Reviewerly/exHarmony huggingface dataset:
Incorporating a multiobjective knowledge-based energy function into differential evolution for protein structure prediction

Abstract Despite the rapid development of computer techniques and the unremitting efforts of researchers, the protein structure prediction (PSP) problem remains challenging in computational biology and bioinformatics. In this study, we model the PSP problem as a multiobjective optimization problem and propose a free modeling approach called MODE-K to solve this problem. Our efforts center on two aspects. First, we use a knowledge-based energy function called RWplus as the evaluation criterion. This function is decomposed into two terms: a distance-dependent energy term and an orientation-dependent energy term. Second, we employ a multiobjective differential evolution coupled with an external archive to perform conformation space searching. After conformation space searching, a cluster method is introduced to select the final predicted structure from a set of decoy structures. We use eighteen test proteins to verify the performance of the proposed approach. The experimental results demonstrate the effectiveness of the proposed approach and indicate that incorporating knowledge-based energy functions into multiobjective approaches to solve the PSP problem is promising.
COVID-19 por SARS-Cov2 también ha afectado a la Educación Médica

El virus, además de provocar una pandemia, con centenares de miles de enfermos y con decenas de miles de fallecidos, ha tenido la capacidad («el poder») de romper las prácticas educativas estándar. Esto ha tenido, o puede tener, un beneficio: reconsiderar lo que es realmente importante y lo que no lo es tanto. Y eso tiene un gran valor para el docente y para el discente. Y también ha servido para reconocer y encontrar formas alternativas con las que se puede transmitir el saber. En definitiva, el profesor ha tenido la oportunidad de contribuir a crear un modelo educativo capaz de impulsar el desarrollo de nuevas formas y métodos de aprendizaje. Ciertas técnicas y recursos educativos han podido ser reconocidos como «presentes» o «ausentes» de nuestro sistema educativo. Esto implica que ciertos recursos han de estar presentes, o que es preciso optimizar los que ya están disponibles para enseñar, para aprender, o para evaluar. La tecnología permite facilitar el contacto «directo» entre profesor y alumno, entre profesores, y entre alumnos. Pero sobra decir que la tecnología ha de estar disponible. The virus, besides causing a pandemic, with hundreds of thousands ill, and with tens of thousands dead, has had the ability (‘the power’) to break standard education practices. This has had, or may have, one benefit: to reconsider that which is really important and that which is not so important. And this is of great value for the teacher and for the student. And it has also served to recognise and find alternative ways to transmit the knowledge. All things considered, the teacher has had the opportunity to contribute in creating an education model capable of boosting the development of new ways and methods of learning. Certain educational techniques and resources have been able to be recognised as ‘present’ or ‘absent’ in our education system. This implies that certain recourses have to be present, or that is required to optimise those that are available in order to teach, to learn, or to evaluate. Technology helps to provide ‘direct’ contact between teacher and student, between teachers and between students. But it goes without saying that the technology has to be available.
Evaluation of Augmented Reality Technologies in Manufacturing – A Literature Review

Augmented Reality (AR) applications are increasingly being used in many different areas, such as architecture, healthcare, manufacturing, and education. With AR, virtual information is merged into the real world. The use of AR systems in manufacturing opens up new ways of providing information and is shaping a new form of interaction of employees. Employees can use AR systems through devices like glasses, smartphones, tablets, and projectors, which are using voice commands and gesture recognition. These features allow hands-free working in manufacturing and offer an attractive advantage of AR glasses and projectors. Despite the apparent advantages of AR, it is used little in industry beyond the prototype stage. This could be explained that so far, the value added doesn’t seem attractive enough. Other influences may be ergonomic aspects, usability, user experience, and acceptance. This paper offers a systematic literature research of evaluation criteria of AR systems published.
M-Evolve: Structural-Mapping-Based Data Augmentation for Graph Classification

Graph classification, which aims to identify the category labels of graphs, plays a significant role in drug classification, toxicity detection, protein analysis etc. However, the limitation of scale in the benchmark datasets makes it easy for graph classification models to fall into over-fitting and undergeneralization. To improve this, we introduce data augmentation on graphs (i.e. graph augmentation) and present four methods:random mapping, vertex-similarity mapping, motif-random mapping and motif-similarity mapping, to generate more weakly labeled data for small-scale benchmark datasets via heuristic transformation of graph structures. Furthermore, we propose a generic model evolution framework, named M-Evolve, which combines graph augmentation, data filtration and model retraining to optimize pre-trained graph classifiers. Experiments on six benchmark datasets demonstrate that the proposed framework helps existing graph classification models alleviate over-fitting and undergeneralization in the training on small-scale benchmark datasets, which successfully yields an average improvement of 3 - 13% accuracy on graph classification tasks.
Computational Cytology: Lessons Learned from Pap Test Computer-Assisted Screening

