Reviewerly/exHarmony huggingface dataset:
Deep Learning Using Bayesian Optimization for Facial Age Estimation

Age Estimation plays a significant role in many real-world applications. Age estimation is a process of determining the exact age or age group of a person depending on his biometric features. Recent research demonstrates that the deeply learned features for age estimation from large-scale data result in significant improvement of the age estimation performance for facial images. This paper propose a Convolutional Neural Network (CNN) - approach using Bayesian Optimization for facial age estimation. Bayesian Optimization is applied to minimize the classification error on the validation set for CNN model. Extensive experiments are done for evaluating Deep Learning using Bayesian Optimization (DLOB) on three datasets: MORPH, FG-NET and FERET. The results show that using Bayesian Optimization for CNN outperforms the state of the arts on FG-NET and FERET datasets with a Mean Absolute Error (MAE) of 2.88 and 1.3, and achieves comparable results compared to the most of the state-of-the-art methods on MORPH dataset with a 3.01 MAE.
A Formal Framework for Coupling Document Spanners with Ontologies

A significant portion of information that is nowa-days collected in enterprises and organizations resides in text documents, and thus is inherently unstructured. Turning it into a structured form is the aim of information extraction (IE). Depending on the approach followed, the output of an IE process can fill forms or populate relational tables, or can be presented through an ontology. This last approach is particularly interesting, since ontologies may facilitate the integration with other corporate and external data, and enable data management and governance at an abstract, conceptual level, as in Ontology-based Data Access (OBDA). To this aim, OBDA uses declarative mappings that specify the relation between the ontology and the database to be accessed. In OBDA, however, only mappings towards relational databases have been so far considered, and how to declaratively relate the ontology to unstructured sources is still unexplored. By leveraging the study on document spanners for IE, in this paper we propose a new framework that allows to map text documents to ontologies, in the spirit of the OBDA approach. We then investigate the problem of answering conjunctive queries (CQs) in our framework, and show that, if the ontology is specified in the lightweight Description Logic DL-LiteR, the problem can be solved by reformulating the user query into a new spanner. Interestingly, both the spanners used in the mapping and the one computed by the rewriting algorithm have the same expressiveness, and CQ answering in this case is polynomial in data complexity.
Cross-registry neural domain adaptation to extract mutational test results from pathology reports

We study the performance of machine learning (ML) methods, including neural networks (NNs), to extract mutational test results from pathology reports collected by cancer registries. Given the lack of hand-labeled datasets for mutational test result extraction, we focus on the particular use-case of extracting Epidermal Growth Factor Receptor mutation results in non-small cell lung cancers. We explore the generalization of NNs across different registries where our goals are twofold: (1) to assess how well models trained on a registry’s data port to test data from a different registry and (2) to assess whether and to what extent such models can be improved using state-of-the-art neural domain adaptation techniques under different assumptions about what is available (labeled vs unlabeled data) at the target registry site. We collected data from two registries: the Kentucky Cancer Registry (KCR) and the Fred Hutchinson Cancer Research Center (FH) Cancer Surveillance System. We combine NNs with adversarial domain adaptation to improve cross-registry performance. We compare to other classifiers in the standard supervised classification, unsupervised domain adaptation, and supervised domain adaptation scenarios. The performance of ML methods varied between registries. To extract positive results, the basic convolutional neural network (CNN) had an F1 of 71.5% on the KCR dataset and 95.7% on the FH dataset. For the KCR dataset, the CNN F1 results were low when trained on FH data (Positive F1: 23%). Using our proposed adversarial CNN, without any labeled data, we match the F1 of the models trained directly on each target registry’s data. The adversarial CNN F1 improved when trained on FH and applied to KCR dataset (Positive F1: 70.8%). We found similar performance improvements when we trained on KCR and tested on FH reports (Positive F1: 45% to 96%). Adversarial domain adaptation improves the performance of NNs applied to pathology reports. In the unsupervised domain adaptation setting, we match the performance of models that are trained directly on target registry’s data by using source registry's labeled data and unlabeled examples from the target registry.
Blind Image Deblurring Based on Local Edges Selection

The edges of images are less sparse when images become blurred. Selecting effective image edges is a vital step in image deblurring, which can help us to build image deblurring models more accurately. While global edges selection methods tend to fail in capturing dense image structures, the edges are easy to be affected by noise and blur. In this paper, we propose an image deblurring method based on local edges selection. The local edges are selected by the difference between the bright channel and the dark channel. Then a novel image deblurring model including local edges regularization term is established. The obtaining of a clear image and blurring kernel is based on alternating iterations, in which the clear image is obtained by the alternating direction method of multipliers (ADMM). In the experiments, tests are carried out on gray value images, synthetic color images and natural color images. Compared with other state-of-the-art blind image deblurring methods, the visualization results and performance verify the effectiveness of our method.
Ontology-Enhanced Educational Annotation Activities

