Reviewerly/exHarmony huggingface dataset:
Operational characterization of general quantum correlation via complex weak value measurement

The last two decades have witnessed significant progress on the understanding the quantum correlation more general than entanglement, wherein even a separable state may yield correlation that cannot be emulated by any classical object. Such a general nonclassical correlation is not only intriguing from the fundamental point of view, but it has also been recognized as a resource in a variety of quantum information processing tasks and quantum technology. Here, we propose a characterization of the general quantum correlation in bipartite system in terms of direct laboratory operations using weak measurement with postselection. We define a quantity based on the imaginary part of weak values obtained via weak measurement of a local basis followed by a postselection of another local basis, and an optimization procedure over all possible choices of the two bases. We show that it satisfies certain desirable requirements for a quantifier of general quantum correlations. It may be statistically interpreted as the minimum genuine quantum share of uncertainty. It is a faithful witness of entanglement for general pure states, giving an observable lower bound to a scaled square root of the linear entropy of entanglement. We then suggest an information theoretic interpretation of the general quantum correlation in a multipartite state as the minimum mean absolute error in an optimal estimation of any local measurement basis, based on the outcomes of local projective measurement on the state, in the worst case scenario.
ANALYSIS OF SELECT INDIAN E-COMMERCE WEBSITES USING PAIRED SIMILARITY INDEX

Online shopping industry is one of the fastest growing industries which has expanded rapidly due to the ever increasing access to internet and rising disposable income levels. It is observed that new age consumers are indulging in impulsive purchase of products in the online shopping website. The information content, features, web design etc. in the websites plays a significant role in drawing shoppers. In this paper the information and features provided in some popular e- commerce websites are analysed using binary representation and developing a similarity index. A Paired Similarity Index (PSI) is created to measure the similarity of information and features between websites through pairs of observations. The study is comprised of selected e-commerce websites, operating in India across a different product categories. This comparison will enable to rank websites based on the information and features provided.
A Numerical Study on Sparse Learning of Interaction Laws in Homogeneous Multiparticle Systems

Multi-agent systems have found wide applications in science and engineering ranging from opinion dynamics to predator-prey systems.A grand challenge encountered in these areas is to reveal the interaction laws between individual agents leading to collective behaviors.In this article, we consider a system of ODEs that is often used in modeling opinion dynamics, where the laws of the interaction are dependent on pairwise distances.We leverage recent advancements in sparsity-promoted algorithms and propose a new approach to learning the interaction laws from a small amount of data.Numerical experiments demonstrate the effectiveness and robustness of the proposed approach in a small, noisy data regime and show the superiority of the proposed approach.
6 Questions for Socially Aware Language Technologies

Over the last few decades, natural language processing (NLP) has dramatically improved performance and produced industrial applications like personal assistants. Despite being sufficient to enable these applications, current NLP systems largely ignore the social part of language. This severely limits the functionality and growth of these applications. This work discusses 6 questions towards how to build socially aware language technologies, with the hope of inspire more research into Social NLP and push our research field to the next level.
Localization and Classification of Parasitic Eggs in Microscopic Images Using an EfficientDet Detector

IPIs caused by protozoan and helminth parasites are among the most common infections in humans in LMICs. They are regarded as a severe public health concern, as they cause a wide array of potentially detrimental health conditions. Researchers have been developing pattern recognition techniques for the automatic identification of parasite eggs in microscopic images. Existing solutions still need improvements to reduce diagnostic errors and generate fast, efficient, and accurate results. Our paper addresses this and proposes a multi-modal learning detector to localize parasitic eggs and categorize them into 11 categories. The experiments were conducted on the novel Chula-ParasiteEgg-11 dataset that was used to train both EfficientDet model with EfficientNet-v2 backbone and EfficientNet-B7+SVM. The dataset has 11,000 microscopic training images from 11 categories. Our results show robust performance with an accuracy of 92%, and an F1 score of 93%. Additionally, the IOU distribution illustrates the high localization capability of the detector.
Sms-based information dissemination system with android application controller for Taguig city university

