Reviewerly/exHarmony huggingface dataset:
Accounting Data Encryption Processing Based on Data Encryption Standard Algorithm

With the application of computer and network technology in the field of accounting, the development of accounting informationization is an inevitable trend, and the construction of accounting statement data into the data warehouse will be the basis of intelligent decision-making. The complexity of industry accounting statements and the arbitrariness and diversity of users’ needs for obtaining information using statements limit the development, popularization, and application of industry accounting statements. As a block encryption algorithm, the Data Encryption Standard (DES) algorithm uses 64-bit packet data for encryption and decryption. Each eighth bit of the key is used as a parity bit; that is, the actual key length is 56 bits. Encryption and decryption use the same algorithm structure, but the order in which the subkeys are used is reversed. Under the control of the subkey, inputting 64-bit plaintext can produce 64-bit ciphertext output; otherwise, inputting 64-bit ciphertext can produce 64-bit plaintext output. The confidentiality of the DES algorithm depends on the key, and only a very small number of keys are considered weak keys, which can be easily avoided in practical applications. The 3DES algorithm is a cascade of the DES algorithm, and its encryption process is based on the DES algorithm principle. This article explains the encryption process of the DES algorithm and introduces the composition of the 3DES algorithm. The experimental results show that the 3DES encryption algorithm still has a better encryption effect and “avalanche effect” than before the improvement. In addition, for the 3DES algorithm, its encryption efficiency has not been greatly affected. The 3DES encryption algorithm achieves one encryption process at a time to some extent, can effectively resist exhaustive search attacks, and enhance the security of the DES algorithm.
MetAdapt: Meta-learned task-adaptive architecture for few-shot classification

• We propose a feed-forward model rewiring its architecture according to few-shot task. • We propose meta-learned NAS-cell controllers for feed-forward predictive rewiring. • We show performance preserving way of pruning feed-forward-adaptive NAS-cells. • Using the proposed approach, we obtain strong results on two popular FSC benchmarks. Recently, great progress has been made in the field of Few-Shot Learning (FSL). While many different methods have been proposed, one of the key factors leading to higher FSL performance is surprisingly simple. It is the backbone network architecture used to embed the images of the few-shot tasks. While first works on FSL resorted to small architectures with just a few convolution layers, recent works show that large architectures pre-trained on the training portion of FSL datasets produce strong features that are more easily transferable to novel few-shot tasks, thus attaining significant gains to methods using them. Despite these observations, little to no work has been done towards finding the right backbone for FSL. In this paper we propose MetAdapt that not only meta-searches for an optimized architecture for FSL using Network Architecture Search (NAS), but also results in a model that can adaptively ‘re-wire’ itself predicting the better architecture for a given novel few-shot task. Using the proposed approach we observe strong results on two popular few-shot benchmarks: mini ImageNet and FC100.
Security analysis of a park-level agricultural energy Internet considering agrometeorology and energy meteorology

China's industrial manufacturing industry is well developed, but its agriculture is primitive. The only way to solve this problem is to improve through modern agriculture. The cross integration of new energy development and modern agriculture is becoming more and more critical. However, the research on the interaction between the meteorological disaster of facility agriculture and the power supply security of the integrated energy supply system has not formed a systematic theoretical system, which challenges the collaborative security of the facility agriculture and energy system. In this paper, energy meteorology and agrometeorology are considered and modeled, and the static security of a park-level agricultural energy network is simulated and analyzed under different weather conditions.
Disentangled Code Representation Learning for Multiple Programming Languages

Developing effective distributed representations of source code is fundamental yet challenging for many software engineering tasks such as code clone detection, code search, code translation and transformation.However, current code embedding approaches that represent the semantic and syntax of code in a mixed way are less interpretable and the resulting embedding can not be easily generalized across programming languages.In this paper, we propose a disentangled code representation learning approach to separate the semantic from the syntax of source code under a multi-programming-language setting, obtaining better interpretability and generalizability.Specially, we design three losses dedicated to the characteristics of source code to enforce the disentanglement effectively.We conduct comprehensive experiments on a real-world dataset composed of programming exercises implemented by multiple solutions that are semantically identical but grammatically distinguished.The experimental results validate the superiority of our proposed disentangled code representation, compared to several baselines, across three types of downstream tasks, i.e., code clone detection, code translation, and code-to-code search.
Using an Optimized Learning Vector Quantization- (LVQ-) Based Neural Network in Accounting Fraud Recognition

