NUAA-QMUL-AIIT at Memotion 3: Multi-modal Fusion with Squeeze-and-Excitation for Internet Meme Emotion Analysis

This paper describes the participation of our NUAA-QMUL-AIIT team in the Memotion 3 shared task on meme emotion analysis. We propose a novel multi-modal fusion method, Squeeze-and-Excitation Fusion (SEFusion), and embed it into our system for emotion classification in memes. SEFusion is a simple fusion method that employs fully connected layers, reshaping, and matrix multiplication. SEFusion learns a weight for each modality and then applies it to its own modality feature. We evaluate the performance of our system on the three Memotion 3 sub-tasks. Among all participating systems in this Memotion 3 shared task, our system ranked first on task A, fifth on task B, and second on task C. Our proposed SEFusion provides the flexibility to fuse any features from different modalities. The source code for our method is published on https://github.com/xxxxxxxxy/memotion3-SEFusion

Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes

Memes have gained popularity as a means to share visual ideas through the Internet and social media by mixing text, images and videos, often for humorous purposes. Research enabling automated analysis of memes has gained attention in recent years, including among others the task of classifying the emotion expressed in memes. In this paper, we propose a novel model, cluster-based deep ensemble learning (CDEL), for emotion classification in memes. CDEL is a hybrid model that leverages the benefits of a deep learning model in combination with a clustering algorithm, which enhances the model with additional information after clustering memes with similar facial features. We evaluate the performance of CDEL on a benchmark dataset for emotion classification, proving its effectiveness by outperforming a wide range of baseline models and achieving state-of-the-art performance. Further evaluation through ablated models demonstrates the effectiveness of the different components of CDEL

Quantum Circuit Implementation and Resource Analysis of LBlock and LiCi

Due to Grover's algorithm, any exhaustive search attack of block ciphers can achieve a quadratic speed-up. To implement Grover,s exhaustive search and accurately estimate the required resources, one needs to implement the target ciphers as quantum circuits. Recently, there has been increasing interest in quantum circuits implementing lightweight ciphers. In this paper we present the quantum implementations and resource estimates of the lightweight ciphers LBlock and LiCi. We optimize the quantum circuit implementations in the number of gates, required qubits and the circuit depth, and simulate the quantum circuits on ProjectQ. Furthermore, based on the quantum implementations, we analyze the resources required for exhaustive key search attacks of LBlock and LiCi with Grover's algorithm. Finally, we compare the resources for implementing LBlock and LiCi with those of other lightweight ciphers.Comment: 29 pages,21 figure

Asymptotic in a class of network models with an increasing sub-Gamma degree sequence

For the differential privacy under the sub-Gamma noise, we derive the asymptotic properties of a class of network models with binary values with general link function. In this paper, we release the degree sequences of the binary networks under a general noisy mechanism with the discrete Laplace mechanism as a special case. We establish the asymptotic result including both consistency and asymptotically normality of the parameter estimator when the number of parameters goes to infinity in a class of network models. Simulations and a real data example are provided to illustrate asymptotic results.Comment: arXiv admin note: text overlap with arXiv:2002.12733 by other author

Correlation between mobile phone addiction tendency and its related risk factor among Chinese college students: A cross-sectional study

Purpose: Mobile phone addiction prevalence is a global concern which has attracted great attention. It is now considered that excessive mobile phone usage is associated with potentially harmful and/or disturbing behaviors. The present study was aimed at exploring the current situation and related factors of mobile phone addiction tendency and providing a scientific suggestion for its prevention among college students. Methods: A cross-sectional study was applied for stratified cluster random sampling among college students, including five survey tools: the basic information questionnaire, UCLA loneliness scale, college students’ interpersonal comprehensive diagnostic scale, the Pittsburgh sleep quality index scale and mobile phone addiction tendency scale (MPATS). SPSS v 17.0 statistical tool was applied to analyze data from the survey. Results: A total of 760 questionnaires were administered of which 735 questionnaires were retrieved and the valid questionnaires were 730. Classification of mobile phone addiction tendency has statistical significance with grade. Also, classification of loneliness has statistical significance with major, grade and home address. Furthermore, classification of interpersonal relationship has statistical significance with romance status and grade. Additionally, classification of MPATS was positively correlated with classification of UCLA loneliness scale, Pittsburgh sleep quality index scale and interpersonal relationship scale. Interpersonal relationship, sleep quality, and loneliness were linearly correlated with mobile phone addiction tendency. Conclusion: Grade, interpersonal relationship, sleep quality and loneliness were positively correlated with mobile phone addiction tendency, which are the associated risk factors. Therefore, concerns and interventions are required to decrease the risk factor for the sake of college students’ health