Reviewerly/exHarmony huggingface dataset:
Privacy-Preserved Image Protection Supporting Different Access Rights

The boom in cloud computing and social networking has led to a large number of online users in the networks. It is necessary to use appropriate privacy protection mechanisms to prevent personal privacy leakage. In general, image privacy protection techniques proceed with the whole image. However, the image may contain multiple users’ information, and different people’s data may require different privacy protection. In addition, cloud servers are semi-trusted instead of trusted service providers due to unknown security vulnerabilities and the possibility of grabbing sensitive data from the cloud server. In order to provide image protection with different privacy-preserved access rights and to prevent the cloud server from retrieving sensitive information from the image, we propose a privacy-preserved scheme that supports different privacy-preserved access rights in a single image based on the difficulty of solving the factorization problem and the discrete logarithm problem. The cloud server performed the image management method without knowing the privacy information contained in the protected images. By working out the proposed scheme in detail, we experimentally validate the scheme and discuss various options of access rights in practice.
Quantum Economic Advantage

A quantum computer exhibits quantum advantage when it can perform a calculation that a classical computer is unable to complete. It follows that a company with a quantum computer would be a monopolist in the market for such a calculation if its only competitor was a company with a classical computer. Conversely, economic outcomes are unclear if quantum computers do not exhibit a quantum advantage, but classical and quantum computers have different cost structures. We model a Cournot duopoly where a quantum computing company competes against a classical computing company. The model features an asymmetric variable cost structure between the two companies and the potential for an asymmetric fixed cost structure, where each firm can invest in scaling its hardware to expand its respective market. We find that even if (1) the companies can complete identical calculations, and thus there is no quantum advantage, and (2) it is more expensive to scale the quantum computer, the quantum computing company may be more profitable and also invest more in market creation due to efficiency gains from using quantum algorithms. Finally, we provide examples of settings where the classical computer can also perform a calculation, but not in a cost-effective enough manner to be commercially viable. In such a setting, the quantum computing company becomes a monopolist despite exhibiting no quantum advantage. Taken together, quantum computers may not need to display a quantum advantage to be able to generate a quantum economic advantage for the companies that deploy them. This paper was accepted by D. J. Wu, information systems. Funding: R. G. Melko is supported by the Natural Sciences and Engineering Research Council of Canada, Canada Research Chair program, and the Perimeter Institute for Theoretical Physics. Research at the Perimeter Institute is supported in part by the Government of Canada through the Department of Innovation, Science and Economic Development Canada and by the Province of Ontario through the Ministry of Colleges and Universities. A. Goldfarb is supported by the Sloan Foundation and the Social Sciences and Humanities Council of Canada. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4578 .
Privatized espionage: <scp>NSO</scp> Group Technologies and its Pegasus spyware

Abstract Advanced cyber technology like NSO Group Technologies' (NSO) controversial Pegasus spyware blurs distinctions between “good” and “bad.” This case follows the Israeli‐based international leader in cyber espionage and developer NSO and one of its co‐founders, Shalev Hulio from its creation in 2010 to the present. It includes NSO's acquisition by US‐based private equity fund Francisco Partners in 2014. NSO's re‐acquisition in 2019 by co‐founders Hulio and Omri Lavie with funding support from London‐based private equity fund Novalpina Capital. During this time, Pegasus had helped capture Mexican drug baron El Chapo, prevented terrorist attacks and broken up pedophilia, sex, and drug‐trafficking rings. But Pegasus also contributed to the murder of Washington Post reporter Jamal Khashoggi as well as other illegal incidents against dissidents, journalist, and governments. As the case suggests, controlling access to such powerful technology that involves accountability, responsibility, and enforceability within a firm and within nations appears illusive.
Quantum receiver enhanced by adaptive learning

Quantum receivers aim to effectively navigate the vast quantum-state space to endow quantum information processing capabilities unmatched by classical receivers. To date, only a handful of quantum receivers have been constructed to tackle the problem of discriminating coherent states. Quantum receivers designed by analytical approaches, however, are incapable of effectively adapting to diverse environment conditions, resulting in their quickly diminishing performance as the operational complexities increase. Here, we present a general architecture, dubbed the quantum receiver enhanced by adaptive learning (QREAL), to adapt quantum receiver structures to diverse operational conditions. QREAL is experimentally implemented in a hardware platform with record-high efficiency. Combining the QREAL architecture and the experimental advances, the error rate is reduced up to 40% over the standard quantum limit in two coherent-state encoding schemes.
Composed query image retrieval based on triangle area triple loss function and combining CNN with transformer

