Reviewerly/exHarmony huggingface dataset:
USIS: A unified semantic image synthesis model trained on a single or multiple samples

Semantic image synthesis methods learn to generate new images conditioned on predefined semantic label maps. Existing methods require access to large-volume samples labeled with semantic maps, which limits their applications. We propose USIS, a Unified Semantic Image Synthesis model which can be trained on only a single or multiple pairs of images and semantic maps. Once trained, a USIS model can generate new images according to unseen semantic maps, as existing semantic image synthesis methods do. Specifically, we design a hierarchical architecture to reconstruct training samples and gradually learn the distributions of multi-scale patches in samples from coarse to fine. To avoid the error accumulation across scales, we propose a mixed training strategy to stabilize the training process. Extensive experiments on one- or multiple-sample datasets show our proposed model achieves state-of-the-art performance in terms of visual fidelity.
Meta-ensemble learning with a multi-headed model for few-shot problems

Recent meta-learning algorithms for few-shot learning are based on episodic training where each episode consists of only a few support and query samples to imitate a target few-shot task. However, due to the limited number of categories and few samples in each category, this framework suffers from over-fitting to both a meta-training dataset and the support set of each episode. It also causes a large variance in the accuracy of each episode, which reduces reliability and confidence in model performance. To address this problem, we propose a novel meta-ensemble learning approach based on a recent ensemble method: a multi-input multi-output (MIMO) configuration. Our approach is simply applied to existing meta-learning algorithms. Multiple subnetworks in a single model simultaneously learn multiple episodes and ensemble the predictions, leveraging the model capacity. We show that meta-ensemble learning achieves significant improvement in generalization. It also improves the performance of meta-learning algorithms on few-shot classification benchmarks.
Quantifying Resource Contention of Co-located Workloads with the System-level Entropy

The workload co-location, such as deploying offline analysis workloads with online service workloads on the same node, has become common for modern data centers. Workload co-location deployment improves data center resource utilization significantly. Still, it also introduces resource contention, resulting in the online service’s quality of service fluctuation, which we call performance interference. As the online service is a tail-latency-sensitive workload, the tail-latency metric can reflect the performance interference degree of the co-location workloads at the application level. However, to guide system design and evaluation, quantitatively evaluating the resource contention of the co-located workloads at the system level is also essential. This article proposes a novel metric called System-Level Entropy (SLE). As a system-level metric, SLE can measure quantitatively resource contention of the co-location systems and perform the apples-to-apples comparison between systems. The experimental results show that SLE can accurately reflect the performance interference of workloads and then evaluate the system resource contention. We also demonstrate two case studies of the SLE. We quantify the affinity of different co-location combinations, including three online services and five offline workloads. Furthermore, we evaluate the effects of state-of-the-art isolated mechanisms (the container and the CPU–affinity binding) with these co-location combinations.
Caring for Analog and Digital Video Art

Video artworks comprise the majority of time-based media artworks in museum collections due to the long and rich history of artists engaging with this medium. This chapter explores the history of video as an artistic medium, gives a short insight on collecting practices, and offers practical advice on the long-term care of this volatile material in a collection. Collection care of time-based media artworks is an ongoing process with a number of important key moments. Acquisition and intake lay the groundwork for creating comprehensive documentation on the concept, production, and display of video artworks. Automated critical viewing is possible with software tools that analyze the video and audio stream and flag certain elements that it has been configured to identify. In the process of digitization, time base correctors (TBCs) are usually required for all analog formats to stabilize the analog signal. A TBC buffers the unstable input analog signal and sends out a stabilized analog signal as a result.
Inventory and Database Registration of Time-Based Media Art

Martina Haidvogl is a lecturer at the Bern University of the Arts’ media conservation program (Switzerland), and Linda Leckart is a registrar at the Los Angeles County Museum of Art (USA). In their previous roles at the San Francisco Museum of Modern Art, both authors collaboratively developed workflows and worked side-by-side to quality control and catalog media artworks. This chapter draws on their respective expertise in media conservation and registration to develop a comprehensive numbering system for inventorying media artworks within a traditional collection management system. A Collection Management System (CMS) is a type of content management system that has been customized to serve museums and collecting institutions. A time-based media artwork may come with multiple information carriers containing analog and digital copies of a single artwork, sculptural installation components and dedicated equipment, and a certificate of authenticity (COA).
Disentangling the correlated continuous and discrete generative factors of data

