Reviewerly/exHarmony huggingface dataset:
A method for forecasting low-temperature disaster based on random forest and LSTM_NN

Aiming at solving the difficulties of modeling factor processing and forecast modeling caused by time correlation and non-linear variation of low-temperature disastrous weather processes during winter, this paper takes the winter low-temperature cold damage index calculated from the daily temperature and precipitation data in Guangxi as the forecast factor. Based on the NCEP/NCAR reanalysis data and forecast field data, a large number of characteristic factors are extracted by using the random forest algorithm (RFS). Combined with the long-short-term memory neural network (LSTM NN) in the nonlinear time series memory characteristics and nonlinear timing dynamic system control advantages, we established a model for low-temperature cold hazard forecast with 72 hours based on the random forest and LSTM_NN. Experimental results indicate that the current method has high prediction accuracy, good stability, and displays better applicability to nonlinear low-temperature cold hazard forecasting.
A multi-block method to improve 2D tracking in outdoor Augmented Reality applications

Augmented reality techniques have recently found many applications in the field of cultural heritage. This work describes an example of application in Pinocchio Park (Collodi, Italy). Several recognition problems arise during the on-site test, due to unstable light conditions. The authors propose a solution based on a multi-blocks image target segmentation and a dedicated add-on for the Unity3D game engine.
Chinese named entity recognition method based on BERT and fusion attention mechanism

To address the existing problems of Chinese named entity recognition, the traditional pre-training model fails to characterize the multiple meanings of words and the model does not sufficiently dig into the potential semantic features at the Chinese word level. This paper proposes a Chinese named entity recognition method based on BERT and fused attention mechanism. First, the word vector features are obtained by the pre-training of large-scale corpus with the use of BERT model to deal with the problem of multiple meanings in one word. Then, the contextual features are recognised through the use of BiLSTM and thus passing the results into the attention layer. This is to exploit the potential semantic features within the text in order to face the shortcomings of unpromising relevance with the given information of semantic feature in previous models. Last, the output results are annotated in sequential order by CRF to reduce the probability of incorrect labelling. Through comparative experiments, the F1 values of this paper's model are 95.12% and 95.43% on MSRA corpus and People's Daily corpus datasets, respectively, which are both better than the comparison models, revealing the effective improvement in the named entity recognition.
The design and simulation of digital filter

The digital filter is widely used in many situations with the development of digital signal process techniques. The basic theory of digital filter is briefly described at first. Then the common design methods of FIR are introduced, and different window functions are demonstrated in detail. And different design method of IIR methods are compared and analyzed. For a given experimental example, the required FIR and IIR low-pass digital filters are designed respectively by MATLAB program function and FDAtool. Finally, the simulation models are constructed in Simulink, and the results show that both designed FIR low-pass digital filter and IIR low-pass digital filter can filter the mixed signals to obtain the required 1 MHz sine wave.
Optimizing human hand gestures for AI-systems

Humans interact more and more with systems containing AI components. In this work, we focus on hand gestures such as handwriting and sketches serving as inputs to such systems. They are represented as a trajectory, i.e. sequence of points, that is altered to improve interaction with an AI model while keeping the model fixed. Optimized inputs are accompanied by instructions on how to create them. We aim to cut on effort for humans and recognition errors while limiting changes to original inputs. We derive multiple objectives and measures and propose continuous and discrete optimization methods embracing the AI model to improve samples in an iterative fashion by removing, shifting and reordering points of the gesture trajectory. Our quantitative and qualitative evaluation shows that mimicking generated proposals that differ only modestly from the original ones leads to lower error rates and requires less effort. Furthermore, our work can be easily adjusted for sketch abstraction improving on prior work.
Range search on encrypted spatial data with dynamic updates1

