Reviewerly/exHarmony huggingface dataset:
Towards Explainable Multi-Party Learning: A Contrastive Knowledge Sharing Framework

Multi-party learning provides solutions for training joint models with decentralized data under legal and practical constraints. However, traditional multi-party learning approaches are confronted with obstacles such as system heterogeneity, statistical heterogeneity, and incentive design. How to deal with these challenges and further improve the efficiency and performance of multi-party learning has become an urgent problem to be solved. In this paper, we propose a novel contrastive multi-party learning framework for knowledge refinement and sharing with an accountable incentive mechanism. Since the existing naive model parameter averaging method is contradictory to the learning paradigm of neural networks, we simulate the process of human cognition and communication, and analogy multi-party learning as a many-to-one knowledge sharing problem. The approach is capable of integrating the acquired explicit knowledge of each client in a transparent manner without privacy disclosure, and it reduces the dependence on data distribution and communication environments. The proposed scheme achieves significant improvement in model performance in a variety of scenarios, as we demonstrated through experiments on several real-world datasets.
ResumeVis Interactive Visualization of Resumes Based on Multi-Source Data

Resumes are critical for individuals to find jobs and for HR to select staffs. To explore the career patterns and demographic information correlation, 372,829 Chinese resumes working in Beijing in 2015 are collected with rich attributes. Besides, 1,837,281 documents in the People's Daily from May 1946 to December 2015 and the national college entrance examination scores of 42 majors in 27 Beijing universities from 2005 to 2015 are collected to build the multi-source dataset to assist resume data mining. The decade characteristics and major characteristics are explored from the multi-source dataset. Based on the data observation, an interactive visualization system called ResumeVis is developed to explore career patterns in the context of the times, especially the correlations among the resume attributes. The system is helpful for both job seekers and human resources.
Toward an Enhanced Dynamic VM Consolidation Approach for Cloud Datacenters Using Continuous Time Markov Chain

Dynamic Virtual Machine (VM) consolidation is an effective manner to reduce energy consumption and balance the resource load of physical machines (PMs) in cloud data centers that guarantees efficient power consumption while maintaining the quality of service requirements. Reducing the number of active PMs using VM live migration leads to prevent inefficient usage of resources. However, high frequency of VM consolidation has a negative effect on the system reliability and we need to deal with the trade-off between energy consumption and system reliability. In recent years many research work has been done to optimize energy management using power management techniques. Although these methods are very efficient from the point of view of energy management, but they ignore the negative impact on the system reliability. In this paper, a novel approach is proposed to achieve a reliable VM consolidation method. In this way, a Markov chain model is designed to determine the reliability of PMs and then it has been prioritized PMs based on their CPU utilization level and reliability status. Two algorithms are presented to determining source and destination servers. The efficiency of our proposed approach is validated by conducting extensive simulations. The results of the evaluation clearly show that the proposed approach significantly improve energy consumption while avoiding the inefficient VM migrations.
The Influence Of Using Video Materials And Attitude In Listening Towards The Listening Ability At The Second Level Cadets Of Diploma III Inland Water And Ferries Transport Palembang

This study investigated to find out whether or not there were any significant differences of the second level cadets’ listening ability and listening attitude using video materials. By using questionnaire forty cadets were selected as the sample and equally divided into experimental and control groups. By applying pretest and post-test control group design the treatment was only given to the experimental group. The data were analyzed by using three techniques: they were the statistical analysis on measuring homogeneity and normality data, independent t-test, and two-way ANOVA. Due to the research findings, it was found that (1) there was significant difference in listening ability between positive and negative attitude students who were taught by using Video materials (0.000&lt;0.05). (2) there was significant difference in listening ability between positive and negative attitude students who were taught by using conventional technique (0.000&lt;0.05). (3) there was significance difference in listening ability between positive and negative attitude students who were taught by using video materials and those who were taught by using conventional technique (0.000&lt;0.05). (4) there was a significant interaction effect of Video materials and listening attitude towards the listening ability at the second level cadets of D III Inland Water and Ferries Transport Palembang (0.020 &lt; 0.05).
Multi-Emotion Classification for Song Lyrics

Song lyrics convey a multitude of emotions to the listener and powerfully portray the emotional state of the writer or singer. This paper examines a variety of modeling approaches to the multi-emotion classification problem for songs. We introduce the Edmonds Dance dataset, a novel emotion-annotated lyrics dataset from the reader’s perspective, and annotate the dataset of Mihalcea and Strapparava (2012) at the song level. We find that models trained on relatively small song datasets achieve marginally better performance than BERT (Devlin et al., 2018) fine-tuned on large social media or dialog datasets.
Analyzing COVID-19 Tweets with Transformer-based Language Models

