Reviewerly/exHarmony huggingface dataset:
Negative Learning Ant Colony Optimization for MaxSAT

Abstract Recently, a new negative learning variant of ant colony optimization (ACO) has been used to successfully tackle a range of combinatorial optimization problems. For providing stronger evidence of the general applicability of negative learning ACO, we investigate how it can be adapted to solve the Maximum Satisfiability problem (MaxSAT). The structure of MaxSAT is different from the problems considered to date and there exists only a few ACO approaches for MaxSAT. In this paper, we describe three negative learning ACO variants. They differ in the way in which sub-instances are solved at each algorithm iteration to provide negative feedback to the main ACO algorithm. In addition to using IBM ILOG CPLEX, two of these variants use existing MaxSAT solvers for this purpose. The experimental results show that the proposed negative learning ACO variants significantly outperform the baseline ACO as well as IBM ILOG CPLEX and the two MaxSAT solvers. This result is of special interest because it shows that negative learning ACO can be used to improve over the results of existing solvers by internally using them to solve smaller sub-instances.
An Oriented Attention Model for Infectious Disease Cases Prediction

Effective infectious disease prediction supports the success of infection prevention and control. Several attention-based predictive models can be applied to undertake the prediction task. However, using a single attention mechanism can only capture local information, i.e. part of the temporal dynamics from time series. In this paper, we take for the hypothesis that using multiple attention from different aspects could improve prediction accuracy. An oriented attention model (OAM) is proposed to draw temporal dynamics in several aspects, via oriented attention units and their aggregation. Firstly, time series are represented as oriented transformations. And then those representations are consolidated to connect with outputs. Intensive experiments on two real infectious disease datasets show OAM's effectiveness.
Optimal User Categorization from a Hierarchical Clustering Tree for Recommendation

Recommender system (RS) and clustering are two main types of data mining techniques that have wide applications. An RS helps users to acquire useful online resources efficiently and effectively, whereas clustering groups similar objects together and separates dissimilar objects as much as possible. Recently, in an increasing number of studies, similar users/items are grouped before recommendation to improve the recommendation quality. Following this routine, we group similar users from a binary cluster tree formed using hierarchical clustering, for which no user-determined number of clusters is required. To extract optimal clusters, we incorporate the formation order of clusters, and propose two cluster quality measures based on lifetime and variance. The first measure favors stable clusters and the second measure tends to group users whose ratings have low variance. Extensive tests on public datasets demonstrate the superiority of the proposed method.
Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation

Artificial Intelligence and Digital Twins play an integral role in driving innovation in the domain of intelligent driving. Long short-term memory (LSTM) is a leading driver in the field of lane change prediction for manoeuvre anticipation. However, the decision-making process of such models is complex and non-transparent, hence reducing the trustworthiness of the smart solution. This work presents an innovative approach and a technical implementation for explaining lane change predictions of layer normalized LSTMs using Layer-wise Relevance Propagation (LRP). The core implementation includes consuming live data from a digital twin on a German highway, live predictions and explanations of lane changes by extending LRP to layer normalized LSTMs, and an interface for communicating and explaining the predictions to a human user. We aim to demonstrate faithful, understandable, and adaptable explanations of lane change prediction to increase the adoption and trustworthiness of AI systems that involve humans. Our research also emphases that explainability and state-of-the-art performance of ML models for manoeuvre anticipation go hand in hand without negatively affecting predictive effectiveness.
Dual-modality synthetic mammogram construction for breast lesion detection using U-DARTS

Multimodal image fusion is an emergent research area for cancer detection. It provides a wide variety of visual qualities for the accurate medical diagnosis. However, this process requires accurate registration of each image modality for its efficient and effective use. To address the aforementioned issue, a novel synthetic mammogram construction model is proposed. An image enhancement approach is applied to enhance the image quality. Dual-modality structural feature (DMSF) based mapping function is designed to transform a mammogram from a thermal image segment. This paper also proposes modified Differentiable ARchiTecture Search (DARTS) named as (U-DARTS) to detect and classify the breast lesion. In U-DARTS, a stochastic gradient descent optimizer is used. The proposed approach is tested over DMR and INbreast datasets. The results exhibit a significant improvement in the performance of the proposed model over the existing techniques. The validation and testing accuracies of 98% and 91%, respectively, are achieved. Overall, the proposed approach establishes supremacy in so far as the mammogram construction and further lesion detection are concerned.
Cross-scale Graph Interaction Network for Semantic Segmentation of Remote Sensing Images

