Reviewerly/exHarmony huggingface dataset:
PADAAV: Enhancing Perception Systems using GAN-generated Adversarial Augmented Domains for Autonomous Vehicles

In the field of autonomous vehicles (AV), it is crucial for the perceptual systems of the AVs to learn inter-domain adaptations in the absence of paired examples for detecting vehicular instances in unstructured real-world scenarios. One straightforward approach is to train the models directly on labeled synthetic datasets. However, this approach usually fails to achieve generality, owing to the domain bias between the real and fake databases of images. We therefore, propose a novel architecture that produces synthetic images based on cycle consistency, in the absence of labeled pair of images. We test the object detectors Detectron and SSD on four types of curated benchmark datasets to evaluate their robustness in detecting objects such as cars, bikes, and pedestrians on road. The four benchmark datasets contain a diversified set of image corruptions and a few variations are built using the proposed framework such as weather variations. The four datasets are PASCAL VOC and SYNTHIA dataset images, weather-translated images, variation-augmented images, and stylized renderings using Adain style transfer. After conducting extensive investigations, we observe a decreased classification loss when exposed to variable image quality. We also witness that augmenting the training datasets with variations in the wild aided in boosting the generalizing capability of the object detectors. The boost in performance is testified by the testing results showing better mAP.
UnStressMe: Explainable Stress Analytics and Self-tracking Data Visualizations

Self-tracking technology for behavior monitoring is prevalent in various aspects of human life. It enables users' activities tracking with data produced “in the wild”, namely capturing real-world physical activity, sleep patterns, and stress levels, among others. Advanced new sensors integrated into commercial self-tracking devices have empowered a new era of sensing data exploration and self-improvement recommendations, aiming to enhance physical and mental well-being. However, the collected data and related inferred knowledge are not always well-explained or well-presented and discourage users' commitment leading to sensing devices' abandonment. To sustain user engagement with self-tracking technology for well-being, this paper introduces a comprehensive framework and respective full-stack web service called “UnStressMe” for the analysis of diverse data modalities tracked in the wild, the prediction of future stress behavior and the production and provision of personalized, model-agnostic explanations and interactive visualizations. We showcase the utility of our framework through a mental health use case, paving the way for explainable, transparent, and human-centric self-tracking technology.
A Spatial-Temporal Graph Convolutional Networks-based Approach for the OpenPack Challenge 2022

We report the proposed method of Team Ritsumei for the OpenPack challenge 2022. In this work, we proposed to use a motion-aware and temporal-enhanced Spatial-Temporal Graph Convolutional Networks for the representation of the keypoint modality features. We also leverage the Accelerometer and Gyroscope modality as auxiliary modalities to improve the performance. Our final result is based on the fusion of four modalities. We report the 92.32% F1 score on the submission set, which won the 3rd place in the OpenPack challenge 2022.
Artifact: Revising Poor Man's Eye Tracker for Crowd-Sourced Studies

We developed a tool to track the test person's gaze while conducting tasks in a web browser [1]. In the browser we can only track the mouse pointer, which is known as poor man's eye tracker. However, the mouse pointer does not necessarily reveal on which area of the web page the user is focusing and therefore it does not allow to conclude the attention of the test person. Therefore, we extended the mouse tracking by applying a fog to the web page. Only an area around the mouse pointer is without fog. Consequently, the mouse pointer must follow the gaze. In addition, we developed a visualisation tool to analyze the focus of the user, i.e. determine the areas where the user's eye had enough time to perceive the visible area. In summary, during our work, we developed two tools. One for conducting the crowd-sourced study (Pipe Maze Study), and one for visualizing and evaluating its findings (Pipe Maze Visualizer). You can find the source code for both tools in the following repository: https://github.com/vs-ude/poor-mans-eyetracker
OpenPack Challenge 2022 Report: Impact of Data Cleaning and Time Alignment on Activity Recognition

This report describes a solution for the OpenPack Challenge 2022 developed by team Malton. Our team mainly focuses on preprocessing of training data for human activity recognition. Because of errors in data collection processes, collected data from subjects can have variety of noises, indicating the importance of data cleaning for the collected data when they are used for training a human activity recognition model. In our solution, we applied two main approaches for the data cleaning. The first approach attempts to remove training data sessions with erroneous data collection procedures. The second approach attempts to align timestamps of different sensors by using binary search. Our experiment revealed that the importance of the preprocessing approaches when we used a standard neural network architecture for activity recognition (DeepConvLSTM). As a result, our solution achieved the F1-score of 91.7%.
PhiNet-GAN: Bringing real-time face swapping to embedded devices

