Reviewerly/exHarmony huggingface dataset:
Cyber Security and Cloud Outsourcing of Payments

We study the incentives of competing banks to outsource their payment services to a common infrastructure, managed by a private third-party provider. The latter stores depositors' information in the cloud and offers compatibility services, but is exposed to cyber risk. In the first-best benchmark, the regulator chooses to build a common payment infrastructure when the marginal social benefits are higher than the marginal social costs, and chooses the welfare-maximizing levels of security investment for all players. If the market is unregulated, without cyber risk, banks outsource excessively to the third-party provider compared to the first-best because network effects soften competition for deposits. However, we show that cyber risk and the costs of security may reduce banks' incentives to join the third-party infrastructure, which may result in an inefficiently low level of interoperability of their payment systems. We examine how the liability regime may improve the players' investment in security. We show that increasing the third-party provider's liability towards depositors has a higher impact on payment system security than increasing its liability towards banks. We discuss how several regulatory options impact the security and compatibility of banks' payment systems: supervision of outsourcing agreements, shared responsibility model, public provision of payment services.
Choosing an Optimal Machine Learning Classifier for Facial Recognition

Facial recognition is vital in today's technological world. There is a need to develop facial recognition systems which have high accuracy and at the same time high input to output response. To overcome the problem of low accuracy of facial recognition system we have attempted to combine different algorithm’s and develop a high accuracy facial recognition system. On combining different algorithms, we have observed that the Random Forest classifier achieved 96% accuracy in 2 secs. On the other hand, the Linear Discriminant Analysis classifier achieved 97% in 0 secs. Here we will compare the 6 classifiers to choose a optimal classifier.
A Knowledge Graph Completion Method for Telecom Metadata Based on the Spherical Coordinate System

In the telecommunications field, the problem of data island is caused by the separation and isolation of data. They are distributed in different systems including the business support system (BSS), management support system (MSS), and operation support system (OSS). The common idea is to use global ID mapping to break data barriers. However, using the direct global ID mapping of raw data has the problems of large data scale and the inability to guarantee privacy and security. With this in mind, constructing and completing a metadata knowledge graph to fabric the data is a feasible approach. Considering the particularity of the telecom metadata knowledge graph and the need for hierarchical distinction in business and semantic abstraction, we propose a deep learning method and framework based on the spherical coordinate system. It can be extended to a poly-spherical coordinate system and add a pre-training process composed of word2vec and a clusterer. Experimental results show that our method achieves state-of-the-art on our dataset, MDCT and has excellent performance on two public datasets, FB15k-237 and WN18RR.
Error Detection Constructions for ITA Finite Field Inversions Over $\text{GF}(2^{m})$ on FPGA Using CRC and Hamming Codes

Finite field arithmetic operations over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{GF}(2^{m})$</tex-math></inline-formula> are widely used in critical applications, such as cryptography, coding theory, error-correcting codes, and digital signal processing. Finite field inversions are the most time-consuming operations among other widely-used ones and require a large footprint as well as power/energy to be performed. To reduce such complexity, the Itoh–Tsujii algorithm (ITA) has received prominent attention in the literature; however, implementations using ITA are still vulnerable to natural very-large-scale integration defects. To overcome the challenge of detecting naturally-induced faults in such constructions, for the first time, we propose error detection schemes based on Hamming codes for architectures performing finite field inversions using the ITA algorithm over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{GF}(2^{m})$</tex-math></inline-formula> with polynomial basis. Additionally, CRC-oriented error detection schemes for inversions in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{GF}(2^{m})$</tex-math></inline-formula> with normal basis are also studied and new approaches to protect them are presented. In this article, general formulations are provided along with different case studies to show the feasibility of our schemes with any finite field size. Moreover, field-programmable gate array (FPGA) implementations are performed on two Xilinx FPGA families, i.e., Kintex UltraScale+ and Xilinx Virtex-7 UltraScale+, to verify that the overheads added by the error detection architectures to provide reliability are suitable for deeply-constrained embedded systems.
Complexity Reduction of Learned In-Loop Filtering in Video Coding

In video coding, in-loop filters are applied on reconstructed video frames to enhance their perceptual quality, before storing the frames for output. Conventional in-loop filters are obtained by hand-crafted methods. Recently, learned filters based on convolutional neural networks that utilize attention mechanisms have been shown to improve upon traditional techniques. However, these solutions are typically significantly more computationally expensive, limiting their potential for practical applications. The proposed method uses a novel combination of sparsity and structured pruning for complexity reduction of learned in-loop filters. This is done through a three-step training process of magnitude-guided weight pruning, insignificant neuron identification and removal, and fine-tuning. Through initial tests we find that network parameters can be significantly reduced with a minimal impact on network performance.
Extracting Graphological Features for Identifying Personality Traits using Agglomerative Hierarchical Clustering Algorithm

