Reviewerly/exHarmony huggingface dataset:
End-to-End Entanglement Generation Strategies: Capacity Bounds and Impact on Quantum Key Distribution

A first quantum revolution has already brought quantum technologies into our everyday life for decades: in fact, electronics and optics are based on the quantum mechanical principles. Today, a second quantum revolution is underway, leveraging the quantum principles of superposition, entanglement and measurement, which were not fully exploited yet. International innovation activities and standardization bodies have identified four main application areas for quantum technologies and services: quantum secure communications, quantum computing, quantum simulation, and quantum sensing and metrology. This paper focuses on quantum secure communications by addressing the evolution of Quantum Key Distribution (QKD) networks (under early exploitation today) towards the Quantum-ready networks and the Quantum Internet based also on entanglement distribution. Assuming that management and control of quantum nodes is a key challenge under definition, today, a main obstacle in exploiting long-range QKD and Quantum-ready networks concerns the inherent losses due to the optical transmission channels. Currently, it is assumed that a most promising way for overcoming this limitation, while avoiding the presence of costly trusted nodes, it is to distribute entangled states by means of Quantum Repeaters. In this respect, the paper provides an overview of current methods and systems for end-to-end entanglement generation, with some simulations and a discussion of capacity upper bounds and their impact of secret key rate in QKD systems.
Learning English with electronic textbooks on mobile devices: Impacts on university students’ vocabulary development

The current study examined the use of electronic textbooks designed as mobile applications for learning vocabulary in English among Iranian university students. To this end, 95 university students in an experimental (N = 50) and a control group (N = 45) participated in the study. An explanatory sequential mixed methods design was employed and over an academic semester, the participants used either traditional materials or mobile-based electronic textbooks for learning 600 words in English. To assess the outcomes from different learning conditions, receptive knowledge of the target vocabulary items was tested in three junctures of time (i.e. pre-, post-, and delayed post-test). Additionally, open-ended questionnaires and interviews were used to collect qualitative data from the experimental group to further investigate their perceptions of using mobile-based electronic textbooks for vocabulary learning. The findings revealed a significant main effect for time and both groups significantly improved their vocabulary knowledge from pre-test to post-test. Moreover, a significant main effect was found for using electronic textbooks on mobile devices, and the experimental group outperformed the control group on the post- and delayed post-tests. The qualitative findings revealed three perceived benefits, namely episodic learning, easy access to materials, and enhanced enjoyment for mobile assisted vocabulary learning through electronic textbooks. The perceived challenges were related to health concerns, distractions associated with mobile environments, and external pressure resulting from excessive mobile use among the participants. In general, the findings of the study shed light on the potential offered by mobile-based textbooks for learning English vocabulary, with implications for teachers and materials developers in language teaching programs.
3D real-time human reconstruction with a single RGBD camera

3D human reconstruction is an important technology connecting the real world and the virtual world, but most of previous work needs expensive computing resources, making it difficult in real-time scenarios. We propose a lightweight human body reconstruction system based on parametric model, which employs only one RGBD camera as input. To generate a human model end to end, we build a fast and lightweight deep-learning network named Fast Body Net (FBN). The network pays more attention on the face and hands to enrich the local details. Additionally, we train a denoising auto-encoder to reduce unreasonable states of human model. Due to the lack of human dataset based on RGBD images, we propose an Indoor-Human dataset to train the network, which contains a total of 2500 frames of action data of five actors collected by Azure Kinect camera. Depth images avoid using RGB to extract depth features, which makes FBN lightweight and high-speed in reconstructing parametric human model. Qualitative and quantitative analysis on experimental results show that our method can improve at least 57% in efficiency with similar accuracy, as compared to state-of-the-art methods. Through our study, it is also demonstrated that consumer-grade RGBD cameras can provide great applications in real-time display and interaction for virtual reality.
A hybrid greedy political optimizer with fireworks algorithm for numerical and engineering optimization problems

This paper proposes a novel hybrid optimization algorithm named GPOFWA, which integrates political optimizer (PO) with fireworks algorithm (FWA) to solve numerical and engineering optimization problems. The original PO uses subgroup optimal solutions such as party leaders and constituency winners to guide the movement of the search agent. However, the number of such subgroup optimal solutions is limited, which leads to insufficient global exploration capabilities of PO. In addition, the recent past-based position updating strategy (RPPUS) of PO lacks effective verification of the updated candidate solutions, which reduces the convergence speed of the algorithm. The proposed hybrid algorithm uses the spark explosion mechanism in FWA to perform explosion spark and Gauss explosion spark operations on the subgroup optimal solutions (party leader and constituency winner) respectively based on the greedy strategy, which optimizes the subgroup optimal solution and enhances the exploitative ability of the algorithm. Moreover, Gaussian explosion sparks are also used to correct the candidate solutions after RPPUS, which makes up for the shortcomings of the original PO. In addition, a new subgroup optimal solution called the Converged Mobile Center (CMC) based on two-way consideration is designed to guide the movement of search agents and maintain the population diversity. We test the presented hybrid algorithm on 30 well-known benchmark functions, CEC2019 benchmark functions and three engineering optimization problems. The experimental results show that GPOFWA is superior to many statE-of-thE-art methods in terms of the quality of the resulting solution.
Open-source quality control routine and multi-year power generation data of 175 PV systems