Abstract The existing typical combined query image retrieval methods adopt Euclidean distance as sample distance measurement method, and the model trained by triple loss function blindly pursues absolute distance between samples, resulting in unsatisfactory image retrieval performance. Meanwhile, these methods singularly adopt Convolutional Neural Network (CNN) to extract reference image features. However, receptive field of convolution operation has the characteristics of locality, which is easy to cause the loss of edge feature information of reference images. In view of shortcomings of these methods, the following improvements are proposed in this paper: (1) We propose Triangle Area Triple Loss Function (TATLF), which adopts Triangle Area (TA) as measurement of sample distance. TA comprehensively considers the absolute distance and included angle between samples, so that the trained model has better retrieval performance; (2) We combine CNN with Transformer to simultaneously extract local and edge features of reference images, which can effectively reduce the loss of reference images information. Specifically, CNN is adopted to extract local feature information of reference images. Transformer is used to pay attention to the edge feature information of reference images. Extensive experiments on two public datasets, Fashion200k and MIT-States, confirm the excellent performance of our proposed method.
Investigating the succession process of native desert plants over hydrocarbon-contaminated soils using remote sensing techniques

Hydrocarbon-contaminated soils are considered as one of the major environmental issues that harm human well-being, particularly in arid regions of the world. Phytoremediation is a possible mitigation measure for this issue and has been suggested as it is cost-effective compared with other remediation technologies for soil clean-up, such as soil thermal treatment and soil washing. However, there are still gaps in the literature regarding the behavior of annual and perennial desert plants and their ability to survive in hydrocarbon-contaminated soils in arid ecosystems. Therefore, this study aims to develop an integrated approach using remote sensing techniques to understand the behavior of annual and perennial desert plants over different types of oil-contaminated soils (oil tarcrete, wet-oil lake, bare soil, and vegetation cover) in the Kuwait Desert and to explore the impact of climate and physical soil properties on the regrowth of native desert plants. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and ferrous iron (Fe2+) index (FI) were used to determine the changes in oil contamination and vegetation cover from 1992 to 2002, and 2013-2020. Subsequently, statistical tests were performed to determine the influence of climatic and soil physical characteristics on changes in hydrocarbon contamination and desert plant behavior. The results showed that hydrocarbon contamination was high at the study sites in the first six years (1992-1997) after contamination, and then decreased in the following years. However, vegetation cover was low in the first six years but significantly increased after 1998, reaching >65%. It was also found that annual plants had the highest distribution rate compared to perennial plants, which mainly depended on the soil type. We concluded that certain annual and perennial plants could successfully grow over tarcrete-contaminated sites, making these sites more suitable for the restoration of native desert plants than hydrocarbon-contaminated sites. We also observed that the succession process of vegetation growth over hydrocarbon-contaminated soils could be associated with vegetation growth on a clean sediment layer covering the oil layer. Additionally, we observed that the remobilization of aeolian sediment over many contaminated sites in Kuwait resulted in the accumulation of organic matter, plant seeds, and dust particles that create layers of nutrient-rich soil for the initial growth of plants.
Intelligent Video Surveillance Platform Based on FFmpeg and Yolov5

With the development of multimedia, video surveillance systems are becoming more popular. However, the current video surveillance systems have a general function and are unable to provide Intelligent perception.
YaConv: Convolution with Low Cache Footprint

This article introduces YaConv , a new algorithm to compute convolution using GEMM microkernels from a Basic Linear Algebra Subprograms library that is efficient for multiple CPU architectures. Previous approaches either create a copy of each image element for each filter element or reload these elements into cache for each GEMM call, leading to redundant instances of the image elements in cache. Instead, YaConv loads each image element once into the cache and maximizes the reuse of these elements. The output image is computed by scattering results of the GEMM microkernel calls to the correct locations in the output image. The main advantage of this new algorithm—which leads to better performance in comparison to the existing im2col approach on several architectures—is a more efficient use of the memory hierarchy. The experimental evaluation on convolutional layers from PyTorch, along with a parameterized study, indicates an average 24% speedup over im2col convolution. Increased performance comes as a result of 3× reduction in L3 cache accesses and 2× fewer branch instructions.
Surface texture analysis in Toothfrax and MountainsMap® SSFA module: Different software packages, different results?