Recent years have seen an unprecedented development of deep learning-based techniques for processing live video from CCTV cameras, causing growing privacy concerns. A possible solution is to ensure that a subject's personal information never leaves the device in which it was collected, thus implementing a Privacy-by-Design (PbD) approach. In live video processing tasks, PbD can be guaranteed through anonymisation techniques, such as face-swapping, performed directly on the end device. This paper, therefore, presents PhiNet-GAN, an extension of the PhiNet family of embedded neural networks applied to generative networks. PhiNet-GAN targets resource-constrained platforms based on low-power microcontrollers. An example is the Kendryte K210, a RISC V dual-core processing unit working at 400MHz on which we tested our network. Overall we achieved a power consumption of less than 300mW, working at more than 15fps with an FID score lower than 150.
An On-Demand Cloud-Native Containerized Storage Design and its Practice of HDFS-on-Kubernetes

Cloud-native big data services become popular in recent years. Two pillars of these services are identified: the separation architecture of compute and storage, and the application-specific controller mechanism. In terms of storage for big data on the cloud, current practices focus on managing a single on-premise storage cluster or building independent PaaS storage services. This paper focuses on the cloud-native containerized storage. An on-demand provisioning design is proposed, which extends the mainstream storage architecture and supports the provisioning of storage clusters for multi-tenancy in a dynamic manner. Its instance of HDFS-on-Kubernetes is implemented. With the mechanisms of global endpoint provisioning and dynamic volume provisioning, this provisioner enables the creation and management of multiple on-demand storage clusters with full-stack resources in an automated way. It guarantees the native performance of host network and local storage, which has been validated through experiments and production applications. It is also easy to use because of its high-level abstraction and single-point configuration mechanism. The design as well as the provisioner has served real business in industrial scenarios.
Efficient Face Generation and Clustering Using Generative Adversarial Networks

Generative Adversarial Network (GAN) is an unsupervised learning technique in performing task such as prediction, classification and clustering. The GAN algorithm can learn the internal representation of data and can act as good features extractor. Training on a dataset of faces, we show convincing evidence that our deep convolutional adversarial pair learnt well and generated new images of fake human faces that look as realistic as possible. The unsupervised clustering model divides and groups faces based on their characteristics. In this paper, we present DCGAN (Deep Convolutional Generative Adversarial Network) in performing classification and clustering.
Multi-view Subspace Clustering with Complex Noise Modeling

Multi-view data clustering often aims to utilize various representations or views of original data to improve the clustering performance compared to the single-view clustering approach. Most multi-view subspace clustering methods are proposed to construct the affinity matrix of each view individually and then implement with spectral clustering for multi-view data clustering. The multi-view low-rank sparse subspace clustering (MLRSSC) is an effective and popular clustering algorithm among multi-view subspace clustering. This method can explore the joint subspace representation through creating an affinity matrix integrated of all views of input data. In addition, the low-rank and sparsity constraints are introduced into this method to enhance the clustering results. However, the original MLRSSC uses the mean square error as the fidelity term while not consider the complex noise pollution in real situations. Therefore, we introduce a complex noise modeling approach, i.e., independent and piecewise identically distributed (IPID) noise model, for MLRSSC to improve its performance. The related experimental results confirm that this proposed algorithm surpasses many state-of-the-art subspace clustering methods on several real-world datasets.
A Comparative Analysis of Non-gradient Methods of Class Activation Mapping

Increased dependency on artificial intelligence (AI) methods for decision-making has emerged in the recent times. The attention lies in the convolutional networks for efficient feature extractions and the generation of valuable insights with high accuracy. Understanding the internal workings of such complex models to ensure trust in the generated systems is challenging for both the developers and the end-users. A growing study on explainability methods with visualizations focuses on model interpretations. Class Activation Mapping (CAM) in the context is used for visualizing the discriminating regions of the images used by the CNNs for classification. Existing gradient-based CAMs like Grad-CAM, Grad-CAM++, XGrad-CAM have certain disadvantages like saturation and false confidence. Hence, we perform a comparative analysis with some of the recently developed non-gradient CAM approaches like Eigen-CAM, Score-CAM, and Ablation-CAM. The efficiency of these methods has been analyzed using the evaluating metrics of remove and debias on the benchmark datasets.
Checkpoint-Based Round-Robin Scheduling Technique Toward Fault-Tolerant Resource Management in Cloud Computing

