Reviewerly/exHarmony huggingface dataset:
The Significance, Role, and Need for Public Sector Enterprises in Economic Growth

Purpose: The purpose of this article is to investigate the role that the public sector plays in the process of creating financial resources for economic growth and employment opportunities. Theoretical framework: One technique to envision the public sector is as an ascending ring of institutions, with the central government at the center, followed by agencies and public corporations. Companies that are either directly or indirectly affiliated with the government and are publicly traded surround the ring. Design/methodology/approach: This is descriptive based study. In this research paper we survey on random sample of private sectors companies data. Findings: As a result, it offers job prospects, which adds to the increase of a nation's financial resources. The public sector is the component of the global economy responsible for supplying important services such as infrastructure, public transit, education reform, and security services such as the military and law enforcement. Conclusion: The scope of government action can now be described in relation to the whole economy. Just one example of the various ways in which the public sector can contribute to economic development is the provision of health and educational services by the public sector, which combined make up a large percentage of the Human Development Index.
The economics of the metaverse: A comparison with the real economy

&lt;p&gt;The economic system is an important component of the metaverse as a virtual world. At the macro level, the metaverse economy operates in three cycles: the inner cycle, the outer cycle, and the virtual-real cycle between the real world and the metaverse. At the micro level, the user is the most important subject of economic activity, acting both as a demander and as a producer of user generated content (UGC). In the metaverse, market demand is based on the use value and scarcity of goods, and market supply is driven by a combination of economic and non-economic motives. The smooth functioning of the metaverse economy relies on property rights and monetary systems, and blockchain technology is increasingly becoming the basis for both of these systems.&lt;/p&gt;
Blind Face Restoration via Integrating Face Shape and Generative Priors

Blind face restoration, which aims to reconstruct high-quality images from low-quality inputs, can benefit many applications. Although existing generative-based methods achieve significant progress in producing high-quality images, they often fail to restore natural face shapes and high-fidelity facial details from severely-degraded inputs. In this work, we propose to integrate shape and generative priors to guide the challenging blind face restoration. Firstly, we set up a shape restoration module to recover reason-able facial geometry with 3D reconstruction. Secondly, a pretrained facial generator is adopted as decoder to generate photo-realistic high-resolution images. To ensure high-fidelity, hierarchical spatial features extracted from the low-quality inputs and rendered 3D images are inserted into the decoder with our proposed Adaptive Feature Fusion Block (AFFB). Moreover, we introduce hybrid-level losses to Jointly train the shape and generative priors together with other network parts such that these two priors better adapt to our blind face restoration task. The proposed Shape and Generative Prior integrated Network (SGPN) can re-store high-quality images with clear face shapes and real-istic facial details. Experimental results on synthetic and real-world datasets demonstrate SGPN performs favorably against state-of-the-art blind face restoration methods.
Multi-UAV Autonomous Path Planning in Reconnaissance Missions Considering Incomplete Information: A Reinforcement Learning Method

Unmanned aerial vehicles (UAVs) are important in reconnaissance missions because of their flexibility and convenience. Vitally, UAVs are capable of autonomous navigation, which means they can be used to plan safe paths to target positions in dangerous surroundings. Traditional path-planning algorithms do not perform well when the environmental state is dynamic and partially observable. It is difficult for a UAV to make the correct decision with incomplete information. In this study, we proposed a multi-UAV path planning algorithm based on multi-agent reinforcement learning which entails the adoption of centralized training–decentralized execution architecture to coordinate all the UAVs. Additionally, we introduced a hidden state of the recurrent neural network to utilize the historical observation information. To solve the multi-objective optimization problem, We designed a joint reward function to guide UAVs to learn optimal policies under the multiple constraints. The results demonstrate that by using our method, we were able to solve the problem of incomplete information and low efficiency caused by partial observations and sparse rewards in reinforcement learning, and we realized kdiff multi-UAV cooperative autonomous path planning in unknown environment.
SoK: Secure Aggregation Based on Cryptographic Schemes for Federated Learning

Secure aggregation consists of computing the sum of data collected from multiple sources without disclosing these individual inputs. Secure aggregation has been found useful for various applications ranging from electronic voting to smart grid measurements. Recently, federated learning emerged as a new collaborative machine learning technology to train machine learning models. In this work, we study the suitability of secure aggregation based on cryptographic schemes to federated learning. We first provide a formal definition of the problem and suggest a systematic categorization of existing solutions. We further investigate the specific challenges raised by federated learning and analyze the recent dedicated secure aggregation solutions based on cryptographic schemes. We finally share some takeaway messages that would help a secure design of federated learning and identify open research directions in this topic. Based on the takeaway messages, we propose an improved definition of secure aggregation that better fits federated learning.
Quantum teleportation in a two-superconducting qubit system under dephasing noisy channel: role of Josephson and mutual coupling energies

