Reviewerly/exHarmony huggingface dataset:
Hier-SFL: Client-edge-cloud collaborative traffic classification framework based on hierarchical federated split learning

Network traffic classification technology is widely noticed by internet service providers(ISP), in order to monitor network security status, improving knowledge of users' traffic demands and then ulterior design policing and prioritization mechanisms. With the continuous development of deep learning technology, network traffic classification technology starts to use supervised learning paradigm to automate traffic identification by extracting some features. However, due to the continuous emergence of new private protocols, network services and access devices, the overall distribution of network traffic data is constantly changing. The training of network traffic classification models usually relies on the labeled data collected by academic organizations, institutions and companies. These data are usually generated in a fixed network environment, and the types of labels collected are limited. Models trained in this way are difficult to adapt to the open and complex network environment. Furthermore, the unevenly distributed computing resources affect the model training efficiency because the collected traffic data is usually concentrated in the end system but the computing power is generally concentrated in the edge computing nodes and cloud centers. Federated learning technology can coordinate data holders to train globally adapted models from discrete data. At the same time, split learning technology can realize more efficient model separation training. In this paper, we designed a new three-tier algorithm Hier-SFL combining and improving these two learning frameworks according to the common client-edge-cloud structure. Hier-SFL algorithm allocated the training task of neural network to each level network device with different computing resources. Also, it took the advantage of federated learning to periodically aggregate network parameters. Thus, we significantly improved the training efficiency of the deep learning model of traffic classification.
Refined SBERT: Representing sentence BERT in manifold space

Distributed sentence representations have shown great power in a wide range of natural language processing (NLP) tasks. Meanwhile, a contextualized sentence representation, called Sentence BERT (SBERT), achieves excellent performance in quite a few NLP tasks. However, Sentence BERT is typically learned in the Euclidean space, the geometric structure of sentence representations and their relations to the representations of sentence’s contexts has not been carefully studied yet. In this paper, we propose a new sentence representation method, named Refined SBERT, which utilizes manifold learning to refine sentence BERT by re-embedding sentence vectors from the original embedding space to a new refined semantic space. In order to map sentences to manifold space, we utilize neighborhood preserving embedding to construct the local manifold structure of the sentences. Our method can discover the local geometric structure and obtain a compact sentence BERT subspace, which can best detect the essential semantic structure. We conduct comprehensive experiments on various sentence embedding tasks including semantic textual similarity tasks, text classification and document clustering, and the experimental results show that the proposed model achieved promising results, comparing to several popular sentence representations.
Deep learning-based counter anti-forensic of GAN-based attack in HEVC compressed domain using coding pattern analysis

Double compression detection is one of the most popular techniques for the authentication of images and videos in the sphere of multimedia forensics. Several forensic methods have been suggested revealing video forgery, particularly high-efficiency video codec (HEVC) double compression detection. Even though forensic methods can effectively validate the integrity of videos, they become more intricate while practicing anti-forensic (AF) on manipulated videos. In this study, we present an innovative counter-anti-forensic (CAF) approach to detect anti-forensically misguided HEVC double compressed videos with the same coding parameters using coding pattern analysis. We mainly focus on employing generative adversarial networks (GANs) attacks to reconstruct artifacts that appear in compressed videos for deceiving forensic methods. In the proposed CAF method, we apply AF attacks twice in primary and secondary compressions and analyze the coding pattern changes to build a CAF method. We extract syntax information, and introduce new syntax map representations to improve distinguishability between single and double-compressed sequences. Additionally, we apply Gaussian and median filtering as AF attacks to show the robustness of the proposed method. The proposed deep learning-based CAF method shows better performance and can overwhelm state-of-the-art works.
Code review guidelines for GUI-based testing artifacts

Review of software artifacts, such as source or test code, is a common practice in industrial practice. However, although review guidelines are available for source and low-level test code, for GUI-based testing artifacts, such guidelines are missing. The goal of this work is to define a set of guidelines from literature about production and test code, that can be mapped to GUI-based testing artifacts. A systematic literature review is conducted, using white and gray literature to identify guidelines for source and test code. These synthesized guidelines are then mapped, through examples, to create actionable, and applicable, guidelines for GUI-based testing artifacts. The results of the study are 33 guidelines, summarized in nine guideline categories, that are successfully mapped as applicable to GUI-based testing artifacts. Of the collected literature, only 10 sources contained test-specific code review guidelines. These guideline categories are: perform automated checks, use checklists, provide context information, utilize metrics, ensure readability, visualize changes, reduce complexity, check conformity with the requirements and follow design principles and patterns. This pivotal set of guidelines provides an industrial contribution in filling the gap of general guidelines for review of GUI-based testing artifacts. Additionally, this work highlights, from an academic perspective, the need for future research in this area to also develop guidelines for other specific aspects of GUI-based testing practice, and to take into account other facets of the review process not covered by this work, such as reviewer selection.
Choice and independence of premise rules in intuitionistic set theory

