Reviewerly/exHarmony huggingface dataset:
Cybercrime aS A transnational organized criminal activity

This chapter puts the spectacle of cybercrime to one side and draws upon existing and recent research to explore the use of the internet as a conduit for transnational organized criminal activity presently. It examines why cybercrimes are different and at how they can be defined. The chapter explores three distinct dimensions of cybercrime to introduce a language framework for explaining its variants and applies the framework to a case study of ransomware as a modern transnational organized advanced cybercrime currently of concern to the governments of nation states. Cyberspace is the environment in which cybercrime takes place and has been written about many times, but briefly, the internet’s qualities that transform human behaviour are also reflected in transforming criminal behaviour into cybercrimes. The final dimension of cybercrime is the issue of who the victim groups are in terms of their wealth, power, and position, each of which is a factor that can motivate offenders.
Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning

Long-horizon planning in realistic environments requires the ability to reason over sequential tasks in high-dimensional state spaces with complex dynamics. Classical motion planning algorithms, such as rapidly-exploring random trees, are capable of efficiently exploring large state spaces and computing long-horizon, sequential plans. However, these algorithms are generally challenged with complex, stochastic, and high-dimensional state spaces as well as in the presence of narrow passages, which naturally emerge in tasks that interact with the environment. Machine learning offers a promising solution for its ability to learn general policies that can handle complex interactions and high-dimensional observations. However, these policies are generally limited in horizon length. Our approach, Broadly-Exploring, Local-policy Trees (BELT), merges these two approaches to leverage the strengths of both through a task-conditioned, model-based tree search. BELT uses an RRT-inspired tree search to efficiently explore the state space. Locally, the exploration is guided by a task-conditioned, learned policy capable of performing general short-horizon tasks. This task space can be quite general and abstract; its only requirements are to be sampleable and to well-cover the space of useful tasks. This search is aided by a task-conditioned model that temporally extends dynamics propagation to allow long-horizon search and sequential reasoning over tasks. BELT is demonstrated experimentally to be able to plan long-horizon, sequential trajectories with a goal conditioned policy and generate plans that are robust.
Unsupervised Object-Based Transition Models For 3D Partially Observable Environments

We present a slot-wise, object-based transition model that decomposes a scene into objects, aligns them (with respect to a slot-wise object memory) to maintain a consistent order across time, and predicts how those objects evolve over successive frames. The model is trained end-to-end without supervision using losses at the level of the object-structured representation rather than pixels. Thanks to its alignment module, the model deals properly with two issues that are not handled satisfactorily by other transition models, namely object persistence and object identity. We show that the combination of an object-level loss and correct object alignment over time enables the model to outperform a state-of-the-art baseline, and allows it to deal well with object occlusion and re-appearance in partially observable environments.
Enhancing banknote authentication by guiding attention to security features and manipulating prevalence expectancy

Abstract All banknotes have security features which are intended to help determine whether they are false or genuine. Typically, however, the general public has limited knowledge of where on a banknote these security features can be found. Here, we tested whether counterfeit detection can be improved with the help of salient elements, designed to guide bottom-up visuospatial attention. We also tested the influence of the participant’s a priori level of trust in the authenticity of the banknote. In an online study ( N = 422), a demographically diverse panel of Dutch participants distinguished genuine banknotes from banknotes with one (left- or right-sided) counterfeited security feature. Either normal banknotes (without novel design elements) or banknotes that contained a salient element (a pink rectangular frame) were presented for 1 s. To manipulate the participant’s level of trust, trials were administered in three blocks, whereby at the start of each block, participants were instructed that either one third, one half, or two thirds of the upcoming banknotes were counterfeit (though the true ratio was always 1:1). We hypothesized (i) that in the presence of a salient element, counterfeits would be better detected when the location of the salient element aligned with the location of the counterfeited security feature—i.e. that it would act as an attentional cue; and (ii) that this effect would be stronger with lower trust. Our hypotheses were partly confirmed: counterfeit detection improved with ‘valid cues’ and decreasing trust, but the level of trust did not modulate the cueing effect. As the overall detection performance was rather poor, we replicated the study with a sample of university students ( N = 66), this time presenting stimuli until response. While indeed observing better overall performance, all other patterns were replicated. Our results provide evidence that attention can be guided to enhance banknote authentication.
Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security more and more [...]
Cascaded Segmented Matting Network for Human Matting

