Reviewerly/exHarmony huggingface dataset:
Deep Learning based Semantic Similarity Detection using Text Data

Similarity detection in the text is the main task for a number of Natural Language Processing (NLP) applications. As textual data is comparatively large in quantity and huge in volume than the numeric data, therefore measuring textual similarity is one of the important problems. Most of the similarity detection algorithms are based upon word to word matching, sentence/paragraph matching, and matching of the whole document. In this research, a novel approach is proposed using deep learning models, combining Long Short Term Memory network (LSTM) with Convolutional Neural Network (CNN) for measuring semantics similarity between two questions. The proposed model takes sentence pairs as input to measure the similarity between them. The model is tested on publicly available Quora’s dataset. The model in comparison to the existing techniques gave 87.50 % accuracy which is better than the previous approaches.
Exploiting Learnable Joint Groups for Hand Pose Estimation

In this paper, we propose to estimate 3D hand pose by recovering the 3D coordinates of joints in a group-wise manner, where less-related joints are automatically categorized into different groups and exhibit different features. This is different from the previous methods where all the joints are considered holistically and share the same feature. The benefits of our method are illustrated by the principle of multi-task learning (MTL), i.e., by separating less-related joints into different groups (as different tasks), our method learns different features for each of them, therefore efficiently avoids the negative transfer (among less related tasks/groups of joints). The key of our method is a novel binary selector that automatically selects related joints into the same group. We implement such a selector with binary values stochastically sampled from a Concretedistribution, which is constructed using Gumbel softmax on trainable parameters. This enables us to preserve the differentiable property of the whole network. We further exploit features from those less-related groups by carrying out an additional feature fusing scheme among them, to learn more discriminative features. This is realized by implementing multiple 1x1 convolutions on the concatenated features, where each joint group contains a unique 1x1convolution for feature fusion. The detailed ablation analysis and the extensive experiments on several benchmark datasets demonstrate the promising performance of the proposed method over the state-of-the-art (SOTA) methods. Besides, our method achieves top-1 among all the methods that do not exploit the dense 3D shape labels on the most recently released FreiHAND competition at the submission date. The source code and models are available at https://github.com/moranli-aca/LearnableGroups-Hand.
A Framework for Detecting and Analyzing Behavior Changes of Elderly People over Time Using Learning Techniques

A sensor-rich environment can be exploited for elder healthcare applications. In this work, our objective was to conduct a continuous and long-term analysis of elderly’s behavior for detecting changes. We indeed did not study snapshots of the behavior but, rather, analyzed the overall behavior evolution over long periods of time in order to detect anomalies. Therefore, we proposed a learning method and formalize a normal behavior pattern for elderly people related to her/his Activities of Daily Living (ADL). We also defined a temporal similarity score between activities that allows detecting behavior changes over time. During the periods of time when behavior changes occurred, we then focused on each activity to identify anomalies. Finally, when a behavior change occurred, it was also necessary to help caregivers and/or family members understand the possible pathology detected in order for them to react accordingly. Therefore, the framework presented in this article includes a fuzzy logic-based decision support system that provides information about the suspected disease and its severity.
Hierarchical Graph Capsule Network

Graph Neural Networks (GNNs) draw their strength from explicitly modeling the topological information of structured data. However, existing GNNs suffer from limited capability in capturing the hierarchical graph representation which plays an important role in graph classification. In this paper, we innovatively propose hierarchical graph capsule network (HGCN) that can jointly learn node embeddings and extract graph hierarchies. Specifically, disentangled graph capsules are established by identifying heterogeneous factors underlying each node, such that their instantiation parameters represent different properties of the same entity. To learn the hierarchical representation, HGCN characterizes the part-whole relationship between lower-level capsules (part) and higher-level capsules (whole) by explicitly considering the structure information among the parts. Experimental studies demonstrate the effectiveness of HGCN and the contribution of each component. Code: https://github.com/uta-smile/HGCN
3D-GAT: 3D-Guided adversarial transform network for person re-identification in unseen domains

