Reviewerly/exHarmony huggingface dataset:
Image processing effects on the deep face recognition system

Face recognition technology has become an important quantitative examination method in the field of forensic identification of human images. However, face image quality affects the recognition performance of face recognition systems. Existing research on the effects of face image denoising and enhancement methods on the face recognition performance are typically based on facial images with manually synthesized noises rather than the noises under natural environmental corruption, and their studied face recognition techniques are limited on the traditional face recognition algorithms rather than state-of-the-art convolutional neural network based face recognition methods. In this work, face image materials from 33 real cases in forensic identification of human images were collected for quantitative analysis of the effects of face image denoising and enhancement methods on the deep face recognition performance of the MXNet system architecture based face recognition system. The results show that face image quality has a significant effect on the recognition performance of the face recognition system, and the image processing techniques can enhance the quality of face images, and then improve the recognition precision of the face recognition system. In addition, the effects of the Gaussian filtering are better than the self-snake model based image enhancement method, which indicates that the image denoising methods are more suitable for performance improvement of the deep face recognition system rather than the image enhancement techniques under the application of the practical cases.
Advanced variations of two-dimensional principal component analysis for face recognition

The two-dimensional principal component analysis (2DPCA) has been one of the basic methods of developing artificial intelligent algorithms. To increase the feasibility, we propose a new general ridge regression model for 2DPCA and variations, with extracting low dimensional features under two projection subspaces. A new relaxed 2DPCA under the quaternion framework is proposed to utilize the label (if known) and color information to compute the essential features of generalization ability with optimization algorithms. The 2DPCA-based approaches for face recognition are also improved by weighting each principle component a scatter measure, which increases efficiently the rate of face recognition. In numerical experiments on well-known standard databases, the R2DPCA approach has high generalization ability and achieves a higher recognition rate than the state-of-the-art 2DPCA-like methods, and has better performance than the basic deep learning methods such as CNNs, DBNs, and DNNs in the small-sample case.
Metaheuristics-based ontology meta-matching approaches

Ontologies have emerged to establish a well-defined meaning for information, solving problems of heterogeneity in data semantics and facilitating the process of information exchange. However, ontologies have generated a new semantic problem, since using more than one ontology can generate ambiguity in the meaning of a given data. The problem of ontology matching is to search for relationships between entities of distinct ontologies, solving the problem of semantic heterogeneity of the data. The problem is a relevant issue in the area of knowledge representation and several approaches have been proposed to solve it. This work presents a systematic mapping of the main works published in the area with an emphasis on metaheuristics-based meta-matching approaches.
Self-adaptive systems: A systematic literature review across categories and domains

Championed by IBM’s vision of autonomic computing paper in 2003, the autonomic computing research field has seen increased research activity over the last 20 years. Several conferences (SEAMS, SASO, ICAC) and workshops (SISSY) have been established and have contributed to the autonomic computing knowledge base in search of a new kind of system — a self-adaptive system (SAS). These systems are characterized by being context-aware and can act on that awareness. The actions carried out could be on the system or on the context (or environment). The underlying goal of a SAS is the sustained achievement of its goals despite changes in its environment. Despite a number of literature reviews on specific aspects of SASs ranging from their requirements to quality attributes, we lack a systematic understanding of the current state of the art. This paper contributes a systematic literature review into self-adaptive systems using the dblp computer science bibliography as a database. We filtered the records systematically in successive steps to arrive at 293 relevant papers. Each paper was critically analyzed and categorized into an attribute matrix. This matrix consisted of five categories, with each category having multiple attributes. The attributes of each paper, along with the summary of its contents formed the basis of the literature review that spanned 30 years (1990–2020). We characterize the maturation process of the research area from theoretical papers over practical implementations to more holistic and generic approaches, frameworks, and exemplars, applied to areas such as networking, web services, and robotics, with much of the recent work focusing on IoT and IaaS. While there is an ebb and flow of application domains, domains like bio-inspired approaches, security, and cyber–physical systems showed promise to grow heading into the 2020s.
Performativity of Materials in Learning: The Learning-Whole in Action

