Reviewerly/exHarmony huggingface dataset:
Ditto: Fair and Robust Federated Learning Through Personalization

Fairness and robustness are two important concerns for federated learning systems. In this work, we identify that robustness to data and model poisoning attacks and fairness, measured as the uniformity of performance across devices, are competing constraints in statistically heterogeneous networks. To address these constraints, we propose employing a simple, general framework for personalized federated learning, Ditto, that can inherently provide fairness and robustness benefits, and develop a scalable solver for it. Theoretically, we analyze the ability of Ditto to achieve fairness and robustness simultaneously on a class of linear problems. Empirically, across a suite of federated datasets, we show that Ditto not only achieves competitive performance relative to recent personalization methods, but also enables more accurate, robust, and fair models relative to state-of-the-art fair or robust baselines.
EMLight: Lighting Estimation via Spherical Distribution Approximation

Illumination estimation from a single image is critical in 3D rendering and it has been investigated extensively in the computer vision and computer graphic research community. On the other hand, existing works estimate illumination by either regressing light parameters or generating illumination maps that are often hard to optimize or tend to produce inaccurate predictions. We propose Earth Mover’s Light (EMLight), an illumination estimation framework that leverages a regression network and a neural projector for accurate illumination estimation. We decompose the illumination map into spherical light distribution, light intensity and the ambient term, and define the illumination estimation as a parameter regression task for the three illumination components. Motivated by the Earth Mover's distance, we design a novel spherical mover's loss that guides to regress light distribution parameters accurately by taking advantage of the subtleties of spherical distribution. Under the guidance of the predicted spherical distribution, light intensity and ambient term, the neural projector synthesizes panoramic illumination maps with realistic light frequency. Extensive experiments show that EMLight achieves accurate illumination estimation and the generated relighting in 3D object embedding exhibits superior plausibility and fidelity as compared with state-of-the-art methods.
Improved Text Classification via Contrastive Adversarial Training

We propose a simple and general method to regularize the fine-tuning of Transformer-based encoders for text classification tasks. Specifically, during fine-tuning we generate adversarial examples by perturbing the word embedding matrix of the model and perform contrastive learning on clean and adversarial examples in order to teach the model to learn noise-invariant representations. By training on both clean and adversarial examples along with the additional contrastive objective, we observe consistent improvement over standard fine-tuning on clean examples. On several GLUE benchmark tasks, our fine-tuned Bert_Large model outperforms Bert_Large baseline by 1.7% on average, and our fine-tuned Roberta_Large improves over Roberta_Large baseline by 1.3%. We additionally validate our method in different domains using three intent classification datasets, where our fine-tuned Roberta_Large outperforms Roberta_Large baseline by 1-2% on average. For the challenging low-resource scenario, we train our system using half of the training data (per intent) in each of the three intent classification datasets, and achieve similar performance compared to the baseline trained with full training data.
Low Resolution Information Also Matters: Learning Multi-Resolution Representations for Person Re-Identification

As a prevailing task in video surveillance and forensics field, person re-identification (re-ID) aims to match person images captured from non-overlapped cameras. In unconstrained scenarios, person images often suffer from the resolution mismatch problem, i.e., Cross-Resolution Person Re-ID. To overcome this problem, most existing methods restore low resolution (LR) images to high resolution (HR) by super-resolution (SR). However, they only focus on the HR feature extraction and ignore the valid information from original LR images. In this work, we explore the influence of resolutions on feature extraction and develop a novel method for cross-resolution person re-ID called Multi-Resolution Representations Joint Learning (MRJL). Our method consists of a Resolution Reconstruction Network (RRN) and a Dual Feature Fusion Network (DFFN). The RRN uses an input image to construct a HR version and a LR version with an encoder and two decoders, while the DFFN adopts a dual-branch structure to generate person representations from multi-resolution images. Comprehensive experiments on five benchmarks verify the superiority of the proposed MRJL over the relevent state-of-the-art methods.
Interval Type-2 Fuzzy Control for HMM-Based Multiagent Systems via Dynamic Event-Triggered Scheme

This article discusses the interval type-2 (IT2) Takagi–Sugeno (T-S) fuzzy asynchronous controller design problem for nonlinear multiagent systems via a dynamic event-triggered scheme in the discrete-time context. To formulate the asynchronous phenomena between the system modes and the anticipant controller modes, the hidden Markov model is proposed. The primary attention is focused on the explicit design of the dynamic event-triggered strategy that can be dynamically adjusted in line with system information, which mitigates the communication burden efficiently. On this occasion, the information renewal of the controller is aperiodic. Furthermore, the nonlinear characteristics are effectually disposed through utilizing a unique IT2 T-S fuzzy model, which is with mismatched membership functions (MFs). As a result, the resulting closed-loop fuzzy multiagent systems are accompanied by mismatched MFs and asynchronous modes, whereafter, via solving the convex optimization problem, the desired controller gains are acquired. Eventually, the validity and practicability of the developed control scheme are illustrated by two examples.
MultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer

We introduce MULTI-EURLEX, a new multilingual dataset for topic classification of legal documents.The dataset comprises 65k European Union (EU) laws, officially translated in 23 languages, annotated with multiple labels from the EUROVOC taxonomy.We highlight the effect of temporal concept drift and the importance of chronological, instead of random splits.We use the dataset as a testbed for zeroshot cross-lingual transfer, where we exploit annotated training documents in one language (source) to classify documents in another language (target).We find that fine-tuning a multilingually pretrained model (XLM-ROBERTA, MT5) in a single source language leads to catastrophic forgetting of multilingual knowledge and, consequently, poor zero-shot transfer to other languages.Adaptation strategies, namely partial fine-tuning, adapters, BITFIT, LNFIT, originally proposed to accelerate finetuning for new end-tasks, help retain multilingual knowledge from pretraining, substantially improving zero-shot cross-lingual transfer, but their impact also depends on the pretrained model used and the size of the label set.
Hybrid Rule-Based Solution for Phishing URL Detection Using Convolutional Neural Network

The phenomenon of phishing has now been a common threat, since many individuals and webpages have been observed to be attacked by phishers. The common purpose of phishing activities is to obtain user’s personal information for illegitimate usage. Considering the growing intensity of the issue, this study is aimed at developing a new hybrid rule-based solution by incorporating six different algorithm models that may efficiently detect and control the phishing issue. The study incorporates 37 features extracted from six different methods including the black listed method, lexical and host method, content method, identity method, identity similarity method, visual similarity method, and behavioral method. Furthermore, comparative analysis was undertaken between different machine learning and deep learning models which includes CART (decision trees), SVM (support vector machines), or KNN ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -nearest neighbors) and deep learning models such as MLP (multilayer perceptron) and CNN (convolutional neural networks). Findings of the study indicated that the method was effective in analysing the URL stress through different viewpoints, leading towards the validity of the model. However, the highest accuracy level was obtained for deep learning with the given values of 97.945 for the CNN model and 93.216 for the MLP model, respectively. The study therefore concludes that the new hybrid solution must be implemented at a practical level to reduce phishing activities, due to its high efficiency and accuracy.
Linear Elliptical Curve Digital Signature (LECDS) With Blockchain Approach for Enhanced Security on Cloud Server

Cloud computing is a continuously evolving technology that can enhance agility, availability, collaboration and scalability of data. Blockchain has a secure, immutable ledger which maintains all the transactions along with the timestamp. The blockchain framework and cloud computing technology jointly provides different ways of computational cost reduction. The existing methods help to identify the anonymous documents which are given in the form of requests from the cloud server. If the anonymized document requests are from the authorized users, then cloud provides better security and hence documents are not available for unauthorized users. But the main issue is access rights available for authorized users on sensitive data of the owner. To maintain the privacy the sensitive data are hidden using cryptographic techniques even for authorized users. The method adopted is Linear Elliptical Curve Digital Signature (LECDS) with Hyperledger blockchain, to prevent private data loss. The Linear regression method is used to classify the user information into two classes namely sensitive and non-sensitive. The non-sensitive data is encrypted using RSA and sensitive data is encrypted using LECC method. Modified Spider optimization search Algorithm (MSOA) is used to verify the integrity of outsourced data and search user query information in a cloud server. The hyper ledger blockchain verifies the user policy to create a private network through which the user communicates the cloud. In the analysis of the proposed method, the results are evaluated using various performance metrics such as security, throughput, classification accuracy and error rate.
Domain-Adaptive Crowd Counting via High-Quality Image Translation and Density Reconstruction

Recently, crowd counting using supervised learning achieves a remarkable improvement. Nevertheless, most counters rely on a large amount of manually labeled data. With the release of synthetic crowd data, a potential alternative is transferring knowledge from them to real data without any manual label. However, there is no method to effectively suppress domain gaps and output elaborate density maps during the transferring. To remedy the above problems, this article proposes a domain-adaptive crowd counting (DACC) framework, which consists of a high-quality image translation and density map reconstruction. To be specific, the former focuses on translating synthetic data to realistic images, which prompts the translation quality by segregating domain-shared/independent features and designing content-aware consistency loss. The latter aims at generating pseudo labels on real scenes to improve the prediction quality. Next, we retrain a final counter using these pseudo labels. Adaptation experiments on six real-world datasets demonstrate that the proposed method outperforms the state-of-the-art methods.
Meta-learning synaptic plasticity and memory addressing for continual familiarity detection

