Reviewerly/exHarmony huggingface dataset:
Experimental data from a quantum computer verifies the generalized Pauli exclusion principle

"What are the consequences ... that Fermi particles cannot get into the same state ... " R. P. Feynman wrote of the Pauli exclusion principle, "In fact, almost all the peculiarities of the material world hinge on this wonderful fact." In 1972 Borland and Dennis showed that there exist powerful constraints beyond the Pauli exclusion principle on the orbital occupations of Fermi particles, providing important restrictions on quantum correlation and entanglement. Here we use computations on quantum computers to experimentally verify the existence of these additional constraints. Quantum many-fermion states are randomly prepared on the quantum computer and tested for constraint violations. Measurements show no violation and confirm the generalized Pauli exclusion principle with an error of one part in one quintillion.
Super-Resolution for Monocular Depth Estimation With Multi-Scale Sub-Pixel Convolutions and a Smoothness Constraint

Depth estimation from a monocular image is of paramount importance in various vision tasks, such as obstacle detection, robot navigation, and 3D reconstruction. However, how to get an accurate depth map with clear details and a fine resolution remains an unresolved issue. As an attempt to solve this problem, we exploit image super-resolution concepts and techniques for monocular depth estimation and propose a novel CNN-based approach, namely <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$MSCN_{NS}$ </tex-math></inline-formula> , which involves multi-scale sub-pixel convolutions and a neighborhood smoothness constraint. Specifically, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$MSCN_{NS}$ </tex-math></inline-formula> makes use of sub-pixel convolutions with multi-scale fusions to retrieve a high-resolution depth map with fine details of the scene. Different from previous multi-scale fusion strategies, those multi-scale features come from supervised scale branches of the network. Furthermore, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$MSCN_{NS}$ </tex-math></inline-formula> incorporates a neighborhood smoothness regularization term to make sure that spatially closer pixels with similar features would have close depth values. The effectiveness and efficiency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$MSCN_{NS}$ </tex-math></inline-formula> have been corroborated through extensive experiments conducted on benchmark datasets.
SCOPE: self-adaptive and policy-based data management middleware for federated clouds

A federated cloud storage setup which integrates and utilizes storage resources from multiple cloud storage providers has become an increasingly popular and attractive paradigm for the persistence tier in cloud-based applications (e.g., SaaS applications, IoT applications, etc). However, federated cloud storage setups are prone to run-time dynamicity: many dynamic properties impact the way such a setup is governed and evolved over time, e.g., storage providers enter or leave the market; QoS metrics and SLA guarantees may change over time; etc. In general, existing federated cloud systems are oblivious to dynamic properties of the underlying operational environment, resulting in both sub-optimal data management decisions and costly SLA violations. Additionally, due to the sheer complexity of cloud-based applications coupled with the heterogeneous and volatile nature of federated cloud setups, the complexity of building, maintaining, and expending such applications increases dramatically and therefore managing them manually is no longer simply an option. To address these concerns, we present SCOPE, a policy-based and autonomic middleware that provides self-adaptiveness for data management in federated clouds. We have validated SCOPE in the context of a realistic SaaS application, performed an extensive functional validation, and conducted a thorough experimental evaluation. The evaluation results demonstrate (i) the ability of the middleware to perform data management decisions that take into account the run-time dynamicity (i.e., dynamic properties) of a federated cloud storage setup to meet the promised SLAs, and (ii) the self-adaptive behavior of SCOPE without the need for operator intervention. In addition, our in-depth performance evaluation results indicate that the benefits are achieved with acceptable performance overhead, and as such highlight the applicability of the proposed middleware for real-world application cases.
Mixed-Reality Learning Environments in Teacher Education: An Analysis of TeachLivE™ Research

TeachLivE™, a mixed-reality simulated classroom technology, has been used in initial teacher education programs to provide repeatable experiential learning opportunities for students now for more than a decade and in more than 80 universities worldwide. However, no broad scale investigation has been conducted into how the platform has been used or what research has been generated as a result. The aim of this study is to provide insight into the types of TeachLivE™ research carried out since its inception and to identify trends and potential gaps in this research. Peer-reviewed academic primary research publications—journal articles (23), conference proceedings (12), and thesis dissertations (20)—were reviewed for participants, research methods, analysis, research design, data collection tools, and design approaches. Of the 102 articles identified as relevant, “instructional skills development” and “integration of TeachLivE™ in teacher education” were the most commonly researched topics. Findings indicate that preservice teachers were the most commonly studied group of participants, research methods were predominately qualitative, single-subject experimental research design was employed most often, and the most used data collection tools were surveys and observation. These findings highlight that the range of topics is increasing, with studies on in-service teachers in school-based contexts beginning to emerge as a new area of interest. This systematic review has implications for researchers and the developers of TeachLivE™. It provides valuable insight and recommendations for future studies in this emerging teacher education field, where technology is not simply used “in the classroom” but rather “as the classroom.”
SDDRS: Stacked Discriminative Denoising Auto-Encoder based Recommender System

