Reviewerly/exHarmony huggingface dataset:
IoT Devices, User Authentication, and Data Management in a Secure, Validated Manner through the Blockchain System

Advancement in technology has led to innovation in equipment, and the number of devices is increasing every day. Industries are introducing new devices every day and predicting 50 billion connected devices by 2022. These devices are deployed through the Internet, called the Internet of Things (IoT). Applications of IoT devices are weather prediction, monitoring surgery in hospitals, identification of animals using biochips, providing tracking connectivity in automobiles, smart home appliances, etc. IoT devices have limitations related to security at both the software and hardware ends. Secure user interfaces can overcome software-level limitations like front-end-user interfaces are accessed easily through public and private networks. The front-end interfaces are connected to the localized storage to contain data produced by the IoT devices. Localized storage deployed in a closed environment connected to IoT devices is more efficient than online servers from a security perspective. Blockchain has emerged as a technology or technique with capabilities to achieve secure administrational authentication and accessibility to IoT devices and their computationally produced data in a decentralized way with high reliability, interrogation, and resilience. In this paper, we propose device, end-user, and transactional authentication techniques using blockchain-embedded algorithms. The localized server interacts with the user interface to authenticate IoT devices, end-users, and their access to IoT devices. The localized server provides efficiency by reducing the load on the IoT devices by carrying out end-user heavy computational data, including end-user, IoT device authentication, and communicational transactions. Authentication data are placed on the public ledger in block form, distributed over the system nodes through blockchain algorithms.
Crowd Density Estimation Method Using Deep Learning for Passenger Flow Detection System in Exhibition Center

Aiming at the problems of crowd distribution, scale feature, and crowd feature extraction difficulties in exhibition centers, this paper proposes a crowd density estimation method using deep learning for passenger flow detection systems in exhibition centers. Firstly, based on the pixel difference symbol feature, the difference amplitude feature and gray feature of the central pixel are extracted to form the CLBP feature to obtain more crowd group description information. Secondly, use the LR activation function to add nonlinear factors to the convolution neural network (CNN) and use dense blocks derived from crowd density estimation to train the LR-CNN crowd density estimation model. Finally, experimental results show that the mean absolute error (MAE) and mean square error (MSE) of the proposed method in the UCF_CC_50 dataset are 325.6 and 369.4, respectively. Besides, MAE and MSE in part_A of the Shanghai Tech dataset are 213.5 and 247.1, respectively, and they in part_B are 85.3 and 99.7, respectively. The proposed method effectively improves the accuracy of crowd density estimation in exhibition centers.
Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages

Abstractive summarization is a technique that allows for extracting condensed meanings from long texts, with a variety of potential practical applications. Nonetheless, today’s abstractive summarization research is limited to testing the models on various types of data, which brings only marginal improvements and does not lead to massive practical employment of the method. In particular, abstractive summarization is not used for social media research, where it would be very useful for opinion and topic mining due to the complications that social media data create for other methods of textual analysis. Of all social media, Reddit is most frequently used for testing new neural models of text summarization on large-scale datasets in English, without further testing on real-world smaller-size data in various languages or from various other platforms. Moreover, for social media, summarizing pools of texts (one-author posts, comment threads, discussion cascades, etc.) may bring crucial results relevant for social studies, which have not yet been tested. However, the existing methods of abstractive summarization are not fine-tuned for social media data and have next-to-never been applied to data from platforms beyond Reddit, nor for comments or non-English user texts. We address these research gaps by fine-tuning the newest Transformer-based neural network models LongFormer and T5 and testing them against BART, and on real-world data from Reddit, with improvements of up to 2%. Then, we apply the best model (fine-tuned T5) to pools of comments from Reddit and assess the similarity of post and comment summarizations. Further, to overcome the 500-token limitation of T5 for analyzing social media pools that are usually bigger, we apply LongFormer Large and T5 Large to pools of tweets from a large-scale discussion on the Charlie Hebdo massacre in three languages and prove that pool summarizations may be used for detecting micro-shifts in agendas of networked discussions. Our results show, however, that additional learning is definitely needed for German and French, as the results for these languages are non-satisfactory, and more fine-tuning is needed even in English for Twitter data. Thus, we show that a ‘one-for-all’ neural-network summarization model is still impossible to reach, while fine-tuning for platform affordances works well. We also show that fine-tuned T5 works best for small-scale social media data, but LongFormer is helpful for larger-scale pool summarizations.
Deep Learning Algorithms for Human Fighting Action Recognition

