Reviewerly/exHarmony huggingface dataset:
Spider webbing the land-atmosphere interface

&amp;lt;p&amp;gt;Worldwide 55-80% of the rainfall evaporates from the surface, making it a major water drain for the earth's water resources and a major supply of moisture to the atmosphere. Evaporation is relevant for crop growth and has a high impact on the severity of drought and floods. Nonetheless, this key process is still a highly uncertain, insufficiently quantified process. Also effecting weather forecasts as the available water is used as their boundary condition in atmospheric models. The persistent problem herein is our restricted understanding of the key processes of the land-atmosphere interface, as well as their interplay with hydrological and atmospheric processes. The major bottleneck is the difficulty to properly measure the land-atmosphere interface at the right spatial and temporal scale.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this talk I will propose an experimental approach that enables data collection for the full surface energy balance at the land-atmosphere interface. This will be achieved by developing and exploiting a 'spider web' - like measurement approach with temperature measuring fibre optic cables (Distributed Temperature Sensing). This will enable simultaneously and continuously measurements of high-resolution temperature, humidity, wind, and soil moisture gradients. Which allows derivaiton of the sensible, latent, and ground heat flux and storage. The spider web offers a better representation of the land-atmosphere interface for the purpose to provide a knowledge base for improving flood and drought predictions and weather forecasts.&amp;lt;/p&amp;gt;
Children Response toward Good Saying in Islamic Picture Book

This research discussed the children response toward good saying in Islamic picture book and aims to know the responses of children as the reader in efferent and aesthetic responses to Islamic picture book. This study focuses on reader response theory. The researcher used video as instrument to find out the valid data. Then. the data was analyzed using a descriptive qualitative method. The researcher was conducted in order to give the reader of Islamic picture book. The finding show that the student used efferent and aesthetic response to constitute the knowledge about tayyibah, especially alhamdulillah sentence in daily life, the character in the Islamic picture book, the moral value, and connecting their prior knowledge after reading the Islamic picture book. The researcher concluded that students have responded using various possibilities, which is related to the content of the text.
Blockchain Applied to the E&amp;P Phase in the Oil &amp; Gas Sector

Summary In a global industry where each year billions of operations move through a complex supply chain involving tens of thousands of suppliers, blockchain’s potential cannot be understated. Blockchain is particularly useful in the seismic sector, an area in which Repsol is pioneering the development of one first blockchain pilots. Seismic rights require complicated processes to ensure the traceability of ownership and their limited use over time, and involve many different suppliers and international teams of workers. As part of the Oil&amp;Gas Blockchain Consortium, Repsol has been responsible for developing this pilot to bring the use of Blockchain technology to the management of seismic rights.
Privacy Evaluation Protocols for the Evaluation of Soft-Biometric Privacy-Enhancing Technologies

Biometric data includes privacy-sensitive information, such as soft-biometrics. Soft-biometric privacy enhancing technologies aim at limiting the possibility of deducing such information. Previous works proposed several solutions to this problem using several different evaluation processes, metrics, and attack scenarios. The absence of a standardized evaluation protocol makes a meaningful comparison of these solutions difficult. In this work, we propose privacy evaluation protocols (PEPs) for privacy-enhancing technologies (PETs) dealing with soft-biometric privacy. Our framework evaluates PETs in the most critical scenario of an attacker that knows and adapts to the systems privacy-mechanism. Moreover, our PEPs differentiate between PET of learning-based or training-free nature. To ensure that our protocol meets the highest standards in both cases, it is based on Kerckhoffs‘s principle of cryptography.
Deep Echo State Q-Network (DEQN) and Its Application in Dynamic Spectrum Sharing for 5G and Beyond

Deep reinforcement learning (DRL) has been shown to be successful in many application domains. Combining recurrent neural networks (RNNs) and DRL further enables DRL to be applicable in non-Markovian environments by capturing temporal information. However, training of both DRL and RNNs is known to be challenging requiring a large amount of training data to achieve convergence. In many targeted applications, such as those used in the fifth generation (5G) cellular communication, the environment is highly dynamic while the available training data is very limited. Therefore, it is extremely important to develop DRL strategies that are capable of capturing the temporal correlation of the dynamic environment requiring limited training overhead. In this paper, we introduce the deep echo state Q-network (DEQN) that can adapt to the highly dynamic environment in a short period of time with limited training data. We evaluate the performance of the introduced DEQN method under the dynamic spectrum sharing (DSS) scenario, which is a promising technology in 5G and future 6G networks to increase the spectrum utilization. Compared to conventional spectrum management policy that grants a fixed spectrum band to a single system for exclusive access, DSS allows the secondary system to share the spectrum with the primary system. Our work sheds light on the application of an efficient DRL framework in highly dynamic environments with limited available training data.
Stone Tools Illustrations with Vector Art: The &amp;#39;STIVA&amp;#39; Method v1

