Reviewerly/exHarmony huggingface dataset:
Translation Principles of Tense Problem in Machine Translation in Process of Chinese-English Translation

When the Chinese language is translated into English, the verb tenses in Chinese are not clearly marked, but the tenses in English are directly indicated by the morphological changes of verbs, which makes it very difficult to keep the tense consistency before and after translation. In this paper, we use the method of transferring the source Chinese tense information to the target English. Firstly, the neural network is used to construct a Chinese tense tagging model to obtain Chinese verb tenses. In the process of translation, the alignment matrix of traditional attention mechanism is used to transfer the source tense to the target, and the probability of translation candidate words in the candidate translation word set that are inconsistent with the corresponding source word tense is reduced. In this way, the consistency of tense translation from Chinese to English can be basically realized. For temporal tagging model  and NMT system combined with temporal tagging, we have carried out detailed experiments, and the final experimental results show that the proposed model is effective.
Inquiries on the Everyday Online Conversations of Design: Typologies, Comments, and Threads

In this article, the author explores the nature of design writing in the online environment by looking at everyday conversations of design: the written conversations that occur as a combination of a design-centred blog or social media post, its reader responses, and affordances of its platforms. This study focuses on the authorial activities of readers on graphic design blogs and social network sites (SNS). These are located, analysed, and traced through three evidence-based inquiries referred to as Typologies, Comments, and Threads, using select English-language graphic design blogs and posts on SNS. Findings are presented visually, followed by a discussion on their disciplinary significance, democratic participation, technological mediation, and issues surrounding reader identity. The study also points toward problems finding and archiving the conversations, and positioning them within the broader context of design writing and literature.
Composition of Quantum Operations and Their Fixed Points

Fixed point sets of quantum operations are very important in correcting quantum errors. In this work, using single qubit two-state quantum system; Bloch sphere, we consider the situation whereby the quantum system undergoes evolution through multiple channels (composition of quantum operations). The outputs of both Bloch sphere deformation and amplitude damping were analyzed using the density matrix representation and the Bloch vector representation respectively. Also, the common fixed point sets were characterized.
A Secure Multi-party Computational Adversary Selection Model Based on Time-Varying of Entropy

In cryptography, secure multi-party computation involves multi-party participant entities, each of them has its own secret input and wants to jointly compute a function through some interaction protocol. In this process, cheating by any of the parties will cause errors in the final result. In order to prevent parties from cheating, many experts have proposed protocols with semi-honest security, malicious security and covert security. However, existing protocols can only detect and deal with malicious attackers after being attacked. This passive approach will not only consume more resources, but also cause excessive losses for participants. To solve the above problems, this paper proposes an optimal participant selection model based on the time-varying entropy. Over time, we evaluate the safety entropy of the secure multi-party computation model. Once the entropy value exceeds the expectation, we will reselect the adversary through the reputation mechanism, thus always ensuring the safe state of the model. Our model can better predict the unsafe state of the system, and the security of the model can be guaranteed by the choice of the adversary. At this same time, it has better efficiency in the design of password protocol.
Passive and Active Reconnaissance: A Social Engineering Case Study

Love scams, online scams and how absurd as it may seem, still causes people to lose their money. This issue happens because some people are still susceptible to social engineering. Our investigation attempts to find out how people could be exploited for this type of social engineering attack on social media. Social engineering could be categorized into passive and active reconnaissance. Our experiment involves individuals such as UNITEN students and Instafamous to find out if such attempts would deceive them. The methodology proposed is the attack cycle that consists of four steps: information gathering, establishing relationship and rapport, exploitation, and execution. This case study will also explore the possible uses of valuable information of target that can be gathered based on passive and active reconnaissance.
A Voltage Pulse Generator for Measurement-Device-Independent Quantum Key Distribution

Measurement-Device-Independent Quantum Key Distribution (MDIQKD) protocol has been proved that it is unaffected by all hacking attacks, and ensures the security of information theory even when the performance of single-photon detectors is not ideal. Fiber channel has been used by the previous MDIQKD experimental device. However, the signal attenuation increases exponentially as the transmission distance increases. In order to overcome this, we regard free space as the channel of signal transmission, and the signal attenuation increases square as the transmission distance increases (regardless of the atmospheric scattering), which can effectively reduce the signal attenuation trend. In order to implement the free space MDIQKD experiments, a modulation module is needed to modulate the wide pulse chopping, decoy-state, normalization, phase encoding and time encoding. In this paper, we present the design of the Voltage Pulse Control Module for the free space MDIQKD.
DAD: Deep Anomaly Detection for Intelligent Monitoring of Expressway Network

