Reviewerly/exHarmony huggingface dataset:
A GUI for Multimodal Sentiment Analysis on Gurmukhi Script

To provide smart access to the results of a sentiment analysis system, a graphical user interface has been developed. A text comment along with an image from Twitter is taken in the existing work by assembling thousands of posts spread on different classes. Twitter found more effective where one can express their feelings in a few words and post it as a text or along with pictures also. It founds more interesting platform where people post their views and others comment on it which makes them related and interactive to each other. It has been carried out by collecting Gurmukhi data from Twitter using Twitter API. Collected content has been filtered, and four categories of data have been generated named as happy, love, sad and angry moods of people. For text feature extraction, TF-IDF and N-gram features are used which are reduced further using mutual information (MI). For image feature extraction, hybrid approach using two different LBP variants is used. The resultant values were recorded to obtain the recall, precision, f-measure, and accuracy to check the efficiency of the proposed system in which three types of classifiers, i.e., Naïve Bayes, k-nearest neighbor and decision tree, have been used. The proposed modal shows decision tree classifier well performed for classifying the emotions of image and text data.
A GPU Scheduling Framework to Accelerate Hyper-Parameter Optimization in Deep Learning Clusters

This paper proposes Hermes, a container-based preemptive GPU scheduling framework for accelerating hyper-parameter optimization in deep learning (DL) clusters. Hermes accelerates hyper-parameter optimization by time-sharing between DL jobs and prioritizing jobs with more promising hyper-parameter combinations. Hermes’s scheduling policy is grounded on the observation that good hyper-parameter combinations converge quickly in the early phases of training. By giving higher priority to fast-converging containers, Hermes’s GPU preemption mechanism can accelerate training. This enables users to find optimal hyper-parameters faster without losing the progress of a container. We have implemented Hermes over Kubernetes and compared its performance against existing scheduling frameworks. Experiments show that Hermes reduces the time for hyper-parameter optimization up to 4.04 times against previously proposed scheduling policies such as FIFO, round-robin (RR), and SLAQ, with minimal time-sharing overhead.
AI2VIS4BigData: A Reference Model for AI-Based Big Data Analysis and Visualization

Public interest in the combined application domain of Artificial Intelligence (AI) - based Big Data Analysis and Visualization is currently peaking. Yet until recently, there was no reference model that provided common ground for information exchange in bigger projects or collaboration scenarios, that prevented scientists and private companies from reinventing the wheel. The introduction of AI2VIS4BigData Reference Model recently filled this gap. This paper targets to introduce the AI2VIS4BigData Reference Model, its elements and all interconnections between them in very detail. It provides an overview of example reference models, their general history and objectives and introduces the reference models that were utilized to derive AI2VIS4BigData.
Stability analysis of intrinsic camera calibration using probability distributions

Abstract The currently most widely used calibration method in geometric computer vision is based on Zhang’s method. This approach is easy to implement and delivers detailed result evaluations. One drawback, however, might be that the unexperienced user may obtain unstable and unrepeatable calibration results despite small re-projection errors. Therefore, the data must be interpreted with caution and there is definitely a need for further consideration beside re-projections to be taken for better estimations. So far, statistical inference has been used for presenting precision measures only. In our work, we extend the statistical approach quantitatively using large bundles of images and get calibrations from randomly chosen subsets. Then a Maximum Likelihood Estimation of intrinsic parameters is implemented and the statistical behaviour is analyzed. In addition, the recovered expected values of parameters are utilized as ground truth to scrutinize the single influencing factors of the imaging configuration. According to the results we found out that the image block plays a significant role for camera calibration with regard to the orientation of the imaging configuration. Including also suitable manufactured calibration boards as well as other operational issues, finally a well-designed image block provides a repeatable and reliable calibration.
A Deep Learning Approach to Malayalam Parts of Speech Tagging

This paper presents a deep learning based approach to Malayalam Parts of Speech (POS) tagging. We applied two neural sequence labelling models long short-term memory (LSTM) and Convolution Neural Network (CNN). The proposed model is an end-to-end deep neural network and that benefits from both word and character level representations. We have studied the performance of a six different combinations of neural sequence labelling models on the ILCI Phase II Malayalam dataset and achieved accuracy up to 87.05% for POS tagging. The proposed Word LSTM model with character LSTM and Softmax gives little improvement than character LSTM and Conditional random Field (CRF) models. Also we demonstrated the effect of word and character embeddings together for Malayalam POS Tagging. The proposed approach can be extended to other languages as well as other sequence labelling tasks like Chunking and Named Entity Recognition, etc.
An improved LeNet-5 model for Image Recognition