Semantic segmentation of remote sensing (RS) images plays a vital role in a variety of fields, including urban planning, natural disaster monitoring, and land resource management. Due to the complexity and low resolution of RS images, many approaches have been proposed to handle the related task. However, these previously developed approaches dedicate to contextual interaction but ignore the cross-scale semantic correlation and multi-scale boundary information. Therefore, we propose a Cross-scale Graph Interaction Network (CGIN) to address semantic segmentation problems of RS images, which consists of a semantic branch and a boundary branch. In the semantic branch, we first apply atrous convolution to extract multi-scale semantic features of RS images. Particularly, based on the multi-scale semantic features, a Cross-scale Graph Interaction (CGI) module is introduced, which establishes cross-scale graph structures and performs adaptive graph reasoning to capture the cross-scale semantic correlation of RS objects. In the boundary branch, we propose a Multi-scale Boundary Feature Extraction (MBFE) module that utilizes atrous convolutions with different dilation rates to extract multi-scale boundary features. Finally, to address the problem of sparse boundary pixels in the fusion process of the two branches, we propose a Multi-scale Similarity-guided Aggregation (MSA) module by calculating the similarity of semantic features and boundary features at the corresponding scale, which can emphasize the boundary information in semantic features. Our proposed CGIN outperforms state-of-the-art approaches in numerical experiments conducted on two benchmark remote sensing datasets.
Hierarchical User Intention–Preference for Sequential Recommendation with Relation-Aware Heterogeneous Information Network Embedding

Existing recommender systems usually make recommendations by exploiting the binary relationship between users and items, and assume that users only have flat preferences for items. They ignore the users' intentions as an origin and driving force for users' performance. Cognitive science tells us that users' preference comes from an explicit intention. They first have an intention to possess a particular (type of) item(s) and then their preferences emerge when facing multiple available options. Most of the data used in recommender systems are composed of heterogeneous information contained in a complicated network's structure. Learning effective representations from these heterogeneous information networks (HINs) can help capture the user's intention and preferences, therefore, improving recommendation performance. We propose a hierarchical user's intention and preferences modeling for sequential recommendation based on relation-aware HIN embedding (HIP-RHINE). We first construct a multirelational semantic space of heterogeneous information networks to learn node embedding based on specific relations. We then model user's intention and preferences using hierarchical trees. Finally, we leverage the structured decision patterns to learn user's preferences and thereafter make recommendations. To demonstrate the effectiveness of our proposed model, we also report on the conducted experiments on three real data sets. The results demonstrated that our model achieves significant improvements in Recall and Mean Reciprocal Rank metrics compared with other baselines.
Iterative photonic processor for fast complex-valued matrix inversion

An <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"><mml:mrow><mml:mi>N</mml:mi><mml:mo>×</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:math> iterative photonic processor is proposed for the first time, we believe, for fast computation of complex-valued matrix inversion, a fundamental but computationally expensive linear algebra operation. Compared to traditional digital electronic processing, optical signal processing has a few unparalleled features that could enable higher representational efficiency and faster computing speed. The proposed processor is based on photonic integration platforms–the inclusion of III-V gain blocks offers net neutral loss in the phase-sensitive loops. This is essential for the Richardson iteration method that is adopted in this paper for complex linear systems. Wavelength multiplexing can be used to significantly improve the processing efficiency, allowing the computation of multiple columns of the inverse matrix using a single processor core. Performances of the key building blocks are modeled and simulated, followed by a system-level analysis, which serves as a guideline for designing an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m2"><mml:mrow><mml:mi>N</mml:mi><mml:mo>×</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:math> Richardson iteration processor. An inversion accuracy of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m3"><mml:mrow><mml:mo form="prefix">&gt;</mml:mo><mml:mn>98</mml:mn><mml:mi>%</mml:mi></mml:mrow></mml:math> can be predicted for a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m4"><mml:mrow><mml:mn>64</mml:mn><mml:mo>×</mml:mo><mml:mn>64</mml:mn></mml:mrow></mml:math> photonic processor with a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m5"><mml:mrow><mml:mo form="prefix">&gt;</mml:mo><mml:mn>80</mml:mn></mml:mrow></mml:math> times faster inversion rate than electronic processors. Including the power consumed by both active components and electronic circuits, the power efficiency of the proposed processor is estimated to be over an order of magnitude more energy-efficient than electronic processors. The proposed iterative photonic integrated processor provides a promising solution for future optical signal processing systems.
Approaching motion planning for mobile manipulators considering the uncertainty of self-positioning and object’s pose estimation

