Reviewerly/exHarmony huggingface dataset:
Near term algorithms for linear systems of equations

Abstract Finding solutions to systems of linear equations is a common problem in many areas of science and engineering, with much potential for a speed up on quantum devices. While the Harrow–Hassidim–Lloyd (HHL) quantum algorithm yields up to an exponential speed up over classical algorithms in some cases, it requires a fault-tolerant quantum computer, which is unlikely to be available in the near-term. Thus, attention has turned to the investigation of quantum algorithms for noisy intermediate-scale quantum (NISQ) devices where several near-term approaches to solving systems of linear equations have been proposed. This paper focuses on the Variational Quantum Linear Solvers (VQLS), and other closely related methods and adaptions. Several contributions are made in this paper, which include: the first application of the Evolutionary Ansatz to the VQLS (EAVQLS), the first implementation of the Logical Ansatz VQLS (LAVQLS), based on the Classical Combination of Quantum States (CQS) method, a proof of principle demonstration of the CQS method on real quantum hardware and a method for the implementation of the Adiabatic Ansatz on the VQLS (AAVQLS). These approaches are implemented and contrasted. The CQS method is run with moderate success on a real quantum device. The EAVQLS and AAVQLS show promise as possible improvements to the standard VQLS algorithm once refined.
Multimodal Extension of the ML-CSC Framework for Medical Image Segmentation

In recent years, Convolutional Neural Networks (CNNs) have led to huge successes across various computer vision applications. However, the lack of interpretability poses a severe barrier for their wider adoption in healthcare. Recently introduced Multilayer Convolutional Sparse Coding (ML-CSC) data model provides a model-based explanation of CNNs. This article aims to extend the ML-CSC framework towards multimodal data processing, which to our knowledge has not been addressed so far. In particular, we focus on interpretable medical image segmentation architecture design for multimodal data. We derive a novel sparse coding algorithm and propose three different CNN architectures with increasing performance, without introducing any additional learnable parameters. Based on the sparse coding theory, our multimodal extension enables the systematic design of interpretable CNN segmentation architectures. Experimental analysis demonstrates that the achieved segmentation results are consistent with the obtained theoretical expectations.
CNN Classifier’s Robustness Enhancement when Preserving Privacy

Laws on privacy preservation challenges supervised learning algorithms in industrial applications and could be an obstacle for the artificial intelligence solutions. In the literature, this issue is never discussed for the algorithm’s design. Indeed, algorithms do not behave the same when the input is modified to protect privacy. Particularly, the unmodified data samples predicts with low confidences show high vulnerability to decision changes. To overcome this challenge, we propose a novel solution that enhances classifier’s robustness by particularly addressing the vulnerable samples. It consists of a novel formulation of the learning objective by hybridizing similarity learning, decision margin and intra-class distance. Experimental results and evaluation on a challenging vehicle image dataset exhibit the high effectiveness and potentials of our method for the privacy preserving classification problems.
PandAna: A Python Analysis Framework for Scalable High Performance Computing in High Energy Physics

Modern experiments in high energy physics analyze millions of events recorded in particle detectors to select the events of interest and make measurements of physics parameters. These data can often be stored as tabular data in files with detector information and reconstructed quantities. Most current techniques for event selection in these files lack the scalability needed for high performance computing environments. We describe our work to develop a high energy physics analysis framework suitable for high performance computing. This new framework utilizes modern tools for reading files and implicit data parallelism. Framework users analyze tabular data using standard, easy-to-use data analysis techniques in Python while the framework handles the file manipulations and parallelism without the user needing advanced experience in parallel programming. In future versions, we hope to provide a framework that can be utilized on a personal computer or a high performance computing cluster with little change to the user code.
Almost sure convergence of randomised‐difference descent algorithm for stochastic convex optimisation

Stochastic gradient descent algorithm is a classical and useful method for stochastic optimisation. While stochastic gradient descent has been theoretically investigated for decades and successfully applied in machine learning such as training of deep neural networks, it essentially relies on obtaining the unbiased estimates of gradients/subgradients of the objective functions. In this paper, by constructing the randomised differences of the objective function, a gradient-free algorithm, named the stochastic randomised-difference descent algorithm, is proposed for stochastic convex optimisation. Under the strongly convex assumption of the objective function, it is proved that the estimates generated from stochastic randomised-difference descent converge to the optimal value with probability one, and the convergence rates of both the mean square error of estimates and the regret functions are established. Finally, some numerical examples are prsented.
Uniformity Testing in the Shuffle Model: Simpler, Better, Faster

