Reviewerly/exHarmony huggingface dataset:
Machine learning–based scoring models to predict hematopoietic stem cell mobilization in allogeneic donors

Mobilized peripheral blood has become the primary source of hematopoietic stem cells for both autologous and allogeneic stem cell transplantation. Granulocyte colony-stimulating factor (G-CSF) is currently the standard agent used in the allogeneic setting. Despite the high mobilization efficacy in most donors, G-CSF requires 4-5 days of daily administration, and a small percentage of the donors fail to mobilize an optimal number of stem cells necessary for a safe allogeneic stem cell transplant. In this study, we retrospectively reviewed 1361 related allogeneic donors who underwent stem cell mobilization at Washington University. We compared the standard mobilization agent G-CSF with five alternative mobilization regimens, including GM-CSF, G-CSF+GM-CSF, GM-CSF + Plerixafor, Plerixafor and BL-8040. Cytokine-based mobilization strategies (G-CSF or in combination with GM-CSF) induce higher CD34 cell yield after 4-5 consecutive days of treatment, while CXCR4 antagonists (plerixafor and BL-8040) induce significantly less but rapid mobilization on the same day. Next, using a large dataset containing the demographic and baseline laboratory data from G-CSF-mobilized donors, we established machine learning (ML)-based scoring models that can be used to predict patients who may have less than optimal stem cell yields after a single leukapheresis session. To our knowledge, this is the first prediction model at the early donor screening stage, which may help identify allogeneic stem cell donors who may benefit from alternative approaches to enhance stem cell yields, thus ensuring safe and effective stem cell transplantation.
Comparison of PPO and SAC Algorithms Towards Decision Making Strategies for Collision Avoidance Among Multiple Autonomous Vehicles

Multiple vehicle collision avoidance strategies with safe lane changing strategy for vehicle control using learning base technique are the most crucial concern in autonomous driving system. Statistics shows that the latest autonomous driving systems are usually prone to rear-end collision. Rear-end collisions often result in severe injuries as well as traffic jam and the consequences are much worse for multiple-vehicle collision. Many previous autonomous driving research focused solely on collision avoidance strategies for two consecutive vehicles. This study proposes a centralised control strategy for multiple vehicles using reinforcement learning focused on partner consideration and goal attainment. The system depicted as a group of vehicles are communicate and coordinate each others by a set of rays and maintain a short following move away. In order to address this challenge, a simulation was implemented in the Unity3D game engine and two state-of-the-art RL algorithms PPO (Proximal Policy Optimization) and SAC (Soft Actor-Critic) were trained by an agent using Unity ML-Agents Toolkit. In terms of success rate, performance, training speed and stability two algorithms are comparable. The potency of algorithms has been assessed by the traffic flow (1) change in vehicle speed, (2) differ in the vehicle beginning positions, and (3) switch to next lane. The agent performed similarly at a 91% success rate in PPO or SAC applications
Blockchain and Artificial Intelligence Applications to Defeat COVID-19 Pandemic

The rapid emergence of novel virus named SARS-CoV2 and unchecked dissemination of this virus around the world ever since its outbreak in 2020, provide critical research criteria to assess the vulnerabilities of our current health system. The paper addresses our preparedness for the management of such acute health emergencies and the need to enhance awareness, about public health and healthcare mechanisms. In view of this unprecedented health crisis, distributed ledger and AI technology can be seen as one of the promising alternatives for fighting against such epidemics at the early stages, and with the higher efficacy. At the implementation level, blockchain integration, early detection and avoidance of an outbreak, identity protection and safety, and a secure drug supply chain can be realized. At the opposite end of the continuum, artificial intelligence methods are used to detect corona effects until they become too serious, avoiding costly drug processing. The paper explores the application of blockchain and artificial intelligence in order to fight with COVID-19 epidemic scenarios. This paper analyzes all possible newly emerging cases that are employing these two technologies for combating a pandemic like COVID-19 along with major challenges which cover all technological and motivational factors. This paper has also discusses the potential challenges and whether further production is required to establish a health monitoring system.
Video summarization with a dual-path attentive network

