Reviewerly/exHarmony huggingface dataset:
Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces

Current state-of-the-art Neural Architecture Search (NAS) methods neither efficiently scale to multiple hardware platforms, nor handle diverse architectural search-spaces. To remedy this, we present DONNA (Distilling Optimal Neural Network Architectures), a novel pipeline for rapid, scalable and diverse NAS, that scales to many user scenarios. DONNA consists of three phases. First, an accuracy predictor is built using blockwise knowledge distillation from a reference model. This predictor enables searching across diverse networks with varying macro-architectural parameters such as layer types and attention mechanisms, as well as across micro-architectural parameters such as block repeats and expansion rates. Second, a rapid evolutionary search finds a set of pareto-optimal architectures for any scenario using the accuracy predictor and on-device measurements. Third, optimal models are quickly fine-tuned to training-from-scratch accuracy. DONNA is up to 100× faster than MNasNet in finding state-of-the-art architectures on-device. Classifying ImageNet, DONNA architectures are 20% faster than EfficientNet-B0 and Mo-bileNetV2 on a Nvidia V100 GPU and 10% faster with 0.5% higher accuracy than MobileNetV2-1.4x on a Samsung S20 smartphone. In addition to NAS, DONNA is used for search-space extension and exploration, as well as hardware-aware model compression.
Complete and nondestructive distinguishment of many-body Rydberg entanglement via robust geometric quantum operations

Practical application of quantum information requires distinguishing multipartite entanglement in a complete, nondestructive, and robust way. Here we explore the possibility to accomplish such a task using Rydberg atoms based on blockade effect and user-defined-passage-based geometric quantum logic gates. Our proposal focuses on distinguishing groups of many-body Greenberger-Horne-Zeilinger (GHZ) states and groups of cluster states, and for bipartite cases distinguishing Bell states, which are basic ingredients of quantum information processing. We validate our proposal by numerical results, showing complete and nondestructive accomplishment of distinguishing above multipartite entangled states and also robustness against decoherence. We exemplify two practical applications regarding quantum teleportation and quantum dense coding to demonstrate the value of our proposal. This idea is scalable and experimentally relevant and can be readily generalized to other quantum information candidates, such as superconducting circuits, NV centers, as well as trapped-ion systems with different-level structures.
Modeling Quantum programs: challenges, initial results, and research directions

Quantum programming languages provide necessary constructs to program quantum computers. To write such programs, one needs to understand the characteristics of quantum computers such as superposition and entanglement, which are novel as compared to programming with classical computers. Understanding these characteristics requires an understanding of quantum physics. Thus, there is a need to build high-level modeling abstractions of quantum programs for software engineers who are used to program on classical computers to understand and model quantum programs at a high-level of abstraction and independent of quantum platforms. To this end, we present some ideas for developing such quantum software modeling languages, by presenting a conceptual model of quantum programs and an example of modeling the state-based behavior of quantum entanglement program. Moreover, we present some open issues in the area of modeling quantum programs.
A Study on Blockchain Scalability

Blockchain is an emerging technology for various business and industrial applications because of its characteristics like security, immutability, and transparency. But still, blockchain faces the main issue of scalability, which influences several factors such as throughput, which is defined as the number of transactions processed per second (tps), the limited size of the block, cost, and capacity (size of the blockchain) and latency. In this paper, a survey of literature on the various solutions for scalability issues, namely cashing, side chain, off chain, inter-chain, child chain, on chain, and also deep learning methods along with their pros and cons.
Fully Convolutional Pyramidal Networks for Semantic Segmentation

Semantic segmentation networks focus on the scene parsing of an unrestricted open scene.The typical segmentation architectures are stacks consisting of convolutional layers, which are used to extract semantic features.The feature map dimension is sharply changed at sampling units for most of networks, which ensure effective propagation of the gradient in deep nets.In this paper, we proposed a state-of-the-art network model named Fully Convolutional Pyramidal Networks (FC-PRNet), which employs pyramidal residual structure to change the feature map dimension at all convolutional layers.This design is an effective way of improving generalization ability and optimizing parameters, and FC-PRNet could achieve excellent capability of semantic extraction.We used urban scene benchmark CamVid and KITTI dataset to test our network, the experimental results show that FC-PRNet achieves better results without any pre-training or post-treatment module.Moreover, due to smart construction of pyramidal residual structures, FC-PRNet has less parameters than other existing networks trained on these datasets.
Toward Quantum Gate-Model Heuristics for Real-World Planning Problems

