Reviewerly/exHarmony huggingface dataset:
A View Invariant Human Action Recognition System for Noisy Inputs

We propose a skeleton-based Human Action Recognition (HAR) system, robust to both noisy inputs and perspective variation. This system receives RGB videos as input and consists of three modules: (M1) 2D Key-Points Estimation module, (M2) Robustness module, and (M3) Action Classification module; of which M2 is our main contribution. This module uses pre-trained 3D pose estimator and pose refinement networks to handle noisy information including missing points, and uses rotations of the 3D poses to add robustness to camera view-point variation. To evaluate our approach, we carried out comparison experiments between models trained with M2 and without it. These experiments were conducted on the UESTC view-varying dataset, on the i3DPost multi-view human action dataset and on a Boxing Actions dataset, created by us. Our system achieved positive results, improving the accuracy by 24&#x0025;, 3&#x0025; and 11&#x0025; on each dataset, respectively. On the UESTC dataset, our method achieves the new state of the art for the cross-view evaluation protocols.
Classification of handwritten annotations in mixed-media documents

Handwritten annotations in documents contain valuable information, but they are challenging to detect and identify. This paper addresses this challenge. We propose an al-gorithm for generating a novel mixed-media document dataset, Annotated Docset, that consists of 14 classes of machine-printed and handwritten elements and annotations. We also propose a novel loss function, Dense Loss, which can correctly identify small objects in complex documents when used in fully convolutional networks (e.g. U-NET, DeepLabV3+). Our Dense Loss function is a compound function that uses local region homogeneity to promote contiguous and smooth segmentation predictions while also using an L1-norm loss to reconstruct the dense-labelled ground truth. By using regression instead of a probabilistic approach to pixel classification, we avoid the pitfalls of training on datasets with small or underrepre-sented objects. We show that our loss function outperforms other semantic segmentation loss functions for imbalanced datasets, containing few elements that occupy small areas. Experimental results show that the proposed method achieved a mean Intersection-over-Union (mIoU) score of 0.7163 for all document classes and 0.6290 for handwritten annotations, thus outperforming state-of-the-art loss functions.
Efficient Hardware Implementation of Real-Time Object Tracking

This paper presents a novel hardware architecture for the minimum output sum of squared error (MOSSE) tracker algorithm. The proposed hardware architecture is built using a high-level synthesis (HLS) approach to generate hardware blocks from C++ code. Furthermore, real-time testing and verification are done using an FPGA device fabricated at a 16nm technology node. The microbolometer thermal imaging sensor which has 17 &#x03BC;m pixel pitch, 640&#x00D7;480 video resolution and 30 frames/second (fps) frame rate is used. Using the HLS tool, the maximum clock frequency and frame rate of 300 MHz and 60 fps are achieved respectively. Generated register transfer level (RTL) and Matlab simulations pose negligible differences between hardware and software implementations. Experimental results reveal that the proposed architecture balances hardware resource utilization and target tracking accuracy for consumer electronics applications.
Understanding the impact of image and input resolution on deep digital pathology patch classifiers

We consider annotation efficient learning in Digital Pathology (DP), where expert annotations are expensive and thus scarce. We explore the impact of image and input resolution on DP patch classification performance. We use two cancer patch classification datasets PCam and CRC, to validate the results of our study. Our experiments show that patch classification performance can be improved by manipulating both the image and input resolution in annotation-scarce and annotation-rich environments. We show a positive correlation between the image and input resolution and the patch classification accuracy on both datasets. By exploiting the image and input resolution, our final model trained on &#x003C; 1&#x0025; of data performs equally well compared to the model trained on 100&#x0025; of data in the original image resolution on the PCam dataset.
Skip Connections for Medical Image Synthesis with Generative Adversarial Networks

Magnetic Resonance Imaging (MRI) is an imaging technique used to produce detailed anatomical images. Acquiring multiple contrast MRI images requires long scan times forcing the patient to remain still. Scan times can be reduced by synthesising unacquired contrasts from acquired contrasts. In recent years, deep generative adversarial networks have been used to synthesise contrasts using one-to-one mapping. Deeper networks can solve more complex functions, however, their performance can decline due to problems such as overfitting and vanishing gradients. In this study, we propose adding skip connections to generative models to overcome the decline in performance with increasing complexity. This will allow the network to bypass unnecessary parameters in the model. Our results show an increase in performance in one-to-one image synthesis by integrating skip connections.
A Permutation Model for the Self-Supervised Stereo Matching Problem

