Reviewerly/exHarmony huggingface dataset:
A New Coupled Method of SINS/DVL Integrated Navigation Based on Improved Dual Adaptive Factors

The integrated navigation of strap-down inertial navigation system (SINS) and Doppler velocity logs (DVL) has the common application in the positioning of autonomous underwater vehicle (AUV). However, DVL may be interrupted for a short time when part of the beam cannot receive the reflected signal and DVL measurement information is easily affected by underwater complex environment and contains outliers. To solve the above problems, a Doppler shifts aided coupled method of SINS/DVL integrated navigation with pare of DVL beam measurements outages based on dual adaptive factors is proposed. In this paper, a tightly coupled approach for SINS/DVL based on Doppler shifts is proposed to solve the short-term failure problem caused by the lack of part measurement in DVL. Then a chi-square detection aided dual factors adaptive filter is used to suppress the outliers. The dual factors are used to adjust the influence of imprecise information of dynamic model and observation model error. The chi-square detection is used to judge the outliers of measurements and the result of judgment is regarded as the precondition of factor selection. In order to verify the robustness of the proposed algorithm to outliers, simulation and Yangtze River experiments are carried out. The results of simulation and river experiments indicate that the tightly coupled approach obtain higher accuracy compared by the loosely coupled model and the proposed adaptive filter can effectively suppress the outliers. The method proposed in this paper can achieve greater robustness in AUV underwater complex environment.
Post-Pandemic Education: Falling or Preparing for a Jump?

Abstract. Relevance. The article provides an overview and analysis of the consequences of changes in the global educational space during the period of action of measures to counter the spread of the new COVID-19 coronavirus infection. The aim of the work was to understand the current state and identify possible trajectories of development of educational systems in the “post-pandemic” era. Research methodology and methods. The main methods used in the preparation of the work were methods of analysis and synthesis. The review and understanding of various standpoints on the educational organizations functioning in the situation of emergency introduction of quarantine measures and the uncertainty of their consequences allowed us to summarize the main challenges facing the education system today. The forecasting method was used to systematize the capabilities of universities in overcoming crisis phenomena and determine the strengths that can be used as the basis for the transformation of University management. Results. The analysis of the trajectories of the prospective organization of the educational process at the University of “post-pandemic” time is carried out. The article highlights the main ways of the educational system adaptation to work in the conditions of an unpredictable crisis consequences and presents the authors’ vision of the future of higher education in Russia, which is inextricably linked with the country’s attractiveness in the global educational space. Scientific novelty. The authors systematize the impact of the pandemic on various sectors of the economy, including the education sector. The results of the analysis of the practical experience of educational systems in the new conditions are presented. Scenarios of educational organizations development in an unfavorable dynamic uncertain environment are shown. Practical significance. The results obtained will be useful in adjusting University management systems and developing strategies for universities to overcome the crisis caused by COVID-19 and its consequences.
Voxel-Based 3D Object Reconstruction from Single 2D Image Using Variational Autoencoders

In recent years, learning-based approaches for 3D reconstruction have gained much popularity due to their encouraging results. However, unlike 2D images, 3D cannot be represented in its canonical form to make it computationally lean and memory-efficient. Moreover, the generation of a 3D model directly from a single 2D image is even more challenging due to the limited details available from the image for 3D reconstruction. Existing learning-based techniques still lack the desired resolution, efficiency, and smoothness of the 3D models required for many practical applications. In this paper, we propose voxel-based 3D object reconstruction (V3DOR) from a single 2D image for better accuracy, one using autoencoders (AE) and another using variational autoencoders (VAE). The encoder part of both models is used to learn suitable compressed latent representation from a single 2D image, and a decoder generates a corresponding 3D model. Our contribution is twofold. First, to the best of the authors’ knowledge, it is the first time that variational autoencoders (VAE) have been employed for the 3D reconstruction problem. Second, the proposed models extract a discriminative set of features and generate a smoother and high-resolution 3D model. To evaluate the efficacy of the proposed method, experiments have been conducted on a benchmark ShapeNet data set. The results confirm that the proposed method outperforms state-of-the-art methods.
DR-Net: dual-rotation network with feature map enhancement for medical image segmentation

