Reviewerly/exHarmony huggingface dataset:
Coreference Resolution for Ambiguous Pronoun with BERT and MLP

The process of identifying all the expressions in a text which refer to the same real-world entity is known as coreference resolution. It is one of the main challenges that are faced in Natural Language Processing. We are focusing on creating a better approach for the task of ambiguous pronoun resolution. We are proposing to make use of a BERT-based approach which makes use of PyTorch pre-trained BERT and PyTorch helper bot along with a custom-made MultiLayerPerceptron model as a classifier to solve this problem. We are using the dataset released by Google AI called Gendered Ambiguous Pronouns. The contextual embedding is received by training the preprocessed data with Pretrained BERT and then the contextual embeddings are passed to the MLP Classifier which is used for classification purposes to get results for coreference resolution for target pronoun.
Age of Exposure 2.0: Estimating word complexity using iterative models of word embeddings

Age of acquisition (AoA) is a measure of word complexity which refers to the age at which a word is typically learned. AoA measures have shown strong correlations with reading comprehension, lexical decision times, and writing quality. AoA scores based on both adult and child data have limitations that allow for error in measurement, and increase the cost and effort to produce. In this paper, we introduce Age of Exposure (AoE) version 2, a proxy for human exposure to new vocabulary terms that expands AoA word lists through training regressors to predict AoA scores. Word2vec word embeddings are trained on cumulatively increasing corpora of texts, word exposure trajectories are generated by aligning the word2vec vector spaces, and features of words are derived for modeling AoA scores. Our prediction models achieve low errors (from 13% with a corresponding R2 of .35 up to 7% with an R2 of .74), can be uniformly applied to different AoA word lists, and generalize to the entire vocabulary of a language. Our method benefits from using existing readability indices to define the order of texts in the corpora, while the performed analyses confirm that the generated AoA scores accurately predicted the difficulty of texts (R2 of .84, surpassing related previous work). Further, we provide evidence of the internal reliability of our word trajectory features, demonstrate the effectiveness of the word trajectory features when contrasted with simple lexical features, and show that the exclusion of features that rely on external resources does not significantly impact performance.
Non-local Attention Improves Description Generation for Retinal Images

Automatically generating medical reports from retinal images is a difficult task in which an algorithm must generate semantically coherent descriptions for a given retinal image. Existing methods mainly rely on the input image to generate descriptions. However, many abstract medical concepts or descriptions cannot be generated based on image information only. In this work, we integrate additional information to help solve this task; we observe that early in the diagnosis process, ophthalmologists have usually written down a small set of keywords denoting important information. These keywords are then subsequently used to aid the later creation of medical reports for a patient. Since these keywords commonly exist and are useful for generating medical reports, we incorporate them into automatic report generation. Since we have two types of inputs expert-defined unordered keywords and images - effectively fusing features from these different modalities is challenging. To that end, we propose a new keyword-driven medical report generation method based on a non-local attention-based multi-modal feature fusion approach, TransFuser, which is capable of fusing features from different types of inputs based on such attention. Our experiments show the proposed method successfully captures the mutual information of keywords and image content. We further show our proposed keyword-driven generation model reinforced by the TransFuser is superior to baselines under the popular text evaluation metrics BLEU, CIDEr, and ROUGE. Trans-Fuser Github: https://github.com/Jhhuangkay/Non-local-Attention-ImprovesDescription-Generation-for-Retinal-Images.
Flesch-Kincaid Measure as Proxy of Socio-Economic Status on Twitter

Social media gives researchers an invaluable opportunity to gain insight into different facets of human life. Researchers put a great emphasis on categorizing the socioeconomic status (SES) of individuals to help predict various findings of interest. Forum uses, hashtags and chatrooms are common tools of conversations grouping. Crowdsourcing involves gathering intelligence to group online user community based on common interest. This paper provides a mechanism to look at writings on social media and group them based on their academic background. We analyzed online forum posts from various geographical regions in the US and characterized the readability scores of users. Specifically, we collected 10,000 tweets from the members of US Senate and computed the Flesch-Kincaid readability score. Comparing the Senators’ tweets to the ones from average internet users, we note 1) US Senators’ readability based on their tweets rate is much higher, and 2) immense difference among average citizen’s score compared to those of US Senators is attributed to the wide spectrum of academic attainment.
Applying BERT Embeddings to Predict Legal Textual Entailment

