Reviewerly/exHarmony huggingface dataset:
Experimental Quantum Communication Overcomes the Rate-Loss Limit without Global Phase Tracking

Secure key rate (SKR) of point-point quantum key distribution (QKD) is fundamentally bounded by the rate-loss limit. Recent breakthrough of twin-field (TF) QKD can overcome this limit and enables long distance quantum communication, but its implementation necessitates complex global phase tracking and requires strong phase references that not only add to noise but also reduce the duty cycle for quantum transmission. Here, we resolve these shortcomings, and importantly achieve even higher SKRs than TF-QKD, via implementing an innovative but simpler measurement-device-independent QKD that realizes repeaterlike communication through asynchronous coincidence pairing. Over 413 and 508 km optical fibers, we achieve finite-size SKRs of 590.61 and 42.64 bit/s, which are respectively 1.80 and 4.08 times of their corresponding absolute rate limits. Significantly, the SKR at 306 km exceeds 5 kbit/s and meets the bitrate requirement for live one-time-pad encryption of voice communication. Our work will bring forward economical and efficient intercity quantum-secure networks.
Learning Behavior Models for Hybrid Timed Systems

A tailored model of a system is the prerequisite for various analysis tasks, such as anomaly detection, fault identification, or quality assurance. This paper deals with the algorithmic learning of a system’s behavior model given a sample of observations. In particular, we consider real-world production plants where the learned model must capture timing behavior, dependencies between system variables, as well as mode switches—in short: hybrid system’s characteristics. Usually, such model formation tasks are solved by human engineers, entailing the well-known bunch of problems including knowledge acquisition, development cost, or lack of experience. Our contributions to the outlined field are as follows. (1) We present a taxonomy of learning problems related to model formation tasks. As a result, an important open learning problem for the domain of production system is identified: The learning of hybrid timed automata. (2) For this class of models, the learning algorithm HyBUTLA is presented. This algorithm is the first of its kind to solve the underlying model formation problem at scalable precision. (3) We present two case studies that illustrate the usability of this approach in realistic settings. (4) We give a proof for the learning and runtime properties of HyBUTLA.
Geostatistical multimodel approach for the assessment of the spatial distribution of natural background concentrations in large-scale groundwater bodies

Quantification of the (spatially distributed) natural contributions to the chemical signature of groundwater resources is an emerging issue in the context of competitive groundwater uses as well as water regulation and management frameworks. Here, we illustrate a geostatistically-based approach for the characterization of spatially variable Natural Background Levels (NBLs) of target chemical species in large-scale groundwater bodies yielding evaluations of local probabilities of exceedance of a given threshold concentration. The approach is exemplified by considering three selected groundwater bodies and focusing on the evaluation of NBLs of ammonium and arsenic, as detected from extensive time series of concentrations collected at monitoring boreholes. Our study is motivated by the observation that reliance on a unique NBL value as representative of the natural geochemical signature of a reservoir can mask the occurrence of localized areas linked to diverse strengths of geogenic contributions to the groundwater status. We start from the application of the typical Pre-Selection (PS) methodology to the scale of each observation borehole to identify local estimates of NBL values. The latter are subsequently subject to geostatistical analysis to obtain estimates of their spatial distribution and the associated uncertainty. A multimodel framework is employed to interpret available data. The impact of alternative variogram models on the resulting spatial distributions of NBLs is assessed through probabilistic weights based on model identification criteria. Our findings highlight that assessing possible impacts of anthropogenic activities on groundwater environments with the aim of designing targeted solutions to restore a good groundwater quality status should consider a probabilistic description of the spatial distribution of NBLs. The latter is useful to provide enhanced information upon which one can then build decision-making protocols embedding the quantification of the associated uncertainty.
Morpheus

Attacks often succeed by abusing the gap between program and machine-level semantics-- for example, by locating a sensitive pointer, exploiting a bug to overwrite this sensitive data, and hijacking the victim program's execution. In this work, we take secure system design on the offensive by continuously obfuscating information that attackers need but normal programs do not use, such as representation of code and pointers or the exact location of code and data. Our secure hardware architecture, Morpheus, combines two powerful protections: ensembles of moving target defenses and churn. Ensembles of moving target defenses randomize key program values (e.g., relocating pointers and encrypting code and pointers) which forces attackers to extensively probe the system prior to an attack. To ensure attack probes fail, the architecture incorporates churn to transparently re-randomize program values underneath the running system. With frequent churn, systems quickly become impractically difficult to penetrate. We demonstrate Morpheus through a RISC-V-based prototype designed to stop control-flow attacks. Each moving target defense in Morpheus uses hardware support to individually offer more randomness at a lower cost than previous techniques. When ensembled with churn, Morpheus defenses offer strong protection against control-flow attacks, with our security testing and performance studies revealing: i) high-coverage protection for a broad array of control-flow attacks, including protections for advanced attacks and an attack disclosed after the design of Morpheus, and ii) negligible performance impacts (1%) with churn periods up to 50 ms, which our study estimates to be at least 5000x faster than the time necessary to possibly penetrate Morpheus.
Cross-Domain Sentiment Encoding through Stochastic Word Embedding

