Reviewerly/exHarmony huggingface dataset:
Review on Sentiment Analysis of Indian Languages with a Special Focus on Code Mixed Indian Languages

Given the wide applicability of social media platforms, individuals increasingly turn to the web to seek and share information, opinions, comments and suggestions which results in proliferation of user generated large volume of text data available for interpretation. A large number of users in India write their feelings or emotions in more than one language, thereby a large volume of text data is made available for Natural Language Processing (NLP) researchers. Sentiment Analysis (SA) of code-mixed text provides useful information in the field of politics, marketing, business, health, sports and what not. During the past decade the work on Sentiment Analysis of Indian language textual data, particularly in Hindi has got momentum in contrast to code-mixed Indian language text. However, due to non-availability of language and vocabulary (linguistic and lexical) tools and annotated resources, the task of Sentiment Analysis of Indian Languages becomes somehow difficult. In this study an attempt has been made to provide a detailed summary of Sentiment Analysis of Indian languages with a special focus on code mixed Indian Languages.
MAP Inference via Block-Coordinate Frank-Wolfe Algorithm

We present a new proximal bundle method for Maximum-A-Posteriori (MAP) inference in structured energy minimization problems. The method optimizes a Lagrangean relaxation of the original energy minimization problem using a multi plane block-coordinate Frank-Wolfe method that takes advantage of the specific structure of the Lagrangean decomposition. We show empirically that our method outperforms state-of-the-art Lagrangean decomposition based algorithms on some challenging Markov Random Field, multi-label discrete tomography and graph matching problems.
Designing Narrative Slideshows for Learning Analytics

The practical power of data visualization is currently attracting much attention in the e-learning domain. A growing number of studies have been conducted in recent years to help instructors better analyze learner behavior and reflect on their teaching. However, current e-learning dashboards and visualization systems usually require a lot of time and effort into the exploration process. Moreover, the lack of communication power of existing systems constrains users from organizing the narrative of information pieces into a compelling data story. In this paper, we have proposed a narrative visualization approach with an interactive slideshow that helps instructors and education experts explore potential learning patterns and convey data stories. This approach contains three key components: guided-tour concept, drill-down path, and dig-in exploration dimension. The use cases further demonstrate the potential of employing this visual narrative approach in the e-learning context.
Portrait Instance Segmentation for Mobile Devices

Accurate and efficient portrait instance segmentation has become a crucial enabler for many multimedia applications on mobile devices. We present a novel convolutional neural network (CNN) architecture to explicitly address the long standing problems in portrait segmentation, i.e., semantic coherence and boundary localization. Specifically, we propose a cross-granularity categorical attention mechanism leveraging the deep supervisions to close the semantic gap of CNN feature hierarchy by imposing consistent category-oriented information across layers. Furthermore, a cross-granularity boundary enhancement module is proposed to boost the boundary awareness of deep layers by integrating the shape context cues from shallow layers of the network. We further propose a novel and efficient non-parametric affinity model to achieve efficient instance segmentation on mobile devices. We present a portrait image dataset with instance level annotations dedicated to evaluating portrait instance segmentation algorithms. We evaluate our approach on challenging datasets which obtains state-of-the-art results.
Constraint-based type-directed program synthesis

We explore an approach to type-directed program synthesis rooted in constraint-based type inference techniques. By doing this, we aim to more efficiently synthesize polymorphic code while also tackling advanced typing features such as GADTs that build upon polymorphism. Along the way, we also present an implementation of these techniques in Scythe, a prototype live, type-directed programming tool for the Haskell programming language and reflect on our initial experience with the tool.
Recognition of Time Expressions in Spanish Electronic Health Records

The widespread adoption of Electronic Health Records (EHRs) is generating an ever-increasing amount of unstructured clinical texts. Processing time expressions from these domain-specific-texts is crucial for the discovery of patterns that can help in the detection of medical events and building the patient's natural history. In medical domain, the recognition of time information from texts is challenging due to their lack of structure; usage of various formats, styles and abbreviations; their domain specific nature; writing quality; and the presence of ambiguous expressions. Furthermore, despite of Spanish occupying the second position in the world ranking of number of native speakers, to the best of our knowledge, no Natural Language Processing (NLP) tools have been introduced for the recognition of time expressions from clinical texts, written in this particular language. Therefore, in this paper, we propose a Temporal Tagger for identifying and normalizing time expressions appeared in Spanish clinical texts. We further compare our Temporal Tagger with the Spanish version of SUTime. By using a large dataset comprising EHRs of people suffering from lung cancer, we show that our developed Temporal Tagger, with an F1 score of 0.93, outperforms SUTime, with an F1 score of 0.797.
A predicate transformer semantics for effects (functional pearl)