The scale-sensitive fractal analysis (SSFA) of dental microwear textures is traditionally performed using the software Toothfrax.SSFA has been recently integrated to the software MountainsMap® as an optional module.Meanwhile, Toothfrax support has ended.Before switching to the new module, the outputs between the two software packages must be compared for consistency.We have performed such a test using Bayesian modelling on three datasets including dental surfaces of sheep (Merceron, Ramdarshan, et al., 2016) and guinea pigs (Winkler, Schulz-Kornas, Kaiser, Cuyper, et al., 2019) from controlled feeding experiments, as well as surfaces of quartzite and flint flakes used in an actualistic archeological experiment on cleaning procedures (Pedergnana, Calandra, Bob, et al., 2020).We found that the two software packages calculate significantly different values for the SSFA parameters epLsar, Asfc, HAsfc9 and R 2 , even when the same settings are used.Nevertheless, the treatments (different diets or cleaning procedures) are discriminated similarly within each dataset.While the new software module is as good as the original software to differentiate treatments, our results imply that the outputs from the two software packages are not directly comparable and, as such, cannot be merged.Surface texture analysts should therefore consider re-analyzing published surfaces before integrating them in their studies.
A deep active learning-based and crowdsourcing-assisted solution for named entity recognition in Chinese historical corpora

Purpose The majority of existing studies about named entity recognition (NER) concentrate on the prediction enhancement of deep neural network (DNN)-based models themselves, but the issues about the scarcity of training corpus and the difficulty of annotation quality control are not fully solved, especially for Chinese ancient corpora. Therefore, designing a new integrated solution for Chinese historical NER, including automatic entity extraction and man-machine cooperative annotation, is quite valuable for improving the effectiveness of Chinese historical NER and fostering the development of low-resource information extraction. Design/methodology/approach The research provides a systematic approach for Chinese historical NER with a three-stage framework. In addition to the stage of basic preprocessing, the authors create, retrain and yield a high-performance NER model only using limited labeled resources during the stage of augmented deep active learning (ADAL), which entails three steps—DNN-based NER modeling, hybrid pool-based sampling (HPS) based on the active learning (AL), and NER-oriented data augmentation (DA). ADAL is thought to have the capacity to maintain the performance of DNN as high as possible under the few-shot constraint. Then, to realize machine-aided quality control in crowdsourcing settings, the authors design a stage of globally-optimized automatic label consolidation (GALC). The core of GALC is a newly-designed label consolidation model called simulated annealing-based automatic label aggregation (“SA-ALC”), which incorporates the factors of worker reliability and global label estimation. The model can assure the annotation quality of those data from a crowdsourcing annotation system. Findings Extensive experiments on two types of Chinese classical historical datasets show that the authors’ solution can effectively reduce the corpus dependency of a DNN-based NER model and alleviate the problem of label quality. Moreover, the results also show the superior performance of the authors’ pipeline approaches (i.e. HPS + DA and SA-ALC) compared to equivalent baselines in each stage. Originality/value The study sheds new light on the automatic extraction of Chinese historical entities in an all-technological-process integration. The solution is helpful to effectively reducing the annotation cost and controlling the labeling quality for the NER task. It can be further applied to similar tasks of information extraction and other low-resource fields in theoretical and practical ways.
Computational modeling of insight processes and artificial cognitive ontogeny

Many approaches were proposed to model complex socially emotional behavior in virtual actors, such as intelligent tutors, creative assistants, or team partners. They still lack the ‘magic’ of human-level cognition: systems built for one paradigm appear clueless outside of its boundary. Natural cognitive systems, on the other hand, can adapt to unexpected environments and paradigms. To capture the robustness of natural cognitive development, a new approach is proposed here that enables the formation of new higher cognitive abilities in a model system, embedded in an unexpected environment. This is achieved based on the naturally developing grounding of innate abstract constructs (schemas). The mechanism producing this binding is that of creative insight. In this study, principles of insight processes borrowed from psychology are formalized and adapted for computer modeling. To do this, several examples of insight phenomena at different evolutionary levels and in different species are analyzed before the model is formulated based on the dual process theory, the signal model of insight, and the eBICA cognitive architecture framework. Results of its computer simulations prove the concept. One specific finding is that the accumulation of activation during the incubation period increases creative abilities of the system. It is argued that the proposed approach can explain a range of facts and mysteries associated with the human cognitive ontogeny and can provide the basis for a self-sustained evolution of future Artificial Intelligence.
Learning-based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracing

Indoor scenes typically exhibit complex, spatially-varying appearance from global illumination, making inverse rendering a challenging ill-posed problem. This work presents an end-to-end, learning-based inverse rendering framework incorporating differentiable Monte Carlo raytracing with importance sampling. The framework takes a single image as input to jointly recover the underlying geometry, spatially-varying lighting, and photorealistic materials. Specifically, we introduce a physically-based differentiable rendering layer with screen-space ray tracing, resulting in more realistic specular reflections that match the input photo. In addition, we create a large-scale, photorealistic indoor scene dataset with significantly richer details like complex furniture and dedicated decorations. Further, we design a novel out-of-view lighting network with uncertainty-aware refinement leveraging hypernetwork-based neural radiance fields to predict lighting outside the view of the input photo. Through extensive evaluations on common benchmark datasets, we demonstrate superior inverse rendering quality of our method compared to state-of-the-art baselines, enabling various applications such as complex object insertion and material editing with high fidelity. Code and data will be made available at https://jingsenzhu.github.io/invrend
Adaptive media streaming over IP multicast (mABR)

