Reviewerly/exHarmony huggingface dataset:
Projected Least-Squares Quantum Process Tomography

We propose and investigate a new method of quantum process tomography (QPT) which we call projected least squares (PLS). In short, PLS consists of first computing the least-squares estimator of the Choi matrix of an unknown channel, and subsequently projecting it onto the convex set of Choi matrices. We consider four experimental setups including direct QPT with Pauli eigenvectors as input and Pauli measurements, and ancilla-assisted QPT with mutually unbiased bases (MUB) measurements. In each case, we provide a closed form solution for the least-squares estimator of the Choi matrix. We propose a novel, two-step method for projecting these estimators onto the set of matrices representing physical quantum channels, and a fast numerical implementation in the form of the hyperplane intersection projection algorithm. We provide rigorous, non-asymptotic concentration bounds, sampling complexities and confidence regions for the Frobenius and trace-norm error of the estimators. For the Frobenius error, the bounds are linear in the rank of the Choi matrix, and for low ranks, they improve the error rates of the least squares estimator by a factor $d^2$, where $d$ is the system dimension. We illustrate the method with numerical experiments involving channels on systems with up to 7 qubits, and find that PLS has highly competitive accuracy and computational tractability.
DeepFaceEditing

Recent facial image synthesis methods have been mainly based on conditional generative models. Sketch-based conditions can effectively describe the geometry of faces, including the contours of facial components, hair structures, as well as salient edges (e.g., wrinkles) on face surfaces but lack effective control of appearance, which is influenced by color, material, lighting condition, etc. To have more control of generated results, one possible approach is to apply existing disentangling works to disentangle face images into geometry and appearance representations. However, existing disentangling methods are not optimized for human face editing, and cannot achieve fine control of facial details such as wrinkles. To address this issue, we propose DeepFaceEditing, a structured disentanglement framework specifically designed for face images to support face generation and editing with disentangled control of geometry and appearance. We adopt a local-to-global approach to incorporate the face domain knowledge: local component images are decomposed into geometry and appearance representations, which are fused consistently using a global fusion module to improve generation quality. We exploit sketches to assist in extracting a better geometry representation, which also supports intuitive geometry editing via sketching. The resulting method can either extract the geometry and appearance representations from face images, or directly extract the geometry representation from face sketches. Such representations allow users to easily edit and synthesize face images, with decoupled control of their geometry and appearance. Both qualitative and quantitative evaluations show the superior detail and appearance control abilities of our method compared to state-of-the-art methods.
LayoutTransformer: Scene Layout Generation with Conceptual and Spatial Diversity

When translating text inputs into layouts or images, existing works typically require explicit descriptions of each object in a scene, including their spatial information or the associated relationships. To better exploit the text input, so that implicit objects or relationships can be properly inferred during layout generation, we propose a LayoutTransformer Network (LT-Net) in this paper. Given a scene-graph input, our LT-Net uniquely encodes the semantic features for exploiting their co-occurrences and implicit relationships. This allows one to manipulate conceptually diverse yet plausible layout outputs. Moreover, the decoder of our LT-Net translates the encoded contextual features into bounding boxes with self-supervised relation consistency preserved. By fitting their distributions to Gaussian mixture models, spatially-diverse layouts can be additionally produced by LT-Net. We conduct extensive experiments on the datasets of MS-COCO and Visual Genome, and confirm the effectiveness and plausibility of our LT-Net over recent layout generation models. Codes will be released at LayoutTransformer.
Graph-based structural difference analysis for video summarization

Keyframe extraction is an effective way to achieve video summarization. More recent studies using deep learning networks are heavily dependent on massive historical datasets for training. For practicality in real applications, we focus more on unsupervised online analysis and present a novel graph-based structural difference analysis method for this purpose. Unlike traditional methods of video representation based on raw features, undirected weighted graphs are constructed from the resulting features to represent video frames. The detailed structural changes between graphs are more consistent with the actual changes between video frames than raw features, thus making the newly proposed method robust for detecting various types of shot transitions, such as hard cuts, dissolves, wipes, and fade-ins/fade-outs. Then, considering the local influence between successive frames, a structural difference analysis of graphs is performed to detect the video shot boundaries. Finally, the median graph of each shot is obtained to extract the corresponding keyframe. Extensive experiments are conducted on three video summarization benchmark datasets. Quantitative and qualitative comparisons are made between the proposed method and other state-of-the-art methods, with the proposed method yielding remarkable improvements from 1.9% to 3.1% in terms of the F-score on the three datasets.
Relative Effectiveness of Print and Digital Advertising: A Memory Perspective