Many challenging scheduling, planning, and resource allocation problems come with realworld input data and hard problem constraints, and reduce to optimizing a cost function over a combinatorially defined feasible set, such as colorings of a graph.Toward tackling such problems with quantum computers using quantum approximate optimization algorithms, we present novel efficient quantum alternating operator ansatz (QAOA) constructions for optimization problems over proper colorings of chordal graphs.As our primary application, we consider the flight-gate assignment problem, where flights are assigned to airport gates as to minimize the total transit time of all passengers, and feasible assignments correspond to proper graph colorings of a conflict graph derived instancewise from the input data.We leverage ideas from classical algorithms and graph theory to show our constructions have the desirable properties of restricting quantum state evolution to the feasible subspace, and satisfying a particular reachability condition for most problem parameter regimes.Using classical preprocessing we show that we can always find and construct a suitable initial quantum (superposition) state efficiently.We show our constructions in detail, including explicit decompositions to a universal set of basic quantum gates, which we use to bound the required resource scaling as low-degree polynomials of the input parameters.In particular, we derive novel QAOA mixing operators and show that their implementation cost is commensurate with that of the QAOA phase operator for flight-gate assignment.A number of quantum circuit diagrams are included such that our constructions may be used as a template toward development and implementation of quantum gate-model approaches for a wider variety of potentially impactful real-world applications.
High-Capacity Image Steganography Based on Improved Xception

The traditional cover modification steganography method only has low steganography ability. We propose a steganography method based on the convolutional neural network architecture (Xception) of deep separable convolutional layers in order to solve this problem. The Xception architecture is used for image steganography for the first time, which not only increases the width of the network, but also improves the adaptability of network expansion, and adds different receiving fields to carry out multi-scale information in it. By introducing jump connections, we solved the problems of gradient dissipation and gradient descent in the Xception architecture. After cascading the secret image and the mask image, high-quality images can be reconstructed through the network, which greatly improves the speed of steganography. When hiding, only the secret image and the cover image are cascaded, and then the secret image can be embedded in the cover image through the hidden network in order to obtain the secret image. After extraction, the secret image can be reconstructed by bypassing the secret image through the extraction network. The results show that the results that are obtained by our model have high peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), and the average high load capacity is 23.96 bpp (bit per pixel), thus realizing large-capacity image steganography surgery.
Property ownership and the legal personhood of artificial intelligence

This paper adds to the discussion on the legal personhood of artificial intelligence by focusing on one area not covered by previous works on the subject – ownership of property. The author discusses the nexus between property ownership and legal personhood. The paper explains the prevailing misconceptions about the requirements of rights or duties in legal personhood, and discusses the potential for conferring rights or imposing obligations on weak and strong AI. While scholars have discussed AI owning real property and copyright, there has been limited discussion on the nexus of AI property ownership and legal personhood. The paper discusses the right to own property and the obligations of property ownership in nonhumans, and applying it to AI. The paper concludes that the law may grant property ownership and legal personhood to weak AI, but not to strong AI.
A toolbox for visualizing trends in large-scale environmental data

Generalized additive models are increasingly used to identify and describe environmental trends. A major advantage of these models, as compared to simpler statistical tools such as linear regression or Mann-Kendall tests, is that they provide estimates of prevailing levels and trend magnitudes at any given point in time instead of an overall measure. For multiple time series, this versatility has to be followed by flexible visualization methods that can summarize and visualize trend analysis results for many series simultaneously. Here, we propose several types of visualizations and illustrate the methods by showing trends in variables related to the recovery from acidification in Swedish riverine data over the period 1988–2017. By this, we show that generalized additive models, together with a small number of selected plots, can comprehensively illustrate prevailing trends and summarize complex information from multiple series.
Clothes image caption generation with attribute detection and visual attention model

Fashion is a multi-billion-dollar industry, which is directly related to social, cultural, and economic implications in the real world. While computer vision has demonstrated remarkable success in the applications of the fashion domain, natural language processing technology has become contributed in the area, so that it can build the connection between clothes image and human semantic understandings. An element work for combing images and language understanding is how to generate a natural language sentence that accurately summarizes the contents of a clothes image. In this paper, we develop a joint attribute detection and visual attention framework for clothes image captioning. Specifically, in order to involve more attributes of clothes to learn, we first utilize a pre-trained Convolutional Neural Network (CNN) to learn the feature that can characterize more information about clothing attribute. Based on such learned feature, we then adopt an encoder/decoder framework, where we first encoder the feature of clothes and then and input it to a language Long Short-Term Memory(LSTM) model for decoding the clothes descriptions. The method greatly enhances the performance of clothes image captioning and reduces the misleading attention. Extensive simulations based on real-world data verify the effectiveness of the proposed method.
Application of information communication network security management and control based on big data technology

