Reviewerly/exHarmony huggingface dataset:
Discrete modulation continuous-variable quantum key distribution based on quantum catalysis

Compared with discrete variable quantum key distribution (DVQKD), continuous variable (CV) QKD has high security bit rate and other advantages, which, however, are slightly insufficient in secure transmission distance. In addition, the application of quantum catalysis has significantly improved the performance of Gaussian modulated (GM) CVQKD, especially in secure transmission distance. Recently, the application of quantum catalysis has significantly improved the performance of GM-CVQKD. However, whether it can be used to improve the performance of discrete modulated (DM) CVQKD protocol is still ambiguous. Therefore, a scheme of DM CVQKD protocol based on quantum catalysis is proposed in this paper to further improve the performance of the proposed protocol in terms of secure key rate, secure transmission distance and maximum tolerable noise. Our results show that under the same parameters, when the transmittance &lt;i&gt;T&lt;/i&gt; introduced by quantum catalysis is optimized, the proposed scheme can effectively further improve the performance of QKD system compared with the original four-state modulation CVQKD scheme. In particular, when the tolerable excess noise is 0.002, the use of quantum catalysis can break the safe communication distance of 300 km with a key rate of 10&lt;sup&gt;–8&lt;/sup&gt; bits/pulse. However, if this noise is too large, the improvement in the effect of quantum catalysis on protocol performance will be restrained. In addition, in order to highlight the advantages of the use of quantum catalysis, the ultimate limit PLOB (Pirandola-Laurenza-Ottaviani-Banchi) bound of point-to-point quantum communication is given in this paper. The simulation results indicate that although neither the original scheme nor the proposed scheme can break the bound, compared with the former, the latter can be close to the boundary in long-distance transmission. These results provide theoretical basis for achieving the ultimate goal of global quantum security communication.
LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization

Reinforcement learning (RL) can enable task-oriented dialogue systems to steer the conversation towards successful task completion.In an end-to-end setting, a response can be constructed in a word-level sequential decision making process with the entire system vocabulary as action space.Policies trained in such a fashion do not require expert-defined action spaces, but they have to deal with large action spaces and long trajectories, making RL impractical.Using the latent space of a variational model as action space alleviates this problem.However, current approaches use an uninformed prior for training and optimize the latent distribution solely on the context.It is therefore unclear whether the latent representation truly encodes the characteristics of different actions.In this paper, we explore three ways of leveraging an auxiliary task to shape the latent variable distribution: via pre-training, to obtain an informed prior, and via multitask learning.We choose response auto-encoding as the auxiliary task, as this captures the generative factors of dialogue responses while requiring low computational cost and neither additional data nor labels.Our approach yields a more action-characterized latent representations which support end-to-end dialogue policy optimization and achieves state-of-the-art success rates.These results warrant a more wide-spread use of RL in end-to-end dialogue models.
Multi-criterial Optimization Problem for Fuzzy If-Then Rules

Fuzzy IF-Then rules as universal approximator very frequently are used for linguistic modelling of real-world complex system. This type of models is characterized mainly two contradictory requirements: Interpretability and accuracy. Unfortunately, researchers in existing works usually focused on the accuracy of the models without paying attention to their interpretability. However, very important problem in developing of fuzzy If-Then rule base is to achieve the best trade-off between interpretability and accuracy. In this paper, we will consider the problem of investigation of trade-off between complexity and semantic-based interpretability and accuracy of fuzzy If-Then knowledge base. The proposed approach is related with differential evolutionary optimization-based multicriteria decision-making problem. Numerical example related to the multi-criterial optimization problem is given.
On Position Embeddings in BERT

Various Position Embeddings (PEs) have been proposed in Transformer based architectures~(e.g. BERT) to model word order. These are empirically-driven and perform well, but no formal framework exists to systematically study them. To address this, we present three properties of PEs that capture word distance in vector space: translation invariance, monotonicity, and symmetry. These properties formally capture the behaviour of PEs and allow us to reinterpret sinusoidal PEs in a principled way. Moreover, we propose a new probing test (called `identical word probing') and mathematical indicators to quantitatively detect the general attention patterns with respect to the above properties. An empirical evaluation of seven PEs (and their combinations) for classification (GLUE) and span prediction (SQuAD) shows that: (1) both classification and span prediction benefit from translation invariance and local monotonicity, while symmetry slightly decreases performance; (2) The fully-learnable absolute PE performs better in classification, while relative PEs perform better in span prediction. We contribute the first formal and quantitative analysis of desiderata for PEs, and a principled discussion about their correlation to the performance of typical downstream tasks.
A BERT-BiGRU-CRF Model for Entity Recognition of Chinese Electronic Medical Records

