Reviewerly/exHarmony huggingface dataset:
Wearable IMU-based Gym Exercise Recognition Using Data Fusion Methods

Gym exercise has become a focus of attention nowadays because of its health benefits. Automatic gym exercise recognition is an emerging research field which aimed at guiding people to keep fit scientifically through gym exercise monitoring. However, as the actions of gym exercise (e.g. barbell bench press, leg extension, etc.) are more diversity and complexity than outdoor exercise (e.g. running, cycling, etc.). Previous studies increase the number of sensors to improve the accuracy gym exercise recognition, while wearing too many sensors make the subjects uncomfortable during gym exercise. In this study, we studied the performance of different classifiers on gym exercise recognition, then the impact of the number of sensors and the positions of the sensor on the body on the recognition performance was analyzed based on the Extra Trees (ET) classifier. Finally, a stratification fusion method using only two sensors was proposed according to the analysis results. The experimental results showed that when two sensors were used to identify eight kinds of gym exercises, the accuracy of the proposed stratification fusion method was 91.26%.
Linear regression on a set of selected templates from a pool of randomly generated templates

We study linear regression for two datasets. For the MNIST dataset we do so using max convolutions, whose parameters are generated directly from training images for the digit recognition problem, hence we call them max convolution templates. From a large pool of randomly generated convolutional templates, we select by iterative process the ones which improve defined linear regression minimization problem the most. With these templates, we use linear and logistic regression and achieve high accuracy, comparable with deep neural networks. We explain why, in a production environment, using this approach has advantages over the use of deep neural networks. On a second dataset ‘Adult Data Set’ of income predictions, we show a similar convolution type approach for generating a pool of random templates and show that the same template selection process and linear regression can be used as for the MNIST dataset.
Clustering of InterCriteria Analysis Data Using a Malignant Neoplasms of the Digestive Organs Data

Very often, very large data tables as results of the InterCriteria Analysis are obtained. In this article, we propose using of one of the types of artificial neural networks that serve to cluster data. The aim is to obtain aggregations of such data. Our example has 9 neurons and 9 clusters along with the centers of gravity of these clusters. To analyze the patients with oncological diseases, registered in Burgas from 2014 to 2018, the InterCriteria Analysis (ICA) approach is applied.
Multi-Objective A* Algorithm for the Multimodal Multi-Objective Path Planning Optimization

In this paper, we consider the multimodal multi-objective path planning (MMOPP) optimization, which is the main topic of a special session in IEEE CEC 2021. The MMOPP aims at finding all the Pareto optimal paths from a start area to a goal area on a grid map, while passing through several designated must-visit areas. We propose an efficient approach based on the multi-objective A* algorithm to exactly solve the MMOPP. Experiments are conducted on the official MMOPP test suite to evaluate the performance of the proposed approach. We also show the admissibility of the proposed approach so that the computational results can be used as the standard answers to the MMOPP test suite.
Improving Answer Type Classification Quality Through Combined Question Answering Datasets

Understanding what a person is asking via a question is one of the first steps that humans use to find the corresponding answer. The same is true for Question Answering (QA) systems. Hence, the quality of the expected answer type classifier (EAT) has a direct influence on QA quality. Many research papers are aiming at improving short text classification quality, however, there is a lack of focus on the impact of training data characteristics on the classification quality as well as effective reuse of datasets through their augmentation and combination. In this work, we propose an approach of analyzing and improving the EAT classification quality via a combination of existing QA datasets. We provide 4 new question classification datasets based on several well-known QA datasets as well as the approach to unify its class taxonomy. We made a sufficient amount of experiments to demonstrate several valuable insights related to the impact of training data characteristics on the classification quality. Additionally, an embedding-based approach for automatic data labeling error detection is demonstrated.
Analysis of Deep Transfer Learning Using DeepConvLSTM for Human Activity Recognition from Wearable Sensors

