Reviewerly/exHarmony huggingface dataset:
Design of a Retrofittable non-Frictional Locking Mechanism for Manual Wheelchairs

With a significant amount of the global population of around sixty-five million people requiring a wheelchair, of which seventy percent need a manual wheelchair, there is a pressing need to address the problems faced by such patients. Paraplegic and some quadriplegic patients who rely on manual wheelchairs find it extremely difficult to exert physical forces to actuate the existing wheelchair locks and brakes. Moreover, they are inefficient in the long run as they rely on friction. Thus, there is a need to develop a better and more efficient locking mechanism that would improve the day-to-day activities of such wheelchair users. This paper discusses the design of a novel locking mechanism that is retrofittable onto any manual wheelchair. As opposed to conventional locks and brakes, the proposed design is non-friction based.
Communication-Efficient Adam-Type Algorithms for Distributed Data Mining

Distributed data mining is an emerging research topic to effectively and efficiently address hard data mining tasks using big data, which are partitioned and computed on different worker nodes, instead of one centralized server. Nevertheless, distributed learning methods often suffer from the communication bottleneck when the network bandwidth is limited or the size of model is large. To solve this critical issue, many gradient compression methods have been proposed recently to reduce the communication cost for multiple optimization algorithms. However, the current applications of gradient compression to adaptive gradient method, which is widely adopted because of its excellent performance to train DNNs, do not achieve the same ideal compression rate or convergence rate as Sketched-SGD. To address this limitation, in this paper, we propose a class of novel distributed Adam-type algorithms (i.e., SketchedAMSGrad) utilizing sketching, which is a promising compression technique that reduces the communication cost from O(d) to $O(\log(d))$ where d is the parameter dimension. In our theoretical analysis, we prove that our new algorithm achieves a fast convergence rate of $O\left(\frac{1}{\sqrt{nT}}+\frac{1}{\left(k/d\right)^{2}T}\right)$ with the communication cost of $O(k\log(d))$ at each iteration. Compared with single-machine AMSGrad, our algorithm can achieve the linear speedup with respect to the number of workers n. The experimental result of training distributed DNN validates the performance of our algorithms.
Inconsistency Distillation For Consistency:Enhancing Multi-View Clustering via Mutual Contrastive Teacher-Student Leaning

Multi-view clustering has attracted more attention recently since many real-world data are comprised of different representations or views. Recent multi-view clustering works mainly exploit the instance consistency to obtain the shared representations across different views, and apply a single-view clustering method to perform data partitions. However, these existing methods often ignore the inconsistency of instance associations within the views, which may enlarge the intra-class diversity among the views and therefore degrade the clustering performance. To address this issue, this paper proposes an efficient mutual contrastive teacher-student leaning (MC-TSL) model to enhance the multi-view clustering, which is the first attempt to study the inconsistency distillation for consistency learning. First, the proposed MC-TSL approach exploits a view-specific encoder with two heads, an instance encoding head and a semantic distillation head, respectively, for capturing the consistent and discriminative feature representations. To be specific, the former head exploits a cross-view contrastive learning method to obtain a redundancy-free consistent representation at the instance level, while the latter head designs a mutual teacher-student learning module to capture the intra-view information at semantic level. By training these two heads in an end-to-end manner, the discriminative multi-view embeddings are efficiently obtained and refined by minimizing the weighted sum of the reconstruction loss, contrastive loss and contrast distillation loss. Extensive experiments verify the superiorities of the proposed MC-TSL framework and show its competitive clustering performances.
Cost-Effective Transfer Learning for Data Streams

In the online data stream environment, a model is typically not effective until a sufficient number of data instances have been seen. One solution to alleviate this issue is using model transfer. A major gap in current research is that they do not address the cost-effectiveness of model transfer in the online context, where processing time is crucial as data instances continuously arrive at high-speed. Model transfer in data streams involves continuously adapting the model to new data in the target stream. Suppose the target stream contains hard-to-learn patterns not covered by the transferred model. In that case, adaptation may incur extra processing time with little accuracy performance gains compared to simply building a new model for the target stream from scratch. Therefore, considering the cost of model transfer and target model construction is essential to balance the accuracy performance gains against computation cost for cost-effective model transfer. To address this gap, we propose a framework called OPERA (Online Transfer using Phantom Tree for Real-Time Adaptation), that orchestrates transfer learning based on the accuracy gain and runtime tradeoffs between transferring and adapting a source model, versus no transfer and constructing a new model for the target stream. We conduct extensive empirical studies to show that our framework can balance accuracy performance gains and runtime for cost-effective transfer learning in the data stream environment.
Research on Evaluation of Undergraduate Training Quality Based on AHP Facing the Construction of "One Belt and One Road"

