Reviewerly/exHarmony huggingface dataset:
Text complexity of open educational resources in Portuguese: mixing written and spoken registers in a multi-task approach

This paper presents a study on text complexity of Open Educational Resources (OER) in Brazilian Portuguese. In a data analysis of the Brazilian Ministry of Education Integrated Platform (MEC-RED) carried out in September 2020, 86% of the resources on the platform did not have any grade level classification, making it difficult to find, use, and expand them. The text complexity task in the Natural Language Processing research area can be used to identify texts that have adequate linguistic complexity for specific grade levels, allowing to complete the stage of education metadata in MEC-RED. However, some types of MEC-RED’s resources do not present any information about their stage of education, making it unfeasible to compile a balanced dataset of OER for training a text complexity predictor. This study is driven and enabled by a recently created corpus of transcribed spoken narratives produced by fourth graders to first graders of high school which were collected to evaluate the development of language abilities. A multi-task learning (MTL) approach via hard parameter sharing of hidden layers was adopted to train three models that share all parameters in their hidden layers. The main objective of this study was to explore the relationship between three text complexity tasks by jointly learning to predict text readability, using coarse and fine-grained datasets of written, spoken and domain texts (a small dataset of OER resources) to overcome the lack of grade classified resources in MEC-RED. Our MTL model with two auxiliary tasks presents a F-measure of 0.955, an improvement of 0.15 points over our previous results.
The Low Computation and Real-Time Shoe Detection with Timestamp for Production Tracking in Shoe Manufacturing

Production planners could not get the update on the actual number of products in real-time. They do not realize the unmatched production until a few days later. Thus, the planners need to revise their production plan with reserve capacity for this unmatched production, and it causes manufacturing to waste a lot of time and money. The production outcome is usually manually counted at the end of the day and recorded on paper. This paper proposes an image processing system for counting products with a timestamp. The YOLOv4-tiny and DNN-OpenCV are utilized to detect an object. The detected object will be counted using the intersection detection and tesseract engine to extract time from the video. The object detection is trained using the 10 folds technique with 106 object photos. The proposed approach is tested with 8 videos for counting accuracy and timestamp accuracy. The testing result reveals that our proposed method achieves 100% of object counting and timestamp accuracy of 80 % compared with the manual counting with the timestamp. The proposed technique is suitable for counting objects with timestamps in real-time.
Opportunities and Challenges of Compensation and Governance of Network Neural Ecological Environment Damage in the Era of Artificial Intelligence

The damage to the ecological environment poses a serious threat to the sustainable development of the country's economy, society, and ecological environment. Based on the artificial intelligence era background, the use of the neural network algorithm to collect and deal with the ecological environment in the network environment damage and compensation for the relevant data, and the use of the habitat equivalent analysis method to analyze the collected experimental data, from the ecological environment of water resources, land resources, biological resources, and damage to the ecological environment in atmospheric resources were analyzed. Then, the legal clauses of compensation for ecological environment damage in developed countries and China are counted and the cases of compensation for ecological environment damage over the last five years in China are analyzed. Most of the land resources are used for building construction, and the proportion of land resources of houses is more than 40%, while the proportion of land occupied by mountain forests is less than 30%. China's legal rights and interests in compensation for ecological and environmental damage are relatively weak. In 2016, there were only three laws in China on compensation for ecological and environmental damage. By 2020, there were 26 laws in China, and within five years, there were 72 laws in China on compensation for ecological and environmental damage. The settlement rate of compensation for ecological environmental damage is 41.96%.
Linked visualisations via Galois dependencies

We present new language-based dynamic analysis techniques for linking visualisations and other structured outputs to data in a fine-grained way, allowing users to explore how data attributes and visual or other output elements are related by selecting (focusing on) substructures of interest. Our approach builds on bidirectional program slicing techiques based on Galois connections, which provide desirable round-tripping properties. Unlike the prior work, our approach allows selections to be negated, equipping the bidirectional analysis with a De Morgan dual which can be used to link different outputs generated from the same input. This offers a principled language-based foundation for a popular view coordination feature called brushing and linking where selections in one chart automatically select corresponding elements in another related chart.
Hard Reliability-Based Ordered Statistic Decoding and Its Application to McEliece Public Key Cryptosystem

This letter constructs the hard reliability from the syndromes to decode low/moderate-density parity-check codes in an ordered statistic way. For regular codes with our proposed decoding algorithm, the error-correction capability can be determined by the column weight and the maximum column intersection of their parity-check matrices. Then, an upper bound is derived to verify their decoding performance in ultra-low region, which may be required by some applications, e.g., McEliece public key cryptosystem and optical communications. It allows us to reduce the size of McEliece public keys with strong security levels due to its good error-correction capability.
Contrastive Goal Grouping for Policy Generalization in Goal-Conditioned Reinforcement Learning

