Reviewerly/exHarmony huggingface dataset:
Mask-aware photorealistic facial attribute manipulation

Abstract The technique of facial attribute manipulation has found increasing application, but it remains challenging to restrict editing of attributes so that a face’s unique details are preserved. In this paper, we introduce our method, which we call a mask-adversarial autoencoder (M-AAE). It combines a variational autoencoder (VAE) and a generative adversarial network (GAN) for photorealistic image generation. We use partial dilated layers to modify a few pixels in the feature maps of an encoder, changing the attribute strength continuously without hindering global information. Our training objectives for the VAE and GAN are reinforced by supervision of face recognition loss and cycle consistency loss, to faithfully preserve facial details. Moreover, we generate facial masks to enforce background consistency, which allows our training to focus on the foreground face rather than the background. Experimental results demonstrate that our method can generate high-quality images with varying attributes, and outperforms existing methods in detail preservation.
Dynamic Scalable Elliptic Curve Cryptographic Scheme and Its Application to In-Vehicle Security

The design of unified, efficient, and lightweight cryptographic platform for resource-constrained on-board devices such as sensors, microcontrollers, and actuators in the context of Internet of Vehicles remains an open and challenging problem, for both academic and industry. Elliptic curve cryptography (ECC) is considered as a promising encryption algorithm for the next generation communications, as it could provide the same strong security level using relatively smaller key size when compared to the currently used Rivest–Shamir–Adleman algorithm. However, traditional ECCs have the disadvantage of using a fixed curve, making it very easy to be intensively analyzed while being hard to construct a united platform for on-board devices with processors of different instruction lengths. To mitigate the above problem, this paper suggests a dynamic scalable elliptic curve cryptosystem. To synchronize the curve in use, a curve list of different security levels is generated and preserved on both parties. Since both parties randomly choose the curve and the prime number, a extra security level could be provided, so that the security level can still remain the same even using smaller key sizes, while the computation efficiency will be enhanced and the power consumption will be reduced, which is especially suitable for the application in on-board embedded devices. Detailed experimental results illustrate that the presented scheme improves the efficiency by 30% in average when compared with traditional ECC implementations on a similar security level. Therefore, the proposed scalable ECC scheme as a unified cryptographic platform is more economic for these on-board devices in vehicles.
Similarity Learning via Kernel Preserving Embedding

Data similarity is a key concept in many data-driven applications. Many algorithms are sensitive to similarity measures. To tackle this fundamental problem, automatically learning of similarity information from data via self-expression has been developed and successfully applied in various models, such as low-rank representation, sparse subspace learning, semisupervised learning. However, it just tries to reconstruct the original data and some valuable information, e.g., the manifold structure, is largely ignored. In this paper, we argue that it is beneficial to preserve the overall relations when we extract similarity information. Specifically, we propose a novel similarity learning framework by minimizing the reconstruction error of kernel matrices, rather than the reconstruction error of original data adopted by existing work. Taking the clustering task as an example to evaluate our method, we observe considerable improvements compared to other state-ofthe-art methods. More importantly, our proposed framework is very general and provides a novel and fundamental building block for many other similarity-based tasks. Besides, our proposed kernel preserving opens up a large number of possibilities to embed high-dimensional data into low-dimensional space.
Image Aesthetic Assessment Assisted by Attributes through Adversarial Learning

The inherent connections among aesthetic attributes and aesthetics are crucial for image aesthetic assessment, but have not been thoroughly explored yet. In this paper, we propose a novel image aesthetic assessment assisted by attributes through both representation-level and label-level. The attributes are used as privileged information, which is only required during training. Specifically, we first propose a multitask deep convolutional rating network to learn the aesthetic score and attributes simultaneously. The attributes are explored to construct better feature representations for aesthetic assessment through multi-task learning. After that, we introduce a discriminator to distinguish the predicted attributes and aesthetics of the multi-task deep network from the ground truth label distribution embedded in the training data. The multi-task deep network wants to output aesthetic score and attributes as close to the ground truth labels as possible. Thus the deep network and the discriminator compete with each other. Through adversarial learning, the attributes are explored to enforce the distribution of the predicted attributes and aesthetics to converge to the ground truth label distribution. Experimental results on two benchmark databases demonstrate the superiority of the proposed method to state of the art work.
Hypergraph Matching With an Entropy Barrier Function