Human Activity Recognition (HAR) from wearable sensors has gained significant attention in the last few decades, largely because of the potential healthcare benefits. For many years, HAR was done using classical machine learning approaches that require extraction of features. With the resurgence of deep learning, a major shift happened and at the moment, HAR researchers are mainly investigating different kinds of deep neural networks. However, deep learning comes with the challenge of having access to large amounts of labeled examples, which in the field of HAR is considered an expensive task, both in terms of time and effort. Another challenge is the fact that the training and testing data in HAR can be different due to the personal preferences of different people when performing the same activity. In order to try and mitigate these problems, in this paper we explore transfer learning, a paradigm for transferring knowledge from a source domain, to another related target domain. More specifically, we explore the effects of transferring knowledge between two open-source datasets, the Opportunity and JSI-FOS datasets, using weight-transfer for the DeepConvLSTM architecture. We also explore the performance of this transfer at different amounts of labeled data from the target domain. The experiments showed that it is beneficial to transfer the weights of fewer layers, and that deep transfer learning can perform better than a domain-specific deep end-to-end model in specific circumstances. Finally, we show that deep transfer learning is a viable alternative to classical machine learning approaches as it produces comparable results and does not require feature extraction.
A Neural Autopilot Training Platform based on a Matlab and X-Plane co-simulation

The main objective of this paper is to describe a tool for the aircraft autopilot deployment only based on a flight database. Flight simulators such as X-Plane turn out to be powerful and efficient tools for creating such database and controller experimentation. The paper outlines the development of a co-simulation framework between Matlab and X-Plane using the User Datagram Protocol (UDP). The flight data collected during a first step are then used for the training of neural controllers. The approach is based on the neural network imitation ability to learn the piloting skills implicitly stored in the dataset. Also, in order to include fault-tolerant control, a Neural Multiple Model Adaptive Control (NMMAC) based on previously learned networks is implemented. This architecture consists of a bank of local controllers and a switching logic using a bank of estimators. As an illustration of the proposed platform, it is assumed that the airspeed is unmeasured for the flight director. A neural guidance autopilot based NMMAC is therefore performed on different airspeed values. Experiments show that the designed neural autopilot can successfully track both heading and altitude reference signals, while the method is not restricted to this scope.
Deepfake Video Detection with Facial Features and Long-Short Term Memory Deep Networks

Generative models have evolved immensely in the last few years. GAN-based video and image generation has become very accessible due to open source software available to anyone, and that may pose a threat to society. Deepfakes can be used to intimidate, blackmail certain public figures or to mislead the public. At the same time, with the rising popularity of deepfakes, detection algorithms have also evolved significantly. The majority of those algorithms focus on images rather than to explore the temporal evolution in the video. In this paper, we explore whether the temporal information of the video can be used to increase the performance of state-of-the-art deepfake detection algorithms. We also investigate whether certain facial regions contain more information about the authenticity of the video by using the entire aligned face as input for our model and by only selecting certain facial regions. We use late fusion to combine those results for increased performance. To validate our solution, we experiment on 2 state-of-the-art datasets, namely FaceForensics++ [1] and CelebDF [2]. The results show that using the temporal dimension can greatly enhance the performance of a deep learning model.
A Study on Automated Grading System for Early Prediction of Rheumatoid Arthritis

The medical diagnosis method is complex and consisting of a lot of vagueness due to imprecision and subjectivity, Magnetic resonance images have a great deal of resonance with this complexity due to its insightful nature. As rheumatoid arthritis is a chronic inflammatory condition, the modality of magnetic resonance imaging plays an important role in delivering in-depth research. Through gathering vast quantities of magnetic resonance data patterns, the early onset of rheumatoid arthritis can be studied. Artificial intelligence approaches have been used to help physicians in addressing these challenges and to make knowledgeable and accurate choices in the diagnosis of diseases. A variety of papers and diverse methods have been written to resolve concerns and problems. Inclusion and exclusion criteria are classified and evaluated to demonstrate the effect of artificial intelligence to enhance the diagnosis of the disease. The paper further explores the common classification methods for the diagnosis of rheumatoid arthritis. This analysis aims to explain new advances to increase the rate of identification and diagnosis of rheumatoid arthritis.
Analysis of Zomato Services using Recommender System Models

The aim of this project is to analyze and derive insightful information from data about restaurants in the city of Bangalore, India that are listed on the popular food reviewing and delivery service Zomato. Using these insights, as well as additional information about the ordering behavior of customers in India (with respect to specific food items), a recommender system approach is presented that aims to recommend restaurants to users based on their individual tastes and recommend food items to be ordered by users at those restaurants. A number of solutions are proposed and discussed with their performance and shortcomings.
DIAN: Differentiable Accelerator-Network Co-Search Towards Maximal DNN Efficiency

