Reviewerly/exHarmony huggingface dataset:
Triple-Sigmoid Activation Function for Deep Open-Set Recognition

Traditional models for various machine learning problems such as image classification perform well only under the assumption of a closed set. This implies that inputs must belong to the classes for which the models were trained. Data collected in the real world may not belong to any finite set of classes, and training a model with an infinite number of classes would obviously be impossible. Rather than incorrectly classifying outlier inputs that belong to unknown classes as members of one of the classes on which the model was trained, learning models must recognize and reject such data samples, or request human assistance in labeling them. For example, when a self-driving vehicle detects unfamiliar scenes or objects, it must notify the driver and hand over control. Various models have been proposed to address the open-set problem. However, the existing models are generally complex or use complicated techniques such as generative adversarial networks and auto-encoders, and their efficiency is not proportional to their complexity. In this study, we propose Triple-Sigmoid as a simple activation function comprised of three Sigmoid functions. Using Triple-Sigmoid in the last activation layer of any deep neural network model enables the model to recognize outliers. Although Triple-Sigmoid can be applied to a variety of machine learning problems, including semi-supervised learning, active learning, and incremental learning, we only investigated the open-set recognition problem in this work. The results of numerous experiments are presented to validate that substituting Triple-Sigmoid for conventional activations such as Softmax and Sigmoid not only retained performance in closed-set settings, but also significantly improved performance in open-set configurations. Furthermore, these results demonstrate that Triple-Sigmoid significantly outperformed existing state-of-the-art methods. Source code for the proposed model is available at https://github.com/dinhtuantran/triple-sigmoid.
Incremental Multilayer Broad Learning System With Stochastic Configuration Algorithm for Regression

Broad learning system (BLS) is a novel randomized learning framework which has a faster modeling efficiency. Although BLS with incremental learning has a better extendibility for updating model rapidly, the incremental mode of BLS lacks a self-supervision mechanism which cannot adjust the structure adaptively. Learning from the idea of stochastic configuration network (SCN), a novel incremental multilayer BLS based on the stochastic configuration (SC) algorithm is proposed for regression, termed as IMLBLS-SC. First, to improve the feature learning ability, the SC algorithm is adopted to configure the parameters of enhancement nodes instead of random weights. Second, the multilayer model with enhancement nodes can be added gradually according to the supervision mechanism without human intervention. Third, all the enhancement nodes and feature nodes are fully connected with output nodes. Finally, two function approximation problems and eight classical data sets are selected to verify the regression performance of IMLBLS-SC, experimental results demonstrate that IMLBLS-SC outperforms the random vector functional-link neural network, SCN, BLS, and broad SCN.
KQI Assessment of VR Services: A Case Study on 360-Video Over 4G and 5G

Extended Reality (XR) arises as one of the current cutting-edge educational and entertainment emergent technologies. This service differs from traditional video streaming approaches due to its immersive experience, which allows the user to enjoy omnidirectional multimedia. However, the service experience must be guaranteed to avoid side effects such as cybersickness or disorientation. This work presents a framework to assess 360-video service streaming performance over mobile networks through Key Quality Indicators (KQIs) using VR (Virtual Reality) HMD (Head Mounted Device). The testbed is composed of a 360-video client for DASH (Dynamic Adaptive Streaming over HTTP), which playbacks multimedia content from a video server located in the cloud while KQI measuring tasks are performed in the user as on the network sides. Various metrics are collected such as resolution, frame rate, initial playback time, throughput, stall events, and round trip time (RTT), among various others. Finally, a performance comparison between LTE and 5G technologies is provided. Results from the KQI measurement highlight the potential of the new generation of mobile networks in the provision of service with high-quality levels of experience.
JPEG Reversible Data Hiding Using Dynamic Distortion Optimizing With Frequency Priority Reassignment

JPEG Reversible data hiding (RDH) aims at correctly extracting hidden data and perfectly recovering the original JPEG image. By combining different frequency selection and block ordering manners, appropriate AC coefficients can be determined to achieve an efficient JPEG RDH scheme using histogram shifting (HS) mechanism. Nevertheless, existing methods only rely on rough frequency band distortion model by involving all DCT blocks, ignoring the dynamic influence of block ordering on embedding block selection. As a result, JPEG image containing hidden data may have an obvious distortion and file size expansion due to an inappropriate frequency band selection. To address the aforementioned problem, we design a new JPEG RDH scheme to optimize distortion model with frequency priority reassignment. Our method firstly orders all DCT blocks by referring to zero AC coefficients of each block. Subsequently, based on the given secret data, only partial ordered blocks are selected to calculate the distortion of each DCT frequency, which can further optimize unit distortion caused by shiftable coefficients. Furthermore, we employ the adjustment parameter to adaptively reassign the priority of each frequency band based on its influence on file size expansion, which can significantly reduce file size expansion of the marked JPEG image. Extensive experiments demonstrate that our method outperforms existing JPEG RDH methods with better visual quality and smaller file size increment of the marked image.
Legal Aspects of IDS: Data Sovereignty—What Does It Imply?

