Reviewerly/exHarmony huggingface dataset:
Next News Recommendation via Knowledge-Aware Sequential Model

A news recommendation system aims to predict the next news based on users' interaction histories. In general, the clicking sequences from the interaction histories indicate users' latent preference, which plays an important role in predicting their future interest. Besides, news articles consist of considerable knowledge entities which have deep connections from common sense of human. In this paper, we propose a Self-Attention Sequential Knowledge-aware Recommendation (Saskr) system consisting of sequential-aware and knowledge-aware modelling. We use the self-attention mechanism to uncover sequential patterns in the sequential-aware modelling. The knowledge-aware modelling leverage the knowledge graph as side information to mine deep connections between news, thus improving diversity and extensibility of recommendation. Content-based news embeddings help to address the item cold-start problem. Through extensive experiments on the real-world news dataset, we demonstrate that the proposed model outperforms state-of-the-art deep neural sequential recommendation systems.
Tools for Visualizing, Annotating and Storing Triangle Meshes in ROS and RViz

Polygonal maps for robotic applications are becoming increasingly popular, but are currently not effectively supported in the Robot Operating System (ROS). In this paper, we introduce the Mesh Tools package consisting of message definitions, RViz plugins and a persistence layer to make the benefits of annotated polygonal maps available in ROS. These tools allow to publish, edit and inspect such maps within the existing ROS context. Our persistence layer efficiently loads and stores large mesh maps. We discuss two application areas as a proof-of-concept: Labeling of triangle clusters for semantic mapping and robot navigation on triangle meshes in a typical outdoor environment by integrating our tools into an existing navigation stack.
Bayesian nonparametric analysis of multivariate time series: A matrix Gamma Process approach

Many Bayesian nonparametric approaches to multivariate time series rely on Whittle's Likelihood, involving the second order structure of a stationary time series by means of its spectral density matrix. In this work, we model the spectral density matrix by means of random measures that are constructed in such a way that positive definiteness is ensured. This is in line with existing approaches for the univariate case, where the normalized spectral density is modeled similar to a probability density, e.g. with a Dirichlet process mixture of Beta densities. We present a related approach for multivariate time series, with matrix-valued mixture weights induced by a Hermitian positive definite Gamma process. The latter has not been considered in the literature, allows to include prior knowledge and possesses a series representation that will be used in MCMC methods. We establish posterior consistency and contraction rates and small sample performance of the proposed procedure is shown in a simulation study and for real data.
Recommendation System for Students’ Course Selection

Graduate students always face a dilemma when it comes to choosing electives every semester. Different data sets have been used in order to avoid this confusion and chaos. In order to help them choose their subjects as per their capability, we use data mining and natural language processing techniques that helps in conversion of human-readable format to machine-readable format, both of which are vastly emerging fields to propose a collaborative recommendation system.
Indic language computing

research-article Share on Indic language computing Authors: Pushpak Bhattacharyya IIT Bombay and IIT Patna IIT Bombay and IIT PatnaView Profile , Hema Murthy IIT Madras IIT MadrasView Profile , Surangika Ranathunga University of Moratuwa University of MoratuwaView Profile , Ranjiva Munasinghe MIND Analytics and Management in Colombo, Sri Lanka MIND Analytics and Management in Colombo, Sri LankaView Profile Authors Info & Claims Communications of the ACMVolume 62Issue 11November 2019 pp 70–75https://doi.org/10.1145/3343456Published:24 October 2019Publication History 4citation5,346DownloadsMetricsTotal Citations4Total Downloads5,346Last 12 Months339Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
GraPASA: Parametric graph embedding via siamese architecture

Graph representation learning or graph embedding is a classical topic in data mining. Current embedding methods are mostly non-parametric, where all the embedding points are unconstrained free points in the target space. These approaches suffer from limited scalability and an over-flexible representation. In this paper, we propose a parametric graph embedding by fusing graph topology information and node content information. The embedding points are obtained through a highly flexible non-linear transformation from node content features to the target space. This transformation is learned using the contrastive loss function of the siamese network to preserve node adjacency in the input graph. On several benchmark network datasets, the proposed GraPASA method shows a significant margin over state-of-the-art techniques on benchmark graph representation tasks.
A comparative study of Different Denoising Techniques in Digital Image Processing