Abstract We investigate the average fidelity of teleportation for a system of two superconducting qubits under a dephasing noisy channel. Using the classical fidelity threshold concept, we disclose the effectiveness of the current channel to remain either in the classical or in the quantum domain. Various parameters of the assumed scheme help us to achieve high average fidelity of teleportation in the current configuration.
The metaverse era: leveraging augmented reality in the creation of novel customer experience

Purpose The main aim of this research is to portray how augmented reality (AR) characteristics (augmentation, interactivity, personalization, spatial presence, novelty, entertainment and informativeness) can enhance online customer experience (OCE). Design/methodology/approach This study conceptualizes a new framework that proposes various relationships between AR characteristics and OCE. Findings This study is extending the relationships between AR and OCE by including various AR characteristics that have not been tackled by the previous research. Originality/value This research provides an original framework on the relationship between AR characteristics and OCE through highlighting the role of media richness theory. The study is considered the first of its kind to combine these AR characteristics and customer experience in a comprehensive framework.
Photonic time crystals: a materials perspective [Invited]

Recent advances in ultrafast, large-modulation photonic materials have opened the door to many new areas of research. One specific example is the exciting prospect of photonic time crystals. In this perspective, we outline the most recent material advances that are promising candidates for photonic time crystals. We discuss their merit in terms of modulation speed and depth. We also investigate the challenges yet to be faced and provide our estimation on possible roads to success.
Augmented reality in liver surgery

During an operation, augmented reality (AR) enables surgeons to enrich their vision of the operating field by means of digital imagery, particularly as regards tumors and anatomical structures. While in some specialties, this type of technology is routinely ustilized, in liver surgery due to the complexity of modeling organ deformities in real time, its applications remain limited. At present, numerous teams are attempting to find a solution applicable to current practice, the objective being to overcome difficulties of intraoperative navigation in an opaque organ.To identify, itemize and analyze series reporting AR techniques tested in liver surgery, the objectives being to establish a state of the art and to provide indications of perspectives for the future.In compliance with the PRISMA guidelines and availing ourselves of the PubMed, Embase and Cochrane databases, we identified English-language articles published between January 2020 and January 2022 corresponding to the following keywords: augmented reality, hepatic surgery, liver and hepatectomy.Initially, 102 titles, studies and summaries were preselected. Twenty-eight corresponding to the inclusion criteria were included, reporting on 183patients operated with the help of AR by laparotomy (n=31) or laparoscopy (n=152). Several techniques of acquisition and visualization were reported. Anatomical precision was the main assessment criterion in 19 articles, with values ranging from 3mm to 14mm, followed by time of acquisition and clinical feasibility.While several AR technologies are presently being developed, due to insufficient anatomical precision their clinical applications have remained limited. That much said, numerous teams are currently working toward their optimization, and it is highly likely that in the short term, the application of AR in liver surgery will have become more frequent and effective. As for its clinical impact, notably in oncology, it remains to be assessed.
Knowledge-Adaptive Contrastive Learning for Recommendation

By jointly modeling user-item interactions and knowledge graph (KG) information, KG-based recommender systems have shown their superiority in alleviating data sparsity and cold start problems. Recently, graph neural networks (GNNs) have been widely used in KG-based recommendation, owing to the strong ability of capturing high-order structural information. However, we argue that existing GNN-based methods have the following two limitations. Interaction domination: the supervision signal of user-item interaction will dominate the model training, and thus the information of KG is barely encoded in learned item representations; Knowledge overload: KG contains much recommendation-irrelevant information, and such noise would be enlarged during the message aggregation of GNNs. The above limitations prevent existing methods to fully utilize the valuable information lying in KG. In this paper, we propose a novel algorithm named Knowledge-Adaptive Contrastive Learning (KACL) to address these challenges. Specifically, we first generate data augmentations from user-item interaction view and KG view separately, and perform contrastive learning across the two views. Our design of contrastive loss will force the item representations to encode information shared by both views, thereby alleviating the interaction domination issue. Moreover, we introduce two learnable view generators to adaptively remove task-irrelevant edges during data augmentation, and help tolerate the noises brought by knowledge overload. Experimental results on three public benchmarks demonstrate that KACL can significantly improve the performance on top-K recommendation compared with state-of-the-art methods.
Less Is More: Learning from Synthetic Data with Fine-Grained Attributes for Person Re-Identification