Reasoning about programs that use effects can be much harder than reasoning about their pure counterparts. This paper presents a predicate transformer semantics for a variety of effects, including exceptions, state, non-determinism, and general recursion. The predicate transformer semantics gives rise to a refinement relation that can be used to relate a program to its specification, or even calculate effectful programs that are correct by construction.
Jetson, Where Is the Ball? Using Neural Networks for Ball Detection at RoboCup 2017

The approach of using neural networks in the RoboCup humanoid league for ball detection is investigated in a case study at the RoboCup 2017 competition. A patch-based classification approach is used. Two different ConvNet architectures, the Inception v3 network by Google and AlexNet are evaluated in the context of a ROS-based architecture on a robot with a Jetson GPU board. The aim is to allow for an efficient re-training of neural networks in the context of the competition.
Efficient separable convolution using field programmable gate arrays

Two-dimensional (2D) convolution is an essential component in several image and video processing applications. The convolution operation is computationally expensive due to the large number of required additions and multiplications. Field Programmable Gate Array (FPGA) architectures have been used to mitigate this problem owing to their parallel processing capabilities. Using separable filter kernels can further improve the convolution operation both in terms of resource utilization and speed. Although several 2D convolution implementations have been presented in the literature, research on separable convolution using FPGA is limited. This paper presents a new separable FPGA-based convolution architecture. The goal is to reduce both on-chip resource utilization and external memory bandwidth for a given processing rate of the convolution unit. External memory bandwidth (EMB) and on-chip resource utilization are reduced by reusing common data shared by consecutive processing windows in a novel way. Comparisons with existing separable convolution methods demonstrate improvements offered by the proposed technique in terms of on-chip resource utilization and power consumption.
Optimizing rewards allocation for privacy-preserving spatial crowdsourcing

Rewards allocation in one of the key issues for ensuring a high task acceptance rate in spatial crowdsourcing applications. Generally, workers who participate in a crowdsourcing project are required to disclose their locations, which may lead to serious privacy threats. Unfortunately, providing a rigid privacy guarantee is incompatible with ensuring a high task acceptance rate in most existing crowdsourcing solutions. Hence, this paper proposes a crowdsourcing framework based on optimized reward allocation strategies. The key idea is to tune the reward for performing each task to the workers’ preferences to attain a high acceptance rate. The first step in the framework is to interrogate the workers’ preferences using a cryptographic protocol that fully preserves the location privacy of the workers. Based on those preferences, two different approaches to reward assignments have been proposed to ensure the rewards are distributed optimally. A theoretical analysis of the privacy protection inherent in the framework demonstrates that the proposed framework guarantee the worker’s location privacy from adversaries including the requester and crowdsourcing server. Further, experiments based on real-world datasets show that the proposed strategies outperform existing solutions in terms of task acceptance rates.
Personalized Training in Fast-Food Restaurants Using Augmented Reality Glasses

Employee training in fast-food restaurants is a long, practice-based process, which is mainly done on the job. Employee performance during training directly affects service quality and customer satisfaction. In this study, we optimize the training process in fast-food restaurants with the use of augmented reality glasses. We increase the quality of the training by gamifying and personalizing the process. We also aim to help businesses lower the labor costs by shortening or possibly fully eliminating one-on-one training sessions instructed by senior employees. We implemented a new training system on a game engine with the aid of various sensors such as gyroscope, accelerometer and audial & visual inputs. We also intend to eliminate the culture of high employee turnover rates of the fast-food sector by increasing the quality of the training. The outcome of this study will be used to fully train and supervise employees in the future.
A Memory-Based Collaborative Filtering Recommender System Using Social Ties

Recent studies have illustrated that social networks are valuable sources of information which can be used for various purposes. In recommender systems, researchers have been motivated to utilize social information not only to enhance the prediction accuracy, but also to address the data sparseness challenges such as cold start problem. In this paper, we propose a social memory-based Collaborative Filtering (CF) approach and examine the impact of integrating social ties on both accuracy and prediction coverage. A simple learning method is used to tune the contribution of social information, in the proposed similarity measure, as a weight parameter. We compared our experimental results with several state-of-the-art memory-based and model-based CF approaches using Epinions dataset to investigate the performance of our method in extreme sparsity of real-world data. Results show that our approach provide high accuracy as well as reasonable prediction coverage comparing with other methods.
A method for virtual machine migration in cloud computing using a collective behavior‐based metaheuristics algorithm

