Reviewerly/exHarmony huggingface dataset:
Deep Learning for Automatic Classification of Identification Documents

This paper presents a general approach for identification documents classification using deep learning models. Our study gives an explanation of the main steps that need to be followed in order to implement a classification deep learning model. We have used convolution neural networks to extract features from raw image pixels on private datasets of identification documents. The implemented models use different techniques to preprocess the images in order to improve the classification performance on the test dataset and also techniques that can offer a better generalization of the models on the classification task. The experiments demonstrate that the training time-efficiency and accuracy of the models depends on the size, numbers of the pattern for each category and type of the image preprocessing. Various techniques of optimization have been applied to improve the model’s performance and as a result we achieved the best classification accuracy of 90.4% on the test dataset.
User profiling based on sentiment analysis and text topic features

In order to better improve the service quality of scenic spots and the travel experience of users, this paper uses sentiment analysis and text theme features to construct scenic spot portraits and user portraits and establishes the coupling relationship between scenic spot portraits and user portraits. The application of scenic spot portraits and user portraits can help tourism companies better understand the characteristics and needs of users and destinations, improve the personalization and accuracy of tourism products and services, and enhance the market competitiveness and development potential of enterprises.
A Predictive Model for Tactile Force Estimation using Audio-Tactile Data

Robust in-hand manipulation of objects with movable content requires estimation and prediction of the contents' motion with enough anticipation to allow time to compensate for resulting internal torques. The quick estimation of the objects' dynamics can be challenging when the objects' motion properties (e.g., type, amount, dynamics) cannot be observed visually due to robot occlusions or opacity of the container. This can be further complicated by the computational requirements of onboard hardware available for real-time processing and control for robotics. In this work, we develop a simple learning framework that uses echo state networks to predict the torques experienced on the robotic hand with enough anticipation to allow for adaptive controls and sufficient efficiency for real-time prediction without GPU processing. We demonstrate the efficacy of this formulation for tactile force prediction on the Allegro robotic hand with a Tekscan tactile skin using both material-specific and material-agnostic learned models. We show that while both are effective, the material-specific models show an improvement in accuracy due to the difference in inertial properties between the different materials. We also develop a prediction model that uses audio feedback to augment the tactile predictions. We show that adding auditory feedback improves the prediction error, though it significantly increases the computation cost of the model. We validate this formulation for online prediction on the robotic hand moving materials in real-time and adapting grip for slip detection.
Presence of VGKC complex: CASPR2/LGI1 antibodies in hydrocele fluid in Morvan’s syndrome (1117)

Saturday, April 17April 13, 2021Free AccessPresence of VGKC complex: CASPR2/LGI1 antibodies in hydrocele fluid in Morvan’s syndrome (1117)Sanjay Sharma, Pranita Sharma, and Chhatrapal Singh SahuAuthors Info & AffiliationsApril 13, 2021 issue96 (15_supplement)https://doi.org/10.1212/WNL.96.15_supplement.1117 Letters to the Editor
Primary Brain Tumor Patients Experience Significant Sleep Disturbance; An Analysis of Clinical Characteristics and Sleep Parameters (4488)

Saturday, April 17April 13, 2021Free AccessPrimary Brain Tumor Patients Experience Significant Sleep Disturbance; An Analysis of Clinical Characteristics and Sleep Parameters (4488)Zaid Soomro, Kristin Alfaro, Dave Balachandran, Charles Bornstein, Meliza Perez, Juhee Song, Garret Williford, Ying Yuan, John De Groot, and Alen JuginovicAuthors Info & AffiliationsApril 13, 2021 issue96 (15_supplement)https://doi.org/10.1212/WNL.96.15_supplement.4488 Letters to the Editor
Structured light vision based pipeline tracking and 3D reconstruction method for underwater vehicle