Choice and independence of premise principles play an important role in characterizing Kreisel's modified realizability and Gödel's Dialectica interpretation. In this paper we show that a great many intuitionistic set theories are closed under the corresponding rules for finite types over N. It is also shown that the existence property (or existential definability property) holds for statements of the form ∃yσφ(y), where the variable y ranges over objects of finite type σ. This applies in particular to CZF (Constructive Zermelo-Fraenkel set theory) and IZF (Intuitionistic Zermelo-Fraenkel set theory), two systems known not to have the general existence property. On the technical side, the paper uses a method that amalgamates generic realizability for set theory with truth, whereby the underlying partial combinatory algebra is required to contain all objects of finite type.
Optimizing the phase sensitivity of Michelson interferometer with two-mode squeezed coherent input in the presence of loss and noise

A Michelson-type interferometer with two-mode squeezed coherent state input is considered. Such an interferometer has a better phase sensitivity over the shot-noise limit by a factor of e2r, where r is the squeezing parameter [Phys. Rev. A 102, 022614 (2020)]. We show that when photon loss and noise in the two arms are asymmetric, an optimal choice of the squeezing angle can allow improvement in phase sensitivity without any increase in input or pump power. In particular, when loss occurs only in one arm of the interferometer, we can have improvement in phase sensitivity for photon loss up to 80%. Hence, a significant improvement can be made in several applications such as LiDAR, gyroscopes, and measuring refractive indices of highly absorptive/reflective materials.
A heterogeneous online/offline signcryption scheme for Internet of Vehicles

With the rapid development of wireless communication and autonomous driving technologies, the Internet of Vehicles (IoV) technology has emerged to build a large network that supports interactive communication between vehicles, roadside infrastructure and the surrounding environment. This allows vehicle sensors to collect massive amount of data. However, data is at risk of attack. In order to ensure data security, in this paper, we design a heterogeneous online/offline signcryption (HGOOSC) scheme. This construction allows a sender in Certificateless Cryptography (CLC) to send messages to a receiver in Public Key Infrastructure (PKI). CLC has neither key escrow problem in Identity-Based Cryptography (IBC) nor public key certificate management problem in PKI. PKI is widely used in the Internet server to ensure its security. For signcryption, the online/offline technology can save a lot of time compared with the ordinary one. It can preprocess complex operations without the message and the receiver's identity in the offline phase. While in the online phase, it just needs some light operations with the message and the receiver's identity. In addition, the proposed scheme can provide the confidentiality, authentication, integrity and non-repudiation in the random oracle model under the Elliptic Curve Discrete Logarithm Problem (ECDLP). Comprehensive analysis shows that our design is the most efficient. Compared with other three schemes, for offline storage and ciphertext size, our scheme is optimal. For computational cost, our scheme reduces by 23%, 17% and 17%, respectively. Therefore, our design is suitable for the IoV application.
Perception-based high quality distributed virtual reality

Abstract Virtual reality has great potential to enable remote collaborative work from anywhere in the world. Developing virtual reality into a platform suitable for natural interaction and immersive collaboration requires the experience to be reliably stable. For a networked collaborative environment, perceived smoothness of motion is limited by the tick rate, that is, the frequency at which information is distributed. As tick rate increases, motion will appear increasingly smooth; however, excessive tick rates may introduce additional load on a network without any perceptible benefit to a user. This paper details two visual psychophysics experiments ( $$N_1 = 16$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>N</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>16</mml:mn> </mml:mrow> </mml:math> , $$N_2 = 11$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>N</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>11</mml:mn> </mml:mrow> </mml:math> ) carried out to evaluate participant sensitivity to tick rate in virtual reality. The influence of three variables, velocity, complexity, and digital medium were investigated. Both velocity and digital medium displayed a significant effect, whilst complexity did not show significance. A model was then built and validated from the results of these experiments. The model predicts for average walking speed within the desktop condition, that 90% of the population will perceive motion to be smooth at 56 Hz, whilst this 90% threshold lies at 113 Hz for the VR condition. This model can predict participant perception of tick rate under given conditions, enabling networks to intelligently optimise participant experience without adding unnecessary further load on the network.
Shaping the creative landscape through the role of digital and computer technologies in advancing art product design and industry applications