With the explosive growth of videos captured everyday, how to efficiently extract useful information from videos has become a more and more important problem. As one of the most effective methods, video summarization aiming to extract the most important frames or shots has attracted more interests recently. Currently, lots of methods employ a recurrent structure. However, due to its step-by-step characteristic, it is difficult to parallelize these models. To address this problem, we propose a dual-path attentive video summarization framework consisting of a temporal spatial encoder, a score-aware encoder and a decoder. And all of them are mainly based on multi-head self-attention and convolutional block attention module. The temporal spatial encoder is to capture the temporal and spatial information while the score-aware encoder incorporates the appearance features with previously predicted frame-level importance scores. By combining the scores and appearance features, our model can better capture the long-range global dependencies and update the importance scores of previous frames continuously. Moreover, entirely based on attention mechanism, our model can be trained in full parallel, which leads to less training time. To validate the method, we employ the two popular datasets SumMe and TVSum. The experimental results show the effectiveness of the proposed method.
Augmented reality for learning in special education: a systematic literature review

The use of technology in special education has been rapidly increased in recent years. Augmented reality (AR) is one of these technologies that combines real world and virtual objects together. AR has the potential to be used as a teaching technique for students with special needs to develop their daily lives skills, supporting learning, increasing comfort and confidence and improving physical, navigation skills and social skills. The aim of this systematic review is to investigate studies on the effects of using AR for the education of students with special needs. Specifically, this literature review investigates the research designs of these studies, the disability groups that AR applications used with, the skills that were taught using AR applications, the opinions of the students and teachers regarding the AR application and influence of AR applications on learning performance of individuals with special needs. Findings show that the use of AR has potential advantages and disadvantages for the education of students with special needs.
Possibilities and Challenges of Online Education in India During the COVID-19 Pandemic

The undergraduate and postgraduate studies of colleges and universities in India have been affected badly amidst the lockdowns for COVID-19 pandemic. The Government has insisted to start the academic activity through online platforms. The biggest concern for the academic institutions now is to select an appropriate e-learning platform. This paper compares different features and facilities available in some widely used online platforms and analyze their suitability from the perspective of socio-economic constraints of students in India. A generic framework for conducting online classes is described that meets the special requirements of the unprivileged students. Some strategic plans to overcome the challenges are identified and suggested. A technical solution for implementation of time-bound assessment module is also proposed.
Automatic Hate Speech Detection in English-Odia Code Mixed Social Media Data Using Machine Learning Techniques

Hate speech on social media may spread quickly through online users and subsequently, may even escalate into local vile violence and heinous crimes. This paper proposes a hate speech detection model by means of machine learning and text mining feature extraction techniques. In this study, the authors collected the hate speech of English-Odia code mixed data from a Facebook public page and manually organized them into three classes. In order to build binary and ternary datasets, the data are further converted into binary classes. The modeling of hate speech employs the combination of a machine learning algorithm and features extraction. Support vector machine (SVM), naïve Bayes (NB) and random forest (RF) models were trained using the whole dataset, with the extracted feature based on word unigram, bigram, trigram, combined n-grams, term frequency-inverse document frequency (TF-IDF), combined n-grams weighted by TF-IDF and word2vec for both the datasets. Using the two datasets, we developed two kinds of models with each feature—binary models and ternary models. The models based on SVM with word2vec achieved better performance than the NB and RF models for both the binary and ternary categories. The result reveals that the ternary models achieved less confusion between hate and non-hate speech than the binary models.
Electric Vehicle Charging Station: Cyber Security Challenges and Perspective

The UK government has set a target to reach net zero greenhouse gas emissions by 2050, and many other countries follow the same goal. In order to support the transition to net zero carbon future, Electric Vehicles (EV) can play a significant role. Providing a secure EV charging station is of high importance as the amount and type of data handled and transmitted via EV charging stations is growing and raising concerns both for the grid and consumers. The objective of this paper is to study the current landscape of EV charging stations in terms of cyber security, identify the cyber vulnerabilities, and present protocols and standards that can address cyber security challenges in such systems to provide a more secure charging infrastructure. Finally, this paper recommends the use of some security measures and techniques to mitigate cyber-attacks on EV charging infrastructure and alleviate the adverse impact of such attacks.
Internet of Behavior and Explainable AI Systems for Influencing IoT Behavior

