Reviewerly/exHarmony huggingface dataset:
Unsupervised Class-Agnostic Instance Segmentation of 3D LiDAR Data for Autonomous Vehicles

Fine-grained scene understanding is essential for autonomous driving. The context around a vehicle can change drastically while navigating, making it hard to identify and understand the different objects that may appear. Although recent efforts on semantic and panoptic segmentation pushed the field of scene understanding forward, it is still a challenging task. Current methods depend on annotations provided before deployment and are bound by the labeled classes, ignoring long-tailed classes not annotated in the training data due to the scarcity of examples. However, those long-tailed classes, such as baby strollers or unknown animals, can be crucial when interpreting the vehicle surroundings, e.g., for safe interaction. We address the problem of class-agnostic instance segmentation in this paper that also tackles the long-tailed classes. We propose a novel approach and a benchmark for class-agnostic instance segmentation and a thorough evaluation of our method on real-world data. Our method relies on a self-supervised trained network to extract point-wise features to build a graph representation of the point cloud. Then, we use GraphCut to perform foreground and background separation, achieving instance segmentation without requiring any label. Our results show that our approach is able to achieve instance segmentation and a competitive performance compared to state-of-the-art supervised methods.
Machine Learning with Distributed Processing using Secure Divided Data: Towards Privacy-Preserving Advanced AI Processing in a Super-Smart Society

Towards the realization of a super-smart society, AI analysis methods that preserve the privacy of big data in cyberspace are being developed. From the viewpoint of developing machine learning as a secure and safe AI analysis method for users, many studies have been conducted in this field on 1) secure multiparty computation (SMC), 2) quasi-homomorphic encryption, and 3) federated learning, among other techniques. Previous studies have shown that both security and utility are essential for machine learning using confidential data. However, there is a trade-off between these two properties, and there are no known methods that satisfy both simultaneously at a high level. In this paper, as a superior method in both privacy-preserving of data and utility, we propose a learning method based on distributed processing using simple, secure, divided data and parameters. In this method, individual data and parameters are divided into multiple pieces using random numbers in advance, and each piece is stored in each server. The learning of the proposed method is achieved by using these data and parameters as they are divided and by repeating partial computations on each server and integrated computations at the central server. The advantages of the proposed method are the preservation of data privacy by not restoring the data and parameters during learning; the improvement of usability by realizing a machine learning method based on distributed processing, as federated learning does; and almost no degradation in accuracy compared to conventional methods. Based on the proposed method, we propose backpropagation and neural gas (NG) algorithms as examples of supervised and unsupervised machine learning applications. Our numerical simulation shows that these algorithms can achieve accuracy comparable to conventional models.
A Bio-Inspired Neural Network Approach to Robot Navigation and Mapping with Nature-Inspired Algorithms

Nature-inspired algorithms have been successfully applied to autonomous robot path planning, vision and mapping. However, concurrent path planning and mapping with replanning feature is still a challenge for autonomous robot navigation. In this paper, a new framework in light of the replanning-based methodology of concurrent mapping and path planning is proposed. It initially performs global path planning through a developed Gravitational Search Algorithm (GSA) to generate a global trajectory. The surrounding environment can then be described through a monocular framework and transformed into occupancy grid maps (OGM) for autonomous robot path planning. With updated moving obstacles and road conditions, the robot can replan the trajectory with the GSA based on the updated map. Local trajectory in the vicinity of the obstacles is generated by a developed bio-inspired neural network (BNN) method integrated with speed profile mechanism, and safe border patrolling waypoints. Simulation and comparative studies demonstrate the effectiveness and robustness of the proposed model.
SMS Fraud Detection Using Machine Learning

The term SMS stands for short message service. It is a message networking method that uses smartphones and cell phones. It is a text messaging system that lets smart phones to communicate with one another. SMS has risen in prominence in current history, with firms adapting and optimizing SMS to be used as a service tool mostly because of its numerous benefits. We are using a unique technique that uses data mining and machine learning method to identify and filter spam communications in this study. We analyzed the characteristics of spam SMS in depth and discovered ten criteria that may effectively distinguish short message (or messaging) service spam from ham. If some adjustments are needed to identify a new scam, they were made manually, either by applying changes to current algorithms or by inventing new algorithms. In this method, as the quantity of clients and data grows, so does the amount of human work, which may also effectively separate spam text messages and ham SMS messages. The random forest classification method, we proposed, is the methodology that produced 99.9% accuracy.
Metaverse—Flight From Dusk to Darkness?

