Reviewerly/exHarmony huggingface dataset:
ThunderNet: A Turbo Unified Network for Real-Time Semantic Segmentation

Recent research in pixel-wise semantic segmentation has increasingly focused on the development of very complicated deep neural networks, which require a large amount of computational resources. The ability to perform dense predictions in real-time, therefore, becomes tantamount to achieving high accuracies. This real-time demand turns out to be fundamental particularly on the mobile platform and other GPU-powered embedded systems like NVIDIA Jetson TX series. In this paper, we present a fast and efficient lightweight network called Turbo Unified Network (ThunderNet). With a minimum backbone truncated from ResNet18, ThunderNet unifies the pyramid pooling module with our customized decoder. Our experimental results show that ThunderNet can achieve 64.0% mIoU on CityScapes, with real-time performance of 96.2 fps on a Titan XP GPU (512x1024), and 20.9 fps on Jetson TX2 (256x512).
Computing and Explaining Query Answers over Inconsistent DL-Lite Knowledge Bases

&#x0D; &#x0D; &#x0D; Several inconsistency-tolerant semantics have been introduced for querying inconsistent description logic knowledge bases. The first contribution of this paper is a practical approach for computing the query answers under three well-known such semantics, namely the AR, IAR and brave semantics, in the lightweight description logic DL-LiteR. We show that query answering under the intractable AR semantics can be performed efficiently by using IAR and brave semantics as tractable approximations and encoding the AR entailment problem as a propositional satisfiability (SAT) problem. The second issue tackled in this work is explaining why a tuple is a (non-)answer to a query under these semantics. We define explanations for positive and negative answers under the brave, AR and IAR semantics. We then study the computational properties of explanations in DL-LiteR. For each type of explanation, we analyze the data complexity of recognizing (preferred) explanations and deciding if a given assertion is relevant or necessary. We establish tight connections between intractable explanation problems and variants of SAT, enabling us to generate explanations by exploiting solvers for Boolean satisfaction and optimization problems. Finally, we empirically study the efficiency of our query answering and explanation framework using a benchmark we built upon the well-established LUBM benchmark.&#x0D; &#x0D; &#x0D;
Classic and inverse compositional Gauss‐Newton in global DIC

Summary Today, effective implementations of digital image correlation (DIC) are based on iterative algorithms with constant linear operators. A relevant idea of the classic finite element (or, more generally, global) DIC solver consists in replacing the gradient of the deformed state image with that of the reference image, so as to obtain a constant operator. Different arguments (small strains, small deformations, equality of the two gradients close to the solution, etc) have been given in the literature to justify this approximation, but none of them are fully accurate. Indeed, the convergence of the optimization algorithm has to be investigated from its ability to produce descent directions. Through such a study, this paper attempts to explain why this approximation works and what is its domain of validity. Then, an inverse compositional Gauss‐Newton implementation of finite element DIC is proposed as a cost‐effective and mathematically sound alternative to this approximation.
A formal framework for measuring technical lag in component repositories — and its application to npm

Abstract Reusable Open Source Software (OSS) components for major programming languages are available in package repositories. Developers rely on package management tools to automate deployments, specifying which package releases satisfy the needs of their applications. However, these specifications may lead to deploying package releases that are outdated, or otherwise undesirable, because they do not include bug fixes, security fixes, or new functionality. In contrast, automatically updating to a more recent release may introduce incompatibility issues. To capture this delicate balance, we formalise a generic model of technical lag , a concept that quantifies to which extent a deployed collection of components is outdated, with respect to the ideal deployment. We operationalise this model for the npm package manager. We empirically analyze the history of package update practices and technical lag for more than 500 K packages with about 4 M package releases over a seven‐year period. We consider both development and runtime dependencies, and study both direct and transitive dependencies. We also analyze the technical lag of external GitHub applications depending on npm packages. We report our findings, suggesting the need for more awareness of, and integrated tool support for, controlling technical lag in software libraries.
Motion-Aware Rapid Video Saliency Detection

In this paper, we propose a computationally efficient and consistently accurate spatiotemporal salient object detection method to identify the most noticeable object in a video sequence. Intuitively, the underlying motion in a video is a more stable saliency indicator than the apparent color cues that often contain significant variations and complex structures. Based on this observation, we build an efficient and accurate spatiotemporal saliency detection method that uses motion information as a leverage to locate the most dynamic regions in a video sequence. We first analyze the optical flow field to obtain foreground priors, and then incorporate spatial saliency features such as appearance contrasts and compactness measures, into a multi-cue integration framework to combine various saliency cues and achieve temporal consistency. Rigorous experiments on the challenging SegTrackV1, SegTrackV2, and FBMS datasets demonstrate that our method generates comparable or superior performance to state-of-the-art methods while running almost 100× faster at only 0.08 sec/frame. Promising performance and rapid speed imply that the proposed spatiotemporal saliency method can be easily involved in various vision applications.
A weighted multiple classifier framework based on random projection