Information and communications technology and technology-enhanced learning have unquestionably transformed traditional teaching–learning processes and are positioned as key factors to promote quality education, one of the basic sustainable development goals of the 2030 agenda. Document annotation, which was traditionally carried out with pencil and paper and currently benefits from digital document annotation tools, is a representative example of this transformation. Using document annotation tools, students can enrich the documents with annotations that highlight the most relevant aspects of these documents. As the conceptual complexity of the learning domain increases, the annotation of the documents may require comprehensive domain knowledge and an expert analysis capability that students usually lack. Consequently, a proliferation of irrelevant, incorrect, and/or poorly decontextualized annotations may appear, while other relevant aspects are completely ignored by the students. The main hypothesis proposed by this paper is that the use of a guiding annotation ontology in the annotation activities is a keystone aspect to alleviate these shortcomings. Consequently, comprehension is improved, exhaustive content analysis is promoted, and meta-reflective thinking is developed. To test this hypothesis, we describe our own annotation tool, @note, which fully implements this ontology-enhanced annotation paradigm, and we provide experimental evidence about how @note can improve academic performance via a pilot study concerning critical literary annotation.
Evolutionary Optimization on k-Nearest Neighbors Classifier for Imbalanced Datasets

Classification is a significant task in machine learning. Common classification algorithms include decision tree, k-nearest neighbors (kNN), support vector machine, and so on. The kNN algorithm is a simple yet useful classification tool in practice. However, its performance may be deeply affected by the properties of dataset, such as imbalance of classes and noisy features. To deal with these problems and enhance the performance of classification, this study proposes optimizing the distance function and class-voting weights of kNN by genetic algorithm. The proposed method is called evolutionary optimized feature and class weights kNN (FCWkNN). The performance of FCWkNN is examined on different datasets. Experimental results show that the combined optimization of feature weights and class weights is capable of improving kNN on imbalanced datasets.
On Round Optimal Statistical Zero Knowledge Arguments

We construct the first three message statistical zero knowledge arguments for all of NP, matching the known lower bound. We do so based on keyless multi-collision resistant hash functions and the Learning with Errors assumption—the same assumptions used to obtain round optimal computational zero knowledge. The main component in our construction is a statistically witness indistinguishable argument of knowledge based on a new notion of statistically hiding commitments with subset opening.
BayesMallows: An R Package for the Bayesian Mallows Model

BayesMallows is an R package for analyzing preference data in the form of rankings with the Mallows rank model, and its finite mixture extension, in a Bayesian framework.The model is grounded on the idea that the probability density of an observed ranking decreases exponentially with the distance to the location parameter.It is the first Bayesian implementation that allows wide choices of distances, and it works well with a large amount of items to be ranked.BayesMallows handles non-standard data: partial rankings and pairwise comparisons, even in cases including non-transitive preference patterns.The Bayesian paradigm allows coherent quantification of posterior uncertainties of estimates of any quantity of interest.These posteriors are fully available to the user, and the package comes with convienient tools for summarizing and visualizing the posterior distributions.
2-Adic and Linear Complexities of a Class of Whiteman’s Generalized Cyclotomic Sequences of Order Four

Although for more than 20 years, Whiteman’s generalized cyclotomic sequences have been thought of as the most important pseudo-random sequences, but, there are only a few papers in which their 2-adic complexities have been discussed. In this paper, we construct a class of binary sequences of order four with odd length (product of two distinct odd primes) from Whiteman’s generalized cyclotomic classes. After that, we determine both 2-adic complexity and linear complexity of these sequences. Our results show that these complexities are greater than half of the period of the sequences, therefore, it may be good pseudo-random sequences.
Image Encryption in IoT Devices Using DNA and Hyperchaotic Neural Network

Encryptions on IoT devices are important because such type of network requires security at its best. In the research conducted by us, we have designed an encryption scheme for images which uses the three techniques, namely, chaos, neural networks, and the DNA encoding scheme. It is a chaos-based pseudorandom sequence generator which uses the neural network for generating the sequence. The chaotic algorithm for encryption is a heterogeneous system as it uses the two types of hyperchaotic maps which are the Lorenz attractor and the Rossler system. In the encryption algorithm, the neural network controls various operations of the encryption scheme. The algorithm further incorporates the use of DNA-based bit permutation and substitution methods for the pixel bits of the image. Every neuron of the neural network uses the equation of chaotic maps as its transfer function. The system performs comparatively better as the two hyperchaotic maps have been used in the process with their nonlinearity property. Security analyses prove the fact with more evidence.
On Evolutionary Classification Ensembles

