Reviewerly/exHarmony huggingface dataset:
Personalized News Headline Generation System with Fine-grained User Modeling

Personalized news headline generation aims to sum-marize a news article as a news headline, according to the preference of a specific user. It can help users filter their interested news quickly and increase the news click rates for news providers. However, in this field, when learning user interests from their historically clicked news, existing research only learned user interests on word and news level, ignoring sentence level informativeness. This paper proposes a user model, adding sentence-level informativeness to learn user interests, and further guide the news headline generation. To be more detailed, based on attention layers, sentence and news are represented as the weighted sum of word and sentence representations, respectively. To further explore the correlation between different news contents (news title, body, and topic information), the query vectors in the attention layers are replaced by news content. Experiments on the dataset PENS show that the performance of these two models is better than the baseline model on the evaluation metrics ROUGE. Finally, some future directions are discussed, including interactions across informativeness levels and contents.
Research on Blockchain-Based Smart Contract Technology

With the continuous development of blockchain technology, smart contract has become an important research object among the achievable technologies on blockchain technology. Based on the characteristics of decentralization, tamper-proof and transparency of blockchain, it provides a reliable technical support for the implementation of smart contract. Based on blockchain smart contract technology, this paper aims to design a smart contract management engine with higher versatility, security, and feasibility to develop a smart contract from the joint participation of multiple users. The smart contract is proliferated through the P2P network and deposited into the blockchain. And the blockchain is designed to automatically execute the smart contract, providing a new solution to the problems of opacity, easy tampering, and low efficiency of the traditional contract. It is safer and more reliable. By specifying the treaty and trigger conditions through the program code, once the conditions are met, the contract will be automatically executed, which greatly reduces the time and space costs. Using Ether and smart contracts to develop distributed applications to realize this technology, the feasibility of applying blockchain technology in this field is explored, and a new technical implementation is provided for traditional contract signing.
DARTS-PAP: Differentiable Neural Architecture Search by Polarization of Instance Complexity Weighted Architecture Parameters

Neural architecture search has attracted much attention because it can automatically find architectures with high performance. In recent years, differentiable architecture search emerges as one of the main techniques for automatic network design. However, related methods suffer from performance collapse due to excessive skip-connect operations and discretization gaps in search and evaluation. To relieve performance collapse, we propose a polarization regularizer on instance-complexity weighted architecture parameters to push the probability of the most important operation in each edge to 1 while the probabilities of other operations to 0. The polarization regularizer effectively removes the discretization gaps between the search and evaluation procedures, and instance-complexity aware learning of the architecture parameters gives higher weights to hard inputs therefore further improves the network performance. Similar to existing methods, the search process is conducted under a differentiable way. Extensive experiments on a variety of search spaces and datasets show our method can well polarize the architecture parameters and greatly reduce the number of skip-connect operations, which contributes to the performance elevation of network search.
Mobile Robot Path Planning Based on Triangular Clip Ant Colony Algorithm

In the running process of mobile robot equipment, the path planning to reach the specified target under the condition of known map modeling is an important topic. Traditional ant colony algorithm is an eight-degree-of-freedom search probabilistic algorithm derived from the foraging behavior of ant colony. Ant colony belongs to a class of intelligent path planning, which has good implementation effect in raster map path planning. However, the traditional ant colony algorithm has a simple choice of step size, which limits the efficiency of path planning of mobile robots. In this article, the triangular pruning ant colony algorithm is proposed, which introduces the guiding factor φ of target points and the inducing factor μ of obstacles, and effectively changes the way of path search. Experimental results show that compared with the traditional ant colony algorithm, it can improve convergence speed, improve the search efficiency and shorten the optimal path length under different grid environments.
An Ultrafast Cryptographically Secure Pseudorandom Number Generator

An ultrafast cryptographically secure pseudorandom number generator, referred to as MaD4, is presented in this paper. MaD4 maintains a small byte-oriented state, whose transition follows a pseudorandom permutation, and a large integer-oriented state, whose transition follows a pseudorandom mapping. The byte-oriented state is initialized from a secret key and then further used to bootstrap and initialize the integer-oriented state. After initialization, both states evolve, with the byte-oriented state serving as a source of entropy and periodically reseeding the integer-oriented state. The combination of slow byte-oriented operations and fast integer-oriented operations renders a nice balance between quality and speed. MaD4 generates high quality pseudorandom numbers as attested by standard statistical testing tools and runs at a speed close to half clock cycle per byte on an Intel Core i7 processor. With a large state space of 10520 bits, MaD4 has an expected period length around 1.00e+1783. It is designed to resist various known cryptographic attacks and withstand state compromise extension attacks as well.
Hybrid Recommendation Algorithm Combining Content and AHP