Real-world data typically include discrete generative factors, such as category labels and the existence of objects, as well as continuous generative factors. Continuous generative factors may be dependent on or independent of discrete generative factors. For instance, an intra-class variation of a category is dependent on the discrete generative factor, whereas a common variation of all categories is not. Most previous attempts to integrate discrete generative factors into disentanglement assumed statistical independence between the continuous and discrete variables. In this paper, we propose a Variational Autoencoder(VAE) model capable of disentangling both continuous generative factors. To represent these generative factors, we introduce two sets of continuous latent variables: a private variable and a public variable. The private and public variables represent the intra-class variations and common variations in categories, respectively. Our proposed framework models the private variable as a Gaussian mixture and the public variable as a Gaussian. Each mode of the private variable is responsible for a class of discrete variables. Our proposed model, called Discond-VAE, DISentangles the class-dependent CONtinuous factors from the Discrete factors by introducing private variables. The experiments showed that Discond-VAE could discover private and public factors from the data. Moreover, even under the dataset with only public factors, Discond-VAE does not fail and adapts private variables to represent public factors.
Homogeneous weighted motion planning for cooperated manipulator systems under position-torque constrained processing Environment

Real-time motion planning under position and torque constraints is a critical challenge for cooperative manipulator efficiency and safety operation. Real-time motion planning at the velocity level improves computation efficiency, eliminates the complex derivative calculation of the Jacobian matrix, and the velocity planning solution can be used directly for robotic kinematic control. However, little research attention has been attached to handling the position and torque constraints simultaneously at velocity level for cooperative manipulator systems. In this paper, we introduce a novel homogeneous weighted least-norm method (HWLN) for joint velocity redistribution of cooperated manipulators. Within the coupled kinematics-dynamics model of cooperated manipulators, joint position and torque constraints are simultaneously homogenized and taken into account by the constraint performance index. To avoid joint's constraint saturations, two real-time weight updating laws are designed for the joint position and driving torque respectively. The joint velocities of cooperated manipulators are then adaptively redistributed using the pseudo-kinetic-energy minimum optimization criteria. When compared to single manipulator regulation, this strategy takes greater advantage of cooperative redundancy and significantly enhances the position-torque planning performance. Mathematical stability proof is presented. In the meanwhile, numerical experiment results under various joint position and torque constraints demonstrate the effectiveness of the proposed HWLN method. The experimental results for motion planning and control of two 6R ABD-20Kg robotic manipulators are provided.
Mind the gap

Abstract Has post-editing changed the nature of translation? Are these tasks two sides of the same coin? These are some of the questions that recent developments in machine translation have brought to translation studies. The quality of the texts rendered by the new neural engines is good enough to challenge the traditional role of the human translator. Some voices even question whether there might be any place left for translators if, in the near future, their role is finally superseded by that of the post-editor. This paper offers a comprehensive view of the many aspects of post-editing with a view to shedding some light on the nature of this task. I first explore how the progress in machine translation has turned post-editing into an essential activity. Then, I present a proposal for a categorization of research areas in post-editing within the framework of translation studies. The central discussion of this paper revolves around three key ideas: (1) the conceptualization of post-editing as more than a simple, fast and inexpensive task; (2) the framing of post-editing as a dynamic process; and (3) the claim that defining quality in machine translation post-editing is not as straightforward as it may seem. The ultimate goal of this paper is to lay the foundations for further discussion into what it is that post-editing means for translation studies.
Economics of Social Media Fake Accounts

Amid the rise of the influencer economy, fake social media accounts have become a prevalent problem on many social media platforms. Yet the problem of fake accounts is still poorly understood and so is the effectiveness of coping strategies. This research models the ecosystem of fake accounts in an influencer economy and obtains insights on fake-account purchasing behaviors, the impact of anti-fake efforts, and the roles of social media literacy, anti-fake technology, and costs of fake accounts. We show that not only low-quality influencers may buy fake accounts to mimic high-quality ones in a “pooling” equilibrium, high-quality influencers may also buy to prevent mimicry in a “costly-separating” equilibrium. There is also a “naturally-separating” equilibrium where the two types are separated without buying fake accounts. We find that increasing anti-fake efforts and social media literacy may cause more fake accounts. The platform generally prefers either a zero-effort pooling equilibrium or a high-effort naturally-separating equilibrium. Compared to the level of anti-fake efforts preferred by consumers, the platform may be overly or insufficiently aggressive. Some anti-fake strategies, such as increasing social media literacy and fake-account costs, may benefit consumers but not the platform. One exception is increasing the effectiveness of anti-fake technology, which benefits both the platform and consumers and reduces the number of fake accounts.
Quantum Computing in Operations Research

