Reviewerly/exHarmony huggingface dataset:
A Robust Visual Loop-Closure Detection Method of vSLAM for Ambiguous Environment

Loop-closure detection is a significant component for autonomous robot SLAM to build a map of the environment. However, the robustness of visual SLAM loop-closure detection in outdoor complex environment is severely restricted by perceptual aliasing, which is caused by changes in illumination, viewpoint and scale. In this paper, we present a novel method with high robustness and low complexity for visual loop-closure detection of autonomous robot. Our method extracts invariable local features, then clusters environmental invariable image descriptors. To reduce the selection complexity of massive loop-closure candidates to sub-linear, hash the image descriptors to Locality-Sensitive Hashing table without losing the invariant ability of image descriptors. The proposed method is tested efficiency and complexity experimentally using a publicly available dataset, results demonstrate that our method can recognize complex surroundings online with good robustness and has good applied value in constructing map accurately for autonomous robot visual SLAM in outdoor environment.
Combination of Support Vector Machine (SVM) and Bayesian Model to Identify Criminal Language

In the last few years, different investigations have been developed related to the use of Natural Language Processing (NLP) and especially of information extraction (IE) in criminal matters (Abbass et al., in 2020 IEEE 14th international conference on semantic computing (ICSC). IEEE, pp 363–368, 2020 []). This paper discusses the creation and characterization of a specialized corpus in criminology. The corpus is made up of plain text news divided into five classes of crimes: killing, attack, abduct, sexual abuse, and blackmail. The classifiers used are the classic support vector machine (SVM) and a Bayesian model. The importance of automatic classification of criminological texts lies in the fact that it can be used for criminalistic analysis (Viloria et al., in Intelligent computing, information and control systems. Springer, Cham, 2020 []) and for the detection of criminal entities (Peersman, in Detecting deceptive behaviour in the wild: text mining for online child protection in the presence of noisy and adversarial social media communications. Lancaster University, 2018 []). News classification can even help to find patterns in crime reports or other criminalistic aspects (Chan et al., Theor Criminol 20:21–39, 2016 []).
Study on Fast-Changing Mixed-Modulation Recognition Based on Neural Network Algorithms

Modulation recognition (MR) plays a key role in cognitive radar, cognitive radio, and some other civilian and military fields.While existing methods can identify the signal modulation type by extracting the signal characteristics, the quality of feature extraction has a serious impact on the recognition results.In this paper, an end-to-end MR method based on long short-term memory (LSTM) and the gated recurrent unit (GRU) is put forward, which can directly predict the modulation type from a sampled signal.Additionally, the sliding window method is applied to fast-changing mixed-modulation signals for which the signal modulation type changes over time.The recognition accuracy on training datasets in different SNR ranges and the proportion of each modulation method in misclassified samples are analyzed, and it is found to be reasonable to select the evenly-distributed and full range of SNR data as the training data.With the improvement of the SNR, the recognition accuracy increases rapidly.When the length of the training dataset increases, the neural network recognition effect is better.The loss function value of the neural network decreases with the increase of the training dataset length, and then tends to be stable.Moreover, when the fast-changing period is less than 20ms, the error rate is as high as 50%.As the fast-changing period is increased to 30ms, the error rates of the GRU and LSTM neural networks are less than 5%.
Prediction of Source Rock Maturity Using Semi Supervised Machine Learning Algorithms

Summary The paper present a semi-supervised machine learning workflow that integrates geochemical measurements, elastic logs, pre-stack seismic inversion parameters and non-seismic measurements to classify source rock maturity, and propagate the classes away from the wells in a controlled manner. Semi-supervised algorithms are able to discover spatial structures in high dimensional space by using the unlabeled data. This type of learning algorithms are useful in situations where data labels are limited. The algorithm spreads labels by constructing a similarity graph over the input items and minimizing a loss function with regularization properties making it robust to noise. Data analysis indicate that maturity is not only an attribute of the rock but it is also an attribute of the intrinsic properties of the location. Using location indicators, the algorithm was able to create a regional distribution of maturity.
When Bitcoin Has the Flu: On Bitcoin’s Performance to Hedge Equity Risk in the Early Wake of the COVID-19 Outbreak

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Video abnormal event detection based on CNN and multiple instance learning