This paper proposes an improved convolutional neural network structure which greatly reduces the scale of network training parameters. It uses the global average pooling algorithm instead of the full connection algorithm to improve the LeNet-5 network. The number of convolution kernels is increased, while the number of subsampling layers is reduced to an optimal value. After verification with MINST handwritten Arabic numeral data set, the results show that the improved network training parameters are only 34.8% of the original, and the recognition accuracy can achieve 99.3%.
An Instance Segmentation Algorithm Based on Improved Mask R-CNN

Mask R-CNN has a high application value in the field of computer vision. However, the Mask R-CNN algorithm has the disadvantages of poor edge segmentation due to the blurred bounding box of the target image and poor segmentation of small targets, which greatly limits its wide application. To solve the above problems, this paper proposes a multi-scale RPN(Region Proposal Network) network structure and adopts KL loss. By building the Tensorflow deep learning framework in the Ubuntu16.04 operating system, the improved algorithm was tested in the MS-COCO data set and the autonomous driving data set Cityscapes which verifies its applicability and effectiveness.
Signal Processing Optimization for Federated Learning over Multi-User MIMO Uplink Channel

In federated learning, remote mobile devices, which are equipped with local datasets, collaborate through a parameter server (PS) in order to train a machine learning model. An advantage of the federated learning is its effectiveness of preserving the privacy of local raw data. However, it is challenging to meet the demands on latency of exchanging data on wireless multiple access channel (MAC) with limited bandwidth. Over-the-air computation (AirComp) is a potential solution to this problem, which leverages the superposition property of MAC channel. This work addresses the signal processing optimization of both digital federated learning and AirComp schemes under multiuser MIMO uplink system. For either system, a mathematical optimization problem is formulated and tackled by deriving an iterative algorithm. Via numerical results, the mean squared error (MSE) performance of the digital and AirComp schemes is compared.
Low Complexity Named-Entity Recognition for Indonesian Language using BiLSTM-CNNs

Named-Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) and information extraction. NER is used to extract information such as names of the people, organizations, and places. NER has been used in many fields of work, one of which is in chatbot development. NLP and machine learning approaches enable a smarter chatbot with better personal analysis to users. This research builds a NER model in Indonesian Language using Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNNs) model architecture. Unlike the former research, this model only uses word-level embedding in the CNNs layer to keep the model simple. The Named-Entities (NEs) used in this study are limited to the name of the person, organization, location, quantity, and time using the BILOU labeling format. The performance of the model built is measured using micro-averaged f1 score evaluation metric. The BiLSTM-CNNs + pretrained word2vec embedding model provides good performance compared to other models with an f1 score of 71.37%.
Cycle-Consistent Generative Rendering for 2D-3D Modality Translation

For humans, visual understanding is inherently generative: given a 3D shape, we can postulate how it would look in the world; given a 2D image, we can infer the 3D structure that likely gave rise to it. We can thus translate between the 2D visual and 3D structural modalities of a given object. In the context of computer vision, this corresponds to a learnable module that serves two purposes: (i) generate a realistic rendering of a 3D object (shape-to-image translation) and (ii) infer a realistic 3D shape from an image (image-to-shape translation). In this paper, we learn such a module while being conscious of the difficulties in obtaining large paired 2D-3D datasets. By leveraging generative domain translation methods, we are able to define a learning algorithm that requires only weak supervision, with unpaired data. The resulting model is not only able to perform 3D shape, pose, and texture inference from 2D images, but can also generate novel textured 3D shapes and renders, similar to a graphics pipeline. More specifically, our method (i) infers an explicit 3D mesh representation, (ii) utilizes example shapes to regularize inference, (iii) requires only an image mask (no keypoints or camera extrinsics), and (iv) has generative capabilities. While prior work explores subsets of these properties, their combination is novel. We demonstrate the utility of our learned representation, as well as its performance on image generation and unpaired 3D shape inference tasks.
Weld Line Detection using RealSense Depth Camera based on Depth Map

Weld line detection is essential to achieve non-destructive inspection automation. Due to the irregularity and the lack of topological information of the point cloud, it is hard to effectively detect the weld line from the 3D weld point cloud. This research investigates a novel method by using the depth map of point cloud to achieve high precision weld line detection of 3D weld point clouds captured by a RealSense depth camera. First, the depth map of the welding height is obtained, which effectively solves the problem caused by the point cloud disorder. Then, based on different welding line extraction algorithms, the detection performance of the algorithm is effectively optimized. Experimental results show that the predicted distance accuracy is 0. 69mm, and the angle accuracy is 0.99 degree. The final weld line extraction method is a random sampling consensus algorithm (RANSAC) based on second-order polynomial function.
A model for analyzing clustered occurrence data

