Reviewerly/exHarmony huggingface dataset:
A comprehensive review on Arabic word sense disambiguation for natural language processing applications

Abstract In communication, textual data are a vital attribute. In all languages, ambiguous or polysemous words' meaning changes depending on the context in which they are used. The ability to determine the ambiguous word's correct meaning is a Know‐distill challenging task in natural language processing (NLP). Word sense disambiguation (WSD) is an NLP process to analyze and determine the correct meaning of polysemous words in a text. WSD is a computational linguistics task that automatically identifies the polysemous word's set of senses. Based on the context some word comes into view, WSD recognizes and tags the word to its correct priori known meaning. Semitic languages like Arabic have even more significant challenges than other languages since Arabic lacks diacritics, standardization, and a massive shortage of available resources. Recently, many approaches and techniques have been suggested to solve word ambiguity dilemmas in many different ways and several languages. In this review paper, an extensive survey of research works is presented, seeking to solve Arabic word sense disambiguation with the existing AWSD datasets. This article is categorized under: Algorithmic Development &gt; Text Mining Technologies &gt; Machine Learning
Adaptive DRL-based Virtual Machine Consolidation in Energy-Efficient Cloud Data Center

The dramatic increasing of data and demands for computing capabilities may result in excessive use of resources in cloud data centers, which not only causes the raising of energy consumption, but also leads to the violation of Service Level Agreement (SLA). Dynamic consolidation of virtual machines (VMs) is proven to be an efficient way to tackle this issue. In this paper, we present an Adaptive Deep Reinforcement Learning (DRL)-based Virtual Machine Consolidation (ADVMC) framework for energy-efficient cloud data centers. ADVMC has two phases. In the first phase, Influence Coefficient is introduced to measure the impact of a VM on producing host overload, and a dynamic Influence Coefficient-based VM selection algorithm (ICVMS) is proposed to preferentially choose those VMs with the greatest impact for migration in order to remove the excessive workloads of the overloaded host quickly and accurately. In the second phase, a Prediction Aware DRL-based VM placement method (PADRL) is further proposed to automatically find suitable hosts for VMs to be migrated, in which a state prediction network is designed based on LSTM to provide DRL-based model more reasonable environment states so as to accelerate the convergence of DRL. Simulation experiments on the real-world workload provided by Google Cluster Trace have shown that our ADVMC approach can largely cut down system energy consumption and reduce SLA violation of users as compared to many other VM consolidation policies.
Moving target defense for the security and resilience of mixed time and event triggered cyber–physical systems

Memory corruption attacks such as code injection, code reuse, and non-control data attacks have become widely popular for compromising safety-critical Cyber–Physical Systems (CPS). Moving target defense (MTD) techniques such as instruction set randomization (ISR), address space randomization (ASR), and data space randomization (DSR) can be used to protect systems against such attacks. CPS often use time-triggered architectures to guarantee predictable and reliable operation. MTD techniques can cause time delays with unpredictable behavior. To protect CPS against memory corruption attacks, MTD techniques can be implemented in a mixed time and event-triggered architecture that provides capabilities for maintaining safety and availability during an attack. This paper presents a mixed time and event-triggered MTD security approach based on the ARINC 653 architecture that provides predictable and reliable operation during normal operation and rapid detection and reconfiguration upon detection of attacks. We leverage a hardware-in-the-loop testbed and an advanced emergency braking system (AEBS) case study to show the effectiveness of our approach.
Development and Application of Digital Maxillofacial Surgery System Based on Mixed Reality Technology

To realize the three-dimensional visual output of surgical navigation information by studying the cross-linking of mixed reality display devices and high-precision optical navigators.Applying quaternion-based point alignment algorithms to realize the positioning configuration of mixed reality display devices, high-precision optical navigators, real-time patient tracking and calibration technology; based on open source SDK and development tools, developing mixed reality surgery based on visual positioning and tracking system. In this study, four patients were selected for mixed reality-assisted tumor resection and reconstruction and re-examined 1 month after the operation. We reconstructed postoperative CT and use 3DMeshMetric to form the error distribution map, and completed the error analysis and quality control.Realized the cross-linking of mixed reality display equipment and high-precision optical navigator, developed a digital maxillofacial surgery system based on mixed reality technology and successfully implemented mixed reality-assisted tumor resection and reconstruction in 4 cases.The maxillofacial digital surgery system based on mixed reality technology can superimpose and display three-dimensional navigation information in the surgeon's field of vision. Moreover, it solves the problem of visual conversion and space conversion of the existing navigation system. It improves the work efficiency of digitally assisted surgery, effectively reduces the surgeon's dependence on spatial experience and imagination, and protects important anatomical structures during surgery. It is a significant clinical application value and potential.
Adaptive grid-based forest-like clustering algorithm