Abstract To obtain more semantic information with small samples for medical image segmentation, this paper proposes a simple and efficient dual-rotation network (DR-Net) that strengthens the quality of both local and global feature maps. The key steps of the DR-Net algorithm are as follows (as shown in Fig. 1). First, the number of channels in each layer is divided into four equal portions. Then, different rotation strategies are used to obtain a rotation feature map in multiple directions for each subimage. Then, the multiscale volume product and dilated convolution are used to learn the local and global features of feature maps. Finally, the residual strategy and integration strategy are used to fuse the generated feature maps. Experimental results demonstrate that the DR-Net method can obtain higher segmentation accuracy on both the CHAOS and BraTS data sets compared to the state-of-the-art methods.
When Idling is Ideal

This paper introduces Perséphone, a kernel-bypass OS scheduler designed to minimize tail latency for applications executing at microsecond-scale and exhibiting wide service time distributions. Perséphone integrates a new scheduling policy, Dynamic Application-aware Reserved Cores (DARC), that reserves cores for requests with short processing times. Unlike existing kernel-bypass schedulers, DARC is not work conserving. DARC profiles application requests and leaves a small number of cores idle when no short requests are in the queue, so when short requests do arrive, they are not blocked by longer-running ones. Counter-intuitively, leaving cores idle lets DARC maintain lower tail latencies at higher utilization, reducing the overall number of cores needed to serve the same workloads and consequently better utilizing the datacenter resources.
Decolonizing Technology and Society: A Perspective from the Global South

Abstract Despite the large impact of digital technology on the lives and future of all people on the planet, many people, especially from the Global South, are not included in the debates about the future of the digital society. This inequality is a systemic problem which has roots in the real world. We refer to this problem as “digital coloniality.” We argue that to achieve a more equitable and inclusive global digital society, active involvement of stakeholders from poor regions of the world as co-researchers, co-creators, and co-designers of technology is required. We briefly discuss a few collaborative, community-oriented technology development projects as examples of transdisciplinary knowledge production and action research for a more inclusive digital society.
Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy

Abstract Subtle differences in a patient’s genetics and physiology may alter radiotherapy (RT) treatment responses, motivating the need for a more personalized treatment plan. Accordingly, we have developed a novel quantum deep reinforcement learning (qDRL) framework for clinical decision support that can estimate an individual patient’s dose response mid-treatment and recommend an optimal dose adjustment. Our framework considers patients’ specific information including biological, physical, genetic, clinical, and dosimetric factors. Recognizing that physicians must make decisions amidst uncertainty in RT treatment outcomes, we employed indeterministic quantum states to represent human decision making in a real-life scenario. We paired quantum decision states with a model-based deep q-learning algorithm to optimize the clinical decision-making process in RT. We trained our proposed qDRL framework on an institutional dataset of 67 stage III non-small cell lung cancer (NSCLC) patients treated on prospective adaptive protocols and independently validated our framework in an external multi-institutional dataset of 174 NSCLC patients. For a comprehensive evaluation, we compared three frameworks: DRL, qDRL trained in a Qiskit quantum computing simulator, and qDRL trained in an IBM quantum computer. Two metrics were considered to evaluate our framework: (1) similarity score, defined as the root mean square error between retrospective clinical decisions and the AI recommendations, and (2) self-evaluation scheme that compares retrospective clinical decisions and AI recommendations based on the improvement in the observed clinical outcomes. Our analysis shows that our framework, which takes into consideration individual patient dose response in its decision-making, can potentially improve clinical RT decision-making by at least about 10% compared to unaided clinical practice. Further validation of our novel quantitative approach in a prospective study will provide a necessary framework for improving the standard of care in personalized RT.
Adaptive Selection of Reference Frames for Video Object Segmentation

Video object segmentation is a challenging task in computer vision because the appearances of target objects might change drastically along the time in the video. To solve this problem, space-time memory (STM) networks are exploited to make use of the information from all the intermediate frames between the first frame and the current frame in the video. However, fully using the information from all the memory frames may make STM not practical for long videos. To overcome this issue, a novel method is developed in this paper to select the reference frames adaptively. First, an adaptive selection criterion is introduced to choose the reference frames with similar appearance and precise mask estimation, which can efficiently capture the rich information of the target object and overcome the challenges of appearance changes, occlusion, and model drift. Secondly, bi-matching (bi-scale and bi-direction) is conducted to obtain more robust correlations for objects of various scales and prevents multiple similar objects in the current frame from being mismatched with the same target object in the reference frame. Thirdly, a novel edge refinement technique is designed by using an edge detection network to obtain smooth edges from the outputs of edge confidence maps, where the edge confidence is quantized into ten sub-intervals to generate smooth edges step by step. Experimental results on the challenging benchmark datasets DAVIS-2016, DAVIS-2017, YouTube-VOS, and a Long-Video dataset have demonstrated the effectiveness of our proposed approach to video object segmentation.
Mapping the quantity, quality and structural indicators of Asian (48 countries and 3 territories) research productivity on cloud computing

