Reviewerly/exHarmony huggingface dataset:
Visualization Mapping Analysis of Emergency Management Scientific Research Big Data during COVID-19

Objective: To better understand the research status of emergency management during the outbreak of COVID-19 in China, and further improve the level of China's emergency management. Method: In this paper, the big data of emergency management research during COVID-19 was visualized and analyzed, and the visual knowledge map was drawn to study the big data of China's emergency management literature. Conclusion: This paper reveals the current situation and characteristics of China's emergency management during COVID-19 by using the visual knowledge map, summarizes the research trend in the future, and points out the new ways to improve China's emergency management ability in the period after the COVID-19.
Systematical Alignment of Business Requirements and System Functions by Linking GQM+Strategies and SysML

Many businesses strive to align their business goals, strategies, and systems before and during system development as the studies of GORE and alignment of business/IT show. However, the level of success derived from their interrelationships may be ambiguous because the relationships between goals, strategies, and systems are often vague. As agile development becomes more prevalent, it is becoming more difficult to adjust a system to accommodate changing goals and strategies. By linking GQM+Strategies and SysML, we propose a method to systematically align business requirements and system functions. Then we evaluate this method from the viewpoint of traceability from business requirements to system functions and the solution to fill the gap between them. This proposed method not only helps maintain consistency from business requirements to system functions but also solves the gapbetween customers' needs and the developed system while simultaneously dealing with changing strategies and requirements.
A Model for Filtering Spam SMS Using Deep Machine Learning Technique

In recent years, a substantial growth has been experienced in the mobile phone market.A cumulative of 432.1 million mobile gadgets has delivered in the second quarter of 2013 with an increment of 6.0% year over year.As the acquisition of cellphone gadgets has become common, Short Message Service (SMS) has developed into a multibillion-dollar business.A rush in the quantity of unwanted business notices sent to cell phones utilizing text messages has additionally expanded due to the increased popularity of mobile platforms.This rise attracted attackers, which have resulted in SMS Spam problem.This study presents model for SMS spam filtering classification using Deep Machine Learning Techniques.The system uses the deep machine learning model(MLPNM) in tensorflow and keras framework to classify SMS Message dataset containing 5574 messages.The dataset was read from directory using the pandas.read_csvfunction.The dataset was cleaned to make sure there are no null values present.The Deep learning model was built with a total of three dense layer with takes in 8672 inputs and 1 output, a batch size (batch size equals the total dataset thus making the iteration and epochs values equivalents) of 32 and epoch value of 50.This trained model was saved and exported into web for easy access and testing with the help of python flask, so that users make various input SMS message.Bootstrap framework (HTML and CSS) was use to design the Front End, while for the Backend, python programming language was use.The results of the test showed accuracy of 99.82% of all input message classified as either ham(legimate) or spam to verify if it's actually a Spam SMS message or a Ham (Legitimate) SMS spam messages.
Inquiry into the Data Availability and Transparency Bill 2020 and the Data Availability and Transparency (Consequential Amendments) Bill 2020

The Hub has made a submission to the Senate Finance and Public Administration Legislation Committee Inquiry regarding the Data Availability and Transparency Bill 2020 and the Data Availability and Transparency (Consequential Amendments) Bill 2020. This is a joint submission by the Allens Hub for Technology, Law and Innovation, the Australian Society for Computers & Laws and the UNSW Institute for Cyber Security.
Improved Video Compression Using Variable Emission Step ConvGRU Based Architecture

Video content over the Internet is growing rapidly. There arises a need for more powerful and proficient video compression techniques to handle beating stress of voluminous video over the limited bandwidth. Traditional video compression mechanisms are hand-designed, and their architecture is an amalgamation of different modules designed in such a way that different modules are optimized individually instead of achieving end-to-end optimization of the whole network. The positive upshots of deep learning in image compression emerged a breakthrough for video compression as well. ConvGRU, a convolutional recurrent neural network, comprises productive edges of both RNN and CNN. The proposed architecture consists of ConvGRU as basic building blocks implemented in both fixed and variable bit rate models. The experimental results demonstrated that randomized emission step ConvGRU-based architecture gives better performance and provides a base framework for further optimization enhancements.
Simulation of generalized Gamma distribution with maximum likelihood estimation and expectation-maximization algorithm on right censored data type 1

