Reviewerly/exHarmony huggingface dataset:
Practical Multi-Party Private Set Intersection Protocols

Privacy-preserving techniques for processing sets of information have attracted the research community’s attention in recent years due to society’s increasing dependency on the availability of data at any time. One of the fundamental problems in set operations is known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Private Set Intersection</i> (PSI). The problem requires two parties to compute the intersection between their sets while preserving correctness and privacy. Although several efficient two-party PSI protocols already exist, protocols for PSI in the multi-party setting (MPSI) currently scale poorly with a growing number of parties, even though this applies to many real-life scenarios. This paper fills this gap by proposing two multi-party protocols based on Bloom filters and threshold homomorphic PKEs, which are secure in the semi-honest model. The first protocol is a multi-party PSI, whereas the second provides a more subtle functionality - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">threshold</i> multi-party PSI (T-MPSI) - which outputs items of the server that appear in at least some number of other private sets. The protocols are inspired by the Davidson-Cid protocol based on Bloom filters. We compare our MPSI protocol against Kolesnikov <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , which is among the fastest known MPSI protocols. Our MPSI protocol performs better than Kolesnikov <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> in terms of run time, given that the sets are small and there is a large number of parties. Our T-MPSI protocol performs better than other existing works: the computational and communication complexities are linear in the number of elements in the largest set given a fixed number of colluding parties. We conclude that our MPSI and T-MPSI protocols are practical solutions suitable for emerging use-case scenarios with many parties, where previous solutions did not scale well.
Quantum speed limits for information and coherence

Abstract The quantum speed limit indicates the maximal evolution speed of the quantum system. In this work, we determine speed limits on the informational measures, namely the von Neumann entropy, maximal information, and coherence of quantum systems evolving under dynamical processes. These speed limits ascertain the fundamental limitations on the evolution time required by the quantum systems for the changes in their informational measures. Erasing of quantum information to reset the memory for future use is crucial for quantum computing devices. We use the speed limit on the maximal information to obtain the minimum time required to erase the information of quantum systems via some quantum processes of interest.
Leukocyte classification based on feature selection using extra trees classifier: a transfer learning approach

The criticality of investigating the white blood cell (WBC) count cannot be underestimated, as white blood cells are an important component of the body's defence system.From helping to diagnose hidden infections to insinuating the presence of comorbidities like immunodeficiency, an accurate white blood cell count can contribute significantly to shape a physician's assessment.The manual process performed by the pathologists for the classification of WBCs is a time consuming and tedious task, which is further disadvantaged by a lack of accuracy.This study concentrates on the automatic detection and classification of WBC without data augmentation into four subtypes such as eosinophil, monocyte, lymphocyte and neutrophil based on images from three different datasets.The methodology adopted in this paper is transfer learning approach in which the features are extracted using ResNet50, DenseNet121, MobileNetv2, Inceptionv3 and Xception deep learning models.The extra trees classifier is used as an intermediate stage for selecting most predominant features, which reduce the execution time.When evaluating the performance on the basis of recall, precision, F-measure and accuracy parameters, the classification of ResNet50 features selected by extra trees classifier using multi-class support vector machine (SVM) provides the highest accuracy of 90.76% .
ConSeC: Word Sense Disambiguation as Continuous Sense Comprehension

Supervised systems have nowadays become the standard recipe for Word Sense Disambiguation (WSD), with Transformer-based language models as their primary ingredient.However, while these systems have certainly attained unprecedented performances, virtually all of them operate under the constraining assumption that, given a context, each word can be disambiguated individually with no account of the other sense choices.To address this limitation and drop this assumption, we propose CONtinuous SEnse Comprehension (CONSEC), a novel approach to WSD: leveraging a recent re-framing of this task as a text extraction problem, we adapt it to our formulation and introduce a feedback loop strategy that allows the disambiguation of a target word to be conditioned not only on its context but also on the explicit senses assigned to nearby words.We evaluate CONSEC and examine how its components lead it to surpass all its competitors and set a new state of the art on English WSD.We also explore how CONSEC fares in the cross-lingual setting, focusing on 8 languages with various degrees of resource availability, and report significant improvements over prior systems.We release our code at https://github.com/SapienzaNLP/consec.
From digital competence to Professional Digital Competence

The authors of this article have collaborated as part of a steering group for Norwegian state-funded research and development project designed to enhance the professional digital competence (PDC) of both teacher educators, practising, and student teachers. In this article, we give voice to students’ experiences of their PDC development during teacher education (TE). We investigate their ideas on how TE might be developed to prepare them better for professional careers in a digital context. The participants are studying at a Norwegian university where, from 2018 to 2021, PDC has become a major area of focus as part of the aforementioned project. The data consist of four group interviews with 17 students from different campuses. We find that student teachers employ a broad range of digital technologies during TE. They experience a diversity of digital didactical practices and engage in thematic discussions concerning digitalization. They also utilise many technologies and apply the digital knowledge they have acquired in their personal lives. While some of them request more technical support during TE, most want to see TE engaging them in more critical discussions about the educational opportunities and challenges that digitalization offers. We discuss some of the dilemmas that TE must address to respond to these findings. In particular, we elaborate on how students’ digital experiences can be used as a resource when preparing for their professional roles as teachers.
Cross-Domain Label-Adaptive Stance Detection

