Reviewerly/exHarmony huggingface dataset:
Utilization of Internet of Thing and Social Media in Designing a Smart System for Identification Pollution Quality of Air, Water, and Temperature

The problem of pollution in big cities is one form of social issues. Natural sources and human activities can cause air pollution. Classification of air pollutants is divided into two, the first is a primary pollutant that is a pollutant which is generated directly from the source of air pollution, and the second is a secondary pollutant that is a pollutant that occurs from primary reactions in the atmosphere. The nature of air causes the effects of air pollution can be direct and local, regional, and global. Air pollution can endanger the health of humans, animals, and plants, disrupt aesthetics and comfort, or damage property. To overcome this social problem, the Government has regulated in Government Regulation Number 41 of 1999 concerning Air Pollution Control. Cooperation is needed for all relevant stakeholders and the community to overcome these social problems. Current conditions both the Government and the public are difficult to find out information about air quality in the surrounding environment, so the role of community functions is very limited in anticipating and overcoming the problem of air pollution. Based on these problems, Machine-to-Machine (M2M) technology and social media networks Twitter can be used as a medium for disseminating information about air quality to trigger public awareness of the condition of air pollution in the surrounding environment and increase the role of community functions in tackling the problem of air pollution.
Web Content Outlier Framework for Enhancing Web Search Results through Mathematical Approaches

Nowadays, plenty, diverse and changing features of web persuades most of the users for seeking and retrieving the interesting information. On searching information on the web through the search engine, the unnecessary irrelevant and redundant information waste the user time and effort, lower the quality of web search results, increase indexing space and time complexity. So, it becomes an important issue to provide high quality and effective search results to retrieve information. Web content outliers mining studies on mining the insignificant and repetitive website pages from the remainder of other website pages under the ideal same class. Removal of content outliers from web enhances and upgrades the quality of web search results providing to the needs of the user. In this paper, the framework for web content outlier mining based on mathematical approaches is used. The experimental results show this framework provides better results in terms of Fl-measure and accuracy.
Michael Asten’s best of <i>Exploration Geophysics</i>

Our ASEG President of the time, Ted Tyne, kicked off this series in February 2020 (Tyne 2020), noting that our journal Exploration Geophysics (EG) is now 50 years old – 50 years that cover three li...
Scaffolding of Interaction Design Education Towards Ethical Design Thinking

The introduction of any technology brings with it a set of assumptions about what is valued by a culture or society. Along with the explosion of design thinking and user experience in industry worldwide, this has led to a rethinking of how to educate design professionals equipped to work in a world of continual technologically driven innovation. We address this challenge by reflecting on our experiences as interaction design educators in an Australian higher education context. We discuss the success and failure of initiatives we have employed in our Master of Interaction Design courses to orient students to a value sensitivity in their design process and practice, such as explicit consideration of personal and professional ethics, value-sensitive design and dystopian critical design proposals. We conclude with suggestions for an educational agenda of ethical design thinking for interaction design, based on a set of six competencies, that can scaffold the development of designers who can advocate for ethical positions and can imbue their design proposals with value systems that support human and planetary flourishing.
A Comparative Study of Audio Encryption Analysis Using Dynamic AES and Standard AES Algorithms

In this paper, we are using Dynamic key dependent AES algorithm and standard AES (Advanced Encryption Standards) to encrypt and decrypt audio file. To analysis the quality of algorithms both algorithms are successfully tested using histogram analysis, peak signal to noise ratio (PSNR), correlation analysis and entropy.
Case study of aeroengine parameter prediction based on MIV and ELM

Aiming at the problems existing in the current prediction methods of aeroengine parameters, such as the difficulty in parameter selection, the slow training speed and the tendency to fall into local optimal solution of traditional BP neural network algorithm, this paper proposes the prediction method of aeroengine performance parameters based on mean influence value (MIV) algorithm and extreme learning machine (ELM). Firstly, we preprocess the sample data. Secondly, screening out the main parameters that affect the predicted parameters by MIV algorithm, attribute reduction is realized, the result of attribute reduction is taken as the input to train an ELM. Finally, using the test samples to do the test. The testing results show that the algorithm is faster and more accurate in parameter prediction.
Silicon Photonic Neural Networks and Applications

Neuromorphic photonic processors promise orders of magnitude improvements in both speed and energy efficiency over purely digital electronic approaches. We will provide an overview of neuromorphic photonic systems and their application to machine learning and specifically deep learning inference with a hybrid digital electronics and analog photonics architecture based on silicon photonics. We will discuss scalability in the context of designing a full-scale neuromorphic photonic processing system, considering aspects such as signal integrity, noise, and hardware fabrication platforms.
An Algorithm for Emotion Evaluation and Analysis Based on CBOW