Ensemble methods train multiple classifiers and combine their outcomes to improve the generalization ability of a learning system. Although there exist methods for automatic creation of ensembles, they require heavy fine-tuning and the impact of their hyper-parameters on the optimization process very often remains unknown. In this paper, we propose a genetic algorithm for evolving classification ensembles. It is coupled with a pre-processing routine which deals with potential data imbalance and redundancy within the training and/or feature sets. Our experimental study, performed over binary and multi-class benchmark datasets and backed up with statistical analysis, revealed that the proposed technique outperforms other algorithms for building multiple classifier systems. It also helped understand the impact of various (hyper-)parameters of our method on its exploration/exploitation capabilities.
RBFA: Radial Basis Function Autoencoders

We are introducing a new variation of the existing autoencoder called Radial Basis Function Autoencoders (RBFA). This version employs radial symmetric functions, in the first step of encoding, to map the input data vectors into a new form. The transformation which relies on the similarity between the input data examples and predefined center points features forces neurons in the first hidden level to respond similarly to similar patterns of the data. This basic idea of radial transformation (activation) can be interpreted as regularization on the hidden neurons which are penalized based on the similarity criterion so only a fraction of them will become highly activated at one time. The paper also introduces a new method for defining Gaussian radial function's parameters using Particle Swarm Optimization (PSO). Results indicate that the accuracy of four different classifiers was significantly affected by the number of layers in the first hidden level of the network. RBFA has shown promising results compared to other state of the art autoencoders using different datasets including high-dimensional small-sized medical data. Optimizing the radial function parameters improved the classification accuracy of the four classifiers in some cases.
Multi-objective cooperative co-evolution of micro for RTS games

We investigate a multi-objective, cooperative, co-evolutionary algorithm to evolve control tactics for groups composed from multiple types of units in real-time strategy games. Such tactical control or micromanagement of units is called micro. Building on prior work, we cooperatively co-evolve micro for a ranged unit using a parameterized control algorithm along with micro for a melee unit using a pure potential fields approach and show that we can simultaneously co-evolve micro for melee and ranged units. These cooperatively co-evolved control algorithms for melee and ranged units evolve to work well together to defeat the default Starcraft II AI, even when outnumbered. We are also able to generate manually co-evolved AI that is significantly better than the default Starcraft II AI and defeat it to generate human competitive micro for controlling multiple types of units. Furthermore, using a multi-objective fitness function leads to a pareto front of near-optimal micro behaviors that range from fleeing while sustaining minimal damage to fighting and maximizing damage to opponents. Such a pareto front naturally provides a user or AI player a variety of micro behaviors suitable for the different types of situations encountered in real-time strategy games. We believe these results indicate the potential of our method for generating effective micro for multiple types of units in real-time strategy games with application in multi-agent control, robotics, and other heterogeneous system control problems.
Development of Path Planning Tool for Unmanned System Considering Energy Consumption

Increasing the flight endurance of unmanned aerial vehicles (UAVs) has received attention recently. To solve this problem, two research topics have generally appeared: Shortest-path planning (SPP) and remaining-flying-range estimation. In this work, energy-efficient path planning by considering the distance between waypoint nodes, the minimum and maximum speed of the UAV, the weight of the UAV, and the angle between two intersecting edges is proposed. The performances of energy-efficient path planning (EEPP) and generic shortest-path planning are compared using extended-Kalman-filter-based state-of-charge and state-of-power estimation. Using this path-planning tool and considering energy consumption during flight operation, two different path plans can be obtained and compared in advance so that the operator can decide which path to choose by consulting a comparison chart. According to the experimental results, the EEPP algorithm results in 0.96% of improved SOC leftover and 11.03 ( W ) of lowered SOP compared to the SPP algorithm.
Augmenting Reality of Tracked Video Objects Using Homography and Keypoints