&#x0D; &#x0D; &#x0D; This study aimed to determine the accessibility of information in Taguig City University in terms of Class Suspensions, Schedule of Enrollment, Meetings and Seminars, Programs/Events, New School Policies and Grade Inquiry. It also focused on the development of SMS-Based Information Dissemination System with Android Application Controller and intended to determine the evaluation of the students, faculty members, non-teaching personnel and IT practitioners on the developed SMS-Based Information Dissemination System with Android Application Controller using the criteria based on ISO 25010 Software Quality Standards. A total of 307 students, 38 faculty members, 38 non-teaching personnel and 20 IT practitioners from Taguig City University participated in the study. In order for the researcher to develop the SMS-Based Information Dissemination System with Android Application Controller, the design covers the model used in the development of the application that is called the Waterfall Model. The results revealed that the level of accessibility of information at Taguig City University as perceived by the respondents was “Accessible”. It was also found out that the evaluation of the respondents on the proposed SMS-Based Information Dissemination System with Android Application Controller was “Very Satisfactory”. The results of one-way ANOVAs revealed that there were no significant differences in the evaluation of SMS-Based Information Dissemination System with Android Application Controller between students, faculty and non-teaching personnel. Lastly, the enhancements that were suggested by the respondents include Improve Graphical User Interface, Security Features, Reports Generation and Develop Android Application for Students.&#x0D; &#x0D; &#x0D;
Research strategy of cleaning airfield navaid lights based on mounted mobile robot group

Based on the landing environment of airport aircraft and the cleaning requirements of a large number of navigation aids, this paper proposes a scheme of intelligent cleaning of navigation aids with mobile robots on the delivery vehicle. The cleaning efficiency of the robot can be effectively improved by optimizing the navigation mode and the moving path of the robot using the feature point navigation according to the layout of the navigation aids. Full automation of navigation light cleaning has been achieved, which saves a lot of manpower and resources for cleaning airports.
SRMDS: Secure and Reliable Medical Data Sharing Using Hybrid Cryptography with Block Chain in E-Health Systems

Abstract The authors have requested that this preprint be removed from Research Square.
Modeling and Simulation of Cloud Computing with iSPD

Cloud Computing, enabled by technological enhancements and the trend for reduction of investments in IT’s physical infrastructure, is the major computing infrastructure nowadays. However, its heterogeneity makes difficult to know if the use of a given environment is efficient or not. In this context, the performance evaluation of cloud systems is useful both to clients, who need to find the best resource configuration for their applications, and to providers, who need to evaluate which scheduling and allocation policies of resources and virtual machines are most cost effective. Simulation is a good approach for this evaluation since it can be done offline. The known cloud computing simulators have issues related to their usability and modeling capability. This work extends the iconic approach for modeling and simulation offered by iSPD to cloud computing, adding icons for virtual machines and VMMs. Our results show that iSPD is faster than Cloudsim with equivalent accuracy, while providing an easier interface to model and simulate IaaS and PaaS environments.
Design Space Exploration of Heterogeneous Systems Applied to the Cloud Resource Allocation Problem

Cloud computing services providers offer on-demand computing resources to applications. Finding the best cloud resource allocation that fits the users’ budget, meets application performance and constraints are still a research challenge. The cloud resource allocation problem is quite akin to the Design Space Exploration (DSE) problem once they both have to find suitable hardware configurations in an ample design space, having incompatible objectives subject to several constraints. This work presents a solution to the cloud resource allocation problem by applying a design space exploration technique. We have designed and developed a software extension, MultiExplorer-VM, from a DSE tool, MultiExplorer, that has a workflow to provide virtual machine configurations according to the users’ requirements and application constraints. A comprehensive set of experiments has been performed to evaluate and validate the proposed tool. We have also compared solutions from our proposal to other existing research work focused on the cloud resource allocation problem based on the Paramount Interaction (PI) technique. The results show that the MultiExplorer-VM achieves significant (better) results than the PI technique. The cost results brought by the MultiExplorer-VM were up to 8.8 times lower compared to the PI technique. The experiments also reveal that for most of the applications, MultiExplorer-VM achieved the optimal cloud configuration.
Certifiers Make Neural Networks Vulnerable to Availability Attacks

To achieve reliable, robust, and safe AI systems, it is vital to implement fallback strategies when AI predictions cannot be trusted. Certifiers for neural networks are a reliable way to check the robustness of these predictions. They guarantee for some predictions that a certain class of manipulations or attacks could not have changed the outcome. For the remaining predictions without guarantees, the method abstains from making a prediction, and a fallback strategy needs to be invoked, which typically incurs additional costs, can require a human operator, or even fail to provide any prediction. While this is a key concept towards safe and secure AI, we show for the first time that this approach comes with its own security risks, as such fallback strategies can be deliberately triggered by an adversary. In addition to naturally occurring abstains for some inputs and perturbations, the adversary can use training-time attacks to deliberately trigger the fallback with high probability. This transfers the main system load onto the fallback, reducing the overall system's integrity and/or availability. We design two novel availability attacks, which show the practical relevance of these threats. For example, adding 1% poisoned data during training is sufficient to trigger the fallback and hence make the model unavailable for up to 100% of all inputs by inserting the trigger. Our extensive experiments across multiple datasets, model architectures, and certifiers demonstrate the broad applicability of these attacks. An initial investigation into potential defenses shows that current approaches are insufficient to mitigate the issue, highlighting the need for new, specific solutions.
Matryoshka Representation Learning

