Reviewerly/exHarmony huggingface dataset:
SOBC: A Smart Ocean Oriented Blockchain System for Cross-organizational Data Sharing

The refinement of the division of labor among organizations in the field of ocean exploration has made the cross-organizational data sharing a common phenomenon. Although the blockchain system can solve the trust problem among organizations, the unstable Underwater Wireless Sensor Network (UWSN) and high network traffic costs of satellite network hindered the application of blockchain in the smart ocean. In this paper, we propose the smart ocean blockchain architecture (SOBC) that supports high transaction concurrency and saves network traffic to achieve trusted cross-organizational data sharing in the ocean exploration field. First, we design the SOBC architecture and use channels to isolate different businesses to ensure data privacy and high transaction concurrency. Second, we design blockchain smart contracts to implement data transmission rules among regulatory agencies, data providers and data demanders to achieve trusted data sharing. Finally, we design a channel-based consistency protocol to reduce the network traffic consumption when nodes reconnect to the core network. We use data collected from the spectrum detection platform to verify the effect of SOBC by simulating the ocean exploration network conditions. The experimental results show that the SOBC is superior to the Ethereum-based blockchain architecture in transactions concurrency supporting. Besides, compared with Ethereum, the SOBC reduces network traffic consumption by 70% under unstable network conditions.
An Adaptive Kalman-Correlation Based Siamese Network Tracker for Visual Object Tracking

Object tracking is important in a variety of applications from surveillance to robotic vision and traffic monitoring. Because of its importance, there has recently been a lot of research and developments in this field. Meanwhile, since the deep convolutional neural networks has shown its impressive potential, Siamese networks have also drawn increasing attention. However, the trackers may fail when there are rapid motions, occlusions, and similar objects in the video. To address the limitation and improve the robustness, this paper takes advantages of both the Kalman filter and the correlation filter, and further develop an adaptive Kalman-Correlation based Siamese network (AKC-SiamTracker). AKC-SiamTracker can automatically make different adjustment strategies to adjust the detected position of the original tracker based on the adaptive influence coefficient decider. The fully connected Siamese network (SiamFC) and Siamese region proposal network (SiamRPN) are selected as the baseline models. Evaluation of our method is carried out on OTB dataset. The promising results have shown better performance and robustness compared to the baselines and other state-of-the-art models. To the best of our knowledge, our work is the first time to propose the adaptive Kalman-Correlation based Siamese tracker.
Method of introducing difficult professional situations for the development of foreign language communicative competence

The paper considers an important pedagogical problem in the sphere of foreign language teaching, i.e. increasing the level of students communicative competence. Case study is a tool for solving this task. The research aim is to develop a set of communicative cases in English for solving difficult professional situations, the implementation of which will contribute to the development of students communicative competence. The following methods are used in the research: analysis of modern scientific and methodological literature on the problem of research; testing; monitoring the effectiveness of students work when using the developed set of communicative cases; statistical data processing. There were elaborated and tested four cases. The unifying topic of them is Difficult professional situations. The choice of the topic can be explained by a stimulating effect of difficult situations in a persons life. The content of each case embraces both productive and receptive skills. The approbation took place at Chelyabinsk State University within the framework of the discipline Foreign language (Applied Mathematics and Informatics group of specialties, the profile of Information and Management Technologies). The testees are the first-year students. One of the cases is presented here as a sample. A particular attention is paid to the study of the influence of difficult situations on human behavior. The paper contains a description of the stages of work on a case, criteria for the materials of cases, an analysis of the skills and abilities being formed, as well as recommendations for working with them. The practical significance of the study lies in the fact that the results of the research can make foreign language teaching more effective.
Detection of Opencast Mining Sites from Multispectral Satellite Images

In this work, we consider the problem of detecting unauthorized opencast mining activities from satellite imagery, which is important for the adverse effect they cause in the environment and the regions where they occur. Detection by traditional methods is cumbersome and costly. For this reason, we propose a solution leveraging machine learning techniques using multispectral satellite imagery. We implemented these techniques and evaluated their performance using real-world examples and considering various important performance metrics. The results show that these techniques provide an accurate detection system and do not require costly computational resources.
Suppressing Style-Sensitive Features via Randomly Erasing for Domain Generalizable Semantic Segmentation

