Reviewerly/exHarmony huggingface dataset:
Explore Deep Learning for Chinese Essay Automated Scoring

Abstract Automated Essay Scoring (AES) has gained increasing attention in recent years. In this paper, we propose a novel automated Chinese essay scoring model, called BLA (BERT and Bi-LSTM with Attention), by using neural network. The model uses a BERT network to obtain the sentence vectors for an essay, and then uses a Bi-LSTM network with two layers to extract the essay vector. We also consider topic instruction as a long sentence and obtain its vector representation by BERT. The obtained topic instruction vector is then used as attention information to further help obtaining more effective essay vector. Moreover, we also present a new dataset that contains topic instruction and related essays collected from Chinese high school. The detailed experimental results show that our proposed model outperforms other baseline methods.
Automatic Requirements Elicitation from Social Media (ARESM)

After software applications are installed and deployed, the software applications enter to maintenance phase. Changes during maintenance phase often occur due to software faults and user new requests. Both software faults and user new requests are user feedbacks that can be normally obtained by interviewing, distributing questionnaires, or setting up JAD meeting. Nowadays, some user feedbacks are scattered online on App Store, Play Store, and social media and normal elicitation methods are not suitable for collecting online user feedbacks. Although some online user feedback elicitation methods are used to collect App Store's reviews and Play Store's reviews, these methods are not designed for eliciting user feedbacks from some social media. Social media contains both positive and negative user feedbacks which are important for software improvement. Positive user feedbacks are tentative new software requirements. Negative user feedbacks are suggestions for fixing existing features. Lacking adequate methods on how to elicit these requirements may eventually result to inefficient software evolution. This paper proposes an approach to extract requirements automatically from user feedbacks on social media and classify user feedbacks to requirements and non-requirements using Naïve Bayes's Machine Learning. The approach is evaluated with an example. A preliminary result is present.
Managing caching strategies for stream reasoning with reinforcement learning

Abstract Efficient decision-making over continuously changing data is essential for many application domains such as cyber-physical systems, industry digitalization, etc. Modern stream reasoning frameworks allow one to model and solve various real-world problems using incremental and continuous evaluation of programs as new data arrives in the stream. Applied techniques use, e.g., Datalog-like materialization or truth maintenance algorithms to avoid costly re-computations, thus ensuring low latency and high throughput of a stream reasoner. However, the expressiveness of existing approaches is quite limited and, e.g., they cannot be used to encode problems with constraints, which often appear in practice. In this paper, we suggest a novel approach that uses the Conflict-Driven Constraint Learning (CDCL) to efficiently update legacy solutions by using intelligent management of learned constraints. In particular, we study the applicability of reinforcement learning to continuously assess the utility of learned constraints computed in previous invocations of the solving algorithm for the current one. Evaluations conducted on real-world reconfiguration problems show that providing a CDCL algorithm with relevant learned constraints from previous iterations results in significant performance improvements of the algorithm in stream reasoning scenarios.
Trust-Driven Vehicle Product-Service System: A Blockchain Approach

For the automotive industry, promoting more value-adding services in usage stage is a long-standing challenge for global manufacturers. As many cars have an unknown history, it is proven difficult, if not impossible, to prevent scams and frauds when offering car services. As an emerging technology, blockchain is characterized by high immutability, transparency, security, and traceability. Such characteristics make blockchain a highly promising empowering technology to maintain mutual trust among stakeholders in vehicle product-service system (PSS). This paper presents a blockchain-based PSS framework, which is intended to address the trust issue vehicle PSS.
Initial Results from Using an Intelligent System to Analyse Powered Wheelchair Users’ Data

Research in this paper presents a technique to collect powered wheelchair users' data using an intelligent system. Python programming language is used to create a program that will collect data for future analysis. The collected data considers driving session details, medication administered that can affect driving ability, and the type of input devices used to control a powered wheelchair. Data is collected on a Raspberry Pi microcomputer and is sent after each session via email. Data is placed in the body of the emails and in an attached file. Data will be used for future analysis and will be considered as a training data set to train an intelligent system to predict future route patterns for different wheelchair users. In addition, data will be used to analyze the ability of a user to operate their wheelchair, and monitor users' progress from one session to another, compare the progress of different users with the same type of disability and identify the most suitable input device for each user and route.
Fitness evaluation reuse for accelerating GPU-based evolutionary induction of decision trees

