Reviewerly/exHarmony huggingface dataset:
Attention-based mixture density recurrent networks for history-based recommendation

Recommendation system has been widely used in search, online advertising, e-Commerce, etc. Most products and services can be formulated as a personalized recommendation problem. Based on users' past behavior, the goal of personalized history-based recommendation is to dynamically predict the user's propensity (online purchase, click, etc.) distribution over time given a sequence of previous activities. In this paper, with an e-Commerce use case, we present a novel and general recommendation approach that uses a recurrent network to summarize the history of users' past purchases, with a continuous vectors representing items, and an attention-based recurrent mixture density network, which outputs each mixture component dynamically, to accurate model the predictive distribution of future purchase. We evaluate the proposed approach on two publicly available datasets, MovieLens-20M and RecSys15. Both experiments show that the proposed approach, which explicitly models the multi-modal nature of the predictive distribution, is able to greatly improve the performance over various baselines in terms of precision, recall and nDCG. The new modeling framework proposed can be easily adopted to many domain-specific problems, such as item recommendation in e-Commerce, ads targeting in online advertising, click-through-rate modeling, etc.
Dynamic Bird Detection Using Image Processing and Neural Network

Collisions of aircraft and birds cause serious flight accidents, and various studies are underway to find a solution to the problem. In recent image recognition studies, state-of-the-art deep learning technologies have been actively applied. This paper proposes image preprocessing and bird detection methods in all dynamic environments using Convolutional Neural Network (CNN) technology. Image preprocessing separates moving creatures from the dynamic background and removes the background. When image preprocessing is complete, the image of the moving object remaining in the frame is used as input data for the learning model to determine whether the bird is in the frame. We used the Inception -v3 neural network model to improve the accuracy of small object classifications.
Design and optimisation methods for interactive mobile VR visualisation

Abstract The article describes the process of creating a VR mobile application for Google VR platform. The application presents a virtual museum exhibition as part of cross-cultural competence and user interface usability research. The process of creating the application requires taking into account the hardware and software limitations and the specificity of mobile devices. In addition, a higher level of optimisation helps build positive user experience, which is essential in VR applications. The process includes, among others: planning activities, preparing assets, implementing VR vision and interaction modules. The paper includes descriptions of individual steps, problems and other non-standard situations that occurred during the creation and testing of the VR application. In addition the results of the initial performance tests and the methods used to optimise and maintain the stability of the application are presented. By taking into account all of the mentioned factors and the fact that many solutions are yet not well described, the description of the process is expected to provide a point of reference and help for future developers of mobile VR applications.
A Secured and Authenticated Internet of Things Model using Blockchain Architecture

Internet of Things (IoT) has created huge vibes in the technology arena. It has been pivotal in providing cutting-edge solutions in diversified fields. Few applications include smart homes, smart cities, medical, transport and logistics. IoT provides real-time data exchange through wired or wireless networks with no standards being developed to regulate the flow of information within system. As such, the security and access control in the IoT has become an issue of prominence. Now days, researchers understood the potentiality of the Blockchain (BC) in context with the security and authentication. More specifically the decentralized nature of BC makes it apt for integration with the IoT networks. In this paper, we propose a model that makes use of the blockchain concept to bridle the security and authentication problems in the IoT network. We consider various factors that effect this integration of blockchain and IoT. Our investigations conclude that this hybrid model is powerful and can provide significant transformations in building novel applications across distributed platforms.
Application of Differential Evolution Algorithm Based on Mixed Penalty Function Screening Criterion in Imbalanced Data Integration Classification

There are some processing problems of imbalanced data such as imbalanced data sets being difficult to integrate efficiently. This paper proposes and constructs a mixed penalty function data integration screening criterion, and proposes Differential Evolution Integration Algorithm Based on Mixed Penalty Function Screening Criteria (DE-MPFSC algorithm). In addition, the theoretical validity and the convergence of the DE-MPFSC algorithm are analyzed and proven by establishing the Markov sequence and Markov evolution process model of the DE-MPFSC algorithm. In this paper, the entanglement degree and enanglement degree error are introduced to analyze the DE-MPFSC algorithm. Finally, the effectiveness and stability of the DE-MPFSC algorithm are verified by UCI machine learning datasets. The test results show that the DE-MPFSC algorithm can effectively improve the effectiveness and application of imbalanced data classification and integration, improve the internal classification of imbalanced data and improve the efficiency of data integration.
Fully Automatic Facial Deformation Transfer

