Reviewerly/exHarmony huggingface dataset:
Clifford+T-based implementation of fault-tolerant quantum circuits over XOR-Majority Graph

To build a scalable quantum computer that is sufficiently large to solve classically intractable problems is one of the biggest engineering quests of current times. But, the fragility of quantum states with short coherence time and vulnerability of quantum components towards external noise becomes an immense challenge to the physical implementation. Thus, the fault-tolerant architecture over the quantum error-correcting code (qecc) such as Surface code in optimal logical level are most desirable and can be achieved transversely using Clifford+T-group. However, the T-gate has high latency. In this paper, we introduce a template matching scheme over the Xor-Majority Graphs to realize a high scalable fault-tolerant quantum circuit. The proposed methodology is validated with multiple quantum benchmarks achieving an improvement of 71.98%, and 78.33% on average T−count, and T−depth respectively over state-of-art, and at the same time completely avoiding any ancillary input. When ancillary inputs are permitted, an improvement of 92.77% on T−depth is obtained.
Fsft-Net: Face Transfer Video Generation With Few-Shot Views

To transfer head pose and expression with few photographs is a novel yet challenging task in deepfake generation. Despite impressive results have been achieved in related works, there are still two limitations in the existing methods: 1) most of the methods are based on computer graphics, which take a lot of computing resources, while lacking of generalization for different identity, 2) few-shot based methods cannot handle the few-shot style transfer video generation. To address these distortion problems, we propose a novel deep learning framework, named as Few-Shot Face Transfer Networks(FSFT-Net) which works for the face transfer video generation. The proposed FSFT-Net driven by arbitrary portrait video involves a cascaded-based style generator to synthesize stable video with few free-view images. In addition, the frame and video discriminators are adopted for optimization of the proposed generator. The FSFT-Net performs long-term adversarial training on large-scale video datasets. Extensive experiments demonstrate that our FSFT-Net outperforms state-of-the-art methods both quantitatively and qualitatively results.
Structuralist Analysis for Neural Network System Diagrams

This paper examines diagrams describing neural network systems in academic conference proceedings. Many aspects of scholarly communication are controlled, particularly with relation to text and formatting, but often diagrams are not centrally curated beyond a peer review. Using a corpus-based approach, we argue that the heterogeneous diagrammatic notations used for neural network systems has implications for signification in this domain. We divide this into two questions (i) what content is being represented and (ii) how relations are encoded. Using a novel structuralist framework, we use a corpus analysis to quantitatively cluster diagrams according to the author’s representational choices. This quantitative diagram classification in a heterogeneous domain may provide a foundation for categorising representational properties of diagrams.
FHSS Signals Classification by Linear Discriminant in a Multi-signal Environment

Frequency-hopping spread spectrum (FHSS) spreads the signal over a large bandwidth where the carrier frequencies change quickly according to a pseudorandom number making signal classification difficult. Furthermore, classification becomes more complex with the presence of additive white Gaussian noise (AWGN) and interference due to background signals. In this paper, a linear discriminant (LD) method based on the Euclidean distance is proposed for the classification of FHSS signals in the presence of AWGN and background signal. Probability of correct classification (PCC) of the FHSS signals is performed by the LD method for the signal-to-noise ratio (SNR) range of −6 to 15 dB. Results show that the proposed method has achieved 90% detection rate at the SNR range of −1.6 to 3.5 dB in the presence of AWGN only, while its performance is degraded to 0.9 to 12 dB when the background signal is present.
Compositional Analysis of Protocol Equivalence in the Applied $$\pi $$-Calculus Using Quasi-open Bisimilarity

Abstract This paper shows that quasi-open bisimilarity is the coarsest bisimilarity congruence for the applied $$\pi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>π</mml:mi> </mml:math> -calculus. Furthermore, we show that this equivalence is suited to security and privacy problems expressed as an equivalence problem in the following senses: (1) being a bisimilarity is a safe choice since it does not miss attacks based on rich strategies; (2) being a congruence it enables a compositional approach to proving certain equivalence problems such as unlinkability; and (3) being the coarsest such bisimilarity congruence it can establish proofs of some privacy properties where finer equivalences fail to do so.
Eavesdropping a quantum key distribution network using sequential quantum unsharp measurement attacks