Graph matching problem has been widely used in many fields. Due to the discrete property of graph matching problem, the continuous relaxation will result in a bad influence on the solution. Thus, we propose a hypergraph matching model with an entropy barrier function considering the discrete property of the assignment matrix. The model is solved by a nonmonotone active set projected Newton method whose nonmonotone line search technique has a good ability to accept the projected Newton stepsize. We evaluate our method through several experiments, which indicates that our method has better matching results than others.
Current and planned adoption of data sharing policies by editors of Korean scholarly journals

Purpose: This study analyzed the present status of data sharing polices and attitudes towards such policies through a web-based survey of editors of scholarly journals published in Korea.Methods: From December 26, 2018 to January 3, 2019, a survey was distributed to 1,055 persons listed in the member directories of both the Korean Council of Science Editors and the Korean Federation of Science & Technology Societies.The survey contained four items on subjects' information, three items that gathered information about the journals, and two further items on reasons for adopting or not adopting a data sharing policy and further opinions about such policies.Results: Of the 100 respondents (from 100 journals), 13 stated that their journals had already adopted a data sharing policy.The strength of the policy was recommendation-only in 10 of those 13 journals.The most frequent reason for adopting a data sharing policy was to follow international trends.The repository sites were the Harvard Dataverse for two journals and Mendeley Data for one.The most common reasons for not adopting a data sharing policy were a lack of knowledge on data sharing, the possibility that submitters would not want to share their data, and the questionable effect of data sharing on scientific development.Conclusion: Data sharing policies were uncommon among Korean scholarly journals.The advantages and disadvantages of adopting such policies should be discussed more actively among editors and researchers.Furthermore, data sharing infrastructure and training courses are required for data sharing policies to be established in scholarly journals in Korea.
Handwriting based writer recognition using implicit shape codebook

Writer characterization from images of handwriting has remained an important research problem in the handwriting recognition community that finds applications in forensics, paleography and neuropsychology. This paper presents a study to evaluate the effectiveness of an implicit shape codebook technique to recognize writer from digitized images of handwriting. The technique relies on identifying the key points in handwriting and clustering the patches around these key points to generate an implicit shape codebook. A writer is then characterized by the probability distribution of producing the codebook patterns. Experiments are carried out in text-dependent as well text-independent mode using the standard BFL and CVL databases of handwriting images. Promising identification and verification performance is reported in a number of interesting experimental scenarios.
Texture-and-Shape Based Active Contour Model for Insulator Segmentation

Insulator segmentation is a critical step for automatic insulator fault diagnosis in high voltage transmission systems. Existing methods fail to segment insulators when they have a low contrast with the surroundings. Considering the unique shape and texture characteristics of insulators, a texture-and-shape based active contour model is proposed for insulator segmentation. The segmentation is achieved by evolving a curve iteratively by the texture features and shape priors. In the texture-driven curve evolution, a semi-local region descriptor is used to extract the texture features of insulators and a new convex energy functional is defined based on the extracted features with the topology-preserving term. The topology-preserving term keeps the curve's topology unchanged as the curve topology is determined by the shape template. In the shape-driven curve evolution, the shape context descriptor is used to align the shape template with the current curve. The semantic transformation between the shape template and the current curve is obtained by Procrustes analysis and then adopted to update the current curve to resemble the shape prior. The proposed method is applied to a set of images, and the experimental results confirm the efficacy and effectiveness of the proposed method for segmenting insulators in cluttered backgrounds.
Shunt connection: An intelligent skipping of contiguous blocks for optimizing MobileNet-V2

Enabling deep neural networks for tight resource constraint environments like mobile phones and cameras is the current need. The existing availability in the form of optimized architectures like Squeeze Net, MobileNet etc., are devised to serve the purpose by utilizing the parameter friendly operations and architectures, such as point-wise convolution, bottleneck layer etc. This work focuses on optimizing the number of floating point operations involved in inference through an already compressed deep learning architecture. The optimization is performed by utilizing the advantage of residual connections in a macroscopic way. This paper proposes novel connection on top of the deep learning architecture whose idea is to locate the blocks of a pretrained network which have relatively lesser knowledge quotient and then bypassing those blocks by an intelligent skip connection, named here as Shunt connection. The proposed method helps in replacing the high computational blocks by computation friendly shunt connection. In a given architecture, up to two vulnerable locations are selected where 6 contiguous blocks are selected and skipped at the first location and 2 contiguous blocks are selected and skipped at the second location, leveraging 2 shunt connections. The proposed connection is used over state-of-the-art MobileNet-V2 architecture and manifests two cases, which lead from 33.5% reduction in flops (one connection) up to 43.6% reduction in flops (two connections) with minimal impact on accuracy.
Dandelion Algorithm With Probability-Based Mutation