In this study, we introduce an open-source dataset holding power measurements of 175 residential photovoltaic (PV) systems that are distributed throughout the province of Utrecht, the Netherlands. The dataset features power measurements with a high temporal resolution, i.e., 1 min, for the period January 2014 until December 2017 (over 260 × 106 data points). Spatial information of the PV systems is mapped through latitude and longitude grids, with a resolution up to 150 × 150 m. In addition, we develop and publish a quality control routine that can be applied to validate and filter PV power measurements. Finally, we propose a method to estimate the rated DC capacity of a PV system based on the power measurements. We have deposited five files into the Zenodo repository [Visser et al. (2022). Zenodo, V. 0.0.1, Dataset https://doi.org/10.5281/zenodo.6906504], which are publicly available. Four numerical datasets are enclosed, holding unfiltered power measurements, filtered power measurements at two different stages and metadata. The latter includes information on the tilt angle, azimuth angle, the estimated DC and AC capacity, and location. Finally, a Python package featuring the quality control routine developed to validate and filter PV power measurements is published.
Multicentric evaluation of the variability of digital morphology performances also respect to the reference methods by optical microscopy

Despite the important diagnostic role of peripheral blood morphology, cell classification is subjective. Automated image-processing systems (AIS) provide more accurate and objective morphological evaluation. The aims of this multicenter study were the evaluation of the intra and inter-laboratory variation between different AIS in cell pre-classification and after reclassification, compared with manual optical microscopy, the reference method.Six peripheral blood samples were included in this study, for each sample, 70 May-Grunwald and Giemsa stained PB smears were prepared from each specimen and 10 slides were delivered to the seven laboratories involved. Smears were processed by both optical microscopy (OM) and AIS. In addition, the assessment times of both methods were recorded.Within-laboratory Reproducibility ranged between 4.76% and 153.78%; between-laboratory Precision ranged between 2.10% and 82.2%, while Total Imprecision ranged between 5.21% and 20.60%. The relative Bland Altman bias ranged between -0.01% and 20.60%. The mean of assessment times were 326 ± 110 s and 191 ± 68 s for AIS post reclassification and OM, respectively.AIS can be helpful when the number of cell counted are low and can give advantages in terms of efficiency, objectivity and time saving in the morphological analysis of blood cells. They can also help in the interpretation of some morphological features and can serve as learning and investigation tools.
Uses of business process modeling in agile software development projects

Agile methodologies and frameworks are widely used in software development projects because of their support for continuous change and delivery. Agile software development advocates de-prioritizing aspects such as processes and documentation. In traditional software engineering methodologies, however, business process models have been extensively used to support these aspects. Up until now, it is unclear to what extent recommendations to focus on code imply that conceptual modeling should be discontinued.
Quasi‐oppositional wild horse optimization based multi‐agent path finding scheme for real time IoT systems

Abstract Multi‐Agent System (MAS) gained significant interest amongst researchers since it provides multiple benefits through several application areas. MAS involves a network of socially‐cooperative smart agents that is conscious about the drastic modifications that occur in the platform at the time of task execution. On the other hand, energy efficiency is a major issue in real‐time IoT systems, since most of the sensor nodes experience energy constraints. Though several works have been conducted earlier, there is a need exists to design an effective solution for simultaneous processing in real‐time environments using multiple agents. The aim of Multi‐Agent Pathfinding (MAPF) process is to provide collision‐free routes so as to divert the agents from original path to the destination. In this view, the current study designs a Quasi‐Oppositional Wild Horse Optimization‐based Multi‐Agent Path Finding (QOWHO‐MAPF) scheme for real‐time IoT systems. The aim of the proposed QOWHO‐MAPF scheme is to determine the optimal set of paths to reach the destination in real‐time IoT networks. QOWHO algorithm is created by integrating the concepts of Quasi‐Oppositional Based Learning (QOBL) and conventional WHO algorithm. In addition, the proposed QOWHO‐MAPF model derives a fitness function that involves two input parameters such as residual energy and distance‐to‐destination. The proposed QOWHO‐MAPF model was experimentally analysed and the results were inspected under several aspects. The simulation results established that QOWHO‐MAPF model is a superior model compared to other state‐of‐the‐art models.
Data-Driven Threat Analysis for Ensuring Security in Cloud Enabled Systems