Spatial or temporal clustering commonly arises in various biological and ecological applications, for example, species or communities may cluster in groups. In this paper, we develop a new clustered occurrence data model where presence-absence data are modeled under a multivariate negative binomial framework. We account for spatial or temporal clustering by introducing a community parameter in the model that controls the strength of dependence between observations thereby enhancing the estimation of the mean and dispersion parameters. We provide conditions to show the existence of maximum likelihood estimates when cluster sizes are homogeneous and equal to 2 or 3 and consider a composite likelihood approach that allows for additional robustness and flexibility in fitting for clustered occurrence data. The proposed method is evaluated in a simulation study and demonstrated using forest plot data from the Center for Tropical Forest Science. Finally, we present several examples using multiple visit occupancy data to illustrate the difference between the proposed model and those of N-mixture models.
WITHDRAWN: A comparative study of augmented reality-based head-worn display devices

It was explored that instructions for manual industrial installation are better than instructions on a stationary monitor in a head-worn Virtual Reality Display (AR-HWD). A prototype consisting of virtual instruction screens was designed for two instance assembly tasks. In a comparative analysis, participants carried out the tasks with instructions through an AR-HWD and a stationary screen. The task performance and user experience were measured through questionnaires, interviews, and observation notes. The study showed that the consumers had the enjoyment of exploring the technology and were enthusiastic. The perceived utility in the current situation was different, but the users saw a tremendous opportunity for the future with AR-HWDs. The accuracy of the task with the ARHWD directions was as strong as on the screen. AR-HWDs are a better solution than a stationary screen, but technical limitations are required and new technology employees need to be educated in order to make their application effective.
ViVA: Semi-Supervised Visualization via Variational Autoencoders

Visualizing latent embeddings is a popular approach to explain classification models, including deep neural networks. However, existing visualization methods such as t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation Projection (UMAP) are often used as a post-processing step which is independent of the classification models. The resulting visualization can be misaligned with the classification models. In this paper, we propose ViVA, a novel method for semi-supervised Visualization via Variational Autoencoders. ViVA learns from both unlabeled and labeled data by jointly optimizing both visualization loss and classification loss. As a parameterized model using neural networks, ViVA can easily project new data to the same embedding space. Experiments show that ViVA can achieve better visualization quality as well as classification accuracy on multiple challenging datasets compared to several visualization baselines, including t-SNE and UMAP.
A comparative study of two meta-heuristic algorithms for MRI and CT images registration

Modern meta-heuristics algorithms have achieved great success on medical image registration task. Due to the difference in performance of these algorithms, we present a comparative study of particle swarm optimization (PSO) and the grey wolf optimizer (GWO) for registration of a section of the human brain. The human brain recognition use using the two methods Magnetic Resonance (MR) and Computed Tomography (CT). The simulation results show that the grey wolf optimizer algorithm approaches achieve a robust registration and high precision compared to that of particle swarm optimization.
Preliminary Insights Into the Adoption of Bitcoin in a Developing Economy

The purpose of this study is to explore the determinants of bitcoin adoption among individuals and also to assess whether the usage of the bitcoin technology for payment of transactions is preferable to other modes of payment. The study proposed a conceptual model analyzing the driving factors that influence a behavior towards the utilization of bitcoins in a developing economy, Ghana, through the lens of the unified theory of acceptance and use of technology model. A qualitative method which employed a purposive sampling technique in the selection of twelve respondents who understand and utilize the bitcoin technology was used. The findings showed that majority of the respondents were exceptionally positive about the idea of bitcoin and had the goal to keep utilizing it, on conditions with respect to the usefulness, the ease of use, the secureness, and the price value of bitcoin. The study offers practitioners with a framework based on theory that goes beyond not only investigating the adoption but also to assess the impact in utilizing bitcoin for organizational benefits.
Automatic Detection of Vehicle Targets Based on CenterNet Model

In the context of the new era, the concept of smart transportation has appeared in people's lives. Detecting vehicles and pedestrians has become a popular application research direction in the field of target detection. Aiming at the problem that traditional methods have low accuracy in vehicle detection in the actual environment, a vehicle detection method based on the CenterNet model in deep learning is proposed. When constructing the model, this paper regards the target as a point- the center point of the target BBox. The detector uses key point estimation to find the center point and returns to other target attributes, such as size, 3D position, direction, and even pose. This paper uses Peking University/Baidu_Autonomous Driving dataset for training and testing. The experimental results show that compared with Inception-ResNet-V2 and Efficient-Det, the method proposed in this paper has significantly improved the average detection accuracy. It has a good detection effect for vehicles in actual scenes, and the network has certain robustness.
Deep learning-based detection and classification of adenocarcinoma cell nuclei