Because of difficulty processing the electronic medical record data of patients with cerebrovascular disease, there is little mature recognition technology capable of identifying the named entity of cerebrovascular disease. Excellent research results have been achieved in the field of named entity recognition (NER), but there are several problems in the pre processing of Chinese named entities that have multiple meanings, of which neglecting the combination of contextual information is one. Therefore, to extract five categories of key entity information for diseases, symptoms, body parts, medical examinations, and treatment in electronic medical records, this paper proposes the use of a BERT-BiGRU-CRF named entity recognition method, which is applied to the field of cerebrovascular diseases. The BERT layer first converts the electronic medical record text into a low-dimensional vector, then uses this vector as the input to the BiGRU layer to capture contextual features, and finally uses conditional random fields (CRFs) to capture the dependency between adjacent tags. The experimental results show that the F1 score of the model reaches 90.38%.
BC-BLPM: A multi-level security access control model based on blockchain technology

Traditional multi-level security (MLS) systems have the defect of centralizing authorized facilities, which is difficult to meet the security requirements of modern distributed peer-to-peer network architecture. Blockchain is widely used in the field of access control with its decentralization, traceability and non-defective modification. Combining the blockchain technology and the Bell-LaPadula model, we propose a new access control model, named BC-BLPM, for MLS environment. The “multi-chain” blockchain architecture is used for dividing resources into isolated access domains, providing a fine-grained data protection mechanism. The access control policies are implemented by smart contracts deployed in each access domain, so that the side chains of different access domains storage access records from outside and maintain the integrity of the records. Finally, we implement the BC-BLPM prototype system using the Hyperledger Fabric. The experimental and analyt-ical results show that the model can adapt well to the needs of multi-level security environment, and it has the feasibility of application in actual scenarios.
The Single Diode Model for PV Characteristics Using Electrical Circuit

A new algorithm of Two-Point Bracketing method based on Newton's algorithm has been developed. The order of convergence of the proposed algorithm is six. Starting with a suitably chosen x_0, this technique generates a sequence of iterations converging to the root. It requires evaluations of only one first order derivatives per iteration. The proposed method is comparable with NRM and does not need an evaluation derivative at the second order of the given function as required in other numerical methods. The efficiency of the technique is tested on a nonlinear equation of a model of a PV cell. It is observed that the proposed method needs lesser number of iterations than NRM.
A nondestructive Bell-state measurement on two distant atomic qubits

Abstract One of the most fascinating aspects of quantum networks is their capability to distribute entanglement as a nonlocal communication resource 1 . In a first step, this requires network-ready devices that can generate and store entangled states 2 . Another crucial step, however, is to develop measurement techniques that allow for entanglement detection. Demonstrations for different platforms 3–13 suffer from being not complete, destructive or local. Here, we demonstrate a complete and nondestructive measurement scheme 14–16 that always projects any initial state of two spatially separated network nodes onto a maximally entangled state. Each node consists of an atom trapped inside an optical resonator from which two photons are successively reflected. Polarization measurements on the photons discriminate between the four maximally entangled states. Remarkably, such states are not destroyed by our measurement. In the future, our technique might serve to probe the decay of entanglement and to stabilize it against dephasing via repeated measurements 17,18 .
A Study on Tiredness Assessment by Using Eye Blink Detection

In this paper, the loss of attention of automotive drivers is studied by using eye blink detection. Facial landmark detection for detecting eye is explored. Afterward, eye blink is detected using Eye Aspect Ratio. By comparing the time of eye closure to a particular period, the driver’s tiredness is decided. The total number of eye blinks in a minute is counted to detect drowsiness. Calculation of total eye blinks in a minute for the driver is done, then compared it with a known standard value. If any of the above conditions fulfills, the system decides the driver is unconscious. A total of 120 samples were taken by placing the light source front, back, and side. There were 40 samples for each position of the light source. The maximum error rate occurred when the light source was placed back with a 15% error rate. The best scenario was 7.5% error rate where the light source was placed front side. The eye blinking process gave an average error of 11.67% depending on the various position of the light source. Another 120 samples were taken at a different time of the day for calculating total eye blink in a minute. The maximum number of blinks was in the morning with an average blink rate of 5.78 per minute, and the lowest number of blink rate was in midnight with 3.33% blink rate. The system performed satisfactorily and achieved the eye blink pattern with 92.7% accuracy.
Annotating Online Misogyny

