Reviewerly/exHarmony huggingface dataset:
Leveraging Secured E-Voting Using Decentralized Blockchain Technology

One of the major causes of election violence is the incompetent existing voting methodologies including the paper and ballot as well as e-voting systems. Electronic voting was suggested as an alternative in order to overcome some of the major shortcomings of the paper and ballot system including the huge manpower required for vote counting. But along with solving such challenges, the e-voting systems raise humongous security threats that may lead to vote tampering and other issues like revealing the identity of the voter. This chapter suggests voting systems based on blockchain as a better alternative to the existing e-voting systems as they would solve the major concerns of the traditional e-voting systems. The decentralized and immutable nature of blockchain will prevent any kind of mischievous activity conducted with the objective of vote tampering as well as it would also protect the anonymity of the user. This chapter provides detailed information on the blockchain concept along with its features and types. This chapter also discusses a few of the major consensus algorithms being frequently utilized in different blockchain networks. Further, the major challenges faced by existing voting methodologies have been addressed and then this chapter explains that integrating the e-voting systems with blockchain technology can help overcome most of these challenges. This chapter concludes that the decentralized voting system using blockchain is the future of e-voting.
A Neuroscience Approach to Women Entrepreneurs’ Pitch Performance: Impact of Inter-Brain Synchrony on Investment Decisions

Making a successful pitch to investors is vital to the success of startups. Improving pitch performance in women entrepreneurs might be an effective mechanism to close gender disparity in entrepreneurship. Drawing on social neuroscience studies, we present our scientific approach to shedding light on the role of “inter-brain synchrony” between women entrepreneurs and investors in pitch performance. Using functional near-infrared spectroscopy (fNIRS) hyperscanning, we will scan 40 entrepreneur-investor dyads who engage in naturalistic pitch events. We will elucidate patterns of inter-brain synchrony that are associated with pitch performance. Additionally, we will assess whether the sex composition of an entrepreneur-investor dyad affects these associations. A better understanding of the inter-brain signatures underlying successful (and unsuccessful) pitches will generate insights into the design of novel and effective interventions that can help catalyze the success of women entrepreneurs.
Big Data in Cloud Today: A Comprehensive Survey

Today, big data and cloud computing are two mainstream techniques in the field of information technology. Big data and cloud computing are concerned with massive scale of data and infrastructure, respectively. The reason for their adoption as a huge enterprise is the ease of simplification provided by these technologies. Big data indicates a large collection of data which cannot be processed by any source of available processing units. Cloud computing refers to handling and operation of data at the remote place. This chapter enunciates the importance, characteristics, and classification of big data with relevant examples. It also presents the tools and techniques used for the processing of big data. In addition, the concept, working, characteristics, and key features of cloud computing are discussed. Ultimately, this chapter correlates both the technologies – big data and cloud computing in today’s scenario with a case study.
An Intelligent Model for Optimizing Sparsity Problem Toward Movie Recommendation Paradigm Using Machine Learning

Recommendation systems for suggesting products are crucial, particularly in streaming services. Recommendation algorithms are crucial for helping viewers find new movies they like on streaming movie platforms like Netflix. In this chapter, we create a smart algorithm that makes an optimistic choice to design a collaborative filtering system that forecasts movie ratings for a user based on a significant database of user ratings. According to the genres that users like to watch, it suggests movies that are the greatest fit for them. The cumulative influence of user ratings and reviews produces the list of suggested films. A statistical analysis is performed to develop a pilot survey model to analyze the real-time dataset. Ant Colony Optimization (ACO) is deployed to determine the rating of the group members’ for future recommendation. In this way, sparsity problems will be optimized in a recommender system. A real-time dataset named as Movielens is used to validate the proposed model. Finally, deploy k-fold cross validation to evaluate the performance metric.
Auto-Scaling Containerized Applications in Geo-Distributed Clouds

