Reviewerly/exHarmony huggingface dataset:
Remote Sensing Image Target Detection inspired by Density Map

Traditional target algorithms are mainly used in the processing of natural images. Due to the many different properties of remote sensing images and natural images, target detection algorithms in natural scenes have not made breakthroughs in remote sensing image research. It also contains a large number of small target instances, complex scenes often flood the feature information of small targets, so they face many difficulties in remote sensing image target detection. This article mainly combines the idea of density map segmentation. Firstly, through the sparse or dense distribution of objects, it guides the image to segment the foreground and background, minimizes the calculation of the background part in target detection, and reduces the impact of complex background on small target detection. Secondly, FPN is used for multi-scale feature extraction of the target, the low-level feature high-resolution and high-level feature high semantic information are combined, and Soft-NMS is used to replace NMS to reduce the false detection rate. Finally, the Faster R-CNN two-stage detector is used for target detection, and the goal of improving the detection accuracy of small targets is achieved.
Securely Improving Stability and Performance of PoW Blockchains Using Anchors

Proof-of-work (PoW) consensus generates blocks at random time instants, and consequently, adds weight to the blockchain at these random instants. This unsteady increase in chain weight over time along with the large network delay of blocks is the root cause of many security and performance problems such as high fork resolution times, vulnerability to double-spend and selfish mining attacks, and also a high block confirmation time. In this paper, we propose a novel signaling scheme of PoW called Anchors that can be implemented on any PoW blockchain system to improve their stability, reduce fork resolution times, prevent forks, strengthen the system against double spend attacks and reduce block confirmation times by half. In a partially synchronous model with a general adversary, we prove that a system with anchors has faster, more stable chain growth as compared to PoW systems without anchors and show that consistency security bounds established by prior work are preserved as in the underlying blockchain. We incorporate anchors in a reference Bitcoin implementation and perform experiments to support our claims in a 210 node test-bed with real world latencies. Our results show that anchors propagate at least 6 times faster than blocks, help resolve forks much faster than Bitcoin (without anchors) does and are able to even prevent forks from occurring.
Cloud-Assisted LLL: A Secure and Efficient Outsourcing Algorithm for Approximate Shortest Vector Problem

Approximating the shortest vector of a given lattice is one of the most important computational problems in public-key cryptanalysis and lattice-based cryptography. However, existing LLL reduction algorithm and its variants for this problem are too time-consuming for resource-constrained clients. To handle this dilemma, in this paper, we propose an efficient and secure outsourcing algorithm under the cloud environment. Compared with the prior Liu et al.’s algorithm, besides realizing the privacy preservation of client’s input/output information, satisfying verifiability and greatly reducing the local-client’s computational overhead, our algorithm is superior in the following aspects. First, our algorithm is technically concise. The main technique ingredient involved in our algorithm is a skillful combination of the unimodular matrix transformation and the Gram matrix, which is concise and effective. Second, our algorithm does not reduce the quality of the reduced basis, that is, the vector finally obtained by the client is as short as that of the vector generated by the client directly performing the existing reduction algorithm. Last but not least, our algorithm not only works for the LLL reduction algorithm, but also for any other algorithms that solve (approximate-)SVP with Euclidean norm.
PEDAGOGICAL DIAGNOSTICS OF STUDENTS' READINESS FOR INDEPENDENT WORK THROUGH BENCHMARKING TECHNOLOGY

This article considers the problem of forming students' readiness for self-organization in the process of independent work in higher educational institutions. The article covers the topic of pedagogical modeling. Comparison of the influence of the invariative and variable parts of the content block of the pedagogical model on the formation of students' readiness for independent work is given. The aim of the article is to analyze the impact of the content block of the model on the effectiveness of students' readiness formation during the organization of the process of self-study. Particular attention is paid to guiding students to perform independent work with a gradual progression to a higher level of independent activity, as well as the introduction of a special course, modern educational programs and electronic courseware, aimed at forming the readiness of university students to work independently through benchmarking technology. The author focuses on the adaptation and implementation of the pedagogical model as a whole, and the content block in particular in the educational process, based on the best experience. The article provides an analysis of the views of researchers Herrera, Stoff, and so on. The issue of the factors influencing the formation of students' readiness for independent work continues to be debatable and requires further research.
Secure Anonymous Query-Based Encryption for Data Privacy Preserving in Cloud