This paper presents an adaptive grid-based clustering algorithm called as “AGFC”, which uses a forest-like query structure to sequentially discovers multiple arbitrary-shaped clusters from the grid. The main advantage of AGFC is that it can effectively generate a reasonable grid division with a simple startup parameter. This method determines the appropriate grid division width through the minimum gap between the peaks and valleys of the density curve in a specific dimension, which depends on the distribution of the sample, to overcome the subjectivity of manual determination to a certain extent. Furthermore, in the forest-like query structure, it constructs a “Aggregation Judgment” criterion for high-density cells to find out the possible clusters through the merging of cells. Finally, using the “Re-clustering process” to eliminate very small clusters and further repairing the edge areas of the main clusters. The experimental results show that the proposed method can obtain competitive results under the premise of automatically determining the grid.
Healthcare robots and human generations: Consequences for nursing and healthcare

<h2>Abstract</h2><h3>Background</h3> Intelligent machines reinforce global technological reliance, maintaining their primacy in healthcare environments. Healthcare robots as intelligent machines foster proficient healthcare, although not much is known about these outcomes. Generation Z, Millennials, and Baby Boomers are expected to value healthcare robots more significantly. <h3>Aim</h3> This opinion paper explores healthcare reliance on technologies particularly intelligent healthcare robots and examines the utilitarian functionalities of technologies impacted by human generations. <h3>Methods</h3> Discursive contents were derived from relevant literature surveyed during the past decade regarding healthcare robots, intelligent machines, and human generational consequences. <h3>Findings</h3> Generational effects of healthcare robots existed. Despite being technology-natives, Generation "Z" were more adaptive, from uncertainty and emotional attachments to fearful engagements; Millennials were more trusting of robots while Baby boomers as target markets of robots showed broad-minded acceptance, especially after experiencing them. Three theories of nursing are advanced to explain the primacy of healthcare robot technologies; Transactive Relationship Theory of Nursing, Model for the Intermediary Role of Nurses in Transactive Relationships with Healthcare Robots and Technological Competency as Caring in Nursing. <h3>Discussion</h3> Reliance on technologies is a significant and egalitarian consequence of technologies, and AI offers inimitable generational consequences. Technology-dependency reflective of generational focus on effectiveness and impact on healthcare foster responsive nursing practice. Prioritising healthcare robot efficiencies in practice engenders nursing as integral to human health and well-being. <h3>Conclusion</h3> Consequences of healthcare robots in practice should distinguish generational positions. Professional nurses and multidisciplinary researchers need to legitimise healthcare robots as integral to nursing and human healthcare.
Second-order statistics of fermionic Gaussian states

Abstract We study the statistical behavior of entanglement in quantum bipartite systems over fermionic Gaussian states as measured by von Neumann entropy and entanglement capacity. The focus is on the variance of von Neumann entropy and the mean entanglement capacity that belong to the so-defined second-order statistics. The main results are the exact yet explicit formulas of the two considered second-order statistics for fixed subsystem dimension differences. We also conjecture the exact variance of von Neumann entropy valid for arbitrary subsystem dimensions. Based on the obtained results, we analytically study the numerically observed phenomena of Gaussianity of von Neumann entropy and linear growth of average capacity.
Visual Relationship Detection: A Survey

Visual relationship detection (VRD) is one newly developed computer vision task, aiming to recognize relations or interactions between objects in an image. It is a further learning task after object recognition, and is important for fully understanding images even the visual world. It has numerous applications, such as image retrieval, machine vision in robotics, visual question answer (VQA), and visual reasoning. However, this problem is difficult since relationships are not definite, and the number of possible relations is much larger than objects. So the complete annotation for visual relationships is much more difficult, making this task hard to learn. Many approaches have been proposed to tackle this problem especially with the development of deep neural networks in recent years. In this survey, we first introduce the background of visual relations. Then, we present categorization and frameworks of deep learning models for visual relationship detection. The high-level applications, benchmark datasets, as well as empirical analysis are also introduced for comprehensive understanding of this task.
Combining Clinical Symptoms and Patient Features for Malaria Diagnosis: Machine Learning Approach

Presumptive treatment and self-medication for malaria have been used in limited-resource countries. However, these approaches have been considered unreliable due to the unnecessary use of malaria medication. This study aims to demonstrate supervised machine learning models in diagnosing malaria using patient symptoms and demographic features. Malaria diagnosis dataset extracted in two regions of Tanzania: Morogoro and Kilimanjaro. Important features were selected to improve model performance and reduce processing time. Machine learning classifiers with the k-fold cross-validation method were used to train and validate the model. The dataset developed a machine learning model for malaria diagnosis using patient symptoms and demographic features. A malaria diagnosis dataset of 2556 patients' records with 36 features was used. It was observed that the ranking of features differs among regions and when combined dataset. Significant features were selected, residence area, fever, age, general body malaise, visit date, and headache. Random Forest was the best classifier with an accuracy of 95% in Kilimanjaro, 87% in Morogoro and 82% in the combined dataset. Based on clinical symptoms and demographic features, a regional-specific malaria predictive model was developed to demonstrate relevant machine learning classifiers. Important features are useful in making the disease prediction.
Novel and secure plaintext‐related image encryption algorithm based on compressive sensing and tent‐sine system

