Reviewerly/exHarmony huggingface dataset:
Dataset Column: Overview, Scope and Call for Contributions

The Dataset Column (https://records.sigmm.org/open-science/datasets/) of ACM SIGMM Records provides timely updates on the developments in the domain of publicly available multimedia datasets as enabling tools for reproducible research in numerous related areas. It is intended as a platform for further dissemination of useful information on multimedia datasets and studies of datasets covering various domains, published in peer-reviewed journals, conference proceedings, dissertations, or as results of applied research in industry. The aim of the Dataset Column is therefore not to substitute already established platforms for disseminating multimedia datasets, e.g., Qualinet Databases (https://qualinet.github.io/databases/) [2], Multimedia Evaluation Benchmark (https://multimediaeval.github.io/), but promote such platforms and particularly interesting datasets and benchmarking challenges associated with them. Multimedia Evaluation Benchmark, MediaEval 2021, registration is now open (https://multimediaeval.github.io). This year's MediaEval features a wide variety of tasks and datasets tackling a large number of domains, including video privacy, social media data analysis and understanding, news items analysis, medicine and wellbeing, affective and subjective content analysis, and game and sports associated media. The Column will also continue reporting of contributions presented within Dataset Tracks at relevant conferences, e.g., ACM Multimedia (MM), ACM Multimedia Systems (MMSys), International Conference on Quality of Multimedia Experience (QoMEX), International Conference on Multimedia Modeling (MMM).
Automatic Areas of Interest Detector for Mobile Eye Trackers

Thе paper deals with automatic areas of interest detection in video streams derived from mobile eye trackers. Defining such areas on a visual stimulus viewed by an informant is an important step in setting up any eye-tracking-based experiment. If the informant’s field of view is stationary, areas of interest can be selected manually, but when we use mobile eye trackers, the field of view is usually constantly changing, so automation is badly needed. We propose using computer vision algorithms to automatically locate the given 2D stimulus template in a video stream and construct the homography transform that can map the undistorted stimulus template to the video frame coordinate system. In parallel to this, the segmentation of a stimulus template into the areas of interest is performed, and the areas of interest are mapped to the video frame. The considered stimuli are texts typed in specific fonts and the interest areas are individual words in these texts. Optical character recognition leveraged by the Tesseract engine is used for segmentation. The text location relies on a combination of Scale-Invariant Feature Transform and Fast Library for Approximate Nearest Neighbors. The homography is constructed using Random Sample Consensus. All the algorithms are implemented based on the OpenCV library as microservices within the SciVi ontology-driven platform that provides high-level tools to compose pipelines using a data-flow-based visual programming paradigm. The proposed pipeline was tested on real eye tracking data and proved to be efficient and robust.
Efficient Dense-Graph Convolutional Network with Inductive Prior Augmentations for Unsupervised Micro-Gesture Recognition

Skeleton-based action/gesture recognition has already witnessed excellent progress on processing large-scale, laboratory-based datasets with pre-defined skeleton joint topology. However, it’s still an unsolved task when it comes to real-world scenarios with practical limitations such as small-scaled dataset sizes, few-labeled samples, and various skeleton topologies. In this paper, we work on the recognition of micro-gestures, which are subtle body gestures collected in real-world scenarios. Specifically, we utilize contrastive learning to heritage the knowledge from known large-scale datasets for enhancing the learning on fewer samples of micro-gestures. To overcome the gap caused by various domain distributions and structure topologies between the datasets, we compute skeleton representations from augmented sequences via momentum-based efficient and scalable encoders as additional inductive priors. Importantly, we propose an effective dense-graph based unsupervised architecture that resorts to a queue-based dictionary to store positive and negative keys for better contrast with queries to learn substantially efficient and discriminant patterns in the feature space. Together with cross-dataset experimental results show that our model significantly improves the accuracies on two micro-gesture datasets, SMG by 7.4% and iMiGUE by 18.41% advocating its superiority.
Student and Teacher Candidate Experiences During the COVID-19 Pandemic

Most recently, the global pandemic of COVID-19 has exacerbated these challenges, making the digital divide greater and the daily struggles even harder to conquer.
Spam Review Detection Using Okapi Relevance Method for Negative Reviews

Human nature is that one takes others’ opinions before deciding to use the product and service. Hotel reviews provide a great deal of help to customers in online hotel booking. Negative reviews have a more devastating effect than positive ones. This paper presents research on a negative reviews dataset; half are fake, and the other half are non-fake. Early research represents that semantic relations are lost if the Bag-of-Words model is replaced by the Lexical Chain-Based Semantic Similarity (LCBSS) algorithm. Further, the okapi relevance method to generate feature vector works better than the TF-IDF method. Thus, the combination of LCBSS and okapi BM25 works best in detecting spam reviews.
Layer-Wisely Supervised Learning For One-Shot Neural Architecture Search