Over the course of a lifetime, we process a continual stream of information. Extracted from this stream, memories must be efficiently encoded and stored in an addressable manner for retrieval. To explore potential mechanisms, we consider a familiarity detection task in which a subject reports whether an image has been previously encountered. We design a feedforward network endowed with synaptic plasticity and an addressing matrix, meta-learned to optimize familiarity detection over long intervals. We find that anti-Hebbian plasticity leads to better performance than Hebbian plasticity and replicates experimental results such as repetition suppression. A combinatorial addressing function emerges, selecting a unique neuron as an index into the synaptic memory matrix for storage or retrieval. Unlike previous models, this network operates continuously and generalizes to intervals it has not been trained on. Our work suggests a biologically plausible mechanism for continual learning and demonstrates an effective application of machine learning for neuroscience discovery.
Computational Intelligence-Based Harmony Search Algorithm for Real-Time Object Detection and Tracking in Video Surveillance Systems

Recently, video surveillance systems have gained significant interest in several application areas. The examination of video sequences for the detection and tracking of objects remains a major issue in the field of image processing and computer vision. The object detection and tracking process includes the extraction of moving objects from the frames and continual tracking over time. The latest advances in computation intelligence (CI) techniques have become popular in the field of image processing and computer vision. In this aspect, this study introduces a novel computational intelligence-based harmony search algorithm for real-time object detection and tracking (CIHSA-RTODT) technique on video surveillance systems. The CIHSA-RTODT technique mainly focuses on detecting and tracking the objects that exist in the video frame. The CIHSA-RTODT technique incorporates an improved RefineDet-based object detection module, which can effectually recognize multiple objects in the video frame. In addition, the hyperparameter values of the improved RefineDet model are adjusted by the use of the Adagrad optimizer. Moreover, a harmony search algorithm (HSA) with a twin support vector machine (TWSVM) model is employed for object classification. The design of optimal RefineDet feature extraction with the application of HSA to appropriately adjust the parameters involved in the TWSVM model for object detection and tracking shows the novelty of the work. A wide range of experimental analyses are carried out on an open access dataset, and the results are inspected in several ways. The simulation outcome reported the superiority of the CIHSA-RTODT technique over the other existing techniques.
Lens-imaging learning Harris hawks optimizer for global optimization and its application to feature selection

• A improve version of HHO, named LIL-HHO, is proposed. • A novel escaping energy parameter strategy is presented. • A modified position search equation is designed. • A lens-imaging learning is proposed to enhance the diversity. • The extensive results show the competitive performance of LIL-HHO. Harris hawks optimizer (HHO) is a recently proposed meta-heuristic algorithm that has been proven to be more competitive than the other meta-heuristic approaches on function optimization and real world applications. However, HHO still has some drawbacks such as stagnation to local optima and low solution precision in solving more complicated optimization problems. To overcome these disadvantages, an improved HHO called LIL-HHO is proposed in this paper. In the LIL-HHO, a modified escaping energy strategy based on sine function for the energy of prey is proposed to achieve a good transition between the exploration phase and exploitation phase. Additionally, the personal best position of each hawk is introduced to modify the position search equation in soft and hard besiege phases to improve the search precision. Finally, a novel lens-imaging learning (LIL) operator is presented to maintain the population diversity and to escape from the local optima. The effective- ness of LIL-HHO is investigated by using 85 benchmark functions including 25 classical functions, 30 more complex functions from CEC2014, and 21 feature selection problems. The results indicate that LIL-HHO shows superior performances on most cases relative to the basic HHO and other compared meta-heuristic algorithms.
Deep Learning-Based Recurrence Prediction of Atrial Fibrillation After Catheter Ablation

Radiofrequency catheter ablation (RFCA) is an effective therapy for atrial fibrillation (AF). However, it the problem of AF recurrence remains. This study investigates whether a deep convolutional neural network (CNN) can accurately predict AF recurrence in patients with AF who underwent RFCA, and compares CNN with conventional statistical analysis.Methods and Results:Three-hundred and ten patients with AF after RFCA treatment, including 94 patients with AF recurrence, were enrolled. Nine variables are identified as candidate predictors by univariate Cox proportional hazards regression (CPH). A CNNSurv model for AF recurrence prediction was proposed. The model's discrimination ability is validated by a 10-fold cross validation method and measured by C-index. After back elimination, 4 predictors are used for model development, they are N-terminal pro-BNP (NT-proBNP), paroxysmal AF (PAF), left atrial appendage volume (LAAV) and left atrial volume (LAV). The average testing C-index is 0.76 (0.72-0.79). The corresponding calibration plot appears to fit well to a diagonal, and the P value of the Hosmer-Lemeshow test also indicates the proposed model has good calibration ability. The proposed model has superior performance compared with the DeepSurv and multivariate CPH. The result of risk stratification indicates that patients with non-PAF, higher NT-proBNP, larger LAAV and LAV would have higher risks of AF recurrence.The proposed CNNSurv model has better performance than conventional statistical analysis, which may provide valuable guidance for clinical practice.
Robust feature selection via nonconvex sparsity-based methods