Cloud computing framework is growing with importance in recent times. Public cloud is more challenging than private cloud. Public cloud framework has inherent challenges of security issues, service reliability, and time-constrained requirement for providing service on demand. Resource management is an important aspect under public cloud. Efficient allocation of resources across different service peers or servers is an important aspect toward error management and fault management. This work proposes a modified round-robin mechanism for resource scheduling with multiple checkpoints which take cares of error handling and fault management. Proposed method has been tested on different benchmarks and establishes its robustness.
A Novel Model for Automated Identification of Terrestrial Species

Artificial intelligence (AI) and machine learning (ML) have slowly but steadily become integral to human lives. While much remains to be learned, it surely has contributed to research works which were beyond the comprehension of the human mind. This paper focuses on the concept of standard machine learning techniques used for terrestrial species identification using 3D images. A novel methodology is proposed for the extraction of the characteristics and structural properties of these species based on images using convolution neural networks (CNNs). This study mainly contributes to disseminating awareness and knowledge to research groups in similar fields of study. Observation has been made that despite the advancement of science and technology, people can still not differentiate a buffalo from a bison. In addition, recent developments in the field of machine learning are still a novice in the field of species studies. The emergence of AI and ML is slowly changing educational entities and industrial services, and rightly so. The proposed methodology can also act as a supplementary tool for restructuring traditional higher education, which is still prevalent in India.
Social Media Fake Profile Classification: A New Machine Learning Approach

Social media fake profile serves various illegal social activities. Therefore, detection and prevention of these profiles are essential. The current approaches based on machine learning (ML) are just considering social media user profile attributes by providing a strict classification. This paper provides a view to utilize a scoring-based fake profile classification technique to monitor the activities of user by using profile attributes and published content. This paper first includes a review to know the dataset to be used and the technique to obtain data from social media platform. Then based on social media user's profile attribute-based ML model has been introduced to classify the fake and legitimate profiles. To train and validate the model, we have used five machine learning algorithms, namely artificial neural network (ANN), support vector machine (SVM), C4.5 decision tree, Bayes classifier, and k-nearest neighbor (k-NN). Here we have found ANN and SVM which is accurate classification technique as compared to others for this task. Finally, by updating the backpropagation neural network and a scoring method for profile a fake profile classification approach has been developed. The developed model is utilizing the content published by users and the basic profile information of public domain. The experiments have been carried out based on real twits and available profile attribute dataset in GitHub. The results are also compared with SVM and ANN algorithms. Based on the precision, recall, and F-score, the proposed technique outperforms as compared to other two implemented models and has been achieved up to 0.94 f-score.
MS3A: Wrapper-Based Feature Selection with Multi-swarm Salp Search Optimization

Feature selection is crucial in improving the effectiveness of classification or clustering algorithms as a large feature set can affect classification accuracy and learning time. The feature selection process includes choosing the most pertinent features from an initial feature set. This work introduces a new feature selection technique using salp swarm algorithm. In particular, an improved variation of the salp swarm algorithm is presented with modifications done to different stages of the algorithm. The proposed work is evaluated by first studying its performance on standard CEC optimization benchmarks. In addition to this, the applicability of the introduced algorithm for feature selection problems is verified by comparing its performance with existing feature selection algorithms. The experimental analysis depicts that the proposed methodology achieves performance improvement over existing algorithms for both numerical optimization and feature selection problems and reduces the feature subset size by 39.1% when compared to the traditional salp swarm algorithm.
Speaker Identification Using Ensemble Learning With Deep Convolutional Features

Speaker identification (SI) is an emerging area of research in the domain of digital speech processing. SI is the process of classification of speakers based on their speech features extracted from the speech utterances. After the recent developments of deep learning (DL) models, deep convolutional neural networks (DCNNs) have been widely used for solving the SI tasks. A CNN model consists of mainly two parts, deep convolutional feature extraction and classification. However, the training process of DCNN models is computationally expensive and time consuming. Therefore, in terms of reducing the computational cost of training a DCNN model an ensemble of machine learning (ML) models is proposed for the speaker identification task. The ensemble model classifies the speakers based on the deep convolutional features extracted from the input speech features. In this work, the deep convolutional features are extracted from mel-spectrograms in terms of flatten vectors (FVs) by utilizing a pre-trained DCNN model (VGG-16 model). The machine learning models that are considered for the hard voting ensemble approach are random forest (RF), extreme gradient boosting (XGBoost) and support vector machine (SVM). The models are trained and tested with voice conversion challenge (VCC) 2016 mono-lingual speech data, VCC 2020 multi-lingual speech data and multi-lingual emotional speech data (ESD). Moreover, three data augmentation techniques are used for increasing the samples of the speech data namely pitch-scaling, amplitude scaling and polarity inversion. The accuracy obtained from the proposed approach is significantly higher than the individual ML models.
Rule Extraction Methods from Neural Networks

