Reviewerly/exHarmony huggingface dataset:
Multi-core fiber integrated multi-port beam splitters for quantum information processing

Multi-port beam splitters are cornerstone devices for high-dimensional quantum information tasks, which can outperform the two-dimensional ones. Nonetheless, the fabrication of such devices has proven to be challenging with progress only recently achieved with the advent of integrated photonics. Here, we report on the production of high-quality <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>N</mml:mi> <mml:mo>×<!-- × --></mml:mo> <mml:mi>N</mml:mi> </mml:math> (with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>4</mml:mn> <mml:mo>,</mml:mo> <mml:mn>7</mml:mn> </mml:math> ) multi-port beam splitters based on a new scheme for manipulating multi-core optical fibers. By exploring their compatibility with optical fiber components, we create four-dimensional quantum systems and implement the measurement-device-independent random number generation task with a programmable four-arm interferometer operating at a 2 MHz repetition rate. Due to the high visibilities observed, we surpass the one-bit limit of binary protocols and attain 1.23 bits of certified private randomness per experimental round. Our result demonstrates that fast switching, low loss, and high optical quality for high-dimensional quantum information can be simultaneously achieved with multi-core fiber technology.
Denoising-Based Multiscale Feature Fusion for Remote Sensing Image Captioning

With the benefits from deep learning technology, generating captions for remote sensing images has become achievable, and great progress has been made in this field in the recent years. However, a large-scale variation of remote sensing images, which would lead to errors or omissions in feature extraction, still limits the further improvement of caption quality. To address this problem, we propose a denoising-based multi-scale feature fusion (DMSFF) mechanism for remote sensing image captioning in this letter. The proposed DMSFF mechanism aggregates multiscale features with the denoising operation at the stage of visual feature extraction. It can help the encoder-decoder framework, which is widely used in image captioning, to obtain the denoising multiscale feature representation. In experiments, we apply the proposed DMSFF in the encoder-decoder framework and perform the comparative experiments on two public remote sensing image captioning data sets including UC Merced (UCM)-captions and Sydney-captions. The experimental results demonstrate the effectiveness of our method.
The spotted hyena optimization algorithm for weight-reduction of automobile brake components

Abstract In recent years, metaheuristic methods have been preferred for the optimum design of automobile components, and important results have been accomplished. In this paper, a comparison of the whale optimization algorithm (WOA), the ant lion algorithm(ALO), and the spotted hyena optimization algorithm (SHOA) are presented to show how these optimization methods have been exploited to achieve weight reduction in an automobile brake pedal while maintaining stress requirements. This research is the first in the literature elucidating the application of the SHOA for the optimum design of automobile components. Optimization using the SHOA results in a reduction of 18.1 % of brake pedal weight.
Robust Kernelized Multiview Self-Representation for Subspace Clustering

In this article, we propose a multiview self-representation model for nonlinear subspaces clustering. By assuming that the heterogeneous features lie within the union of multiple linear subspaces, the recent multiview subspace learning methods aim to capture the complementary and consensus from multiple views to boost the performance. However, in real-world applications, data feature usually resides in multiple nonlinear subspaces, leading to undesirable results. To this end, we propose a kernelized version of tensor-based multiview subspace clustering, which is referred to as Kt-SVD-MSC, to jointly learn self-representation coefficients in mapped high-dimensional spaces and multiple views correlation in unified tensor space. In view-specific feature space, a kernel-induced mapping is introduced for each view to ensure the separability of self-representation coefficients. In unified tensor space, a new kind of tensor low-rank regularizer is employed on the rotated self-representation coefficient tensor to preserve the global consistency across different views. We also derive an algorithm to efficiently solve the optimization problem with all the subproblems having closed-form solutions. Furthermore, by incorporating the nonnegative and sparsity constraints, the proposed method can be easily extended to a useful variant, meaning that several useful variants can be easily constructed in a similar way. Extensive experiments of the proposed method are tested on eight challenging data sets, in which a significant (even a breakthrough) advance over state-of-the-art multiview clustering is achieved.
Chance Constrained Extreme Learning Machine for Nonparametric Prediction Intervals of Wind Power Generation

