Reviewerly/exHarmony huggingface dataset:
DPC: An amalgamated approach for drone package classification system

The logistics and e-commerce sectors have always incorporated parcel delivery and packing as their integral part. With the high amount of demand and supply in the domain of e-commerce, there is a high demand for drone-based delivery systems through which a certain amount of parcel packages can be delivered using drones. To assist the aforementioned domain, the proposed work aims to develop a drone package classification (DPC) system for the classification of packages according to their weight and the type of drone that can be employed to deliver the particular class of packages. The complete experiment has been executed using DL based hybrid approach consisting of convolutional neural network (CNN) and long short-term memory (LSTM) technique on a self-developed package5k dataset resulting in an overall accuracy of 93.82%. The entire investigation and suggested DL-based hybrid strategy have shown to be a successful, efficient, and sustainable method for the identification and classification of drone packages to create a successful and sustainable DPC system.
The Synthetic Data Application in the UAV Recognition Systems Development

The increasing popularity and accessibility of un-manned aerial vehicles (UAVs) presents both opportunities and challenges. On the one hand, UAVs has a wide range of civilian, industrial, and military applications. On the other hand, the popularity of UAVs can lead to illegal or dangerous usage. Thus, the development of UAV recognition systems is crucial for ensuring safety and security. However, collecting and labeling large amounts of real-world data for training these systems can be time-consuming and labor-intensive.In this study, we propose a methodology, which can help to accelerate the development of new UAV recognition systems. This work demonstrates the effectiveness of training a neural network using a combination of real-world and synthetic data that can achieve similar performance to a network trained on real-world data only.
Application of Hopfield Neural Network Algorithm in Mathematical Modeling

Neural network model is an important research direction in mathematical modeling, also known as artificial neural net, which is composed of artificial neural network, brain and other systems, and its main research object is pattern recognition, which has a wide range of applications in mathematical modeling. It can simulate the human brain’s information processing process by learning through its structure and function, and has strong self-adaptive ability. It can simulate various patterns that may appear in the human brain’s information processing process, and can make predictions and decisions. The neural network model can be a good solution to the traditional mathematical modeling methods, and also has the characteristics of better fault tolerance and generalization ability, which can easily transform the mathematical problems into computer solutions, and can make the model can be solved in a limited time by processing the data.
Mining and Utilization of English Learning Resources Using the Python NLTK

In the age of big data, language learning increasingly rely on a large number of text-based corpora. Massive and authentic texts provide an essential English language learning context for English learners in China. Python NLTK (Natural Language Toolkit) is helpful for language learners for its large number of labeled English texts and its support for loading additional texts. These texts are so massive and authentic that they provide a language corpus for English learners. Furthermore, NLTK can extract useful language features with simple set of commands. A quick and systematic analysis of language features make language learning more efficient. This paper tries to describe the basic operation methods of Python NLTK in the mining and utilization of English learning resources, at lexical level, syntactic level and textual level. The research is not only beneficial to individual language learners, but also conducive to the development of corpus research towards systematization and intelligence.
AI-Enable Generating Human Faces using Deep Learning

Lately, sensible Image processing using deep neural networks has become a fervently discussed issue in machine learning and computer vision. Image can be made at the pixel level by learning from a gigantic variety of pictures. Learning to make splendid movement pictures from highdifference draws is not only a captivating investigation issue yet also a reasonable application in innovative delight. In this research, we research the sketch-to-picture mix issue by using prohibitive generative poorly arranged networks. The model can normally deliver reasonable shadings for a sketch. The new model is not only prepared for painting hand-drawn sketches with real tones, yet also allows customers to exhibit supported tones. Test results on two sketch datasets show that the autopainter performs better contrasted with existing picture-topicture methodologies. With creating interest in the development of film, the interest in building a computerized structure to change over the authentic video into action is higher than at some other time. The edge-by-diagram modification of the action age measure is costly and dreary. To help with moving quickly and with no issue in a robotized collaboration we proposed a generative model that changes over genuine pictures into contrasting energy pictures without losing critical nuances of the source picture. We used an assortment of the generative hostile association as a fundamental plan with the custom incident ability to ensure the substance of the source picture, which changed over to an exuberance image.
A new Polynomial based Symmetric Key Algorithm using Polynomial Interpolation Methods

