Reviewerly/exHarmony huggingface dataset:
Improving Cause-of-Death Classification from Verbal Autopsy Reports

In many lower-and-middle income countries including South Africa, data access in health facilities is restricted due to patient privacy and confidentiality policies. Further, since clinical data is unique to individual institutions and laboratories, there are insufficient data annotation standards and conventions. As a result of the scarcity of textual data, natural language processing (NLP) techniques have fared poorly in the health sector. A cause of death (COD) is often determined by a verbal autopsy (VA) report in places without reliable death registration systems. A non-clinician field worker does a verbal autopsy (VA) report using a set of standardized questions as a guide to uncover symptoms of a COD. This analysis focuses on the textual part of the VA report as a case study to address the challenge of adapting NLP techniques in the health domain. We present a system that relies on two transfer learning paradigms of monolingual learning and multi-source domain adaptation to improve VA narratives for the target task of the COD classification. We use the Bidirectional Encoder Representations from Transformers (BERT) and Embeddings from Language Models (ELMo) models pre-trained on the general English and health domains to extract features from the VA narratives. Our findings suggest that this transfer learning system improves the COD classification tasks and that the narrative text contains valuable information for figuring out a COD. Our results further show that combining binary VA features and narrative text features learned via this framework boosts the classification task of COD.
Analysis of a New Practical SPN-Based Scheme in the Luby-Rackoff Model

Many modern block cipher schemes are constructed based on substitution-permutation networks (SPNs). Their provable security is often evaluated by idealizing S-boxes as underlying primitives (public or secret). This limits the security bound to the domain-size of the S-boxes, and it will not make much sense when this size is small. In this paper, we propose an SPN-based scheme, namely FLC, to achieve provable security in the Luby-Rackoff model in which the round functions are underlying primitives and secret. Concretely, the 3-round FLC scheme is pseudorandom, while the 5-round FLC scheme is super pseudorandom. Both of these results are capped at birthday-bound security up to $$O(2^{\frac{w}{2}})$$ , when w is the size of the round functions. These are the best results for SPN-based schemes in the Luby-Rackoff model.
Modeling the Fork Probability of Blockchains: Did EIP-1559 Improve Ethereum?

There is a natural tendency for many public blockchain systems to undergo forks. When they are resolved, they result in a chain reorganization (reorg) where the transactions from the side fork are discarded. This causes delays since clients are required to wait for more confirmations before processing transactions. In order to improve security, speed, and efficiency of any blockchain network, it is desirable to reduce the probability of forking as much as possible. For this purpose, it is necessary to have a thorough understanding of the behavior of blockchain systems in order to optimize parameters in accordance with this goal [1], [2]. This study proposes a new fork model that incorporates parameters not previously considered, such as network delay and validation degree. We have conducted several experiments to verify the validity of our proposed method, using the BlockSim simulator on the Ethereum network and EIP-1559. According to the results of this study, decreasing the validation degree and increasing the miners' marginal cost (as in EIP-1559) reduces the likelihood of a fork occurring by around 10 percent. Additionally, the experimental results validate the precision of our method in predicting the probability of forking.
Recent Trends and Challenges in Analysis of UAV Aerial Images for Post-Disaster Scene Understanding

Last few years have seen a tremendous increase in natural disasters such as earthquakes, floods, hurricanes, etc., primarily due to climate change. Apart from the steps taken to mitigate climate change, there is also a need for rapid and efficient planning and management of post-disaster relief and rescue efforts. Unmanned Aerial Vehicles (UAV) based system offers mobility, rapid deployment and customized flight path for surveying the disaster affected area. Therefore, images acquired from Unmanned Aerial Vehicles (UAV) may be utilized for post-disaster damage assessment. This work summarizes current methods for assessing damage after an earthquake and flood by analyzing optical UAV aerial images. Moreover, this work highlights the challenges encountered and discusses the possible way forward for a robust postdisaster scene understanding from UAV aerial images.
Research progress on chemical composition of apatite: Application in petrogenesis, ore genesis and mineral exploration