In this paper, we propose a new model for supervised multiclass feature selection which has the 2,1 -norm in both the fidelity loss and the regularization terms with an additional 2,0 -constraint.This problem is challenging for applying available optimization methods because of the discontinuous and nonconvex nature of the 2,0 -norm.We first convert the constraint defined by the 2,0 -norm into a new constraint defined by a difference of two matrix norms.Then we reformulate the problem as an unconstrained problem using the exact penalty method.Based on a derived formula for the proximal mapping of this difference of matrix norms and Nesterov's smoothing techniques, the nonmonotonic accelerated proximal gradient method is applied to solve the unconstrained problem.Numerical experiments are conducted on many benchmark data sets to show the effectiveness of our proposed method in comparison with existing methods.
The impact of financial development and corruption on foreign direct investment in developing countries

Foreign direct investment (FDI) inflows into developing countries play an important role in the dynamics of economic growth. Meanwhile, financial development (FDV) and corruption have been considered a determinant of FDI. Therefore, this study aims to assess the effect of FDV and corruption on FDI in developing countries. In addition, this study explores the combined impact of FDV and corruption on FDI. Furthermore, the data for 108 developing countries were collected from the World Development Indicators (WDI) of the World Bank from 1993 to 2017. The results showed that FDV has a positive and significant effect on FDI, while corruption does not have a statistically significant impact. This demonstrates that FDV has contributed to the growth of foreign investment and the important sources of financing for developing countries. However, the interaction between FDV and corruption has a negative effect on FDI. This implies that FDV followed by an increase in corruption tends to reduce FDI inflows. These results encourage policymakers to address issues regarding the joint impact of FDV and corruption on FDI in developing countries. AcknowledgmentThe authors would like to express their gratitude to three anonymous reviewers and seminar participants at Mulawarman University for their insightful comments.
Authorship identification using ensemble learning

Abstract With time, textual data is proliferating, primarily through the publications of articles. With this rapid increase in textual data, anonymous content is also increasing. Researchers are searching for alternative strategies to identify the author of an unknown text. There is a need to develop a system to identify the actual author of unknown texts based on a given set of writing samples. This study presents a novel approach based on ensemble learning, DistilBERT , and conventional machine learning techniques for authorship identification. The proposed approach extracts the valuable characteristics of the author using a count vectorizer and bi-gram Term frequency-inverse document frequency (TF-IDF). An extensive and detailed dataset, “All the news” is used in this study for experimentation. The dataset is divided into three subsets (article1, article2, and article3). We limit the scope of the dataset and selected ten authors in the first scope and 20 authors in the second scope for experimentation. The experimental results of proposed ensemble learning and DistilBERT provide better performance for all the three subsets of the “All the news” dataset. In the first scope, the experimental results prove that the proposed ensemble learning approach from 10 authors provides a better accuracy gain of 3.14% and from DistilBERT 2.44% from the article1 dataset. Similarly, in the second scope from 20 authors, the proposed ensemble learning approach provides a better accuracy gain of 5.25% and from DistilBERT 7.17% from the article1 dataset, which is better than previous state-of-the-art studies.
Pseudorandom number generator based on novel 2D Hénon-Sine hyperchaotic map with microcontroller implementation

Recently, chaotic maps have been considered to design pseudorandom number generator (PRNG). However, some chaotic maps present security disadvantages, such as low uniformity and low randomness properties. Nowadays, chaos-based PRNGs are used as the main source for the development of cryptographic algorithms. In this work, to overcome such weaknesses, a novel 2D hyperchaotic map is proposed based on discrete-time feedback by using Hénon map and Sine map. In addition, the dynamics of the hyperchaotic map are enhanced by using the remainder after division function (rem), where better random statistical properties are obtained. A comparison is made between the enhanced Hénon-Sine hyperchaotic map (EHSHM) and the Hénon-Sine hyperchaotic map through Lyapunov exponent analysis, attractor trajectory, histograms and sensitivity at initialization. Then, 8-bit pseudorandom number generator based on the proposed hyperchaotic map (PRNG-EHSHM) is designed and the initial seed of the PRNG is calculated by a secret key of 60 hexadecimal characters. It is implemented in both MATLAB and Arduino Mega microcontroller for experimental results. A complete security analysis is presented from a cryptographic point of view, such as key space, floating frequency, histograms and entropy of the information. Moreover, the randomness is verified with the tests of the National Institute of Standards and Technology (NIST 800-22). Based on the security results obtained, the proposed PRNG-EHSHM can be implemented in embedded cryptographic applications based on chaos.