Reviewerly/exHarmony huggingface dataset:
IP Traffic Classification of 4G Network using Machine Learning Techniques

In today's world, the number of internet services and users is increasing rapidly. This leads to a significant rise in the internet traffic. Thus, the task of classifying IP traffic is essential for internet service providers or ISP, as well as various government and private organizations in order to have better network management and security. IP traffic classification involves identification of user activity using network traffic flowing through the system. This will also help in enhancing the performance of the network. The use of traditional IP traffic classification mechanisms which are based on inspection of packet payload and port numbers has decreased drastically because there are many internet applications nowadays which use port numbers which are dynamic in nature rather than well-known port numbers. Also, there are several encryption techniques nowadays due to which the inspection of packet payload is hindered. Presently, various machine learning techniques are generally used for classifying IP traffic. However, not much research has been conducted for the classification of IP traffic for a 4G network. During this research, we developed a new dataset by capturing packets of real-time internet traffic data of a 4G network using a tool named Wireshark. After that, we extracted the inferred features of the captured packets by using a python script. Then we applied five machine learning models, i.e., Decision Tree, Support Vector Machines, K Nearest Neighbours, Random Forest, and Naive Bayes for classifying IP traffic. It was observed that Random Forest gave the best accuracy of approximately 87%.
Gaussian Process Temporal-Difference Learning with Scalability and Worst-Case Performance Guarantees

Value function approximation is a crucial module for policy evaluation in reinforcement learning when the state space is large or continuous. The present paper revisits policy evaluation via temporal-difference (TD) learning from the Gaussian process (GP) perspective. Leveraging random features to approximate the GP prior, an online scalable (OS) approach, termed OS-GPTD, is developed to estimate the value function for a given policy by observing a sequence of state-reward pairs. To benchmark the performance of OS-GPTD even in the adversarial setting, where the modeling assumptions are violated, complementary worst-case analyses are performed. The cumulative Bellman error, as well as the long-term reward prediction error, are upper bounded relative to their counterparts from a fixed value function estimator with the entire state-reward trajectory in hindsight. Performance of the novel OS-GPTD is evaluated on two benchmark problems.
Smart Grid Using Internet of Things

The IoT (internet of things) is a network of people and stuff at any moment, anytime, for anyone, with any network or service. IoT is therefore a major complex worldwide network backbone for online service providers. The smart grid (SG) is one of IoT's main applications. SG is an interconnected data exchange network that gathers and analyzes data obtained from transmission lines, generation stations, and customers through the power grid. The internet of things has risen as the basis of creativity for energy grids. The chapter is based on the idea that, if one grid station transmitting electricity to customers is cut off due to some defects of IoT-based systems, all grid station loads can be connected to another system so that power is not disrupted. The authors discuss the IoT and SG and their relationship in this chapter. The best advantages for SG and specifications can be addressed in the SG works, creative innovations using IoT in SG, IoT software, and facilities in SG.
Lightweight Human Pose Estimation under Resource-Limited Scenes

Recent research on human pose estimation has achieved significant improvement. However, most existing methods tend to pursue higher scores on benchmark datasets using complex architecture, ignoring the deployment costs in practice. In this paper, we investigate the problem of lightweight human pose estimation under resource-limited scenes.We first redesign a lightweight bottleneck block with two concepts: depthwise convolution and attention mechanism. And then, based on the lightweight block, we present a single-stage Lightweight Pose Network (LPN). Our small network LPN-50 only has 2.7M parameters and 1.0G FLOPs, which is much more lightweight than other popular networks. In order to overcome the training barrier, we propose an iterative training strategy that can give full play to our LPNs’ potential to get more accurate predicted results. We empirically demonstrate the effectiveness and efficiency of our methods on the benchmark dataset: the COCO keypoint detection dataset. Besides, we show the speed superiority of our lightweight network at inference time on a non-GPU platform. Specifically, our LPN-50 can achieve 68.7 in AP score on the COCO test-dev set, with 17 FPS inference speed on an Intel i7-8700K (6 cores) CPU machine.
Fighting Fires and Powering Steam Locomotives: Distribution of Control and Its Role in Social Interaction at Tangible Interactive Museum Exhibits