Handwriting/graphology is a unique and exclusive tool that describes one's non-verbal expression, which indirectly portrays the mental state and psychological state of a writer in a subconscious manner. The graphology analysis has been proven to identify and predict the signs of mental health disorders. This study explores the distinctive graphological features in Malaysian handwritings towards the identification of early sign of mental health disorders. The Agglomerative Hierarchical Clustering algorithm was proposed to build up clusters over the handwriting data. The promising finding suggests that the distinctive features could be useful in the personality traits analysis. The results from this study could be extended and further explored for identifying the early signs of depression through one's handwriting.
Correlations In Spatial Variability When Accounting For Cloud Advection

Spatiotemporal variability of irradiance has been a topic of interest in the literature. This study attempts to separate advective and uncorrelated portions of the spatially distributed irradiance by comparing cross correlations between site pairs within an irradiance measurement network and accounting for time lag when calculating those cross correlations. Following techniques in the literature, cross correlations are computed as a function of site pair separation distance for multiple wavelet transform timescales. Results show that the well known form of the correlation's decline can be maintained by considering the lagging correlation and the component of the site pair separation distance perpendicular to the overall cloud motion. This may open the door for additional fidelity in modeling of spatiotemporal irradiance that more formally represents how the loss of correlation with distance depends on the cloud motion vector.
Cost-Oriented Candidate Screening Using Machine Learning Algorithms

Choosing the right candidates for any kind of position, whether it is for academic studies or for a professional job, is not an easy task, since each candidate has multiple traits, which may impact her or his success probability in a different way. Furthermore, admitting inappropriate candidates and leaving out the right ones may incur significant costs to the screening organization. Therefore, such a candidate selection process requires a lot of time and resources. In this paper, we treat this task as a cost optimization problem and use machine learning methods to predict the most cost-effective number of candidates to admit, given a ranked list of all candidates and a cost function. This is a general problem, which applies to various domains, such as: job candidate screening, student admission, document retrieval, and diagnostic testing. We conduct comprehensive experiments on two real-world case studies that demonstrate the effectiveness of the proposed method in finding the optimal number of admitted candidates.
Research on Data Governance and Data Migration based on Oracle Database Appliance in campus

With the rapid development of the current informatization construction in campus, this paper discusses the situation and stages in Data Governance which are the Data Migration of transaction databases, the construction of public data platforms, and the establishment of Data Warehouses. In the step of Data Migration, this paper explores the application path of a flexible management constructed on the basis of oracle multi-tenant architecture with fast I/O and high availability that depended on the Oracle Database Appliance, so as to achieves a highly centralized management platform of the database and brings the convenience of benefit for college education reform and innovation.
Engineering Practical Rank-Code-Based Cryptographic Schemes on Embedded Hardware. A Case Study on ROLLO

In this paper, we investigate the practical performance of rank-code based cryptography on FPGA platforms by presenting a case study on the quantum-safe KEM scheme based on LRPC codes called ROLLO, which was among NIST post-quantum cryptography standardization round-2 candidates. Specifically, we present an FPGA implementation of the encapsulation and decapsulation operations of the ROLLO KEM scheme with some variations to the original specification. The design is fully parameterized, using code-generation scripts to support a wide range of parameter choices for security levels specified in ROLLO. At the core of the ROLLO hardware, we presented a generic approach for hardware-based Gaussian elimination, which can process both non-singular and singular matrices. Previous works on hardware-based Gaussian elimination can only process non-singular ones. However, a plethora of cryptosystems, for instance, quantum-safe key encapsulation mechanisms based on rank-metric codes, ROLLO and RQC, which are among NIST post-quantum cryptography standardization round-2 candidates, require performing Gaussian elimination for random matrices regardless of the singularity. To the best of our knowledge, this work is the first hardware implementation for rank-code-based cryptographic schemes. The experimental results suggest rank-code-based schemes can be highly efficient.
Evaluation Method of Medical Students' Public English Learning Engagement Based on Machine Learning