A dandelion algorithm (DA) is a recently-proposed intelligent optimization algorithm and shows an excellent performance in solving function optimization problems.However, like other intelligent algorithms, it converges slowly and falls into local optima easily.To overcome these two flaws, a dandelion algorithm with probability-based mutation (DAPM) is proposed in this paper.In DAPM, both Gaussian and Levy mutations can be used interchangeably according to a given probability model.In this paper, three probability models are discussed, namely linear, binomial, and exponential models.The experiments show that DAPM achieves better overall performance on standard test functions than DA.
Genetic programming hyper-heuristic with knowledge transfer for uncertain capacitated arc routing problem

The Uncertain Capacitated Arc Routing Problem (UCARP) is an important combinatorial optimisation problem. Genetic Programming (GP) has shown effectiveness in automatically evolving routing policies to handle the uncertain environment in UCARP. However, when the scenario changes, the current routing policy can no longer work effectively, and one has to retrain a new policy for the new scenario which is time consuming. On the other hand, knowledge from solving the previous similar scenarios may be helpful in improving the efficiency of the retraining process. In this paper, we propose different knowledge transfer methods from a source scenario to a similar target scenario and examine them in different settings. The experimental results showed that by knowledge transfer, the retraining process is made more efficient and the same performance can be obtained within a much shorter time without having any negative transfer.
A STAMP-based ontology approach to support safety and security analyses

Considerations of safety and security in the early stage of system life cycle are essential to collect and prioritize operation needs, determine feasibility of the desired system, and identify technology gaps. Experts from many disciplines are needed to perform the safety and security analyses, ensuring that a system has the necessary attributes. Safety assessment is usually conducted in the concept stage. On the order hand, security assessment is performed in design stage usually when an initial architecture along with the logical and physical components are defined. Systems-Theoretic Process Analysis (STPA) is a new hazard analysis technique based on systems thinking and is built on top of a new causality model of accident, which stands for Systems-Theoretic Accident Model and Processes (STAMP), grounded in systems theory. STPA for Security (STPA-Sec) is an extension of STPA that proposes to include security concerns into the analysis. STPA-Sec helps identifying some hazardous control actions, causal scenarios, and casual factors; however, no emphasis is placed on security threat scenarios. In this paper we propose an ontology-based technique that extends STPA-Sec to improve identification of causal scenarios and associated casual factors, specifically those related to security. We propose an approach that assists safety and security experts conducting safety and security analyses using STPA-Sec with a supporting ontology. First, we present an ontology representing the safety and security knowledge through STPA-Sec process, and provide a tool that implements the proposed ontology. We then propose a process to capture safety and security knowledge into the proposed ontology to identify causal scenarios. We perform a preliminary evaluation of the ontology and the process using an aeronautic case study. The results show that the ontology-based approach helps systems engineers to identify more security scenarios compared to the case where they use only STPA-Sec. Furthermore, some hazardous control actions are not addressed if the systems engineer uses the basic STPA-Sec.
An information-theoretic treatment of quantum dichotomies

Given two pairs of quantum states, we want to decide if there exists a quantum channel that transforms one pair into the other. The theory of quantum statistical comparison and quantum relative majorization provides necessary and sufficient conditions for such a transformation to exist, but such conditions are typically difficult to check in practice. Here, by building upon work by Matsumoto, we relax the problem by allowing for small errors in one of the transformations. In this way, a simple sufficient condition can be formulated in terms of one-shot relative entropies of the two pairs. In the asymptotic setting where we consider sequences of state pairs, under some mild convergence conditions, this implies that the quantum relative entropy is the only relevant quantity deciding when a pairwise state transformation is possible. More precisely, if the relative entropy of the initial state pair is strictly larger compared to the relative entropy of the target state pair, then a transformation with exponentially vanishing error is possible. On the other hand, if the relative entropy of the target state is strictly larger, then any such transformation will have an error converging exponentially to one. As an immediate consequence, we show that the rate at which pairs of states can be transformed into each other is given by the ratio of their relative entropies. We discuss applications to the resource theories of athermality and coherence.
Technology-Enhanced Learning and the Pursuit of Sustainability

