Reviewerly/exHarmony huggingface dataset:
Tight Bounds on the Convergence of Noisy Random Circuits to the Uniform Distribution

We study the properties of output distributions of noisy random circuits. We obtain upper and lower bounds on the expected distance of the output distribution from the "useless" uniform distribution. These bounds are tight with respect to the dependence on circuit depth. Our proof techniques also allow us to make statements about the presence or absence of anticoncentration for both noisy and noiseless circuits. We uncover a number of interesting consequences for hardness proofs of sampling schemes that aim to show a quantum computational advantage over classical computation. Specifically, we discuss recent barrier results for depth-agnostic and/or noise-agnostic proof techniques. We show that in certain depth regimes, noise-agnostic proof techniques might still work in order to prove an often-conjectured claim in the literature on quantum computational advantage, contrary to what has been thought prior to this work.Received 31 January 2022Revised 9 September 2022Accepted 31 October 2022DOI:https://doi.org/10.1103/PRXQuantum.3.040329Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum benchmarkingQuantum channelsQuantum computationQuantum information theoryQuantum protocolsQuantum Information
On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond

Real-world data often exhibit imbalanced label distributions. Existing studies on data imbalance focus on single-domain settings, i.e., samples are from the same data distribution. However, natural data can originate from distinct domains, where a minority class in one domain could have abundant instances from other domains. We formalize the task of Multi-Domain Long-Tailed Recognition (MDLT), which learns from multi-domain imbalanced data, addresses label imbalance, domain shift, and divergent label distributions across domains, and generalizes to all domain-class pairs. We first develop the domain-class transferability graph, and show that such transferability governs the success of learning in MDLT. We then propose BoDA, a theoretically grounded learning strategy that tracks the upper bound of transferability statistics, and ensures balanced alignment and calibration across imbalanced domain-class distributions. We curate five MDLT benchmarks based on widely-used multi-domain datasets, and compare BoDA to twenty algorithms that span different learning strategies. Extensive and rigorous experiments verify the superior performance of BoDA. Further, as a byproduct, BoDA establishes new state-of-the-art on Domain Generalization benchmarks, highlighting the importance of addressing data imbalance across domains, which can be crucial for improving generalization to unseen domains. Code and data are available at: https://github.com/YyzHarry/multi-domain-imbalance .
An Augmented Reality-Based Approach for Designing Interactive Food Menu of Restaurant Using Android

The food industry is becoming competitive on a daily basis and introducing newer cuisines to the menu in an attempt to rise up the ladder. But they still are not being able to improve their performances because customers often only have the waiters to describe the dishes to them and thus, most of the time results in not fulfilling their expectations. Thus, to allow the customers to visualize their orders more informatively, this paper presents an android application that overlays digital three-dimensional (3D) food models onto a quick responsible (QR) code image marker on a food menu using augmented reality (AR) technology through the camera of the system. Moreover, the price and a detailed list of the ingredients used to prepare the dish, along with the nutritional and calorie content, will also appear beside the 3D food model to keep the customers completely informed of what they will be ordering. This work focused on designing the 3D food models in the Blender 3D tool, which were then imported into the Unity 3D application with the Vuforia software development kit preinstalled, and Figma has been utilized for designing the user interface of the system. The study’s outcome is an AR application that provides the customer with a more engaging approach to visualize the dishes in 3D form, which can improve customer sales and restaurant loyalty.
Modality Synergy Complement Learning with Cascaded Aggregation for Visible-Infrared Person Re-Identification

Visible-Infrared Re-Identification (VI-ReID) is challenging in image retrievals. The modality discrepancy will easily make huge intra-class variations. Most existing methods either bridge different modalities through modality-invariance or generate the intermediate modality for better performance. Differently, this paper proposes a novel framework, named Modality Synergy Complement Learning Network (MSCLNet) with Cascaded Aggregation. Its basic idea is to synergize two modalities to construct diverse representations of identity-discriminative semantics and less noise. Then, we complement synergistic representations under the advantages of the two modalities. Furthermore, we propose the Cascaded Aggregation strategy for fine-grained optimization of the feature distribution, which progressively aggregates feature embeddings from the subclass, intra-class, and inter-class. Extensive experiments on SYSU-MM01 and RegDB datasets show that MSCLNet outperforms the state-of-the-art by a large margin. On the large-scale SYSU-MM01 dataset, our model can achieve 76.99% and 71.64% in terms of Rank-1 accuracy and mAP value. Our code will be available at https://github.com/bitreidgroup/VI-ReID-MSCLNet .
Social networking of innovative farmers through WhatsApp messenger for learning exchange: A study of content sharing

