Reviewerly/exHarmony huggingface dataset:
Performance of underwater quantum key distribution with polarization encoding

Underwater quantum key distribution (QKD) has potential applications in absolutely secure underwater communication. However, the performance of underwater QKD is limited by the optical elements, background light, and dark counts of the detector. In this paper, we propose a modified formula for the quantum bit error rate (QBER), which takes into account the effect of detector efficiency on the QBER caused by the background light. Then we calculate the QBER of the polarization encoding BB84 protocol in Jerlov-type seawater by analyzing the effect of the background light and optical components in a more realistic situation. Finally, we further analyze the final key rate and the maximum secure communication distance in three propagation modes, i.e., upward, downward, and horizontal modes. We find that secure QKD can be performed in the clearest Jerlov-type seawater at a distance of hundreds of meters, even in the worst downward propagation mode. Specifically, by optimizing the system parameters, it is possible to securely transmit information with a rate of 67 kbits/s at a distance of 100 m in the seawater channel with an attenuation coefficient of 0.03/m at night. For practical underwater QKD, the performance can also be improved by using decoy states. Our results are useful for long-distance underwater quantum communication.
TherModule

Humans have specific sensory organs and they can feel tactile sensation on the whole body. However, many haptic devices have limitations due to the location of the body part and might not provide natural haptic feedback. Thus, we propose a novel interface, TherModule, which is a wearable and modular thermal feedback system for embodied interactions based on a wireless platform. TherModule can be worn on multiple body parts such as the wrist, forearm, ankle, and neck. In this paper, we describe the system concept, module implementation, and applications. To demonstrate and explore the embodied interaction with thermal feedback, we implemented prototype applications, such as movie experiences, projector-based augmented reality, navigation, and notification based on a wireless platform, with TherModule on multiple parts of the body. The result of an experiment on movie experience showed that participants felt more interactions between temperature and visual stimulus.
Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks

The task of Language-Based Image Editing (LBIE) aims at generating a target image by editing the source image based on the given language description. The main challenge of LBIE is to disentangle the semantics in image and text and then combine them to generate realistic images. Therefore, the editing performance is heavily dependent on the learned representation. In this work, conditional generative adversarial network (cGAN) is utilized for LBIE. We find that existing conditioning methods in cGAN lack of representation power as they cannot learn the second-order correlation between two conditioning vectors. To solve this problem, we propose an improved conditional layer named Bilinear Residual Layer (BRL) to learning more powerful representations for LBIE task. Qualitative and quantitative comparisons demonstrate that our method can generate images with higher quality when compared to previous LBIE techniques.
Optimal joint estimation of multiple Rabi frequencies

We study the joint estimation of multiple Rabi frequencies in a multilevel system. By analytically identifying the optimal probe state and the optimal measurement, we obtain the highest precision limit for the joint estimation of multiple Rabi frequencies. We show that the joint estimation does not always outperform the separate estimation. In different regimes the joint estimation can either outperform or underperform the separate estimation. We further show that if additional adaptive quantum controls are allowed then the advantage of the joint estimation over the separate estimation can be reestablished.
Experimental realization of a momentum-space quantum walk

We report on a discrete-time quantum walk that uses the momentum of ultra-cold rubidium-87 atoms as the walk space and two internal atomic states as the coin degree of freedom. Each step of the walk consists of a coin toss (a microwave pulse) followed by a unitary shift operator (a resonant ratchet pulse). We carry out a comprehensive experimental study on the effects of various parameters, including the strength of the shift operation, coin parameters, noise, and initialization of the system on the behavior of the walk. The walk dynamics can be well controlled in our experiment; potential applications include atom interferometry and engineering asymmetric walks.
Embedding Equality Constraints of Optimization Problems into a Quantum Annealer

Quantum annealers such as D-Wave machines are designed to propose solutions for quadratic unconstrained binary optimization (QUBO) problems by mapping them onto the quantum processing unit, which tries to find a solution by measuring the parameters of a minimum-energy state of the quantum system. While many NP-hard problems can be easily formulated as binary quadratic optimization problems, such formulations almost always contain one or more constraints, which are not allowed in a QUBO. Embedding such constraints as quadratic penalties is the standard approach for addressing this issue, but it has drawbacks such as the introduction of large coefficients and using too many additional qubits. In this paper, we propose an alternative approach for implementing constraints based on a combinatorial design and solving mixed-integer linear programming (MILP) problems in order to find better embeddings of constraints of the type ∑ x i = k for binary variables x i. Our approach is scalable to any number of variables and uses a linear number of ancillary variables for a fixed k.
A Quantum-Inspired Similarity Measure for the Analysis of Complete Weighted Graphs