Stance detection concerns the classification of a writer's viewpoint towards a target.There are different task variants, e.g., stance of a tweet vs. a full article, or stance with respect to a claim vs. an (implicit) topic.Moreover, task definitions vary, which includes the label inventory, the data collection, and the annotation protocol.All these aspects hinder cross-domain studies, as they require changes to standard domain adaptation approaches.In this paper, we perform an in-depth analysis of 16 stance detection datasets, and we explore the possibility for cross-domain learning from them.Moreover, we propose an end-to-end unsupervised framework for outof-domain prediction of unseen, user-defined labels.In particular, we combine domain adaptation techniques such as mixture of experts and domain-adversarial training with label embeddings, and we demonstrate sizable performance gains over strong baselines, both (i) indomain, i.e., for seen targets, and (ii) out-ofdomain, i.e., for unseen targets.Finally, we perform an exhaustive analysis of the crossdomain results, and we highlight the important factors influencing the model performance.
MSˆ2: Multi-Document Summarization of Medical Studies

To assess the effectiveness of any medical intervention, researchers must conduct a timeintensive and manual literature review.NLP systems can help to automate or assist in parts of this expensive process.In support of this goal, we release MSˆ2 (Multi-Document Summarization of Medical Studies), a dataset of over 470k documents and 20K summaries derived from the scientific literature.This dataset facilitates the development of systems that can assess and aggregate contradictory evidence across multiple studies, and is the first large-scale, publicly available multi-document summarization dataset in the biomedical domain.We experiment with a summarization system based on BART, with promising early results, though significant work remains to achieve higher summarization quality.We formulate our summarization inputs and targets in both free text and structured forms and modify a recently proposed metric to assess the quality of our system's generated summaries.
American Sign Language Words Recognition Using Spatio-Temporal Prosodic and Angle Features: A Sequential Learning Approach

Most of the available American Sign Language (ASL) words share similar characteristics. These characteristics are usually during sign trajectory which yields similarity issues and hinders ubiquitous application. However, recognition of similar ASL words confused translation algorithms, which lead to misclassification. In this paper, based on fast fisher vector (FFV) and bi-directional Long-Short Term memory (Bi-LSTM) method, a large database of dynamic sign words recognition algorithm called bidirectional long-short term memory-fast fisher vector (FFV-Bi-LSTM) is designed. This algorithm is designed to train 3D hand skeletal information of motion and orientation angle features learned from the leap motion controller (LMC). Each bulk features in the 3D video frame is concatenated together and represented as an high-dimensional vector using FFV encoding. Evaluation results demonstrate that the FFV-Bi-LSTM algorithm is suitable for accurately recognizing dynamic ASL words on basis of prosodic and angle cues. Furthermore, comparison results demonstrate that FFV-Bi-LSTM can provide better recognition accuracy of 98.6% and 91.002% for randomly selected ASL dictionary and 10 pairs of similar ASL words, in leave-one-subject-out cross-validation on the constructed dataset. The performance of our FFV-Bi-LSTM is further evaluated on ASL data set, leap motion dynamic hand gestures data set (LMDHG), and Semaphoric hand gestures contained in the Shape Retrieval Contest (SHREC) dataset. We improve the accuracy of the ASL data set, LMDHG, and SHREC data sets by 2%, 2%, and 3.19% respectively.
VR in Tourism: A New Call for Virtual Tourism Experience amid and after the COVID-19 Pandemic

Virtual reality has become a more common phenomenon in both destination marketing and on-site experience. The recent challenges such as overtourism and the COVID-19 pandemic have created a pressing need to examine virtual tourism as an alternative to traditional travel. This conceptual article aims at clarifying virtual experience in tourism, discussing the main antecedents and outcomes of virtual experience, and proposing a conceptual model of virtual tourism experience. The review of the literature revealed that virtual experience in tourism is influenced by factors related to information, quality, technology acceptance, and affective involvement and has significant effects on tourists’ attitudes and behavioral intentions. This paper contributes to knowledge and practice by classifying the main groups of factors influencing virtual tourism experience, introducing the conceptual model, discussing opportunities for future research, and providing recommendations for tourism practitioners.
A Deep Learning Approach for IoT Traffic Multi-Classification in a Smart-City Scenario