The inspection of underwater pipeline by underwater vehicles is of great significance to ensure safe transportation. However, most of underwater pipeline inspection methods have disadvantages such as low precision, low resolution and less information, and cannot realize the fine three-dimensional (3D) reconstruction of underwater pipelines. In order to address these problems, an underwater pipeline tracking and 3D reconstruction method for underwater vehicle based on structured light vision (SLV) is proposed. Firstly, a dual-line laser SLV is developed, and a new underwater pipeline positioning method is proposed, which can simultaneously obtain the lateral deviation, height deviation and heading deviation of underwater vehicle and underwater pipeline under weak light water environment. Then, by combining laser stripe image feature points, refracted underwater SLV model and Doppler Velocity Log (DVL) information, underwater pipeline tracking and dense 3D reconstruction are realized. Finally, by integrating the self-designed underwater SLV sensor with the underwater vehicle BlueROV, an underwater pipeline tracking and 3D reconstruction system is developed. A series of planar and spatial pipeline tracking and 3D reconstruction experiments demonstrate the effectiveness of the proposed method.
Meta-Heuristic Guided Feature Optimization for Enhanced Authorship Attribution in Java Source Code

Source code authorship attribution is the task of identifying who develops the code based on learning based on the programmer style. It is one of the critical activities which used extensively in different aspects such as computer security, computer law, and plagiarism. This paper attempts to investigate source code authorship attribution by capturing natural language aspects of the code rather than only using minimal set of syntactic and stylistic code features as explored in the previous literature. It proposes an evolutionary feature selection model to improve the accuracy of authorship attribution by implementing two language models (uni-gram and bi-gram). The proposed approach uses K-Nearest Neighbor as a classifier and Genetic Algorithm as a feature selection technique. Two experiments have been demonstrated on a public Authorship Attribution dataset on GitHub, the experiments include various evolutionary feature selection models. Notably, the obtained results in both experiments were compared with the related studies, and show a significant improvement in terms of accuracy.
Towards Digital Sobriety: Why Improving the Energy Efficiency of Video Streaming is Not Enough

IPCC conclusions are unequivocal: we must divide our greenhouse gas emissions by two before 2030 if we want to maintain the global warming below 1.5째C in 2100. Hence, it becomes urgent to aim sobriety. Contrary to what is often claimed, digital technologies must also target global emission reduction, as their impact on the climate is huge and exploding every year. Among the digital world's emissions, those related to video processing and streaming are significant. At the same time, a lot of research efforts are currently done to reduce the energy consumed by video transmission algorithms or infrastructures. In this paper, we demonstrate that, even though such research works are crucial, they are not sufficient to enable global video streaming emissions reductions. The conclusion is that we must collectively think of other complementary solutions.
Semantic Based Generative Compression of Images for Extremely Low Bitrates

We propose a framework for image compression in which the fidelity criterion is replaced by a semantic and quality preservation objective. Encoding the image thus becomes a simple extraction of semantic, enabling to reach drastic compression ratio. The decoding side is handled by a generative model relying on the diffusion process for the reconstruction of images. We first propose to describe the semantic using low resolution segmentation maps as guide. We further improve the generation, introducing colors map guidance without retraining the generative decoder. We show that it is possible to produce images of high visual quality with preserved semantic at extremely low bitrates when compared with classical codecs.
On the Impact of AI-Based Compression on Deep Learning-Based Source Social Network Identification

Recognition of the social network of origin of an image is a relatively recent topic that is part of the techniques that fall under the umbrella of digital image forensics. It consists of the classification of images according to the social network on which they were posted. In contrast with other topics of digital image forensics, there are no works addressing counter forensic for source social network identification. Thus, we analyse the impact of image manipulations on its performances. We focus our study on AI-based compression, which tends to become the new compression solution with the upcoming standard JPEG AI. To conduct a fair analysis, we compare the AI-based compression with the conventional legacy JPEG compression, and also include three other manipulations: median filtering, Gaussian blurring, and additional white Gaussian noise, which are often used to assess the robustness of digital image forensic methods. We define two sets of parameters based on the resulting image quality in terms of structural similarity, which correspond respectively to attacks with strong and limited image degradation. In the context of strong downgrade of the image quality, all the manipulations lead to similar decrease in performance, while for attacks that preserve image quality, AI-based compression is able to reach a drop in identification rate twice higher than the other manipulations.
From VMs to Containers

