Reviewerly/exHarmony huggingface dataset:
24-Gaze-Point Calibration Method for Improving the Precision of AC-EOG Gaze Estimation

This paper sought to improve the precision of the Alternating Current Electro-Occulo-Graphy (AC-EOG) gaze estimation method. The method consisted of two core techniques: To estimate eyeball movement from EOG signals and to convert signals from the eyeball movement to the gaze position. In conventional research, the estimations are computed with two EOG signals corresponding to vertical and horizontal movements. The conversion is based on the affine transformation and those parameters are computed with 24-point gazing data at the calibration. However, the transformation is not applied to all the 24-point gazing data, but to four spatially separated data (Quadrant method), and each result has different characteristics. Thus, we proposed the conversion method for 24-point gazing data at the same time: To assume an imaginary center (i.e., 25th point) on gaze coordinates with 24-point gazing data and apply an affine transformation to 24-point gazing data. Then, we conducted a comparative investigation between the conventional method and the proposed method. From the results, the average eye angle error for the cross-shaped electrode attachment is x = 2.27 ° ± 0.46 ° and y = 1.83 ° ± 0.34 ° . In contrast, for the plus-shaped electrode attachment, the average eye angle error is is x = 0.94 ° ± 0.19 ° and y = 1.48 ° ± 0.27 ° . We concluded that the proposed method offers a simpler and more precise EOG gaze estimation than the conventional method.
HABLex: Human Annotated Bilingual Lexicons for Experiments in Machine Translation

Bilingual lexicons are valuable resources used by professional human translators.While these resources can be easily incorporated in statistical machine translation, it is unclear how to best do so in the neural framework.In this work, we present the HABLex dataset, designed to test methods for bilingual lexicon integration into neural machine translation.Our data consists of humangenerated alignments of words and phrases in machine translation test sets in three language pairs (Russian→English, Chinese→English, and Korean→English), resulting in clean bilingual lexicons which are well matched to the reference.We also present two simple baselines-constrained decoding and continued training-and an improvement to continued training to address overfitting.
DEFYING THE CERTIFICATION DIPLOMA FORGERY WITH BLOCKCHAIN PLATFORM: A PROPOSED MODEL

In the University setting, part of management recorded all education transaction since the students register until they graduate.In this point, the transaction raises several new and crucial challenges.At a closer look, the concerns appeared due to no data integration, verification, and validation from parties both inside or outside of the university.In this respect, the data is not centralized or integrated.As this backdrop, this causes repeated data confirmation, and no one can authenticate the data validity when distributed to other parties.In this point of entry, the diploma certification forgery has become one of the significant issues in education in Indonesia.As a framework that enables the data integration, Blockchain contains verified data on every single of a transaction, so the data is trustworthy.With this in mind, this paper aims to discuss the best blockchain model for the university level.This study is qualitative-based research.As a result, McRhys model is introduced as a potential and implementable blockchain model in university in Indonesia.
MOPSO-Based CNN for Keyword Selection on Google Ads

Google Ads is an advertising agency that provides ads to advertisers. Advertisers match the user's search terms and push ads by selecting keywords related to their ad content. Keywords can determine the type of users an advertiser pushes, the effectiveness of the ad promotion, and the sales of the ad product. Automatically selecting keywords that are satisfactory to advertisers from a large number of keywords provided by Google Ads is the main task of this paper. But there is not too much time for the model to judge whether keywords are selected, choosing correct keywords in the shortest time is another task of this paper. Therefore, a structure of the model that can get some useful keywords for advertisers is designed and an improved multi-objective particle swarm optimization algorithm is proposed to achieve this multi-objective task. These are also the main contributions of this paper. To accomplish this multi-objective task, many technical issues need to be overcome, such as the mixed language problem, the imbalance problem, the problem of extracting features from corpora and so on. This paper proposes a corpus selection method to solve the mixed problem of Chinese and English in keywords, word embedding method to solve the representation of keywords, re-sampling to solve data imbalance problem, improved convolutional neural network (CNN) to solve classification problem, and a multi-objective particle swarm optimization algorithm (MOPSO) to achieve neural structure search of CNN so that the effect of the classification is improved and the training time is reduced. The keyword selection problem is solved with the combination of evolutionary computing, deep learning, machine learning, and text processing techniques. Experimental results show that the proposed algorithm greatly improved the accuracy of keyword selection and shortened the time of selecting keywords. Therefore, this algorithm has a good application value.
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment

Empowerment is an information-theoretic method that can be used to intrinsically motivate learning agents. It attempts to maximize an agent's control over the environment by encouraging visiting states with a large number of reachable next states. Empowered learning has been shown to lead to complex behaviors, without requiring an explicit reward signal. In this paper, we investigate the use of empowerment in the presence of an extrinsic reward signal. We hypothesize that empowerment can guide reinforcement learning (RL) agents to find good early behavioral solutions by encouraging highly empowered states. We propose a unified Bellman optimality principle for empowered reward maximization. Our empowered reward maximization approach generalizes both Bellman’s optimality principle as well as recent information-theoretical extensions to it. We prove uniqueness of the empowered values and show convergence to the optimal solution. We then apply this idea to develop off-policy actor-critic RL algorithms which we validate in high-dimensional continuous robotics domains (MuJoCo). Our methods demonstrate improved initial and competitive final performance compared to model-free state-of-the-art techniques.
A Practical Dialogue-Act-Driven Conversation Model for Multi-Turn Response Selection

Dialogue Acts play an important role in conversation modeling.Research has shown the utility of dialogue acts for the response selection task, however, the underlying assumption is that the dialogue acts are readily available, which is impractical, as dialogue acts are rarely available for new conversations.This paper proposes an end-to-end multi-task model for conversation modeling, which is optimized for two tasks, dialogue act prediction and response selection, with the latter being the task of interest.It proposes a novel way of combining the predicted dialogue acts of context and response with the context (previous utterances) and response (follow-up utterance) in a crossway fashion, such that, it achieves at par performance for the response selection task compared to the model that uses actual dialogue acts.Through experiments on two well known datasets, we demonstrate that the multi-task model not only improves the accuracy of the dialogue act prediction task but also improves the MRR for the response selection task.Also, the cross-stitching of dialogue acts of context and response with the context and response is better than using either one of them individually.
Advancing user quality of experience in 360-degree video streaming

Conventional streaming solutions for streaming 360- degree panoramic videos are inefficient in that they download the entire 360-degree panoramic scene, while the user views only a small sub-part of the scene called the viewport. This can waste over 80% of the network bandwidth. We develop a comprehensive approach called Mosaic that combines a powerful neural network-based viewport prediction with a rate control mechanism that assigns rates to different tiles in the 360-degree frame such that the video quality of experience is optimized subject to a given network capacity. We model the optimization as a multi-choice knapsack problem and solve it using a greedy approach.We also develop an end-to-end testbed using standardscompliant components and provide a comprehensive performance evaluation of Mosaic along with four other streaming techniques – two for conventional adaptive video streaming and two for 360- degree tile-based video streaming. Mosaic outperforms the best of the competition by as much as 50% in terms of median video quality.
Development of Cyber-Physical Speech-Controlled Wheelchair for Disabled Persons

The three-stage development of a cyber-physical speech-controlled wheelchair for disabled persons is described. Initially, a small prototype was built to test the feasibility of using cloud-based speech recognition systems for real-time wheelchair maneuvering. In the second stage, the full-sized prototype was built and tested in a laboratory and in a clinical environment. In the third stage, the full-scale prototype was equipped with distance sensors and an advanced control algorithm for semi-autonomous drive. The system architecture is described with the important addition of edge computing for speech recognition. Six cloud speech recognition services and two offline services were used, as using multiple speech recognition systems improves system reliability and latency. The software and hardware technologies are described, in addition to an innovative application of multiple cloud/edge systems for wheelchair motion control.
Privacy and Policy in Polystores: A Data Management Research Agenda

Modern data-driven technologies are providing new capabilities for working with data across diverse storage architectures and analyzing it in unified frameworks to yield powerful insights. These new analysis capabilities, which rely on correlating data across sources and types and exploiting statistical structure, have challenged classical approaches to privacy, leading to a need for radical rethinking of the meaning of the term. In the area of federated database technologies, there is a growing recognition that new technologies like polystores must incorporate the mitigation of privacy risks into the design process.
An Empirical Framework for Code Smell Prediction using Extreme Learning Machine