This paper describes a method for using Transformer-based Language Models (TLMs) to understand public opinion from social media posts. In this approach, we train a set of GPT models on several COVID-19 tweet corpora that reflect populations of users with distinctive views. We then use prompt-based queries to probe these models to reveal insights into the biases and opinions of the users. We demonstrate how this approach can be used to produce results which resemble polling the public on diverse social, political and public health issues. The results on the COVID-19 tweet data show that transformer language models are promising tools that can help us understand public opinions on social media at scale.
Efficient normalization techniques to optimize AI models for deployment in tactical edge

Normalizations used in model reduction can be chosen to emphasize anything from computation reduction to parameter reduction. Choosing a normalization that emphasizes a model with a small number of parameters is useful when deploying a model onto machines with a limited communication rate, while choosing a normalization that emphasizes a model with a small computational cost is useful when deploying a model onto a machine for real-time sensor analysis. As such, we explore the effect of various normalizations used to prune kernel parameters on models trained on the ImageNet database.
Dual Head Adversarial Training

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples/attacks, raising concerns about their reliability in safety-critical applications. A number of defense methods have been proposed to train robust DNNs resistant to adversarial attacks, among which adversarial training has so far demonstrated the most promising results. However, recent studies have shown that there exists an inherent tradeoff between accuracy and robustness in adversarially-trained DNNs. In this paper, we propose a novel technique Dual Head Adversarial Training (DH-AT) to further improve the robustness of existing adversarial training methods. Different from existing improved variants of adversarial training, DH-AT modifies both the architecture of the network and the training strategy to seek more robustness. Specifically, DH-AT first attaches a second network head (or branch) to one intermediate layer of the network, then uses a lightweight convolutional neural network (CNN) to aggregate the outputs of the two heads. The training strategy is also adapted to reflect the relative importance of the two heads. We empirically show, on multiple benchmark datasets, that DH-AT can bring notable robustness improvements to existing adversarial training methods. Compared with TRADES, one state-of-the-art adversarial training method, our DH-AT can improve the robustness by 3.4% against PGD40 and 2.3% against AutoAttack, and also improve the clean accuracy by 1.8%.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study

This paper introduces a novel dataset for video enhancement and studies the state-of-the-art methods of the NTIRE 2021 challenge on quality enhancement of compressed video. The challenge is the first NTIRE challenge in this direction, with three competitions, hundreds of participants and tens of proposed solutions. Our newly collected Large-scale Diverse Video (LDV) dataset is employed in the challenge. In our study, we analyze the proposed methods of the challenge and several methods in previous works on the proposed LDV dataset. We find that the NTIRE 2021 challenge advances the state-of-the-art of quality enhancement on compressed video. The proposed LDV dataset is publicly available at the homepage of the challenge: https://github.com/RenYang-home/NTIRE21_VEnh
Subgoal-based Reward Shaping to Improve Efficiency in Reinforcement Learning

Reinforcement learning, which acquires a policy maximizing long-term rewards, has been actively studied. Unfortunately, this learning type is too slow and difficult to use in practical situations because the state-action space becomes huge in real environments. Many studies have incorporated human knowledge into reinforcement Learning. Though human knowledge on trajectories is often used, a human could be asked to control an AI agent, which can be difficult. Knowledge on subgoals may lessen this requirement because humans need only to consider a few representative states on an optimal trajectory in their minds. The essential factor for learning efficiency is rewards. Potential-based reward shaping is a basic method for enriching rewards. However, it is often difficult to incorporate subgoals for accelerating learning over potential-based reward shaping. This is because the appropriate potentials are not intuitive for humans. We extend potential-based reward shaping and propose a subgoal-based reward shaping. The method makes it easier for human trainers to share their knowledge of subgoals. To evaluate our method, we obtained a subgoal series from participants and conducted experiments in three domains, four-rooms(discrete states and discrete actions), pinball(continuous and discrete), and picking(both continuous). We compared our method with a baseline reinforcement learning algorithm and other subgoal-based methods, including random subgoal and naive subgoal-based reward shaping. As a result, we found out that our reward shaping outperformed all other methods in learning efficiency.
The Pandemic Crisis: Time for Digitization of Higher Education in Tunisia