Previous chapter Next chapter Full AccessProceedings Symposium on Simplicity in Algorithms (SOSA)Uniformity Testing in the Shuffle Model: Simpler, Better, FasterClement L. Canonne and Hongyi LyuClement L. Canonne and Hongyi Lyupp.182 - 202Chapter DOI:https://doi.org/10.1137/1.9781611977066.13PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Uniformity testing, or testing whether independent observations are uniformly distributed, is the prototypical question in distribution testing. Over the past years, a line of work has been focusing on uniformity testing under privacy constraints on the data, and obtained private and data-efficient algorithms under various privacy models such as central differential privacy (DP), local privacy (LDP), pan-privacy, and, very recently, the shuffle model of differential privacy. In this work, we considerably simplify the analysis of the known uniformity testing algorithm in the shuffle model, and, using a recent result on “privacy amplification via shuffling,” provide an alternative algorithm attaining the same guarantees with an elementary and streamlined argument. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-706-6 https://doi.org/10.1137/1.9781611977066Book Series Name:ProceedingsBook Code:SOSA22Book Pages:v + 320
An environmental approach to developing and applying smart complexes of academic disciplines in professional training of future specialists

The paper discloses modern approaches to creating integrated information environments of SMART complexes of academic disciplines through integrating creative, authorial, non-verbal, encyclopaedic, information-and-communication, self-realization, self-assessment components in professional (vocational) and pre-university professional education. It lists the advantages of such complexes compared to e-textbooks and reveals the requirements for developing them. It highlights the ways of considering future specialists’ psycho-physiological development when selecting and structuring educational information. It recommends applying constructive equalization of students’ cognitive activity based on the Kosko’s quasi-neural network model in their designing an educational trajectory. It shows conditions for ensuring equal opportunities for students’ learning within such complexes. Both selection and structurization of such complexes’ educational information follow students’ psychological development in perceiving it and focus on critical feature patterns of the adaptive resonance theory and the Hopfield model for associative memory. The paper suggests evaluating students’ activities within such complexes by comparing each participant’s achievements with the parameters of completed projects, using the index method based on qualimetric measurements. It specifies the features of an environmental approach in developing such complexes; elaborating their educational material; determining types of learning tasks; creating means of monitoring students’ knowledge. It justifies the results of experimental work, which involved surveying 442 teachers and analyzes the influence of such complexes on the effectiveness of the educational process. It highlights the importance of introducing heuristic forms, methods and techniques of students’ learning to help students obtain, systematize and consolidate educational information and acquire practical skills in performing creative projects and professional tasks.
Photonic processors light the way

Highly efficient light-based processors can overcome the bottlenecks of today's electronics.
Protean Kinematics: A Blended Model of VR Physics

Avatar research largely focuses on the effects of the appearance and external characteristics of avatars, but may also warrant further consideration of the effects of avatar movement characteristics. With Protean kinematics , we offer an expansion the avatar-user appearances-based effects of the Proteus Effect to a systematic exploration into the role of movement in affecting social perceptions (about others) and idealized perceptions (about self). This work presents both a theoretical (typology) and methodological (physics-based measurement) approach to understanding the complex blend of physical inputs and virtual outputs that occur in the perceptual experience of VR, particularly in consideration of the collection of hippocampal (e.g., place cells, grid cells) and entorhinal neurons (e.g., speed cells) that fire topologically relative to physical movement in physical space. Offered is a novel method that distills the blend of physical and virtual kinematics to contribute to modern understandings of human-agent interaction and cognitive psychology.
An Analysis of Privacy Preserving Data Storage and Retrieval Approaches in Heterogeneous Multi-cloud Architectures

Cloud storage and retrieval are considered essential services of cloud computing (CC), which enables the data owner to store the data from the local computing systems to the cloud.Though cloud storage provides several benefits such as minimal cost and short turnaround time, data security is found to be a highly challenging and difficult process.One of the effective solutions to avoid data loss related to security risks is the concurrent usage of multi-cloud.The multi-cloud allows the data owner to easily access their data from remote areas via the web interfaces offered by Amazon EC2.However, even if such multi-cloud environments are susceptible to security attacks, it would bring irreversible challenges to the owners namely integrity, availability, and confidentiality of data.In order to resolve these issues, several privacy preserving data storage and retrieval models are presented in the literature to store data securely and reduce the computational resources.In this view, this study performs a review of existing state of art data storage and retrieval approaches for heterogeneous multi-cloud architectures.The reviewed methods are investigated based on their objectives, underlying techniques, implementation data, and evaluation parameters.Besides, a comparative results analysis of few of the reviewed models takes place.Finally, a discussion along with the possible future direction is given to enable the readers to find the research problem.
Metrics for Automatic Evaluation of Text from NLP Models for Text to Scene Generation

