Reviewerly/exHarmony huggingface dataset:
On the Generalization of Detecting Face Morphing Attacks as Anomalies: Novelty vs. Outlier Detection

Face morphing attacks are verifiable to multiple identities, leading to faulty identity links. Recent works have studied the face morphing attack detection performance over variations in morphing approaches, pointing out low generalization. This work studies detecting these attacks as anomalies and discusses the performance and generalization over different morphing types. We also analyze the accuracy and generalization effect of including different amounts of attack contamination in the anomaly training data (novelty vs. outlier). This is performed with two baseline 2-class classifiers, two approaches for anomaly detection, two image feature extractions, two morphing types, and variations in contamination levels and tolerated training errors. The results points out the relative lower performance, but higher generalization ability, of anomaly detection in comparison to 2-class classifiers, along with the benefits of contaminating the anomaly training data.
Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis

Multitasking optimization is an incipient research area which is lately gaining a notable research momentum. Unlike traditional optimization paradigm that focuses on solving a single task at a time, multitasking addresses how multiple optimization problems can be tackled simultaneously by performing a single search process. The main objective to achieve this goal efficiently is to exploit synergies between the problems (tasks) to be optimized, helping each other via knowledge transfer (thereby being referred to as Transfer Optimization). Furthermore, the equally recent concept of Evolutionary Multitasking (EM) refers to multitasking environments adopting concepts from Evolutionary Computation as their inspiration for the simultaneous solving of the problems under consideration. As such, EM approaches such as the Multifactorial Evolutionary Algorithm (MFEA) has shown a remarkable success when dealing with multiple discrete, continuous, single-, and/or multi-objective optimization problems. In this work we propose a novel algorithmic scheme for Multifactorial Optimization scenarios - the Multifactorial Cellular Genetic Algorithm (MFCGA) - that hinges on concepts from Cellular Automata to implement mechanisms for exchanging knowledge among problems. We conduct an extensive performance analysis of the proposed MFCGA and compare it to the canonical MFEA under the same algorithmic conditions and over 15 different multitasking setups (encompassing different reference instances of the discrete Traveling Salesman Problem). A further contribution of this analysis beyond performance benchmarking is a quantitative examination of the genetic transferability among the problem instances, eliciting an empirical demonstration of the synergies emerged between the different optimization tasks along the MFCGA search process.
Privacy Threats and Protection in Machine Learning

With the improvement of computing power and storage level, Machine Learning (ML), especially Deep Learning (DL), has shown its capabilities beyond humans in areas such as image recognition, speech processing, and content recommendation. However, the data collected to build ML models often contains sensitive information, and models may have high commercial value. Compared with the security problem of model prediction errors caused by malicious external influences, privacy threats have not attracted widespread attention, and they have characteristics that are difficult to define and detect. This article reviews recent research progress on ML privacy. First, the privacy threats on data and models in different scenarios are described in detail. Then, typical privacy protection methods are introduced. Finally, the limitations and future development trends of ML privacy research are discussed.
The Effects of Learning Objectives on Searchers' Perceptions and Behaviors

In recent years, the "search as learning" community has argued that search systems should be designed to support learning. We report on a lab study in which we manipulated the learning objectives associated with search tasks assigned to participants. We manipulated learning objectives by leveraging Anderson and Krathwohl's taxonomy of learning (A&K's taxonomy)[2], which situates learning objectives at the intersection of two orthogonal dimensions: the cognitive process and the knowledge type dimension. Participants in our study completed tasks with learning objectives that varied across three cognitive processes (apply, evaluate, and create) and three knowledge types (factual, conceptual, and procedural knowledge). We focus on the effects of the tasks cognitive process and knowledge type on participants' pre-/post-task perceptions and search behaviors. Our results found that the three knowledge types considered in our study had a greater effect than the three cognitive processes. Specifically, conceptual knowledge tasks were perceived to be more difficult and required more search activity. We discuss implications for designing search systems that support learning.
Zero-Shot Deep Hashing and Neural Network Based Error Correction for Face Template Protection