Our article highlights the negative impacts of Covid-19 on higher education in Tunisia. The future is uncertain for everyone, especially students who face a world paralyzed by the pandemic. Based on a qualitative study based on the Focus Group method among students, teachers, researchers, directors, a number of parents and members of civil society, we want to know how the Coronavirus has affected education and how universities cope with different changes. Our work focuses on the lessons learned from containment as well as the potential positive benefits to higher education. Our results show that the pandemic has forced universities to explore new methods of teaching and learning, including through distance and online education. This solution has proven difficult for students and teachers, who not only have to cope with the emotional, social and economic challenges posed by Covid-19 by doing their best to curb the spread of the virus. The higher education sector has made remarkable headway through accelerating digitization and the pursuit of distance education during the pandemic, but constraints persist. Our article will provide interested parties with a set of courses of action helping to structure such a policy of transformation towards digital education in a changing university landscape. The challenge is to invent a more flexible higher education that offers active pedagogies by integrating the contributions of digital technology and relying on research, and which can meet the different expectations of students.
Study of EFL Teachers’ Plan for Macro-scaffolding for Writing Classes: The Case of Wollega University

This study aimed at exploring how EFL teachers plan for scaffolding in writing classrooms. Mixed method was used to gather and analyze data. Two EFL teachers at Wollega University were interviewed for the activities they accomplish before going to the classroom for the actual teaching and learning. Besides, 100 students were provided a questionnaire to triangulate data generated through interview. The data were transcribed and were entered into an open code project. Then analysis was made thematically. The students’ questionnaire was analyzed using mean, frequency and percentage. The finding indicated that the teachers identify content to be taught; modify the pre-existing content, and consider students’ knowledge level, the complexity of the content and the time and resources available for the teaching and learning before going to the actual classroom. From this, it was concluded that teachers’ activities could facilitates the implementation of micro-scaffolding which is provided during the actual classroom teaching and learning.
BROADCAST: Reducing Both Stochastic and Compression Noise to Robustify Communication-Efficient Federated Learning

Communication between workers and the master node to collect local stochastic gradients is a key bottleneck in a large-scale federated learning system. Various recent works have proposed to compress the local stochastic gradients to mitigate the communication overhead. However, robustness to malicious attacks is rarely considered in such a setting. In this work, we investigate the problem of Byzantine-robust compressed federated learning, where the attacks from Byzantine workers can be arbitrarily malicious. We theoretically point out that different to the attacks-free compressed stochastic gradient descent (SGD), its vanilla combination with geometric median-based robust aggregation seriously suffers from the compression noise in the presence of Byzantine attacks. In light of this observation, we propose to reduce the compression noise with gradient difference compression so as to improve the Byzantine-robustness. We also observe the impact of the intrinsic stochastic noise caused by selecting random samples, and adopt the stochastic average gradient algorithm (SAGA) to gradually eliminate the inner variations of regular workers. We theoretically prove that the proposed algorithm reaches a neighborhood of the optimal solution at a linear convergence rate, and the asymptotic learning error is in the same order as that of the state-of-the-art uncompressed method. Finally, numerical experiments demonstrate the effectiveness of the proposed method.
Multi-task Learning with Attention for End-to-end Autonomous Driving

Autonomous driving systems need to handle complex scenarios such as lane following, avoiding collisions, taking turns, and responding to traffic signals. In recent years, approaches based on end-to-end behavioral cloning have demonstrated remarkable performance in point-to-point navigational scenarios, using a realistic simulator and standard benchmarks. Offline imitation learning is readily available, as it does not require expensive hand annotation or interaction with the target environment, but it is difficult to obtain a reliable system. In addition, existing methods have not specifically addressed the learning of reaction for traffic lights, which are a rare occurrence in the training datasets. Inspired by the previous work on multi-task learning and attention modeling, we propose a novel multi-task attention-aware network in the conditional imitation learning (CIL) framework. This does not only improve the success rate of standard benchmarks, but also the ability to react to traffic lights, which we show with standard benchmarks.
Higher Order Recurrent Space-Time Transformer.

Endowing visual agents with predictive capability is a key step towards video intelligence at scale. The predominant modeling paradigm for this is sequence learning, mostly implemented through LSTMs. Feed-forward Transformer architectures have replaced recurrent model designs in ML applications of language processing and also partly in computer vision. In this paper we investigate on the competitiveness of Transformer-style architectures for video predictive tasks. To do so we propose HORST, a novel higher order recurrent layer design whose core element is a spatial-temporal decomposition of self-attention for video. HORST achieves state of the art competitive performance on Something-Something-V2 early action recognition and EPIC-Kitchens-55 action anticipation, without exploiting a task specific design. We believe this is promising evidence of causal predictive capability that we attribute to our recurrent higher order design of self-attention.
Low Row Rank Parity Check Codes

