Reviewerly/exHarmony huggingface dataset:
Learning deep feature fusion for traffic light detection

Traffic light detection in real-world conditions is challenging because of the positioning of lights, variety in shapes and scales, and similarity with other objects. The paper presents a deep learning-based traffic light detection system by learning the fusion of handcrafted features. The handcrafted features for object detection focus on specific attributes such as shape, color, or texture. The objective of this work is to incorporate handcrafted features into the network learning process such that the resulting detector parameters are robust to input variations, sensor noise, and atmospheric noise. The proposed detection framework is based on the latest You only look once (YOLO) architecture, trained with the fusion of different information channels in the Integral Channel Features (ICF). The approach demonstrates a qualitative approach for identifying the optimal layer for additional feature injection in the network, and the selection of ICF channels to be applied for fusion. The validation of the proposed detector on the Bosch small traffic light dataset achieved the best mAP score of 55.70 % on the testing set. Further, a qualitative comparison of the proposed detector’s performance with that of other recent methods is presented, along with an analysis using auxiliary experiments.
Labeling confidence for uncertainty-aware histology image classification

Deep learning-based models applied to digital pathology require large, curated datasets with high-quality (HQ) annotations to perform correctly. In many cases, recruiting expert pathologists to annotate large databases is not feasible, and it is necessary to collect additional labeled data with varying label qualities, e.g., pathologists-in-training (henceforth, non-expert annotators). Learning from datasets with noisy labels is more challenging in medical applications since medical imaging datasets tend to have instance-dependent noise and suffer from high inter/intra-observer variability. In this paper, we design an uncertainty-driven labeling strategy with which we generate soft labels from 10 non-expert annotators for multi-class skin cancer classification. Based on this soft annotation, we propose an uncertainty estimation-based framework to handle these noisy labels. This framework is based on a novel formulation using a dual-branch min-max entropy calibration to penalize inexact labels during the training. Comprehensive experiments demonstrate the promising performance of our labeling strategy. Results show a consistent improvement by using soft labels with standard cross-entropy loss during training (∼4.0% F1-score) and increases when calibrating the model with the proposed min-max entropy calibration (∼6.6% F1-score). These improvements are produced at negligible cost, both in terms of annotation and calculation.
Text Matching in Insurance Question-Answering Community Based on an Integrated BiLSTM-TextCNN Model Fusing Multi-Feature

Along with the explosion of ChatGPT, the artificial intelligence question-answering system has been pushed to a climax. Intelligent question-answering enables computers to simulate people's behavior habits of understanding a corpus through machine learning, so as to answer questions in professional fields. How to obtain more accurate answers to personalized questions in professional fields is the core content of intelligent question-answering research. As one of the key technologies of intelligent question-answering, the accuracy of text matching is related to the development of the intelligent question-answering community. Aiming to solve the problem of polysemy of text, the Enhanced Representation through Knowledge Integration (ERNIE) model is used to obtain the word vector representation of text, which makes up for the lack of prior knowledge in the traditional word vector representation model. Additionally, there are also problems of homophones and polyphones in Chinese, so this paper introduces the phonetic character sequence of the text to distinguish them. In addition, aiming at the problem that there are many proper nouns in the insurance field that are difficult to identify, after conventional part-of-speech tagging, proper nouns are distinguished by especially defining their parts of speech. After the above three types of text-based semantic feature extensions, this paper also uses the Bi-directional Long Short-Term Memory (BiLSTM) and TextCNN models to extract the global features and local features of the text, respectively. It can obtain the feature representation of the text more comprehensively. Thus, the text matching model integrating BiLSTM and TextCNN fusing Multi-Feature (namely MFBT) is proposed for the insurance question-answering community. The MFBT model aims to solve the problems that affect the answer selection in the insurance question-answering community, such as proper nouns, nonstandard sentences and sparse features. Taking the question-and-answer data of the insurance library as the sample, the MFBT text-matching model is compared and evaluated with other models. The experimental results show that the MFBT text-matching model has higher evaluation index values, including accuracy, recall and F1, than other models. The model trained by historical search data can better help users in the insurance question-and-answer community obtain the answers they need and improve their satisfaction.
Research on Hierarchical Knowledge Graphs of Data, Information, and Knowledge Based on Multiple Data Sources