We develop a novel method for measuring the similarity between complete weighted graphs, which are probed by means of the discrete-time quantum walks. Directly probing complete graphs using discrete-time quantum walks is intractable due to the cost of simulating the quantum walk. We overcome this problem by extracting a commute time minimum spanning tree from the complete weighted graph. The spanning tree is probed by a discrete-time quantum walk which is initialized using a weighted version of the Perron-Frobenius operator. This naturally encapsulates the edge weight information for the spanning tree extracted from the original graph. For each pair of complete weighted graphs to be compared, we simulate a discrete-time quantum walk on each of the corresponding commute time minimum spanning trees and, then, compute the associated density matrices for the quantum walks. The probability of the walk visiting each edge of the spanning tree is given by the diagonal elements of the density matrices. The similarity between each pair of graphs is then computed using either: 1) the inner product or 2) the negative exponential of the Jensen-Shannon divergence between the probability distributions. We show that in both cases the resulting similarity measure is positive definite and, therefore, corresponds to a kernel on the graphs. We perform a series of experiments on publicly available graph datasets from a variety of different domains, together with time-varying financial networks extracted from data for the New York Stock Exchange. Our experiments demonstrate the effectiveness of the proposed similarity measures.
Person Detection from Overhead View: A Survey

In recent years, overhead view based person detection gained importance, due to handling occlusion problem and providing better coverage in scene, as compared to frontal view.In computer vision, overhead based person detection holds significant importance in many applications including person detection, person counting, person tracking, behavior analysis and occlusion free surveillance system, etc.This paper aims to provide a comprehensive survey on recent development and challenges related to person detection from top view.To the best of our knowledge, it is the first attempt which provides the survey of different overhead person detection techniques.This paper provides an overview of state of the art overhead based person detection methods and guidelines to choose the appropriate method in real life applications.The techniques are divided into two main categories: the blob-based techniques and the feature-based techniques.Various detection factors such as field of view, region of interest, color space, image resolution are also examined along with a variety of top view datasets.
LODESTAR

The performance of sparse problems suffers from lack of spatial locality and low memory bandwidth utilization. However, the distribution of non-zero values in the data structures of a class of sparse problems, such as matrix operations in neural networks, is modifiable so that it can be matched with an efficient underlying hardware, such as systolic arrays. Such modification helps addressing the challenges coupled with sparsity. To efficiently execute sparse neural network inference on systolic arrays, we propose a structured pruning algorithm that increases the spatial locality in neural network models, while maintaining the accuracy of inference.
Using Tri-Relation Networks for Effective Software Fault-Proneness Prediction

Software modules and developers are two core elements during the process of software development.Previous studies have shown that analyzing relations between 1) software modules; (2) developers; and (3) modules and developers, is critical to understand how they interact with each other, which ultimately affects software quality.Specifically, relations such as developer contribution relation, module dependency relation, and developer collaboration relation have been used independently or in pairs to build networks for software fault-proneness prediction.However, none of them investigate the joint effort of these three relations.Bearing this in mind, in this paper, we propose a tri-relation network, a weighted network that integrates developer contribution, module dependency, and developer collaboration relations to study their combined impact on software quality.Four network node centrality metrics are further derived from the proposed network to predict the fault-proneness of a given software module at the file level.Moreover, we have explored a mechanism to refine current networks in order to further improve the effectiveness of software fault-proneness prediction.
Secure and Efficient Data Sharing Scheme Based on Certificateless Hybrid Signcryption for Cloud Storage

As cloud service providers are not completely trusted, people are increasingly concerned about security issues such as data confidentiality and user privacy. In many existing schemes, the private key generator (PKG) generates a full private key for each user, which means that the PKG can forge a valid signature or decrypt the ciphertext. To address the issue, we first present a novel certificateless hybrid signcryption (CL-HSC) scheme without pairing, in which the PKG only generates the partial private keys for users. It is provably secure under the Elliptic Curve Computational Diffie-Hellman (EC-CDH) assumption in the random oracle model. Then, we propose a key derivation method by which the data owner only needs to maintain the master key to get rid of the complex key management. By combining our proposed CL-HSC scheme and the key derivation method, we present a secure and efficient data-sharing scheme for cloud storage, which can resist collusion attacks, spoofing attacks, and replay attacks and makes user revocation easier. In addition, compared with some existing schemes, our scheme has a lower computational complexity.
Considerations for handling updates in learned index structures