This paper proposes a novel permutation formulation to the stereo matching problem. Our proposed approach introduces a permutation volume which provides a natural representation of stereo constraints and disentangles stereo matching from monocular disparity estimation. It also has the benefit of simultaneously computing disparity and a confidence measure which provides explainability and a simple confidence heuristic for occlusions. In the context of self-supervised learning, the stereo performance is validated for standard testing datasets and the confidence maps are validated through stereo-visibility. Results show that the permutation volume increases stereo performance and features good generalization behaviour. We believe that measuring confidence is a key part of explainability which is instrumental to adoption of deep methods in critical stereo applications such as autonomous navigation.
Virtual Reality current trends and proposed research agenda

Among the hottest fields in Information Technology, together with AI/Machine Learning and Blockchain, just to name of couple more, Virtual Reality (as well as Augmented Reality and Extended Reality) are gaining momentum all the more every day. This is mainly because it is heavily applicable on a broad range of business and scientific domains reshaping the way human activities are done now and leaving promises for the near and far future. This short review study aims to provide some insights on the status of the main aspects of VR and some challenges to address as well as provide some possible research directions for anyone interested to delve into the field.
Improving Image Synthesis Quality in Multi-Contrast MRI Using Transfer Learning via Autoencoders

The capacity of magnetic resonance imaging (MRI) to capture several contrasts within a session enables it to obtain increased diagnostic information. However, such multi-contrast MRI tests take a long time to scan, resulting in acquiring just a part of the essential contrasts. Synthetic multi-contrast MRI has the potential to improve radiological observations and consequent image analysis activities. Because of its ability to generate realistic results, generative adversarial networks (GAN) have recently been the most popular choice for medical imaging synthesis. This paper proposes a novel generative adversarial framework to improve the image synthesis quality in multi-contrast MRI. Our method uses transfer learning to adapt pre-trained autoencoder networks to the synthesis task and enhances the image synthesis quality by initializing the training process with more optimal network parameters. We demonstrate that the proposed method outperforms competing synthesis models by 0.95 dB on average on a well-known multi-contrast MRI dataset.
Learned Intrinsic Auto-Calibration From Fundamental Matrices

Auto-calibration that relies on unconstrained image content and epipolar relationships is necessary in online operations, especially when internal calibration parameters such as focal length can vary. In contrast, traditional calibration relies on a checkerboard and other scene information and are typically conducted offline. Unfortunately, auto-calibration may not always converge when solved traditionally in an iterative optimization formalism. We propose to solve for the intrinsic calibration parameters using a neural network that is trained on a synthetic Unity dataset that we created. We demonstrate our results on both synthetic and real data to validate the generalizability of our neural network model, which outperforms traditional methods by 2&#x0025; to 30&#x0025;, and outperforms recent deep learning approaches by a factor of 2 to 4 times.
MA-Sapa: A Temporal Planner for Multiple Agents

Multi-agent planning is an important task in the domain of Artificial Intelligence that poses a number of challenges. Firstly, there must be no conflict (negative interactions) among the plans of different agents, in order to ensure their smooth execution. Secondly, redundant actions should be avoided. And finally, cooperative opportunities (positive interactions) should be created and utilized in order to optimize the global work. The problem becomes more challenging if the actions in the agents' domains are temporal rather than the instantaneous ones. This paper solves this problem by presenting a multi-agent temporal planner called MA-Sapa. This planner extends a well known single agent heuristic based temporal planner Sapa for solving the multi-agent planning problem. Experimental results are also presented which demonstrate the effectiveness of MA-Sapa.
Adaptive Memory Management for Video Object Segmentation

Matching-based networks have achieved state-of-the-art performance for video object segmentation (VOS) tasks by storing every-k frames in an external memory bank for future inference. Storing the intermediate frames&#x0027; predictions provides the network with richer cues for segmenting an object in the current frame. However, the size of the memory bank gradually increases with the length of the video, which slows down inference speed and makes it impractical to handle arbitrary length videos. This paper proposes an adaptive memory bank strategy for matching-based networks for semi-supervised video object segmentation (VOS) that can handle videos of arbitrary length by discarding obsolete features. Features are indexed based on their importance in the segmentation of the objects in previous frames. Based on the index, we discard unimportant features to accommodate new features. We present our experiments on DAVIS 2016, DAVIS 2017, and Youtube-VOS that demonstrate that our method outperforms state-of-the-art that employ first-and-latest strategy with fixed-sized memory banks and achieves comparable performance to the every-k strategy with increasing-sized memory banks. Furthermore, experiments show that our method increases inference speed by up to 80&#x0025; over the every-k and 35&#x0025; over first-and-latest strategies.
Performance Evaluation of Metaheuristic Optimization Techniques in Insulation Problem

