Reviewerly/exHarmony huggingface dataset:
Development of Interactive Mobile Application with Augmented Reality for Tourism Sites in Batam

Batam is one of the tourist destinations in Indonesia, with the arrival of foreign tourists' numbers, reaches above one million. The COVID-19 pandemic brings a negative impact on tourism in Indonesia, including Batam. The focus of this study is to propose an interactive mobile application and Augmented Reality for tourism sites in Batam. The interactive mobile app uses the marker-based Augmented Reality and provides information and map of tourism sites in Batam. This study utilizes the Android Studio to develop the mobile application prototype, Kudan AR SDK for the Augmented Reality, and Adobe XD to create the interface design. The target users of this interactive application are foreign tourists as well as local tourists. The Augmented Reality provides interactive participation of visitors in the tourism site. The proposed mobile apps expect to be an approach of new normal in the tourism sector after the COVID-19 pandemic in Batam.
An Ensemble of Knowledge Sharing Models for Dynamic Hand Gesture Recognition

The focus of this paper is dynamic gesture recognition in the context of the interaction between humans and machines. We propose a model consisting of two sub-networks, a transformer and an ordered-neuron long-short-term-memory (ON-LSTM) based recurrent neural network (RNN). Each sub-network is trained to perform the task of gesture recognition using only skeleton joints. Since each sub-network extracts different types of features due to the difference in architecture, the knowledge can be shared between the sub-networks. Through knowledge distillation, the features and predictions from each sub-network are fused together into a new fusion classifier. In addition, a cyclical learning rate can be used to generate a series of models that are combined in an ensemble, in order to yield a more generalizable prediction. The proposed ensemble of knowledge-sharing models exhibits an overall accuracy of 86.11% using only skeleton information, as tested using the Dynamic Hand Gesture-14/28 dataset.
A Spell-checker Integrated Machine Learning Based Solution for Speech to Text Conversion

Speaking is undeniably an effective way of communication in any language, which is now an essential feature in various sectors of technology. This study proposes to build such a speech to text conversion system for the Bengali language because Bengali, being one of the most popular languages worldwide, is very little explored in this research arena. Hence, the study aims to provide a platform to detect speech and recognize its content as text automatically. This work promises to help the mass people who may not be educated enough to write fluently and accurately. In this study, the focus is on processing Bengali speech data using ‘DeepSpeech’, which creates a neural network to recognize the audio files containing speech and then, to transform the audio speech into its text format. Moreover, the performance evaluation of the proposed system is accomplished after integrating it with the proposed Bengali spell corrector mechanism to achieve significant improvement over the existing approach.
A History-Based Auto-Tuning Framework for Fast and High-Performance DNN Design on GPU

While Deep Neural Networks (DNNs) are becoming increasingly popular, there is a growing trend to accelerate the DNN applications on hardware platforms like GPUs, FPGAs, etc., to gain higher performance and efficiency. However, it is time-consuming to tune the performance for such platforms due to the large design space and the expensive cost to evaluate each design point. Although many tuning algorithms, such as XGBoost tuner and genetic algorithm (GA) tuner, have been proposed to guide the design space exploring process in the previous work, the timing issue still remains a critical problem. In this work, we propose a novel auto-tuning framework to optimize the DNN operator design on GPU by leveraging the tuning history efficiently in different scenarios. Our experiments show that we can achieve superior performance than the state-of-the-art work, such as auto-tuning framework TVM and the handcraft optimized library cuDNN, while reducing the searching time by 8.96x and 4.58x comparing with XGBoost tuner and GA tuner in TVM.
Input-Dependent Edge-Cloud Mapping of Recurrent Neural Networks Inference

Given the computational complexity of Recurrent Neural Networks (RNNs) inference, IoT and mobile devices typically offload this task to the cloud. However, the execution time and energy consumption of RNN inference strongly depends on the length of the processed input. Therefore, considering also communication costs, it may be more convenient to process short input sequences locally and only offload long ones to the cloud. In this paper, we propose a low-overhead runtime tool that performs this choice automatically. Results based on real edge and cloud devices show that our method is able to simultaneously reduce the total execution time and energy consumption of the system compared to solutions that run RNN inference fully locally or fully in the cloud.
An artificial intelligence recognition model for English online teaching

