Reviewerly/exHarmony huggingface dataset:
Using Neural Encoder-Decoder Models With Continuous Outputs for Remote Sensing Image Captioning

Remote sensing image captioning involves generating a concise textual description for an input aerial image. The task has received significant attention, and several recent proposals are based on neural encoder-decoder models. Most previous methods are trained to generate discrete outputs corresponding to word tokens that match the reference sentences word-by-word, thereby optimizing the generation locally at token-level instead of globally at sentence-level. This paper explores an alternative generation method based on continuous outputs, which generates sequences of embedding vectors instead of directly predicting word tokens. We argue that continuous output models have the potential to better capture the global semantic similarity between captions and images, e.g. by facilitating the use of loss functions matching different views of the data. This includes comparing representations for individual tokens and for the entire captions, and also comparing captions against intermediate image representations. We experimentally compare discrete versus continuous output captioning methods over the UCM and RSICD datasets, which are extensively used in the area despite some issues which we also discuss. Results show that the alternative encoder-decoder framework with continuous outputs can indeed lead to better results on the two datasets, compared to the standard approach based on discrete outputs. The proposed approach is also competitive against the state-of-the-art model in the area.
BPR algorithm: New broken plural rules for an Arabic stemmer

One of the most important phases in text processing is stemming, whose aim is to aggregate all variations in a word into one group to aid natural language processing. The morphological structure of the Arabic language is more challenging than that of the English language; thus, it requires superior stemming algorithms for Arabic stemmers to be effective. One of the challenges is the irregular broken plural, which has been a problematic issue in Arabic natural language processing that affects the performance of Arabic information retrieval and other Arabic language engineering applications. Several studies have attempted to develop solutions to irregular plural problems, but the challenge remains, especially in extracting correct Arabic root words. In this paper, the broken plural rule (BPR) algorithm introduces new solutions to solve the problem in which an existing root-based method cannot extract correct roots by using their proposed rules. The BPR algorithm introduces several rules (main rules and subrules) to extract the correct roots of the Arabic irregular broken plural words. To evaluate the effectiveness of the BPR algorithm, we extracted roots from an Arabic standard dataset and applied the BPR algorithm as an enhancement to a root-based Arabic stemmer, ISRI. The obtained results from both evaluations showed encouraging results: (i) Only a few numbers of incorrect roots were stemmed on the large-sized Arabic word dataset. (ii) The enhanced root-based Arabic stemmer, ISRI + BPR, exhibited the best performance compared with the original ISRI stemmer and a well-known Arabic stemmer, ARLS 2. Thus, the proposed BPR algorithm has solved some of the irregular broken plural problems that eventually increase the performance of a root-based Arabic stemmer.
Application of Fuzzy and Rough Logic to Posture Recognition in Fall Detection System

Considering that the population is aging rapidly, the demand for technology for aging-at-home, which can provide reliable, unobtrusive monitoring of human activity, is expected to expand. This research focuses on improving the solution of the posture detection problem, which is a part of fall detection system. Fall detection, using depth maps obtained by the Microsoft Kinect sensor, is a two-stage method. We concentrate on the first stage of the system, that is, pose recognition from a depth map. For lying pose detection, a new hybrid FRSystem is proposed. In the system, two rule sets are investigated, the first one created based on a domain knowledge and the second induced based on the rough set theory. Additionally, two inference aggregation approaches are considered with and without the knowledge measure. The results indicate that the new axiomatic definition of knowledge measures, which we propose has a positive impact on the effectiveness of inference and the rule induction method reducing the number of rules in a set maintains it.
Endoscopic image luminance enhancement based on the inverse square law for illuminance and retinex

In a single-port robotic system where the 3D endoscope possesses two bending segments, only point light sources can be integrated at the tip due to space limitations. However, point light sources usually provide non-uniform illumination, causing the endoscopic images to appear bright in the centre and dark near the corners.Based on the inverse square law for illuminance, an initial luminance weighting is first proposed to increase the image luminance uniformity. Then, a saturation-based model is proposed to finalise the luminance weighting to avoid overexposure and colour discrepancy, while the single-scale retinex (SSR) scheme is employed for noise control.Via qualitative and quantitative comparisons, the proposed method performs effectively in enhancing the luminance and uniformity of endoscopic images, in terms of both visual perception and objective assessment.The proposed method can effectively reduce the image degradation caused by point light sources.
Augmented Reality for Presenting Real-Time Data During Students’ Laboratory Work: Comparing a Head-Mounted Display With a Separate Display