The software containing code smells indicates the violation of standard design and coding practices by developer during the development of the software system. Recent empirical studies observed that classes having code smells have higher probability of change proneness or fault proneness with respect to classes having no code smells [1]. The effort of removing bugs due to code smells increases exponentially if the smells are not identified during the earlier phases of software development. The code smell prediction using source code metrics can be used in starting phases of software development life cycle to reduce the maintenance and testing effort of software and also help in improving the quality of the software. The work in this paper empirically investigates and evaluates different classification techniques, feature selection techniques, and data sampling techniques to handle imbalance data in predicting 7 different types of code smell. The conclusion of this research is assessed over 629 application packages. The experimental finding confirms the estimating capability of different classifiers, feature selection, and data imbalance techniques for developing code smell prediction models. Our analysis also reveals that the models developed using one technique are superior than the models developed using other techniques.
Robust and Undetectable White-Box Watermarks for Deep Neural Networks.

Watermarking of deep neural networks (DNN) can enable their tracing once released by a data owner. In this paper we generalize white-box watermarking algorithms for DNNs, where the data owner needs white-box access to the model to extract the watermark, and attack and defend them using DNNs. White-box watermarking algorithms have the advantage that they do not impact the accuracy of the watermarked model. We demonstrate a new property inference attack using a DNN that can detect watermarking by any existing, manually designed algorithm regardless of training data set and model architecture. We then use a new training architecture and a further DNN to create a new white-box watermarking algorithm that does not impact accuracy, is undetectable and robust against moderate model transformation attacks.
Learned Collaborative Stereo Refinement

In this work, we propose a learning-based method to denoise and refine disparity maps of a given stereo method. The proposed variational network arises naturally from unrolling the iterates of a proximal gradient method applied to a variational energy defined in a joint disparity, color, and confidence image space. Our method allows to learn a robust collaborative regularizer leveraging the joint statistics of the color image, the confidence map and the disparity map. Due to the variational structure of our method, the individual steps can be easily visualized, thus enabling interpretability of the method. We can therefore provide interesting insights into how our method refines and denoises disparity maps. The efficiency of our method is demonstrated by the publicly available stereo benchmarks Middlebury 2014 and Kitti 2015.
Higher-order nonclassical properties of nonlinear charge pair cat states

The concept of nonlinear charge pair cat states is introduced and their higher-order nonclassical properties are studied. By comparing the nonlinear cases of photon-added states f_2(n) and a laser-driven trapped ion f_3(n) with the linear case f_1(n), it is shown that these states exhibit two-mode higher-order antibunching to all orders and the degree of antibunching depends on the suitable variables, especially on the nonlinear functions. We also show that in such states, the higher-order squeezing appears only in the even orders and the squeezing behaviours for the nonlinear cases f_2(n) and f_3(n) are quite opposite. These states are two-mode genuinely entangled regardless of the nonlinear functions and their entanglement properties are more obvious in the photon-added states f_2(n) case.
Deep learning apporach for image captioning in Hindi language

Generating image description automatically from the content of an image is one of the fundamental problems in artificial intelligence. This task involves the knowledge of both computer vision and natural language processing, called “Image caption generation” Many researches have been carried out in this field, but it was mainly focused on generating image descriptions in English, as existing image caption datasets are mostly in English. However, the image captioning should not be restricted by language. The lack of image captioning dataset other than English is a problem, especially for a morphologically rich language such as Hindi. Thus, to tackle this problem, this research constructed Hindi image caption dataset based on images from Flickr8k dataset using Google cloud translator, which is called Flickr8k-Hindi Datasets The Flickr8k-Hindi Datasets consist of four datasets based on a number of descriptions per image and clean or unclean descriptions. This study also finds the best effective method to generate image description using the encoder-decoder neural network model. The experiments showed that training the model with a single clean description per image generates higher-quality caption than a model trained with five uncleaned descriptions per image. Although model trained with five uncleaned descriptions per image achieved BLEU-I score of 0.585, which is the current state of the art.
Education, privacy, and big data algorithms: Taking the persons out of personalized learning

In this paper, we review the literature on philanthropy in education to provide a larger context for the role that technology company foundations, such as the Bill and Melinda Gates Foundation and Chan Zuckerberg Initiative, are playing with respect to the development and implementation of personalized learning. We then analyze the ways that education magazines and tech company foundation outreach discuss personalized learning, paying special attention to issues of privacy. Our findings suggest that competing discourses on personalized learning revolve around contested meanings about the type of expertise needed for twenty-first century learning, what self-directed learning should look like, whether education is about process or content, and the type of evidence that is required to establish whether or not personalized learning leads to better student outcomes. Throughout, privacy issues remain a hot spot of conflict between the desire for more efficient outcomes and a whole child approach that is reminiscent of John Dewey’s insight that public education plays a special role in creating citizens.
Deep neural network based calibration for freeform surface misalignments in general interferometer

