Reviewerly/exHarmony huggingface dataset:
Sensors to Sign Language: A Natural Approach to Equitable Communication

Sign Language Recognition (SLR) aims to improve the equity of communication with the hearing impaired. However, SLR typically relies on having recorded videos of the signer. We develop a more natural solution by fitting a signer with arm sensors and classifying the sensor signals directly into language. We refer to this task as Sensors-to-Sign-Language (STSL). While existing STSL systems demonstrate effectiveness with small vocabularies of fewer than 100 words, we aim to determine if STSL can scale to larger, more realistic lexicons. For this purpose, we introduce a new dataset, SignBank, which consists of exactly 6,000 signs, spans 558 distinct words from 15 different novice signers, and constitutes the largest such dataset. By using a simple but effective model for STSL, we demonstrate a strong baseline performance on SignBank. Notably, despite our model having trained on only four signings of each word, it is able to correctly classify new signings with 95.1% accuracy (out of 558 candidate words). This work enables and motivates further development of lightweight, wearable hardware and real-time modelling for SLR.
Generative Adversarial Network Including Referring Image Segmentation For Text-Guided Image Manipulation

This paper proposes a novel generative adversarial network to improve the performance of image manipulation using natural language descriptions that contain desired attributes. Text-guided image manipulation aims to semantically manipulate an image aligned with the text description while preserving text-irrelevant regions. To achieve this, we newly introduce referring image segmentation into the generative adversarial network for image manipulation. The referring image segmentation aims to generate a segmentation mask that extracts the text-relevant region. By utilizing the feature map of the segmentation mask in the network, the proposed method explicitly distinguishes the text-relevant and irrelevant regions and has the following two contributions. First, our model can pay attention only to the text-relevant region and manipulate the region aligned with the text description. Second, our model can achieve an appropriate balance between the generation of accurate attributes in the text-relevant region and the reconstruction in the text-irrelevant regions. Experimental results show that the proposed method can significantly improve the performance of image manipulation.
Simulation-and-Mining: Towards Accurate Source-Free Unsupervised Domain Adaptive Object Detection

Vanilla unsupervised domain adaptive (UDA) object detection typically requires the labeled source data for joint-training with the unlabeled target data, which is usually unavailable in real-world scenarios due to data privacy, leading to source data-free UDA object detection. Herein, we first analyze the phenomenon of cross-domain detection degradation varying from easy to hard samples (e.g. the objects with different scales or occlusion degrees), termed as domain generalization differentiation. In detail, the ability to detect easy samples is well transferred while the one to detect hard samples is dramatically degraded. To this end, we then revisit the existing self-training method, which is of great challenge to deal with the abundant false negatives (hard samples). Assumed that true positives (easy samples) labeled by the source model can be exploited as supervision cues. UDA is finally modeled into an unsupervised false negatives mining problem. Thus, we propose a Simulation-and-Mining (S&M) framework, which simulates false negatives by augmenting true positives and mines back false negatives alternatively and iteratively. Experimental results show the effectiveness.
Adversarial Examples for Image Cropping in Social Media

It is well known that constructing adversarial examples is important to make a machine learning system more stable by pointing out its vulnerabilities. We present an approach to produce the adversarial examples to image cropping systems. In this method, we propose a method to calculate repeated perturbations based on the gradient information calculated using a model similar to the cropping system provided by a social network service, and give the cropping model an attack to change regions that should be cropped. We measure the amount of shift in the peak value of the saliency map and quantitatively verify the effectiveness of the system by this amount. We demonstrate that the proposed method outperforms baseline methods.
Multi-Feature Integration for Speaker Embedding Extraction

The performance of the automatic speaker recognition system is becoming more and more accurate, with the advancement in deep learning methods. However, current speaker recognition system performances are subjective to the training conditions, thereby decreasing the performance drastically even on slightly varied test data. A lot of methods such as using various data augmentation structures, various loss functions, and integrating multiple features systems have been proposed and shown a performance improvement. This work focuses on integrating multiple features to improve speaker verification performance. Speaker information is commonly represented in the different kinds of features, where the redundant and irrelevant information such as noise and channel information will affect the dimensions of different features in a different manner. In this work, we intend to maximize the speaker information by reconstructing the extracted speaker information in one feature from the other features while at the same time minimizing the irrelevant information. The experiments with the multi-feature integration model demonstrate improved performance than the stand-alone models by significant margins. Also, the extracted speaker embeddings are found to be noise-robust.
Data-informed knowledge and strategies

