Reviewerly/exHarmony huggingface dataset:
An Embedded Deep Learning Object Detection Model For Traffic In Asian Countries

Deep learning-based object detection algorithms have been developed through extensive research and achieved impressive performance. However, due to the heavy computation of deep convolutional neural networks, it is not suitable to deploy all object detection algorithms on resource-constrained embedded systems, such as NVIDIA Jetson TX2. In this paper, a size-efficient YOLO v3-based model is adopted with two key points, a better backbone convolutional neural network and a fusing structure for the multiscale feature map. We have deployed the model on NVIDIA Jetson TX2 to test the performance of accuracy, model size, computational complexity and detecting time. Furthermore, this methodology has been awarded as the best bicycle detector in the 2020 embedded deep learning object detection model compression competition for traffic in Asian countries.
News recommendation system using collaborative filtering method

Abstract In the era of Internet of Things (IoT), various facts, news, and up-to-date information are presented online that can be accessed quickly, anytime, and anywhere. However, sometimes the news which displayed is not up-to-date or not in accordance with the interests of the reader. This study aims to build a system that can recommend the latest news, which is most often accessed, and that is in accordance with the interests of the reader. The method that used in this study is Collaborative Filtering (CF) to rank news as the best recommendation for readers. Based on the results of experiments conducted on 19 examples of news, the percentage of accuracy results of the recommendations was around 84.2% compared to the manual calculation. This shows that CF capable to provide news recommendations good enough.
Some Aspects of Individual Approach to Learning Based on Tutoring Integrated Expert Systems

Issues of intelligent individual education are of particular relevance today. This paper describes some aspects of building an individual strategy of education and training of young specialists with the help of tutoring integrated expert systems, which have been developed and used in the educational process of MEPhI for many years.
G2T: Generating Fluent Descriptions for Knowledge Graph

Generating natural language descriptions for knowledge graph (KG) is an important category for intelligent writing. Recent models on this task substitute the sequence encoder in a commonly used encoder-decoder framework with a graph encoder. However, these models suffer from entity missing and repetition. In this paper, we propose a novel end-to-end generation model named G2T, which integrates a novel Graph Structure Enhanced Mechanism (GSEM) and a Copy Coverage Loss (CCL). Instead of just considering graph structure in the encoding phase in most existing methods, our GSEM fully utilizes graph structure in the decoding phase and helps to mitigate entity missing problem. Moreover, our CCL can further improve performance by avoiding generating repeated entities. With their help, our model is capable of generating fluent description for KG. The results of automatic and human evaluations show that our model outperforms the state-of-the-art models.
Configuration-Dependent Characteristics of Virtual-Mode Quantum Sensing Systems

Previous works have examined the use of a distributed network of quantum illumination and sensing nodes to augment the available number of distinguishable modes with an additional set of virtual modes. It has been shown that this approach in theory can be applied to achieve a required level of target detection performance within the constraints imposed by the limited rate at which entangled microwave photons can be generated. In this paper we examine how this performance is impacted by the spatial geometry of the network of nodes with respect to the target's range and cross section.
P-frame Coding Proposal by NCTU: Parametric Video Prediction through Backprop-based Motion Estimation

This paper presents a parametric video prediction scheme with backprop-based motion estimation, in response to the CLIC challenge on P-frame compression. Recognizing that most learning-based video codecs rely on optical flow-based temporal prediction and suffer from having to signal a large amount of motion information, we propose to perform parametric overlapped block motion compensation on a sparse motion field. In forming this sparse motion field, we conduct the steepest descent algorithm on a loss function for identifying critical pixels, of which the motion vectors are communicated to the decoder. Moreover, we introduce a critical pixel dropout mechanism to strike a good balance between motion overhead and prediction quality. Compression results with HEVC-based residual coding on CLIC validation sequences show that our parametric video prediction achieves higher PSNR and MS-SSIM than optical flow-based warping. Moreover, our critical pixel dropout mechanism is found beneficial in terms of rate-distortion performance. Our scheme offers the potential for working with learned residual coding.
A Context-Aware Collaborative Filtering Recommender System Based on GCNs

In order to solve the problem of data sparsity inherent in collaborative filtering systems, this paper proposes a context-aware collaborative filtering method based on user similarity measures. This method can effectively integrate context information, social information and other information into the recommendation process. First, a graph convolutional network (GCN) is used to learn the relationship between the context information of each user node and the user evaluation, and consider the potential social network structure in the recommendation system. Secondly, the direct similarity between neighborhoods is measured by representing the multi-source data as user scores of popular items, and then the indirect similarity between non-neighborhood users is calculated based on the similarity propagation. Finally, two similarity values are used to normalize the content in the user item scoring matrix to prevent overfitting. Experimental results on actual data sets show that this method can effectively alleviate the problem of data sparsity in the collaborative filtering system, and can integrate multi-source data, effectively improving the accuracy of the recommendation system.
JA-POLS: A Moving-Camera Background Model via Joint Alignment and Partially-Overlapping Local Subspaces