Person Re-identification (ReID) has witnessed remarkable improvements in the past couple of years. However, its applications in real-world scenarios are limited by the disparity among different cameras and datasets. In general, it remains challenging to generalize ReID algorithms from one domain to another, especially when the target domain is unknown. To solve this issue, we develop a 3D-guided adversarial transform (3D-GAT) network which explores the transfer ability of source training data to facilitate learning domain-independent knowledge. Being aware of a 3D model and human poses, 3D-GAT makes use of image-to-image translation to synthesize person images in different conditions whilst preserving features for identification as much as possible. With these augmented training data, it is easier for ReID approaches to perceive how a person can appear differently under varying viewpoints and poses, most of which are not seen in the training data, and thus achieve higher ReID accuracy especially in an unknown domain. Extensive experiments conducted on Market-1501, DukeMTMC-reID and CUHK03 demonstrate the effectiveness of our proposed approach, which is competitive to the baseline models in the original dataset and sets the new state-of-the-art in direct transfer to other datasets.
Semi-automatic generation of multilingual datasets for stance detection in Twitter

Popular social media networks provide the perfect environment to study the opinions and attitudes expressed by users. While interactions in social media such as Twitter occur in many natural languages, research on stance detection (the position or attitude expressed with respect to a specific topic) within the Natural Language Processing field has largely been done for English. Although some efforts have recently been made to develop annotated data in other languages, there is a telling lack of resources to facilitate multilingual and crosslingual research on stance detection. This is partially due to the fact that manually annotating a corpus of social media texts is a difficult, slow and costly process. Furthermore, as stance is a highly domain- and topic-specific phenomenon, the need for annotated data is specially demanding. As a result, most of the manually labeled resources are hindered by their relatively small size and skewed class distribution. This paper presents a method to obtain multilingual datasets for stance detection in Twitter. Instead of manually annotating on a per tweet basis, we leverage user-based information to semi-automatically label large amounts of tweets. Empirical monolingual and cross-lingual experimentation and qualitative analysis show that our method helps to overcome the aforementioned difficulties to build large, balanced and multilingual labeled corpora. We believe that our method can be easily adapted to easily generate labeled social media data for other Natural Language Processing tasks and domains.
Universal Sentence Representation Learning with Conditional Masked Language Model

This paper presents a novel training method, Conditional Masked Language Modeling (CMLM), to effectively learn sentence representations on large scale unlabeled corpora.CMLM integrates sentence representation learning into MLM training by conditioning on the encoded vectors of adjacent sentences.Our English CMLM model achieves state-ofthe-art performance on SentEval (Conneau and Kiela, 2018), even outperforming models learned using supervised signals.As a fully unsupervised learning method, CMLM can be conveniently extended to a broad range of languages and domains.We find that a multilingual CMLM model co-trained with bitext retrieval (BR) and natural language inference (NLI) tasks outperforms the previous state-of-the-art multilingual models by a large margin, e.g.10% improvement upon baseline models on cross-lingual semantic search.We explore the same language bias of the learned representations, and propose a simple, post-training and model agnostic approach to remove the language identifying information from the representation while still retaining sentence semantics.
Multilingual Neural RST Discourse Parsing

Text discourse parsing plays an important role in understanding information flow and argumentative structure in natural language.Previous research under the Rhetorical Structure Theory (RST) has mostly focused on inducing and evaluating models from the English treebank.However, the parsing tasks for other languages such as German, Dutch, and Portuguese are still challenging due to the shortage of annotated data.In this work, we investigate two approaches to establish a neural, cross-lingual discourse parser via: (1) utilizing multilingual vector representations; and(2) adopting segment-level translation of the source content.Experiment results show that both methods are effective even with limited training data, and achieve state-of-the-art performance on cross-lingual, document-level discourse parsing on all sub-tasks.
LISAC FSDM-USMBA Team at SemEval-2020 Task 12: Overcoming AraBERT’s pretrain-finetune discrepancy for Arabic offensive language identification

AraBERT is an Arabic version of the state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) model.The latter has achieved good performance in a variety of Natural Language Processing (NLP) tasks.In this paper, we propose an effective AraBERT embeddingsbased method for dealing with offensive Arabic language in Twitter.First, we pre-process tweets by handling emojis and including their Arabic meanings.Next, to overcome the pretrain-finetune discrepancy, we substitute each detected emojis by the special token [MASK] into both fine tuning and inference phases.Then, we represent tweets tokens by applying AraBERT model.Finally, we feed the tweet representation into a sigmoid function to decide whether a tweet is offensive or not.The proposed method achieved the best results on OffensEval 2020: Arabic task and reached a macro F1 score equal to 90.17%.
Adapting Bidirectional Encoder Representations from Transformers (BERT) to Assess Clinical Semantic Textual Similarity: Algorithm Development and Validation Study