In interferometric testing for optical freeform surfaces, the calibration of surface misalignment aberrations is a tremendous challenge. A complex surface figure with a six-axis degree of freedom introduces complex non-linear misaligned aberration variations with slight misalignments. The traditional sensitive matrix method provides considerable precision in only the linear area, which is unacceptable. In this paper, a deep neural network (DNN)-based calibration method is introduced. The well-trained DNN can treat this non-linear relation and, thus, yield accurate misalignment estimation. Subsequently, the estimated misalignments are simulated in a model to predict all the misalignment aberrations by ray tracing. These aberrations are then removed by a simple wavefront data subtraction. The simulation and experimental results show the feasibility of the proposed DNN-based method.
Reinforcement Learning Content Generation for Virtual Reality Applications

Abstract This work presents a Procedural Content Generation (PCG) method based on a Neural Network Reinforcement Learning (RL) approach that generates new environments for Virtual Reality (VR) learning applications. The primary objective of PCG methods is to algorithmically generate new content (e.g., environments, levels) in order to improve user experience. Researchers have started exploring the integration of Machine Learning (ML) algorithms into their PCG methods. These ML approaches help explore the design space and generate new content more efficiently. The capability to provide users with new content has great potential for learning applications. However, these ML algorithms require large datasets to train their generative models. In contrast, RL based methods take advantage of simulation to train their models. Moreover, even though VR has become an emerging technology to engage users, there have been few studies that explore PCG for learning purposes and fewer in the context of VR. Considering these limitations, this work presents a method that generates new VR environments by training an RL agent using a simulation platform. This PCG method has the potential to maintain users’ engagement over time by presenting them with new environments in VR learning applications.
Secure end-to-end processing of smart metering data

Abstract Cloud computing considerably reduces the costs of deploying applications through on-demand, automated and fine-granular allocation of resources. Even in private settings, cloud computing platforms enable agile and self-service management, which means that physical resources are shared more efficiently. Cloud computing considerably reduces the costs of deploying applications through on-demand, automated and fine-granular allocation of resources. Even in private settings, cloud computing platforms enable agile and self-service management, which means that physical resources are shared more efficiently. Nevertheless, using shared infrastructures also creates more opportunities for attacks and data breaches. In this paper, we describe the SecureCloud approach. The SecureCloud project aims to enable confidentiality and integrity of data and applications running in potentially untrusted cloud environments. The project leverages technologies such as Intel SGX, OpenStack and Kubernetes to provide a cloud platform that supports secure applications. In addition, the project provides tools that help generating cloud-native, secure applications and services that can be deployed on potentially untrusted clouds. The results have been validated in a real-world smart grid scenario to enable a data workflow that is protected end-to-end: from the collection of data to the generation of high-level information such as fraud alerts.
Pattern Sequence Neural Network for Solar Power Forecasting

We propose a new approach for time series forecasting, called PSNN, which combines pattern sequences with neural networks. It is a general approach that can be used with different pattern sequence extraction algorithms. The main idea is to build a separate prediction model for each pattern sequence type. PSNN is applicable to multiple related time series. We demonstrate its effectiveness for predicting the solar power output for the next day using Australian data from three data sources - solar power, weather and weather forecast. In our case study, we show three instantiations of PSNN by employing the pattern sequence extraction algorithms PSF, PSF1 and PSF2. The results show that PSNN achieved the most accurate results.
The College English Teaching Reform Supported by Multimedia Teaching Technology and Immersive Virtual Reality Technology

At present, due to various reforms and developments, such as the reform of educational technology, the reform of the overall teaching mode, the reform of teaching methods, the improvement of teachers'comprehensive quality, etc., the important content of college English teaching reform under the current new situation has been created. Supported by multimedia teaching technology and immersive virtual reality technology, China will surely achieve the important goal of teaching reform. Immersion virtual English teaching environment refers to a new type of college English teaching environment under the modern mode. Its main purpose is to solve the current dilemma in the implementation of immersion English teaching. Supported by the current environment, the traditional immersion college English teaching mode can break through the constraints of time and space, and gradually become a new immersion college English teaching mode supported by high-end computer science and technology.