In this chapter, we will explain the benefits of deploying an application via containers vs. the traditional method of using VMs while observing the benefits containers offer over the VMs for solving the same problem set.
CLEANSE – Cluster-based Undersampling Method

Class imbalance is a common problem with datasets relating to various areas of life. It causes many traditional machine learning algorithms to tend to misclassify minority samples as majority ones. Despite various studies, the class imbalance still remains a relevant problem for which no one-size-fits-all solution has been found. In this paper, an undersampling method based on clustering is presented. In the proposed approach K-means algorithm is used to cluster data. In homogenous "majority" clusters, i.e., clusters containing objects of only the majority class, objects within the specified distance from the center are removed. In the case of non-homogeneous clusters, objects located at the class decision boundary are removed using the KNN algorithm. As tests have shown, the clustering-based solution can improve classification quality. The results of experiments show that in many cases the proposed solution outperformed other undersampling techniques described in the literature.
Publish/Subscribe Method for Real-Time Data Processing in Massive IoT Leveraging Blockchain for Secured Storage

In the Internet of Things (IoT) era, the surge in Machine-Type Devices (MTDs) has introduced Massive IoT (MIoT), opening new horizons in the world of connected devices. However, such proliferation presents challenges, especially in storing and analyzing massive, heterogeneous data streams in real time. In order to manage Massive IoT data streams, we utilize analytical database software such as Apache Druid version 28.0.0 that excels in real-time data processing. Our approach relies on a publish/subscribe mechanism, where device-generated data are relayed to a dedicated broker, effectively functioning as a separate server. This broker enables any application to subscribe to the dataset, promoting a dynamic and responsive data ecosystem. At the core of our data transmission infrastructure lies Apache Kafka version 3.6.1, renowned for its exceptional data flow management performance. Kafka efficiently bridges the gap between MIoT sensors and brokers, enabling parallel clusters of brokers that lead to more scalability. In our pursuit of uninterrupted connectivity, we incorporate a fail-safe mechanism with two Software-Defined Radios (SDR) called Nutaq PicoLTE Release 1.5 within our model. This strategic redundancy enhances data transmission availability, safeguarding against connectivity disruptions. Furthermore, to enhance the data repository security, we utilize blockchain technology, specifically Hyperledger Fabric, known for its high-performance attributes, ensuring data integrity, immutability, and security. Our latency results demonstrate that our platform effectively reduces latency for 100,000 devices, qualifying as an MIoT, to less than 25 milliseconds. Furthermore, our findings on blockchain performance underscore our model as a secure platform, achieving over 800 Transactions Per Second in a dataset comprising 14,000 transactions, thereby demonstrating its high efficiency.
Innovative and entrepreneurial education reform strategy based on algorithmic recommendation of social media information sharing characteristics in colleges and universities

Abstract In this paper, the student documents are processed by experimental strategy, with the topic of college innovation and entrepreneurship resources recommendation, word diffusion and improved LDA topic model distribution training, set the training topic model and the number of topic top-words, and compare the high-frequency vocabulary and the probability distribution error of the 80% user coverage case and 90% user coverage case. Explore the stability of social media information pushing, the performance comparison results of both combine LDA based on user comment information text and LDA modeling directly using user comment information text as a reference. Select the experimental objects of innovation and entrepreneurship resources in colleges and universities, obtain the recommendation information of innovation and entrepreneurship resources in social media, and analyze the accuracy and satisfaction of algorithmic recommendation. Analyze the sharing characteristics of algorithmic recommendation information on social media and propose educational countermeasures against its adverse effects. According to the analysis, the improved LDA topic model results in a 15% and 85% increase in the number of same words in each topic when top_words is 10. When top_words is 20, the proportion of the same words in each theme is 15% and 78%, respectively. This indicates that the innovation and entrepreneurship education topic features are stable, and the accuracy and satisfaction of the algorithmic recommendation have improved.
Methods for Developing Semantically Oriented Virtual Assistants