Background Natural Language Understanding enables automatic extraction of relevant information from clinical text data, which are acquired every day in hospitals. In 2018, the language model Bidirectional Encoder Representations from Transformers (BERT) was introduced, generating new state-of-the-art results on several downstream tasks. The National NLP Clinical Challenges (n2c2) is an initiative that strives to tackle such downstream tasks on domain-specific clinical data. In this paper, we present the results of our participation in the 2019 n2c2 and related work completed thereafter. Objective The objective of this study was to optimally leverage BERT for the task of assessing the semantic textual similarity of clinical text data. Methods We used BERT as an initial baseline and analyzed the results, which we used as a starting point to develop 3 different approaches where we (1) added additional, handcrafted sentence similarity features to the classifier token of BERT and combined the results with more features in multiple regression estimators, (2) incorporated a built-in ensembling method, M-Heads, into BERT by duplicating the regression head and applying an adapted training strategy to facilitate the focus of the heads on different input patterns of the medical sentences, and (3) developed a graph-based similarity approach for medications, which allows extrapolating similarities across known entities from the training set. The approaches were evaluated with the Pearson correlation coefficient between the predicted scores and ground truth of the official training and test dataset. Results We improved the performance of BERT on the test dataset from a Pearson correlation coefficient of 0.859 to 0.883 using a combination of the M-Heads method and the graph-based similarity approach. We also show differences between the test and training dataset and how the two datasets influenced the results. Conclusions We found that using a graph-based similarity approach has the potential to extrapolate domain specific knowledge to unseen sentences. We observed that it is easily possible to obtain deceptive results from the test dataset, especially when the distribution of the data samples is different between training and test datasets.
Hand Gestures for Elderly Care Using a Microsoft Kinect

The link between humans and computers, called human-computer interaction (HCI) techniques, has the potential to improve quality of life, where analysis of the information collected from humans through computers allows personal patient requirements to be achieved.Among them, a computer vision system for helping elderly patients currently attracts a large amount of research interest to avail of personal requirements.This paper proposes a real-time computer vision system to recognize hand gestures for elderly patients who are disabled or unable to translate their orders or feelings into words.The proposed system uses a Microsoft Kinect v2 Sensor, installed in front of the elderly patient, to recognize hand signs that correspond to a specific request and sends their meanings to the care provider or family member through a microcontroller and global system for mobile communications (GSM).The experimental results illustrated the effectiveness of the proposed system, which showed promising results with several hand signs, whilst being reliable, safe, comfortable, and cost-effective.
Photometric transfer for direct visual odometry

Due to efficient photometric information utilization, direct visual odometry (DVO) is getting widely used to estimate the ego-motion of moving cameras as well as map the environment from videos simultaneously, especially in challenging weak-texture scenarios. However, DVO suffers from brightness discrepancies since it directly utilizes intensity patterns of pixels to register frames for camera pose estimation. Most existing brightness transfer methods build a fixed transfer function which is inappropriate for successive and inconsistent brightness changes in practice. To overcome this problem, we propose a Photometric Transfer Net (PTNet) which is trained to pixel-wisely remove brightness discrepancies between two frames without ruining the context information. Photometric consistency in DVO is obtained by adjusting the source frame according to the reference frame. Since no dataset is available for training the photometric transfer model, we augment the EuRoC dataset by generating a certain number of frames with different brightness levels for each original frame by a nonlinear transformation. Afterwards, required training data containing various brightness changes and scene movements along with ground truth can be collected from the extended sequences. Evaluations on both real-world and synthetic datasets demonstrate the effectiveness of the proposed model. Assessment on an unseen dataset with fixed model parameters trained on another dataset proves the generalization ability of the model. Furthermore, we embed the model into DVO to preprocess input data with brightness discrepancies. Experimental results show that PTNet-based DVO achieves more robust initialization and accurate pose estimation than the original one.
Digital Development of Education and Universities: Global Challenges of the Digital Economy

The article deals with the main aspects of the digital technology impact on education.The aim of the research is to collect and analyze most of digital technologies that have been implemented into educational system of Uzbekistan recently and to find out how they have influenced upon the development of the modern educational methods.The paper presents an assessment of the quality of education on the basis of system performance indicators, which makes it possible to evaluate education in the framework of management criteria based on the results of the participants' preparedness for practical activities.Analysing the foreign and domestic experience, the solution was found out to manage the educational system of the higher education organizations.At the same time, the work stipulates that such an approach should not violate the existing academic traditions and prevent commercial orientation of university management.
Applications of Clustering Techniques in Data Mining: A Comparative Study

