Reviewerly/exHarmony huggingface dataset:
The individual’s role in cybercrime prevention: internal spheres of protection and our ability to safeguard them

Purpose As a well-known social institution, crime prevention has traditionally been in the purview of public authorities. However, the ceaseless increase in the use of online resources and governments’ responsibilisation approach to cybercrime prevention has created an ecosystem, which necessitates the empowerment of individuals. By introducing the concept of internal spheres of protection, the purpose of this paper is to show how traditionally public responsibilities require increased facilitation by individuals to adequately safeguard what they value. Design/methodology/approach This is a brief conceptual paper, which recasts the individual’s role in cybercrime prevention through a critique of the routine activity theory (Cohen and Felson, 1979) and explains how responsibilisation (Garland, 1996) has created an unavoidable need to provide individuals with the knowledge and tools required to avoid online victimisation. Findings In the context of cybercrime, criminological theories that are limited in considering the individual both as the target of crime and the person likeliest to prevent it are quickly becoming outdated. Public authorities either cannot intervene or are moving away from intervening on citizens’ behalf to effectively stymie the pressure from cybercriminals. Thus, there is a need of an approach that starts with individuals and their value-based motivations. Originality/value The concept of internal spheres of protection is a novel way of looking at cybercrime prevention. The internal spheres are based on individuals’ values, and the value of security in particular, and take cyber-knowledge as a point of departure towards safeguarding such values, i.e. through risk-decreasing actions and the use of relevant tools.
G-LBM: Generative Low-Dimensional Background Model Estimation from Video Sequences

In this paper, we propose a computationally tractable and theoretically supported non-linear low-dimensional generative model to represent real-world data in the presence of noise and sparse outliers. The non-linear low-dimensional manifold discovery of data is done through describing a joint distribution over observations, and their low-dimensional representations (i.e. manifold coordinates). Our model, called generative low-dimensional background model (G-LBM) admits variational operations on the distribution of the manifold coordinates and simultaneously generates a low-rank structure of the latent manifold given the data. Therefore, our probabilistic model contains the intuition of the non-probabilistic low-dimensional manifold learning. G-LBM selects the intrinsic dimensionality of the underling manifold of the observations, and its probabilistic nature models the noise in the observation data. G-LBM has direct application in the background scenes model estimation from video sequences and we have evaluated its performance on SBMnet-2016 and BMC2012 datasets, where it achieved a performance higher or comparable to other state-of-the-art methods while being agnostic to different scenes. Besides, in challenges such as camera jitter and background motion, G-LBM is able to robustly estimate the background by effectively modeling the uncertainties in video observations in these scenarios. (The code and models are available at: https://github.com/brezaei/G-LBM.)
Non-Classical Semiconductor Photon Sources Enhancing the Performance of Classical Target Detection Systems

We demonstrate and analyze how deploying non-classical intensity correlations obtained from a monolithic semiconductor quantum photon source can enhance classical target detection systems. This is demonstrated by examining the advantages offered by the utilization of the non-classical correlations in a correlation based target detection protocol. We experimentally demonstrate that under the same condition, the target contrast obtained from the protocol when non-classical correlations are utilized exhibits an improvement of up to 17.79dB over the best classical intensity correlation-based target detection protocol, under 29.69dB channel loss and excess noise 13.40dB stronger than the probe signal. We also assessed how the strong frequency correlations within the non-classical photon pairs can be used to further enhance this protocol.
On the cyclicity of the rational points group of abelian varieties over finite fields

We propose a simple criterion to know if an abelian variety A defined over a finite field Fq is cyclic, i.e., it has a cyclic group of rational points; this criterion is based on the endomorphism ring EndFq(A). We also provide a criterion to know if an isogeny class is cyclic, i.e., all its varieties are cyclic; this criterion is based on the characteristic polynomial of the isogeny class. We find some asymptotic lower bounds on the fraction of cyclic Fq-isogeny classes among certain families of them, when q tends to infinity. Some of these bounds require an additional hypothesis. In the case of surfaces, we prove that this hypothesis is achieved and, over all Fq-isogeny classes with endomorphism algebra being a field and where q is an even power of a prime, we prove that the one with maximal number of rational points is cyclic and ordinary.
Power of a shared singlet state in comparison to a shared reference frame

We show that a Shared Singlet State (SSS) can supersede a Shared Reference Frame (SRF) in certain quantum communication tasks, i.e. tasks in which two remote players are required to estimate certain parameters of a two particle state sent to them. This shows that task specific value of resources maybe some what different from their common values based on their exchange possibilities.
Saliency detection based on structural dissimilarity induced by image quality assessment model

