Reviewerly/exHarmony huggingface dataset:
Survey of Automated Waste Segregation Methods

Waste segregation is a major aspect of any efficient waste management system. Since manual segregation is such a tedious task, there has been extensive research into the development of automated techniques for the same. The techniques are categorized as two distinct approaches—hardware-based approach which primarily employs the use of sensors, and software-based approach, which uses image processing and deep learning algorithms to perform the classification. This paper aims to highlight and study all the existing techniques and analyse the advantages and disadvantages of each approach.
Towards Enabling Secure Web-Based Cloud Services using Client-Side Encryption

The recent years have brought an influx of privacy conscious applications, that enable strong security guarantees for end-users via end-to-end or client-side encryption. Unfortunately, this application paradigm is not easily transferable to web-based cloud applications. The reason for this lies within adversary's enhanced control over client-side computing through application provided JavaScript. In this paper, we propose CryptoMembranes - a set of native client-side components that allow the development of web applications which provide a robust isolation layer between the client-side encrypted user data and the potentially untrusted JavaScript, while maintaining full interoperability with current client-side development practices. In addition, to enable a realistic transition phase, we show how CryptoMembranes can be realized for currently existing web browsers via a standard browser extension.
Customized Robot-Assisted Language Learning to Support Immigrants at Work

There is a strong societal pressure for the immigrants to learn the local language to be able to properly enter the work-life and social life in the new culture. The limit of resources in second language teaching and the challenges related to language practicing with native people, encourages us to search for alternative options for the second language learning. Robot-assisted language learning (RALL) provides one potential option. In this article, we focus on exploring the potentials of customized language robots for immigrants to support their professional development and adaptation to the local work contexts. We describe our qualitative and exploratory user study with 10 immigrants as participants, focusing on their user experience (UX) of the customized RALL solution for language learning at work settings. We collected data through 1) field observations of the authentic RALL sessions in a user pilot, and 2) user interviews in the end of the pilot. The research shows that customization of several aspects of RALL is needed to support immigrants? language learning at work. These include, e.g. the vocabulary and tasks, the level, the speed of language, and the timing of the RALL. The optimal customization can be achieved by co-designing the RALL within the multidisciplinary team including the workplace representatives, language trainer and programmer. The RALL provides a lot of potential for immigrants? language learning as it offers unique learning experiences and many possibilities to keep up the learner?s attention and motivation. However, there are also challenging aspects, which need further consideration.
A Multi-Feature Fusion Based Pedestrian Detection Method

This paper proposes an improved pedestrian detection algorithm, the Haar-like feature and Adaboost cascade classifier Histogram of Oriented Gradient (HOG) feature are utilizing to construct hybrid features library, then the weak classifiers in Adaboost are trained by using the combined hybrid features. And the strong classifiers are built based on the weak classifiers. In the proposed algorithm, For improving the detection speed, the HOG feature is rapidly calculated by integral histogram instead of gradient histogram features, and the experimental results showed that the method proposed improved the detection performance and detection accuracy of pedestrians.
ATOM: HMD-VR Interface to Learn Atomic Structure, Bonding and Historical Research Experiments.

Head-Mounted Displays (HMDs) based Virtual Reality (VR) has shown promising results in training and education. We present ATOM, an HMD-VR interface to educate students about atoms, atomic structures and historical research experiments conducted in understanding atomic structures. ATOM is designed to complement the classroom learning for grade 9 students through an interactive and practice-based learning experience. Preliminary evaluation with 10 students revealed higher interest, increase engagement and playfulness. The students also pointed out a few difficult user interactions.
On stability of exactness properties under the pro-completion

In this paper we formulate and prove a general theorem of stability of exactness properties under the pro-completion, which unifies several such theorems in the literature and gives many more. The theorem depends on a formal approach to exactness properties proposed in this paper, which is based on the theory of sketches. Our stability theorem has applications in proving theorems that establish links between exactness properties, as well as in establishing embedding (representation) theorems for classes of categories defined by exactness properties.
Why Do Deep Neural Networks with Skip Connections and Concatenated Hidden Representations Work?

