Reviewerly/exHarmony huggingface dataset:
2D Semantic Segmentation: Recent Developments and Future Directions

Semantic segmentation is a critical task in computer vision that aims to assign each pixel in an image a corresponding label on the basis of its semantic content. This task is commonly referred to as dense labeling because it requires pixel-level classification of the image. The research area of semantic segmentation is vast and has achieved critical advances in recent years. Deep learning architectures in particular have shown remarkable performance in generating high-level, hierarchical, and semantic features from images. Among these architectures, convolutional neural networks have been widely used to address semantic segmentation problems. This work aims to review and analyze recent technological developments in image semantic segmentation. It provides an overview of traditional and deep-learning-based approaches and analyzes their structural characteristics, strengths, and limitations. Specifically, it focuses on technical developments in deep-learning-based 2D semantic segmentation methods proposed over the past decade and discusses current challenges in semantic segmentation. The future development direction of semantic segmentation and the potential research areas that need further exploration are also examined.
Hardware Architecture Design for Hand Gesture Recognition System on FPGA

Hand gesture recognition (HGR) is one of the widely-used human-computer interaction technology. With HGR, the user can operate the interaction system without touching any devices. For a better experience, recognition accuracy and computational speed are especially important. In this work, a small-footprint HGR model and its hardware architecture design are proposed. The model first processes the hand segmentation and uses the feature to recognize the hand gesture. The model mainly consists of depthwise separable convolution to reduce the overall parameters and computations. We transfer the hand segmentation task with some features to the hand gesture recognition task as a single-stage model. Based on this hardware-efficient model, we propose the hardware architecture of the whole neural model including depthwise convolution, pointwise convolution, batch normalization, and max-pooling. We also demonstrate it on the evaluation board. The whole system is implemented on the Xilinx ZCU106 evaluation board. The implemented system can achieve the performance of 52.6 fps and 65.6 GOPS based on the evaluation board.
Distribution Reliability Assessment-Based Incremental Learning for Automatic Target Recognition

In order to rapidly improve the automatic target recognition (ATR) system when new unknown samples are constantly captured, it is necessary to examine the existing training samples and recognition model so that the ATR system could autonomously assess new unknown samples with low predictive reliability during the recognition process and learn them preferentially. Incremental learning methods generally consider forming key exemplar set from existing known samples, but rarely managing updates of unknown samples. In this paper, an incremental samples’ evaluation and management method from the perspective of distribution reliability (DRaIL) is proposed, which realizes the retention of existent reliable exemplars and the predictive-reliability-assessment-based updating of new unknown samples simultaneously. DRaIL preserves the prior distribution in the high-density and overlap regions first, and then the classification reliability and “in-of-distribution" reliability of new unknown samples are evaluated based on the consistency between the new and the preserved distribution. Updating the new samples with low reliability using new labels could rapidly improve the classification surface and add new classes. Experimental results for the practical incremental learning scenario demonstrate the validity of the proposed DRaIL on representative exemplar selection and reliability ranking performance.
PERCY: A post-hoc explanation-based score for logic rule dissemination consistency assessment in sentiment classification

Disseminating and incorporating logic rules into deep neural networks has been extensively explored for sentiment classification in recent years. In particular, most methods and algorithms proposed for this purpose rely on a specific component that aims to capture and model logic rules, followed by a sequence model to process the input sequence. While the authors of these methods claim that they effectively capture syntactic structures that affect sentiment classification, they only show improvement in accuracy to support their claims without further analysis. Focusing on various syntactic structures, particularly contrastive discourse relations such as the A-but-B structure, we introduce the PERCY score, a novel Post-hoc Explanation-based Rule ConsistencY Score to analyze and study the ability of several of these methods to identify these structures in a given sentence, and to make their classification decisions based on the appropriate conjunct. Specifically, we explore the use of model-agnostic post-hoc explanation frameworks to explain the predictions of any classifier in an interpretable and faithful manner. These model explainability frameworks provide feature attribution scores to estimate each word’s impact on the final classification decision. Then, they are combined to check whether the model has based its decision on the right conjunct. Our experiments show that (a) accuracy – or any other performance metric – can be misleading in assessing the ability of logic rule dissemination methods to base their decisions on the right conjunct, (b) not all analyzed methods effectively capture syntactic structures, (c) often, the underlying sequence model is what captures the structure, and (d) for the best method, less than 25% of the test examples are classified based on the appropriate conjunct, indicating that a lot of research needs to be done on this topic. Finally, we experimentally demonstrate that the PERCY scores calculated are robust and stable w.r.t. the feature-attribution frameworks used.
Development of Interactive E-Modules to Increase Learning Motivation and Science Literacy in Elementary School Students