The distinctiveness of image regions is widely used as the cue of saliency. Generally, the distinctiveness is computed according to the absolute difference of features. However, according to the image quality assessment (IQA) studies, the human visual system is highly sensitive to structural changes rather than absolute difference. Accordingly, we propose the computation of the structural dissimilarity between image patches as the distinctiveness measure for saliency detection. Similar to IQA models, the structural dissimilarity is computed based on the correlation of the structural features. The global structural dissimilarity of a patch to all the other patches represents saliency of the patch. We adopt two widely used structural features, namely the local contrast and gradient magnitude, into the structural dissimilarity computation in the proposed model. Without any postprocessing, the proposed model based on the correlation of either of the two structural features outperforms 11 state-of-the-art saliency models on three saliency databases.
Partial distinguishability as a coherence resource in boson sampling

Quantum coherence is a useful resource that is consumed to accomplish several tasks that classical devices are hard to fulfill. Particularly, it is considered to be the origin of quantum speedup for many computational algorithms. In this work, we interpret the computational time cost of boson sampling with partially distinguishable photons from the perspective of coherence resource theory. With incoherent operations that preserve the diagonal elements of quantum states up to permutation, which we name permuted genuinely incoherent operation, we present some evidence that the decrease of coherence corresponds to a computationally less complex system of partially distinguishable boson sampling. Our result shows that coherence is one of crucial resources for the computational time cost of boson sampling. We expect our work presents an insight to understand the quantum complexity of the linear optical network system.
Learning to Incentivize Other Learning Agents

The challenge of developing powerful and general Reinforcement Learning (RL) agents has received increasing attention in recent years. Much of this effort has focused on the single-agent setting, in which an agent maximizes a predefined extrinsic reward function. However, a long-term question inevitably arises: how will such independent agents cooperate when they are continually learning and acting in a shared multi-agent environment? Observing that humans often provide incentives to influence others' behavior, we propose to equip each RL agent in a multi-agent environment with the ability to give rewards directly to other agents, using a learned incentive function. Each agent learns its own incentive function by explicitly accounting for its impact on the learning of recipients and, through them, the impact on its own extrinsic objective. We demonstrate in experiments that such agents significantly outperform standard RL and opponent-shaping agents in challenging general-sum Markov games, often by finding a near-optimal division of labor. Our work points toward more opportunities and challenges along the path to ensure the common good in a multi-agent future.
Fast Indexes for Gapped Pattern Matching

We describe indexes for searching large data sets for variable-length-gapped (VLG) patterns. VLG patterns are composed of two or more subpatterns, between each adjacent pair of which is a gap-constraint specifying upper and lower bounds on the distance allowed between subpatterns. VLG patterns have numerous applications in computational biology (motif search), information retrieval (e.g., for language models, snippet generation, machine translation) and capture a useful subclass of the regular expressions commonly used in practice for searching source code. Our best approach provides search speeds several times faster than prior art across a broad range of patterns and texts.
Class-specific residual constraint non-negative representation for pattern classification

Representation-based classification methods (RBCM) remain one of the hottest topics in the community of pattern recognition, and the recently proposed non-negative representation-based classification (NRC) achieved impressive recognition results in various classification tasks. However, NRC ignores the relationship between the coding and classification stages. Moreover, there is no regularization term other than the reconstruction error term in the formulation of NRC, which may result in an unstable solution leading to misclassification. To overcome these drawbacks of NRC, we propose a class-specific residual constraint non-negative representation (CRNR) for pattern classification. CRNR introduces a class-specific residual constraint into the formulation of NRC, which encourages training samples from different classes to competitively represent the test sample. Based on the proposed CRNR, we develop a CRNR-based classifier (CRNRC) for pattern classification. Experimental results on several benchmark datasets demonstrate the superiority of CRNRC over conventional RBCM as well as the recently proposed NRC. Moreover, CRNRC works better than or comparable to some state-of-the-art deep approaches on diverse challenging pattern classification tasks.
Open-Destination Measurement-Device-Independent Quantum Key Distribution Network

Quantum key distribution (QKD) networks hold promise for sharing secure randomness over multi-partities. Most existing QKD network schemes and demonstrations are based on trusted relays or limited to point-to-point scenario. Here, we propose a flexible and extensible scheme named as open-destination measurement-device-independent QKD network. The scheme enjoys security against untrusted relays and all detector side-channel attacks. Particularly, any users can accomplish key distribution under assistance of others in the network. As an illustration, we show in detail a four-user network where two users establish secure communication and present realistic simulations by taking into account imperfections of both sources and detectors.
Rigorous characterization method for photon-number statistics

