Factors Impacting Teacher Stress and Mitigation of This Stress in UK School System

Teacher plays an essential role in the lives of children. In addition to promoting their learning and improving academic performance, teachers have a major character in developing student’s creative potential and growing into responsible citizens. However, in recent years, a report by National Foundation for Educational (2019) claimed that the teacher has faced many challenges than other occupations. Stress is one of the most challenges for the teacher to overcome. The main sources of teacher stress generally come from personal, interpersonal as well as organization.This essay explores and discusses the factors that impact or affect teacher stress, exploring the theories and concepts associated with teacher stress and mitigation practices for teacher stress in the United Kingdom school system- from primary and secondary school

A Study on the Subtitle Translation of A Long Cherished Dream From the Perspective of Multimodal Discourse Analysis

The documentary is an important medium for cross-cultural communication, and its subtitle translation has gradually become increasingly important. The emergence of multimodal discourse analysis theory extends the study of single discourse to multimodal discourse, and at the same time broadens the depth and breadth of subtitle translation research. This paper selects the subtitle translation of the documentary A Long Cherished Dream as a case study for analysis. Based on the multimodal discourse analysis framework proposed by Zhang Delu, this paper explores how translators translate the subtitles according to the coordination of various modalities to convey the overall meaning of the documentary at the cultural level, the contextual level, the content level and the expression level respectively. The main research results are as follows. Firstly, translators should pay attention to cultural phenomena at the cultural level and the literal translation method is usually adopted. Secondly, the translator should consider the contextual background to adopt an addition or omission translation method at the contextual level. Thirdly, the translator can adopt the omission translation method to maximize the complementary effect of the English subtitles and multiple modalities at the content level. This study will be expected to be useful for the subtitle translation study of documentaries

On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks

Large-scale deep neural networks are both memory intensive and computation-intensive, thereby posing stringent requirements on the computing platforms. Hardware accelerations of deep neural networks have been extensively investigated in both industry and academia. Specific forms of binary neural networks (BNNs) and stochastic computing based neural networks (SCNNs) are particularly appealing to hardware implementations since they can be implemented almost entirely with binary operations. Despite the obvious advantages in hardware implementation, these approximate computing techniques are questioned by researchers in terms of accuracy and universal applicability. Also it is important to understand the relative pros and cons of SCNNs and BNNs in theory and in actual hardware implementations. In order to address these concerns, in this paper we prove that the "ideal" SCNNs and BNNs satisfy the universal approximation property with probability 1 (due to the stochastic behavior). The proof is conducted by first proving the property for SCNNs from the strong law of large numbers, and then using SCNNs as a "bridge" to prove for BNNs. Based on the universal approximation property, we further prove that SCNNs and BNNs exhibit the same energy complexity. In other words, they have the same asymptotic energy consumption with the growing of network size. We also provide a detailed analysis of the pros and cons of SCNNs and BNNs for hardware implementations and conclude that SCNNs are more suitable for hardware.Comment: 9 pages, 3 figure

A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection

The recent decade witnessed a surge of increase in financial crimes across the public and private sectors, with an average cost of scams of $102m to financial institutions in 2022. Developing a mechanism for battling financial crimes is an impending task that requires in-depth collaboration from multiple institutions, and yet such collaboration imposed significant technical challenges due to the privacy and security requirements of distributed financial data. For example, consider the modern payment network systems, which can generate millions of transactions per day across a large number of global institutions. Training a detection model of fraudulent transactions requires not only secured transactions but also the private account activities of those involved in each transaction from corresponding bank systems. The distributed nature of both samples and features prevents most existing learning systems from being directly adopted to handle the data mining task. In this paper, we collectively address these challenges by proposing a hybrid federated learning system that offers secure and privacy-aware learning and inference for financial crime detection. We conduct extensive empirical studies to evaluate the proposed framework's detection performance and privacy-protection capability, evaluating its robustness against common malicious attacks of collaborative learning. We release our source code at https://github.com/illidanlab/HyFL .Comment: PETs prize challenge versio

