Abstract In recent years, while algorithm-driven recommendation applications have seen widespread use, their negative impacts have also increasingly raised concerns. To gain a more comprehensive understanding of the impact of different recommendation algorithms, we explored the phenomenon of information cocoons, where users are enveloped by homogenized recommended content, in different algorithm-driven recommender systems. We simulated long-term interactions between users and various algorithm-driven recommender systems, trying to recreate multi-stage recommendation scenarios under the influence of complex factors, and explored whether and to what extent users would fall into information cocoons while analyzing the underlying reasons from the perspective of algorithms. We conducted simulation experiments on two real-world recommendation datasets from different fields. The results show that information cocoons is prevalent across different algorithm-driven recommender systems, and the extent of its occurrence varies. Diversity-oriented recommendations can help alleviate information cocoons but are limited in effectiveness. The ability of diversity-aware re-ranking frameworks to alleviate information cocoons is influenced by the basic recommendation models. Not only considering the diversity of the current recommendation list but also the similarity between items and users’ historical consumption content, we proposed a simple and lightweight re-ranking framework called ICMF. Compared to other re-ranking methods, ICMF avoids an average of 12.48% of users encountering homogenized recommended content.

Clinically, histopathology images always offer a golden standard for disease diagnosis. With the development of artificial intelligence, digital histopathology significantly improves the efficiency of diagnosis. Nevertheless, noisy labels are inevitable in histopathology images, which lead to poor algorithm efficiency. Curriculum learning is one of the typical methods to solve such problems. However, existing curriculum learning methods either fail to measure the training priority between difficult samples and noisy ones or need an extra clean dataset to establish a valid curriculum scheme. Therefore, a new curriculum learning paradigm is designed based on a proposed ranking function, which is named The Ranking Margins (TRM). The ranking function measures the 'distances' between samples and decision boundaries, which helps distinguish difficult samples and noisy ones. The proposed method includes three stages: the warm-up stage, the main training stage and the fine-tuning stage. In the warm-up stage, the margin of each sample is obtained through the ranking function. In the main training stage, samples are progressively fed into the networks for training, starting from those with larger margins to those with smaller ones. Label correction is also performed in this stage. In the fine-tuning stage, the networks are retrained on the samples with corrected labels. In addition, we provide theoretical analysis to guarantee the feasibility of TRM. The experiments on two representative histopathologies image datasets show that the proposed method achieves substantial improvements over the latest Label Noise Learning (LNL) methods.

Complex networks have been used widely to model a large number of relationships. The outbreak of COVID-19 has had a huge impact on various complex networks in the real world, for example global trade networks, air transport networks, and even social networks, known as racial equality issues caused by the spread of the epidemic. Link prediction plays an important role in complex network analysis in that it can find missing links or predict the links which will arise in the future in the network by analyzing the existing network structures. Therefore, it is extremely important to study the link prediction problem on complex networks. There are a variety of techniques for link prediction based on the topology of the network and the properties of entities. In this work, a new taxonomy is proposed to divide the link prediction methods into five categories and a comprehensive overview of these methods is provided. The network embedding-based methods, especially graph neural network-based methods, which have attracted increasing attention in recent years, have been creatively investigated as well. Moreover, we analyze thirty-six datasets and divide them into seven types of networks according to their topological features shown in real networks and perform comprehensive experiments on these networks. We further analyze the results of experiments in detail, aiming to discover the most suitable approach for each kind of network.

Recently, sparse arrays such as nested array and coprime array have attracted much attention in the field of array signal processing. In this letter, we develop a symmetric coprime array (SCA) whose sensor locations satisfy the nesting property, so it can be used as a dense subarray of nested array. Based on this observation, we propose a new sparse array named coprime nested array, which can achieve the same number of uniform degrees of freedom (uDOFs) as the prototype nested array, while the mutual coupling effect is at the same level as the coprime arrays. Moreover, an improved coprime nested array (ICNA) is proposed by rearranging some sensors in SCA to the right side of the sparse subarray. ICNA possesses more uDOFs than the existing nested arrays with further reduced mutual coupling effect. Numerical simulations verify the effectiveness of the proposed configurations.

In recent years, task placement technology for reconfigurable FPGA has been developed into 2-D arrays. In this paper, we propose a methodology to pre-place hardware resource into multi-area to achieve the high utilization of hardware resource and reduce used area. The method solves the task type placement problems on the partial dynamic reconfigurable systems. The proposed placement method can provide multi reconfigurable area reusable which depends on each request to load corresponding reconfigurable module into pre-place reconfigurable area. In this experiment, the task placement into the configuration area is using by the tool of Xilinx Plan Ahead 14.1 [15] to analyze and verify on the Xilinx Virtex-6 system development platform. Comparison of related work, the experiment results shown that the proposed placement methodology increases 20.1% the utilization of hardware resources and reduces 61.1% the area of hardware resources.

This paper presents a low complexity digit-serial GF(2m) multiplier. The proposed architecture use digit-serial combination Karatsuba multiplier to reduce area complexity of the circuit. This circuit is suitable for elliptic curve cryptography (ECC) technology. We know that the password system operation core is a multiplier. However that password system multiplier is very big, so it is necessary to reduce the area and time complexity. Therefore, this paper design and implement three smaller multipliers and digit-serial in FPGA to reduce time and area complexity. This method uses 3dm/2 ANDs, (6m+n+3dm/2+m/2+d-7) XORs and (3m-3) registers. Take GF(2340) example, the proposed method compares with related works [12] and [14] which can reduce 70.7% and 50.79% on area, respectively and reduce 50.9% and 73.5% on time, respectively.