We present a video-analysis study of museum visitors’ interactions at two tangible interactive exhibits in a transport museum. Our focus is on groups’ social and shared interactions, in particular how exhibit setup and structure influence collaboration patterns. Behaviors at the exhibits included individuals focusing beyond their personal activity towards companions’ interaction, adults participating via physical interaction, and visitors taking opportunities to interact when companions moved between sections of the exhibit or stepped back from interaction. We demonstrate how exhibits’ physical configuration and interactive control engendered behavioral patterns. Systematic analysis reveals how different configurations (concerning physical-spatial hardware and interactive software) distribute control differently amongst visitors. We present four mechanisms for how control can be distributed at an interactive installation: functional, temporal, physical and indirect verbal. In summary, our work explores how mechanisms that distribute control influence patterns of shared interaction with the exhibits and social interaction between museum visitor companions.
Label-Aware Text Representation for Multi-Label Text Classification

Multi-label text classification (MLTC) is an important task in natural language processing (NLP), which is appealing to researchers in both academia and industry. However, few of studies have been conducted on the relations among the labels. Most of existing methods tend to neglect the semantic information between labels and words. In this paper, we propose a label-aware network to obtain both the label correlation and text representation. A heterogeneous graph is built from words and labels to learn the label representation by metap-ath2vec, since two nearby labels or words in the graph have similar relation and the graph structure is beneficial for label representation as well. Each part of the text contributes differently to label inference, therefore bidirectional attention flow is exploited for label-aware text representation in two directions: from text to label and from label to text. Experimental evaluations illustrate that the proposed method outperforms various baselines on both offline benchmarks and real-world online systems.
Give-Me-A-Hand: The Effect of Partner’s Gender on Collaboration Quality in Virtual Reality

Avatar appearance, especially gender, influences user behavior in virtual environments (VE). However the effect is often examined only as a co-variant. In this paper, we use technology to empower individuals beyond traditional societal gender roles to effectively collaborate in virtual environments. We specifically investigate the impact of the partner's avatar gender on the quality of collaboration in the VE, while performing an inherently male-dominated task. We designed a virtual garage, where pairs of same (C1) and mixed (C2) gender repair cars collaboratively. We evaluated the interaction using a collaboration questionnaire adopted from Team Effective Questionnaire (TEQ). Our results show that same-gender pairs were perceived as more productive and supportive. We envision that our work aids developers and researchers in enhancing the collaboration quality by supporting group cohesion and positive interactions.
CoCapture: Effectively Communicating UI Behaviors on Existing Websites by Demonstrating and Remixing

User Interface (UI) mockups are commonly used as shared context during interface development collaboration. In practice, UI designers often use screenshots and sketches to create mockups of desired UI behaviors for communication. However, in the later stages of UI development, interfaces can be arbitrarily complex, making it labor-intensive to sketch, and static screenshots are limited in the types of interactive and dynamic behaviors they can express. We introduce CoCapture, a system that allows designers to easily create UI behavior mockups on existing web interfaces by demonstrating and remixing, and to accurately describe their requests to helpers by referencing the resulting mockups using hypertext. We showed that participants could more accurately describe UI behaviors with CoCapture than with existing sketch and communication tools and that the resulting descriptions were clear and easy to follow. Our approach can help teams develop UIs efficiently by bridging communication gaps with more accurate visual context.
Virus as Quasi-Living Bio-Material for Interaction Design: Practical, Ethical, and Philosophical Implications

The interaction design research community continues to benefit from material-focused approaches, and from the diversity of materials under investigation. One category of such material is bio-materials of microbial origin, such as bacteria, mycelium, moulds, and Euglena. However, despite the increasing momentum towards bio-material based research, one type that is yet to be investigated in HCI, is viruses; an infectious, sub-microscopic, quasi-living, computational bio-agent. This paper initiates exploration of Human-Virus Interaction (HVI), through a material lens. This was achieved first by generating a literature-based material profile sketch of viruses, highlighting some of their distinct and/or unique material properties, characteristics, composition, and meaning. The components of the profile were then used as anchor points, to unpack the practical, ethical, and philosophical implications that are associated with viruses, and those that could be considered by researchers to help in their preparation of working with viruses in interaction design.
L-Red: Efficient Post-Training Detection of Imperceptible Backdoor Attacks Without Access to the Training Set

Backdoor attacks (BAs) are an emerging form of adversarial attack typically against deep neural network image classifiers. The attacker aims to have the classifier learn to classify to a target class when test images from one or more source classes contain a backdoor pattern, while maintaining high accuracy on all clean test images. Reverse-Engineering-based Defenses (REDs) against BAs do not require access to the training set but only to an independent clean dataset. Unfortunately, most existing REDs rely on an unrealistic assumption that all classes except the target class are source classes of the attack. REDs that do not rely on this assumption often require a large set of clean images and heavy computation. In this paper, we propose a Lagrangian-based RED (L-RED) that does not require knowledge of the number of source classes (or whether an attack is present). Our defense requires very few clean images to effectively detect BAs and is computationally efficient. Notably, we detect 56 out of 60 BAs using only two clean images per class in our experiments on CIFAR-10.
Sentiment Analysis of COVID-19 Tweets: How Does BERT Perform?