Neural architecture search aims to automatically discover both efficient and effective neural architectures. Recently, one-shot neural architecture search (one-shot NAS) has drawn great attention due to its high efficiency and competitive performance. One of the most important problems in one-shot NAS is to evaluate the capabilities of architecture candidates. In particular, a pre-trained super-net is served as an evaluator. Due to the large weight-sharing space, current one-shot methods suffer from the ranking disorder issue, that is, the ranking correlation between estimated capabilities and true capabilities of candidates is incorrect. Moreover, the super-net in search is dense thus it is inefficient to train with end-to-end back-propagation. In this paper, we propose to modularize the large weight-sharing space of one-shot NAS into layers by introducing layer-wisely supervised learning. But we discover that greedy layer-wise learning that learns each layer separately with a local objective hurts super-net performance as well as ranking correlation. Instead, we learn each layer by using the gradients propagated from the objective associated with the adjacent upper layer. The simple proposal reduces the representation shift and improves the ranking correlation. In addition, it reduces 47.4% memory footprint and gets a faster convergence of super-net training compared with the strong baseline. Extensive experiments on ImageNet with both supervised and self-supervised objectives demonstrate the effectiveness of our proposal.
Classification of Student Mental Health Prediction Using LSTM

A novel approach for identifying the metal health of the students that are indicative of depression due to Covid-19 Pandemic, In this study Natural Language Processing techniques such as embedding of words, tokenizing, and vectorization to are used extract sentiment from the data that emotions of sadness, depression, and similar emotions. The prediction of sentiment in the text is made accurately using Deep Learning, that eliminates false positives using the immediate context of words. Dataset for the study has been collected for over more than 2 lakhs tweets and posts from public forums and social media website for training the model developed. Labelling is done before data analyzing, which permits posts that contain common topics are grouped together. data from many groups expressing about depression and sadness are considered as the positive class, where as the posts from any random sites, miscellaneous are considered as the negative class. The dataset may be said to be indicative of a real-world environment due to the diversity of the negative class. The model developed in this study has applications across a broad spectrum of domains, such as deployment on online platforms and communication forums used by students to detect potentially harmful tendencies. This study helps various educational institutes, private and governments organisations in analysing student and public sentiments, as well as their attitude in response to the pandemic, and then take precautions and actions accordingly, the algorithm that used here is LSTM, In this study it is observed that LSTM provides accuracy of 88%.
Does the Proteus Effect with Elderly Simulation Kit Improve Empathy Enhancement to the Elderly?

The purpose of this study was to construct an experience system using an elderly avatar that produces the Proteus effect, which promotes empathy for the elderly and to evaluate whether the system can improve empathy for the elderly compared to conventional methods. In this experiment, the following three conditions were evaluated for the between-participants experiment: (1) young avatars and a kit for simulating the elderly, (2) elderly avatars and no kit for simulating the elderly, and (3) elderly avatars and a kit for simulating the elderly. The participants were asked to answer a questionnaire to measure their empathy for the elderly and evaluate the difficulty of their movements, which was used in the conventional simulated experience of the elderly, before moving on to the experiment in the virtual space. In the virtual space, it was confirmed that the participants’ movements matched those of the avatar and asked them to perform the task for 20 min to generate sense of embodiment. The results showed no change in empathy toward the elderly in the condition using the conventional experience kit. However, empathy toward the elderly increased significantly using the elderly avatar and no experience kit. In conclusion, it was revealed that the simulated experience of the elderly using the Proteus effect proposed in this study may be sufficiently valuable in terms of effectiveness. However, for the system to be used in various fields, there is still room for improvement, such as understanding the decline of physical functions and reproducing the vision of cataract patients using VR.
UAV Aerial Photography Traffic Object Detection Based on Lightweight Design and Feature Fusion

Real-time and accurate traffic object detection is one of the important technologies to support intelligent traffic management. Focusing on the problem of poor detection accuracy in aerial photography scenes of drones, the algorithm of lightweight design and multi-scale feature fusion for traffic object detection based on YOLOv5s (LMF-YOLOv5s) is proposed. First, a lightweight network is designed. Then a fast-spatial pyramid convolution module based on dilated convolution is constructed. Finally, an improved spatial pyramid pooling layer module is introduced before the detection layers of different scales, which can enhance the multi-scale feature fusion ability of the network. The experimental results in the public dataset VisDrone show that the detection accuracy of the proposed method is improved by 7.4% compared with YOLOv5s. The model's parameters are reduced by 67.3%, and the model's size is reduced by 63.2% compared with YOLOv5s.
Defensive Bit Planes: Defense Against Adversarial Attacks