Confronted with considerable intermittence and variability of wind power, prediction intervals (PIs) serve as a crucial tool to assist power system decision-making under uncertainties. Conventional PIs rely on predetermining the lower and upper quantile proportions and therefore suffer from conservative interval width. This paper innovatively develops a chance constrained extreme learning machine (CCELM) model to generate quality nonparametric proportion-free PIs of wind power generation, which minimizes the expected interval width subject to the PI coverage probability constraint. Due to the independency on the preset PI bounds proportions, the proposed CCELM model merits high adaptivity and taps the latent potentialities for PI shortening. The convexity of extreme learning machine renders the sample average approximation counterpart of stochastic CCELM model equivalent to a parameter searching task in parametric optimization problem with polyhedral feasible region. A novel difference of convex functions optimization based bisection search (DCBS) algorithm is proposed to efficiently construct the CCELM model, which successfully realizes machine learning by means of solving linear programming problems sequentially. Comprehensive numerical experiments based on actual wind farm data demonstrate the significant effectiveness and efficiency of the developed CCELM model and DCBS algorithm.
Secure and Efficient Distributed Network Provenance for IoT: A Blockchain-Based Approach

Network provenance is essential for Internet-of-Things (IoT) network administrators to conduct the network diagnostics and identify root causes of network errors. However, the distributed nature of the IoT network results in the management of the provenance data at different trust domains, which poses concerns on the security and trustworthiness of the cross-domain network diagnostics. In this article, we propose a blockchain-based architecture for secure and efficient distributed network provenance (SEDNP) in the IoT. Instead of directly storing and querying the whole provenance data on the blockchain with prohibitive implementation cost, we introduce a unified provenance query model and develop a provenance digest strategy that: 1) enables compact (constant size) on-blockchain digests of provenance data and a multilevel index regardless of provenance data volume and 2) ensures the correctness and integrity of provenance query results through the verification of the on-blockchain digests. We formally define the security requirements as Archiving Security along with thorough security analysis. Moreover, we conduct extensive experiments with the integration of a verifiable computation (VC) framework and a blockchain testing network. The experimental results are provided as performance benchmarks to demonstrate the application feasibility of SEDNP.
A brief survey of visual saliency detection

Salient object detection models mimic the behavior of human beings and capture the most salient region/object from the images or scenes, this field contains many important applications in both computer vision and pattern recognition tasks. Despite hundreds of models that have been proposed in this field, but still, it requires a large room for research. This paper demonstrates a detailed overview of the recent progress of saliency detection models in terms of heuristic-based techniques and deep learning-based techniques. we have discussed and reviewed its co-related fields, such as Eye-fixation-prediction, RGBD salient-object-detection, co-saliency object detection, and video-saliency-detection models. We have reviewed the key issues of the current saliency models and discussed future trends and recommendations. The broadly utilized datasets and assessment strategies are additionally investigated in this paper.
Blind quality assessment for image superresolution using deep two-stream convolutional networks

Numerous image superresolution (SR) algorithms have been proposed for reconstructing high-resolution (HR) images from input images with lower spatial resolutions. However, effectively evaluating the perceptual quality of SR images remains a challenging research problem. In this paper, we propose a no-reference/blind deep neural network-based SR image quality assessor (DeepSRQ). To learn more discriminative feature representations of various distorted SR images, the proposed DeepSRQ is a two-stream convolutional network including two subcomponents for distorted structure and texture SR images. Different from traditional image distortions, the artifacts of SR images cause both image structure and texture quality degradation. Therefore, we choose the two-stream scheme that captures different properties of SR inputs instead of directly learning features from one image stream. Considering the human visual system (HVS) characteristics, the structure stream focuses on extracting features in structural degradations, while the texture stream focuses on the change in textural distributions. In addition, to augment the training data and ensure the category balance, we propose a stride-based adaptive cropping approach for further improvement. Experimental results on three publicly available SR image quality databases demonstrate the effectiveness and generalization ability of our proposed DeepSRQ method compared with state-of-the-art image quality assessment algorithms.
Homomorphic encryption systems statement: Trends and challenges

For securing our own systems, encryption got the major interest, especially when talking about homomorphic encryption, which has spread like wildfire. Therefore, in this study, we will be presenting different known cryptosystems based, in a great part of its construction, on the homomorphic encryption, all joined with other techniques to enhance the cryptosystem performance and the privacy ratio. In addition, the homomorphic encryption has the feature to be highly adequate with any field it is used in, giving numerous advantages and a tremendous performance. Hence, the following survey presents different domains using homomorphic encryption and a final comparison between the adopted techniques.
Acoustic information about upper limb movement in voicing

We show that the human voice has complex acoustic qualities that are directly coupled to peripheral musculoskeletal tensioning of the body, such as subtle wrist movements. In this study, human vocalizers produced a steady-state vocalization while rhythmically moving the wrist or the arm at different tempos. Although listeners could only hear and not see the vocalizer, they were able to completely synchronize their own rhythmic wrist or arm movement with the movement of the vocalizer which they perceived in the voice acoustics. This study corroborates recent evidence suggesting that the human voice is constrained by bodily tensioning affecting the respiratory-vocal system. The current results show that the human voice contains a bodily imprint that is directly informative for the interpersonal perception of another's dynamic physical states.
Credit Card Fraud Detection Based on Machine and Deep Learning