— Human action recognition using skeletons has been employed in various applications, including healthcare robots, human-computer interaction, and surveillance systems. Recently, deep learning systems have been used in various applications, such as object classification. In contrast to conventional techniques, one of the most prominent convolutional neural network deep learning algorithms extracts image features from its operations. Machine learning in computer vision applications faces many challenges, including human action recognition in real time. Despite significant improvements, videos are typically shot with at least 24 frames per second, meaning that the fastest classification technologies take time. Object detection algorithms must correctly identify and locate essential items, but they must also be speedy at prediction time to meet the real-time requirements of video processing. The fundamental goal of this research paper is to recognize the real-time state of human fighting to provide security in organizations by discovering and identifying problems through video surveillance. First, the images in the videos are investigated to locate human fight scenes using the YOLOv3 algorithm, which has been updated in this work. Our improvements to the YOLOv3 algorithm allowed us to accelerate the exploration of a group of humans in the images. The center locator feature in this algorithm was adopted as an essential indicator for measuring the safety distance between two persons. If it is less than a specific value specified in the code, they are tracked. Then, a deep sorting algorithm is used to track people. This framework is filtered to process and classify whether these two people continue to exceed the programmatically defined minimum safety distance. Finally, the content of the filter frame is categorized as combat scenes using the OpenPose technology and a trained VGG-16 algorithm, which classifies the situation as walking, hugging, or fighting. A dataset was created to train these algorithms in the three categories of walking, hugging, and fighting. The proposed methodology proved successful, exhibiting a classification accuracy for walking, hugging, and fighting of 95.0%, 87.4%, and 90.1%, respectively.
Offline Scaling of IoT Devices in IOTA Blockchain

An increased pattern of hidden Internet of Things (IoT) devices has been observed. Due to the increased number of security attacks, a large number of IoT devices are disappearing from the public internet. Operating blockchain operations in such ad hoc connectivity becomes challenging. However, multiple past studies have pointed towards IOTA Distributed Ledger Technology (DLT) that closely caters to offline blockchain use cases. However, there has been little to no empirical study or introduction to time bounds on transaction confirmation. Therefore, this study explains what provisions the existing IOTA blockchain has to accommodate the increased pattern of hidden IoT devices, and if IOTA is truly sufficient as a solution. In summary, we approach research questions by analyzing the studies that explore the trend of offline IoT devices and evaluating the relevance of offline blockchains, assessing the IOTA specification and codebase around offline transaction-making capabilities and pointing out some bounds that IOTA blockchain nodes must follow towards incoming transactions. Furthermore, we confirm by experimental runs that outside and within the tight time bounds transactions in offline Tangle can become stale and not get confirmed, and the effective time-bound can be even less. Realizing the need for a better offline blockchain scalability solution.
Dataset Bias in Few-Shot Image Recognition

The goal of few-shot image recognition (FSIR) is to identify novel categories with a small number of annotated samples by exploiting transferable knowledge from training data (base categories). Most current studies assume that the transferable knowledge can be well used to identify novel categories. However, such transferable capability may be impacted by the dataset bias, and this problem has rarely been investigated before. Besides, most of few-shot learning methods are biased to different datasets, which is also an important issue that needs to be investigated deeply. In this paper, we first investigate the impact of transferable capabilities learned from base categories. Specifically, we use the relevance to measure relationships between base categories and novel categories. Distributions of base categories are depicted via the instance density and category diversity. The FSIR model learns better transferable knowledge from relevant training data. In the relevant data, dense instances or diverse categories can further enrich the learned knowledge. Experimental results on different sub-datasets of Imagenet demonstrate category relevance, instance density and category diversity can depict transferable bias from distributions of base categories. Second, we investigate performance differences on different datasets from the aspects of dataset structures and different few-shot learning methods. Specifically, we introduce image complexity, intra-concept visual consistency, and inter-concept visual similarity to quantify characteristics of dataset structures. We use these quantitative characteristics and eight few-shot learning methods to analyze performance differences on multiple datasets. Based on the experimental analysis, some insightful observations are obtained from the perspective of both dataset structures and few-shot learning methods. We hope these observations are useful to guide future few-shot learning research on new datasets or tasks. Our data is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://123.57.42.89/dataset-bias/dataset-bias.html</uri> .
Weakly Supervised Video Object Segmentation via Dual-attention Cross-branch Fusion

