Reviewerly/exHarmony huggingface dataset:
Biased compensation adaptive gradient algorithm for rational model with time-delay using self-organising maps

This paper develops a biased compensation adaptive gradient decent algorithm for rational models with unknown time-delay. Owing to the unknown time-delay, traditional identification methods cannot be directly applied for such models. To overcome this difficulty, the self-organised maps are proposed, which can obtain the estimates of the time-delay based on the residual errors. Then, an adaptive gradient descent algorithm is introduced to obtain the parameter estimates. Compared with the traditional gradient descent and redundant rule based methods, the proposed method has two advantages: (1) each element in the parameter vector has its own step-size, thus it is more effective than the traditional gradient descent method; (2) the number of the unknown parameters is unchanged, therefore, it has less computational effort than the redundant rule based method. Finally, a simulation experiment is given to show the effectiveness of the proposed algorithm.
TokenCut: Segmenting Objects in Images and Videos With Self-Supervised Transformer and Normalized Cut

In this paper, we describe a graph-based algorithm that uses the features obtained by a self-supervised transformer to detect and segment salient objects in images and videos. With this approach, the image patches that compose an image or video are organised into a fully connected graph, in which the edge between each pair of patches is labeled with a similarity score based on the features learned by the transformer. Detection and segmentation of salient objects can then be formulated as a graph-cut problem and solved using the classical Normalized Cut algorithm. Despite the simplicity of this approach, it achieves state-of-the-art results on several common image and video detection and segmentation tasks. For unsupervised object discovery, this approach outperforms the competing approaches by a margin of 6.1%, 5.7%, and 2.6% when tested with the VOC07, VOC12, and COCO20 K datasets. For the unsupervised saliency detection task in images, this method improves the score for Intersection over Union (IoU) by 4.4%, 5.6% and 5.2%. When tested with the ECSSD, DUTS, and DUT-OMRON datasets. This method also achieves competitive results for unsupervised video object segmentation tasks with the DAVIS, SegTV2, and FBMS datasets.
A Universal Self-Correcting Approach for Abnormal Jump Errors in Absolute Phase Retrieval

Codeword-assisted temporal phase unwrapping (TPU) is extensively applied in absolute phase retrieval, in which the fringe order embedded in the temporal sequence identifies the interval of the wrapped phase. However, owing to inevitable image blurring and random noise, the fringe order cannot be aligned with the interval of the wrapped phase at the pixel level and tends to fail easily under high-frequency measurements, thereby yielding abnormal jump errors. Existing approaches suffer from the limitations of universality, efficiency, and fringe frequency. To this end, this work proposes a universal self-correcting approach to remove abnormal jump errors efficiently and accurately. Specifically, the virtual fringe order is introduced by analysing the intensity changes of the phase-shifted patterns, and then it is combined with the real fringe order to form a complementary fringe order pair. Additionally, to enhance the anti-interference ability of the virtual fringe order, the fringe order histogram check technique (FOHCT) is proposed to eliminate fringe order errors. The proposed approach can efficiently conduct various codeword-assisted absolute phase retrieval without introducing additional patterns. Furthermore, benefitting from no error accumulation, our approach is applicable for high-frequency measurements, thereby improving the measurement capability. The measurement results validated the universality and efficiency of the proposed approach. High-frequency measurements demonstrated that the proposed approach outperformed the median filtering and the phase coding approach in terms of error rate (0.009% vs. 3.13% vs. 1.76%).
Increasing trust and fairness in machine learning applications within the mortgage industry

The integration of machine learning in applications provides opportunities for increased efficiency in many organisations. However, the deployment of such systems is often hampered by the lack of insight into how their decisions are reached, resulting in concerns about trust and fairness. In this article, we investigate to what extent the addition of explainable AI components to ML applications can contribute to alleviating these issues. As part of this research, explainable AI functionality was developed for an existing ML model used for mortgage fraud detection at a large international financial institution based in The Netherlands A system implementing local explanation techniques was deployed to support the day-to-day work of fraud detection experts working with the model. In addition, a second system implementing global explanation techniques was developed to support the model management processes involving data-scientists, legal experts and compliance officers. A controlled experiment using actual mortgage applications was carried out to measure the effectiveness of these two systems, using both quantitative and qualitative assessment methods. Our results show that the addition of explainable AI functionality results in a statistically significant improvement in the levels of trust and usability by its daily users. The explainable AI system implementing global interpretability was found to considerably increase confidence in the ability to perform the processes focused on compliance and fairness. In particular, bias detection towards demographic groups successfully aided in the identification and removal of bias towards applicants with a migration background.
IDT: An incremental deep tree framework for biological image classification