The exponential growth in digital media has recently challenged the value of print media in the overall marketing mix. Across three studies, the authors evaluate the relative effectiveness of print ads versus digital ads. In Study 1, using eye tracking and biometric measures during exposure, the authors find stronger encoding and engagement for print ads over digital ads. A week later, participants showed no significant difference in recognizing ads across format, though they better remembered the encoding context of print ads. Notably, using functional magnetic resonance imaging, the authors find greater activation in hippocampus and parahippocampal regions for print ads relative to digital ads. Extending these insights, Study 2 demonstrates that participants better remember print ads across contents, context, and brand associations when using snippets as retrieval cues. In addition to establishing the robustness of previous findings, Study 3 provides further evidence that the observed memory advantage for print ads is primarily due to superior encoding during initial exposure. From a practical perspective, these findings suggest that marketers should not discount the value of print media in advertising, despite the rapid growth of digital media and communications.
Toward hierarchical classification of imbalanced data using random resampling algorithms

Although the class imbalance issue affects hierarchical datasets, the literature has few studies that deal with it, especially when it comes to methods to pre-process the training dataset as a whole. In this study, we present novel resampling algorithms to deal with imbalance in hierarchical datasets. To be able to process the imbalanced data, we first propose methods to retrieve the set of majority and minority label paths. The resampling algorithms were designed with respect to the depth of the label path prediction and may be used for hierarchical classification problems with single- and multiple-path labels. We propose two oversampling and two undersampling algorithms: HROS-FD/PD – Random Oversampling for Full/Partial Depth Hierarchical problems, and HRUS-PD/FD – Random Undersampling for Full/Partial Depth Hierarchical problems. While the resampling algorithms for full-depth problems deal with the data by simply processing the set of majority/minority paths, the partial depth approaches process the data in a leaf-node order way, recalculating the set of majority/minority paths at each step until reaching the root label. The experimental evaluation with 23 hierarchical datasets across different domains and characteristics, supported by statistical analysis, showed that the proposed resampling algorithms significantly improve the classification performance.
Co-adaptive visual data analysis and guidance processes

Mixed-initiative visual data analysis processes are characterized by the co-adaptation of users and systems over time. As the analysis progresses, both actors – users and systems – gather information, update their analysis behavior, and work on different tasks towards their respective goals. In this paper, we contribute a multigranular model of co-adaptive visual analysis that is centered around incremental learning goals derived from a hierarchical taxonomy of learning goals from pedagogy. Our model captures how both actors adapt their data-, task-, and user/system-models over time. We characterize interaction patterns in terms of the dynamics of learning and teaching that drive adaptation. To demonstrate our model's applicability, we outline aspects of co-adaptation in related models of visual analytics and highlight co-adaptation in existing applications. We further postulate a set of expectations towards adaptation in mixed-initiative processes and identify open research questions and opportunities for future work in co-adaptation.
AdaptNet: Human Activity Recognition via Bilateral Domain Adaptation Using Semi-Supervised Deep Translation Networks

This study demonstrates robust human activity recognition from a single triaxial accelerometer via bilateral domain adaptation using semi-supervised deep translation networks. Datasets were obtained from previously published studies: University of Michigan (Domain 1) and Georgia Institute of Technology (Domain 2) where triaxial accelerometry was obtained on subjects during defined conditions with the goal of recognizing standing rest, walking (level ground), walking (decline), and walking (incline) with and without stairs (activity classes). Collected accelerometer data was preprocessed then analyzed by AdaptNet, a deep translation network composed of Variational Autoencoders and Generative Adversarial Networks trained with additional cycle-consistency losses to combine information from two data domains over shared latent space. Visualization and quantitative analyses demonstrated that AdaptNet successfully reconstructs self-domain wavelet scalogram inputs and generates realistic cross-domain translations. We found AdaptNet provides up to 36 percentage points (0.75 compared to 0.39) better classification performance measured by average macro-F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score compared to the existing domain adaptation methods when a small amount of labeled data is provided for both domains. AdaptNet yielded more robust performance than other methods when the sensor placements are different across two domains. By enabling improved ability to fuse datasets with scarce and weak labels, AdaptNet provides valid recognition of real-world locomotor activities, which can be further utilized in digital health tools such as status assessment of patients with chronic diseases.
The Role of ICT and Effect of National Culture on Human Development

