to-sequence and Transformer models used for NMT by a factor of four. Finally, we show evidence suggesting that our VVMA extends to other domains, and we discuss novel hardware for its efficient use.
Incorporating Count-Based Features into Pre-Trained Models for Improved Stance Detection

The explosive growth and popularity of Social Media has revolutionised the way we communicate and collaborate. Unfortunately, this same ease of accessing and sharing information has led to an explosion of misinformation and propaganda. Given that stance detection can significantly aid in veracity prediction, this work focuses on boosting automated stance detection, a task on which pre-trained models have been extremely successful on, as on several other tasks. This work shows that the task of stance detection can benefit from feature based information, especially on certain under performing classes, however, integrating such features into pre-trained models using ensembling is challenging. We propose a novel architecture for integrating features with pre-trained models that address these challenges and test our method on the RumourEval 2019 dataset. This method achieves state-of-the-art results with an F1-score of 63.94 on the test set.
Aplikasi K-means cluster untuk pengelompokan unit kerja yang beresiko Covid-19 pada industri layanan kesehatan

The pandemic of COVID-19 infection is an epidemic that is of major concern now more than the dengue epidemic that continues to occur today. The focus of risk, especially on medical and non-medical personnel working specifically in health services, is very high because it deals directly with co-19 patients or other patients who are likely to suffer but have not been detected with other illnesses. Related to this, the human resources working in the health service are affected as well as the health conditions and habits that are carried out daily that affect the level of risk that might occur. Covid-19 positive patients who must be hospitalized usually have acute respiratory distress which can be fatal. Identification of patients with Covid-19 infection is usually obtained after laboratory examinations which in less than half are based on congenital (comorbid) diseases such as diabetes, hypertension and those related to the heart and blood vessels.
Usage of basic problem-solving techniques in the discipline of general physics in higher school

The subject of this research is the process of teaching students to solve problems in the discipline of general physics on their own, as well as develop skills of future engineers to break up the solution of the problem into stages. The article is aimed ad generalization, elaboration and implementation of the basic problem-solving techniques higher school based on the preliminary compiled classification of problems in accordance with certain characteristics. The author develops educational and methodological support for the discipline of general physics as the means of training students to solve problems on their own. The following methods were applied in the course of this work: analysis of psychological, pedagogical and scientific-methodical literature; analysis of curricula, textbooks, problem books, guidebooks on natural science disciplines, modeling of class activity of the students; empirical methods of observation, conversation, survey. As a result, the author incorporates the existing problem-solving techniques into the system, based on which students are capable to go through all stages of solving the problem on their own, better understand the study material, and acquire essential skills for articulation of the problem. The examples are provided on step-by-step solution of different types of problems on the topic &amp;ldquo;Calculation of an electrical network&amp;rdquo;.
THE TACIT CURRICULUM: UNEXPLORED OPPORTUNITY OR APPARENT CHALLENGE IN NURTURING RESILIENCE AND TRUST IN GOD’S SUPERNATURAL POWER AMONG YOUTH?

This article investigated on the role played by the unseen curriculum in developing resilience and real honor for God among youth to help them holistically define their way of life and aspirations amid numerous life challenges. Since time immemorial significant attention has been directed towards the official curricular, but the role played by the veiled curriculum in nurturing young people to endure predicaments and genuinely trust in God’s supernatural power has barely featured in many schooling discourses. This means a good number of Christians are burdened by how best they can inspire young people to trust in the omniscient, omnipresent, omnipotent Creator of the universe but very few, if any, ever think about how the veiled curriculum, can hinder or facilitate young people’s development of resilience and trust in God. In this article the meaning of the concept unstated curriculum and its influences on nurturing resilience and teaching about God’s supernatural power—which when invoked in faith causes the kingdom of darkness to tremble, are discussed. The research employed a phenomenological design, guided by an interview guide with 12 open-ended items followed by extensive probing questions. Two experts examined the interview guide items to determine their clarity, but the researcher also conducted a pilot study among 3 participants (one lecturer and two students) to determine participants’ level of understanding of the interview guide items. The results from the research indicated a remarkable connection between hidden curriculum aspects and young people’s resilience and their grasp of how deep, wide, and high God’s power is in overcoming life challenges. Article visualizations:
A reproducible solar irradiance estimation process using Recurrent Neural Network