Personalized recommendation system is an effective way to help users quickly obtain demand data in the era of information overload, but there are still problems such as sparse data, cold start, inaccurate similarity calculation, etc. In order to solve the above problems, this paper proposes a hybrid recommendation algorithm that combines content and analytic hierarchy process. First, the user profile constructed by user information, project information and scoring information is fused with the content based recommendation algorithm. Then, we use AHP to improve the similarity calculation and prediction score of the traditional collaborative filtering algorithm. Finally, the content recommendation algorithm based on user profile and the improved collaborative filtering algorithm are mixed in a weighted way to obtain a hybrid recommendation algorithm that combines content and analytic hierarchy process. Experiments show that, compared with other comparison algorithms, the algorithm proposed in this paper can overcome the limitations of a single algorithm, effectively improve the accuracy of the recommendation results, and thus improve the recommendation quality.
Research on BERT-based Text2SQL Multi-task Learning

With the continuous development of computer technology, the Internet has entered the era of big data, which has spawned massive data in various fields and stored it in the database in a structured form. As an important national infrastructure, the power system is also constantly generating various types of data, and has become an important resource for daily operation and maintenance. During the interaction with the database, business personnel need to manually write SQL statements, which poses a challenge to a large number of nonprofessionals; At the same time, for experienced professionals, the conditions of SQL statements are complex and changeable, which is easy to make mistakes, thus affecting the utilization of data and the efficiency of queries. Therefore, how to fully explore the relationship between natural language and SQLstatements and apply them to power operation and maintenance is an important research topic. This paper studies the method of transforming natural language into SQL statement according to the current situation of power field. In terms of solution strategy, this paper divides the problem into several subproblems and combines BERT to carry out multi task learning. The experimental results show that this method is effective on Text2SQL tasks.
Instance Weighting Methods

Instance weighting methods are one of the most effective methods for transfer learning. Technically speaking, any weighting methods can be used for evaluating the importance of each instance. In this chapter, we mainly focus on two basic methods: instance selection and instance weight adaptation. These two kinds of methods are widely adopted in existing transfer learning research and also act as the basic module for more complicated systems.
Research on image classification algorithm of domestic garbage based on deep learning method

Garbage classification and treatment is a necessary measure to fully implement garbage reduction, recycling, harmlessness and the construction of ecological civilization. With the rapid development of artificial intelligence, the use of artificial intelligence technologies such as deep learning to automatically classify images has become possible, and the classification of domestic garbage based on images has also become a specific problem in the field of deep learning. Therefore, this paper focuses on the widely concerned domestic garbage image classification problem, adopts the open source domestic garbage image dataset and the self-made unbalanced domestic garbage image dataset as the research object, studies the image classification task based on deep learning, and builds a deep learning-based domestic garbage image classification system based on the research results of this paper for users to quickly and accurately classify domestic garbage. The research results of this paper will provide valuable reference and reference for the research and practice of garbage image classification method based on machine vision, as well as the application of Flask framework and My SQL database design framework in garbage classification tasks.
An Effective Incentive Mechanism for Individual Data Sharing

In this paper, an elegant mechanism of data-sharing and incentive is constructed based on personalized local differential privacy. We also proved that the mechanism is perfectly data privacy, arbitrage-free, ex-interim individually rational, Bayesian incentive compatible, and equilibrium. By this way, data consumer can maximize estimation accuracy with the lowest capital budget or minimize capital budget with the considerable accuracy.
Construction of Image Recognition Model Based on Second-Order Local Feature Aggregation

