Reviewerly/exHarmony huggingface dataset:
Zero-Shot Learning via Category-Specific Visual-Semantic Mapping and Label Refinement

Zero-Shot Learning (ZSL) aims to classify a test instance from an unseen category based on the training instances from seen categories, in which the gap between seen categories and unseen categories is generally bridged via visual-semantic mapping between the low-level visual feature space and the intermediate semantic space. However, the visual-semantic mapping (i.e., projection) learnt based on seen categories may not generalize well to unseen categories, which is known as the projection domain shift in ZSL. To address this projection domain shift issue, we propose a method named Adaptive Embedding ZSL (AEZSL) to learn an adaptive visual-semantic mapping for each unseen category, followed by progressive label refinement. Moreover, to avoid learning visual-semantic mapping for each unseen category in the large-scale classification task, we additionally propose a deep adaptive embedding model named Deep AEZSL (DAEZSL) sharing the similar idea (i.e., visual-semantic mapping should be category-specific and related to the semantic space) with AEZSL, which only needs to be trained once, but can be applied to arbitrary number of unseen categories. Extensive experiments demonstrate that our proposed methods achieve the state-of-theart results for image classification on three small-scale benchmark datasets and one large-scale benchmark dataset.
Fault-tolerant protection of near-term trapped-ion topological qubits under realistic noise sources

The quest of demonstrating beneficial quantum error correction in near-term noisy quantum processors can benefit enormously from a low-resource optimization of fault-tolerant schemes, which are specially designed for a particular platform considering both state-of-the-art technological capabilities and main sources of noise. In this work, we show that flag-qubit-based fault-tolerant techniques for active error detection and correction, as well as for encoding of logical qubits, can be leveraged in current designs of trapped-ion quantum processors to achieve this break-even point of beneficial quantum error correction. Our improved description of the relevant sources of noise, together with detailed schedules for the implementation of these flag-based protocols, provide one of the most complete microscopic characterizations of a fault-tolerant quantum processor to date. By extensive numerical simulations, we provide a comparative study of flag- and cat-based approaches to quantum error correction, and show that the superior performance of the former can become a landmark in the success of near-term quantum computing with noisy trapped-ion devices.
Video Super-Resolution Using Non-Simultaneous Fully Recurrent Convolutional Network

Video super-resolution (SR) aims at restoring fine details and enhancing visual experience for low-resolution videos. In this paper, we propose a very deep non-simultaneous fully recurrent convolutional network for video SR. To make full use of temporal information, we employ motion compensation, very deep fully recurrent convolutional layers, and late fusion in our system. Residual connection is also employed in our recurrent structure for more accurate SR. Finally, a new model ensemble strategy is used to combine our method with a single-image SR method. Experimental results demonstrate that the proposed method is better than that of the state-of-the-art SR methods on quantitative visual quality assessment.
Implementing homomorphic encryption based secure feedback control

This paper is about an encryption based approach to the secure implementation of feedback controllers for physical systems. Specifically, Paillier's homomorphic encryption is used to digitally implement a class of linear dynamic controllers, which includes the commonplace static gain and PID type feedback control laws as special cases. The developed implementation is amenable to Field Programmable Gate Array (FPGA) realization. Experimental results, including timing analysis and resource usage characteristics for different encryption key lengths, are presented for the realization of an inverted pendulum controller; as this is an unstable plant, the control is necessarily fast.
Shadow Detection in Single RGB Images Using a Context Preserver Convolutional Neural Network Trained by Multiple Adversarial Examples

Automatic identification of shadow regions in an image is a basic and yet very important task in many computer vision applications, such as object detection, target tracking, and visual data analysis. Although shadow detection is a well-studied topic, current methods for identification of shadow are not as accurate as required. In this paper, we propose a deep-learning method for shadow detection at a pixel-level that is suitable for single RGB images. The proposed CNN-based method benefits from a novel architecture through which global and local shadow attributes are identified using a new and efficient mapping scheme in the skip connection. It extracts and preserves shadow context in multiple layers and utilizes them gradually in multiple blocks to generate final shadow masks. The training phase of the network is simple and can be directly and easily adapted for other image segmentation tasks. The performance of the proposed system is evaluated on three publicly available datasets, where it outperforms the state-of-the-art balanced error rates (BER) by 3%, 6.2%, and 11.4%.
Coherence of quantum channels

We investigate the coherence of quantum channels and establish a resource theory for quantifying the coherence of quantum channels via Choi matrix. To this aim, we define the incoherent channels and incoherent superchannels. This theory recovers the case of quantum states when we view quantum states as a special case of quantum channels and also, this theory allows some analytical expressions for coherence measures.
Density Matching for Bilingual Word Embedding