This study focuses on the problem of determining the posture of a mobile manipulator aiming to reach a target end-effector pose. Any posture of a mobile manipulator can be described using two parameters: the pose of the mobile platform and the joint angles of the manipulator. This study proposes a posture evaluation method for determining the pose of the mobile platform while focusing on two types of uncertainty: (i) the pose error associated with the end-effector caused by the accumulated positioning error of the mobile platform and (ii) the pose estimation error of objects to be grasped. In addition to using pose errors, the proposed method considers the manipulation ability of the end-effector and the tolerance region for grasping an object by the end-effector. Furthermore, this study proposes a method based on Bayes optimization for finding acceptable moving paths for the mobile platform in environments with sparse obstacles. The effectiveness of the proposed methods was demonstrated through simulations using a mobile manipulator. According to simulation results, the proposed methods can find both moving trajectories for the mobile platform and postures for the mobile manipulator within several seconds.
Sentiment Analysis of Twitter Data Using Deep Learning

In today's world, the use of social networking websites is in the next level. People express their thoughts and opinions on any brand, product or any social events via these sites. Sentimental analysis often uses natural language processing (NLP) to obtain a sentiment behind the text, tweet or comments. In this modern world, sentiment analysis has become one of the most efficient way to mine the public emotions, opinions based on their particular topic of their interest. We have used datasets which consists of tweets from Twitter which contains tweets from various domains. This paper described an approach where a stream of tweets is pre-processed then classified based on the emotion within the text. We used sequential model, with long short-term memory (LSTM) as a layer to train our model. This trained model can be used to analyse any tweets or blogs to obtain sentiments behind that. We have achieved an accuracy of 81.5% from our developed model.
Low-Light Image Enhancement via Gradient Prior-Aided Network

Low-light images have low brightness and low contrast, which bring huge obstacles to the intelligent video surveillance system.The enhancement of low-light images must simultaneously consider the interference of factors such as brightness, contrast, artifacts, and noise.To this end, in this study, we propose a gradient prior-aided low-light enhancement network (GPANet).The main idea is to improve the network's ability to extract edge features and remove unwanted noise by introducing first-order (i.e., Sobel Filter) and second-order gradient (i.e., Laplacian Filter) features.Unlike in previous methods, in the proposed study, we first extract the first-order and second-order gradient information of low-light images and concatenate them with low-light images for multi-view feature analysis in the multi-view fusion encoder (MFE).Then, we suggest the multi-branch topology module (MTM) to fuse and decompose the multi-view features.Finally, we reconstruct the multi-view features through multi-view decomposition decoders (MDDs, including three sub-decoders) to generate potentially normal-light images.The firstand second-order gradient decoders will provide the enhancement decoder with multi-scale gradient prior features.Furthermore, we suggest a residual network to speed up network convergence while ensuring stable enhancement performance.We conduct experiments on widely adopted datasets.The results demonstrate the advantages of our method compared to other methods from both qualitative and quantitative perspectives.The source code is available at https://github.com/LouisYuxuLu/GPANet.
Proper Definitions of Dirichlet Conditions and Convergence of Fourier Representations [Lecture Notes]

Fourier theory is the backbone of signal processing (SP) and communication engineering. It has been widely used in almost all branches of science and engineering in numerous applications since its inception. However, Fourier representations such as Fourier series (FS) and Fourier transform (FT) may not exist for some signals that fail to fulfill a predefined set of Dirichlet conditions (DCs). We note a subtle gap in explaining these conditions as available in popular SP literature. For example, original DCs require a signal to have bounded variations (BVs) over one time period for the convergence of FS, where there can be, at most, a countably infinite number of maxima and minima and, at most, a countably infinite number of discontinuities of finite magnitude. However, a large body of the literature replaces this statement with the requirements of a finite number of maxima and minima over one time period, and a finite number of finite discontinuities over one time period. Due to the latter, some signals fulfilling the original DCs are incorrectly perceived as not having convergent FS (CFS) representation. A similar problem holds in the description of DCs for the FT. Likewise, although it is easy to relate the first DC with the finite value of FS or FT coefficients, the relevance of second and third DCs, as specified in SP literature, is hard to assimilate.
Toward Enabling Next-Generation Societal Virtual Reality Applications for Virtual Human Teleportation: A novel future system concept and computation-communication-signal representation trade-offs