Contemporary educational practices have been calling for pedagogical models that foreground flexibility, agency, ubiquity, and connectedness in learning. These models have, in turn, been stimulating redevelopments of educational infrastructure –with physical contours reconfigured into novel complex learning spaces at universities, schools, museums, and libraries. Understanding the complexity of these innovative learning spaces requires an acknowledgement of the material and digital as interconnected. A ‘physical’ learning space is likely to involve a range of technologies and in addition to paying attention to these ‘technologies’ one must understand and account for their physical sites of use as well. This paper discusses the influence of materiality in learning, using an analytical approach that situates learning activity as an emergent process. Drawing on theories that foreground socio-materiality in learning and on the relational perspective offered by networked learning, we call for a deeper understanding of the interplay between the physical (material and digital), conceptual, and social aspects of learning, and their combined influence on emergent activity. The paper argues that in order to successfully design for innovative learning, educators need to develop their capacity to trace the intricate connections between people, ideas, digital and material tools, and tasks –to see the learning-whole in action.
A Novel Four-Way Approach Designed With Ensemble Feature Selection for Code Smell Detection

Purpose: Code smells are residuals of technical debt induced by the developers.They hinder evolution, adaptability and maintenance of the software.Meanwhile, they are very beneficial in indicating the loopholes of problems and bugs in the software.Machine learning has been extensively used to predict Code Smells in research.The current study aims to optimise the prediction using Ensemble Learning and Feature Selection techniques on three open-source Java data sets.Design and Results: The work Compares four varied approaches to detect code smells using four performance measures Accuracy(P1), G-mean1 (P2), G-mean2 (P3), and F-measure (P4).The study found out that values of the performance measures did not degrade it instead of either remained same or increased with feature selection and Ensemble Learning.Random Forest turns out to be the best classifier while Correlation-based Feature selection(BFS) is best amongst Feature Selection techniques.Ensemble Learning aggregators, i.e.ET5C2 (BFS intersection Relief with classifier Random Forest), ET6C2 (BFS union Relief with classifier Random Forest), and ET5C1 (BFS intersection Relief with Bagging) and Majority Voting give best results from all the aggregation combinations studied.Conclusion: Though the results are good, but using Ensemble learning techniques needs a lot of validation for a variety of data sets before it can be standardised.The Ensemble Learning techniques also pose a challenge concerning diversity and reliability and hence needs exhaustive studies.
FedLearnSP: Preserving Privacy and Security Using Federated Learning and Edge Computing

Enormous amount of information is processed at different websites, on a number of different AI tools and in multiple data silos. Sharing data between various sources, this is a significant obstacle, due to administrative, organizational, and security considerations. One possible solution is federated machine learning (FML), a system that simultaneously sends machine learning algorithms to all data sources, trains models at each source, and aggregates the learned models. This technique ensures consumer-influenced solution by processing the data locally. This work is the first to investigate the applicability of Internet attack detection through FML, to the best of our knowledge. Our primary contributions include the application of federated learning to satisfy customer search queries by detecting malicious spam images, which may lead these AI systems to retrieve irrelevant information. We assess and analyze the FML-entangled learning output comprehensively in different ways adjustments including balanced and imbalanced customer data distribution, scalability, and overhead communication. Our measuring results show that FML suits practical scenarios, where the variable image size, including the animation ratio to legitimate samples of images, present among the advertisements that may distract consumer from fetching relevant results. With the evaluated results, the state-of-the-art FedLearnSP proved significant image spam detection.
Machine Learning for Identifying Group Trajectory Outliers

Prior works on the trajectory outlier detection problem solely consider individual outliers. However, in real-world scenarios, trajectory outliers can often appear in groups, e.g., a group of bikes that deviates to the usual trajectory due to the maintenance of streets in the context of intelligent transportation. The current paper considers the Group Trajectory Outlier (GTO) problem and proposes three algorithms. The first and the second algorithms are extensions of the well-known DBSCAN and k NN algorithms, while the third one models the GTO problem as a feature selection problem. Furthermore, two different enhancements for the proposed algorithms are proposed. The first one is based on ensemble learning and computational intelligence, which allows for merging algorithms’ outputs to possibly improve the final result. The second is a general high-performance computing framework that deals with big trajectory databases, which we used for a GPU-based implementation. Experimental results on different real trajectory databases show the scalability of the proposed approaches.
SmsNet: A New Deep Convolutional Neural Network Model for Adversarial Example Detection

The emergence of adversarial examples has had a significant impact on the development and application of deep learning. In this paper, a novel convolutional neural network model, the stochastic multifilter statistical network (SmsNet), is proposed for the detection of adversarial examples. A feature statistical layer is constructed to collect statistical data of feature map output from each convolutional layer in SmsNet by combining manual features with a neural network. The entire model is an end-to-end detection model, so the feature statistical layer is not independent of the network, and its output is directly transmitted to the fully connected layer by a short-cut connection called the SmsConnection. Additionally, a dynamic pruning strategy is introduced to simplify the model structure for better performance. The experiments demonstrate the effectiveness of the network structure and pruning strategy, and the proposed model achieves high detection rates against state-of-the-art adversarial attacks.
Medical concept normalization in French using multilingual terminologies and contextual embeddings