Human development is one of the prominent factors that captures the essence of livelihoods in societies. In the current information era, the unprecedented reach and diffusion of information and communication technologies (ICT) to the remotest countries of the world make it a catalyst to achieve the desired target of human development. But ICT cannot be highly overrated, while the world acknowledges the benefits of incorporating ICT to solve societal issues, the world reports also indicate that nearly 70% of ICT designed projects for development are mere failures. The reason can be attributed to the societal norms and cultural aspects of the community toward the usage of technology. This study builds on the thought that the sustained usage of ICT is highly dependent on the various factors and facets of the society. Using national culture as a societal facet, the study demonstrates the necessity of congruence between ICT usage and national culture values to attain the desired human development. The results indicate that national culture dimensions of low-power distance, collectivism, femininity, short-term orientation, and restraint with ICT have significant influence on human development index. The study demonstrates the variations in using ICT for human development depending on cultures in different regions of the world. The study has implications for policymakers at national and international levels regarding treatment of ICT for human development.
A distributed set-membership estimator for linear systems with reduced computational requirements

In this paper, a distributed set-membership estimator for linear full-coupled systems affected by bounded disturbances is presented. The estimator uses a multi-hop staircase decomposition, capturing the locally unobservable subspaces in a cascaded fashion with the information incoming from other agents involved in the network. Each agent has to find different sets for each subspace, that are mathematically described by zonotopes. The observer gains that minimize the size of those sets, i.e. the estimation uncertainty, can be designed in independent distributed steps by means of simple algebraic equations. Simulations are given to compare the proposed solution with others in the field. An important benefit of the proposed structure is the reduction of the computational requirements with respect to existing solutions.
Cross-domain sentiment classification via parameter transferring and attention sharing mechanism

Training data in a specific domain are often insufficient in the area of text sentiment classifications. Cross-domain sentiment classification (CDSC) is usually utilized to extend the application scope of transfer learning in text-based social media and effectively solve the problem of insufficient data marking in specific domains. Hence, this paper aims to propose a CDSC method via parameter transferring and attention sharing mechanism (PTASM), and the presented architecture includes the source domain network (SDN) and the target domain network (TDN). First, hierarchical attentional network with pre-training language model on training data, such as global vectors for word representation and bidirectional encoder representations from transformers (BERT), are constructed. The word and sentence levels of parameter transferring mechanisms are introduced in the model transfer. Then, parameter transfer and fine-tuning techniques are adopted to transfer network parameters from SDN to TDN. Moreover, sentiment attention can serve as a bridge for sentiment transfer across different domains. Finally, word and sentence level attention mechanisms are introduced, and sentiment attention is shared from the two levels across domains. Extensive experiments show that the PTASM-BERT method achieves state-of-the-art results on Amazon review cross-domain datasets.
A Lightweight Full Entropy TRNG With On-Chip Entropy Assurance

True random number generator (TRNG) as one essential hardware primitive is widely used in cryptography, Monte Carlo simulation, and gambling. To evaluate the security of TRNG, the entropy of the TRNG&#x2019;s output is usually estimated by the stochastic model in theory or measured off-chip after fabrication. However, the sufficiency of entropy is difficult to be guaranteed in practice due to the facts: 1) the inaccuracy of the model-based jitter measurement method; 2) the variations of the chip manufacturing process and operating environments (such as supply voltage and temperature); and 3) malicious attacks. In this work, we design a novel TRNG architecture with on-chip entropy assurance to properly solve practical security problems. In the design, we propose an on-chip entropy estimator for measuring independent jitter to quantify true randomness, which enables continuous monitoring of TRNG at runtime. Furthermore, with the cooperation of the proposed on-chip entropy estimator and a rational self-adaptive mechanism, the designed TRNG can steadily generate bitstreams with sufficient entropy (&#x2265; 0.999 per bit) against PVT variations. We implement the TRNG architecture in FPGAs with different technology nodes (45 and 65 nm) and SMIC 130 nm chips. Experimental results validate that the designed TRNG has an excellent performance in terms of technology independence and environmental robustness. The generated bitstreams pass the NIST SP800-22 and Diehard statistical test suites successfully without any post-processing.
The more the merrier! Barriers and facilitators to the general public’s use of a COVID-19 contact tracing app in New Zealand