Undergraduate training is the core teaching task of colleges and universities, and it is also an important support for the development of higher education and the cultivation of social talents. From the perspective of the "Belt and Road" construction, the Analytic Hierarchy Process has been used to construct a quality evaluation system for undergraduate training in colleges and universities covering 3 first-level indicators, 9 second-level indicators, and 40 third-level indicators, including student abilities, resource input, and multiple evaluations. Then, 20 experts and professors participating in undergraduate education in colleges and universities will score the indicators, and the comprehensive weights of the evaluation index system will be obtained through calculation, and a complete evaluation index system will be established.
Understanding digits in identifier names

Before any software maintenance can occur, developers must read the identifier names found in the code to be maintained. Thus, high-quality identifier names are essential for productive program comprehension and maintenance activities. With developers free to construct identifier names to their liking, it can be difficult to automatically reason about the quality and semantics behind an identifier name. Studying the structure of identifier names can help alleviate this problem. Existing research focuses on studying words within identifiers, but there are other symbols that appear in identifier names- such as digits. This paper explores the presence and purpose of digits in identifier names through an empirical study of 800 open-source Java systems. We study how digits contribute to the semantics of identifier names and how identifier names that contain digits evolve over time through renaming. We envision our findings improving the efficiency of name appraisal and recommendation tools and techniques.
An Improvement on Mapless Navigation with Deep Reinforcement Learning: A Reward Shaping Approach

This paper presents an algorithm for mapless motion planning with deep reinforcement learning. Focusing mainly on reward shaping, possible reward functions for the navigation problem are investigated with the deep deterministic policy gradient method. Accordingly, a new reward function to improve the robot's performance is proposed. The input vector consists of 20 distance laser data, the relative goal position, and the last step velocity, while the output is the velocity command. The proposed method improves previous similar algorithms and shows an acceptable performance in a dynamic environment. The results reveal that the new reward function's effect on the algorithm generates smoother moves of the robot, which results in a 40 percent time reduction and a 30 percent total error reduction compared to the original method. Besides, the learning is completed much faster and takes only 5 hours, while the original method takes 16 hours. All the training and testing processes are conducted on a standard personal laptop.
Privacy-Preserving Split Learning via Patch Shuffling over Transformers

We focus on the privacy-preserving problem in split learning in this work. In vanilla split learning, a neural network is split to different devices to be trained, risking leaking the private training data in the process. We novelly propose a patch shuffling scheme on transformers to preserve training data privacy, yet without degrading overall model performance. Formal privacy guarantees are provided and we further introduce the batch shuffling and the spectral shuffling schemes to enhance the guarantee. We show through experiments that our methods successfully defend the black-box, white-box, and adaptive attacks in split learning, with superior performance over baselines, and are efficient to deploy with negligible overhead compared to the vanilla split learning.
Research on intelligent management strategy of college counselor team based on data mining model

According to the law of daily management and the key points of work assessment of College Counselors in China, this paper explores the means of electronic information technology to fully tap the data information in the daily management of counselors. Establish a data mining model, traverse and analyze the collected data information, and analyze the logical relationship they reflect. Then, based on the data mining model, the intelligent management system is designed to carry out scientific, efficient and fine management and supervision, so as to realize the intelligent management of college counselors.
Research on knowledge query network model with self-attention mechanism

As an important research area for providing a personalized education platform to students, knowledge tracking (KT) aims to model the learner's knowledge state and predict the probability of the learner's answer to the next question. In recent years, KT models based on deep learning outperformed all traditional KT models because of their ability to capture complex representations of human learning. After combing through the literature on the knowledge tracking model based on deep learning, we combine the advantages of knowledge query network (KQN) and self-attention knowledge tracking (SAKT) to propose a deep knowledge tracking model of a self-attention knowledge query network (SAKQN) that introduces a self-attention mechanism. The introduction of the self-attention mechanism in KQN can not only retain the ability to model sequences, but also enhance the correlation of different positions of a single sequence to calculate the representation of the sequence to obtain more accurate key characteristics of the internal key characteristics of the learner's historical question record. On four public datasets, we have carried out experiments to show that the SAKQN model proposed in this paper has the highest average AUC value on the 4 datasets of 86.39%, which is 6.18% higher than the KQN model and 1.93% higher than the SAKT model.
D.MCA: Outlier Detection with Explicit Micro-Cluster Assignments

