Reviewerly/exHarmony huggingface dataset:
BIAS-3D: Brain inspired attentional search model fashioned after what and where/how pathways for target search in 3D environment

We propose a brain inspired attentional search model for target search in a 3D environment, which has two separate channels-one for the object classification, analogous to the "what" pathway in the human visual system, and the other for prediction of the next location of the camera, analogous to the "where" pathway. To evaluate the proposed model, we generated 3D Cluttered Cube datasets that consist of an image on one vertical face, and clutter or background images on the other faces. The camera goes around each cube on a circular orbit and determines the identity of the image pasted on the face. The images pasted on the cube faces were drawn from: MNIST handwriting digit, QuickDraw, and RGB MNIST handwriting digit datasets. The attentional input of three concentric cropped windows resembling the high-resolution central fovea and low-resolution periphery of the retina, flows through a Classifier Network and a Camera Motion Network. The Classifier Network classifies the current view into one of the target classes or the clutter. The Camera Motion Network predicts the camera's next position on the orbit (varying the azimuthal angle or "θ"). Here the camera performs one of three actions: move right, move left, or do not move. The Camera-Position Network adds the camera's current position (θ) into the higher features level of the Classifier Network and the Camera Motion Network. The Camera Motion Network is trained using Q-learning where the reward is 1 if the classifier network gives the correct classification, otherwise 0. Total loss is computed by adding the mean square loss of temporal difference and cross entropy loss. Then the model is trained end-to-end by backpropagating the total loss using Adam optimizer. Results on two grayscale image datasets and one RGB image dataset show that the proposed model is successfully able to discover the desired search pattern to find the target face on the cube, and also classify the target face accurately.
A fuzzy cognitive map of the quality of user experience determinants in mobile application design

The users of mobile phone are exponentially increasing. The applications are developed every day in a variety of domains to enhance the Quality of User Experience (QoUE) along with utility determinants. The design of the mobile application impacts the QoUE. QoUE in mobile applications is a measure that describes the appropriateness of the purpose of the application and the need for user retention. However, the challenge is to identify, understand, focus and interconnect the variety of determinants influencing the QoUE based on mobile application design. These determinants are based on the diversity of users and the related functional needs, user-specific needs, and background functioning of the application. The modelling and analysis help mobile application developers to improve, increase and retain user engagement on the app based on improved QoUE. To do so, a qualitative analytical method is employed in the following steps. The first ever Fuzzy Cognitive Map (FCM) is proposed to show the causal-effect links of the interdependent determinants in mobile applications based on QoUE. In our model, the existence of relationships between determinants relies on a thorough literature review. The weight of these links is estimated by users of different ages and lines of work. This is performed by an empirical study based on a questionnaire filled by experts. The questionnaire is based on the formal utility and perceived QoUE-based topics. Finally, scenario-based analysis on formed FCM based on these inputs is performed. We show that small changes in cases using different direct determinants can be used to enhance QoUE. These changes can be studied before launching an application for the user, thereby limiting the need to rework the improvements based on QoUE and providing useful guidance for the possible increase in user base and behaviour change.
PERSPECTIVES OF FORMAL EDUCATION WITH CHILDREN IN THE FACE OF COVID-19: THE CONTEXT OF CHIAPAS

ABSTRACT This article recovers the narratives of preschool and elementary education teachers in Chiapas, Mexico, through which describes and analyzes how educational processes are developing with children of rural and indigenous communities in the pandemic. The teachers narrate their personal and teaching experiences as complex, impregnated with negative emotions, as well as the difficult interactions with parents in the negotiation of formal educational practice to develop at home, and report social inequality and its impact on children’s educational attention.
Collapse-revival of entanglement in a non-commutative harmonic oscillator revealed via coherent states and path integral

Abstract We develop an approach using coherent states and path integral to investigate the dynamics of entanglement in a simple two-dimensional non-commutative harmonic oscillator. We start by employing a Bopp shift to convert the Hamiltonian describing the system into a commutative equivalent one. This allows us to construct coherent states and calculate the propagator in standard way. By deriving the explicit expression of the time-dependent coherent states and considering its connection with the number states, we provide exact results for evaluating the degree of entanglement between the ground state and any excited state through the purity function. The interesting emerging result is that, as long as the non-commutativity parameter is non-zero, our system exhibits the phenomenon of collapse and revival of entanglement.
Improving Multi-Class Code Readability Classification with An Enhanced Data Augmentation Approach (130)

