Reviewerly/exHarmony huggingface dataset:
Approximating Decoherence Processes for the Design and Simulation of Quantum Error Correction Codes on Classical Computers

Quantum information is prone to suffer from errors caused by the so-called decoherence, which describes the loss in coherence of quantum states associated to their interactions with the surrounding environment.This decoherence phenomenon is present in every quantum information task, be it transmission, processing or even storage of quantum information.Consequently, the protection of quantum information via quantum error correction codes (QECC) is of paramount importance to construct fully operational quantum computers.Understanding environmental decoherence processes and the way they are modeled is fundamental in order to construct effective error correction methods capable of protecting quantum information.Moreover, quantum channel models that are efficiently implementable and manageable on classical computers are required in order to design and simulate such error correction schemes.In this article, we present a survey of decoherence models, reviewing the manner in which these models can be approximated into quantum Pauli channel models, which can be efficiently implemented on classical computers.We also explain how certain families of quantum error correction codes can be entirely simulated in the classical domain, without the explicit need of a quantum computer.A quantum error correction code for the approximated channel is also a correctable code for the original channel, and its performance can be obtained by Monte Carlo simulations on a classical computer.
Multi-Person Pose Estimation Using Thermal Images

Human pose estimation is an important task for several applications, such as video surveillance systems. However, color (i.e., RGB) images may not always be available under certain conditions, such as privacy issues and lack of illumination. In these scenarios, thermal images are more prominent than color images. We introduce in this study ThermalPose, which is a neural network system that parses thermal images and extracts accurate 2D human poses. ThermalPose uses lightweight neural network models that can be easily matched to the design requirements for Internet-of-Things applications. The performance of ThermalPose in visible scenes only slightly decreases compared with that of the state-of-the-art vision-based pose estimator. Meanwhile, in complex scenes with masking background textures or lack of illumination, ThermalPose does not degenerate in terms of performance, whereas the vision-based system completely fails.
SAFNet: A Semi-Anchor-Free Network With Enhanced Feature Pyramid for Object Detection

In recent years, the field of object detection has made significant progress. The success of most of the state-of-the-art object detectors is derived from the use of feature pyramid and the carefully designed anchor boxes. However, the current methods of constructing feature pyramid usually blindly integrate multi-scale representations on each feature hierarchy. Furthermore, these detectors also suffer from some drawbacks brought by the hand-designed anchors. To mitigate the adverse effects caused thereby, we introduce a one-stage object detector, named as the semi-anchor-free network with enhanced feature pyramid (SAFNet). Specifically, to better construct feature pyramid, we propose a novel enhanced feature pyramid generation paradigm, which mainly consists of two modules, i.e., adaptive feature fusion module (AFFM) and self-enhanced module (SEM). The paradigm adaptively integrates multi-scale representations in a non-linear method meanwhile suppress the redundant semantic information for each pyramid level, such that a clean and enhanced feature pyramid could be obtained. In addition, an adaptive anchor generator (AAG) is designed to yield fewer but more suitable anchor boxes for each input image. Benefiting from the enhanced feature pyramid, AAG is capable of generating more accurate anchor boxes by introducing few priors. Thus, AAG has the ability to alleviate the drawbacks caused by the preset anchor hyper-parameters and helps to decrease the computation cost. Extensive experiments demonstrate the effectiveness of our approach. Profited from the proposed modules, SAFNet significantly boosts the detection performance, i.e., achieving 2 points and 2.1 points higher Average Precision (AP) than RetinaNet (our baseline) on PASCAL VOC and MS COCO respectively. Codes will be publicly available soon.
Exploring Children’s Creative Self-Efficacy Affected by After-School Program and Parent–Child Relationships

This study aimed to verify the relationship among children's creative self-efficacy, parenting style, parent-child relationship, and after-school program. A judgmental sampling was used for subject selection from Taiwan. A total of 550 valid participants were elementary school to junior high school students then their data were put into the statistical process. The multiple regression analysis was applied in this study. The survey tool was developed base on literature review and related articles. Research result supported that the after-school program was the most significant variable that affected the student's creative self-efficacy. Then the 'punitive discipline' and 'autonomy support' of the parenting style can affect positive parent-child relationships then it will impact students' creative self-efficacy. Evidence supported that the 'negative parent-child relationships' will not motivate students' creative self-efficacy. Besides, the after-school program plays an important role in the students' creative self-efficacy independently.
Path Planning of Mobile Robot Using Improved Artificial Bee Colony Algorithm