Nowadays, breast and cervical cancers are respectively the first and fourth most common causes of cancer death in females. It is believed that, automated systems based on artificial intelligence would allow the early diagnostic which increases significantly the chances of proper treatment and survival. Although Convolutional Neural Networks (CNNs) have achieved human-level performance in object classification tasks, the regular growing of the amount of medical data and the continuous increase of the number of classes make them difficult to learn new tasks without being re-trained from scratch. Nevertheless, fine tuning and transfer learning in deep models are techniques that lead to the well-known catastrophic forgetting problem. In this paper, an Incremental Deep Tree (IDT) framework for biological image classification is proposed to address the catastrophic forgetting of CNNs allowing them to learn new classes while maintaining acceptable accuracies on the previously learnt ones. To evaluate the performance of our approach, the IDT framework is compared against with three popular incremental methods, namely iCaRL, LwF and SupportNet. The experimental results on MNIST dataset achieved 87 % of accuracy and the obtained values on the BreakHis, the LBC and the SIPaKMeD datasets are promising with 92 %, 98 % and 93 % respectively.
Impact of neovascular age-related macular degeneration on eye-movement control during scene viewing: Viewing biases and guidance by visual salience

Human vision requires us to analyze the visual periphery to decide where to fixate next. In the present study, we investigated this process in people with age-related macular degeneration (AMD). In particular, we examined viewing biases and the extent to which visual salience guides fixation selection during free-viewing of naturalistic scenes. We used an approach combining generalized linear mixed modeling (GLMM) with a-priori scene parcellation. This method allows one to investigate group differences in terms of scene coverage and observers’ well-known tendency to look at the center of scene images. Moreover, it allows for testing whether image salience influences fixation probability above and beyond what can be accounted for by the central bias. Compared with age-matched normally sighted control subjects (and young subjects), AMD patients’ viewing behavior was less exploratory, with a stronger central fixation bias. All three subject groups showed a salience effect on fixation selection—higher-salience scene patches were more likely to be fixated. Importantly, the salience effect for the AMD group was of similar size as the salience effect for the control group, suggesting that guidance by visual salience was still intact. The variances for by-subject random effects in the GLMM indicated substantial individual differences. A separate model exclusively considered the AMD data and included fixation stability as a covariate, with the results suggesting that reduced fixation stability was associated with a reduced impact of visual salience on fixation selection.
Learning to Discriminate Information for Online Action Detection: Analysis and Application

Online action detection, which aims to identify an ongoing action from a streaming video, is an important subject in real-world applications. For this task, previous methods use recurrent neural networks for modeling temporal relations in an input sequence. However, these methods overlook the fact that the input image sequence includes not only the action of interest but background and irrelevant actions. This would induce recurrent units to accumulate unnecessary information for encoding features on the action of interest. To overcome this problem, we propose a novel recurrent unit, named Information Discrimination Unit (IDU), which explicitly discriminates the information relevancy between an ongoing action and others to decide whether to accumulate the input information. This enables learning more discriminative representations for identifying an ongoing action. In this paper, we further present a new recurrent unit, called Information Integration Unit (IIU), for action anticipation. Our IIU exploits the outputs from IDN as pseudo action labels as well as RGB frames to learn enriched features of observed actions effectively. In experiments on TVSeries and THUMOS-14, the proposed methods outperform state-of-the-art methods by a significant margin in online action detection and action anticipation. Moreover, we demonstrate the effectiveness of the proposed units by conducting comprehensive ablation studies.
Work-in-Progress—Blower VR: A Virtual Reality Experience To Support the Training of Forest Firefighters

First responders require adequate training to operate safely in dangerous situations. However, low-fidelity exercises used in courses are an approximation of real scenarios. This is particularly true for firefighters since practice sessions often do not include real fire. A way to overcome these limitations consists in using Virtual Reality (VR) to create experiences where trainees can face detailed simulations of actual risks. The system presented in this work-in-progress paper aims to support practice training on the use of the blower as a firefighting tool and offers two training modes to assist trainees in learning the procedure and assessing their knowledge.
Design and implementation of an efficient CNN accelerator for low-cost FPGAs