Driven by the cloud-first initiative taken by various governments and companies, it has become a common practice to outsource spatial data to cloud servers for a wide range of applications such as location-based services and geographic information systems. Searchable encryption is a common practice for outsourcing spatial data which enables search over encrypted data by sacrificing the full security via leaking some information about the queries to the server. However, these inherent leakages could equip the server to learn beyond what is considered in the scheme, in the worst-case allowing it to reconstruct of the database. Recently, a novel form of database reconstruction attack against such kind of outsourced spatial data was introduced (Markatou and Tamassia, IACR ePrint 2020/284), which is performed using common leakages of searchable encryption schemes, i.e., access and search pattern leakages. An access pattern leakage is utilized to achieve an order reconstruction attack, whereas both access and search pattern leakages are exploited for the full database reconstruction attack. In this paper, we propose two novel schemes for outsourcing encrypted spatial data supporting dynamic range search. Our proposed schemes leverage R+tree to partition the dataset and binary secret sharing to support secure range search. They further provide backward and content privacy and do not leak the access pattern, therefore being resilient against the above mentioned database reconstruction attacks. The evaluations and results on the real-world dataset demonstrate the practicality of our schemes, due to (a) the minimal round-trip between the client and server, and (b) the low computation and storage overhead on the client side.
A Diffraction Service Composition Approach Based on S-ABCPC

In recent years, research on the QoS-aware service composition problem often assumes that each component service in the process to be solved is equally essential. They do not consider the impact of core component services and other component services on problem-solving, or even though their impact is considered, they are not fully considered. So this paper first proposes a diffractive method based on them. Considering the advantages of artificial bee colony (ABC) such as simplicity, this paper chooses it as the basic algorithm. In addition, with the continuous development of service ecosystem, it gradually formed a variety of domain features. They have an important influence on problem-solving, but the existing research has not explored this influence in-depth. Therefore, this paper digs deep into this influence. Given the characteristics of the problem to be solved in this paper, the S-ABCPC algorithm is designed. At last, experiments have proved the effectiveness of the method proposed in this paper. The impact factors of this method have been studied.
Text region extraction method for historical Tibetan document based on border detection

The text region extraction is the key step for optical character recognition (OCR) operation. According to the layout characteristics of historical Tibetan document, this paper proposes a text region extraction method based on border detection to extract text region from documents. Firstly, the character height and stroke width are estimated and the border region position is detected. Then, the position of decorative lines surrounding the body text region is detected by heuristic search. Finally, the mask image of the document is generated according to the position relationship between the text regions and border, then different text regions are extracted. Experiments on dataset of historical Tibetan document show that this method can effectively overcome the problems of page tilt, border and decorative lines fracture, and demonstrate the effectiveness of the proposed method.
AdaptiveResonance.jl: A Julia Implementation of Adaptive Resonance Theory (ART) Algorithms

AdaptiveResonance.jl is a Julia package for machine learning with adaptive resonance theory (ART) algorithms, written in the numerical computing language Julia.ART is a neurocognitive theory of how competitive cellular networks can learn distributed patterns without supervision through recurrent field connections, eliciting the mechanisms of perception, expectation, and recognition (Grossberg, 1980(Grossberg, , 2013)).Engineering algorithms based upon ART are principally in the class of competitive, incremental, single-layer, neurogenesis clustering algorithms, but they have been adapted for multimodal learning in diverse applications.
Video super-resolution enhancement based on 3D spatio-temporal fusion model

In the field of video super-resolution, multiple frames provide more scene information, not only in intra-frame spatial dependence, but also inter-frame temporal dependence (for example motion, brightness, and color changes). Therefore, existing work mainly focuses on making better use of spatio-temporal dependence, including explicit motion compensation (for example based on optical flow, based on learning) and cyclic methods. After years of research by predecessors, video super-resolution has achieved good results, but there are still some difficulties and shortcomings that need to be resolved. Generally speaking, SR algorithm series using deep learning technology are different from each other in the following main aspects: different types of network architectures, different types of loss functions, and different types of learning strategies. In this paper, the spatio-temporal fusion module is improved on the basis of a two-stage 3D convolutional network. The adjacent frame and the current frame are merged in space and time, and the generated residual block is added to the feature map. The two-stage 3D convolution is applied to the residual block in the backbone network DenseNet to better process the pre-deblurring module.
Multi-View Visual Relationship Detection with Estimated Depth Map

The abundant visual information contained in multi-view images is widely used in computer vision tasks. Existing visual relationship detection frameworks have extended the feature vector to improve model performance. However, single view information can not fully reveal the visual relationships in complex visual scenes. To solve this problem and explore the multi-view information in a visual relationship detection (VRD) model, a novel multi-view VRD framework based on a monocular RGB image and an estimated depth map is proposed. The contributions of this paper are threefold. First, we construct a novel multi-view framework which fuses information of different views extracted from estimated RGB-D images. Second, a multi-view image generation method is proposed to transfer flat visual space to 3D multi-view space. Third, we redesign the visual relationship balanced classifier which can process multi-view feature vectors simultaneously. Detailed experiments were conducted on two datasets to demonstrate the effectiveness of the multi-view VRD framework. The experimental results showed that the multi-view VRD framework resulted in state-of-the-art zero-shot learning performance in specific depth conditions.
A privacy-preserving attribute-based access control system based on blockchain