With the rapid evolution of the technology, the world is turning to use credit cards instead of cash in their daily life, which opens the door to many new ways for fraudulent people to use these cards in a bad way. According to the Nilson report, global card losses are expected to exceed $35 billion by 2020. To ensure the safety of users for these credit cards, the credit card’s provider should provide a service to protect users from any risk they may face. Consequently, we present our approach to predict legitimate or fraud transactions on the IEEE-CIS Fraud Detection dataset provided by Kaggel. Our model is BiLSTM- MaxPooling-BiGRU-MaxPooling which based on bidirectional Long short-term memory (BiLSTM) and bidirectional Gated recurrent unit (BiGRU). We also applied six machine learning classifiers which are: Naïve base, Voting, Ada boosting, Random Forest, Decision Tree, and Logistic Regression. Comparing the results from machine learning classifiers and our model the results show that our model achieved better as we got 91.37% score.
Interactive autodidactic school: A new metaheuristic optimization algorithm for solving mathematical and structural design optimization problems

A new efficient and robust metaheuristic algorithm called “Interactive Autodidactic School (IAS)” is proposed in this paper to solve numerical optimization and structural design optimization problems. IAS is a population-based algorithm on the basis of the interactions between students in an autodidactic school with the goal of increasing their knowledge through a combination of self-teaching/self-learning, interactive discussion, criticism, and the competition. IAS is tested in twenty mathematical optimization and seven structural optimization problems. Subsequently, its optimum solution is compared with other well-known optimization algorithms. The obtained results confirmed that the proposed IAS algorithm gives best optimal solution and has excellent performance compared with other optimization methods.
A fault-tolerance shim for serverless computing

Serverless computing has grown in popularity in recent years, with an increasing number of applications being built on Functions-as-a-Service (FaaS) platforms. By default, FaaS platforms support retry-based fault tolerance, but this is insufficient for programs that modify shared state, as they can unwittingly persist partial sets of updates in case of failures. To address this challenge, we would like atomic visibility of the updates made by a FaaS application.
Adversarial attacks on deep-learning-based radar range profile target recognition

Target recognition based on a high-resolution range profile (HRRP) has always been a research hotspot in the radar signal interpretation field. Deep learning has been an important method for HRRP target recognition. However, recent research has shown that optical image target recognition methods based on deep learning are vulnerable to adversarial samples. Whether HRRP target recognition methods based on deep learning can be attacked remains an open question. In this paper, four methods of generating adversarial perturbations are proposed. Algorithm 1 generates the nontargeted fine-grained perturbation based on the binary search method. Algorithm 2 generates the targeted fine-grained perturbation based on the multiple-iteration method. Algorithm 3 generates the nontargeted universal adversarial perturbation (UAP) based on aggregating some fine-grained perturbations. Algorithm 4 generates the targeted universal perturbation based on scaling one fine-grained perturbation. These perturbations are used to generate adversarial samples to attack HRRP target recognition methods based on deep learning under white-box and black-box attacks. The experiments are conducted with actual radar data and show that the HRRP adversarial samples have certain aggressiveness. Therefore, HRRP target recognition methods based on deep learning have potential security risks.
Performance Evaluation of Hyperledger Fabric

Blockchain is a distributed secure ledger that eliminates the need for centralized authority to store data. The centralized approach has several limitations as it is a Single-Point-of-Failure and a third-party might be needed. Blockchain, on the other hand, provides decentralized, secure and trustless framework that eliminates the need for a third party and enhances fault tolerance. In this paper, we investigate the potentials of customizing the behavior of Blockchain network based on the applications' requirements. In particular, we conduct several experiments to evaluate the performance of the Hyperledger Fabric (HLF) - a permissioned blockchain framework. Seven different scenarios were tested to depict the Blockchain behavior in terms of end-to-end transaction latency and network throughput. Moreover, in these scenarios, the impact of different parameters, such as the batch-timeout, batch size, and number of endorsing peers, has been studied.
Feature selection based on improved binary global harmony search for data classification