As the number of Internet of Things (IoT) devices and applications increases, the capacity of the IoT access networks is considerably stressed. This can create significant performance bottlenecks in various layers of an end-to-end communication path, including the scheduling of the spectrum, the resource requirements for processing the IoT data at the Edge and/or Cloud, and the attainable delay for critical emergency scenarios. Thus, a proper classification or prediction of the time varying traffic characteristics of the IoT devices is required. However, this classification remains at large an open challenge. Most of the existing solutions are based on machine learning techniques, which nonetheless present high computational cost, whereas they are not considering the fine-grained flow characteristics of the traffic. To this end, this paper introduces the following four contributions. Firstly, we provide an extended feature set including, flow, packet and device level features to characterize the IoT devices in the context of a smart environment. Secondly, we propose a custom weighting based preprocessing algorithm to determine the importance of the data values. Thirdly, we present insights into traffic characteristics using feature selection and correlation mechanisms. Finally, we develop a two-stage learning algorithm and we demonstrate its ability to accurately categorize the IoT devices in two different datasets. The evaluation results show that the proposed learning framework achieves 99.9% accuracy for the first dataset and 99.8% accuracy for the second. Additionally, for the first dataset we achieve a precision and recall performance of 99.6% and 99.5%, while for the second dataset the precission and recall attained is of 99.6% and 99.7% respectively. These results show that our approach clearly outperforms other well-known machine learning methods. Hence, this work provides a useful model deployed in a realistic IoT scenario, where IoT traffic and devices’ profiles are predicted and classified, while facilitating the data processing in the upper layers of an end-to-end communication model.
Robust spin squeezing from the tower of states of U(1)-symmetric spin Hamiltonians

Spin squeezing---a central resource for quantum metrology---can be generated via the nonlinear, entangling evolution of an initially factorized spin state. Here we show that robust (i.e., persistent) squeezing dynamics is generated by a very large class of $S=1/2$ spin Hamiltonians with axial symmetry, in relationship with the existence of a peculiar structure of the low-lying Hamiltonian eigenstates---the so-called Anderson's tower of states. Such states are fundamentally related to the appearance of spontaneous symmetry breaking in quantum systems; and, for models with sufficiently high connectivity, they are parametrically close to the eigenstates of a planar rotor (Dicke states), in that they feature an anomalously large value of the total angular momentum. Our central insight is that starting from a coherent spin state, a generic U(1)-symmetric Hamiltonian featuring the Anderson's tower of states generates the same squeezing evolution at short times as the one governed by the paradigmatic one-axis-twisting (or planar-rotor) model of squeezing dynamics. The full squeezing evolution of the planar-rotor model is seemingly reproduced for interactions decaying with distance $r$ as ${r}^{\ensuremath{-}\ensuremath{\alpha}}$ when $\ensuremath{\alpha}&lt;5d/3$ in $d$ dimensions. Our results connect quantum simulation with quantum metrology by unveiling the squeezing power of a large variety of Hamiltonian dynamics that are currently implemented by different quantum simulation platforms.
CodeFill

Code completion is an essential feature of IDEs, yet current auto-completers are restricted to either grammar-based or NLP-based single token completions. Both approaches have significant drawbacks: grammar-based autocompletion is restricted in dynamically-typed language environments, whereas NLP-based autocompleters struggle to understand the semantics of the programming language and the developer's code context.
Solar Irradiance Forecasting to Short-Term PV Power: Accuracy Comparison of ANN and LSTM Models

The use of renewable energies, such as Photovoltaic (PV) solar power, is necessary to meet the growing energy consumption. PV solar power generation has intrinsic characteristics related to the climatic variables that cause intermittence during the generation process, promoting instabilities and insecurity in the electrical system. One of the solutions for this problem uses methods for the Prediction of Solar Photovoltaic Power Generation (PSPPG). In this context, the aim of this study is to develop and compare the prediction accuracy of solar irradiance between Artificial Neural Network (ANN) and Long-Term Short Memory (LSTM) network models, from a comprehensive analysis that simultaneously considers two distinct sets of exogenous meteorological input variables and three short-term prediction horizons (1, 15 and 60 min), in a controlled experimental environment. The results indicate that there is a significant difference (p &lt; 0.001) in the prediction accuracy between the ANN and LSTM models, with better overall prediction accuracy skill for the LSTM models (MAPE = 19.5%), except for the 60 min prediction horizon. Furthermore, the accuracy difference between the ANN and LSTM models decreased as the prediction horizon increased, and no significant influence was observed on the accuracy of the prediction with both sets of evaluated meteorological input variables.
Parallel quantum annealing