Abstract Textual entailment classification is one of the hardest tasks for the Natural Language Processing community. In particular, working on entailment with legal statutes comes with an increased difficulty, for example in terms of different abstraction levels, terminology and required domain knowledge to solve this task. In course of the COLIEE competition, we develop three approaches to classify entailment. The first approach combines Sentence-BERT embeddings with a graph neural network, while the second approach uses the domain-specific model LEGAL-BERT, further trained on the competition’s retrieval task and fine-tuned for entailment classification. The third approach involves embedding syntactic parse trees with the KERMIT encoder and using them with a BERT model. In this work, we discuss the potential of the latter technique and why of all our submissions, the LEGAL-BERT runs may have outperformed the graph-based approach.
3DMesh-GAR: 3D Human Body Mesh-Based Method for Group Activity Recognition

Group activity recognition is a prime research topic in video understanding and has many practical applications, such as crowd behavior monitoring, video surveillance, etc. To understand the multi-person/group action, the model should not only identify the individual person's action in the context but also describe their collective activity. A lot of previous works adopt skeleton-based approaches with graph convolutional networks for group activity recognition. However, these approaches are subject to limitation in scalability, robustness, and interoperability. In this paper, we propose 3DMesh-GAR, a novel approach to 3D human body Mesh-based Group Activity Recognition, which relies on a body center heatmap, camera map, and mesh parameter map instead of the complex and noisy 3D skeleton of each person of the input frames. We adopt a 3D mesh creation method, which is conceptually simple, single-stage, and bounding box free, and is able to handle highly occluded and multi-person scenes without any additional computational cost. We implement 3DMesh-GAR on a standard group activity dataset: the Collective Activity Dataset, and achieve state-of-the-art performance for group activity recognition.
Ruled Path Planning Framework for Safe and Dynamic Navigation

The need for vehicle autonomy is constantly increasing, requiring increased load of on-board processing. However, most of low-cost agents are not able to handle the workload in real-time. In this paper, a path planning framework is presented for safe and dynamic navigation in multi-dimension environments. It is design to minimise the computational power required while remaining able to quickly adapt. For this, a highly Paramatrised Rapidly-exploring Random Graph (PRRG) is developed along side a system of rules allowing dynamic node selection and the D* Lite search algorithm to create the path. All of the graph parameters can be changed dynamically, making the graph able to adapt to the environment in real-time. Eventually, an extension for custom node generation is proposed, enabling specific routes to be created online which is especially relevant for survey and inspection applications. Although this framework was made for navigation of Autonomous Underwater Vehicles (AUVs), it can find applications to other domains such as navigation of Unmanned Aerial Vehicles (UAVs). The capabilities of the framework are demonstrated considering an underwater ship hull inspection scenario and confirmed its usefulness and effectiveness for real-time operations.
Adapt or Perish? Exploring the Effectiveness of Adaptive DoF Control Interaction Methods for Assistive Robot Arms

Robot arms are one of many assistive technologies used by people with motor impairments. Assistive robot arms can allow people to perform activities of daily living (ADL) involving grasping and manipulating objects in their environment without the assistance of caregivers. Suitable input devices (e.g., joysticks) mostly have two Degrees of Freedom (DoF), while most assistive robot arms have six or more. This results in time-consuming and cognitively demanding mode switches to change the mapping of DoFs to control the robot. One option to decrease the difficulty of controlling a high-DoF assistive robot arm using a low-DoF input device is to assign different combinations of movement-DoFs to the device’s input DoFs depending on the current situation (adaptive control). To explore this method of control, we designed two adaptive control methods for a realistic virtual 3D environment. We evaluated our methods against a commonly used non-adaptive control method that requires the user to switch controls manually. This was conducted in a simulated remote study that used Virtual Reality and involved 39 non-disabled participants. Our results show that the number of mode switches necessary to complete a simple pick-and-place task decreases significantly when using an adaptive control type. In contrast, the task completion time and workload stay the same. A thematic analysis of qualitative feedback of our participants suggests that a longer period of training could further improve the performance of adaptive control methods.
Code-quality evaluation scheme for assessment of student contributions to programming projects

Project-based learning is the most common approach to software engineering education, due to its emphasis on the teamwork skills essential to real-world collaborations. This study developed an automated programming assessment system (APAS) featuring a code-quality evaluation scheme to overcome difficulties in assessing the contribution of individual team members. Team participation is visualized on a weekly basis to provide insights pertaining to team dynamics, and metrics based on code quality allow the segmentation of students by level of contribution. Insights provided by the proposed system were also shown to facilitate interventions aimed at improving code quality. Empirical analysis based on submission data from 146 students (41 teams) demonstrated the feasibility of the proposed system in monitoring group-based learning projects at the university level, particularly in detecting free-riders.
Learning under unawareness