The article aimed to produce interactive e-modules from my country that meets the qualified criteria and to know the effectiveness of the interactive e-modules of my country produced to improve the learning motivation and science literacy of elementary school students of grade IV in the nearby. The research used research and development (R&amp;D), with ADDIE model. The subject of a pilot project for a small group trial consists of 15 students and 1teacher in SD Negeri Soka. Next, the test subject of the field trial consists of 20 Muhammadiyah Kalinampu 1 students as a control class and 20 students IV grade SD Muhammadiyah Kalipakem 1 as an experimental class. Collecting data used interview guide, observation guidelines, product assessment sheet, teacher-response scale, student response scale, and the motivational scale of study and science literary test. The results showed that: (1) the resulting interactive e-module met the criteria of "very appropriate" according to the assessment of media experts and material experts; in the one-to-one trial, it obtained the "Decent" category from the responses given by the teacher and obtained the "Easy" category. Very Feasible" from the responses of students, while in the small group trial, the category "Very Eligible" was obtained from the responses given by the teacher and students; (2) the interactive e-module was found effectively increase learning motivation and science literacy of fourth-grade primary school students. The results of the t-test and the MANOVA test showed a significant level of 0.05, indicating the interactive e-modules, 'the richness of my country,' can significantly increase the learning motivation and science literacy of fourth-grade elementary school students with a significance value of 0.000 &lt;0.05. Keywords : Interactive E-module, Learning Motivation, Science Literacy
Research on modulation recognition method of multi‐component radar signals based on deep convolution neural network

Abstract Modulation recognition of single‐component radar signals has been widely studied in the fewer years. Meanwhile, as the electromagnetic environment in the actual battlefields is ever‐increasingly complicated, the demand for the modulation recognition of multi‐component radar signals is also increasing. However, the modulation recognition of single‐component radar signals performs poorly on multi‐component radar signals. To address this problem, a novel modulation recognition method of multi‐component radar signals based on the deep convolutional neural network with a convolutional block attention module (DCNN‐CBAM) is proposed, and the network architecture mainly includes mobile‐inverted bottleneck convolution (MBConv), Fused‐MBConv, and convolutional block attention module (CBAM). An adaptive optimization algorithm Adam with weighted gradient and dynamic bound is proposed for accelerating convergence speed and generalisation performance. The proposed method can not only recognise dual‐component and three‐component radar signals but also four‐component radar signals. The experimental results demonstrate that the recognition accuracies of all radar signals modulation types are more than 83.57% at −8 dB for dual‐component radar signals, 67.41% for three‐component radar signals, and 57.95% for four‐component radar signals, which verify the better recognition performance of DCNN‐CBAM for multi‐component radar signals. The recognition accuracy of the proposed DCNN‐CBAM for dual‐component radar signals can be up to 95.95% at −6 dB, which shows superior recognition performance over the residual method. This work provides an essential guidance for enhancing the recognition performance of multi‐component radar signals.
A comparative study of evolutionary computation and swarm-based methods applied to color quantization

Color Quantization (CQ) is a complex and hard problem because selecting the best set of colors from many available colors and using that set to obtain a good quality image is an NP-complete problem. The use of evolutionary computation and swarm-based methods to solve search and optimization problems has increased dramatically in recent years. This article compares some of these methods in order to solve the CQ problem. The following methods were used to generate CQ images: Particle swarm optimization, Artificial bee colony, Adaptive differential evolution, Success-history based adaptive differential evolution (with and without linear population size reduction), Cuckoo search, Firefly algorithm and Shuffled-frog leaping algorithm. For the first two methods, two variants were considered. Thus, a total of ten metaheuristics were compared with four classical CQ methods (Variance-based, Median-cut, Binary splitting and Wu’s methods) applying them to a set of benchmark images and considering four different palette sizes (32, 64, 128, and 256 colors). Three error measures were considered to compare the methods: the mean squared error, the mean absolute error and the peak signal-to-noise ratio. Some of the swarm-based methods analyzed include a recently proposed CQ method using ants. Although they have a slow computational speed in the experimental studies, the ant-based methods are significantly better than all other methods according to the Wilcoxon signed rank test. In general, despite their speed, classical methods underperform the other ten methods both qualitatively and quantitatively.
Conformal Predictive Distribution Trees

