Reviewerly/exHarmony huggingface dataset:
University Students’ Perceptions on the Use of Google Translate: Problems and Solutions

Machine learning has globally become a trend in most educational settings. This study aims to explore students’ perceptions when using Google Translate (GT) to support their learning as well as their problems and solutions from GT. With the participation of 250 university students at a private educational institution, a 5-point Likert-scale questionnaire and a semi-structured interview were employed to examine how students perceived the use of GT in their learning process. The findings revealed that practically students had positive perceptions on GT’s use in learning. Several major problems were recorded when they used GT, and some recommended solutions were also considered for improvement. Specifically, students utilized GT as a learning tool, particularly for language study, because of its useful features such as multi-language translation, time saving, ease of use, and improving pronunciation. Although Google Translate has a number of advantages for students, several problems such as erroneous grammar and semantics on a frequent basis have led to misunderstanding of original words. As a result, students discovered that they needed to deal with these problems by double checking the results in a dictionary or other translation programs, as well as the help from a peer or supervisor. It is suggested that GT is a helpful machine translator, but students are encouraged to know how to make some judgement on its results for a better translation version.
Handling Sparse Rating Matrix for E-commerce Recommender System Using Hybrid Deep Learning Based on LSTM, SDAE and Latent Factor

E-commerce is the most essential application for conducting business transactions.Delivering product information to customers require an essential machine called recommender system.Recommender systems have been adopted in many large e-commerce companies such as Amazon, e-Bay, Alibaba, YouTube, iTunes, and so on.Ratings have become an essential factor to calculate product information.They are users' expressions about their satisfaction regarding a product or service.Unfortunately, the number of ratings is extremely sparse.Generating rating prediction is a major issue in the recommender system research field.The most popular model using latent factor or matrix factorization to generate rating prediction faced the problem in accuracy performance.This research aimed to develop a novel model to generate rating prediction using two deep learning variants based on Stack Denoising Auto Encoder (SDAE), Long Short Term Memory (LSTM), and combining with a latent factor model based on Probabilistic Matrix Factorization (PMF).This study considered integrated information resources, including user information and document product information.Following the experiment report involved in Movielens and Amazon Information Video dataset, our model outperformed previous works using PMF, Collaborative Deep Learning (CDL), Probabilistic Hybrid Deep Learning (PHD) and LSTM-PMF model, with more than 5% in average using Root Mean Square Error (RMSE) evaluation metrices.
STARE: Augmented Reality Data Visualization for Explainable Decision Support in Smart Environments

The Internet of Things (IoT) provides unprecedented opportunities for the access to and conflation of a myriad of heterogeneous data to support real-time decision-making within smart environments.Augmented Reality (AR) is on cusp of becoming mainstream and will allow for the ubiquitous visualization of IoT derived data.Such visualization will simultaneously permit the cognitive and visual binding of information to the physical object(s) to which they pertain.Important questions exist as to how one can efficiently filter, prioritize, determine relevance and adjudicate on individual information needs in support of real-time decision making.To this end, this paper proposes a novel AR decision support framework (STARE) to support immediate decisions within a smart environment by augmenting the user's focal objects with assemblies of semantically relevant IoT data and corresponding suggestions.In order to evaluate this technique, a remote user study was undertaken within a simulated smart home environment.The evaluation results demonstrate that the proposed Semantic Augmented Reality decision support framework leads to a reduction in information overloading and enhanced effectiveness, both in terms of IoT data interpretation and decision support.
The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

Using a Web search engine is one of today's most frequent activities. Exploratory search activities which are carried out in order to gain knowledge are conceptualized and denoted as Search as Learning (SAL). In this paper, we introduce a novel framework model which incorporates the perspective of both psychology and computer science to describe the search as learning process by reviewing recent literature. The main entities of the model are the learner who is surrounded by a specific learning context, the interface that mediates between the learner and the information environment, the information retrieval (IR) backend which manages the processes between the interface and the set of Web resources, that is, the collective Web knowledge represented in resources of different modalities. At first, we provide an overview of the current state of the art with regard to the five main entities of our model, before we outline areas of future research to improve our understanding of search as learning processes.
A Novel Deep Learning-Enabled Physical Education Mechanism