Decision trees (DTs) are one of the most popular white-box machine-learning techniques. Traditionally, DTs are induced using a top-down greedy search that may lead to sub-optimal solutions. One of the emerging alternatives is an evolutionary induction inspired by the biological evolution. It searches for the tree structure and tests simultaneously, which results in less complex DTs with at least comparable prediction performance. However, the evolutionary search is computationally expensive, and its effective application to big data mining needs algorithmic and technological progress. In this paper, noting that many trees or their parts reappear during the evolution, we propose a reuse strategy. A fixed number of recently processed individuals (DTs) is stored in a so-called repository. A part of the repository entry (related to fitness calculations) is maintained on a CPU side to limit CPU/GPU memory transactions. The rest of the repository entry (tree structures) is located on a GPU side to speed up searching for similar DTs. As the most time-demanding task of the induction is the DTs’ evaluation, the GPU first searches similar DTs in the repository for reuse. If it fails, the GPU has to evaluate DT from the ground up. Large artificial and real-life datasets and various repository strategies are tested. Results show that the concept of reusing information from previous generations can accelerate the original GPU-based solution further. It is especially visible for large-scale data. To give an idea of the overall acceleration scale, the proposed solution can process even billions of objects in a few hours on a single GPU workstation.
RecSys 2020 Challenge Workshop: Engagement Prediction on Twitter’s Home Timeline

The workshop features presentations of accepted contributions to the RecSys Challenge 2020, organized by Politecnico di Bari, Free University of Bozen-Bolzano, TU Wien, University of Colorado, Boulder, and Universidade Federal de Campina Grande, and sponsored by Twitter. The challenge focuses on a real-world task of Tweet engagement prediction in a dynamic environment. The goal is to predict the probability for different types of engagement (Like, Reply, Retweet, and Retweet with comment) of a target user for a set of Tweets, based on heterogeneous input data. To this end, Twitter has released a large public dataset of ~160M public Tweets, obtained by subsampling within ~2 weeks, that contains engagement features, user features, and Tweet features. A peculiarity of this challenge is related to the recent regulations on data protection and privacy. The challenge data set was compliant: if a user deleted a Tweet, or their data from Twitter, the dataset was promptly updated. Moreover, each change in the dataset implied new evaluations of all submissions and the update of the leaderboard metrics.
Robust automatic modulation classification based on convolutional and recurrent fusion network

Automatic modulation classification (AMC) is a critical step to recognize the unknown signal modulation types. It is widely applied in non-cooperative communication systems with diverse modulation types and complex communication environments. In recent years, existing deep learning based AMC methods have been developed to improve the performance of classification in additive white Gaussian noise (AWGN) or Rayleigh channel. However, these methods do not consider the interference problem caused by the Doppler shift in high mobile communication environment. In this paper, we propose a deep learning based robust AMC (RAMC) method to effectively suppress the Doppler shift and achieve classification performance. The proposed convolutional and recurrent fusion network (CRFN) consists of the combination of convolutional neural network (CNN) and simple recurrent unit (SRU) to classify eight types of modulation signal under additive white Gaussian noise (AWGN) and different Doppler shifts. We consider four kinds of fusion networks, i.e., SRU series CNN (SSC), CNN series SRU (CSS), CNN parallel SRU (CPS) and weight average (WA). Simulation results show that the proposed CRFN-CSS method achieves the best performance and brings better performance than either CNN or SRU in 100 Hz Doppler shift. What is more, CRFN-CSS is also robust in the small range of Doppler shifts.
Measure It Super Simple (MISS) activity tracker: (re)design of a user-friendly interface and evaluation of experiences in daily life

The purposes of this study were, first, to (re)design the user-interface of the activity tracker known as the MOX with the help of input from elderly individuals living independently and, second, to assess the use of and experiences with the adapted Measure It Super Simple (MISS) activity tracker in daily life.The double diamond method, which was used to (re)design the user-interface, consists of four phases: discover, define, develop, and deliver. As a departure point, this study used a list of general design requirements that facilitate the development of technology for the elderly. Usage and experiences were assessed through interviews after elderly individuals had used the activity tracker for 2 weeks.In co-creation with thirty-five elderly individuals (65 to 89-years-old) the design, feedback system, and application were further developed into a user-friendly interface: the Measure It Super Simple (MISS) activity. Twenty-eight elderly individuals (65 to 78-years-old) reported that they found the MISS activity easy to use, needed limited help when setting the tracker up, and required limited assistance when using it during their daily lives.This study offers a generic structured methodology and a list of design requirements to adapt the interface of an existing activity tracker consistent with the skills and needs of the elderly. The MISS activity seemed to be successfully (re)designed, like the elderly who participated in this pilot study reported that anyone should be able to use it.IMPLICATIONS FOR REHABILITATIONThis study provides an overview of important characteristics of the activity tracker interface for elderly individuals that can be used when choosing an appropriate activity tracker.This study can serve as a model that demonstrates how to adapt other eHealth and mHealth tools to improve the user-centred design.The MISS activity seems to be an elderly-friendly activity tracker that can facilitate a meaningful experience.
Interfaces and Human Decision Making for Recommender Systems