Online misogyny, a category of online abusive language, has serious and harmful social consequences.Automatic detection of misogynistic language online, while imperative, poses complicated challenges to both data gathering, data annotation, and bias mitigation, as this type of data is linguistically complex and diverse.This paper makes three contributions in this area: Firstly, we describe the detailed design of our iterative annotation process and codebook.Secondly, we present a comprehensive taxonomy of labels for annotating misogyny in natural written language, and finally, we introduce a high-quality dataset of annotated posts sampled from social media posts.
5G based Blockchain network for authentic and ethical keyword search engine

The evolution of 4G telecommunication propagated various resource-crunched clients to experience rate-effective resources at ease. However, it extends its underlying centralised architecture, which arouses various challenges correlated with network data availability, network information protection, and operational infrastructure charges. With the recent revolution of telecommunication, 5G networks promised to provide credible schemes like the high quality of service, ultra-low latency, and much security over the pre-existing architecture. However, the deployment of end-to-end 5G network cutting-edge systems in the present heterogeneous world limits its core idea of extensive data privacy, native interoperability, risk-free interference, and radio spectrum sharing. Perhaps, to achieve its true capability, improved versions of blockchain technology could be aligned to strengthen various real-time complex applications at a flourishing rate. One of the multiplexed real-time enterprise applications is a keyword search engine where the integrity of user data files and keyword searches are bound to come under cyber hackers. On the one hand, it was found that when a 5G-based blockchain emulated network gets deployed with intact encryption techniques, the entire system facilitates to give reliable, efficient, and risk-free keyword search over variegated 5G network data and its complex computational calculations. Consequently, the use of blockchain-based decentralised cloud orchestration scheme at various levels enabled the architecture to remain incorruptible and protects all the confidential files and keywords in a fully controlled file access environment. The results of the simulation kernel shows that proposed architecture which, when combined with blockchain-based decentralised cloud orchestration network system, justify all the essential characteristics and effectuates the optimal use of 5G network sharing by each network entity.
Multi-SpectroGAN: High-Diversity and High-Fidelity Spectrogram Generation with Adversarial Style Combination for Speech Synthesis

While generative adversarial networks (GANs) based neural text-to-speech (TTS) systems have shown significant improvement in neural speech synthesis, there is no TTS system to learn to synthesize speech from text sequences with only adversarial feedback. Because adversarial feedback alone is not sufficient to train the generator, current models still require the reconstruction loss compared with the ground-truth and the generated mel-spectrogram directly. In this paper, we present Multi-SpectroGAN (MSG), which can train the multi-speaker model with only the adversarial feedback by conditioning a self-supervised hidden representation of the generator to a conditional discriminator. This leads to better guidance for generator training. Moreover, we also propose adversarial style combination (ASC) for better generalization in the unseen speaking style and transcript, which can learn latent representations of the combined style embedding from multiple mel-spectrograms. Trained with ASC and feature matching, the MSG synthesizes a high-diversity mel-spectrogram by controlling and mixing the individual speaking styles (e.g., duration, pitch, and energy). The result shows that the MSG synthesizes a high-fidelity mel-spectrogram, which has almost the same naturalness MOS score as the ground-truth mel-spectrogram.
Code Summarization with Structure-induced Transformer

Code summarization (CS) is becoming a promising area in recent language understanding, which aims to generate sensible human language automatically for programming language in the format of source code, serving in the most convenience of programmer developing.It is well known that programming languages are highly structured.Thus previous works attempt to apply structurebased traversal (SBT) or non-sequential models like Tree-LSTM and graph neural network (GNN) to learn structural program semantics.However, it is surprising that incorporating SBT into advanced encoder like Transformer instead of LSTM has been shown no performance gain, which lets GNN become the only rest means modeling such necessary structural clue in source code.To release such inconvenience, we propose structureinduced Transformer, which encodes sequential code inputs with multi-view structural clues in terms of a newly-proposed structureinduced self-attention mechanism.Extensive experiments show that our proposed structureinduced Transformer helps achieve new stateof-the-art results on benchmarks.
Trident‐YOLO: Improving the precision and speed of mobile device object detection

This paper introduce an efficient object detection network named Trident-You Only Look Once (YOLO), which is designed for mobile devices with limited computing power. The new architecture is improved based on YOLO v4-tiny. The authors redesign the network structure and propose a trident feature pyramid network (Trident-FPN), which can improve the precision and recall of lightweight object detection. Specifically, Trident-FPN increases the computational complexity by only a small amount of floating point operations per second (FLOPs) and obtains a multi-scale feature map of the model, which significantly lightweight object detection performance. To enlarge the receptive field of the network with the fewest FLOPs, this paper redesign the receptive field block (RFB) and spatial pyramid pooling (SPP) layer and propose tinier cross-stage partial RFBs and smaller cross-stage partial SPPs. This paper present extensive experiments, and Trident-YOLO shows strong performance compared to that of other popular models on the PASCAL VOC and MS COCO. On the MS COCO and PASCAL VOC 2007 test sets, the mean average precision (mAP) of Trident-YOLO improved by 4.5% and 5.0%, respectively. Trident-YOLO also reduce the network size by more than 54.4% compared to YOLO v4-tiny. With a 23.7% FLOP reduction, the FPS is improved by 1.9 on an Nvidia Jetson Xavier NX.
Gender and Age Classification Enabled Blockschain Security Mechanism for Assisting Mobile Application