Data privacy protection is a paramount issue in cloud applications for the last decade. In addition, data encryption, which is the primary method to impart security in clouds, is proved insufficient to guarantee data privacy protection from some security issues like homogeneity and background knowledge attacks. Therefore, it is important to provide a security mechanism that provide not only anonymous data but also anonymous continuous queries. So, this paper proposes a new scheme (Moye) that tackles this challenge by protecting queries to be linked to specific sensitive data. Specifically, the proposed solution is based on the design of a hybrid implementation of public key encryption with keyword search (PEKS) and subset membership encryption (SME) cryptosystem to enhance both data and query privacy protection. In addition, this approach provides an efficient and anonymous data processing by using an optimized k-anonymity scheme. Doing so, the authors protect searchable keywords and queries from inside and outside guessing attacks for the effectiveness of the proposed solution.
合成性はいかなるいみで必要なのか

This paper is about the status of compositionality in our linguistic communication and its theorizing. The principle of compositionality has been regarded as an essential precondition under which semantic theories are built since the principle seems to properly explain some important facts about natural language, such as learnability and novelty. According to recent discussions, however, the precise understanding of compositionality casts doubt on its necessity. In this paper, I take a closer look at how we have started and improved our semantic investigations, and present a new argument for compositionality which is based on our linguistic intuition about the notion of un-synonymy and substitution. My point of the argument is that compositionality can serve as a useful tool for theorizing natural language semantics even if semantic rules themselves are described non-compositionally.
MTdt:Enabling Meta-Transactions for Data Tracking of Industrial Workflow

The decentralization and traceability of blockchain technology can realize the supervision of industrial Internet industry processes. As a jointly maintained distributed ledger, blockchain can provide an open, transparent and tamper proof information publicity platform. However, due to the huge amount of data and complex data interface of IOT equipment, blockchain cannot become a platform for direct access of IOT system. Nowadays, more and more decentralized applications are born. Building such services using emerging blockchain technology is a promising direction. We attempt to use the latest blockchain proxy strategy to solve data management problems in the system. We propose a blockchain sub-proxy model that traces workflows. Host data links to proxies, blockchain only store necessary information to ensure that subsequent transactions can be traced back to previous operations. Multiple tasks are represented by distributed agents to form a complete industry chain. In this model, industrial processes can be recorded asynchronously and on a large scale. Unlike centralized management processes, agents and blocks work together to ensure data storage efficiency and security.
내러티브 탐구를 통한 찾아가는 특허정보검색 및 전자출원 교육의 활성화 방안 연구

The purpose of this study is to analyze from the perspective of lifelong education the activation method of field education of intellectual property rights currently being implemented by the Korean Intellectual Property Office to reduce the information gap among the IP vulnerable. The conclusion of this study are as follows. First, it is required to develop a patented information search and e-application education program. Second, practical education that can be used in practice is necessary. Third, it is necessary to apply adult learning methods that are being studied in the lifelong education field. Fourth, it should also be improved to improve the quality and internalization of the patent information search and electronic filing education. Fifth, it is necessary to develop education contents and education methods centered on adult learners. Sixth, the demand for on-site training for patent information retrieval and electronic filing education has been high in universities and corporations, but it has not fully satisfied the quality and quantity. Seventh, it is necessary to expand educational opportunities by strengthening public relations with educational institutions and intellectual property rights education institutions that target to the local citizens.
A Self-Adaptive and Multiple Activation Function Neural Network for Facial Expression Recognition

Most neural networks are constructed by a large number of neurons with single activation function, in this way, huge number of neurons are used to fit the training data. It is very easy to overfit the training samples, and consequently resulting in poor generalization ability. Moreover, the more neurons are included in the network, the greater the computing power and storage are needed. In this paper, a Self-Adaptive and Multiple Activation Function Neural Network with a small number of neurons is proposed to recognize facial expression images. The experiment demostrates that the accuracy of our network is competitive with the existing networks with the accurancy is improved by 9.85% while the VRAM (Video RAM) and the GPU usage is reduced by 61.74% and 38.5% in average. When the noise perturbation is introduced into the neuron activation function group to enhance the generalization ability of network, the VRAM is kept the same, the average GPU usage is increased by 2.5% and the average accuracy is improved by 3.83% further comparing with the results of the proposed network without noise perturbation.
Education in the Age of Marginalism and the Alienation of Knowledge