As an interactive intelligent system, recommender systems are developed to give recommendations that match users’ preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from users’ perspectives. The field has reached a point where it is ready to look beyond algorithms, into users’ interactions, decision making processes, and overall experience. The series of workshops on Interfaces and Human Decision Making for Recommender Systems focuses on the ”human side” of recommender systems. The goal of the research stream featured at the workshop is to improve users’ overall experience with recommender systems by integrating different theories of human decision making into the construction of recommender systems and exploring better interfaces for recommender systems. In this summary, we introduce 7th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’20, review its history, and discuss most important topics considered at the workshop.
Convolutional neural network with adaptive inferential framework for skeleton-based action recognition

In the task of skeleton-based action recognition, CNN-based methods represent the skeleton data as a pseudo image for processing. However, it still remains as a critical issue of how to construct the pseudo image to model the spatial dependencies of the skeletal data. To address this issue, we propose a novel convolutional neural network with adaptive inferential framework (AIF-CNN) to exploit the dependencies among the skeleton joints. We particularly investigate several initialization strategies to make the AIF effective with each strategy introducing the different prior knowledge. Extensive experiments on the dataset of NTU RGB+D and Kinetics-Skeleton demonstrate that the performance is improved significantly by integrating the different prior information. The source code is available at: https://github.com/hhe-distance/AIF-CNN.
Identifying noisy labels with a transductive semi-supervised leave-one-out filter

Obtaining data with meaningful labels is often costly and error-prone. In this situation, semi-supervised learning (SSL) approaches are interesting, as they leverage assumptions about the unlabeled data to make up for the limited amount of labels. However, in real-world situations, we cannot assume that the labeling process is infallible, and the accuracy of many SSL classifiers decreases significantly in the presence of label noise. In this work, we introduce the LGC_LVOf, a leave-one-out filtering approach based on the Local and Global Consistency (LGC) algorithm. Our method aims to detect and remove wrong labels, and thus can be used as a preprocessing step to any SSL classifier. Given the propagation matrix, detecting noisy labels takes O(cl) per step, with c the number of classes and l the number of labels. Moreover, one does not need to compute the whole matrix, but only a l × l submatrix corresponding to interactions between labeled instances. As a result, our approach is best suited to datasets with a large amount of unlabeled data but not many labels. Results are provided for a number of datasets, including MNIST and ISOLET. LGC_LVOf appears to be equally or more precise than the adapted gradient-based filter, and thus can be used in practice for active learning, where it may iteratively send labels for re-evaluation. We show that the best-case accuracy of the embedding of LGC_LVOf into LGC yields performance comparable to the best-case of ℓ1-based classifiers designed to be robust to label noise.
Modeling Vocal Entrainment in Conversational Speech Using Deep Unsupervised Learning

In interpersonal spoken interactions, individuals tend to adapt to their conversation partner's vocal characteristics to become similar, a phenomenon known as entrainment. A majority of the previous computational approaches are often knowledge driven and linear and fail to capture the inherent nonlinearity of entrainment. In this article, we present an unsupervised deep learning framework to derive a representation from speech features containing information relevant for vocal entrainment. We investigate both an encoding based approach and a more robust triplet network based approach within the proposed framework. We also propose a number of distance measures in the representation space and use them for quantification of entrainment. We first validate the proposed distances by using them to distinguish real conversations from fake ones. Then we also demonstrate their applications in relation to modeling several entrainment-relevant behaviors in observational psychotherapy, namely agreement, blame and emotional bond.
Second Workshop on Recommender Systems in Fashion – fashionXrecsys2020

Online Fashion retailers have significantly increased in popularity over the last decade, making it possible for customers to explore hundreds of thousands of products without the need to visit multiple stores or stand in long queues for checkout. Recommender Systems are often used to solve different complex problems in this domain, such as social fashion-aware recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. Moreover, the research interest on this area is increasing, demonstrated by the success of the first edition of the fashionXrecsys Workshop in 2019. The second edition of the workshop aims at providing an avenue for continuing the discussion of novel approaches and applications of recommendation systems in fashion and e-commerce.
Using MyLSU app to enhance student engagement and promote a smart town at a rural university in Zimbabwe