With the wide application of blockchain, the access control of on-chain data and privacy protection are two urgent concerns to be solved. Although many access control systems based on attribute-based encryption (ABE) have emerged, they are not satisfactory for achieving the privacy protection of data owners’ attributes and the revocation of data consumers’ attribute sets. An anonymous ciphertext policy ABE with attribute revocation (AR-ACP-ABE) was unveiled by adopting the idea of attribute revocation based on broadcast encryption. Then, a new privacy-preserving attributebased access control system was constructed. It is an extension of the AR-ACP-ABE scheme by adopting the technique of blockchain and outsourced computation. The new system supports completely hidden access policies and allows data owners to revoke data consumers’ attribute sets. In addition, data consumers only need to perform one exponent operation on the target group during the access phase. Security analysis shows that the new system meets the properties of correctness, confidentiality, and integrity.
Comparative Approaches to Data and Methods in Corpus Linguistics

In the Introduction, the editors describe the motivations and aims underlying the publication of the book against the background of the importance of corpus linguistics in current research and the associated methodological diversification. The didactic orientation of the book is outlined, as is its organization into four major parts and the contribution made by each chapter. Going beyond a summary of the contents, the editors voice some reflections on the state of the art in corpus linguistics and desiderata for future research. Finally, recommendations for further reading are listed and provided with comments.
LUNAR: A Practical Anonymous Network Simulation Platform

With the expansion of cyberspace and the increasing importance of users for privacy protection, anonymous network research has been further developed, especially for the numerous internet of things (IoT) devices. However, when we repeat the existing experiment in an anonymous network, there are many problems such as too old version, low realism, poor operability, and so on. In this paper, we analyzed the design requirements and topology of the new experimental platform. Two topologies with different levels of complexity are designed. We also set up a practical anonymous network simulation platform called LUNAR with virtualization, software-defined networking (SDN), and other technologies to solve those problems. The platform we proposed supports multiprotocol and reproducible complex networks with centralized management. Finally, we implement our simulation platform and reproduce two typical attacks, that is, time-linked Tor node reset attack and website fingerprint attacks on The Onion Router (Tor) network, to evaluate the platform. Experiments results indicate the practicality and superiority of our simulation platform in terms of anonymous network simulation.
A remote attestation model and protocol for multimedia mobile edge computing security

In the Multimedia Mobile Edge Computing (MMEC) environment, cryptography technology is used to ensure secure transmission of multimedia data when Terminal accesses to MMEC or Terminal accesses to the central cloud. However, the problem of the participants’ identity authentication and integrity measurement can not be solved. Therefore, in this paper, a novel remote attestation model and protocol is proposed, which introduces mutual anonymous identity authentication, mutual integrity measurement and verification between Terminal and MMEC as well as between MMEC and cloud. Moreover, the proposed protocol guarantees data security and identity security of the platform, as well as integrity state maintenance when MMEC interacts with Terminal. We make a thorough security analysis and performance simulation over the proposed protocol. Security analysis shows that the proposed protocol is superior to the other two similar schemes in terms of privacy protection ability and anti-attack ability. Performance simulation results demonstrate that the remote attestation protocol with non-intensive requests state achieve much higher efficiency.
Correntropy-Based Low-Rank Matrix Factorization With Constraint Graph Learning for Image Clustering

This article proposes a novel low-rank matrix factorization model for semisupervised image clustering. In order to alleviate the negative effect of outliers, the maximum correntropy criterion (MCC) is incorporated as a metric to build the model. To utilize the label information to improve the clustering results, a constraint graph learning framework is proposed to adaptively learn the local structure of the data by considering the label information. Furthermore, an iterative algorithm based on Fenchel conjugate (FC) and block coordinate update (BCU) is proposed to solve the model. The convergence properties of the proposed algorithm are analyzed, which shows that the algorithm exhibits both objective sequential convergence and iterate sequential convergence. Experiments are conducted on six real-world image datasets, and the proposed algorithm is compared with eight state-of-the-art methods. The results show that the proposed method can achieve better performance in most situations in terms of clustering accuracy and mutual information.
Resource-efficient Pervasive Smart Camera Networks

