Reviewerly/exHarmony huggingface dataset:
BASIC MIND MAPPING ON MASTERING LISTENING SKILLS FOR COMMUNICATION ON ASEAN ECONOMIC COMMUNITY (AEC) AND 4.0 ERA (PEMETAAN KONSEP DASAR PENGUASAAN KEAHLIAN MENYIMAK UNTUK KOMUNIKASI PADA MASYARAKAT EKONOMI ASEAN DAN ERA 4.0)

Since 2015, Asean Economic Community has +been implemented in Indonesia. One of the great changes is using English for communication. English has still been a foreign language for Indonesian people. Consequently, English is learned and started in kindergarten from university to make Indonesian people familiar with English. Listening becomes a difficult subject for the students because they could not answer the questions and they got bad grades for examination. It was to find out what the need analysis important for the teacher and students to study English. It used a descriptive qualitative method with the instruments such as questionnaires, document checklists, and interviews. The population of the research was 72 students and the sampling was total sampling. After analyzing the data, from the questionnaire filled up by students, it is found that almost all students agree about the items of teaching materials proposed by Hutchinson and Water. The results are 92.75% for 2016 A, 98.87% for 2016 B, and 92.46 % for 2016 C. The total is 94.69% of students agree with the teaching material. From the document, checklist shows that the teaching materials used on the Listening subject have not fulfilled provide a stimulus to learning, help to organize the teaching-learning process, use nature of language and learning, provide nature of learning task, broadening the teacher’s knowledge, and provide correct and appropriate language use. Interview information shows that teaching materials for Listening subjects are a combination of many sources without being analyzed and investigated by lecturers. Finally, the most important aspect is an appropriate teaching material must be made to fulfill the learning outcomes.
Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model

Inspired by biological evolution, we explain the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derive that both of them have consistent mathematical representation. Analogous to the dynamic local population in EA, we improve the existing transformer structure and propose a more efficient EAT model, and design task-related heads to deal with different tasks more flexibly. Moreover, we introduce the spatial-filling curve into the current vision transformer to sequence image data into a uniform sequential format. Thus we can design a unified EAT framework to address multi-modal tasks, separating the network architecture from the data format adaptation. Our approach achieves state-of-the-art results on the ImageNet classification task compared with recent vision transformer works while having smaller parameters and greater throughput. We further conduct multi-modal tasks to demonstrate the superiority of the unified EAT, e.g., Text-Based Image Retrieval, and our approach improves the rank-1 by +3.7 points over the baseline on the CSS dataset.
Research on Improvement and Parallelization of Canopy-KMeans Clustering Algorithm

For the traditional K-Means algorithm in the face of large-scale data, due to the randomness of the initial cluster centers selected lead to local optima, slow speed clustering and other issues, this paper presents an improved algorithm Canopy-KMeans. First use the “minimum maximum principle” to optimize the selection of the center point of the Canopy algorithm, obtain a more accurate set of cluster center points and the number of clusters k; then use the “triangular inequality principle” to optimize the K-Means algorithm, reduce unnecessary distance calculation in the iterative process, finally combined with the Hadoop platform Mapreduce calculation framework for the parallel design and implementation of the algorithm. By experimental tests show, K-Means optimized analysis and comparison before optimization algorithm, fast convergence, you can get better clustering quality, suitable for large-scale data clustering.
Disaggregation via Gaussian regression for robust analysis of heterogeneous data

Quantitative analysis of large-scale data is often complicated by the presence of diverse subgroups, which reduce the accuracy of inferences they make on held-out data. To address the challenge of heterogeneous data analysis, we propose a method that discovers latent confounders by simultaneously partitioning the data into overlapping clusters (disaggregation) and modeling the behavior within them (regression). When applied to real-world data, our method discovers meaningful clusters and their characteristic behaviors, thus giving insight into group differences and their impact on the outcome of interest. By accounting for latent confounders, our framework facilitates exploratory analysis of noisy, heterogeneous data and can be used to learn predictive models that better generalize to new data.
Teaching-learning Multi-tasks Programming Using Python

Algorithmic thinking is part of human nature. Nonetheless, writing algorithms in programming languages can be not trivial, mainly for syntax issues. Multi-tasks thinking seems natural for human teams. Nonetheless, implementing multi-tasks solutions can be a complex task for people and students who learn programming languages mainly for syntax issues. Python is a real solution to simplify their programming learning. This article presents and highlights the learning experience of Chilean students to implement multi-task solutions, that is, concurrent and parallel programs, in Python. Those benefits invite us to promote programming using Python in other courses, majors, and institutions. Programming for everybody is nearest now.
Semi-Supervised Exaggeration Detection of Health Science Press Releases