Farm Innovators are regularly exchanging information and their experiences using WhatsApp messenger on their mobile phones. Most of the content shared was knowledge intensive with a mix of personal farming experiences. As Social constructivist learning theory seeks to improve socialinteractions to construct and share knowledge and the social networking through WhatsApp has proved to be potential to construct knowledge. Learning being the outcome of interactions between cognitive and psychological and the WhatsApp being the potential source for socialization and internalization promoted the creation ofsocial wealth in the form of discussion forums of Innovative farmers for learning exchange. The extension mechanism for purposeful farmer to farmer learning exchange has been created which in turnis a step towards innovative farmer led extension delivery mechanism. The potential of not only WhatsApp but other social media need to be exploited to bring location specific and commodity oriented transformative changes in the agriculture extension delivery system. The experimentation with innovative farmers is not only helping in scaling the farmersâ€™ innovations but also institutional innovations at large. As all human resources (labour, management, innovation, creativity) are products of social relationships, no one can reach maturity without the help of personally caring people, including their families, friends, neighbors, and communities. Farms and agricultural enterprises also depend on the ability of people to work together toward the common goal of ecological, social, and economic sustainability through social networks.
Blockchained supply chain management based on IoT tracking and machine learning

When it comes to running and managing modern supply chains, 6G Internet of things (IoT) is of utmost importance. To provide IoT with security and automation, blockchain and machine learning are two upper-layer technology that can help. First, we propose to utilize blockchain in modern supply chains to ensure efficient collaboration between all parties. Second, we adopt multi-head attention (MHA)-based gated recurrent unit (GRU) to do inbound logistics task prediction. Finally, numerical results justify that multi-head attention-based GRU model has better fitting efficiency and prediction accuracy than its counterparts.
Automated Dynamic Algorithm Configuration

The performance of an algorithm often critically depends on its parameter configuration. While a variety of automated algorithm configuration methods have been proposed to relieve users from the tedious and error-prone task of manually tuning parameters, there is still a lot of untapped potential as the learned configuration is static, i.e., parameter settings remain fixed throughout the run. However, it has been shown that some algorithm parameters are best adjusted dynamically during execution. Thus far, this is most commonly achieved through hand-crafted heuristics. A promising recent alternative is to automatically learn such dynamic parameter adaptation policies from data. In this article, we give the first comprehensive account of this new field of automated dynamic algorithm configuration (DAC), present a series of recent advances, and provide a solid foundation for future research in this field. Specifically, we (i) situate DAC in the broader historical context of AI research; (ii) formalize DAC as a computational problem; (iii) identify the methods used in prior art to tackle this problem; and (iv) conduct empirical case studies for using DAC in evolutionary optimization, AI planning, and machine learning.
Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems

In Nature Machine Intelligence 4, 367 (2022), Schuetz et al provide a scheme to employ graph neural networks (GNN) as a heuristic to solve a variety of classical, NP-hard combinatorial optimization problems. It describes how the network is trained on sample instances and the resulting GNN heuristic is evaluated applying widely used techniques to determine its ability to succeed. Clearly, the idea of harnessing the powerful abilities of such networks to ``learn'' the intricacies of complex, multimodal energy landscapes in such a hands-off approach seems enticing. And based on the observed performance, the heuristic promises to be highly scalable, with a computational cost linear in the input size $n$, although there is likely a significant overhead in the pre-factor due to the GNN itself. However, closer inspection shows that the reported results for this GNN are only minutely better than those for gradient descent and get outperformed by a greedy algorithm, for example, for Max-Cut. The discussion also highlights what I believe are some common misconceptions in the evaluations of heuristics.
Statistical Decoding 2.0: Reducing Decoding to LPN

The security of code-based cryptography relies primarily on the hardness of generic decoding with linear codes. The best generic decoding algorithms are all improvements of an old algorithm due to Prange: they are known under the name of information set decoders (ISD). A while ago, a generic decoding algorithm which does not belong to this family was proposed: statistical decoding. It is a randomized algorithm that requires the computation of a large set of parity-checks of moderate weight, and uses some kind of majority voting on these equations to recover the error. This algorithm was long forgotten because even the best variants of it performed poorly when compared to the simplest ISD algorithm. We revisit this old algorithm by using parity-check equations in a more general way. Here the parity-checks are used to get LPN samples with a secret which is part of the error and the LPN noise is related to the weight of the parity-checks we produce. The corresponding LPN problem is then solved by standard Fourier techniques. By properly choosing the method of producing these low weight equations and the size of the LPN problem, we are able to outperform in this way significantly information set decoders at code rates smaller than 0.3. It gives for the first time after 60 years, a better decoding algorithm for a significant range which does not belong to the ISD family.
Adaptive Hyperparameter Fine-Tuning for Boosting the Robustness and Quality of the Particle Swarm Optimization Algorithm for Non-Linear RBF Neural Network Modelling and Its Applications