As a lightweight and flexible infrastructure solution, containers have increasingly been used for application deployment on a global scale. By rapidly scaling containers at different locations, the deployed applications can handle dynamic workloads from the worldwide user community. Existing studies usually focus on the (dynamic) container scaling within a single data center or the (static) container deployment across geo-distributed data centers. This article studies an increasingly important container scaling problem for application deployment in geo-distributed clouds. Reinforcement learning (RL) has been widely used in container scaling due to its high adaptability and robustness. To handle high-dimensional state spaces in geo-distributed clouds, we propose a deep RL algorithm, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepScale</i> , to auto-scale containerized applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepScale</i> innovatively utilizes multi-step predicted future workloads to train a holistic scaling policy. It features several newly designed algorithmic components, including a domain-tailored state constructor and a heuristic-based action executor. These new algorithmic components are essential to meet the requirements of low deployment costs and achieve desirable application performance. We conduct extensive simulation studies using real-world datasets. The results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepScale</i> can significantly outperform an industry-leading scaling strategy and two state-of-the-art baselines in terms of both cost-effectiveness and constraint satisfaction.
Tighter Regret Analysis and Optimization of Online Federated Learning

In federated learning (FL), it is generally assumed that all data are placed at clients in the beginning of machine learning (ML) optimization (i.e., offline learning). However, in many real-world applications, ML tasks are expected to proceed in an online fashion, wherein data samples are generated as a function of time and each client has to predict a label (or make a decision) upon receiving an incoming data. To this end, online FL (OFL) has been introduced, which aims at learning a sequence of global models from distributed streaming data such that a cumulative regret is minimized. In this framework, the vanilla method (named FedOGD) by combining online gradient descent and model averaging, which is regarded as the counterpart of FedSGD in the standard FL. Despite its asymptotic optimality, FedOGD suffers from high communication costs. In this paper, we present a communication-efficient OFL method by means of intermittent transmission (enabled by client subsampling and periodic transmission) and gradient quantization. For the first time, we derive the regret bound which can reflect the impact of data-heterogeneity and communication-efficient techniques. Based on our tighter analysis, we optimize the key parameters of OFedIQ such as sampling rate, transmission period, and quantization bits. Also, we prove that the optimized OFedIQ asymptotically achieves the performance of FedOGD while reducing the communication costs by 99%. Via experiments with real datasets, we validate the effectiveness of our algorithm on various online ML tasks.
Offline Model-Based Adaptable Policy Learning for Decision-Making in Out-of-Support Regions

In reinforcement learning, a promising direction to avoid online trial-and-error costs is learning from an offline dataset. Current offline reinforcement learning methods commonly learn in the policy space constrained to in-support regions by the offline dataset, in order to ensure the robustness of the outcome policies. Such constraints, however, also limit the potential of the outcome policies. In this paper, to release the potential of offline policy learning, we investigate the decision-making problems in out-of-support regions directly and propose offline Model-based Adaptable Policy LEarning (MAPLE). By this approach, instead of learning in in-support regions, we learn an adaptable policy that can adapt its behavior in out-of-support regions when deployed. We give a practical implementation of MAPLE via meta-learning techniques and ensemble model learning techniques. We conduct experiments on MuJoCo locomotion tasks with offline datasets. The results show that the proposed method can make robust decisions in out-of-support regions and achieve better performance than SOTA algorithms.
Unsupervised Lighting Reflectance Estimation for Robot Monitoring Under Poor Illuminance

Accurate visual detection under poor illuminance is arguably an important and challenging practice for robot monitoring. The purpose of this study is to build detectors for inspection robot under nonuniform and poor lighting conditions. To this end, we propose an unsupervised lighting reflectance estimation framework for dark image detection unsupervised lighting reflectance estimation network (ULRE-Net). Based on lightness-color consistency, the “true color” of objects merely depends on the reflectance regardless of the variations of illuminance. The proposed ULRE-Net extracts robust features for detection by estimating a simple and differentiable expression of reflectance, and the method formulates reflectance estimation in an unsupervised learning manner without relying on image pairs. Thorough experiments are conducted to validate the veracity and generality of the proposed method over existing ones.
Sign Language Recognition With Self-Learning Fusion Model

Sign language recognition (SLR) is the task of recognizing human actions that represent the language, which is not only helpful for deaf–mute people but also a means for human–computer interaction. Although data from wearable sensors have been proven useful for this task, it is still difficult to collect such data for training deep fusion models. In this study, our contributions are twofold: 1) we collect and release a dataset for SLR consisting of both video and sensor data obtained from wearable devices and 2) we propose the first self-learning fusion model for SLR, termed STSLR, that utilizes a portion of annotated data to simulate sensor embedding vectors. By virtue of the simulated sensor features, the video features from video-only data are enhanced to allow the fusion model to recognize the annotated actions more effectively. We empirically demonstrate the superiority of STSLR over competitive benchmarks on our newly released dataset and well-known publicly available ones.
Making Machine Learning More Energy Efficient by Bringing It Closer to the Sensor