The main attributes of a security framework are Confidentiality, Authentication, and Data integrity. There are two main standards of cryptography algorithms categorized as private or symmetric and public or asymmetric algorithms, symmetric algorithms use one secret key to encrypt and decrypt payloads, the list includes Data Encryption Standard (DES), Advanced Encryption Standard (AES), RC5, Blowfish, and others, and asymmetric algorithms use key pairs consists of public and private keys, RSA, and Elliptic Curve Cryptography are two best examples of the same. In spite of their successful implementations, these standards suffer from various attacks, and the beginning of large-scale quantum is the biggest threat to the commonly used cryptography algorithms. In general, Public-key cryptography relies upon mathematical problems that are hard to be broken with the available computational power but it is believed that quantum computers can break them at polynomial solvable time in future. Quantum-safe cryptography refers to a new generation of algorithms that are resilient against attacks launched by both classical and quantum computers. This work designs and implements a novel cryptography standard using polynomial interpolations to make it both supercomputer and quantum safe.
Implementation of Comprehensive Ability Evaluation System for Business Comprehensive Ability Evaluation Based on Intelligent Algorithm

With the increasing demand for business English talents, how to evaluate the effect of business English Teaching (ET) effectively has attracted increasing attention. Traditional evaluation methods are time-consuming and labor-intensive, and at the same time problems such as incomplete information and delayed feedback go along with them. In regard to this situation, this paper proposes to construct a comprehensive ability evaluation (CAE) system for business ET based on the perspective of intelligent algorithm. The article first states the current situation of business ET, then discusses the role of teaching CAE. Next it studies the application of intelligent algorithms, and on this basis, tests for the security of the evaluation system have been conducted. The test results show that the system has a high utilization rate of resources, which means the system can aid students’ learning effectively. Also, the system runs well, has high security, and is convenient to operate, which can help teachers master students’ learning situation and improve teaching level with efficiency.
A Non-Interactive Key-Evolving Protocol to Provide the Intrusion Resilience

Key-evolving techniques defend from secret key exposures and allow to extend the basic properties of cryptographic schemes by updating the keys time after time. The intrusion resilience is an example of properties that can be achieved by the use of this concept. Intrusion-resilient cryptosystems imply the presence of a sequence of keys for different time periods and allow to provide the security of unexposed secret keys even if a subset of secret keys of that sequence is compromised. In this paper, we propose a generic construction over underlying key exchange and digital signature schemes that allows to achieve the security level comparable to intrusion-resilient cryptosystems. The proposed key-evolving protocol is intended to use in systems, where a direct interactive channel between users can be absent and the frequency of message exchange is relatively small (for example, like in e-mail applications), so it is not possible to apply usual interactive methods providing the intrusion resilience. The protocol described in this paper allows to provide an adequate security level in terms of key evolving in systems with limited possibilities to perform interactive cryptographic protocols.
AI Computing in Large-Scale Era: Pre-trillion-scale Neural Network Models and Exa-scale Supercomputing

The development of AI computing has reached a critical inflection point. The scale of large-scale AI neural network model parameters has grown rapidly to “pre-trillion-scale” level. The computing needs of training large-scale AI neural network models have reached “exa-scale” level. Besides, AI Foundation Model also affects the correctness of AI applications, and becoming a new information security issue. Future AI development will be pushed by progress of computing power (supercomputer), algorithm (neural network model and parameter scale), and application (foundation model and downstream fine tuning). In particular, the computational efficiency of AI will be a key factor in the commercialization and popularization of AI applications.
A Next-Generation Side-Channel Detector for General-Purpose Processors

Common techniques that accelerate general-purpose processors in modern systems can leave trails of data that are detectable even after speculative execution returns to the correct path of execution. To mitigate these data leaks, or side-channels, both speculative defences, as well as detectors (that can selectively enable defenses during an attack) aim to restore the integrity of the system while maintaining high performance. Unfortunately, recent work has demonstrated that a DNN-based attack detector can show significantly reduced accuracy in the presence of new adversarial attacks. To combat these limitations, a new work, Spectify, aims to directly monitor side-channel leaks, and can raise an alarm before data can leak to the attacker.
A Novel Unified Modular Arithmetic Unit for Elliptic Curve Cryptography

Elliptic Curve Cryptography (ECC) is widely used in asymmetric cryptographic systems. The efficiency of an ECC processor highly depends on its modular arithmetic units responsible for modular multiplications, additions, and subtractions. Previous ECC processor designs perform these modular operations in separate modules and may lead to low hardware utilization efficiency. In this paper, we propose a novel modular arithmetic unit that implements all the required modular arithmetic operations in one unified module and can perform all the operations in a highly-efficient manner. As a result, this design can greatly enhance the performance of an ECC processor with very low hardware complexity. Experimental results show that this design achieves the lowest area-time product and the highest operating frequency compared to the currently available modular multipliers, even though there are additional functions in the proposed design.
Minimizing Computation in Binarized Neural Network Inference using Partial-Filter Sharing