Recommender systems are widely used in our life for automatically recommending items relevant to our preference. Collaborative Filtering (CF) is one of the most successful methods in recommendation field. Matrix Factorization (MF) based recommender system is designed according to the basic strategy of the CF algorithm, which is widely adopted recently. However, the rating matrix utilized by these models is usually sparse, so it is of vital significance to integrate the side information to provide relatively effective knowledge for modeling the user or item features. The key problem is to extract effective features from the noisy side information. However, the side information contains a lot of noise except rating knowledge, which makes it a challenging issue for extracting effective features. In this paper, we propose Stacked Discriminative Denoising Auto-Encoder based Recommender System (SDDRS) by integrating deep learning model with MF based recommender system to effectively incorporate side information with rating information. Extensive top-N recommendation experiments conducted on three real-world datasets empirically demonstrate that SDDRS outperforms several state-of-the-art methods.
Group Optimization to Maximize Peer Assessment Accuracy Using Item Response Theory and Integer Programming

With the wide spread large-scale e-learning environments such as MOOCs, peer assessment has been popularly used to measure the learner ability.When the number of learners increases, peer assessment is often conducted by dividing learners into multiple groups to reduce the learner's assessment workload.However, in such cases, the peer assessment accuracy depends on the method of forming groups.To resolve that difficulty, this study proposes a group formation method to maximize peer assessment accuracy using item response theory and integer programming.Experimental results, however, have demonstrated that the method does not present sufficiently higher accuracy than a random group formation method does.Therefore, this study further proposes an external rater assignment method that assigns a few outside-group raters to each learner after groups are formed using the proposed group formation method.Through results of simulation and actual data experiments, this study demonstrates that the proposed external rater assignment can substantially improve peer assessment accuracy.
Modeling Dyadic and Group Impressions with Intermodal and Interperson Features

This article proposes a novel feature-extraction framework for inferring impression personality traits, emergent leadership skills, communicative competence, and hiring decisions. The proposed framework extracts multimodal features, describing each participant’s nonverbal activities. It captures intermodal and interperson relationships in interactions and captures how the target interactor generates nonverbal behavior when other interactors also generate nonverbal behavior. The intermodal and interperson patterns are identified as frequent co-occurring events based on clustering from multimodal sequences. The proposed framework is applied to the SONVB corpus, which is an audiovisual dataset collected from dyadic job interviews, and the ELEA audiovisual data corpus, which is a dataset collected from group meetings. We evaluate the framework on a binary classification task involving 15 impression variables from the two data corpora. The experimental results show that the model trained with co-occurrence features is more accurate than previous models for 14 out of 15 traits.
Polynomial Controller Synthesis for Uncertain Large-Scale Polynomial T–S Fuzzy Systems

This paper proposes a novel method to synthesize a controller for stabilizing the nonlinear large-scale system which is represented by a large-scale polynomial Takagi-Sugeno (T-S) fuzzy system. The large-scale system consists of a set of the uncertain polynomial T-S fuzzy system with interconnection terms. Modeling the large-scale nonlinear system under the framework of the polynomial form will decrease both the modeling errors and the number of fuzzy rules with respect to the conventional large-scale T-S fuzzy system. In addition, because of the existence of uncertainties, the synthesizing controller for the large-scale polynomial fuzzy system becomes much more challenging and has not been investigated in the previous studies. In this paper, a controller is synthesized to simultaneously eliminate the impact of the uncertainties and stabilize the system. With the aid of Lyapunov theory, sum-of-square technique, and S-procedure, the conditions for controller synthesis are derived in the main theorems. Finally, two examples are illustrated to show the effectiveness and merit of the proposed method.
Optimum Circuits for Bit-Dimension Permutations

In this paper, we present a systematic approach to design hardware circuits for bit-dimension permutations. The proposed approach is based on decomposing any bit-dimension permutation into elementary bit-exchanges. Such decomposition is proven to achieve the theoretical minimum number of delays required for the permutation. This offers optimum solutions for multiple well-known problems in the literature that make use of bit-dimension permutations. This includes the design of permutation circuits for the fast Fourier transform, bit reversal, matrix transposition, stride permutations, and Viterbi decoders.
CDbin: Compact Discriminative Binary Descriptor Learned With Efficient Neural Network