Chunting Zhou, Xuezhe Ma, Di Wang, Graham Neubig. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Efficient hyperentanglement purification for three-photon systems with the fidelity-robust quantum gates and hyperentanglement link

We present an efficient and faithful hyperentanglement purification protocol (hyper-EPP) for three-photon system in mixed hyperentangled Greenberger-Horne-Zeilinger states with bit-flip errors in both spatial-mode and polarization degrees of freedom (DOFs), resorting to the fidelity-robust quantum gates and hyperentanglement link. Our high-efficiency hyper-EPP comes from two aspects. One is to pump the higher-fidelity hyperentanglement from different three-photon systems into the same three-photon system with fidelity-robust swap gates, the other is to reproduce some hyperentangled three-photon systems from hyperentangled two-photon subsystems based on hyperentanglement link. Moreover, as the infidelity originating from imperfect single-photon scattering can be heralded as a failure by triggering a detector, our hyper-EPP operates faithfully with the present quantum circuits. Furthermore, our hyper-EPP can be directly extended to purify multiple photon systems entangled in one DOF or hyperentangled in multiple DOFs.
Analysis and Impact of Vulnerability Assessment and Penetration Testing

The rapid increase of the machinery whether its mobile or computer systems have brought more advance and efficient Windows, Web and Mobile applications but it also increases the complexity in systems which ultimately leads to vulnerabilities that attackers use to exploit the victim systems. In decades, the uses of web applications and web hacking activities have been amplified swiftly. In today's world organizations and institution find themselves in complex situation for securing their system and data from increasing vulnerabilities that is why it would be better to discover and identify these vulnerabilities in advance before attacker can exploit them. Thus vulnerability assessment and penetration testing techniques helps it to determine whether the arrangements in securing system are working properly or not by fixing those security gaps This paper will be on discussing and analyzing about life cycle of VAPT process and VAPT tools for finding vulnerabilities in system. We will also focus its importance at various organizational levels for adoption requirement of updating security measures in order to provide protection from various cyber-attacks.
Pigeon

In today's datacenters, job heterogeneity makes it difficult for schedulers to simultaneously meet latency requirements and maintain high resource utilization. The state-of-the-art datacenter schedulers, including centralized, distributed, and hybrid schedulers, fail to ensure low latency for short jobs in large-scale and highly loaded systems. The key issues are the scalability in centralized schedulers, ineffective and inefficient probing and resource sharing in both distributed and hybrid schedulers.
Segmentation of MR Brain Images Through Hidden Markov Random Field and Hybrid Metaheuristic Algorithm

Image segmentation is one of the most critical tasks in Magnetic Resonance (MR) images analysis. Since the performance of most current image segmentation methods is suffered by noise and intensity non-uniformity artifact (INU), a precise and artifact resistant method is desired. In this work, we propose a new segmentation method combining a new Hidden Markov Random Field (HMRF) model and a novel hybrid metaheuristic method based on Cuckoo search (CS) and Particle swarm optimization algorithms (PSO). The new model uses adaptive parameters to allow balancing between the segmented components of the model. In addition, to improve the quality of searching solutions in the Maximum a posteriori (MAP) estimation of the HMRF model, the hybrid metaheuristic algorithm is introduced. This algorithm takes into account both the advantages of CS and PSO algorithms in searching ability by cooperating them with the same population in a parallel way and with a solution selection mechanism. Since CS and PSO are performing exploration and exploitation in the search space, respectively, hybridizing them in an intelligent way can provide better solutions in terms of quality. Furthermore, initialization of the population is carefully taken into account to improve the performance of the proposed method. The whole algorithm is evaluated on benchmark images including both the simulated and real MR brain images. Experimental results show that the proposed method can achieve satisfactory performance for images with noise and intensity inhomogeneity, and provides better results than its considered competitors.
Using Diaries to Probe the Illness Experiences of Adolescent Patients and Parental Caregivers

Adolescents with chronic conditions must work with family caregivers to manage their illness experiences. To explore how technology can support collaborative documentation of these experiences, we designed and distributed a paper diary probe kit in a two-week field deployment with 12 adolescent-parent dyads (24 participants). Three insights emerged from the study that highlight how technology can support shared illness management: 1) provide scaffolds to recognize physical and emotional experiences in the context of daily activities; 2) help families reconstruct patient experiences; and 3) adapt to individual preferences for capturing, representing and sharing experiences. We discuss opportunities for HCI research that follow from these findings and conclude by reflecting on the benefits and limitations of using diary probes with adolescent patients and their parental caregivers.
Industry-Academia research collaboration in software engineering: The Certus model