Binarized Neural Network (BNN), which is a variant of Convolutional Neural Network (CNN) with binary weights and binary outputs on a neuron, has emerged as a promising approach to deploy artificial intelligence on resource-restricted devices in recent years. Due to the binarized weights, there are no longer required multipliers for computation, and there exist relatively high similarities among filters as well. In this work, we propose a partial-filter sharing approach and integrate it with the state-of-the-art to reduce the hardware cost and the synthesis time onto Field Programmable Gate Arrays (FPGAs). As compared to the state-of-the-art, the LUT reduction ratio by our approach is 47.71% on average without any accuracy loss, and 62.5% synthesis time in the Tiny ImageNet layers can be also saved on average.
Analysis and Design of Personalized Learning Resources Recommendation System Based on Collaborative Filtering Algorithm

Personalized recommendation is a powerful means to solve information overload. By analysing the historical data of users, it can recommend the learning resources that users may be interested in. Currently, the commonly used collaborative filtering recommendation algorithm can well support users to discover potential interests and preferences, and provide users with personalized learning resource recommendation. This paper builds a basic algorithm model based on the collaborative filtering algorithm flow. In view of the problems existing in practical application, the popular resource penalty and time attenuation function are introduced to improve the basic algorithm model. The use case model of the system is constructed based on the use case modelling method. On this basis, each subsystem and its function are designed. The application of the research results in this paper can reduce the user’s time loss in choosing learning resources, quickly obtain the required learning resources, and improve the user’s sense of experience on the application system.
Accelerating Binarized Neural Network Inference by Reusing Operation Results and Elevating Resource Utilization on Edge devices

Convolutional Neural Networks (CNNs) have shown their abilities in computer vision such as self-driving car and image classification applications. However, computation, power consumption, and memory requirement remain challenges for CNNs when applied in the domains of edge devices. To address these challenges, one of the possible solutions is using Binarized Neural Networks (BNNs). Researchers have demonstrated that BNN models dramatically reduce computational complexity and memory requirements with acceptable accuracy loss. This paper presents an accelerator design for BNN inference that minimizes the number of operations and elevates resource utilization on edge devices. We implemented our accelerator on the Xilinx ZCU104 FPGA and evaluated it with VGG-13 like BNN networks for Tiny ImageNet. Experimental results show that our accelerator can reduce, on average, 99.6% operations and achieve up to 1974 speedup on an FPGA platform compared to state-of-the-art design.
Suspect Face Detection by Auto Sketching

In the Modern world, the crime rate has been increasing day by day. To find the suspect face is a completely different task. So, the police want to draw the face sketch by the eye-witness. But it takes a lot of time and so to finish the sketch. Sometimes the face sketches are inaccurate because of the artist’s incorrect drawing and the eye-witness’s incorrect description. The main objective of the proposed work is to create an application using Generative Adversarial Networks (GAN) technology to generate unique images of human faces. our program will be generating more realistic images according to the inputs given by the user. This would be possible when the different slides of facial features such as Skin Tone, Facial Hair Length, Shape of the Head, etc are present in the application. These images are fake but it is similar to human faces. Our experimental outcomes are shown that the sketches generated by this application are more accurate and matched than the normal hand-drawn sketches. This application is directly used by the police to sketch persons. These sketches are better than hand draws due to the images created by using this application. It is convenient and more particle to use.
Cybersecurity Education: Understanding the knowledge gaps based on cyber security policy, challenge, and knowledge

Cyber security is a critical aspect of modern information technology as it helps protect sensitive data, programs, files, and software from unauthorized access and potential harm from cyber attackers. This can include data theft, system disruption, or other malicious activities. To ensure a safe and secure environment, organizations and companies adopt various strategies and practices to prevent cyber-attacks. The study of cyber security involves understanding different types of cyber-attacks, their mechanisms, and the challenges that need to be overcome. With the growing use of online resources and the rapid advancement of technology, cyber security remains a top concern for individuals, businesses, and governments. Cyberattack disrupts their basic functions, causing widespread economic damage and societal disruptions. Hence, governments, organizations, and individuals must take proactive measures to address these threats and improve their cyber security posture. This involves implementing effective security policies and technologies, raising awareness of safe online practices, and investing in research and development to stay ahead of the constantly evolving cyber threats. In addition, international cooperation and collaboration among countries, industries, and communities are necessary to tackle the global challenges posed by cybercrime and cyber-attacks. organizations are working towards developing effective solutions to mitigate the risks associated with cybercrime and ensure a secure digital environment.
Optimization Design and Implementation of Web Fingerprint Identification