We propose Contrastive Goal Grouping (COGOAL), a self-supervised goal embedding algorithm for learning a well-structured latent goal space to simplify goal-conditioned reinforcement learning. Compared to conventional reconstruction-based methods such as variational autoencoder, our approach can benefit from previously learnt goals and achieve better generalizability. More specifically, we theoretically prove a sufficient condition for determining whether goals share similar optimal policies, and propose COGOAL that groups goals satisfying the condition in the latent space via contrastive learning. The learnt goal embeddings enable a fully-trained policy for a goal to reach new goals which are adjacent in the latent space. We conduct experiments on visual navigation and visual object search tasks. COGOAL significantly outperforms the baseline methods in terms of sample efficiency in the visual object search task, in which a previously learnt policy is adaptively transferred to reach new goals with fine-tuning.
Stable Algorithms for Solving the Problem of Determining the Weighting Coefficients of Neural Networks with Radial-Basis Activation Functions

The article proposes computational procedures for the formation and construction of stable algorithms for solving the problem of determining the weight coefficients of neural networks with radial-basis activation functions. The approximation properties of multilayer neural networks are analyzed. It was revealed that neural networks with radial-basis activation functions are universal approximators. It is noted that the problem of RBF networks is the choice of the number of radially based functions required for the approximation. When solving this problem for the purpose of quality approximation, the network output error is minimized. Regularized iterative algorithms for determining the weight coefficients of neural networks with radial-basis activation functions based on projection methods are proposed. To solve the extended regularized normal system of equations, the block version of the Kachmazh's algorithm and the pseudo-inversion of rectangular matrices proposed by Greville is used. To give stability to the procedure for determining the weight coefficients of neural networks, an algorithm for finding a pseudoinverse matrix was also used using the representation of a symmetric matrix in a diagonal form.
Considerations on creating conversational agents for multiple environments and users

Abstract Advances in artificial intelligence algorithms and expansion of straightforward cloud‐based platforms have enabled the adoption of conversational assistants by both, medium and large companies, to facilitate interaction between clients and employees. The interactions are possible through the use of ubiquitous devices (e.g., Amazon Echo, Apple HomePod, Google Nest), virtual assistants (e.g., Apple Siri, Google Assistant, Samsung Bixby, or Microsoft Cortana), chat windows on the corporate website, or social network applications (e.g. Facebook Messenger, Telegram, Slack, WeChat). Creating a useful, personalized conversational agent that is also robust and popular is nonetheless challenging work. It requires picking the right algorithm, framework, and/or communication channel, but perhaps more importantly, consideration of the specific task, user needs, environment, available training data, budget, and a thoughtful design. In this paper, we will consider the elements necessary to create a conversational agent for different types of users, environments, and tasks. The elements will account for the limited amount of data available for specific tasks within a company and for non‐English languages. We are confident that we can provide a useful resource for the new practitioner developing an agent. We can point out novice problems/traps to avoid, create consciousness that the development of the technology is achievable despite comprehensive and significant challenges, and raise awareness about different ethical issues that may be associated with this technology. We have compiled our experience with deploying conversational systems for daily use in multicultural, multilingual, and intergenerational settings. Additionally, we will give insight on how to scale the proposed solutions.
Contextualized Embedding based Approaches for Social Media-specific Sentiment Analysis

Social media-specific Sentiment Analysis has a wide range of applications in various domains like Business Intelligence, Marketing, Politics and Psychology, to mention a few. Irony Detection and Emotion Recognition, two of Sentiment Analysis' significant pillars have become increasingly important as a result of the continued growth of social media. Previous approaches for the two tasks have yielded promising results, but have often relied on recurrence and pre-trained word- embedding ensembles. In this paper, we propose two novel contextual embedding-based approaches for Irony Detection and Emotion Recognition. We leverage social media-specific pre- training in the form of BERTweet - A language model pre-trained on English Tweets, along with either a Convolutional Neural Network or a Transformer Encoder. We empirically show that the addition of Convolutional Neural Networks or a Transformer Encoder results in improved performance when compared to a vanilla BERTweet model. Furthermore, we also compare CNNs and the Transformer Encoder as feature extractors, assessing the trade-off between the number of learnable parameters and performance. Finally, we also investigate the impact of partial and complete fine-tuning and analyze the trade-off between computational power and accuracy in the process. Experimental results demonstrate that our proposed methods achieve state-of- the-art performance on two standard benchmark datasets.
Weighted expectation-maximisation algorithm for parameter estimation of misspecified dynamical models: application to vehicle dynamics