Multimedia learning theories suggest presenting associated pieces of information in spatial and temporal contiguity. New technologies like Augmented Reality allow for realizing these principles in science laboratory courses by presenting virtual real-time information during hands-on experimentation. Spatial integration can be achieved by pinning virtual representations of measurement data to corresponding real components. In the present study, an Augmented Reality-based presentation format was realized via a head-mounted display and contrasted to a separate display, which provided a well-arranged data matrix in spatial distance to the real components and was therefore expected to result in a spatial split-attention effect. Two groups of engineering students (N = 107; Augmented Reality vs. separate display) performed six experiments exploring fundamental laws of electric circuits. Cognitive load and conceptual knowledge acquisition were assessed as main outcome variables. In contrast to our hypotheses and previous findings, the Augmented Reality group did not report lower extraneous load and the separate display group showed higher learning gains. The pre- and posttest assessing conceptual knowledge were monitored by eye tracking. Results indicate that the condition affected the visual relevancy of circuit diagrams to final problem completion. The unexpected reverse effects could be traced back to emphasizing coherence formation processes regarding multiple measurements.
Fault tolerant multi-sensor data fusion for autonomous navigation in future civil aviation operations

The problem addressed in this paper is that of fault-tolerant multi-sensor data-fusion for flight navigation during approach and landing of a civil jetliner. The work falls within the scope of a large collaborative project on future single pilot operations issues. The proposed methodology is based on a multi-layer hierarchical architecture which can integrate several heterogeneous information sources. The study is restricted to three classical information sources, namely, Inertial Reference System (IRS), Global Positioning System (GPS) and Instrument Landing System (ILS). The system is designed to meet the specified navigation performance requirements in terms of validity, accuracy, continuity and on-board implementation issues. The methodology takes advantage of recently developed set-membership tools, and paves the way toward a new and simple tuning of global merging filter.
LFM signal optimization time-fractional-frequency analysis: Principles, method and application

The classical time-frequency analysis method is not the optimal signal representation method for LFM signal, fractional Fourier transform (FRFT) can represent signal from any fractional domain between time domain and frequency domain. Inspired by this characteristic, an optimization time-fractional-frequency (TFF) analysis method for LFM signal is built in this paper. First, a new simple fractional wavelet transform (FRWT) is proposed using generalized fractional convolution, which can provide joint time and fractional-frequency information of signal. Furthermore, an optimization TFF analysis method based on FRWT is established. It can provide a robust and high-resolution TFF representation for LFM signal, especially for multi-component LFM signal with close or intersecting components. Finally, the FRWT-based TFF analysis method is successfully applied to the parameter estimation of LFM signal. Experimental results show that the FRWT-based parameter estimation method is superior to some existing methods in terms of accuracy and noise robustness.
CTRF: Ethereum-Based Ponzi Contract Identification

In recent years, blockchain technology has been developing rapidly. More and more traditional industries are using blockchain as a platform for information storage and financial transactions, mainly because of its new characteristics of non-tamperability and decentralization compared with the traditional systems. As a representative of blockchain 2.0, Ethereum has gained popularity upon its introduction. However, because of the anonymity of blockchain, Ethereum has also attracted the attention of some unscrupulous people. Currently, millions of contracts are deployed on Ethereum, many of which are fraudulent contracts deployed by unscrupulous people for profit, and these contracts are causing huge losses to investors worldwide. Ponzi contracts are typical of these contracts, which mainly reward the funds invested by later investors to early investors, and later investors will have no gain. However, although there are some studies for identifying Ponzi contracts on Ethereum, there is some room for progress in the research. Therefore, we propose a method to detect Ponzi scheme contracts on Ethereum-CTRF. This method forms a dataset by extracting the word features and sequence features of the smart contract’s code and the features of transactions. The dataset is divided into a training set and a test set. Oversampling is performed on the training set to deal with the problem of positive and negative sample imbalance. Finally, the model is trained on the training set and tested on the test set. The experimental results show that the model has significantly improved recall compared with existing Ponzi contract detection methods.
Artificial Intelligence techniques based on the integration of symbolic logic and deep neural networks: A systematic review of the literature