Summary Due to the growth of applications and the integration of new customers into the world of computing systems, computing needs to be changed and to become more powerful and flexible than before. Meanwhile, cloud computing is presented as a model beyond a system that is currently capable of answering most request needs. Flexible infrastructure for cloud computing and virtualization technology provide new features to support business activities. Clouds are a very important topic that used secure management tools for storage, security and securing data centers in a flexible manner. One of the important matters in cloud technologies is virtual machine migration (VMM). There are different ways to implement the VMM, but because of the limitation of resources' energy, energy management is also very important and challenging. Due to the NP‐hard nature of this problem, this paper presents an energy‐aware VMM engine for cloud computing using the discrete bacterial foraging algorithm as a new collective behavior‐based metaheuristics algorithm. The CloudSim simulator is employed to investigate the efficiency of this method. The obtained results have shown that the proposed method improves the energy consumption and the migration count.
Haze removal from a single remote sensing image based on a fully convolutional neural network

In many remote sensing (RS) applications, haze greatly affects the quality of optical RS images, but we do not always have the conditions to acquire multiple images in the same area for haze removal tasks. Therefore, the research on haze removal from a single RS image is necessary. Previous haze-removal methods introduce various prior knowledge to solve this problem, and thus, the quality of these methods largely depends on the reliability and validity of prior knowledge, which brings various limitations. We propose and validate a deep-learning-based model for haze removal, named haze removal fully convolutional network, to estimate transmission maps and generate corresponding haze-removed images via an atmospheric scattering model. Moreover, we propose an approximate method to produce hazy-and-clear image pairs as a dataset for training and validation. Experiments using this dataset demonstrated that the proposed model achieved the desired results in both visual effect and quantitative measurement.
Particle Filter-Based Prediction for Anomaly Detection in Automatic Surveillance

Automatic surveillance of abnormal events is a major unsolved problem in city management. By successful implementation of automatic surveillance of abnormal events, a significant amount of human resources in video monitoring can be economized. One solution to this application is computer vision technology. This approach utilizes an image processing algorithm to extract specific features and then uses discriminator algorithms to give an alert. In this paper, we propose to apply a particle filter-based algorithm to feature series extracted from videos in order to give alerts when abnormal events occur. The whole process consists of feature series generation and particle filter tracking. To represent the features of a video, an L2-norm extractor is designed based on the optical flow. Then, the particle filter keeps track of these feature series. The occurrence of abnormal events will cause the shift of feature series and a large error in PF tracking. This, in turn, will allow computers to understand and define the occurrences of anomalies. Experiments on UMN dataset show that our algorithm reaches 90% accuracy in frame-level detection.
Online Learning Model for Handling Different Concept Drifts Using Diverse Ensemble Classifiers on Evolving Data Streams

The rapid growth of the information technology accelerates organizations to generate vast volumes of high-velocity data streams. The concept drift is a crucial issue, and discovering the sequential patterns over data streams are more challenging. The ensemble classifiers incrementally learn the data for providing quick reaction to the concept drifts. The ensemble classifiers have to process both the gradual and sudden concept drifts that happen in the real-time data streams. Thus, a novel ensemble classifier is essential that significantly reacting to various types of concept drifts quickly and maintaining the classification accuracy. This work proposes the stream data mining on the fly using an adaptive online learning rule (SOAR) model to handle both the gradual and sudden pattern changes and improves mining accuracy. Adding the number of classifiers fails because the ensemble tends to include redundant classifiers instead of high-quality ones. Thus, the SOAR includes different diversity levels of classifiers in the ensemble to provide fast recovery from both the concept drifts. Moreover, the SOAR synthesizes the essential features of the block and online-based ensemble and updates the weight of each classifier, regarding its quality. It facilitates adaptive windowing to handle both gradual and sudden concept drifts. To reduce the computational cost and analyze the data stream quickly, the SOAR caches the occurred primitive patterns into a bitmap with the internal relationship. Finally, the experimental results show that the SOAR performs better classification and accuracy over data streams.
The ideal effect of Gabor filters and Uniform Local Binary Pattern combinations on deformed scanned paper images