We present DIAN, a Differentiable Accelerator-Network Co-Search framework for automatically searching for matched networks and accelerators to maximize both the accuracy and efficiency. Specifically, DIAN integrates two enablers: (1) a generic design space for DNN accelerators that is applicable to both FPGA- and ASIC-based DNN accelerators; and (2) a joint DNN network and accelerator co-search algorithm that enables the simultaneous search for optimal DNN structures and their accelerators. Experiments and ablation studies based on FPGA measurements and ASIC synthesis show that the matched networks and accelerators generated by DIAN consistently outperform state-of-the-art (SOTA) DNNs and DNN accelerators (e.g., 3.04× better FPS with a 5.46% higher accuracy on ImageNet), while requiring notably reduced search time (up to $1234.3\times)$ over SOTA co-exploration methods, when evaluated over ten SOTA baselines on three datasets.
Full-Abstract Biomedical Relation Extraction with Keyword-Attentive Domain Knowledge Infusion

Relation extraction (RE) is an essential task in natural language processing. Given a context, RE aims to classify an entity-mention pair into a set of pre-defined relations. In the biomedical field, building an efficient and accurate RE system is critical for the construction of a domain knowledge base to support upper-level applications. Recent advances have witnessed a focus shift from sentence to document-level RE problems, which are more challenging due to the need for inter- and intra-sentence semantic reasoning. This type of distant dependency is difficult to understand and capture for a learning algorithm. To address the challenge, prior efforts either attempted to improve the cross sentence text representation or infuse domain or local knowledge into the model. Both strategies demonstrated efficacy on various datasets. In this paper, a keyword-attentive knowledge infusion strategy is proposed and integrated into BioBERT. A domain keyword collection mechanism is developed to discover the most relation-suggestive word tokens for bio-entities in a given context. By manipulating the attention masks, the model can be guided to focus on the semantic interaction between bio-entities linked by the keywords. We validated the proposed method on the Biocreative V Chemical Disease Relation dataset with an F1 of 75.6%, outperforming the state-of-the-art by 5.6%.
Cross-Chain-Based Decentralized Identity for Mortgage Loans

Decentralized Identity (DID) has been deemed an ideal mechanism for identity sharing and authentication, which allows users to process control over identity information. However, the centralized storage of DID hinders further expansion due to security issues. Combining the existing blockchain technology with the DID theory has a great development prospect in terms of privacy protection and security enhancement. In order to address the identity authentication issue of collateral in financial loans, this paper proposes the Cross-chain Channel-based DID (C3-DID) model, which realizes the distributed storage of DID and allows relay-based cross-chain data exchange. We deploy the loan process and the identity verification process on consortium blockchains, separately. In addition, a permission chain channel, called peer-to-peer Matching Channel (MC), is established to accomplish cross-chain data exchange. Our security analysis indicates that the proposed model can resist multiple threats.
Cryptocurrency Security and Privacy Issues: A Research Perspective

Cryptocurrency has developed as a new mode of money exchange since it has become easier, faster and safer. The first cryptocurrency was introduced in 2009 and since then, the growth rate of cryptocurrency has been increasing drastically. As of 2020, the cryptocurrency exchange all over the world has exceeded 300%. The researchers face many challenges during their research on the various cryptocurrencies. For example, most of the high-tech companies still do not support bitcoin on mobile platforms. High-tech companies like Google and Apple are also thinking into banning the bitcoin wallet from their app stores. The work provides a review of cryptocurrency and its types, scope on the investment plans and its advantages also discussed. The growth and comparison between bitcoins and gold is also discussed. The challenges researchers face and the security issues concerning it. This review provides an overview of how the different forms of cryptocurrency are increasing from over a decade. It explains the different types and the year in which they were invented. It also gives a brief comparison with respect to bitcoin, which is one of the most used cryptocurrency. Furthermore, it gives a brief explanation on investments, and schemes for those who are new in the cryptomarket. Later emphasizes on the security issues faced by this technology. It talks about proof of work and the different data attacks the software faced and how the issues were overcome. In the end, it talks about the challenges researchers face while researching cryptocurrency.
A positive/unlabeled approach for the segmentation of medical sequences using point-wise supervision

The ability to quickly annotate medical imaging data plays a critical role in training deep learning frameworks for segmentation. Doing so for image volumes or video sequences is even more pressing as annotating these is particularly burdensome. To alleviate this problem, this work proposes a new method to efficiently segment medical imaging volumes or videos using point-wise annotations only. This allows annotations to be collected extremely quickly and remains applicable to numerous segmentation tasks. Our approach trains a deep learning model using an appropriate Positive/Unlabeled objective function using sparse point-wise annotations. While most methods of this kind assume that the proportion of positive samples in the data is known a-priori, we introduce a novel self-supervised method to estimate this prior efficiently by combining a Bayesian estimation framework and new stopping criteria. Our method iteratively estimates appropriate class priors and yields high segmentation quality for a variety of object types and imaging modalities. In addition, by leveraging a spatio-temporal tracking framework, we regularize our predictions by leveraging the complete data volume. We show experimentally that our approach outperforms state-of-the-art methods tailored to the same problem.
Robust Engineering-based Unified Biomedical Imaging Framework for Liver Tumor Segmentation