Human matting, high quality extraction of humans from natural images, is crucial for a wide variety of applications such as virtual reality, augmented reality, entertainment and so on. Since the matting problem is an ill-posed problem, most previous methods rely on extra user inputs such as trimap or scribbles as guidance to estimate alpha value for the pixels that are in the unknown region of the trimap. This phenomenon makes it difficult to be applied to large scale data. In order to solve these problems, we studied the unique role of semantics and details in image matting, and decomposed the matting task into two sub-tasks: trimap segmentation based on high-level semantic information and alpha regression based on low-level detailed information. Specifically, we proposed a novel Cascaded Segmented Matting Network (CSMNet), which uses a shared encoder and two separate decoders to learn these two tasks in a collaborative way to achieve the end-to-end human image matting. In addition, we established a large-scale dataset with 14,000 fine-labeled human matting images. A background dataset is also built to simulate real pictures. Comprehensive empirical studies on above datasets demonstrate that CSMNet could produce a stable and accurate alpha matte without the input of trimap and achieve an evaluation value that is comparable to the algorithm that requires trimap.
Non-Markovian dynamics of a single excitation within many-body dissipative systems

We explore the dynamics of $N$ coupled atomic two-level systems embedded within a generic bosonic reservoir under specific system symmetries. In the regime of many atoms identically coupled to a single reservoir, we identify remarkable effects, notably that the initial configuration of the atomic excited-state amplitudes strongly impacts the dynamics of the system and can even fully sever the system from its environment. Additionally, we find that steady-state amplitudes of the excited states become independent of the specific structure of the bosonic reservoirs considered. The framework introduced is deployed to model a structured photonic reservoir associated with a photonic crystal, where it recaptures previous theoretical and experimental results for the superradiance rates even within the single-excitation regime. For the photonic band-gap system, our formalism predicts the generation of pairwise entanglement between initially uncorrelated atomic systems. Furthermore, it suggests that---with respect to a non-Markovian metric---the non-Markovianity of the aggregated many-atom system is modulated by the total number of atoms. This is due to a stark interplay between the Lamb shifting of the atomic transition energies due to their varying number and the increased number of atomic systems with resonant transition energies.
The Synergy between Governance and Economic Integration in Promoting Female Economic Inclusion in Sub-Saharan Africa

The debate on the need for Sub-Saharan African (SSA) countries to increase female participation in the economic sector has intensified the coming into force of the African Continental Free Trade Area (AfCFTA) and good governance. This study investigates the joint effects of governance (comprising of political, economic and institutional governance) and economic integration on female economic participation in sub-Saharan Africa (SSA). The study employs panel data of 42 countries in SSA spanning 1996-2020 for the analysis. The empirical strategy uses the dynamic System Generalized Method of Moments (SGMM) estimation technique. The findings reveal that the single effect of economic integration on female economic participation is necessary but not sufficient. Hence, complementing economic integration with good governance further enhances female economic participation in SSA. In general, the joint effect of economic integration and good governance should be a concern for policymakers to promote female economic inclusion.
MedicareVis: a Joint Visual Analytics Approach for Anti-Fraud in Medical Insurance

<p indent=0mm>Medical insurance fraud causes serious economic losses, which has a great impact on the safety and stability of the medical insurance system. However, existing work does not support the joint analysis and exploration of various types of fraud. Based on the medical insurance data related to multi-dimensional time series, a visual analytics approach for anti-fraud in medical insurance is proposed. It can perform spatio-temporal filtering of medical insurance data, locate fraud quickly, and discover hidden frauds by performing the correlation analysis between different types and different subjects of fraud. We design and develop MedicareVis, a visual analysis system for medical insurance anti-fraud. We demonstrate the usefulness and effectiveness of our approach in helping detect the association of fraud through a case study on real-world medical insurance data and interviews with domain experts.
A Consensus-Based Load-Balancing Algorithm for Sharded Blockchains

Public blockchains are decentralized networks where each participating node executes the same decision-making process. This form of decentralization does not scale well because the same data are stored on each network node, and because all nodes must validate each transaction prior to their confirmation. One solution approach decomposes the nodes of a blockchain network into subsets called "shards", each shard processing and storing disjoint sets of transactions in parallel. To fully benefit from the parallelism of sharded blockchains, the processing load of shards must be evenly distributed. However, the problem of computing balanced workloads is theoretically hard and further complicated in practice as transaction processing times are unknown prior to be assigned to shards. In this paper we introduce a dynamic workload-balancing algorithm where the allocation strategy of transactions to shards is periodically adapted based on the recent workload history of shards. Our algorithm is an adaptation to sharded blockchains of a consensus-based load-balancing algorithm. It is a fully distributed algorithm inline with network based applications such as blockchains. Some preliminary results are reported based on simulations that shard transactions of three well-known blockchain platforms.
State-of-the-Art Dynamic Load Balancing Algorithms for Cloud Computing