Context: Research collaborations between software engineering industry and academia can provide significant benefits to both sides, including improved innovation capacity for industry, and real-world environment for motivating and validating research ideas. However, building scalable and effective research collaborations in software engineering is known to be challenging. While such challenges can be varied and many, in this paper we focus on the challenges of achieving participative knowledge creation supported by active dialog between industry and academia and continuous commitment to joint problem solving. Objective: This paper aims to understand what are the elements of a successful industry-academia collaboration that enable the culture of participative knowledge creation. Method: We conducted participant observation collecting qualitative data spanning 8 years of collaborative research between a software engineering research group on software V&V and the Norwegian IT sector. The collected data was analyzed and synthesized into a practical collaboration model, named the Certus Model. Results: The model is structured in seven phases, describing activities from setting up research projects to the exploitation of research results. As such, the Certus model advances other collaborations models from literature by delineating different phases covering the complete life cycle of participative research knowledge creation. Conclusion: The Certus model describes the elements of a research collaboration process between researchers and practitioners in software engineering, grounded on the principles of research knowledge co-creation and continuous commitment to joint problem solving. The model can be applied and tested in other contexts where it may be adapted to the local context through experimentation.
Quantitative wave–particle duality relations from the density matrix properties

We derive upper bounds for Hilbert–Schmidt’s quantum coherence of general states of a d-level quantum system, a qudit, in terms of its incoherent uncertainty, with the latter quantified using the linear and von Neumann’s entropies of the corresponding closest incoherent state. Similar bounds are obtained for Wigner–Yanase’s coherence. The reported inequalities are also given as coherence–populations trade-off relations. As an application example of these inequalities, we derive quantitative wave–particle duality relations for multi-slit interferometry. Our framework leads to the identification of predictability measures complementary to Hilbert–Schmidt’s, Wigner–Yanase’s, and $$l_{1}$$ -norm quantum coherences. The quantifiers reported here for the wave and particle aspects of a quanton follow directly from the defining properties of the quantum density matrix (i.e., semi-positivity and unit trace), contrasting thus with most related results from the literature.
Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network

High accuracy video label prediction (classification) models are attributed to large scale data. These data could be frame feature sequences extracted by a pre-trained convolutional-neural-network, which promote the efficiency for creating models. Unsupervised solutions such as feature average pooling, as a simple label-independent parameter-free based method, has limited ability to represent the video. While the supervised methods, like RNN, can greatly improve the recognition accuracy. However, the video length is usually long, and there are hierarchical relationships between frames across events in the video, the performance of RNN based models are decreased. In this paper, we proposes a novel video classification method based on a deep convolutional graph neural network(DCGN). The proposed method utilize the characteristics of the hierarchical structure of the video, and performed multi-level feature extraction on the video frame sequence through the graph network, obtained a video representation re ecting the event semantics hierarchically. We test our model on YouTube-8M Large-Scale Video Understanding dataset, and the result outperforms RNN based benchmarks.
Classification-Specific Parts for Improving Fine-Grained Visual Categorization

Fine-grained visual categorization is a classification task for distinguishing categories with high intra-class and small inter-class variance. While global approaches aim at using the whole image for performing the classification, part-based solutions gather additional local information in terms of attentions or parts. We propose a novel classification-specific part estimation that uses an initial prediction as well as back-propagation of feature importance via gradient computations in order to estimate relevant image regions. The subsequently detected parts are then not only selected by a-posteriori classification knowledge, but also have an intrinsic spatial extent that is determined automatically. This is in contrast to most part-based approaches and even to available ground-truth part annotations, which only provide point coordinates and no additional scale information. We show in our experiments on various widely-used fine-grained datasets the effectiveness of the mentioned part selection method in conjunction with the extracted part features.
Hamiltonian Engineering for Adiabatic Quantum Computation: Lessons from Shortcuts to Adiabaticity

We discuss applications of shortcuts to adiabaticity (STA) to adiabatic quantum computation.After reviewing the fundamental properties and the present status of STA from the author's personal point of view, we apply the method to the adiabatic algorithm of the Grover's problem.We discuss two possible implementations of STA for the adiabatic quantum computations: the method of quantum adiabatic brachistochrone and the Lewis-Riesenfeld invariant-based inverse engineering.
Rank-metric codes and q-polymatroids

