Reviewerly/exHarmony huggingface dataset:
Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learning

Offline Reinforcement learning (RL) has shown potent in many safe-critical tasks in robotics where exploration is risky and expensive. However, it still struggles to acquire skills in temporally extended tasks. In this paper, we study the problem of offline RL for temporally extended tasks. We propose a hierarchical planning framework, consisting of a low-level goal-conditioned RL policy and a high-level goal planner. The low-level policy is trained via offline RL. We improve the offline training to deal with out-of-distribution goals by a perturbed goal sampling process. The high-level planner selects intermediate sub-goals by taking advantages of model-based planning methods. It plans over future sub-goal sequences based on the learned value function of the low-level policy. We adopt a Conditional Variational Autoencoder to sample meaningful high-dimensional sub-goal candidates and to solve the high-level long-term strategy optimization problem. We evaluate our proposed method in long-horizon driving and robot navigation tasks. Experiments show that our method outperforms baselines with different hierarchical designs and other regular planners without hierarchy in these complex tasks.
A Deep Learning Model for Opinion mining in Twitter Combining Text and Emojis

Several approaches have been proposed to study opinions on Social Network Sites (SNS). Unfortunately, those works are not topic-sensitive and do not investigate the impact of emojis on text-based classification. In this paper, we propose a novel approach to predict the users' opinions expressed through textual tweets and emojis. Thus, we construct an emoji sentiment lexicon. Then, we extract opinions from the text before considering both the text and emojis to see how they enhance the expression of opinions in SNS discussions. We conduct a set of benchmarks using several well-known machine learning algorithms, leading to an accuracy of 83, 7%.
Modeling the Remote Associates Test as Retrievals from Semantic Memory

The Remote Associates Test (RAT) is a word association retrieval task that consists of a series of problems, each with three seemingly unrelated prompt words. The subject is asked to produce a single word that is related to all three prompt words. In this paper, we provide support for a theory in which the RAT assesses a person's ability to retrieve relevant word associations from long-term memory. We present a computational model of humans solving the RAT and investigate how prior knowledge and memory retrieval mechanisms influence the model's ability to match human behavior. We expand prior modeling attempts by investigating multiple large knowledge bases and by creating a cognitive process model that uses long-term memory spreading activation retrieval processes inspired by ACT-R and implemented in Soar. We evaluate multiple model variants for their ability to model human problem difficulty, including the incorporation of noise and base-level activation into memory retrieval. We conclude that the main factors affecting human difficulty are the existence of associations between prompt words and solutions, the relative strengths and directions of those associations compared to associations to other words, and the ability to perform multiple retrievals.
Data Availability of Open T-Cell Receptor Repertoire Data, a Systematic Assessment

Modern data-driven research has the power to promote novel biomedical discoveries through secondary analyses of raw data. Therefore, it is important to ensure data-driven research with great reproducibility and robustness for promoting a precise and accurate secondary analysis of the immunogenomics data. In scientific research, rigorous conduct in designing and conducting experiments is needed, specifically in scientific writing and reporting results. It is also crucial to make raw data available, discoverable, and well described or annotated in order to promote future re-analysis of the data. In order to assess the data availability of published T cell receptor (TCR) repertoire data, we examined 11,918 TCR-Seq samples corresponding to 134 TCR-Seq studies ranging from 2006 to 2022. Among the 134 studies, only 38.1% had publicly available raw TCR-Seq data shared in public repositories. We also found a statistically significant association between the presence of data availability statements and the increase in raw data availability ( p = 0.014). Yet, 46.8% of studies with data availability statements failed to share the raw TCR-Seq data. There is a pressing need for the biomedical community to increase awareness of the importance of promoting raw data availability in scientific research and take immediate action to improve its raw data availability enabling cost-effective secondary analysis of existing immunogenomics data by the larger scientific community.
A comparative evaluation of biomedical similar article recommendation

Biomedical sciences, with their focus on human health and disease, have attracted unprecedented attention in the 21st century. The proliferation of biomedical sciences has also led to a large number of scientific articles being produced, which makes it difficult for biomedical researchers to find relevant articles and hinders the dissemination of valuable discoveries. To bridge this gap, the research community has initiated the article recommendation task, with the aim of recommending articles to biomedical researchers automatically based on their research interests. Over the past two decades, many recommendation methods have been developed. However, an algorithm-level comparison and rigorous evaluation of the most important methods on a shared dataset is still lacking. In this study, we first investigate 15 methods for automated article recommendation in the biomedical domain. We then conduct an empirical evaluation of the 15 methods, including six term-based methods, two word embedding methods, three sentence embedding methods, two document embedding methods, and two BERT-based methods. These methods are evaluated in two scenarios: article-oriented recommenders and user-oriented recommenders, with two publicly available datasets: TREC 2005 Genomics and RELISH, respectively. Our experimental results show that the text representation models BERT and BioSenVec outperform many existing recommendation methods (e.g., BM25, PMRA, XPRC) and web-based recommendation systems (e.g., MScanner, MedlineRanker, BioReader) on both datasets regarding most of the evaluation metrics, and fine-tuning can improve the performance of the BERT-based methods. Our comparison study is useful for researchers and practitioners in selecting the best modeling strategies for building article recommendation systems in the biomedical domain. The code and datasets are publicly available.
Selection of suitable IoT-based End-devices, tools, and technologies for implementing Smart Farming: Issues and Challenges