Race walking is one of the key events in the Tokyo Olympic Games, and also one of the strengths of China in athletics events. In recent years, China has made remarkable achievements in various race-walking competitions. However, with the improvement of the performance of race walkers, more and more technical problems have emerged, and the number of fouls due to nonstandard movements has increased significantly. It is a pity that athletes are disqualified for technical fouls in long-distance race-walking competitions. Therefore, it is necessary to introduce scientific training methods to help coaches strictly monitor the training process of athletes and accurately detect their standard degree of action in real-time. This paper mainly proposes a novel mechanism for foul recognition in race walking based on deep learning. Firstly, the image frames in the video are preprocessed by the Yolo algorithm to obtain the athletes' separated images. The U-Net network mixed with the attention mechanism is used to detect the athletes’ actions to identify fouls and nonstandard actions, so as to assist the coach to identify the athletes’ nonstandard actions in training and adjust them in time. Experiments show that the above method can identify the foul actions and nonstandard actions of multiple athletes in training at the same time quickly, and the recognition accuracy is higher than human eyes. It is more conducive to assist the coach to monitor and standardize the athletes’ actions in the long-term training process, so as to reduce the error rate and improve the performance.
Co-De|GT: The Gamification and Tokenisation of More-Than-Human Qualities and Values

The article explores how the quality of life within a deprived urban environment might be improved through the ‘gamification’ of and interaction with, more-than-human elements within the environment. It argues that such quality may be achieved through the community’s multicentered value from the bottom up. This is shown through the case study of the Co-De|GT urban mobile application that was developed in the Synergetic Landscapes unit through real-life research by design experimental studio teaching. Complimentary experimentation took place during the Relating Systems Thinking and Design 10 symposium in the Co-De|BP workshop, where experts were able to be collocated for interactive real-time data gathering. This application addresses the need for collective action towards more-than-human synergy across an urban ecosystem through gamification, community collaboration and DIY culture. It intends to generate a sustainable, scalable token economy where humans and non-humans play equal roles, earning, trading and being paid for goods and services to test such potentials for future economies underpinned by blockchain. This work diverges from dominant economic models that do not recognise the performance of and the limits to, material extraction from the ecosystem. The current economic model has led to the global financial crisis (GFC). Furthermore, it is based on the unsustainable perpetual consumption of services and goods, which may lead to the untangling and critical failure of the market system globally. Therefore, this work investigates how gamification and tokenization may support a complementary and parallel economic market that sustains and grows urban ecosystems. While the research does not speculate on policy implications, it posits how such markets may ameliorate some of the brittleness apparent in the global economic model. It demonstrates a systemic approach to urban ecosystem performance for the future post-Anthropocene communities and economies.
Learning Unsupervised Visual Representations using 3D Convolutional Autoencoder with Temporal Contrastive Modeling for Video Retrieval

The rapid growth of tag-free user-generated videos (on the Internet), surgical recorded videos, and surveillance videos has necessitated the need for effective content-based video retrieval systems. Earlier methods for video representations are based on hand-crafted, which hardly performed well on the video retrieval tasks. Subsequently, deep learning methods have successfully demonstrated their effectiveness in both image and video-related tasks, but at the cost of creating massively labeled datasets. Thus, the economic solution is to use freely available unlabeled web videos for representation learning. In this regard, most of the recently developed methods are based on solving a single pretext task using 2D or 3D convolutional network. However, this paper designs and studies a 3D convolutional autoencoder (3D-CAE) for video representation learning (since it does not require labels). Further, this paper proposes a new unsupervised video feature learning method based on joint learning of past and future prediction using 3D-CAE with temporal contrastive learning. The experiments are conducted on UCF-101 and HMDB-51 datasets, where the proposed approach achieves better retrieval performance than state-of-the-art. In the ablation study, the action recognition task is performed by fine-tuning the unsupervised pre-trained model where it outperforms other methods, which further confirms the superiority of our method in learning underlying features. Such an unsupervised representation learning approach could also benefit the medical domain, where it is expensive to create large label datasets.
Automatic Events Recognition in Low SNR Microseismic Signals of Coal Mine Based on Wavelet Scattering Transform and SVM