&lt;b&gt;&lt;i&gt;Background:&lt;/i&gt;&lt;/b&gt; In the face of rapid technological advances in computational cytology including artificial intelligence (AI), optimization of its application to clinical practice would benefit from reflection on the lessons learned from the decades-long journey in the development of computer-assisted Pap test screening. &lt;b&gt;&lt;i&gt;Summary:&lt;/i&gt;&lt;/b&gt; The initial driving force for automated screening in cytology was the overwhelming number of Pap tests requiring manual screening, leading to workflow backlogs and incorrect diagnoses. Several companies invested resources to address these concerns utilizing different specimen processing techniques and imaging systems. However, not all companies were commercially prosperous. Successful implementation of this new technology required viable use cases, improved clinical outcomes, and an acceptable means of integration into the daily workflow of cytopathology laboratories. Several factors including supply and demand, Food and Drug Administration (FDA) oversight, reimbursement, overcoming learning curves and workflow changes associated with the adoption of new technology, and cytologist apprehension, played a significant role in either promoting or preventing the widespread adoption of automated screening technologies. &lt;b&gt;&lt;i&gt;Key Messages:&lt;/i&gt;&lt;/b&gt; Any change in health care, particularly those involving new technology that impacts clinical workflow, is bound to have its successes and failures. However, perseverance through learning curves, optimizing workflow processes, improvements in diagnostic accuracy, and regulatory and financial approval can facilitate widespread adoption of these technologies. Given their history with successfully implementing automated Pap test screening, cytologists are uniquely positioned to not only help with the development of AI technology for other areas of pathology, but also to guide how they are utilized, regulated, and managed.
Controlling the Output Length of Neural Machine Translation

The recent advances introduced by neural machine translation (NMT) are rapidly expanding the application fields of machine translation, as well as reshaping the quality level to be targeted. In particular, if translations have to fit some given layout, quality should not only be measured in terms of adequacy and fluency, but also length. Exemplary cases are the translation of document files, subtitles, and scripts for dubbing, where the output length should ideally be as close as possible to the length of the input text. This paper addresses for the first time, to the best of our knowledge, the problem of controlling the output length in NMT. We investigate two methods for biasing the output length with a transformer architecture: i) conditioning the output to a given target-source length-ratio class and ii) enriching the transformer positional embedding with length information. Our experiments show that both methods can induce the network to generate shorter translations, as well as acquiring interpretable linguistic skills.
Efficient Attribute-Based Encryption Outsourcing Scheme With User and Attribute Revocation for Fog-Enabled IoT

With the rapid growth of Internet of Things (IoT) applications, fog computing enables the IoT to provide efficient services by extending the cloud computing paradigm to the edge of the network. However, the existing attribute-based encryption schemes rarely focus on user and attribute revocation in fog computing and most of them still impose high computational and storage overhead on resource-limited IoT devices. In this article, we propose an efficient attribute-based encryption outsourcing scheme with user and attribute revocation for fog-enabled IoT. The proposed scheme improves the existing encryption scheme that uses the concept of attribute groups to achieve attribute revocation, making it suitable for fog computing and improving the efficiency of ciphertext update. In addition, it implements a novel method of user revocation in fog computing based on the characteristics of fog computing. In order to reduce the computing and storage burden of IoT devices, the heavy computation operations of encryption and decryption are outsourced to fog nodes and part of secret keys are stored in fog nodes. The security analysis shows that the proposed scheme is secure under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. The performance analysis shows that the proposed scheme has high revocation efficiency and is efficient enough to be deployed in practical fog computing.
TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling

Reconstructing Portal Vein and Hepatic Vein trees from contrast enhanced abdominal CT scans is a prerequisite for preoperative liver surgery simulation. Existing deep learning based methods treat vascular tree reconstruction as a semantic segmentation problem. However, vessels such as hepatic and portal vein look very similar locally and need to be traced to their source for robust label assignment. Therefore, semantic segmentation by looking at local 3D patch results in noisy misclassifications. To tackle this, we propose a novel multi-task deep learning architecture for vessel tree reconstruction. The network architecture simultaneously solves the task of detecting voxels on vascular centerlines (i.e. nodes) and estimates connectivity between center-voxels (edges) in the tree structure to be reconstructed. Further, we propose a novel connectivity metric which considers both inter-class distance and intra-class topological distance between center-voxel pairs. Vascular trees are reconstructed starting from the vessel source using the learned connectivity metric using the shortest path tree algorithm. A thorough evaluation on public IRCAD dataset shows that the proposed method considerably outperforms existing semantic segmentation based methods. To the best of our knowledge, this is the first deep learning based approach which learns multi-label tree structure connectivity from images.
Design Of Web Based LMS (Learning Management System) in SMAN 1 Kampar Kiri Hilir