Global changes of contemporary education are so large-scale that they need a se­rious reconsideration. Philosophy of education has viewed education as a highly valued human activity and form of socialization, though philosophers may have had different views on the aims and methods of education. The importance of ed­ucation was based on the fact that a society could develop if and only if its mem­bers had an appropriate education. Nowadays education is discussed in journals, like the Philosophy of Education published in Novosibirsk, in annual confer­ences and by various associations of thinkers, teachers and professors. The abso­lute majority of philosophers since Plato emphasizes the fundamental value of education and wants to make its quality better. But a current state of humankind does not require a universal education of population. Robots and neural networks force the humans out of production. The humans are lacking a long-time mem­ory as information turns easily accessible by means of Internet. Growth rates of innovations, discoveries and changes are so high that adaptation to them requires daily efforts of any individual. Many professions are dying. The current trend of social development is growing marginalization of humans, their societies and countries. Knowledge alienates from its creators and, in a much greater degree, from public. Humans are first of all consumers but consumers do not need edu­cation: why one should spend many years in school and institute to buy food or see a clip on a smartphone? A multistage system of education looks outdated. The need of such an institution is dying step by step, as complex operations may be more effectively made by machines with stable, reliable and efficient neural networks, than by humans who have to spend years to become specialists. Colos­sal growth of information makes its learning by an individual impossible. Alien­ated knowledge turns external force regarding an individual.
Dynamic Graph Attention Recurrent Neural Networks for Fact Verification

Fact verification is a recently introduced task, which is quired to judge the authenticity of a claim. Previous works mainly leverage extracting the semantic relations of claim and evidences, e.g., using the graph to model their relation. However, these graph-based models ignore the features evolution over different step graph propagation. In addition, they fail to capture the sequence features of evidence graph. To deal with the above issues, we propose a dynamic graph attention recurrent neural network for fact verification. Specifically, we design a dynamic memory mechanism to retain the node features in the updating process. Moreover, we propose a graph attention recurrent neural networks to aggregate features based on the logical order. Experimental results on FEVER dataset demonstrate the effectiveness of our model, especially in the multi-evidence scenario.
Using InterCloudFramework to migrate application and data between two public clouds

Cloud computing is an emerging computing platform for sharing resources over the internet, including infrastructure, applications, software, and business processes. Cloud computing is popular because of its public presentation, least cost, high availability. These days, many organizations use cloud services for their business activities to enhance their computing resource capabilities at lower prices. Cloud computing plays a vital role in research and academia to share resources and improve the teaching-learning process. One of the Cloud platforms' drawbacks is the data and application tightly coupled with the Cloud Service called Vendor Lock-In. Methods and procedures are available to migrate live data within the same cloud service providers. But to migrate the applications or data from one cloud service provider to another cloud service provider is complex. The paper proposes a novel methodology for migrating the application or data from one cloud service provider to another cloud service provider. The proposed approach presents CPU and RAM usage for the migration of applications or data. The paper also provides insights into the time taken for the Live Migration between heterogeneous clouds.
Distributed dynamic semantic query and reasoning for graph data

With the massive increase of graph data such as RDF and knowledge graphs, how to effectively improve the query efficiency of massive graph data has become a research hotspot in graph data management. However, most of the existing graph query research has the problem of lack of support for dynamic data query, and usually requires a high upfront cost to semantically integrate the data to provide effective semantic query and reasoning. In response to the above problems, this paper proposes a distributed dynamic semantic query and reasoning framework for graph data. Firstly, an initial query is created according to the user's search requirements, and then based on the ontology, Wikipedia, and other knowledge bases, the initial query is expanded by semantic concepts and semantic associations to create an expanded query. In a distributed environment, the expanded query is decomposed into multiple sub-queries. Then, the relevant semantic inference rules are matched, and query processing is performed on the distributed data so that multiple sub-query results will be generated. Finally, we evaluate and sort these results, aggregate the results of sub-queries with high scores and return them to the user. The framework supports dynamic semantic query and reasoning in a distributed environment, and can dynamically optimize and adjust the extended query and semantic reasoning rules in time, so as to provide support for real-time graph data query.
A new framework for deniable secure key exchange

A deniable secure key exchange protocol allows two parties to agree on a common secret while achieving two seemingly contradictory functionalities: authentication and deniability. The former requires each party to confirm the identity of the other while the latter requires any attacker (e.g., participant or eavesdropper) be unable to prove to a third party an honest party's participation. Designing an efficient secure key exchange with deniability is a challenging problem. In this paper, we first formalize the deniability model by requiring information theoretic deniability with an eavesdropping attack. The information theoretic deniability has the advantage that it can hold forever without any computational assumption. An eavesdropping attack (Di Raimondo et al., CCS'06) allows an attacker to apply eavesdropped transcripts into an active attack session. This gives an attacker more power to make the victim undeniable as he does not know the randomness of the transcript. We then propose an efficient, provably deniable secure framework of key exchange. Our deniability holds non-adaptively in the eavesdropping model. However, if we consider a model without an eavesdropping attack (which is practical in many scenarios), then our framework is proven adaptively deniable. This is important since no previous key exchange protocols can satisfy our adaptive and information theoretical deniability. We give a concrete realization for our framework that is more efficient than SKEME (Krawczyk, NDSS'96).
Explaining Neural Network Results by Sensitivity Analysis for Deception Detection