Processing data close to the sensor on a low-cost, low power embedded device has the potential to unlock new areas for machine learning (ML). Whether it is possible to deploy such ML applications or not depends on the energy efficiency of the solution. One way to realize a lower energy consumption is to bring the application as close as possible to the sensor. We demonstrate the concept of transforming an ML application running near to the sensor into a hybrid near-sensor in-sensor application. This approach aims to reduce the overall energy consumption and we showcase it using a motion classification example, which can be considered as a simpler sub-problem of activity recognition. The reduction of energy consumption is achieved by combining a convolutional neural network with a decision tree. Both applications are compared in terms of accuracy and energy consumption, illustrating the benefits of the hybrid approach.
Human Activity Recognition Using 2-D LiDAR and Deep Learning Technology

In this letter, a home care assistance system is proposed for elder human activity recognition that combines 2-D LiDAR and deep learning technology. First, the 2-D LiDAR is used to scan the room's interior data, and cluster algorithms are used to identify high-density areas that may be objects. The resulting data are then classified into human and nonhuman clusters. The coordinates of the human clusters are sequentially recorded to generate trajectory graphs. These trajectory graphs possess both spatial and temporal attributes and are processed using spatial–temporal graph convolutional networks to achieve accurate classification of human activities. Furthermore, the system is designed to detect abnormal trajectories that may indicate a fall and can send out a warning signal to notify the caregiver for emergency assistance. With these advanced features, the home care assistance system can improve the safety and well-being of elderly individuals living alone or with limited assistance.
Unsupervised State Representation Learning in Partially Observable Atari Games

State representation learning aims to capture latent factors of an environment. Although some researchers realize the connections between masked image modeling and contrastive representation learning, the effort is focused on using masks as an augmentation technique to represent the latent generative factors better. Partially observable environments in reinforcement learning have not yet been carefully studied using unsupervised state representation learning methods. In this article, we create an unsupervised state representation learning scheme for partially observable states. We conducted our experiment on a previous Atari 2600 framework designed to evaluate representation learning models. A contrastive method called Spatiotemporal DeepInfomax (ST-DIM) has shown state-of-the-art performance on this benchmark but remains inferior to its supervised counterpart. Our approach improves ST-DIM when the environment is not fully observable and achieves higher F1 scores and accuracy scores than the supervised learning counterpart. The mean accuracy score averaged over categories of our approach is $$\sim $$ 66%, compared to $$\sim $$ 38% of supervised learning. The mean F1 score is $$\sim $$ 64% to $$\sim $$ 33%. The code can be found on https://github.com/mengli11235/MST_DIM .
Biometric Recognition of African Clawed Frogs

The African clawed frog (Xenopus laevis) is a commonly used model organism for cell biological, developmental, and biomedical research. For health monitoring and experimental quality control purposes, it is desirable to identify individual frogs regularly throughout their life. Current methods for identification are often invasive and associated with significant investment costs. Identification based on images of the biometric pattern on a frog’s back has been implemented in some laboratories, but so far has been performed manually and therefore is time-consuming and limited to small group sizes. This work proposes a novel pipeline for data acquisition, pre-processing, and training of a classification model based on pattern recognition. The pipeline is structured around laboratory frog colonies and smartphone usage. In order to achieve a lightweight system in our evaluation we consider a MobileNet ConvNet pre-trained on ImageNet. Two feature sets are evaluated on a new data set of 1,647 image samples collected from 160 frogs: RGB images, and 3-channel contour maps (i.e. CORF3D). The results indicate that the CORF3D feature set is favoured over RGB. CORF3D achieved the best performance of 99.94% average accuracy, while RGB had the best performance of 98.79%. Analysis of misclassifications shows that bad predictions are often caused by bad lens focus, light reflections, and positional inconsistency in pattern extraction, which can be addressed during data acquisition. The proposed methodology is, therefore, an effective solution for the recognition of Xenopus laevis.
Knowledge Guided Deep Learning for General-Purpose Computer Vision Applications