Quantum computing has sparked a tremendous interest from governments, academics, and the private sector alike. According to a 2021 McKinsey report, governments have announced to invest almost $30 billion to develop quantum technologies. Companies such as IBM, Google, Amazon, and Microsoft are also investing heavily, and have already launched commercial quantum computing services. Research on quantum computing is also on the rise with hundreds of publications in Nature, Science, and PNAS. Despite this enormous interest, quantum computing has received little or no attention in the OR community. This is somewhat surprising given the potential that has been ascribed to quantum computers to solve Operations Research (OR) problems. To investigate the potential of quantum computing from an OR perspective, we discuss the most important quantum algorithms, and use them to effectively solve the knapsack problem for the very first time. We verify our results using Qiskit (IBM's software development kit for quantum computing), and make available templates that allow to solve other OR problems. In addition, we highlight a number of important limitations and drawbacks of quantum computing (when compared to classical computing), and conclude that quantum computing indeed shows promise, albeit not for every application.
“The meal is served, but who are we serving it to?” Challenges in a Maths professional development program during COVID-19 and the strategies developed to address them

This qualitative exploratory case study focused on a Professional Development (PD) program for mathematics teachers in the public system, which is an underserved community of teachers in Chile. The study aimed to understand how the program addressed the challenges brought by the pandemic. Using semi-structured interviews and drawing from a pedagogy of connection, this study analysed data from nine participants, who were facilitators and coordinators of the PD program. The findings showed that participants identified three sources of challenges: personal, professional, and operational and that they presented the strategies used for addressing them. The strategies, in general, involved a better understanding of the contexts and teachers they served even though doing so prevented them from meeting some curricular goals. Their strategies highlighted the notions of flexibility, humanising aspects of communication, and innovation to overcome the limitation of using technology in pedagogical contexts.
Monitoring Suspicious Discussion on Online Forum

The forum may be a huge place where people can express their distinct thoughts and views inducing their part of life for the objective of communication and marketing to live the loyalty of users one can keep eye on their everyday posts and monitor for any suspicious discussions. These discussion forums are employed by people for illegal purposes by posting suspicious chats within the sort of text, video, images and are also interchanging them online with other users. The prosecution agencies are discovery answers to suspect such malicious posts which can be within the sort of text for criminal investigation. As mostly the info in chatting forums are stored within the sort of text, the proposed system focuses on text posts.
Detection of Cyber Threats in Application Platforms

Illicit behavior on application platform, the gadget’s popular social platform, promotes misuse of ‘search rankings’ and the spread of malware. The evidence that has been left by the scammers is detected by fraud rank detector. It detects vulnerable malware and application to hunt for malware manipulation. A Pseudo Clique Finder program is proposed which tracks the behavior of deceiver enlisted to download/rate a technology that is suitably to advertise those rating with comparably less interval of time or download the app using symmetric key. The server needs to permit the key to access the secret key as referred to PCF takes the input as set of downloads of an app in and a threshold value u (u = 3). Whenever the threshold limit exceeds by downloading the application more than the u times, the user is said to be fraudster.
Uncovering data relationships to tackle fraud

Graph technology has reached a point of maturity where it can run off-the-shelf algorithms over massive networks to discover previously undetectable patterns. These algorithms also permit security professionals to take the necessary actions to render systems more robust and secure. As a result, financial and government institutions are making increasing use of graph database technology to tackle fraud.
Are cyber security breaches unavoidable?

The increased likelihood of a security breach is a natural consequence of the widespread availability of an organisation's IT systems and data via digital and mobile technologies. Criminals look for the weakest link in the chain of security, whether it be through applications, an increased number of portal entry points, or an employee who is unaware of the potential danger. So are we at the point where a breach is inevitable?
An Optimized LIME Scheme for Medical Low Light Level Image Enhancement

The role of medical image technology in the medical field is becoming more and more obvious. Doctors can use medical image technology to more accurately understand the patient's condition and make accurate judgments and diagnosis and treatment in order to make a large number of medical blurred images clear and easy to identify. Inspired by the human vision system (HVS), we propose a simple and effective method of low-light image enhancement. In the proposed method, first a sampler is used to get the optimal exposure ratio for the camera response model. Then, a generator is used to synthesize dual-exposure images that are well exposed in the regions where the original image is underexposed. Next, the enhanced image is processed by using a part of low-light image enhancement via the illumination map estimation (LIME) algorithm, and the weight matrix of the two images will be determined when fusing. After that, the combiner is used to get the synthesized image with all pixels well exposed, and finally, a postprocessing part is added to make the output image perform better. In the postprocessing part, the best gray range of the image is adjusted, and the image is denoised and recomposed by using the block machine 3-dimensional (BM3D) model. Experiment results show that the proposed method can enhance low-light images with less visual information distortions when compared with those of several recent effective methods. When it is applied in the field of medical images, it is convenient for medical workers to accurately grasp the details and characteristics of medical images and help medical workers analyze and judge the images more conveniently.
Ethereum Blockchain-Based Authentication Approach for Data Sharing in Cloud Storage Model