In modern scientific research, data analyses are often used as a popular tool across computer science, communication science, and biological science.Clustering plays a significant role in the reference composition of data analysis.Clustering, recognized as an essential issue of unsupervised learning, deals with the segmentation of the data structure in an unknown region and is the basis for further understanding.Among many clustering algorithms, "more than 100 clustering algorithms known" because of its simplicity and rapid convergence, the K-means clustering algorithm is commonly used.This paper explains the different applications, literature, challenges, methodologies, considerations of clustering methods, and related key objectives to implement clustering with big data.Also, presents one of the most common clustering technique for identification of data patterns by performing an analysis of sample data.
Mapping hydrothermal alteration mineral deposits from Landsat 8 satellite data in Pala, Mayo Kebbi Region, Southwestern Chad

The different image attribute extracting techniques, spectral enhancement ability, pixel classification methods have been applied on the Landsat 8 OLI satellite image data to map geological features and spatial distribution of hydrothermal alteration zones, which have been recorded to be associated with gold mineralization in the Pala, Mayo Kebbi region of southwestern Chad. Radiometric calibrated, noise filtered and image enhanced Landsat 8 OLI data were subjected to colour composite, band rationing and crosta analyses in order to enhance spectral reflectance and compositional information, create colourful multispectral images which enable the mapping of desired spectral bands into principal component images for the purpose of accentuating salient information needful to delineate hydrothermal alteration zones within the study area. Composite ratios 4/2-6/7-6/5 indicate relative spatial distribution of iron-oxide in the southeast while clays and hydroxyl minerals occur in the west and northwestern part of the study area. The eigenvectors and eigenvalues for hydroxyl minerals from the crosta analysis indicate highest loadings of opposite signs (-0.721498 & 0.649465; bands 6 & 7) in PC3, representing ''H'' image. The eigenvectors and eigenvalues for iron-oxides on the other hand present highest loading of opposite signs (0.895721 & -0.443283; bands 2 & 4) in PC4, representing the "F" image. The white coloured pixels within the bright reflections generated from Crosta composite image of H, H+F & F in RGB delineate hydrothermal alteration zones. The plot of delineated zones of hydrothermal alteration on the Landsat 8 true colour indicate spatial distribution of the alteration areas within the host greenstone belt belonging to the Precambrian Goueygoudoum series and they plot mainly along major structural features, suggesting structural control. Field control revealed that some areas delineated by band rationing as hydrothermal alteration zones appear to be the result of intense weathering. Accordingly, we recommend that remote sensing delineation of hydrothermal alteration zones should be checked by a relevant ground-observation based control procedure.
Multiclass Event Classification from Text

Social media has become one of the most popular sources of information. People communicate with each other and share their ideas, commenting on global issues and events in a multilingual environment. While social media has been popular for several years, recently, it has given an exponential rise in online data volumes because of the increasing popularity of local languages on the web. This allows researchers of the NLP community to exploit the richness of different languages while overcoming the challenges posed by these languages. Urdu is also one of the most used local languages being used on social media. In this paper, we presented the first-ever event detection approach for Urdu language text. Multiclass event classification is performed by popular deep learning (DL) models, i.e.,Convolution Neural Network (CNN), Recurrence Neural Network (RNN), and Deep Neural Network (DNN). The one-hot-encoding, word embedding, and term-frequency inverse document frequency- (TF-IDF-) based feature vectors are used to evaluate the Deep Learning(DL) models. The dataset that is used for experimental work consists of more than 0.15 million (103965) labeled sentences. DNN classifier has achieved a promising accuracy of 84% in extracting and classifying the events in the Urdu language script.
Stochastic proximal gradient methods for nonconvex problems in Hilbert spaces

Abstract For finite-dimensional problems, stochastic approximation methods have long been used to solve stochastic optimization problems. Their application to infinite-dimensional problems is less understood, particularly for nonconvex objectives. This paper presents convergence results for the stochastic proximal gradient method applied to Hilbert spaces, motivated by optimization problems with partial differential equation (PDE) constraints with random inputs and coefficients. We study stochastic algorithms for nonconvex and nonsmooth problems, where the nonsmooth part is convex and the nonconvex part is the expectation, which is assumed to have a Lipschitz continuous gradient. The optimization variable is an element of a Hilbert space. We show almost sure convergence of strong limit points of the random sequence generated by the algorithm to stationary points. We demonstrate the stochastic proximal gradient algorithm on a tracking-type functional with a $$L^1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mn>1</mml:mn> </mml:msup> </mml:math> -penalty term constrained by a semilinear PDE and box constraints, where input terms and coefficients are subject to uncertainty. We verify conditions for ensuring convergence of the algorithm and show a simulation.