Reviewerly/exHarmony huggingface dataset:
WITHDRAWN: Secure information hiding using LSB features in an image

This paper proposes an information hiding technique using the concept of the modified least significant bit substitution (MLSBS). The proposed technique utilizes the XOR feature of the original image (OI) pixel for embedding the secret bits. At the embedding end, the last three LSBs of the OI image pixels are considered for finding the featured bits. The obtained bits are replaced with the least significant bit (LSB) of each pixel. The proposed MLSBS technique does not directly embed the secret bits, therefore, it produces a superior quality of stego-image (SI) compared to other existing state-of-art LSBS based techniques. Experiment results show that the proposed MLSBS technique does not only improve the peak-signal-to noise ratio (PSNR) of the SI but also robust to various stego-attacks.
Adaptive Network Latency Prediction From Noisy Measurements

Recent decades have observed an exponential growth in network traffic, thanks to the increased popularity of real-time applications, such as live video chat and gaming. The resulting growth in the network infrastructure has made it difficult for the service providers to abide by the service level agreements, especially with regards to the quality-of-service guarantees. Predicting network latencies from noisy and missing measurements has therefore emerged as an important problem, and a plethora of solutions have been proposed for the same. Existing network latency predictions rely either on Euclidean embedding or matrix completion methods. This work considers the estimation and prediction of network latencies from a sequence of noisy and incomplete latency matrices collected over time. An adaptive matrix completion algorithm is proposed that can handle streaming data at low computational complexity. The performance of the proposed algorithm is characterized both in theory and using a real dataset, demonstrating its viability as a network monitoring tool.
Combined Selection and Parameter Control of Meta-heuristics

Parameter control and online algorithm selection are well-studied problems in heuristic-based optimization. The former adapts algorithm parameters at runtime, improving its quality; while the latter is often tackled with hyper-heuristics: problem-independent optimization algorithms, operating problem-dependent heuristics. Combining these two approaches is challenging: parameter control is algorithm-dependent, while the search space comprising multiple algorithms and their parameters is highly hierarchical and hard to model and traverse. In this paper we provide an approach to automatically select the best-performing algorithm and its configuration at runtime, combining the benefits of both parameter control and hyper-heuristics. Parameter control is realized with a surrogate-model-based approach, while the complex search space is treated by using individual surrogate models for each level in the hierarchy. We evaluate our approach with four instances of the symmetric traveling salesman problem. Our findings show that the presented approach is comparable with heuristics tuned offline and is highly recommended in cases when the best-performing heuristic is not known in advance.
Multi-Task Learning with Deep Dual-Path Network for Facial Attribute Recognition

Facial attribute recognition is a popular and challenging research topic in computer vision. In the traditional deep learning based attribute recognition methods, the mid-level network features and the differences between attribute groups are not fully explored. To solve the above problem, a deep dual-path network is proposed for facial attribute recognition. In the multi-task learning framework, two sub-networks are employed to respectively extract the features of two attribute groups, i.e., local attributes and global ones, and designed with both different scale images and different depth networks. Furthermore, an adaptive Focal loss penalty scheme is developed to automatically assign weights to handle the class imbalance problem for facial attribute recognition. Experimental results on the challenging CelebA dataset show that the proposed method achieves the better performance than state-of-the-art methods.
Evaluating Input Perturbation Methods for Interpreting CNNs and Saliency Map Comparison

Input perturbation methods occlude parts of an input to a function and measure the change in the function’s output. Recently, input perturbation methods have been applied to generate and evaluate saliency maps from convolutional neural networks. In practice, neutral baseline images are used for the occlusion, such that the baseline image’s impact on the classification probability is minimal. However, in this paper we show that arguably neutral baseline images still impact the generated saliency maps and their evaluation with input perturbations. We also demonstrate that many choices of hyperparameters lead to the divergence of saliency maps generated by input perturbations. We experimentally reveal inconsistencies among a selection of input perturbation methods and find that they lack robustness for generating saliency maps and for evaluating saliency maps as saliency metrics.
Improving screening systems of autism using data sampling