The article proposes a new approach to reasoning about knowledge and strategies in multiagent systems. It emphasizes data, not agents, as the source of strategic knowledge. The approach brings together Armstrong's functional dependency from database theory, a data-informed knowledge modality based on a recent work by Baltag and van Benthem, and a newly proposed data-informed strategy modality. The main technical result is a sound and complete logical system that describes the interplay between these three logical operators.
Map: Multispectral Adversarial Patch to Attack Person Detection

Recently, multispectral person detection has shown great performance in real world applications such as autonomous driving and security systems. However, the reliability of person detection against physical attacks has not been fully explored yet in multispectral person detectors. To evaluate the robustness of multispectral person detectors in the physical world, we propose a novel Multispectral Adversarial Patch (MAP) generation framework. MAP is optimized with a Cross-spectral Mapping(CSM) and Material Emissivity(ME) loss. This paper is the first to evaluate the reliability of a multispectral person detector against physical attack. Throughout experiment, our proposed adversarial patch successfully attacks the person detector and the Average Precision (AP) score is dropped by 90.79% in digital space and 73.34% in physical space.
A Survey on Decentralized Application based On Blockchain Platform

Blockchain is distributed network technology which stores information in decentralized manner. Blockchain makes transaction transparent and provides enhanced security using cryptography. It has some properties including immutability, decentralization and auditability which makes transaction tamper proof and more secure. Decentralization is becoming popular way to develop applications and for data management. This paper has conducted a survey on the working of decentralized application based on Ethereum. Further, this research study has also analyzed the working of smart contract in Blockchain. Apart from that, InterPlanetary File System is used to for storing and accessing files in a distributed system. Moreover, this research study has covered different tools and techniques, which are used to develop DApps based on Ethereum network.
DACFL: Dynamic Average Consensus-Based Federated Learning in Decentralized Sensors Network

Federated Learning (FL) is a privacy-preserving way to utilize the sensitive data generated by smart sensors of user devices, where a central parameter server (PS) coordinates multiple user devices to train a global model. However, relying on centralized topology poses challenges when applying FL in a sensors network, including imbalanced communication congestion and possible single point of failure, especially on the PS. To alleviate these problems, we devise a Dynamic Average Consensus-based Federated Learning (DACFL) for implementing FL in a decentralized sensors network. Different from existing studies that replace the model aggregation roughly with neighbors' average, we first transform the FL model aggregation, which is the most intractable in a decentralized topology, into the dynamic average consensus problem by treating a local training procedure as a discrete-time series.We then employ the first-order dynamic average consensus (FODAC) to estimate the average model, which not only solves the model aggregation for DACFL but also ensures model consistency as much as possible. To improve the performance with non-i.i.d data, each user also takes the neighbors' average model as its next-round initialization, which prevents the possible local over-fitting. Besides, we also provide a basic theoretical analysis of DACFL on the premise of i.i.d data. The result validates the feasibility of DACFL in both time-invariant and time-varying topologies and declares that DACFL outperforms existing studies, including CDSGD and D-PSGD, in most cases. Take the result on Fashion-MNIST as a numerical example, with i.i.d data, our DACFL achieves 19∼34% and 3∼10% increases in average accuracy; with non-i.i.d data, our DACFL achieves 30∼50% and 0∼10% increases in average accuracy, compared to CDSGD and D-PSGD.
Spam Email Detection with Affect Intensities using Recurrent Neural Network Algorithm

A large number of email users triggers an increase in the occurrence of spam in emails to gain benefits for some parties but harm others and also email users. Spam emails usually contain advertisements or criminal acts such as phishing which implicitly contain human emotions in them. It is quite difficult and takes time to differentiate between a large number of spam and ham emails. This problem can be overcome by using deep learning technology. One of which is a neural network that can classify spam emails. This paper uses the spam and ham Enron email corpus dataset. This study will add emotional features in extracting its features. The steps taken include text preprocessing, feature extraction using tf-idf, and lexicon-based emotion features, followed by classification using RNN to detect spam in emails. A comparison with other methods is also provided by comparing the proposed method to Naïve Bayes and Support-Vector Machine (SVM) algorithm based on precision and accuracy. In addition, this study also compares the effect of using affect intensities on the performance of algorithms. The results show that RNN outperforms other methods by showing the highest accuracy 99% and the precision of 99.1%. Adding effect intensities to the model would increase the model recognition results.
Social Media Fake News Detection using mNB in Blockchain