Domain generalization aims to enhance robustness of models to different domains, which is crucial for safety-critical systems in practice. In this paper, we propose a simple plug-in module to promote the ability of generalization for semantic segmentation networks without extra loss function. Firstly, we rethink the relationship between semantics and style in the sight of feature maps, and divide the channels of them into two kinds (i.e. style-sensitive channels and semantic-sensitive channels) via the variance of Gram matrix. Secondly, with the assumption that the domain shift mainly lies in style, a random erasure method is proposed to style-sensitive-channel features with the hope of learning domain invariant features and preventing model from over-fitting to specific domain. Extensive experiments demonstrate that the generalization of our proposed method outperforms existing approaches.
Jasmine

Distributed shared memory abstraction can coordinate a cluster of machine nodes to empower performance-critical queries with the scalable memory space and abundant parallelism. But to deploy the query under such an abstraction, the general execution model just makes operators expressed as multiple subtasks and sequentially schedule them in parallel, while neglecting those vital dependencies between subtasks and data. In this paper, we conduct the in-depth researches about the issues (i.e., low CPU Utilization and poor data locality) raised by the ignorance of dependencies, and then propose a dependency-aware query execution model called Jasmine, which can (i) help users explicitly declare the dependencies and (ii) take these declared dependencies into the consideration of execution to address the issues. We invite our audience to use the rich graphical interfaces to interact with Jasmine to explore the dependency-aware query execution on distributed shared memory.
Head Movement Detection using Deep Learning and Face Edge Detection (FED) Method

The main problem of online learning is the effectiveness and the hesitation of student focus during the activities. Lot of things could be the trigger for student to lost their focuses. One of the ways to deal with these problem is to monitor the movement of their head during class to ensure that they still keep on their focus. But, it is a problem when we deal with lot of students. In this research we proposed a method to identified and detect the movement of the head such as rightward, leftward and forward by utilizing the distance of the eye location. Deep Learning method such as MTCNN is utilized, furthermore, Face Edge Detection (FED) also added to obtain a better approach to detect the head movement. Using the combination of MTCNN and FED, we also produce of accuracy.
Object tracking based on response maps fusion Siamese network

The full convolution Siamese network for object tracker formulate tracking as convolutional feature cross-correlation between a target template and a search region. This tracker realizes real-time object tracking. However, when there are interference factors similar to the target object, Siamese trackers still have an accuracy gap compared with state-of-theart algorithms. Therefore, we proposes an object tracking based on response maps fusion Siamese network(Siam-RMF ). Different from the full convolution Siamese network for object tracker, when the Siam-RMF tracker performs similarity learning, it no longer uses the features extracted by the last layer of the network, but extracts the features of the last three-layer network. Moreover, we propose a new model architecture to perform layer-wise and depth-wise aggregations, the depth-wise separable convolution is used to learn the similarity respectively to obtain the effective fusion of the corresponding depth cross-correlation response map. The fusion response maps can effectively avoid the loss of spatial information after multi-layer feature extraction. Experimental results on TB50 and UAV123L demonstrate the effectiveness of the proposed tracker without decreasing the tracking speed, and show stronger robustness and better tracking performance in complex environments.
Learning compositional programs with arguments and sampling

One of the most challenging goals in designing intelligent systems is empowering them with the ability to synthesize programs from data. Namely, given specific requirements in the form of input/output pairs, the goal is to train a machine learning model to discover a program that satisfies those requirements. A recent class of methods exploits combinatorial search procedures and deep learning to learn compositional programs. However, they usually generate only toy programs using a domain-specific language that does not provide any high-level feature, such as function arguments, which reduces their applicability in real-world settings. We extend upon a state of the art model, AlphaNPI, by learning to generate functions that can accept arguments. This improvement will enable us to move closer to real computer programs. Moreover, we investigate employing an Approximate version of Monte Carlo Tree Search (A-MCTS) to speed up convergence. We showcase the potential of our approach by learning the Quicksort algorithm, showing how the ability to deal with arguments is crucial for learning and generalization.
From Driver Talk To Future Action: Vehicle Maneuver Prediction by Learning from Driving Exam Dialogs