Machine learned models have recently been suggested as a rival for index structures such as B-trees and hash tables. An optimized learned index potentially has a significantly smaller memory footprint compared to its algorithmic counterparts, which alleviates the relatively high computational complexity of ML models. One unexplored aspect of learned index structures, however, is handling updates to the data and hence the model. In this paper we therefore discuss updates to the data and their implications for the model. Moreover, we suggest a method for eliminating the drift - the error of learned index models caused by the updates to the index- so that the learned model can maintain its performance under higher update rates.
Applicability of Immersive Analytics in Mixed Reality: Usability Study

Nowadays, visual analytics is mainly performed by programming approaches and viewing the results on a desktop monitor. However, due to the capabilities of smart glasses, new user interactions and representation possibilities become possible. This refers especially to 3D visualizations in the medical field, as well as, the industry domain, as valuable depth information can be related to the complex real-world structures and related data, which is also denoted as immersive analytics. However, the applicability of immersive analytics and its drawbacks, especially in the context of mixed reality, are quite unexplored. In order to validate the feasibility of immersive analytics for the aforementioned purposes, we designed and conducted a usability study with 60 participants. More specifically, we evaluated the effects of spatial sounds, performance changes from one analytics task to another, expert status, and compared an immersive analytics approach (i.e., a mixed-reality application) with a desktop-based solution. Participants had to solve several data analytics tasks (outlier's detection and cluster recognition) with the developed mixed-reality application. Thereby, the performance measures regarding time, errors, and movement patterns were evaluated. The separation into groups (low performer vs. high performer) was performed using a mental rotation pretest. When solving analytic tasks in mixed reality, participants changed their movement patterns in the mixed reality setting significantly, while the use of spatial sounds reduced the handling time significantly, but did not affect the movement patterns. Furthermore, the usage of mixed reality for cluster recognition is significantly faster than the desktop-based solution (i.e., a 2D approach). Moreover, the results obtained with self-developed questionnaires indicate 1) that wearing smart glasses are perceived as a potential stressor and 2) that the utilization of sounds is perceived very differently by the participants. Altogether, industry and researchers should consider immersive analytics as a suitable alternative compared to the traditional approaches.
Deploying Blockchain Technology in the Supply Chain

In the rapidly evolving environment of the international supply chain, the traditional network of manufacturers and suppliers has grown into a vast ecosystem made of various products that move through multiple parties and require cooperation among stakeholders. Additionally, the demand for improved product visibility and source-to-store traceability has never been higher. However, traditional data sharing procedures in today’s supply chain are inefficient, costly, and unadaptable as compared to new and innovative technology. Blockchain technology has shown promising results for improving supply chain networks in recent applications and has already impacted our society and lifestyle by reshaping many business and industry processes. In an effort to understand the integration of blockchain technology in the supply chain, this paper systematically summarizes its current status, key characteristics, potential challenges, and pilot applications.
Microphone Identification Using Convolutional Neural Networks

The ability to identify mobile phones through their built-in components has been demonstrated in the literature for various types of sensors including charge coupled devices (CCD)/complementary metal-oxide semiconductors (CMOS), accelerometers, magnetometers, and microphones. The identification is performed by exploiting small but significant differences in the electronic circuits generated during the manufacturing process. Thus, these distinctive traces become an intrinsic property of the electronic components, which can be detected and exploited as a unique fingerprints associated with the mobile phone. Such fingerprints can be used in various scenarios, especially in security and forensics related applications. In this article, the identification of mobile phones through their built-in microphone by means of convolutional neural networks (CNNs) is investigated. In this specific context, CNNs have received very limited attention by the research community so far. An experimental dataset is created by collecting microphone responses from 34 different mobile phones. These responses are then used to perform classification through CNNs. On different experiments, the proposed CNN is able to provide encouraging results; in particular, the achieved identification accuracy of CNNs is superior to the one obtained with more conventional machine learning algorithms like the K-nearest neighbor and support vector machine, also in the presence of additive white Gaussian noise.
Cross-Sentence Grammatical Error Correction