Previous researches show that people are only able to recognise deception with an accuracy of 54%. In this paper, using physiological signals from observers, we train a three-layer neural network, a long short-term memory (LSTM) and a multi-tasking learning neural network (MTL-NN). We demonstrate that examined models are able to identify deception with an accuracy up to 62%, surpassing the average accuracy of human deception detection. The superior deception recognition ability shows that these tools are capable of helping people discriminate against deception. Further, to improve the interpret-ability of neural networks, we extract rules from the trained models using sensitivity analysis. We find that the rule extraction methods using sensitivity analysis along with genetic algorithm (GA) based data reduction successfully explain all three neural network models. We hope the rule extraction methods can help to improve the interpret-ability of neural networks.
Denoising LAPAN A3 High-Resolution Digital Camera Images: A Comparative Study

Satellite images are usually degraded due to inaccuracy or limitations of the transmission and storage devices. Many researchers are still trying to remove noise from satellite images. Satellite image denoising techniques should not distort edges in an image. FFDNet is an image denoising solution that was recently developed. FFDNet has several advantages over other existing neural network denoisers, including a faster execution time and reduced memory footprint, as well as the ability to properly handle a wide variety of noise levels with a single network model. This technique is appealing for practical denoising applications because of its denoising performance and minimal computing load. High-Resolution LAPAN-A3 imagery has great potential for use in various remote sensing applications. But it has a problem related to its dynamic range which causes very high noise that will appear when we try to increase the brightness of the image. In this paper a different approach for LAPAN-A3 satellite imagery denoising, namely BM3D, FastNLM, and FFDNet. This method has tried to compare in terms of denoising performance, with three different cases of AWGN, model, and mixed noise. The result shows that FFDNet has the best performance among others, especially when recovering images from AWGN noise. But when trying to remove the model and mixed noise, all of these methods have almost the same output value, but FFDNet is slightly better than others.
Neural Networks Applied for Broadcast Channels

The famous mathematical definition of the capacity region of broadcast channels assumes that the channel is a degraded. However, not all wireless communication channels can be mathematically represented as degrades ones. Motivated by the artificial intelligence revolution, this paper provides a novel approach for tackling this problem. In particular, this paper utilizes artificial neural networks for providing a solution for the problem of converting a broadcast channel into a degraded one. Creating a degraded channel implies modeling the broadcast channel as a series of cascaded channels connecting the transmitter to the receivers sequentially which in turn implies finding a middle (relay) channel connecting the receivers. This process of finding the middle channel is performed in this paper utilizing different models of neural networks. This approach is applied for binary symmetric channels and Gaussian channel. For the Gaussian case, this paper provides a novel approach for establishing the middle channel by providing an estimation of the distribution of the middle channel itself, not only the crossover probability or the variance of the distribution. As a result, several possible extensions for practical channels (like Rayleigh) is suggested. Furthermore, this paper provides an abundant evidence that artificial intelligent is capable of modernizing classical information theory that faces major problems with the complexity of the analytical solutions.
Pre-trained Models and Evaluation Data for the Myanmar Language

Pre-trained language models (PLMs), which working for downstream natural language processing (NLP) tasks with the large corpus ubiquitously and favorably, have outstanding effectiveness due to their ability to learn universal language contexts as well as representations. Despite their success, most available models have trained on universal language, especially English. This phenomenon brings restriction to practical use of other low-resource languages, like Myanmar which is a language with underdeveloped electric resources and the previous work for it have been hampered by. Our aims in this paper are to train a monolingual pre-trained model for the Myanmar language and improve the state-of-the-art in its NLP tasks including Part-of-speech tagging and news classification. We presented transformer-based PTMs with four versions: BERT-small, BERT-base, ELECTRA-small and ELECTRA-base. Also we are the first to public large-scale Myanmar-specific models. Experimental results demonstrate the outperforms of our models. Our models and datasets will be available at the community to facilitate future research and downstream application for Myanmar NLP.