Recently, concerning the challenge of collecting large-scale explicitly annotated videos, weakly supervised video object segmentation (WSVOS) using video tags has attracted much attention. Existing WSVOS approaches follow a general pipeline including two phases, i.e., a pseudo masks generation phase and a refinement phase. To explore the intrinsic property and correlation buried in the video frames, most of them focus on the later phase by introducing optical flow as temporal information to provide more supervision. However, these optical flow-based studies are greatly affected by illumination and distortion and lack consideration of the discriminative capacity of multi-level deep features. In this article, with the goal of capturing more effective temporal information and investigating a temporal information fusion strategy accordingly, we propose a unified WSVOS model by adopting a two-branch architecture with a multi-level cross-branch fusion strategy, named as dual-attention cross-branch fusion network (DACF-Net). Concretely, the two branches of DACF-Net, i.e., a temporal prediction subnetwork (TPN) and a spatial segmentation subnetwork (SSN), are used for extracting temporal information and generating predicted segmentation masks, respectively. To perform the cross-branch fusion between TPN and SSN, we propose a dual-attention fusion module that can be plugged into the SSN flexibly. We also pose a cross-frame coherence loss (CFCL) to achieve smooth segmentation results by exploiting the coherence of masks produced by TPN and SSN. Extensive experiments demonstrate the effectiveness of proposed approach compared with the state-of-the-arts on two challenging datasets, i.e., Davis-2016 and YouTube-Objects.
Blockchain for Real Estate Industry

Blockchain technology is one of the latest technologies on the horizon and has evolved over the last 7-10 years. There is tremendous potential for usage of Blockchain technology in today’s Real Estate industry. This paper aims to analyze the disruptive power of Blockchain digital technologies in the real estate industry. This paper will start with an introduction of the blockchain technology and high-level technical overview. It will then go on to discuss the various benefits the Real Estate industry can reap from this technology. It will also put forth various use cases or opportunities in the Real Estate sector for this technology. In the end, the paper will also discuss the present-day limitations and the questions marks over this technology in relation to its usage for the Real Estate industry.
Multi-granularity Brushstrokes Network for Universal Style Transfer

Neural style transfer has been developed in recent years, where both performance and efficiency have been greatly improved. However, most existing methods do not transfer the brushstrokes information of style images well. In this article, we address this issue by training a multi-granularity brushstrokes network based on a parallel coding structure. Specifically, we first adopt the content parsing module to obtain the spatial distribution of content image and the smoothness of different regions. Then, different brushstrokes features are transformed by a multi-granularity style-swap module guided by the region content map. Finally, the stylized features of the two branches are fused to enhance the stylized results. The multi-granularity brushstrokes network is jointly supervised by a new multi-layer brushstroke loss and pre-existing loss. The proposed method is close to the artistic drawing process. In addition, we can control whether the color of the stylized results tend to be the style image or the content image. Experimental results demonstrate the advantage of our proposed method compare with the existing schemes.
Towards Low-Latency Implementation of Linear Layers

Lightweight cryptography features a small footprint and/or low computational complexity. Low-cost implementations of linear layers usually play an important role in lightweight cryptography. Although it has been shown by Boyar et al. that finding the optimal implementation of a linear layer is a Shortest Linear Program (SLP) problem and NP-hard, there exist a variety of heuristic methods to search for near-optimal solutions. This paper considers the low-latency criteria and focuses on the heuristic search of lightweight implementation for linear layers. Most of the prior approach iteratively combines the inputs (of linear layers) to reach the output, which can be regarded as the forward search. To better adapt the low-latency criteria, we propose a new framework of backward search that attempts to iteratively split every output (into an XORing of two bits) until all inputs appear. By bounding the time of splitting, the new framework can find a sub-optimal solution with a minimized depth of circuits.We apply our new search algorithm to linear layers of block ciphers and find many low-latency candidates for implementations. Notably, for AES Mixcolumns, we provide an implementation with 103 XOR gates with a depth of 3, which is among the best hardware implementations of the AES linear layer. Besides, we obtain better implementations in XOR gates for 54.3% of 4256 Maximum Distance Separable (MDS) matrices proposed by Li et al. at FSE 2019. We also achieve an involutory MDS matrix (in M4(GL(8, F2))) whose implementation uses the lowest number (i.e., 86, saving 2 from the state-of-the-art result) of XORs with the minimum depth.
LASense