Abstract Quantum annealers of D-Wave Systems, Inc., offer an efficient way to compute high quality solutions of NP-hard problems. This is done by mapping a problem onto the physical qubits of the quantum chip, from which a solution is obtained after quantum annealing. However, since the connectivity of the physical qubits on the chip is limited, a minor embedding of the problem structure onto the chip is required. In this process, and especially for smaller problems, many qubits will stay unused. We propose a novel method, called parallel quantum annealing, to make better use of available qubits, wherein either the same or several independent problems are solved in the same annealing cycle of a quantum annealer, assuming enough physical qubits are available to embed more than one problem. Although the individual solution quality may be slightly decreased when solving several problems in parallel (as opposed to solving each problem separately), we demonstrate that our method may give dramatic speed-ups in terms of the Time-To-Solution (TTS) metric for solving instances of the Maximum Clique problem when compared to solving each problem sequentially on the quantum annealer. Additionally, we show that solving a single Maximum Clique problem using parallel quantum annealing reduces the TTS significantly.
Uncertainty-Aware Multi-View Representation Learning

Learning from different data views by exploring the underlying complementary information among them can endow the representation with stronger expressive ability. However, high-dimensional features tend to contain noise, and furthermore, quality of data usually varies for different samples (even for different views), i.e., one view may be informative for one sample but not the case for another. Therefore, it is quite challenging to integrate multi-view noisy data under unsupervised setting. Traditional multi-view methods either simply treat each view with equal importance or tune the weights of different views to fixed values, which are insufficient to capture the dynamic noise in multi-view data. In this work, we devise a novel unsupervised multi-view learning approach, termed as Dynamic Uncertainty-Aware Networks (DUA-Nets). Guided by the uncertainty of data estimated from the generation perspective, intrinsic information from multiple views is integrated to obtain noise-free representations. Under the help of uncertainty estimation, DUA-Nets weigh each view of individual sample according to data quality so that the high-quality samples (or views) can be fully exploited while the effects from the noisy samples (or views) will be alleviated. Our model achieves superior performance in extensive experiments and shows the robustness to noisy data.
Human Detection in Aerial Thermal Images Using Faster R-CNN and SSD Algorithms

The automatic detection of humans in aerial thermal imagery plays a significant role in various real-time applications, such as surveillance, search and rescue and border monitoring. Small target size, low resolution, occlusion, pose, and scale variations are the significant challenges in aerial thermal images that cause poor performance for various state-of-the-art object detection algorithms. Though many deep-learning-based object detection algorithms have shown impressive performance for generic object detection tasks, their ability to detect smaller objects in the aerial thermal images is analyzed through this study. This work carried out the performance evaluation of Faster R-CNN and single-shot multi-box detector (SSD) algorithms with different backbone networks to detect human targets in aerial view thermal images. For this purpose, two standard aerial thermal datasets having human objects of varying scale are considered with different backbone networks, such as ResNet50, Inception-v2, and MobileNet-v1. The evaluation results demonstrate that the Faster R-CNN model trained with the ResNet50 network architecture out-performed in terms of detection accuracy, with a mean average precision (mAP at 0.5 IoU) of 100% and 55.7% for the test data of the OSU thermal dataset and AAU PD T datasets, respectively. SSD with MobileNet-v1 achieved the highest detection speed of 44 frames per second (FPS) on the NVIDIA GeForce GTX 1080 GPU. Fine-tuning the anchor parameters of the Faster R-CNN ResNet50 and SSD Inception-v2 algorithms caused remarkable improvement in mAP by 10% and 3.5%, respectively, for the challenging AAU PD T dataset. The experimental results demonstrated the application of Faster R-CNN and SSD algorithms for human detection in aerial view thermal images, and the impact of varying backbone network and anchor parameters on the performance improvement of these algorithms.
An Improved CenterNet Model for Insulator Defect Detection Using Aerial Imagery

For the issue of low accuracy and poor real-time performance of insulator and defect detection by an unmanned aerial vehicle (UAV) in the process of power inspection, an insulator detection model MobileNet_CenterNet was proposed in this study. First, the lightweight network MobileNet V1 was used to replace the feature extraction network Resnet-50 of the original model, aiming to ensure the detection accuracy of the model while speeding up its detection speed. Second, a spatial and channel attention mechanism convolutional block attention module (CBAM) was introduced in CenterNet, aiming to improve the prediction accuracy of small target insulator position information. Then, three transposed convolution modules were added for upsampling, aiming to better restore the semantic information and position information of the image. Finally, the insulator dataset (ID) constructed by ourselves and the public dataset (CPLID) were used for model training and validation, aiming to improve the generalization ability of the model. The experimental results showed that compared with the CenterNet model, MobileNet_CenterNet improved the detection accuracy by 12.2%, the inference speed by 1.1 f/s for FPS-CPU and 4.9 f/s for FPS-GPU, and the model size was reduced by 37 MB. Compared with other models, our proposed model improved both detection accuracy and inference speed, indicating that the MobileNet_CenterNet model had better real-time performance and robustness.