Characterization of photon statistics of a light source is one of the most basic tools in quantum optics. Although the outcome from existing methods is believed to be a good approximation when the measured light is sufficiently weak, there is no rigorous quantitative bounds on the degree of the approximation. As a result, they fail to fulfill the demand arising from emerging applications of quantum information such as quantum cryptography. Here, we propose a calibration method to produce rigorous bounds for a photon-number probability distribution by using a conventional Hanbury-Brown-Twiss setup with threshold photon detectors. We present a general framework to treat any number of detectors and non-uniformity of their efficiencies. The bounds are conveniently given as closed-form expressions of the observed coincidence rates and the detector efficiencies. We also show optimality of the bounds for light with a small mean photon number. As an application, we show that our calibration method can be used for the light source in a decoy-state quantum key distribution protocol. It replaces the a priori assumption on the distribution that has been commonly used, and achieves almost the same secure key rate when four detectors are used for the calibration.
The <i>m</i>-least significant bits operation for quantum random number generation

Quantum random number generators (QRNGs) can provide genuine randomness based on the inherent unpredictable nature of quantum physics. The extracted randomness relies not only on the physical parts of the QRNG, such as the entropy source and the measurement device, but also on appropriate postprocessing method. The m-least significant bits (m-LSBs) operation is one of the simplest randomness extraction methods, which has the advantage of easy implementation. Nonetheless, a detailed analysis of the m-LSBs operation in QRNGs is still lacking. In this work we give a physical explanation of the m-LSBs operation by introducing a new positive operator-valued measurement operator, which is obtained by regrouping the results of coarse-grained measurements. Both trusted- and untrusted-source scenarios are discussed. The results show that the m-LSBs operation can extract randomness effectively under the condition of the trusted source, while it is not effective under the untrusted-source scenario.
Validation of quantum adiabaticity through non-inertial frames and its trapped-ion realization

Validity conditions for the adiabatic approximation are useful tools to understand and predict the quantum dynamics. Remarkably, the resonance phenomenon in oscillating quantum systems has challenged the adiabatic theorem. In this scenario, inconsistencies in the application of quantitative adiabatic conditions have led to a sequence of new approaches for adiabaticity. Here, by adopting a different strategy, we introduce a validation mechanism for the adiabatic approximation by driving the quantum system to a non-inertial reference frame. More specifically, we begin by considering several relevant adiabatic approximation conditions previously derived and show that all of them fail by introducing a suitable oscillating Hamiltonian for a single quantum bit (qubit). Then, by evaluating the adiabatic condition in a rotated non-inertial frame, we show that all of these conditions, including the standard adiabatic condition, can correctly describe the adiabatic dynamics in the original frame, either far from resonance or at a resonant point. Moreover, we prove that this validation mechanism can be extended for general multi-particle quantum systems, establishing the conditions for the equivalence of the adiabatic behavior as described in inertial or non-inertial frames. In order to experimentally investigate our method, we consider a hyperfine qubit through a single trapped Ytterbium ion $^{171}$Yb$^{+}$, where the ion hyperfine energy levels are used as degrees of freedom of a two-level system. By monitoring the quantum evolution, we explicitly show the consistency of the adiabatic conditions in the non-inertial frame.
Numerical investigation on image degradation caused by aero-optical effects of the supersonic mixing layer

The supersonic mixing layer over the optic window of a high-speed imaging guided vehicle is the primary turbulent flow causing aero-optical effects such as the target image blurring, jittering, and offsetting. Employing the large eddy simulation and ray tracing methods, this paper presents a numerical investigation on target image degradation of a beam passing through the supersonic mixing layer under different conditions. The distorted wavefront, degraded target image, and peak signal-to-noise ratio of the degraded image are proposed to quantitatively evaluate the target image degradation. On the whole, the degraded target image is mainly shown as image blurring, and the larger the convective Mach number of the flow field, the more severe the image degradation. It is found that the beam incident position should be away from transition area of the supersonic mixing layer for a clearer image, the larger inlet fluid density difference results in a more severe target image degradation due to the stronger density fluctuation of turbulent flow structures, and the optimal beam incident angle is about 30° for the current calculation conditions.
Instruction-level design of local optimisers using push GP

This work uses genetic programming to explore the design space of local optimisation algorithms. Optimisers are expressed in the Push programming language, a stack-based language with a wide range of typed primitive instructions. The evolutionary framework provides the evolving optimisers with an outer loop and information about whether a solution has improved, but otherwise they are relatively unconstrained in how they explore optimisation landscapes. To test the utility of this approach, optimisers were evolved on four different types of continuous landscape, and the search behaviours of the evolved optimisers analysed. By making use of mathematical functions such as tangents and logarithms to explore different neighbourhoods, and also by learning features of the landscapes, it was observed that the evolved optimisers were often able to reach the optima using relatively short paths.
Resource Allocation in Volunteered Cloud Computing and Battling COVID-19