Background models are widely used in computer vision. While successful Static-camera Background (SCB) models exist, Moving-camera Background (MCB) models are limited. Seemingly, there is a straightforward solution: 1) align the video frames; 2) learn an SCB model; 3) warp either original or previously-unseen frames toward the model. This approach, however, has drawbacks, especially when the accumulative camera motion is large and/or the video is long. Here we propose a purely-2D unsupervised modular method that systematically eliminates those issues. First, to estimate warps in the original video, we solve a joint-alignment problem while leveraging a certifiably-correct initialization. Next, we learn both multiple partially-overlapping local subspaces and how to predict alignments. Lastly, in test time, we warp a previously-unseen frame, based on the prediction, and project it on a subset of those subspaces to obtain a background/foreground separation. We show the method handles even large scenes with a relatively-free camera motion (provided the camera-to-scene distance does not change much) and that it not only yields State-of-the-Art results on the original video but also generalizes gracefully to previously-unseen videos of the same scene. Our code is available at https://github.com/BGU-CS-VIL/JA-POLS.
A Word Representation Method Based on Glyph of Chinese Character

The quality of words representation has an important impact on natural language processing tasks. Aiming at the problems in the current Chinese word representation method: the training data set is huge, the model quality depends on the data set, and the model stability is poor, a word representation method based on the glyph of Chinese character, Glyph2Vec, is proposed. Taking full advantage of the semantic information contained in Chinese characters, a glyph auto-encoder is constructed based on a convolutional auto-encoder. The glyph auto-encoder is used to obtain Chinese character embedding by mapping the glyph of Chinese character in the potential low-dimensional semantic space. In the Chinese named entity recognition task experiment, Glyph2Vec improves the accuracy to F1 score by 0.77%, 1.84%, and 1.31% respectively, compared with Word2Vec. The experimental results show that the method proposed is better than the existing results, which proves the effectiveness of this method.
Multi-Level Multimodal Transformer Network for Multimodal Recipe Comprehension

Multimodal Machine Comprehension ($\rm M^3C$) has been a challenging task that requires understanding both language and vision, as well as their integration and interaction. For example, the RecipeQA challenge, which provides several $\rm M^3C$ tasks, requires deep neural models to understand textual instructions, images of different steps, as well as the logic orders of food cooking. To address this challenge, we propose a Multi-Level Multi-Modal Transformer (MLMM-Trans) framework to integrate and understand multiple textual instructions and multiple images. Our model can conduct intensive attention mechanism at multiple levels of objects (e.g., step level and passage-image level) for sequences of different modalities. Experiments have shown that our model can achieve the state-of-the-art results on the three multimodal tasks of RecipeQA.
Collaborative Filtering Recommendation Algorithm Fuses Semantic Nearest Neighbors Based on Knowledge Graph

To solve the problem that collaborative filtering only uses the items-users rating matrix, a collaborative filtering recommendation algorithm fuses semantic nearest neighbors based on Knowledge Graph is presented. This method fuses semantic nearest neighbors and embeds the existing semantic database into a low-dimensional semantic space. It combines the semantic information of items and collaborative filtering using calculating the semantic similarity between items. The shortcoming of collaborative filtering which does not consider the semantic information of items is overcame, and therefore the effect of collaborative filtering result is improved on the semantic level. Experimental results show that the proposed methods can get higher values on Recall, Accuracy and F-value for collaborative filtering systems.
Learning Clasiffier Systems with Hebbian Learning for Autonomus Behaviors

One of the main characteristics of multi-agent systems is the ability to solve problems achieving objectives. This is possible because of the learning mechanisms that are embedded in the systems and go from neural networks up to vector support machines. Agent-based systems stand out for their autonomy and adaptation of dynamic conditions of the environment. This article presents the Hebbian theory, which is one of the learning methods from the neuroscience field. A particularity presented by the Hebbian theory from the computer since field perspective is the primary mechanism of synaptic plasticity where the value of a synaptic connection increases if neurons on both sides of a said synapse are activated repeatedly, creating a new one simultaneously. This mechanism is integrated into the Learning Classifier Systems (LCS) to validate its effectiveness in the solution task, and can be used in multi-agent systems.
Cut Elimination for Systems of Transparent Truth with Restricted Initial Sequents

