Reviewerly/exHarmony huggingface dataset:
Mechanical Characteristic and Water Absorption Property of Bio Composite from Sago Starch and Jute Fiber (Boehmeria Nivea) as the filler

Environmentally friendly plastics can be degraded biologically in an anaerobic environment. This plastic is synthesized from starch such as sago starch which is available in abundance. In the form of bioplastics, its mechanical properties are still not compared to conventional plastics derived from crude oil, so its application is limited. The incorporation of filler material increases its mechanical properties, one of the selected fillers is hemp fiber as used in this study. Thermoplastic starch from sago with flax fiber as a filler and the addition of Polypropylene to improve mechanical properties with a certain composition to maintain its natural biodegradability. The mechanical properties analyzed were tensile strength, elongation and modulus of elasticity. Water absorption tests were also carried out to observe the water resistance properties. The results of the tensile strength test showed that the best tensile strength value of 9.32 Mpa was obtained at the addition of 35% fiber with a TPS: PP ratio of 1:1.5. The same conditions were obtained for the percent elongation with the results of 10.16% and the modulus of elasticity was 91.73 Mpa. Water absorption showed that 55% filler gave the lowest water absorption, namely 4.41% at a ratio of TPS: PP 1:0.5. The addition of fiber filler into the bio-composite affects the tensile strength, elongation and modulus of elasticity, the higher the volume of filler entering the bio-composite, the lower the value of tensile strength, elongation and modulus of elasticity, or vice versa. The ratio of addition of polypropylene matrix is also influential, the higher the ratio contributes to the tensile strength, elongation and higher modulus of elasticity. High water absorption capacity will reduce the performance of biocomposite, so the lower the water absorption ability, the better the quality of the biocomposite product and the wider its application
Nonlinear interferometry in all spatiotemporal degrees of freedom

The effects of the spatiotemporal degrees of freedom on the practical implementation of an SU(1,1) interferometry is investigated. A recently developed Wigner functional approach is used to obtain the phase sensitivity of such an SU(1,1) interferometry in terms of all the spatiotemporal degrees of freedom. It reveals how experimental scale parameters affect the performance of the interferometer. The analysis provides information that would be useful for quantum metrology applications.
GiBERT: Enhancing BERT with Linguistic Information using a Lightweight Gated Injection Method

Large pre-trained language models such as BERT have been the driving force behind recent improvements across many NLP tasks.However, BERT is only trained to predict missing words -either through masking or next sentence prediction -and has no knowledge of lexical, syntactic or semantic information beyond what it picks up through unsupervised pre-training.We propose a novel method to explicitly inject linguistic information in the form of word embeddings into any layer of a pre-trained BERT.When injecting counter-fitted and dependency-based embeddings, the performance improvements on multiple semantic similarity datasets indicate that such information is beneficial and currently missing from the original model.Our qualitative analysis shows that counter-fitted embedding injection is particularly beneficial, with notable improvements on examples that require synonym resolution.
Semi-supervised Thai Sentence Segmentation Using Local and Distant Word Representations

A sentence is typically treated as the minimal syntactic unit used to extract valuable information from long text.However, in written Thai, there are no explicit sentence markers.Some prior works use machine learning; however, a deep learning approach has never been employed.We propose a deep learning model for sentence segmentation that includes three main contributions.First, we integrate n-gram embedding as a local representation to capture word groups near sentence boundaries.Second, to focus on the keywords of dependent clauses, we combine the model with a distant representation obtained from self-attention modules.Finally, due to the scarcity of labeled data, for which annotation is difficult and time-consuming, we also investigate two techniques that allow us to utilize unlabeled data: Cross-View Training (CVT) as a semi-supervised learning technique, and a pre-trained language model (ELMo) to improve word representation.In the experiments, our model reduced the relative error by 7.4% and 18.5% compared with the baseline models on the Orchid and UGWC datasets, respectively.Ablation studies revealed that the main contributing factor was adopting n-gram features, which were further analyzed using the interpretation technique and indicated that the model utilizes the features in the same way that humans do.
A Layered Approach for Quality Assessment of DIBR-Synthesized Images