Training the classical-vanilla deep neural networks (DNNs) with several layers is problematic due to optimization problems. Interestingly, skip connections of various forms (e.g. that perform the summation or concatenation of hidden representations or layer outputs) have been shown to allow the successful training of very DNNs. Although there are ongoing theoretical works to understand very DNNs that employ the summation of the outputs of different layers (e.g. as in the residual network), there is none to the best of our knowledge that has studied why DNNs that concatenate of the outputs of different layers (e.g. as seen in Inception, FractalNet and DenseNet) works. As such, we present in this paper, the first theoretical analysis of very DNNs with concatenated hidden representations based on a general framework that can be extended to specific cases. Our results reveal that DNNs with concatenated hidden representations circumnavigate the singularity of hidden representation, which is catastrophic for optimization. For substantiating the theoretical results, extensive experiments are reported on standard datasets such as the MNIST and CIFAR-10.
Detecting Online Fake Reviews via Hierarchical Neural Networks and Multivariate Features

In recent years, as the value and credibility of online reviews tend to influence people's shopping feelings and consumption decisions, various online fake reviews have been constantly emerging. Detecting online fake reviews has attracted widespread attention from both the business and research communities. The existing methods are usually to detect fake reviews on off-the-shelf algorithms using kinds of linguistic and behavioral features respectively. That ignores the fusion of different features and does not take into account that different features have different effects on model performance. In this research, a set of linguistic and non-linguistic features is explored, and an optimal feature subset is selected by using random forest algorithm and the sequential backward selection strategy. Then, a hierarchical neural networks for detecting online fake reviews is empirically proposed, which can learn local and global information from multivariate features. Experimental results show that the model proposed in this paper on multiple datasets is superior to the traditional discrete model and the existing neural network benchmark model, and has a good generalization ability.
Developing manufacturing execution system with predictive analysis

Abstract Digitalisation is currently developing in many sectors of the economy. The success of a manufacturing enterprise requires a transition to digital control of production and business processes, to the greatest extent possible without human intervention using artificial intelligence technologies. This research applies a Manufacturing Execution System (MES) with Predictive Analysis to an automatic production line (Chemical Line) which contains sensors, actuators and pumps controlled by four Programmable Logic Controllers, linked together, and being monitored through a Supervisory Control and Data Acquisition system. The production line composed of four different subsystems, responsible for filtration to bottling the chemical product. This paper tries to join the MES system with Artificial Neural Networks (ANN) in order to not only monitor the system but having predictive analysis to plan the future. In such way we will take advantage of the benefits of the ANN, such as Long-Short Term Memory architectures. The experimental data will be compared with other usual platforms, such as the Master SCADA itself, through the course of this research.
WITHDRAWN: Research on experimental tourism teaching mode based on modern educational technology

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
The Go Transformer: Natural Language Modeling for Game Play

This work applies natural language modeling to generate plausible strategic moves in the ancient game of Go. We train the Generative Pretrained Transformer (GPT-2) to mimic the style of Go champions as archived in Smart Game Format (SGF), which offers a text description of move sequences. The trained model further generates valid but previously unseen strategies for Go. Because GPT-2 preserves punctuation and spacing, the raw output of the text generator provides inputs to game visualization and creative patterns, such as the Sabaki project's game engine using auto-replays. Results demonstrate that language modeling can capture both the sequencing format of championship Go games and their strategic formations. Compared to random game boards, the GPT-2 fine-tuning shows efficient opening move sequences favoring corner play over less advantageous center and side play. Game generation as a language modeling task offers novel approaches to more than 40 other board games where historical text annotation provides training data (e.g., Amazons & Connect 4/6).
A citizen science approach for supporting rapid earthquake impact assessments

After a strong earthquake, the rapid identification of the extent of the affected area is the most crucial task of civil protection authorities. The information provided by those directly affected by an event makes the difference since it offers the opportunity to take advantage of direct observations and, in the case alert systems are available, to verify if they worked properly. Thanks to the fruitful collaboration between the Regional Civil Protection of Friuli Venezia Giulia and the National Institute of Oceanography and Applied Geophysics – OGS in Italy, a procedure for collecting rapid information about the impact of an earthquake using an expert crowdsourcing approach has been in place for some years. The volunteers of the civil protection, appropriately trained, upon receipt of the seismic event notification, are asked to rapidly compile a specific questionnaire, describing the impact of the event. Data are rapidly coded, and the results are summarized in a seismic impact map showing the effect of the earthquake throughout the territory in a simplified way. This map complements the impact estimation provided by the analysis of recorded shaking data. This paper describes the results obtained by analyzing the "Trained Volunteers Seismic Evaluation" questionnaire (TVSE) completed by volunteers for some of the most significant events in recent years.
When causation does not imply correlation