Since the implementation of the ‘‘Internet plus’’ strategy, a large number of open-source frameworks have been used to build Web sites, that’s bringing convenience as well as security risks. As the key technology of the network security evaluation system, Web fingerprint identification technology can effectively provide whether the target system exists the risk of Web service security vulnerabilities and conduct hidden trouble troubleshooting. Traditional Web fingerprint identification and detection has many problems, such as slow scanning speed, low identification accuracy, limited number of identification fingerprints, the need to construct a large number of abnormal HTTP requests, easy to trigger the firewall, and unable to timely alert the potential vulnerabilities of the website. To solve the above problems, this paper proposes a Web fingerprint identification system optimization and development. After a lot of testing and analysis, it can effectively improve the port scanning speed and improve the accuracy of Web fingerprint identification.
Key Technologies of Constructing Parallel Corpus for Chinese-English Intertranslation Based on Web

With the development of network technology, natural language technology has also flourished, and translation methods based on large-scale corpora have developed rapidly. Bilingual corpora has become an indispensable and important resource for research on machine translation, machine-assisted translation and translation knowledge acquisition. This paper constructs a parallel corpus of Chinese-English translation based on Web, obtains corpus from massive Web resources, and improves the level of Chinese-English translation. The corpus acquisition based on web crawler technology studied in this paper creates conditions for acquiring large-scale Chinese-English translation corpus. Sentence alignment based on Attention-BiLSTM technology can give full play to the role of machine learning. The index construction based on Lucence technology is suitable for fulltext retrieval of document sets, and solves the application problem of massive corpus.
FPGA Accelerated Automotive ADAS Sensor Fusion

Fusion of multi-modal sensor data is crucial to improve the performance of advanced driver assistance and safety systems. Usually, sensors such as radars, lidars, and cameras work individually to provide the time-varying kinematics (also referred to as tracks) of other vehicles and objects in the field of view of the ego vehicle. Further, these individual tracks are fused in a decentralized manner to achieve the fused tracks. For an automotive vehicle, low-latency and high-speed sensor fusion is a requirement to improve the overall safety. Rather than running the entire fusion algorithm in the central processing unit, some portion of the code or the whole fusion algorithm can be accelerated on a field programmable gate array to improve the overall functionality. With this objective, in this paper we propose a dedicated digital signal processing (DSP) architecture to realize the fusion algorithm which involves computation of fused state and covariance matrix by making use of matrix-to-matrix multiplications and matrix inversion. The matrix inversion is carried out using an efficient LU decomposition method, and matrix multiplication is realized as a vector-to-vector multiplication DSP architecture. Moreover, a folded DSP architecture is proposed for the state and covariance sub-modules to accelerate the overall functionality. Simulations are presented for two-dimensional constant velocity (CV) model with 4-dimensional state space, three-dimensional CV model with 6-dimensional state space, and three-dimensional constant acceleration (CA) model with 9-dimensional state space. Our results indicate that the proposed architecture is well suited for automotive sensor fusion.
Research on Secure Interactive System of Video Surveillance Data

With the continuous improvement of China’s scientific and technological level in recent years, the monitoring technology has achieved significant development, so it has been widely used in many fields, such as large shopping malls, offices, banks and enterprises. Combined with the actual situation, the effect of the traditional safety monitoring system is relatively poor, and the monitoring is mainly carried out in a human way, resulting in the relevant data interaction ability can not meet the actual needs. In this paper, a new secure interactive system of video surveillance data is designed for the secure interaction of video surveillance data. By using boosting algorithm to process dynamic video images, users can obtain more effective video data. The cleaned video data is shared through the blockchain platform. In the blockchain network, the video is encrypted through the encryption algorithm, and the data is decrypted through the decryption algorithm, so that the video data provides a security level in the sharing process. Experiments show that the sharing rate of video data and the degree of confidentiality of data between different regions are relatively high, which realizes the sharing and encryption of data transmission process and greatly improves the security performance of video surveillance data interaction. Therefore, it is necessary to study and design the interactive ability and data sharing ability of the current video surveillance system.