In this paper, we propose a weighted multiple classifier framework based on random projections. Similar to the mechanism of other homogeneous ensemble methods, the base classifiers in our approach are obtained by a learning algorithm on different training sets generated by projecting the original up-space training set to lower dimensional down-spaces. We then apply a Least SquarE−based method to weigh the outputs of the base classifiers so that the contribution of each classifier to the final combined prediction is different. We choose Decision Tree as the learning algorithm in the proposed framework and conduct experiments on a number of real and synthetic datasets. The experimental results indicate that our framework is better than many of the benchmark algorithms, including three homogeneous ensemble methods (Bagging, RotBoost, and Random Subspace), several well-known algorithms (Decision Tree, Random Neural Network, Linear Discriminative Analysis, K Nearest Neighbor, L2-loss Linear Support Vector Machine, and Discriminative Restricted Boltzmann Machine), and random projection-based ensembles with fixed combining rules with regard to both classification error rates and F1 scores.
A decomposition clustering ensemble learning approach for forecasting foreign exchange rates

A decomposition clustering ensemble (DCE) learning approach is proposed for forecasting foreign exchange rates by integrating the variational mode decomposition (VMD), the self-organizing map (SOM) network, and the kernel extreme learning machine (KELM). First, the exchange rate time series is decomposed into N subcomponents by the VMD method. Second, each subcomponent series is modeled by the KELM. Third, the SOM neural network is introduced to cluster the subcomponent forecasting results of the in-sample dataset to obtain cluster centers. Finally, each cluster's ensemble weight is estimated by another KELM, and the final forecasting results are obtained by the corresponding clusters' ensemble weights. The empirical results illustrate that our proposed DCE learning approach can significantly improve forecasting performance, and statistically outperform some other benchmark models in directional and level forecasting accuracy.
Just gaze and wave

Eye-gaze and mid-air gestures are promising for resisting various types of side-channel attacks during authentication. However, to date, a comparison of the different authentication modalities is missing. We investigate multiple authentication mechanisms that leverage gestures, eye gaze, and a multimodal combination of them and study their resilience to shoulder surfing. To this end, we report on our implementation of three schemes and results from usability and security evaluations where we also experimented with fixed and randomized layouts. We found that the gaze-based approach outperforms the other schemes in terms of input time, error rate, perceived workload, and resistance to observation attacks, and that randomizing the layout does not improve observation resistance enough to warrant the reduced usability. Our work further underlines the significance of replicating previous eye tracking studies using today's sensors as we show significant improvement over similar previously introduced gaze-based authentication systems.
Integrating UX principles and practices into software development organizations: A case study of influencing events

Current studies on User eXperience (UX) integration often do not investigate or reflect on the transition companies go through from only developing Graphical User Interfaces (GUI) to also considering usability and more recently UX. Understanding this transition provides a more holistic and realistic picture of integration and can be a rich source of knowledge for improving UX integration in the software industry. Applying case study and grounded theory research we show that UX integration, like other organizational changes, can include a mixture of planned and emergent initiatives, and is influenced by various intertwined events; not only those that reside inside an organization but also those external to it. We also show that different decisions that are made outside the authority of UX practitioners have an inevitable impact on enabling or prohibiting UX integration. In addition, we found that for a successful integration, practitioners need to explicitly consider and address the characteristics of UX, otherwise, the integration efforts may have a lopsided focus on the pragmatic aspect of UX, consequently, leave the hedonic aspect unaddressed. Based on our findings, we present four lessons learned and five pitfalls companies should consider to go beyond GUI design and usability to also address UX.
Head Pose Estimation in the Wild Assisted by Facial Landmarks Based on Convolutional Neural Networks

Convolutional neural networks (CNNs) exhibit excellent performance on the head pose estimation problem under controllable conditions, but their generalization ability in the wild needs to be improved. To address this issue, we propose an approach involving the introduction of facial landmark information into the task simplifier and landmark heatmap generator constructed before the feed-forward neural network, which can use this information to normalize the face shape into a canonical shape and generate a landmark heatmap based on the transformed facial landmarks to assist in feature extraction, for enhancing generalization ability in the wild. Our method was trained on 300W-LP and tested on AFLW2000-3D. The result shows that for the same feed-forward neural network when our method is used to introduce facial landmark information into a CNN, accuracy improves from 88.5% to 99.0% and mean average error decreases from 5.94° to 1.46° on AFLW2000-3D. Furthermore, we evaluate our method on several datasets used for pose estimation and compare the result with AFLW2000-3D, finding that the features extracted by a CNN could not reflect the head pose efficiently, which limits the performance of the CNN on the head pose estimation problem in wild. By introducing facial landmarks, the CNN could extract features that reflect head pose more efficiently, thereby significantly improving the accuracy of head pose estimation in the wild.
Drone.io: A Gestural and Visual Interface for Human-Drone Interaction