Aiming at the need of video abnormal events to be located in pixel-level regions, a video abnormal event detection method based on CNN (Convolutional Neural Networks) and multiple instance learning is proposed. Firstly, the Gaussian background model is used to extract the moving targets in the video, and the connected regions of the moving targets are obtained by the image processing method. Secondly, the pre-trained VGG16 model is used to extract the features of the connected regions what construct multiple instance learning packages. Finally, the multiple instance learning model is trained using MISVM (Multiple-Instance Support Vector Machines) and NSK (Normalized Set Kernel) algorithms and predicted at the pixel-level. The experimental results show that the video anomaly detection method based on CNN and multiple instance learning can accurately locate the abnormal events in the pixel-level region.
Scoring Systems for Predicting Mortality for Severe Patients with COVID-19

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Pelatihan penggunaan aplikasi Mendeley bagi mahasiswa Sekolah Tinggi Ilmu Pembangunan Indonesia (STIE-PI) Makassar dalam penulisan karya ilmiah

Partners of the Community Partnership Program (PKM) are students of Sekolah Tinggi Ilmu Ekonomi Pembangunan Indonesia (STIE-PI) Makassar. The problem is the lack of understanding of students in reference writing when writing a thesis / final project. Generally, students do not know the difference between reference writing and citation style. The used method are lecture, question and answer, simulation, and practice. The achieved results were students able to use the Mendeley application as a reference management software and academic social network and in reference writing.
Generational Gaps: Women, Rural Traditions and Community Networks*

Abstract Community Networks (CNs) can provide access to telecommunications in low-income rural areas that are excluded by dominant connectivity models. Women and older people often constitute relatively higher proportions of these populations, thus this paper explores interactions between technology, gender and age in three CNs in rural Africa, Latin America and South Asia. All cases are situated both in local governance structures dominated by men, including a tribal authority, indigenous assembly, and a village council; and in collectivist cultures where women are involved in community work but not in decision-making. I generated data about people’s everyday practices and opinions in relation to their local CNs in focus group discussions and interviews of different sorts with 76 men and 60 women, including network initiators, champions, operators, users and non-users. Older women significantly contributed volunteer labour but were less likely to use their CN, for instance because they did do not own/know how to use devices, or because the location of hotspots was unsuited to their daily lives. Meanwhile, younger women frequently used alternatives to the CNs for connectivity and sometimes established their own enterprises in this; which contributed to some older women’s perspectives that younger women were increasingly separated from communal traditions. This has the potential to amplify generational gaps amongst women and patriarchy within CNs. Such divisive potential may be further exacerbated by masculine bias of priorities in global discourse on telecommunications technology and policy, which tends to emphasise certain concerns about access over concerns about power relations embedded in infrastructure.
M2FN: Multi-step modality fusion for advertisement image assessment

Assessing advertisements, specifically on the basis of user preferences and ad quality, is crucial to the marketing industry. Although recent studies have attempted to use deep neural networks for this purpose, these studies have not utilized image-related auxiliary attributes, which include embedded text frequently found in ad images. We, therefore, investigated the influence of these attributes on ad image preferences. First, we analyzed large-scale real-world ad log data and, based on our findings, proposed a novel multi-step modality fusion network (M2FN) that determines advertising images likely to appeal to user preferences. Our method utilizes auxiliary attributes through multiple steps in the network, which include conditional batch normalization-based low-level fusion and attention-based high-level fusion. We verified M2FN on the AVA dataset, which is widely used for aesthetic image assessment, and then demonstrated that M2FN can achieve state-of-the-art performance in preference prediction using a real-world ad dataset with rich auxiliary attributes.
IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot Learning

The need of collecting large quantities of labeled training data for each new task has limited the usefulness of deep neural networks. Given data from a set of source tasks, this limitation can be overcome using two transfer learning approaches: few-shot learning (FSL) and self-supervised learning (SSL). The former aims to learn `how to learn' by designing learning episodes using source tasks to simulate the challenge of solving the target new task with few labeled samples. In contrast, the latter exploits an annotation-free pretext task across all source tasks in order to learn generalizable feature representations. In this work, we propose a novel Instance-level and Episode-level Pretext Task (IEPT) framework that seamlessly integrates SSL into FSL. Specifically, given an FSL episode, we first apply geometric transformations to each instance to generate extended episodes. At the instance-level, transformation recognition is performed as per standard SSL. Importantly, at the episode-level, two SSL-FSL hybrid learning objectives are devised: (1) The consistency across the predictions of an FSL classifier from different extended episodes is maximized as an episode-level pretext task. (2) The features extracted from each instance across different episodes are integrated to construct a single FSL classifier for meta-learning. Extensive experiments show that our proposed model (i.e., FSL with IEPT) achieves the new state-of-the-art.
Climatic traits on daily clearness and cloudiness indices