In this paper we present an extended variant of low rank parity check matrix (LRPC) codes that have received significant interests in recent years. It is shown that the extension indeed yields a superfamily of LRPC codes, which are termed low row rank parity check codes. The decoding method of the proposed codes is also investigated.
Word Picker as a Vocabulary Enhancer for Weak Students in a Secondary Classroom

Students love to take part in classroom discussions and activities, but when English as Second Language (ESL) students with limited English vocabulary are ostracized because they cannot properly participate, this causes their confidence to suffer. The challenges usually lie in the lack of vocabulary among students which lead to the poor performances in the English language and this calls for immediate attention. This paper will discuss the use of a strategy the Word Picker to improve the vocabulary among the ESL weak students in a secondary school in Malaysia. Data collected via questionnaire, document analysis and video recordings were analysed qualitatively. The findings suggested that the Word Picker strategy has helped the weak ESL students to enhance their vocabulary skills.
Learning on Hardware: A Tutorial on Neural Network Accelerators and Co-Processors

Deep neural networks (DNNs) have the advantage that they can take into account a large number of parameters, which enables them to solve complex tasks. In computer vision and speech recognition, they have a better accuracy than common algorithms, and in some tasks, they boast an even higher accuracy than human experts. With the progress of DNNs in recent years, many other fields of application such as diagnosis of diseases and autonomous driving are taking advantage of them. The trend at DNNs is clear: The network size is growing exponentially, which leads to an exponential increase in computational effort and required memory size. For this reason, optimized hardware accelerators are used to increase the performance of the inference of neuronal networks. However, there are various neural network hardware accelerator platforms, such as graphics processing units (GPUs), application specific integrated circuits (ASICs) and field programmable gate arrays (FPGAs). Each of these platforms offer certain advantages and disadvantages. Also, there are various methods for reducing the computational effort of DNNs, which are differently suitable for each hardware accelerator. In this article an overview of existing neural network hardware accelerators and acceleration methods is given. Their strengths and weaknesses are shown and a recommendation of suitable applications is given. In particular, we focus on acceleration of the inference of convolutional neural networks (CNNs) used for image recognition tasks. Given that there exist many different hardware architectures. FPGA-based implementations are well-suited to show the effect of DNN optimization methods on accuracy and throughput. For this reason, the focus of this work is more on FPGA-based implementations.
직무스트레스에 따른 감정노동이 보육교사의 이직의도에 미치는 영향

If The purpose of this study was to examine the direct relationship between emotional labor and turnover intention of childcare teachers, and to examine the indirect effects of job stress as a mediator and social support as a moderating variable in the relationship between emotional labor and turnover intention. The results showed that emotional labor of child care teachers had a direct effect on turnover intention, and job stress of child care teachers had a mediating effect on the relationship between emotional labor and turnover intention. In addition, the social support of child care teachers showed a moderating effect on emotional labor and turnover intention. In other words, the effect of childcare teachers who receive much social support on the intention to turnover by emotional labor was relatively smaller than that of teachers who did not.
Predictive classification of breast cancer using machine learning

Breast cancer is a disease in which cells in the breast grow out of control. There are different kinds of breast cancer. The kind of breast cancer depends on which cells in the breast turn into cancer. Breast cancer can begin in different parts of the breast. A breast is made up of three main parts: lobules, ducts, and connective tissue. The lobules are the glands that produce milk. The ducts are tubes that carry milk to the nipple. The connective tissue (which consists of fibrous and fatty tissue) surrounds and holds everything together. Most breast cancers begin in the ducts or lobules. Breast cancer can spread outside the breast through blood vessels and lymph vessels. When breast cancer spreads to other parts of the body, it is said to have metastasized. Advances in screening and treatment for breast cancer have improved survival rates dramatically since 1989. According to the American Cancer Society (ACS), there are more than 3.1 million breast cancer survivors in the United States. The chance of any woman dying from breast cancer is around 1 in 38 (2.6%). The ACS estimate that 268,600 women will receive a diagnosis of invasive breast cancer and 62,930 people will receive a diagnosis of noninvasive cancer in 2019. In the same year, the ACS report that 41,760 women will die as a result of breast cancer. However, due to advances in treatment, death rates from breast cancer have been decreasing since 1989. However, The required facility for diagnosing cancer accurately and at the earliest stage using the results of the biopsy is not available to all general hospitals. Identifying and diagnosing cancer at the earliest stage is crucial as the possibility of cancer spreading increases. Therefore, A computerized system that identifies cancer at the earliest stage with minimal time with the greatest accuracy and which reduces cancer recurrence and mortality has to be developed. This paper concentrates and summarises the different machine learning algorithms which may be implied in cancer diagnosis to improve the accuracy of the diagnosis and identification.