This paper analyzes the mathematical model constructed by the Net VLAD method, and proposes a second-order function based on the Net VLAD method to solve the problem that the obtained image feature encoding is a first-order function of the local information of the image. Image feature encoding learning dictionary. Use the Net VLAD classifier as the fully connected layer of the CNN model, design the reconstruction residual weight, and redesign the image feature extraction module for the control of the reconstruction residual value range; when the image feature encoding in the Net VLAD learning dictionary is described by a first-order vector, there is a problem that it is difficult to derive and calculate the minimum value of image features. In the image dimensionality reduction transformation kernel, the first-order linear function is promoted to a second-order nonlinear function, and the image dimensionality reduction transformation kernel is used as a breakthrough, and the second-order nonlinear transformation kernel is used. The activation function has the characteristic of being derivable, and the local features of the image samples are subjected to dimensionality reduction processing to achieve more accurate dimensionality reduction calculations for image features. In the experiment, the accuracy of image feature classification, the recognition rate of image samples, and the training and testing efficiency of image samples are used as verification data. By using the CNN deep convolutional neural network model, the Net VLAD learning dictionary and the improved learning dictionary proposed in this paper, the Multiple image sample sets are compared for training and testing, which verifies that the improved learning dictionary based on Net VLAD proposed in this paper has obvious advantages.
Research of Duplicate Requirement Detection Method

With the rapid development of the software industry, the number of requirements included in the software is also increasing . In particular, in a complex software system such as an avionics software system, dozens of subsystems are usually included, and each subsystem includes hundreds of requirements, so the entire software system will include thousands of requirements. Furthermore, during software development, requirements will undergo frequent changes. When the number of requirements is huge and there are many people from different backgrounds participating in requirements development, it is easy to duplicate requirements. Therefore, in order to prevent redundant development, in the requirements analysis stage, requirements analysts usually need to detect all duplicate requirements in the requirements document and eliminate them, thereby improving the quality of software requirements and saving software development costs. However, when the number of requests is large, manually detecting duplicate requests can be a time-consuming and error-prone task. Aiming at this problem, this paper studies the method of duplicate requirements detection in English requirements.
See-In-The-Middle Attacks on Blockciphers ARIA and DEFAULT

See-In-The-Middle (SITM) is an analysis technique that utilizes side-channel information for differential cryptanalysis. The SITM attack exploits side-channel leakage in the middle round of blockcipher implementations. The blockcipher ARIA proposed at ICISC 2003 is a Korean national standard, and the blockcipher DEFAULT proposed at Asiacrypt 2021 offers proctection against differential fault analysis. In this study, we propose SITM attacks on the ARIA-128, 192, 256 and DEFAULT. Consequently, it is demonstrated that for these blockciphe r based on look-up-table implementations and no masking technique SITM attacks are possible with practical attack complexities.
Federated Learning Based User Scheduling for Real-Time Multimedia Tasks in Edge Devices

Edge networks are highly volatile and the quality of device communication and computational resources change not only over time but also according to the movement of users. Current federation learning suffers from poor device network state and failure of devices to upload models in a timely manner. To address these problems, an intelligent scheduling mechanism that uses the predicted device state based on device information to select the appropriate device for federated learning is proposed in this paper. By focusing on information such as communication quality, computational resources, and location information, the information of edge devices is collected to analyze and predict the device network and computing resources to further analyze the state of devices in depth. Experiments are conducted on real datasets, and the experimental results show that the proposed scheduling method can make the global model fit faster than without the algorithm, which significantly improves the training efficiency of federated learning.
Multi-scale and Multi-stage Deraining Network with Fourier Space Loss

The goal of rain streak removal is to recover the rain-free background scenes of an image degraded by rain streaks. Most current deep convolutional neural networks methods have achieved dramatic performance. However, these methods still cannot capture the discriminative features to well distinguish the rain streaks and the important image content. To solve this problem, we propose a Multi-scale and Multi-stage deraining network in the end-to-end manner. Specifically, we design a multi-scale rain streak extraction module to capture complex rain streak features across different scales through the multi-scale selection kernel attention mechanism. In addition, multi-stage learning is used to extract deeper feature representations of rain streak and fuse different stages of background information. Furthermore, we introduce a Fourier space loss function to reduce the loss of high-frequency information in the background image and improve the quality of deraining results. Extensive experiments demonstrate that our network performs favorably against the state-of-the-art deraining methods.
Cooperative Coevolutionary Genetic Programming Hyper-Heuristic for Budget Constrained Dynamic Multi-workflow Scheduling in Cloud Computing