Abstract. Solar radiation has a crucial role in photosynthesis, evapotranspiration and other biogeochemical processes. The amount of solar radiation reaching the Earth's surface is a function of astronomical geometry and atmospheric optics. While the first is deterministic, the latter has a random behaviour caused by highly variable atmospheric components such as water and aerosols. In this study, we use daily radiation data (1978–2014) from 37 FLUXNET sites distributed across the globe to inspect for climatic traits in the shape of the probability density function (PDF) of the clear-day (c) and the clearness (k) indices. The analysis was made for shortwave radiation (SW) at all sites and for photosynthetically active radiation (PAR) at 28 sites. We identified three types of PDF, unimodal with low dispersion (ULD), unimodal with high dispersion (UHD) and bimodal (B), with no difference in the PDF type between c and k at each site. Looking for regional patterns in the PDF type, we found that latitude, global climate zone and Köppen climate type have a weak relation and the Holdridge life a stronger relation with c and k PDF types. The existence and relevance of a second mode in the PDF can be explained by the frequency and meteorological mechanisms of rainy days. These results are a frame to develop solar radiation stochastic models for biogeochemical and ecohydrological modelling.
The measurement model of geo-education among trainee teachers in Malaysia

&lt;p&gt;This study conducted to develop a measurement model for measuring Geo-Education in Malaysia context. This cross-sectional survey involved 245 trainee teachers in Universities and Institut Pendidikan Guru Malaysia (IPGM). The data collection was made through a set of questionnaires and analyzed using SEM-AMOS. There are four main elements measured, which are loading factors, convergent validity, discriminant validity, and composite reliability. The findings indicated that Geo-Education had a significant contribution to the proposed constructs, namely primer, issues, ecosystem, lifestyle, and cross-curricular elements. A model of Geo-Education was successfully developed in this study using these five constructs, namely primer, issues, ecosystem, lifestyle, and cross-curricular elements. This study also identified twenty-five behaviours of Geo-Education among the trainee teachers in Malaysia. The findings of this study are essential as a guideline for Malaysian teachers to implement the concept of Geo-Education in Malaysia. Additionally, the application of this subject as cross-curricular elements in the Malaysian curriculum is essential to ensure the success of the implementation of Education Sustainable Development (ESD) in the school environment.&lt;/p&gt;
The large learning rate phase of deep learning

The choice of initial learning rate can have a profound effect on the performance of deep networks. We present empirical evidence that networks exhibit sharply distinct behaviors at small and large learning rates. In the small learning rate phase, training can be understood using the existing theory of infinitely wide neural networks. At large learning rates, we find that networks exhibit qualitatively distinct phenomena that cannot be explained by existing theory: The loss grows during the early part of training, and optimization eventually converges to a flatter minimum. Furthermore, we find that the optimal performance is often found in the large learning rate phase. To better understand this behavior we analyze the dynamics of a two-layer linear network and prove that it exhibits these different phases. We find good agreement between our analysis and the training dynamics observed in realistic deep learning settings.
Extreme low-light image enhancement using deep neural network

Low-light images are inappropriate for human observation and computer processing. Multiple enhancement techniques have been proposed but most of them are based on synthetic datasets. In this paper, an image enhancement method using deep convolutional neural network is introduced. The model is expert at merging multi-scale information and it is flexible for image of arbitrary size without pre- or post-processing. Different from previous work, we deliberately address those extreme low-light images which cannot be identified by human eyes. For doing so, a Real Extreme Low-Light Images (RELLI) dataset is collected to validate the method, and will be contributed to related research. We also analyze some factors that affect the model’s performance to achieve the best results. Furthermore, the generalization ability beyond RELLI dataset and the superiority over several state-of-the-art methods are also confirmed.
One Vertex Attack on Graph Neural Networks-based Spatiotemporal Forecasting

Spatiotemporal forecasting plays an essential role in intelligent transportation systems (ITS) and numerous applications, such as route planning, navigation, and automatic driving. Deep Spatiotemporal Graph Neural Networks, which capture both spatial and temporal patterns, have achieved great success in traffic forecasting applications. Though Deep Neural Networks (DNNs) have been proven to be vulnerable to carefully designed perturbations in multiple domains like objection classification and graph classification, these adversarial works cannot be directly applied to spatiotemporal GNNs because of their causality and spatiotemporal mechanism. There is still a lack of studies on the vulnerability and robustness of spatiotemporal GNNs. Particularly, if spatiotemporal GNNs are vulnerable in real-world traffic applications, a hacker can easily cause serious traffic congestion and even a city-scale breakdown. To fill this gap, we design One Vertex Attack to break deep spatiotemporal GNNs by attacking a single one vertex. To achieve this, we apply the genetic algorithm with a universal attack method as the evaluation function to locate the weakest vertex; then perturbations are generated by solving an optimization problem with the inverse estimation. Empirical studies prove that perturbations in one vertex can be diffused into most of the graph when spatiotemporal GNNs are under One Vertex Attack.
The accuracy of transfer learning using neural network method for sentiment analysis problem on Indonesian tweets