In this paper, we present a novel architecture that integrates a deep hashing framework with a neural network decoder (NND) for application to face template protection. It improves upon existing face template protection techniques to provide better matching performance with one-shot and multi-shot enrollment. A key novelty of our proposed architecture is that the framework can also be used with zero-shot enrollment. This implies that our architecture does not need to be re-trained even if a new subject is to be enrolled into the system. The proposed architecture consists of two major components: a deep hashing (DH) component, which is used for robust mapping of face images to their corresponding intermediate binary codes, and a NND component, which corrects errors in the intermediate binary codes that are caused by differences in the enrollment and probe biometrics due to factors such as variation in pose, illumination, and other factors. The final binary code generated by the NND is then cryptographically hashed and stored as a secure face template in the database. The efficacy of our approach with zero-shot, one-shot, and multi-shot enrollments is shown for CMU-PIE, Extended Yale B, WVU multimodal and Multi-PIE face databases. With zero-shot enrollment, the system achieves approximately 85% genuine accept rates (GAR) at 0.01% false accept rate (FAR), and with one-shot and multi-shot enrollments, it achieves approximately 99.95% GAR at 0.01% FAR, while providing a high level of template security.
Gray-level image denoising with an improved weighted sparse coding

The nonlocal self-similarity of images means that groups of similar patches have low-dimensional property. The property has been previously used for image denoising, with particularly notable success via sparse coding. However, only a few studies have focused on the varying statistics of noise in different similar patches during the iterative denoising process. This has motivated us to introduce an improved weighted sparse coding for gray-level image denoising in this paper. On the basis of traditional sparse coding, we introduce a weight matrix to account for the noise variation characteristics of different similar patches, while introduce another weight matrix to make full use of the sparsity priors of natural images. The Maximum A-Posterior estimation (MAP) is used to obtain the closed-form solution of the proposed method. Experimental results demonstrate the competitiveness of the proposed method compared with that of state-of-the-art methods in both the objective and perceptual quality.
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing

Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can hardly afford complex DNN models, and offloading anomaly detection tasks to the cloud incurs long delay. In this paper, we propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems to solve this problem, for both univariate and multivariate IoT data. First, we construct multiple anomaly detection DNN models with increasing complexity, and associate each model with a layer in HEC from bottom to top. Then, we design an adaptive scheme to select one of these models on the fly, based on the contextual information extracted from each input data. The model selection is formulated as a contextual bandit problem characterized by a single-step Markov decision process, and is solved using a reinforcement learning policy network. We build an HEC testbed, implement our proposed approach, and evaluate it using real IoT datasets. The demo shows that our proposed approach significantly reduces detection delay (e.g., by 71.4% for univariate dataset) without sacrificing accuracy, as compared to offloading detection tasks to the cloud. We also compare it with other baseline schemes and demonstrate that it achieves the best accuracy-delay tradeoff <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Statistical hierarchical clustering algorithm for outlier detection in evolving data streams

Anomaly detection is a hard data analysis process that requires constant creation and improvement of data analysis algorithms. Using traditional clustering algorithms to analyse data streams is impossible due to processing power and memory issues. To solve this, the traditional clustering algorithm complexity needed to be reduced, which led to the creation of sequential clustering algorithms. The usual approach is two-phase clustering, which uses online phase to relax data details and complexity, and offline phase to cluster concepts created in the online phase. Detecting anomalies in a data stream is usually solved in the online phase, as it requires unreduced data. Contrarily, producing good macro-clustering is done in the offline phase, which is the reason why two-phase clustering algorithms have difficulty being equally good in anomaly detection and macro-clustering. In this paper, we propose a statistical hierarchical clustering algorithm equally suitable for both detecting anomalies and macro-clustering. The proposed algorithm is single-phased and uses statistical inference on the input data stream, resulting in statistical distributions that are constantly updated. This makes the classification adaptable, allowing agglomeration of outliers into clusters, tracking population evolution, and to be used without knowing the expected number of clusters and outliers. The proposed algorithm was tested against typical clustering algorithms, including two-phase algorithms suitable for data stream analysis. A number of typical test cases were selected, to show the universality and qualities of the proposed clustering algorithm.
Real-time sepsis severity prediction on knowledge graph deep learning networks for the intensive care unit

Sepsis is the third-highest mortality disease in intensive care units (ICUs). In this paper, we proposed a deep learning model for predicting the severity of sepsis patients. Most existing models based on attention mechanisms do not fully utilize knowledge graph based information for different organ systems, such that might constitute crucial features for predicting the severity of sepsis patients. Therefore, we have employed a medical knowledge graph as a reliable and robust source of side information. End-to-end neural networks that incorporate analyses of various organ systems simultaneously and intuitively were developed in the proposed model to reflect upon the condition of patients in a timely fashion. We have developed a pre-training technique in the proposed model to combine it with labeled data by multi-task learning. Experimental results on real-world clinical datasets, MIMIC-III and eIR, demonstrate that our model outperforms state-of-the-art models in predicting the severity of sepsis patients.
An Experimental Comparison between Deep Learning and Classical Machine Learning Approaches for Writer Identification in Medieval Documents