In the existing medical knowledge graphs, there are problems concerning inadequate knowledge discovery strategies and the use of single sources of medical data. Therefore, this paper proposed a research method for multi-data-source medical knowledge graphs based on the data, information, knowledge, and wisdom (DIKW) system to address these issues. Firstly, a reliable data source selection strategy was used to assign priorities to the data sources. Secondly, a two-step data fusion strategy was developed to effectively fuse the processed medical data, which is conducive to improving the quality of medical knowledge graphs. The proposed research method is for the design of a multi-data-source medical knowledge graph based on the DIKW system. The method was used to design a set of DIK three-layer knowledge graph architectures according to the DIKW system in line with the medical knowledge discovery strategy, employing a scientific method for expanding and updating knowledge at each level of the knowledge graph. Finally, question and answer experiments were used to compare the two different ways of constructing knowledge graphs, validating the effectiveness of the two-step data fusion strategy and the DIK three-layer knowledge graph.
Active Jamming Mitigation for Short-Range Detection System

The emergence of active jamming technology has led to a surge of interest in jamming suppression. This work proposes a one-dimensional adaptive multi-scale residual autoencoder (1-DAMRAE) to take overlapping signals (echo signals and jamming signals) as input and restore correspondingly-sized echo signals, which frames the jamming suppression as a regression problem. 1-DAMRAE incorporates three key components. First, adaptive multi-scale convolutional blocks are developed to adaptively fuse multi-scale information for a more robust representation learning. Second, a fully residual design is explored to improve information flow within and across blocks. Third, given the unknown of jamming sources, our method encodes contextual dependencies and produces a plausible hypothesis for the potential missing information to handle out-of-distribution (OOD) jamming. To verify the reliability of 1-DAMRAE, the dynamic short-range detection system model is established. Experimental results show that 1-DAMRAE achieves excellent anti-jamming performance while keeping complexity and computation cost at a low level. Even facing unseen OOD jamming, 1-DAMRAE can still achieve desirable anti-jamming performance.
Text Categorization By Content using Naïve Bayes Approach

The goal of text classification is to mechanically place documents (such as plain text files and Web pages) into certain categories determined by their content. The main objectives aimed towards the execution of the work include. Firstly, exploration and conducted studies to learn how to effectively extract data that may be utilized to symbolize a document. The next step is to Figure out how to make the most informed choice when assigning a document to a particular category. Implementing a text filter was the last objective. To prove the worth by successfully completing the tasks set out in this project. In the proposed work Naïve Bayes approach is used to show the effective result.
Purity in the QTMS radar

Abstract In this paper, we analyze the purity and decoherence effects in quantum two-mode squeezed (QTMS) radar as a function of the squeezing parameter and temperature, using quantum information processing tools. The squeezing parameter is an important key to improving the performance of the QTMS radar. We investigate the response to the squeezing parameter controlling to system state of the QTMS radar. In this work, we deal with the QTMS radar with two cases of the transmitted signal, the presence or the absence of the target. The squeezing parameter controls the power of the generated signal and idler, the correlation between signal and idler, as well as the coherence and state of the system. We show that the decoherence effects are low at low temperatures, low squeezing parameters, and low power. In addition, we demonstrate that the purity and, consequently, the coherence of the QTMS radar are better when the target is absent than when it is present. However, the coherence and purity are maintained at high temperatures in both cases. In addition, by calculating the entropy of formation as a tool to investigate the qualitative behavior of entanglement in QTMS radar, we show that the behaviors of purity and entropy are similar. Finally, we show that the proportion of received photons in the QTMS radar is an important factor in improving the radar performance.
Teaching and Learning with AI: How Artificial Intelligence is Transforming the Future of Education

editorial Free Access Share on Teaching and Learning with AI: How Artificial Intelligence is Transforming the Future of Education Author: Amy Adair Rutgers University Rutgers UniversityView Profile Authors Info & Claims XRDS: Crossroads, The ACM Magazine for StudentsVolume 29Issue 3Spring 2023pp 7–9https://doi.org/10.1145/3589252Published:12 April 2023Publication History 0citation2,170DownloadsMetricsTotal Citations0Total Downloads2,170Last 12 Months2,170Last 6 weeks305 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Discourse anaphoricity vs. perspective sensitivity in emoji semantics

This paper aims to provide a foundation for studying the interplay between emoji and linguistic (natural language) expressions; it does so by proposing a formal semantic classification of emoji- text combinations, focusing on two core sets of emoji: face emoji and activity emoji. Based on different data sources (introspective intuitions, naturalistic Twitter examples, and experimental evidence), we argue that activity emoji (case study I) are essentially event descriptions that serve as separate discourse units (similar to free adjuncts) and connect to the accompanying (linguistic) text by virtue of suitable discourse relations. By contrast, face emoji (case study II) are expressive elements that are anchored to an attitude holder and comment on a proposition provided by the accompanying text. We provide further evidence for the distinct behavior of face emoji and activity emoji by looking at their scopal behavior with respect to linguistically- expressed negation. In particular, we probe interactions of emoji and texts that contain clausal negation, and conclude that both face emoji and activity emoji generally do not scope under negation. However, the appearance of such a scope relation arises with activity emoji when the emoji are connected to the accompanying text by virtue of an Explanation discourse relation. With face emoji, scopal interactions seem to appear in cases where the default interpretation would result in a discourse contribution that is pragmatically infelicitous, and also in cases that involve a specialized emoji-repetition construction where a repeated alternation of face emoji with words assumes a scope-marking role.
Uncertainty parameter weighted entropy-based fuzzy c-means algorithm using complemented membership functions for noisy volumetric brain MR image segmentation

