Reviewerly/exHarmony huggingface dataset:
Gesture driven smart home solution for bedridden people

Conversion of ordinary houses into smart homes has been a rising trend for past years. Smart house development is based on the enhancement of the quality of the daily activities of normal people. But many smart homes have not been designed in a way that is user friendly for differently-abled people such as immobile, bedridden (disabled people with at least one hand movable). Due to negligence and forgetfulness, there are cases where the electrical devices are left switched on, regardless of any necessity. It is one of the most occurred examples of domestic energy wastage. To overcome those challenges, this research represents the improved smart home design: MobiGO that uses cameras to capture gestures, smart sockets to deliver gesture-driven outputs to home appliances, etc. The camera captures the gestures done by the user and the system processes those images through advanced gesture recognition and image processing technologies. The commands relevant to the gesture are sent to the specific appliance through a specific IoT device attached to them. The basic literature survey content, which contains technical words, is analyzed using Deep Learning, Convolutional Neural Network (CNN), Image Processing, Gesture recognition, smart homes, IoT. Finally, the authors conclude that the MobiGO solution proposes a smart home system that is safer and easier for people with disabilities.
Comovement and instability in cryptocurrency markets

We analyze the extent of comovement between daily price returns of nine major cryptocurrencies from April 2013 to February 2021. We assess its evolution using bivariate and multivariate modeling approaches, and detect pronounced time variation. Generally, comovement is initially low and positive, but increases substantially between early 2017 and late 2018 and stays high until the end of the sample. We then adopt a right-tail version of the Augmented Dickey–Fuller unit root test to identify periods of mildly explosive behavior (statistical instability) in the Network Value to Transactions (NVT) ratio of seven cryptocurrencies. The NVT ratio is a novel measure of the dollar value of cryptocurrency transaction activity relative to network value. We show evidence of significant mild explosiveness in all seven cryptocurrencies. In 2018 and in the second half of 2019, several cryptocurrencies experience often simultaneous instability. Instability is a dominant feature of these markets.
Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild

With the exponential growth of online marketplaces and user-generated content therein, aspect-based sentiment analysis has become more important than ever. In this work, we critically review a representative sample of the models published during the past six years through the lens of a practitioner, with an eye towards deployment in production. First, our rigorous empirical evaluation reveals poor reproducibility: an average 4-5% drop in test accuracy across the sample. Second, to further bolster our confidence in empirical evaluation, we report experiments on two challenging data slices, and observe a consistent 12-55% drop in accuracy. Third, we study the possibility of transfer across domains and observe that as little as 10-25% of the domain-specific training dataset, when used in conjunction with datasets from other domains within the same locale, largely closes the gap between complete cross-domain and complete in-domain predictive performance. Lastly, we open-source two large-scale annotated review corpora from a large e-commerce portal in India in order to aid the study of replicability and transfer, with the hope that it will fuel further growth of the field.
A Study on Different Aspects of Web Mining and Research Issues

Abstract Web is huge and dynamically increasing day by day with exponential rate. If query is little bit complex then often or sometimes it becomes difficult to get relevant information in response. Several search engines try to optimize the users query and/or search results to provide the relevant information. Also many websites are ecommerce sites which are also competing to attract their customers and some are recommendation based. Now almost all good sites are using web mining techniques to improve their performance. In this paper, an attempt has been made to cover the key aspects used in web mining research. Initially problems faced by users and/or owners have been highlighted. Further it discusses types of data and techniques used by different categories of web mining are discussed. Finally research issues and application areas have been discussed.
EdgeAvatar: An Edge Computing System for Building Virtual Beings

Dialogue systems, also known as conversational agents, are computing systems that use algorithms for speech and language processing to engage in conversation with humans or other conversation-capable systems. A chatbot is a conversational agent that has, as its primary goal, to maximize the length of the conversation without any specific targeted task. When a chatbot is embellished with an artistic approach that is meant to evoke an emotional response, then it is called a virtual being. On the other hand, conversational agents that interact with the physical world require the use of specialized hardware to sense and process captured information. In this article we describe EdgeAvatar, a system based on Edge Computing principles for the creation of virtual beings. The objective of the EdgeAvatar system is to provide a streamlined and modular framework for virtual being applications that are to be deployed in public settings. We also present two implementations that use EdgeAvatar and are inspired by historical figures to interact with visitors of the Venice Biennale 2019. EdgeAvatar can adapt to fit different approaches for AI powered conversations.
Collaborative knowledge distillation for incomplete multi-view action prediction