The need for neural-symbolic integration becomes apparent as more complex problems are tackled, and they go beyond limited domain tasks such as classification. In this sense, understanding the state of the art of hybrid technologies based on Deep Learning and augmented with logic based systems, is of utmost importance. As a consequence, we seek to understand and represent the current state of these technologies that are highly used in intelligent systems engineering.This work aims to provide a comprehensive view of the solutions available in the literature, within the field of applied Artificial Intelligence (AI), using technologies based on AI techniques that integrate symbolic and non-symbolic logic (in particular artificial neural networks), making them the subject of a systematic literature review (SLR). The resulting technologies are discussed and evaluated from both perspectives: symbolic and non-symbolic AI.In this work, we use the PICOC &amp; Limits method to define the research questions and analyze the results.Out of a total of 65 candidate studies found, 24 articles (37%) relevant to this study were selected. Each study also focuses on different application domains. Conclusion: Through the analysis of the selected works throughout this review, we have seen different combinations of logical systems with some form of neural network and, although we have not found a clear architectural pattern, efforts to find a model of general purpose combining both worlds drive trends and research efforts.
Tumor cell identification and classification in esophageal adenocarcinoma specimens by hyperspectral imaging

Esophageal cancer is the sixth leading cause of cancer-related death worldwide. Histopathological confirmation is a key step in tumor diagnosis. Therefore, simplification in decision-making by discrimination between malignant and non-malignant cells of histological specimens can be provided by combination of new imaging technology and artificial intelligence (AI). In this work, hyperspectral imaging (HSI) data from 95 patients were used to classify three different histopathological features (squamous epithelium cells, esophageal adenocarcinoma (EAC) cells, and tumor stroma cells), based on a multi-layer perceptron with two hidden layers. We achieved an accuracy of 78% for EAC and stroma cells, and 80% for squamous epithelium. HSI combined with machine learning algorithms is a promising and innovative technique, which allows image acquisition beyond Red-Green-Blue (RGB) images. Further method validation and standardization will be necessary, before automated tumor cell identification algorithms can be used in daily clinical practice.
Local and Global GANs With Semantic-Aware Upsampling for Image Generation

In this paper, we address the task of semantic-guided image generation.One challenge common to most existing imagelevel generation methods is the difficulty in generating small objects and detailed local textures.To address this, in this work we consider generating images using local context.As such, we design a local class-specific generative network using semantic maps as guidance, which separately constructs and learns subgenerators for different classes, enabling it to capture finer details.To learn more discriminative class-specific feature representations for the local generation, we also propose a novel classification module.To combine the advantages of both global image-level and local class-specific generation, a joint generation network is designed with an attention fusion module and a dual-discriminator structure embedded.Lastly, we propose a novel semantic-aware upsampling method, which has a larger receptive field and can take far-away pixels that are semantically related for feature upsampling, enabling it to better preserve semantic consistency for instances with the same semantic labels.Extensive experiments on two image generation tasks show the superior performance of the proposed method.State-of-the-art results are established by large margins on both tasks and on nine challenging public benchmarks.The source code and trained models are available at https://github.com/Ha0Tang/LGGAN.
Sample Efficient Grasp Learning Using Equivariant Models

In planar grasp detection, the goal is to learn a function from an image of a scene onto a set of feasible grasp poses in SE(2).In this paper, we recognize that the optimal grasp function is SE(2)-equivariant and can be modeled using an equivariant convolutional neural network.As a result, we are able to significantly improve the sample efficiency of grasp learning, obtaining a good approximation of the grasp function after only 600 grasp attempts.This is few enough that we can learn to grasp completely on a physical robot in about 1.5 hours.Code is available at https://github.com/ZXP-S-works/SE2-equivariant-grasp-learning.
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation

Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE).This paper explores a deeper relationship between Transformer and numerical ODE methods.We first show that a residual block of layers in Transformer can be described as a higher-order solution to ODE.Inspired by this, we design a new architecture, ODE Transformer, which is analogous to the Runge-Kutta method that is well motivated in ODE.As a natural extension to Transformer, ODE Transformer is easy to implement and efficient to use.Experimental results on the large-scale machine translation, abstractive summarization, and grammar error correction tasks demonstrate the high genericity of ODE Transformer.It can gain large improvements in model performance over strong baselines (e.g., 30.77 and 44.11BLEU scores on the WMT'14 English-German and English-French benchmarks) at a slight cost in inference efficiency.
Artificial intelligence-based prediction for cancer-related outcomes in Africa: Status and potential refinements