Nowadays, various technologies are used to create intelligent assistants, based both on research in the field of neural networks, natural language text analysis tools, and on the use of semantic modeling tools. Each of these approaches allows you to qualitatively solve certain problems. As part of this work, an intelligent assistant is being developed that combines all these approaches. The purpose of the work is to create an intelligent assistant that performs as a virtual consultant on the organization’s work processes. The development is based on the use of the semantic model of the organization and business processes. To recognize user intents, we use homomorphic and generalized user intents. The system allows decomposing user tasks and creating a consistency of their execution based on the user semantic models and the subject area.
Strongest nonlocal sets with small sizes

A set of orthogonal states is the strongest nonlocal set if it is locally stable in every bipartition, which shows the strong quantum nonlocality proposed by Halder et al. [Phys. Rev. Lett. 122, 040403 (2019)]. The existence of the strongest nonlocal sets with the minimum size is an open question. In this work, we partially solve this question by constructing the strongest nonlocal sets with the minimum size in $2\ensuremath{\bigotimes}2\ensuremath{\bigotimes}2\ensuremath{\bigotimes}2$ and $2\ensuremath{\bigotimes}{d}_{2}\ensuremath{\bigotimes}{d}_{3}$, where $2\ensuremath{\le}{d}_{2}\ensuremath{\le}{d}_{3}$. Moreover, we also give the strongest nonlocal sets with size ${d}_{2}{d}_{3}+{d}_{1}\ensuremath{-}1$ in ${d}_{1}\ensuremath{\bigotimes}{d}_{2}\ensuremath{\bigotimes}{d}_{3}$ and the strongest nonlocal sets with size ${d}^{3}+d\ensuremath{-}1$ in $d\ensuremath{\bigotimes}d\ensuremath{\bigotimes}d\ensuremath{\bigotimes}d$. All the sizes of the strongest nonlocal sets are close to the minimum size and are smaller than all previously known constructions. As an application, our strongest nonlocal sets can be used to construct partially genuinely entangled subspaces in ${d}_{1}\ensuremath{\bigotimes}{d}_{2}\ensuremath{\bigotimes}{d}_{3}$ when $2\ensuremath{\le}{d}_{1}\ensuremath{\le}{d}_{2}\ensuremath{\le}{d}_{3}$ and ${d}_{2}\ensuremath{\ge}3$.
Deep Learning-Based Video Compression for Surveillance Footage

The utilization of closed-circuit television (CCTV) monitoring plays a pivotal role in the realm of video processing, providing an effective means for vigilant surveillance. However, a significant challenge associated with this practice is the substantial demand it places on storage space. Traditionally, surveillance footage is stored on hard disk drives, and due to limited storage capacities, it often necessitates periodic deletion. To tackle this issue, we have introduced an innovative method for compressing CCTV video, named “Detection-Based video Compression” (DBVC). Our DBVC model is a two-step process. In the first step, we employ advanced neural network approaches such as Mask-RCNN and YOLOv4 to determine active and idle frames within the surveillance video. These cutting-edge techniques enable precise identification of objects and events of interest in the video feed. In the second step, we construct a new video composed solely of the active frames, eliminating redundant or uneventful segments. After conducting a comprehensive analysis of the experimental results, it is evident that Mask-RCNN stands out with an impressive detection accuracy of 98% on the COCO dataset, making it a robust choice for identifying objects and events in the surveillance footage. Consequently, we chose to leverage the output generated by Mask-RCNN and YOLOv4 for subsequent processing stages. Our DBVC approach is a breakthrough in video compression technology, significantly reducing the storage space required for CCTV footage. In fact, it achieves an average compression ratio of up to 85% when using YOLOv4, surpassing the capabilities of existing state-of-the-art compression methods. This innovation not only optimizes storage efficiency but also maintains a high level of surveillance data integrity, making it a valuable advancement in the field of CCTV video processing and storage management.
Improving sentiment analysis on PeduliLindungi comments: a comparative study with CNN-Word2Vec and integrated negation handling