The widespread introduction of artificial intelligence systems in all areas of human activity imposes certain requirements on these systems. In particular, systems operating in critical areas such as health care, economics, and security systems based on artificial neural network models should have an explanatory apparatus in order to be able to evaluate not only the recognition, prediction, or recommendation accuracy familiar to everyone, but also to show the algorithm for getting result of neural network working. In this paper, we suggest to investigate methods for rule extraction from ANN, which are based on the fuzzy logic. In our research, we will consider several possible methods for extracting rules, such as the mining clustering algorithm and decision trees.
Simulation Tools for Cloud Computing: A Comparative Study

Computing is evolving toward a paradigm that consists of commoditized services supplied in a way similar to conventional utilities. A variety of computing paradigms have been proposed to realize this goal, the most recent of which is known as cloud computing. It is capable of performing complex and massive scale computing, which has changed roles and responsibilities in IT services. It relieves the consumers from the burden of maintaining massive computing hardware, applications, and data storage systems. As many people have moved to cloud computing because of its elastic resource allocations and pay-for-per-usage features, and also because the cloud environment is very complex and sensitive, it is essential to study, analyze, and evaluate the performance, security, and other features that might be confronted in cloud computing. It is expensive to set up and perform these tests in actual cloud systems. However, modeling and simulation tools are often viable alternatives to cloud computing environment emulation. There are numerous simulation programs available, and they all have different features and applications. As a contribution to knowledge, in this paper, we first present a brief introduction to cloud computing, describe its fundamentals, and present a detailed comparative study of some popular cloud computing simulation tools.
An Overview of Hybridization of Differential Evolution with Opposition-Based Learning

The paper is focused on comprehensive survey on the concept of hybridizing opposition-based learning with Differential Evolution Algorithm. In this paper, we focused on all phases such as population initialization, mutation, crossover and generation jumping, on which the methods applied, application part and motivation.
Neuro-Fuzzy-Based Supervised Feature Selection: An Embedded Approach

In problems of machine learning, selection of important features is an important pre-processing step. Many large datasets contain redundant information in the form of non-informative features. Sometimes the features can be irrelevant and noisy. Feature selection is the process of obtaining a minimal feature subset that sufficiently represents the original set of features. This subset of features can subsequently be used for tasks such as classification, clustering and inference. Most of the work on feature selection found in literature is either supervised or unsupervised. In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based feature ranking and selection approach which is supervised in nature is presented. For feature ranking, keep one-leave the rest and leave one-keep the rest strategies are used. A greedy forward feature selection approach is also presented, which aims to select a feature subset. The results of the proposed approaches are compared with the results obtained by a well-known unsupervised neuro-fuzzy feature selection algorithm and the supervised Relief-F algorithm. The findings are quite encouraging.
Sentiment Analysis Integrating with Machine Learning and Their Diverse Application

Sentiment analysis has risen to prominence as a discipline that has attracted a significant amount of attention due to the vast range of applications that could benefit from its findings. Despite this, this region is still in the early stages of development, with urgent upgrades necessary on a variety of concerns, particularly in the display of opinion groups. Three main testing concerns influencing opinion arrangement are presented in this proposal, along with creative techniques to resolving these issues. First and foremost, message pre-handling has been identified as a vital component of the sensation grouping execution. As a result, for the opinion characterization procedure, a combination of a few known pre-processing techniques is presented. Second, the message qualities of monetary news are utilized to create models that predict how people will feel. Two distinct models are proposed, one that uses monetary events to predict financial news feelings, and another intriguing viewpoint that considers the evaluation per user view, rather than the example methodology that considers the opinion holder view. Another approach for capturing the individual user's mood is offered. Third, one of the characteristics of financial news is that it covers a wide range of topics; also, predicting public opinion across many disciplines is quite difficult. Different cross-space opinion examination methodologies have been suggested and fundamentally evaluated. And based on the evaluation and research results that show, the efficacy is about 95.6%.