Increasingly, major live events are being delivered using HTTP Adaptive Streaming (HAS), which, being a unicast technology, causes such events to generate huge traffic demand peaks on broadband networks which in turn drive significant investment in the network to increase capacity. Multicast ABR (mABR) is a streaming technology that inserts multicast into the path of a unicast HAS stream, allowing the traffic peaks to be reduced while requiring little or no change to the client players. But the unmodified clients continue to behave as HAS clients, asynchronously requesting content segments, adapting the quality of content requested according to perceived network conditions, totally unaware of the use of multicast to deliver synchronously one or more of the available encoded representations of the content to a local proxy. This creates inefficiencies and challenges in system design. Segments delivered synchronously must be cached locally ready for asynchronous requests from the client. Adaptation by the client will cause unwanted segments to be received by multicast, and alternative representations to be requested by unicast. We exploit CDN log data for BT Sport channels delivered by HAS to explore these inefficiencies and challenges. We show that unicast traffic in the core network could be reduced by more than 75% by delivering a single HAS representation by multicast to each client proxy, with larger savings for HD than UHD streams. We show that a cache at the client proxy capable of storing seven segments of 6s duration would be sufficient to be able to satisfy the majority of asynchronous requests from clients. Finally, we evaluate the performance of four policies for a client proxy to join and leave multicast groups in terms of the competing metrics of savings in unicast traffic in the core network and the quantity of data delivered over the access network.
On implementing ChaCha on a programmable switch

This paper presents an implementation of a practical cryptographic primitive based on ChaCha on a Tofino programmable switch. A key challenge is optimizing the implementation by leveraging the structure of ChaCha operations and hardware features of Tofino. Our implementation outperforms the AES-based approach in terms of performance and small memory footprint and achieves up to 203 Gbps of throughput.
Idle-parked vehicles assisted collaborative resource allocation in VEC based on Stackelberg game

With the advent of autonomous vehicles, how to meet the computing demands of vehicles with computing-intensive and latency-sensitive tasks becomes a challenge. Vehicular Edge Computing (VEC) is an advanced computing paradigm that improves the Quality of Service (QoS) of vehicles by offloading tasks to VEC servers. However, the computing resources of the VEC server are limited and thus cannot meet the offloading needs of the vehicle. This paper proposes to use idle-parked vehicles to assist the VEC server to offload computing tasks, which increases the resource capacity and expands the communication range. Firstly, we propose an idle-parked vehicle-assisted VEC model, which incentivizes idle-parked vehicles to assist VEC server computing tasks by obtaining rewards from VEC servers. Secondly, this paper proposes a more flexible dynamic offloading scheme based on comprehensive consideration of the selection strategy and pricing strategy. Then, based on the Stackelberg game, the interaction between the VEC server and the idle-parked vehicle is analyzed, and utilize backward induction to prove that there is a unique Nash equilibrium in this game. Finally, an improved joint selection decision and pricing decision algorithm based on the Branch and Bound (JSPBB) algorithm is proposed, which achieves the optimal pricing under the condition of maximizing the utility of idle-parked vehicles and each VEC server under this pricing optimal offloading strategy to maximize system utility. The simulation results can show that the proposed algorithm can solve the problem effectively and has good performance so that the system can obtain higher benefits.
Learning to measure infrared properties of street views from visible images

With the rapid development of infrared cameras, infrared images which have the unique advantage of all-weather detection ability are more and more incorporated into practical applications. However, it is hard to develop infrared-domain artificial intelligence with the shortage of labeled infrared data. Traditional infrared image simulation techniques face the problem of strict parameters, slow solution speed, and limited scenarios. In this paper, we tackle the problem of simulating infrared image proprieties with generative networks via reference to visible images. We propose a novel infrared image generation model that complied with the infrared imaging principle, the V2IR-GAN, consisting of spontaneous emission, reflected radiation and transmission coefficient modules. Extensive experiments and analyses are conducted to demonstrate the high stimulative performance of the V2IR-GAN, showing that it achieves the best SSIM of 88.2% against the state-of-the-art generative models. We also provide a specific application to validate the practicability of the V2IR-GAN.