Pandemics and natural disasters over the years have changed the behavior of people, which has had a tremendous impact on all life aspects. With the technologies available in each era, governments, organizations, and companies have used these technologies to track, control, and influence the behavior of individuals for some benefits. Nowadays, the use of the Internet of Things (IoT), cloud computing, and Artificial Intelligence (AI) have made it easier to track and change the behavior of users through changing the behavior of IoT devices. This article introduces and discusses the concept of the Internet of Behavior (IoB) and its integration with Explainable AI (XAI) techniques to provide trusted and evident experience in the process of changing IoT behavior to ultimately influence user behavior. Therefore, We propose a system based on IoB and XAI that aims to influence user behavior and showcase its effectiveness in an electrical power consumption use case. Through our experiments, we are able to demonstrate a reduction in both power consumption and cost. The scenario results showed a decrease of 522.2 kW of active power based on the original historical average consumption over a 200-hours period. It also showed a total power cost saving of €95.04 for the same period.
ICDAR 2021 Competition on Scientific Literature Parsing

Scientific literature contain important information related to cutting-edge innovations in diverse domains. Advances in natural language processing have been driving the fast development in automated information extraction from scientific literature. However, scientific literature is often available in unstructured PDF format. While PDF is great for preserving basic visual elements, such as characters, lines, shapes, etc., on a canvas for presentation to humans, automatic processing of the PDF format by machines presents many challenges. With over 2.5 trillion PDF documents in existence, these issues are prevalent in many other important application domains as well.A critical challenge for automated information extraction from scientific literature is that documents often contain content that is not in natural language, such as figures and tables. Nevertheless, such content usually illustrates key results, messages, or summarizations of the research. To obtain a comprehensive understanding of scientific literature, the automated system must be able to recognize the layout of the documents and parse the non-natural-language content into a machine readable format.Our ICDAR 2021 Scientific Literature Parsing Competition (ICDAR2021-SLP) aims to drive the advances specifically in document understanding. ICDAR2021-SLP leverages the PubLayNet and PubTabNet datasets, which provide hundreds of thousands of training and evaluation examples. In Task A, Document Layout Recognition, submissions with the highest performance combine object detection and specialised solutions for the different categories. In Task B, Table Recognition, top submissions rely on methods to identify table components and post-processing methods to generate the table structure and content. Results from both tasks show an impressive performance and opens the possibility for high performance practical applications.
Named Entity Recognition of BERT-BiLSTM-CRF Combined with Self-attention

Neural network models are often used in Chinese named entity recognition methods, but this model has the problem of too single character vector representation in the training process, and cannot handle the ambiguity of words well. Therefore, this paper uses the pre-trained model BERT (bidirectional encoder representation from transformers) as the embedding layer of the model, adds a self-attention layer, and proposes a Chinese named entity recognition research method based on the Bert-BiLSTM-CRF model combined with self-attention. The semantic vector of the word is enriched according to the context information of the word, and the word vector sequence output by BERT is sent as input to the BiLSTM-CRF model for training. Experimental results show that this method has achieved 94.80%, 95.44% and 95.12% in accuracy, recall and F1 value on the task of Chinese named entity recognition. Compared with other traditional methods, the effect of this method is significantly improved.
Statistical Federated Learning for Beyond 5G SLA-Constrained RAN Slicing

A key enabler for both scalability and sustainability in beyond 5G (B5G) network slicing consists on minimizing the exchange of raw monitoring data across different domains. This is achieved by bringing the analysis functions closer to the data collection points. To this end, we introduce in this paper <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">statistical federated learning</i> (SFL) provisioning models that can learn over a live network non independent identically distributed (non-IID) datasets in an offline fashion while respecting slice-level service level agreement (SLA) long-term statistical constraints. Specifically, we consider three resource SLA metrics, namely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cumulative distribution function</i> (CDF), <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th percentile</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">maximum/minimum bounds</i> . These metrics are dataset-dependent and non-convex non-differentiable and, to sidestep the inaccuracy of settling only for surrogates, we propose a novel formulation that jointly considers the statistical objective and constraints as well as their smooth approximation using the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">proxy-Lagrangian</i> framework, which we solve via a non-zero sum two-player game strategy. Numerical results on various slice-level resources show that SFL enables SLA enforcement while significantly reducing the overhead compared to both state-of-the-art FedAvg and centralized constrained deep learning schemes. Finally, we provide an analysis for the lower bound of the so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reliable convergence probability</i> in the SFL setup.
The coming tsunami of digital artefacts

Contemporary life has become deeply entangled with the digital. Every day, huge quantities of data, or digital artefacts, are generated. To date, the archaeological study of these artefacts has been limited, its potential demonstrated by a few initial studies and, more recently, the emergence of archaeogaming. If archaeology seeks to understand human culture, however, the study of these digital artefacts must become mainstream. This will require collaboration with computer scientists to bring an archaeological sensibility to understanding the virtual. Ultimately, the aims and methods required resemble those that archaeologists have already developed, but require a change of mindset if they are to be widely deployed for the study of digital artefacts.
Mask-RCNN with spatial attention for pedestrian segmentation in cyber–physical systems