A method is proposed for recognizing and predicting non-linear systems employing a radial basis function neural network (RBFNN) and robust hybrid particle swarm optimization (HPSO) approach. A PSO is coupled with a spiral-shaped mechanism (HPSO-SSM) to optimize the PSO performance by mitigating its constraints, such as sluggish convergence and the local minimum dilemma. Three advancements are incorporated into the hypothesized HPSO-SSM algorithms to achieve remarkable results. First, the diversity of the search process is promoted to update the inertial weight ω based on the logistic map sequence. Then, two distinct parameters are trained in the original position update algorithm to enhance the work efficiency of the successive generation. Finally, the proposed approach employs a spiral-shaped mechanism as a local search operator inside the optimum solution space. Moreover, the HPSO-SSM method concurrently improves the RBFNN parameters and network size, building a model with a compact configuration and higher precision. Two non-linear benchmark functions and the total phosphorus (TP) modelling issue in a waste water treatment process (WWTP) are utilized to assess the overall efficacy of the creative technique. The results of testing indicate that the projected HPSO-SSM-RBFNN algorithm performed very effectively.
Load balancing and service discovery using Docker Swarm for microservice based big data applications

Abstract Big Data applications require extensive resources and environments to store, process and analyze this colossal collection of data in a distributed manner. Containerization with cloud computing provides a pertinent remedy to accommodate big data requirements, however requires a precise and appropriate load-balancing mechanism. The load on servers increases exponentially with increased resource usage thus making load balancing an essential requirement. Moreover, the adjustment of containers accurately and rapidly according to load as per services is one of the crucial aspects in big data applications. This study provides a review relating to containerized environments like Docker for big data applications with load balancing. A novel scheduling mechanism of containers for big data applications established on Docker Swarm and Microservice architecture is proposed. The concept of Docker Swarm is utilized to effectively handle big data applications' workload and service discovery. Results shows that increasing workloads with respect to big data applications can be effectively managed by utilizing microservices in containerized environments and load balancing is efficiently achieved using Docker Swarm. The implementation is done using a case study deployed on a single server and then scaled to four instances. Applications developed using containerized microservices reduces average deployment time and continuous integration.
The Use of Virtual Worlds in the Field of Education: A Bibliometric Study

The purpose of this study is to examine the publications conducted in education related to virtual worlds with the bibliometric analysis method. This study employed the appropriate keywords determined via digging the Web of Science database. A total of 2,978 publications meeting the criteria of the current study were examined. VOSviewer software was used for the analysis. We utilized co-authorship analysis to see the descriptive characteristics of the examined publications. The most conducted type was proceedings and the number of publications fluctuated over time with a peak in 2012. England, USA, and Spain had the most publications among the countries, and Open University, Coventry University, and the Polytechnic University of Bucharest had the most publications among the organizations. The majority of the studies were published in the journal of Computers &amp;amp; Education, British Journal of Educational Technology, and Interactive Learning Environments. For revealing the most common keywords, we used the co-occurrence analysis. “e-learning”, “second life” and “higher education” were the most occurred keywords in the publications. The citations analysis was used to see which authors and publications were mostly cited in the examined papers. The most cited authors were Dede, C., Lan, Y. and Ketelhut, D. J. According to the analysis of the cited references, Dalgarno and Lee, (2010), Merchant, Goetz, Cifuentes, Keeney-Kennicutt, and Davis, (2014) and Dunleavy et al. (2008) were the most cited studies. Technological developments have affected the interest in the use of VWs in education. As a new type or extension of virtual worlds, metaverse promises new opportunities in education. The findings of this study would present a road map for those future research to see the general picture of the relevant area.
BeHere: a VR/SAR remote collaboration system based on virtual replicas sharing gesture and avatar in a procedural task