Deep learning-based models are vulnerable to adversarial examples crafted with different adversarial attack techniques. Numerous attack methods have been proposed that utilize gradient information of deep model to craft an adversarial examples. Amongst the existing defense mechanisms, adversarial training has gained considerable attention in building robust deep models that remain effective against different adversarial attacks. However, adversarial training demands high computational cost during the development of a robust deep model. In this paper, we present a simple yet effective defense mechanism against adversarial attacks. The proposed defense mechanism uses the concept of bit plane slicing for de-noising of an input image. The efficacy of the proposed defense technique has been evaluated on two benchmark image datasets, viz. MNIST and Fashion-MNIST datasets. The experiments and results show that the proposed defence technique yields comparable and competitive performance to state-of-the-art defense techniques against adversarial attacks.
Design Explorations for 3D-Printed Modular Markers for eXtended-Reality Tangible User Interfaces

Various materials, objects, and sensors have been explored earlier for creating tangible user interfaces (TUIs). However, there is little work on 3D-printed TUIs based on visual markers for smartphone-based extended reality (XR) experiences. The combination of visual markers and smartphones results in cheap, accessible XR systems within reach of many people. Combined with 3D printing, it could foster do-it-yourself (DIY) projects for XR experiences, which may further expand and open-up possibilities for accessible and tangible interaction. This work explores the design space of modular 3D-printed tangibles for smartphone-based XR. The authors report the design exploration process, provide several interactive 3D-printed markers, and reflect on the resulting possibilities.
MEinVR: Multimodal Interaction Paradigms in Immersive Exploration

Immersive environments have become increasingly popular for visu-alizing and exploring large-scale, complex scientific datasets because of their inherent features: immersion, engagement, awareness, etc. Virtual Reality has brought rich opportunities for supporting a wide variety of novel interaction techniques, such as tactile and tangible interactions, gesture interactions, voice commands, etc. Multimodal interaction refers to that users are equipped with multiple modes for interacting with data. However, it is still important to determine how these techniques can be used and combined as a more natural interaction metaphor. In this paper, we aim to explore interaction techniques combining VR controller with voice input for a novel multimodal experience. We present MEin VR, a multimodal interaction technique that enables users to manipulate 3D molecular data in the virtual environment. Users can use VR controller to specify location or region of interest, and use voice command to express the tasks that they intend to perform on the data. This combination can serve as an intuitive means for users to perform complex data exploration tasks in immersive settings. We believe that our work can help inform the design of multimodal interaction techniques that incorporate multiple inputs for 3D data exploration in immersive environments.
Public Randomness Extraction with Ephemeral Roles and Worst-Case Corruptions

We distill a simple information-theoretic model for randomness extraction motivated by the task of generating publicly verifiable randomness in blockchain settings and which is closely related to You-Only-Speak-Once (YOSO) protocols (CRYPTO 2021). With the goal of avoiding denial-of-service attacks, parties speak only once and in sequence by broadcasting a public value and forwarding secret values to future parties. Additionally, an unbounded adversary can corrupt any chosen subset of at most t parties. In contrast, existing YOSO protocols only handle random corruptions. As a notable example, considering worst-case corruptions allows us to reduce trust in the role assignment mechanism, which is assumed to be perfectly random in YOSO. We study the maximum corruption threshold t which allows for unconditional randomness extraction in our model:
Estimation of Convolutional Neural Network for sign Language Recognition on Twitter Data

As technology has increased these social media has became vast plat for people to share their views, thought , and twitter has become one the best social media plat form where people are sharing their thoughts ,views and their emotions publically , that’s why twitter has become one of the best platform where group of people will share their thought about leader , or a trending news or about politics and religion, education etc the fast in the number of web user, In today’s world has an huge measure of data produce by the inhabitants. This information plays vital role when it comes for selling a product, country’s election, differentiating fake news and real news through twitter. Bias detection where we identify the statement To analysis these kind of data, deep estimation analysis is used. The aim is to find the opinion of the people by taking the data and classify it according to its divergence, i.e. the tweets will get divided into positive negative and neutral, it will help to know the opinion of the people ,and mentality of the people.
Neural Embedding Extractors for Text-Independent Speaker Verification