Facial Animation is a serious and ongoing challenge for the Computer Graphic industry. Because diverse and complex emotions need to be expressed by different facial deformation and animation, copying facial deformations from existing character to another is widely needed in both industry and academia, to reduce time-consuming and repetitive manual work of modeling to create the 3D shape sequences for every new character. But transfer of realistic facial animations between two 3D models is limited and inconvenient for general use. Modern deformation transfer methods require correspondences mapping, in most cases, which are tedious to get. In this paper, we present a fast and automatic approach to transfer the deformations of the facial mesh models by obtaining the 3D point-wise correspondences in the automatic manner. The key idea is that we could estimate the correspondences with different facial meshes using the robust facial landmark detection method by projecting the 3D model to the 2D image. Experiments show that without any manual labelling efforts, our method detects reliable correspondences faster and simpler compared with the state-of-the-art automatic deformation transfer method on the facial models.
Online Courses in the Context of Inclusive Education

One of the areas of the theory and practice in the modern pedagogy is providing conditions and guarantees in socialization and equal access to education for all members of the society including persons with special educational needs. Persons with special educational needs (SEN) are the subjects of inclusion who have atypical features in their development or living conditions. These atypical features may be the cause of uncomplete realization of the right to education. Online courses may be viewed as a resource of inclusive education since they have great potential for educational process organization and implementation for the learners with special educational needs – subjects of inclusion. The paper discusses the possibility and potential benefits of using online courses for different categories of SEN learners. In particular, online courses can serve as instruments of additional pedagogical support in designing student’s individual educational trajectory as well as instruments of socialization.
Automatic Identification of Assumptions from the Hibernate Developer Mailing List

During the software development life cycle, assumptions are an important type of software development knowledge that can be extracted from textual artifacts. Analyzing assumptions can help to, for example, comprehend software design and further facilitate software maintenance. Manual identification of assumptions by stakeholders is rather time-consuming, especially when analyzing a large dataset of textual artifacts. To address this problem, one promising way is to use automatic techniques for assumption identification. In this study, we conducted an experiment to evaluate the performance of existing machine learning classification algorithms for automatic assumption identification, through a dataset extracted from the Hibernate developer mailing list. The dataset is composed of 400 "Assumption" sentences and 400 "Non-Assumption" sentences. Seven classifiers using different machine learning algorithms were selected and evaluated. The experiment results show that the SVM algorithm achieved the best performance (with a precision of 0.829, a recall of 0.812, and an F1-score of 0.819). Additionally, according to the ROC curves and related AUC values, the SVM-based classifier comparatively performed better than other classifiers for the binary classification of assumptions.
Sampling Subgraphs with Guaranteed Treewidth for Accurate and Efficient Graphical Inference

How can we run graphical inference on large graphs efficiently and accurately? Many real-world networks are modeled as graphical models, and graphical inference is fundamental to understand the properties of those networks. In this work, we propose a novel approach for fast and accurate inference, which first samples a small subgraph and then runs inference over the subgraph instead of the given graph. This is done by the bounded treewidth (BTW) sampling, our novel algorithm that generates a subgraph with guaranteed bounded treewidth while retaining as many edges as possible. We first analyze the properties of BTW theoretically. Then, we evaluate our approach on node classification and compare it with the baseline which is to run loopy belief propagation (LBP) on the original graph. Our approach can be coupled with various inference algorithms: it shows higher accuracy up to 13.7% with the junction tree algorithm, and allows faster inference up to 23.8 times with LBP. We further compare BTW with previous graph sampling algorithms and show that it gives the best accuracy.
Prospects for the Digital Education Development: Analysis from the Standpoint of System-Activity Approach

Traditional "analog" form of the educational environment (textbook, system of classroom lessons) undergoes significant transformation driven by innovative educational configuration ("human – computer – digital educational environment"). Numerous electronic systems, resources, online-courses, mobile applications are developed and included in educational process. Immediate availability of considerable informational volumes and the content variability creates new operating conditions of personal cognitive processes. Since the possibilities and diversity of digital technologies are rapidly growing, and there are no adequate methodologies for studying them, studies of the problems of modern digital education – including its effectiveness – are still insufficient to develop generalized requirements for the effectiveness of e-learning. The fundamental issues of creating generalized model of digital education and digital educational environment on the basis of system and activity approach are discussed. Besides its theoretical importance for fundamental psychology and modern neuroscience and pedagogics, the model is important in terms of digital economy advancement and information society development in Russian Federation.
Asymptotic consistency of loss‐calibrated variational Bayes