Dynamic Multi-workflow Scheduling (DMWS) in cloud computing is a well-known combinatorial optimisation problem. It is a great challenge to tackle this problem by scheduling multiple workflows submitted at different times and meet user-defined quality of service objectives. Scheduling with user-defined budget constraints is becoming increasingly important due to cloud dynamics associated with on-demand provisioning, instance types, and pricing. To address the Budget-Constrained Dynamic Multi-workflow Scheduling (BC-DMWS) problem, a novel Cooperative Coevolution Genetic Programming (CCGP) approach is proposed. Two heuristic rules, namely VM Selection/Creation Rule (VMR) and Budget Alert Rule (BAR), are learned automatically by CCGP. VMR is used to allocate ready tasks to either existing or newly rented VM instances, while BAR makes decisions to downgrade VM instances so as to meet the budget constraint. Experiments show significant performance and success rate improvement compared to state-of-the-art algorithms.
Precise Segmentation on Poly-Yolo, YoloV5 and FCN

Nowadays, computer vision is becoming more and more popular and is applied to lots of fields. It can be used to detect safe and unsafe behavior happening in construction area [6]. It can also used for auto-vehicle driving, object detection, and so on. Currently, there are several ways to realize this like FCN and YOLO. However, they all exist some limitation. For example, the bounding box of YOLO is always being castigated by the user. There are a lot of versions of YOLO with different solutions to this kind of issues aiming to be used in different circumstances. Poly-YOLO is one of them. It decently solved the bounding box issue by using polygons rather than rectangles. In the paper, we focused on several techniques to achieve object detection. The YOLO approach will be mainly talked about for application, since it can be easily used combined with other algorithms like semantic segmentation, FCN, and etc. to enhance its performance. What’s more, the computation speed of Poly-YOLO is the main advantage why people choose YOLO.
A Game-Theoretic Approach for Pricing and Determining Quality Levels of Cybersecurity Products Under an Exogenous Information-Sharing Program

Price and quality level of products are two important decisions of any business. This paper provides equilibrium solutions for these decisions of two players for a cybersecurity ecosystem, including a solution provider and an information provider. We assume that end users join a cybersecurity ecosystem based on the prices and qualities of the solution provider’s and information provider’s products; so that, the increasing of the prices or/and decreasing the qualities will reduce the number of end-users of that ecosystem. Also, it is assumed that there is an exogenous information-sharing program under which the effect of quality of the information provider’s product on the cybersecurity level depends on the level of information-sharing. Under this information-sharing program, the equilibrium solutions of prices and qualities of these two players are given. Also, some results and insights are given with a numerical example.
Memory-Based DCNs

This chapter first provides a brief overview of memory-based Deep Cognitive Networks (DCNs). Then, representative models from two aspects in terms of short-term memory and long-term memory are introduced and analyzed, as well as their relation to important theories, computational models and experimental evidences in cognitive psychology. At last, this chapter is briefly summarized.
Transformations of Trust Under Caused by Instabilities of Educational Interactions

The digital mass media update is taking place in almost all spheres of society's life and causes an expanded interest in the consequences of this truly global process. For such spheres of public activity as production (business), science, and education, which particularly determine the "breakthrough" nature of the technologies used today, the consequences of the development, and the digital mass media [DMM] usage volume have a differentiated nature. This differentiation reflects "many-in-one," as it is clearly (especially in the advanced organizational forms of consolidation of business—science—education) combined with the integration of the mentioned industries and their "attachment" to changing human potential and the processes of its capitalization. The development of distance/e-learning is one of the consequences of using the DMM in education. Considering the DMM as the material basis for a new education ecosystem, we can clearly see the need to research the consequences of this process to manage and forecast the learning and education itself and determine the future architecture of educational interactions. In this regard, arises the significant research trend—to forecast the attitude of the main subjects of educational interactions (individuals (educators and learners), family, civil society, government, and business) towards the updated DMM. The present article aims to trace a trend based on sociological research that can be characterized as a "form of interaction with a teacher" or consulting activity. Consultation is considered a special type of informative and teaching activity distinguished by two qualities—individualization and a high degree of trust. Besides, the consultation is a universal type of pedagogical work with many levels (student–teacher; scientific supervisor—postgraduate student; head of the laboratory—intern, etc.). Naturally arises the question of dominant trust: whom to trust—the educator ("live" communication) or the technologies and information of the Internet (final form of formalized algorithms)? As a result of independently conducted and involved research, the authors formulated a hypothesis that with the development of DMM usage volume and distance learning, the need for live communication with the educator decreases, and "digital literacy" both among learners and educators—increases.