The problem of parameter estimation for nonlinear state-space models is addressed using the expectation-maximisation algorithm. Model states and parameters are iteratively estimated using cubature Kalman smoothing and maximum a posteriori estimation. A modification to this technique is proposed by weighting measurement samples so the algorithm equally tries to approximate all system dynamics, even those poorly represented in the measurements. The method is applied to parameter estimation of a vehicle dynamics model.
Research on Hybrid Discrete Particle Swarm Algorithm to Solve UAV Task Assignment Problem

Aiming at the target allocation problem of multiple UAVs executing multiple combat tasks at the same time, the traditional particle swarm algorithm has problems such as local optimal solution and unbalanced task load. In order to solve this problem, this paper proposes an unmanned aerial vehicle task allocation model based on hybrid discrete particle swarm algorithm. Compared with the traditional discrete particle swarm, the improvement of the algorithm is mainly reflected in the following two aspects: the introduction of the simulated annealing algorithm search mechanism to enhance the local search capability of the algorithm; the use of a two-dimensional vector coding scheme based on task sequence and UAV coding, the introduction of Transformation, reconstruction and other operational procedures, enhance the algorithm’s global search capabilities. The simulation results show that the algorithm proposed in this paper can effectively avoid local optimal solutions and improve the balance of task allocation, which proves the feasibility and effectiveness of the algorithm.
A Social Human-Robot Interaction Simulator for Reinforcement Learning Systems

Social robotics represents a branch of human-robot interaction that aims to develop robots to operate in unstructured environments in direct partnership with human beings. Social robots must interact with human beings by understanding social signals and responding appropriately to promote a natural and socially acceptable interaction among humans and robots. In this article, we propose a simulator for Deep Reinforcement Learning and Social Robotics, Sim-DRLSR, aiming to provide a system development tool for human-robot interaction with a self-learning paradigm. The simulator SimDRLSR is capable of providing an environment for social robots to learn and to identify, through vision, human interactive behaviors and to act accordingly to them. We use the Multimodal Deep Reinforcement Learning (MDQN) architecture for training and validating the simulated robot. Preliminary experiments show the proposed simulator can assist in testing and developing phases of social robots for interactions using vision, saving the use of real robots in the early stages of projects.
UET-Headpose: A sensor-based top-view head pose dataset

Head pose estimation is a challenging task that aims to solve problems related to predicting three dimensions vector, that serves for many applications in human-robot interaction or customer behavior. Previous researches have proposed some precise methods for collecting head pose data. But those methods require either expensive devices like depth cameras or complex laboratory environment setup. In this research, we introduce a new approach with efficient cost and easy setup to collecting head pose images, namely UET-Headpose dataset, with top-view head pose data. This method uses an absolute orientation sensor instead of Depth cameras to be set up quickly and small cost but still ensure good results. Through experiments, our dataset has been shown the difference between its distribution and available dataset like CMU Panoptic Dataset [6]. Besides using the UET-Headpose dataset and other head pose datasets, we also introduce the full-range model called FSANet-Wide, which significantly outperforms head pose estimation results by the UET-Headpose dataset, especially on top-view images. Also, this model is very lightweight and takes small size images.
Distributed Three-Dimensional Pseudo Measurement Kalman Filter with Angle-Only Measurements

This paper addresses the problem of three-dimensional target localization using a network of sensors with bearing-only measurements. We derive a distributed pseudo measurement Kalman filter by utilizing the concept of information weighted consensus. Theoretical analysis reveals that the estimation output is biased due to the correlation between the pseudo measurement matrix and the pseudo measurement noise. We then propose a distributed instantaneous bias compensation algorithm to mitigate this issue. Extensive numerical simulations reveal that the proposed approach can approximately recover the performance of the centralized solution and outperforms other mainstream distributed algorithms.
Medical Specialty Classification from A Bangla Dataset: A Token Level Approach Using Several Machine And Deep Learning Algorithms

With the outbreak of the current pandemic, the health care sector is going through significant changes throughout the world. Doctors and patients are preferring online services from both ends to avoid unnecessary hassles and risks of contamination. As a result, Electronic Health Records (EHR) are getting inundated in large numbers. For proper and easy maintenance of the system, sorting of the text-based records according to medical specialty is a primary concern and automation is a key requirement for achieving the purpose. As a matter of fact, the machine as well as deep learning methods can work significantly with proper training and accurate data. Correspondingly, the models can be able to understand the context of each patient in their natural language and provide with classified medical specialization of the respective dataset. Here, we have used the Bangla language for the training of the machine and deep learning models and achieved an accuracy of 82% using the Bidirectional LSTM model. Although complete dependence on the machine for the healthcare sector is contentious, it can definitely be a very good prospect in a world where each sector is being digitalized every now and then.
Network Analysis Application to Analyze the Activities and Artifacts in the Core Business Analysis Cycle