Current methods of detecting causal relationships from data rely on analysing the patterns of correlation among the variables. Given some basic assumptions about how causal relationships constrain these patterns, this allows causal inferences to be made. I demonstrate that one commonly used assumption, called Faithfulness (roughly, where there is causation there must be correlation), is robustly violated for a large class of systems of a type that occurs throughout the life and social sciences: control systems. These systems exhibit correlations indistinguishable from zero between variables that are directly causally connected, and can show very high correlations between variables that have no direct causal connection, only a connection via causal links between uncorrelated variables. Their patterns of correlation are robust, in that they remain unchanged when their parameters are varied. The violation of Faithfulness is fundamental to what a control system does: hold some variable constant despite the disturbing influences on it. No method of causal analysis that requires Faithfulness is applicable to such systems.
Illumination-Invariant Feature Point Detection Based on Neighborhood Information

Feature point detection is the basis of computer vision, and the detection methods with geometric invariance and illumination invariance are the key and difficult problem in the field of feature detection. This paper proposes an illumination-invariant feature point detection method based on neighborhood information. The method can be summarized into two steps. Firstly, the feature points are divided into eight types according to the number of connected neighbors. Secondly, each type of feature points is classified again according to the position distribution of neighboring pixels. The theoretical deduction proves that the proposed method has lower computational complexity than other methods. The experimental results indicate that, when the photometric variation of the two images is very large, the feature-based detection methods are usually inferior, while the learning-based detection methods performs better. However, our method performs better than the learning-based detection method in terms of the number of feature points, the number of matching points, and the repeatability rate stability. The experimental results demonstrate that the proposed method has the best illumination robustness among state-of-the-art feature detection methods.
Learning Tools of KVK Module using Augmented Reality Mobile Application for Remedial Education Program (REP)

Augmented Reality (AR) is one of the technologies that has increased popularity in industry revolution 4.0 (IR 4.0). This technology is widely used in the education sector that can help the students easily acquire, process, and remember the information. However, due to the lack of introduction to new technology in some education fields, primary students tend to only use conventional learning methods such as books or card games. By looking at this loophole, this project is focusing on the primary students that being called remedial students in Remedial Education Program (RP). We choose the consonant vowel consonant (KVK) module after a deep discussion with the remedial teacher and regional education officer in Batu Pahat. The record shows that remedial students are facing hardship to spell, read, and pronounce the KVK. We have successfully developed the AR application education platform apps by using the Unity Real-Time Development platform (Unity 3D) and Vuforia. Inside this application, there are two menus for learning purposes which are KVK and Suku Kata and one menu for evaluation which is Kuiz. This module has been tested to 25 remedial students and the results show that the evaluation time taken to answer the Kuiz from the apps has been reduced around 10% to 50% compared to the conventional method. We hope that this application can be applied widely for remedial students in elementary school in year one that has a problem in mastering the KVK for REP.
Study on collaborative filtering recommendation algorithm based on prediction for item rating

As the Internet is developing rapidly and people are becoming increasingly dependent on e-commerce, whether the users can be provided with accurate recommendation information has become the key to attracting users. Under this background, corresponding personalized recommendation systems have been proposed, and collaborative filtering algorithm is one of the algorithms commonly used in the recommendation systems. Collaborative filtering recommends the information in which the user is interested through analyzing the preferences of a group with similar interests and common experience. With the increasing number of users and items in the e-commerce system, the collaborative filtering algorithm is limited by data sparsity in operation, which will influence the quality of the recommendation results. This paper studied the collaborative filtering recommendation algorithm based on the prediction for item rating, and enhanced the accuracy of the recommendation algorithm by designing an improved similarity-based algorithm for predicting ratings based on the Pearson correlation coefficient.
A Web-based Data Visualization Tool Regarding School Dropouts and User Asssesment