In a world, where microblogging is a regular component of modern life, COVID-19, a sudden pandemic, made its own way with public opinion in social networking sites too. People maintaining social distancing and self-quarantining to avoid Coronavirus had no other option than to express their fear and concern by discussing this on social media platforms. To have a proper outlook of the impact of this pandemic on people's lives and surroundings, we cannot deny the significance of social media acting as the sentiment pool. In this paper, we focused on performing sentiment analysis on these opinions posted on Twitter. We have processed these tweets as our data and converted them into tokens. BERT, a pre-trained model after fine-tuning and with an additional classifier layer, has been used to classify these sentiments into three classes namely positive, negative and neutral and analyzed how it performed in doing the task. BERT model showed 92.22% accuracy on our dataset followed by high scores in evaluation metrices.
Weakly Supervised Attention Rectification for Scene Text Recognition

Scene text recognition has become a hot topic in recent years due to its booming real-life applications. Attention-based encoder-decoder framework has become one of the most popular frameworks especially in the irregular text scenario. However, the “attention drift” problem hinders the recognition performance for most existing attention-based scene text recognition methods. To solve this problem, we propose an auxiliary supervision branch along with the attention-based encoder-decoder framework. A new loss function is designed to refine the feature map and to help the attention region align the target character area. Compared with existing attention rectification mechanisms, our method does not require character-level annotations or introduce any additional trainable parameter. Furthermore, our method can improve the performance for both RNN-Attention and Scaled Dot-Product Attention. The experiment results on various benchmarks have demonstrated that the proposed approach outperforms the state-of-the-art methods in both regular and irregular text recognition scenarios.
Study and Analysis of Pedestrian Detection in Thermal Images Using YOLO and SVM

Pedestrian detection and driver assistance applications draws an important role on safety measure such as speed control, crash control, sensing crash and occupant location detection. The pedestrian accidents occurs due to the vulnerable traffic users such as humans, stranded or moving vehicle or other obstacles. To avoid the pedestrian accident, this paper proposes a model that can accomplish pedestrian detection automatically using Histogram of Gradient (HOG) and You Only Look Once (YOLO) algorithm. The experiments are carried out on Forward Looking Infrared Radar (FLIR) starter thermal dataset consisting of 5000 images. The HOG algorithm is implemented on these thermal image samples and is classified using Support Vector Machine (SVM) classifier. The accuracy of YOLO is calculated using intersection over union method between the ground truth and the predicted bounding box. To further improve the safety of the user an alarm is designed to alert the user on sight of pedestrians during night.
Fine-Tuning of Pre-Trained End-to-End Speech Recognition with Generative Adversarial Networks

Adversarial training of end-to-end (E2E) ASR systems using generative adversarial networks (GAN) has recently been explored for low-resource ASR corpora. GANs help to learn the true data representation through a two-player min-max game. However, training an E2E ASR model using a large ASR corpus with a GAN framework has never been explored, because it might take excessively long time due to high-variance gradient updates and face convergence issues. In this paper, we introduce a novel framework for fine-tuning a pre-trained ASR model using the GAN objective where the ASR model acts as a generator and a discriminator tries to distinguish the ASR output from the real data. Since the ASR model is pre-trained, we hypothesize that the ASR model output (soft distribution vectors) helps to get higher scores from the discriminator and makes the task of the discriminator harder within our GAN framework, which in turn improves the performance of the ASR model in the fine-tuning stage. Here, the pre-trained ASR model is fine-tuned adversarially against the discriminator using an additional adversarial loss. Experiments on full LibriSpeech dataset show that our proposed approach outperforms baselines and conventional GAN-based adversarial models.
A Legendre polynomial based activation function: An aid for modeling of max pooling

In this paper, we propose a novel differentiable activation function for convolutional neural networks. This function is proposed using an orthogonal Legendre polynomial based linear-in-parameter model. First, we present mathematical modeling for max pooling operation in convolutional neural networks and derive the relationship between max pooling and average pooling. The representation of max pooling using signal processing elements is presented. Further, we study approximations of various nonlinearities using the proposed linear-in-parameter model. The proposed activation function can be used as an alternative in place of existing activation functions in convolutional neural networks. Finally, the effectiveness of the proposed activation function is shown with empirical evaluations on benchmark image classification datasets using convolutional neural networks.
Student Physical Fitness Test System and Test Data Analysis System Based on Computer Vision