We investigate the possibility of eavesdropping on a quantum key distribution network by local sequential quantum unsharp measurement attacks by the eavesdropper. In particular, we consider a pure two-qubit state shared between two parties Alice and Bob, sharing quantum steerable correlations that form the one-sided device-independent quantum key distribution network. One qubit of the shared state is with Alice and the other one while going to Bob’s place is intercepted by multiple sequential eavesdroppers who perform quantum unsharp measurement attacks thus gaining some positive key rate while preserving the quantum steerable correlations for Bob. In this way, Bob will also have a positive secret key rate although reduced. However, this reduction is not that sharp and can be perceived due to the decoherence and imperfection of the measurement devices. We found that there is a possibility of sequential eavesdropping even after setting a minimum threshold on the observed secure key rate by Alice and Bob. In the end, we show that an unbounded number of eavesdroppers can also get secret information in some specific scenario.
Universal Graph Compression: Stochastic Block Models

Motivated by the prevalent data science applications of processing large-scale graph data such as social networks, web graphs, and biological networks, as well as the high I/O and communication costs of storing and transmitting such data, this paper investigates universal compression of data appearing in the form of a labeled graph. In particular, we consider a widely used random graph model, stochastic block model (SBM), which captures the clustering effects in social networks. A universal graph compressor is proposed, which achieves the optimal compression rate for a wide family of SBMs with edge probabilities from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O$</tex> (1) to Ω(1/ <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2-∊</sup> ) for any 0 < ∊ < 1. Existing universal compression techniques are developed mostly for stationary ergodic one-dimensional sequences with entropy linear in the number of variables. However, the adjacency matrix of SBM has complex two-dimensional correlations and sublinear entropy in the sparse regime. These challenges are alleviated through a carefully designed transform that converts two-dimensional correlated data into almost i.i.d. blocks. The blocks are then compressed by a Krichevsky-Trofimov compressor, whose length analysis is generalized to arbitrarily correlated processes with identical marginals.
Building Medical Claims-Processing Software From 12 Time Zones Away

Each of the 50 U.S. states offers subsidized health insurance (Medicaid) to certain sections of its population. This, in turn, means that each of the 50 states needs to implement and maintain an IT system, the Medicaid Management Information System (MMIS), to enable its Medicaid processes. From fiscal year 2008 through fiscal year 2018, states spent close to US$40 billion on their MMIS systems—out of which, close to US$30 billion was reimbursed by the federal government as per prevailing funding norms.<sup>1</sup>
Context-Based Deep Learning Approach for Named Entity Recognition in Hindi

Named Entity Recognition is a crucial step in Natural Language. For a language like Hindi, this task is difficult due to complex linguistic features. To overcome the difficulties posed by the nature of the language we propose a deep learning method for performing Named Entity Recognition in Hindi. In our method, we use contextualized word embeddings and a Bi-LSTM neural network. The results observed have an average accuracy of 98.67%. The model successfully captures the context of ambiguous words and classifies them correctly.
Intelligent Door Lock System Based on Raspberry Pi

In recent years, with the development of network technology and the development of real estate industry, intelligent apartments are becoming more and more popular. The intelligent door lock equipment is becoming more and more popular. Compared with the traditional door lock, the intelligent door lock has higher security and convenience. The user controls the door lock through the mobile device, and the apartment controls the door lock through the computer. Compared with the traditional apartment door lock or electronic access card, it is more convenient. Therefore, a management system that can manage the door locks intelligently through the Internet is of great significance to the real estate industry and social development. In this paper, using the existing knowledge of electronic door lock in the industry, combined with the author's professional ability, an intelligent door lock management system based on raspberry pie, Java EE and Android is designed. The door lock end is controlled by raspberry pie, which scans fingerprint data and enters fingerprint data by as608 fingerprint module. Rpi-lcd35b display screen is used as password input interface, RPI camera is used as monitoring equipment of door lock. Raspberry pie controls electronic door lock and sends door opening record to server. Users can get the information of the door lock through the mobile app, modify the door lock password, save the household information and perform other operations, which can be monitored by the camera on the door lock.
Binarisation of photographed documents image quality and processing time assessment

Smartphones with cameras are omnipresent in today's world and are very often used to photograph documents. Document binarization is a key process in many document processing platforms. This competition on binarizing photographed documents assessed the quality and time performance of 13 new algorithms and 50 existing algorithms. The evaluation dataset is composed of offset, laser, and deskjet printed documents, photographed using four widely-used mobile devices with the strobe flash on and off, under two different angles and places of capture.
Constructions for measuring error syndromes in Calderbank-Shor-Steane codes between Shor and Steane methods

