Reviewerly/exHarmony huggingface dataset:
Image Super-Resolution Reconstruction Based on Dual Residual Network

Research shows that deep learning algorithms can effectively improve a single image's super-resolution quality.However, if the algorithm is solely focused on increasing network depth and the desired result is not achieved, difficulties in the training process are more likely to arise.Simultaneously, the function space that can be transferred from a low-resolution image to a high-resolution image is enormous, making finding a satisfactory solution difficult.In this paper, we propose a deep learning method for single image super-resolution.The MDRN network framework uses multi-scale residual blocks and dual learning to fully acquire features in low-resolution images.Finally, these features will be sent to the image reconstruction module to restore high-quality images.The function space is constrained by the closedloop formed by dual learning, which provides additional supervision for the super-resolution reconstruction of the image.The up-sampling process includes residual blocks with short-hop connections, so that the network focuses on learning high-frequency information, and strives to reconstruct images with richer feature details.The experimental results of ×4 and ×8 super-resolution reconstruction of the image show that the quality of the reconstructed image with this method is better than some existing experimental results of image super-resolution reconstruction in subjective visual effects and objective evaluation indicators.
Mapping breakpoint types: an exploratory study

Debugging is a relevant task for finding bugs during software development, maintenance, and evolution. During debugging, developers use modern IDE debuggers to analyze variables, step execution, and set breakpoints. Observing IDE debuggers, we find several breakpoint types. However, what are the breakpoint types? The goal of our study is to map the breakpoint types among IDEs and academic literature. Thus, we mapped the gray literature on the documentation of the nine main IDEs used by developers according to the three public rankings. In addition, we performed a systematic mapping of academic literature over 68 articles describing breakpoint types. Finally, we analyzed the developers understanding of the main breakpoint types through a questionnaire. We present three main contributions: (1) the mapping of breakpoint types (IDEs and literature), (2) compiled definitions of breakpoint types, (3) a breakpoint type taxonomy. Our contributions provide the first step to organize breakpoint IDE taxonomy and lexicon, and support further debugging research.
Demonstration of an Algorithm for Quantum State Generation in Polarization-Encoding QKD Systems

We experimentally demonstrate a polarization-state generation algorithm using off-the-shelf components. The method was implemented using a laboratory QKD testbed running for 21 hours with an average QBER of 1.8%.
Toward an African Seismological Data Center

In recent years, Africa was hit by several severe earthquakes. Although the seismic monitoring in Africa has been evolving during the last decade, there has been no joint effort to collect, archive, and process the data, in order to publish earthquake bulletins at the continental or world level. Scientific projects (e.g., IGCP-601–659) dedicated to the seismotectonics and seismic hazard assessment of Africa allowed for the compilation of a database at the continental and regional levels. These research projects have exposed conspicuous aspects of the seismic activity, but also uncovered a severe deficiency of seismic and geophysical equipment and limited capacity throughout the continent. The newly established African Seismological Commission (established in 2014; https://www.afsc-web.org.za/ ) has highlighted the importance of having an African Seismological Data Center (Meghraoui et al., in Episodes 39:9, 2016) in its first General Assembly in Luxor, Egypt, and the second one in Hoceima Morocco. Many scientific, logistic, and financial challenges face this goal. An experiment has been initiated to collect and process data from seismological stations with open access located within Africa and to provide a platform for supplying a generic seismological bulletin using a SeisComp3-based system. The system is a real-time processing server at the Egyptian National Data Center. This system is currently under alpha testing utilizing internal auditing. The second stage will be in the first quarter of 2020 through beta testing after selecting a closed group to test the system’s efficiency and stability to be ready for the validation phase and then commissioning.
Reverse Attention U-Net for Brain Grey Matter Nuclei Segmentation

It has been reported that abnormal high iron deposition in brain grey matter nuclei is closely related to neurodegenerative diseases. Therefore, precise nuclei segmentation is beneficial to further explore the pathological mechanism of neurodegenerative diseases. However, segmenting nuclei manually is an extremely time-consuming work. To this end, we proposed a Reverse Attention U-Net (RAU-Net) for segmenting nuclei automatically, where multiple reverse attention (RA) modules are added between the encoder and decoder to aggregate different features. In the meanwhile, we use the estimations of segmentation maps at multiple levels as the guide information for the RA module. The model with RA module implicitly erases the predicted nuclei regions while highlighting background, which guides the network to explore the missing nuclei parts sequentially. Experimental results on our nuclei dataset imply that the RAU-Net performs favorably against the state-of-the-art methods.
Comprehensive Review of Various Optimization Algorithms for Image Captioning