The quality of image usually depends on various factors including noise, light, and temperature. Noise has a decisive role in the image as it is constantly present in different digital images. Noise implication may present during the image coding, transmission and development process. Noise removal has become an eye-catching and dynamic field in the Image processing domain. There is a significant recent advance in filtering of different kinds of noises for digital image processing. In this paper the problem of de-blurring in the presence of various noises is studied. A comparison among various filtering techniques is made in the presence of different kinds of noises. The experiments show that the Adaptive filter method performance is appealing both visually and in terms of objective quality measures that as the color peak-signal-to-noise ratio (CPSNR) and the mean square error (MSE) for Gaussian noise, Speckle noise, Poisson noise, while median filter demonstrates the visual objectives for salt and pepper noise.
Sensor Based Dynamic Hand Gesture Recognition by PairNet

This paper presents a novel feedforward neural network for sensor-based dynamic hand gesture recognition. The algorithm, termed PairNet, is capable of carrying out accurate gesture spotting for the sensory data produced by basic accelerators and gyroscopes, which are commonly deployed in internet of things devices. The gesture classification outcomes are then obtained from the spotting results by the Maximum A Posteriori (MAP) estimation. To illustrate the effectiveness of the proposed algorithm, a prototype system based on a mobile phone has been implemented. Experimental results reveal that, while attaining realtime operations, the proposed algorithm has superior accuracy over existing sensor-based counterparts for hand gesture recognition.
CNN-Based Ternary Classification for Image Steganalysis

This study proposes a convolutional neural network (CNN)-based steganalytic method that allows ternary classification to simultaneously identify WOW and UNIWARD, which are representative adaptive image steganographic algorithms. WOW and UNIWARD have very similar message embedding methods in terms of measuring and minimizing the degree of distortion of images caused by message embedding. This similarity between WOW and UNIWARD makes it difficult to distinguish between both algorithms even in a CNN-based classifier. Our experiments particularly show that WOW and UNIWARD cannot be distinguished by simply combining binary CNN-based classifiers learned to separately identify both algorithms. Therefore, to identify and classify WOW and UNIWARD, WOW and UNIWARD must be learned at the same time using a single CNN-based classifier designed for ternary classification. This study proposes a method for ternary classification that learns and classifies cover, WOW stego, and UNIWARD stego images using a single CNN-based classifier. A CNN structure and a preprocessing filter are also proposed to effectively classify/identify WOW and UNIWARD. Experiments using BOSSBase 1.01 database images confirmed that the proposed method could make a ternary classification with an accuracy of approximately 72%.
Requirements Dependency Graph Modeling on Software Requirements Specification Using Text Analysis

Understanding interdependency among requirements is one of the success factors in software development. Information on requirements interdependency explicitly and implicitly resided various design artifacts or diagrams. A software requirements specification document is the artifact delivered in the early phase of development. It drives its following development processes. It also contains information on interdependencies among the requirements, such as similar, part-of, and elaborate. This study proposes an approach to model the requirement dependency graph for a software requirements specification document. There is an extraction process for Text Preprocessing, which includes of Tokenization, Stopword Removal, and Stemming. Besides, there is a process of measuring semantic similarity through WS4J (WordNet Similarity for Java). The results of the extraction process, combined with Greedy Algorithms as the optimum value solution approach. Besides, a method for calculating similarity was used through the practices of Wu Palmer and Levenshtein. At the end of this process, Reliability is performed using the Gwet’s AC1 formula.
Combining dissimilarity measures for image classification

The local dissimilarity has been verified as one of effective metrics for pattern classification. For high-dimensional data, because of the property of high-dimension, even if there are a number of available samples, they are still only resultant observations of a sampling process of the high-dimensional population. As a consequence, available samples at least partially possess the property of randomness and are not “accurate” representations of the true and total sample space. Besides the prevailing local dissimilarity measure, global dissimilarity measures might also be exploited for improving the classification approach. In this paper, we propose to directly exploit global and local dissimilarity measures to efficiently perform image classification. The proposed method proposes to simultaneously use three dissimilarities derived from the original and transform sample space. These dissimilarities including the elaborated distance ratio enable space relations of the probe sample and gallery samples to be measured from three viewpoints, so the combination of them provide us with more reliable measurements on spatial geometric relationship of samples. An obvious advantage of this combination is that we can attain a very robust evaluation on the space distance between samples and the consequent classification decision will be less affected by the noise in data. The experiments prove that the proposed method does achieve the desired goal, i.e., very satisfactory accuracy improvement in comparison with the previous state-of-the art methods.
A Hybrid Multiple Parallel Queuing Model to Enhance QoS in Cloud Computing