Existing scanners produce paper images with different types of deformations such as noise, rotation and shear. These deformations affect the accuracy of the fingerprinting the document images, which entails utilizing advanced feature extraction operators. Existing feature extractor such as the Uniform Local Binary Patterns (ULBP) has been found to be limited in dealing with the global view of the texture and neglecting useful information about the images. This article presents an Automated Paper Fingerprinting (APF) method that deploys a combination approach for Gabor Filters (GF) and Uniform Local Binary Patterns (ULBP) called the GFULBP operator to cater for both local and global image information during the feature extraction process for higher texture classification accuracy. The APF method is evaluated by a standard dataset of 306 blank paper images derived from pre-existing scanner image dataset from Universiti Kebangsaan Malaysia (UKM) with properties ranges from 50 DPI, 100 DPI, and 150 DPI respectively. The images are captured by a flatbed scanner with 50 DPI, 100 DPI, and 150 DPI resolutions. Each image is represented by four patches that are segmented from specific locations of the image. The test results of the APF show that GFULBP is able to outperform the ULBP alone by 30.68% when the GF has a 5 scale and π/2 orientation degree. This work finds that the integration of Gabor filters and ULBP significantly enhances the feature extraction quality and fingerprinting accuracy.
Exploranative Code Quality Documents

Good code quality is a prerequisite for efficiently developing maintainable software. In this paper, we present a novel approach to generate exploranative (explanatory and exploratory) data-driven documents that report code quality in an interactive, exploratory environment. We employ a template-based natural language generation method to create textual explanations about the code quality, dependent on data from software metrics. The interactive document is enriched by different kinds of visualization, including parallel coordinates plots and scatterplots for data exploration and graphics embedded into text. We devise an interaction model that allows users to explore code quality with consistent linking between text and visualizations; through integrated explanatory text, users are taught background knowledge about code quality aspects. Our approach to interactive documents was developed in a design study process that included software engineering and visual analytics experts. Although the solution is specific to the software engineering scenario, we discuss how the concept could generalize to multivariate data and report lessons learned in a broader scope.
Adversarially Learned Abnormal Trajectory Classifier

We address the problem of abnormal event detection from trajectory data. In this paper, a new adversarial approach is proposed for building a deep neural network binary classifier, trained in an unsupervised fashion, that can distinguish normal from abnormal trajectory-based events without the need for setting manual detection threshold. Inspired by the generative adversarial network (GAN) framework, our GAN version is a discriminative one in which the discriminator is trained to distinguish normal and abnormal trajectory reconstruction errors given by a deep autoencoder. With urban traffic videos and their associated trajectories, our proposed method gives the best accuracy for abnormal trajectory detection. In addition, our model can easily be generalized for abnormal trajectory-based event detection and can still yield the best behavioural detection results as demonstrated on the CAVIAR dataset.
A Cost Effective Design and Implementation of Arduino Based Sign Language Interpreter

People suffering from listening impairment and voice disability usually make use of different sign of symbols and languages for their communication purpose. Sign languages are generally dependent on hand-driven gesticulations with various motions explicit to that particular language by which these people communicate. In sign language, gesticulation is basically specific movements of our hands with an explicit form build out of them. Current research focuses on converting the hand gesticulations based on electronic devices. The devices will basically convert the sign language into its speech form to build the communication gap among the voiceless societies with the normal people. Here cloth driven gloves are being used which are Bluetooth-enabled. The glove is tailored with one accelerometer and five flexible sensors. The sensor placement is length ways of each of the fingers with the thumb. This work will facilitate the silent people to make different hand gesticulations wearing this gloves, and these intern will be transformed into respective speeches to the normal peoples recognition. In this case flexible sensors has a key role to play. Also the resistance value change generating from the extent of curvature of the sensors in combination with accelerometer value of slant position of hand to the land surface is measured. This acquired data is further managed by microcontroller module and can be transmitted to any smart-phone user via Bluetooth connectivity. A developed application can further be used to transform the data into text liable to the hand shape detected and will produce a voice signal.
Coverage Path Planning for Complex Structures Inspection Using Unmanned Aerial Vehicle (UAV)

The most critical process in the inspection is the structure coverage which is a time and resource intensive task. In this paper, Search Space Coverage Path Planning (SSCPP) algorithm for inspecting complex structure using a vehicular system consisting of Unmanned Aerial Vehicle (UAV) is proposed. The proposed algorithm exploits our knowledge of the structure model, and the UAV’s onboard sensors to generate coverage paths that maximizes coverage and accuracy. The algorithm supports the integration of multiple sensors to increase the coverage at each viewpoint and reduce the mission time. A weighted heuristic reward function is developed in the algorithm to target coverage, accuracy, travelled distance and turning angle at each viewpoint. The iterative processes of the proposed algorithm were accelerated exploiting the parallel architecture of the Graphics Processing Unit (GPU). A set of experiments using models of different shapes were conducted in simulated and real environments. The simulation and experimental results show the validity and effectiveness of the proposed algorithm.