Multiview video plus depth (MVD) is a popular video format that supports three-dimensional television (3DTV) and free viewpoint television (FTV). 3DTV and FTV provide depth sensation to the viewer by presenting two views of the same scene but with slightly different angles. In MVD, few views are captured, and each view has the color image and the corresponding depth map which is used in depth image-based rendering (DIBR) to generate views at novel viewpoints. The DIBR can introduce various artifacts in the synthesized view resulting in poor quality. Therefore, evaluating the quality of the synthesized image is crucial to provide an appreciable quality of experience (QoE) to the viewer. In a 3D scene, objects are at a different distance from the camera, characterized by their depth. In this paper, we investigate the effect that objects at a different distance make on the overall QoE. In particular, we find that the quality of the closer objects contributes more to the overall quality as compared to the background objects. Based on this phenomenon, we propose a 3D quality assessment metric to evaluate the quality of the synthesized images. The proposed metric using the depth of the scene divides the image into different layers where each layer represents the objects at a different distance from the camera. The quality of each layer is individually computed, and their scores are pooled together to obtain a single quality score that represents the quality of the synthesized image. The performance of the proposed metric is evaluated on two benchmark DIBR image databases. The results show that the proposed metric is highly accurate and performs better than most existing 2D and 3D quality assessment algorithms.
Inclusión de estudiantes con discapacidad diferencial auditiva a través de la aplicación móvil ListenApp

The objective of the development of the mobile application in education inclusion is the transformation of the teacher's discourse in real time, providing a tool that helps in the teaching-learning process of students with hearing disabilities, favoring inclusion in the academic process, improving the access to opportunities and finally, the quality of life. Therefore, the information transmitted to the non-hearing person, each phrase or word spoken in which voice recognition is made, it is sent in text synchronously using a cellphone, offering support in teaching between the hearing-impaired student and the teacher in the classroom. The research focus is quantitative and descriptive – exploration type. In addition, the application was developed under the Android mobile operating system since it currently provides with a free and open software development environment. Finally, it is significant to implement strategies with ICT since it is a key point in the coexistence of virtual society through the ListenApp application.
Threat and Vulnerability Analysis of IoT and IIoT Concepts

IoT and IIoT are new information technologies. They are very efficient solutions for home, industry and infrastructure. A lot of complex processes can be implemented using this systems. The popularity of the industrial Internet of things is steadily growing along with the development of the Internet of things. Both of these approaches involve the exchange of data over the Internet, use of common hardware platforms and are managed by using specialized software, and this leads to an increase in the number of common vulnerabilities and possible attacks on industrial facilities. The Frost &amp; Sullivan report shows that industrial and IT infrastructures are becoming more transparent. First of all, this is due to the development of the Industrial 4.0 standard and the refusal to isolate industrial facilities, which entails common vulnerabilities, the use of security services based on the SaaS model for industrial facilities, as well as the use of hardware devices that a potential attacker can access quite easily. But very actual problems of IoT and IIoT are information security. Many of this systems are critical and little error can stop the entire system. This is not hard for hackers because that complex system has sensitive components usually. For example simple router can have a lot of vulnerabilities. There an attacker takes a root easily in every system. To solve the problem successfully it is recommended to use complex security actions. These are secure configurations of network devices, using safe devices and protocols, regular audit, using backups, using actual politics of information security.
Generating Tailored Advice in Video Games through Play-Style Identification and Player Modelling

Recent advances in fields such as reinforcement learning have enabled the development of systems that are able to achieve super-human performance on a number of domains, specifically in complex games such as Go and StarCraft. Based on these successes, it is reasonable to ask if these learned behaviours could be utilised to improve the performance of humans on the same tasks. However, the types of models used in these systems are typically not easily understandable, and can not be directly used to improve the performance of a human. My research looks to address these difficulties by developing a system that can provide advice tailored to a player's style in a video game setting. This system would be particularly useful for improving tutorial systems that quickly become redundant lacking any personalization. I have already developed an unsupervised approach to identifying different play-styles present in a video game. I look to use this knowledge to train agents which can demonstrate optimal behaviour for each style. It is hoped that this information can be utilised to generate useful advice based on the differences between the current performance of a player and the corresponding expert agent in their style.
An integrated Fuzzy DEMATEL and Fuzzy Cognitive Maps approach for the assessing of the Industry 4.0 Model

Today, organizations are experiencing problems due to advancements in the environment, community, economy, and technology. In the future, physical and virtual systems, including the whole chain from product design to the distribution to customers and feedback will be required to overcome problems for organizations. Thus, companies need to comprehend and apply Industry 4.0. The most frequently encountered situation in the literature is the absence of research based on the practical experience of Industry 4.0 processes. In this study, the analysis of 9 concepts (Strategy, Leadership, Customers, Products, Operations, Culture, People, Governance, and Technology) is performed by utilizing the fuzzy DEMATEL and fuzzy AHP methods. As a result of both methods, Technology, Operations and Strategy concepts are obtained as the three most important concepts in the same rank in both methods. There is no significant change in the rank of the other concepts. Although close rankings are obtained from both methods, considering the interaction between the concepts and avoid shortcomings of consensus in the method, the ranking obtained by the fuzzy DEMATEL method is preferred to use as input in Fuzzy Cognitive Maps (FCMs). Then a relation map is formed based on their interaction and three alternative scenarios are created for FCMs from defuzzified total relation matrix data. As a result of scenarios, it is more useful for organizations to apply the Industry 4.0 processes to transfer their resources to their activities concerning the Strategies, Technology, and Operations concepts. The aim is to predict the maturity levels of Industry 4.0 of organizations for the future by employing the FCMs methodology. Thanks to the introduced model based on the integration of fuzzy DEMATEL and FCMs, an insight is provided into which concepts should be prioritized about Industry 4.0.
Seasonal variations of textural and heavy minerals of the Karamana river sediments and beach sediments of Trivandrum, Southern Kerala