Cloud computing, as the name implies, is a new age (modern) technology which has huge potentials in markets and enterprises. It is continuously becoming of the major growing systems in the figuring computing industry. In this paper, we overview the allocation of different cloud resources in next-generation, tactile internet for volunteer computing. Companies or organizations can use these resources for computational/storage purposes and reduce their infrastructure cost significantly. We overview critical scheduling and resource allocation methods for a vast number of aggregated services on the web, which is a vital and demanding challenge in the cloud. We classify the various areas of resource allocation and its implementation in the cloud volunteer computing environment. We also outline the main issues identified with security and intelligence that are mainly considered while allocating these resources. This paper also emphasizes and addresses the foremost challenges that are normal to the progress of resource allocation in the cloud. This survey study is believed to benefit both users of cloud resources and researchers in overpowering the difficulties they face.
Students' Project Competency within the Framework of STEM Education

The article is devoted to formation of students' project competence within the framework of STEM education. Project competence is an important component of professional competence of a future specialist and is applicable to designate professionalism. The article identifies the main characteristics of STEM education as means of formation of project competence of students. Project competence is characterized by possession of special knowledge, skills and abilities (to solve problems on the basis of putting forward and substantiating hypotheses, to set an activity goal, to plan activities, to collect and to analyze necessary information, to carry out an experiment, to present research results). Structure of project competence is presented by information, research, and analytical components. The authors conclude that STEM education provides formation of students' project competence.
Policy Improvement via Imitation of Multiple Oracles

Despite its promise, reinforcement learning’s real-world adoption has been hampered by the need for costly exploration to learn a good policy. Imitation learning (IL) mitigates this shortcoming by using an oracle policy during training as a bootstrap to accelerate the learning process. However, in many practical situations, the learner has access to multiple suboptimal oracles, which may provide conflicting advice in a state. The existing IL literature provides a limited treatment of such scenarios. Whereas in the single-oracle case, the return of the oracle’s policy provides an obvious benchmark for the learner to compete against, neither such a benchmark nor principled ways of outperforming it are known for the multi-oracle setting. In this paper, we propose the state-wise maximum of the oracle policies’ values as a natural baseline to resolve conflicting advice from multiple oracles. Using a reduction of policy optimization to online learning, we introduce a novel IL algorithm MAMBA, which can provably learn a policy competitive with this benchmark. In particular, MAMBA optimizes policies by using a gradient estimator in the style of generalized advantage estimation (GAE). Our theoretical analysis shows that this design makes MAMBA robust and enables it to outperform the oracle policies by a larger margin than the IL state of the art, even in the single-oracle case. In an evaluation against standard policy gradient with GAE and AggreVaTe(D), we showcase MAMBA’s ability to leverage demonstrations both from a single and from multiple weak oracles, and significantly speed up policy optimization
Building High-Quality Auction Fraud Dataset

Given the magnitude of online auction transactions, it is difficult to safeguard consumers from dishonest sellers, such as shill bidders. To date, the application of Machine Learning Techniques (MLTs) to auction fraud has been limited, unlike their applications for combatting other types of fraud. Shill Bidding (SB) is a severe auction fraud, which is driven by modern-day technologies and clever scammers. The difficulty of identifying the behavior of sophisticated fraudsters and the unavailability of training datasets hinder the research on SB detection. In this study, we developed a high-quality SB dataset. To do so, first, we crawled and preprocessed a large number of commercial auctions and bidders&amp;rsquo; history as well. We thoroughly preprocessed both datasets to make them usable for the computation of the SB metrics. Nevertheless, this operation requires a deep understanding of the behavior of auctions and bidders. Second, we introduced two new SB patterns and implemented other existing SB patterns. Finally, we removed outliers to improve the quality of training SB data.
An upper bound on the time required to implement unitary operations

We derive an upper bound for the time needed to implement a generic unitary transformation in a $d$ dimensional quantum system using $d$ control fields. We show that given the ability to control the diagonal elements of the Hamiltonian, which allows for implementing any unitary transformation under the premise of controllability, the time $T$ needed is upper bounded by $T\leq \frac{\pi d^{2}(d-1)}{2g_{\text{min}}}$ where $g_{\text{min}}$ is the smallest coupling constant present in the system. We study the tightness of the bound by numerically investigating randomly generated systems, with specific focus on a system consisting of $d$ energy levels that interact in a tight-binding like manner.