Concept normalization is the task of linking terms from textual medical documents to their concept in terminologies such as the UMLS®. Traditional approaches to this problem depend heavily on the coverage of available resources, which poses a problem for languages other than English. We present a system for concept normalization in French. We consider textual mentions already extracted and labeled by a named entity recognition system, and we classify these mentions with a UMLS concept unique identifier. We take advantage of the multilingual nature of available terminologies and embedding models to improve concept normalization in French without translation nor direct supervision. We consider the task as a highly-multiclass classification problem. The terms are encoded with contextualized embeddings and classified via cosine similarity and softmax. A first step uses a subset of the terminology to finetune the embeddings and train the model. A second step adds the entire target terminology, and the model is trained further with hard negative selection and softmax sampling. On two corpora from the Quaero FrenchMed benchmark, we show that our approach can lead to good results even with no labeled data at all; and that it outperforms existing supervised methods with labeled data. Training the system with both French and English terms improves by a large margin the performance of the system on a French benchmark, regardless of the way the embeddings were pretrained (French, English, multilingual). Our distantly supervised method can be applied to any kind of documents or medical domain, as it does not require any concept-labeled documents. These experiments pave the way for simpler and more effective multilingual approaches to processing medical texts in languages other than English.
Learning Data Augmentation with Online Bilevel Optimization for Image Classification

Data augmentation is a key practice in machine learning for improving generalization performance. However, finding the best data augmentation hyperparameters requires domain knowledge or a computationally demanding search. We address this issue by proposing an efficient approach to automatically train a network that learns an effective distribution of transformations to improve its generalization. Using bilevel optimization, we directly optimize the data augmentation parameters using a validation set. This framework can be used as a general solution to learn the optimal data augmentation jointly with an end task model like a classifier. Results show that our joint training method produces an image classification accuracy that is comparable to or better than carefully hand-crafted data augmentation. Yet, it does not need an expensive external validation loop on the data augmentation hyperparameters.
Usability Evaluation of Virtual Reality-Based Fire Training Simulator Using a Combined AHP and Fuzzy Comprehensive Evaluation Approach

With the continuous advances in virtual reality (VR) technology and the widespread use of VR-based fire training simulators that provide users with an effective training system, developing effective and usable interactive VR-based fire training simulators is a novel challenge for systems designers and ergonomics. Traditional usability standards do not consider features specific to virtual environments (VEs). Therefore, it is crucial to improve and use relevant research techniques that can estimate the usability of VE simulators. An efficient usability assessment should be capable to receive feedback from users on how efficiently they use a VR simulator and how they prefer to use it. This article intends to introduce a new framework for conducting a usability assessment for a VR fire training simulator using a combined analytical hierarchy process (AHP) and a fuzzy comprehensive evaluation approach (FCE).
Consistency guarantees for greedy permutation-based causal inference algorithms

Summary Directed acyclic graphical models are widely used to represent complex causal systems. Since the basic task of learning such a model from data is NP-hard, a standard approach is greedy search over the space of directed acyclic graphs or Markov equivalence classes of directed acyclic graphs. As the space of directed acyclic graphs on $p$ nodes and the associated space of Markov equivalence classes are both much larger than the space of permutations, it is desirable to consider permutation-based greedy searches. Here, we provide the first consistency guarantees, both uniform and high dimensional, of a greedy permutation-based search. This search corresponds to a simplex-like algorithm operating over the edge-graph of a subpolytope of the permutohedron, called a directed acyclic graph associahedron. Every vertex in this polytope is associated with a directed acyclic graph, and hence with a collection of permutations that are consistent with the directed acyclic graph ordering. A walk is performed on the edges of the polytope maximizing the sparsity of the associated directed acyclic graphs. We show via simulated and real data that this permutation search is competitive with current approaches.
Semantic-Level Maneuver Sampling and Trajectory Planning for On-Road Autonomous Driving in Dynamic Scenarios