OBJECTIVES: Autism Spectrum Disorder (ASD) is a complex range of neurodegenerative conditions that impact individuals’ social behaviour and communication skills. However, ASD data often contains far more controls than cases. This poses a serious challenge when creating classification models due to deriving models that favour controls during the classification of individuals. This problem is known as class imbalance, and it may reduce the performance in classification models derived by machine learning (ML) techniques due to individuals may remain undetected. METHODS: ML appears to help in the distressing disorder by improving outcome quality besides speeding up the access to early diagnosis and consequential treatment. A screening dataset that consists of over 1100 instances was used to perform extensive quantitative analysis using different data resampling techniques and according to specific evaluation metrics. We measure the effect of class imbalance on autism screening performance using different data resampling techniques with a ML classifier and with respect to sensitivity, specificity, and F1-measure. We would like to know which resampling methods work well in balancing autism screening data. RESULTS: The results reveal that data resampling, and especially oversampling, improve results derived by the considered ML classifier. More importantly, there was superiority in terms of sensitivity and specificity for models derived by Naive Bayes classifier when oversampling methods have been used for data pre-processing on the autism data considered. CONCLUSION: The results reported encourages further improvement of the design and implementation of ASD screening systems using intelligent technology.
Fully Automated Traffic Sign Substitution in Real-World Images for Large-Scale Data Augmentation

Video-based traffic sign recognition is a key ability of autonomous vehicles but a demanding challenge due to the enormous number of classes and natural conditions in the wild. We address this problem with a fully automatic close-to-life image-to-image translation technique for traffic sign substitution in natural images (cf. Fig. 1). The work is intended as data augmentation technique and allows for training rare or unavailable traffic sign classes, or otherwise uncommon cases in visual traffic sign detection and classification. To this end, we extend our previous data generation model [1] and propose a rendering pipeline to create convincing traffic sign images with realistic background and camera recording artifacts. Experiments are conducted by exchanging traffic sign classes on different parts of the German Traffic Sign Recognition Benchmark (GTSRB) [2]. We demonstrate that the pipeline is well-suited for generating representative images of unseen traffic sign classes. A baseline image classification setup trained on real data shows an overall performance similar to being trained with a comparable number of artificial data samples. Our code is made publicly available under an open source license.
Evaluation of unsupervised anomaly detection approaches on photovoltaic monitoring data

Fault detection in photovoltaic is a challenging task, our project <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">"</sub> PV Digital 4.0” addreses this challenge by using machine learning techniques on monitoring data from different systems to detect faults on photovoltaic modules. This paper is embedded in this project and addresses faults for which we have little data or which may occur in the future. For this we use different state of the art unsupervised anomaly detection approaches (e.g. LSCP, VAE or MOGAAL) and compare them in terms of their applicability to our problem. We combine the monitoring data of 70 PV plants with weather data and preprocess them for the usage in multiple models. Each model is elaborated with the same dataset and afterwards compared according to its accuracy. In our scenario we only want to detect new occurring faults and not long term existing faults like shadows. The models should be able to detect the actual behavior of the PV system.
Estimating Security Risk of Healthcare Web Applications: A Design Perspective

In the recent years, the booming web-based applications have attracted the hackers’ community. The security risk of the web-based hospital management system (WBHMS) has been increasing rapidly. In the given context, the main goal of all security professionals and website developers is to maintain security divisions and improve on the user’s confidence and satisfaction. At this point, the different WBHMS tackle different types of security risks. In WBHMS, the security of the patients’ medical information is of utmost importance. All in all, there is an inherent security risk of data and assets in the field of the medical industry as a whole. The objective of this study is to estimate the security risk assessment of WBHMS. The risks assessment pertains to securing the integrity of the information in alignment with the Health Insurance Portability and Accountability Act. This includes protecting the relevant financial records, as well as the identification, evaluation, and prevention of a data breach. In the past few years, according to the US-based cyber-security firm <i>Fire-eye</i>, 6.8 million data thefts have been recorded in the healthcare sector in India. The breach barometer report mentions that in the year 2019, the data breaches found were up to 48.6% as compared to the year 2018. Therefore, it is very important to assess the security risk in WBHMS. In this research, we have followed the hybrid technique fuzzy analytic hierarchy process-technique for order of preference by similarity to ideal solution (F-AHPTOPSIS) approach to assess the security risk in WBHMS. The place of this empirical database is at the local hospital of Varanasi, U.P., India. Given the affectability of WBHMS for its board framework, this work has used diverse types of web applications. The outcomes obtained and the procedure used in this assessment would support future researchers and specialists in organizing web applications through advanced support of safety and security.
The New Frontier: Implementing OpenAthens at UNC Charlotte

As vendors move away from IP authentication due to security concerns and a need for a more reliable method, libraries need to start considering other options for making their electronic resources available to users remotely. In 2018, the University of North Carolina, Charlotte (UNC Charlotte) made the decision to move from EZproxy to OpenAthens. This presentation will discuss the decision to make this change, the implementation process, and the ongoing maintenance and troubleshooting of OpenAthens.
A Novel Deep-Learning-Based Model for Medical Text Classification