Computer vision in general and semantic segmentation has experienced many achievements in recent years. Consequently, the emergence of medical imaging has provided new opportunities for conducting artificial intelligence research. Since cancer is the second-leading cause of death in the world, early-stage diagnosis is an essential process that directly slows down the development speed of cancer.Deep neural network-based methods are anticipated to reduce diagnosis time for pathologists.In this research paper, an approach to liver tumor identification based on two types of medical images has been presented: computed tomography scans and whole-slide. It is constructed based on the improvement of U-Net and GLNet architectures. It also includes sub-modules that are combined with segmentation models to boost up the overall performance during inference phases.Based on the experimental results, the proposed unified framework has been emerging to be used in the production environment.
A new methodology for secured inventory management by average fitness‐based colliding bodies optimization integrated with block chain under cloud

Abstract This article tries to develop the intelligent secured inventory management strategy by blockchain technology under cloud sector. Multiple suppliers, a manufacturer and multiple distributors are considered in the proposed model. The proposed inventory management is derived as a multi‐objective model, which is solved by the average fitness‐based colliding bodies optimization. A set of cost parameters like “transaction cost, inventory holding cost, shortage cost, transportation cost, time cost, setup cost, backordering cost, quality improvement cost, procurement cost, and stock‐out cost” is used for deriving the multi‐objective function. The optimal solution with the reduced cost function is stored in the block chain under cloud sector by each distributor, where a hash function is used by the distributor to secure the data that could not be visible to any other distributors. The proposed methodology aims to provide an important tool for the management of proposed echelon tree structured supply chains with block chain security under cloud sector that overcomes some of the deficiencies of competing methodologies.
Amplitude based keyless optical encryption system using deep neural network

Double random phase encryption (DRPE) system is a simple and powerful encoding technique that consists of only two lenses and two random phase masks. However, there are many issues for applying to actual security systems such as phase acquisition, vulnerability to phase retrieval techniques, and data throughput. Although various extensions of DRPE have addressed each issue, there is no comprehensive solution. To tackle all the issues of DRPE, we propose a new amplitude-based DRPE (ADRPE) system using deep learning. The encoding is the same as the current ADRPE system, and the decoding is achieved by an inverse ADRPE system using convolution neural networks. Our system can achieve a real-time end-to-end encryption system without any additional optical devices and exposure of the keys. To demonstrate our method, we applied it to simulations with various datasets such as passwords, Quick-Response (QR) codes, and fingerprints.
Face recognition using facial features

Face recognition is in demand today due to its utility in person identification, where a small input image or a glimpse can help recognise the person from a database. In India this method of identification is not that popular but it is the need of the hour, biometric identification systems should also have facial recognition, so as to make the system contactless, and more robust in terms of security. Therefore, in this paper the authors have tried to analyse the aptness of the two face recognition algorithms namely facenet and facial landmarks for (i) Crime detection, (ii) face verification and (iii) human tracking. The features are extracted and pre-processed, converted into an array and the region of interest is matched to identify the person. The features selected are Euclidean distance between eyes, structure of nose, lip to lip distance. This project will help in securing industrial area where there is possibility of trespassing, person identification from the crowd, tracking missing personal and help keep a track on some anti-social elements of the society. It has a great application in places with high security like the parliament or bank vault etc.
A Short Survey on Machine Learning Explainability: An Application to Periocular Recognition

Interpretability has made significant strides in recent years, enabling the formerly black-box models to reach new levels of transparency. These kinds of models can be particularly useful to broaden the applicability of machine learning-based systems to domains where—apart from the predictions—appropriate justifications are also required (e.g., forensics and medical image analysis). In this context, techniques that focus on visual explanations are of particular interest here, due to their ability to directly portray the reasons that support a given prediction. Therefore, in this document, we focus on presenting the core principles of interpretability and describing the main methods that deliver visual cues (including one that we designed for periocular recognition in particular). Based on these intuitions, the experiments performed show explanations that attempt to highlight the most important periocular components towards a non-match decision. Then, some particularly challenging scenarios are presented to naturally sustain our conclusions and thoughts regarding future directions.