Reviewerly/exHarmony huggingface dataset:
Fake Account Detection in Social Media Using Big Data Analytics

Social media has changed the environment by increasing the number of social media users; the advantage of online social media is that it is an easy way of communication between individuals in an efficient manner. This ends up in potential attacks, like fake identity or pretend and larva accounts, unfolding of info etc. In step with the knowledge shared during a survey, the amount of actual accounts in social media is far lesser than the present users, what is more is that this means the increasing quantity of pretended or fake accounts in recent years. The detection of pretended accounts in social media platforms, like Twitter, has been an important task in huge amounts of information. Online social media owners or suppliers face issues in pretend accounts detections. Old strategies cannot distinguish between real and pretended accounts expeditiously. To overcome the problem, new strategies were created that used completely different approaches like bots or automatic comments and posts, rotating false data or info unfold and spreading spam messages within the type of advertisements. These strategies are a unit accustomed to observe pretend accounts in online social media. The large increase within the pretend accounts has reduced the potency of classification algorithms such as support vector machines, naive Bayes and random forest. In this work, an innovative methodology to observe pretend accounts needs to be developed by employing a variation of gradient boosting algorithmic program with a call tree consisting of a group of attributes. This may lead to rising overall potency and subsume scalability because of the increasing range of users using social media. So, there is a necessity for a tool which can identify and detect the fake or pretend account and accurately make the difference between pretend and genuine accounts.
FLUID: Few-Shot Self-Supervised Image Deraining

Self-supervised methods have shown promising results in denoising and dehazing tasks, where the collection of the paired dataset is challenging and expensive. However, we find that these methods fail to remove the rain streaks when applied for image deraining tasks. The method’s poor performance is due to the explicit assumptions: (i) the distribution of noise or haze is uniform and (ii) the value of a noisy or hazy pixel is independent of its neighbors. The rainy pixels are non-uniformly distributed, and it is not necessarily dependant on its neighboring pixels. Hence, we conclude that the self-supervised method needs to have some prior knowledge about rain distribution to perform the deraining task. To provide this knowledge, we hypothesize a network trained with minimal supervision to estimate the likelihood of rainy pixels. This leads us to our proposed method called FLUID: Few Shot Sel f-Supervised Image Deraining.We perform extensive experiments and comparisons with existing image deraining and few-shot image-to-image translation methods on Rain 100L and DDN-SIRR datasets containing real and synthetic rainy images. In addition, we use the Rainy Cityscapes dataset to show that our method trained in a few-shot setting can improve semantic segmentation and object detection in rainy conditions. Our approach obtains a mIoU gain of 51.20 over the current best-performing deraining method. [Project Page]
Sense understanding of text conversation using temporal convolution neural network

This paper proposes a model which uses Spatio Temporal features for real-time sense understanding of a text conversation. The proposed model uses CNN along with the concept of LSTM to create a new Spatio temporal cell. Furthermore, the proposed model is used to classify the sentences into eight senses. The model achieved an F-Score around 0.984 on sense classification. Additionally, the efficiency and capabilities of the model are also tested on a standard IMDB sentiment classification dataset. On the IMDB dataset, the model gave an accuracy of 89.27. The experimental results show that the proposed model works better than a CNN model, a Bi-LSTM model, and a combination of CNN & LSTM model in terms of a number of parameters and execution time.
cStick: A Calm Stick for Fall Prediction, Detection and Control in the IoMT Framework

Falls are constant threats to older adults and can minimize their ability to live independently. To help mitigate the occurrences and effects of such unfortunate accidents, it is imperative to find an accurate, reliable, robust and convenient solution to make life easier for elder adults who may have visual or hearing impairments. In order to reduce such occurrences, a calm stick, cStick is proposed. cStick is an IoT (Internet of Things) enabled system which has a capability to predict falls before their occurrence, to warn the user that there may be an incident of fall, to detect falls and also provide control remedies to reduce their impact. cStick monitors the location of the user, the physiological changes that occur when a person is about to fall and also monitors the surroundings the user is in when having an incident of fall. Based on the changes in the monitored parameters, the decision of fall i.e., a prediction, warning or detection of fall is made with an accuracy of approximately 95%. Control mechanisms to reduce the impact of the fall along with connection capabilities to the help unit are provided with the cStick system.
Analyzing Student Reviews on Teacher Performance Using Long Short-Term Memory