In recent years, with development of the Internet hospitals and natural language processing technology, intelligent medical guidance based on machine learning has been gained increasing attentions. Medical text classification is indispensable for intelligent medical guidance. In this paper, we propose a novel deep-learning-based model named CNN-MHA-BLSTM for medical text classification. The model combines the characteristics of CNN, Multi-Head Attention and Bidirectional LSTM to capture local potential features, contextual information and contribution of each feature to the classification. We conduct numerical simulations using real dataset to verify the validation of the model. The results show that the proposed model achieves a performance with accuracy of 91.99% and F1-score of 92.03%, which outperforms some typical methods for text classification.
Machine Learning Inspired Phishing Detection (PD) for Efficient Classification and Secure Storage Distribution (SSD) for Cloud-IoT Application

Cloud-IoT data security and privacy have become a major problem due to its sensitivity, which curbs multiple cloud applications. In addition, if the encrypted data lives in one place, in many fields, such as the financial industry and government agencies, the man-in-the-middle-attack (MMA) and phishing attack (PA) may have chances of realising the extraction. The phishing goal is evaluated and predicted by most previous machine learning models through a discrete or continuous result. The current models lag in accurately determining both attacks because of this approach. We developed a three-step phishing detection (PD) framework inspired by machine learning and a secure storage distribution (SSD) for cloud to improve model accuracy and storage security. The partition-based selection of features is designed for phishing detection (PD) with a hybrid classifier approach and hyper-parameter classifier tuning. Initially, the entire data set is partitioned by entropy and is hybridised for each performing model partition. In order to reduce the complexity, the next entropy is applied to decrease the dimension of each partition. Finally, to improve precision, the performing model is optimised with hyper-parameter tuning. The partition-based feature choice with the hybrid classifier approach outperforms with 97.86% accuracy for both attack detection from the experimental and comparative results of SVM, LM, NN and RF. Atlast, SSD performance is evaluated against other storage models where SSD outperforms other models.
Receding Horizon Extended Linear Quadratic Regulator for RFS-Based Swarms With Target Planning and Automatic Cost Function Scaling

A cost function is constructed for the random finite-set-based swarm guidance problem mechanized by Gaussian mixtures. This cost function uses an automated problem-dependent scaling and introduces an activator function for quadratic convergence of far off Gaussians. The cost function also depends on a target planner to transform user-defined waypoints into “way-areas” by calculating a covariance matrix. These covariances are based upon the given distribution of the target/reference geometry. Then an extended linear-quadratic regulator (LQR) is defined for the swarm problem as an improvement to the iterative LQR (ILQR). The algorithm is tested in a software simulation, and it is found to have easier tuning than ILQR and generates smooth trajectories toward the targets.
A multilevel fusion network for 3D object detection

3D object detection is an important yet challenging problem in a myriad of vision, robotics, and human–machine interaction applications. Given an RGB-D image, the task is to infer the class labels and the 3D bounding boxes of the objects in the image. While the previous studies have made remarkable progress over the past decade, how to effectively exploit the feature fusion with neural networks for boosting 3D object detection performance remains an open problem. This paper proposes a multilevel fusion network (MFN) model to detect 3D objects in RGB-D images. The MFN model contains two streams of neural networks which respectively extracts the RGB and depth features with cascaded convolutional modules. To effectively exploit the information of 3D objects, a multilevel fusion mechanism is adopted to fuse the convolutional RGB and depth features at multiple levels. To train the network, we propose a new weighted loss function by encoding the difference of geometric attributes on 3D bounding box regression. Since the original depth data is full of noisy holes, we also develop an adaptive filtering algorithm to restore and correct the depth images. We test the proposed model on challenging RGB-D datasets. The experimental results on the datasets prove the strength and advantage of the proposed model.
Efficient Threshold Private Set Intersection