In order to improve the real-time efficiency of expressway operation monitoring and management, the anomaly detection in intelligent monitoring network (IMN) of expressway based on edge computing and deep learning is studied. The video data collected by the camera equipment in the IMN structure of the expressway is transmitted to the edge processing server for screening and then sent to the convolution neural network. Then video data was preprocessed after the edge calculation to generate the training sample set, then send it to the AlexNet model for feature extraction. SVM classifier model is used to train the feature data set and input the features of the test samples into the trained SVM classifier model to realize the anomaly detection in the IMN of expressway. The experimental results showed that the method has better detection effect than the machine learning method and the small block learning method, and the detection time is greatly shortened.
Automatic Parameter Selection of Granual Self-organizing Map for Microblog Summarization

In this paper, a neural-network-based unsupervised classification technique is proposed for summarizing a set of tweets where informative tweets are selected based on their importance. The approach works in two stages: in the first stage, the concept of a self-organizing map (SOM) is utilized to reduce the number of tweets. In the second stage, a granular self-organizing map (GSOM), which is a 2-layer feed-forward neural network utilizing the fuzzy rough set theory for its training, is considered for clustering the reduced set of tweets. Then, a fixed length summary is generated by selecting tweets from the obtained clusters. GSOM is having a set of parameters; proper selection of these parameter values influences the performance. Therefore an evolutionary optimization technique is utilized for the selection of the optimal parameter combinations. We have evaluated the efficacy of the proposed approach on four disaster-related microblog datasets. Results obtained clearly illustrate that our proposed method outperforms the state-of-the-art methods.
“Towards Re-Inventing Psychohistory”: Predicting the Popularity of Tomorrow’s News from Yesterday’s Twitter and News Feeds

Rapid advances in machine learning combined with wide availability of online social media have created considerable research activity in predicting what might be the news of tomorrow based on an analysis of the past. In this work, we present a deep learning forecasting framework which is capable to predict tomorrow's news topics on Twitter and news feeds based on yesterday's content and topic-interaction features. The proposed framework starts by generating topics from words using word embeddings and K-means clustering. Then temporal topic-networks are constructed where two topics are linked if the same user has worked on both topics. Structural and dynamic metrics calculated from networks along with content features and past activity, are used as input of a long short-term memory (LSTM) model, which predicts the number of mentions of a specific topic on the subsequent day. Utilizing dependencies among topics, our experiments on two Twitter datasets and the HuffPost news dataset demonstrate that selecting a topic's historical local neighbors in the topic-network as extra features greatly improves the prediction accuracy and outperforms existing baselines.
eBook surveillance and the threat to freedom of association

eBook reader surveillance can have a strong negative effect on freedom of association and freedom of expression. In this paper we consider the potential for Kindle surveillance through a review of two of Amazon’s patents. We then consider the impact of reader surveillance on freedom of association through a study of US and European law. Particular emphasis is placed on the chilling effect of loss of anonymity in a group setting.
Systematic Literature Review on Global Software Development Risks in Agile Methodology

Background: The word “Global Software Development” can be described as the development of software, with development teams spread across different geographical locations. Problem statement: The issues arise when there are gaps in information, workflows or processes, policies and others in the world. Objective: This paper aims to build an understanding of the risk in Global Software Development. Then, to identify category risk in Global Software Development and how Agile can reduce or mitigate their challenges. Method: This review paper using the standard systematic literature review method by Kitchehamm by reviewing and analyzing the relevant state-of -art techniques and approaches in the journal libraries based on the research questions. Results: The findings show that communication in Agile Global Software development is the main risk challenge. Contribution: The contributions of this paper may support the academician to propose and validate an enhanced approach to solve the issues if risk management in Agile GSD environment and assist the practitioners in choosing the most suitable and relevant method based on the requirement of Agile GSD project. This review paper is also focusing on the contribution to the Software Engineering Management and Software Engineering Models and Methods Knowledge Area.
Constructing a Data Visualization Recommender System