Lithic illustrations are often used in scientific publications to efficiently communicate the technological and morphological characteristics of stone tools. They offer invaluable information and insights not only on how stone raw materials were transformed into their final form, but also on the individuals that made them. Here, the “Stone Tools Illustrations with Vector Art” (STIVA) Method is presented, which involves the illustration of lithic artefacts using vectorial graphics software (Adobe® Illustrator®). This protocol follows an optimised step-by-step method, presenting ten major sections that constitute the creation of a lithic illustration: photography, vectorial software configuration, scale, outline, scar borders, ripples, cortex, symbols, composition, and export. This method has been developed to allow researchers, students and educators to create clear and competent illustrations for any application, from scientific publications to public outreach.
Innovation for the Engaged Librarian

Abstract As librarians, we constantly innovate to meet the needs of our users and to utilize new technology. Discovery is an important part of this process. When we discover our patrons’ pain points, we can more easily adapt to their needs. As the National Science Foundation’s Innovation Corps program is implemented in more and more universities, engineering and patent librarians facilitate faculty and scientists in using the Business Model Canvas. Librarians can help faculty and scientists with familiarizing themselves with library resources to fill key parts of the Canvas. Additionally, this canvas is often implemented in innovation centers across campuses, and becoming familiar with this canvas can assist students as well. Understanding the innovation ecosystem at one’s university is fundamentally important, since intellectual property plays an important role in innovation. This Business Model Canvas, as well as understanding the innovation ecosystem, can provide librarians with useful tools to engage with innovators and entrepreneurs.
A Generalizable Deepfake Detector based on Neural Conditional Distribution Modelling

Photo- and video-realistic generation techniques have become a reality following the advent of deep neural networks. Consequently, there are immense concerns regarding the difficulty in differentiating what content is real from what is synthetic. An example of video-realistic generation techniques is the infamous Deepfakes, which exploit the main modality by which humans identify each other. Deepfakes are a category of synthetic face generation methods and are commonly based on generative adversarial networks. In this article, we propose a novel two-step synthetic face image detection method in which general-purpose features are extracted in a first step, trivializing the task of detecting synthetic images. The anomaly detector predicts the conditional probabilities for observing every individual pixel in the image and is trained on pristine data only. The extracted anomaly features demonstrate true generalization capacity across widely different unknown synthesis methods while showing a minimal loss in performance with regard to the detection of known synthetic samples.
Predicting Clinical Trial Results by Implicit Evidence Integration

Clinical trials provide essential guidance for practicing Evidence-Based Medicine, though often accompanying with unendurable costs and risks. To optimize the design of clinical trials, we introduce a novel Clinical Trial Result Prediction (CTRP) task. In the CTRP framework, a model takes a PICO-formatted clinical trial proposal with its background as input and predicts the result, i.e. how the Intervention group compares with the Comparison group in terms of the measured Outcome in the studied Population. While structured clinical evidence is prohibitively expensive for manual collection, we exploit large-scale unstructured sentences from medical literature that implicitly contain PICOs and results as evidence. Specifically, we pre-train a model to predict the disentangled results from such implicit evidence and fine-tune the model with limited data on the downstream datasets. Experiments on the benchmark Evidence Integration dataset show that the proposed model outperforms the baselines by large margins, e.g., with a 10.7% relative gain over BioBERT in macro-F1. Moreover, the performance improvement is also validated on another dataset composed of clinical trials related to COVID-19.
Extending finite mixtures of [formula omitted] linear mixed-effects models with concomitant covariates

The issue of model-based clustering of longitudinal data has attracted increasing attention in past two decades. Finite mixtures of Student’s-t linear mixed-effects (FM-tLME) models have been considered for implementing this task especially when data contain extreme observations. This paper presents an extended finite mixtures of Student’s-t linear mixed-effects (EFM-tLME) model, where the categorical component labels are assumed to be influenced by the observed covariates. As compared with the naive methods assuming the mixing proportions to be fixed but unknown, the proposed EFM-tLME model exploits a logistic function to link the relationship between the prior classification probabilities and the covariates of interest. To carry out maximum likelihood estimation, an alternating expectation conditional maximization (AECM) algorithm is developed under several model reduction schemes. The technique for extracting the information-based standard errors of parameter estimates is also investigated. The proposed method is illustrated using simulation experiments and real data from an AIDS clinical study.
A Comparison of Selected Optimization Methods for Neural Networks