Summary With the continuous development of communication technology and computer network technology and the continuous progress of social economy, information technology has penetrated into all aspects of people's lives. The basic task of communication network is to ensure the reliability and security of information transmission. Based on the above background, the purpose of this study is to study the application of information communication network security management and control based on big data technology. Based on the concept and management and control structure of information communication network, combined with the data collection and preprocessing in big data technology, this study analyzes the feasibility of big data technology applied in communication network management and control and expounds the specific application of big data technology from the four aspects of troubleshooting, security protection, business management, and resource allocation. At last, the test environment of information communication network security management and control platform based on big data technology is built in the laboratory to test the network management performance and routing transmission performance of the system. The experimental results show that the communication success rate of the information communication network security management and control platform based on big data technology remains to 91.78%. It can be seen from the test results that the information communication network security management and control platform based on big data technology can not only accurately collect sensor data but also have the function of real‐time monitoring network status and reconfiguration of nodes.
Learning a deep network with cross-hierarchy aggregation for crowd counting

Crowd counting, a significant but challenging task in computer vision, aims at estimating the number of people in an image or video. Recent methods for crowd counting have obtained promising performance due to deep neural networks but most of them ignore the abundant conducive information in hierarchical features. In this paper, a novel Cross-Hierarchy Aggregation Network (CHANet) is proposed to exploit multi-hierarchy information in the crowd features from each hierarchy and aggregate cross-hierarchy features to generate reasonable density maps. Firstly, we propose a CHA module to fully extract local hierarchical features and capture maximum information of the crowd features. The CHA module combines residual and dense connections without over-assigning parameters for feature reuse. Then, we utilize the global hierarchical features from the shallow hierarchies to obtain a more powerful representation ability with a global residual connection. Experimental evaluations on four publicly available crowd counting datasets (ShanghaiTech, UCF-QNRF, WorldExpo’10, and Beijing BRT) demonstrate that the proposed CHANet achieves superior performance compared to other state-of-the-art methods.
Enhancing Participation Experience in VR Live Concerts by Improving Motions of Virtual Audience Avatars

While participating in live concerts is a promising application of virtual reality (VR), it falls short of our participation experience in the real world. In particular, to increase the engagement of participants, previous studies emphasized the importance of social experience among audience members, such as the sense of co-presence elicited by sharing physical reactions or body movements synchronized with music. In this respect, a common strategy in existing platforms is to present avatars of remote human participants in a VR venue and make every avatar imitate movements of the corresponding participant. However, this strategy implicitly assumes that a not small number of users connect simultaneously to watch the same content and thus is not applicable when only a few users gather or a user is watching alone. Therefore, with the aim of providing better experience to a user who participates in live concerts as one of the audience, we examine computational approaches to enhancing the sense of co-presence through virtual audience avatars. We propose four methods of presenting avatar movements: copying the user’s own movements, copying other users’ movements, repeating beat-synchronous movements, and synthesizing machine-learning-based movements. We compare their effectiveness in a user experiment and discuss application scenarios and design implications that open up new ways of active media consumption in VR environments.
SciKGraph: A knowledge graph approach to structure a scientific field

Understanding the structure of a scientific domain and extracting specific information from it is laborious. The high amount of manual effort required to this end indicates that the way knowledge has been structured and visualized until the present day should be improved in software tools. Nowadays, scientific domains are organized based on citation networks or bag-of-words techniques, disregarding the intrinsic semantics of concepts presented in literature documents. We propose a novel approach to structure scientific fields, which uses semantic analysis from natural language texts to construct knowledge graphs. Then, our approach clusters knowledge graphs in their main topics and automatically extracts information such as the most relevant concepts in topics and overlapping concepts between topics. We evaluate the proposed model in two datasets from distinct areas. The results achieve up to 84% of accuracy in the task of document classification without using annotated data to segment topics from a set of input documents. Our solution identifies coherent keyphrases and key concepts considering the dataset used. The SciKGraph framework contributes by structuring knowledge that might aid researchers in the study of their areas, reducing the effort and amount of time devoted to groundwork.
Multi-Function Radar Signal Sorting Based on Complex Network