磷灰石广泛分布于各类岩石和矿床中, 根据其F、OH以及Cl阴离子种类可分为氟磷灰石、氯磷灰石和羟基磷灰石。磷灰石对其自身结构的变形以及化学取代有良好的耐受性, 一系列元素可以替代磷灰石中的Ca、P、F、Cl和OH, 因此其化学组成复杂。磷灰石中Ca位存在两种不同的阳离子位置, 影响不同元素在这两种位置取代的因素包括离子半径、价态以及磷灰石的种类等。岩浆过程中, 稀土(REE)、Y、Sr是磷灰石中的相容元素, 而F、Cl、OH的相容性随物理化学条件不同而变化。相较于元素在磷灰石与熔体相间的分配研究, 目前对于元素在磷灰石与流体相间的分配研究较少。磷灰石化学组成可以判别母岩类型: 如F、Cl、As、V含量可以区分Ⅰ型与S型花岗岩; REE、Sr、Y、Mn、As、Th的含量、轻稀土(LREE)的富集程度和球粒陨石标准化稀土元素配分模式可以用于判别碳酸岩、二辉橄榄岩、镁铁质岩石等岩石类型。利用磷灰石的F、Cl和CO<sub>3</sub>等挥发分的含量, 并结合Sr-Nd同位素组成, 可以识别地幔源区及其流体交代作用。磷灰石Sr/Th与La/Sm的变化可用于指示岩浆来自脱水板块或熔融沉积物。磷灰石的主微量元素(Mn、Eu)含量或元素比值(F/Cl)能够反映岩浆的分异程度, 而且Ce/Pb和Th/U比值可以反映岩浆形成过程中流体参与程度。磷灰石中的变价元素如Eu、Ce、Ga和S含量及<i>δ</i>Eu和<i>δ</i>Ce可用于推断岩浆的氧化还原状态。由于磷灰石对热液交代作用非常敏感, 所以磷灰石的结构和化学组成已广泛用于成矿过程研究。在与花岗岩有关的关键金属矿床中, 磷灰石的主、微量元素含量和Sr-Nd同位素比值常常用于区分不同花岗质岩浆的源区特征, <i>δ</i>Ce、<i>δ</i>Eu也可以用于指示花岗质侵入体的氧化还原状态; 斑岩有关的多金属矿床F、Cl、S、Mn含量以及F/Cl比值能够指示成矿岩浆-热液的来源及演化, <i>δ</i>Ce、<i>δ</i>Eu可以用于指示岩浆热液的氧逸度; 铁氧化物-铜-金和铁氧化物-磷灰石矿床中磷灰石结构、共生稀土矿物结合Na、F、Cl以及REE含量能够反映与成矿有关的岩浆-流体作用以及流体性质。磷灰石中Mn、Fe、REE、Y、F、Cl、SO<sub>3</sub>的含量或比值的差异可用于判别矿化与未矿化的岩石或者蚀变类型, 并用于区分不同矿床类型等, 对矿产勘查具有一定指示意义。磷灰石化学组成结合机器学习方法, 可以更好地识别磷灰石的来源。综上所述, 磷灰石作为一种新兴的指示矿物, 除了在成岩和成矿过程示踪方面体现出独特优势, 在矿产勘查应用方面也具有重要的应用前景。
Ensemble Based Anomaly Detection for Legged Robots to Explore Unknown Environments

Exploring unknown environments, such as caves or planetary surfaces, requires a quick understanding of the surroundings. Beforehand, only aerial footage from satellites or images from previous missions might be available. The proposed ensemble based anomaly detection framework utilizes previously gained knowledge and incorporates it with insights gained during the mission. The modular system consists of different networks which are combined to determine anomalies in the current surroundings. By utilizing data from other missions, simulations or aerial photos, a precise anomaly detection can be achieved at the start of a mission. The system can further be improved by training new networks during the mission, which can be incorporated into the ensemble at runtime. This allows for synchronous execution of mission and training of models on a base station. The proposed system is tested and evaluated on an ANYmal C walking robot in different scenarios, however the approach is applicable for different kinds of mobile robots. The results show a clear improvement of ensembles compared to individual networks, while keeping a small memory footprint and low inference time on the mobile system.
An Improved Dynamic Window Approach for Mobile Robot Dynamic Path Planning