eMotions: A Large-Scale Dataset for Emotion Recognition in Short Videos

Nowadays, short videos (SVs) are essential to information acquisition and sharing in our life. The prevailing use of SVs to spread emotions leads to the necessity of emotion recognition in SVs. Considering the lack of SVs emotion data, we introduce a large-scale dataset named eMotions, comprising 27,996 videos. Meanwhile, we alleviate the impact of subjectivities on labeling quality by emphasizing better personnel allocations and multi-stage annotations. In addition, we provide the category-balanced and test-oriented variants through targeted data sampling. Some commonly used videos (e.g., facial expressions and postures) have been well studied. However, it is still challenging to understand the emotions in SVs. Since the enhanced content diversity brings more distinct semantic gaps and difficulties in learning emotion-related features, and there exists information gaps caused by the emotion incompleteness under the prevalently audio-visual co-expressions. To tackle these problems, we present an end-to-end baseline method AV-CPNet that employs the video transformer to better learn semantically relevant representations. We further design the two-stage cross-modal fusion module to complementarily model the correlations of audio-visual features. The EP-CE Loss, incorporating three emotion polarities, is then applied to guide model optimization. Extensive experimental results on nine datasets verify the effectiveness of AV-CPNet. Datasets and code will be open on https://github.com/XuecWu/eMotions

Multi-Objective Feature Selection With Missing Data in Classification

Feature selection (FS) is an important research topic in machine learning. Usually, FS is modelled as a bi-objective optimization problem whose objectives are: 1) classification accuracy; 2) number of features. One of the main issues in real-world applications is missing data. Databases with missing data are likely to be unreliable. Thus, FS performed on a data set missing some data is also unreliable. In order to directly control this issue plaguing the field, we propose in this study a novel modelling of FS: we include reliability as the third objective of the problem. In order to address the modified problem, we propose the application of the non-dominated sorting genetic algorithm-III (NSGA-III). We selected six incomplete data sets from the University of California Irvine (UCI) machine learning repository. We used the mean imputation method to deal with the missing data. In the experiments, k-nearest neighbors (K-NN) is used as the classifier to evaluate the feature subsets. Experimental results show that the proposed three-objective model coupled with NSGA-III efficiently addresses the FS problem for the six data sets included in this study

Genome-wide analysis of the nucleotide binding site leucine-rich repeat genes of four orchids revealed extremely low numbers of disease resistance genes

Orchids are one of the most diverse flowering plant families, yet possibly maintain the smallest number of the nucleotide-binding site-leucine-rich repeat (NBS-LRR) type plant resistance (R) genes among the angiosperms. In this study, a genome-wide search in four orchid taxa identified 186 NBS-LRR genes. Furthermore, 214 NBS-LRR genes were identified from seven orchid transcriptomes. A phylogenetic analysis recovered 30 ancestral lineages (29 CNL and one RNL), far fewer than other angiosperm families. From the genetics aspect, the relatively low number of ancestral R genes is unlikely to explain the low number of R genes in orchids alone, as historical gene loss and scarce gene duplication has continuously occurred, which also contributes to the low number of R genes. Due to recent sharp expansions, Phalaenopsis equestris and Dendrobium catenatum having 52 and 115 genes, respectively, and exhibited an "early shrinking to recent expanding" evolutionary pattern, while Gastrodia elata and Apostasia shenzhenica both exhibit a "consistently shrinking" evolutionary pattern and have retained only five and 14 NBS-LRR genes, respectively. RNL genes remain in extremely low numbers with only one or two copies per genome. Notably, all of the orchid RNL genes belong to the ADR1 lineage. A separate lineage, NRG1, was entirely absent and was likely lost in the common ancestor of all monocots. All of the TNL genes were absent as well, coincident with the RNL NRG1 lineage, which supports the previously proposed notion that a potential functional association between the TNL and RNL NRG1 genes