The article studies the phenomenon of the metaverses, that is, projects developed by technology companies, where physical, virtual and augmented (extended) realities are brough together in digital space. Apologists for the metaverses argue that soon the Internet will cease to exist in the usual sense, and its place will be taken by the metaverses as global virtual spaces for communication, work, and leisure. This vision is compared with the concept of the Panopticon, formulated by Jeremy Bentham and subsequently interpreted by Michel Foucault. The growing dependence of the state, the society, and the individual on technology and their increasing interconnection forms a set of problems with an ever increasing acuteness, yet no obvious solution has been found so far. The article demonstrates how technology manipulates human weaknesses, increasing its power and gaining control over society. Among the major risks related to the metaverses are the decay of values and morality in the virtual world, and the collapse of the individual’s identity. Possible scenarios of interaction between human and technology are illustrated by Tarkovsky’s Solaris, Cronenberg’s Videodrome, and Folman’s Congress. The study ends with an assumption that a new socio-humanitarian rationality must be formed to enable control over the logic of the development of tech companies promoting the metaverses.
QuoteR: A Benchmark of Quote Recommendation for Writing

It is very common to use quotations (quotes) to make our writings more elegant or convincing.To help people find appropriate quotes efficiently, the task of quote recommendation is presented, aiming to recommend quotes that fit the current context of writing.There have been various quote recommendation approaches, but they are evaluated on different unpublished datasets.To facilitate the research on this task, we build a large and fully open quote recommendation dataset called QuoteR, which comprises three parts including English, standard Chinese and classical Chinese.Any part of it is larger than previous unpublished counterparts.We conduct an extensive evaluation of existing quote recommendation methods on QuoteR.Furthermore, we propose a new quote recommendation model that significantly outperforms previous methods on all three parts of QuoteR.All the code and data of this paper can be obtained at https://github.com/thunlp/QuoteR.
Cartographic scripts for seismic and geophysical mapping of Ecuador

This research describes a script-based method of Generic Mapping Tools (GMT) for mapping the seismicity, geophysics, geology and topography of Ecuador. The advances of GMT include the following points: (1) automation of workflow; (2) refined aesthetics of graphics; (3) speed console-based mapping; (4) multi-format data handling; (5) advanced syntax. An explanation of scripting with the examples of code snippets is provided. The results present six new maps of Ecuador. The distribution of geophysical phenomena and seismicity is compared to the terrain elevation, showing remarkable correlations with the topography and geoid. The data demonstrated low values in the depression of the Andes, the Gulf of Guayaquil, and the Peru-Chile Trench. The peaks in gravity are representative for the Andean topography. Local decreases in gravity correspond to the depressions in the coastal shelf and the Gulf of Guayaquil. The increase in seismicity is detected along the tectonically active areas. The GMT is appropriate to the geological risk assessment of Ecuador.
The analysis and optimization of CNN Hyperparameters with fuzzy tree modelfor image classification

The meaningful performance of convolutional neural network (CNN) has enabled the solution of various state-of-the-art problems.Although CNNs achieve satisfactory results in computer-vision problems, they still have some difficulties.As the designed CNN models are deepened to achieve much better accuracy, computational cost and complexity increase.It is significant to train CNNs with suitable topology and training hyperparameters that include initial learning rate, minibatch size, epoch number, filter size, number of filters, etc. because the initialization of hyperparameters affects classification results.On the other hand, it is not possible to make a definite inference for the hyperparameter initialization and there is uncertainty.This study is carried out to model uncertainty using fuzzy inference system (FIS).The designed fuzzy model provides estimation of classification result depending on CNN topology and training hyperparameters.GoogleNet and Inceptionv3 that contain inception-modules, ShuffleNet that contains shuffleblocks, DenseNet201 that contains dense-blocks, EfficientNet, ResNet18, ResNet50, ResNet101, and MobileNetv2 that contain residual-blocks, and InceptionResNetv2 that includes both inception-modules and residual-blocks were evaluated as CNN models.Test sample dataset was obtained by training CNN models with various training hyperparameter combinations.CNN models were trained on Animal Diagnostics Lab (ADL) which is a histopathological dataset includes healthy and inflamed kidney, lung, and spleen images.A new FIS tree model that is more computationally efficient and easier to understand than a single FIS was designed and classification accuracy prediction of CNN models depending on hyperparameter combinations was performed.The best, the worst, and the average classification accuracies obtained with CNN models that use best training hyperparameter set are 97.70%,93.60%, and 96.30%, respectively.Moreover, Cifar10 and Cifar100 benchmark datasets were experimented to reveal true capability and limitations of the proposed approach.Experimental results indicate that the designed FIS tree model provides a successful hyperparameter evaluation mechanism with an average RMSE value of 1.2652.
Multi-Information Preprocessing Event Extraction With BiLSTM-CRF Attention for Academic Knowledge Graph Construction