Automatic Image captioning is a prominent and challenging research problem in the field of computer vision and natural language processing with applications ranging in biology, automotive and several other domains. Human brain being a complex organ, is able to constantly identify and establish all the intricate details of a scene in real-time, which a machine fails to imitate. However, with neoteric advances in Artificial Intelligence, image captioning has witnessed significant progress. This paper provides a comprehensive review of the latest advancements in caption generation with an emphasis on encoder-decoder and generative adversarial network frameworks. A comparative analysis is performed on existing approaches and methodologies along with an overview of commonly used datasets and evaluation metrics.
Physical Weaponization of a Smartphone by a Third Party

In the literature and media, the treatment of the dangers and exposures posed by smartphones has generally focused on information security or privacy concerns. There have also been reports of fires, explosions, electric shocks, or loss of phone functionality due to faulty design or manufacture. This article provides an overview of acute physical and physiological dangers of smartphones that could be induced or triggered by a third party. It proposes a categorical discussion framework to describe and define the dangers in terms of attack vectors, effects on the smartphone, harms, and potential culprits/instigators. Counterfeit smartphones are themselves a significant potential threat in this context. Finally, some possible solutions and mitigation are suggested as preventive measures. Some templates for threat assessment forms are also proposed.
Efficient Few-Shot Classification via Contrastive Pretraining on Web Data

Few-shot classification is challenging due to the limited data and labels. The existing algorithms usually resolve this problem by pretraining models with a considerable amount of annotated data, which share knowledge with the target domain. Nevertheless, large quantities of homogenous data samples are not always available. To tackle this obstacle, we develop a few-shot learning framework that prepares data automatically and still produces well-behaved models. This framework is implemented through conducting contrastive learning on unlabeled web images. Instead of requiring manually annotated data, this framework trains models via constructing pseudolabels. Additionally, since online data are virtually limitless and continue to be generated, the model can, thus, be empowered to constantly obtain up-to-date knowledge from the Internet. Furthermore, we observe that the generalization ability of learned representation is crucial for self-supervised learning. To present its importance, a naive yet efficient normalization strategy is proposed. Consequentially, this strategy boosts the accuracy of trained models significantly. We demonstrate the superiority of the proposed framework with experiments on miniImageNet, tieredImageNet, and Omniglot. The results indicate that our method has surpassed previous unsupervised counterparts by large margins and obtained performance comparable with some supervised ones.
Analysis and research on abnormal causes of lightning impulse test of 500kV transformer

Lightning impulse test of transformer is an essential item of equipment delivery test, and it is also to assess the bearing capacity of transformer insulation under lightning impulse. Taking an abnormal lightning impulse test of 500 <tex>$\text{kV}$</tex> transformer as an example, the background and method of lightning impulse test are described, and the treatment of lightning impulse anomaly is described in detail. In the process of abnormal cause judgment, the approximate location of the fault is preliminarily judged by repeating the test and changing the pressurization position, further disassembly and inspection shall be carried out to find out the discharge trace left on the device and determine the discharge path, then accurately judge the cause of the fault, At the same time, a complete solution to the internal discharge problem is provided, and the test is carried out again to verify the rationality and effectiveness of the conclusions and treatment measures.
Artificial Intelligence and Machine Learning-Based Security Enforcement Techniques for 6G Communication

The next generation 6G era is considered to be highly coupled with intelligent network management and orchestration, while 5G is completely renowned for micro-service architecture-based network cloudification. 6G has been revolutionized for satisfying the mandatory services and carry forwarding the potentialities of 5G to superior and intelligent level. 6G network structure is determined to be dynamic, densely deployed and extremely heterogeneous, and when integrated with a high degree of Quality of Service (QoS) completely transforms the complex architecture into a seamless operating process of classical networks. The immense role of Artificial Intelligence (AI) and Machine Learning (ML) is required for improving the paradigm of 6G for learning information from uncertain and dynamic environments. This integration of AI and 6G resembles a double-edged sword since the application of AI may positively influences the privacy or security of 6G on one side, and negatively introduces the possibility of security infringement into 6G on the other side. In specific, the self-sustaining networks in 6G are obtained by guaranteed application of intelligent security attack mitigation schemes and proactive threat discovery approaches that facilitate end-to-end future network automation. In this Chapter, a comprehensive review of AI and ML-based security enforcement techniques are contributed for improving reliability during robust data dissemination in 6G communications. It presents consolidated and solidified role of AI and ML towards the enforcement of security in 6G networks. In addition, it also demonstrates the challenges and solutions that are handled by the inclusion of AI and ML-based attack mitigation approaches concerning energy and security-based ultra-massive access.
Optimizing Constrained Engineering Problems using Opposition based Swarm Algorithms