The Generalized Gamma distribution is very suitable for modeling data with various forms of hazard (risk) functions, which makes the Generalized Gamma distribution useful in survival analysis. Survival analysis aims are to predict chances of survival, disease recurrence, death, and other events over a period of time. One characteristic of survival data is the possibility of sensors. Let X be the life span of the person being studied and the right censorship time of Cr, X is assumed to be independent with the probability density function f(x), the survival function S(x), and the hazard function h(x). A person's X life span will be known if X is less than or equal to Cr. If X is greater than Cr, the individual X survives or is censored right now. Statistical inference, especially parameter estimation is needed in analyzing empirical data. Obviously the estimation results obtained are expected to be a good estimator, namely to meet the nature of unbiased and minimum variance. This paper will discuss the results of the estimation of Generalized Gamma distribution parameters with type 1 right censored data through simulations using the Expectation Maximization method and the Maximum Likelihood Estimation method. The simulation is conducted by generating data with the sample size: 25, 50, 100, 200, 500, 1000, 1500 and 2000 as well as determining censored data of 10%, 20% and 30% by first setting the parameters used which are obtained from the data of patients with gastric cancer namely α = 1.0649, β = 1,072, θ = 59.766. Based on the results obtained from the simulations on the two estimation methods that the parameter estimation using the Maximum Likelihood Estimation method is better than the Expectation Maximization method because it provides a smaller bias and MSE value where the larger the sample size used, the estimated parameter value will get closer to the parameter in fact. In addition, the Expectation Maximization method can also be used as an alternative estimation of generalized gamma distribution parameters with type 1 right censored data because it has a bias value and MSE approaching the MLE method.
Video topic detection on Micro-Blog using Relational Topic Model

To enhance the performance of the personalized tag recommendation method, a microblog video topic discovery algorithm based on common knowledge atlas analysis was proposed.Firstly, graphical form was used for expressing latent local expression in the microblog video topic and top-k similar user discovery of users represented by user topic distribution.Then, the frequency of all the tags occurring in these users was calculated and the tags most relevant with users were recommended.Next, to mine potential topic information, enhanced cosine similarity RTM model with penalty term was used for naming the tag of topic for microblog video, which enhanced the impact of united modeling on tag generation for potential topic and might discover the relationship between global tab and topic.At last, the real experiment result showed that the recommended method was superior to typical tag recommendation algorithms of several selected microblog video topics.Meanwhile, the validity of the algorithm was verified.
Inferring Personality from GitHub Communication Data: Promises a Perils

Personality plays a significant role in our lives; it does not only influence what we think, feel, and do, but also affects what we say about what we think, feel, and do. In software engineering (SE), it might help in improving team composition through a combination of personalities within a team, and it could help explain work preferences and work satisfaction. Earlier studies in the field of software engineering have focused on extracting personality from developers with the use of questionnaires and automatic tools such as psycholinguistic tests. Psycholinguistic tests infer personality based on the words people use. As taking questionnaires is time-consuming, the interest in automated tools has grown. However, there is a lack of studies comparing different psycholinguistic models on actual SE data to validate to what extent these tools apply to software engineering. In this study, we compare two well-established academical models proposed by Yarkoni and Golbeck et al. and the popular industrial model Personality Insights by IBM. We use the three models to infer personality from comments on open-source projects on GitHub and compare the found scores to a ground-truth obtained through a questionnaire among software developers. In this study, we establish a baseline and compare three models on their performance to this baseline. We show three methods to perform almost equally when mean-centered, indicating the three methods may work on different scales. We show log-transformations to improve LIWC category scores found by reducing the effect of outliers and give recommendations for thirteen preprocessing steps to improve the inference on SE data. We found 600 to 1200 words per person to provide sufficient accuracy while remaining resource-aware and recommend a minimum of a hundred words for all three methods. Furthermore, we do not find enough evidence for discrimination by all three methods for people proficient in English compared to those who found themselves non-proficient in English. We find existing psycholinguistic models to be most useful for software engineering when used on a group or team level. When used on an individual level, one should take into account possible inaccuracies and consider the potentially harmful impact the misuse or misinterpretation of scores may have on an individual.
Analysis of Web-based Learning Interface Design based on Expertsâ€™ Verification for Higher Education