Dynamic window approach (DWA) is a local obstacle avoidance algorithm based on velocity sampling of a mobile robot. When the mobile robot based on the DW A algorithm faces moving obstacles,the obstacle can easily collide with the robot if the speed of the obstacle is too fast. Based on the traditional dynamic window approach, the weighting value of its evaluation function is modified to optimize the obstacle avoidance performance of the robot. The simulation results show that the proposed method can avoid dynamic obstacles safely.
COTA 2.0: an Automatic Corrector of Tunisian Arabic Social Media Texts

In written text, orthographic noise is a common concern for NLP, especially when operating social network comments and raw documents. This is mainly due to its orthographic conventions and morphological ambiguity. We propose to automatically normalize the social media dialect corpora by following CODA-TUN, the Conventional Orthography for Tunisian Arabic (TA). The existing system developed for TA &lt;&lt;COTA Orthography 1.0&gt;&gt; is not able to handle all forms of TA. Therefore, we propose to extend its rules and lexicons to address the peculiarities of social media dialect. In certain words, the COTA Orthography 1.0 system provides the user with several correction possibilities. Therefore, in the new version, we incorporated a trigram language model to automatically select the right correction. Our results show that the system can reduce transcription errors by 95.72%.
Short-term Photovoltaic Power Forecasting Using the Combined KSVD and LSTM Method

A sparse representation model strategy based on feature extraction techniques is proposed for predicting solar power. To begin, all of these factors that affect photovoltaic power output are considered input data for the model, and dictionary learning techniques such as the K-mean singular value decomposition (KSVD) algorithm are applied to extract the corresponding sparse coding from all of the input data. Second, the sparse coding vectors are utilized as the model's input, and the Long Short-Term Memory (LSTM) algorithm is employed to construct the global prediction model. The proposed approach is used to a solar power forecasting to demonstrate the usefulness of the combined KSVD and LSTM method. Experiments reveal that several combination sparse representation approaches generate superior prediction results than SVM and LSTM under the same settings, with the combined KSVD and LSTM method providing the best prediction outcomes and model accuracy.
Toxic leadership and knowledge hiding behaviour: examining a serial mediation mechanism

Knowledge management is the need of today's complex business environment. Effective knowledge sharing develops cooperation among employees which enhances firm performance and creativity. Leaders are the main players which transform their followers and develop sharing habits among them. Knowledge is required by co-workers to accomplish a particular task. Like positive, dark leadership phenomenon also exists in the organisations and affects the knowledge sharing process by forecasting knowledge hiding behaviour. Therefore, the purpose of this research is to find out the effect of toxic leadership on knowledge hiding behaviour. Moreover, this study investigates the underlying mechanism that how leader toxicity affects employee organisational identification and self-esteem which urges them to involve in knowledge hiding behaviour. Based on the leader-member exchange theory, current research suggests that knowledge hiding behaviour is the outcome of leader toxicity. A survey of 158 employees from the education sector has empirically validated the hypotheses. SEM was used to analyse structural paths using Smart PLS 3.0. The result shows that toxic leadership has a direct effect on knowledge hiding behaviour whereas dual mediation unveils the underlying mechanism. Current research has enriched the literature on toxic leadership and provided important managerial implications to practitioners.
Intelligent path planning of robotic arm considering dynamics properties

In order to improve the intelligence of robotic arm, this paper investigates an intelligent path planning method based on Deep Deterministic Policy Gradient. The dynamics model of a 2 degree-of-freedom robotic arm is established first. On this basis, the joint driven torque is chosen as the action space, and the set of joint angle, joint angular velocity and the vector from the end-effector position to the target position is chosen as the state space. Considering the continuous characteristics of the action space and the state space, Deep Deterministic Policy Gradient method is adopted to train the robotic arm agent. The training results prove that this method can improve the intelligence of the robotic arm.
Profile - Decentralized Application for Education