Choosing a suitable visualization for data is a difficult task. Current data visualization recommender systems exist to aid in choosing a visualization, yet suffer from issues such as low accessibility and indecisiveness. In this study, we first define a step-by-step guide on how to build a data visualization recommender system. We then use this guide to create a model for a data visualization recommender system for non-experts that aims to resolve the issues of current solutions. The result is a question-based model that uses a decision tree and a data visualization classification hierarchy in order to recommend a visualization. Furthermore, it incorporates both task-driven and data characteristics-driven perspectives, whereas existing solutions seem to either convolute these or focus on one of the two exclusively. Based on testing against existing solutions, it is shown that the new model reaches similar results while being simpler, clearer, more versatile, extendable and transparent. The presented guide can be used as a manual for anyone building a data visualization recommender system. The resulting model can be applied in the development of new data visualization software or as part of a learning tool.
Filler-gaps that neural networks fail to generalize

It can be difficult to separate abstract linguistic knowledge in recurrent neural networks (RNNs) from surface heuristics.In this work, we probe for highly abstract syntactic constraints that have been claimed to govern the behavior of filler-gap dependencies across different surface constructions.For models to generalize abstract patterns in expected ways to unseen data, they must share representational features in predictable ways.We use cumulative priming to test for representational overlap between disparate filler-gap constructions in English and find evidence that the models learn a general representation for the existence of filler-gap dependencies.However, we find no evidence that the models learn any of the shared underlying grammatical constraints we tested.Our work raises questions about the degree to which RNN language models learn abstract linguistic representations.
How to Accurately Estimate the Cost of Cloud Migration

Many organizations underestimate the true costs of moving to the cloud, but it's possible to effectively and accurately plan for a migration.
Categorizing Volcanic Seismic Events with Unsupervised Learning

We explored three different clustering-based classifiers to categorize two different volcanic seismic events and to find possible overlapping signals that could occur at the same time or immediately after seismic events occurrence. The BFR classifier with k = 2 was chosen as the best out of 27 explored models statistically (p <; 0.05), reaching a mean of accuracy score of 88%. This result represents a satisfactory and competitive classification performance when compared to the state of art methods. The CURE classifier with k = 3 attained a mean of accuracy value of 87% at p <; 0.05, allowing it to be the only model capable of detecting seismic events with overlapping signals. Therefore, the proposed clustering-based exploration was effective in providing competitive models for seismic events classification and overlapped signal detection.
중국인 한국어 학습자의 모국어 전이 연구 - 2인칭 대명사를 중심으로 -

This study amid to examine how the translative mode of native language make an effect on Chinese Learners when studying Korean language, expecially on the second person. Due to the culture's difference, it is one of the most difficult part to use the second person expression exactly. so two forms of mistakes were pointed out, which is addition, substitution and form error. Also, based on this, some suggestions were given to improve the ability of Chinese learners on using the second person expression.
Enhancing IoT-based critical diagnosis in emergency rooms through intelligent video surveillance

The detection of critical patients in Emergency Departments is often a critical task, especially in situations in which the number of patients to be monitored is high with respect to the available medical personnel. To this end, IoT data analytics can provide a useful support in automatically monitoring the status of patients, and detect the most critical ones. This paper presents a knowledge representation frame-work enabling the intelligent video surveillance of patients, which can be used in combination with IoT-based systems to enhance the detection of critical patients in emergency departments, and alert medical personnel. We also describe a clinical scenario related to the early treatment of sepsis in the emergency department, and show how the proposed framework can enhance the detection of such critical disease.
Strategic Deconfliction of Heterogeneous Multiple Unmanned Aerial Vehicles using Direct Trajectory Optimization

This paper deals with a strategic deconfliction of heterogeneous multiple unmanned aerial vehicles (UAVs) via trajectory optimization. A trajectory optimization problem for multiple UAVs with different specifications is defined with constraints to accomplish deconfliction between UAVs and avoidance against obstacles in the environment and the objective function to minimize the total control effort of the UAVs. The takeoff and landing times of UAVs are considered as optimization parameters and the optimization problem is constructed with multiple phases defined with take-off and landing times of UAVs. Gauss pseudospectral optimization method considered in this paper decides not only the guidance commands for UAVs but also the take-off and landing times of UAVs by determining the durations of phases to minimize the objective function. A case study is performed to illustrate how a given strategic deconfliction problem is defined as a trajectory optimization problem for Gauss pseudospectral optimization method and to show the example result of the strategeic deconfliction with trajectory optimization.
Assuming Identities Online: How Linguistics Is Helping the Policing of Online Grooming and the Distribution of Abusive Images