Predicting future actions is a key in visual understanding, surveillance, and human behavior analysis. Current methods for video-based prediction are primarily using single-view data, while in the real world multiple cameras and produced videos are readily available, which may potentially benefit the action prediction tasks. However, it may bring up a new challenge: subjects in the videos are more likely to be occluded by objects when captured from different angles, or suffer from signal jittering in transmission. To that end, in this paper we propose a novel student network called Collaborative Knowledge Distillation (CKD) to predict human actions with missing information under a multi-view setting, i.e., incomplete multi-view action prediction. First, we create a graph attention based teacher model capable of fusing multi-view video features for prediction task. Second, we construct a corruption pattern bank (CPB) to simulate various missing segments in multi-view video, and each student model will manage one pattern through privileged information and knowledge distillation. Third, to account for arbitrary missing video segments in real-world, the ensemble of student models will be developed to make a joint prediction. The proposed framework has been extensively evaluated on popular multi-view visual action datasets, including PKU-MMD and NTU-RGB to validate the effectiveness of our approach and to the best of our knowledge action prediction has not yet been explored in the multi-view setting.
Multimodal Affective Pedagogical Agents for Different Types of Learners

The paper reports progress on an NSF-funded project whose goal is to research and develop multimodal affective animated pedagogical agents (APA) for different types of learners. Although the preponderance of research on APA tends to focus on the cognitive aspects of online learning, this project explores the less-studied role of affective features. More specifically, the objectives of the work are to: (1) research and develop novel algorithms for emotion recognition and for life-like emotion representation in embodied agents, which will be integrated in a new system for creating APA to be embedded in digital lessons; and (2) develop an empirically grounded research base that will guide the design of affective APA that are effective for different types of learners. This involves conducting a series of experiments to determine the effects of the agent’s emotional style and emotional intelligence on a diverse population of students. The paper outlines the work conducted so far, e.g., development of a new system (and underlying algorithms) for producing affective APA. It also reports the findings from two preliminary studies.
Interactive information module for person re-identification

In person re-identification (Re-ID) task, multi-branch networks acquire better performance by combining global features and local features. Obviously, local branch can obtain detailed information of person pictures but may work on invalid regions when person pictures have imprecise bounding boxes. On the contrary, global branch can be aware of the position of person but hard to acquire detailed information of person pictures. Meanwhile, lots of multi-branch networks ignore mutual information among different branches. Therefore, it is necessary to enhance interaction of global branch and local branch. For this purpose, we propose Interactive Information Module (IIM). IIM includes two components named Global-map Attention Module (GAM) and Labeled-class Mutual Learning (LML), respectively. GAM leverages heatmaps generated by global branch to guide calculation of local attention and obtains a composite global feature by combining local features. GAM relys more on the performance of global branch which decides the quality of heatmaps. To improve performance of global branch, we propose LML to promote convergent rate of global branch. Extensive experiments implemented on Market-1501, DukeMTMC-ReID, and CUHK03-NP datasets confirm that our method achieves state-of-the-art results.
Privacy-preserving chi-squared test of independence for small samples

Abstract Background The importance of privacy protection in analyses of personal data, such as genome-wide association studies (GWAS), has grown in recent years. GWAS focuses on identifying single-nucleotide polymorphisms (SNPs) associated with certain diseases such as cancer and diabetes, and the chi-squared ( χ 2 ) hypothesis test of independence can be utilized for this identification. However, recent studies have shown that publishing the results of χ 2 tests of SNPs or personal data could lead to privacy violations. Several studies have proposed anonymization methods for χ 2 testing with ε -differential privacy, which is the cryptographic community’s de facto privacy metric. However, existing methods can only be applied to 2×2 or 2×3 contingency tables, otherwise their accuracy is low for small numbers of samples. It is difficult to collect numerous high-sensitive samples in many cases such as COVID-19 analysis in its early propagation stage. Results We propose a novel anonymization method (RandChiDist), which anonymizes χ 2 testing for small samples. We prove that RandChiDist satisfies differential privacy. We also experimentally evaluate its analysis using synthetic datasets and real two genomic datasets. RandChiDist achieved the least number of Type II errors among existing and baseline methods that can control the ratio of Type I errors. Conclusions We propose a new differentially private method, named RandChiDist, for anonymizing χ 2 values for an I × J contingency table with a small number of samples. The experimental results show that RandChiDist outperforms existing methods for small numbers of samples.
Wilderness Search and Rescue Missions using Deep Reinforcement Learning