Among the different QoS metrics and parameters considered in cloud computing are the waiting time of cloud tasks, execution time of tasks in VM's, and the utilization rate of servers. The proposed model was developed to overcome some of the pitfalls in the existing systems among which are sub-optimal markdown in the queue length, waiting time, response time, and server utilization rate. The proposed model contemplates on the enhancement of these metrics using a Hybrid Multiple Parallel Queuing approach with a joint implementation of M/M/1: ∞ and M/M/s: N/FCFS to achieve the desired objectives. A neoteric set of mathematical equations have been formulated to validate the efficiency and performance of the hybrid queuing model. The results have been validated with reference to the workload traces of Bit Brains infrastructure provider. The results obtained indicate the significant reduction in the queue length by 60.93 percent, waiting time in the queue by 73.85 percent, and total response time by 97.51%.
Double-layer conditional random fields model for human action recognition

The conditional random fields (CRFs) model, as one of the most successful discriminative approaches, has received renewed attention recently for human action recognition. However, the existing CRFs model formulations have typically limited capabilities to capture higher order dependencies among the given states and deeper intermediate representations within the target states, which are potentially useful and significant to model the complex action recognition scenarios. In this paper, we present a novel double-layer CRFs (DL-CRFs) model for human action recognition in the graphical model framework. In problem formulation, an augmented top layer as the high-level and global variable is designed in the DL-CRFs model, with the global perception perspective to acquire higher-order dependencies between the target states. Meanwhile, we exploit the additional intermediate variables to explicitly perceive the intermediate representations between the target states and observation features. We then propose to decompose the DL-CRFs model in two parts, that are the top linear-chain CRFs model and the bottom one, in order to execute ease inference both during the parameter learning phase and test time. Lastly, the assumed DL-CRFs model parameters can be learned with block-coordinate primal–dual Frank–Wolfe algorithm with gap sampling scheme in a structured support vector machine framework. Experimental results and discussions on two public benchmark datasets demonstrate that the proposed approach performs better than other state-of-the-art methods in several evaluation criteria.
SPARQL2Hive: An approach to processing SPARQL queries on Hive based on meta-models

The growth of Web data has presented new challenges in terms of the ability to effectively query RDF data. Traditional relational database systems efficiently adapt and query distributed data. With the development of Hadoop, its implementation of the MapReduce Framework with Hive, a data warehouse, the semantics of data processing and querying has changed. We present in this article, SPARQL2Hive a competitive SPARQL Query Processing System on MapReduce that allows ad hoc SPARQL query processing on large RDF graphs. Instead of a direct mapping, SPARQL2Hive uses the query language of Hive, a data warehouse system that queries systems using HDFS, located above Hadoop MapReduce, as an intermediate layer between SPARQL and MapReduce. This additional level of abstraction makes our approach independent of the current version of Hadoop and thus ensures compatibility with future changes to the Hadoop framework as they will be covered by the underlying Hive layer. Our approach is to use the two meta-models of SPARQL and Hive, and propose a transformation between these two meta-models using the ATL language. We compare SPARQL2Hive with MapReduce-based SPARQL implementations.
Detecting Non-Atomic Requirements in Software Requirements Specifications Using Classification Methods

Requirements engineering is the most important stage in software engineering, one of which is to carry out specifications on requirements. Errors that occur at this stage will have a very bad impact on the next stages. A mistake that often occurs is a misunderstanding between stakeholders regarding the document specifications, and this is due to different backgrounds or fields of science. In addition, errors can also occur when making specification documents, for example, there are still non-atomic requirements in the document. Non-atomic requirements are a statement of requirements in which there is not only one element/function of the system. This research was conducted to develop a model that can detect non-atomic requirements in the software specification requirements written in natural languages. The initial stage of this research was to make a list of expert annotations (corpus) containing statements of atomic and non-atomic requirements. This Corpus later used as training data and test data in this study. Based on the corpus created, feature extraction and keyword generation carried out. The best model built in this research was produced by the classification method that used the Bayes Net algorithm. The result of the classification model was evaluated against human annotator using Cohen Kappa. The reliability of the model is considered fair for non-balance data in detecting non-atomic requirements in the software requirements specification. The reliability of the model is considered moderate for balance data in detecting non-atomic requirements.
A self-organizing developmental cognitive architecture with interactive reinforcement learning