This study investigates sentiment analysis in Google Play reviews of the PeduliLindungi application, focusing on the integration of negation handling into text preprocessing and comparing the effectiveness of two prominent methods: CNN-Word2Vec CBOW and CNN-Word2Vec SkipGram. Through a meticulous methodology, negation handling is incorporated into the preprocessing phase to enhance sentiment analysis. The results demonstrate a noteworthy improvement in accuracy for both methods with the inclusion of negation handling, with CNN-Word2Vec SkipGram emerging as the superior performer, achieving an impressive 76.2% accuracy rate. Leveraging a dataset comprising 13,567 comments, this research introduces a novel approach by emphasizing the significance of negation handling in sentiment analysis. The study not only contributes valuable insights into the optimization of sentiment analysis processes but also provides practical considerations for refining methodologies, particularly in the context of mobile application reviews.
Machine to Machine Authenticated Key Agreement with Forward Secrecy for Internet of Things

Internet of things (IoT), is the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data. The communication is through the internet hence susceptible to security and privacy attacks. Consequently, authenticated key agreement (AKA) of communicating entities in IoT is of paramount importance as a security and privacy credential. However, IoT devices have resource-constrained feature, hence implementation of heavy security and privacy features becomes a challenge. Research on AKA in IoT has been done since year 2006. Current research trends on AKA are together with forward secrecy (FS) feasibility, which ensures that future SKs remain safe even if the long-term master keys get compromised. However, most of researches use public key cryptosystems to achieve FS, which requires heavy computations that is not good for the resource-constrained IoT environment. The main purpose of this Thesis is to devise a new machine AKA with FS for IoT, denoted as M2MAKA-FS. To design M2MAKA-FS, we devise a new lightweight FS framework first, which does not rely on the public key cryptosystem but based on a hash chain. The security and privacy building blocks of M2MAKA-FS and the FS framework are symmetric key cryptosystem, one-way hash function, fuzzy commitment and challenge-response mechanism. Results of formal security and privacy analysis show that M2MAKA-FS provides mutual authentication, SK agreement with FS, anonymity and unlinkability and is resilient against various active attacks. Performance analysis shows that M2MAKA-FS achieves the lightweight requirements for IoT environments compared to the related protocols.
Not Wacky vs. Definitely Wacky: A Study of Scalar Adverbs in Pretrained Language Models

Vector-space models of word meaning all assume that words occurring in similar contexts have similar meanings. Words that are similar in their topical associations but differ in their logical force tend to emerge as semantically close – creating well-known challenges for NLP applications that involve logical reasoning. Pretrained language models such as BERT, RoBERTa, GPT-2, and GPT-3 hold the promise of performing better on logical tasks than classic static word embeddings. However, reports are mixed about their success. Here, we advance this discussion through a systematic study of scalar adverbs, an under-explored class of words with strong logical force. Using three different tasks involving both naturalistic social media data and constructed examples, we investigate the extent to which BERT, RoBERTa, GPT-2 and GPT-3 exhibit knowledge of these common words. We ask: 1) Do the models distinguish amongst the three semantic categories of MODALITY, FREQUENCY and DEGREE? 2) Do they have implicit representations of full scales from maximally negative to maximally positive? 3) How do word frequency and contextual factors impact model performance? We find that despite capturing some aspects of logical meaning, the models still have obvious shortfalls.