Purpose of Study: In the contemporary world, Internet-of-Things have become an indispensable part of our lives. The present era is of sensor-based electronic gadgets. Nowadays, more or less, every aspect of human life, from Home Automation, Healthcare, and Fitness, Agriculture, Education, Entertainment, Locomotive, Smart Farming, Smart Digital Cities, towards Industrial IoT, each one is redesigned from statistical to qualitative mechanisms. With the growing trends and technological transformations, the reality is that Internet-of-Things play a vital role. Currently, the agriculture industry is precise, and data-centered than traditional farming. Such revolutionary changes in the smart farming in the last decades are shaking the existing agriculture methods and creating new opportunities in the specific agriculture domains like crop status, insect and pest detection, and removal, irrigation, soil preparation, crop surveillance along with a range of unfolded issues and challenges. This study highlights the potential unfolded issues and challenges in the domain specific and precise selection of Internet-of-Thing based devices with supportive technologies and tools for instigating Smart Farming.&#x0D; Methodology: a systematic data processing approach with literature search and screening to review, is used in this study to identify selection of suitable iot-based end-devices, tools and technologies for implementing smart farming&#x0D; Main Findings: This study discussed the various IoT end-devices, tools and technologies in a well-organized manner. This study also highlights the foremost outspread challenges during selection of IoT-based resources for smart farming.&#x0D; Implications: This article is beneficial to all concerned stakeholders involved in agricultural activities like farmers, agricultural institutions, professional decision makers, who are passionate for enhancing the smart farming.&#x0D; Novelty/Originality of the study: Various scholars/researchers have been making efforts for smart farming by using IoT concepts in agriculture. This study make some efforts to discussed past research on IoT tools and Technology and challenges in selection of IoT devices for implementing an Iot based infrastructure for smart farming.
Real-Time Object Detection for the Running Train Based on the Improved YOLO V4 Neural Network

Matching the detection speed and accuracy in practical applications is considered to improve the speed of video object detection in front of trains. Lightweight convolutional neural network MobileNet and clustering ideas are combined to improve the object detection algorithm, and the MYOLO-lite model object detection algorithm is designed. The self-made object dataset of a forward-moving train is combined with the K-means clustering idea. The transcendental frame is redesigned to enhance scale adaptability. The nonmaximum suppression and loss function improvement methods of the MYOLO-lite network model are proposed for occluded tracks in front of the train, obstruction of trains, low detection accuracy of large object coincidence, and uneven distribution of positive and negative samples. The mean average precision value of the experimentally designed MYOLO-lite model object detection algorithm reaches 95.74%, with 42.04 frames per second. Detecting a picture only takes 0.024 s.
Scheimpflug Camera-Based Technique for Multi-Point Displacement Monitoring of Bridges

Owing to the limited field of view (FOV) and depth of field (DOF) of a conventional camera, it is quite difficult to employ a single conventional camera to simultaneously measure high-precision displacements at many points on a bridge of dozens or hundreds of meters. Researchers have attempted to obtain a large FOV and wide DOF by a multi-camera system; however, with the growth of the camera number, the cost, complexity and instability of multi-camera systems will increase exponentially. This study proposes a multi-point displacement measurement method for bridges based on a low-cost Scheimpflug camera. The Scheimpflug camera, which meets the Scheimpflug condition, can enlarge the depth of field of the camera without reducing the lens aperture and magnification; thus, when the measurement points are aligned in the depth direction, all points can be clearly observed in a single field of view with a high-power zoom lens. To reduce the impact of camera motions, a motion compensation method applied to the Scheimpflug camera is proposed according to the characteristic that the image plane is not perpendicular to the lens axis in the Scheimpflug camera. Several tests were conducted for performance verification under diverse settings. The results showed that the motion errors in x and y directions were reduced by at least 62% and 92%, respectively, using the proposed method, and the measurements of the camera were highly consistent with LiDAR-based measurements.
Robust sparse Bayesian learning for broad learning with application to high-speed railway track monitoring