Nowadays, clinical practice uses digital pathology for examining digitized microscopic images to identify diseases like cancers. The main challenge in examining microscopic pictures is the need to dissect every single individual cell for precise analysis because identification of cancerous diseases depends emphatically on cell-level data. Due to this reason, the detection of cells is a significant point in medical image examination, and it is regularly the essential prerequisite for disease classification techniques. Cell detection and then classification is a problematic issue because of diverse heterogeneity in the characteristics of the cells. Deep learning methodologies have appeared to deliver empowering results on analyzing digital pathology pictures. This chapter introduces an approach by using region-based convolution neural networks for locating the cell nuclei. The region-based convolution neural network estimates the probability of a pixel belonging to the core of the cell nuclei. Pixels with maximum probability indicate the location of the core of the cell nucleus. After finding the cells, they are classified as healthy or malicious cells by training a deep convolution neural network. The proposed approach for cell detection and classification is tested with the adenocarcinoma dataset. Cell image analysis based on deep learning techniques shows good results in both the identification and classification of the cell nucleus.
Hy-FiX: Fast In-Place Upgrades of KVM Hypervisors

Maintaining up-to-date KVM hypervisors requires regular upgrades to the host kernel, hence rebooting the physical host with the consequent termination of running Virtual Machines (VMs). Cloud platforms capable of massive large-scale live migrations evacuate VMs from the hosts before rebooting, minimizing the impact over VM up-time. However, scenarios exist where resource constraints make live migration undesirable, or the presence of fault-tolerant instances (e.g., replicated services) favors the adoption of VM termination, a simpler but more disruptive strategy. In this article, we present Hy-FiX, a fast in-place upgrade solution for KVM hypervisors. Hy-FiX preserves VM memory across host reboots, protecting the execution state of running guests while hypervisor upgrades are applied. Hy-FiX memory preservation across reboot, combined with a mixed suspend-to-disk/suspend-to-RAM technique, achieves a 2.31-second checkpoint/restore time for a 256 GB VM, and Hy-FiX lazy memory initialization reboots an enterprise-class host in constant time (7.6 seconds) regardless of its equipped memory. Hy-FiX is, therefore, a better alternative to classical VM termination and restart.
A Dual Residual Network with Channel Attention for Image Restoration

Deep learning models have achieved significant performance on image restoration task. However, restoring the images with complicated and combined degradation types still remains a challenge. For this purpose, we proposed a dual residual network with channel attention (DRANet) to address complicated degradation in the real world. We further exploit the potential of encoder-decoder structure. To fuse feature more efficiently, we adopt the channel attention module with skip-connections. To better process low- and high-level information, we introduce the dual residual connection into the network architecture. And we explore the effect of multi-level connection to image restoration. Experimental results demonstrate the superiority of our proposed approach over state-of-the-art methods on the UDC T-OLED dataset.
Effect of Stemming Nazief &amp; Adriani on the Ratcliff/Obershelp algorithm in identifying level of similarity between slang and formal words

The slang word is a word that is widely used everyday by social media users, especially in Indonesia. The language generally contains abbreviations or strange words that have absolutely no connection with the word in question, this often leads to ambiguity when analysis such as sentiment analysis and Information Retrieval (IR). But lately there has been a slang dictionary that keeps a list of slang words that are often used by internet users, the dictionary informs slang words into words in formal Indonesian. But there are still many studies, especially text mining that ignores or does not filter out the word slang so that it will likely affect the results in research that has been done. The Nazief & Adriani algorithm is an algorithm to transform basic forms using Indonesian morphology by eliminating prefixes and suffixes. The Ratcliff/Obershelp algorithm can determine the similarity between two strings by matching the two strings. The purpose of this study is to compare the word slang to the formal word Indonesian, it aims to find out the similarity between the word slang with the formal word so that it will be known the percentage level of slang similarity with the formal word to find out whether the formal basic word can indirectly representing the word slang with the degree of similarity. The dataset used by Kaggle and Github, the results of the study of the use of text preprocessing and stemming of the Nazief & Adriani algorithm affect the similarity of slang and formal words with the most frequent distribution in the range of values 0.8-0.89 (80% -89.99%) in three different tests with two types of datasets and affect the level of similarity of strings precisely on the dataset from Kaggle at the value of similarity 1 (100%) which previously amounted to 40 data to 927 data, the dataset from Github also increased to the value of similarity 1 (100%) which previously did not have data to 409 data.