Contact tracing for infectious diseases can be partially automated using mobile applications. However, the success of these tools is dependent on significant uptake and frequent use by the public. This study explored the barriers and facilitators to the New Zealand (NZ) general public's use of the COVID-19 contact NZ COVID Tracer app. Adults (≥18 years, N = 373) in NZ. Qualitative and quantitative data were gathered from a nation-wide online survey. App use and frequency of use were presented as descriptive statistics. Qualitative data were analyzed thematically. 31% reported using the app frequently, 24% used it sometimes, 21% had installed but not used it, and 24% had not installed it. Barriers to use include technical issues, privacy and security concerns, forgetfulness and a lack of support from businesses. The perceived risk of contracting COVID-19, government recommendations and communications, and the importance of contact tracing facilitated use. Technical, user, business, and government factors influenced the public's use of a COVID-19 contact tracing app. The development of apps requiring minimal user effort and initial user testing may improve uptake. Enabling environments and better risk communication may improve uptake of similar community-driven contact tracing apps during future pandemics.
Bayer CFA Pattern Compression With JPEG XS

While traditional image compression algorithms take a full three-component color representation of an image as input, capturing of such images is done in many applications with Bayer CFA pattern sensors that provide only a single color information per sensor element and position.In order to avoid additional complexity at the encoder side, such CFA pattern images can be compressed directly without prior conversion to a full color image.In this paper, we describe a recent activity of the JPEG committee (ISO SC 29 WG 1) to develop such a compression algorithm in the framework of JPEG XS.It turns out that it is important to understand the "development process" from CFA patterns to full color images in order to optimize the image quality of such a compression algorithm, which we will also describe shortly.We introduce (1) a novel decorrelation step upfront processing (the so-called Star-Tetrix transform), along with (2) a pre-emphasis function to improve the compression efficiency of the subsequent compression algorithm (here, JPEG XS).Our experiments clearly indicate a gain over a RGB compression workflow in terms of complexity and quality (between 1.5dB and more than 4dB depending on the target bitrate).A comparison is also made with other state-of-the-art CFA compression techniques.
A low cost fault-attack resilient AES for IoT applications

The Internet of Things (IoT) as an emerging infrastructure has an essential rule in daily lives in many domains, ranging from healthcare wearable devices to complex industrial systems. Nevertheless, its security is a challenging issue that has to be addressed. The security can be settled by utilizing cryptographic techniques such as Advanced Encryption Standard (AES) for encryption and authentication. In this paper, we propose 32-bit architecture AES encryption/decryption for utilizing in IoT infrastructure and similar resource-constrained applications. On the other hand, providing robustness against existing malicious attacks is a significant factor in ensuring communication reliably and so securely. Therefore, we propose a low-cost fault-resilient integrated architecture, named LC-FRAES, for data-path and also on-the-fly key expansion unit by exploiting of resource sharing between encryption and decryption processes. The results of both ASIC and FPGA implementations of the proposed architecture are reported and also compared with those of similar recent designs. The comparisons illustrate that the LC-FRAES outperforms its counterparts in many architectural features which make it suitable for IoT applications. Moreover, we provide a comparison between our proposal and lightweight cryptographic designs from literature. The comparisons verify the consistency and appropriateness of proposed architecture for IoT applications. Finally, through the extensive experimental results, we show that LC-FRAES can detect almost all injected faults.
A minority oversampling approach for fault detection with heterogeneous imbalanced data

• Feature heterogeneity problem in imbalanced data is considered. • Two new minority oversampling methods are proposed. • Various public imbalanced datasets are considered in the experiment. • A real case study on fault detection is considered. • Results show the effectiveness of the proposed methods. Between-class imbalance and feature heterogeneity commonly coexist in monitoring data collected from engineering systems. The decision hyperplanes of data-driven methods when adopted for fault detection with imbalanced data may be biased to the majority class, resulting in a low fault-detection rate. Various data- and algorithm-level methods have been proposed, with minority oversampling methods among the most popular and successful. However, state-of-the-art minority oversampling methods are unsuitable for imbalanced data with heterogeneous features, including both numeric and nominal variables. There are two main drawbacks: 1) taking a nominal variable as a numeric variable is not trivial, and synthetic minority samples may exceed the value range of nominal variables; 2) conventional distance measures, e.g., Euclidean distance, cannot properly measure the similarity of samples with heterogeneous features. For these considerations, this work proposes new fault-detection methods. The methodological contributions include: 1) two different distance measures adopted for heterogeneous features in the minority class; 2) a new method for coordinate calculation of synthetic samples considering feature heterogeneity; and 3) a new strategy to encode nominal variables into numeric data for data-driven models. Several public heterogeneous imbalanced datasets and a real case study considering fault detection in high-speed trains are considered to verify the effectiveness of the proposed methods. To the knowledge of the author, this is also the first time that the effectiveness of diverse oversampling methods on heterogeneous imbalance data are specifically discussed.