In this paper, we propose an uncertainty parameter weighted entropy-based fuzzy c-means clustering algorithm for noisy volumetric (3D) brain MR image segmentation using complemented global and spatially constraint local membership functions. Due to inherent noise and intensity inhomogeneity (IIH), the acquired MR images have blurry tissue boundaries. This leads to a situation where uncertainty arises while labeling a pixel/voxel into its proper tissue region. Further, it magnifies in the regions of different tissue boundaries. The proposed algorithm addresses this issue by introducing a class-level uncertainty parameter for each voxel and weightedly incorporating in the fuzzy objective function using complemented global and spatially constraint local fuzzy membership functions. It also incorporates total uncertainties in the 3D image domain by means of Shannon entropy. The complemented local fuzzy membership function estimates the degree of non-association, constraint by the local region-level intensity distribution. This framework allows the algorithm to utilize the spatial intensity distribution both in locally and globally within the image domain and produce more accurate cluster prototypes. We validate the proposed algorithm qualitatively and quantitatively with 10 3D simulated brain MR images, contaminated by noise and intensity inhomogeneity, 4 volumes of clinical real patient 3D brain MR images and a synthetic 3D image with added Rician noise. The simulation results in several evaluation indices indicate its superiority over state-of-the-art algorithms developed in recent past.
Mobile Technology as an Alternative Teaching Strategy Amidst COVID-19 Hiatus: Exploring Pedagogical Possibilities and Implications for Teacher Development

Amidst the COVID-19 pandemic, the education sector worldwide had to adapt rapidly from in-person to virtual modes of teaching and learning to mitigate the spread of the virus. In a short period of time, teachers were forced to find new and innovative ways of delivering education to their students to ensure the continuation of education. In this context, this paper investigates how teachers in Pakistan leveraged mobile technology as an alternative teaching strategy to provide access to and to ensure the continuation of education. Through in-depth interviews with 13 selected teachers, this study explores the potential of mobile technology to offer alternative teaching and learning arenas. The findings underscore the importance of embracing new pedagogical possibilities offered by mobile technology and the significance of effective teacher professional development in the post-pandemic era. This study provides valuable insights into the utilization of mobile technology in the education sector, even in the most challenging circumstances, and highlights the potential for mobile learning to contribute to education reform.
Hybrid Learning Moth Search Algorithm for Solving Multidimensional Knapsack Problems

The moth search algorithm (MS) is a relatively new metaheuristic optimization algorithm which mimics the phototaxis and Lévy flights of moths. Being an NP-hard problem, the 0–1 multidimensional knapsack problem (MKP) is a classical multi-constraint complicated combinatorial optimization problem with numerous applications. In this paper, we present a hybrid learning MS (HLMS) by incorporating two learning mechanisms, global-best harmony search (GHS) learning and Baldwinian learning for solving MKP. (1) GHS learning guides moth individuals to search for more valuable space and the potential dimensional learning uses the difference between two random dimensions to generate a large jump. (2) Baldwinian learning guides moth individuals to change the search space by making full use of the beneficial information of other individuals. Hence, GHS learning mainly provides global exploration and Baldwinian learning works for local exploitation. We demonstrate the competitiveness and effectiveness of the proposed HLMS by conducting extensive experiments on 87 benchmark instances. The experimental results show that the proposed HLMS has better or at least competitive performance against the original MS and some other state-of-the-art metaheuristic algorithms. In addition, the parameter sensitivity of Baldwinian learning is analyzed and two important components of HLMS are investigated to understand their impacts on the performance of the proposed algorithm.
Constant-round linear-broadcast secure computation with penalties