Acoustic signals have been widely adopted in sensing fine-grained human activities, including respiration monitoring, finger tracking, eye blink detection, etc. One major challenge for acoustic sensing is the extremely limited sensing range, which becomes even more severe when sensing fine-grained activities. Different from the prior efforts that adopt multiple microphones and/or advanced deep learning techniques for long sensing range, we propose a system called LASense, which can significantly increase the sensing range for fine-grained human activities using a single pair of speaker and microphone. To achieve this, LASense introduces a virtual transceiver idea that purely leverages delicate signal processing techniques in software. To demonstrate the effectiveness of LASense, we apply the proposed approach to three fine-grained human activities, i.e., respiration, finger tapping and eye blink. For respiration monitoring, we significantly increase the sensing range from the state-of-the-art 2 m to 6 m. For finer-grained finger tapping and eye blink detection, we increase the state-of-the-art sensing range by 150% and 80%, respectively.
Collaborative Society

The MIT Press Essential Knowledge series provide the reader with accessible, concise, yet interesting and completely up-to-date information. (Some examples: Neuroplasticity, Critical Thinking, Anticorruption etc.) Each part was written by excellent experts on the subject, in a language understood by non-experts, too. In this way, the current research data and results in the field of eachtopic can be really used. Nowadays, it is not easy to find in the endless set of information obtainable on the World Wide Web those that essentially provide the fundamental knowledge on a particular topic. The MIT series fill a gap in this. The topic of the present volume of the series is the Collaborative Society by Dariusz Jemielnaiak &amp; Aleksandra Przegalinska. And where does human cooperation come from? ‘Being collaborative distinguishes us as human even more than our opposable thumbs; the drive to cooperate significantly sets us apart from chimpanzees, our closest cousins in terms of DNA similarity, and this difference is already visible in young children. In fact, cooperation with nonkin, so typical for humans, is relatively rare in the animal kingdom.’ (Jemielnaiak &amp; Przegalinska, 2020). Whether new technologies, for example online communication tools, help to develop a more positive and stronger human cooperation or amplify the currently existing social differences? The authors examine these key important questions.
WearSign

Sign language translation (SLT) is considered as the core technology to break the communication barrier between the deaf and hearing people. However, most studies only focus on recognizing the sequence of sign gestures (sign language recognition (SLR)), ignoring the significant difference of linguistic structures between sign language and spoken language. In this paper, we approach SLT as a spatio-temporal machine translation task and propose a wearable-based system, WearSign, to enable direct translation from the sign-induced sensory signals into spoken texts. WearSign leverages a smartwatch and an armband of ElectroMyoGraphy (EMG) sensors to capture the sophisticated sign gestures. In the design of the translation network, considering the significant modality and linguistic gap between sensory signals and spoken language, we design a multi-task encoder-decoder framework which uses sign glosses (sign gesture labels) for intermediate supervision to guide the end-to-end training. In addition, due to the lack of sufficient training data, the performance of prior studies usually degrades drastically when it comes to sentences with complex structures or unseen in the training set. To tackle this, we borrow the idea of back-translation and leverage the much more available spoken language data to synthesize the paired sign language data. We include the synthetic pairs into the training process, which enables the network to learn better sequence-to-sequence mapping as well as generate more fluent spoken language sentences. We construct an American sign language (ASL) dataset consisting of 250 commonly used sentences gathered from 15 volunteers. WearSign achieves 4.7% and 8.6% word error rate (WER) in user-independent tests and unseen sentence tests respectively. We also implement a real-time version of WearSign which runs fully on the smartphone with a low latency and energy overhead.
Multi-Dimensional and Objective Assessment of Motion Sickness Susceptibility Based on Machine Learning

Background As human transportation, recreation, and production methods change, the impact of motion sickness (MS) on humans is becoming more prominent. The susceptibility of people to MS can be accurately assessed, which will allow ordinary people to choose comfortable transportation and entertainment and prevent people susceptible to MS from entering provocative environments. This is valuable for maintaining public health and the safety of tasks. Objective To develop an objective multi-dimensional MS susceptibility assessment model based on physiological indicators that objectively reflect the severity of MS and provide a reference for improving the existing MS susceptibility assessment methods. Methods MS was induced in 51 participants using the Coriolis acceleration stimulation. Some portable equipment were used to digitize the typical clinical manifestations of MS and explore the correlations between them and Graybiel's diagnostic criteria. Based on significant objective parameters and selected machine learning (ML) algorithms, several MS susceptibility assessment models were developed, and their performances were compared. Results Gastric electrical activity, facial skin color, skin temperature, and nystagmus are related to the severity of MS. Among the ML assessment models based on these variables, the support vector machine classifier had the best performance with an accuracy of 88.24%, sensitivity of 91.43%, and specificity of 81.25%. Conclusion The severity of symptoms and signs of MS can be objectively quantified using some indicators. Multi-dimensional and objective assessment models for MS susceptibility based on ML can be successfully established.