Real-world engineering problems involve complicated mathematical functions with strict constraints and are difficult to solve analytically due to their complexity. Conventional methods fail to handle problems with multiple local optimum values and thus cannot guarantee global optimum solutions. Two-Phase Opposition-based PSO-GWO (TPOPSOGWO) is a novel hybrid optimization method that combines the exploration and exploitation capabilities of the Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) algorithms. The two optimizer techniques are carefully applied to the same search agents at different stages in this algorithm without changing the essential operations of the classical algorithms. When opposition-based learning is integrated to improve the diversity of the population, the system's performance increases even more. We evaluated the proposed approach's performance by comparing its mean and best function values to existing state-of-the-art methods. TPOPSOGWO surpasses all traditional swarm optimizers in many engineering issues, demonstrating its potential to aid industrial development whose challenges reside in limited and otherwise unexplored search areas.
Research on DM Algorithm Based on Association Rules and Its Application

In recent years, DM technology has attracted extensive attention in the information industry, which is the inevitable result of the contradiction between the rapidly growing amount of data and the increasingly poor amount of information. Systematic and in-depth research on DM technology is the objective requirement of global information development. There are many research fields of DM technology, among which association rules is an important research direction and has extremely important application value. DM is to mine useful information from a large amount of data. Its main goal is to mine valuable information for users from a large database. Facing the situation of &#x201C;a large amount of data, but little knowledge&#x201D;, DM technology came into being. This discipline originated in the field of database and artificial intelligence has formed a system and began to play a great role in practical application. This paper is mainly engaged in the research related to association rule DM algorithm.
Identity-Based Remote Data Integrity Checking Using Lattice Approach by Third-Party Auditor

In the environment of cloud computing, one of the old techniques is the identity-based signature scheme, where people who can access the signature can verify the person’s signature identity. In the due course of time, PKI has become one of the most well-known technologies, with applications in a broad range of fields. The PKI scheme works with certificate creation and management which helps the user to store the information in the cloud and whereas the organization to spread its data across multiple servers for security reasons. The major drawback to working with the certification process is it sometimes gets hacked by black hackers which results in violation of security policies. So, in recent days, identity-based cryptography came into existence which does not rely on this certification process. It only considers a unique ID that solely determines the user’s authorization, and this is treated as a public key for exchanging the information from the cloud. This mechanism reduced the cost of the certification process and the time involved by the user to activate their accounts. These are considered to be efficient and scalable systems, but they need a third-party module to verify the authentication of the user known as “Third_Party_Auditor” (TPA). In this paper, an enhanced approach based on the lattice is implemented, which verifies the user identity by combining the authentication elements like name (or) email id with a secret message.
A Geographical Analysis of the Systemic Risk by a Compositional Data (CoDa) Approach

After the financial crises in the last decades, the systemic risk is now recognized as an unavoidable issue to be constantly monitored. In this paper the contributions to the global systemic risk of different geographic areas are investigated. The analysis is performed by considering the aggregate systemic risk of the firms located in a specific geographic area as a part of the distribution of the global systemic risk. The techniques used for a such investigation are based on Compositional Data (CoDa) methodology, a quite recent approach very useful when the relevant information convoyed by the data is in the proportions among the parts and not in their absolute values or in their sum.
Turing Completeness and <i>Sid Meier’s Civilization</i>

We prove that three strategy video games from the <i>Sid Meier's Civilization</i> series: <i>Sid Meier's Civilization: Beyond Earth</i>, <i>Sid Meier's Civilization V</i>, and <i>Sid Meier's Civilization VI</i>, are Turing-complete. We achieve this by building three universal Turing machines--one for each game--using only the elements present in the games, and using their internal rules and mechanics as the transition function. We use these machines to prove the undecidability of these games under the unlimited turns, infinite map assumptions; and conclude by providing a sample execution of an algorithm--the three-state Busy Beaver--with one of our machines.
Threats in Open Environment

In the previous chapters, we have introduced solutions of protecting privacy against eavesdroppers in machine learning tasks. In this chapter, we will describe attack techniques of other three types of adversaries.
User Expectations When Augmented Reality Mediates Historical Artifacts

