Reviewerly/exHarmony huggingface dataset:
ParaPose: Parameter and Domain Randomization Optimization for Pose Estimation using Synthetic Data

Pose estimation is the task of determining the 6D position of an object in a scene. Pose estimation aid the abilities and flexibility of robotic set-ups. However, the system must be configured towards the use case to perform adequately. This configuration is time-consuming and limits the usability of pose estimation and, thereby, robotic systems. Deep learning is a method to overcome this configuration procedure by learning parameters directly from the dataset. However, obtaining this training data can also be very time-consuming. The use of synthetic training data avoids this data collection problem, but a configuration of the training procedure is necessary to overcome the domain gap problem. Additionally, the pose estimation parameters also need to be configured. This configuration is jokingly known as grad student descent as parameters are manually adjusted until satisfactory results are obtained. This paper presents a method for automatic configuration using only synthetic data. This is accomplished by learning the domain randomization during network training, and then using the domain randomization to optimize the pose estimation parameters. The developed approach shows state-of-the-art performance of 82.0 % recall on the challenging OCCLUSION dataset, outperforming all previous methods with a large margin. These results prove the validity of automatic set-up of pose estimation using purely synthetic data.
Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction

In this paper, we investigate improvements to the GEC sequence tagging architecture with a focus on ensembling of recent cutting-edge Transformer-based encoders in Large configurations. We encourage ensembling models by majority votes on span-level edits because this approach is tolerant to the model architecture and vocabulary size. Our best ensemble achieves a new SOTA result with an $F_{0.5}$ score of 76.05 on BEA-2019 (test), even without pre-training on synthetic datasets. In addition, we perform knowledge distillation with a trained ensemble to generate new synthetic training datasets, "Troy-Blogs" and "Troy-1BW". Our best single sequence tagging model that is pretrained on the generated Troy-datasets in combination with the publicly available synthetic PIE dataset achieves a near-SOTA (To the best of our knowledge, our best single model gives way only to much heavier T5 model result with an $F_{0.5}$ score of 73.21 on BEA-2019 (test). The code, datasets, and trained models are publicly available).
Multi-scale selection pyramid networks for small-sample target detection algorithms

Target detection is to detect the specified target in the image. This technology has been widely used in automatic driving, face recognition, and other fields and has become a significant research hotspot in the field of computer vision at home and abroad. Traditional target detection often requires many annotated data sets, so detecting targets with only a small number of annotated samples is challenging. We propose a multi-scale selection pyramid algorithm for small sample target detection to address this problem so that detection no longer relies on large-scale labeled datasets. First, we designed a multi-scale selection pyramid network for small sample target detection, consisting of three components: Contextual Layer Attention, Feature Scale Enhancement, and Feature Scale Selection module. After the RPN network generates the RoI features, the max-pooling and average-pooling are used to extract the features and then merge them. The model's sensitivity to the new class of parameters was improved with the stability of the sample parameters. In addition, the orthogonal mapping loss function is used to constrain the features before the classification layer, which can reasonably measure the similarity between features even in the case of a small number of samples.
Separate Exchangeability as Modeling Principle in Bayesian  Nonparametrics

We argue for the use of separate exchangeability as a modeling principle in Bayesian inference, especially for nonparametric Bayesian models. While in some areas, such as random graphs, separate and (closely related) joint exchangeability are widely used, and it naturally arises for example in simple mixed models, it is curiously underused for other applications. We briefly review the definition of separate exchangeability. We then discuss two specific models that implement separate exchangeability. One example is about nested random partitions for a data matrix, defining a partition of columns and nested partitions of rows, nested within column clusters. Many recently proposed models for nested partitions implement partially exchangeable models. We argue that inference under such models in some cases ignores important features of the experimental setup. The second example is about setting up separately exchangeable priors for a nonparametric regression model when multiple sets of experimental units are involved.
Estimating Gaussian Copulas with Missing Data

In this work we present a rigorous application of the Expectation Maximization algorithm to determine the marginal distributions and the dependence structure in a Gaussian copula model with missing data. We further show how to circumvent a priori assumptions on the marginals with semiparametric modelling. The joint distribution learned through this algorithm is considerably closer to the underlying distribution than existing methods.
Masked Measurement Prediction: Learning to Jointly Predict Quantities and Units from Textual Context