A rapidly growing amount of content posted online inherently holds knowledge about concepts of interest, i.e. driver actions. We leverage methods at the intersection of vision and language to surpass costly annotation and present the first automated framework for anticipating driver intention by learning from recorded driving exam conversations. We query YouTube and collect a dataset of posted mock road tests comprising student-teacher dialogs and video data, which we use for learning to foresee the next maneuver without any additional supervision. However, instructional conversations give us very loose labels, while casual chat results in a high amount of noise. To mitigate this effect, we propose a technique for automatic detection of smalltalk based on the likelihood of spoken words being present in everyday dialogs. While visually recognizing driver's intention by learning from natural dialogs only is a challenging task, learning from less but better data via our smalltalk refinement consistently improves performance.
Professional Training of Future Primary School Teachers for the Organization of Exhibition Activities

The article describes the technology of professional training of future primary school teachers for the organization of exhibition expositions&#x0D; of creative works of younger schoolchildren. The conclusion is made about the importance of involving primary school students in exhibition&#x0D; activities. The characteristics of the types of exhibitions, which are most expedient to use in school practice, are given, since in different situations of the educational process they allow solving the problems of the educational plan: to consider the work of other children and get new impressions; show your work and listen to feedback about them; talk about your own idea and talk about the work of other children. The substantiation of the exhibition is given as a condition for the development of a culture of perception, aesthetic taste, the ability to empathize and enjoy the success of classmates in younger schoolchildren, to increase their own self-esteem and the desire for creative search.
Cyberspace, Cybercrime, Cyberterrorism

Abstract The purpose of this chapter is to synthetically characterize the phenomenon of cybercrime, cyberterrorism and cyberwar. It presents attempts to define computer crimes and their classification, history of criminalization of this phenomenon together with related difficulties. The author consistently distinguishes cybercrime from cyberterrorism and cyberwar.
Discovering Latent Information from Noisy Sources in the Cultural Heritage Domain

Today, there are many publicly available data sources, such as online museum catalogues, Wikipedia, and social media, in the cultural heritage domain. Yet, the data is heterogeneous and complex (diverse, multi-modal, sparse, and noisy). In particular, availability of social media (such as Twitter messages) is both a boon and a curse in this domain: social media messages can potentially provide information not available otherwise, yet such messages are short, noisy, and are dominated by grammatical and linguistic errors. The key claim of this research is that the availability of publicly available information related to the cultural heritage domain can be improved with tools capable of signaling to the various classes of users (such as the public, local governments, researchers) the entities that make up the domain and the relationships existing among them. To achieve this goal, I focus on developing novel algorithms, techniques, and tools for leveraging multi-modal, sparse, and noisy data available from multiple public sources to integrate and enrich useful information available to the public in the cultural heritage domain. In particular, research aims to develop novel models that take advantage of multi-modal features extracted by deep neural models to improve the performance for various underlying tasks.
Evaluating emergency distance education and support for people with intellectual disabilities during the COVID-19 pandemic

This article engages with evaluating the usefulness of distance education for people with intellectual disabilities. It focuses primarily on the situation of students in special schools during the pandemic. In order to recognise the contexts and determinants of digital inclusion, the research makes use of the results of expert interviews and questionnaires completed by heads of special schools. Schools and other institutions usually evaluate the support of both local and national governments in remote education negatively. The pandemic has confirmed and emphasised the existing digital inequalities (also in terms of hardware). The usefulness of distance learning for people with intellectual disabilities is mostly rated as low. Its main advantage is perceived to be the cooperation between the teachers. The pandemic has, to a certain extent, contributed to raising awareness about the need for developing digital skills by all those involved in education. The article concludes with proposed solutions for diminishing digital inequalities, marginalisation and exclusion of people with intellectual disabilities.
Novel object motion estimation method for industrial vision systems in aligning machines

Industrial vision systems have been adopted in many manufacturing processes for monitoring manufacturing procedures. With the recent proposed concept of industrial information integration engineering (IIIE) (Da Xu, 2020 [1]), the adoption of industrial vision systems involves a wider IIIE context as a data acquisition measurement, and thus higher level intelligence like control automation is expected. Industrial vision systems have been used for object alignment in aligning machines. By coping with a pneumatic device, misaligned objects can be detected by vision systems and rejected. Since alignment efficiency depends on the appropriateness of the blown timing/pressure, which is casually manifested in the motion of the observed blown objects, a motion estimation method for industrial vision systems must be adopted. Although conventional motion estimation methods in this field typically use pre-prepared templates, this paper proposed a novel object motion estimation method based on the properties of industrial images. A bounding box within a confined observation area is first initialized for each object, and then motion estimation is achieved by updating the bounding box following the expectation–maximization principle. Experiments revealed that our proposed method achieved robust and continuous motion estimation of the parts and reduced the processing time compared to conventional template matching methods.
Research on a Face Recognition Algorithm Based on Joint Supervised Learning