Abstract In this paper, sentiment analysis is applied to one social media called Twitter. Sentiment analysis is categorized as a classification problem that can be solved using one of machine learning methods, namely Neural Network. If machine learning is applied, it is necessary to rebuilt the model from scratch using new training data that requires manual labelling process. Hence, it is better to apply other learning besides machine learning, such as transfer learning. The simulation in this research yielded an accuracy of transfer learning using Neural Network which will be tested by N-grams (bigram and trigram) feature and one of feature selection method, namely Extra-Trees Classifier. The highest value of transfer learning accuracy is obtained when one hidden layer, 250 neurons on hidden layer, and tanh activation function are used. The use of feature selection method in simulation can also improve the transfer learning performance, so that the accuracy value is higher than the one that does not use feature selection method.
Academic Freedom for the University as an Intellectual and Creative Organization

The article reveals the essence of the “academic freedom” concept and phenomenon, develops the directions for the development of universities as an intellectual and creative organizations based on the implementation of the academic freedoms’ principle, and offers a matrix for the realization of academic freedoms and autonomy of the university. The practice-oriented matrix presents the infrastructural components, the development of which creates the transformational basis of the university, the principle of academic freedom autonomy.The author considers the features of the university as an intellectual and creative organization, determining the specifics of its management based on the implementation of the principle of academic freedom. This approach involves the transformation of the marketing paradigm of the university as a management tool that activates the intellectual and creative potential of its employees based on the implementation of the principle of academic freedoms in the context of the digitalization of the scientific and educational process.The main function of the “academic freedom” concept is revealed, which consists in the formation of a theoretical and ideological paradigm, which serves as the basis for creating sociocultural, economic and managerial mechanisms to counter the most powerful negative impact vector on the university environment, which has a destructive effect on the essence of university science and education.The specifics of the concept’s contents and the essence of the managerial tool “academic freedom” are studied in relation to the current stage of the university as a social institution development, which allows them to be used as the basis for transformational processes aimed at de-bureaucratizing the university and building a future university self-government system.
Improving Abstractive Dialogue Summarization with Conversational Structure and Factual Knowledge

Recently, people have been paying more attention to the abstractive dialogue summarization task. Compared with news text, the information flows of the dialogue exchange between at least two interlocutors, which leads to the necessity of capturing long-distance cross-sentence relations. In addition, the generated summaries commonly suffer from fake facts because the key elements of dialogues often scatter in multiple utterances. However, the existing sequence-to-sequence models are difficult to address these issues. Therefore, it is necessary for researchers to explore the implicit conversational structure to ensure the richness and faithfulness of generated contents. In this paper, we present a Knowledge Graph Enhanced Dual-Copy network (KGEDC), a novel framework for abstractive dialogue summarization with conversational structure and factual knowledge. We use a sequence encoder to draw local features and a graph encoder to integrate global features via the sparse relational graph self-attention network, complementing each other. Besides, a dual-copy mechanism is also designed in decoding process to force the generation conditioned on both the source text and extracted factual knowledge. The experimental results show that our method produces significantly higher ROUGE scores than most of the baselines on both SAMSum corpus and Automobile Master corpus. Human judges further evaluate that outputs of our model contain more richer and faithful information.
Intuiciones en lógica: una propuesta moderada

Intuitions play a significant role in debates about logic. In this paper, I analyze how legitimate is that practice. In the first part of the paper, I distinguish between theoretical and pretheoretical intuitions, and argue that some pretheoretical intuitions are not to be taken into account in logic. Particularly, our pretheoretical intuitions about the concept of validity are not of much importance, since we don’t have a uniform or clear concept of validity in the natural language to be elucidated. Nevertheless, I argue that, since logical connectives are more homogeneously used in our ordinary speech, we can appeal to pretheoretical intuitions to establish their meaning in a logical theory. In the second part of the paper, I consider and reply to four objections to this moderate proposal. Two of them try to show that, if this position is adopted, then the pretheoretical intuitions about the connectives are completely unreliable and useless. One of them argues that this mixed position is unstable: pretheoretical intuitions about the connectives are also pretheoretical intuitions about validity. The last problem is related to the definition of validity and the possibility of revising it.