Recently, the Web-based learning (WBL) platform, particularly for higher education, has become more crucial due to the Covid-19 pandemic. Thus, due to the increased use of WBLÂ Â in higher education, an effective WBL interface design for higher education is truly important in order to attract students to use WBL and to further keep them engaged during learning via the Web-based platform. Therefore, the aim of this study was to determine the aesthetics of web interfaces based on expertsâ€™ opinions. This study adopted a quantitative research approach involving a data-gathering survey. Fifteen (15) WBL interfaces were designed based on nine (9) design principles which were balance, proportion, simplicity, alignment, movement, hierarchy, consistency, contrast, and proximity. The results of this study discovered that nine (9) WBL interfaces were determined by the experts as aesthetic interfaces, five (5) WBL interfaces as non-aesthetic and 1 (one) WBL interface was considered neither aesthetic nor non-aesthetic. This finding revealed that six (6) out of nine (9) interfaces had the balance design principle. However, balance was also in most non-aesthetic interfaces. A possible reason that balance was the most design principle in both the aesthetic and the non-aesthetic interfaces is that when designing WBL interfaces, there is a need to consider the combination of the design principles as a whole, and not count the design principles individually. In conclusion, this study's findings could contribute to the knowledge in the Human Computer Interaction domain, specifically in the interface design area.
A Generalized Framework for Preserving Both Privacy and Utility in Data Outsourcing

Property preserving encryption techniques have significantly advanced the utility of encrypted data in data outsourcing. However, while preserving certain properties (e.g., the prefixes or order of the data) in the encrypted data, such encryption schemes are typically limited to specific data types (e.g., IP addresses) or applications (e.g., range queries over order-preserved data), and highly vulnerable to the emerging inference attacks which may greatly limit their applications in practice. In this paper, to the best of our knowledge, we make the first attempt to generalize the prefix-preserving encryption to make it applicable to more general data types (e.g., geo-locations, market basket data, DNA sequences, numerical data and timestamps) and secure against the inference attacks. Furthermore, we present a generalized multi-view outsourcing framework that generates multiple indistinguishable data views in which one view fully preserves the utility for data analysis, and its accurate analysis result can be obliviously retrieved. We empirically evaluate the performance of our outsourcing framework against two common inference attacks on two different real datasets: the check-in location dataset and network traffic dataset. The experimental results demonstrate that our proposed framework preserves both privacy (with bounded leakage and indistinguishable data views) and utility (with 100% analysis accuracy).
A Novel Scheme for Encryption and Decryption of 3D Point and Mesh Cloud Data in Cloud Computing

With the rapid evolution of Internet technology, cloud computing has taken a major role in managing large amounts of data. The major concerns in this domain are security and privacy. Therefore, attaining a reliable level of confidentiality in the cloud computing environment is a pivotal task. Among different types of data stored in the cloud, the 3D point and mesh cloud data are increasingly popular in recent days, due to the growth of 3D modelling and 3D printing technologies. Hence, in this research, we propose a novel scheme for preserving the privacy of 3D point and mesh cloud data. Chaotic map-based data encryption is a recently trending research area due to its unique properties like pseudo-randomness, deterministic nature, sensitivity to initial conditions, ergodicity, etc. Hence, chaotic systems can be regarded as systems of deterministic randomness. We exploit these properties of chaotic systems to encrypt the 3D point and mesh cloud data. To boost encryption efficiency significantly, in this work, we propose a novel chaotic map. The sequence generated by this map is used to transform the coordinates of the cloud data. The improved range of the proposed map is depicted using bifurcation analysis. The quality of the proposed chaotic map is also analyzed using metrics like Lyapunov exponent and approximate entropy. We also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical attack. The experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.
COVID-19 Decision-Making Model using Extended Plithogenic Hypersoft Sets with Dual Dominant Attributes