Drones are becoming ubiquitous and offer support to people in various tasks, such as photography, in increasingly interactive social contexts. We introduce drone.io, a projected body-centric graphical user interface for human-drone interaction. Using two simple gestures, users can interact with a drone in a natural manner. drone.io is the first human-drone graphical user interface embedded on a drone to provide both input and output capabilities. This paper describes the design process of drone.io. We present a proof of concept, drone-based implementation, as well as a fully functional prototype for a drone tour-guide scenario. We report drone.io's evaluation in three user studies (N=27) and show that people were able to use the interface with little prior training. We contribute to the field of human-robot interaction and the growing field of human-drone interaction.
Modified diffractive-imaging-based image encryption

The diffractive-imaging-based image encryption (DIBE) has been breached by chosen-plaintext attack (CPA) due to the straightforward relation between the plaintext and the ciphertext. In this paper, we report a modified diffractive-imaging-based image encryption (MDIBE) scheme which inherits the merits of conventional DIBE (e. g. concise optical setup) and has higher security level. The ciphertext of the proposal is a synthetic image of multiple sparse diffractions obtained at different positions along the axis. As a result, no explicit mathematic relation between the plaintext and the ciphertext can be utilized by the attacker, and CPA therefore no longer works. For decryption, a novel algorithm containing a median-filter-based iterative procedure (MFBIP) and an ordinary iterative procedure (OIP), is proposed to recover the primary image from these sparse patterns. In addition, the size of the ciphertext does not increase with the enhancement of the security. Furthermore, the additional keys can create a huge key space to resist the brute-force attack. The proposal opens up a new way for reinforcing the security of DIBE. A series of numerical simulations are presented to support the proposal.
A true random number generator architecture based on a reduced number of FPGA primitives

This paper reports on the design, realization and characterization of a True Random Number Generator (TRNG) that operates using as seeds of entropy, the jitter and the metastability introduced by primitives of a Field Programmable Gate Arrays (FPGA) board. In particular, the TRNG architecture has been implemented on a Xilinx Ultrascale XCKU040 FPGA board. Generally, the implementations on FPGA of fully-digital TRNGs make use of ring oscillators employing a large number of Look-Up-Table (LUT) blocks. Differently from this approach, this paper demonstrates that a reliable FPGA-based TRNG architecture can be realized mainly employing only a single PLL and three on-board primitives together with other few basic logic elements (i.e., 8 D-type Flip-Flop, 17 LUT and 2 Counters) used only for the initial overall system synchronization and post-processing operations. In this way, the proposed solution largely reduces the employed number of the FPGA Configurable Logic Blocks (CLB), the circuitry complexity and the overall power consumption without affecting the achievable output bit rate so resulting suitable for full-custom VLSI implementations. The random and statistical properties of the generated 100 Mbps output bitstreams have been validated by passing all the National Institute of Standards and Technology (NIST) tests as well as the Anderson-Darling and the Kolmogorov-Smirnov tests so demonstrating that the proposed TRNG architecture can be suitably employed in security/cybersecurity network systems as well as, once integrated, in Internet-of-Things (IoT) and Industrial-Internet-of-Things (IIoT) applications.
Improving Human-Robot Interaction Through Explainable Reinforcement Learning

Gathering the most informative data from humans without overloading them remains an active research area in AI, and is closely coupled with the problems of determining how and when information should be communicated to others [12]. Current decision support systems (DSS) are still overly simple and static, and cannot adapt to changing environments we expect to deploy in modern systems [3], [4], [9], [11]. They are intrinsically limited in their ability to explain rationale versus merely listing their future behaviors, limiting a human's understanding of the system [2], [7]. Most probabilistic assessments of a task are conveyed after the task/skill is attempted rather than before [10], [14], [16]. This limits failure recovery and danger avoidance mechanisms. Existing work on predicting failures relies on sensors to accurately detect explicitly annotated and learned failure modes [13]. As such, important non-obvious pieces of information for assessing appropriate trust and/or course-of-action (COA) evaluation in collaborative scenarios can go overlooked, while irrelevant information may instead be provided that increases clutter and mental workload. Understanding how AI models arrive at specific decisions is a key principle of trust [8]. Therefore, it is critically important to develop new strategies for anticipating, communicating, and explaining justifications and rationale for AI driven behaviors via contextually appropriate semantics.
A New Transfer Learning Algorithm in Semi-Supervised Setting