Smart cameras integrate sensing, processing, and communication on embedded platforms and serve as key components at the sensory edge of many Internet of Things (IoT) and cyber-physical systems (CPS) applications. Although impressive technological progress has been achieved over the last decade, smart cameras still face resource limitations, particularly when used for advanced networked data processing. This position paper briefly summarizes trends in pervasive smart cameras, presents general strategies for improving resource efficiency, and discusses the resource challenges of person re-identification; a prototypical example of surveillance applications for smart cameras based on advanced machine learning techniques. Lastly, we present our concept for resource-efficient decentralized person re-identification in smart camera networks as a work in progress.
A New Competitive Neural Architecture for Object Classification

In this paper, we propose a new neural architecture for object classification, made up from a set of competitive layers whose number and size are dynamically learned from training data using a two-step process that combines unsupervised and supervised learning modes. The first step consists in finding a set of one or more optimal prototypes for each of the c classes that form the training data. For this, it uses the unsupervised learning and prototype generator algorithm called fuzzy learning vector quantization (FLVQ). The second step aims to assess the quality of the learned prototypes in terms of classification results. For this, the c classes are reconstructed by assigning each object to the class represented by its nearest prototype, and the obtained results are compared to the original classes. If one or more constructed classes differ from the original ones, the corresponding prototypes are not validated and the whole process is repeated for all misclassified objects, using additional competitive layers, until no difference persists between the constructed and the original classes or a maximum number of layers is reached. Experimental results show the effectiveness of the proposed method on a variety of well-known benchmark data sets.
Are the statistical tests the best way to deal with the biomarker selection problem?

Abstract Statistical tests are a powerful set of tools when applied correctly, but unfortunately the extended misuse of them has caused great concern. Among many other applications, they are used in the detection of biomarkers so as to use the resulting p -values as a reference with which the candidate biomarkers are ranked. Although statistical tests can be used to rank, they have not been designed for that use. Moreover, there is no need to compute any p -value to build a ranking of candidate biomarkers. Those two facts raise the question of whether or not alternative methods which are not based on the computation of statistical tests that match or improve their performances can be proposed. In this paper, we propose two alternative methods to statistical tests. In addition, we propose an evaluation framework to assess both statistical tests and alternative methods in terms of both the performance and the reproducibility. The results indicate that there are alternative methods that can match or surpass methods based on statistical tests in terms of the reproducibility when processing real data, while maintaining a similar performance when dealing with synthetic data. The main conclusion is that there is room for the proposal of such alternative methods.
A Generalized Search Construct for Imperative Languages to Facilitate Declarative Programming

Search is one of the more common strategies used by problem-solving agents. For many hard problems, a backtracking search can be the most effective approach for finding a solution. Logic programming languages provide, as a core feature of the language, the mechanics for the search behind the scenes without requiring the user to specify the algorithm. However, this functionality has been generally left out of the core features of imperative programming languages. In this work, we propose integrating a search agent into an imperative programming language through a search construct in the syntax. By doing so, our intention is that the user may apply a declarative approach to problem solving. We extend the Go programming language with this search construct and develop an agent to perform the search. The algorithm behind the search is implicitly handled by the agent. We demonstrate the effectiveness of this search construct through solutions to an exhaustive search over a file-system, the NQueens problem, and the monkey and the bananas problem. To evaluate the performance of our agent, we compare its runtime performance against two of the common Prolog implementations, SWI-Prolog and GNU Prolog.
The Appearance of Digital Competence in the Work of Health Sciences Educators

The digital competence of health sciences educators is important for the delivery and development of modern education and lifelong learning. The aim of the study was to assess the appearance of digital competence in the work of Finnish health sciences educators and to determine whether educators' background factors are related to the areas of digital competence appearance. The European Framework for the Digital Competence of Educators was used as a theoretical background. The participants were Finnish health sciences educators (n = 388). Data were collected by quantitative survey and statistically analyzed. Results show that health sciences educators had participated in continuing education to develop their expertise and used a variety of digital methods and materials. Educators need more competence to improve healthcare students' ability to use digital technology. In the area of Teaching and Learning, educators younger than 40 years rated the appearance of digital competence as better than did those between the ages of 40 and 49 years. In the future, health sciences educators' basic and continuing education could take into account the competence requirements for digital competence, and educators' expertise must be increased in areas where digital competence does not appear strong.