Learning Management System appears as a solution to the many problems that arise due to limited time, place and the number of meetings between teacher and student. Learning Management System as a tool in the learning process offers several advantages so that it can solve problems that often arise in the learning process. Based on the limited number and time of meetings, a web-based Learning Management System was built by using the PHP programming language and MySQL Using the Waterfall Model Method. The result has been developed a Learning Management System for SMA NEGERI 1 KAMPAR KIRI HILIR with facilities as a medium for the dissemination of subject matter, media for the distribution of assignments, and as a media for student discussion forums. In this Learning Management System application has 3 admin actors, teachers, students. The admin has the duty as an elearning application manager to manage the application so that it can run well. The admin manages curriculum data, semester years, teachers, students, subjects. The teacher gives the material and assignments done by students, while students will work on assignments uploaded by the teacher and download the material that has been uploaded by the teacher. Learning Management System application as a support for learning activities, because schools are able to carry out learning activities not only teaching in the classroom. By creating learning media through the Learning Management System website in SMA NEGERI 1 KAMPAR KIRI HILIR, teacher and student interaction is increasing.
Secure Edge-Aided Computations for Social Internet-of-Things Systems

Devices in the Internet-of-Things (IoT) are networked and perform massive computations to support various social IoT systems. Applications in social IoT systems often involve complicated computations that are out of the computation capacity of some resource-constrained IoT devices. Thus, how to enable resource-constrained IoT devices to accomplish complex computations efficiently and securely is of significant importance. To address this problem, we develop a secure edge-aided computation scheme for the social IoT systems. We scope the framework of edge-aided computations and identify the security threats in such a system. We define the security requirements that the outsourcing algorithms should meet. Then, we provide two examples of secure outsourcing algorithms (matrix multiplication and modular exponentiation) that meet the given security requirements. The efficiency and security of the proposed algorithms are supported through the theoretical analysis and experimental results.
Curious Hierarchical Actor-Critic Reinforcement Learning

Hierarchical abstraction and curiosity-driven exploration are two common paradigms in current reinforcement learning approaches to break down difficult problems into a sequence of simpler ones and to overcome reward sparsity. However, there is a lack of approaches that combine these paradigms, and it is currently unknown whether curiosity also helps to perform the hierarchical abstraction. As a novelty and scientific contribution, we tackle this issue and develop a method that combines hierarchical reinforcement learning with curiosity. Herein, we extend a contemporary hierarchical actor-critic approach with a forward model to develop a hierarchical notion of curiosity. We demonstrate in several continuous-space environments that curiosity can more than double the learning performance and success rates for most of the investigated benchmarking problems. We also provide our source code (https://github.com/knowledgetechnologyuhh/goal_conditioned_RL_baselines) and a supplementary video (https://www2.informatik.uni-hamburg.de/wtm/videos/chac_icann_roeder_2020.mp4).
Multi-AUVs Cooperative Target Search Based on Autonomous Cooperative Search Learning Algorithm

As a new type of marine unmanned intelligent equipment, autonomous underwater vehicle (AUV) has been widely used in the field of ocean observation, maritime rescue, mine countermeasures, intelligence reconnaissance, etc. Especially in the underwater search mission, the technical advantages of AUV are particularly obvious. However, limited operational capability and sophisticated mission environments are also difficulties faced by AUV. To make better use of AUV in the search mission, we establish the DMACSS (distributed multi-AUVs collaborative search system) and propose the ACSLA (autonomous collaborative search learning algorithm) integrated into the DMACSS. Compared with the previous system, DMACSS adopts a distributed control structure to improve the system robustness and combines an information fusion mechanism and a time stamp mechanism, making each AUV in the system able to exchange and fuse information during the mission. ACSLA is an adaptive learning algorithm trained by the RL (Reinforcement learning) method with a tailored design of state information, reward function, and training framework, which can give the system optimal search path in real-time according to the environment. We test DMACSS and ACSLA in the simulation test. The test results demonstrate that the DMACSS runs stably, the search accuracy and efficiency of ACSLA outperform other search methods, thus better realizing the cooperation between AUVs, making the DMACSS find the target more accurately and faster.
Power-Aware Allocation of Graph Jobs in Geo-Distributed Cloud Networks