How can we detect outliers, both scattered and clustered, and also explicitly assign them to respective micro-clusters, without knowing apriori how many micro-clusters exist? How can we perform both tasks in-house, i.e., without any post-hoc processing, so that both detection and assignment can benefit simultaneously from each other? Presenting outliers in separate micro-clusters is informative to analysts in many real-world applications. However, a naïve solution based on post-hoc clustering of the outliers detected by any existing method suffers from two main drawbacks: (a) appropriate hyperparameter values are commonly unknown for clustering, and most algorithms struggle with clusters of varying shapes and densities; (b) detection and assignment cannot benefit from one another. In this paper, we propose D.MCA to Detect outliers with explicit Micro-Cluster Assignment. Our method performs both detection and assignment iteratively, and in-house, by using a novel strategy that prunes entire micro-clusters out of the training set to improve the performance of the detection. It also benefits from a novel strategy that avoids clustered outliers to mask each other, which is a well-known problem in the literature. Also, D.MCA is designed to be robust to a critical hyperparameter by employing a hyperensemble “warm up” phase. Experiments performed on 16 real-world and synthetic datasets demonstrate that D.MCA outperforms 8 state-of-the-art competitors, especially on the explicit outlier micro-cluster assignment task.
The Application of Information Technology in Multicultural Education of Business English Professionals

Under the background of Globalization, Chinese culture going abroad has been an important way for China’s publicity and impact on the world stage. Chinese culture plays a great important role in national moral education for their further understand the mother tongue culture to build up the confidence in the fine traditional Chinese culture and stimulate their patriotic enthusiasm. With the unprecedented rapid development and popularization of information technology, great changes have been brought into the field of education. The application of information technology in teaching has become a hot topic for teachers of college and universities. In Business English education, information-based teaching is an efficient way for Chinese culture training of Business English talents. This paper explores the importance of the emerging information technology in multicultural education through describing the positive role of information technology in education, analyzing the problems in the information-based teaching and putting forward some effective ways to promote the deep combination of the information technology and Chinese culture education for Business English professionals. The organic combination between Chinese culture and information technology in Business English teaching is revealed to be a beneficial method to the cultivation of Business English professionals.
Research on the Application of Big Data Technology in English Corpus and Teaching

In the era of big data, it is the general trend to optimize and innovate English teaching. To achieve this goal, building an English corpus is also an indispensable step. Based on this, based on big data technology, the paper discusses the construction and operation and maintenance process of English corpus, and the actual application process of big data technology in English teaching. The paper aims to create a more advanced English learning model to meet the diverse needs of English learners.
Learning from the Multi-Level Abstraction of the Control Flow Graph via Alternating Propagation for Bug Localization

Bug localization aims to automatically locate the buggy source files according to the bug report, which plays an important role in software maintenance. Recent studies indicate that exploiting the program structure is beneficial for bug localization. Benefiting from the rich statement-level implementation detail in the Control Flow Graph (CFG), CFG-based bug localization methods have achieved state-of-the-art performance. However, due to the huge semantic gap between the high-level description of the unexpected program behavior in the bug report and the low-level implementation detail in the CFG, it is challenging to directly establish the match between the bug report and the CFG. In this paper, we argue that this gap can be bridged through the multi-level abstraction of the CFG, where each node in an abstraction level corresponds to a code block with a certain granularity. The multi-level abstraction of the CFG reflects the essence of the structured programming paradigm. We further propose a novel model named MLA (Multi-Level Abstraction of the control flow graph) for bug localization, which contains a particularly designed model that alternately propagates the block feature within and between abstraction levels, corresponding to reflecting the control flow and summarizing the block functionality, respectively. Experimental results on four widely-used open-source software projects show that MLA outperforms the state-of-the-art bug localization methods.
Supporting systematic literature reviews using deep-learning-based language models