Abstract Being able to understand the logic behind predictions or recommendations on the instance level is at the heart of trustworthy machine learning models. Inherently interpretable models make this possible by allowing inspection and analysis of the model itself, thus exhibiting the logic behind each prediction, while providing an opportunity to gain insights about the underlying domain. Another important criterion for trustworthiness is the model’s ability to somehow communicate a measure of confidence in every specific prediction or recommendation. Indeed, the overall goal of this paper is to produce highly informative models that combine interpretability and algorithmic confidence. For this purpose, we introduce conformal predictive distribution trees, which is a novel form of regression trees where each leaf contains a conformal predictive distribution. Using this representation language, the proposed approach allows very versatile analyses of individual leaves in the regression trees. Specifically, depending on the chosen level of detail, the leaves, in addition to the normal point predictions, can provide either cumulative distributions or prediction intervals that are guaranteed to be well-calibrated. In the empirical evaluation, the suggested conformal predictive distribution trees are compared to the well-established conformal regressors, thus demonstrating the benefits of the enhanced representation.
Fuzzy Image Enhancement Based on an Adjustable Intensifier OperatorFuzzy Image Enhancement Based on an Adjustable Intensifier Operator

Fuzzy image enhancement is an important method in the process of image processing. Fuzzy image enhancement includes steps: gray-level fuzzification, modifying membership using intensifier (INT) operator, and obtaining new gray-levels by defuzzification. This paper proposed an adjustable INT operator with parameter k. Firstly, the image’s pixels are divided into two regions by the OTSU method (low and high region), and calculate the pixels’ membership by fuzzification in each region. Then, the INT operator reduce pixels’ membership in the low region and enlarge pixels’ membership in the high region. The parameter k is determined base on the pixel’s location information (neighborhood information), and plays an adjusting role when the INT operator is working. And finally, the result image is obtained by the defuzzification process. In the experiment results, the fuzzy image enhancement with the adjustable intensifier operator achieves a better performance.
IP packet-level encrypted traffic classification using machine learning with a light weight feature engineering method

As the importance of personal data privacy increases, traffic encryption has become an important topic in network communication. In the field of network security and management, the development of encrypted traffic classification technology has drawn attention to deep learning methods. For raw encrypted traffic, deep learning models can realize end-to-end classification with high accuracy. However, deep learning methods do not explain which part of the encrypted traffic is critical to classification, and that will limit their application in some cyber security scenarios that demand high interpretability. The approach proposed in this paper features a novel feature engineering method named "BITization" to determine the encoding method of features and a sliding window technique to explore which bytes contribute the most to the classification. The accuracy of classical machine learning methods in encrypted traffic is improved by at least 14.1% through the proposed feature engineering approach. In the experiments, the enhanced methods achieve a 98.6% average accuracy and a 98.5% average F1-score on the ISCX-VPN-Service, Cross-Platform-IOS, Cross-Platform-Android, and USTC-TFC2016 datasets, from which we believe a state-of-the-art performance is achieved.
Batch Integrated Gradients: Explanations for Temporal Electronic Health Records

eXplainable Artifical Intelligence (XAI) is integral for the usability of black-box models in high-risk domains. Many problems in such domains are concerned with analysing temporal data. Namely, we must consider a sequence of instances that occur in time, and explain why the prediction transitions from one time point to the next. Currently, XAI techniques do not leverage the temporal nature of data and instead treat each instance independently. Therefore, we introduce a new approach advancing the Integrated Gradients method developed in the literature, namely the Batch-Integrated Gradients (Batch-IG) technique that (1) produces explanations over a temporal batch for instance-to-instance state transitions and (2) takes into account features that change over time. In Electronic Health Records (EHRs), we see patient records can be stored in temporal sequences. Thus, we demonstrate Batch-Integrated Gradients in producing explanations over a temporal sequence that satisfy proposed properties corresponding to XAI for EHR data.
Representational change is integral to reasoning

Reasoning is the derivation of new knowledge from old. The reasoner must represent both the old and new knowledge. This representation will change as reasoning proceeds. This change will not just be the addition of the new knowledge. We claim that the representation of the old knowledge will also often change as a side effect of the reasoning process. For instance, the old knowledge may contain errors, be insufficiently detailed or require new concepts to be introduced. Representational change triggered by reasoning is a common feature of human reasoning but it has been neglected both in Cognitive Science and Artificial Intelligence. We aim to put that right. We exemplify this claim by analysing Imre Lakatos's rational reconstruction of the evolution of mathematical methodology. We then describe the abduction, belief revision and conceptual change (ABC) theory repair system, which can automate such representational change. We further claim that the ABC system has a diverse range of applications to successfully repair faulty representations. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.
Low-rank learning for feature selection in multi-label classification