The traditional English online teaching model is limited by the teaching location and the difficulty of online teaching, which prevents teachers from controlling students. In order to improve the ability of the English online teaching model to supervise and recognize the status of students, this paper proposes an English online teaching model based on artificial intelligence technology, and adopts a positioning method based on an improved deep belief network for real-time position control and status recognition for students in online learning. Moreover, this study combines intelligent algorithms to build the model structure and verify the performance of the model. The results show that the performance of the model is good. In addition, on the basis of performance testing, the recognition effect of the artificial intelligence-based student online learning recognition model constructed in this paper is recognized. The results show that the model proposed in this paper has a certain effect and meets the actual needs of intelligent teaching.
Fast Distributed <i>k</i> NN Graph Construction Using Auto-tuned Locality-sensitive Hashing

The k -nearest-neighbors ( k NN) graph is a popular and powerful data structure that is used in various areas of Data Science, but the high computational cost of obtaining it hinders its use on large datasets. Approximate solutions have been described in the literature using diverse techniques, among which Locality-sensitive Hashing (LSH) is a promising alternative that still has unsolved problems. We present Variable Resolution Locality-sensitive Hashing, an algorithm that addresses these problems to obtain an approximate k NN graph at a significantly reduced computational cost. Its usability is greatly enhanced by its capacity to automatically find adequate hyperparameter values, a common hindrance to LSH-based methods. Moreover, we provide an implementation in the distributed computing framework Apache Spark that takes advantage of the structure of the algorithm to efficiently distribute the computational load across multiple machines, enabling practitioners to apply this solution to very large datasets. Experimental results show that our method offers significant improvements over the state-of-the-art in the field and shows very good scalability as more machines are added to the computation.
An End-to-End Multi-task Learning Network with Scope Controller for Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE) aims to extract all potential pairs of emotions and corresponding causes in a document. It has an advantage over traditional emotion cause extraction (ECE) that it does not require annotating emotions manually. Existing methods for ECPE task are based on two-step framework. However, they ignore the fact that the emotion-cause pair is regarded as a whole unit and there are cascading errors in two-step framework. In this paper, we propose an end-to-end hierarchical neural network model, which directly extracts emotion-cause pairs and enhances mutual interaction between emotions and causes via multi-task learning. In addition, we introduce a scope controller to constrain the emotion-cause pair predictions in a high probability area, according to the position correlation between emotions and causes. The experimental results demonstrate that our method achieves the state-of-the-art performance and improves F-measure by 2.24%.
Unmanned Aerial Vehicle Trajectory Planning via Staged Reinforcement Learning

Unmanned Aerial Vehicle (UAV) trajectory planning problem has always been a popular but still an open topic, where online planning is desired in unknown environments. This paper investigates how to combine human knowledge with reinforcement learning to train the UAV in a staged manner. With the novel framework we design, the UAV learns well to avoid densely arranged no-fly-zones and reach stationary or moving targets via calling the trained policy online. We demonstrate the advantages of our approach in terms of the flight time and the success rate of reaching target and avoiding no-fly-zones. The experimental results are performed in a set of new designed environments including dynamic no-fly-zones and moving targets.
Cloud Based Collaborative Filtering Algorithm for Library Book Recommendation System

Library book recommendation system is an automated system which helps to carry out the library's everyday work in a productive manner. A library is a website where the admin develops a system to store and retrieve books can from the database. This cloud-based library recommendation system makes use of a collaborative filtering algorithm where the admin adds the books based on categories and also recommend the top-rated (5-star rating) books to the user. The user can sign in into their account using the autogenerated password which is sent to their authentic email ids. Although after the user can perform many functions such as update his/her profile and also view the books in a subject-wise manner. Each category recommends one or more top-rated books to the user where the user can find the related books faster without wasting much time in searching the book. The user can even have a live chat with other users to take suggestions over the respective books. He/she can also place a borrow and return request to the admin to ship the book and also allow the user to rate the book and give user feedback which he/she is borrowed. Overall, our system is been developed u sing the collaborative filtering methodology to recommend the books to all age group category which can reduce the human effort of the user where he/she going to a public library in search of books. This system isfree of cost and reliable and also provide the books directly to their house just by placing the borrow request.
A Simple Pipeline for Coherent Grid Maps

Grid maps are spatial arrangements of simple tiles (often squares or hexagons), each of which represents a spatial element. They are an established, effective way to show complex data per spatial element, using visual encodings within each tile ranging from simple coloring to nested small-multiples visualizations. An effective grid map is coherent with the underlying geographic space: the tiles maintain the contiguity, neighborhoods and identifiability of the corresponding spatial elements, while the grid map as a whole maintains the global shape of the input. Of particular importance are salient local features of the global shape which need to be represented by tiles assigned to the appropriate spatial elements. State-of-the-art techniques can adequately deal only with simple cases, such as close-to-uniform spatial distributions or global shapes that have few characteristic features. We introduce a simple fully-automated 3-step pipeline for computing coherent grid maps. Each step is a well-studied problem: shape decomposition based on salient features, tile-based Mosaic Cartograms, and point-set matching. Our pipeline is a seamless composition of existing techniques for these problems and results in high-quality grid maps. We provide an implementation, demonstrate the efficacy of our approach on various complex datasets, and compare it to the state-of-the-art.
On Diverse Asynchronous Activity Anticipation