Abstract The claim of data sovereignty is inherently linked to putting the legal instruments and tools in the hands of each participant in the ecosystem, allowing freedom of contract as well as ensuring that exercising data exchange and consorted data usage in the data economy is in compliance with general and specific regulations, ranging from anti-trust to GDPR and cyber-security regulations as well as sector specific regulations. The IDS provides a framework and a technology to allow the parties to limit their transaction costs and to ensure effective enforcement through the concept of usage control. In a future world, this will include increased automation of contract execution (conclusion, performance, and enforcement), whereas the steps to reach that goal are plentiful and, as of now, still require to “set the scene” with the means of the traditional contractual agreements. This article provides an overview and orientation on the key legal areas and aspects to consider for stakeholders, participants, and the business more generally and in the application of the IDS architecture.
Nonminimality of the Realizations and Possessing State Matrices With Integer Elements in Linear Discrete-Time Controllers

It is known that discrete-time controllers, whose state matrices have no noninteger element, are beneficial in homomorphic-based encrypted control systems. Nevertheless, it has been recently shown that possessing state matrices with integer elements usually yields unstable discrete-time controllers. In this article, we investigate the problem from a nonminimality perspective. It is shown that nonminimal realizations, in comparison to minimal ones, can theoretically provide a wider framework to obtain controllers having state matrices with integer elements. However, in the case of dealing with bounded-input bounded-output (BIBO) stable controllers, this framework cannot preserve internal stability. But, benefiting from the introduced framework, a class of unstable controllers is introduced, which can be realized by state-space forms having state matrices with integer elements. Numerical examples are presented to verify the usefulness of the introduced framework in the realization of unstable controllers with integer state matrices.
Named Entity Recognition for Chinese Electronic Medical Records Based on Multitask and Transfer Learning

Current work on named entities for Chinese electronic medical records requires training a separate model for each different type of electronic medical record, the performance of which depends on the amount of training data available for each dataset. However, different types of electronic medical records share similar semantic information with each other, while current models do not take full advantage of this potentially common knowledge. To overcome the mentioned problem, we propose a multi-task learning framework to transfer multiple types of electronic medical records through a shared encoder. Experiments demonstrate that our model achieves substantially better performance compared with the single-task model based on BERT. F1 scores improved by more than 1% on average across the four datasets, with individual datasets improving precision by more than 3.5%. Further analysis shows that our model still achieves better F1 scores on long tail datasets and small size datasets.
Document Recapture Detection Based on a Unified Distortion Model of Halftone Cells

In recent years, digital copies of paper documents are used widely with the prevalence of various online services. As a result, it is critical to validate the authenticity of the uploaded document images to protect against attacks from malicious users. Out of various types of attacks, the recapture attack (by reprinting and recapturing) is effective in concealing the trace of document forgeries. However, detecting the recaptured document images is challenging. To address this problem, we first study the halftone cell distortion introduced in both the genuine and recaptured document images. Based on our study, a unified model that characterizes the halftone cell distortion (e.g., errors in size and displacement) is then proposed for accurate estimation of the distortion parameters. The statistics of the estimated parameters are then exploited in a hypothesis testing framework to detect the recaptured document images. The questioned document image can be authenticated by testing against the null hypothesis, i.e., the image is a genuine sample. To evaluate the performance of the proposed approach under different application scenarios, extensive experiments are conducted with different prior knowledge of printers (known printer model, known printing technique, and In-The-Wild (unknown printing device and document contents)). The experiment results show that the proposed approach outperforms the data-driven benchmark approaches by a significant margin. Specifically, under the In-The-Wild experiment protocol, the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of the proposed approach is above 0.87 while the AUC of the benchmark approaches (even some utilize both genuine and recaptured samples) degrades to less than 0.77.
Multimodal Semantic Consistency-Based Fusion Architecture Search for Land Cover Classification