A modified version of the artificial Bee Colony Algorithm (ABC) was suggested namely Adaptive Dimension Limit- Artificial Bee Colony Algorithm (ADL-ABC). To determine the optimum global path for mobile robot that satisfies the chosen criteria for shortest distance and collision–free with circular shaped static obstacles on robot environment. The cubic polynomial connects the start point to the end point through three via points used, so the generated paths are smooth and achievable by the robot. Two case studies (or scenarios) are presented in this task and comparative research (or study) is adopted between two algorithm’s results in order to evaluate the performance of the suggested algorithm. The results of the simulation showed that modified parameter (dynamic control limit) is avoiding static number of limit which excludes unnecessary Iteration, so it can find solution with minimum number of iterations and less computational time. From tables of result if there is an equal distance along the path such as in case A (14.490, 14.459) unit, there will be a reduction in time approximately to halve at percentage 5%.
Analysis of Face-Touching Behavior in Large Scale Social Interaction Dataset

We present the first publicly available annotations for the analysis of face-touching behavior. These annotations are for a dataset composed of audio-visual recordings of small group social interactions with a total number of 64 videos, each one lasting between 12 to 30 minutes and showing a single person while participating to four-people meetings. They were performed by in total 16 annotators with an almost perfect agreement (Cohen's Kappa=0.89) on average. In total, 74K and 2M video frames were labelled as face-touch and no-face-touch, respectively. Given the dataset and the collected annotations, we also present an extensive evaluation of several methods: rule-based, supervised learning with hand-crafted features and feature learning and inference with a Convolutional Neural Network (CNN) for Face-Touching detection. Our evaluation indicates that among all, CNN performed the best, reaching 83.76% F1-score and 0.84 Matthews Correlation Coefficient. To foster future research in this problem, code and dataset were made publicly available (github.com/IIT-PAVIS/Face-Touching-Behavior), providing all video frames, face-touch annotations, body pose estimations including face and hands key-points detection, face bounding boxes as well as the baseline methods implemented and the cross-validation splits used for training and evaluating our models.
A Feature-Importance-Aware and Robust Aggregator for GCN

Neighborhood aggregation is a key step in Graph Convolutional Networks (GCNs) for graph representation learning. Two commonly used aggregators, sum and mean, are designed with the homophily assumption that connected nodes are likely to share the same label. However, real-world graphs are noisy and adjacent nodes do not necessarily imply similarity.Learnable aggregators are proposed in Graph Attention Network (GAT) and Learnable Graph Convolutional Layer (LGCL). However, GAT considers node importance but not the importance of different features. The convolution aggregator in LGCL considers feature importance but it can not directly operate on graphs due to the irregular connectivity and lack of orderliness. In this paper, we firstly unify the current learnable aggregators in a framework: Learnable Aggregator for GCN (LA-GCN) by introducing a shared auxiliary model that provides a customized schema in neighborhood aggregation. Under this framework, we propose a new model called LA-GCNMask consisting of a new aggregator function,mask aggregator. The auxiliary model learns a specific mask for each neighbor of a given node, allowing both node-level and feature-level attention. This mechanism learns to assign different importance to both nodes and features for prediction, which provides interpretable explanations for prediction and increases the model robustness. Experiments on seven graphs for node classification and graph classification tasks show that LA-GCNMask outperforms the state-of-the-art methods. Moreover, our aggregator can identify both the important nodes and node features simultaneously, which provides a quantified understanding of the relationship between input nodes and the prediction. We further conduct experiments on noisy graphs to evaluate the robustness of our model. Experiments show that LA-GCNMask consistently outperforms the state-of-the-art methods, with up to 15% improvements in terms of accuracy compared to the second best.
Delivering Critical Stimuli for Decision Making in VR Training: Evaluation Study of a Firefighter Training Scenario

The goal for a virtual reality (VR) training system is to enable trainees to acquire all the knowledge they need to perform effectively in a real environment. Such a system should provide an experience so authentic that no further real-world training is necessary, meaning that it is sufficient to train in VR. We evaluate the impact of a haptic thermal stimulus, which is of paramount importance to decision making, on trainees performance and knowledge acquisition. A thermal device was created to deliver the stimulus. As a proof of concept, a procedure from firefighter training is selected, in which sensing the temperature of a door with one's hand is essential. The sample consisted of 48 subjects divided among three experimental scenarios: one in which a virtual thermometer is used (visual stimulus), another in which the temperature is felt with the hand (thermal stimulus) and a third in which both methods are used (visual + thermal stimuli). For the performance evaluation, we measured the total time taken, the numbers of correctly executed procedures and identified neutral planes, the deviation from the target height, and the responses to a knowledge transfer questionnaire. Presence, cybersickness, and usability are measured to evaluate the impact of the haptic thermal stimulus. Considering the thermal stimulus condition as the baseline, we conclude that the significantly different results in the performance among the conditions indicate that the better performance in the visual-only condition is not representative of the real-life performance. Consequently, VR training applications need to deliver the correct stimuli for decision making.
Using Eye- Tracking to Examine Query Syntax and Method Syntax Comprehension in LINQ