Physical measurements constitute a large portion of numbers in academic papers, engineering reports, and web tables. Current benchmarks fall short of properly evaluating numeracy of pretrained language models on measurements, hindering research on developing new methods and applying them to numerical tasks. To that end, we introduce a novel task, Masked Measurement Prediction (MMP), where a model learns to reconstruct a number together with its associated unit given masked text. MMP is useful for both training new numerically informed models as well as evaluating numeracy of existing systems. In order to address this task, we introduce a new Generative Masked Measurement (GeMM) model that jointly learns to predict numbers along with their units. We perform fine-grained analyses comparing our model with various ablations and baselines. We use linear probing of traditional pretrained transformer models (RoBERTa) to show that they significantly underperform jointly trained number-unit models, highlighting the difficulty of this new task and the benefits of our proposed pretraining approach. We hope this framework accelerates the progress towards building more robust numerical reasoning systems in the future.
UNIVERSITY STUDENTS’ TRAINING IN CROSS-CULTURAL SCIENTIFIC COMMUNICATION VIA VIRTUAL INTERACTION

The topicality of university students’ training in cross-cultural scientific communication can be seen at the state, theoretical and practical levels. The significance of its solution aims at finding the best ways to organize the educational process at the university. The specifics of communication between the representatives of the scientific community is the existence of direct and indirect (virtual) ways of interaction. Accordingly, when organizing the process of university students’ training in cross-cultural scientific communication, it is necessary to build the process both with direct and indirect interaction. The paper describesthe algorithm of university students’ training in cross-cultural scientific communication via virtual interaction, which includes the stages (preparatory, acquisition and expertize), the content of the online course (“Academic English for Masters”) created in the information-educational system of the university, and e-learning tools (simulator tools, control tools, information and reference tools, demonstration tools) used for this type of training. The principle of implementing the preparation of university students for crosscultural scientific communication via virtual interaction is the adaptability of building the educational process in the e-learning system, which consists in adaptive presentation of the material, adaptive navigation, and adaptive testing. The solution of this problem has been carried out using theoretical and empirical methods: documentation analysis, literature analysis, ascertaining experiment, comparison, synthesis, generalization, induction, deduction.
(Thesis) Reservoir Computing With Dynamical Systems

A reservoir computer is a special type of neural network, where most of the weights are randomly fixed and only a subset are trained. In this thesis we prove results about reservoir computers trained on deterministic dynamical systems, and stochastic processes. We focus mostly on a special type of reservoir computer called an Echo State Network (ESN). In the deterministic case, we prove (under some assumptions) that if a reservoir computer has the Echo State Property (ESP), then there is a C1 generalised synchronisation between the input dynamical system and the dynamics in the reservoir space. Furthermore, we prove that a reservoir computer with the local ESP in several disjoint subsets of the reservoir space will admit several distinct generalised synchronisations. In the special case that the reservoir map is linear, and has the ESP, we prove that the generalised synchronisation is generically an embedding. This result admits Takens' embedding Theorem as a special case. We go to show that ESNs trained on scalar observations of an ergodic dynamical system can approximate an arbitrary target function, including the next step map used in time series forecasting. This universal approximation property holds despite the training process being entirely linear. We prove analogous results for ESNs trained on observations of a stochastic process, which are not be Markovian in general. We use these results to develop supervised learning, and reinforcement learning algorithms supported by an ESN. In the penultimate chapter of this thesis, we use a reservoir computer to numerically solve linear PDEs. In the final chapter, we conclude and discuss directions for future work.
Intelligent Decision Support Systems

In this chapter, the main characteristics of Intelligent Decision Support Systems (IDSS) are described. Starting with a definition of an IDSS, next an introduction to the Artificial Intelligence (AI) field is provided. Main AI paradigms or approaches to solve complex problems are presented. This way the readers can have a better understanding of the AI methods used in Intelligent Decision Support Systems. These methods will be detailed in the next chapters. Next, the different typology of IDSSs according to the nature of the problem at hand is detailed, and possible classification of IDSS regarding the type of knowledge and models used: model-driven IDSSs and data-driven IDSSs. Afterwards, the different conceptual components of an IDSS are described and the main open challenges in IDSS field are explained. The main requirements and considerations prior to the construction of an IDSS are detailed. Next, a proposal for a general architecture for an IDSS is presented, jointly with a design and cognitive-oriented approach for the development process for an IDSS. In addition, some checklist of features to be taken into account for the validation of an IDSS are described, even though this is an open challenge which will be addressed in Chap. 10. Finally, a first real case study of the development of an IDSS is detailed to illustrate the approaches previously presented in this chapter.
Problem based learning didactic program to develop critical thinking in high school students