The inroads of sophisticated technologies and related applications in the field of education trigger several developments related not only to the processes of managing education institutions across levels and domains but also pertaining to approaches to teaching and learning. As advances in technology impact all aspects of life, when adopting and adapting to these advances, the education sector is expected to respond to issues and processes that current technological revolution triggers in the entire society. Hence, effective and forward-looking manner of managing technological advances in the education sector today is a necessity to ensure sustainability of that sector in the future. The objective of this Special Issue was to reflect on these issues, to identify the key questions that have to be addressed in this context, and to encourage new critical insights into these developments.
Robust Flexible Feature Selection via Exclusive L21 Regularization

Recently, exclusive lasso has demonstrated its promising results in selecting discriminative features for each class. The sparsity is enforced on each feature across all the classes via L12-norm. However, the exclusive sparsity of L12-norm could not screen out a large amount of irrelevant and redundant noise features in high-dimensional data space, since each feature belongs to at least one class. Thus, in this paper, we introduce a novel regularization called "exclusive L21", which is short for "L21 with exclusive lasso", towards robust flexible feature selection. The exclusive L21 regularization is the mix of L21-norm and L12-norm, which brings out joint sparsity at inter-group level and exclusive sparsity at intra-group level simultaneously. An efficient augmented Lagrange multipliers based optimization algorithm is proposed to iteratively solve the exclusive L21 regularization in a row-wise fashion. Extensive experiments on twelve benchmark datasets demonstrate the effectiveness of the proposed regularization and the optimization algorithm as compared to state-of-the-arts.
Improving HPC System Performance by Predicting Job Resources via Supervised Machine Learning

High-Performance Computing (HPC) systems are resources utilized for data capture, sharing, and analysis. The majority of our HPC users come from other disciplines than Computer Science. HPC users including computer scientists have difficulties and do not feel proficient enough to decide the required amount of resources for their submitted jobs on the cluster. Consequently, users are encouraged to over-estimate resources for their submitted jobs, so their jobs will not be killing due insufficient resources. This process will waste and devour HPC resources; hence, this will lead to inefficient cluster utilization. We created a supervised machine learning model and integrated it into the Slurm resource manager simulator to predict the amount of required memory resources (Memory) and the required amount of time to run the computation. Our model involves using different machine learning algorithms. Our goal is to integrate and test the proposed supervised machine learning model on Slurm. We used over 10000 tasks selected from our HPC log files to evaluate the performance and the accuracy of our integrated model. The purpose of our work is to increase the performance of the Slurm by predicting the amount of require jobs memory resources and the time required for each particular job in order to improve the utilization of the HPC system using our integrated supervised machine learning model.
A Study of x-Vector Based Speaker Recognition on Short Utterances

The aim of this work is to gain insights into how the deep neural network (DNN) models should be trained for short utterance evaluation conditions in an x-vector based speaker verification system.The study suggests that the speaker embedding can be extracted with reduced dimensions for short utterance evaluation conditions.When the speaker embedding is extracted from deeper layer which has lower dimension, the x-vector system achieves 14% relative improvement over baseline approach on EER on NIST2010 5sec-5sec truncated conditions.We surmise that since short utterances have less phonetic information speaker discriminative x-vectors can be extracted from a deeper layer of the DNN which captures less phonetic information.Another interesting finding is that the x-vector system achieves 5% relative improvement on NIST2010 5sec-5sec evaluation condition when the back-end PLDA is trained using short utterance development data.The results confirms the intuitive expectation that duration of development utterances and the duration of evaluation utterances should be matched.Finally, for the duration mismatch condition, we propose a variance normalization approach for PLDA training that provides a 4% relative improvement on EER over baseline approach.
Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement

The aim of this paper is to improve the dynamic window approach algorithm for mobile robots by implementing a prediction window with a fuzzy inference system to adapt to fixed parameters, depending on the surrounding conditions. The first implementation shows the advantage of the prediction step in terms of optimizing the path selection. The second improvement uses fuzzy inference to optimize each of the fixed parameters’ values to increase the algorithm performance. Nevertheless, a simple fuzzy inference system (FIS) was not used for this particular study; instead, an artificial neuro-fuzzy inference system (ANFIS) was used, thus making it possible to develop a FIS system with a back-propagation technique. Each parameter would have a particular ANFIS, in order to modify the α D , β D , and γ D parameters individually. At the end of the article, different scenarios are analyzed to determine whether the developments in this article have improved the DWA behavior. The results show that the prediction step and ANFIS adapt DWA performance by optimizing the path resolution.