Computer vision technology is one of the main research directions of artificial intelligence. With the rapid growth of image or video data scale and the improvement of computing power, computer vision technology has achieved unprecedented development in recent years and is widely used in a variety of scenes. This study mainly discusses the design of student physical fitness test system and test data analysis system based on computer vision. This study is mainly based on the motion attitude determination algorithm to identify the motion. In hardware configuration, the key is CPU and GPU. The model realizes large-scale matrix computation based on the parallel computing power provided by GPU and uses CPU to realize data reading and preprocessing. The assessment controller is responsible for the transmission of instructions and status information and controls the operation of the entire pitch assessment system. It is the control center of the entire system. ZigBee wireless communication technology is adopted as the communication method of human posture measurement terminal and assessment controller. The input image is preprocessed through scaling and standardization. The image is scaled to the resolution of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>224</mn> <mo>×</mo> <mn>224</mn> </math> when input, which is performed to realize data parallel training. The image was changed by means of random horizontal flip, random rotation, and color change to achieve the effect of expanding the dataset. Then, the test evaluation module was used to evaluate various test indexes of the body. During the sit-up test, nine out of 10 sit-ups can be accurately counted and the recognition rate reaches 90 percent. The results show that the system designed in this study has high accuracy and good performance, which can be used for the physical fitness test and test data analysis of students.
Joint Alignment Learning-Attention Based Model for Grapheme-to-Phoneme Conversion

Sequence-to-sequence attention-based models for grapheme-to-phoneme (G2P) conversion have gained significant interests. The attention-based encoder-decoder framework learns the mapping of input to output tokens by selectively focusing on relevant information, and has been shown well performance. However, the attention mechanism can result in non-monotonic alignments, resulting in poor G2P conversion performance. In this paper, we present a novel approach to optimize the G2P conversion model directly alignment grapheme-phoneme sequence by using alignment learning (AL) as the loss function. Besides, we propose a multi-task learning method that uses a joint alignment learning model and attention model to predict the proper alignments and thus improve the accuracy of G2P conversion. Evaluations on Mongolian and CMUDict tasks show that alignment learning as the loss function can effectively train G2P conversion model. Further, our multi-task method can significantly outperform both the alignment learning-based model and attention-based model.
ValveCare: A Fuzzy Based Intelligent Model for Predicting Heart Diseases Using Arduino Based IoT Infrastructure

IoT-based portable medical diagnostic tools like smartwatches, health monitors, etc. are extensively used for real-time data collection and monitoring. There are a plethora of options available for tech stacks to be used and open source frameworks for managing a complete internet of things system. Recognizing the pattern of fluctuation of medical data and making a decision on the probability of disease before the onset of any symptom, puts forth a big challenge for the medical practitioners. We propose our model ValveCare which leverages the power of machine learning to find patterns in the data for finding thresholds of optimal values of each parameter and use those thresholds to predict with a certain probability whether a subject is going to have cardiovascular diseases (CVD) in near future. We have opted to use fuzzy logic to find the probability of chances of CVD of the user directly in an android app. ValveCare uses an Arduino based microcontroller to take the heart rate of a subject through a pulse sensor, and temperature through a temperature sensor. The Arduino is linked with our ValveCare app where the user inputs other details like total cholesterol levels, age, and other relevant parameters and our model computes and shows the inference instantly in the app with the likelihood of the subject having CVD in the future.
ASAP - Academic Support Aid Proposal for Student Recommendations

Some research works point out that university students are increasingly presenting depression, anxiety, and even suicidal scenarios during their studies. Numbers indicate that these issues affect both undergraduate and graduate students, dropping their academic performance steadily. However, these disorders can be captured in advance through, for example, heart bit rates and changes in blood pressure. This paper presents the Academic Support Aid Proposal (ASAP), characterized by a model capable of getting data, such as location and body signals, from students and afterward provides some recommendations and advice to them during their period inside the university. The body signals give an emotional context from a specific student and his/her location. These elements help the ASAP in terms of accuracy to the recommendation approach, which is based upon IoT (smart bands) and a machine learning paradigm. The differentiated aspect of the present contribution is based on the use of ubiquitous computing and the proposed architecture.