Multimodal Land Cover Classification (MLCC) using the optical and Synthetic Aperture Radar (SAR) modalities has resulted in outstanding performances over using only unimodal data due to their complementary information on land properties. Previous multimodal deep learning (MDL) methods have relied on handcrafted multi-branch convolutional neural networks (CNN) to extract the features of different modalities and merged them for land cover classification. However, natural images-oriented handcrafted CNN models may not the optimal strategies to handle Remote Sensing (RS) image interpretation problems, due to the huge difference in terms of imaging angles and imaging ways. Furthermore, few MDL methods have analyzed optimal combinations of hierarchical features from different modalities. In this article, we propose an efficient multimodal architecture search framework, namely Multimodal Semantic Consistency-Based Fusion Architecture Search (M<sup>2</sup>SC-FAS) in continuous search space with the gradient-based optimization method, which can not only discover optimal optical- and SAR-specific architectures according to the different characteristics of the optical and SAR images, respectively, but also realizes the search of optimal multimodal dense fusion architecture. Specifically, the semantic-consistency constraint is introduced to guarantee dense fusion between hierarchical optical and SAR features with high semantic consistency and then capture the complementary performance on land properties. Finally, the basis of curriculum learning strategy is adopted on the M<sup>2</sup>SC-FAS. Extensive experiments show superior performances of our work on three broad co-registered optical and SAR datasets.
FASNet: Feature Aggregation and Sharing Network for Image Inpainting

Image inpainting is a reconstruction method, where a corrupted image consisting of holes is filled with the most relevant contents from the valid region of an image. To inpaint an image, we have proposed a lightweight cascaded architecture with <i>2.5M parameters</i> consisting of encoder feature aggregation block (FAB) with decoder feature sharing (DFS) inpainting network followed by a refinement network. Initially, the FAB with DFS (inpainting) generator network is proposed which comprises of multi-level feature aggregation mechanism and feature sharing decoder. The FAB makes use of multi-scale spatial channel-wise attention to fuse weighted features from all the encoder levels. The DFS reconstructs the inpainted image with multi-scale and multi-receptive feature sharing in order to inpaint the image with smaller to larger hole regions effectively. Further, the refinement generator network is proposed for refining the inpainted image from the inpainting generator network. The effectiveness of proposed architecture is verified on CelebA-HQ [1], [2], Paris Street View (PARIS_SV) [3] and Places2 [4] datasets corrupted using publicly available NVIDIA mask dataset [5]. Extensive result analysis with detailed ablation study prove the robustness of the proposed architecture over state-of-the-art methods for image inpainting.
Are link-based and citation-based journal metrics correlated? An Open Access megapublisher case study

Abstract The current value of link counts as supplementary measures of the formal quality and impact of journals is analyzed, considering an open access megapublisher (MDPI) as a case study. We analyzed 352 journals through 21 citation-based and link-based journal-level indicators, using Scopus (523,935 publications) and Majestic (567,900 links) as data sources. Given the statistically significant strong positive Spearman correlations achieved, it is concluded that link-based indicators mainly reflect the quality (indexed in Scopus), size (publication output), and impact (citations received) of MDPI’s journals. In addition, link data are significantly greater for those MDPI journals covering many subjects (generalist journals). However, nonstatistically significant differences are found between subject categories, which can be partially attributed to the “series title profile” effect of MDPI. Further research is necessary to test whether link-based indicators can be used as informative measures of journals’ current research impact beyond the specific characteristics of MDPI.
Entity alignment based on relational semantics augmentation for multilingual knowledge graphs

Seeking similar entity pairs pointing to their counterpart real-world objects in knowledge graphs (KGs) is one of the most challenging and critical steps for entity alignment (EA), also known as KG integration. EA has prompted the development of knowledge-based technologies such as recommender systems, semantic search engines, chatbot systems, and knowledge reasoning. Recent years have witnessed increasing interest in representation learning-based entity alignment methods. Most of these represent different KG entities as low-dimensional vector embeddings via their neighborhood structure and then find counterpart entities by estimating the similarities between entity representations. However, most of the studies on the mainstream methods for entity alignment have paid little attention to abundant information on entity one-hop bidirectional neighbors and relational semantics centrality in KGs. Due to the heterogeneity of the knowledge graph, it is difficult to achieve satisfactory alignment results for entity alignment methods based on structure encoding. This paper investigates multilingual entity alignment strategies and proposes a novel relational semantics augmentation (RSA) model to alleviate these issues. RSA can fuse the entity’s bidirectional neighborhood path information and their connected relational semantics contexts. Comprehensive experiments on five benchmark EA datasets with the comparison of state-of-the-art entity alignment methods demonstrate the better performance of our RSA model.
Improving Nurse and Patient Experiences with Voice-Controlled Intelligent Personal Assistants