With the continuous development and wide application of artificial intelligence technology, artificial neural network technology has begun to be used in the field of fraud identification. Among them, learning vector quantization (LVQ) neural network is the most widely used in the field of fraud identification, and the fraud identification rate is relatively high. In this context, this paper explores this neural network technology in depth, uses the same fraud sample to test the fraud recognition rate of these two models, and proposes an optimized LVQ-based combined neural network fraud risk recognition model on this basis. This paper selects 550 listed companies that have committed fraud from 2015 to 2019 as the fraud samples, determines 550 nonfraud matching sample companies in accordance with the Beasley principle one-to-one, and uses this as the research sample. The fraud risk identification indicators with better identification effects combed out according to the literature were used as the initial indicator system. After the collinearity problem was eliminated through the paired sample T test and principal component analysis, the five indicators with the best identification effects were finally selected. Finally, based on the above theoretical analysis and empirical research summarizing the full text, it analyzes the shortcomings of this research and puts forward prospects for the future development of fraud risk identification models.
Unsupervised azimuth estimation of solar arrays in low-resolution satellite imagery through semantic segmentation and Hough transform

This paper explains the use of a convolutional neural network (CNN) to segment solar panels in a satellite image containing solar arrays, and extract associated metadata from the arrays. A novel unsupervised technique is introduced to estimate the azimuth of each individual solar panel from the predicted mask of the convolutional neural network. This pipeline was developed with the aim of extracting necessary metadata for a solar installation, using only a set of latitude–longitude coordinates. Azimuth prediction results for 669 individual solar installations associated with 387 sites located across the United States are provided. A mean average error and median average error of 21.65 degrees and 1.0 degrees were obtained, respectively, when predicting the azimuth of the solar fleet data set, with about 80% of the results within an error of zero degrees of the ground truth azimuth value and about 85% within an error of 25 degrees. The predicted azimuth was then used to estimate the energy conversion of the solar arrays. Results show a 90.9 and 90.6 R-squared value for estimating alternating current (AC) and direct current (DC) energy, respectively, and a mean absolute percentage error (MAPE) of 1.70% in estimating the alternating current (AC) energy using the fully automated algorithm.
A Flexibly Assembled and Maneuverable Reconfigurable Modular Multirotor Aerial Vehicle

This article introduces a novel modular reconfigurable multirotor aerial vehicle that can flexibly configure the amount and position of rotors. According to different actual task requirements, such aerial vehicles can be flexibly assembled into bicopters, tricopters, quadcopters, and omnidirectional aerial vehicles. These multirotor aerial vehicles can independently control their position and attitude and track arbitrary trajectories in a 3-D space. Such maneuverability owes to a double-gimbal thrust module (DGTM), which can independently regulate the direction and magnitude of propeller thrust. The reconfigurable multirotor aerial vehicle’s dynamic model is established based on the analysis of the rotor distribution’s vector thrust and topological structure. Then, a hierarchical cooperative control framework is applied for the pose tracking of the vehicle. In the top level, a Lyapunov-based pose controller is proposed to calculate the desired force and torque. In the bottom level, a control allocation that maps the desired force and torque to desired input of the DGTM is used. An experimental prototype is built to verify the feasibility of the design scheme.
Experimental demonstration of robustness of Gaussian quantum coherence

Besides quantum entanglement and steering, quantum coherence has also been identified as a useful quantum resource in quantum information. It is important to investigate the evolution of quantum coherence in practical quantum channels. In this paper, we experimentally quantify the quantum coherence of a squeezed state and a Gaussian Einstein–Podolsky–Rosen (EPR) entangled state transmitted in Gaussian thermal noise channel. By reconstructing the covariance matrix of the transmitted states, quantum coherence of these Gaussian states is quantified by calculating the relative entropy. We show that quantum coherence of the squeezed state and the Gaussian EPR entangled state is robust against loss and noise in a quantum channel, which is different from the properties of squeezing and Gaussian entanglement. Our experimental results pave the way for application of Gaussian quantum coherence in lossy and noisy environments.
End-to-End Hierarchical Reinforcement Learning With Integrated Subgoal Discovery

Hierarchical reinforcement learning (HRL) is a promising approach to perform long-horizon goal-reaching tasks by decomposing the goals into subgoals. In a holistic HRL paradigm, an agent must autonomously discover such subgoals and also learn a hierarchy of policies that uses them to reach the goals. Recently introduced end-to-end HRL methods accomplish this by using the higher-level policy in the hierarchy to directly search the useful subgoals in a continuous subgoal space. However, learning such a policy may be challenging when the subgoal space is large. We propose integrated discovery of salient subgoals (LIDOSS), an end-to-end HRL method with an integrated subgoal discovery heuristic that reduces the search space of the higher-level policy, by explicitly focusing on the subgoals that have a greater probability of occurrence on various state-transition trajectories leading to the goal. We evaluate LIDOSS on a set of continuous control tasks in the MuJoCo domain against hierarchical actor critic (HAC), a state-of-the-art end-to-end HRL method. The results show that LIDOSS attains better goal achievement rates than HAC in most of the tasks.
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus

We introduce the largest transcribed Arabic speech corpus, QASR 1 , collected from the broadcast domain.This multi-dialect speech dataset contains 2, 000 hours of speech sampled at 16kHz crawled from Aljazeera news channel.The dataset is released with lightly supervised transcriptions, aligned with the audio segments.Unlike previous datasets, QASR contains linguistically motivated segmentation, punctuation, speaker information among others.QASR is suitable for training and evaluating speech recognition systems, acoustics-and/or linguistics-based Arabic dialect identification, punctuation restoration, speaker identification, speaker linking, and potentially other NLP modules for spoken data.In addition to QASR transcription, we release a dataset of 130M words to aid in designing and training a better language model.We show that end-to-end automatic speech recognition trained on QASR reports a competitive word error rate compared to the previous MGB-2 corpus.We report baseline results for downstream natural language processing tasks such as named entity recognition using speech transcript.We also report the first baseline for Arabic punctuation restoration.We make the corpus available for the research community.
Generating Relevant and Coherent Dialogue Responses using Self-Separated Conditional Variational AutoEncoders

Conditional Variational AutoEncoder (CVAE) effectively increases the diversity and informativeness of responses in open-ended dialogue generation tasks through enriching the context vector with sampled latent variables.However, due to the inherent one-to-many and many-toone phenomena in human dialogues, the sampled latent variables may not correctly reflect the contexts' semantics, leading to irrelevant and incoherent generated responses.To resolve this problem, we propose Self-separated Conditional Variational AutoEncoder (abbreviated as SepaCVAE) that introduces group information to regularize the latent variables, which enhances CVAE by improving the responses' relevance and coherence while maintaining their diversity and informativeness.SepaCVAE actively divides the input data into groups, and then widens the absolute difference between data pairs from distinct groups, while narrowing the relative distance between data pairs in the same group.Empirical results from automatic evaluation and detailed analysis demonstrate that SepaC-VAE can significantly boost responses in wellestablished open-domain dialogue datasets.
Frustratingly Simple Few-Shot Slot Tagging

We propose a simple and effective few-shot model for slot tagging.Recent work shows that it is promising to extend standard fewshot classification methods to sequence labeling with CRF-specific augmentations.Such methods show strengths in encoding slot name semantics and slot dependencies.However, we find these strengths can be obtained by a much simpler method, which casts slot tagging into machine reading comprehension (MRC).We fine-tune a standard BERT-based MRC model with a mixture of source domain and (few-shot) target domain data.Such simple method outperforms state-of-the-art methods by a large margin on the SNIPS dataset.
A blockchain-based credible and secure education experience data management scheme supporting for searchable encryption

With the in-depth application of new technologies such as big data in education fields, the storage and sharing model of student education records data still faces many challenges in terms of privacy protection and efficient transmission. In this paper, we propose a data security storage and sharing scheme based on consortium blockchain, which is a credible search scheme without verification. In our scheme, the implementation of data security storage is using the blockchain and storage server together. In detail, the smart contract provides protection for data keywords, the storage server stores data after data masking, and the blockchain ensures the traceability of query transactions. The need for precise privacy data is achieved by constructing a dictionary. Cryptographic techniques such as AES and RSA are used for encrypted storage of data, keywords, and digital signatures. Security analysis and performance evaluation shows that the availability, high efficiency, and privacy-preserving can be achieved. Meanwhile, this scheme has better robustness compared to other educational records data sharing models.
Tightening Robustness Verification of Convolutional Neural Networks with Fine-Grained Linear Approximation

The robustness of neural networks can be quantitatively indicated by a lower bound within which any perturbation does not alter the original input’s classification result. A certified lower bound is also a criterion to evaluate the performance of robustness verification approaches. In this paper, we present a tighter linear approximation approach for the robustness verification of Convolutional Neural Networks (CNNs). By the tighter approximation, we can tighten the robustness verification of CNNs, i.e., proving they are robust within a larger 10 perturbation distance. Furthermore, our approach is applicable to general sigmoid-like activation functions. We implement DeepCert, the resulting verification toolkit. We evaluate it with open-source benchmarks, including LeNet and the models trained on MNIST and CIFAR. Experimental results show that DeepCert outperforms other state-of-the-art robustness verification tools with at most 286.28% improvement to the certified lower bound and 1566.76 times speedup for the same neural networks.
Employing Argumentation Knowledge Graphs for Neural Argument Generation