In this paper, a secure plaintext-related image encryption scheme based on compressive sensing and a tent-sine system is proposed. First, the discrete wavelet transform (DWT) is used to transform the plain image to get a coefficient matrix. Second, several chaotic sequences generated by the tent-sine chaotic map are used to scramble the coefficient matrix and construct a measurement matrix. Afterward, compressive sensing is performed on the coefficient matrix to obtain a small-sized encrypted image. Finally, an image encryption scheme related to plaintext is designed. In particular, the proposed system uses the original image information to participate in the encryption process, ensuring the high sensitivity of the cryptosystem to minor differences in the plain image and good performance on resisting known/selected plaintext attacks. Furthermore, to convey the plaintext-related parameters to the receiver, the dimension of the ciphertext image is expanded, and the parameters are embedded into the ciphertext image. Simulation results and security analysis show that the proposed image encryption system has strong plaintext sensitivity and robustness for effectively resisting various typical attacks such as brute-force attacks, statistical attacks, and differential attacks.
On Addressing Practical Challenges for RNN-Transducer

In this paper, several works are proposed to address practi-cal challenges for deploying RNN Transducer (RNN-T) based speech recognition systems. These challenges are adapting a well-trained RNN-T model to a new domain without col-lecting the audio data, obtaining time stamps and confidence scores at word level. We solve the first challenge with a splicing data method which concatenates the speech segments ex-tracted from the source domain data. To get time stamps, a phone prediction branch is added to the RNN-T model by sharing the encoder for the purpose of forced alignment. Fi-nally, we obtain word level confidence scores by utilizing sev-eral types of features calculated during decoding and from a confusion network. Evaluated with Microsoft production data, the splicing data adaptation method improves the base-line and adaptation with the text to speech method by 58.03% and 15.25% relative word error rate reduction, respectively. The proposed time stamping method can get less than 50 mil-lisecond word timing difference from the ground truth align-ment on average while maintaining the recognition accuracy. We also obtain high confidence annotation performance with limited computation cost.
Personalized Federated Learning

Owing to the universal availability of 5G networking networks, both business and academia have started to explore 6G interchanges. 6G is widely intended to be based on artificial intelligence's pervasiveness in order to accomplish machine learning dependent on evidence (ML) arrangements in a diverse environment and large-scale organizations (AI). Traditional machine learning procedures, on the other side, include centralized data storage and processing by a single employee. Escalating security issues have increasingly become restriction on a wide range of applications in daily life. Federated learning, as a growing distributed AI method with a security-conscious design, is particularly appealing for a variety of remote applications and is being viewed as one of the critical solutions for achieving universal AI in 6G. We'll go into the partnership between 6G and federated learning in this article, as well as some future 6G federated learning implementations. Then, in relation to 6G interchanges, we go through the main technical problems, compare federated learning approaches, and answer unanswered questions for potential federated learning analysis.
CoVAC: A P2P smart contract-based intelligent smart city architecture for vaccine manufacturing

With the Corona Virus Disease 2019 (COVID-19) outbreak, vaccination is an urgent need worldwide. Internet of Things (IoT) has a vital role in the smart city for vaccine manufacturing with wearable sensors. According to the advanced services in intelligent manufacturing, the fourth resolution is also changing in Industry 5.0 and utilizes high-definition connectivity sensors. Traditional manufacturing companies rely on trusted third parties, which may act as a single point of failure. Access control, big data, and scalability are also challenging issues in existing systems because of the demand response data (DRD) in advanced manufacturing. To mitigate these challenges, CoVAC: A P2P Smart Contract-based Intelligent Smart City Architecture for Vaccine Manufacturing is proposed with three layers, including connection, conversion, and intelligent cloud layer. Smart contract-based blockchain is utilized at the conversion layer for resolving access control, security, and privacy issues. Deep learning is adopted in the intelligent cloud layer for big data analysis and increasing production for vaccine manufacturing in smart city environments. A case study is carried out wherein access data are collected from the various smart plants for vaccines using smart manufacturing to validate the effectiveness of the proposed architecture. Simulation of the proposed architecture is performed on the collected advanced sensor IoT plants data to address the challenges above, offering scalable production in the vaccine manufacturing for the smart city.
A Proposal-Free One-Stage Framework for Referring Expression Comprehension and Generation via Dense Cross-Attention