Being a critical factor affecting the maintainability and reusability of the software, code readability is growing crucial in modern software development, where a metric for classifying code readability levels is both applicable and desired. However, most prior research has treated code readability classification as a binary classification task due to the lack of labeled data. To support the training of multi-class code readability classification models, we propose an enhanced data augmentation approach that could be used to generate sufficient readability data and well train a multi-class code readability model. The approach includes the use of domain-specific data transformation and GAN-based data augmentation. We conduct a series of experiments to verify our augmentation approach and gain a state-of-the-art multi-class code readability classification performance with 69.5% Micro-F1, 54.0% Macro-F1 and 67.7% Macro-AUC. Compared to the results where no augmented data is used, the improvements on Micro-F1, Macro-F1 and Macro-AUC are significant with 6.9%, 11.3% and 11.2%, respectively. As an innovative work of proposing multi-class code readability classification and an enhanced code readability data augmentation approach, our method is proved to be effective.
Multilingual News Feed Analysis using Intelligent Linguistic Particle Filtering Techniques

Analyzing real-time news feeds and their impacts in the real world is a complex task in the social networking arena. Particularly, countries with a multilingual environment have various patterns and perceptions of news reports considering the diversity of the people. Multilingual and multimodal news analysis is an emerging trend for evaluating news source neutralities. Therefore, in this work, four new deep news particle filtering techniques were developed, including generic news analysis, sequential importance re-sampling (SIR) -based news particle filtering analysis, reinforcement learning (RL) -based multimodal news analysis, and deep Convolution neural network (DCNN) -based multi-news filtering approach, for news classification. Results indicate that these techniques, which primarily employ particle filtering with multilevel sampling strategies, produce 15% to 20% better performance than conventional news analysis techniques.
Development of empirical solar radiation models with genetic algorithm and extended validation procedure

The strong dependence of the accuracy of empirical solar radiation models on natural and climatic conditions causes their diversity and ambiguity of choice. The paper proposes an approach to choosing two most accurate models from a variety of empirical models designed to estimate global and diffuse solar radiation over the entire territory based on data from several locations. The approach focuses on the improvement of the accuracy and adaptability of models to a variety of natural and climatic conditions. To improve the adaptability of models, their coefficients are determined using a genetic algorithm. In contrast to the least-squares method, the objective function is aimed at minimizing the maximum mean absolute error (MAE) in estimating solar radiation at locations. A new procedure for extended validation of empirical models is proposed to improve their accuracy. In addition to the standard validation based on estimates of global and diffuse radiation, the accuracy of direct solar radiation estimates obtained as the difference between models’ estimates is checked. A numerical example is given for the Baikal natural territory. Based on the proposed approach, 18 empirical models are calibrated and the 2 most accurate models are selected. The maximum MAE in estimates of global, diffuse, and direct radiation for nine locations of the territory was 0.51, 0.57, and 0.6 MJ/m2day, respectively. The least-squares method of calibration yielded higher estimates of the maximum MAE: 0.64, 0.64, 0.88 MJ/m2day. The estimates of the developed models are more accurate compared to those provided by ERA5 and NASA POWER databases.
Interaction between grammatical accuracy and syntactic complexity at different proficiency levels

This learner corpus study tracks the development and interactional dynamics between grammatical accuracy (GrA) and syntactic complexity (SC) at four Common European Framework proficiency levels (intermediate and advanced levels B1/B2/C1/C2). Data from the International Corpus of Learner English (Granger et al. 2020) were error-tagged and submitted to the automatic L2 Syntactic Complexity Analyzer (Lu 2010). Interaction between B1 and B2 is characterised by a competitive relationship between GrA and SC, with the most marked improvement displayed in accuracy. Development between B2 and C1 displays subtle shifts, with GrA improving slightly and SC showing mild signs of reorganisation. Between C1 and C2, a more supportive relationship appears: SC shows the most marked signs of development, with GrA continuing to subtly improve.
Triggered together: the circulation of emotions in the digital virtual during the COVID-19 pandemic