As one of the important groups in higher education, it is very important for medical students to pay attention to their learning path, learning state and learning effect in Public English curriculum. Based on the theory of learning engagement, this study chooses medical students' Public English learning engagement as the research object, and construct the learning engagement evaluation index system based on machine learning. This study selects 131 students majoring in clinical medicine and medical imaging of 2020 as the research objects, and conducts mixed Public English teaching practice for medical students and conducts relevant research on learning engagement on this basis. The results show that the selected medical students' behavioral engagement, cognitive engagement and emotional engagement in Public English learning are at the upper middle level as a whole. Teachers and schools should pay attention to these students' engagement in the three dimensions, especially in emotional engagement, and pay special attention to students' learning emotional experience.
Decomposing the Tangent of Occluding Boundaries According to Curvatures and Torsions

This paper develops new insight into the local structure of occluding boundaries on 3D surfaces. Prior literature has addressed the relationship between 3D occluding boundaries and their 2D image projections by radial curvature, planar curvature, and Gaussian curvature. Occluding boundaries have also been studied implicitly as intersections of level surfaces, avoiding their explicit description in terms of local surface geometry. In contrast, this work studies and characterizes the local structure of occluding curves explicitly in terms of the local geometry of the surface. We show how the first order structure of the occluding curve (its tangent) can be extracted from the second order structure of the surface purely along the viewing direction, without the need to consider curvatures or torsions in other directions. We derive a theorem to show that the tangent vector of the occluding boundary exhibits a strikingly elegant decomposition along the viewing direction and its orthogonal tangent, where the decomposition weights precisely match the geodesic torsion and the normal curvature of the surface respectively only along the line-of-sight! Though the focus of this paper is an enhanced theoretical understanding of the occluding curve in the continuum, we nevertheless demonstrate its potential numerical utility in a straight-forward marching method to explicitly trace out the occluding curve. We also present mathematical analysis to show the relevance of this theory to computer vision and how it might be leveraged in more accurate future algorithms for 2D/3D registration and/or multiview stereo reconstruction.
Learning Bi-directional Feature Propagation with Latent Layout Modeling for Group Re-identification

Group re-identification (G-ReID) aims to identify the same group of persons across the disjoint cameras. The key challenge of G-ReID is the robust feature extraction against the potential group layout and membership varitions. However, previous works focus more on the appearance modeling and less on the importance of group layout. In this paper, we propose a bi-directional feature propagation framework, which propagates information between group layout and member appearance. In addition, we propose the spatial generation framework, which analyses the group image and generates new images with different group layouts to simulate various layouts in the real world. Moreover, we propose a network that learns latent layout representations and propagates the layout representations with the member appearance representations. The proposed network achieves SOTA performance on two widely used G-ReID datasets, i.e., 87.9% mAP and 89.2% Rank-1 on CSG, 92.7% mAP and 90.1% Rank-1 on RoadGroup.
A Simulation Software Model of the ⊕ HUGO Stegosystem

In this article, the authors consider the problems of modern steganography. Starting with presenting a historical example of steganography, the authors classify contemporary steganography methods. The authors also offer a structural diagram of the steganographic system based on further research. Further, the authors describe a simulation software model called a “$\oplus$ Highly undetectable stegosystem” or “$\oplus$ HUGO stegosystem” for short, implementing a steganographic method of transmitting a secret message embedded in a fixed digitized image. The article also discusses the principle of operation of the simulation software model and its steganographic justification. As an implementation algorithm, the authors used a cryptographic gamming algorithm using the function of bijective addition modulo two, conventionally denoted $\oplus$. The authors determine the difficulty of detecting container change in this embedding method by calculating the Pearson correlation coefficient. The authors show that this model successfully improved information security when transmitting classified information in various electronic document management systems. The developed software model is much more efficient than the algorithm (LSB), which is determined by higher performance and provides higher resistance to detection.
Kalman Filter Algorithm Based on Sheep Herding Optimization

When dealing with the real track, the environment is often an unpredictable factor, so filtering is very important. We can use the filter to eliminate the influence of noise as much as possible. Kalman filter is one of them. In this work, we proposed a new Particle Swarm Optimization algorithm, called the Sheep Herding Optimization algorithm, which can obtain higher quality solutions with faster convergence speed and better stability. Besides, in order to improve the performance of Kalman filter, we apply the Sheep Herding Optimization algorithm to the filter. The improved Kalman filter can fuse and predict the track, and has higher computational performance and smaller error.
BASNEA: Threat Hunting for Ethereum Smart Contract Based on Backtrackless Aligned-Spatial Network Entity Alignment