In the framework of palaeography, the availability of both effective image analysis algorithms, and high-quality digital images has favored the development of new applications for the study of ancient manuscripts and has provided new tools for decision-making support systems. The quality of the results provided by such applications, however, is strongly influenced by the selection of effective features, which should be able to capture the distinctive aspects to which the paleography expert is interested in. This process is very difficult to generalize due to the enormous variability in the type of ancient documents, produced in different historical periods with different languages and styles. The effect is that it is very difficult to define standard techniques that are general enough to be effectively used in any case, and this is the reason why ad-hoc systems, generally designed according to paleographers’ suggestions, have been designed for the analysis of ancient manuscripts. In recent years, there has been a growing scientific interest in the use of techniques based on deep learning (DL) for the automatic processing of ancient documents. This interest is not only due to their capability of designing high-performance pattern recognition systems, but also to their ability of automatically extracting features from raw data, without using any a priori knowledge. Moving from these considerations, the aim of this study is to verify if DL-based approaches may actually represent a general methodology for automatically designing machine learning systems for palaeography applications. To this purpose, we compared the performance of a DL-based approach with that of a “classical” machine learning one, in a particularly unfavorable case for DL, namely that of highly standardized schools. The rationale of this choice is to compare the obtainable results even when context information is present and discriminating: this information is ignored by DL approaches, while it is used by machine learning methods, making the comparison more significant. The experimental results refer to the use of a large sets of digital images extracted from an entire 12th-century Bibles, the “Avila Bible”. This manuscript, produced by several scribes who worked in different periods and in different places, represents a severe test bed to evaluate the efficiency of scribe identification systems.
Comprehensive comparative evaluation of background subtraction algorithms in open sea environments

In autonomous-ship and maritime security surveillance operations involving electro-optical sensors, the first phase of foreground segmentation and change detection using background subtraction (BS) algorithms is crucial. However, it is also the most complex in terms of execution time. Despite the development of several BS algorithms, maritime foreground segmentation and change detection remain major challenges owing to the complex, unconstrained, and diverse nature of ocean scenarios. However, only a few studies have investigated the applications of BS algorithms in maritime environments, especially those involving boats in the open sea. This study compares BS methods involving use of a non-static electro-optical sensor in combination with visible-light and infrared cameras to identify the best method for use in open sea scenarios, especially from the viewpoint of avoiding piracy and armed robbery. Thirty-seven methods, ranging from simple temporal differencing to more sophisticated ones, were validated via extensive experiments and analyses using realistic maritime datasets and practical maritime applications. In addition, because most methods considered in this study were not previously evaluated at the pixel level on open sea datasets, this paper proposes an appropriate maritime BS benchmark, based on which the 37 methods were compared to compensate for their prior lack of detailed analyses. The experimental results indicate that BS algorithms of the multiple features category can better handle maritime challenges, thereby realizing higher accuracies when analyzing visible-light and thermal videos. The proposed evaluation, therefore, complements those reported previously. Consequently, the proposed study enables users to identify the most suitable BS algorithm for use in intelligent maritime transportation, maritime security surveillance systems, and autonomous ships in the open sea.
A probability-based core dandelion guided dandelion algorithm and application to traffic flow prediction

The Dandelion Algorithm (DA) is a recently proposed intelligent optimization algorithm inspired by dandelion sowing. For enhancing its exploitation ability and speeding up its convergence, this work proposes a probability-based core dandelion guided dandelion algorithm (GDA). Specifically, the probability of dandelions being selected is calculated firstly. Then the dandelions need to learn from the core dandelion based on previously calculated probability in the process of sowing. Meanwhile, a greedy selection strategy is applied to GDA. Experimental results show that the proposed algorithm not only outperforms DA and its variants, but also outperforms eight state-of-the-art intelligent optimization algorithms on most functions and three real world problems. In addition, the proposed algorithm is applied to optimize kernel extreme learning machine (KELM) for traffic flow prediction, and the results show that the proposed model has a smaller prediction error than its peers.
Using Augmented Reality to Reduce Fear and Promote Cooperation During Pediatric Otolaryngologic Procedures