The technology of microseismic monitoring, the first step of which is event recognition, provides an effective method for giving early warning of dynamic disasters in coal mines, especially mining water hazards, while signals with a low signal-to-noise ratio (SNR) usually cannot be recognized effectively by systematic methods. This paper proposes a wavelet scattering decomposition (WSD) transform and support vector machine (SVM) algorithm for discriminating events of microseismic signals with a low SNR. Firstly, a method of signal feature extraction based on WSD transform is presented by studying the matrix constructed by the scattering decomposition coefficients. Secondly, the microseismic events intelligent recognition model built by operating a WSD coefficients calculation for the acquired raw vibration signals, shaping a feature vector matrix of them, is outlined. Finally, a comparative analysis of the microseismic events and noise signals in the experiment verifies that the discriminative features of the two can accurately be expressed by using wavelet scattering coefficients. The artificial intelligence recognition model developed based on both SVM and WSD not only provides a fast method with a high classification accuracy rate, but it also fits the online feature extraction of microseismic monitoring signals. We establish that the proposed method improves the efficiency and the accuracy of microseismic signals processing for monitoring rock instability and seismicity.
StickyLand: Breaking the Linear Presentation of Computational Notebooks

How can we better organize code in computational notebooks? Notebooks have become a popular tool among data scientists, as they seamlessly weave text and code together, supporting users to rapidly iterate and document code experiments. However, it is often challenging to organize code in notebooks, partially because there is a mismatch between the linear presentation of code and the non-linear process of exploratory data analysis. We present StickyLand, a notebook extension for empowering users to freely organize their code in non-linear ways. With sticky cells that are always shown on the screen, users can quickly access their notes, instantly observe experiment results, and easily build interactive dashboards that support complex visual analytics. Case studies highlight how our tool can enhance notebook users's productivity and identify opportunities for future notebook designs. StickyLand is available at https://github.com/xiaohk/stickyland.
ATMChain: Blockchain-Based Security Framework for Cyber-Physics System in Air Traffic Management

The air traffic management (ATM) system is an intelligent system that integrates the ground computer network, the airborne network, and the space satellite (communication and navigation) network. It has remarkable characteristics of cyber-physical system (CPS). The development of ATM system is inseparable from the application of new technologies. This paper proposes a security framework based on blockchain for CPS in the ATM system. Through the research on the characteristics of blockchain and CPS, this paper analyzes the necessity of integrating them into the research of the ATM system, demonstrates the feasibility of combining them, and then constructs the ATMChain framework mechanism to realize the in-depth integration of blockchain and CPS in ATM. On this basis, this paper gives the overall design architecture and implementation steps of the scheme. In addition, this paper also makes a series of analysis and demonstration from the perspective of scheme security. The research scheme will help to improve the security, reliability, and scalability of ATM services and provide a new reference for establishing a more security and efficient ATM system.
DyFiP

Filter pruning is one of the most effective ways to accelerate Convolutional Neural Networks (CNNs). Most of the existing works are focused on the static pruning of CNN filters. In dynamic pruning of CNN filters, existing works are based on the idea of switching between different branches of a CNN or exiting early based on the hardness of a sample. These approaches can reduce the average latency of inference, but they cannot reduce the longest-path latency of inference. In contrast, we present a novel approach of dynamic filter pruning that utilizes explainable AI along with early coarse prediction in the intermediate layers of a CNN. This coarse prediction is performed using a simple branch that is trained to perform top-k classification. The branch either predicts the output class with high confidence, in which case the rest of the computations are left out. Alternatively, the branch predicts the output class to be within a subset of possible output classes. After this coarse prediction, only those filters that are important for this subset of classes are then evaluated. The importances of filters for each output class are obtained using explainable AI. Using this concept of dynamic pruning, we are able not only to reduce the average latency of inference, but also the longest-path latency of inference. Our proposed architecture for dynamic pruning can be deployed on different hardware platforms.
Project IRL: Playful Co-Located Interactions with Mobile Augmented Reality

We present Project IRL (In Real Life), a suite of five mobile apps we created to explore novel ways of supporting in-person social interactions with augmented reality. In recent years, the tone of public discourse surrounding digital technology has become increasingly critical, and technology's influence on the way people relate to each other has been blamed for making people feel "alone together," diverting their attention from truly engaging with one another when they interact in person. Motivated by this challenge, we focus on an under-explored design space: playful co-located interactions. We evaluated the apps through a deployment study that involved interviews and participant observations with 101 people. We synthesized the results into a series of design guidelines that focus on four themes: (1) device arrangement (e.g., are people sharing one phone, or does each person have their own?), (2) enablers (e.g., should the activity focus on an object, body part, or pet?), (3) affordances of modifying reality (i.e., features of the technology that enhance its potential to encourage various aspects of social interaction), and (4) co-located play (i.e., using technology to make in-person play engaging and inviting). We conclude by presenting our design guidelines for future work on embodied social AR.
Multi-relation Graph Summarization