Generating high-quality arguments, while being challenging, may benefit a wide range of downstream applications, such as writing assistants and argument search engines.Motivated by the effectiveness of utilizing knowledge graphs for supporting general text generation tasks, this paper investigates the usage of argumentation-related knowledge graphs to control the generation of arguments.In particular, we construct and populate three knowledge graphs, employing several compositions of them to encode various knowledge into texts of debate portals and relevant paragraphs from Wikipedia.Then, the texts with the encoded knowledge are used to fine-tune a pre-trained text generation model, GPT-2.We evaluate the newly created arguments manually and automatically, based on several dimensions important in argumentative contexts, including argumentativeness and plausibility.The results demonstrate the positive impact of encoding the graphs' knowledge into debate portal texts for generating arguments with superior quality than those generated without knowledge.
Detection, Attribution and Localization of GAN Generated Images

Recent advances in Generative Adversarial Networks (GANs) have led to the creation of realistic-looking digital images that pose a major challenge to their detection by humans or computers. GANs are used in a wide range of tasks, from modifying small attributes of an image (StarGAN [14]), transferring attributes between image pairs (CycleGAN [91]), as well as generating entirely new images (ProGAN [36], StyleGAN [37], SPADE/GauGAN [64]). In this paper, we propose a novel approach to detect, attribute and localize GAN generated images that combines image features with deep learning methods. For every image, co-occurrence matrices are computed on neighborhood pixels of RGB channels in different directions (horizontal, vertical and diagonal). A deep learning network is then trained on these features to detect, attribute and localize these GAN generated/manipulated images. A large scale evaluation of our approach on 5 GAN datasets comprising over 2.76 million images (ProGAN, StarGAN, CycleGAN, StyleGAN and SPADE/GauGAN) shows promising results in detecting GAN generated images.
Gravitational Search Algorithm

Today, many metaheuristics algorithms have been developed are inspired by the physical phenomena or behaviors of natural creatures that are very effective in solving complex engineering optimization problems. These algorithms seek to find optimal solutions in various engineering problems such as machine learning, image processing, pattern matching, decision making, and so on. These algorithms can be implemented in both single-objective and multi-objective optimization problems. Among the most famous techniques in this category include the ant colony optimization (ACO) algorithm, which is designed based on the navigation of ants; particle swarm optimization (PSO), which is based on the movement of organisms in a bird flock; and gravitational search algorithm (GSA). GSA is one of the most popular metaheuristics methods. It is inspired by the law of gravity and motion and creating the appropriate balance between exploration and exploitation capabilities. In this chapter, we are going to introduce the basic GSA and review the newer versions. First, we will introduce the original GSA that is proposed for continuous problems. Then we will discuss other versions of GSA for various optimization problems and analyze the convergence properties of this algorithm. We will also review the GSA in various engineering applications.
Recommendation System Based on Generative Adversarial Network with Graph Convolutional Layers

A graph convolutional generative adversarial network (GCGAN) is proposed to provide recommendations for new users or items. To maintain scalability, the discriminator was improved to capture the latent features of users and items, using graph convolution from a minibatch-sized bipartite graph. In the experiment using MovieLens, it was confirmed that the proposed GCGAN had better performance than the conventional CFGAN, when MovieLens 1M was employed with sufficient data. The proposed method is characterized in such a manner that it can learn domain information of both, users and items, and it does not require to relearn a model for a new node. Further, it can be developed for any service having such conditions, in the information recommendation field.
Recent Neural Methods on Dialogue State Tracking for Task-Oriented Dialogue Systems: A Survey

This paper aims at providing a comprehensive overview of recent developments in dialogue state tracking (DST) for task-oriented conversational systems. We introduce the task, the main datasets that have been exploited as well as their evaluation metrics, and we analyze several proposed approaches. We distinguish between static ontology DST models, which predict a fixed set of dialogue states, and dynamic ontology models, which can predict dialogue states even when the ontology changes. We also discuss the model’s ability to track either single or multiple domains and to scale to new domains, both in terms of knowledge transfer and zero-shot learning. We cover a period from 2013 to 2020, showing a significant increase of multiple domain methods, most of them utilizing pre-trained language models.