Person re-identification (ReID) plays an important role in applications such as public security and video surveillance. Recently, learning from synthetic data [ 9 ], which benefits from the popularity of the synthetic data engine, has attracted great attention from the public. However, existing datasets are limited in quantity, diversity, and realisticity, and cannot be efficiently used for the ReID problem. To address this challenge, we manually construct a large-scale person dataset named FineGPR with fine-grained attribute annotations. Moreover, aiming to fully exploit the potential of FineGPR and promote the efficient training from millions of synthetic data, we propose an attribute analysis pipeline called AOST based on the traditional machine learning algorithm, which dynamically learns attribute distribution in a real domain, then eliminates the gap between synthetic and real-world data and thus is freely deployed to new scenarios. Experiments conducted on benchmarks demonstrate that FineGPR with AOST outperforms (or is on par with) existing real and synthetic datasets, which suggests its feasibility for the ReID task and proves the proverbial less-is-more principle. Our synthetic FineGPR dataset is publicly available at https://github.com/JeremyXSC/FineGPR .
Linear Optical Universal Quantum Gates with Higher Success Probabilities

Abstract Universal quantum gates lie at the heart of designing quantum computer. Here two compact quantum circuits for implementing post‐selected controlled‐phase‐flip (CPF) gate and Toffoli gate with linear optics are constructed assisted by one and two single photons, respectively. The current existing maximum success probability of 1/4 for linear optical CPF gate is achieved by resorting to an ancillary single photon rather than an entangled photon pair or two single photons. Remarkably, the presented Toffoli gate is accomplished with current maximum success probability of 1/30 and unity fidelity in principle, without using additional entangled photon pairs and the standard decomposition‐based approach. Linear optical implementations of the presented two universal gates are feasible and economical under current technology, and provide a potential application in large‐scale optical quantum computing.
Few-Shot Object Detection: A Comprehensive Survey

Humans are able to learn to recognize new objects even from a few examples. In contrast, training deep-learning-based object detectors requires huge amounts of annotated data. To avoid the need to acquire and annotate these huge amounts of data, few-shot object detection (FSOD) aims to learn from few object instances of new categories in the target domain. In this survey, we provide an overview of the state of the art in FSOD. We categorize approaches according to their training scheme and architectural layout. For each type of approach, we describe the general realization as well as concepts to improve the performance on novel categories. Whenever appropriate, we give short takeaways regarding these concepts in order to highlight the best ideas. Eventually, we introduce commonly used datasets and their evaluation protocols and analyze the reported benchmark results. As a result, we emphasize common challenges in evaluation and identify the most promising current trends in this emerging field of FSOD.
Blockchain in health sciences: Research trends in Scopus

Objective. The scientific production indexed in Scopus on Blockchain in health sciences was characterized from 2012 to 2021. Design/Methodology/Approach. A bibliometric study of the scientific production related to Blockchain technology in health sciences was carried out and indexed in Scopus from 2012 to 2021. Bibliometric indicators of productivity, impact, and collaboration were used. The SciVal, VOSviewer, and Microsoft Excel tools were used to process and present the information. Results/Discussion. Between 2012 and 2021, 1,543 articles were published on the subject studied, with an average of 14.9 citations per publication. Scientific production increased until 2021, as did the number of authors. International collaboration fluctuated. The most frequent terms related to Blockchain were the Internet of Things, smart contracts, and cryptography. India, China, and the United States led the collaborations, and authors of Asian origin predominated. Conclusions. The scientific production of Blockchain in health has grown due to its transformative potential in health systems, with an interest in the security and traceability of medical data.
Document-Level Machine Translation with Large Language Models

Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing (NLP) tasks. Taking document-level machine translation (MT) as a testbed, this paper provides an in-depth evaluation of LLMs’ ability on discourse modeling. The study focuses on three aspects: 1) Effects of Context-Aware Prompts, where we investigate the impact of different prompts on document-level translation quality and discourse phenomena; 2) Comparison of Translation Models, where we compare the translation performance of ChatGPT with commercial MT systems and advanced document-level MT methods; 3) Analysis of Discourse Modelling Abilities, where we further probe discourse knowledge encoded in LLMs and shed light on impacts of training techniques on discourse modeling. By evaluating on a number of benchmarks, we surprisingly find that LLMs have demonstrated superior performance and show potential to become a new paradigm for document-level translation: 1) leveraging their powerful long-text modeling capabilities, GPT-3.5 and GPT-4 outperform commercial MT systems in terms of human evaluation; 2) GPT-4 demonstrates a stronger ability for probing linguistic knowledge than GPT-3.5. This work highlights the challenges and opportunities of LLMs for MT, which we hope can inspire the future design and evaluation of LLMs (We release our data and annotations at https://github.com/longyuewangdcu/Document-MT-LLM).
Supporting Software Design: Integrating Design Methods and Design Rationale