In this study, we focus on non-parametric probabilistic modeling for general regression analysis with large amounts of data and present an algorithm called the robust sparse Bayesian broad learning system. Robust sparse Bayesian learning is employed to infer the posterior distribution of the sparse connecting weight parameters in broad learning system. Regardless of the number of candidate features, our algorithm can always produce a compact subset of hidden-layer neurons of almost the same size learned from the data, which allows the algorithm to automatically adjust the model complexity of the network. This algorithm not only solves the regression problem of large amounts of data robustly but also possesses high computational efficiency and low requirements for computing hardware. Moreover, as a Bayesian probabilistic algorithm, it can provide the posterior uncertainty quantification of the predicted output, giving a measure of prediction confidence. The proposed algorithm is verified using simulated data generated by a benchmark function and also applied in non-parametric probabilistic modeling using high-speed railway track monitoring data. The results show that compared with several existing neural network algorithms, our proposed algorithm has strong model robustness, excellent prediction accuracy, and computational efficiency for regression analysis with large amounts of data, and has the potential to be widely used in general regression problems in science and engineering.
New Key-Recovery Attack on Reduced-Round AES

A new fundamental 4-round property of AES, called the zero-difference property, was introduced by Rønjom, Bardeh and Helleseth at Asiacrypt 2017. Our work characterizes it in a simple way by exploiting the notion of related differences which was introduced and well analyzed by the AES designers. We extend the 4-round property by considering some further properties of related differences over the AES linear layer, generalizing the zero-difference property. This results in a new key-recovery attack on 7-round AES which is the first attack on 7-round AES by exploiting the zero-difference property.
Scalable and Effective Bipartite Network Embedding

Given a bipartite graph G consisting of inter-set weighted edges connecting the nodes in two disjoint sets U and V, bipartite network embedding (BNE) maps each node ui in U and vj in V to compact embedding vectors that capture the hidden topological features surrounding the nodes, to facilitate downstream tasks. Effective BNE should preserve not only the direct connections between nodes but also the multi-hop relationships formed alternately by the two types of nodes in G, which can incur prohibitive overheads, especially on massive bipartite graphs with millions of nodes and billions of edges. Existing solutions are hardly scalable to massive bipartite graphs, and often produce low-quality results. This paper proposes GEBE, a generic BNE framework achieving state-of-the-art performance on massive bipartite graphs, via four main algorithmic designs. First, we present two generic measures to capture the multi-hop similarity/proximity between homogeneous/heterogeneous nodes respectively, and the measures can be instantiated with three popular probability distributions, including Poisson, Geometric, and Uniform distributions. Second, GEBE formulates a novel and unified BNE objective to preserve the two measures of all possible node pairs. Third, GEBE includes several efficiency designs to get high-quality embeddings on massive graphs. Finally, we observe that GEBE achieves the best performance when instantiating MHS and MHP using a Poisson distribution, and thus, we further develop GEBEp based on Poisson-instantiated MHS and MHP, with non-trivial efficiency optimizations. Extensive experiments, comparing 15 competitors on 10 real datasets, demonstrate that our solutions, especially GEBEp, obtain superior result utility than all competitors for top-N recommendation and link prediction, while being up to orders of magnitude faster.
The Artificial Intelligence and Neural Network in Teaching

This study aims to explore the application of artificial intelligence (AI) and network technology in teaching. By studying the AI-based smart classroom teaching mode and the advantages and disadvantages of network teaching using network technology and taking the mathematics classroom as an example, this study makes an intelligent analysis of the questioning link of classroom teachers in the teaching process. For the questions raised by teachers, the network classification models of convolutional neural network (CNN) and long short-term memory (LSTM) are used to classify the questions according to the content and types of questions and carry out experimental verification. The results show that the overall performance of the CNN model is better than that of the LSTM model in the classification results of the teacher’s question content dimension. CNN has higher accuracy, and the classification accuracy of essential knowledge points reaches 86.3%. LSTM is only 79.2%, and CNN improves by 8.96%. In the classification results of teacher question types, CNN has higher accuracy. The classification accuracy of the prompt question is the highest, reaching 87.82%. LSTM is only 83.2%, and CNN improves by 4.95%. CNN performs better in teacher question classification results.
Distributed big data analysis using spark parallel data processing