Cloud computing is a technological commitment that allows it to achieve a set goal, improving business performance. Cloud computing offers many applications like scalability, extensive infrastructure, storage, resource pooling, virtualization, and a wide range of online services. Cloud service providers provide a variety of technologies to cloud computing users all the time. Algorithms for load balancing were applied to enhance the speed and performance of nodes in cloud environments and protect each device from affecting their threshold by reducing their performance. Classification of different algorithms was made according to two factors, i.e., the person initiating the algorithms and the current state of the process. Dynamic load balancing algorithms are based on challenges like high availability, scalability, fault tolerance, virtual machine migration, and low response time. Dynamic load balancing algorithms apply some policies because they use the current state of a system. This paper provides the study of state-of-the-art algorithms used in cloud computing and performance parameters, which are based on different load balancing systems.
Conditional Deep Gaussian Processes: Multi-Fidelity Kernel Learning

Deep Gaussian Processes (DGPs) were proposed as an expressive Bayesian model capable of a mathematically grounded estimation of uncertainty. The expressivity of DPGs results from not only the compositional character but the distribution propagation within the hierarchy. Recently, it was pointed out that the hierarchical structure of DGP well suited modeling the multi-fidelity regression, in which one is provided sparse observations with high precision and plenty of low fidelity observations. We propose the conditional DGP model in which the latent GPs are directly supported by the fixed lower fidelity data. Then the moment matching method is applied to approximate the marginal prior of conditional DGP with a GP. The obtained effective kernels are implicit functions of the lower-fidelity data, manifesting the expressivity contributed by distribution propagation within the hierarchy. The hyperparameters are learned via optimizing the approximate marginal likelihood. Experiments with synthetic and high dimensional data show comparable performance against other multi-fidelity regression methods, variational inference, and multi-output GP. We conclude that, with the low fidelity data and the hierarchical DGP structure, the effective kernel encodes the inductive bias for true function allowing the compositional freedom.
OsaMOT: Occlusion and scale‐aware multi‐object tracking algorithm for low viewpoint

Multi-object tracking (MOT), which uses the context information of image sequences to locate, maintain identities and generate trajectories of multiple targets in each frame, is key technology in the field of computer vision. To address the problems of occlusion and scale variation in low-viewpoint MOT, OsaMOT is proposed here. First, according to the global occlusion state of each frame, OsaMOT proposes the adaptive anti-occlusion feature to enhance the awareness and adaptability for occlusion. At the same time, OsaMOT uses the cascade screening mechanism to reduce the “virtual new target” phenomenon due to the dramatic change in target features caused by scale variation and occlusion. Finally, considering that the occluded templates will affect the tracking performance, OsaMOT proposes an adaptive anti-noise template update mechanism according to the partial occlusion state of the target, which improves the purity of the template library and further enhances the applicability to occlusion. The experimental results show that OsaMOT can weaken the influence of scale variation, partial occlusion, short-term full occlusion and long-term full occlusion in the low-viewpoint tracking scenes. Most evaluation indexes of OsaMOT under low-viewpoint tracking scenario are superior to those of some typical algorithms proposed in recent years, and the tracking robustness is improved.
Online cleaning method of power grid energy anomaly data based on improved random forest

Abstract Aiming at the problem of high root mean square error of traditional power grid energy anomaly data online cleaning, a power grid energy anomaly data online cleaning method based on improved random forest is designed. Firstly, an outlier data recognition model of isolated forest is designed to identify outliers in the data. Secondly, an improved random forest regression model is established to improve the adaptability of random forest to mixed abnormal data, and the data trend is fitted and predicted. Finally, the improved random forest data cleaning method is used to compensate the missing data after removing the mixed abnormal data, so as to clean the abnormal energy data of the power grid. The experimental results show that when the amount of power grid energy anomaly data increases, the cleaning root mean square error of the experimental group is significantly lower than that of the control group. The method in this paper solves the problem of high root-mean-square error in the online cleaning of abnormal data of traditional grid energy.
SpArc: A VR Animating Tool at Your Fingertips

3D animation is becoming a popular form of storytelling in many fields, bringing life to games, films, and advertising. However, the complexity of conventional 3D animation software poses steep learning curves for novices. Our work aims to lower such barriers by creating a simple yet immersive interface that users can easily interact with. Based on the focus-group interviews, we identified key functionalities in animation workflows. The resulting tool, SpArc, is designed for two-handed setups and allows users to dive into animating without complex rigging and skinning process or learning multiple menu interactions. Instead of using a conventional horizontal slider, we designed a radial time slider to reduce possible arm fatigue and enhance the accuracy of keyframe selection. The demo will showcase this interactive 3D animation tool.
Non-Gaussian entanglement swapping between three-mode spontaneous parametric down-conversion and three qubits