Virtual reality (VR) is an emerging technology of great societal potential. Some of its most exciting and promising use cases include remote scene content and untethered lifelike navigation. This article first highlights the relevance of such future societal applications and the challenges ahead toward enabling them. It then provides a broad and contextual high-level perspective of several emerging technologies and unconventional techniques and argues that only by their synergistic integration can the fundamental performance bottlenecks of hyperintensive computation, ultrahigh data rate, and ultralow latency be overcome to enable untethered and lifelike VR-based remote scene immersion. A novel future system concept is introduced that embodies this holistic integration, unified with a rigorous analysis, to capture the fundamental synergies and interplay among communications, computation, and scalable signal representation that arises in this context, and advance its performance. Several representative results highlighting these tradeoffs and the benefits of the envisioned system are presented at the end.
Performance Impact of Header-Based Network Steganographic Countermeasures

Cybersecurity executives, engineers, and decision-makers should not presume effective network steganographic countermeasures come at the cost of performance. A performance assessment of network steganographic countermeasures during an experimental data exfiltration attack revealed a negligible influence on latency. Despite the advanced nature of the attack, preventative controls did not degrade packet round-trip times to a statistically significant degree. During the experiment, synthetic credit card numbers were exfiltrated at a rate equivalent to (US)&#x0024;74,702 per second. The measured round trip time between a covert transmitting host and covert receiving host was nearly identical regardless of whether interventions were active or inactive. The de minimis effect suggests similar preventative controls in an enterprise environment would not have an adverse effect on large volumes of business network traffic in the presence of an actual attack.
Textline alignment on the image domain

Editing and publishing a historical manuscript involves a research phase to recover the original manuscript and reconstruct the transmission of its text based on the relations between its surviving copies. Manuscript alignment, which aims to locate the shared and the different text among a set of copies of the same manuscript, is essential for this phase. In this paper, we present an alignment algorithm for historical handwritten documents that works directly on the image domain due to the absence of an accurate handwritten text recognition (HTR) system for handwritten historical documents and the necessity to visualize the original manuscripts in parallel to examine features beyond the transcribed text. Our approach extracts subwords, estimates the similarity among these subwords, and establishes an alignment among them. We extract subwords from textlines images and convert them into sequences of subword images. It estimates the similarity between two subwords using a Siamese network model and applies Longest Common Subsequence (LCS) to establish the alignment between two image sequences. We have implemented our algorithm, trained the Siamese model, and evaluate its performance using textline images from historical documents. Our algorithm outperformed the state-of-the-art by large margins. Unlike the state-of-the-art, the framework builds the alignment from scratch without requiring any prior knowledge concern subwords boundaries. In addition, we build a new dataset for textline alignment for historical documents, which include ten pairs of pages taken from two copies of two Arabic manuscripts and annotated at the subword level.
BugPre: an intelligent software version-to-version bug prediction system using graph convolutional neural networks

Abstract Since defects in software may cause product fault and financial loss, it is essential to conduct software defect prediction (SDP) to identify the potentially defective modules, especially in the early stage of the software development lifecycle. Recently, cross-version defect prediction (CVDP) began to draw increasing research interests, employing the labeled defect data of the prior version within the same project to predict defects in the current version. As software development is a dynamic process, the data distribution (such as defects) during version change may get changed. Recent studies utilize machine learning (ML) techniques to detect software defects. However, due to the close dependencies between the updated and unchanged code, prior ML-based methods fail to model the long and deep dependencies, causing a high false positive. Furthermore, traditional defect detection is performed on the entire project, and the detection efficiency is relatively low, especially on large-scale software projects. To this end, we propose BugPre , a CVDP approach to address these two issues. BugPre is a novel framework that only conducts efficient defect prediction on changed modules in the current version. BugPre utilizes variable propagation tree-based associated analysis method to obtain the changed modules in the current version. Besides, BugPre constructs graph leveraging code context dependences and uses a graph convolutional neural network to learn representative characteristics of code, thereby improving defect prediction capability when version changes occur. Through extensive experiments on open-source Apache projects, the experimental results indicate that our BugPre outperforms three state-of-the-art defect detection approaches, and the F1-score has increased by higher than 16%.
Towards Black-Box Adversarial Attacks on Interpretable Deep Learning Systems

Recent works have empirically shown that neural network interpretability is susceptible to malicious manipulations. However, existing attacks against Interpretable Deep Learning Systems (IDLSes) all focus on the white-box setting, which is obviously unpractical in real-world scenarios. In this paper, we make the first attempt to attack IDLSes in the decision-based black-box setting. We propose a new framework called Dual Black-box Adversarial Attack (DBAA) which can generate adversarial examples that are misclassified as the target class, yet have very similar interpretations to their benign cases. We conduct comprehensive experiments on different combinations of classifiers and interpreters to illustrate the effectiveness of DBAA. Empirical results show that in all the cases, DBAA achieves high attack success rates and Intersection over Union (IoU) scores.