Ethereum, a blockchain-based platform with a large number of decentralized applications, has been facing vast attacks and suffered significant financial losses. Threat hunting on Ethereum fails to detect attacks in time, resulting in abundant attacks being discovered only after vendors or developers take property inventory count. We propose BASNEA, a backtrackless aligned-spatial network entity alignment algorithm, to identify attacks, suspicious, and benign behaviors by comparing the attack provenance graphs constructed by the Ethereum threat intelligence with transaction provenance graphs generated from the Ethereum sync node. We also use attack investigation to the analysis of suspicious behaviors, and feedback is given to the analysis model to identify more potential threats. The experiments show that based on the collected 1,220 attack events, BASNEA can show more accurate and robust results in Ethereum smart contract threat hunting, which identifies 14 vulnerability types, and 8,814 attack events, including 1,122 known attack behaviors, and 7692 suspected attack behaviors. After the attack investigation, we discovered the hidden information behind the attack, which can help us better identify unknown threats.
General Image Fast Encryption Algorithm for Deep Learning Compression and Reconstruction

Aiming at the problems of long time-consuming in image compression encryption and color distortion of the decrypted image, a fast and safe general image encryption algorithm is proposed. The compression network uses bilinear interpolation and a fully connected layer for compression preprocessing to prevent the loss of color information. Convolution layer improves compression rate and reduces time complexity in this algorithm. In the encryption part, the chaotic sequence dynamically selects the pixel block and the pixel points in the block combining the XOR operation to perform pixel diffusion, which can improve the randomness of the algorithm. In the improved bilinear convolution neural network, bilinear and convolutional layers are used to obtain the image contour features. And the fully connected layer obtains color information for image reconstruction. This algorithm realizes the integration of compression, encryption, decryption and restoration of grayscale and RGB format color images. The experimental results show that the various encryption indexes of this algorithm are close to ideal values. It has good encryption security and anti-attack ability. Moreover, the encryption time is greatly shortened. On the 500 test set (BSD500) commonly used in image processing, when the sampling rate is 0.25, 0.10, 0.04 and 0.01, the peak signal-to-noise ratio can reach to 29.01 dB, 25.69 dB, 23.75 dB and 21.45 dB, which is better than FCLBCNN. This encryption algorithm effectively solves the color distortion problem of reconstructed images and has better encryption effects and wider applicability.
HiFi-GAN based Text-to-Speech Synthesis in Serbian

In this paper we present a deep neural network based text-to-speech system in the Serbian language, which converts generated acoustic features into a speech signal using the HiFi-GAN vocoder. The HiFi-GAN model was fine-tuned using an existing multi-speaker model trained on an English speech corpus. To overcome the problem of inadequate training data, we introduce a data generation technique based on a guided acoustic neural network, which attempts to minimize the mis-match between data used in HiFi-GAN training and inference. The outputs of the acoustic network are intended to represent a trade-off between original feature trajectories and trajectories generated by the standard text-to-speech system. The results of subjective evaluation through listening tests show that the proposed system produces speech whose quality significantly surpasses the quality of speech generated by the best existing speech synthesis for Serbian, and that its MOS score is very close to the score given to natural speech.
Blockchain-inspired Distributed Trust in a Smart Vehicular Network System

A smart vehicular network aims at goals of safe operation, and aggregate efficiency, by sharing information between cars, so that traffic information observed by some cars can be used by others out of observation range. However, as is common in multi-agent complex IoT systems, the priorities and intents of the various vehicles are distinct, such as different desired speed, or in contesting rights of way. This leads to a need for security, specifically, establishing a trust in the data generated by vehicles. This security need to be operative at the network level because the individual device security is expensive and not practical. We propose using a blockchain-inspired solution for achieving distributed trust in the data generated in a multi-agent system. We design and implement a discrete-event vehicular network simulator, and demonstrate how it achieves an all-to-all trust when the network is connected, by using just the time and location of generation of a message or data. We conduct simulation experiments to evaluate the impact of using blockchain on network latency of this resource-constrained IoT system.
DPlanner: A Privacy Budgeting System for Utility

Differential mymargin privacy has been deployed to machine learning platforms to preserve the privacy of data in use. A long neglected but important fact is that data privacy is a non-replenishable resource and should be carefully scheduled to maximize its utility gain. In this work, we propose a new privacy budgeting system—DPlanner, which estimates data blocks’ importance to queries and assigns fractional privacy budget to those data blocks contributing most to a query. The scheduler is novelly designed to include two-fold randomness, which satisfies differential privacy with tight budgets, at the same time guarantees the expected utility in the worst-case query sequence when queries arrive in an online fashion. Experiments in a variety of machine learning settings have shown that our DPlanner outperforms the state-of-the-art schedulers by serving at least 25% more queries, or reducing the total privacy consumption by over 50%.