The aim of this research was to determine the influence of the problem-based learning didactic program on the improvement of critical thinking of third grade high school students, in the area of Social Studies, of San Juan High School, Trujillo, 2020. The methodology was based on an experimental research design, in its preexperimental form, for which a single group consisting of 20 students was considered. To identify the level of critical thinking, before and after applying the program, whose learning sessions were carried out through the Zoom virtual videoconferencing, the data collection instrument called “Questionnaire to measure the level of critical thinking in the area of Social studies” was used. It should be noted that this instrument was validated by expert judgment and obtained an acceptable reliability coefficient with Cronbach's alpha consistency index. Hypothesis testing was done through the “student's t” test. It was concluded that the problem-based learning didactic program influences the improvement of critical thinking in Social studies of third grade students from San Juan High School, 2020, significantly.
Gas Gauge: A Security Analysis Tool for Smart Contract Out-of-Gas Vulnerabilities

In recent years we have witnessed a dramatic increase in the adoption and application of smart contracts in a variety of contexts such as decentralized finance, supply chain management, and identity management. However, a critical stumbling block to the further adoption of smart contracts is their security. A particularly widespread class of security vulnerabilities that afflicts Ethereum smart contracts is the gas limit denial of service(DoS) on a contract via unbounded operations. These vulnerabilities result in a failed transaction with an out-of-gas error and are often present in contracts containing loops whose bounds are affected by end-user input. Note that such vulnerabilities differ from gas limit DoS on the network via block stuffing. Therefore, we present Gas Gauge, a tool aimed at detecting Out-of-Gas DoS vulnerabilities in Ethereum smart contracts. Gas Gauge consists of three major components: the Detection, Identification, and Correction Phases. The Detection Phase consists of an accurate static analysis approach that finds and summarizes all the loops in a smart contract. The Identification Phase uses a white-box fuzzing approach to generate a set of inputs that causes the contract to run out of gas. The Correction Phase uses static analysis and run-time verification to predict the maximum loop bounds consistent with allowable gas usage and suggest appropriate repairs to the user of the tool. Each part of the tool can be used separately for different purposes or all together to detect, identify and help repair the contracts vulnerable to Out-of-Gas DoS vulnerabilities. Gas Gauge was tested on 1,000 real-world solidity smart contracts deployed on the Ethereum Mainnet. The results were compared to seven state-of-the-art static and symbolic tools, and it was empirically demonstrated that Gas Gauge is far more effective than competing state-of-the-art tools.
Improved Dual Correlation Reduction Network

Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different clusters without human annotations, is a fundamental yet challenging task. However, we observed that the existing methods suffer from the representation collapse problem and easily tend to encode samples with different classes into the same latent embedding. Consequently, the discriminative capability of nodes is limited, resulting in sub-optimal clustering performance. To address this problem, we propose a novel deep graph clustering algorithm termed Improved Dual Correlation Reduction Network (IDCRN) through improving the discriminative capability of samples. Specifically, by approximating the cross-view feature correlation matrix to an identity matrix, we reduce the redundancy between different dimensions of features, thus improving the discriminative capability of the latent space explicitly. Meanwhile, the cross-view sample correlation matrix is forced to approximate the designed clustering-refined adjacency matrix to guide the learned latent representation to recover the affinity matrix even across views, thus enhancing the discriminative capability of features implicitly. Moreover, we avoid the collapsed representation caused by the over-smoothing issue in Graph Convolutional Networks (GCNs) through an introduced propagation regularization term, enabling IDCRN to capture the long-range information with the shallow network structure. Extensive experimental results on six benchmarks have demonstrated the effectiveness and the efficiency of IDCRN compared to the existing state-of-the-art deep graph clustering algorithms.
Combinatorial Aspects of the Card Game War

This paper studies a single-suit version of the card game War on a finite deck of cards. There are varying methods of how players put the cards that they win back into their hands, but we primarily consider randomly putting the cards back and deterministically always putting the winning card before the losing card. The concept of a \emph{passthrough} is defined, which refers to a player playing through all cards in their hand from a particular point in the game. We consider games in which the second player wins during their first passthrough. We introduce several combinatorial objects related to the game: game graphs, win-loss sequences, win-loss binary trees, and game posets. We show how these objects relate to each other. We enumerate states depending on the number of rounds and the number of passthroughs.
A Theoretical Approach for Structuring and Analysing Knowledge Provenance for Visual Analytics