This paper discusses the evaluation of an app that was developed to enhance student engagement, thereby transforming a rural university into a smart town. This account provides an opportunity to strengthen the deployment of smart services and digital technologies that enhance student engagement. Developed countries have integrated virtual and physical systems to support education and industrial processes while developing countries such as Zimbabwe seem to be lagging. Government and funding organisations for higher education institutions have expressed interest in clear strategies that encompass student engagement in their policies and activities. The study focused on developing an app that enhanced student engagement aspects related to off-the classroom activities such as voting for leaders, sports and social activities. The study employed design science research methodology, a paradigm used for the development of artefacts that are aimed at solving real-world problems. A user-centred design methodology was adopted to guide the app development process as it enhances user acceptance. A total of 220 participants successfully evaluated the app through a questionnaire. The results of the evaluation revealed that the app was usable, easy to learn, and they would recommend it to others and agreed that they could use the app with no assistance. The results show that the app promoted student engagement and promoted concepts of a smart town. These results will help policymakers and educators to refine the app features and content to improve student engagement. Recommendations proffered can help researchers to explore how mobile apps can be used to promote smart-university towns.
Tripped at the Finishing Line: The Åland Islands Internet Voting Project

The Åland Islands spent years preparing an internet voting system, to be implemented for the first time in October 2019 for Parliamentary Elections. Despite this, the project was canceled the evening before the expected release date. In this paper, we explore the causes of this failure using a two-pronged approach including Information System failure perspectives and the approach to e-voting Mirabilis, focusing on organizational elements which provoked the decision not to use the system.
High-Quality Single-Model Deep Video Compression with Frame-Conv3D and Multi-frame Differential Modulation

Deep learning (DL) methods have revolutionized the paradigm of computer vision tasks and DL-based video compression is becoming a hot topic. This paper proposes a deep video compression method to simultaneously encode multiple frames with Frame-Conv3D and differential modulation. We first adopt Frame-Conv3D instead of traditional Channel-Conv3D for efficient multi-frame fusion. When generating the binary representation, the multi-frame differential modulation is utilized to alleviate the effect of quantization noise. By analyzing the forward and backward computing flow of the modulator, we identify that this technique can make full use of past frames' information to remove the redundancy between multiple frames, thus achieves better performance. A dropout scheme combined with the differential modulator is proposed to enable bit rate optimization within a single model. Experimental results show that the proposed approach outperforms the H.264 and H.265 codecs in the region of low bit rate. Compared with recent DL-based methods, our model also achieves competitive performance.
Solar Power Generation Analysis and Forecasting Real-World Data Using LSTM and Autoregressive CNN

Generated power of a solar panel is volatile and susceptible to environmental conditions. In this study, we have analyzed variables affecting the generated power of a 17.5 kW real-world solar power plant with respect to five independent variables over the generated power: irradiance, time of measurement, panel's temperature, ambient temperature and cloudiness of the weather at the time of measurement. After our analysis, we have trained three different models to predict intra-day solar power forecasts of the plant. Our models are able to predict future power output of the solar power plant with less than 10% RMSE without requiring additional sensor data, e.g. a camera to observe clouds. Based on our forecasting accuracy, our study promises: fast, scaleable and effective solutions to solar power plant maintainers and may facilitate grid safety on a large scale.
Fairness-aware Recommendation with librec-auto

Comparative experimentation is important for studying reproducibility in recommender systems. This is particularly true in areas without well-established methodologies, such as fairness-aware recommendation. In this paper, we describe fairness-aware enhancements to our recommender systems experimentation tool librec-auto. These enhancements include metrics for various classes of fairness definitions, extension of the experimental model to support result re-ranking and a library of associated re-ranking algorithms, and additional support for experiment automation and reporting. The associated demo will help attendees move quickly to configuring and running their own experiments with librec-auto.
SSFNET-VOS: Semantic segmentation and fusion network for video object segmentation

Most of the recent successful approaches for video object segmentation are highly complex, heavily rely on fine-tuning of the first frame, are slow, and henceforth, are of constrained practical use. In this work, we introduce a novel approach of video object segmentation using unsupervised learning. The complete process is divided into two phases where base frame and current frame are considered for segmentation. In the first phase, we generate the coarse region proposals, bounding boxes and scores, then in the next phase, the feature extraction process is carried out where attention network is incorporated for feature encoding. Finally, these features are scaled and fused using Softmax operation to generate the object segmentation. The performance of proposed approach is compared with several state-of-art techniques on challenging DAVIS 2016 & 2017 datasets. The experimental study shows that the proposed semantic segmentation and fusion network for video object segmentation (SSFNET-VOS) achieves better segmentation with less error in terms of segmentation accuracy.