The paper studies a cluster of systems for fully disquotational truth based on the restriction of initial sequents. Unlike well-known alternative approaches, such systems display both a simple and intuitive model theory and remarkable proof-theoretic properties. We start by showing that, due to a strong form of invertibility of the truth rules, cut is eliminable in the systems via a standard strategy supplemented by a suitable measure of the number of applications of truth rules to formulas in derivations. Next, we notice that cut remains eliminable when suitable arithmetical axioms are added to the system. Finally, we establish a direct link between cut-free derivability in infinitary formulations of the systems considered and fixed-point semantics. Noticeably, unlike what happens with other background logics, such links are established without imposing any restriction to the premises of the truth rules.
Research on multi‐source mobile commerce service recommendation model of data fusion based on tree network

Summary With the rapid development of e‐commerce and computer technology, the recommendation service in business activities has no longer implemented by people, but by software technology. In general, the e‐commerce website recommendation service can have divided into two types: standardization and personalization. The design of the recommendation service should be as follows, the content presentation should meet the consumer's shopping needs; we need to provide different processing level information according to the consumer's cognitive level; appropriately select the expression and strengthen the credibility of the recommendation. This article proposes a multi‐source data fusion strategy of mobile commerce service recommendation for tree‐based networks. On this basis, the tree‐node relationship is stored through redundant data, thus achieving complete decoupling between tree‐type nodes and reaching the tree. The purpose is to have the efficient storage and access of structured data. For the multimedia mobile device is limited by hardware conditions, limited storage capacity, weak computing power, and so on, using client‐side cached partial data and on‐demand sequence request server data and other strategies, making full use of the hierarchical structure of tree structure to design efficient. The tree synchronization model enables efficient buffering and accessing of server data by mobile devices.
Features of Teaching Economic Disciplines to Students of Engineering Specialties

the authors raise the problem of teaching economic disciplines to students of engineering specialties; prove the necessity and effectiveness of using modern interactive teaching methods, namely, business computer games. The article proposes five formats for conducting training sessions using business computer games, and evaluates the effectiveness of such classes.
Development of an improved view registration method with application in optical velocimetry

View registration (VR) refers to the process of determining the orientations and locations of the images captured by one or more cameras. It represents a fundamental procedure underpinning a wide spectrum of applications, ranging from optical sensing, object identification, and image processing. Existing VR procedures typically rely on a set of images captured on a fixed calibration target (referred to as FVM, fixed view method, hereafter). This work reports the development of a new view registration procedure, referred to as the variable view method (VVM). The VVM procedure relies on a set of images taken on a calibration target at various orientations and/or distances. As shown in this work, both numerically and experimentally, the VVM can significantly improve the VR accuracy compared to the FVM, by more than 40% in the tests conducted here. Such enhanced accuracy is expected to benefit a range of applications, and a specific example is demonstrated involving three-dimensional velocity measurement based on particle imaging velocimetry.
PhD Forum Abstract: Understanding Deep Model Compression for IoT Devices

Today’s deep neural networks require substantial computation resources for their training, storage, and inference, which limits their effective use on resource-constrained devices. Many recent research activities explore different options for compressing and optimizing deep models for the Internet of Things (IoT). Our work so far has focused on two important aspects of deep neural network compression: class-dependent model compression and explainable compression. We shortly summarize our contributions and conclude with an outline of our future research directions.
Multi-Directional Projection Transformations for Machine Learning based Object Matching

In case of object matching on low-quality images, projection-based similarity measurement is an often used approach. In recent advances, deep convolutional neural networks are used to extract the features from the input data. In this paper, an analysis is done to compare end-to-end methods with projection-based preprocessing methods, including the Radon transform and the Trace transform. The paper explains the method of convolutional neural architecture generation based on input sizes, followed by the parallel processing on a cluster of workstations. To show the applicability of multi-directional projections as a preprocessing method on small storage units, model memory cost and prediction performance are both measured and optimal results are selected accordingly.
Creating a Children-Friendly Reading Environment via Joint Learning of Content and Human Attention