Which numerical methods are ideal for training a neural network? In this report four different optimization methods are analysed and compared to each other. First, the most basic method Stochastic ...
Secure Information Hiding Based on Random Similar Bit Mapping

The goal of cryptography is to maintain the secrecy of information while steganography aims to hide the information.A hybrid steganography and cryptography method was introduced to increase the security of data transmission.Random Similar Bit Mapping (RSBM) was proposed to hide a secret message without modifying the image and generates a Position File (PF) which stores the positions of a hidden message.PF is encrypted using Advanced Encryption Standard (AES) algorithm before being sent to the receiver.Two security measures were proposed to estimate the message location in PF based on Correct Position Finding (CPF) for message detection against a brute force attack.This paper compared related works using the proposed security measures to evaluate its security.From the CPF probability analysis, RSBM produced the lowest CPF probability results, while recording the highest CPF time complexity results in CPF time complexity analysis.In conclusion, RSBM ensured high data security which can be implemented in any information hiding application.
GreedyFool: An Imperceptible Black-box Adversarial Example Attack against Neural Networks.

Deep neural networks (DNNs) are inherently vulnerable to well-designed input samples called adversarial examples. The adversary can easily fool DNNs by adding slight perturbations to the input. In this paper, we propose a novel black-box adversarial example attack named GreedyFool, which synthesizes adversarial examples based on the differential evolution and the greedy approximation. The differential evolution is utilized to evaluate the effects of perturbed pixels on the confidence of the DNNs-based classifier. The greedy approximation is an approximate optimization algorithm to automatically get adversarial perturbations. Existing works synthesize the adversarial examples by leveraging simple metrics to penalize the perturbations, which lack sufficient consideration of the human visual system (HVS), resulting in noticeable artifacts. In order to sufficient imperceptibility, we launch a lot of investigations into the HVS and design an integrated metric considering just noticeable distortion (JND), Weber-Fechner law, texture masking and channel modulation, which is proven to be a better metric to measure the perceptual distance between the benign examples and the adversarial ones. The experimental results demonstrate that the GreedyFool has several remarkable properties including black-box, 100% success rate, flexibility, automation and can synthesize the more imperceptible adversarial examples than the state-of-the-art pixel-wise methods.
MedICaT: A Dataset of Medical Images, Captions, and Textual References

Understanding the relationship between figures and text is key to scientific document understanding. Medical figures in particular are quite complex, often consisting of several subfigures (75% of figures in our dataset), with detailed text describing their content. Previous work studying figures in scientific papers focused on classifying figure content rather than understanding how images relate to the text. To address challenges in figure retrieval and figure-to-text alignment, we introduce MedICaT, a dataset of medical images in context. MedICaT consists of 217K images from 131K open access biomedical papers, and includes captions, inline references for 74% of figures, and manually annotated subfigures and subcaptions for a subset of figures. Using MedICaT, we introduce the task of subfigure to subcaption alignment in compound figures and demonstrate the utility of inline references in image-text matching. Our data and code can be accessed at this https URL.
Testing the Quantitative Spacetime Hypothesis using Artificial Narrative Comprehension (I) : Bootstrapping Meaning from Episodic Narrative viewed as a Feature Landscape

The problem of extracting important and meaningful parts of a sensory data stream, without prior training, is studied for symbolic sequences, by using textual narrative as a test case. This is part of a larger study concerning the extraction of concepts from spacetime processes, and their knowledge representations within hybrid symbolic-learning `Artificial Intelligence'. Most approaches to text analysis make extensive use of the evolved human sense of language and semantics. In this work, streams are parsed without knowledge of semantics, using only measurable patterns (size and time) within the changing stream of symbols -- as an event `landscape'. This is a form of interferometry. Using lightweight procedures that can be run in just a few seconds on a single CPU, this work studies the validity of the Semantic Spacetime Hypothesis, for the extraction of concepts as process invariants. This `semantic preprocessor' may then act as a front-end for more sophisticated long-term graph-based learning techniques. The results suggest that what we consider important and interesting about sensory experience is not solely based on higher reasoning, but on simple spacetime process cues, and this may be how cognitive processing is bootstrapped in the beginning.
A Scale and Rotational Invariant Key-point Detector based on Sparse Coding