Threshold private set intersection (TPSI) allows a receiver to obtain the intersection when the cardinality of the intersection is greater or equal to the threshold, which has a wide range of applications such as fingerprint matching, online dating and ridesharing.Existing TPSI protocols are inefficient because almost all of them rely on lots of expensive public-key techniques or require an exponential number of possible combinations among the shares.In this work, we design an efficient TPSI protocol, which achieves computational security in semi-honest model.To improve the efficiency of the TPSI protocol, we design a new TPSI protocol based on garbled Bloom filter (GBF) and threshold secret sharing, which uses a small amount of public-key operations.Moreover, our protocol combines with the Reed-Solomon decoding algorithm to reconstruct the secret which is a feasible method to avoid calculating all possible combinations among the shares.The performance analysis shows that our protocol is more efficient than the previous TPSI protocols.To the best of our knowledge, the optimal TPSI protocol implemented by Zhao and Chow (WPES'18) has an online time of 78 seconds to compute the intersection of two datasets of 100 elements each with threshold t = 50.In contrast, our protocol has a total time of 2.988 seconds.
Non-discrete Sentiment Dataset Annotation: Case Study for Lövheim Cube Emotional Model

The research project we are conducting is devoted to text emotional analysis. In this paper, we report the preliminary results of the non-discrete data assessment method, which uses an original interface developed to annotate texts according to emotion model known as Lövheim Cube. Swedish neurophysiologist H. Lövheim put eight basic emotions in the cube vertices according to the particular combination of three monoamines triggers each of them. We took four supporting diagonals of the cube and mapped them onto assessment scales: Distress/Enjoyment, Rage/Disgust, Shame/Excitement, Fear/Surprise. 172 human assessors were asked to adjust the pointer of a slider between two opposite emotions on the scales after having been read each of 48 text fragments retrieved from Russian social network VKontakte. By converting labeled scalars into spatial coordinates in the cube space, we obtained a set of comparable evaluations. The effectiveness of the approach has been validated using the Intra-class correlation metric. The proposed method offers noticeable benefits when compared to the discrete assessment procedure, giving to each text a multidimensional evaluation, which is closer to the natural text perception while reading.
Bayesian network models with decision tree analysis for management of childhood malaria in Malawi

Abstract Background Malaria is a major cause of death in children under five years old in low- and middle-income countries such as Malawi. Accurate diagnosis and management of malaria can help reduce the global burden of childhood morbidity and mortality. Trained healthcare workers in rural health centers manage malaria with limited supplies of malarial diagnostic tests and drugs for treatment. A clinical decision support system that integrates predictive models to provide an accurate prediction of malaria based on clinical features could aid healthcare workers in the judicious use of testing and treatment. We developed Bayesian network (BN) models to predict the probability of malaria from clinical features and an illustrative decision tree to model the decision to use or not use a malaria rapid diagnostic test (mRDT). Methods We developed two BN models to predict malaria from a dataset of outpatient encounters of children in Malawi. The first BN model was created manually with expert knowledge, and the second model was derived using an automated method. The performance of the BN models was compared to other statistical models on a range of performance metrics at multiple thresholds. We developed a decision tree that integrates predictions with the costs of mRDT and a course of recommended treatment. Results The manually created BN model achieved an area under the ROC curve (AUC) equal to 0.60 which was statistically significantly higher than the other models. At the optimal threshold for classification, the manual BN model had sensitivity and specificity of 0.74 and 0.42 respectively, and the automated BN model had sensitivity and specificity of 0.45 and 0.68 respectively. The balanced accuracy values were similar across all the models. Sensitivity analysis of the decision tree showed that for values of probability of malaria below 0.04 and above 0.40, the preferred decision that minimizes expected costs is not to perform mRDT. Conclusion In resource-constrained settings, judicious use of mRDT is important. Predictive models in combination with decision analysis can provide personalized guidance on when to use mRDT in the management of childhood malaria. BN models can be efficiently derived from data to support clinical decision making.
Intrinsically Motivated Exploration of Learned Goal Spaces

Finding algorithms that allow agents to discover a wide variety of skills efficiently and autonomously, remains a challenge of Artificial Intelligence. Intrinsically Motivated Goal Exploration Processes (IMGEPs) have been shown to enable real world robots to learn repertoires of policies producing a wide range of diverse effects. They work by enabling agents to autonomously sample goals that they then try to achieve. In practice, this strategy leads to an efficient exploration of complex environments with high-dimensional continuous actions. Until recently, it was necessary to provide the agents with an engineered goal space containing relevant features of the environment. In this article we show that the goal space can be learned using deep representation learning algorithms, effectively reducing the burden of designing goal spaces. Our results pave the way to autonomous learning agents that are able to autonomously build a representation of the world and use this representation to explore the world efficiently. We present experiments in two environments using population-based IMGEPs. The first experiments are performed on a simple, yet challenging, simulated environment. Then, another set of experiments tests the applicability of those principles on a real-world robotic setup, where a 6-joint robotic arm learns to manipulate a ball inside an arena, by choosing goals in a space learned from its past experience.