Plithogenic Hypersoft set is the multi-argument function with plithogenic universe of discourse and single dominant attribute value. The theory of plithogenic sets deals with the attributes and it is of the form (P, a, V, d, c) characterized by the degree of appurtenance and contradiction. This paper introduces the approach of plithogenic hypersoft sets with two independent dominant attribute values pertaining to each attribute to handle the dual system of decision making. The proposed decision-making model is validated with the data of the present COVID -19 pandemic situations. The objective of the model is to rank the patients being identified as asymptotic and affected using the Frequency Matrix Multi Attributes Decision-making system. Combined plithogenic hypersoft representations of the degree of appurtenance between the patients and the attribute values make the decision-making model more comprehensive and feasible. The developed model can be extended to another decision-making environment with various forms of the degree of appurtenance.
Inner Workings of Collateral-based Stablecoins and its Implications

While early cryptoassets such as Bitcoin have reputation for high price fluctuation and limited scalability, stablecoin, a new class of cryptographic token, emerged with the purpose of mitigating price volatility. What distinguishes stablecoin from pre-existing cryptocurrency is its use of collateral and use of specific pegging mechanism to mitigate volatility risks. The paper sheds light on the inner workings of stablecoins by analyzing stabilizing property with various aspects of collateral mix as well as pegging mechanism. Specifically, it evaluates initiatives to effectively utilize gold and government bonds as combinatorial collateral.
Inter-Language Translation from Chinese to Indonesian: Strategies and Adjustments

Translation usually involves two languages, the source language, and the target language. However, there is a certain situation that compels the use of an intermediary language for translating a source language (SL) text into a target language (TL) text. For example, when the source language texts (such as texts in Chinese) are not easily available in the target language country (such as Indonesia). This paper analyzed the strategies and adjustments made by the translators in doing inter-language translation or translation through an intermediary language, from Chinese to English to Indonesian. The purpose of this research is to compare two versions of translation, the first being the Indonesian translation from English as an intermediary language (IT) and the second is the Indonesian translation directly from Chinese (DT). Research revealed that IT used loss strategy while DT used gain strategy in the form of syntactic and semantic adjustments because there are many culture-specific items of the source language text that are unknown to either English or Indonesian. The findings imply that the translators should have enough skill and knowledge to enable them to accurately communicate the message of the source text to the target text audience.
LDLE: Low Distortion Local Eigenmaps.

We present Low Distortion Local Eigenmaps (LDLE), a manifold learning technique which constructs a set of low distortion local views of a data set in lower dimension and registers them to obtain a global embedding. The local views are constructed using the global eigenvectors of the graph Laplacian and are registered using Procrustes analysis. The choice of these eigenvectors may vary across the regions. In contrast to existing techniques, LDLE can embed closed and non-orientable manifolds into their intrinsic dimension by tearing them apart. It also provides gluing instruction on the boundary of the torn embedding to help identify the topology of the original manifold. Our experimental results will show that LDLE largely preserved distances up to a constant scale while other techniques produced higher distortion. We also demonstrate that LDLE produces high quality embeddings even when the data is noisy or sparse.
LNL-FPC: The Linear/Non-linear Fixpoint Calculus

We describe a type system with mixed linear and non-linear recursive types called LNL-FPC (the linear/non-linear fixpoint calculus). The type system supports linear typing, which enhances the safety properties of programs, but also supports non-linear typing as well, which makes the type system more convenient for programming. Just as in FPC, we show that LNL-FPC supports type-level recursion, which in turn induces term-level recursion. We also provide sound and computationally adequate categorical models for LNL-FPC that describe the categorical structure of the substructural operations of Intuitionistic Linear Logic at all non-linear types, including the recursive ones. In order to do so, we describe a new technique for solving recursive domain equations within cartesian categories by constructing the solutions over pre-embeddings. The type system also enjoys implicit weakening and contraction rules that we are able to model by identifying the canonical comonoid structure of all non-linear types. We also show that the requirements of our abstract model are reasonable by constructing a large class of concrete models that have found applications not only in classical functional programming, but also in emerging programming paradigms that incorporate linear types, such as quantum programming and circuit description programming languages.
A dissemination workshop for introducing young Italian students to NLP

We describe and make available the gamebased material developed for a laboratory run at several Italian science festivals to popularize NLP among young students.