This paper establishes the asymptotic consistency of the loss‐calibrated variational Bayes (LCVB) method. LCVB is a method for approximately computing Bayesian posterior approximations in a “loss aware” manner. This methodology is also highly relevant in general data‐driven decision‐making contexts. Here, we establish the asymptotic consistency of both the loss‐ calibrated approximate posterior and the resulting decision rules. We also establish the asymptotic consistency of decision rules obtained from a “naive” two‐stage procedure that first computes a standard variational Bayes approximation and then uses this in the decision‐making procedure.
Knowledge Representation for Infectious Disease Risk Prediction System: A Literature Review

This article contains a literature review to seek knowledge representation for infectious disease risk prediction system. The knowledge representation should be able to encode knowledge related to infectious diseases and usable by experts. 188 articles are collected through several constraints. From these articles, 14 knowledge representations are gathered. Ontology comes out as the most used knowledge representation related to disease, followed by fuzzy and rules. This leads to the next step of the research, on how to encode knowledge using these representations.
K-Best Transformation Synchronization

In this paper, we introduce the problem of K-best transformation synchronization for the purpose of multiple scan matching. Given noisy pair-wise transformations computed between a subset of depth scan pairs, K-best transformation synchronization seeks to output multiple consistent relative transformations. This problem naturally arises in many geometry reconstruction applications, where the underlying object possesses self-symmetry. For approximately symmetric or even non-symmetric objects, K-best solutions offer an intermediate presentation for recovering the underlying single-best solution. We introduce a simple yet robust iterative algorithm for K-best transformation synchronization, which alternates between transformation propagation and transformation clustering. We present theoretical guarantees on the robust and exact recoveries of our algorithm. Experimental results demonstrate the advantage of our approach against state-of-the-art transformation synchronization techniques on both synthetic and real datasets.
A Probabilistic Zero-Shot Learning Method via Latent Nonnegative Prototype Synthesis of Unseen Classes

Zero-shot learning (ZSL), a type of structured multioutput learning, has attracted much attention due to its requirement of no training data for target classes. Conventional ZSL methods usually project visual features into semantic space and assign labels by finding their nearest prototypes. However, this type of nearest neighbor search (NNS)-based method often suffers from great performance degradation because of the nonuniform variances between different categories. In this article, we propose a probabilistic framework by taking covariance into account to deal with the above-mentioned problem. In this framework, we define a new latent space, which has two characteristics. The first is that the features in this space should gather within the classes and scatter between the classes, which is implemented by triplet learning; the second is that the prototypes of unseen classes are synthesized with nonnegative coefficients, which are generated by nonnegative matrix factorization (NMF) of relations between the seen classes and the unseen classes in attribute space. During training, the learned parameters are the projection model for triplet network and the nonnegative coefficients between the unseen classes and the seen classes. In the testing phase, visual features are projected into latent space and assigned with the labels that have the maximum probability among unseen classes for classic ZSL or within all classes for generalized ZSL. Extensive experiments are conducted on four popular data sets, and the results show that the proposed method can outperform the state-of-the-art methods in most circumstances.
Measuring the Impact of HPC Training

The demand for computational power is rising rapidly in science and engineering and is now expanding into areas from the social sciences and humanities. This is met with the use of large-scale, and increasingly complex, High-Performance Computing (HPC) systems. Programming such heterogeneous and quickly evolving systems efficiently has always been challenging, particularly for novice HPC programmers, but is continually increasing in complexity. HPC service providers are therefore growing their training offerings as part of their overall service provision. However, there is no standard for understanding the impact and effectiveness of this training. Traditional training and education metrics, such as examination, are typically not applicable. Additionally, the key criteria for service providers are not only knowledge gained but the impact that the knowledge has on HPC usage. To capture and evaluate the effectiveness of training, a novel methodology with two key components is proposed: progress productivity and training productivity. The methodology includes a process for deriving an activity-specific weighted selection of the metrics that comprise the productivity measures. The proposed productivity criteria could then be applied to improve the training of HPC application programmers taking the participant's pre-knowledge and focus areas into account.
Handling data skew at reduce stage in Spark by ReducePartition