This paper contributes to the study of rank-metric codes from an algebraic and combinatorial point of view. We introduce q-polymatroids, the q-analogue of polymatroids, and develop their basic properties. We associate a pair of q-polymatroids with a rank-metric code and show that several invariants and structural properties of the code, such as generalized weights, the property of being MRD or an optimal anticode, and duality, are captured by the associated combinatorial object.
Quantum Encryption with Certified Deletion

Given a ciphertext, is it possible to prove the deletion of the underlying plaintext? Since classical ciphertexts can be copied, clearly such a feat is impossible using classical information alone. In stark contrast to this, we show that quantum encodings enable certified deletion. More precisely, we show that it is possible to encrypt classical data into a quantum ciphertext such that the recipient of the ciphertext can produce a classical string which proves to the originator that the recipient has relinquished any chance of recovering the plaintext should the decryption key be revealed. Our scheme is feasible with current quantum technology: the honest parties only require quantum devices for single-qubit preparation and measurements; the scheme is also robust against noise in these devices. Furthermore, we provide an analysis that is suitable in the finite-key regime.
Building A blockchain-based decentralized digital asset management system for commercial aircraft leasing

The blockchain technology has the potential to drastically change the way record-keeping and asset management is done. While more and more aerospace companies are starting to adopt the new technology, commercial aircraft leasing can still be considered uncharted territory for blockchain applications. The present paper analyzes how the sector stands to benefit from such a solution and proposes a suitable blockchain application design, focusing both on the regulatory and business needs as well as the implementation from a technological perspective. Our proof-of-concept demonstrates that asset management in commercial aircraft leasing can realistically be handled by a blockchain solution such as the one proposed in this paper.
Particle swarm optimization with state-based adaptive velocity limit strategy

Velocity limit (VL) has been widely adopted in many variants of particle swarm optimization (PSO) to prevent particles from searching outside the solution space. Several adaptive VL strategies have been introduced with which the performance of PSO can be improved. However, the existing adaptive VL strategies simply adjust their VL based on iterations, leading to unsatisfactory optimization results because of the incompatibility between VL and the current searching state of particles. To deal with this problem, a novel PSO variant with state-based adaptive velocity limit strategy (PSO-SAVL) is proposed. In the proposed PSO-SAVL, VL is adaptively adjusted based on the evolutionary state estimation (ESE) in which a high value of VL is set for global searching state and a low value of VL is set for local searching state. Besides that, limit handling strategies have been modified and adopted to improve the capability of avoiding local optima. The good performance of PSO-SAVL has been experimentally validated on a wide range of benchmark functions with 50 dimensions. The satisfactory scalability of PSO-SAVL in high-dimension and large-scale problems is also verified. Besides, the merits of the strategies in PSO-SAVL are verified in experiments. Sensitivity analysis for the relevant hyper-parameters in state-based adaptive VL strategy is conducted, and insights in how to select these hyper-parameters are also discussed.
Incompatibility in quantum parameter estimation

In this paper we introduce a measure of genuine quantum incompatibility in the estimation task of multiple parameters, that has a geometric character and is backed by a clear operational interpretation. This measure is then applied to some simple systems in order to track the effect of a local depolarizing noise on the incompatibility of the estimation task. A semidefinite program is described and used to numerically compute the figure of merit when the analytical tools are not sufficient, among these we include an upper bound computable from the symmetric logarithmic derivatives only. Finally we discuss how to obtain compatible models for a general unitary encoding on a finite dimensional probe.
Unconstrained binary models of the travelling salesman problem variants for quantum optimization

Abstract Quantum computing is offering a novel perspective for solving combinatorial optimization problems. To fully explore the possibilities offered by quantum computers, the problems need to be formulated as unconstrained binary models, taking into account limitation and advantages of quantum devices. In this work, we provide a detailed analysis of the travelling salesman problem with time windows (TSPTW) in the context of solving it on a quantum computer. We introduce quadratic unconstrained binary optimization and higher-order binary optimization formulations of this problem. We demonstrate the advantages of edge-based and node-based formulations of the TSPTW problem. Additionally, we investigate the experimental realization of the presented methods on a quantum annealing device. The provided results pave the path for utilizing quantum computer for a variety of real-world tasks which can be cast in the form of travelling salesman problem with time windows.
Reinforcement Learning With Human Advice: A Survey

In this paper, we provide an overview of the existing methods for integrating human advice into a reinforcement learning process. We first propose a taxonomy of the different forms of advice that can be provided to a learning agent. We then describe the methods that can be used for interpreting advice when its meaning is not determined beforehand. Finally, we review different approaches for integrating advice into the learning process.