Nowadays, the big data marketplace is rising rapidly. The big challenge is finding a system that can store and handle a huge size of data and then processing that huge data for mining the hidden knowledge. This paper proposed a comprehensive system that is used for improving big data analysis performance. It contains a fast big data processing engine using Apache Spark and a big data storage environment using Apache Hadoop. The system tests about 11 Gigabytes of text data which are collected from multiple sources for sentiment analysis. Three different machine learning (ML) algorithms are used in this system which is already supported by the Spark ML package. The system programs were written in Java and Scala programming languages and the constructed model consists of the classification algorithms as well as the pre-processing steps in a figure of ML pipeline. The proposed system was implemented in both central and distributed data processing. Moreover, some datasets manipulation manners have been applied in the system tests to check which manner provides the best accuracy and time performance. The results showed that the system works efficiently for treating big data, it gains excellent accuracy with fast execution time especially in the distributed data nodes.
Multi Layered Feature Explanation Method for Convolutional Neural Networks

The most popular methods for Artificial Intelligence such as Deep Neural Networks are, for the vast majority, considered black boxes. It is necessary to explain their decisions to understand the input data which influence most the result. Methods presented in this paper aim at an explanation in image classification tasks: which data in the input are the most important for the result. We further extend the Feature Explanation Method (FEM) from our previous work, transforming it into a multi-layered FEM (MLFEM). The evaluation of the method is designed by comparison of explanation maps with human Gaze Fixation Density maps (GFDM). We show that proposed MLFEM outperforms FEM and popular DNN explanation methods in terms of classical comparison metrics with GFDM.
On removing conflicts for machine learning

A Machine Learning (ML) system learns from a set of samples called the training set. In some cases, the training set may have learning conflicts that affect the performance of the machine learning system. A learning conflict is produced when two or more samples in a dataset have similar input values but different target values. We propose a method to remove learning conflicts from a dataset in this work. Our method is based on a genetic algorithm tries to keep those samples that free of conflicts and intents to remove those samples with conflicts. Each individual in the genetic algorithm represents a possible dataset. We introduce the concept of retention error in the fitness function, which describes how many samples are kept while removing learning conflicts. Additionally, the fitness function comprises the Mean-Squared Error (MSE) that validates the machine learning performance. The algorithm is designed to keep as many samples as possible while the machine learning system exhibits the highest possible performance. Therefore, the proposal consists in cleaning first the dataset that compares and highlights the individual with the best performance in the Genetic Algorithm (GA), recommending which samples must be included for training and testing. Three different datasets with learning conflicts are used to test the proposed methodology. Besides, one artificial neural network is trained using the datasets with learning conflicts for each dataset. After removing the conflicts, a second artificial neural network is trained using the cleaned datasets. A noticeable reduction in the mean-square error is observed when the neural network is trained using the cleaned dataset.
Image Moment-Based Features for Mass Detection in Breast US Images via Machine Learning and Neural Network Classification Models

Differentiating between malignant and benign masses using machine learning in the recognition of breast ultrasound (BUS) images is a technique with good accuracy and precision, which helps doctors make a correct diagnosis. The method proposed in this paper integrates Hu’s moments in the analysis of the breast tumor. The extracted features feed a k-nearest neighbor (k-NN) classifier and a radial basis function neural network (RBFNN) to classify breast tumors into benign and malignant. The raw images and the tumor masks provided as ground-truth images belong to the public digital BUS images database. Certain metrics such as accuracy, sensitivity, precision, and F1-score were used to evaluate the segmentation results and to select Hu’s moments showing the best capacity to discriminate between malignant and benign breast tissues in BUS images. Regarding the selection of Hu’s moments, the k-NN classifier reached 85% accuracy for moment M1 and 80% for moment M5 whilst RBFNN reached an accuracy of 76% for M1. The proposed method might be used to assist the clinical diagnosis of breast cancer identification by providing a good combination between segmentation and Hu’s moments.
Motion inspires notion

Environment depth estimation by fusing camera and radar enables a broad spectrum of applications such as autonomous driving, environmental perception, context-aware localization and navigation. Various pioneering approaches have been proposed to achieve accurate and dense depth estimation by integrating vision and LiDAR through deep learning. However, due to the challenges of sparse sampling of in-vehicle LiDARs, high ground-truth annotation overhead, and severe dynamics in real environments, existing solutions have not yet achieved widespread deployment on commercial autonomous vehicles. In this paper, we propose LeoVR, a visual-LiDAR fusion based self-supervised approach that enables accurate environment depth estimation. LeoVR digs into the vehicle's motion information and designs two effective system frameworks based on it to (i) optimize the depth estimation results, and (ii) provide supervision signals to train a DNN. We fully implement LeoVR on a robotic testbed and commercial vehicle to conduct extensive experiments across 6 months. The results demonstrate that LeoVR achieves remarkable performance with an average depth estimation error of 0.17m, outperforming existing state-of-the-art solutions by > 43%. Besides, even cold-start in real environments by self-supervised training, LeoVR still achieves an average error of 0.21m, outperforming the related works by > 45% and comparable to those supervised training methods.