In another work [S. Huang and K. R. Brown, Phys. Rev. Lett. 127, 090505 (2021)] we introduced syndrome extraction methods for Calderbank-Shor-Steane quantum error-correction codes that interpolate between the well-known Shor and Steane syndrome extraction methods. Here we provide detailed proofs of the main theorems and show that up to gate ordering there is a one-to-one correspondence between extraction gadgets and partitions of the parity check matrix. Operationally, all the circuits in our framework can be obtained by merging ancilla qubits of Shor error correction with certain rules, which enables us to design fault-tolerant syndrome extraction circuits for given specific codes. We then apply our construction to the toric code and provide a detailed analysis of the time overhead of fault tolerance. In particular, we construct a syndrome extraction family whose time overhead smoothly varies from Shor to Steane error correction. To understand the potential advantage, we consider two simplified error models: no errors on the ancilla block and uncorrelated errors on the ancilla block. We study the threshold behavior of the family for both error models and show its potential advantage for quantum architectures with long coherence times.
Research on intelligent somatosensory interaction devices based on virtual reality technology

Abstract Contemporary students have developed a dependence on electronic devices and developed the habit of sitting with screens for a long time, which has led to a trend of gradual deterioration of health conditions, declining physical quality and rejuvenation of diseases. Therefore, in this paper, based on the interactive and experiential characteristics of intelligent somatic interactive devices with virtual reality technology, the intelligent somatic interactive device body is improved according to the functions of virtual reality device body shape and experience, based on the handling needs, experiential needs and functional needs, and the educational interactive device experience strategy is designed to meet individual needs for the student group, and through comparison experiments with two groups of students, it is concluded that The interactive device experience strategy proposed in this paper outperforms the comparison group in terms of learning interest, motor interest, psychological anxiety, and emotional communication, verifying the effectiveness of the interactive device experience strategy, thus promoting students' physical health and improving individual physical signs, and also providing reference and learning for researchers engaged in intelligent somatic interactive devices based on virtual reality technology, from form design to development ideas.
Adaptive Label Smoothing for Classifier-based Mutual Information Neural Estimation

Estimating the mutual information (MI) by neural networks has achieved significant practical success, especially in representation learning. Recent results further reduced the variance in the neural estimation by training a probabilistic classifier. However, the trained classifier tends to be overly confident about some of its predictions, which results in an overestimated MI that fails to capture the desired representation. To soften the classifier, we propose a novel scheme that smooths the label adaptively according to how extreme the probability estimates are. The resulting MI estimate is unbiased under a mild assumption on the model. Experimental results on MNIST and CIFAR10 datasets confirmed that our method yields better representation and achieves higher classification test accuracy among existing approaches in self-supervised representation learning.
Faster enclave transitions for IO-intensive network applications

Process-based confidential computing enclaves such as Intel SGX have been proposed for protecting the confidentiality and integrity of network applications, without the overhead of virtualization. However, these solutions introduce other types of overhead, particularly the cost transitioning in and out of an enclave context. This makes the use of enclaves impractical for running IO-intensive applications, such as network packet processing. We build on earlier approaches to improve the IO performance of workloads in Intel SGX enclaves and propose the HotCall-Bundler library that helps reduce the cost of individual single enclave transitions and the total number of enclave transitions in trusted applications running in Intel SGX enclaves. We describe the implementation of the HotCall-Bundler library, evaluate its performance and demonstrate its practicality using the case study of Open vSwitch, a widely used software switch implementation.
Ransomware: A Darwinian Opportunity for Cyber Insurance

Darwin’s survival of the fittest theory maintains that an organism's ability to adapt to changes in its environment and adjust accordingly over time determines its survival success. This process of adaptation at the heart of Darwinism is apropos for the cyber insurance industry amidst the selective pressures introduced by ransomware (RW) incidents and claims. This case study proffers adaptations to the changes wrought by ransomware in order to increase cyber insurance resiliency to this peril and prevent coverage extinction. These adaptations exist on a spectrum of controllability and speed of impact, including: risk management guidance, mandatory ransomware incident disclosure regulation, security controls failure reporting, infosec prevention and mitigation controls incentives, data-driven risk models, and cyber extortion policy reform.
A Systematic Review and Analysis of Blockchain Technology for Corporate Remittance and Settlement Process

Unlike traditional remittance approaches, blockchain infrastructure fundamentally alters international transfers. This is because blockchain provides an effective, open, and reliable method of facilitating international payments. Centered on a distributed network of computers, it may conduct global financial transactions. It helps streamline the whole payments chain by eliminating redundant intermediaries and provides near-instant payment solutions. The importance of research lies in avoiding many of the obstacles those current remittance technologies, such as reconciliation, transaction accessibility, legal record sharing, the implementation of a consistent and real-time workflow, and so on. This literature survey will allow for the development of various products and workflows in accordance with the demands of various corporate remittance applications. The corporate remittance is all about exchange of documents and information about the transacting parties for compliance purpose. In addition, we discovered more incidents of criminal activity there than in retail remittances. As a result, we discovered blockchain technologies for handling these payments. The literature review filtered all practicable methods of remittance and arbitration present in the sector. All potential solutions are represented using blockchain technology, which is capable of handling payments and records.
Frequent Itemsets Mining with a Guaranteed Local Differential Privacy in Small Datasets