Decentralized applications have evolved in the recent past. Many sectors have shown focus towards the Decentralized applications. This paper extends the application to the education sector. The work is associated with the creation of a Decentralized Application on the Ethereum Blockchain, which includes the decentralized way of storing data such as the student details. Face detection feature has been incorporated to tackle the problem of proxy attendance and other malpractices. The Decentralized Application interacts with the smart contract written on the Ethereum network. The smart contract created involves in storing the details of the student’s education timeline, such as his/ her marks, institution information, etc. Cloud technology has been used to store large files which cannot be stored in the smart contracts. Many natural calamities and other causes have led to the destruction of the stored details in the traditional databases, these problems have been overcome with this use of Cloud technology, where it provides features like replicability to avoid loses, reliability to avoid failures, scalability to avoid shortage of resources, etc. Authentication has been addressed with the help of face recognition tool which detects the identity of a person. This attempt of solving the above problems have been stated in this paper. Addition of a security layer with face recognition, storage of media files in the AWS S3 cloud, and interaction with the Decentralized Application, makes this paper unique from the other papers.
A New Dense Hybrid Stereo Visual Odometry Approach

Visual odometry is an important part of the perception module of autonomous robots. Recent advances in deep learning approaches have given rise to hybrid visual odometry approaches that combine both deep networks and traditional pose estimation methods. One limitation of deep learning approaches is the availability of ground truth data needed to train the neural networks. For example, it is extremely difficult, if not impossible, to obtain a ground truth dense depth map of the environment to be used for stereo visual odometry. Even if unsupervised training of networks has been investigated, supervised training remains more reliable and robust. In this paper, we propose a new hybrid dense stereo visual odometry approach in which a dense depth map is obtained with a network that is supervised using ground truth poses that can be more easily obtained than ground truth depths maps. The depth map obtained from the neural network is used to warp the current image into the reference frame and the optimal pose is obtained by minimizing a cost function that encodes the similarity between the warped image and the reference image. The experimental results show that the proposed approach, not only improves state-of-the-art depth maps estimation networks on some of the standard benchmark datasets, but also outperforms the state-of-the-art visual odometry methods.
Attention Mechanism Based Full-face Gaze Estimation for Human-computer Interaction

Due to the expanding application field of gaze estimation, gaze estimation has become an important research topic in human-computer interaction and computer vision. Most of the current mainstream methods for gaze estimation during HCI are based on a sing-camera and single-screen system. In order to address the issues of gaze estimation for multi-camera multi-screen systems, this paper explores the use of a deep learning model, the residual neural network (ResNet50) plus an attention mechanism, to estimate the operator's point of gaze (PoG) on the screen in the flight simulator. First, the front face image is obtained from the three images by preprocessing, and then ResNet is used to regress the PoG. The results show that the inclusion of the attention mechanism enhances the network effect, in which the smallest prediction error is found for on-screen PoG using ResNet50+ECANet compared to SENet and CBAM.
Controllable Abstractive Summarization Using Multilingual Pretrained Language Model

By leveraging a multilingual language model, we show that CTRLSum [1], an abstractive summarization approach that can be controlled by keywords, improves baseline summarization system in four languages: English, Indonesian, Spanish, and French by 1.57 in terms of average ROUGE-1, with the Indonesian model achieving state-of-the-art results. We further provide novel analysis about the importance of keywords fed to CTRLSum which (1) shows hypothetical upper-bound results that outperform the state-of-the-art in all four languages by a large margin and (2) provides natural direction for future work to improve CTRLSum by improving the keyword prediction model.
Target-Side Language Model for Reference-Free Machine Translation Evaluation

With the rapid progress of deep learning in multilingual language processing, there has been a growing interest in reference-free machine translation evaluation, where source texts are directly compared with system translations. In this paper, we design a reference-free metric that is based only on a target-side language model for segment-level and system-level machine translation evaluations respectively, and it is found out that promising results could be achieved when only the target-side language model is used in such evaluations. From the experimental results on all the 18 language pairs of the WMT19 news translation shared task, it is interesting to see that the designed metrics with the multilingual model XLM-R get very promising results (best segment-level mean score on the from-English language pairs, and best system-level mean scores on the from-English and none-English language pairs) when the current SOTA metrics that we know are chosen for comparison.
Designing Gender Ambiguous Voice Agents