Educational institutions collect feedback from students and analyze them to provide effective teaching experience and knowledge to students. The laborious process of present traditional way of collecting, summarizing and abstracting feedback manually is overcome by online process. There are many existing online methods. But obtaining an effective method is the main problem. In this paper, a sentimental analysis model has been proposed for reviewing student's feedback using long short-term memory. A comparison is done between the proposed model and Naive Bayes, decision tree and random forest methods. The results obtained in this work show that the proposed method is more accurate than the other considered machine learning methods.
Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques

This paper presents, a new approach of Medical Image Pixels Clustering (MIPC), aims to trace the dissimilar patterns over the Magnetic Resonance (MR) image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment, pattern predication and deeper investigation. The proposed MIPC consists of two stages: clustering and validation. In the clustering stage, the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering (iLIAC), Dynamic Automatic Agglomerative Clustering (DAAC) and Optimum N-Means (ONM). In the second stage, the performance of MIPC approach is estimated by measuring Intra intimacy and Intra contrast of each individual cluster in the result of MR image based on proposed validation method namely Shreekum Intra Cluster Measure (SICM). Experimental results show that the MIPC approach is better suited for automatic identification of highly relative dissimilar clusters over the MR cancer images with higher Intra closeness and lower Intra contrast based on improved unsupervised clustering schemes.
Knowledge-Driven Analytics and Systems Impacting Human Quality of Life

The advent of artificial intelligence (AI), Internet of Things (IoT), powerful computational hardwares like graphics processing units, affordable sensing devices like smart bands, wearables, smartphones pave ways for large number of useful and intelligent applications hitherto never commonly envisaged. However, it is felt that applications, which positively influence human life and society, need distinct attention from the perspective of the researchers, application developers as well as industry. It is understood that knowledge-driven initiatives in terms of technology, application and practical deployment have strong capability to enable long term human-centric convergence of cyber-physical systems. Our endeavor is to discuss those finer details, research directions and application development aspects of analytics and systems intended for impacting human quality of life.
Mapping Across Relational Domains for Transfer Learning with Word Embeddings-Based Similarity

Statistical machine learning models are a concise representation of probabilistic dependencies among the attributes of an object. Most of the models assume that training and testing data come from the same distribution. Transfer learning has emerged as an essential technique to handle scenarios where such an assumption does not hold, as it relies on leveraging the knowledge acquired in one or more learning tasks as a starting point to solve a new task. Statistical Relational Learning (SRL) extends statistical learning to represent and learn from data with several objects and their relations. In this way, SRL deals with data with a rich vocabulary composed of classes, objects, their properties, and relationships. When employing transfer learning to SRL, the primary challenge is to transfer the learned structure, mapping the vocabulary from a source domain to a different target domain. To address the problem of transferring across domains, we propose TransBoostler, which uses pre-trained word embeddings to guide the mapping as the name of a predicate usually has a semantic connotation that can be mapped to a vector space model. After transferring, TransBoostler employs theory revision operators further to adapt the mapped model to the target data. In the experimental results, TransBoostler has successfully transferred trees from a source to a distinct target domain, performing equal or better than previous work but requiring less training time.
Automatic parking paths planning research based on scattering points six-degree polynomial and easement curve

Existing automatic parking path planning methods usually use circular curves and lines. At the junction of parking paths, the curves are not smooth. Based on a low-speed kinematic model of a vehicle, this study redesigned parking paths. Sixth-degree polynomial based on scattering points and easement curve are used in parking path planning. At the junction of two curved segments, the paths are continuous in the original, first-order and second-order equations. Script programming was used in MATLAB to draw the parking paths and the area swept by the vehicle. Parking simulations were conducted using Carsim to demonstrate the parking process. The parallel and vertical parking path simulation results obtained using MATLAB and Carsim showed that the designed parking paths were smooth.
Input Data Modeling: An Approach Using Generative Adversarial Networks

Input data modeling varies according to the modeler&#x0027;s objectives and may be a simple or complex task. Despite great advances in data collection techniques, the input data analysis remains a challenge, especially when the input data is complex and cannot be modeled by standard solutions offered by commercial simulation software. Therefore, this paper focuses on how Generative Adversarial Networks (GANs) may support input data modeling, especially when traditional approaches are insufficient or inefficient. We evaluate the adoption of GANs for modeling correlated data as well as independent and identically distributed data. As results, GAN input models were able to generate highly accurate synthetic samples (average accuracies&#x003E; 97.0&#x0025;). For univariate distributions, we found no significant difference between standard and GAN input models performances. On the other hand, for correlated data, GAN input models outperformed standard ones. The most relevant accuracy gain was observed for the bivariate normal.
Next-generation reservoir computing based on memristor array