Nowadays, due to the high usage of social media-based global news, verification and authentication is a very challenging task. Most social media platforms are easily enabled to access news anytime, anywhere over the internet, but it also produces a lot of false news and false information simultaneously. Therefore, in such a case, it is necessary to determine whether available information is genuine, whether it is fake or real. This allows users to make confused and lose the trust of social media. A blockchain-based fake news detection can better handle such problems. The proposed classification algorithm is used to detect fake news in training and testing evolution. Another major objective of this work is to revoke the attackers who update the published news. The blockchain-based decentralized peer-to-peer environment has been used to protect the published data even in a vulnerable environment Various features extraction and selection techniques have been used to generate effective training rules and validate the test classifier accordingly. An extensive experimental analysis demonstrates the classification accuracy of fake news detection on the LIAR dataset. The system achieves 95.20% average accuracy for training as well as testing, which is higher than conventional machine learning algorithms like SVM, ANN, NB etc.
Remote Sensing Survey of Altiplano-Puna Volcanic Complex Rocks and Minerals for Planetary Analog Use

The Altiplano-Puna Volcanic Complex (APVC) of the Central Andes is an arid region with extensive volcanism, possessing various geological features comparable to those of other solar system objects. The unique features of the APVC, e.g., hydrothermal fields and evaporite salars, have been used as planetary analogs before, but the complexity of the APVC presents a wealth of opportunities for more analog studies that have not been exploited previously. Motivated by the potential of using the APVC as an analog of the volcanic terrains of solar system objects, we mapped the mineralogy and silica content of the APVC up to ~100,000 km2 in northern Chile based on a combination of remote sensing data resembling those of the Moon and Mars. The band ratio indices of Landsat 8 Operational Land Imager multispectral images and mineral classifications based on spectral hourglass approach using Earth Observing-1 Hyperion hyperspectral images (both in the visible to shortwave infrared wavelengths) were used to map iron-bearing and alteration minerals. We also used Hyperion imagery to detect feldspar spectral signatures and demonstrated that feldspar minerals can be detected on non-anorthosites, which may influence interpretations of feldspar spectral signatures on Mars. From the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset, we derived the silica percentage of non-evaporite rocks within errors of approximately 2–3 wt.% SiO2 for those in the 60–70 wt.% range (about 8 wt.% errors for the 50–60 wt.% range). Based on an integrated assessment of the three datasets, we highlighted three regions of particular interest worthy of further field investigation. We also evaluated the benefits and limitations of all three remote sensing methods for mapping key minerals and capturing rock diversity, based on available samples and existing geological maps.
Identification of Missing Person Using Convolutional Neural Networks

There are large number of people missing in India every year. Many missing instances cannot be tracked. The current existing models are limited to regional search of missing persons. The image of person is matched using the Deep learning model, VGG16. This CNN model can be used for Face recognition in images. VGG-16 is a pre-trained model which is run on millions of images. Those weights are used in our proposed work. The NumPy module is used to represent mathematical and logical operations on arrays. This Predictive model probably identifies the missing person if the image matches and is found in the database. This methodology uses several steps which are data preprocessing, data augmentation, VGG-16, training and testing the model. Using this model, the accuracy for the training data is 90.01%. On testing, the accuracy obtained is 85.21%.
ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services

Dynamic resource provisioning is made more accessible with cloud computing. Monitoring a running service is critical, and modifications are performed when specific criteria are exceeded. It is a standard practice to add or delete resources in such situations. We investigate the method to ensure the Quality of Service (QoS), estimate the required resources, and modify allotted resources depending on workload, serialization, and parallelism due to resources. This article focuses on cloud QoS violation remediation using resource planning and scaling. A Resource Quantified Scaling for QoS Violation (ReSQoV) model is proposed based on the Universal Scalability Law (USL), which provides cloud service capacity for specific workloads and generates a capacity model. ReSQoV considers the system overheads while allocating resources to maintain the agreed QoS. As the QoS violation detection decision is Probably Violation and Definitely Violation, the remedial action is triggered, and required resources are added to the virtual machine as vertical scaling. The scenarios emulate QoS parameters and their respective resource utilization for ReSQoV compared to policy-based resource allocation. The results show that after USLbased Quantified resource allocation, QoS is regained, and validation of the ReSQoV is performed through the statistical test ANOVA that shows the significant difference before and after implementation.
Learning Monocular Mesh Recovery of Multiple Body Parts Via Synthesis

In this paper, we focus on simultaneously recovering the 3D mesh of multiple body parts from a single RGB image. One of the main challenges is that available datasets with full-body 3D annotations are very limited. This results in poor generalization ability of existing learning-based methods. Existing optimization-based methods iteratively fit the 3D mesh to the 2d pose, which is very time-consuming. To address these limitations, we propose to integrate multiple 3D single-body-part datasets to create a highly diverse whole-body 3D motion space for learning from controllable synthetics. Compared with the learning-based approaches, the proposed method greatly alleviates the reliance on training data. Compared with the optimization-based approaches, the proposed method is a hundred times faster. Our proposed method also outperforms previous state-of-the-art methods on CMU Panoptic dataset.
Correlation Assessment of the Performance of Associative Classifiers on Credit Datasets Based on Data Complexity Measures