In this paper, we focus on the significance of remote collaboration using virtual replicas, avatar, and gesture on a procedural task in industry; thus, we present a Virtual Reality (VR)/Spatial Augmented Reality (SAR) remote collaboration system, BeHere, based on 3D virtual replicas and sharing gestures and avatar. BeHere enables a remote expert in VR to guide a local worker in real-time to complete a procedural task in the real-world. For the remote VR site, we construct a 3D virtual environment using virtual replicas, and the user can manipulate them by using gestures in an intuitive interaction and see their partners' 3D virtual avatar. For the local site, we use SAR to enable the local worker to see instructions projected onto the real-world based on the shared virtual replicas and gestures. We conducted a formal user study to evaluate the prototype system in terms of performance, social presence, workload, and ranking and user preference. We found that the combination of visual cues of gestures, avatar, and virtual replicas plays a positive role in improving user experience, especially for remote VR users. More significantly, our study provides useful information and important design implications for further research on the use of gesture-, gaze- and avatar-based cues as well as virtual replicas in VR/AR remote collaboration on a procedural task in industry.The online version contains supplementary material available at 10.1007/s10055-023-00748-5.
Extraction of Activity Diagrams Based on Steps Performed in Use Case Description Using Text Mining (Case Study: SRS Myoffice Application)

In this research, extraction is carried out to change the text on an artifact to be processed through Text Mining to process and analyze the text whose results are validated, using the coefficient equation formula. There is a discrepancy between the flow in the Activity Diagram and the steps performed in the Use Case Description. Based on these problems, this study aims to extract an artifact to find similarities between the Activity Diagram and the actions taken in the Use Case Description and perform text analysis using the Text Mining method to obtain similarities, then validation and reliability are carried out. The extraction process is broken down into several documents according to the artifacts used, for Use Case Description, namely: UC01, UC02, UC03, UC04, and UC05. As for the Activity Diagram, namely: AD1, AD2, AD3, AD4, and AD5. The results obtained from this study are the highest similarity between activity diagrams and use case descriptions in sentences UC04 and AD4 with a total similarity of 0.51394086. The highest similarity between words lies in the words “request” and “application” with a complete similarity of 0.9231. Kappa's score with the Gwet's AC1 formula using Python programming is “Less than the Opportunity Agreement” with a number of −008583, while the questionnaire filled out by the Expert is “Almost perfect” with a score of 0.92246.
Characterisation of a single photon event camera for quantum imaging

We show a simple yet effective method that can be used to characterize the per pixel quantum efficiency and temporal resolution of a single photon event camera for quantum imaging applications. Utilizing photon pairs generated through spontaneous parametric down-conversion, the detection efficiency of each pixel, and the temporal resolution of the system, are extracted through coincidence measurements. We use this method to evaluate the TPX3CAM, with appended image intensifier, and measure an average efficiency of [Formula: see text]% and a temporal resolution of 7.3 ns. Furthermore, this technique reveals important error mechanisms that can occur in post-processing. We expect that this technique, and elements therein, will be useful to characterise other quantum imaging systems.
Kinematic Modelling and Position Control of A 3-DOF Parallel Stabilizing Robot Manipulator

Abstract This paper focuses on investigating a parallel camera stabilizing manipulator with three angular degrees of freedom controlled by three linear actuators. An experimental setup is designed and manufactured to actively isolate the host vehicle's disturbing motions. The kinematic analysis of the manipulator combined with a controller is used to disturbance rejection coming from the base platform. Two inertia measurement units (IMU) are used for real-time feedback from the base and up-per platforms' orientation. A Kalman filter is implemented for handling the noises and drifts of the IMUs data. Inverse kinematics of the manipulator is used for calculating the actuating commands and velocity control of the linear motors. The experimental results of the proposed camera stabilizing system are shown. The results indicate its good capability in following the reference input of the controller. Considering the closed kinematic chain of the system and its stiff parallel architecture, this system can be a good choice for the stabilizing system of ground and aerial vehicles.
Blockchain-based DDoS attack mitigation protocol for device-to-device interaction in smart home

Smart home devices are vulnerable to a variety of attacks. The matter gets more complicated when a number of devices collaborate to launch a colluding attack (e.g. Distributed-Denial-of-Service (DDoS)) in a network (e.g., Smart home). To handle these attacks, most studies have hitherto proposed authentication protocols that cannot necessarily be implemented in devices, especially during Device-to-Device (D2D) interactions. Tapping into the potential of Ethereum blockchain and smart contracts, this work proposes a lightweight authentication mechanism that enables safe D2D interactions in a smart home. The Ethereum blockchain enables the implementation of a decentralized prototype as well as a peer-to-peer distributed ledger system. The work also uses a single server queuing system model and the authentication mechanism to curtail DDoS attacks by controlling the number of service requests in the system. The simulation was conducted twenty times, each with varying number of devices chosen at random (ranging from 1 to 30). Each requester device sends an arbitrary request with a unique resource requirement at a time. This is done to measure the system's consistency across a variety of device capabilities. The experimental results show that the proposed protocol not only prevents colluding attacks, but also outperforms the benchmark protocols in terms of computational cost, message processing, and response times.