Graph summarization is beneficial in a wide range of applications, such as visualization, interactive and exploratory analysis, approximate query processing, reducing the on-disk storage footprint, and graph processing in modern hardware. However, the bulk of the literature on graph summarization surprisingly overlooks the possibility of having edges of different types. In this article, we study the novel problem of producing summaries of multi-relation networks, i.e., graphs where multiple edges of different types may exist between any pair of nodes. Multi-relation graphs are an expressive model of real-world activities, in which a relation can be a topic in social networks, an interaction type in genetic networks, or a snapshot in temporal graphs. The first approach that we consider for multi-relation graph summarization is a two-step method based on summarizing each relation in isolation, and then aggregating the resulting summaries in some clever way to produce a final unique summary. In doing this, as a side contribution, we provide the first polynomial-time approximation algorithm based on the k -Median clustering for the classic problem of lossless single-relation graph summarization. Then, we demonstrate the shortcomings of these two-step methods, and propose holistic approaches, both approximate and heuristic algorithms, to compute a summary directly for multi-relation graphs. In particular, we prove that the approximation bound of k -Median clustering for the single relation solution can be maintained in a multi-relation graph with proper aggregation operation over adjacency matrices corresponding to its multiple relations. Experimental results and case studies (on co-authorship networks and brain networks) validate the effectiveness and efficiency of the proposed algorithms.
Improving Mobile Game Performance with Basic Optimization Techniques in Unity

Creating video games can be a very complex process, which requires taking into account various hardware and software limitations. This process is even more complex for mobile games, which are limited to the resources that their platforms (mobile devices) offer in comparison to game consoles and personal computers. This restriction makes performance one of the top critical requirements, meaning that a videogame should be designed and developed more carefully. In order to reduce the resources that a game uses, there are optimization techniques that can be applied in different stages of the development. For the purposes of this article, we designed and developed a simple shooter videogame, intended for Android mobile devices. The game was developed with the Unity game engine and most of the models were designed with the 3D computer graphics software Blender. Two versions of the game were developed in order to study the differences in performance: one version that applies basic optimization techniques, such as low poly count for the models and the object pooling algorithm for the enemy’s spawn; and one where the aforementioned optimizations were not used. Even though the game is not large in scale, the optimized version achieves a better user experience and needs less resources in order to run smoothly. This means that in larger and more complex video games these optimizations could have a bigger impact on the performance of the final product. To measure how the techniques affected the two versions of the game, the values of frames per second, batches and triangles/polygons were taken under consideration and used as metrics for game performance in terms of CPU usage, rendering (GPU usage) and memory usage.
Towards training reproducible deep learning models

Reproducibility is an increasing concern in Artificial Intelligence (AI), particularly in the area of Deep Learning (DL). Being able to reproduce DL models is crucial for AI-based systems, as it is closely tied to various tasks like training, testing, debugging, and auditing. However, DL models are challenging to be reproduced due to issues like randomness in the software (e.g., DL algorithms) and non-determinism in the hardware (e.g., GPU). There are various practices to mitigate some of the aforementioned issues. However, many of them are either too intrusive or can only work for a specific usage context. In this paper, we propose a systematic approach to training reproducible DL models. Our approach includes three main parts: (1) a set of general criteria to thoroughly evaluate the reproducibility of DL models for two different domains, (2) a unified framework which leverages a record-and-replay technique to mitigate software-related randomness and a profile-and-patch technique to control hardware-related non-determinism, and (3) a reproducibility guideline which explains the rationales and the mitigation strategies on conducting a reproducible training process for DL models. Case study results show our approach can successfully reproduce six open source and one commercial DL models.
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning

Abstract Generalization is a major challenge for multi-agent reinforcement learning. How well does an agent perform when placed in novel environments and in interactions with new co-players? In this paper, we investigate and quantify the relationship between generalization and diversity in the multi-agent domain. Across the range of multi-agent environments considered here, procedurally generating training levels significantly improves agent performance on held-out levels. However, agent performance on the specific levels used in training sometimes declines as a result. To better understand the effects of co-player variation, our experiments introduce a new environment-agnostic measure of behavioral diversity. Results demonstrate that population size and intrinsic motivation are both effective methods of generating greater population diversity. In turn, training with a diverse set of co-players strengthens agent performance in some (but not all) cases.