The rapid evolution of digital and computer technologies has transformed the creative landscape, influencing art product design and industry applications. This study aims to explore the impact of these technologies in shaping contemporary art product design and identifying the key trends driving industry growth. Utilizing a mixed-methods research approach, the study examines the integration of advanced software, hardware, and innovative techniques in the art and design process. The findings reveal that digital fabrication, 3D printing, virtual and augmented reality, and artificial intelligence have revolutionized the design process, enabling artists to create complex, multi-dimensional, and interactive works. Furthermore, the integration of technology has fostered new avenues for resource sharing, and global connectivity, significantly altering the industry’s traditional boundaries. The study concludes that embracing digital and computer technologies is vital for continued innovation in the creative sector and highlights potential future developments that can further advance art product design and industry applications.
Circular Costas maps: A multidimensional analog of circular Costas sequences

A unifying theoretical framework is presented, in which the connections among Costas sequences, circular Costas sequences, Costas polynomials, the shifting property, and Welch sequences are extended to the multidimensional context. Several conjectures on multidimensional periodic Costas arrays by Ortiz-Ubarri et al. are proved. Furthermore, a conjecture on Costas polynomials over extension fields presented by Muratović-Ribić et al. is showed to be a multidimensional extension of a conjecture by Golomb and Moreno on circular Costas sequences. A weaker version of said conjecture is proved by considering a multidimensional extension of the shifting Costas property defined by Moreno.
Artificial key fingerprints for continuous-variable quantum key distribution

With rapid progress and significant successes in quantum hacking attacks, machine learning is being applied in continuous-variable quantum key distribution (CVQKD) systems. However, most machine-learning networks are shown to be vulnerable to adversarial examples. Thus, we propose a defense scheme with artificial key fingerprints for CVQKD systems. We first embed artificial key fingerprints into quadrature values, then exploit changes of fingerprints in the communication process to probe whether the samples have been manipulated. The paper verifies the effectiveness of our proposal based on four groups of samples that consist of normal measurement values and abnormal measurement values under four attacks. Simulation results show that the model can monitor sample changes in real time with high precision, and our scheme stays robust against adversarial examples attack. This proposal provides a different idea to address the potential vulnerability between machine-learning models used in CVQKD systems and their possible attacked training and test data by adversarial examples.
High-performance phase measuring profilometry architecture based on Zynq SoC

This paper presents a novel high-performance heterogeneous computation architecture, to the best of our knowledge, for stereo structure light using the phase measuring profilometry (PMP) algorithm based on a Zynq UltraScale+ system on chip (SoC). The proposed architecture aims to achieve real-time and high-accuracy 3D shape measurement. The experiment results indicate that the calculation time of a standard four-step PMP algorithm with a resolution of 1280×1024 is 14.11 ms. It is nearly 51 times faster than the well-optimized software implementation running on a Raspberry Pi and nearly three times faster than a high-end PC, with 15 times less power consumption. Consequently, the proposed architecture is deemed suitable for real-time 3D measurements in embedded applications.
Homomorphic Logical Measurements

A generalization of the Shor and Steane constructions allows for measurement of select logical Pauli operators in error-correcting codes with reduced time and space overhead and opens the door to fault-tolerant implementations of logical gates.
AN ONTOLOGY BASED PRODUCT RECOMMENDATION SYSTEM FOR NEXT GENERATION E-RETAIL

The number of e-commerce resources has increased considerably. Thus, it has become important for sellers to be able to quickly recommend products to potential buyers. Some product recommendation systems developed for this purpose. However, due to the lack of semantics, the systems’ success in recommending accurate products according to user preferences is low. In this study carried out within the scope of a state-funded R&D project, an ontology-based personalized product recommendation system named E-Prod was developed. E-Prod tracks various e-commerce systems in real time and transfers the product information to the ontology model. E-Prod uses a novel recommendation approach that combines machine learning and semantic matching to provide personalized recommendations. The system learns user’s preferences based on semantic relationships between products by monitoring their behaviors. In this way, accurate recommendations are made by semantic matching between products and user preferences. E-Prod has been tested with over 250 registered users and compared to traditional collaborative recommendations in terms of accuracy, precision, and recall. As a result, E-Prod outperformed traditional methods by 92.79% accuracy, 92.93% precision, and 90.58% recall. Within the scope of this study, E-Prod covers the clothing, shoes, and bag retail sectors. However, it provides a generic infrastructure for new generation e-commerce systems. Its reusable modules can be adapted to any domain.
Gaussian conversion protocol for heralded generation of generalized Gottesman-Kitaev-Preskill states