As a tool to study people's views and opinions on things and events around them, sentiment analysis is widely used in the analysis and processing of mass evaluation information. Traditional emotion analysis is generally based on emotion dictionary, but the construction of emotion dictionary needs a lot of artificial time, and in different application fields need to establish different emotion dictionaries. Meanwhile, emotion dictionary can't contain the semantic information of words and it also ignore the role of non-emotional words in the expression of emotion. This paper proposes a new emotion analysis algorithm (CBOW-PE) based on word embedding and part of speech. First, in the stage of pre-processed experiment, we use part of speech to preprocess the experimental data, fully consider the role of non-emotional words on emotion analysis. And then we add emotional information to assist word vector training, so that the word embedding of words related to emotion has both semantic information and emotional information. Finally, this paper makes a lot of experimental analysis and comparison of this paper and its related algorithms. The results show that the algorithm is effective and efficient in Chinese emotion analysis.
Policy Reuse for Dialog Management Using Action-Relation Probability

We study the problem of policy adaptation for reinforcement-learning-based dialog management. Policy adaptation is a commonly used technique to alleviate the problem of data sparsity when training a goal-oriented dialog system for a new task (the target task) by using knowledge when learning policies in an existing task. The methods used by current works in dialog policy adaptation need much time and effort for adapting because they use reinforcement learning algorithms to train a new policy for the target task from scratch. In this paper, we show that a dialog policy can be learned without training by reinforcement learning in the target task. In contrast to existing works, our proposed method learns the relation in the form of probability distribution between the action sets of the source and the target tasks. Thus, we can immediately derive a policy for the target task, which significantly reduces the adaptation time. Our experiments show that the proposed method learns a new policy for the target task much more quickly. In addition, the learned policy achieves higher performance than policies created by fine-tuning when the amount of available data on the target task is limited.
Quantitative analysis of lexical complexity in contemporary Russian novels

Abstract This research presents a set of methods for measuring the lexical complexity against the culture at large. We applied a range of statistical measures upon a set of texts. For the analysis, we selected 50 famous Russian novels of the 20th century based on their historical and generic variety. The main approach consists of the comparison of the relative frequency of a work’s words against the Google Books dataset. This dataset represents a remarkable resource, with the Russian Google Books corpus in the period 1800 - 2012, containing approximately 4.7 billion 1-grams. Relative frequencies distribution for a novels words were compared with frequencies of Google Books corpus of different years using JensenShannon divergence, KullbackLeibler divergence and other information measures. Also, so-called a Flesch reading ease scale was calculated, such measure was used in similar research of modern English fiction. It was shown that the lexical complexity of individual texts should be measured against the culture at large. It was found that a writer who commonly seems to be difficult, verbose or notional, can in fact use language that is more ‘common’ (relative to the culture at large) than any other texts.
Compression with wildcards: All exact or all minimal hitting sets

Abstract Our objective is the compressed enumeration (based on wildcards) of all minimal hitting sets of general hypergraphs. To the author’s best knowledge, the only previous attempt towards compression, due to Toda, is based on binary decision diagrams and much different from our techniques. Traditional one-by-one enumeration schemes cannot compete when the number of minimal hitting sets is large and the degree of compression is high. Our method works particularly well in these two cases: either compressing all minimum cardinality hitting sets, or compressing all exact hitting sets.
Mixed LFM Signal Estimation Based on Radon-Wigner Transform and Matching Pursuit

Abstract Parameter estimation of mixed signals is a key problem in electronic reconnaissance. Based on Radon-Wigner transform (RWT) and Matching Pursuit (MP) algorithm, a parameter estimation method for mixed LFM signals is proposed in this paper. The core of the method is to separate signal components from the mixed signal and estimate their parameters one by one. Firstly, a rough parameter estimation of the strongest signal is obtained by RWT. After that an optimized estimation based on MP algorithm is performed to fine-tune the estimation result. Then, the strongest signal component is reconstructed with the optimized estimation, and it is separated from the mixed signal. Therefore, by iteratively estimating and separating the stronger signal within the residual mixed signal, all of the signal components can be precisely estimated. Experimental results show that the proposed method is able to achieve satisfactory performance on a lower signal-to-noise ratio.
Augmented Reality Technology for Predictive Maintenance Education: A Pilot Case Study