This case series examines interactive AR during minor otolaryngologic procedures. Although VR has been successfully used for pediatric vascular access, removing children from comforting people in the real world has resulted in patient anxiety. AR offers a potential advantage, utilizing distracting holographic images when patients maintain eye contact with parents. The primary objective was to determine the effect of AR on fear during pediatric otolaryngologic procedures. Secondary objectives included evaluating pain; procedure compliance; and patient, parent and physician attitudes toward AR, as well as assessing the feasibility of adding AR to a busy outpatient otolaryngologic clinic. Laryngoscope , 131:E1342–E1344, 2021
Weakly supervised single image dehazing

Single image dehazing is a critical image pre-processing step for many practical vision systems. Most existing dehazing methods solve this problem utilizing various of hand-crafted priors or by supervised training on the synthetic hazy image information (such as haze-free image, transmission map and atmospheric light). However, the assumptions on the hand-crafted priors are easily violated and collecting realistic transmission map and atmospheric light are unpractical. In this paper, we propose a novel weakly supervised network based on the multi-level multi-scale block. The proposed network reduces the constraint on the training data and automatically estimates the transmission map and the atmospheric light as well as the intermediate haze-free image without using any realistic transmission map and atmospheric light as supervision. Moreover, the estimated intermediate haze-free image helps to generate accurate transmission map and atmospheric light by embedding the physical-model, which presents reliable restoration of the final haze-free image. In particular, our network also can be trained on the real-world dataset to fine-tune the model and the fine-tuning operation improves the dehazing performance on the real-world dataset. Quantitative and qualitative experimental results demonstrate the proposed method performs on par with the supervised methods.
Recovering the finance-growth nexus

Abstract We show that the finance-growth nexus can be recovered by using quality adjusted measures of financial development. Specifically, we utilize three World Bank financial fragility indicators – the Z-score, a measure of liquidity and a measure of impaired loans – to construct quality adjusted measures of private credit to GDP. Our findings suggest that the finance-growth nexus is alive and kicking, as long as banks use sound lending practices to prevent the buildup of non-performing loans. We also show that our results hold in Sub-Saharan Africa — a region where the finance-growth nexus could potentially make a big difference
A Domain-Independent Classification Model for Sentiment Analysis Using Neural Models

Most people nowadays depend on the Web as a primary source of information. Statistical studies show that young people obtain information mainly from Facebook, Twitter, and other social media platforms. By relying on these data, people may risk drawing the incorrect conclusions when reading the news or planning to buy a product. Therefore, systems that can detect and classify sentiments and assist users in finding the correct information on the Web is highly needed in order to prevent Web surfers from being easily deceived. This paper proposes an intensive study regarding domain-independent classification models for sentiment analysis that should be trained only once. The study consists of two phases: the first phase is based on a deep learning model which is training a neural network model once after extracting robust features and saving the model and its parameters. The second phase is based on applying the trained model on a totally new dataset, aiming at correctly classifying reviews as positive or negative. The proposed model is trained on the IMDb dataset and then tested on three different datasets: IMDb dataset, Movie Reviews dataset, and our own dataset collected from Amazon reviews that rate users’ opinions regarding Apple products. The work shows high performance using different evaluation metrics compared to the stat-of-the-art results.
Enhanced Rolling Horizon Evolution Algorithm With Opponent Model Learning: Results for the Fighting Game AI Competition

The Fighting Game AI Competition (FTGAIC) provides a challenging benchmark for two-player video game artificial intelligence. The challenge arises from the large action space, diverse styles of characters and abilities, and the real-time nature of the game. In this article, we propose a novel algorithm that combines the rolling horizon evolution algorithm (RHEA) with opponent model learning. The approach is readily applicable to any two-player video game. In contrast to conventional RHEA, an opponent model is proposed and is optimized by supervised learning with cross-entropy and reinforcement learning with policy gradient and Q-learning respectively, based on history observations from opponent. The model is learned during the live gameplay. With the learned opponent model, the extended RHEA is able to make more realistic plans based on what the opponent is likely to do. This tends to lead to better results. We compared our approach directly with the bots from the FTGAIC 2018 competition and found our method to significantly outperform all of them for all three characters. Furthermore, our proposed bot with the policy gradient based opponent model is the only one without using Monte Carlo tree search among the top five bots in the 2019 competition in which it achieved second place, while using much less domain knowledge than the winner.