Learned representations are a central component in modern ML systems, serving a multitude of downstream tasks. When training such representations, it is often the case that computational and statistical constraints for each downstream task are unknown. In this context rigid, fixed capacity representations can be either over or under-accommodating to the task at hand. This leads us to ask: can we design a flexible representation that can adapt to multiple downstream tasks with varying computational resources? Our main contribution is Matryoshka Representation Learning (MRL) which encodes information at different granularities and allows a single embedding to adapt to the computational constraints of downstream tasks. MRL minimally modifies existing representation learning pipelines and imposes no additional cost during inference and deployment. MRL learns coarse-to-fine representations that are at least as accurate and rich as independently trained low-dimensional representations. The flexibility within the learned Matryoshka Representations offer: (a) up to 14x smaller embedding size for ImageNet-1K classification at the same level of accuracy; (b) up to 14x real-world speed-ups for large-scale retrieval on ImageNet-1K and 4K; and (c) up to 2% accuracy improvements for long-tail few-shot classification, all while being as robust as the original representations. Finally, we show that MRL extends seamlessly to web-scale datasets (ImageNet, JFT) across various modalities -- vision (ViT, ResNet), vision + language (ALIGN) and language (BERT). MRL code and pretrained models are open-sourced at https://github.com/RAIVNLab/MRL.
Techniques for Secure Data Contribution and Retrieval in Social Networks Using Effective Privacy-Preserving Data Mining

Abstract: The major area of data-mining methods that focuses on protecting personal information from unauthorised or unsolicited exposure is called Privacy Preserving Data-Mining (PPDM). The most valuable information is analysed and predicted using data-mining methods. The security of confidential information from unwanted access is at the core of PPDM abstraction. The Secure Data Contribution Retrieval Algorithm (SDCRA), Enhanced-Attribute Based Encryption (E-ABE), Level by Level Security Optimization and Content Visualization (LSOCV) algorithm, and Privacy Preserved Hadoop Environment are just a few of the proposed methods in this research work to increase privacy and security (PPHE). To address the immediate difficulties, the proposed SDCRA is first taken into consideration. Based on specifications and application compatibility, the SDCRA algorithm determines a privacy policy and sets up security. The accuracy requirements for numerous datasets may be met by this approach. Online social networks (OSNs) are presently favoured interactive medium for establishing communication, sharing, and disseminating a sizable quantity of data on human existence.
Task Formulation Matters When Learning Continually: A Case Study in Visual Question Answering

Continual learning aims to train a model incrementally on a sequence of tasks without forgetting previous knowledge. Although continual learning has been widely studied in computer vision, its application to Vision+Language tasks is not that straightforward, as settings can be parameterized in multiple ways according to their input modalities. In this paper, we present a detailed study of how different settings affect performance for Visual Question Answering. We first propose three plausible task formulations and demonstrate their impact on the performance of continual learning algorithms. We break down several factors of task similarity, showing that performance and sensitivity to task order highly depend on the shift of the output distribution. We also investigate the potential of pretrained models and compare the robustness of transformer models with different visual embeddings. Finally, we provide an analysis interpreting model representations and their impact on forgetting. Our results highlight the importance of stabilizing visual representations in deeper layers.
Calibrating Sequence likelihood Improves Conditional Language Generation

Conditional language models are predominantly trained with maximum likelihood estimation (MLE), giving probability mass to sparsely observed target sequences. While MLE trained models assign high probability to plausible sequences given the context, the model probabilities often do not accurately rank-order generated sequences by quality. This has been empirically observed in beam search decoding as output quality degrading with large beam sizes, and decoding strategies benefiting from heuristics such as length normalization and repetition-blocking. In this work, we introduce sequence likelihood calibration (SLiC) where the likelihood of model generated sequences are calibrated to better align with reference sequences in the model's latent space. With SLiC, decoding heuristics become unnecessary and decoding candidates' quality significantly improves regardless of the decoding method. Furthermore, SLiC shows no sign of diminishing returns with model scale, and presents alternative ways to improve quality with limited training and inference budgets. With SLiC, we exceed or match SOTA results on a wide range of generation tasks spanning abstractive summarization, question generation, abstractive question answering and data-to-text generation, even with modest-sized models.
Improving Policy Learning via Language Dynamics Distillation