Performance metrics give us an indication of which model is better for which task. Researchers attempt to apply machine learning and deep learning models to measure the performance of models through cost function or evaluation criteria like Mean square error (MSE) for regression, accuracy, and f1-score for classification tasks Whereas in NLP performance measurement is a complex due variation of ground truth and results obta.
Data Deduplication Techniques: A Comparative Analysis

Cloud Storage Service affords users with plentiful storage space andprovides users accessibility in synchronizing files across platform and devices fromanyplace. In today’s world, emerging technologies are relying on a large number ofdata, hence data management and storage becomes a serious problem. Thereforedata deduplication significantly reduces the storage overhead particularly in cloudenvironment. Deduplication mechanism applied on server-side and client-side. In thispaper, we discussed about various types and mechanisms of data deduplication,importance of client-side deduplication and additionally comparative analysis of thefew among the different existing client-side deduplication schemes are done.
Embedding of particle tracking data using hybrid quantum-classical neural networks

The High Luminosity Large Hadron Collider (HL-LHC) at CERN will involve a significant increase in complexity and sheer size of data with respect to the current LHC experimental complex. Hence, the task of reconstructing the particle trajectories will become more involved due to the number of simultaneous collisions and the resulting increased detector occupancy. Aiming to identify the particle paths, machine learning techniques such as graph neural networks are being explored in the HEP.TrkX project and its successor, the Exa.TrkX project. Both show promising results and reduce the combinatorial nature of the problem. Previous results of our team have demonstrated the successful attempt of applying quantum graph neural networks to reconstruct the particle track based on the hits of the detector. A higher overall accuracy is gained by representing the training data in a meaningful way within an embedded space. That has been included in the Exa.TrkX project by applying a classical MLP. Consequently, pairs of hits belonging to different trajectories are pushed apart while those belonging to the same ones stay close together. We explore the applicability of variational quantum circuits that include a relatively low number of qubits applicable to NISQ devices within the task of embedding and show preliminary results.
Learning Sensor Interdependencies for IMU-to-Segment Assignment

Due to the recent technological advances in inertial measurement units (IMUs), many applications for the measurement of human motion using multiple body-worn IMUs have been developed. In these applications, each IMU has to be attached to a predefined body segment. A technique to identify the body segment on which each IMU is mounted allows users to attach inertial sensors to arbitrary body segments, which avoids having to remeasure due to incorrect attachment of the sensors. We address this IMU-to-segment assignment problem and propose a novel end-to-end learning model that incorporates a global feature generation module and an attention-based mechanism. The former extracts the feature representing the motion of all attached IMUs, and the latter enables the model to learn the dependency relationships between the IMUs. The proposed model thus identifies the IMU placement based on the features from global motion and relevant IMUs. We quantitatively evaluated the proposed method using synthetic and real public datasets with three sensor configurations, including a full-body configuration mounting 15 sensors. The results demonstrated that our approach significantly outperformed the conventional and baseline methods for all datasets and sensor configurations.
A Benchmark Evaluation of Adaptive Image Compression for Multi Picture Object Stereoscopic Images

A stereopair consists of two pictures related to the same subject taken by two different points of view. Since the two images contain a high amount of redundant information, new compression approaches and data formats are continuously proposed, which aim to reduce the space needed to store a stereoscopic image while preserving its quality. A standard for multi-picture image encoding is represented by the MPO format (Multi-Picture Object). The classic stereoscopic image compression approaches compute a disparity map between the two views, which is stored with one of the two views together with a residual image. An alternative approach, named adaptive stereoscopic image compression, encodes just the two views independently with different quality factors. Then, the redundancy between the two views is exploited to enhance the low quality image. In this paper, the problem of stereoscopic image compression is presented, with a focus on the adaptive stereoscopic compression approach, which allows us to obtain a standardized format of the compressed data. The paper presents a benchmark evaluation on large and standardized datasets including 60 stereopairs that differ by resolution and acquisition technique. The method is evaluated by varying the amount of compression, as well as the matching and optimization methods resulting in 16 different settings. The adaptive approach is also compared with other MPO-compliant methods. The paper also presents an Human Visual System (HVS)-based assessment experiment which involved 116 people in order to verify the perceived quality of the decoded images.
Image Cropping Assisted By Modeling Inter-Patch Relations