This paper proposes a computation-array-centered dataflow, which adjusts the convolution with different kernel sizes to a unified computing manner and reduces the dimension of computation array from 2D to 1D, so as to maximize the utilization of the computation elements offered by the accelerator. Furthermore, a single unit multiple data (SUMD) strategy is proposed to effectively alleviate the mismatch between the quantized data and the hardware resources with fixed bit width on FPGA. As a case study, an 8-bit MobileNetV2 model has been implemented on the low-cost ZYNQ XC7Z020 FPGA, whose FPS/DSP and GOPS/DSP achieve upto 0.55 and 0.35 respectively.
Emotion Analysis Based on Deep Learning With Application to Research on Development of Western Culture

Cultural development is often reflected in the emotional expression of various cultural carriers, such as literary works, movies, etc. Therefore, the cultural development can be analyzed through emotion analysis of the text, so as to sort out its context and obtain its development dynamics. This paper proposes a text emotion analysis method based on deep learning. The traditional neural network model mainly deals with the classification task of short texts in the form of word vectors, which causes the model to rely too much on the accuracy of word segmentation. In addition, the short texts have the characteristics of short corpus and divergent features. A text emotion classification model combing the Bidirectional Encoder Representations from Transformers (BERT) and Bi-directional Long Short-Term Memory (BiLSTM) is developed in this work. First, the BERT model is used to convert the trained text into a word-based vector representation. Then, the generated word vector is employed as the input of the BiLSTM to obtain the semantic representation of the context of the relevant word. By adding random dropout, the mechanism prevents the model from overfitting. Finally, the extracted feature vector is input to the fully connected layer, and the emotion category to which the text belongs is calculated through the Softmax function. Experiments show that in processing short texts, the proposed model based on BERT-BiLSTM is more accurate and reliable than the traditional neural network model using word vectors. The proposed method has a better analysis effect on the development of western culture.
A State Health Department and Health Information Exchange Partnership: An Effective Collaboration for a Data-Driven Response for COVID-19 Contact Tracing in Maryland

Background Accurate, complete, timely data were essential to effective contact tracing for COVID-19. Maryland Department of Health partnered with Maryland's designated health information exchange, Chesapeake Regional Information System for Our Patients (CRISP), to establish data enhancement processes that provided the foundation for Maryland's successful contact tracing program. Methods Hourly, electronic positive COVID-19 test results were routed through CRISP to the contact tracing data platform. The CRISP matched reports against its master patient index to enhance the record with demographic, locating, fatality, vaccination, and hospitalization data. Records were deduplicated and flagged if associated with a congregate setting, select state universities, or recent international travel. χ 2 Tests were used to assess if CRISP-added phone numbers resulted in better contact tracing outcomes. Results During June 15, 2020, to September 1, 2021, CRISP pushed 531,094 records to the state's contact tracing data platform within an hour of receipt; of those eligible for investigation, 99% had a phone number. The CRISP matched 521,731 (98%) records to their master patient index, allowing for deduplication and enrichment. The CRISP flagged 15,615 cases in congregate settings and 3304 cases as university students; these records were immediately routed for outbreak investigation. Records with an added phone number were significantly more likely to be successfully reached compared with cases with no added phone number ( P = 0.01). Conclusions The CRISP enhanced COVID-19 electronic laboratory reports with a near-instant impact on public health actions. The partnership and data processing workflows can serve as a blueprint for data modernization in public health agencies across the United States.
Quantitative investment model based on LightGBM algorithm

Quantitative investment refers to the trading method through quantitative way and computer program issued orders, in order to obtain stable income.Firstly, this paper completes Pearson correlation analysis.Finally, the correlation coefficients of 50 indicators were calculated according to relevant data, and 8 indicators with the highest correlation were obtained.Secondly, according to the LightGBM model, multiple linear regression model and BP neural network model, the three prediction models are used to forecast the trading volume of the "digital economy" plate from January 4, 2022 to January 28, 2022 respectively.Thirdly, the mean absolute value error, mean square error, R and other model evaluation indexes of the three prediction models are analyzed.Finally, the accuracy of LightGBM model is better, followed by BP neural network model, and multiple linear regression is the worst.
RI-MFM: A Novel Infrared and Visible Image Registration with Rotation Invariance and Multilevel Feature Matching