Many software development organizations are now using standard design methods. Because a method concentrates the designer's attention on different issues at different times, it is fruitful to analyze, compare, and support different methods in an issue-based framework. In our work, we have extended the IBIS model in several ways to accommodate design methodological concepts. In this chapter three such extensions are reviewed, illustrated, and compared: (a) the embellishment of IBIS by method-specific constructs, (b) the specialization of IBIS in method-specific ways, and (c) the incorporation into IBIS of the results of objective analysisand simulation. We conclude that the second approach is the most extensible, but that relaxation of the IBIS model (as is possible in the third approach) is necessary for practical use on design projects.
On-the-Fly Garbage Collection: An Exercise in Cooperation

chapter Share on On-the-Fly Garbage Collection: An Exercise in Cooperation Authors: Edsger W. Dijkstra View Profile , Leslie Lamport View Profile , A.J. Martin View Profile , C.S. Scholten View Profile , E.F.M. Steffens View Profile Authors Info & Claims Edsger Wybe Dijkstra: His Life,Work, and LegacyJuly 2022 Pages 339–358https://doi.org/10.1145/3544585.3544607Published:13 July 2022Publication History 0citation1DownloadsMetricsTotal Citations0Total Downloads1Last 12 Months1Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Institutional Policy for ALN

For the past five years, Stanford has been involved in developing the capability to offer courses to remote learners over the Internet. This has evolved into a robust operation, which over the past year has offered 180 courses to approximately 4000 distance learners. More recently, the Department of Electrical Engineering has extended its offerings so that it is now possible to earn course credits sufficient to obtain a Master’s degree and academic certificates entirely online. In this paper, we discuss the issues of institutional policy which have emerged as we have gone through this evolution. Our experience at Stanford will be discussed within the broader framework of institutional policy and of the general institutional resistance to change in higher education. A version of this paper was presented at the Fourth International Conference on Asynchronous Learning Networks held in New York in November 1998.
A Virtual Laboratory On Fluid Mechanics

This paper describes the development of an interactive Web-based virtual laboratory on fluid mechanics at Stevens Institute of Technology (SIT), 1 which integrates animations, graphics and analysis results in order to achieve a realistic feel of the experiment and to enhance the students' understanding of some complex concepts of fluid mechanics.Based on existing real experimental setups, different experiment simulations were implemented using the Python programming language, such as a wind tunnel and an air flow rig.In the wind tunnel experiment, the lift forces for several kinds of airfoils can be experimented with virtually.The users can specify certain parameters for controlling the simulations and obtain the corresponding lift force outputs, including tables, figures and data listings.Furthermore, the users can display a 3-D rendering of the wind tunnel equipment in the graphical user interface as well as a 2-D animation of the stream lines.The simulation achieves a good degree of accuracy for steady state conditions over a wide range of parameters.
Patchmatch-Based Robust Stereo Matching Under Radiometric Changes

In the real world, the two challenges of stereo vision system include a robust system under various radiometric changes and real-time process. To extract depth information from stereoscopic images, this paper proposes Patchmatch-based robust and fast stereo matching under radiometric changes. For this, a cost function was designed and minimized for estimating an accurate disparity map. Specifically, we used a prior probability to minimize the occlusion region and a smoothness term that considers convexity of objects to extract a fine disparity map. For evaluating the performance of the proposed scheme, we used Middlebury stereo data sets with radiometric changes. The experimental result showed that the proposed method outperforms state-of-the-art methods by up to 3.35 percent better and a range of 4.71 - 27.24 times faster result in terms of bad pixel error and processing time, respectively. Therefore, we believe that the proposed scheme can be a useful tool for computer vision-based applications.
A Non–Convex Optimization Approach to Correlation Clustering

We develop a non-convex optimization approach to correlation clustering using the Frank-Wolfe (FW) framework. We show that the basic approach leads to a simple and natural local search algorithm with guaranteed convergence. This algorithm already beats alternative algorithms by substantial margins in both running time and quality of the clustering. Using ideas from FW algorithms, we develop subsampling and variance reduction paradigms for this approach. This yields both a practical improvement of the algorithm and some interesting further directions to investigate. We demonstrate the performance on both synthetic and real world data sets.