Selection of insulation material and determination of its thickness are the two most important parameters that prevent heat loss. Too much thickness complicates the price and use. The low coefficient of thermal conductivity of the insulation material reduces heat loss and increases the cost. One of the most important factors when determining the material thickness and conductivity coefficient is the price. In this study, two cases were examined under certain conditions, independent of the price. In the first case, the insulation material is determined, whereas the material thickness is determined in the second case. Six different current metaheuristic algorithms were used to calculate the two cases. It has been observed that harris hawks optimization (HHO), salp swarm optimization (SSO), and whale optimization (WO) algorithms determine the global optimum point without any error in determining the targets of the problem while the processing times were short. Overall, it has been seen that all six algorithms can create solutions to heat transfer problems with very low errors.
User Experience Analysis on e-Wallet Using a Combination of Heuristic Evaluation and UMUX

Usability is the level of ease of users in using the interface on a system. Usability can be measured using expert judgment or user testing. One of the techniques in usability measurement that can be is Heuristic Evaluation (measured by the expert) and Usability Metric for User Experience or as known as UMUX (measured by user). Heuristic Evaluation is an interface evaluation process that aims to measure an interface's usability, efficiency, and effectiveness based on ten heuristic rules. Meanwhile, UMUX is a short-level instrument method or rating level used to collect quantitative user data about the usability of an application. Therefore, the combination of expert judgment and user assessment will provide rich and complementary findings. In this study, we used “CARDS” as the research object. “CARDS” is a digital card application or e-wallet used to pay bills, top-up card balances, online stores, and Payment Point Online Banks. This study aims to improve the quality of service to users of the “CARDS” application by testing the user experience. The result shows that the UMUX score is not equal to 74, so it is necessary to make improvements, with recommendations from experts by the Heuristic Evaluation method based on the lowest assessment score, namely the Consistency and Standards category.
A non-interactive hybrid method for structured offline documents.: Application on the mathematical expression

This article describes a novel method for interpreting sketches. This method is based on a breadth-first competitive analysis of the analysis tree. In contrast to well-known structural approaches, this method enables the simultaneous evaluation of multiple possible recognition hypotheses within the dynamic local context of a document. The decision process selects the best hypotheses at each stage of the analysis. This article demonstrates this novel approach to mathematical expression recognition.
Intent Detection on Indonesian Text Using Convolutional Neural Network

NLP (Natural Language Processing) has become the focus of research in recent years. NLP tasks have been implemented in various sectors and fields. The chatbot system is one of the NLP tasks, which functions to communicate with humans using natural language. Many researchers build models to represent the chatbot. To make a chatbot more powerful, the intent of the conversation a set of sentences representing a specific user's intention when interacting with the chatbot, must be classified. This classification will make the chatbot system more focused, which leads to providing appropriate answers. Humans can simply understand the meaning of different sentences with the same intent. However, a chatbot system will require a complex technique. Therefore, our work uses the CNN (Convolutional Neural Network) for intent detection in Indonesian Language Text using ATIS (Airline Travel Information System) dataset. CNN was selected because it can extract important features from input data, which makes it more efficient than other deep learning algorithms, in terms of memory and complexity. In our work, we also used GloVe (Global Vectors) embedding for generating an optimal intent classification model. The result shows that the GloVe model and CNN produce the best accuracy of 95.84%.
Using Artificial Intelligence for Reverse Osmosis Desalination

The use of artificial intelligence in modeling different processes and systems have recently become an essential need. In this paper, artificial neural network, will be used to model a desalination system that uses reverse osmosis principle. The purity of the water will be predicted and calculated using neural network technology. This research aims to reveal the hidden patterns and relationships in data obtained from the reverse osmosis process, by training and testing the data set to enhance the prediction of extracting clean water. The performance of the artificial neural network was very promising as obtained from some of the metrics, like the high value of correlation factor between the actual output data and the output data obtained from the neural network model.
New Approach to M-OFS Quality Analysis: Focus on Current Issues

several recent studies on multiple-choice questions online feedback systems (OFS) show their high efficiency for student tutoring. AI algorithms are applied to obtain efficient feedback, better quality and optimal course structure. However, the fundamental Classical Testing Theory (CTT) is left behind by many authors. This article introduces current M-OFS issues, applies the CTT concept of validity and reliability and drives the readers to the problems that really require AI methods. The paper presents the decision tree-based quality analysis. This is a new approach, that might enrich the standard procedure.
Clustering Analysis of Bangla News Articles with TF-IDF &amp; CV Using Mini-Batch K-Means and K-Means