Data visualization is important for understanding the enormous amount of data generated daily. The education domain generates and owns huge amounts of data. Presentation of these data in a way that gives users quick and meaningful insights is very important. One of the biggest challenges in education is school dropouts, which is observed from basic education levels to colleges and universities. This paper presents a web-based data visualization tool for school dropouts in Tanzania targeting primary and secondary schools, together with the users’ feedback regarding the developed tool. We collected data from the United Republic of Tanzania Government Open Data Portal and the President’s Office - Regional Administration and Local Government (PO-RALG). Python was then used to preprocess the data, and finally, with JavaScript, a web-based tool was developed for data visualization. User acceptance testing was conducted and the majority agreed that data visualization is very helpful for quickly understanding data, reporting, and decision making. It was also noted that the developed tool could be useful not only in the education domain but it could also be adopted by other departments and organizations of the government.
VRFCNN: Virtual Reference Frame Generation Network for Quality SHVC

For more efficient inter prediction in quality scalable high efficiency video coding (SHVC), a learning-based framework for generating a virtual reference frame (VRF) is proposed in this letter. In our method, reconstructed base layer (BL) and enhancement layer (EL) frames are employed to make the generated VRF and current EL frame as same as possible. To this end, this letter proposes a novel VRF generation convolutional neural network (VRFCNN) to jointly handle enhancement of corresponding BL frame and compensation of previous EL frame. Specifically, the VRFCNN consists of BL enhancement, EL compensation and feature fusion subnets. Previous EL frames are firstly compensated with the learned coarse flow between two adjacent BL frames and then adopted to provide interlayer information for corresponding BL enhancement. The learned finer flow between the EL and enhanced BL features is adopted to provide temporal information for previous EL compensation. For efficiently handling slow- and fast-motion videos, the enhanced BL and compensated EL features are fused to generate a VRF. Experimental results show that VRFCNN averagely achieves 11.8% BD-rate reduction under low delay P configuration, which outperforms other methods. The code of our VRFCNN approach is available at https://github.com/dq0309/VRFCNN.
The Design and Implementation of a Lightweight CoAP-based IoT Framework with Smart Contract Security Guarantee

With the growth of Internet of Things (IoT), users develop many different IoT applications in web cloud platforms. However, cloud-based web services do not usually apply to the constrained networks because of HyperText Transfer Protocol (HTTP). In addition, distributed mechanisms to achieve identity authentication or data confidentiality are becoming more and more important when the number of devices located in IoT networks increases. The paper proposes the IoT Framework based on IoTtalk. IoTtalk is a web platform for users to quickly develop IoT applications. To enhance the applicability of IoTtalk in IoT networks and support identity authentication as well as data confidentiality, we introduce CoAP-based IoT Proxy and smart contract-based IoT Chain respectively. The IoT Proxy promotes the applicability of IoTtalk in IoT networks through the Constrained Application Protocol (CoAP). The IoT Chain provides identity authentication as well as data confidentiality to IoTtalk through the blockchain and the smart contract. Our experiments show that the IoT Framework promotes the security of the IoTtalk and keeps the performance of IoTtalk.
On Robust Grouping Active Learning

Early active learning, in a common paradigm, usually selects representative samples for human annotating. This aligns with the goal of minimizing the overall reconstruction error in an unsupervised manner. While existing methods mainly focus on data samples that are drawn from individual yet high-dimensional feature space, they can hardly handle the real-world scenario where samples are often represented by low-dimensional features drawn from multiple groups (subspaces). In this case, how to leverage the grouping structure to select most representative samples becomes the key point to success. In this paper, we propose an unsupervised active learning framework, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Robust Grouping Active Learning</i> (RGAL), to achieve this goal. The key idea is to take into account of different degrees of information shared across data groups. Specifically in RGAL, we assume data from some group can be embedded in a low-dimensional space, as well as that the data distributions of different groups can overlap with each other to a certain degree. And RGAL controls such group overlaps by imposing sparsity constraints on a matrix of reconstruction coefficients. To encourage a smooth coefficient space, we also enforce a robust loss with Laplacian regularization for noise suppression. We perform extensive experiments on multiple tasks which normally require costly human annotation, including facial age estimation, video action recognition and medical image classification. Results on benchmark datasets clearly demonstrate the efficacy of our RGAL method compared state-of-the-art methods.