It is known that Bitcoin enables achieving fairness in secure computation by imposing monetary penalties on adversarial parties. This functionality is called secure computation with penalties. Bentov and Kumaresan (2014) [9] introduced the claim-or-refund functionality that can be implemented via Bitcoin. They achieved secure computation with penalties with O(n) rounds and O(n) broadcasts for any function, where n is the number of parties. After that, Kumaresan and Bentov (2014) [8] showed a constant-round protocol. Unfortunately, this protocol requires O(n2) broadcasts. As far as we know, no protocol achieves O(1) rounds and O(n) broadcasts based on Bitcoin. This work accomplishes such efficiency in secure computation with penalties. We first show a protocol in a slightly relaxed setting called secure computation with non-equivalent penalties. This setting is the same as secure computation with penalties except that every honest party receives more than a predetermined amount of compensation, while the previous one requires that every honest party receives the same amount of compensation. Namely, our setting allows the compensations for honest parties to be non-equivalent. Moreover, we present a technique to remove the non-equivalence of our protocol without sacrificing efficiency. We then propose a new ideal functionality called claim-refund-or-give that can be implemented via Bitcoin.
PolyHope: Two-level hope speech detection from tweets

Hope is characterized as openness of spirit towards the future, a desire, expectation, and wish for something to happen or to be true that remarkably affects human's state of mind, emotions, behaviors, and decisions. Hope is usually associated with concepts of desired expectations and possibility/probability concerning the future. Despite its importance, hope has rarely been studied as a social media analysis task. This paper presents a hope speech dataset that classifies each tweet first into "Hope" and "Not Hope", then into three fine-grained hope categories: "Generalized Hope", "Realistic Hope", and "Unrealistic Hope" (along with "Not Hope"). English tweets in the first half of 2022 were collected to build this dataset. Furthermore, we describe our annotation process and guidelines in detail and discuss the challenges of classifying hope and the limitations of the existing hope speech detection corpora. In addition, we reported several baselines based on different learning approaches, such as traditional machine learning, deep learning, and transformers, to benchmark our dataset. We evaluated our baselines using averaged-weighted and averaged-macro F1-scores. Observations show that a strict process for annotator selection and detailed annotation guidelines enhanced the dataset's quality. This strict annotation process yielded promising performance for simple machine learning classifiers with only uni-grams; however, binary and multiclass hope speech detection results reveal that contextual embedding models have higher performance in this dataset.
Interpretable attention-based deep learning ensemble for personalized ovarian cancer treatment without manual annotations

Inhibition of pathological angiogenesis has become one of the first FDA approved targeted therapies widely tested in anti-cancer treatment, i.e. VEGF-targeting monoclonal antibody bevacizumab, in combination with chemotherapy for frontline and maintenance therapy for women with newly diagnosed ovarian cancer. Identification of the best predictive biomarkers of bevacizumab response is necessary in order to select patients most likely to benefit from this therapy. Hence, this study investigates the protein expression patterns on immunohistochemical whole slide images of three angiogenesis related proteins, including Vascular endothelial growth factor, Angiopoietin 2 and Pyruvate kinase isoform M2, and develops an interpretable and annotation-free attention based deep learning ensemble framework to predict the bevacizumab therapeutic effect on patients with epithelial ovarian cancer or peritoneal serous papillary carcinoma using tissue microarrays (TMAs). In evaluation with five-fold cross validation, the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 achieves a notably high F-score (0.99±0.02), accuracy (0.99±0.03), precision (0.99±0.02), recall (0.99±0.02) and AUC (1.00±0). Kaplan-Meier progression free survival analysis confirms that the proposed ensemble is able to identify patients in the predictive therapeutic sensitive group with low cancer recurrence (p<0.001), and the Cox proportional hazards model analysis further confirms the above statement (p=0.012). In conclusion, the experimental results demonstrate that the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 can assist treatment planning of bevacizumab targeted therapy for patients with ovarian cancer.
Probability-weighted tensor robust PCA with CP decomposition for hyperspectral image restoration

This paper presents a novel probability-weighted tensor robust principal component analysis (TRPCA) method based on CANDECOMP/PARAFAC decomposition (CPD) for hyperspectral image (HSI) restoration with low computational complexity from the coexistence of dense noise and sparse outliers. In the proposed method, via the CPD property, the tensor nuclear norm (TNN) optimization object is replaced as the CPD factor matrix of HSI with lower dimensions without losing low-rank property, so as to the computational complexity could be reduced during optimizing TNN in TRPCA model. To demonstrate the different effects of noise and outliers on HSI restoration, two weighted sets are defined with probability by the prior knowledge of the occurrence of outliers, which are used to analyze the probability whether the HSI is corrupted by dense noise or outliers. Via the probability theory, information entropy is employed to measure the uncertainty of noise and outlier occurrence in received data, so that to demonstrate their difference impacts on HSI restoration to improve the recovery accuracy. Theoretical analysis and simulation results show that the proposed method has lower computational complexity and better restoration performance compared with other related methods.