In neural network-based speaker verification tasks, extraction of speaker embeddings plays a vital role. In this work, in order to extract speaker discriminant utterance level representations, we propose to employ two neural speaker embedding extractors that incorporate multi-stream hybrid neural network (MSHNN) and ensemble of neural speaker embedding networks. In the proposed MSHNN approach, an input acoustic feature frame is processed in multiple parallel hybrid neural network (HNN) pipelines where each stream has a unique dilation rate for incorporating diversified temporal resolution in embedding processing. The proposed ensemble neural speaker embedding extractor employs a hybrid neural network, a Time Delay Neural Network - Long Short-Term Memory (TDNN-LSTM) hybrid network and a time delay neural network (TDNN) in parallel manner for including diversified temporal resolution as well as for capturing the complementarity exists in different architectures. A set of speaker verification experiments were carried out on the CNCeleb and VoxCeleb corpora for evaluating the performances of the proposed systems. The proposed multi-stream hybrid neural network performs better than the conventional approaches trained on the same dataset. The ensemble approach is found to yield the best performance in terms of equal error rates (EER) and minimum detection cost function (minDCF) evaluation metrics.
A Novel Global Prototype-Based Node Embedding Technique

Node embedding refers to learning or generating low-dimensional representations for nodes in a given graph. In the era of big data and large graphs, there has been a growing interest in node embedding across a wide range of applications, ranging from social media to healthcare. Numerous research efforts have been invested in searching for node embeddings that maximally preserve the associated graph properties. However, each embedding technique has its own relative disadvantages. This paper presents a method for generating deep neural node embeddings that encode dissimilarity scores between pairs of nodes with the help of prototype nodes spread throughout the target graph. The technique we develop is adaptable to various notions of dissimilarity and yields efficient embeddings capable of estimating the dissimilarity between any two pairs of nodes in a graph. We compare our technique against some state-of-the-art similar embedding techniques and demonstrate superior results in a number of experiments using several benchmark datasets.
The Effect of Laxity of Real-Time Workflow Applications on the Performance of Elastic Cloud Resources

An increasing number of workflow applications with varying deadlines are processed on cloud platforms. One of the main characteristics of such environments is the elasticity of the virtualized resources, which allows them to be dynamically scaled in or out. Consequently, it is imperative to examine in this context how the laxity (slack time) of the workload affects performance. To this end, in this paper we investigate the effect of laxity of real-time workflow jobs on the performance of elastic cloud resources. We formulate the problem and evaluate through simulation the impact of laxity on the deadline miss ratio, energy consumption and monetary cost associated with the resources, considering workloads with different slack times. The simulation results provide useful insights into how each performance metric is affected by the laxity of the real-time workflow jobs.
A Model Frame Work To Segregate Clusters Through K-Means Method

The financial fraud has more imperative effect on today's world. Credit card information security, as well as techniques to detect and divide illegal credit card transactions, as well as hacking and phishing methods, are all quite popular nowadays. A variety of data mining approaches have been used to find/detect these frauds over the years. The focus of this paper is to create a systematic model, to find the detection and minimize the detection towards the credit card fraud by generating the clusters and analyze and comprise these normal clusters through segregation and using k-means techniques in more prominent way. The purpose of this research is to compare the performance of two of the most important fraud detection systems. The paper also employs k-means clustering algorithm into 5 folds of k folds into detection of fraud. Here, the cluster is divided into 80% split and to is been modeled to have an accuracy of 80%. The accuracy of 2 hybrid models has been compared on the split of 5 folds on the basis of 60 and 40 trained test model. The most extensive testing with much larger data set with round about 2 lakh of data is required to validate these results. The digitization of world has brought many issues along with it. Intense research is been conducted how to confront the financial fraud, credit card fraud, including the hackers and criminal anomaly detection. Data mining techniques has done an incredible work in this regard. The algorithms played a credible role of solving the issues.
Wi-Fi Frame Detection Via Spiking Neural Network with Memristive Synapses

With increasing performance of deep learning, researchers have employed Deep Neural Networks (DNNs) for wireless communications. In particular, mechanisms for detecting Wi-Fi frames using DNNs demonstrate excellent performances in terms of detection accuracy. However, DNN requires significant amount of energy and computation resources. Thus, if the mechanisms using DNN are used in mobile devices or low-end devices, their battery would be quickly depleted. Spiking Neural Networks (SNNs), which are regarded as next generation of neural network, have advantages over DNNs - low energy consumption and limited computational complexity. Motivated by thses advantages, in this paper, we propose a mechanism to detect a Wi-Fi frame using SNN and show the feasibility of SNN for Wi-Fi detection. The mechanism is composed of a preprocessing module for collecting an actual RF signal and an SNN module for detecting a Wi-Fi frame. The SNN module employs Leaky Integrate and Fire (LIF) neurons and Spike-Timing Dependent Plasticity (STDP) learning rule. To reflect the features of an actual neuromorphic system, our SNN module considers memristive synaptic features such as non-linear weight update. Experimental study demonstrates that the detection capabilities of the proposed mechanism are comparable to those of a previous mechanism using DNN, while consuming much less energy than the previous mechanism.