Augmented reality (AR) technology has been investigated previously to enhance the experience within various tourism contexts. This paper reports on a study that aims to investigate user expectations when AR mediates exploration of historical artifacts in a museum context. The users in this study used an iOS software application to explore several historical artifacts using an Augmented Reality experience. Twenty-four participants were interviewed to evaluate their expectations and satisfaction with the experience. Analysis of the responses showed that users prioritised storytelling techniques within the AR experience to understand the history and significance of the artifacts.
Interest Points Analysis for Internet Forum Based on Long-Short Windows Similarity

For Internet forum Points of Interest (PoI), existing analysis methods are usually lack of usability analysis under different conditions and ignore the long-term variation, which lead to blindness in method selection.To address this problem, this paper proposed a PoI variation prediction framework based on similarity analysis between long and short windows.Based on the framework, this paper presented 5 PoI analysis algorithms which can be categorized into 2 types, i.e., the traditional sequence analysis methods such as autoregressive integrated moving average model (ARIMA), support vector regressor (SVR), and the deep learning methods such as convolutional neural network (CNN), long-short term memory network (LSTM), Transformer (TRM).Specifically, this paper firstly divides observed data into long and short windows, and extracts key words as PoI of each window.Then, the PoI similarities between long and short windows are calculated for training and prediction.Finally, series of experiments is conducted based on real Internet forum datasets.The results show that, all the 5 algorithms could predict PoI variations well, which indicate effectiveness of the proposed framework.When the length of long window is small, traditional methods perform better, and SVR is the best.On the contrary, the deep learning methods show superiority, and LSTM performs best.The results could provide beneficial references for PoI variation analysis and prediction algorithms selection under different parameter configurations.
The Effectiveness of Using Fables in Developing EFL Elementary Students' Speaking Skills

The aim of this study was to investigate the effectiveness of using fables in developing EFL elementary students' speaking skills.This study used mixed method research, where one class (N=17) in the first term of the academic school year 2020/ 2021 was randomly selected from El-Massria Integrated Schools, 10 th of Ramadan.Quantitative and qualitative were collected through a speaking pre-posttest, a teacher log, speaking rubrics, and a satisfaction questionnaire.The results indicated that the program has a large effect on promoting the experimental group students' speaking skill in general and the speaking sub-skills in particular.All students of the experimental group showed an increase in their performance during the oral post-test.In addition, there was a high level of student satisfaction which indicates the extent of students' satisfaction and engagement with the proposed program.As to the qualitative data, it was found that the students were involved, enthusiastic, motivated, and interested in the program.It can be concluded that students' speaking skill through using fables was improved.Based on the results of the study, the researcher provided some recommendations and suggestions for further studies.
Virtual ASICs: Generalized Proof-of-Stake Mining in Cryptocurrencies

In proof-of-work based cryptocurrencies, miners invest computing power to maintain a distributed ledger. One known drawback of such a consensus protocol is its immense energy consumption. To prevent this waste of energy various consensus mechanism such as proof-of-space or proof-of-stake have been proposed. In proof-of-stake, block creators are selected based on the amounts of currency they stake instead of their expanded computing power. In this work we study Virtual ASICs–a generalization of proof-of-stake. Virtual ASICs are essentially a virtualized version of proof-of-work. Miners can buy on-chain virtual mining machines which can be powered by virtual electricity. Similar to their physical counterparts, each powered virtual ASIC has a certain chance to win the right to create the next block. In the boundary case where virtual electricity is free, the protocol corresponds to proof-of-stake using an ASIC token which is separate from the currency itself (the amount of stake equals your virtual computing power). In the other boundary case where virtual computers are free, we get a proof-of-burn equivalent. That is, a consensus mechanism in which miners ‘burn’ currency to obtain lottery tickets for the right to create the next block. From a technical point of view, we provide the following contributions:
A Denoising Method for Distant Supervised Relation Extraction Based on Deep Clustering

Distant supervision for relation extraction uses an external knowledge base as supervision signals to automatically label corpus and has attracted more and more attention. However, this method has an ideal hypothesis that all instances containing the same entity pairs represent the same relation, which will lead to a lot of noisy data and affect the training effect of classifier. Aiming at the noise problem of distant supervised dataset, we propose a denoising method based on deep clustering. First, the deep clustering method is used to train the text features and clustering centers, and the effect of clustering is improved by using the information of mask entity pairs. Then high quality training samples are obtained by removing the noisy data, so as to achieve the effect of denoising. Experimental results show that the proposed model can effectively improve the performance of relation extraction by comparing with the traditional denoising methods.