We investigate the joint anticipation of long-term activity labels and their corresponding times with the aim of improving both the naturalness and diversity of predictions. We address these matters using Conditional Adversarial Generative Networks for Discrete Sequences. Central to our approach is a reexamination of the unavoidable sample quality vs. diversity tradeoff of the recently emerged Gumbel-Softmax relaxation based GAN on discrete data. In particular, we ameliorate this trade-off with a simple but effective sample distance regularizer. Moreover, we provide a unified approach to inference of activity labels and their times so that a single integrated optimization succeeds for both. With this novel approach in hand, we demonstrate the effectiveness of the resulting discrete sequential GAN on multimodal activity anticipation. We evaluate the approach on three standard datasets and show that it outperforms previous approaches in terms of both accuracy and diversity, thereby yielding a new state-of-the-art in activity anticipation.
Vehicle Detection Based on Multifeature Extraction and Recognition Adopting RBF Neural Network on ADAS System

A region of interest (ROI) that may contain vehicles is extracted based on the composite features on vehicle’s bottom shadow and taillights by setting a gray threshold on vehicle shadow region and a series of constraints on taillights. In order to identify the existence of target vehicle in front of Advanced Driver Assistance System (ADAS) for the extracted ROI, a neural network recognizer of the Radial Basis Function (RBF) is found by extracting a series of parameters on the vehicle’s edge and region features. Using a large amount of collected images, the ROI that may contain vehicles is verified to be effective by extracting composite features of the shadow at the bottom of vehicle and taillights. Based on the positive and negative sample base of vehicles, the neural network recognizer is trained and learned, which can quickly realize network convergence. Furthermore, the vehicle can be effectively identified in the region of interest using the trained network. Test results show that the vehicle detection method based on multifeature extraction and recognition method based on RBF network have stable performance and high recognition accuracy.
A Novel Convolutional Neural Network-Gated Recurrent Unit approach for Image Captioning

Image captioning is a concept of generating a textual description for an image. It involves Machine Learning techniques like Natural Language Processing and Computer Vision to produce appropriate descriptions for images. Image Captioning has several applications in today's world of ever-expanding data such as Application Recommendation, Virtual Assistance, Image Indexing, and in Social Media. Image captioning can also help us in automating the job of interpreting images and in describing a visual scene to the visually impaired. Image Captioning has been dispensable in driving the Human-Computer Interaction field. Our Research paper proposes a CNN-GRU based framework for training using large datasets of Images and Captions and generating accurate caption descriptions for new images. A dictionary of photo identifiers is built based on descriptions to convert these descriptions into a vocabulary of words and built their list. A VGG-16 Convolution Neural Network has been proposed as our feature extractor and a Gated Recurrent Unit - Recurrent Neural Network as our Sequence Processor. Our model gives us an accuracy of 82.39%.
Workload prediction of cloud computing based on SVM and BP neural networks

In this study, support vector machine (SVM) and back-propagation (BP) neural networks were combined to predict the workload of cloud computing physical machine, so as to improve the work efficiency of physical machine and service quality of cloud computing. Then, the SVM and BP neural network was simulated and analyzed in MATLAB software and compared with SVM, BP and radial basis function (RBF) prediction models. The results showed that the average error of the SVM and BP based model was 0.670%, and the average error of SVM, BP and RBF was 0.781%, 0.759% and 0.708%, respectively; in the multi-step prediction, the prediction accuracy of SVM, BP, RBF and SVM + BP in the first step was 89.3%, 94.6%, 96.3% and 98.5%, respectively, the second step was 87.4%, 93.1%, 95.2% and 97.8%, respectively, the third step was 83.5%, 90.3%, 93.1% and 95.7%, the fourth step was 79.1%, 87.4%, 90.5% and 93.2%, respectively, the fifth step was 75.3%, 81.3%, 85.9% and 91.1% respectively, and the sixth step was 71.1%, 76.6%, 82.1% and 89.4%, respectively.
Analysis and safety engineering of fuzzy string matching algorithms