Image cropping is a common way to enhance the aesthetic quality of images. Huge industrial demand and the tediousness of image cropping make automatic image cropping a prosperous task. Existing works, however, face two difficulties: objects are easily truncated and key components of images are discarded by the model. The key to solving this problem is to understand the relations between different components of an image. These relations break the limit of spatial distance and reflect the contextual information in images, which help the model decide whether to retain a component. Motivated by this, a patch-related graph module is proposed to model the relations between different patches of an image. The patch-related features are extracted by a graph convolution layer and then fused with the original local features by a proposed gated unit. Moreover, a gradient layer is designed to embed the edge information in the input. The edge-prior input helps the model read the contents of images and reserve the main objects completely. Experimental results show that our model grasps the inter-patch relations well and performs competitively with other state-of-the-art approaches.
Who cares about the digital culture at school?

The article assumes that the expansion of cultural content in basic education implies a qualitative change in the entire curriculum system. It rescues the idea of social micro spaces of common curriculum selection and reconfiguration and underlines the role of decision-makers in different groups. Namely, experts and specialists who, within the scope of the Learning Outcomes Project launched by the Portuguese Ministry of Education, supported the legitimation of ICT as a transdisciplinary learning area, consenting to shared responsibility in promoting digital culture. Based on the study carried out, involving 11 interviews, the article reveals the reasons that justify the detachment from a social practice that, paradoxically, was desired to be more articulated, integrated, and open to digital culture.
ACTION RECOGNITION IN LOW RESOLUTION VIDEOS USING FO-SVM

The majority of extant in recognition of action research is focused on high-quality videos with clearly apparent activity.The actions in videos are collected at a variety of resolutions in real-world surveillance situations.Most actions take place at a distance with a low resolution, and identifying such events is a difficult task.In this paper, we take a look at the effect of low video quality on human activity location from two points: recordings that are weakly gathered topographically (low level resolution) and transiently (lower frame rate), and packed recordings with movement obscuring and artifacts.To recognize the action four main steps are considered.First one is background subtraction using LPB operator.Second, the features are extracted using histogram of gradients (HOG) and Histogram of optical flow (HOF) algorithm which is used to estimate the motion of a person and eigen value algorithm which is used to recognize the person.These features are fused together and perform Firefly optimization (FO) technique to obtain optimized features.Thirdly, generate a code book for feature encoding.In feature encoding, the process is performed using bag of words.Finally action recognition is done using Support vector machine (SVM) classifier.The experimental results are performed on low resolution datasets like VIRAT dataset, KTH dataset and Soccer dataset.The result obtained using VIRAT is 91.46%,KTH is 92.40 % and Soccers is 90.51% in terms of accuracy.
Result Prediction for Political Parties Using Twitter Sentiment Analysis

Elections in general is a decision making process by which people choose an individual to hold power of public office. Recently, social media has a huge impact on people's opinion towards political parties, candidates and elections. Twitter is a blogging service which has millions of tweets over particular topic. Moreover, each tweet may have either a positive, negative or neutral sentiment towards the topic, which needs to be considered. We use different machine learning algorithms like Naive Baye’s, Support Vector Machine both linear and radial basis kernel function. By gathering a large dataset of more than 500,000 tweets out of which 80% of data is taken for training algorithms and remaining 20% for testing. By applying this technique to Indian parliamentary and state elections we predict which political party has more influence on social media.
Graph-Convolution Network for Image Compression

Currently, convolution neural network is widely applied in image compression frameworks. However, classical convolution can only capture local information because of the heavy restriction of the fixed-shape receptive field. In this paper, we propose a novel image compression network, which introduces graph convolution block (GCB) to enhance the capability of extracting image information in the encoder. In GCB, the graph convolution and residual block are utilized to acquire local and global image features at the same time. Furthermore, an effective dequantization strategy is developed so that the decoder can learn better parameters to reconstruct more image information that is lost in quantization. Extensive experiments demonstrate that our model has outstanding performance, which outperforms existing excellent classical and learned image compression frameworks.