Sentiment analysis is an important topic concerning identification of feelings, attitudes, emotions and opinions from text. To automate such analysis, a large amount of example text needs to be manually annotated for model training. This is laborious and expensive, but the cross-domain technique is a key solution to reducing the cost by reusing annotated reviews across domains. However, its success largely relies on the learning of a robust common representation space across domains. In the recent years, significant effort has been invested to improve the cross-domain representation learning by designing increasingly more complex and elaborate model inputs and architectures. We support that it is not necessary to increase design complexity as this inevitably consumes more time in model training. Instead, we propose to explore the word polarity and occurrence information through a simple mapping and encode such information more accurately whilst managing lower computational costs. The proposed approach is unique and takes advantage of the stochastic embedding technique to tackle cross-domain sentiment alignment. Its effectiveness is benchmarked with over ten data tasks constructed from two review corpora and it is compared against ten classical and state-of-the-art methods.
Multi-target Tracking in Multiple Non-overlapping Cameras Using Fast-Constrained Dominant Sets

In this paper, a unified three-layer hierarchical approach for solving tracking problem in a multiple non-overlapping cameras setting is proposed. Given a video and a set of detections (obtained by any person detector), we first solve within-camera tracking employing the first two layers of our framework and then, in the third layer, we solve across-camera tracking by associating tracks of the same person in all cameras simultaneously. To best serve our purpose, we propose fast-constrained dominant set clustering (FCDSC), a novel method which is several orders of magnitude faster (close to real time) than existing methods. FCDSC is a parameterized family of quadratic programs that generalizes the standard quadratic optimization problem. In our method, we first build a graph where nodes of the graph represent short-tracklets, tracklets and tracks in the first, second and third layer of the framework, respectively. The edge weights reflect the similarity between nodes. FCDSC takes as input a constrained set, a subset of nodes from the graph which need to be included in the extracted cluster. Given a constrained set, FCDSC generates compact clusters by selecting nodes from the graph which are highly similar to each other and with elements in the constrained set. We have tested this approach on a very large and challenging dataset (namely, MOTchallenge DukeMTMC) and show that the proposed framework outperforms the state-of-the-art approaches. Even though the main focus of this paper is on multi-target tracking in non-overlapping cameras, the proposed approach can also be applied to solve video-based person re-identification problem. We show that when the re-identification problem is formulated as a clustering problem, FCDSC can be used in conjunction with state-of-the-art video-based re-identification algorithms, to increase their already good performances. Our experiments demonstrate the general applicability of the proposed framework for multi-target multi-camera tracking and person re-identification tasks.
A Novel Improved Bat Algorithm in UAV Path Planning

Path planning algorithm is the key point to UAV path planning scenario. Many traditional path planning methods still suffer from low convergence rate and insufficient robustness. In this paper, three main methods are contributed to solving these problems. First, the improved artificial potential field (APF) method is adopted to accelerate the convergence process of the bat’s position update. Second, the optimal success rate strategy is proposed to improve the adaptive inertia weight of bat algorithm. Third chaos strategy is proposed to avoid falling into a local optimum. Compared with standard APF and chaos strategy in UAV path planning scenarios, the improved algorithm CPFIBA (The improved artificial potential field method combined with chaotic bat algorithm, CPFIBA) significantly increases the success rate of finding suitable planning path and decrease the convergence time. Simulation results show that the proposed algorithm also has great robustness for processing with path planning problems. Meanwhile, it overcomes the shortcomings of the traditional meta-heuristic algorithms, as their convergence process is the potential to fall into a local optimum. From the simulation, we can see also obverse that the proposed CPFIBA provides better performance than BA and DEBA in problems of UAV path planning.
Embodiment and Presence in Virtual Reality After Stroke. A Comparative Study With Healthy Subjects