In this paper we explore fuzzy string matching in an automatic ticket classification and processing system. We compare performance of the following string similarity algorithms: Longest Common Subsequence (LCS), Dice coefficient, Cosine Similarity, Levenshtein (edit) distance and Damerau distance. Through optimisation, we accomplished a 15% improvement in the ratio of false positives to true positive classifications over the existing approach used by a customer support system for free customers. To introduce greater safety; we compliment fuzzy string matching algorithms with a second layer Convolutional Neural Network (CNN) binary classifier, achieving an improved keyword classification ratio for two ticket categories by a relative 69% and 78%. Such an approach allows for classification to only be applied where a desired level of safety achieved, such as in instances where automated answers.
Facial Beauty Prediction Using Transfer and Multi-task Learning Techniques

The objective of facial beauty prediction, which is a significant yet challenging problem in the domains of computer vision and machine learning, is to develop a human-like model that automatically evaluates facial attractiveness. Using deep learning methods to enhance facial beauty prediction is a promising and important area. This study provides a new framework for simultaneous facial attractiveness assessment, gender recognition as well as ethnicity identification using deep Convolutional Neural Networks (CNNs). Specifically, a deep residual network originally trained on massive face datasets is utilized which is capable of learning high-level and robust features from face images. Furthermore, a multi-task learning algorithm that operates on the effective features, exploits the synergy among the tasks. Said differently, a multi-task learning scheme is employed by our model to learn optimal shared features for these correlated tasks in an end-to-end manner. Interestingly, prediction correlation of 0.94 is achieved by our method for the SCUT-FBP5500 benchmark dataset (spanning 5500 facial images), which would certainly support the efficacy of our proposed model. This would also indicate significant improvement in accuracy over the other state-of-the-art methods.
Capacitive Swipe Gesture Based Smartphone User Authentication and Identification

Weaknesses in smartphone security pose a severe privacy threat to users. Currently, smartphones are secured through methods such as passwords, fingerprint scanners, and facial recognition cameras. To explore new methods and strengthen smartphone security, we developed a capacitive swipe based user authentication and identification technique. Swipe is a gesture that a user performs throughout the usage of a smartphone. Our methodology focuses on using the capacitive touchscreen to capture the user's swipe. While the user swipes, a series of capacitive frames are captured for each swipe. We developed an algorithm to process this series of capacitive frames pertaining to the swipe. While different swipes may contain different numbers of capacitive frames, our algorithm normalizes the frames by constructing the same number of frames for every swipe. After applying the algorithm, we transform the normalized frames into gray scale images. We apply principal component analysis (PCA) to these images to extract principal components, which are then used as features to authenticate/identify the user. We tested random forest (RF) and support vector machine (SVM) algorithms as classifiers. For authentication, the performance of SVM (tested with left swipes) was more promising than RF, yielding a maximum accuracy of 79.88% with an FAR and FRR of 15.84% and 50%, respectively. SVM (tested with right swipes) produced our maximum identification accuracy at 57.81% along with an FAR and FRR of 0.60% and 42.18%, respectively.
Energy-Aware Multi-Goal Motion Planning Guided by Monte Carlo Search

Autonomous robots need a reliable way to preserve their energy level while performing a persistent task such as inspection or surveillance. Toward this objective, this paper considers the multi-goal motion-planning problem with multiple recharging stations where a robot operating in a complex environment has to reach each goal while reducing the travel distance and the number of times it recharges. This paper develops an integrated approach that couples sampling-based motion planning with Monte-Carlo Tree Search (MCTS). The proposed MCTS searches over a discrete abstraction, which is obtained via a probabilistic roadmap, and uses a reward function to calculate when, where, and whether it is beneficial to recharge. This results in short tours that also reduce the number of recharges. Such tours are used to guide sampling-based motion planning as it expands a tree of collision-free and dynamically-feasible motions. Experiments with nonlinear dynamical robot models operating in obstacle-rich environments demonstrate the efficiency of the approach.
Decoupling Inherent Risk and Early Cancer Signs in Image-Based Breast Cancer Risk Models

The ability to accurately estimate risk of developing breast cancer would be invaluable for clinical decision-making. One promising new approach is to integrate image-based risk models based on deep neural networks. However, one must take care when using such models, as selection of training data influences the patterns the network will learn to identify. With this in mind, we trained networks using three different criteria to select the positive training data (i.e. images from patients that will develop cancer): an inherent risk model trained on images with no visible signs of cancer, a cancer signs model trained on images containing cancer or early signs of cancer, and a conflated model trained on all images from patients with a cancer diagnosis. We find that these three models learn distinctive features that focus on different patterns, which translates to contrasts in performance. Short-term risk is best estimated by the cancer signs model, whilst long-term risk is best estimated by the inherent risk model. Carelessly training with all images conflates inherent risk with early cancer signs, and yields sub-optimal estimates in both regimes. As a consequence, conflated models may lead physicians to recommend preventative action when early cancer signs are already visible.