Summary As a typical representative of distributed computing framework, Spark has been continuously developed and popularized. It reduces the data transmission time through efficient memory‐based operations and solves the shortcomings of the traditional MapReduce computation model in iterative computation. In Spark, data skew is very prominent due to the uneven distribution of input data and the unbalanced allocation of default partitioning algorithm. When data skew occurs, the execution efficiency of the program will be reduced, especially in the reduce stage of Spark. Therefore, this paper proposes ReducePartition to solve data skew problem at reduce stage of Spark platform. First, the compute node samples the local data according to the sampling algorithm to predict the overall characteristics of data distribution. Then, to take full use of cluster resources, ReducePartition divides data into multiple partitions evenly. Next, taking into account the differences in computational capabilities among Executors, each task is assigned to Executor with the highest performance factor according to the greedy strategy. Finally, the results of the related algorithms and ReducePartition are compared by using WordCount benchmark and Sort benchmarks on heterogeneous Spark standalone cluster. The performance of the ReducePartition under different degree of data skew and different data size is analyzed. Experimental results show that the proposed algorithm can effectively reduce the impact of data skew on the total makespan of Spark big data applications, and the average total makespan is reduced by 30% to 50% while resource utilization is increased by 20%‐30% on average.
First steps to control a digitalized factory via Augmented Reality

The technological capability of augmented reality (AR) is still increasing. Modern devices have available a huge computing power and several sensors like cameras etc. The communication possibilities cover all actual communication standards - also the industrial standards (except safety). The programming of AR applications is simplified by the engines known from the gaming fields. This paper describes the first steps enabling the use of AR in the field of a production system. The production system is represented by a model factory in a laboratory format with a sufficient complexity. The communication between the AR device and the model factory is realized via WLAN, a typical field bus and OPC UA.
MSIR@FIRE: A Comprehensive Report from 2013 to 2016

India is a nation of geographical and cultural diversity where over 1600 dialects are spoken by the people. With the technological advancement, penetration of the internet and cheaper access to mobile data, India has recently seen a sudden growth of internet users. These Indian internet users generate contents either in English or in other vernacular Indian languages. To develop technological solutions for the contents generated by the Indian users using the Indian languages, the Forum for Information Retrieval Evaluation (FIRE) was established and held for the first time in 2008. Although Indian languages are written using indigenous scripts, often websites and user-generated content (such as tweets and blogs) in these Indian languages are written using Roman script due to various socio-cultural and technological reasons. A challenge that search engines face while processing transliterated queries and documents is that of extensive spelling variation. MSIR track was first introduced in 2013 at FIRE and the aim of MSIR was to systematically formalize several research problems that one must solve to tackle the code mixing in Web search for users of many languages around the world, develop related data sets, test benches and most importantly, build a research community focusing on this important problem that has received very little attention. This document is a comprehensive report on the 4 years of MSIR track evaluated at FIRE between 2013 and 2016.
A Standardized ICS Network Data Processing Flow With Generative Model in Anomaly Detection

Industrial control systems (ICS) now usually connect to Wireless Sensor Networks and the Internet, exposing them to security threats resulting from cyber-attacks.However, detecting such attacks is non-trivial task.The high-dimensional network data pose significant challenges on security anomaly detection.In this work, we propose a network flow data processing method, which can make the complex network data more standardized and unified to assist security anomaly detection.Then, data generation method is applied to collect enough training data.We also propose a evaluation method for generated data.Finally, the bidirectional recurrent neural networks with attention mechanism is proposed to extract the latent feature, and give an explainable results in identifying the dominant attributes.Empirical results show our method outperforms the state-of-the-art models.
3D Face Shape Regression From 2D Videos with Multi-Reconstruction and Mesh Retrieval

This paper introduces our submission to the 2nd 3DFAW Challenge. To get a high-accuracy 3D dense face shape based on 2D videos or multiple images, a framework which is consist of multi-reconstruction branches and a mesh retrieval module, is proposed to effectively utilize the information of all frames and the results predicted by all branches. The recent state-of-the-art methods based on single-view and multi-view are introduced to form an ensemble of independent regression networks. The candidate 3D shape of each branch is synthesized by weighted linear combination of the results on all frames to boost the depth estimation and invisible regions reconstruction. Finally, the best fitting mesh is retrieved according to the distance between the synthesized texture and the ground truth texture. Experiment results show that our approach obtains competitive results near the accuracy of "pseudo" ground truths, and achieves superior performance over most of submissions by other teams in the testing phases.