As a kind of brain-inspired computing, reservoir computing (RC) has great potential applications in time sequence signal processing and chaotic dynamics system prediction due to its simple structure and few training parameters. Since in the RC randomly initialized network weights are used, it requires abundant data and calculation time for warm-up and parameter optimization. Recent research results show that an RC with linear activation nodes, combined with a feature vector, is mathematically equivalent to a nonlinear vector autoregression (NVAR) machine, which is named next-generation reservoir computing (NGRC). Although the NGRC can effectively alleviate the problems which traditional RC has, it still needs vast computing resources for multiplication operations. In the present work, a hardware implementation method of using computing-in memory paradigm for NGRC is proposed for the first time. We use memristor array to perform the matrix vector multiplication involved in the nonlinear vector autoregressive process for the improvement of the energy efficiency. The Lorenz63 time series prediction task is performed by simulation experiments with the memristor array, demonstrating the feasibility and robustness of this method, and the influence of the weight precision of the memristor devices on the prediction results is discussed. These results provide a promising way of implementing the hardware NGRC.
Cross-Mix Monitoring for Medical Image Segmentation With Limited Supervision

Image segmentation is a fundamental building block of automatic medical applications. It has been greatly improved since the emergence of deep neural networks. However, deep-learning based models often require a large number of manual annotations, which has seriously hindered its practical usage. To alleviate this problem, numerous works were proposed by utilizing unlabeled data based on semi-supervised frameworks. Recently, the Mean-Teacher (MT) model has been successfully applied in many scenarios due to its effective learning strategy. Nevertheless, the existing MT model still have certain limitations. Firstly, various sorts of perturbations are often added to the training data to gain extra generalization ability through consistency training. However, if the variation is too weak, it may cause the Lazy Student Phenomenon, and bring large fluctuations to the learning model. On the contrary, large image perturbations may enlarge the performance gap between the teacher and student. In this case, the student may lose its learning momentum, and more seriously, drag down the overall performance of the whole system. In order to address these issues, we introduce a novel semi-supervised medical image segmentation framework, in which a Cross-Mix Teaching paradigm is proposed to provide extra data flexibility, thus effectively avoid Lazy Student Phenomenon. Moreover, a lightweight Transductive Monitor is applied to server as the bridge that connect the teacher and student for active knowledge distillation. In the light of this cross-network information mixing and transfer mechanism, our method is able to continuously explore the discriminative information contained in unlabeled data. Extensive experiments on challenging medical image data sets demonstrate that our method is able to outperform current state-of-the-art semi-supervised segmentation methods under severe lack of supervision.
Will Virtual Reality Connect or Isolate Students?

Virtual reality intends to transport individuals or groups from their corporal space to another real and imagined past, present, and future places and people. With the growing popularity of virtual reality and the ideations of implementations for education, it is important to consider both the possible applications and unintended consequences of the technology. Critics of proposed educational implementations of virtual reality are concerned with the risks of escapism, isolation, and loss of connection with reality. While the domain of virtual reality for education is relatively new, there are already many instances of efficacious educational applications. These applications exist in a range of costs, from the low-cost experiences driven by a low-cost VR headset powered by a mobile device to the high-end highly immersive interactive (HII) virtual reality headsets powered by a high-end computer. The quality of virtual reality experiences for education will certainly be measured by learning outcomes, they will also be measured by the traditional measures of virtual reality: presence, co-presence, and social presence. The most prevalent virtual reality learning experiences include virtual field trips, social learning, experiential VR, and building empathy. These experiences can both connect and isolate students, depending on the design. The paradox of the question of whether virtual reality will isolate or connect students is that success lies in the balance between experiences that separate students to focus and the experiences that connect learners with each other and peers. It is important to understand the ways virtual reality can be used to connect students or isolate them in order to inform our design and implementation before we forge ahead with this technology.
Response Time Estimate for a Fork-Join System with Pareto Distributed Service Time as a Model of a Cloud Computing System Using Neural Networks

A cloud computing system that receives complex user tasks involving several subtasks is studied from the point of view of the response time. In order to reduce the service time, the tasks are divided into smaller components and processed in parallel. As a cloud center model we use a fork-join queuing system with Pareto distribution of the service time on the servers. To analyze the mean response time and its standard deviation, a new approach is used combining simulation modeling with one of the machine learning methods. The estimates obtained are much more accurate than the earlier analytical results on fork-join systems.
Automatic Dynamic Range Adjustment for Pedestrian Detection in Thermal (Infrared) Surveillance Videos