With the application of industrial cyber–physical systems in various fields such as transportation systems, smart cities, and medical systems, pedestrian scenarios are becoming more and more complex, which brings difficulties to pedestrian segmentation. The difficulty of pedestrian segmentation lies in the scene's complexity where the pedestrian is located, including the pedestrian's shooting angle, light, and obstructions, which makes it difficult to distinguish accurately. This paper proposes an S-Mask-RCNN network that integrates spatial attention mechanisms for pedestrian segmentation. Mask-RCNN uses residual neural networks in the feature extraction network, and the effect of model feature extraction is not ideal. Based on transfer learning, a spatial attention mechanism is introduced to focus more spatially on areas that need attention. The force mechanism focuses more on the areas that need attention in space. Experiments show that the S-Mask-RCNN method proposed in this paper has better performance than traditional Mask-RCNN in pedestrian segmentation. Experiments show that the S-Mask-RCNN method proposed in this paper has better performance than traditional Mask-RCNN in pedestrian segmentation, also can provide more comprehensive and practical information for the construction of cyber–physical systems.
Incomplete Multi-View Clustering with Reconstructed Views

As one category of important incomplete multi-view clustering methods, subspace based methods seek the common latent representation of incomplete multi-view data by matrix factorization and then partition the latent representation to get clustering results. However, these methods ignore missing views in the process of matrix factorization, which makes the connection of different views be exploited inadequately. This paper proposes Incomplete Multi-view Clustering with Reconstructed Views (IMCRV), which utilizes the incomplete examples sufficiently. In IMCRV, the missing views of incomplete examples are reconstructed and the reconstructed views are also used to seek the common latent representation. IMCRV also involves the Laplacian regularization to preserve the global property of the latent representation. A novel gradient descent method with the multiplicative update rule is designed to solve the objective function of IMCRV. The corresponding iterative algorithm is developed and the convergence of the algorithm is proved. IMCRV is compared with many state-of-the-art incomplete multi-view clustering methods under different Incomplete Example Rates (IER) on public multi-view datasets. The experimental results demonstrate the superior effectiveness of IMCRV.
Mitigating the Backdoor Attack by Federated Filters for Industrial IoT Applications

The federated learning provides an effective solution to train collaborative models over a large scale of participated Industrial Internet of Things (IIoT) applications with the help of a global server, building an intelligent life. However, the federated learning is vulnerable to the backdoor attack from strong malicious participants. The backdoor attack is inconspicuous and may result in devastating consequences. To resist the attack on IIoT applications, we propose the federated backdoor filter defense that can identify backdoor inputs and restore the data to availability by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">blur-label-flipping</i> strategy. We build multiple filters with eXplainable AI models on the server and send them to clients randomly, preventing advanced attackers from evading the defense. Our backdoor filters show significant backdoor recognition with the accuracy up to 99%. After the implementation of the blur-label-flipping strategy, victim's local model on suspicious backdoor samples can achieve the accuracy up to 88%.
Remote Sensing Image Super-Resolution via Residual Aggregation and Split Attentional Fusion Network

Remote sensing images contain various land surface scenes and different scales of ground objects, which greatly increases the difficulty of super-resolution tasks.The existing deep learning-based methods cannot solve this problem well.To achieve high-quality super-resolution of remote sensing images, a residual aggregation and split attentional fusion network (RASAF) is proposed in this article.It is mainly divided into the following three parts.First, a split attentional fusion block is proposed.It uses a basic split-fusion mechanism to achieve cross-channel feature group interaction, allowing the method to adapt to various land surface scene reconstructions.Second, to fully exploit multi-scale image information, a hierarchical loss function is used.Third, residual learning is used to reduce the difficulty of training in super-resolution tasks.However, the respective residual branch features are used quite locally and fail to represent the real value.A residual aggregation mechanism is used to aggregate the local residual branch features to generate higher quality local residual branch features.The comparison of RASAF with some classical super-resolution methods using two widely used remote sensing datasets showed that the RASAF achieved better performance.And it achieves a good balance between performance and model parameter number.Meanwhile, the RASAF's ability to support multi-label remote sensing image classification tasks demonstrates its usefulness.