Gender ambiguous voice agents have been considered as a way to reduce gender bias and harmful stereotyping on Voice User Interfaces (VUI). This study explores a method for designing gender ambiguous voice agents for VUIs and investigates its effects on two usability factors: Ease of Understanding and Pleasantness. To conduct this investigation, pitch shift is applied to morph nine voice agents towards the opposite gender, which six are natural voice agents from different positions in the gender spectrum and three are artificial. N=37 participants completed an online experiment to evaluate their perception of gender and usability factors in eighteen scenarios. Our findings indicate that pitch shift is effective in morphing the voice agents to sound as gender ambiguous, and that these voice agents have an effect on perception of the usability factors. Also, results showed that the participant gender impact on how the usability factors are perceived, mainly for Pleasantness.
Stacked Feature Selection and C5.0 Classification Model with Tsallis Entropy for Medical Dataset

Feature selection is considered to be one of the important tasks in data mining.It identifies the subset of features from the dataset that are best related to the response variable.Here, the five different medical dataset such as Pima diabetes, Liver, Hepatitis, Chronic kidney disease and Breast cancer are considered.The optimal subset of features for the various dataset is obtained by the intersection of top 'n' features returned by feature selection algorithms such as CMIM, JMI, mRMR, CFS, Boruta and SVM-RFE.Then, the method of C5.0 algorithm with Tsallis entropy and Association function is tested with these top n features selected.Accuracy value obtained for the proposed method is of 61% for Pima diabetes dataset, 85% for Liver dataset, 95% for Hepatitis dataset, 99.5% for Chronic Kidney disease dataset and 97% for Breast cancer dataset.The performance measures show that the proposed method works better than that of SVM, Naïve Bayes, KNN and Random forest.
Aspect Based Multilabel Text Classification for Identifying Dangerous Speech Twitter Text

As part of hate speech, dangerous speech is any expression that can increase the risk of committing violence against other people. So far, hate speech research only explains whether some sentences is categorized as hate speech. It does not explain aspects of the sentences that make them called dangerous speech. Aspects of dangerous speech are social context, historical context, dehumanization, the accusation in the mirror, women and children attack, loyalty to the group, and group threat. This study uses the multi-label text classification method to determine dangerous speeches on Twitter texts based on seven aspects. Then, we assign a weighted score from those aspects to differentiate dangerous and hate speech. Based on the test results show the best performance is the Naive Bayes method with label-based subset accuracy (±36 %), instance-based (average) accuracy (±86 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) and classification accuracy (±77 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ). However, even though Naive Bayes has the best performance in terms of instance based (average) accuracy, the average difference between all methods with Naive Bayes is only ± 0.014, this indicates that other methods also produce quite good performance.
Wubble World

We introduce Wubble World, a virtual environment for learning situated language. In Wubble World children create avatars, called wubbles, which can interact with other other children's avatars through free-form spontaneous play or structured language games. Wubbles can also learn language from direct interaction with children, since the system uses principles from developmental psychology to restrict the complexity of this learning task: a shared attention model that includes deictic pointing, and a concept acquisition system that allows for rapid learning of new words from a limited number of exposures.Since we have complete knowledge of the state and structure of the virtual environment, we are able to track correspondences between utterances and the scene in which they are uttered. This sentence/scene corpus will be a valuable resource as we attempt to tackle more sophisticated language learning tasks, such as the acquisition of syntax and verb semantics from world dynamics.
Knowledge Management Orientation: A Synthesized Window for Future Research

The knowledge management behavior of an organization is discussed under the concept of knowledge Management Orientation (KMO). As one of the most important strategic resources of an organization, there is a growing interest among scholars and practitioners about KMO. Although the interest in KMO is widely spread all over the world, little attention has been given to integrate the findings of former surveys and assess the prevailing status of the research in this field. This study aims to conduct a systematic review of KMO research, to identify the various kinds of definitions of KMO, measurements of KMO, areas in which KMO studies are conducted, research gaps addressed in KMO research, limitations confronted by the former studies and the future focuses of the KMO research. By following multiple filtering processes, 35 scholarly works on KMO published during the period from 2015 to 2020 were identified. The study reveals that there is a high potential for future studies in KMO in association with numerous fields of study. Future scholars and practitioners who wish to conduct their studies on KMO can use the findings of this study as a motive to initiate their research work.