This paper presents a novel candidate generation algorithm for pedestrian detection in infrared surveillance videos. The proposed method uses a combination of histogram specification and iterative histogram partitioning to progressively adjust the dynamic range and efficiently suppress the background of each video frame. This pairing eliminates the general-purpose nature associated with histogram partitioning where chosen thresholds, although reasonable, are usually not suitable for specific purposes. Moreover, as the initial threshold value chosen by histogram partitioning is sensitive to the shape of the histogram, specifying a uniformly distributed histogram before initial partitioning provides a stable histogram shape. This ensures that pedestrians are present in the image at the convergence point of the algorithm. The performance of the method is tested using four publicly available thermal datasets. Experiments were performed with images from four publicly available databases. The results show the improvement of the proposed method over thresholding with minimum-cross entropy, the robustness across images acquired under different conditions, and the comparable results with other methods in the literature.
Data Farming Output Analysis Using Explainable AI

Data Farming combines large-scale simulation experiments with high performance computing and sophisticated big data analysis methods. The portfolio of analysis methods for those large amounts of simulation data still yields potential to further development, and new methods emerge frequently. Especially the application of machine learning and artificial intelligence is difficult, since a lot of those methods are very good at approximating data for prediction, but less at actually revealing their underlying model of rules. To overcome the lack of comprehensibility of such black-box algorithms, a discipline called explainable artificial intelligence (XAI) has gained a lot of traction and has become very popular recently. This paper shows how to extend the portfolio of Data Farming output analysis methods using XAI.
Design of a Wearable Healthcare Emergency Detection Device for Elder Persons

Improving quality of life in geriatric patients is related to constant physical activity and fall prevention. In this paper, we propose a wearable system that takes advantage of sensors embedded in a smart device to collect data for movement identification (running, walking, falling and daily activities) of an elderly user in real-time. To provide high efficiency in fall detection, the sensor’s readings are analysed using a neural network. If a fall is detected, an alert is sent though a smartphone connected via Bluetooth. We conducted an experimental session using an Arduino Nano 33 BLE Sense board in inside and outside environments. The results of the experiment have shown that the system is extremely portable and provides high success rates in fall detection in terms of accuracy and loss.
Face Recognition Using Machine Learning Models - Comparative Analysis and impact of dimensionality reduction

Face Recognition is considered a biometric technique where it is capable of uniquely identifying and verifying a person just by analysing and comparing the facial patterns on the facial contours. Face Recognition has gained significant importance in security aspects and it has been widely used and accepted biometric. It has given greater importance during pandemic situations in terms of cheapest and widely accepted touchless biometrics. This paper studies the impact of dimensionality reduction on the efficiency or accuracy of machine learning algorithms in face recognition. The analysis is carried out over various algorithms include Random Forests, Support Vector Machine, Linear Regression, Logistic Regression, K-Nearest Neighbor. Based on the analysis, Logistic Regression gives better performance in terms of accuracy and time with an accuracy score of 0.97 within a time of 5.74 sec when implemented without principal component analysis whereas with principal component analysis, Logistic Regression achieved an accuracy score of 0.93 within a time of 0. 15sec. There is a huge difference in computation time approximately 20 times, the difference in accuracy is minimal.
Hyperbolic Geometrically Uniform Codes and Ungerboeck Partitioning on the Double Torus

Current research builds labelings for geometrically uniform codes on the double torus through tiling groups. At least one labeling group was provided for all of the 11 regular tessellations on the double torus, derived from triangular Fuchsian groups, as well as extensions of these labeling groups to generate new codes. An important consequence is that such techniques can be used to label geometrically uniform codes on surfaces with greater genera. Furthermore, partitioning chains are constructed into geometrically uniform codes using soluble groups as labeling, which in some cases results in an Ungerboeck partitioning for the surface. As a result of these constructions, it is demonstrated that, as in Euclidean spaces, modulation and encoding can be combined in a single step in hyperbolic space.
Multi-phase Fine-Tuning: A New Fine-Tuning Approach for Sign Language Recognition

Abstract In this paper, we propose multi-phase fine-tuning for tuning deep networks from typical object recognition to sign language recognition (SLR). It extends the successful idea of transfer learning by fine-tuning the network’s weights over several phases. Starting from the top of the network, layers are trained in phases by successively unfreezing layers for training. We apply this novel training approach to SLR, since in this application, training data is scarce and differs considerably from the datasets which are usually used for pre-training. Our experiments show that multi-phase fine-tuning can reach significantly better accuracy in fewer training epochs compared to previous fine-tuning techniques