Globally, cancer ranks among the most common causes of death, especially among people under 70 years of age [1].With this burden rapidly increasing globally, utilizing prediction tools to assist decision-making and encourage individualized treatment planning is gradually becoming paramount in cancer diagnosis and management.Notably, many tools constructed on the backend of artificial intelligence (AI) algorithms have been shown to improve the predictive accuracy and clinical impact of risk prediction compared to clinical scenarios not utilizing these models [2].However, the actualisation of the potential of health care AI has mostly been assessed in high-income and resource-driven centres.The impact and efficiency of oncological AI-based prediction tools are expected to be better realised when applied in low-resource and rural settings fraught with a paucity of experienced clinicians and specialists.Africa is considered the most vulnerable continent in the world with regards to the availability of resources for optimal health care provision and comprises about 85.2% of countries that belong to the low-income/ lower-middle-income economies [3].Also, many individuals have to rely on underfunded public health care systems, even for lethal diseases like cancer, which may be further constrained by access to secondary or tertiary health centers and financial limitations [4].Therefore, AI prediction tools would prove beneficial in triaging patients before they present to health institutions, as well as for treatment selection of patients with precancer and cancer in such settings.Furthermore, these platforms could potentially be employed alongside other digitized systems such as telemedicine for remote consultations and Internet of Medical Things (IoMT) to improve referral strategies [5,6].We examine the implementation status of AI platforms for cancer outcomes in Africa and provide pointers that could maximize their utilization.
GRAND+: Scalable Graph Random Neural Networks

Graph neural networks (GNNs) have been widely adopted for semi-supervised learning on graphs. A recent study shows that the graph random neural network (GRAND) model can generate state-of-the-art performance for this problem. However, it is difficult for GRAND to handle large-scale graphs since its effectiveness relies on computationally expensive data augmentation procedures. In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning. To address the above issue, we develop a generalized forward push (GFPush) algorithm in GRAND+ to pre-compute a general propagation matrix and employ it to perform graph data augmentation in a mini-batch manner. We show that both the low time and space complexities of GFPush enable GRAND+ to efficiently scale to large graphs. Furthermore, we introduce a confidence-aware consistency loss into the model optimization of GRAND+, facilitating GRAND+’s generalization superiority. We conduct extensive experiments on seven public datasets of different sizes. The results demonstrate that GRAND+ 1) is able to scale to large graphs and costs less running time than existing scalable GNNs, and 2) can offer consistent accuracy improvements over both full-batch and scalable GNNs across all datasets.
Uncertainty-Based Rejection in Machine Learning: Implications for Model Development and Interpretability

Uncertainty is present in every single prediction of Machine Learning (ML) models. Uncertainty Quantification (UQ) is arguably relevant, in particular for safety-critical applications. Prior research focused on the development of methods to quantify uncertainty; however, less attention has been given to how to leverage the knowledge of uncertainty in the process of model development. This work focused on applying UQ into practice, closing the gap of its utility in the ML pipeline and giving insights into how UQ is used to improve model development and its interpretability. We identified three main research questions: (1) How can UQ contribute to choosing the most suitable model for a given classification task? (2) Can UQ be used to combine different models in a principled manner? (3) Can visualization techniques improve UQ’s interpretability? These questions are answered by applying several methods to quantify uncertainty in both a simulated dataset and a real-world dataset of Human Activity Recognition (HAR). Our results showed that uncertainty quantification can increase model robustness and interpretability.
Online Teaching Skills and Competencies

The technique of instructing people using virtual networks is known as online teaching. Live courses, video calls, lectures, and other internet technologies are all used in this form of education. The web programs are created and intended to make studying and comprehension easier. The goal of this study and analysis was to narrow down the abilities and competencies that professors need to successfully instruct in virtual training contexts. Six groups of skills and competencies have been identified: Instructional skills, subject abilities, creative ability, technical abilities, managerial and organizational skills, and verbal ability are all important. Online education allows both the instructor and the learner to establish their teaching speed, with the additional benefit of being able to create a timetable that works for everybody. As a consequence, adopting a virtual learning environment provides for a healthier work combination, but there is no need to sacrifice everything.
Institutional threshold in the nexus between financial openness and TFP in Africa: A dynamic panel analysis

The challenge of “growth without development” in most developing countries particularly in the African region has brought to fore the discussion of the effects of macroeconomic policies on the key determinants of growth, namely Total Factor Productivity (TFP). This study investigated the threshold or sustainable level of institutional development necessary for financial openness to stimulate TFP growth by employing a Dynamic Panel Threshold Model (DPTM) for the panel of 28 selected African countries from 1996 to 2019. The findings showed that there exists a threshold or sustainable level of institutional development (4.73 out of 10-points scale) which African countries must attain to reap the full benefits of financial openness in terms of TFP growth because the quality of institutions in most African countries are pervasively weak and poorly developed to checkmate excesses, corruptions and political interferences in the financial markets. Therefore, the study recommended that there should be a deliberate attempt by relevant authorities to block all inherent loopholes in the institutional settings of African countries which give room for shady dealings in the financial markets, to ensure institutional environments (political, legal, regulatory, and economic institutions) are healthy enough to absorb the negative influence or the risks associated with financial openness that may hinder growth and development in the long-run by hindering TFP growth.