Industry 4.0 is an industrial era where several disruptive technologies e.g. Internet of Things, cloud technology, 3D printed, advanced robotics and materials have merged or integrated to enables a new level of organising and controlling the entire valve chain with the product lifecycle by creating dynamic and real-time understanding of cross-company behaviours. Such collaboration between physical and cyber spaces is rapidly growing and the benefits becoming more feasible. However, the interaction of human (in case automation is not applicable) at physical space with digital contents requires special facilitation where argumentation is the key. Predictive maintenance which is based on real-time measurements e.g. health/process parameters requires high level of expertise to understand and diagnose the present status of machine health. Therefore, the purpose of this paper is to explore, through a pilot project, how could augmented reality technology be utilised to assist novice learners to gain deep understanding of predictive maintenance process and practical skills as well. The case study is developed to assist the student of industrial asset management programme to learn the machine fault simulator and the analysed content of vibration measurements. Such contribution aims to enhance the training technology and accelerate the learning process of novice technician/engineers to be expert.
AutoIDL: Automated Imbalanced Data Learning via Collaborative Filtering

AutoML aims to select an appropriate classification algorithm and corresponding hyperparameters for an individual dataset. However, existing AutoML methods usually ignore the intrinsic imbalance nature of most real-world datasets and lead to poor performance. For handling imbalanced data, sampling methods have been widely used since their independence of the used algorithms. We propose a method named AutoIDL for selecting the sampling methods as well as classification algorithms simultaneously. Particularly, AutoIDL firstly represents datasets as graphs and extracts their meta-features with a graph embedding method. In addition, meta-targets are identified as pairs of sampling methods and classification algorithms for each imbalanced dataset. Secondly, the user-based collaborative filtering method is employed to train a ranking model based on the meta repository to select appropriate sampling methods and algorithms for a new dataset. Extensive experimental results demonstrate that AutoIDL is effective for automated imbalanced data learning and it outperforms the state-of-the-art AutoML methods.
A Secure Multi-Party Computing System Based on SGX Technology for Trusted Data Circulation

The security of data circulation is the core technology of data fusion and sharing service. The paper proposes multi-party circulation mechanism of the trusted data using to MPI communication. In order to achieve the trusted computing, this study proposes the computing service platform based on SMPC, which encapsulates the operation of sensitive data such as encryption key, password, user data, and etc.by trusted hardware using the security extension of Intel SGX. Meanwhile, aiming at these problems of semantic security and efficient processing ability, we chooses ElGamal homomorphic encryption system. In additional, SGX is extended to the remote authentication mechanism. System can support the deployment of hybrid cloud mode. Thus, the data security circulation can be satisfied. Compared to other methods, it has the advantage of model security and efficient communication.
Formative Assessment as A Factor in The Development of Student Personality

The article is devoted to the development of the personality of a university student in the training process and the importance of finding an effective way to train future specialists. The formative assessment method is considered as an important factor in the development of self-esteem and motivational sphere of a young person. The study purpose was to reveal the current level of self-esteem and the value orientations specifics of first-year students of the faculty of pedagogy and psychology. In the course of an empirical study, a formative assessment method was used in the learning process of an experimental group of students (100 people). This method is associated with the immersion of future specialists in the process of evaluating educational material, developing their methods of testing knowledge, etc. The results of the study indicate the effectiveness of formative assessment for the development of personality and professional motivation of young people.
Being Through Interaction

Auguste Comte claimed that we have reached the stage of scientific progress based on a linear progression through history. It is very difficult for design, in our current information age, to identify new goals if we accept this linear and monodirectional hypothesis of progress. In this chapter, we examine the way we interact(ed) based on a circular view of history, indicating a return from intellectual doing and moving further towards being. Being through interaction is a consequence of the turn to the tangible in HCI, which reflects a sense of presence (of being) in a digital world, through interaction, that is essentially the same as our sense of presence in the physical world. We consider the importance of subjective time to the nature of being and discuss how designers can design to affect this. We present and compare dimensions of mind and of interaction, pointing to the parallels and the directions in which being through interaction is taking us. Finally, we present some of the main challenges for the primitive interaction design approach.
Modernization of The Management of a University Educational Structural Unit Based on The Distributed Leadership Model