Eye movement tracking systems offer the opportunity to observe and study a complex cognitive process such as programming. As a consequence of the growing number of program systems, software developers need to be able to use more and more programming technologies effectively. The paper analyses one of the possibilities of the C# programming language, the two types of the Language-Integrated Query data abstraction layer, that is the readability and comprehensibility of query and method syntax-based queries by evaluating the knowledge level and evaluating eye movement parameters.
Why are Adaptive Methods Good for Attention Models

While stochastic gradient descent (SGD) is still the \emph{de facto} algorithm in deep learning, adaptive methods like Clipped SGD/Adam have been observed to outperform SGD across important tasks, such as attention models. The settings under which SGD performs poorly in comparison to adaptive methods are not well understood yet. In this paper, we provide empirical and theoretical evidence that a heavy-tailed distribution of the noise in stochastic gradients is one cause of SGD's poor performance. We provide the first tight upper and lower convergence bounds for adaptive gradient methods under heavy-tailed noise. Further, we demonstrate how gradient clipping plays a key role in addressing heavy-tailed gradient noise. Subsequently, we show how clipping can be applied in practice by developing an \emph{adaptive} coordinate-wise clipping algorithm (ACClip) and demonstrate its superior performance on BERT pretraining and finetuning tasks.
RecoBERT: A Catalog Language Model for Text-Based Recommendations

Language models that utilize extensive selfsupervised pre-training from unlabeled text, have recently shown to significantly advance the state-of-the-art performance in a variety of language understanding tasks.However, it is yet unclear if and how these recent models can be harnessed for conducting text-based recommendations.In this work, we introduce RecoBERT, a BERT-based approach for learning catalog-specialized language models for text-based item recommendations.We suggest novel training and inference procedures for scoring similarities between pairs of items, that don't require item similarity labels.Both the training and the inference techniques were designed to utilize the unlabeled structure of textual catalogs, and minimize the discrepancy between them.By incorporating four scores during inference, RecoBERT can infer text-based item-to-item similarities more accurately than other techniques.In addition, we introduce a new language understanding task for wine recommendations using similarities based on professional wine reviews.As an additional contribution, we publish annotated recommendations dataset crafted by human wine experts.Finally, we evaluate Re-coBERT and compare it to various state-of-theart NLP models on wine and fashion recommendations tasks.
A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies.

In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods.First, the quality of the existing DG methods are still far from practical use.There are still room for DG quality improvement.Second, the existing DG designs are mainly for single distractor generation.However, for practical MCQ preparation, multiple distractors are desired.Aiming at these goals, in this paper, we present a new distractor generation scheme with multi-tasking and negative answer training strategies for effectively generating multiple distractors.The experimental results show that (1) our model advances the state-of-the-art result from 28.65 to 39.81 (BLEU 1 score) and (2) the generated multiple distractors are diverse and shows strong distracting power for multiple choice question.
Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements

Natural language descriptions of user interface (UI) elements such as alternative text are crucial for accessibility and language-based interaction in general.Yet, these descriptions are constantly missing in mobile UIs.We propose widget captioning, a novel task for automatically generating language descriptions for UI elements from multimodal input including both the image and the structural representations of user interfaces.We collected a largescale dataset for widget captioning with crowdsourcing.Our dataset contains 162,859 language phrases created by human workers for annotating 61,285 UI elements across 21,750 unique UI screens.We thoroughly analyze the dataset, and train and evaluate a set of deep model configurations to investigate how each feature modality as well as the choice of learning strategies impact the quality of predicted captions.The task formulation and the dataset as well as our benchmark models contribute a solid basis for this novel multimodal captioning task that connects language and user interfaces.
On Long-Tailed Phenomena in Neural Machine Translation

State-of-the-art Neural Machine Translation (NMT) models struggle with generating lowfrequency tokens, tackling which remains a major challenge.The analysis of long-tailed phenomena in the context of structured prediction tasks is further hindered by the added complexities of search during inference.In this work, we quantitatively characterize such longtailed phenomena at two levels of abstraction, namely, token classification and sequence generation.We propose a new loss function, the Anti-Focal loss, to better adapt model training to the structural dependencies of conditional text generation by incorporating the inductive biases of beam search in the training process.We show the efficacy of the proposed technique on a number of Machine Translation (MT) datasets, demonstrating that it leads to significant gains over cross-entropy across different language pairs, especially on the generation of low-frequency words.We have released the code to reproduce our results. 1
Continual Adaptation for Efficient Machine Communication