Purpose The purpose of this study was to map the quantity (frequency), quality (impact) and structural indicators (correlations) of research produced on cloud computing in 48 countries and 3 territories in the Asia continent. Design/methodology/approach To achieve the objectives of the study and scientifically map the indicators, data were extracted from the Scopus database. The extracted bibliographic data was first cleaned properly using Endnote and then analyzed using Biblioshiny and VosViewer application software. In the software, calculations include citations count; h , g and m indexes; Bradford's and Lotka's laws; and other scientific mappings. Findings Results of the study indicate that China remained the most productive, impactful and collaborative country in Asia. All the top 20 impactful authors were also from China. The other most researched areas associated with cloud computing were revealed to be mobile cloud computing and data security in clouds. The most prominent journal currently publishing research studies on cloud computing was “Advances in Intelligent Systems and Computing.” Originality/value The study is the first of its kind which identified the quantity (frequencies), quality (impact) and structural indicators (correlations) of Asian (48 countries and 3 territories) research productivity on cloud computing. The results are of great importance for researchers and countries interested in further exploring, publishing and increasing cross country collaborations related to the phenomenon of cloud computing.
Blockchain-Aided Edge Computing Market: Smart Contract and Consensus Mechanisms

Building upon the prevailing concept of edge computing (EC), a distributed EC market requires decentralized and verified transaction management to trade computing resources.Towards this goal, we study a blockchain-aided EC market wherein each data service operator (DSO) rents a group of edge computing nodes (ECNs) and leases the ECNs to the user terminals (UTs) to provide computation offloading services.A trustworthiness model is introduced to evaluate the quality of each network entity throughout the transactions.We develop a two-level trading mechanism over smart contract to enable the automatic and efficient transactions among the network entities and provide high quality services.First, we propose a smart contract based matching mechanism to establish the renting association between the DSOs and ECNs with the aim of maximizing the social welfare.Second, we propose a social welfare improved double auction (SWIDA) mechanism to build up the leasing association between the DSOs and UTs, and determine the pricing of the winners.We show that the proposed double auction mechanism can achieve individual rationality, balanced budget, truthfulness in expectation, and an improved social welfare than the benchmark mechanisms.Moreover, we put forth a trustworthiness driven Proof-of-Stake (PoS) consensus mechanism to enable verified transaction and fair allocation of block generation reward.Following the principle of PoS, we formulate the block generation as a coalitional game, wherein each stakeholder votes according to its trustworthiness and coinage, and shares the reward among the coalition according to the Shapley values.The simulation results show that the proposed PoS consensus mechanism can reduce the wealth inequality among the network entities compared with the conventional consensus mechanisms.
Information and Communication Technologies (ICT) in the processes of distribution and use of knowledge in Higher Education Institutions (HEIs)

This research aims to relate Information and Communication Technologies (ICT) in the processes of distribution and use of knowledge in Higher Education Institutions (HEI). The methodology used was based on the quantitative approach, descriptive scope, and non-experimental cross-sectional design, the sample was non-probabilistic, convenience sampling. We used the survey as a technique for data collection, through a questionnaire with Likert and ordinal scales applied to the directors and vice-rectors of research in the research departments. The methodology developed allowed us to show that the profiles of the universities to carry out knowledge management focus on two components: distribution and use of knowledge.
An unsupervised generative adversarial network based-host intrusion detection system for internet of things devices

Machine learning (ML) and deep learning (DL) have achieved amazing progress in diverse disciplines. One of the most efficient approaches is unsupervised learning (UL), a sort of algorithms for analyzing and clustering unlabeled data; it allows identifying hidden patterns or performing data clustering over provided data without the need for human involvement. There is no prior knowledge of actual abnormalities when using UL methods in anomaly detection (AD); hence, a DL-intrusion detection system (IDS)- based on AD depends intensely on their assumption about the distribution of anomalies. In this paper, we propose a novel unsupervised AD Host-IDS for internet of things (IoT) based on adversarial training architecture using the generative adversarial network (GAN). Our proposed IDS, called “EdgeIDS”, targets mostly IoT devices because of their limited functionality; IoT devices send and receive only specific data, not like traditional devices, such as servers or computers that exchange a wide range of data. We benchmarked our proposed “EdgeIDS” on the message queuing telemetry transport (MQTTset) dataset with five attack types, and our obtained results are promising, up to 0.99 in the ROC-AUC metric, and to just 0.035 in the ROC-EER metric. Our proposed technique could be a solution for detecting cyber abnormalities in the IoT.
MEduKG: A Deep-Learning-Based Approach for Multi-Modal Educational Knowledge Graph Construction