This article examined the emotionality of the COVID-19 pandemic discourse in the digital space of social media, contending that emotions are not just psychological responses to stimuli but are constitutive of relational, ethical, and political bodies produced in its circulation. Informed by Koschut’s methodological framework for studying emotions in discourse and Fairclough’s analytical framework for Critical Discourse Analysis, select “landmark” texts were analyzed for their emotion discourse. It discusses the securitization of the COVID-19 pandemic through an exercise of ontopower, that is, by preempting the threat from the hateful pasaway. By problematizing the phenomenon of being “triggered,” it also argues for the potentiality of any affected individuals or groups to be activated to perform their ethical responsibility towards their Others, and to resist regimes of power through assemblages. The affective structures of social media allow for the reverberation of affect from the digital virtual to the actual world. Hence, this article also argues that to be “triggered” together is an invitation to participate in the revolutionary moments of solidarity and compassion. It concludes with a discussion on the potentials of the circulation of emotions to “trigger” individuals to participate in different forms of digitally networked actions.
Measuring lexical accuracy

This study seeks to contribute to the dialogue about the creation and application of lexical error annotation schemes for the purpose of studying learner language. It outlines some of the common issues that the creators of such lexical error annotation schemes face on the theoretical level, as well as the less theoretical issues that annotators encounter in practice. The chapter further illustrates the importance of a clean separation of codes that pertain to error description from those that seek to explain errors. In doing so, the study also highlights the importance of the hierarchical structure of error tag sets and how this (previously under-utilized) concept has implications for the comparability of results across learner corpora and their respective annotation schemes.
Accuracy, syntactic complexity and task type at play in examination writing

This chapter explores the association between syntactic complexity and syntactic accuracy in essays written by Russian learners of English in reply to two examination task types: a description of graphical material (Task 1) and an opinion essay (Task 2). A Poisson regression model served to predict the number of syntactic errors. Two syntactic complexity parameters were statistically significant in predicting syntactic accuracy in both tasks: the numbers of sentences and adverbial clauses. Three more parameters predicted the accuracy in Task 1 only: maximum depth of syntactic trees, and the numbers of adjective + noun and noun + infinitive constructions. Six parameters were related to syntactic accuracy in Task 2: the numbers of all clauses, of tokens and of T- units; the average length of sentence; and the numbers of coordinated and of participle + noun constructions.
Testing idealness in the filter oracle model

A filter oracle for a clutter consists of a finite set V and an oracle which, given any set X⊆V, decides in unit time whether X contains a member of the clutter. Let A2n be an algorithm that, given any clutter C over 2n elements via a filter oracle, decides whether C is ideal. We prove that in the worst case, A2n makes at least 2n−1 calls to the filter oracle. Our proof uses the theory of cuboids.
AugPrompt: Knowledgeable augmented-trigger prompt for few-shot event classification

Few-Shot Event Classification (FSEC) aims at assigning event labels to unlabeled sentences when limited annotated samples are available. Existing works mainly focus on using meta-learning to overcome the low-resource problem that still requires abundant held-out classes for model learning and selection. Thus we propose to deal with the low-resource problem by utilizing prompts. Further, existing methods suffer from severe trigger biases that may result in ignorance of the context. That is, the correct classifications are gained by looking at only the triggers, which hurts the model’s generalization ability. Thus, we propose a knowledgeable augmented-trigger prompt FSEC framework (AugPrompt), which can overcome the bias issues and alleviates the classification bottleneck brought by insufficient data. In detail, we first design an External Knowledge Injection (EKI) module to incorporate an external knowledge base (Related Words) for trigger augmentation. Then, we propose an Event Prompt Generation (EPG) module to generate appropriate discrete prompts for initializing the continuous prompts. After that, we propose an Event Prompt Tuning (EPT) module to automatically search prompts in the continuous space for FSEC and finally predict the corresponding event types of the inputs. We conduct extensive experiments on two public English datasets for FSEC, i.e., FewEvent and RAMS. The experimental results show the superiority of our proposal over the competitive baselines, where the maximum accuracy increase compared to the strongest baseline reaches 10.8%.
Ethics and interventions: A commentary on how to “improve” prediction using behavior modification

We respond to Tetlock et al. (2022) showing (1) how expert judgment fails to reflect tail risk and (2) the lack of compatibility between forecasting tournaments and tail-risk assessment methods (such as extreme value theory). More importantly, we communicate a new result showing a greater gap between the properties of tail expectation and those of the corresponding probability.
Microblog sentiment analysis based on deep memory network with structural attention