In this paper, we propose an iterative approach to estimate the frequent itemsets with high accuracy while satisfying the local differential privacy (LDP). The key component behind the improved accuracy of the estimated frequent itemsets by our approach is our novel two-level randomization technique for guaranteeing the LDP. Our randomization technique exploits the correlation of the presence of items in a user's itemset, which has not been considered before. We present a mathematical proof that shows that our approach satisfies the LDP constraint. Extensive experiments are performed to validate the effectiveness and efficiency of our proposed algorithms using real datasets.
A Study of the Impact of Crypto Currency on the Indian Payment system

Payment systems form an integral part of any emerging economy. A payment system should be safe, secure, reliable, and accessible. It will help in expanding financial inclusion and bringing financial stability. An efficient payment system helps in the smooth flow of payments and mitigation of risks and smooth functioning of the economy. It helps in fostering confidence in individuals about the use of payment services. Technological development has helped in changing the face of payments system from cards (credit/debit card) to wallets (Paytm/Phonepe etc.) to Unified Payments Interface (UPI) and Quick Response (QR)codes. It has not only introduced us to new payment methods but also strengthen the traditional payment methods. It has become an important part of our daily life. It has empowered us and made our life easier by offering services at our fingertips round the clock. The latest addition to these is cryptocurrencies like Bitcoin, Ether, Ripple, etc. Cryptocurrencies are one of the first applications of Blockchain technologies.it has removed the need for intermediaries and exert pressure on the existing framework. The attributes of cryptocurrency framework like decentralized network, no intermediaries, and the lack of stable pricing factors do not let it unlock its true potential. The future of Cryptocurrency is uncertain. Whether it will be accepted globally or still be traded via unauthorized means. Every problem allows for finding a solution. The regulators should come up with policies, which will help in shaping the payments system for the betterment of the people, by using the positive attributes of cryptocurrencies and coordinating with the Global peers.
GNN-DBSCAN: A new density-based algorithm using grid and the nearest neighbor

DBSCAN (density-based spatial clustering of applications with noise) is one of the most widely used density-based clustering algorithms, which can find arbitrary shapes of clusters, determine the number of clusters, and identify noise samples automatically. However, the performance of DBSCAN is significantly limited as it is quite sensitive to the parameters of eps and MinPts. Eps represents the eps-neighborhood and MinPts stands for a minimum number of points. Additionally, a dataset with large variations in densities will probably trap the DBSCAN because its parameters are fixed. In order to overcome these limitations, we propose a new density-clustering algorithm called GNN-DBSCAN which uses an adaptive Grid to divide the dataset and defines local core samples by using the Nearest Neighbor. With the help of grid, the dataset space will be divided into a finite number of cells. After that, the nearest neighbor lying in every filled cell and adjacent filled cells are defined as the local core samples. Then, GNN-DBSCAN obtains global core samples by enhancing and screening local core samples. In this way, our algorithm can identify higher-quality core samples than DBSCAN. Lastly, give these global core samples and use dynamic radius based on k-nearest neighbors to cluster the datasets. Dynamic radius can overcome the problems of DBSCAN caused by its fixed parameter eps. Therefore, our method can perform better on dataset with large variations in densities. Experiments on synthetic and real-world datasets were conducted. The results indicate that the average Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Adjusted Mutual Information (AMI) and V-measure of our proposed algorithm outperform the existing algorithm DBSCAN, DPC, ADBSCAN, and HDBSCAN.
Statistical Prediction of Facial Emotions Using Mini Xception CNN and Time Series Analysis

The growing era of facial recognition has opened a large area of computational study. Facial emotion recognition has always been a challenging task in the fields of deep learning. In this work, we have proposed a better approach to not only study human facial emotion, but also predict one’s emotion by collecting the same personal data. One part of the article represents the usage of CNN for detecting facial emotions in which it takes in real-time video frames and predicts the probabilities of the seven basic emotion states. Output data of the CNN model serves as the input for the time-series analysis model, and the task of predicting one’s future emotions has been accomplished. The two-step hierarchical structure helps in studying human behaviour to predict future outcomes. Finally, the model can be used for continuous monitoring and predicting a person’s behaviour providing constant emotional parameters. This work will be used in many interrogatory procedures or as a preventive measure by collections of the convict’s facial data.