For the last ten years, leading professional associations and institutes have developed a set of practice standards in business analysis and requirement engineering. The best-known standard is a guide to the business analysis body of knowledge version 3 (BABOK), created by the International Institute of Business Analysis. The article is aimed to identify the core business analysis cycle's central elements via network analysis and understand the main directions of the business analysis model development. The focus is set to dependencies of tasks, deliverables, and work products to highlight the core activities and artifacts of the BABOK. The influences and linkage tasks and artifacts significantly impact pre-project analysis, requirement analysis, and business solution evaluation. This work allows rethinking the scope of business analysis by creating a conceptual model and defining the most influential business analysis tasks and artifacts that business analysts should concentrate on.
Compound Projection Learning for Bridging Seen and Unseen Objects

Zero-shot Learning (ZSL) aims to transfer knowledge from seen image categories to unseen ones by leveraging semantic information. It is generally assumed that the seen and unseen objects share a common semantic space. Most of existing ZSL methods focus on how to connect the visual space and the semantic space. However, since there are some visual distribution differences between seen and unseen objects, the projection function learned by those seen classes is biased when transferring knowledge to unseen classes. We argue that, although the unseen objects are class-agnostic, the visual distribution information of unseen samples can be generated by exploiting semantic features. In this paper, we propose a \underline{C}ompound \underline{P}rojection \underline{L}earning (CPL) model to transfer knowledge from seen to unseen objects by exploiting the information of both seen and class-agnostic samples. With the projected semantic representation by CPL, effective constraints such as projection loss and semantic reconstruction loss can be explored for seen and unseen objects, respectively, such that the semantic ambiguity across seen and unseen objects is reduced. Additionally, we utilize a similarity network to further explore the inter-class relationship by employing the labels and the similarities between seen and unseen classes. Extensive experiments on three benchmark datasets show the effectiveness of our proposed approach.
Self-Configuring Silicon-Photonic Receiver for Multimode Free Space Channels

A self-configuring mesh of silicon Mach-Zehnder Interferometers is employed to receive two spatially overlapped orthogonal beams modulated at 10 Gbit/s. These beams, sharing the same wavelength and state of polarization, are separated with more than 30 dB isolation, and sorted out with no signal degradation.
A Low-Cost Radar-based Domain Adaptive Breast Cancer Screening System

Over the past three decades, the advancements of breast cancer screening technologies such mammography, ultrasound, Magnetic resonance imaging (MRI) saved countless lives. The invention of mammography screening of breast cancer in the 1990s led a technological revolution which is, now-a-days, coupled with MRI and/or ultrasound to achieve diagnoses of much greater accuracy than previously attainable. However, these technologies (mammography, ultrasound, MRI) are not as widely available to patients as one might assume less fortunate countries (such as Bangladesh) often cannot afford the potentially enormous price tag that several ultrasound machines of sufficient quality for accurate diagnoses incurs. In this paper, we present a low-cost (&#x003C;100 USD) millimeter Wave (mmWave) Radar sensor array (3&#x2013; 10 GHz) imaging technology and a deep learning domain adaptation model-based breast cancer screening system. More specifically, (i) we develop a mmWave Radar sensor array (18 sensor antennas) based 2D imaging system; (ii) we develop a deep learning based domain adaptation model that can learn breast segmentation and cancer detection from expensive source data (mammography, ultrasound) and transfer the knowledge to less expensive target data (Radar images), (iii) we validated our system and methods by utilizing our existing mammography and ultrasound breast cancer screening data as well as 14 patients&#x0027; Radar images collected from a third world country (Bangladesh).
Predicting the Movement of Cryptocurrency “Bitcoin” Using Random Forest

Predicting cryptocurrency is a challenging and interesting job for traders, investors and researchers because of the cost and complexity involved. The current study focuses on predicting the direction of the cryptocurrency ‘Bitcoin’ for trading window of 3 days, 5 days, and 10 days ahead of the current day. The predictive model proposed is built using ensemble learning via random forest. The model uses feature matrix drawn on technical indicators as input for learning and predicting. The proposed model is empirically evaluated and achieves good levels of accuracy 92%, 85% and 87% for trading windows of 3 days, 5 days and 10 days ahead respectively.