The study proposes an original version of the reorganization of the management of an educational structural unit of a university based on the “distributed leadership model”. The purpose of the study is to compare the effectiveness of bachelor’s and master’s programs in the traditional and based on the “distributed leadership model” management systems. The study showed that the qualitative transformation of the management of the university’s educational unit based on the “distributed leadership model” made it possible to delegate a significant part of the teachers’ managerial powers, which allowed positive changes in the effectiveness of education programs (quantitative and qualitative characteristics of applicants, improvement of the quality of teaching, the competitiveness of education programs etc.) implemented at the institute.
A Study of Efficient Methods for Selecting Quasi-identifier for Privacy-Preserving Data Mining

A voluminous amount of data regarding users’ location services is being generated and shared every second. The anonymization plays a major role in data sanitization before sharing it to the third party by removing directly linked personal identifiers of an individual. However, the rest of the non-unique attributes, i.e., quasi-identifiers (QIDs) can be used to identify unique identities in a dataset or linked with other dataset attributes to infer the identity of users. These attributes can lead to major information leakage and also generate threat to user data privacy and security. So, the selection of QID from users’ data acts as a first step to provide individual data privacy. This paper provides an understanding of the quasi-identifier and discusses the importance to select QID efficiently. The paper also presents the different methods to select quasi-identifier efficiently in order to provide privacy that eliminates re-identification risk on user data.
An Inclination based Progressive Resource Allotment Strategy in Cloud Computing Systems

Cloud computing context procurements provider about registering resources based interest, as and while required. It constructs about virtualization as well as distributed computing towards help price effective utilization about registering resources, underlining scheduled resource adaptability in addition to on request benefits. It enables commerce range awake with downward their resources dependent on requirements. Dealing with the client request makes the difficulties about resource distribution. Virtual Machine (VIM) innovation has been utilized as resource procuring. It remains estimated such utilizing virtualized context would diminish the moderate task reaction time just as implements the task as indicated by the accessibility of resources. Henceforth ViMs were apportioned to client dependent on attributes of the job. Powerful as well as dynamic use about resources in cloud can adjust the heap and maintain a strategic distance from circumstances like moderate keep running of frameworks. This manuscript predominantly focuses allotment of VIM toward the client, supported on considering the qualities of the activity. Primary guideline of the present effort remain that small preference tasks (due date of task remains large) shall not postpone the implementation of high preference tasks (due date of the task remains small) also to dynamically allots VIM resources in support of a client work inside time limit. The ‘Pay according to utilize’ form of distributed computing necessitates a client to lease cloud resources otherwise utilities in support of a timeframe also create instalment as needs be. The present manuscript suggests an interest -based preferred resource distribution method that plans a market-driven sale method to distinguish clients for resource portion dependent on their instalment limits also actualizes an instalment procedure dependent on a purchaser’s utility preferences. A correlation remains drawn among planned allotment system moreover the well known off-line VCG sale method as well as outcomes demonstrate a performance advantage in incomes to utilities supplier, instalments of cloud clients other than guaranteeing an excellent resources usage.
Medical Imaging Data Security using Effective Honey with Ant-Lion Optimization Algorithm

Security is the one of the significant thing for transmitting the restorative information,in the same time it contains some extra information of patients. Medical Imaging Data Security is a Necessary system for secure the sensitive information and their related patient data and the information are put away and transmitted over the open system. Numerous cryptographic calculations are accessible, and they fall under symmetric, asymmetric and other procedures. To choose an algorithm for secure information correspondence, the algorithm ought to give higher Privacy, security and better effectiveness. Here in Medical Imaging data, Honey encryption is used to scramble the restorative medical data. After that the Ant-Lion optimization is used to overhaul the keys. The presentation of the future methodology is assessed utilizing different estimates such as, Peak Signal to Noise ratio (PSNR), entropy, Mean Square error (MSE) and Correlation Coefficient (CC).
Solar Signals in Observation Indeed Implied Enhanced Predictability Since 1977

This is a Correspondence following the undermentioned paper: Chiodo et al. (Nat Geosci 12:94–99, 2019).
An Overview of Feature-Based Shadow Detection Methods

Shadows occur very frequently in digital images while considering them for various important applications. Shadow is considered as a source of noise and can cause false image colors, loss of information, and false image segmentation. Thus, it is required to detect and remove shadows from images. This chapter addresses the problem of shadow detection in high-resolution aerial images. It presents the required main concepts to introduce for the subject. These concepts are the main knowledge units that provide for the reader a better understanding of the subject of shadow detection and furthering the research. Additionally, an overview of various shadow detection methods is provided together with a detailed comparative study. The results of these methods are also discussed extensively by investigating their main features used in the process to detect the shadows accurately.