Transfer Learning is an effective method of dealing with real-world problems where the training and test data are drawn from different distributions. Transfer learning methods use a labeled source domain to boost the task in a target domain that may be unsupervised or semi-supervised. However, the previous transfer learning algorithms use Euclidean distance or Mahalanobis distance formula to represent the relationships between instances and to try and capture the geometry of the manifold. In many real-world scenarios, this is not enough and these functions fail to capture the intrinsic geometry of the manifold that the data exists in. In this paper, we propose a transfer learning framework called Semi-Supervised Metric Transfer Learning with Relative Constraints (SSMTR), that uses distance metric learning with a set of relative distance constraints that capture the similarities and dissimilarities between the source and the target domains better. In SSMTR, instance weights are learned for different domains which are then used to reduce the domain shift while a Relative Distance metric is learned in parallel. We have developed SSMTR for classification problems as well, and have conducted extensive experiments on several real-world datasets; particularly, the PIE Face, Office-Caltech, and USPS-MNIST datasets to verify the accuracy of our proposed algorithm when compared to the current transfer learning algorithms.
No-reference synthetic image quality assessment with convolutional neural network and local image saliency

Depth-image-based rendering (DIBR) is widely used in 3DTV, free-viewpoint video, and interactive 3D graphics applications. Typically, synthetic images generated by DIBR-based systems incorporate various distortions, particularly geometric distortions induced by object dis-occlusion. Ensuring the quality of synthetic images is critical to maintaining adequate system service. However, traditional 2D image quality metrics are ineffective for evaluating synthetic images as they are not sensitive to geometric distortion. In this paper, we propose a novel no-reference image quality assessment method for synthetic images based on convolutional neural networks, introducing local image saliency as prediction weights. Due to the lack of existing training data, we construct a new DIBR synthetic image dataset as part of our contribution. Experiments were conducted on both the public benchmark IRCCyN/IVC DIBR image dataset and our own dataset. Results demonstrate that our proposed metric outperforms traditional 2D image quality metrics and state-of-the-art DIBR-related metrics.
The Expanded Evidence-Centered Design (e-ECD) for Learning and Assessment Systems: A Framework for Incorporating Learning Goals and Processes Within Assessment Design

Evidence-centered design (ECD) is a framework for the design and development of assessments that ensures consideration and collection of validity evidence from the onset of the test design. Blending learning and assessment requires integrating aspects of learning at the same level of rigor as aspects of testing. In this paper, we describe an expansion to the ECD framework (termed e-ECD) such that it includes the specifications of the relevant aspects of learning at each of the three core models in the ECD, as well as making room for specifying the relationship between learning and assessment within the system. The framework proposed here does not assume a specific learning theory or particular learning goals, rather it allows for their inclusion within an assessment framework, such that they can be articulated by researchers or assessment developers that wish to focus on learning.
New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection

Forgery in digital images is immensely affected by the improvement of image manipulation tools. Image forgery can be classified as image splicing or copy-move on the basis of the image manipulation type. Image splicing involves creating a new tampered image by merging the components of one or more images. Moreover, image splicing disrupts the content and causes abnormality in the features of a tampered image. Most of the proposed algorithms are incapable of accurately classifying high-dimension feature vectors. Thus, the current study focuses on improving the accuracy of image splicing detection with low-dimension feature vectors. This study also proposes an approximated Machado fractional entropy (AMFE) of the discrete wavelet transform (DWT) to effectively capture splicing artifacts inside an image. AMFE is used as a new fractional texture descriptor, while DWT is applied to decompose the input image into a number of sub-images with different frequency bands. The standard image dataset CASIA v2 was used to evaluate the proposed approach. Superior detection accuracy and positive and false positive rates were achieved compared with other state-of-the-art approaches with a low-dimension of feature vectors.
Continuous Variable Neighborhood Search (C-VNS) for Solving Systems of Nonlinear Equations

In this paper, we propose the continuous variable neighborhood search method for finding all the solutions to a nonlinear system of equations (NSEs). We transform the NSE problem into an equivalent optimization problem, and we use a new objective function that allows us to find all the zeros. Instead of the usual sum-of-squares objective function, our objective function is presented as the sum of absolute values. Theoretical investigation confirms that our objective function provides more accurate solutions regardless of the optimization method used. In addition, we achieve a trade-off (i.e., increased precision at the expense of reduced smoothness). Computational analysis of standard test instances shows that the proposed method is more precise and much faster than two recently developed methods. Similar conclusions are drawn by comparing the proposed method with many other methods in the literature. The online supplement is available at https://doi.org/10.1287/ijoc.2018.0876 .