research-article Research on a Face Recognition Algorithm Based on Joint Supervised Learning Share on Authors: Ruifang Zhang Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Feng Dong Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Tianyi Ji Guilin University of Technology, China Guilin University of Technology, ChinaView Profile Authors Info & Claims ICIIP 2021: 2021 6th International Conference on Intelligent Information ProcessingJuly 2021 Pages 181–186https://doi.org/10.1145/3480571.3480604Online:29 October 2021Publication History 0citation10DownloadsMetricsTotal Citations0Total Downloads10Last 12 Months10Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Multi-feature weighted cross-fusion target tracking algorithm based on kernel correlation filtering

The Kernel Correlation Filtering Algorithm (KCF) is one of the popular algorithms in the field of target tracking. It has been widely used in many fields because of its advantages in tracking effect and tracking speed. However, KCF uses a single directional gradient histogram (HOG) as the feature descriptor, when the tracking background is complex, the target moves too fast, the target rotates or is blocked, the tracking frame drifts and even the target is lost. In order to improve the robustness of the tracking effect, a method of weighted cross-fusing the directional gradient histogram and the color features to form a fusion feature as the new feature descriptor is proposed, which can describe the target feature more accurately, so as to achieve target tracking in more complex scenarios. Finally, the paper tests the tracking effects on multiple OTB standard data sets, and compares them with the KCF using a single feature, which shows that the algorithm using the fusion feature has good robustness in multiple complex backgrounds.
Research on Knowledge Map Completion with Entity Description Information based on Neural Network

Knowledge map is a large-scale semantic network that represents knowledge in the form of triples (entity, relationship, entity). It is widely used in information search, question answering system, e-commerce, recommendation system and other fields. In order to alleviate the problem of data sparsity in knowledge map with large amount of data, knowledge map representation learning represents entities and relationships as low dimensional real valued vectors, which effectively reduces the computational complexity and improves the efficiency of knowledge reasoning and knowledge map completion. In order to solve the problem that DKRL does not consider the relationship between sentences, which leads to the serious loss of entity description information, this paper proposes a new model T-CGRU. The model takes advantage of recurrent neural network, which can capture the context information of entity description. Convolution and maximum pooling are used to obtain the feature of entity description, and then the context word order of entity description information is extracted by cyclic operation, and finally the semantic representation of entity description information is obtained.
Risk Analysis for Abusing Blockchain Poisoning

In recent years, blockchain has become widely used as a new platform for information systems. Blockchain differs from conventional server-based systems in that it is tamper-resistant and highly available. However, the advantages of blockchain also work to the advantage of attackers. There is an area in the blockchain where users can embed arbitrary data via transactions, and prior works have shown that this area can be exploited for botnet C&C communications. In order to analyze what other types of attacks could develop from the contamination of the blockchain, we constructed specific attack scenarios and analyzed the ease of such attacks by conducting demonstrations experiments. We also proposed countermeasures against the attack scenarios and verified the effectiveness of these by conducting demonstration experiments.
Identifying Maturity Rating Levels of Online Books

With the huge amount of books available nowadays, it is a challenge to determine appropriate reading materials that are suitable for a reader, especially books that match the maturity levels of children and adolescents. Analyzing the age-appropriateness for books can be a time-consuming process, since it can take up to three hours for a human to read a book, and the relatively low cost of creating literary content can cause it to be even more difficult to discover age-suitable materials to read. In order to solve this problem, we propose a maturity-rating-level detection tool based on neural network models. The proposed model predicts a book’s content rating level within each of the seven categories: (i) crude humor/language; (ii) drug, alcohol, and tobacco use; (iii) kissing; (iv) profanity; (v) nudity; (vi) sex and intimacy; and (vii) violence and horror, given the text of the book. The empirical study demonstrates that mature content of online books can be accurately predicted by computers through the use of natural language processing and machine learning techniques. Experimental results also verify the merit of the proposed model that outperforms a number of baseline models and well-known, existing maturity ratings prediction tools.