Developmental cognitive systems can endow robots with the abilities to incrementally learn knowledge and autonomously adapt to complex environments. Conventional cognitive methods often acquire knowledge through passive perception, such as observing and listening. However, this learning way may generate incorrect representations inevitably and cannot correct them online without any feedback. To tackle this problem, we propose a biologically-inspired hierarchical cognitive system called Self-Organizing Developmental Cognitive Architecture with Interactive Reinforcement Learning (SODCA-IRL). The architecture introduces interactive reinforcement learning into hierarchical self-organizing incremental neural networks to simultaneously learn object concepts and fine-tune the learned knowledge by interacting with humans. In order to realize the integration, we equip individual neural networks with a memory model, which is designed as an exponential function controlled by two forgetting factors to simulate the consolidation and forgetting processes of humans. Besides, an interactive reinforcement strategy is designed to provide appropriate rewards and execute mistake correction. The feedback acts on the forgetting factors to reinforce or weaken the memory of neurons. Therefore, correct knowledge is preserved while incorrect representations are forgotten. Experimental results show that the proposed method can make effective use of the feedback from humans to improve the learning effectiveness significantly and reduce the model redundancy.
Towards a Standardized Grammar for Navigation Systems for Persons with Visual Impairments

Pedestrian navigation systems are rarely accessible or suit the needs of persons with visual impairments. They usually lack a standardized grammar for their speech instructions, forcing users to learn new types of instructions for each new system. Thus, we propose (1) a German grammar with syntax rules and vocabulary for mobile pedestrian navigation systems that take into account the special requirements of people with visual impairments. (2) a set of rules for specifying what should be spoken and when, given GPS accuracy in a city [18]. We describe (3) the methodology used to obtain the grammar as well as (4) a qualitative evaluation with orientation and mobility experts and with people with visual impairments who deployed our grammar during a user study. Our approach is the first of its kind, as there is no such grammar neither for German nor for English, as far as we know. It serves as a contribution to standardize pedestrian navigation speech instructions for people with visual impairments.
Oui, Chef!!: Supervised Learning for Novel Gameplay with Believable AI

The use of artificial intelligence for the creation of game agents is fundamental to digital game development, enabling the design of new styles of games offering novel play experiences. Machine learning has long been used in the creation of digital games, most often for the purpose of creating game agent controllers that are trained off-line and do not learn during play. Little attention has been given to the possibility of game designs or mechanics that use machine learning to allow the player to train game agents on-line. This document outlines the design and implementation of Oui, Chef!!, a supervised learning game in which the player trains neural networks to map recipes to the ingredients necessary to make dishes in a restaurant. The game demonstrates that training game agents in a supervised manner provides a fun and engaging player experience wherein the effects of training are easily recognizable, and sometimes delightfully surprising.
Multiple Object Tracking with Dynamic Fuzzy Cognitive Maps Using Deep Learning

Object tracking is the process of matching objects detected on image sequences onto image frames. There are different types of object tracking applications used for different scenarios. For example, if a single object is being traced on an image, this is a single object tracking application. Tracking multiple objects on an image is called multiple object tracking. Fuzzy cognitive maps, on the other hand, form the model of a system by using the features of a system and the relationships between these features. Here, the single object tracking process is a matching problem, so FCM assumes a classifier role. In conventional operations, FCMs use the same weight matrix for all initial concept values. This can reduce the performance of the solution that the FCM produces for the problem it tackles. The FCM structure we use here takes advantage of the dynamic learning of FCM weights with deep learning. The study was tested on different image sequences and the performance of the proposed method were very satisfactory.
A Quantitative Performance Assessment of surface EMG and Accelerometer in Sign Language Recognition

Sign language predominantly makes use of different hand postures and hand motions. The combined use of surface electromyogram (sEMG) and accelerometer sensors in a wearable sign language recognition system has been shown to enhance performance as compared to either of the sensing modalities. However, since sEMG is prone to motion artefacts, noises arising due to sweat on skin surface besides being costlier as compared to accelerometers, there has been a discussion in literature regarding the development of sign language recognition system with only motion sensors. In this work, the utilities of sEMG and accelerometer are analysed to reveal the scenario under which each of these modalities contribute the most. For a set of 1200 recordings, the overall accuracy when only sEMG or accelerometer signals are used is found to be 86.3% and 82.1% respectively, whereas their combined use yields an accuracy of 87.5%. Although, the inclusion of accelerometer data with the sEMG signals in a sign language recognition system is found to improve the overall recognition accuracy of the signs, it is demonstrated that under certain conditions the accelerometer data does not contribute much towards sign recognition. In fact, inclusion of accelerometer data with the sEMG signals under these conditions may adversely affect the classification accuracy of the sign. However, for another category of signs, accelerometers are sufficient for classification and sEMG is not required. The supporting results are also tested for significance using statistical analysis.