Wilderness Search and Rescue (WiSAR) requires navigating large regions - often in rugged, remote areas - searching for missing people or animals. Because of the large regions and potentially limited mobility of ground vehicles, WiSAR missions are frequently carried out with the help of Unmanned Aerial Vehicles (UAVs). However, the ability to autonomously execute WiSAR remains an unsolved challenge. In this paper, we take advantage of Deep Reinforcement Learning (DRL) to develop an autonomous WiSAR controller for UAVs. We improve the learning and understanding of a UAV agent to explore a partially observable environment in search of a victim trapped in the wild. The proposed approach breaks up this difficult problem into 4 sub-tasks: tractable mapping of the environment in small regions, region selection, target search, and region exploration. Quad-Tree is utilized offline to decompose the environment map into smaller, tractable maps. Then, an efficient cost function is repeatedly computed to determine the best target region to search in each iteration of the process. Recurrent-DDQN and A2C algorithms are trained to generate optimal policies for the target search and regions exploration tasks, respectively. We tested our approach against a baseline of a hard-coded policy of navigating the map in a zigzag fashion and another baseline of using the same sub-tasks but instead of using the DRL algorithms, randomly selecting an action at each time step. The results demonstrate that our proposed approach is capable of navigating through 25 randomly generated environments and finding the missing victim faster than the baselines by 46%.
A Text classification algorithm based on topic model and convolutional neural network

Abstract Based on the neural topic model ProdLDA and convolutional neural network, this paper proposes a text classification algorithm based on topic model and convolutional neural network. Firstly, the text information is modeled on the word vector model, then the convolutional neural network is used to extract the granularity features of high-dimensional text, and the neural topic model ProdLDA is used to extract the potential topic features. Then, the connection layer is established to connect the text features, and finally the classification layer is processed. At the same time, a new topic feature introduction method is used in the process of extracting topic features. Experimental results show that this algorithm can effectively improve the performance of text classification.
Auto-encoder based structured dictionary learning for visual classification

Dictionary learning and deep learning can be combined to boost the performance of classification tasks. However, existing combined methods often learn multi-level dictionaries each of which is embedded in a network layer, involve a large number of parameters (elements of many dictionaries) and thus easily result in prohibitive computational cost and even overfitting. In this paper, we present a novel deep Auto-Encoder based Structured Dictionary (AESD) learning model, where we need to learn only one dictionary which is composed of class-specific sub-dictionaries, and supervision is introduced by imposing discriminative category constraints to empower the dictionary with discrimination. The encoding layers are designed with shared parameters which are exactly dependent on the dictionary carried by the decoding layer. This characterizes the learning process by forward-propagation based optimization w.r.t the dictionary only, leading to a light-weight network training. In addition to utilizing directly the trained encoding network combined with a minimum-reconstruction-residual scheme for single image based classification, to expand the application spectrum of our method, in the testing phase, we extend the proposed prototype into a Convolutional Encoder based Block Sparse Representation (CEBSR) model to promote the latent block sparsity in the joint representation of an image set, achieving improved image set based classification. Extensive experiments verify the performance of the learned dictionary for image classification, and the superiority of our extended model over the state-of-the-art image set classification methods.
The Role of Artificial and Nonartificial Intelligence in the New Product Success with Moderating Role of New Product Innovation: A Case of Manufacturing Companies in China

Currently, there is an increasing trend in the organizations towards examining the artificial intelligence and nonartificial intelligence for the innovation and success of the new product, as well as getting the intentions of the upcoming researchers. Thus, the purpose of the ongoing study is to examine the role of artificial and nonartificial intelligence in the new product success along with the moderating role of new product innovation in the manufacturing organizations of China. The quantitative methods have been followed by the study and gathered the responses from the respondents using questionnaires, and analysis has been conducted by using the smart-PLS. The results exposed that artificial intelligence and nonartificial intelligence have positive and significant nexus with the new product success. The outcomes also revealed that the new product innovation significantly moderated the links among the nonartificial intelligence and new product success, but it insignificantly moderated the links among the artificial intelligence and new product success in the manufacturing organizations of China. These findings have provided the guidelines to the manufacturing companies and their policies developing authorities that they should be developed and implement the suitable policies regarding the adoption of artificial intelligence and nonartificial intelligence that enhance the success of the new product, which ultimately enhances the success of the organization.
Velocity Planning Method Base on Fuzzy Neural Network for Autonomous Vehicle