Academic knowledge graph is an important application of knowledge graph in the vertical field of academia. At present, the construction of the academic knowledge graph is mainly completed by extracting published academic papers, authors, publications, and other information from related databases. However, academic information is not just information of published papers. Scholars’ academic activities include participation in academic conferences, visiting and making presentation, and so on. However, the above academic information is hidden in natural language texts and cannot be directly stored in academic knowledge graph. This article proposes an approach named construct-SCHOLAT knowledge graph to construct an academic event knowledge graph based on academic social network SCHOLAT. The construction framework mainly consists of two parts: data preprocessing and event extraction. In the data preprocessing, we propose a knowledge graph embedding method to represent scholars’ academic social feature. In the event extraction, we concatenate the preprocessed scholar vector with academic we-media blog text into the extraction model based on BiLSTM-CRF fused with attention mechanism. The extracted events are added to academic knowledge graph, and a public relationship exists between the event and the scholar. Compared to the previous methods, our framework has an excellent performance after experimental verification. To the best of our knowledge, this is the first study to use the scholar academic social information of the scholar who edited the text as the event extraction input information. In addition, we publish a Chinese event extraction dataset SCHOLAT academic event extraction. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</xref> The dataset includes academic we-media blog and the social behavior embedding vector of the scholar. All the data in this dataset are derived from the academic social network SCHOLAT. <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label><sup>1</sup></label> <uri>https://www.scholat.com/research/opendata</uri> </fn>
Sentiment Analysis of Elon Musk’s Twitter Data Using LSTM and ANFIS-SVM

Social media plays a huge role spreading words to millions and influencing their opinions. Twitter is one of the most essential platform that reach over 300 million active users and 500 million tweets per day, it plays a significant role spreading the word around the world,. These tweets covers a various subjects from personal conversations to globally important topics such as updates about Covid19 and macroeconomic subjects. Especially in financial matters, it is a very common situation that business owners, even politicians report the news on Twitter first. The Tesla's and SpaceX's CEO and owner Elon Musk's tweets had a huge impact on coin market or even stock exchanges. Although many accused him of market manipulation his tweets impact cannot be underestimated. In 2020 and 2021 there are various tweets that strike the stock market instantly both in the positive and negative direction. This study aims to predict the direction of his tweets and perform a sentiment analysis using both Long-Short Term Memory (LSTM) and Adaptive Neuro Fuzzy Interface Systems (ANFIS)-SVM(Support Vector Machines) models. The dataset is obtained by using Twitter API which spans a time horizon of 5 years. In order to compare the results under same conditions same preprocessing steps are performed for both models. According to the results, LSTM performs a superior performance with its 72.2% accuracy against ANFIS-SVM model with 74.1%.
Real-Time and Cost-Effective Smart Mat System Based on Frequency Channel Selection for Sleep Posture Recognition in IoMT

Sleep posture, which affects the quality of sleep and could lead to medical conditions, such as pressure ulcers, is a key metric for sleep analysis in Internet of Medical Things (IoMT). In this article, a real-time and low-cost smart mat system for sleep posture recognition based on frequency channel selection is proposed. The system can recognize postures unobtrusively with a dense flexible sensor array. In addition, to enable real-time recognition with a relatively low-cost STM32 processor system, a lightweight algorithm that includes frequency channel selection, model pretraining, and real-time classification is proposed. Through a series of short-term and overnight experiments with 21 subjects, the feasibility and reliability of the proposed system were evaluated. Experimental results show that the accuracy of the short-term experiment is up to 95.43% and of the overnight experiment is up to 86.80% for four posture categories (supine, prone, right, and left) classification. The model size is just 56 kB which is much smaller than other methods. The runtime of the complete algorithm is about 6 ms with a low-power STM32 embedded system, which shows the system’s ability to provide real-time posture recognition. As an edge device, the proposed system could lead to the development of fast, convenient, and low-cost sleep posture recognition products for IoMT.
TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval

Entity retrieval-retrieving information about entity mentions in a query-is a key step in open-domain tasks, such as question answering or fact checking.However, state-of-the-art entity retrievers struggle to retrieve rare entities for ambiguous mentions due to biases towards popular entities.Incorporating knowledge graph types during training could help overcome popularity biases, but there are several challenges: (1) existing type-based retrieval methods require mention boundaries as input, but open-domain tasks run on unstructured text, (2) type-based methods should not compromise overall performance, and (3) type-based methods should be robust to noisy and missing types.In this work, we introduce TABi, a method to jointly train bi-encoders on knowledge graph types and unstructured text for entity retrieval for open-domain tasks.TABi leverages a typeenforced contrastive loss to encourage entities and queries of similar types to be close in the embedding space.TABi improves retrieval of rare entities on the Ambiguous Entity Retrieval (AmbER) sets, while maintaining strong overall retrieval performance on open-domain tasks in the KILT benchmark compared to state-of-the-art retrievers.TABi is also robust to incomplete type systems, improving rare entity retrieval over baselines with only 5% type coverage of the training dataset.We make our code publicly available.
Toward achieving sustainable development agenda: Nexus between agriculture, trade openness, and oil rents in Nigeria

Abstract Over the years, agriculture has been considered as a panacea for long-term economic growth as believed by the physiocracy school of thought. Aligning this with the United Nations’ Sustainable Development Goals (specifically UN-SDG-2 which highlights zero hunger), the present study empirically complements existing studies by exploring the interactions between agriculture, trade openness, and oil rents using annual time frequency series data from 1981–2017. A series of analyses is conducted. First, a battery of non-stationarity and stationarity unit root tests are performed; these range from the traditional Augmented Dickey-Fuller (ADF) and Phillips–Perron (PP) techniques to the relatively recent Zivot Andrews (ZA) unit root test which accounts for a single structural break to ascertain stationarity properties in the variables under review. Subsequently, the recent Bayer–Hanck cointegration test in conjunction with the Johansen cointegration test was used for the cointegration analysis. Furthermore, to detect the direction of causality, the Toda-Yamamoto Granger Causality test alongside the impulse response function technique shows insightful outcomes. From the empirical results, cointegration is apparent and a long-run equilibrium relationship is traced between the outlined variables over the investigated period. The causality results and impulse response analysis highlight the existence of one-way causality links running from agriculture to trade and from trade to oil rents. The causality test results are informative, and the plausible explanation might be due to the dwindling oil market prices. More insights are elucidated in the conclusion section accordingly.
Improving the Accuracy of Progress Indication for Constructing Deep Learning Models

For many machine learning tasks, deep learning greatly outperforms all other existing learning algorithms. However, constructing a deep learning model on a big data set often takes days or months. During this long process, it is preferable to provide a progress indicator that keeps predicting the model construction time left and the percentage of model construction work done. Recently, we developed the first method to do this that permits early stopping. That method revises its predicted model construction cost using information gathered at the validation points, where the model&#x2019;s error rate is computed on the validation set. Due to the sparsity of validation points, the resulting progress indicators often have a long delay in gathering information from enough validation points and obtaining relatively accurate progress estimates. In this paper, we propose a new progress indication method to overcome this shortcoming by judiciously inserting extra validation points between the original validation points. We implemented this new method in TensorFlow. Our experiments show that compared with using our prior method, using this new method reduces the progress indicator&#x2019;s prediction error of the model construction time left by 57.5% on average. Also, with a low overhead, this new method enables us to obtain relatively accurate progress estimates faster.
Indonesian EFL Learners' Ability to Comprehend and Produce Implicature

To achieve a successful course of communication, one must master pragmatics, the study of context. To be able to have a proficient pragmatic competence, as one of macrolinguistics’ fields, getting acquainted with the target language’s cultural background is a must for foreign language learners. In a situation where someone is offered something, Indonesia has its own culture where the polite way to respond is to not directly accept or refuse the offer. This is actually considered as using implicature, one of the subfields of pragmatics. According to Stanford Encyclopedia of Philosophy (2019), implicature can be defined as the act of implying meaning by saying something else. The aim of using implicature is to mislead a response without lying while also maintaining a good social interaction. The objective of this study is to identify if EFL learners are capable of comprehending and producing implicature, one of pragmatics subfields or not. The method used in this study is descriptive qualitative research. The data were collected through assigning questionnaires and doing interviews on Brawijaya University students. The result of the study shows how most students are able to comprehend and produce implicature in English. The type of implicature they used was conversational implicature, one of the principal subjects of pragmatics.
Deep Learning on Automatic Fall Detection

Nowadays, independent older people stay alone for long periods, which increases the risk of being seriously damaged after a fall without the quick attendance of medical services. Several smart clothing equipment was created to detect these falls using sensors such as accelerometers and gyroscopes, allowing a short intervention to the victims. This work considers the Sisfall database, where 23 adults and 15 older people performed several daily living simulations. The signals registered by three sensors were used to train a Long Short-Term Memory network and a Bi-Long Short-Term Memory network to detect everyday activities and falls. Several experiments were performed, where the BiLSTM model outperforms the LSTM model with a mean accuracy of 99.21% on the testing set.