Our large quaternary-care medical center partnered with a technology start-up company to implement an innovative project using voice-controlled intelligent personal assistants (VIPAs) to improve patient communication workflow, medication education and ultimately hospital efficiency. Specifically, the team utilized 2<sup>nd</sup> generation Amazon Echo dots for the VIPAs. Nursing leaders and frontline staff developed a workflow map of patient requests, identifying tasks for specific roles with timed escalation. From December 2019 to September 2021, 746 VIPA devices documented hundreds of thousands of interactions. Most patient requests were for music and other entertainment (84%), followed by requests to control the television (9%), which the VIPA handled through Aiva's integration with Sonifi, Cedars-Sinai's patient experience platform. Nursing-related requests accounted for 3%. The VIPAs streamlined workflow by directly addressing entertainment and non-medical-related requests, allowing nurses to focus on health care–related issues and thus practice at the top of their scope.
Towards a unified framework for identity documents analysis and recognition

Identity documents recognition is far beyond classical optical character recognition problems. Automated ID document recognition systems are tasked not only with the extraction of editable and transferable data but with performing identity validation and preventing fraud, with an increasingly high cost of error. A significant amount of research is directed to the creation of ID analysis systems with a specific focus for a subset of document types, or a particular mode of image acquisition, however, one of the challenges of the modern world is an increasing demand for identity document recognition from a wide variety of image sources, such as scans, photos, or video frames, as well as in a variety of virtually uncontrolled capturing conditions. In this paper, we describe the scope and context of identity document analysis and recognition problem and its challenges; analyze the existing works on implementing ID document recognition systems; and set a task to construct a unified framework for identity document recognition, which would be applicable for different types of image sources and capturing conditions, as well as scalable enough to support large number of identity document types. The aim of the presented framework is to serve as a basis for developing new methods and algorithms for ID document recognition, as well as for far more heavy challenges of identity document forensics, fully automated personal authentication and fraud prevention.
A Feature Selection-Based K-NN Model for Fast Software Defect Prediction

Software Defect Prediction (SDP) is an advanced technological method of predicting software defects in the software development life cycle. Various research works have been previously being done on SDP but the performance of these methods varied from several datasets, hence, making them inconsistent for SDP in the unknown software project. But the hybrid technique using feature selection enabled with machine learning for SDP can be very efficient as it takes the advantage of various methods to come up with better prediction accuracy for a given dataset when compared with an individual classifier. The major issues with individual ML-based models for SDP are the long detection time, vulnerability of the software project, and high dimensionality of the feature parameters. Therefore, this study proposes a hybrid model using a feature selection enabled Extreme Gradient Boost (XGB) classifier to address these mentioned challenges. The cleaned NASA MDP datasets were used for the implementation of the proposed model, and various performance metrics like F-score, accuracy, and MCC were used to reveal the performance of the model. The results of the proposed model when compared with state-of-the-art methods without feature selection perform better in terms of the metrics used. The results reveal that the proposed model outperformed all other prediction techniques.
NFT Performance and Security Review

In this article we review NFT token architecture and metadata storage options. We show the main disadvantages and risks of the existing NFT architecture and infrastructure. NFT minting performance was analysed on various distributed ledger technologies. The main problem of NFT technology that we highlight is its associated metadata storage options. Since in-secure Web2 based servers are allowed as a storage solution data that is associated with the NFT tokens can be corrupted, modified and lost. Thus harming the end user. We see the main solution for such issues in data blockchains proliferation as they would provide data immutability.
A Systematic Literature Review of Question Answering: Research Trends, Datasets, Methods

Answering questions, finding the most appropriate answer to the question given by the user as input are among the important tasks of natural language processing. Many studies have been done on question answering and datasets, methods have been published. The aim of this article is to reveal the studies done in question answering and to identify the missing research topics. In this literature review, it is tried to determine the datasets, methods and frameworks used for question answering between 2000 and 2022. From the articles published between these years, 91 papers are selected based on inclusion and exclusion criteria. This systematic literature review consists of research analyzes such as research questions, search strategy, inclusion and exclusion criteria, data extraction. We see that the selected final study focuses on four topics. These are Natural Language Processing, Information Retrieval, Knowledge Base, Hybrid Based.