Abstract We propose a model of learning when experimentation is possible, but unawareness and ambiguity matter. In this model, complete lack of information regarding the underlying data generating process is expressed as a (maximal) family of priors. These priors yield posterior inferences that become more precise as more information becomes available. As information accumulates, however, the individual’s level of awareness as encoded in the state space may expand. Such newly learned states are initially seen as ambiguous, but as evidence accumulates there is a gradual reduction of ambiguity.
Multi-Extended Target Tracking Algorithm Based on VBEM-CPHD

Considering that the extended targets tracking problem of the measurement is a glint noise with an unknown inverse covariance, a new algorithm from a multi-extended target tracking based on the Variational Bayesian Expectation Maximization (VBEM) is proposed. To improve the variational Bayesian technique, a modeled Student’s t distribution is proposed based on multiple Gaussian mixture terms in order to replace the probability hypothesis density (PHD) intensity. The extended target was modeled with a Student’s t distribution with the glint noise, using Gauss-Gamma distribution combining the variational Bayesian technique to obtain an approximate distribution and applying the expectation-maximization algorithm for iterative estimation. The two experiments are compared and analyzed with the VBEM-CPHD algorithm and the traditional extended target tracking algorithm. Experiment 1 estimated the trajectory of the target, compared algorithms of VBEM-CPHD and GM-CPHD with OSPA distance, varied glint noise with the three different measurement noise standard deviations. Experiment 2 completed the examination of the tracking performance and stability of the proposed method and performed to compare the VBEM-CPHD algorithm proposed with the VB-based GM-CPHD (GM-VBCPHD) algorithm under an unknown measurement noise covariance. The experimental results indicate that the algorithm of VBEM-CPHD has high tracking accuracy, good adaptability, and a strong antijamming ability for multiple extended targets under glint noise conditions.
Deep embedding kernel mixture networks for conditional anomaly detection in high-dimensional data

In various industrial problems, sensor data are often used to detect the abnormal state of manufacturing systems. Sensor data are sometimes influenced by contextual variables that are not related to the system health status and may exhibit different behaviours depending on their values, even if the system is in a normal condition. In this case, a conditional anomaly detection method should be used to consider the effects of contextual variables. In this study, we propose a conditional anomaly detection method, particularly for high-dimensional and complex data, using a deep embedding kernel mixture network. The proposed method comprises embedding and kernel mixture networks. The embedding network learns low-dimensional embeddings from high-dimensional data, and the kernel mixture network models the distribution of the learned embeddings conditional on contextual variables. The two networks enable a flexible estimation of conditional density using the high expressive power of deep neural networks. The two networks are trained simultaneously such that the high-dimensional data are embedded into a low-dimensional space, to assist conditional density estimation. The effectiveness of the proposed model is demonstrated using real data examples from the UCI repository and a case study from a tire company.
Evaluation of News's Website with Webqual And Importance Performance Analysis

The phenomenon of today&#x0027;s society tends to trust the news on social media more than the news published by online news portals. For this reason, it is necessary to measure the quality of the news portal website. journalistic activities are conducted by utilizing print, electronic, and online media. Modern societyoften employs online media to read information. Therefore, the purposes of this study were to measurethe users&#x0027;satisfactionwithportal websites. Following this, performance achievement on the news portal website was also evaluated. By using WebQual and Importance Performance Analysis (IPA), we performed a subjective test. Data questionaries were collected from 97 respondents in which the sample data was simple random sampling. The news portal websites that have been reviewed are Tribunnews, Terkini, Inikata, Rakyatku and Fajar. The overall analysis results showed the Fajar news portal website is the best in usability (0.76), Information Quality (0.76), and Service Interaction Quality (0.72) categories. Indicators need to be improved about the positive experience given, the accuracy of the information, and the level of trust in the news, while the good indicators relate to ease of use, information is easy to understand and presented in detail.
Deep Learning-based detection of psychiatric attributes from German mental health records