In this work we study the production and swapping of non-gaussian multipartite entanglement in a setup containing a parametric amplifier which generates three photons in different modes coupled to three qubits. We prove that the entanglement generated in this setup is of nongaussian nature. We introduce witnesses of genuine tripartite nongaussian entanglement, valid both for mode and qubit entanglement. Moreover, those witnesses show that the entanglement generated among the photons can be swapped to the qubits, and indeed the qubits display nongaussian genuine tripartite entanglement over a wider parameter regime, suggesting that our setup could be a useful tool to extract entanglement generated in higher-order parametric amplification for quantum metrology or quantum computing applications.
Mapping the spread of Dutch non-standard language use on corporate Facebook pages

This paper examines the online spread of non-standard Dutch in an unexplored communicative setting, i.e. complaint management on corporate Facebook pages. Based on a self-compiled corpus of consumer-company interactions taken from 6 corporate Facebook pages, we investigate to what extent typical features of informal social media communication spill over into more sensitive contexts of complaint management. We do so by mapping the presence and frequency of 27 Flemish old vernacular and 13 new vernacular features (cf. Androutsopoulos 2011) in consumer-initiated posts, company replies and consumer-to-consumer interactions. The results show that some, though not all, new vernacular features occur relatively frequently in both consumer and company messages. Consumers predominantly use new vernacular features for expressive compensation, while companies appear to incorporate them in their webcare (especially emoji and English insertions) as functional operationalisations of conversational human voice (Kelleher 2009) and to support their desired brand identity. In contrast, old vernacular features rarely occur in the dataset. This suggests a different status of old and new vernacular features, where the former are deemed less appropriate or more limited in terms of functionality in this communicative setting. Despite the often informal register of the consumer messages, Standard Dutch still seems the preferred or safer option for company-addressed posts while companies, too, cling to Standard Dutch as the benchmark for professional written communication in this online context.
Tight Security for Key-Alternating Ciphers with Correlated Sub-keys

A substantial effort has been devoted to proving optimal bounds for the security of key-alternating ciphers with independent sub-keys in the random permutation model (e.g., Chen and Steinberger, EUROCRYPT ’14; Hoang and Tessaro, CRYPTO ’16). While common in the study of multi-round constructions, the assumption that sub-keys are truly independent is not realistic, as these are generally highly correlated and generated from shorter keys.In this paper, we show the existence of non-trivial distributions of limited independence for which a t-round key-alternating cipher achieves optimal security. Our work is a natural continuation of the work of Chen et al. (CRYPTO ’14) which considered the case of \(t = 2\) when all-subkeys are identical. Here, we show that key-alternating ciphers remain secure for a large class of \((t-1)\)-wise and \((t-2)\)-wise independent distribution of sub-keys.Our proofs proceed by generalizations of the so-called Sum-Capture Theorem, which we prove using Fourier-analytic techniques.
Recursive Prediction Error Algorithm for the Nufzy System to Identify Nonlinear Systems

In our early work, an integrated neural-fuzzy approach named NUFZY has been developed for nonlinear system identification. From the literature recursive methods have been applied with success to neural networks. Due to the resemblance of the NUFZY system and multi-layered neural networks recursive methods may also be suited for the neural-fuzzy approach. In this paper the recursive prediction error algorithm is adapted for on-line parameter tuning of the NUFZY system. Examples are presented to demonstrate the feasibility of this recursive identification to models of the NUFZY type.
Performance Evaluation of Selected Encryption Algorithms

Data security is a key aspect of today’s communication trend and growth. Various mechanisms have been developed to achieve this security. One is cryptography, which represents a most effective method of enhancing security and confidentiality of data. In this work, a hybrid based 136bit key algorithm involving a sequential combination of XOR (Exclusive –Or) encryption and AES (Advanced Encryption Standard) algorithm to enhance the security strength is developed. The hybrid algorithm performance is matched with XOR encryption and AES algorithm using encryption and decryption time, throughput of encryption, space complexity and CPU process time.
Understanding and correcting wavenumber error in interference pattern structured illumination imaging

The impacts of uncertainty in mirror movements in mechanically scanned interference pattern structured illumination imaging (IPSII) are discussed. It is shown that uncertainty in IPSII mirror movements causes errors in both the phase and amplitude of the Fourier transform of the resulting imaging. Finally, we demonstrate that iterative phase retrieval algorithms can improve the quality of IPSII images by correcting the phase errors caused by mirror movement uncertainties.