In this chapter we seek to elucidate the potential of linguistic analysis in the undercover pursuit of criminals online. We examine the relationship between language and online identity performance and address the question of what linguistic analysis is necessary and sufficient to describe an online linguistic persona to the extent it could be successfully assumed by another individual. We compare the performance of trainee undercover officers (UCOs) at online impersonation of a specific individual before receiving any linguistic training to their performance after such input. We also report on a series of experiments in which participants engaged with each other over Instant Messaging (IM), before an impersonation situation was engineered. Information was collected about what level of accuracy and confidence individuals can detect the substitution of one interlocutor with another, what linguistic criteria give rise to such suspicions, and how individuals prepare for impersonation tasks. We also draw on other data sets including from police training and operational settings where identity disguise is employed online in the context of investigations into child grooming and the distribution of abusive images.
Developing Digital Fluency among Teacher Educators: Evidence from Tanzanian Schools of Education.

This study explores how teacher educators develop digital fluency as a key competence in the contemporary world. Largely, the teacher education curriculum that the educators studied did not integrate digital fluency as a key competence. The study established dimensions of digital fluency as a concept through reviewing the literature. The study was qualitative, with data collected through interviews with ninety educators, including the management of university schools of education. The findings show that both individual mechanisms, particularly individualized learning, practice, engagement in research and consultancy, and institutional mechanisms - specifically training, infrastructure and the delivery of online programmes - contribute to the development of the digital fluency of teacher educators. The study contributes to the existing body of literature regarding digital fluency as a key competence for teacher educators. As regards practice, on-going training should be aimed at professional development, supported by continuous practice.Keywords: Digital Fluency, digital skills, digital competencies, teacher educators, pre-service teachers, Tanzania
사회적 공헌을 위한 정부의 문화예술교육 현황 및 발전방안

Since the social value estimation of how culture and art education contributes to our society is an important subject of study, the current condition of social and cultural art education implemented by the government was analyzed and the future development plan was studied based on this. Social and cultural art education support project could be categorized by class, support project, participating institution, the number of participants, and the number of programs.According to the result, even though the overall growth trend has been shown, issues such as the change of business direction and lack of connection among government departments have been addressed with lack of stability and persistency of support project until now.Considering the mobility of life and the change in values, the officials should reestablish the direction to reconstruct the education promotion system and try to connect with other departments so that culture and art education could perform its capability more effectively in corporate social responsibility.
Interpretability of Black Box Models Through Data-View Extraction and Shadow Model Creation

Deep learning models trained using massive amounts of data, tend to capture one view of the data and its associated mapping. Different deep learning models built on the same training data may capture different views of the data based on the underlying techniques used. For explaining the decisions arrived by Black box deep learning models, we argue that it is essential to reproduce that model’s view of the training data faithfully. This faithful reproduction can then be used for explanation generation. We investigate two methods for data-view extraction: Hill Climbing approach and a GAN-driven approach. We then use this synthesized data for explanation generation by using methods such as Decision-Trees and Permutation Importance. We evaluate these approaches on a Black box model trained on public datasets and show its usefulness in explanation generation.
Semantic Segmentation for Visually Adverse Images – A Critical Review

Semantic Segmentation is one of the high-end visual tasks that has remained a topic of interest in various domains. Segmentation of visual scenes was confined to the extraction of object boundaries present in the image data. However, with the progressive developments in technology, machines are expected to produce assistive decisions to aid versatile tasks. Subsequently, these assistive decisions are dependent on efficient results and must project information on a granular level from the visual scenes. The visual scenes are usually of vast variety depending on the scenarios in which the image data is captured. As per recent trends, semantic segmentation is still an open area of research, one of its worth mentioning challenges is to handle the visually adverse images. These visually adverse images are the result of low light/ high light, rain, fog and sometimes in the form of too many objects present in the scene. The study sheds light on the non-trivial problem and diverts attention to the gaps present in literature by providing in-depth critical analysis. This study comprehensively presents unidentified problems prevailing in existing semantic segmentation techniques. A critical literary study is conducted to examine the working mechanics of existing solutions to identify their limitations to produce accurate results for the visually adverse scenarios. The study discusses some of the possible reasons which result in erroneous semantic segmentation results for visually adverse images. Finally, the problems and challenges to be tackled are concluded which highlight the future direction of analysis.