This research targets general-purpose smart computer vision that eliminates reliance on domain-specific knowledge to reach adaptable generic models for flexible applications. It proposes a novel approach in which several deep learning models are trained for each image. Statistical information of each trained image is then calculated and stored with the loss values of each model used in the training phase. The stored information is finally used to select the appropriate model for each new image data in the testing phase. To efficiently select the appropriate model, a kNN (k Nearest Neighbors) strategy is used to select the best model in the testing phase. The developed framework called KGDL (Knowledge Guided Deep Learning) was evaluated and tested using two computer vision benchmarks, 1) ImageNet for image classification, and 2) COCO for object detection. The results reveal the effectiveness of KGDL in terms of accuracy and competitiveness of inference runtime. In particular, it achieved $$94\%$$ of classification rate in ImageNet, and 92% of intersection over union in COCO dataset.
Generalized Median Computation for Consensus Learning: A Brief Survey

Computing a consensus object from a set of given objects is a core problem in machine learning and pattern recognition. A popular approach is the formulation of generalized median as an optimization problem. The concept of generalized median has been studied for numerous problem domains with a broad range of applications. Currently, the research is widely scattered in the literature and no comprehensive survey is available. This brief survey contributes to closing this gap and systematically discusses the relevant issues of generalized median computation. In particular, we present a taxonomy of computation frameworks and methods. We also outline a number of future research directions.
Explainability-Enhanced Neural Network for Thoracic Diagnosis Improvement

Thoracic problems are medical conditions that affect the area behind the sternum and include the heart, lungs, trachea, bronchi, esophagus and other structures of the respiratory and cardiovascular system. These problems can be caused by a variety of conditions, such as respiratory infections, lung conditions, heart conditions, autoimmune diseases, or anxiety disorders, and can vary in symptoms and severity. In this paper, we introduce a supervised neural network model that is trained to predict these problems and to further more increase the level of accuracy by using explainability methods. We chose to use the attention mechanism to be able to get a higher weight after training the data set. The accuracy of the trained model reached the value of more than 80%. To be able to analyze and explain each feature, we use Local Interpretable Model-Agnostic Explanations, which is a post-hoc model agnostic technique. Our experiments showed that by using explainability results as feedback signal, we were able to increase the accuracy of the base model with more than 20% on a small medical dataset.
Explaining StyleGAN Synthesized Swimmer Images in Low-Dimensional Space

In many existing AI methods, the reasons behind the decisions made by a trained model are not easy to explain. This often leads to a black-box design that is not interpretable, which makes it a delicate issue to adopt such methods in an application related to safety. We consider generative adversarial networks that are often used to generate data for further use in deep learning applications where not much data is available. In particular, we deal with the StyleGAN approach for generating synthetic observations of swimmers. This paper provides a pipeline that can clearly explain the synthesized images after projecting them to a lower dimensional space. These understood images can later be chosen to train a swimmer safety observation framework. The main goal of our paper is to achieve a higher level of abstraction by which one can explain the variation of synthesized swimmer images in low dimension space. A standard similarity measure is used to evaluate our pipeline and validate a low intra-class variation of established swimmer clusters representing similar swimming style within a low dimensional space.
Towards Robust Colour Texture Classification with Limited Training Data

Texture classification plays an important role in different domains of healthcare, agriculture, and industry. In this contribution, we propose an interpretable and efficient texture classification framework that considers colour or channel information and does not require much data to produce accurate results. Therefore, such a classifier can be suitable for medical applications and resource-limited hardware. We base our work on a Generalized Matrix Learning Vector Quantization (GMLVQ) and introduce a special matrix format for multi-channel images. We compare the performance of different model designs on two data sets emphasising the role of the dissimilarity measure used. We demonstrate that our extension of parametrized angle dissimilarity measure leads to better model generalization and improved robustness against varying lighting conditions than its Euclidean counterpart.
Evaluation of a Visual Question Answering Architecture for Pedestrian Attribute Recognition

Pedestrian attribute recognition (PAR) ensures public safety and security. By automatically detecting attributes such as clothing color, accessories, and hairstyles, surveillance systems can provide valuable information for criminal investigations, aiding in identifying suspects based on their appearances. Additionally, in crowd management scenarios, PAR enables monitoring of specific groups, such as individuals wearing safety gear at construction sites or identifying potential threats in sensitive areas. Real-time attribute recognition enhances situational awareness and facilitates rapid response during emergencies, thereby contributing to public spaces’ overall safety and security. This work proposes applying the BLIP-2 Visual Question Answering (VQA) framework to address the PAR problem. By employing Large Language Models (LLMs), we have achieved an accuracy rate of 92% in the private set. This combination of VQA and LLMs makes it possible to effectively analyze visual information and answer questions related to pedestrian attributes, improving the accuracy and performance of PAR systems.