In order to improve the comfort performance and reduce the planning algorithm complexity in autonomous vehicle, an intelligent longitudinal velocity planning method based on fuzzy neural network (FNN) is proposed. With the manual driving experience, fuzzy planning model is established. By utilizing the self-learning function of neural network, fuzzy planning model is modified, which is attempted to establish FNN planning model. The planning method is applied to velocity planning. Three kinds of driving scenes are analyzed, and velocity planning models based on FNN are established accordingly. The simulation and experiment results indicate that acceleration generated by FNN planning model has good smooth property, and it is easy to be tracked by the subsequent control module. Compared with traditional method, the proposed method has certain anti-disturbance ability and self-adaptability. Also, the proposed method is convenient for engineering application, which ensures both the real-time performance and stability of the algorithm.
Performance Optimization for Standalone Virtual Reality Headsets

This paper focuses on the performance optimization process for standalone virtual reality headsets. With the release of the Oculus Quest on May 2019, and its wide adoption, developing a VR application is not as simple as it used to be on regular PCVR headsets, which run on VR ready desktops powered by dedicated processors and graphics cards. The mobile processor of standalone headsets is much weaker than the ones on a dedicated VR computer. Therefore, heavy performance optimization is needed in order to run full scale applications on the Oculus Quest without facing application slow downs and performance drops. PlayBricks; A VR application running on the Oculus Quest using Unreal Engine 4 and C++, is developed to simulate real life LEGOs for architectural design and educational applications. The hardest problem faced in development is the performance problem, where the number of blocks the user places before the application starts slowing down is not enough to build anything meaningful. Through the optimization of the CPU, GPU and draw call threads, the paper reveals how the number of building blocks can be increased from 100 blocks to reach 12,000 blocks.
Unsupervised Root-Cause Analysis for Integrated Systems

The increasing complexity and high cost of integrated systems has placed immense pressure on root-cause analysis and diagnosis. In light of artificial intelligent and machine learning, a large amount of intelligent root-cause analysis methods have been proposed. However, most of them need historical test data with root-cause labels from repair history, which are often difficult and expensive to obtain. In this paper, we propose a two-stage unsupervised root-cause analysis method in which no repair history is needed. In the first stage, a decision-tree model is trained with system test information to roughly cluster the data. In the second stage, frequent-pattern mining is applied to extract frequent patterns in each decision-tree node to precisely cluster the data so that each cluster represents only a small number of root causes. In additional, L-method and cross validation are applied to automatically determine the hyper-parameters of our algorithm. Two industry case studies with system test data demonstrate that the proposed approach significantly outperforms the state-of-the-art unsupervised root-cause analysis method.
A Critical Evaluation of Dyslexia Information on the Internet

The internet is a common source of information for parents, educators, and the general public. However, researchers who analyze the quality of internet sources have found they often contain inaccurate and misleading information. Here, we present an analysis of dyslexia on the internet. Employing disability studies in education (DSE), disability critical race studies (DisCrit), and Bakhtin’s construct of ideological becoming, we examined the credibility of sources, the quality of information, and the discourse in which the information is presented. We found the majority of webpages do not meet basic source credibility criteria, much of the content contradicts or is unsupported by research, and most pages convey information in an authoritative discourse, making it seem irreproachable. Building on the findings, we offer criteria for evaluating dyslexia information and suggestions for research and practice. We focus on the need for less divisive, more collaborative dialogue, along with research among stakeholders with multiple perspectives.
Experimental observation of quantum nonlocality in general networks with different topologies

Nonlocal correlation plays an important role in device independent quantum information processing. The standard Bell nonlocality has been well studied with single local hidden variables, however the nonlocal correlations in general networks with several independent quantum sources and distant observers have been far less explored. Here, by using three independent entangled photon sources and recently constructed nonlinear Bell inequalities, we experimentally test the nonlocal correlations in the network scenario with different topologies. The violation of the inequalities can be obtained simply with separate measurements, which is much more favourable from the practical point of view. Our experiment results show a violation as 0.7779±0.0093 for the star network, and 0.7303±0.0024 for the chain network. Furthermore, we demonstrate that more measurement settings for each observer can bound more information against an eavesdropper.