Kerala, south western state of India is known for its abundant heavy mineral deposits along the coast.Beach placer deposits in Kerala is known for ilmenite, rutile, zircon, monazite, sillimanite and garnet.The grain size study of the sediments from Karamana river, flowing in the southern part of Kerala.Sediments from different parts of the river basin is studied, which enabled to understand the grain size variations with course of the river from the Peppara dam to Thiruvallam, very near to coast.Mineralogy of the river vary from one place to another depending different factors like host rocks in the province, climate conditions of the area, agents of transport and hydraulic condition during deposition, also can predict the actual source area associated with heavy mineral.Textural and heavy mineral studies of the placers of beach and Karamana river basins indicating the river is one of the important contributors of sediments to the western coast of the southern part of Kerala.Samples from various parts of the river indicate that the sediment is medium sand, moderately sorted, fine skewed and platykurtic.The characteristics of grain size distribution are related to the source rocks, process of weathering, and different sorting processes during transportation and deposition.The total heavy mineral percentage shows it is prominently enrichment in fine sand and very fine sands.
LAMAD: A Linguistic Attentional Model for Arabic Text Diacritization

In Arabic Language, diacritics are used to specify meanings as well as pronunciations.However, diacritics are often omitted from written texts, which increases the number of possible meanings and pronunciations.This leads to an ambiguous text and makes the computational process on undiacritized text more difficult.In this paper, we propose a Linguistic Attentional Model for Arabic text Diacritization (LAMAD).In LAMAD, a new linguistic feature representation is presented, which utilizes both word and character contextual features.Then, a linguistic attention mechanism is proposed to capture the important linguistic features.In addition, we explore the impact of the linguistic features extracted from the text on Arabic text diacritization (ATD) by introducing them to the linguistic attention mechanism.The extensive experimental results on three datasets with different sizes illustrate that LAMAD outperforms the existing state-of-theart models.
Crime prevention through digitalization

Common crime prevention measures have not yielded the expected results. It is important to examine the possibilities of using the latest achievements in crime prevention. One of the available options is digitalization. Starting from the definition of digitalization as the use of digital technologies to change the business model and provide new opportunities for generating income and value, the author emphasizes that digitalization can be used as a measure to prevent crime. In this regard, some solutions for preventing crime through digitalization are embodied in the use of digital currencies, digital identities and signatures, smart devices, and mobile applications. The author elaborates on each of these solutions, focusing on specific crime prevention measures and examples from different countries worldwide. It may help crime prevention experts perceive digitalization as a measure for reducing the volume of crime. If the benefits of digitalization are put into good use, we can expect that the volume of property-related crimes, violence-related crimes and traffic delinquency will drop in the forthcoming period. On the other hand, the implementation of these measures may give rise to the commission of Internet-related crimes, thus leading to the increase in computer crime.
Blockchain-native mechanism supporting the circulation of complex physical assets

An increasing number of physical assets, such as academic certificates and credentials, are being represented in the form of digital assets. These assets always involve multiple statuses, rules, and complex associations. It is challenging to determine the transaction execution order using the existing blockchains because they lack the ability to record and execute associations between assets, which results in problems of inconsistency of assets, such as the loss of asset status changes and status conflict between transactions. This study proposes a blockchain-native mechanism supporting complex physical assets, including a novel digital asset model and transaction validity guarantee protocol. Under the premise of ensuring safety and performance, the proposed mechanism can fully describe different associations and their influence on the transaction execution order. This study builds a global deterministic transaction execution sequence that can be executed in parallel to guarantee the consistency of complex digital assets based on partial orders. Theoretical analysis of the proposed mechanism demonstrates that it can guarantee digital asset consistency and can protect against attacks such as reentrant and transaction sequence attacks. Furthermore, experiments show that the proposed mechanism can achieve a transaction execution speed of up to 10,000 transactions per second, and this speed can be further increased by increasing the number of CPU cores.
Wavelet-Based Denoising Of Images