Background: Systematic Literature Reviews are an important research method for gathering and evaluating the available evidence regarding a specific research topic. However, the process of conducting a Systematic Literature Review manually can be difficult and time-consuming. For this reason, researchers aim to semi-automate this process or some of its phases. Aim: We aimed at using a deep-learning based contextualized embeddings clustering technique involving transformer-based language models and a weighted scheme to accelerate the conduction phase of Systematic Literature Reviews for efficiently scanning the initial set of retrieved publications. Method: We performed an experiment using two manually conducted SLRs to evaluate the performance of two deep-learning-based clustering models. These models build on transformer-based deep language models (i.e., BERT and S-BERT) to extract contextualized embeddings on different text levels along with a weighted scheme to cluster similar publications. Results: Our primary results show that clustering based on embedding at paragraph-level using S-BERT-paragraph represents the best performing model setting in terms of optimizing the required parameters such as correctly identifying primary studies, number of additional documents identified as part of the relevant cluster and the execution time of the experiments. Conclusions: The findings indicate that using natural-language-based deep-learning architectures for semi-automating the selection of primary studies can accelerate the scanning and identification process. While our results represent first insights only, such a technique seems to enhance SLR process, promising to help researchers identify the most relevant publications more quickly and efficiently.
Voice Your Opinion! Young Voters’ Usage and Perceptions of a Text-Based, Voice-Based and Text-Voice Combined Conversational Agent Voting Advice Application (CAVAA)

Conversational Agent Voting Advice Applications (CAVAAs) are chatbot-based information retrieval systems for citizens who aim to inform themselves about the political issues at stake in times of political elections. Previous studies investigating these relatively young tools primarily focused on the effects of CAVAAs that include a text-based chatbot. In order to further optimize their design, current research compared the effects of CAVAAs with a text, voice, and combined chatbot. In an experimental lab study among young voters (N = 60) these three modalities have been compared on usage measures (the amount of information retrieved from the chatbot, and miscommunication), evaluation measures (ease of use, usefulness, and enjoyment), and political measures (perceived and factual political knowledge). Results show that the three CAVAA modalities score equally high on political measures and the perception of enjoyment. At the same time, the textual and combined CAVAA outperform the voice CAVAA on several aspects: the voice CAVAA received lower ease of use and usefulness scores, respondents requested less additional information, and they experienced more miscommunication when interacting with the voice chatbot. Analyses of the usage data also indicate that in the combined condition users hardly use the voice-option and instead almost exclusively rely on text-functionalities like clicking on suggestion buttons. This seems to suggest that using voice is too much of an effort for CAVAA users; we therefore recommend the usage of text-bots in this specific usage context.
The Effects of Married Child-Care Teachers' Work-Family Conflict and Enrichment on The Work and Family Life Satisfaction: The Mediating Effects of Job Resources

Objectives This study has the purpose of researching the mediating effects of job resources of the effects of married child-care teachers’ work-family conflict and enrichment on the work and family life satisfaction.&#x0D; Methods The subjects of this study were 534 married childcare teachers working at daycare centers in 5 cities in Gyeonggi-do, and the data was collected from the 1st week of August to the 2nd week of September 2019 through questionnaires. For data processing, frequency analysis, confirmatory factor analysis, correlation analysis, and structural equation model were performed using SPSS 23.0 and AMOS 23.0 programs.&#x0D; Results The results of this study are the following. First, the work-family conflict of married childcare teachers was found to have an negative effect on family life satisfaction, and work-family improvement was found to have an positive effect on work and family life satisfaction. Second, In the work-family conflict of married child-care teachers’ effects on work-life satisfaction, the ego-resilience statistically functioned as a mediating role and it did not have any mediating effects on spouse support and social support. In the effects of married child-care teachers’ work-family conflict on family-life satisfaction, ego-resilience and social support have partial mediating effects and spouse support did not have any mediating effects. In the effects of married child-care teachers’ work-family enrichment on work-life satisfaction, ego-resilience, spouse support, and social support do not have any mediating effects. In the effects of married child-care teachers’ work-family enrichment on family-life satisfaction, ego-resilience, spouse support, and social support statistically have mediating effects.&#x0D; Conclusions Based on the findings, it was confirmed the importance of a plan to respond appropriately for the satisfaction of work and family life through effective management of work-family conflict of married child-care teachers.