Augmented reality (AR) and object tracking are active research fields and few research exists that combines both. We propose a markerless video AR system that applies video-processing tasks on the input video and virtual-data processing tasks on the virtual data and then adaptively relates both virtual and real data. The proposed system tracks a target and enables the user to select virtual data such as objects, images, or texts, for augmentation but adapted to the current state of the target. For adaptation, the virtual data is segmented to be overlaid on the target. To account for zoom, scaling, translation, and rotation of tracked object, and to make a proper alignment of virtual data, the system applies a homography estimation based on a feature point detector. Augmentation is performed on extracted keyframes of the input video. Experimental results show promising different AR applications of the proposed system, e.g., social media or industrial maintenance. No objective measures are known for video AR systems. We propose to use the image histogram to objectively measure performance by showing the histograms of the segmented virtual object before and after applying estimated homography is preserved.
Learning of Boosting Fuzzy Cognitive Maps Using a Real-coded Genetic Algorithm

Fuzzy cognitive maps (FCMs) are generally applied to model and analyze complex dynamical systems. However, the accuracy of population-based FCM learning algorithms is relatively low. Boosting is an effective method to improve the accuracy of any learning algorithm. To this end, we combine FCMs with boosting, termed as boosting fuzzy cognitive maps (BFCMs). The BFCM is an extension of FCMs and has a better performance on fast numerical reasoning than FCMs. In this paper, a real-coded genetic algorithm, which is a popular population-based learning algorithm, is improved on mutation operator and applied to learn the BFCM models, termed as RCGA-BFCM. In the experiments, RCGA-BFCM is applied to learn the BFCM from synthetic data with varying sizes and densities. The experimental results show that RCGA-BFCM can learn BFCMs with high accuracy from synthetic data. In addition, the performance of RCGA-BFCM is validated on the benchmark datasets DREAM3 and DREAM4. The experimental results show that RCGA-BFCM outperforms other learning algorithms obviously, which illustrates that RCGA-BFCM can reconstruct gene regulatory networks (GRNs) effectively.
A Conceptual Framework for Pragmatic Interoperability

Interoperability is the ability of heterogeneous systems to communicate with another system transparently. Usually, interoperability is classified into syntactic, semantic, and pragmatic. The syntactic level is related to the grammar and vocabulary of the message swapped, the semantic level with the meaning of the data and the pragmatic level with the understanding of the messages sent and received. A set of systems is pragmatically interoperable when they share the same expectations about the effect of messages exchanged between them. Due to the vast diversity of definitions and no consensus, provide a pragmatic interoperability solution is a challenge. In this paper, we propose a conceptual framework that aims to contribute to the unification of the concept of pragmatic interoperability and common elements necessary for its realization. For this, a unified definition and conceptual framework are presented. The framework was applied in three different scenarios to demonstrate its applicability and, consequently, validation of the unified concept.
Memory-Hard Functions from Cryptographic Primitives

Memory-hard functions (MHFs) are moderately-hard functions which enforce evaluation costs both in terms of time and memory (often, in form of a trade-off). They are used e.g. for password protection, password-based key-derivation, and within cryptocurrencies, and have received a considerable amount of theoretical scrutiny over the last few years. However, analyses see MHFs as modes of operation of some underlying hash function $$\mathcal {H}$$ , modeled as a monolithic random oracle. This is however a very strong assumption, as such hash functions are built from much simpler primitives, following somewhat ad-hoc design paradigms. This paper initiates the study of how to securely instantiate $$\mathcal {H}$$ within MHF designs using common cryptographic primitives like block ciphers, compression functions, and permutations. Security here will be in a model in which the adversary has parallel access to an idealized version of the underlying primitive. We will provide provably memory-hard constructions from all the aforementioned primitives. Our results are generic, in that we will rely on hard-to-pebble graphs designed in prior works to obtain our constructions. One particular challenge we encounter is that $$\mathcal {H}$$ is usually required to have large outputs (to increase memory hardness without changing the description size of MHFs), whereas the underlying primitives generally have small output sizes.
Recommendation Systems for Interactive Multimedia Entertainment

In the era of internet has embarked on World Wide Web for searching or knowing any information about any topic which has resulted in humongous information load. Access to open source creates ever-growing content on web, where user will succumb to thousands of arrays of options available, when seeking for any product or services. As the entertainment industry is no way an exception, optimization of user's choice is of utmost importance. Recommendation system (RS) is the application that provides decision tool for tracking the user's previous choices about products or browsing web pages history, clicks and choices of similar users. The main purpose of RS is to support the user to select his desired product among the multiple, equally-competitive choices available. It can be employed for diverse set of item recommendation such as books, songs, movies, restaurants, gadgets, e-learning materials etc. This chapter attempts to explore the various techniques and reveals the human–machine cooperation by implementing recommendation systems, especially meant for entertainment industry. In summary, the essence of this treatise is the soft computing/knowledge engineering approaches (such as filtering techniques, machine learning algorithms) guided by internet of things to provide prediction that can be displayed by data visualization in entertainment landscape convincingly.