One of the four basic machine learning tasks is pattern classification. The selection of the proper learning algorithm for a given problem is a challenging task, formally known as the algorithm selection problem (ASP). In particular, we are interested in the behavior of the associative classifiers derived from Alpha-Beta models applied to the financial field. In this paper, the behavior of four associative classifiers was studied: the One-Hot version of the Hybrid Associative Classifier with Translation (CHAT-OHM), the Extended Gamma (EG), the Naïve Associative Classifier (NAC), and the Assisted Classification for Imbalanced Datasets (ACID). To establish the performance, we used the area under the curve (AUC), F-score, and geometric mean measures. The four classifiers were applied over 11 datasets from the financial area. Then, the performance of each one was analyzed, considering their correlation with the measures of data complexity, corresponding to six categories based on specific aspects of the datasets: feature, linearity, neighborhood, network, dimensionality, and class imbalance. The correlations that arise between the measures of complexity of the datasets and the measures of performance of the associative classifiers are established; these results are expressed with Spearman’s Rho coefficient. The experimental results correctly indicated correlations between data complexity measures and the performance of the associative classifiers.
Object Detection by Attention-Guided Feature Fusion Network

One of the most noticeable characteristics of security issues is the prevalence of “Security Asymmetry”. The safety of production and even the lives of workers can be jeopardized if risk factors aren’t detected in time. Today, object detection technology plays a vital role in actual operating conditions. For the sake of warning danger and ensuring the work security, we propose the Attention-guided Feature Fusion Network method and apply it to the Helmet Detection in this paper. AFFN method, which is capable of reliably detecting objects of a wider range of sizes, outperforms previous methods with an mAP value of 85.3% and achieves an excellent result in helmet detection with an mAP value of 62.4%. From objects of finite sizes to a wider range of sizes, the proposed method achieves “symmetry” in the sense of detection.
Impressions that last: representing the meaningful museum experience

Research in human–computer interaction (HCI) has identified meaning as an important, yet poorly understood concept in interaction design contexts. Central to this development is the increasing emphasis on designing products and technologies that promote leisure, personal fulfillment, and well-being. As spaces of profound historical significance and societal value, museums offer a unique perspective on how people construct meaning during their interactions in museum spaces and with collections, which may help to deepen notions of the content of meaningful interaction and support innovative design for cultural heritage contexts. The present work reports on the results of two studies that investigate meaning-making in museums. The first is an experience narrative study (N = 32) that analyzed 175 memorable museum visits, resulting in the establishment of 23 triggers that inform meaningful interaction in museums. A second study (N = 354) validated the comprehensiveness and generalisability of the triggers by asking participants to apply them to their own memorable museum experiences. We conclude with a framework of meaning in museums featuring the 23 triggers and two descriptive categories of temporality and scope. Our findings contribute to meaning research in HCI for museums through an articulation of the content of meaning-making in the cultural sector.
The Normal Natural Troubles of Virtual Reality in Mixed-Reality Performances

Performing with technology is a complex and challenging task. Artists who use novel technologies, such as Virtual Reality, have to develop strategies of monitoring, maintenance, and recovery from errors with as minimal impact on the ongoing performance as possible. In this paper we draw on two case studies of mixed-reality performances and document strategies of Stage Managing VR Performance, Choreographing for Cables, Consistency & Charging, Improvising Interventions, and Priming Participants. We discuss how these practices expose areas ripe with potential for tool development, and how they can also be used to inform the design of interaction with other technologies, such as the Internet of Things.
Rapid Vehicle Detection in Aerial Images under the Complex Background of Dense Urban Areas

Vehicle detection on aerial remote sensing images under the complex background of urban areas has always received great attention in the field of remote sensing; however, the view of remote sensing images usually covers a large area, and the size of the vehicle is small and the background is complex. Therefore, compared with object detection in the ground view images, vehicle detection in aerial images remains a challenging problem. In this paper, we propose a single-scale rapid convolutional neural network (SSRD-Net). In the proposed framework, we design a global relational (GR) block to enhance the fusion of local and global features; moreover, we adjust the image segmentation method to unify the vehicle size in the input image, thus simplifying the model structure and improving the detection speed. We further introduce an aerial remote sensing image dataset with rotating bounding boxes (RO-ARS), which has complex backgrounds such as snow, clouds, and fog scenes. We also design a data augmentation method to get more images with clouds and fog. Finally, we evaluate the performance of the proposed model on several datasets, and the experimental results show that the recall and precision are improved compared with existing methods.