Technological advancements have led to increasing availability of erotic literature and pornography novels online, which can be alluring to adolescence and children. Unfortunately, because of the inherent complexity of these indecent contents and training data sparseness, it is a challenging task to detect these readings in the Cyberspace while children can easily access them. In this study, we propose a novel framework, Joint Learning of Content and Human Attention (GoodMan), to identify indecent readings by augmenting natural language understanding models with large scale human reading behaviors (dwell time per page) on portable devices. From the text modeling viewpoint, the innovative joint attention trained by joint learning is employed to orchestrate the content attention and human behavior attention via the BiGRU. From the data augmentation perspective, various users' reading behaviors on the same text can generate considerable training instances with joint attention, which can be effective to address the cold start problem. We conduct an extensive set of experiments on an online ebook dataset (with human reading behaviors on portable devices). The experimental results show insights into the task and demonstrate the superiority of the proposed model against alternative solutions.
Implementation of artificial neural networks for very short range weather prediction

Abstract Weather conditions are a significant factor for various sectors such as transportation safety, development, health, etc. Therefore, high development is needed in forecasting future weather conditions. Many ways are used to predict weather conditions. Along with the development of technology now, weather prediction can be made using Artificial Intelligence (AI) technology or artificial intelligence so that the results obtained are more optimal. In this study, the artificial neural network used has a feedforward neural network algorithm using training data consisting of temperature, air pressure, air humidity, wind speed, hourly wind speed at the Juanda Meteorological Station in Surabaya in Januari 2019 with the target is intensity of rainfall. Furthermore, the data was released in the period of 1 January 2019 to 31 Januari 2019. Based on the analysis results, the Artificial Neural Network model has a fairly good performance in predicting an increase in rainfall in Surabaya. The best model is considered by a model with architecture 7 - 60 - 1 with an estimate correlation is 0.87, with an error value of -0.03. With this model, it is expected to become one of the forecaster considerations in making special weather forecasts at intervals every hour.
HAF-SVG: Hierarchical Stochastic Video Generation with Aligned Features

Stochastic video generation methods predict diverse videos based on observed frames, where the main challenge lies in modeling the complex future uncertainty and generating realistic frames. Numerous of Recurrent-VAE-based methods have achieved state-of-the-art results. However, on the one hand, the independence assumption of the variables of approximate posterior limits the inference performance. On the other hand, although these methods adopt skip connections between encoder and decoder to utilize multi-level features, they still produce blurry generation due to the spatial misalignment between encoder and decoder features at different time steps. In this paper, we propose a hierarchical recurrent VAE with a feature aligner, which can not only relax the independence assumption in typical VAE but also use a feature aligner to enable the decoder to obtain the aligned spatial information from the last observed frames. The proposed model is named Hierarchical Stochastic Video Generation network with Aligned Features, referred to as HAF-SVG. Experiments on Moving-MNIST, BAIR, and KTH datasets demonstrate that hierarchical structure is helpful for modeling more accurate future uncertainty, and the feature aligner is beneficial to generate realistic frames. Besides, the HAF-SVG exceeds SVG on both prediction accuracy and the quality of generated frames.
SparkAIBench: A Benchmark to Generate AI Workloads on Spark

With the rapid development of artificial intelligence and cloud computing technologies, more and more workloads embed AI algorithms are deploying on cloud systems. For lack of sufficient achievements on generating AI workloads in recent years, designing and developing an efficient benchmark for AI workloads will be significant helpful for optimization of job execution time and cluster resource utilization. SparkAIBench proposed in this paper is a user customized benchmark and is able to automatically generate a variety of AI workloads by transforming user requirements into JSON objects. Besides, cooperated with a DRL-based job scheduling optimizer, a real scenario is introduced in this paper to demonstrate how SparkAIBench works.
Poster Abstract: Using Deep Learning to Classify The Acceleration Measurement Devices

Recent work has shown that two wearable devices worn on the same user can exploit gait as a secret source to generate a common key for secure pairing. The main threat of using gait comes from side-channel attackers who can use cameras to record the walking user and extract accelerations from the video to pair with legitimate devices. We propose a novel pre-step that uses a CNN-LSTM deep learning model to classify the acceleration measurement devices, i.e., between IMU vs. Camera. We prototype the pre-step and evaluate it using real subjects. Our results show that the proposed pre-step can achieve high classification success rates. The experiments with different cut-off frequencies show that the higher acceleration frequencies appear to contain more distinguishable features to classify camera from IMU.
Confronting the Challenges of Computational and Social Perspectives of the Data Continuum

As the availability of data is increasing everyday, the need to reflect on how to make these data meaningful and impactful becomes vital. Current data paradigms have provided data life cycles that often focus on data acumen and data stewardship approaches. In an effort to examine the convergence, tensions, and harmonies of these two approaches, a group of researchers participated in an interactive panel session at the Association of Information Science and Technology Annual meeting in 2019. The panel presenters described their various research activities in which they confront the challenges of the computational and social perspectives of the data continuum. This paper provides a summary of this interactive panel.