Public trust in science depends on honest and factual communication of scientific papers. However, recent studies have demonstrated a tendency of news media to misrepresent scientific papers by exaggerating their findings. Given this, we present a formalization of and study into the problem of exaggeration detection in science communication. While there are an abundance of scientific papers and popular media articles written about them, very rarely do the articles include a direct link to the original paper, making data collection challenging, and necessitating the need for few-shot learning. We address this by curating a set of labeled press release/abstract pairs from existing expert annotated studies on exaggeration in press releases of scientific papers suitable for benchmarking the performance of machine learning models on the task. Using limited data from this and previous studies on exaggeration detection in science, we introduce MT-PET, a multi-task version of Pattern Exploiting Training (PET), which leverages knowledge from complementary cloze-style QA tasks to improve few-shot learning. We demonstrate that MT-PET outperforms PET and supervised learning both when data is limited, as well as when there is an abundance of data for the main task.
BREAST CANCER PREDICTION USING DEEP LEARNING TECHNIQUE

Breast cancer is the second most prevalent kind of cancer globally, behind lung cancer.Breast cancer is the most common disease affecting women worldwide.There is always the potential for progress and development in the field of medical imaging.Cancer mortality is expected to reduce if the disease is detected and treated early and adequately.Doctors may be able to improve their diagnostic accuracy with the assistance of machine education.Deep learning, also known as neural networking, is one technique for distinguishing between benign and cancerous breasts.As a result, CNN may be used.The Mammograms-MINI-DDSM data-collection used in this study consisted of 5358 mammograms, with about 2180 benign and 2998 malignant breast images being obtained.Deep learning in the diagnosis of mammogram cancer has been demonstrated in mammograms in promising experimental findings that encourage the use of in-depth learning techniques based on current characteristics and classification of a range of applications, particularly in the detection of breast cancer demonstrated in mammograms.Though work has to be done, improvements in CNN design and the use of pretrained networks allow for more development, which should result in improved accuracy over the next several years.To extract and categorize features efficiently, proper segmentation is required.
Surface Shape Reconstruction of Transparent Objects using Structured Light

Reconstruction of a transparent phase object based on the varying periodicity of an incident sinusoidal intensity pattern is proposed and verified by using a cm -thick glass cylinder.
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection

The ability to detect Out-of-Domain (OOD) inputs has been a critical requirement in many real-world NLP applications. For example, intent classification in dialogue systems. The reason is that the inclusion of unsupported OOD inputs may lead to catastrophic failure of systems. However, it remains an empirical question whether current methods can tackle such problems reliably in a realistic scenario where zero OOD training data is available. In this study, we propose ProtoInfoMax, a new architecture that extends Prototypical Networks to simultaneously process in-domain and OOD sentences via Mutual Information Maximization (InfoMax) objective. Experimental results show that our proposed method can substantially improve performance up to 20% for OOD detection in low resource settings of text classification. We also show that ProtoInfoMax is less prone to typical overconfidence errors of Neural Networks, leading to more reliable prediction results.
Dimensional Emotion Detection from Categorical Emotion

We present a model to predict fine-grained emotions along the continuous dimensions of valence, arousal, and dominance (VAD) with a corpus with categorical emotion annotations. Our model is trained by minimizing the EMD (Earth Mover’s Distance) loss between the predicted VAD score distribution and the categorical emotion distributions sorted along VAD, and it can simultaneously classify the emotion categories and predict the VAD scores for a given sentence. We use pre-trained RoBERTa-Large and fine-tune on three different corpora with categorical labels and evaluate on EmoBank corpus with VAD scores. We show that our approach reaches comparable performance to that of the state-of-the-art classifiers in categorical emotion classification and shows significant positive correlations with the ground truth VAD scores. Also, further training with supervision of VAD labels leads to improved performance especially when dataset is small. We also present examples of predictions of appropriate emotion words that are not part of the original annotations.
Abnormal Behavior Detection in Farming Stream Data

Farmers have been able to detect, quantify, and respond to spatial and temporal variation in crops thanks to a variety of technological advancements in recent years. Precision farming aims to provide precise targeting of agricultural inputs while reducing waste and negative consequences. Precision agriculture technologies are effective instruments for increasing farm sustainability and production. These technologies provide ways to create more with less resources. Nowadays, improving agricultural production efficiency and crop yields is impossible without the use of contemporary digital technologies and smart machinery. The introduction of high-accuracy GPS technology into farm machinery, such as combine harvesters, has been a significant component of precision farming. In this paper, we present a streaming-based methodology for detecting anomalies in combine harvester GPS recordings. The key hypothesis is that, “similar points in a feature space have similar anomaly scores”. We examine a data-driven strategy with the goal of applying unsupervised detection algorithms to find anomalies on the fly. Based on the results of the experiments, we can conclude that LSCP beats all other strategies when the number of base detectors is varied. The AUCPR performance of LSCP with two base detectors (HBOS and MCD) is 8.02% better than the second best technique MCD.
A Mixed Method For Internal Threat Detection