The ability of virtual reality (VR) to recreate controlled, immersive, and interactive environments that provide intensive and customized exercises has motivated its therapeutic use after stroke. Interaction and bodily presence in VR-based interventions is usually mediated through virtual selves, which synchronously represent body movements or responses to events on external input devices. Embodied self-representations in the virtual world not only provide an anchor for visuomotor tasks, but their morphologies can have behavioral implications. While research has focused on the underlying subjective mechanisms of exposure to VR on healthy individuals, the transference of these findings to individuals with stroke is not evident and remains unexplored, which could affect the experience and, ultimately, the clinical effectiveness of neurorehabilitation interventions. This study determined and compared the sense of embodiment and presence elicited by a virtual environment under different perspectives and levels of immersion in healthy subjects and individuals with stroke. Forty-six healthy subjects and 32 individuals with stroke embodied a gender-matched neutral avatar in a virtual environment that was displayed in first-person perspective with a head-mounted display and in third-person perspective with a screen, and were asked to interact in a virtual task for 10 minutes under each condition in counterbalanced order, and complete two questionnaires about the sense of embodiment and presence experienced during the interaction. The sense of body-ownership, self-location, and presence were more vividly experienced in first-person than in third-person perspective by both healthy subjects (p<.001, ƞ2p=.212; p=.005, ƞ2p=.101; p=.001, ƞ2p=.401, respectively) and individuals with stroke (p<.019, ƞ2p=.070; p=.001, ƞ2p=.135; p=.014, ƞ2p=.077, respectively). In contrast, no perspective-related differences were found for agency in any group. All measures were consistently higher for healthy controls than for individuals with stroke, but differences between groups only reached statistical significance in presence under the first-person condition (p<.010, ƞ2p=.084). In spite of these differences, the participants experienced a vivid sense of embodiment and presence in almost all conditions. These results provide first evidence that, although less intensively, embodiment and presence are similarly experienced after stroke as in healthy individuals, which could support the vividness of their experience and, consequently, the effectiveness of VR-based interventions.
High-speed robust polarization modulation for quantum key distribution

Polarization modulation plays a key role in polarization-encoding quantum key distribution (QKD). Here, we report a new, to the best of our knowledge, polarization modulation scheme based on an inherently stable Sagnac interferometer. The presented scheme is free of polarization mode dispersion and calibration as well as insensitive to environmental influences. Successful experiments at a repetition frequency of 1.25 GHz have been conducted to demonstrate the feasibility and stability of the scheme. The measured average quantum bit-error rate of the four polarization states is as low as 0.27% for 80 consecutive minutes without any adjustment. This high-speed intrinsically stable polarization modulation can be widely applied to many polarization-encoding QKD systems, such as BB84, MDI, etc.
Type-Aware Anchor Link Prediction across Heterogeneous Networks Based on Graph Attention Network

Anchor Link Prediction (ALP) across heterogeneous networks plays a pivotal role in inter-network applications. The difficulty of anchor link prediction in heterogeneous networks lies in how to consider the factors affecting nodes alignment comprehensively. In recent years, predicting anchor links based on network embedding has become the main trend. For heterogeneous networks, previous anchor link prediction methods first integrate various types of nodes associated with a user node to obtain a fusion embedding vector from global perspective, and then predict anchor links based on the similarity between fusion vectors corresponding with different user nodes. However, the fusion vector ignores effects of the local type information on user nodes alignment. To address the challenge, we propose a novel type-aware anchor link prediction across heterogeneous networks (TALP), which models the effect of type information and fusion information on user nodes alignment from local and global perspective simultaneously. TALP can solve the network embedding and type-aware alignment under a unified optimization framework based on a two-layer graph attention architecture. Through extensive experiments on real heterogeneous network datasets, we demonstrate that TALP significantly outperforms the state-of-the-art methods.
Ant Colony Optimization Algorithms for Dynamic Optimization: A Case Study of the Dynamic Travelling Salesperson Problem [Research Frontier]

Ant colony optimization is a swarm intelligence metaheuristic inspired by the foraging behavior of some ant species. Ant colony optimization has been successfully applied to challenging optimization problems. This article investigates existing ant colony optimization algorithms specifically designed for combinatorial optimization problems with a dynamic environment. The investigated algorithms are classified into two frameworks: evaporation-based and population-based. A case study of using these algorithms to solve the dynamic traveling salesperson problem is described. Experiments are systematically conducted using a proposed dynamic benchmark framework to analyze the effect of important ant colony optimization features on numerous test cases. Different performance measures are used to evaluate the adaptation capabilities of the investigated algorithms, indicating which features are the most important when designing ant colony optimization algorithms in dynamic environments.
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes

Off-policy evaluation (OPE) in reinforcement learning allows one to evaluate novel decision policies without needing to conduct exploration, which is often costly or otherwise infeasible. We consider for the first time the semiparametric efficiency limits of OPE in Markov decision processes (MDPs), where actions, rewards, and states are memoryless. We show existing OPE estimators may fail to be efficient in this setting. We develop a new estimator based on cross-fold estimation of $q$-functions and marginalized density ratios, which we term double reinforcement learning (DRL). We show that DRL is efficient when both components are estimated at fourth-root rates and is also doubly robust when only one component is consistent. We investigate these properties empirically and demonstrate the performance benefits due to harnessing memorylessness.
Memristor-based hyper-chaotic circuit for image encryption*

The memristor is a kind of non-linear element with memory function, which can be applied to chaotic systems to increase signal randomness and complexity. In this paper, a new four-dimensional hyper-chaotic system is designed based on a flux controlled memristor model, which can generate complex chaotic attractors. The basic dynamic theory analysis and numerical simulations of the system, such as the stability of equilibrium points, the Lyapunov exponents and dimension, Poincare maps, the power spectrum, and the waveform graph prove that it has rich dynamic behaviors. Then, the circuit implementation of this system is established. The consistency of simulation program with integrated circuit emphasis (SPICE) simulation and numerical analysis proves the ability to generate chaos. Finally, a new image encryption scheme is designed by using the memristor-based hyper-chaotic system proposed in this paper. The scheme involves a total of two encryptions. By using different security analysis factors, the proposed algorithm is compared with other image encryption schemes, including correlation analysis, information entropy, etc. The results show that the proposed image encryption scheme has a large key space and presents a better encryption effect.
Datasets for phishing websites detection

Phishing stands for a fraudulent process, where an attacker tries to obtain sensitive information from the victim. Usually, these kinds of attacks are done via emails, text messages, or websites. Phishing websites, which are nowadays in a considerable rise, have the same look as legitimate sites. However, their backend is designed to collect sensitive information that is inputted by the victim. Discovering and detecting phishing websites has recently also gained the machine learning community's attention, which has built the models and performed classifications of phishing websites. This paper presents two dataset variations that consist of 58,645 and 88,647 websites labeled as legitimate or phishing and allow the researchers to train their classification models, build phishing detection systems, and mining association rules.
Teaching and learning process to enhance teaching effectiveness: literature review

Teaching and learning process can be defined as a transformation process of knowledge from teachers to students. It is referred as the combination of various elements within the process where an educator identifies and establish the learning objectives and develop teaching resources and implement the teaching and learning strategy. On the other hand, learning is a cardinal factor that a teacher must consider while teaching students. The paper evaluated various academic journals, pedagogy and inclusive practices to assess the teaching effectiveness within the higher education setting. The objective of the research is to assess the teaching effectiveness in higher education setting. The research used experimental research methods (primarily reflection) using literary forms to analyse the theory with the reinforcement of the practice from the university experiences. The research findings suggests that, providing positive and adequate formative and developmental feedback, introduction of role play have profound positive impact on the confidence and self-esteem of the students. It was also revealed that, active learning environment promotes inclusivity and improve the faculty and student academic performances. The research findings will enable the educators to help create and implement an inclusive teaching and learning environment to improve the learner’s expectation and academic performance.
Adoption of Virtual and Augmented Reality for Mathematics Education: A Scoping Review

Augmented reality and virtual reality, collectively called extended reality (XR), has made substantial strides in the education sector in both theory and practice. Existing active research focuses on implementation by educators to teach real-world phenomena, and for students to learn through an immersive experience. This article surveys existing research in XR with special focus on the implications of immersive extended realities for teaching and learning engineering mathematics in institutes of higher learning. We also survey various interactive multimedia associated with XR before examining the implications of XR as an educational tool for existing mathematics pedagogy. Finally, the contribution of this scoping review is to provide an adaptable framework on XR implementation for educators, and potential academic advances for researchers.
Packed Levitated Marker for Entity and Relation Extraction

Recent entity and relation extraction works focus on investigating how to obtain a better span representation from the pre-trained encoder.However, a major limitation of existing works is that they ignore the interrelation between spans (pairs).In this work, we propose a novel span representation approach, named Packed Levitated Markers (PL-Marker), to consider the interrelation between the spans (pairs) by strategically packing the markers in the encoder.In particular, we propose a neighborhood-oriented packing strategy, which considers the neighbor spans integrally to better model the entity boundary information.Furthermore, for those more complicated span pair classification tasks, we design a subject-oriented packing strategy, which packs each subject and all its objects to model the interrelation between the same-subject span pairs.The experimental results show that, with the enhanced marker feature, our model advances baselines on six NER benchmarks, and obtains a 4.1%-4.3%strict relation F1 improvement with higher speed over previous state-of-the-art models on ACE04 and ACE05.Our code and models are publicly available at