The development of the cloud plays a wide role in commencing secured communication among users. However, privacy and managing large databases is a major problem in a cloud model. This affects the data sharing due to a lack of trust amongst the stakeholders. Thus, we proposed a new blockchain-assisted authentication and access control technique to provide reliable data integrity verification between the cloud owners and the requester without relying on end-users. The proposed blockchain-based authentication and access control mechanism effectively increases the throughput and reduces the cost by addressing the issues of failure in the cloud model. This method integrates the Interplanetary File System (IPFS) and Ethereum blockchain for sharing the data effectively between the cloud owner and the requester. This framework distributes the secret key to the requester and the data is encrypted by adapting data policies to attain access controls over data. The keyword search function on ciphertext is executed on the basis of smart contract and Ethereum blockchain. The proposed Blockchain-based access control and authentication mechanism obtained better results with a genuine user detection rate of 95%, maximum search accuracy of 95%, and minimum responsiveness of 2.25 sec for 500 Blockchain size.
Irregular message passing networks

The graph neural network (GNN) is a widely adopted technique to process graph-structured data. Despite its pervasiveness, the exact reasons for the message aggregator’s effectiveness are still poorly understood. The popular belief is that this effectiveness stems from optimizing edge weights to improve the local fusion of node information. In this study, we demonstrate that such propagation weight optimization has a limited contribution to the success of message passing. Instead, we find that any normalized random attention (or edge weights) can have a similar and, sometime, even stronger effect. We refer to these randomly initialized propagations as irregular message passing. Experiments conducted on our random edge weight and random attention models verified the positive impact of weight randomness, uncovering the importance of the topology itself in achieving superior results for message iterations. Our code is available at https://github.com/Eigenworld/RAN.
Construction and Application of Video Big Data Analysis Platform for Smart City Development

With the progress of society and the rapid development of science and technology, daily data volume also shows an exponential upward trend. From the research report of the Internet data center, we can see that the growth rate of data will change from the original slow growth to a sharp rise within 10 years. This shows that the era of big data has arrived, and video data plays an important role in it. Video comes from all aspects of life. As a typical unstructured data, video has the characteristics of large memory, and with the leap of society, this characteristic is becoming increasingly obvious. Taking video data analysis as the starting point, this paper proposes a long-term and short-term memory neural network integrating attention mechanism and verifies it in the experimental data set. The experiment shows that this method has superior performance in model accuracy and work efficiency. Therefore, the application of this method to the construction and application of video big data analysis platform is an important step to promote the development of smart cities.
Specific Emitter Identification Based on a Hybrid Deep Neural Network for ACARS Authentication

With the increasing variety and quantity of aircraft, there is a potential threat to the security of the Aircraft Communications Addressing and Reporting System (ACARS) due to the lack of reliable authentication measures. This paper proposes a novel specific emitter identification (SEI) algorithm based on a hybrid deep neural network (DNN) for ACARS authentication. Our deep learning architecture is a combination of Deep Residual Shrinkage Network (DRSN), Bidirectional-LSTM (Bi-LSTM), and attention mechanism (AM), which perform the functions of local and global feature learning and feature focusing, respectively, so that the individual information hidden in the signal waveform can be thoroughly mined. We introduce soft thresholding as a nonlinear transformation in the DSRN to enhance robustness against noise and adopt a low-cost training strategy for new data using transfer learning. The proposed SEI algorithm is optimized and evaluated based on real-world ACARS signals captured in the Xianyang airport. Experimental results demonstrate that our algorithm can distinguish authorized entities from unauthorized entities and obtain an identification accuracy of up to 0.980. In addition, the design rationality and the superiority over other algorithms are verified through the experiments.
SVD Entropy Indicates Coded Diffraction Generalized Reconstruction Accuracy

We study encoded diffraction with shallow neural networks using singular value decomposition entropy ( H SVD ) to measure image span. Higher- H SVD synthetic training images are learned more slowly; their generalized models generally attain higher reconstruction accuracy.