Considering several research areas such as data mining, pattern recognition, and machine learning, it is difficult to handle high-dimensional data sets because of time consumption, large memory, and inefficient algorithms. Therefore, the feature selection method is widely used to address these problems. Several conventional feature selection methods have selected features by calculating the relationship between features and labels using some measures such as a mutual information. However, it is difficult to calculate mutual information because it requires to joint probability from a high-dimensional data set. Therefore, only few features can be considered, and a global search cannot be used. Moreover, calculating the correlation among labels using mutual information is limited because of insufficient label sets. In this study, we propose a feature selection method for multi-label classification based on low-rank learning. Unlike conventional methods, which use restricted search space, the proposed method considers a low-rank space from the entire feature space provided and the relationship between features and labels simultaneously. Therefore, we design a regression-based objective function using a nuclear norm and propose an efficient algorithm based on the gradient descent method to solve the optimization problem. The proposed method shows a better performance than the existing feature selection methods. This is based on the results of the multi-label classification experiments with five data sets and five multi-label classification performances.
Transformer Based Semantic Relation Typing for Knowledge Graph Integration

More and more knowledge graphs (KGs) are generated in various domains. Applications using more than one KG require an integrated view of those KGs, which, in the first place, requires a common schema or ontology. Merging schemas requires not only equivalence mappings between classes but also other semantic relations, like subclass, superclass, etc. In this paper, we introduce TaSeR, a Transformer based model for Semantic Relation Typing, which is able to decide which type of relation holds between two given classes. The approach can differentiate between equivalent class, sub-/superclass, part of/has part, cohyponym, and no relation at all. With the latter outcome, it is not only possible to refine given class alignments, but also filter incorrect correspondences. The models are trained based on examples from general knowledge graphs as well as fine-tuned on the test case at hand. The former models can be directly used to predict a relation without further training. We show that those models are able to outperform other approaches which solve a similar task. For the evaluation, a new measure is introduced which credits for proximal matches.
MOSAIK: An Agent-Based Decentralized Control System with Stigmergy for a Transportation Scenario

We investigate possibilities for implementing the decentralized control of transporters with Semantic Web agents to fulfill a given transportation task. We present the MOSAIK framework as a system to build and simulate agents to control transporters using stigmergy for communication, and self-organize based on local decisions. Our framework uses Semantic Web technologies because the communication paradigm of stigmergy directly maps to the REST constraints of the application architecture of the web. The system achieves self-organization by implementing a combination of simple reflex web agents that coordinate using web resources as environment for stigmergy. Finally, we evaluate our system compared to an agent-based simulation and discuss requirements of decentralized systems on the Semantic Web using stigmergy.
Understanding Customer Requirements

Understanding customers demands and needs is one of the keys to success for large enterprises. Customers come to a large enterprise with a set of requirements and finding a mapping between the needs they are expressing and the scale of available products and services within the enterprise is a complex task. Formalizing the two sides of interaction - the requests and the offerings - is a way to achieve the matching. Enterprise Knowledge Graphs (EKG) are an effective method to represent enterprise information in ways that can be more easily interpreted by both humans and machines. In this work, we propose a solution to identify customer requirements from free text to represent them in terms of an EKG. We demonstrate the validity of the approach by matching customer requirements to their appropriate business units, using a dataset of historical requirement-offering records in IBM spanning over 10 years.
Structural Bias in Knowledge Graphs for the Entity Alignment Task

Knowledge Graphs (KGs) have recently gained attention for representing knowledge about a particular domain and play a central role in a multitude of AI tasks like recommendations and query answering. Recent works have revealed that KG embedding methods used to implement these tasks often exhibit direct forms of bias (e.g., related to gender, nationality, etc.) leading to discrimination. In this work, we are interested in the impact of indirect forms of bias related to the structural diversity of KGs in entity alignment (EA) tasks. In this respect, we propose an exploration-based sampling algorithm, SUSIE, that generates challenging benchmark data for EA methods, with respect to structural diversity. SUSIE requires setting the value of a single hyperparameter, which affects the connectivity of the generated KGs. The generated samples exhibit similar characteristics to some of the most challenging real-world KGs for EA tasks. Using our sampling, we demonstrate that state-of-the-art EA methods, like RREA, RDGCN, MultiKE and PARIS, exhibit different robustness to structurally diverse input KGs.