&amp;lt;p&amp;gt;The Sun have a constant action on Earth, interfering in different ways on life in our planet. The physical, chemical and biological processes that occur on Earth are directly influenced by the variation of solar irradiance, which is a function of the activity in the Sun&amp;amp;#8217;s different atmospheric layers and their rapid variation. Studying this relationship may require the availability of a large amount of collected data, without significant gaps that could be caused from many kinds of issues. In this work, we present a Recurrent Neural Network as an option for estimating the Total Solar Irradiance (TSI) and the Spectral Solar Irradiance (SSI) variability. Solar images collected on different wave components were preprocessed and used as the input parameters, and TSI and SSI data collected by instruments onboard of SORCE were used as reference of the results we expected to achieve. Complementary to this approach, we opted for developing a reproducible procedure, for which we chose a free programming language, in attempt to offer the same kind of results, with same accuracy, for future studies which would like to reproduce our procedure. To achieve this, reproducible notebooks will be generated with the intention of providing transparency in the data analysis process and allowing the process and the results to be validated, modified and optimized by those who would like to do it. This approach aims to obtain a good accuracy in estimating the TSI and SSI, allowing its reconstruction in gap scales and also the forecast of their values six hours ahead.&amp;lt;/p&amp;gt;
LibGuides: Construction: Accessibility

A comprehensive guide to all of the Library Service's resources which are available to support you with your studies.
Some Estimates of the Labor Costs for Creating Electronic Libraries

The issues of creation of books digital copies and 3D-models of museum objects as a content of integrated digital libraries are discussed in the paper. The process steps to take a digital content for digital library are presented and analyzed in terms of the Scientific Heritage of Russia digital library (SHR DL). Librarians, museum employees, editors and technical specialists carry out these steps. Time labor costs for performing certain operations such as the formation of digital objects and collections of these objects are calculated based on approved standards for library and museum works and on the basis of 10 years of provisioning experience with SHR DL. The analysis allows us to optimize the time spent on the formation of digital library elements by parallelizing technological processes.
PEMETAAN DIGITAL FAKULTAS TEKNIK UNIVERSITAS TADULAKO BERBASIS GEOGRAPHIC INFORMATION SYSTEM (GIS)

Tadulako University is the largest tertiary institution in Central Sulawesi and every year new students are registered to Tadulako University comes from inside and outside Central Sulawesi Province. Prospective students from various regions certainly many are looking for information on the campus area, especially in the Faculty of Engineering, the University of Tadulako through existing media. Seeing this, the authors conducted a research aiming to make a digital mapping of the Tadulako University Faculty of Engineering based on the Geographic Information System (GIS) application using the website, and at the same time giving directions to the location of the room using 360-degree photos and how to utilize the Tadulako University Faculty of Engineering Mapping for determine the location on the website. The method used is to use Geographic Information Systems (GIS) and the results of the study that the author found is using the HyperText Preprocessor (PHP) programming language and JavaScript and retrieve data from 360 degrees as many as 245 photo locations in each Department of Engineering at the University of Tadulako with 1-time taking photos requires 40 times the taking of 360-degree objects with a distance between one room with another so that it connects the existing mapping at the Department of Engineering, Tadulako University using the website.
Aitac: an identity-based traceable anonymous communication model

In the big data background, data privacy becomes more and more important when data leakage and other security events occur more frequently. As one of the key means of privacy protection, anonymous communication attracts large attention. Aiming at the problems such as low efficiency of message forwarding, high communication delay and abusing of anonymity, this paper presents an identity-based traceable anonymous communication model by adding a preprocessing phase, modifying the ciphertext structure and increasing the controllability of anonymity. Firstly, a new identity-based signature algorithm is proposed, and its security is proved via existential unforgeability against chosen-message attacks (EU-CMA). The signature algorithm is further applied to the anonymous communication model to implement the controllability of revocable anonymity. Secondly, by adding a preprocessing Setup phase, the operations of identifications distribution and user authentication are launched before the anonymous communication phase starts, and this practice significantly improves the efficiency of the anonymous communication model. Finally, by adding the hash value of the message and the user identification as the message authentication code, we design a new ciphertext structure, which can efficiently guarantee the integrity of the ciphertext. Performance analysis and simulation results show that the proposed anonymous communication model has high message forwarding efficiency and better security and controllability of anonymity.
Continual learning using hash-routed convolutional neural networks