As an important computer vision task, image matching requires efficient and discriminative local descriptors. Most of the existing descriptors like SIFT and ORB are hand-crafted; therefore it is necessary to study more optimized descriptors through end-to-end learning. This paper proposes the compact binary descriptors learned with a lightweight Convolutional Neural Network (CNN), which is efficient for training and testing. Specifically, we propose a CNN with no larger than five layers for descriptor learning. The resulting descriptors, i.e., Compact Discriminative binary descriptors (CDbin) are optimized with four complementary loss functions, i.e., 1) triplet loss to ensure the discriminative power; 2) quantization loss to decrease the quantization error; 3) correlation loss to ensure the feature compactness; and 4) even-distribution loss to enrich the embedded information. The extensive experiments on two image patch datasets and three image retrieval datasets show that the CDbin exhibits competitive performance compared with the existing descriptors. For example, the 64-bit CDbin substantially outperforms the 256-bit ORB and 1024-bit SIFT on Hpatches dataset. Although generated by a shallow CNN, CDbin also outperforms several recent deep descriptors.
OPTICS-Based Template Matching for Vision Sensor-Based Shoe Detection in Human–Robot Coexisting Environments

In human-robot coexisting environment, one of the primary objectives is to equip robots with the facilities of detecting and following a human being in front, using various sensors. Within this genre, developing algorithms for vision sensor-based shoe detection and subsequently following is considered an active problem. Considering that the shoe poses, during the pursuit, undergo different transformations, this paper presents how the vision sensor based shoe detection problem can be treated as analogous to template matching under general conditions. This paper first shows how a popular fast randomized template-matching algorithm, called FAsT-Match algorithm, and its contemporary variant for color images, called CFAsT-Match algorithm, can be implemented in real robots with success for detecting shoes in subsequent frames. Then, this paper proposes a new density-based clustering method, called ordering points to identify the clustering structure (OPTICS), based template-matching algorithm that is specifically developed to overcome several implementation problems associated with the FAsT-Match and CFAsT-Match algorithms, in real life. Experimental results and performance evaluations demonstrate the superiority of the proposed OPTICS-based algorithm for shoe detection during human tracking in human-robot coexisting environments.
Watermarking scheme using local binary pattern for image authentication and tamper detection through dual image

Watermarking scheme is an efficient solution to protect the multimedia document from unauthorized modification. Several watermarking scheme have been developed but authentication and tamper detection is still an important research issue. In this paper, a dual image‐based watermarking scheme has been developed using local binary pattern (LBP) to protect the multimedia document from illegal modification. The suggested method includes the following procedure: During watermark encoding the host image is partitioned into (3 × 3) nonoverlapping blocks. Then generate system vector (s) using LBP and perform XOR with secret watermark bits. Two bits authentication code is generated from S vector and embed within dual image depending in a shared secret key ( δ ). At the receiver end, the embedded watermark, authentication code, and original cover image can be successfully recovered from dual watermarked image. After that authentication process has been carried out by comparing extracted authentication code and the regenerated authentication code. The experimental results are compared with the state‐of‐the‐art methods to show the effectiveness of our proposed scheme. It is observed that the proposed scheme is secure and robust against different standard attacks meanwhile it can detect message integrity within the watermarked object.
Refining Vision Videos

[Context and motivation] Complex software-based systems involve several stakeholders, their activities and interactions with the system. Vision videos are used during the early phases of a project to complement textual representations. They visualize previously abstract visions of the product and its use. By creating, elaborating, and discussing vision videos, stakeholders and developers gain an improved shared understanding of how those abstract visions could translate into concrete scenarios and requirements to which individuals can relate. [Question/problem] In this paper, we investigate two aspects of refining vision videos: (1) Refining the vision by providing alternative answers to previously open issues about the system to be built. (2) A refined understanding of the camera perspective in vision videos. The impact of using a subjective (or "ego") perspective is compared to the usual third-person perspective. [Methodology] We use shopping in rural areas as a real-world application domain for refining vision videos. Both aspects of refining vision videos were investigated in an experiment with 20 participants. [Contribution] Subjects made a significant number of additional contributions when they had received not only video or text but also both – even with very short text and short video clips. Subjective video elements were rated as positive. However, there was no significant preference for either subjective or non-subjective videos in general.
Robust URL Classification With Generative Adversarial Networks