Maneuver decision-making and trajectory planning play important roles in autonomous driving since a safe and flexible decision module is indispensable for navigation. Typical algorithms apply sampling methods to generate feasible trajectories. However, the fixed sampling distance and maneuver execution time in a sampling approach sacrifice the flexibility of algorithm. Moreover, since motion planning can be represented as a high-dimensional problem, it usually results in unnecessary samples that require additional resources to search the solution. Therefore, a semantic-level maneuver sampling and trajectory planning algorithm is proposed to solve the above problems. In the upper-level maneuver decision, the decision-making problem is formulated as a selection of the forward leading object. A semantic-level decision tree is built to sample long-term maneuver sequences, and the safety corridor of each maneuver sequence is calculated according to the surrounding environment. In the lower-level trajectory planning, the process is decoupled into longitudinal and lateral directions. First, a heuristic search method is proposed to generate longitudinal trajectory candidates for each maneuver sequence. Then, an exhaustive search algorithm is employed to synchronously generate the lateral trajectory within safety corridor. Among the generated trajectory candidates, the one with minimum cost will be chosen as the searching result. Furthermore, in order to improve the driving comfort, numerical optimization is adopted to refine the result by accounting for the constraints of kinematics and safety. Finally, the proposed method was evaluated through simulations of typical on-road dynamic scenarios, which help verify its performance with desirable computation efficiency of less than 32 ms.
PF -cpGAN: Profile to Frontal Coupled GAN for Face Recognition in the Wild

In recent years, due to the emergence of deep learning, face recognition has achieved exceptional success. However, many of these deep face recognition models perform relatively poorly in handling profile faces compared to frontal faces. The major reason for this poor performance is that it is inherently difficult to learn large pose invariant deep representations that are useful for profile face recognition. In this paper, we hypothesize that the profile face domain possesses a gradual connection with the frontal face domain in the deep feature space. We look to exploit this connection by projecting the profile faces and frontal faces into a common latent space and perform verification or retrieval in the latent domain. We leverage a coupled generative adversarial network (cpGAN) structure to find the hidden relationship between the profile and frontal images in a latent common embedding subspace. Specifically, the cp-GAN framework consists of two GAN-based sub-networks, one dedicated to the frontal domain and the other dedicated to the profile domain. Each sub-network tends to find a projection that maximizes the pair-wise correlation between two feature domains in a common embedding feature subspace. The efficacy of our approach compared with the state-of-the-art is demonstrated using the CFp, CMU Multi-PIE, IJB-A, and IJB-C datasets.
Dynamics of Open Innovation in Small- and Medium-Sized Enterprises: A Metacognitive Approach

Open innovation (OI) is clearly becoming increasingly important to organizations. Studying its impacts has become a challenge as assessing OI possible effects on not only performance but also processes, time, and people is difficult and complex. To move beyond these barriers, this article develops a metacognitive framework that combines cognitive mapping—supported by the strategic options development and analysis approach—and the decision-making trial and evaluation laboratory technique. This dual methodology facilitates analyses of specific OI-related impacts on small- or medium-sized enterprises (SMEs). The data were collected from SME managers and entrepreneurs who agreed to participate in an expert panel in face-to-face group meetings. The findings include that the methodologies used facilitate the identification and understanding of cause-and-effect relationships among OI determinants in SMEs, and were validated by the participants and a project manager at COTEC Portugal— <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Associação Empresarial para a Inovação</i> (Business Association for Innovation). This organization is a leading think-and-action network for advancing technology diffusion and business innovation cooperation. The contributions and limitations of the proposed methodology are also analyzed.
Dual-Representation-Based Autoencoder for Domain Adaptation

Domain adaptation aims to facilitate the learning task in an unlabeled target domain by leveraging the auxiliary knowledge in a well-labeled source domain from a different distribution. Almost existing autoencoder-based domain adaptation approaches focus on learning domain-invariant representations to reduce the distribution discrepancy between source and target domains. However, there is still a weakness existing in these approaches: the class-discriminative information of the two domains may be damaged while aligning the distributions of the source and target domains, which makes the samples with different classes close to each other, leading to performance degradation. To tackle this issue, we propose a novel dual-representation autoencoder (DRAE) to learn dual-domain-invariant representations for domain adaptation. Specifically, DRAE consists of three learning phases. First, DRAE learns global representations of all source and target data to maximize the interclass distance in each domain and minimize the marginal distribution and conditional distribution of both domains simultaneously. Second, DRAE extracts local representations of instances sharing the same label in both domains to maintain class-discriminative information in each class. Finally, DRAE constructs dual representations by aligning the global and local representations with different weights. Using three text and two image datasets and 12 state-of-the-art domain adaptation methods, the extensive experiments have demonstrated the effectiveness of DRAE.