To communicate with new partners in new contexts, humans rapidly form new linguistic conventions.Recent neural language models are able to comprehend and produce the existing conventions present in their training data, but are not able to flexibly and interactively adapt those conventions on the fly as humans do.We introduce an interactive repeated reference task as a benchmark for models of adaptation in communication and propose a regularized continual learning framework that allows an artificial agent initialized with a generic language model to more accurately and efficiently communicate with a partner over time.We evaluate this framework through simulations on COCO and in real-time reference game experiments with human partners.
Viral Data

We are experiencing a historical moment characterized by unprecedented conditions of virality: a viral pandemic, the viral diffusion of misinformation and conspiracy theories, the viral momentum of ongoing Hong Kong protests, and the viral spread of #BlackLivesMatter demonstrations and related efforts to defund policing. These co-articulations of crises, traumas, and virality both implicate and are implicated by big data practices occurring in a present that is pervasively mediated by data materialities, deeply rooted dataist ideologies that entrench processes of datafication as granting objective access to truth and attendant practices of tracking, data analytics, algorithmic prediction, and data-driven targeting of individuals and communities. This collection of papers explores how data (and their absences) is figuring in the making of the discourses, lived realities, and systemic inequalities of the uneven impacts of the coronavirus pandemic.
Unstructured Text Documents Summarization With Multi-Stage Clustering

In natural language processing, text summarization is an important application used to extract desired information by reducing large text.Existing studies use keyword-based algorithms for grouping text, which do not give the documents' actual theme.Our proposed dynamic corpus creation mechanism combines metadata with summarized extracted text.The proposed approach analyzes the mesh of multiple unstructured documents and generates a linked set of multiple weighted nodes by applying multistage Clustering.We have generated adjacency graphs to link the clusters of various collections of documents.This approach comprises of ten steps: pre-processing, making multiple corpuses, first stage clustering, creating sub-corpuses, interlinking sub-corpuses, creating page rank keyword dictionary of each sub-corpus, second stage clustering, path creation among clusters of sub-corpuses, text processing by forward and backward propagation for results generation.The outcome of this technique consists of interlinked subcorpuses through clusters.We have applied our approach to a News dataset, and this interlinked corpus processing follows step by step clustering to search the most relevant parts of the corpus with less cost, time, and improve content detection.We have applied six different metadata processing combinations over multiple text queries to compare results during our experimentation.The comparison results of text satisfaction show that Page-Rank keywords give 38% related text, single-stage Clustering gives 46%, twostage Clustering gives 54%, and the proposed technique gives 67% associated text.Furthermore, this approach covers/searches the relevant data with a range of most to less relevant content.It provides the systematic query-relevant corpus processing mechanism, which automatically selects the most relevant subcorpus through dynamic path selection.We used the SHAP model to evaluate the proposed technique, and our evaluation results proved that the proposed mechanism improved text processing.Moreover, combining text summarization features, shown satisfactory results compared to the summaries generated by general models of abstractive & extractive summarization.
An IoT and Blockchain Based System for Monitoring and Tracking Real-Time Occupancy for COVID-19 Public Safety

The COVID-19 pandemic has brought several limitations regarding physical distancing in order to reduce the interactions among large groups that could have prolonged close contact.For health reasons, such physical distancing requirements should be guaranteed in private and public spaces.In Spain, occupancy is restricted by law but, in practice, certain spaces may become overcrowded, existing law infringements in places that rely on occupancy estimations that are not accurate enough.For instance, although the number of passengers who enter a public transportation service is known, it is difficult to determine the actual occupancy of such a vehicle, since it is commonly unknown when and where passengers descend.Despite a number of counting systems existing, they are either prone to counting errors in overcrowded scenarios or require the active involvement of the people to be counted (e.g., going through a lathe or tapping a card when entering or exiting a monitored area) or of a person who manages the entering/exit process.This paper presents a novel IoT occupancy system that allows estimating in real time the people occupancy level of public spaces such as buildings, classrooms, businesses or moving transportation vehicles.The proposed system is based on autonomous wireless devices that, after powering them on, do not need active actions from the passengers/users and require a minimum amount of infrastructure.The system does not collect any personal information to ensure user privacy and includes a decentralized traceability subsystem based on blockchain, which guarantees the availability, security and immutability of the collected information.Such data will be shared among smart city stakeholders to ensure public safety and then deliver transparent decision-making based on data-driven analysis and planning.