Classifying URLs is essential for different applications, such as parental control, URL filtering and Ads/tracking protection. Such systems historically identify URLs by means of regular expressions, even if machine learning alternatives have been proposed to overcome the time-consuming maintenance of classification rules. Classical machine learning algorithms, however, require large samples of URLs to train the models, covering the diverse classes of URLs (i.e., a ground truth), which somehow limits the applicability of the approach. We here give a first step towards the use of Generative Adversarial Neural Networks (GANs) to classify URLs. GANs are attractive for this problem for two reasons. First, GANs can produce samples of URLs belonging to specific classes even if exposed to a limited training set, outputting both synthetic traces and a robust discriminator. Second, a GAN can be trained to discriminate a class of URLs without being exposed to all other URLs classes - i.e., GANs are robust even if not exposed to uninteresting URL classes during training. Experiments on real data show that not only the generated synthetic traces are somehow realistic, but also the URL classification is accurate with GANs.
Group Sparse Representation Based Dictionary Learning for SAR Image Despeckling

Since the sparse representation coefficients of synthetic aperture radar (SAR) images often appear in clusters with intrinsic structure, traditional sparse representation theory cannot capture this property.In this paper, the concept of group sparse representation (GSR) is utilized to exploit the intrinsic structure of SAR images.Different from traditional patch-based sparse representation theory, GSR is able to sparsely represent images in the domain of group which contains the image patches with similar structure.Based on the multiplicative speckle noise model, a novel dictionary learning algorithm based on GSR (GSR-DL) for SAR image despeckling is proposed.The proposed algorithm mainly consists of three steps.First, in order to realize the recovery of despeckled SAR image by the GSR model, a mean filter is included in the modeling process.Second, the proposed GSR-DL algorithm is used to calculate the optimal dictionary and group sparse representation coefficients.Third, the despeckled SAR image is reconstructed by the learned dictionary and coefficients.The experimental results on SAR images manifest that the proposed GSR-DL algorithm achieves a better performance than other state-of-the-art despeckling algorithms.
Discrete Robust Supervised Hashing for Cross-Modal Retrieval

Cross-modal hashing has been studied extensively in the past decades for its significant advantage in computation and storage cost. For heterogeneous data points, the cross-modal hashing aims at learning a sharing Hamming space in where one query from one modality can retrieve relevant items of another modality. Although the cross-modal hashing method has achieved significant progress, there are some limitations that need to be further solved. First, to leverage the semantic information in hash codes, most of them learn hash codes from a similarity matrix, which is constructed by class labels directly, ignoring the fact that the class labels may contain noises in the real world. Second, most of them relax the discrete constraint on hash codes, which may cause large quantization error and inevitably results in suboptimal performance. To address the above issues, we propose a discrete robust supervised hashing (DRSH) algorithm in this paper. Specifically, both the class labels and features from different modalities are first fused to learn a robust similarity matrix through low-rank constraint that can reveal its structure and capture the noises in it. And then, hash codes are generated by preserving the robust similarity matrix-based similarities in the sharing Hamming space. The optimization is challenging due to the discrete constraint on hash codes. Finally, a discrete optimal algorithm is proposed to address this issue. We evaluate the DRSH on three real-world datasets, and the results demonstrate the superiority of DRSH over several existing hashing methods.
Lightweight and Effective Facial Landmark Detection using Adversarial Learning with Face Geometric Map Generative Network

Facial landmark detection plays an important role in face analysis tasks. Moreover, it is used as a prerequisite in many facial related applications, the simplicity, as well as effectiveness, is essential in the facial landmark detection. In this paper, we propose an effective facial landmark detection network and an associated learning framework with the geometric prior-generative adversarial network. The geometric prior-generative adversarial network consists of one generator and two discriminators. The generator consists of an encoder and two decoders. The encoder predicts facial landmark points. The decoders generate a facial inner and contour geometric map from predicted landmark points. Generating face geometric maps from predicted landmark points helps the predicted landmark points to represent the face geometric information, including shape and configuration. The discriminators determine that the given geometric maps are generated from actual landmark points or estimated landmark points. Our proposed network is end-to-end trainable, and only the encoder part is used simply as the facial landmark detector in the testing stage. To verify the effectiveness of the proposed method, we have conducted comprehensive experiments with benchmark data sets. The results have shown that the proposed method achieves comparable performances over recently proposed facial landmark detection methods with a simple and effective facial landmark detection network.