Abstract Microblog sentiment analysis has important applications in many fields, such as social media analysis and online product reviews. However, the traditional methods may be challenging to compute the long dependencies between them and easy to lose some semantic information due to low standardization of text and emojis in microblogs. In this paper, we propose a novel deep memory network with structural self-attention, storing long-term contextual information and extracting richer text and emojis information from microblogs, which aims to improve the performance of sentiment analysis. Specifically, the model first utilizes a bidirectional long short-term memory network to extract the semantic information in the microblogs, and considers the extraction results as the memory component of the deep memory network, storing the long dependencies and free of syntactic parser, sentiment lexicon and feature engineering. Then, we consider multi-step structural self-attention operations as the generalization and output components. Furthermore, this study also employs a penalty mechanism to the loss function to promote the diversity across different hops of attention in the model. This study conducted extensive comprehensive experiments with eight baseline methods on real datasets. Results show that our model outperforms those state-of-the-art models, which validates the effectiveness of the proposed model.
Including Transfer Learning and Synthetic Data in a Training Process of a 2D Object Detector for Autonomous Driving

Nowadays the use of deep learning (DL) based systems is widely extended in several areas such as facial recognition, voice and audio processing or perception systems. The training process that must be performed for proper functionality requires a large amount of data with the required characteristics needed for the task to be executed. The process of obtaining new adequate training data is complex and tedious, therefore multiple techniques such as data augmentation or transfer learning have been developed in order to have a greater amount of knowledge in the network without the need to search for new data sources. The aim of this paper is to study the effect of the inclusion of knowledge from transfer learning in a 2D image detector trained with real world and synthetic data from multiple sources. The detector that is going to be trained will be focused on autonomous driving tasks, therefore we decide to use KITTI as the real world data source and our AD PerDevkit (based on CARLA) and Virtual-KITTI as the synthetic sources.
YOLO-Based Object Detection and Tracking for Autonomous Vehicles Using Edge Devices

One of the essential tasks for Autonomous Driving and Driving Assistance systems is the detection and tracking of Vulnerable Road Users (VRU) and traffic objects. Many recent developments in this area have been leveraging Deep Learning techniques. However, these Deep Learning models require heavy computational power. For this reason, optimising software components coupled with adequate hardware choices is crucial in the development of a system that can infer in real-time. This paper proposes solutions for object detection and tracking in an Autonomous Driving scenario by comparing and exploring the applicability of different State-of-the-art object detectors trained on the BDD100K dataset, namely YOLOv5, Scaled-YOLOv4 and YOLOR. In addition, the paper explores the deployment of these algorithms on Edge Devices, more specifically, the NVIDIA Jetson AGX Xavier. Furthermore, it examines the use of the DeepStream technology for real-time inference by comparing different object trackers, such as NvDCF and DeepSORT, in the KITTI tracking dataset. The proposed solution considers a YOLOR-CSP architecture with a DeepSORT tracker running at 33.3 FPS with a detection interval of one and 17 FPS with an interval of one.
Recurrent Graph Neural Networks for Video Instance Segmentation

Abstract Video instance segmentation is one of the core problems in computer vision. Formulating a purely learning-based method, which models the generic track management required to solve the video instance segmentation task, is a highly challenging problem. In this work, we propose a novel learning framework where the entire video instance segmentation problem is modeled jointly. To this end, we design a graph neural network that in each frame jointly processes all detections and a memory of previously seen tracks. Past information is considered and processed via a recurrent connection. We demonstrate the effectiveness of the proposed approach in comprehensive experiments. Our approach operates online at over 25 FPS and obtains 16.3 AP on the challenging OVIS benchmark, setting a new state-of-the-art. We further conduct detailed ablative experiments that validate the different aspects of our approach. Code is available at https://github.com/emibr948/RGNNVIS-PlusPlus .
Hand Gestures Recognition for an Intelligent Wheelchair Steering Command

The independence and autonomy of both elderly and disabled people have been a growing concern of today’s society. Consequently, the increase in life expectancy combined with the ageing of the population has created the ideal conditions for the introduction of Intelligent Wheelchairs (IWs). For this purpose, several adapted sensors should be used to optimize the control of a wheelchair. During this work, the Leap Motion sensor was analyzed to convert the user’s will into one of four fundamental driving commands, move forward, turn right, left, or stop. Leap Motion aims to determine the direction to follow according to the hand gesture identified. For this task, data was collected from volunteers while they were performing certain gestures. Thereby it was possible to produce a data set that after being processed and extracted some features enabled the classification of the data with an F1-Score higher than 0.97. Additionally, when tested in a real-time application, this sensor reinforced its high performance.