Most popular hand-crafted key-point detectors such as Harris corner, SIFT, SURF aim to detect corners, blobs, junctions or other human defined structures in images. Though being robust with some geometric transformations, unintended scenarios or non-uniform lighting variations could significantly degrade their performance. Hence, a new detector that is flexible with context change and simultaneously robust with both geometric and non-uniform illumination variations is very desirable. In this paper, we propose a solution to this challenging problem by incorporating Scale and Rotation Invariant design (named SRI-SCK) into a recently developed Sparse Coding based Key-point detector (SCK). The SCK detector is flexible in different scenarios and fully invariant to affine intensity change, yet it is not designed to handle images with drastic scale and rotation changes. In SRI-SCK, the scale invariance is implemented with an image pyramid technique while the rotation invariance is realized by combining multiple rotated versions of the dictionary used in the sparse coding step of SCK. Techniques for calculation of key-points' characteristic scales and their sub-pixel accuracy positions are also proposed. Experimental results on three public datasets demonstrate that significantly high repeatability and matching score are achieved.
The Extraordinary Failure of Complement Coercion Crowdsourcing

Crowdsourcing has eased and scaled up the collection of linguistic annotation in recent years. In this work, we follow known methodologies of collecting labeled data for the complement coercion phenomenon. These are constructions with an implied action -- e.g., "I started a new book I bought last week", where the implied action is reading. We aim to collect annotated data for this phenomenon by reducing it to either of two known tasks: Explicit Completion and Natural Language Inference. However, in both cases, crowdsourcing resulted in low agreement scores, even though we followed the same methodologies as in previous work. Why does the same process fail to yield high agreement scores? We specify our modeling schemes, highlight the differences with previous work and provide some insights about the task and possible explanations for the failure. We conclude that specific phenomena require tailored solutions, not only in specialized algorithms, but also in data collection methods.
The POA Application in the Teaching of Chinese Writing as a Foreign Language

The old style of traditional teaching mode is to take “text as the core”. A whole new teaching approach was put forward by a famous professor and the approach is named as production oriented approach (POA), which is pulling people’s attention on both “input” and “production”. From the essence of these two elements, we know that it is for sure the innovation and development of traditional teaching mode is great. Also, there is also a disjointed situation between learning and using during the teaching. Based on the POA, this paper selects lesson 25 “developing Chinese intermediate Writing II” as an example, expects to explore the effective way of production so as to provide a series of reference for the subsequent writing teaching design from three aspects including driving, facilitating and evaluating.
Kajian Pendahuluan Untuk Analisis Kebutuhan Pengembangan E-book Berbasis Android

ABSTRACT This study aims to determinate the potential use of Android-based E-books. By using Android students can repeat lessons at home that are not yet understood because of limited face to face time at school and can be an indpendent learning medium that is easily accessible using Android. This research is a qualitative descriptive study that describes the collectnaled findings, analyzed and concluded according to the research objectives desired. The sampling technique used was purposive sampling. Data collection technique in this study were trough questionnaires and interviews. Android-based e-book learning media has the potential to be applied to support distance learning, 92,2% according to teacers of android -based e-book effectively used for learning. Keyword :Learning media, e-book, Android
A symmetric attractor-decomposition lifting algorithm for parity games

Progress-measure lifting algorithms for solving parity games have the best worst-case asymptotic runtime, but are limited by their asymmetric nature, and known from the work of Czerwi\'nski et al. (2018) to be subject to a matching quasi-polynomial lower bound inherited from the combinatorics of universal trees. Parys (2019) has developed an ingenious quasi-polynomial McNaughton- Zielonka-style algorithm, and Lehtinen et al. (2019) have improved its worst-case runtime. Jurdzi\'nski and Morvan (2020) have recently brought forward a generic attractor-based algorithm, formalizing a second class of quasi-polynomial solutions to solving parity games, which have runtime quadratic in the size of universal trees. First, we adapt the framework of iterative lifting algorithms to computing attractor-based strategies. Second, we design a symmetric lifting algorithm in this setting, in which two lifting iterations, one for each player, accelerate each other in a recursive fashion. The symmetric algorithm performs at least as well as progress-measure liftings in the worst-case, whilst bypassing their inherent asymmetric limitation. Thirdly, we argue that the behaviour of the generic attractor-based algorithm of Jurdzinski and Morvan (2020) can be reproduced by a specific deceleration of our symmetric lifting algorithm, in which some of the information collected by the algorithm is repeatedly discarded. This yields a novel interpretation of McNaughton-Zielonka-style algorithms as progress-measure lifting iterations (with deliberate set-backs), further strengthening the ties between all known quasi-polynomial algorithms to date.