In recent years, the development of deep learning has brought new ideas to internal threat detection. In this paper, three common deep learning algorithms for threat detection are optimized and innovated, and feature embedding, drift detection and sample weighting are introduced into FCNN. Adaptive multi-iteration method is introduced into Support Vector Data Description (SVDD). A dynamic threshold adjustment mechanism is introduced in VAE. In threat detection, three methods are used to detect the abnormal behavior of users, and the intersection of output results is taken as the final threat judgment basis. Experiments on cert r6.2 data set show that this method can significantly reduce the false positive rate.
Machine Learning-Based Font Recognition and Substitution Method for Electronic Publishing

This study proposes a font recognition algorithm based on a deep convolution neural network and a font substitution algorithm based on texture and grayscale features. The experiments show that the proposed font recognition method can effectively extract font features with a high recognition rate, without the prior knowledge of the text content and with good versatility. The substitution effect of the proposed font replacement method can better satisfy the subjective visual perception of the human eyes and easily expand. The research results can be used to improve the publication quality; ensure the best presentation effect when presented in different platforms; facilitate font retrieval and effectively protect font copyright.
IMPACTOS DO CONTEXTO PANDÊMICO NA SAÚDE MENTAL DOS PROFISSIONAIS DA ÁREA DA SAÚDE

The COVID-19 pandemic reflects in an intense and complex way in social, economic and cultural processes globally, also due to its sanitary nature in the work environment, a phenomenon that impacts and can have repercussions on workers' health. The general objective of this article was to analyze the influence that the pandemic scenario has on the mental health of professionals working in the health area, taking into account their work, quality of life and satisfaction with the organization. The methodology selected for the research was descriptive, qualitative, classified as a bibliographic research, in which the following descriptors were applied: satisfaction and quality of life in the work context, selecting academic sources of information that responded to the proposed objectives and that presented in the context of the pandemic. The results point to the fact that teams from healthcare organizations can be more affected in the context of the scenario, such as the nurses' class. It is portrayed that these professionals are doubly affected by the pandemic, in their work environment, while occupying the front line in the fight against the virus, and in their personal life, when they face the effects of social isolation, the absence of schools as partners, financial insecurity and related issues. Thus, it is concluded that the satisfaction and quality of life in the work environment was significantly affected by the pandemic, contributing to the development even psychological suffering in various spheres, including work stress, especially in environments where professionals from health.
Explanation Of Marine Lake Formation At Misool Raja Ampat West Papua, Indonesia

Marine lake in a karst landscape is one of the macro karst forms known as doline and is only found in some locations in the world. Moreover, the theory of marine doline formation is always associated with global sea-level rise which differs from one place to another due to several factors. This research was conducted to understand the formation process of marine lakes in Misool and how the water fills the basins formed especially at Holocene time. This was achieved by obtaining information on the longest underwater terrace which is also the longest standing water position recorded on the sea wall. The marine terraces were measured by sounding profiles to the sea bordering the seven marine lakes including Lenmakana, Balbullol, Lenkafal, Keramat, Karawapop, Keramat-2, and Keramat-3 as well as Harapan Jaya Sea. A total of 24 profiles were measured and stable isotopes δ18O and δD of water samples were used to determine the origin of water in the lakes. The results showed the longest terrace was at the depth of ˗33 and ˗3 m while the references from the area closest to Misool showed the same water level positions at 10,500 BP and 6,985 BP. Furthermore, the composition of δ18O and δD from lake water indicated the water samples were a mixture of groundwater and seawater with the seawater having the more dominant concentration and this allows it to fill the lake first through a previously formed cavity system.
A New Representation for Span-based CCG Parsing

This paper proposes a new representation for CCG derivations. CCG derivations are represented as trees whose nodes are labeled with categories strictly restricted by CCG rule schemata. This characteristic is not suitable for span-based parsing models because they predict node labels independently. In other words, span-based models may generate invalid CCG derivations that violate the rule schemata. Our proposed representation decomposes CCG derivations into several independent pieces and prevents the span-based parsing models from violating the schemata. Our experimental result shows that an off-the-shelf span-based parser with our representation is comparable with previous CCG parsers.
Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization

We provide a first-order oracle complexity lower bound for finding stationary points of min-max optimization problems where the objective function is smooth, nonconvex in the minimization variable, and strongly concave in the maximization variable. We establish a lower bound of $\Omega\left(\sqrt{\kappa}\epsilon^{-2}\right)$ for deterministic oracles, where $\epsilon$ defines the level of approximate stationarity and $\kappa$ is the condition number. Our analysis shows that the upper bound achieved in (Lin et al., 2020b) is optimal in the $\epsilon$ and $\kappa$ dependence up to logarithmic factors. For stochastic oracles, we provide a lower bound of $\Omega\left(\sqrt{\kappa}\epsilon^{-2} + \kappa^{1/3}\epsilon^{-4}\right)$. It suggests that there is a significant gap between the upper bound $\mathcal{O}(\kappa^3 \epsilon^{-4})$ in (Lin et al., 2020a) and our lower bound in the condition number dependence.
Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stability

We are motivated by the problem of providing strong generalization guarantees in the context of meta-learning. Existing generalization bounds are either challenging to evaluate or provide vacuous guarantees in even relatively simple settings. We derive a probably approximately correct (PAC) bound for gradient-based meta-learning using two different generalization frameworks in order to deal with the qualitatively different challenges of generalization at the and levels. We employ bounds for uniformly stable algorithms at the base level and bounds from the PAC-Bayes framework at the meta level. The result of this approach is a novel PAC bound that is tighter when the base learner adapts quickly, which is precisely the goal of meta-learning. We show that our bound provides a tighter guarantee than other bounds on a toy non-convex problem on the unit sphere and a text-based classification example. We also present a practical regularization scheme motivated by the bound in settings where the bound is loose and demonstrate improved performance over baseline techniques.
Early-stage development of the SignON application and open framework – challenges and opportunities

SignON is an EU Horizon 2020 Research and Innovation project, that is developing a smartphone application and an open framework to facilitate translation between different European sign, spoken and text languages. The framework will incorporate state of the art sign language recognition and presentation, speech processing technologies and, in its core, multi-modal, cross-language machine translation. The framework, dedicated to the computationally heavy tasks and distributed on the cloud powers the application – a lightweight app running on a standard mobile device. The application and framework are being researched, designed and developed through a co-creation user-centric approach with the European deaf and hard of hearing communities. In this session, the speakers will detail their progress, challenges and lessons learned in the early-stage development of the application and framework. They will also present their Agile DevOps approach and the next steps in the evolution of the SignON project.
A Never-Ending Masquerade: Luc Besson’s Fifth Element

This article shows Luc Besson’s Fifth Element as a hybrid fi lm, exploiting different genre formulas. Besson uses genre conventions as if they screened off the fi lm’s deep structure. Seemingly, the science fi ction formula determines its iconographic dimension. At the same time, melodrama is the principal hidden genre of Fifth Element. The main characters in the fi lm imitate typical heroes of science fi ction and action movies, and Besson gradually dismantles this camouflage.
Bayesian Bellman Operators

We introduce a novel perspective on Bayesian reinforcement learning (RL); whereas existing approaches infer a posterior over the transition distribution or Q-function, we characterise the uncertainty in the Bellman operator. Our Bayesian Bellman operator (BBO) framework is motivated by the insight that when bootstrapping is introduced, model-free approaches actually infer a posterior over Bellman operators, not value functions. In this paper, we use BBO to provide a rigorous theoretical analysis of model-free Bayesian RL to better understand its relationshipto established frequentist RL methodologies. We prove that Bayesian solutions are consistent with frequentist RL solutions, even when approximate inference isused, and derive conditions for which convergence properties hold. Empirically, we demonstrate that algorithms derived from the BBO framework have sophisticated deep exploration properties that enable them to solve continuous control tasks at which state-of-the-art regularised actor-critic algorithms fail catastrophically
Virutal Reality as an Educational and Training Tool in Medicine

Use of computers is tremendously increasing in medical education. Learning Process is moving from textbook to electronic mode, a interactive way in real sense to fulfill the educational needs of the graduate and post graduate learners. In virtual reality learners can learn according to their own time and also contents are reusable. It is not limited to any geographical area and time slots. It is a valuable learning tool for medical students, can be used appropriately when and where required. This article reveals the role of virtual reality for learning and training in medical field.
AI Algorithm Based Jacket Ejection For Automatic Security System in Water Bodies

It is tedious process and takes time to detect the person on water by usual ways like with the help of rescue teamand divers. To overcome this, we have introduced this work with a life camera and edge detection algorithm for detectingdrowning person. When raspberry PI identifies the drowning person, it will initialize the servo motor which will eject the lifejacket automatically. The greatest advantage of this proposed work is that this whole system is made into a boat shaped botand is completely automatic process. Python language is preferred for AI algorithm and to specify edge detection process.