Wavelet-analysis has become a powerful tool for denoising images. It represents a new way to achieve better noise reduction and increased contrast.Here, experimentally demonstrate abilities of discrete wavelet transform with Daubechies basis functions for improving the quality of noisy images.inthis research two methods has been compaired for modify the coefficients using soft and hard threshold to improv the visual fineness of noisy image depend on Root-Mean-Square error (RMS).The low RMS value and better noise reduction find in soft threshold method which is based on Daubechies wavelet (db8) for first example image RMS=0.101 and second example RMS=0.109.
Humanitarian Communication Through the Lens of Feminist Ethics of Care

Ethics are the driving force of the humanitarian field, a domain that has been governed by general and universal ethical principles. Researchers have largely focused on studying the organizational commitment to these principles, paying less attention to the role-specific ethics of this field. Moreover, researchers who consider the humanitarian field from a media studies lens have often focused on media representation, while questions about communication as practice are sidelined. In this paper, I approach humanitarian ethics with a particular focus on role morality and communication practices. With a particular focus on the role of a humanitarian communications specialist, I argue, in this paper, that the feminist ethics of care is a useful ethical framework that can guide communication specialists to better practices when they are in the field of operation. I also answer the following research questions: What are the main ethical principles that humanitarian communication specialists are expected to observe as humanitarians? Why are these principles insufficient? How might feminist ethics of care fill the gap left by current humanitarian principles and what would be the added value of this framework for practicing humanitarian communication? To answer, I ground my approach in an experiential understanding built from my personal experience as a humanitarian communications specialist. Second, I offer a literature review to highlight the common ground between humanitarian ethics and the feminist ethics of care and the added value of the feminist ethics of care why applied by humanitarian communication specialists. Third, I provide some examples of communications practices that may follow the feminist ethics of care model.&#x0D;
ANALISIS RISIKO KEKERINGAN DI KABUPATEN BANTUL PROVINSI D.I. YOGYAKARTA

Bantul Regency is a district in Yogyakarta Province which has geographic, geological, hydrological, and demographic characteristics that are likely to cause drought. Drought event in Bantul Regency may have significant impacts on various aspects in line with the characteristics of drought impacts which are complex and cross-sectoral. This study addresses to analyze the level of risk of drought with observation units in 75 villages in the Bantul Regency. The risk analysis was carried out by comparing the time period of the 10 years, i.e. 2008 and 2018 to observe the shift of risk areas of drought in Bantul Regency. The research was conducted using quantitative research methods with quantitative descriptive and mapping analysis. The analysis steps are drought hazard analysis, vulnerability analysis, and drought risk analysis. The analysis shows that during the last 10 years, Kabupaten Bantul has been experiencing an increasing number of villages classified as high risk of drought, both in urban and rural areas. In 2008 there were 15 villages (20%) and increased to 21 villages (28%) in 2018 that were classified as very very high level. Meanwhile, in 2008 there were 30 villages (40%) in 2008 and increased to 32 villages (42.7%) in 2018 that were classified as very high level. It caused by the increasing probability of drought as well as vulnerability. The analysis results can be used as input for stakeholders to take mitigation and anticipation actions to reduce the impact of drought based on the spatial characteristics of the risk areas.
Prediction of Hospital Readmission from Longitudinal Mobile Data Streams

Hospital readmissions impose an extreme burden on both health systems and patients. Timely management of the postoperative complications that result in readmissions is necessary to mitigate the effects of these events. However, accurately predicting readmissions is very challenging, and current approaches demonstrated a limited ability to forecast which patients are likely to be readmitted. Our research addresses the challenge of daily readmission risk prediction after the hospital discharge via leveraging the abilities of mobile data streams collected from patients devices in a probabilistic deep learning framework. Through extensive experiments on a real-world dataset that includes smartphone and Fitbit device data from 49 patients collected for 60 days after discharge, we demonstrate our framework's ability to closely simulate the readmission risk trajectories for cancer patients.
Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning

Automatic fact-checking is crucial for recognizing misinformation spreading on the internet.Most existing fact-checkers break down the process into several subtasks, one of which determines candidate evidence sentences that can potentially support or refute the claim to be verified; typically, evidence sentences with gold-standard labels are needed for this.In a more realistic setting, however, such sentence-level annotations are not available.In this paper, we tackle the natural language inference (NLI) subtask-given a document and a (sentence) claim, determine whether the document supports or refutes the claimonly using document-level annotations.Using fine-tuned BERT and multiple instance learning, we achieve 81.9% accuracy, significantly outperforming the existing results on the WikiFactCheck-English dataset.