The popularity of information technology has given rise to a growing interest in smart education and has provided the possibility of combining online and offline education. Knowledge graphs, an effective technology for knowledge representation and management, have been successfully utilized to manage massive educational resources. However, the existing research on constructing educational knowledge graphs ignores multiple modalities and their relationships, such as teacher speeches and their relationship with knowledge. To tackle this problem, we propose an automatic approach to construct multi-modal educational knowledge graphs that integrate speech as a modal resource to facilitate the reuse of educational resources. Specifically, we first propose a fine-tuned Bidirectional Encoder Representation from Transformers (BERT) model based on education lexicon, called EduBERT, which can adaptively capture effective information in the education field. We also add a Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) to effectively identify educational entities. Then, the locational information of the entity is incorporated into BERT to extract the educational relationship. In addition, to cover the shortage of traditional text-based knowledge graphs, we focus on collecting teacher speech to construct a multi-modal knowledge graph. We propose a speech-fusion method that links these data into the graph as a class of entities. The numeric results show that our proposed approach can manage and present various modes of educational resources and that it can provide better education services.
Efficient Graph Convolution for Joint Node Representation Learning and Clustering

Attributed graphs are used to model a wide variety of real-world networks. Recent graph convolutional network-based representation learning methods have set state-of-the-art results on the clustering of attributed graphs. However, these approaches deal with clustering as a downstream task while better performances can be attained by incorporating the clustering objective into the representation learning process. In this paper, we propose, in a unified framework, an objective function taking into account both tasks simultaneously. Based on a variant of the simple graph convolutional network, our model does clustering by minimizing the difference between the convolved node representations and their reconstructed cluster representatives. We showcase the efficiency of the derived algorithm against state-of-the-art methods both in terms of clustering performance and computational cost on thede facto benchmark graph clustering datasets. We further demonstrate the usefulness of the proposed approach for graph visualization through generating embeddings that exhibit a clustering structure.
Crystal structure optimization approach to problem solving in mechanical engineering design

Purpose In this paper, the authors aim to examine and comparatively evaluate a recently-developed metaheuristic called crystal structure algorithm (CryStAl) – which is inspired by the symmetries in the internal structure of crystalline solids – in solving engineering mechanics and design problems. Design/methodology/approach A total number of 20 benchmark mathematical functions are employed as test functions to evaluate the overall performance of the proposed method in handling various functions. Moreover, different classical and modern metaheuristic algorithms are selected from the optimization literature for a comparative evaluation of the performance of the proposed approach. Furthermore, five well-known mechanical design examples are utilized to examine the capability of the proposed method in dealing with challenging optimization problems. Findings The results of this study indicated that, in most cases, CryStAl produced more accurate outputs when compared to the other metaheuristics examined as competitors. Research limitations/implications This paper can provide motivation and justification for the application of CryStAl to solve more complex problems in engineering design and mechanics, as well as in other branches of engineering. Originality/value CryStAl is one of the newest metaheuristic algorithms, the mathematical details of which were recently introduced and published. This is the first time that this algorithm is applied to solving engineering mechanics and design problems.
FlatMagic: Improving Flat Colorization through AI-driven Design for Digital Comic Professionals

Creating digital comics involves multiple stages, some creative and some menial. For example, coloring a comic requires a labor-intensive stage known as 'flatting,' or masking segments of continuous color, as well as creative shading, lighting, and stylization stages. The use of AI can automate the colorization process, but early efforts have revealed limitations—technical and UX—to full automation. Via a formative study of professionals, we identify flatting as a bottleneck and key target of opportunity for human-guided AI-driven automation. Based on this insight, we built FlatMagic, an interactive, AI-driven flat colorization support tool for Photoshop. Our user studies found that using FlatMagic significantly reduced professionals' real and perceived effort versus their current practice. While participants effectively used FlatMagic, we also identified potential constraints in interactions with AI and partially automated workflows. We reflect on implications for comic-focused tools and the benefits and pitfalls of intermediate representations and partial automation in designing human-AI collaboration tools for professionals.