In the field of fault-tolerant quantum computing, continuous-variable systems can be utilized to protect quantum information from noise through the use of bosonic codes. These codes map qubit-type quantum information onto the larger bosonic Hilbert space, and can be divided into two main categories: translational-symmetric codes, such as Gottesman-Kitaev-Preskill (GKP) codes, and rotational-symmetric codes, including cat and binomial codes. The relationship between these families of codes has not yet been fully understood. We present an iterative protocol for converting between two instances of these codes GKP qunaught states and four-foldsymmetric binomial states corresponding to a zero-logical encoded qubit - using only Gaussian operations. This conversion demonstrates the potential for universality of binomial states for all-Gaussian quantum computation and provides a new method for the heraladed preparation of GKP states. Through numerical simulation, we obtain GKP qunaught states with a fidelity of over 98% and a probability of approximately 3.14%, after only two steps of our iterative protocol, though higher fidelities can be achieved with additional iterations at the cost of lower success probabilities.
Effects of non-Markovianity on daemonic ergotropy in the quantum switch

The quantum switch implements indefinite causal order via a coherent control of the orderings of multiple quantum operations, leading to various advantages and applications, one of which being in work extraction where enhancements to daemonic ergotropy is possible. Motivated by recent developments in the connections between non-Markovianity and the quantum switch, we construct a non-Markovian process that reduces to the two-party quantum switch in the fully non-Markovian limit. By controlling the amount of non-Markovianity in the process, we identified two operational regimes with differing behaviors. One has its daemonic ergotropy dependent on the presence and amount of non-Markovianity, achieving the maximum in the quantum switch case of full non-Markovianity. The other regime, however, has no advantages from non-Markovianity. We compare this non-Markovian process with the case of a superposition of independent channels, where two channels are placed in a coherent superposition without indefinite causal order, uncovering the advantages of non-Markovianity. Finally, the conditions required for the production of positive daemonic ergotropy are also derived for the case of fully non-Markovian and fully Markovian limits, where we compare against the conditions required for the superposition of independent channels.
Fennoscandic comparison on KYC obligations of a virtual currency provider

Purpose This paper aims to analyse the virtual currency regulation especially in Finland, Sweden and Norway. Different member states had a bit differently incorporated regulation of AMLD5. Finland has gone the furthest in regulation and even issuers of virtual currency are under the Finnish regulation. Design/methodology/approach In one hand, the study approach is legal dogmatics, but in other hand it is comparative legal research. Both approaches can be found in this paper. Findings The EEA is going from a more fragmented regulatory landscape based on 5th Anti-Money Laundering Directive to a more uniform regulatory approach provided by a legislative package that regulates crypto assets more broadly, coupled with an overhaul of the anti-money laundering rules, bringing them into a single European rulebook. Finland has taken a step further in this matter. Therefore, it would be reasonable for the AMLD5 scope to be expanded in this respect. It is a welcome development that the regulation will be unified and that investor protection will be better taken into account in the future as well. Originality/value This paper gives a picture of what kind of challenges is there in Fennoscandic in terms of money laundering regulation of virtual currencies. On the other hand, this paper brings into the discussion the rather clever solutions of Fennoscandic (especially Finland) regarding money laundering of virtual currencies.
Perancangan Awal Sistem Automatic Self-Checkout Untuk Produk Buah Berbasis CNN dan Sensor Berat Loadcell

&#x0D; &#x0D; &#x0D; The check-out system is one of the most important systems when we make purchases at a shopping center, shops, supermarkets, or mini markets. Determining different types of fruit is one of the problems faced in making payments, especially in supermarkets, because cashiers must determine the category labeling and weight of a particular fruit to determine the price. In this research, a design is proposed which utilizes Deep Learning in the field of image processing using the Convolutional Neural Network (CNN) algorithm utilizing RaspberryPi3. In making a classification system that uses deep learning, there are several main process stages, such as dataset collection, system design, training, and testing to see the results of the designed CNN model. The processed dataset in this research is a fruit image dataset derived from the Fruits-360. The results of the learning process obtained CNN models with 100% accuracy and loss of ≤ 0.026. The results of the CNN model that has been carried out will be combined with the load cell sensor which is expected to be used as an automatic self-checkout, especially for fruits. On the other hand, the calibration process of the loadcell weight measurement sensor is conducted which resulting an error measurement rate of 0.2 grams.&#x0D; &#x0D; &#x0D;
Confirmation of the Acquired Knowledge for Recognition of the Geometric Triangle Figure Using Information Technologies

The article is dedicated to the use of information technology in the creation of educational games for acquiring novelties in primary school. Specialized Scratsh software for the inclusion of children in computer culture is considered. The basis for the developed animated modules is the textbook in mathematics for third grade with authors Prof. Dr. Vladimira Angelova and Jana Koleva, Prosveta Plus Publishing House, Sofia, 2017