Continual learning could shift the machine learning paradigm from data centric to model centric. A continual learning model needs to scale efficiently to handle semantically different datasets, while avoiding unnecessary growth. We introduce hash-routed convolutional neural networks: a group of convolutional units where data flows dynamically. Feature maps are compared using feature hashing and similar data is routed to the same units. A hash-routed network provides excellent plasticity thanks to its routed nature, while generating stable features through the use of orthogonal feature hashing. Each unit evolves separately and new units can be added (to be used only when necessary). Hash-routed networks achieve excellent performance across a variety of typical continual learning benchmarks without storing raw data and train using only gradient descent. Besides providing a continual learning framework for supervised tasks with encouraging results, our model can be used for unsupervised or reinforcement learning.
On uncertainty in geoscience decision making

&amp;lt;p&amp;gt;Uncertainty is a feature of all science, but geoscientists tend to operate under a higher degree of uncertainty at every level of scientific decision making (B&amp;amp;#225;rdossy and Fodor,&amp;amp;#160;2001). Geoscientists work in fundamentally less predictable environments, where direct observation and experimental control are difficult or impossible due to the large time spans of geologic processes. We are cognitive psychologists who collaborate with geoscientists to better understand the influence of uncertainty on geologic decision making, and to identify the ways expert scientists constrain decision making using heuristics (i.e., rules of thumb).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This presentation will review our work on geologic decision making under uncertainty, focusing on how scientists use heuristics when making spatiotemporal data collection decisions. Our research demonstrates that, when the gradient of data value in the environment is uncertain, scientists use heuristics to decide where to go (in space) to collect data. Heuristics are efficient and effective in many circumstances, but can leave experts vulnerable to decision bias. In ongoing research, we explore whether new field workflows (uncertainty mapping) or mobile robotic platforms (terrestrial, aerial) can debias decision making. Our research agenda is translational, with the goal of improving scientists&amp;amp;#8217; interpretation of geologic phenomena in the field.&amp;lt;/p&amp;gt;
Achieving Message-Encapsulated Leveled FHE for IoT Privacy Protection

The rapid development of the Internet of Things has made the issue of privacy protection even more concerning. Privacy protection has affected the large-scale application of the Internet of Things. Fully Homomorphic Encryption (FHE) is a newly emerging public key encryption scheme, which can be used to prevent information leakage. It allows performing arbitrary algebraic operations on data which are encrypted, such that the operation performed on the ciphertext is directly transformed into the corresponding plaintext. Recently, overwhelming majority of FHE schemes are confined to single-bit encryption, whereas how to achieve a multibit FHE scheme is still an open problem. This problem is partially (rather than fully) solved by Hiromasa-Abe-Okamoto (PKC′15), who proposed a packed message FHE scheme which only supports decryption in a bit-by-bit manner. Followed by that, Li-Ma-Morais-Du (Inscrypt′16) proposed a multibit FHE scheme which can decrypt the ciphertext at one time, but their scheme is based on dual LWE assumption. Armed with the abovementioned two schemes, in this paper, we propose an efficient packed message FHE that supports the decryption in two ways: single-bit decryption and one-time decryption.
Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification

Groundbreaking successes have been achieved by Deep Reinforcement Learning (DRL) in solving practical decision-making problems. Robotics, in particular, can involve high-cost hardware and human interactions. Hence, scrupulous evaluations of trained models are required to avoid unsafe behaviours in the operational environment. However, designing metrics to measure the safety of a neural network is an open problem, since standard evaluation parameters (e.g., total reward) are not informative enough. In this paper, we present a semi-formal verification approach for decision-making tasks, based on interval analysis, that addresses the computational demanding of previous verification frameworks and design metrics to measure the safety of the models. Our method obtains comparable results over standard benchmarks with respect to formal verifiers, while drastically reducing the computation time. Moreover, our approach allows to efficiently evaluate safety properties for decision-making models in practical applications such as mapless navigation for mobile robots and trajectory generation for manipulators.
Rooting Formal Methods within Higher Education Curricula for Computer Science and Software Engineering -- A White Paper

This white paper argues that formal methods need to be better rooted in higher education curricula for computer science and software engineering programmes of study. To this end, it advocates (i) improved teaching of formal methods; (ii) systematic highlighting of formal methods within existing, `classical' computer science courses; and (iii) the inclusion of a compulsory formal methods course in computer science and software engineering curricula. These recommendations are based on the observations that (a) formal methods are an essential and cost-effective means to increase software quality; however (b) computer science and software engineering programmes typically fail to provide adequate training in formal methods; and thus (c) there is a lack of computer science graduates who are qualified to apply formal methods in industry. This white paper is the result of a collective effort by authors and participants of the 1st International Workshop on Formal Methods, Fun for Everybody which was held in Bergen, Norway, 2-3 December 2019. As such, it represents insights based on learning and teaching computer science and software engineering (with or without formal methods) at various universities across Europe.
USING UNBALANCED EMBRYOS AS REFERENCES IN DISTINGUISHING CARRIER AND NORMAL EMBRYOS BY SNP ARRAY FOR RECIPROCAL TRANSLOCATIONS

To validate the feasibility of using unbalanced embryos as references in the detection of normal euploid embryos by SNP array based preimplantation genetic haplotyping (PGH) in preimplantation genetic testing (PGT) for reciprocal translocations. The purpose of this study was to compare the consistency of results from using unbalanced embryos as references with family members, thus to evaluate the feasibility of only using unbalanced embryo as a reference to distinguish normal and carrier embryos, for the situation when no family member was available. Haplotypes were first established based on SNPs from family members. Blastocysts were biopsied and screened by SNP array based comprehensive chromosome screening (CCS). After CCS, balanced embryos were further tested to identify normal euploidies by haplotyping using either family member as a reference, or unbalanced embryos generated by adjacent-1 or adjacent-2 segregation mode. Karyotypes of transferred embryos were validated by prenatal diagnosis. A total of 110 couples with reciprocal translocations were analyzed. After CCS, 288 out of 995 embryos were found balanced. Using family members as references, 142 of 288 balanced embryos were tested to be normal, 144 carriers, and the other 2 undetermined. Next, unbalanced embryos were selected as references, and all the results of carrier and normal embryos were consistent with the previous results. A total of 107 embryos were transferred, resulting in 66 clinical pregnancies. Of the 66 clinical pregnancies, 34 received prenatal diagnosis. Karyotypes from prenatal diagnosis were all in accordance with the results of tested embryos. PGH based SNP array is a rapid and effective way to identify normal embryos from balanced ones for translocation carriers. In case no family member was available as a reference, unbalanced embryos can be used for distinguishing normal and carrier embryos.
Can Generative Colourisation Help Face Recognition

Generative colourisation methods can be applied to automatically convert greyscale images to realistically looking colour images. In a face recognition system, such techniques might be employed as a pre-processing step in scenarios where either one or both face images to be compared are only available in greyscale format. In an experimental setup which reflects said scenarios, we investigate if generative colourisation can improve face sample utility and overall biometric performance of face recognition. To this end, subsets of the FERET and FRGCv2 face image databases are converted to greyscale and colourised applying two versions of the DeOldify colourisation algorithm. Face sample quality assessment is done using the FaceQnet quality estimator. Biometric performance measurements are conducted for the widely used ArcFace system and reported according to standardised metrics. Obtained results indicate that, for the tested systems, the application of generative colourisation does neither improve face image quality nor recognition performance. However, generative colourisation was found to aid face detection and subsequent feature extraction of the used face recognition system which results in a decrease of the overall false reject rate.