In the past ten years, multimodal image registration technology has been continuously developed, and a large number of researchers have paid attention to the problem of infrared and visible image registration. Due to the differences in grayscale distribution, resolution and viewpoint between two images, most of the existing infrared and visible image registration methods are still insufficient in accuracy. To solve such problems, we propose a new robust and accurate infrared and visible image registration method. For the purpose of generating more robust feature descriptors, we propose to generate feature descriptors using a concentric-circle-based feature-description algorithm. The method enhances the description of the main direction of feature points by introducing centroids, and, at the same time, uses concentric circles to ensure the rotation invariance of feature descriptors. To match feature points quickly and accurately, we propose a multi-level feature-matching algorithm using improved offset consistency for matching feature points. We redesigned the matching algorithm based on the offset consistency principle. The comparison experiments with several other state-of-the-art registration methods in CVC and homemade datasets show that our proposed method has significant advantages in both feature-point localization accuracy and correct matching rate.
Comparative Analysis of Automated Text Summarization Techniques: The Case of Ethiopian Languages

Nowadays, there is an abundance of information available from both online and offline sources. For a single topic, we can get more than hundreds of sources containing a wealth of information. The ability to extract or generate a summary of popular content allows users to quickly search for content and obtain preliminary data in the shortest amount of time. Manually extracting useful information from them is a difficult task. Automatic text summarization (ATS) systems are being developed to address this issue. Text summarization is the process of extracting useful information from large documents and compressing it into a summary while retaining all the relevant contents. This review paper provides a broad overview of ATS research works in various Ethiopian languages such as Amharic, Afan Oromo, and Tigrinya using different text summarization approaches. The work has identified the novel and recommended state-of-the-art techniques and methods for future researchers in the area and provides knowledge and useful support to new researchers in this field by providing a concise overview of the various feature extraction methods and classification techniques required for different types of ATS approaches applied to the Ethiopian languages. Finally, different recommendations for future researchers have been forwarded.
Evaluating perceptual and semantic interpretability of saliency methods: A case study of melanoma

Abstract In order to be useful, XAI explanations have to be faithful to the AI system they seek to elucidate and also interpretable to the people that engage with them. There exist multiple algorithmic methods for assessing faithfulness, but this is not so for interpretability, which is typically only assessed through expensive user studies. Here we propose two complementary metrics to algorithmically evaluate the interpretability of saliency map explanations. One metric assesses perceptual interpretability by quantifying the visual coherence of the saliency map. The second metric assesses semantic interpretability by capturing the degree of overlap between the saliency map and textbook features—features human experts use to make a classification. We use a melanoma dataset and a deep‐neural network classifier as a case‐study to explore how our two interpretability metrics relate to each other and a faithfulness metric. Across six commonly used saliency methods, we find that none achieves high scores across all three metrics for all test images, but that different methods perform well in different regions of the data distribution. This variation between methods can be leveraged to consistently achieve high interpretability and faithfulness by using our metrics to inform saliency mask selection on a case‐by‐case basis. Our interpretability metrics provide a new way to evaluate saliency‐based explanations and allow for the adaptive combination of saliency‐based explanation methods.
Effectiveness of Robot Motion Block on A-Star Algorithm for Robotic Path Planning

Efficient path planning and minimization of path movement costs for collision-free faster robot movement are very important in the field of robot automation. Several path planning algorithms have been explored to fulfill these requirements. Among them, the A-star (A*) algorithm performs better than others because of its heuristic search guidance. However, the performance, effectiveness, and searching time complexity of this algorithm mostly depends on the robot motion block to search for the goal by avoiding obstacles. With this challenge kept in mind, this paper proposes an efficient robot motion block with different block sizes for the A* path planning algorithm. The proposed approach reduces robots’ path cost and time complexity to find the goal position as well as avoid obstacles. In this proposed approach, grid-based maps are used where the robot’s next move is decided by searching eight directions among the surrounding grid points. However, the proposed robot motion blocks size has a significant effect on path cost and time complexity of the A* path planning algorithm. For the experiment and to validate the efficiency of the proposed approach, an online benchmarked dataset is used. The proposed approach is applied on thousands of different grid maps with various obstacles, starting, and goal positions. The obtained results from the experiment show that the presented robot motion blocks reduce the robot’s pathfinding time complexity and number of search nodes by maintaining a minimum path cost towards the goal position.