Recent work has shown that augmenting environments with language descriptions improves policy learning. However, for environments with complex language abstractions, learning how to ground language to observations is difficult due to sparse, delayed rewards. We propose Language Dynamics Distillation (LDD), which pretrains a model to predict environment dynamics given demonstrations with language descriptions, and then fine-tunes these language-aware pretrained representations via reinforcement learning (RL). In this way, the model is trained to both maximize expected reward and retain knowledge about how language relates to environment dynamics. On SILG, a benchmark of five tasks with language descriptions that evaluate distinct generalization challenges on unseen environments (NetHack, ALFWorld, RTFM, Messenger, and Touchdown), LDD outperforms tabula-rasa RL, VAE pretraining, and methods that learn from unlabeled demonstrations in inverse RL and reward shaping with pretrained experts. In our analyses, we show that language descriptions in demonstrations improve sample-efficiency and generalization across environments, and that dynamics modelling with expert demonstrations is more effective than with non-experts.
Learning Robust Kernel Ensembles with Kernel Average Pooling

Model ensembles have long been used in machine learning to reduce the variance in individual model predictions, making them more robust to input perturbations. Pseudo-ensemble methods like dropout have also been commonly used in deep learning models to improve generalization. However, the application of these techniques to improve neural networks' robustness against input perturbations remains underexplored. We introduce Kernel Average Pooling (KAP), a neural network building block that applies the mean filter along the kernel dimension of the layer activation tensor. We show that ensembles of kernels with similar functionality naturally emerge in convolutional neural networks equipped with KAP and trained with backpropagation. Moreover, we show that when trained on inputs perturbed with additive Gaussian noise, KAP models are remarkably robust against various forms of adversarial attacks. Empirical evaluations on CIFAR10, CIFAR100, TinyImagenet, and Imagenet datasets show substantial improvements in robustness against strong adversarial attacks such as AutoAttack without training on any adversarial examples.
Contrastive Graph Few-Shot Learning

Prevailing deep graph learning models often suffer from label sparsity issue. Although many graph few-shot learning (GFL) methods have been developed to avoid performance degradation in face of limited annotated data, they excessively rely on labeled data, where the distribution shift in the test phase might result in impaired generalization ability. Additionally, they lack a general purpose as their designs are coupled with task or data-specific characteristics. To this end, we propose a general and effective Contrastive Graph Few-shot Learning framework (CGFL). CGFL leverages a self-distilled contrastive learning procedure to boost GFL. Specifically, our model firstly pre-trains a graph encoder with contrastive learning using unlabeled data. Later, the trained encoder is frozen as a teacher model to distill a student model with a contrastive loss. The distilled model is finally fed to GFL. CGFL learns data representation in a self-supervised manner, thus mitigating the distribution shift impact for better generalization and making model task and data-independent for a general graph mining purpose. Furthermore, we introduce an information-based method to quantitatively measure the capability of CGFL. Comprehensive experiments demonstrate that CGFL outperforms state-of-the-art baselines on several graph mining tasks in the few-shot scenario. We also provide quantitative measurement of CGFL's success.
Multi-Task Option Learning and Discovery for Stochastic Path Planning

This paper addresses the problem of reliably and efficiently solving broad classes of long-horizon stochastic path planning problems. Starting with a vanilla RL formulation with a stochastic dynamics simulator and an occupancy matrix of the environment, our approach computes useful options with policies as well as high-level paths that compose the discovered options. Our main contributions are (1) data-driven methods for creating abstract states that serve as endpoints for helpful options, (2) methods for computing option policies using auto-generated option guides in the form of dense pseudo-reward functions, and (3) an overarching algorithm for composing the computed options. We show that this approach yields strong guarantees of executability and solvability: under fairly general conditions, the computed option guides lead to composable option policies and consequently ensure downward refinability. Empirical evaluation on a range of robots, environments, and tasks shows that this approach effectively transfers knowledge across related tasks and that it outperforms existing approaches by a significant margin.
Exotic Behavior of Parity in the Superradiant Phase of Quantum Rabi Model

Parity describing the symmetry of quantum mechanics wavefunction under space inversion transformation not only plays an essential role in solving quantum systems but also can be used to manipulate and measure the motional quantum states of such hybrid quantum systems as quantum Rabi model(QRM) and/or its variants through parity measurements. Here we address an exotic parity behavior of the QRM in its superradiant phase by numerical exact diagonalization, namely, the parities of eigenstates of the QRM behave irregular in the strong coupling regime but the sum of parities for each pair of eigenstates beginning from the ground state remains vanishing. It is found that this exotic behavior originates from the comparability of the photon distribution in the odd and even components of Fock basis when the eigenenergies of each pair of eigenstates approach enough to each other and physically is due to the emergent double-well potential induced by the strong coupling between the single-mode photon field and the two-level atom. The result not only uncovers the physics not known previously in the QRM but also makes an intrinsic limitation on the measurement precision of motional quantum states through parity measurements in modern quantum science and technologies.