This research paper focus on a Machine learning model named Face Lock Algorithm with Gender and Age Classifier which will detect the face of the user using a face classifier called Haar Cascade Frontal Face classifier and will also provide an extra layer of security to mobile applications and websites by unlocking them only when the algorithm confirms the person as actual user. For this, it will first take the training data as an input from the camera of the device, then train the model based on the input and detect the face according to the training. Training will be done using LBPH (Local Binary Pattern Histogram) model which uses the concept of sliding window and applies the LBP operation on the image, which includes calculating pixel values of the image, finding threshold and then convert the image into binary. By doing so, all the important features of the image are extracted. Then it makes histograms for face recognition. In addition to this, after detecting the person’s face, this algorithm will also classify the gender and gives a prediction about the age of the person. The primary advantage of this model is it makes use of a library called OpenCV for image capturing, which makes the implementation very simple as compared to the one’s that use neural networks. Moreover, this algorithm can be used for a variety of purposes such as face lock gender and age classifier.
Edge-Aware Multi-Level Interactive Network for Salient Object Detection of Strip Steel Surface Defects

The performance of the salient object detection of strip surface defects has been promoted largely by deep learning based models. However, due to the complexity of strip surface defects, the existing models perform poorly in the challenging scenes such as noise disturbance, and low contrast between defect regions and background. Meanwhile, the detection results of existing models often suffer from coarse boundary details. Therefore, we propose a novel saliency model, namely an Edge-aware Multi-level Interactive Network, to detect the defects from the strip steel surface. Concretely, our model adopts the U-shape architecture where the two crucial points are the interactive feature integration and the edge-guided saliency fusion. Firstly, except the skip connection that combines the same stage of encoder and decoder, we deploy another connection, where the features from adjacent levels of encoder are transferred to the same stage of decoder. By this way, we are able to provide an effective fusion of multi-level deep features, yielding a well depiction for defects. Secondly, to give well-defined boundaries for prediction results, we add the edge extraction branch after each decoder block, where the progressive feature aggregation endows the edge with precise details and complete object cues. Meanwhile, together with the edge extraction branches, we deploy the saliency prediction branch at each decoder stage. After that, coupled with the fine edge information, we fuse all outputs of saliency prediction branches into the final saliency map, where the edge cue steers the saliency result to pay more attention to the boundary details. Following this way, we can provide a high-quality saliency map which can accurately locate and segment the defects. Extensive experiments are performed on the public dataset, and the results prove the effectiveness and robustness of our model which consistently outperforms the state-of-the-art models.
Construction of S-Boxes Using Different Maps Over Elliptic Curves for Image Encryption

In this article, we present an encryption technique that can encrypt any digital data. The proposed scheme basically depends on a substitution-permutation network. Two separate bijective maps are used in the proposed algorithm. Firstly, the substitution boxes are used to perform the substitution process. This technique uses elliptic curves (ECs) to create several substitution boxes with good cryptographic properties. The generated substitution boxes are utilized to replace the arrange data that produces the most uncertainty in the plain image data. Further, we used the permutation process to generate strong randomness in the proposed technique. For the evaluation of the stability of the proposed algorithm; nonlinearity, linear approximation probability, bit independence criterion, strict avalanche criterion and differential approximation probability were performed on the substitution boxes. Differential attacks ( Number of Changing Pixel Rate (NPCR), Uniform Average Change Intensity (UACI)) and statistical tests ( Entropy, Correlation) were performed on the encrypted images to check their resistance against different attacks. The execution and image analysis reveals that the proposed scheme attains good encryption results while requiring minimal computing ability and has efficient potential in real-time image encryption applications.
Improved U-Net based insulator image segmentation method based on attention mechanism

To realize the accurate identification and segmentation of the insulator string in the complex background image with diverse appearance and obscuration, this paper proposes an insulator segmentation method based on improved U-Net. The algorithm embeds the attention mechanism ECA-Net (Efficient Channel Attention Neural Networks) in the coding stage of U-Net to improve the model’s ability to extract semantic features, thereby improving the accuracy of insulator detection. Experimental results show that the average overlap IOU of the proposed method is 96.8%, which can more accurately segment different types of insulators in complex backgrounds.