Health care records provide large amounts of data with real-world and longitudinal aspects, which is advantageous for predictive analyses and improvements in personalized medicine. Text-based records are a main source of information in mental health. Therefore, application of text mining to the electronic health records - especially mental state examination - is a key approach for detection of psychiatric disease phenotypes that relate to treatment outcomes.We focused on the mental state examination (MSE) in the patients' discharge summaries as the key part of the psychiatric records. We prepared a sample of 150 text documents that we manually annotated for psychiatric attributes and symptoms. These documents were further divided into training and test sets. We designed and implemented a system to detect the psychiatric attributes automatically and linked the pathologically assessed attributes to AMDP terminology. This workflow uses a pre-trained neural network model, which is fine-tuned on the training set, and validated on the independent test set. Furthermore, a traditional NLP and rule-based component linked the recognized mentions to AMDP terminology. In a further step, we applied the system on a larger clinical dataset of 510 patients to extract their symptoms.The system identified the psychiatric attributes as well as their assessment (normal and pathological) and linked these entities to the AMDP terminology with an F1-score of 86% and 91% on an independent test set, respectively.The development of the current text mining system and the results highlight the feasibility of text mining methods applied to MSE in electronic mental health care reports. Our findings pave the way for the secondary use of routine data in the field of mental health, facilitating further clinical data analyses.
Transformer Fault Diagnosis Technology Based on Maximally Collapsing Metric Learning and Parameter Optimization Kernel Extreme Learning Machine

Aiming at the problem of unsatisfactory diagnosis performance of conventional fault diagnosis methods for transformer, a novel method based on maximally collapsing metric learning algorithm (MCML) and parameter optimization kernel extreme learning machine (KELM) is proposed in this study. First, a new set of dissolved gas analysis (DGA) features combination, which can reflect the transformer fault information, is used to form the input feature space. Then, the MCML is employed to reduce the feature space dimension to extract a set of optimal DGA features combination. Finally, the salp swarm algorithm (SSA) is utilized to optimize the parameters in KELM to establish an SSA‐KELM model, which is adopted to diagnose and identify transformer faults. The proposed method is applied to the International Electrotechnical Commission (IEC) TC 10 database, and the results show that the feature extraction effect of MCML is superior than that of linear discriminant analysis, neighborhood preserving embedding, and Laplacian eigenmaps. The optimal DGA feature set is more advantageous than the frequent‐used DGA data, IEC ratios, Rogers ratios, and Doernenburg Ratios. The diagnosis accuracy of SSA‐KELM is better than that of KELM, particle swarm optimization‐KELM, genetic algorithm‐KELM, and loin swarm optimization‐KELM. Furthermore, the generalization and robustness ability of the MCML and SSA‐KELM is confirmed by the China DGA samples, the obtained results verify the reliability and validity of the proposed method again. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Fast and Lightweight Online Person Search for Large-Scale Surveillance Systems

The demand for methods for video analysis in the field of surveillance technology is rapidly growing due to the increasing amount of surveillance footage available. Intelligent methods for surveillance software offer numerous possibilities to support police investigations and crime prevention. This includes the integration of video processing pipelines for tasks such as detection of graffiti, suspicious luggage, or intruders. Another important surveillance task is the semi-automated search for specific persons-of-interest within a camera network. In this work, we identify the major obstacles for the development of person search systems as the real-time processing capability on affordable hardware and the performance gap of person detection and re-identification methods on unseen target domain data. In addition, we demonstrate the current potential of intelligent online person search by developing a real-world, large-scale surveillance system. An extensive evaluation is provided for person detection, tracking, and re-identification components on affordable hardware setups, for which the whole system achieves real-time processing up to 76 FPS.
Efficient Layout Hotspot Detection via Neural Architecture Search

Layout hotspot detection is of great importance in the physical verification flow. Deep neural network models have been applied to hotspot detection and achieved great success. Despite their success, high-performance neural networks are still quite difficult to design. In this article, we propose a bayesian optimization-based neural architecture search scheme to automatically do this time-consuming and fiddly job. Experimental results on ICCAD 2012 and ICCAD 2019 Contest benchmarks show that the architectures designed by our proposed scheme achieve higher performance on hotspot detection task compared with state-of-the-art manually designed neural networks.
Video and Text Matching with Conditioned Embeddings

We present a method for matching a text sentence from a given corpus to a given video clip and vice versa. Traditionally video and text matching is done by learning a shared embedding space and the encoding of one modality is independent of the other. In this work, we encode the dataset data in a way that takes into account the query’s relevant information. The power of the method is demonstrated to arise from pooling the interaction data between words and frames. Since the encoding of the video clip depends on the sentence compared to it, the representation needs to be recomputed for each potential match. To this end, we propose an efficient shallow neural network. Its training employs a hierarchical triplet loss that is extendable to paragraph/video matching. The method is simple, provides explainability, and achieves state-of-the-art results for both sentence-clip and video-text by a sizable margin across five different datasets: ActivityNet, DiDeMo, YouCook2, MSR-VTT, and LSMDC. We also show that our conditioned representation can be transferred to video-guided machine translation, where we improved the current results on VATEX. Source code is available at https://github.com/AmeenAli/VideoMatch.