Document clustering is the compilation of docu-ments relating to textual content into classes or clusters. The primary objective is to group the documents that are internally logical but substantially different from each other. It is a vital method used in the retrieval of information, extraction of information and organization of records. Around 210 million people worldwide speak Bangla as a first or second language. With the passage of time, these computer-assisted approaches were also used in the Bangla language. However, not enough paper has represented the current state of research in Bangla Document Clustering. The ultimate aim of this work is to achieve the objective of testing K-Means clustering and Mini-Batch K-Means clustering algorithms and analysing the performance with silhouette score and homogeneity score of these algorithms for Bangla news text data. The findings shows that with TF-IDF both K-Mean and MiniBatch K-Mean algorithms gives silhouette score of 0.031 & 0.015 and homogeneity score of 0.33 & 0.27 for 11 clusters which is better than the results with CountVectorizer.
Comparison of Support Vector Machine and Neural Network Algorithm in Drone Detection System

With the increase in the number of drones, it is possible to have the danger of using drones illegally. It is crucial to detect adverse events or conditions so that security operators can obtain that information and situational identification of drones. This paper proposes two methods of classifying acoustic sensor data in a UAV detection system, using Support Vector Machine and Neural Network, that will be compared. This research shows that the accuracy achieved in predicting acoustic sensor data is 82.27% in the SVM method. The accuracy achieved is 90.58% for the NN method under the same input conditions and amount of training data. This comparation needs to do to choose the best accuracy in a public safety environment.
A Dependent Feature Weighting Filter for Naive Bayes Classifier

Naive Bayes (NB) classification is one of the most extensively used algorithms in data mining and machine learning due to its high efficiency and structural simplicity based on conditional independence of attributes. In this paper, we present a dependence metric to quantify the dependence among attributes and class attributes and propose feature-feature significance (FFS) and feature-class significance(FCS)to discover highly predictive attributes over less predictive ones in NB classification. We show how to get feature weights from FFS and FCS and propose a novel dependent feature weighted (DFW) NB classification. To increase performance further, we recommend clustering the random sample of interest due to the non-homogeneous dependence nature of features, and then using feature weighting to alleviate the conditional independence. As a consequence, we propose a cluster-based DFW (CDFW) NB as a result of weighting the DFW filters of random sub-samples by their accuracy and then merging them for performance augmentation. The experimental results show that the NB with DFW filter provides good results when compared to the conventional NB and all other feature weighting techniques.
HemiPhysioData - A SmartWatch App and Methodolgy to collect sensor data of Upper Limbs

Consumer smartwatches come with various sensors that provide opportunities for collecting data of daily physical activities and upper limb range of motion. The data can be used for various purposes, such as constrained movements and disability assessment, physical recovery, and achieving fitness goals. In this paper, we introduce a methodology and a HemiPhysioData app developed for the Apple Watch to collect physical activities and movements data involving upper limbs. The app provides an interactive method to collect labelled and clean data. These features minimize the data collection and data cleaning efforts for analysis, machine-learning models, and developing apps to monitor and recognize physical activities and range of motion. The HemiPhysioData app is available in App Store.
Evaluation of Decision Matrix, Hash Rate and Attacker Regions Effects in Bitcoin Network Securities

Bitcoin is a famously decentralized cryptocurrency. Bitcoin is excellent because it is a digital currency that provides convenience and security in transactions. Transaction security in Bitcoin uses a consensus involving a distributed system, the security of this system generates a hash sequence with a Proof of Work (PoW) mechanism. However, in its implementation, various attacks appear that are used to generate profits from the existing system. Attackers can use various types of methods to get an unfair portion of the mining income. Such attacks are commonly referred to as Mining attacks. Among which the famous is the Selfish Mining attack. In this study, we simulate the effect of changing decision matrix, attacker region, attacker hash rate on selfish miner attacks by using the opensource NS3 platform. The experiment aims to see the effect of using 1%, 10%, and 20% decision matrices with different attacker regions and different attacker hash rates on Bitcoin selfish mining income. The result of this study shows that regional North America and Europe have the advantage in doing selfish mining attacks. This advantage is also supported by increasing the decision matrix from 1%, 10%, 20%. The highest attacker income, when using decision matrix 20% in North America using 16 nodes on 0.3 hash rate with income 129 BTC. For the hash rate, the best result for a selfish mining attack is between 27% to 30% hash rate.
A Novel Approach to Detect Mathematical Expressions: Recognition Based Convex Hulls

For mathematical expression recognition, a novel strategy with a global perspective is described in this study, including the deployment of convex hull and CD-CMG grammars. Even though the proposed model is generic and new, it has only been tested on two-dimensional organized mathematical statements. Uncertainty must be managed throughout the examination of complicated documents. Model-based grammar rules are a set of optimization algorithms we suggest to reduce the combinatory typically generated by similar methods in practice. A time analysis that is in line with user expectations is the purpose of these modifications. In addition, the grammar rules applied help determine the context of the document. This article aims to develop a convex hull-based recognition model that efficiently segmented distinct mathematical symbols from the expression using the grammars concept. Because of this result, we arrive at an approach for recognizing complicated offline documents based on mathematical expressions that rely on the analysis process of user solicitation