Cloud computing offers many benefits including business flexibility, scalability and cost savings but despite these benefits, there exist threats that require adequate attention for secure service delivery. Threats in a cloud-based system need to be considered from a holistic perspective that accounts for data, application, infrastructure and service, which can pose potential risks. Data certainly plays a critical role within the whole ecosystem and organisations should take account of and protect data from any potential threats. Due to the variation of data types, status, and location, understanding the potential security concerns in cloud-based infrastructures is more complex than in a traditional system. The existing threat modeling approaches lack the ability to analyse and prioritise data-related threats. The main contribution of the paper is a novel data-driven threat analysis (d-TM) approach for the cloud-based systems. The main motivation of d-TM is the integration of data from three levels of abstractions, i.e., management, control, and business and three phases, i.e., storage, process and transmittance, within each level. The d-TM provides a systematic flow of attack surface analysis from the user agent to the cloud service provider based on the threat layers in cloud computing. Finally, a cloud-based use case scenario was used to demonstrate the applicability of the proposed approach. The result shows that d-TM revealed four critical threats out of the seven threats based on the identified assets. The threats targeted management and business data in general, while targeting data in process and transit more specifically.
PeriodicSketch: Finding Periodic Items in Data Streams

In this paper, we study periodic items in data streams, which refer to those items arriving with a fixed interval. All existing works involving mining periodic patterns does not fit for data stream scenarios. To find periodic items in real time, we propose a novel sketch, PeriodicSketch, aiming to accurately record top- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> periodic items. To the best of our knowledge, this is the first work to find periodic items in data streams. Any interval may occur many times, and we use frequency to denote the number of an interval occurred. To pick out periodic items with high frequency, we propose a key technique called Guaranteed Soft Uniform (GSU) replacement strategy. Our theoretical proofs show that when replacement is successful, it is more likely that the new item has a higher frequency than the current smallest frequency; and GSU can ensure that our items in the sketch will approach the true periodic items closer and closer. And as soon as we get all the periodic items, the state would not change worse with high probability. We conduct extensive experiments, and the experimental results show that the Average Absolute Error (AAE) of our sketch using 1/10 memory is around 737 times (up to 2019 times) lower than the baseline solution. Finally, we provide a concrete case: Cache prefetch, which proves that PeriodicSketch can significantly improve the Cache hit ratio. All related codes of PeriodicSketch are open-sourced and available at GitHub [1].
Self-Supervised Dual-Channel Attentive Network for Session-based Social Recommendation

The task of Session-based Social Recommendation (SSR) aims to utilize the social networks to make recommendations in session-based scenarios. Existing SSR methods mainly focused on using graph networks to capture complex item transition patterns, ignoring the sequential information. Few studies combined two aspects of features to enhance session preferences, resulting in information loss. Besides, modeling the entire session that some items are invalid or repeatedly clicked will interfere with the results. In this paper, to address the information loss issue in SSR, we propose a novel Dual-Channel Attentive Network (DCAN) to leverage both sequential infor-mation and complex item transitions. Specifically, we construct one channel by a light graph attention layer to capture item transitions, and we elaborate a concise attention-based layer to build the other channel to learn sequential information. To solve the invalid or repeatedly clicked problem in the session, we introduce new self-supervised learning (SSL) learning method, which allows model learning to distinguish and discard these items. However, the effect of SSL in SSR has not been investigated yet. Besides, these studies require negative sampling, which makes its performance depend on negative sampling strategies. Then, we investigate the effect of adding existing SSL frameworks in DCAN, but it has not achieved good results. Besides, we propose a novel SSL framework that does not require negative sampling for SSR, denoted as Positive sampling SSL (PSSL). Furthermore, we combined DCAN and PSSL to make more accurate recommendations, denoted as DCAN - PSSL. Extensive experiments on three public benchmark datasets demonstrate that both DCAN and DCAN - PSSL consistently outperform the state-of-the-art models.
Black-box Adversarial Attack and Defense on Graph Neural Networks