The primary goal of Visual Analytics (VA) is to enable user-guided knowledge generation. Theoretical VA works to explain how the different aspects of a VA tool bring forth new insights through user interactivity, which itself can be captured through tracking methods for reproduction or evaluation. However, the process of automatically capturing the user's thought process, such as intent and insights, and associating it with user's interaction events are largely ignored. Also, two forms of interactivity capture are typically ambiguous and intermixed: the temporal aspect, which indicates sequences of events, and the atemporal aspect, which explains the workflow as sequences of states within a state-space. In this work, we propose Visual Analytics Knowledge Graph (VAKG), a conceptual framework that brings VA modeling theory to practice through a novel Set-Theory formalization of knowledge modeling. By extracting such a model from a VA tool, VAKG structures a 4-way temporal knowledge graph that describes user behavior and its associated knowledge gain process. Such knowledge graphs can be populated manually or automatically during user analysis sessions, which can then be analyzed using graph analysis methods. VAKG is demonstrated by modeling and collecting Tableau and visual text-mining workflows, where comparative user satisfaction, tool efficacy, and overall workflow shortcomings can be extracted from the knowledge graph.
Named Entity Disambiguation Based on Bidirectional Semantic Path

In the existing entity disambiguation algorithms, the full connection method is adopted to measure the association between candidate entities, ignoring the ambiguity of entity mention, and the shortest path feature used for feature extraction does not consider the influence of different path starting and ending points. Therefore, this paper proposes a progressive named entity disambiguation (NED) algorithm PNED, which uses the bidirectional semantic association feature and topic relevance feature extracted from the disambiguated entity to calculate the association relation between entities. The algorithm fully considers the difficulty of different entity mention disambiguation, and gradually completes the NED task from easy to difficult, which improves the implementation efficiency of NED. Experimental results show the effectiveness of the algorithm.
Gesture based Arabic Sign Language Recognition for Impaired People based on Convolution Neural Network

The Arabic Sign Language has endorsed outstanding research achievements for identifying gestures and hand signs using the deep learning methodology. The term "forms of communication" refers to the actions used by hearing-impaired people to communicate. These actions are difficult for ordinary people to comprehend. The recognition of Arabic Sign Language (ArSL) has become a difficult study subject due to variations in Arabic Sign Language (ArSL) from one territory to another and then within states. The Convolution Neural Network has been encapsulated in the proposed system which is based on the machine learning technique. For the recognition of the Arabic Sign Language, the wearable sensor is utilized. This approach has been used a different system that could suit all Arabic gestures. This could be used by the impaired people of the local Arabic community. The research method has been used with reasonable and moderate accuracy. A deep Convolutional network is initially developed for feature extraction from the data gathered by the sensing devices. These sensors can reliably recognize the Arabic sign language's 30 hand sign letters. The hand movements in the dataset were captured using DG5-V hand gloves with wearable sensors. For categorization purposes, the CNN technique is used. The suggested system takes Arabic sign language hand gestures as input and outputs vocalized speech as output. The results were recognized by 90% of the people.
Speech + Posture: A Method for Interaction with Multiple and Large Interactive Displays

Multiple or Large displays play an important role in collaboration scenarios, because they can provide more display space. However, they are challenging concerning effective manipulating and managing the display contents, particularly when the displays are beyond the users' reachable region and operational limit region. In this work, we explore a particular interactive input combination for multiple or large displays, the Speech + Posture interactive input mode. We integrate postures to point to target display areas and phonetic keywords to designate display contents. This method makes interactive input commands concise and explicit, and it can support interaction with multiple or large interactive displays effectively.
Ensemble Method for Estimating Individualized Treatment Effects

In many medical and business applications, researchers are interested in estimating individualized treatment effects using data from a randomized experiment. For example in medical applications, doctors learn the treatment effects from clinical trials and in technology companies, researchers learn them from A/B testing experiments. Although dozens of machine learning models have been proposed for this task, it is challenging to determine which model will be best for the problem at hand because ground-truth treatment effects are unobservable. In contrast to several recent papers proposing methods to select one of these competing models, we propose an algorithm for aggregating the estimates from a diverse library of models. We compare ensembling to model selection on 43 benchmark datasets, and find that ensembling wins almost every time. Theoretically, we prove that our ensemble model is (asymptotically) at least as accurate as the best model under consideration, even if the number of candidate models is allowed to grow with the sample size.