In the era of big-data, the jobs submitted to the clouds exhibit complicated structures represented by graphs, where the nodes denote the sub-tasks each of which can be accommodated at a slot in a server, while the edges indicate the communication constraints among the sub-tasks. We develop a framework for efficient allocation of graph jobs in geo-distributed cloud networks (GDCNs), explicitly considering the power consumption of the datacenters (DCs). We address the following two challenges arising in graph job allocation: i) the allocation problem belongs to NP-hard nonlinear integer programming; ii) the allocation requires solving the NP-complete sub-graph isomorphism problem, which is particularly cumbersome in large-scale GDCNs. We develop a suite of efficient solutions for GDCNs of various scales. For small-scale GDCNs, we propose an analytical approach based on convex programming. For medium-scale GDCNs, we develop a distributed allocation algorithm exploiting the processing power of DCs in parallel. Afterward, we provide a novel low-complexity (decentralized) sub-graph extraction method, based on which we introduce cloud crawlers aiming to extract allocations of good potentials for large-scale GDCNs. Given these suggested strategies, we further investigate strategy selection under both fixed and adaptive DC pricing schemes, and propose an online learning algorithm for each.
Implicit Graph Neural Networks

Graph Neural Networks (GNNs) are widely used deep learning models that learn meaningful representations from graph-structured data. Due to the finite nature of the underlying recurrent structure, current GNN methods may struggle to capture long-range dependencies in underlying graphs. To overcome this difficulty, we propose a graph learning framework, called Implicit Graph Neural Networks (IGNN), where predictions are based on the solution of a fixed-point equilibrium equation involving implicitly defined state vectors. We use the Perron-Frobenius theory to derive sufficient conditions that ensure well-posedness of the framework. Leveraging implicit differentiation, we derive a tractable projected gradient descent method to train the framework. Experiments on a comprehensive range of tasks show that IGNNs consistently capture long-range dependencies and outperform the state-of-the-art GNN models.
DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search

Efficient search is a core issue in Neural Architecture Search (NAS). It is difficult for conventional NAS algorithms to directly search the architectures on large-scale tasks like ImageNet. In general, the cost of GPU hours for NAS grows with regard to training dataset size and candidate set size. One common way is searching on a smaller proxy dataset (e.g., CIFAR-10) and then transferring to the target task (e.g., ImageNet). These architectures optimized on proxy data are not guaranteed to be optimal on the target task. Another common way is learning with a smaller candidate set, which may require expert knowledge and indeed betrays the essence of NAS. In this paper, we present DA-NAS that can directly search the architecture for large-scale target tasks while allowing a large candidate set in a more efficient manner. Our method is based on an interesting observation that the learning speed for blocks in deep neural networks is related to the difficulty of recognizing distinct categories. We carefully design a progressive data adapted pruning strategy for efficient architecture search. It will quickly trim low performed blocks on a subset of target dataset (e.g., easy classes), and then gradually find the best blocks on the whole target dataset. At this time, the original candidate set becomes as compact as possible, providing a faster search in the target task. Experiments on ImageNet verify the effectiveness of our approach. It is $$\mathbf{2} \varvec{\times }$$ faster than previous methods while the accuracy is currently state-of-the-art, at 76.2% under small FLOPs constraint. It supports an argument search space (i.e., more candidate blocks) to efficiently search the best-performing architecture.
Research and application of data sharing platform integrating Ethereum and IPFs Technology

The development of the Internet has increased the amount of data exponentially, and the reliable transmission, sharing, and storage of data have become the primary problems of data sharing. This article combines the decentralized and irreversible characteristics of the Ethereum blockchain, integrates IPFS distributed storage technology, and proposes a data sharing platform under a new technology environment to ensure data security, user rights protection, and high-speed data processing. Provide a new technology application environment for current data sharing.
Practical Membership Inference Attack Against Collaborative Inference in Industrial IoT

The effectiveness of state-of-the-art deep learning (DL) models has empowered the development of industrial Internet of things (IIoT). Recently, considering resource-constrained and privacy-required IIoT devices, collaborative inference has been proposed, which splits DL models and deploys them in IIoT devices and an edge server separately. However, in this article, we argue that there are still severe privacy vulnerabilities in collaborative inference systems. And we devise the first membership inference attack (MIA) against collaborative inference, to infer whether a particular data sample is used for training the model of IIoT systems. Existing MIAs either assume full access to the systems’ APIs or availability of the target model's parameters, which is not applicable in realistic IIoT environments. In contrast to prior works, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transfer-inherit</i> shadow learning and thus relax these key assumptions. We evaluate our attack on different datasets and various settings, and the results show it has high effectiveness.