Harmony search (HS) is an effective meta-heuristic algorithm inspired by the music improvisation process, where musicians search for a pleasing harmony by adjusting their instruments' pitches. The HS algorithm and its variants have been widely used to solve binary and continuous optimization problems. In this paper, we propose an improved binary global harmony search algorithm, called IBGHS, to undertake feature selection problems. A modified improvisation step is introduced to enhance the global search ability and increase the convergence speed of the algorithm. In addition, the K-nearest neighbor (KNN) is used as an underlying learning model to evaluate the effectiveness of the selected feature subsets. The experimental results on eighteen benchmark problems indicate that the proposed IBGHS algorithm is able to produce comparable results as compared with other state-of-the-art population-based methods such as genetic algorithm (GA), particle swarm optimization (PSO), antlion optimizer (ALO), novel global harmony search (NGHS) and whale optimization algorithm (WOA) in solving feature selection problems.
Decentralized Access Control for IoT Data Using Blockchain and Trusted Oracles

The Internet of Things (IoT) is a network of connected electromechanical devices that have limited computational, networking, and storage capabilities. IoT is now widely used in healthcare, smart cars, smart grids, smart homes, smart manufacturing, and smart cities. IoT devices sense, monitor, and collect data where it can be shared with legitimate users. IoT data can be aggregated, stored and made available by multiple IoT data hosting providers. IoT data storage, management, and access involve multiple stakeholders that many include admins, owners of IoT devices, data repository hosts and providers, normal users, etc. Decentralized control and trusted management of such IoT data become critical, in which the management and access control of data is not centralized, i.e., under the control of a single entity. To date, the available methods for for access control in IoT systems are mainly centralized. In this paper, we propose a decentralized access control system for IoT data using blockchain and trusted oracles. We use features of blockchain and smart contracts to propose a decentralized, scalable, and secure management solution for accessing IoT data. In addition, we use oracles as gateways that interface with the blockchain, IoT data hosts, and remote users to provide decentralized, trusted, and uniform source feeds for IoT data. The paper also presents architectural design, interactions, logic flow, algorithms, implementation details, along with cost, computation, and security evaluation. The full code of the developed smart contracts is made publicly available at GitHub.
Using Ethereum blockchain to store and query pharmacogenomics data via smart contracts

Abstract Background As pharmacogenomics data becomes increasingly integral to clinical treatment decisions, appropriate data storage and sharing protocols need to be adopted. One promising option for secure, high-integrity storage and sharing is Ethereum smart contracts. Ethereum is a blockchain platform, and smart contracts are immutable pieces of code running on virtual machines in this platform that can be invoked by a user or another contract (in the blockchain network). The 2019 iDASH (Integrating Data for Analysis, Anonymization, and Sharing) competition for Secure Genome Analysis challenged participants to develop time- and space-efficient Ethereum smart contracts for gene-drug relationship data. Methods Here we design a specific smart contract to store and query gene-drug interactions in Ethereum using an index-based, multi-mapping approach. Our contract stores each pharmacogenomics observation, a gene-variant-drug triplet with outcome, in a mapping searchable by a unique identifier, allowing for time and space efficient storage and query. This solution ranked in the top three at the 2019 IDASH competition. We further improve our ”challenge solution” and develop an alternate ”fastQuery” smart contract, which combines together identical gene-variant-drug combinations into a single storage entry, leading to significantly better scalability and query efficiency. Results On a private, proof-of-authority network, both our challenge and fastQuery solutions exhibit approximately linear memory and time usage for inserting into and querying small databases (&lt;1,000 entries). For larger databases (1000 to 10,000 entries), fastQuery maintains this scaling. Furthermore, both solutions can query by a single field (”0-AND”) or a combination of fields (”1- or 2-AND”). Specifically, the challenge solution can complete a 2-AND query from a small database (100 entries) in 35ms using 0.1 MB of memory. For the same query, fastQuery has a 2-fold improvement in time and a 10-fold improvement in memory. Conclusion We show that pharmacogenomics data can be stored and queried efficiently using Ethereum blockchain. Our solutions could potentially be used to store a range of clinical data and extended to other fields requiring high-integrity data storage and efficient access.
The Politics of Privacy Theories: Moving from Norms to Vulnerabilities

Privacy and surveillance are central features of public discourse around use of computing systems. As the systems we design and study are increasingly used and regulated as potential instruments of surveillance, HCI researchers-even those whose focus is not privacy-find themselves needing to understand privacy in their work. Concepts like contextual integrity and boundary regulation have become touchstones for thinking about privacy in HCI. In this paper, we draw on HCI and privacy literature to understand the limitations of commonly used theories and examine their assumptions, politics, strengths, and weaknesses. We use a case study from the HCI literature to illustrate conceptual gaps in existing frameworks where privacy requirements can fall through. Finally, we advocate vulnerability as a core concept for privacy theorizing and examine how feminist, queer-Marxist, and intersectional thinking may augment our existing repertoire of privacy theories to create a more inclusive scholarship and design practice.