Referring Expression Comprehension (REC) and Generation (REG) have become one of the most important tasks in visual reasoning, since it is an essential step for many vision-and-language tasks such as visual question answering or visual dialogue. However, it has not been widely used in many downstream tasks, mainly for the following reasons: 1) mainstream two-stage methods rely on additional annotations or off-the-shelf detectors to generate proposals. It would heavily degrade the generalization ability of models and lead to inevitable error accumulation. 2) Although one-stage strategies for REC have been proposed, these methods have to depend on lots of hyper-parameters (such as anchors) to generate bounding box. In this paper, we present a proposal-free one-stage (PFOS) framework that can directly regress the region-of-interest from the image or generate unambiguous descriptions in an end-to-end manner. Instead of using the dominant two-stage fashion, we take the dense-grid of images as input for a cross-attention transformer that learns multi-modal correspondences. The final bounding box or sentence is directly predicted from the image without the anchor selection or the computation of visual difference. Furthermore, we expand the traditional two-stage listener-speaker framework to jointly train by a one-stage learning paradigm. Our model achieves state-of-the-art performance on both accuracy and speed for comprehension and competitive results for generation.
ETA: An Efficient Training Accelerator for DNNs Based on Hardware-Algorithm Co-Optimization

Recently, the efficient training of deep neural networks (DNNs) on resource-constrained platforms has attracted increasing attention for protecting user privacy. However, it is still a severe challenge since the DNN training involves intensive computations and a large amount of data access. To deal with these issues, in this work, we implement an efficient training accelerator (ETA) on field-programmable gate array (FPGA) by adopting a hardware-algorithm co-optimization approach. A novel training scheme is proposed to effectively train DNNs using 8-bit precision with arbitrary batch sizes, in which a compact but powerful data format and a hardware-oriented normalization layer are introduced. Thus the computational complexity and memory accesses are significantly reduced. In the ETA, a reconfigurable processing element (PE) is designed to support various computational patterns during training while avoiding redundant calculations from nonunit-stride convolutional layers. With a flexible network-on-chip (NoC) and a hierarchical PE array, computational parallelism and data reuse can be fully exploited, and memory accesses are further reduced. In addition, a unified computing core is developed to execute auxiliary layers such as normalization and weight update (WU), which works in a time-multiplexed manner and consumes only a small amount of hardware resources. The experiments show that our training scheme achieves the state-of-the-art accuracy across multiple models, including CIFAR-VGG16, CIFAR-ResNet20, CIFAR-InceptionV3, ResNet18, and ResNet50. Evaluated on three networks (CIFAR-VGG16, CIFAR-ResNet20, and ResNet18), our ETA on Xilinx VC709 FPGA achieves 610.98, 658.64, and 811.24 GOPS in terms of throughput, respectively. Compared with the prior art, our design demonstrates a speedup of 3.65× and an energy efficiency improvement of 8.54× on CIFAR-ResNet20.
Multi-Agent Coverage Path Planning via Proximity Interaction and Cooperation

In multi-agent systems, the decision of an agent will be affected by the behaviors of others. Therefore, from the perspective of an agent, the situation is uncertain and random. Inspired by the social behaviors in the biological world, a novel multi-agent coverage path planning algorithm is proposed. Based on the positions of agents, the problem is decoupled, which can effectively reduce the dimension of the decision space. The behavior-guide-point is introduced to guide agents in making decisions, and a new motion mode is presented. To avoid falling into the local optimum, a cooperation mechanism is designed, which can improve the adaptability of the system. Through proximity interaction, the prediction results obtained via the model predictive control (MPC) technology are fused, evaluated, and sorted within the neighborhood, based on which decisions are gained. The proposed algorithm can handle emergencies in unknown environments such as body damage and moving obstacles, and can also be applied to heterogeneous systems. Simulation shows that compared with other algorithms, it has advantages in terms of the makespan and the coverage repetition rate.
Emoji as Icebreakers? Emoji can signal distinct intentions in first time online interactions

Very few studies have examined if emoji play a role in first-time interactions and whether their use conveys information about relationship initiation intentions. In two experimental studies (N = 758), we explored if using different emoji to initiate interactions with strangers on Facebook and Tinder were perceived to signal distinct intentions from the sender (Studies 1 and 2) and which emoji (if any) would be used to convey distinct intentions when sending a message to a stranger (Study 2). Results from Study 1 showed that participants perceived messages with (or without emoji) to signal friendship intentions, with (or ) to signal romantic intentions, and with (or ) to signal sexual intentions. Results were consistent across social platforms. Study 2 replicated the results regarding the impact of emoji use on perceived intentions. When asked to take the sender’s perspective, participants selected for friendship intentions, or for romantic intentions, and for sexual intentions. Still, about one-third of participants chose not to include emoji in initial interactions. Taken together, these findings suggest that emoji play an important role in online interpersonal communications and potentially shape the initiation of new relationships.