Automatic grammatical error correction (GEC) research has made remarkable progress in the past decade.However, all existing approaches to GEC correct errors by considering a single sentence alone and ignoring crucial cross-sentence context.Some errors can only be corrected reliably using cross-sentence context and models can also benefit from the additional contextual information in correcting other errors.In this paper, we address this serious limitation of existing approaches and improve strong neural encoder-decoder models by appropriately modeling wider contexts.We employ an auxiliary encoder that encodes previous sentences and incorporate the encoding in the decoder via attention and gating mechanisms.Our approach results in statistically significant improvements in overall GEC performance over strong baselines across multiple test sets.Analysis of our cross-sentence GEC model on a synthetic dataset shows high performance in verb tense corrections that require cross-sentence context.
Improving Visual Question Answering by Referring to Generated Paragraph Captions

Paragraph-style image captions describe diverse aspects of an image as opposed to the more common single-sentence captions that only provide an abstract description of the image.These paragraph captions can hence contain substantial information of the image for tasks such as visual question answering.Moreover, this textual information is complementary with visual information present in the image because it can discuss both more abstract concepts and more explicit, intermediate symbolic information about objects, events, and scenes that can directly be matched with the textual question and copied into the textual answer (i.e., via easier modality match).Hence, we propose a combined Visual and Textual Question Answering (VTQA) model which takes as input a paragraph caption as well as the corresponding image, and answers the given question based on both inputs.In our model, the inputs are fused to extract related information by cross-attention (early fusion), then fused again in the form of consensus (late fusion), and finally expected answers are given an extra score to enhance the chance of selection (later fusion).Empirical results show that paragraph captions, even when automatically generated (via an RL-based encoderdecoder model), help correctly answer more visual questions.Overall, our joint model, when trained on the Visual Genome dataset, significantly improves the VQA performance over a strong baseline model.
Look Harder: A Neural Machine Translation Model with Hard Attention

Soft-attention based Neural Machine Translation (NMT) models have achieved promising results on several translation tasks.These models attend all the words in the source sequence for each target token, which makes them ineffective for long sequence translation.In this work, we propose a hard-attention based NMT model which selects a subset of source tokens for each target token to effectively handle long sequence translation.Due to the discrete nature of the hard-attention mechanism, we design a reinforcement learning algorithm coupled with reward shaping strategy to efficiently train it.Experimental results show that the proposed model performs better on long sequences and thereby achieves significant BLEU score improvement on English-German (EN-DE) and English-French (EN-FR) translation tasks compared to the soft-attention based NMT.
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In this paper we investigate the geometric properties of the configuration consisting of a $k$-subspace $\Gamma$ and a canonical subgeometry $\Sigma$ in $\mathrm{PG}(n-1,q^n)$, with $\Gamma\cap\Sigma=\emptyset$. The idea motivating is that such properties are reflected in the algebraic structure of the linear set which is projection of $\Sigma$ from the vertex $\Gamma$. In particular we deal with the maximum scattered linear sets of the line $\mathrm{PG}(1,q^n)$ found by Lunardon and Polverino and recently generalized by Sheekey. Our aim is to characterize this family by means of the properties of the vertex of the projection as done by Csajb\'ok and the first author of this paper for linear sets of pseudoregulus type. With reference to such properties, we construct new examples of scattered linear sets in $\mathrm{PG}(1,q^6)$, yielding also to new examples of MRD-codes in $\mathbb F_q^{6\times 6}$ with left idealiser isomorphic to $\mathbb F_{q^6}$.
Achieving GWAS with homomorphic encryption

Abstract Background One way of investigating how genes affect human traits would be with a genome-wide association study (GWAS). Genetic markers, known as single-nucleotide polymorphism (SNP), are used in GWAS. This raises privacy and security concerns as these genetic markers can be used to identify individuals uniquely. This problem is further exacerbated by a large number of SNPs needed, which produce reliable results at a higher risk of compromising the privacy of participants. Methods We describe a method using homomorphic encryption (HE) to perform GWAS in a secure and private setting. This work is based on a proposed algorithm. Our solution mainly involves homomorphically encrypted matrix operations and suitable approximations that adapts the semi-parallel GWAS algorithm for HE. We leverage upon the complex space of the CKKS encryption scheme to increase the number of SNPs that can be packed within a ciphertext. We have also developed a cache module that manages ciphertexts, reducing the memory footprint. Results We have implemented our solution over two HE open source libraries, HEAAN and SEAL. Our best implementation took 24.70 minutes for a dataset with 245 samples, over 4 covariates and 10643 SNPs. Conclusions We demonstrate that it is possible to achieve GWAS with homomorphic encryption with suitable approximations.