In complex electromagnetic environments, the challenge of signal sorting task for multi-function radars (MFRs) with various work modes has arisen. The previous methods are prone to cause the so-called “increasing batch” problem, which means that the work modes of one MFR may be sorted into multiple emitters. In this letter, a MFR signal sorting method based on complex network is proposed to tackle the problem mentioned above. The novel method utilizes limited penetrable visibility graph to construct the network from interleaved radar pulse sequences, then employs label propagation algorithm and density peak clustering to detect community structures, thus fulfilling deinterleaving of pulses from several MFRs. Simulation results show that the proposed method is effective to alleviate the “increasing batch” problem, and is also robust under non-ideal conditions.
Digital image steganalysis: A survey on paradigm shift from machine learning to deep learning based techniques

Steganography, a branch of data hiding techniques aims to hide confidential information within any digital media by obscuring the existence of hidden information. On the contrary, steganalysis aims to detect steganography. With the advent of powerful steganographic algorithms, the process of cracking them became very challenging. Traditional steganalysis following machine learning principle employs a two-step process, with first process extracting highly sophisticated features capable of discriminating hidden message from original data and second process classifying the input as innocent or guilty. In recent years, deep learning which has its roots in artificial neural networks emerged as a brilliant alternative for many computer vision tasks. A review of recent research works in deep learning based digital image steganalysis is presented here. The paradigm shift from machine learning approaches to employing more promising deep learning architectures, observed with the current research community and hence in literature has been presented here in chronological order. Deep learning can unify the two-step process into a single process by giving the ability for machine to learn end-to-end by itself. The use of Convolutional Neural Networks to perform steganalysis in spatial or transform or combination of both domains has effectively lowered the detection error rates.
Implications of emerging technologies on the future of work

Technology pessimists believe that emerging technologies will take away jobs. However, technology optimists suggest that "non-routine" jobs may not only exist but also surge. To better understand the implications of emerging technologies on the future of work, we conducted a panel discussion with senior industry leaders. The discussion was focussed on understanding what opportunities get created, how jobs get transformed, what competencies become essential, and what challenges are posed by emerging technologies. The emerging themes from the discussion highlighted creative tensions in the way technology interacts with work, opening a plethora of opportunities for shaping a desired future.
Development of surface reconstruction algorithms for optical interferometric measurement

Abstract Optical interferometry is a powerful tool for measuring and characterizing areal surface topography in precision manufacturing. A variety of instruments based on optical interferometry have been developed to meet the measurement needs in various applications, but the existing techniques are simply not enough to meet the ever-increasing requirements in terms of accuracy, speed, robustness, and dynamic range, especially in on-line or on-machine conditions. This paper provides an in-depth perspective of surface topography reconstruction for optical interferometric measurements. Principles, configurations, and applications of typical optical interferometers with different capabilities and limitations are presented. Theoretical background and recent advances of fringe analysis algorithms, including coherence peak sensing and phase-shifting algorithm, are summarized. The new developments in measurement accuracy and repeatability, noise resistance, self-calibration ability, and computational efficiency are discussed. This paper also presents the new challenges that optical interferometry techniques are facing in surface topography measurement. To address these challenges, advanced techniques in image stitching, on-machine measurement, intelligent sampling, parallel computing, and deep learning are explored to improve the functional performance of optical interferometry in future manufacturing metrology.
Crafting performance-based cryptocurrency mining strategies using a hybrid analytics approach

Crafting and executing the best cryptocurrency mining strategy is vital to succeeding in cryptocurrency market investments. This study aims to identify the best cryptocurrency mining strategy based on service providers' performance for cryptocurrency mining using a hybrid analytics approach, which integrates the Analytic Hierarchy Process (AHP) and Fuzzy-TOPSIS techniques, along with sensitivity analysis. The results show that hosted mining is the overall best cryptocurrency mining strategy, followed by home mining and cloud mining, based on both total cost of operations and cryptocurrency payout criteria. The empirical findings also suggest that the critical features of the highest performing service providers (i.e., hosted mining strategies and cloud mining) were their flexibility of contracts and the superior efficiency in terms of the daily payout. Finally, of the three location alternatives for home mining, Turkey ranks first compared to the U.S. and Europe.