Graph neural networks (GNNs) have achieved great success on various graph tasks. However, recent studies have re-vealed that GNNs are vulnerable to adversarial attacks, including topology modifications and feature perturbations. Regardless of the fruitful progress, existing attackers require node labels and GNN parameters to optimize a bi-level problem, or cannot cover both topology modifications and feature perturbations, which are not practical, efficient, or effective. In this paper, we propose a black-box attacker PEEGA, which is restricted to access node features and graph topology for practicability. Specifically, we propose to measure the negative impact of various adversarial attacks from the perspective of node representations, thereby we formulate a single-level problem that can be efficiently solved. Furthermore, we observe that existing attackers tend to blur the context of nodes through adding edges between nodes with different labels. As a result, GNNs are unable to recognize nodes. Based on this observation, we propose a GNN defender GNAT, which incorporates three augmented graphs, i.e., a topology graph, a feature graph, and an ego graph, to make the context of nodes more distinguishable. Extensive experiments on three real-world datasets demonstrate the effectiveness and efficiency of our proposed attacker, despite the fact that we do not access node labels and GNN parameters. Moreover, the effectiveness and efficiency of our proposed defender are also validated by substantial experiments.
Robust Attributed Network Embedding Preserving Community Information

Network embedding, also known as network repre-sentation, has attracted a surge of attention in data mining and machine learning community as a fundamental tool to treat net-work data. Most existing deep learning-based network embedding approaches focus on reconstructing the pairwise connections of micro-structure, which are easily disturbed by network anomaly or attack. Thus, to address the aforementioned challenge, we pro-pose a novel robust framework for attributed network embedding by preserving Community Information (AnECI). Rather than using pairwise connection-based micro-structure, we try to guide the node embedding by the underlying community structure learned from data itself as an unsupervised learning, as to own stronger anti-interference ability. Specially, we put forward a new modularity function for high-order proximity and overlapped community to guide the network embedding of an attributed graph encoder. We conducted extensive experiments on node classification, anomaly detection and community detection tasks on real benchmark data sets, and the results show that AnECI is superior to the state-of-art attributed network embedding methods.
DB-LSH: Locality-Sensitive Hashing with Query-based Dynamic Bucketing

Among many solutions to the high-dimensional approximate nearest neighbor (ANN) search problem, locality sensitive hashing (LSH) is known for its sub-linear query time and robust theoretical guarantee on query accuracy. Traditional LSH methods can generate a small number of candidates quickly from hash tables but suffer from large index sizes and hash boundary problems. Recent studies to address these issues often incur extra overhead to identify eligible candidates or remove false positives, making query time no longer sub-linear. To address this dilemma, in this paper we propose a novel LSH scheme called DB-LSH which supports efficient ANN search for large high-dimensional datasets. It organizes the projected spaces with multi-dimensional indexes rather than using fixed-width hash buckets. Our approach can significantly reduce the space cost by avoiding the need to maintain many hash tables for different bucket sizes. During the query phase of DB-LSH, a small number of high-quality candidates can be generated efficiently by dynamically constructing query-based hypercubic buckets with the required widths through index-based window queries. For a dataset of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> d-dimensional points with approximation ratio <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$c$</tex> , our rigorous theoretical analysis shows that DB-LSH achieves a smaller query cost <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(n^{\rho}d\log n)$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\rho^{\ast}$</tex> is bounded by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1/c^{\alpha}$</tex> versus a bound of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1/c$</tex> in the existing work. An extensive range of experiments on real-world data demonstrate the superiority of DB-LSH over state-of-the-art methods on both efficiency and accuracy.
Characterizing Coherent Integrated Photonic Neural Networks Under Imperfections

Integrated photonic neural networks (IPNNs) are emerging as promising successors to conventional electronic AI accelerators as they offer substantial improvements in computing speed and energy efficiency. In particular, coherent IPNNs use arrays of Mach-Zehnder interferometers (MZIs) for unitary transformations to perform energy-efficient matrix-vector multiplication. However, the underlying MZI devices in IPNNs are susceptible to uncertainties stemming from optical lithographic variations and thermal crosstalk and can experience imprecisions due to non-uniform MZI insertion loss and quantization errors due to low-precision encoding in the tuned phase angles. In this paper, we, for the first time, systematically characterize the impact of such uncertainties and imprecisions (together referred to as imperfections) in IPNNs using a bottom-up approach. We show that their impact on IPNN accuracy can vary widely based on the tuned parameters (e.g., phase angles) of the affected components, their physical location, and the nature and distribution of the imperfections. To improve reliability measures, we identify critical IPNN building blocks that, under imperfections, can lead to catastrophic degradation in the classification accuracy. We show that under multiple simultaneous imperfections, the IPNN inferencing accuracy can degrade by up to 46%, even when the imperfection parameters are restricted within a small range. Our results also indicate that the inferencing accuracy is sensitive to imperfections affecting the MZIs in the linear layers next to the input layer of the IPNN.