Reviewerly/exHarmony huggingface dataset:
An Adaptive Information Reconciliation Protocol for Physical-Layer Based Secret Key Generation

Physical-layer based secret key generation (SKG) becomes a research focus as it solves key distribution problem which is difficult in traditional cryptographic mechanism. To remove the disagreements caused by imperfect reciprocity of communicating parties, information reconciliation is a critical process in SKG to obtain symmetric keys. Various reconciliation schemes are proposed, e.g. BBBSS, Cascade, BCH code and Turbo code. Reconciliation efficiency is a common evaluation index which takes reconciliation success rate and information leakage rate into account. However, time delay caused by information interaction and computation overhead may affect reconciliation performance under specific scenarios. Therefore, a comprehensive evaluation metric is required to compare existing schemes. Besides, channel condition changes all the time in real mobile communication systems, and most existing reconciliation schemes only work well in certain channel conditions. Hence, a reconciliation scheme which can adapt to time-varying channel condition is required. In this paper, we introduce a novel comprehensive reconciliation efficiency index (CREI) to evaluate existing reconciliation schemes and propose an adaptive information reconciliation scheme selection (AIRSS) protocol to maximize CREI. The simulation results show the superiority of AIRSS and present recommendations of reconciliation scheme selection in different scenarios.
A Hierarchical Agent Decision Support Model and Its Clinical Application

In this paper, a hierarchical agent decision support model is proposed. The model helps to provide multifaceted decision support: grouping of specialist towards complex problems at the organisational level, planning and argumentation at the individual level, and service binding at the computational level. The approach includes an agent scheme, a conceptual decision model, and a set of functional signatures that drive the decision inference, accompanied with algorithms that guide their implementation. The model is specially designed to be adaptive. A clinical application of triple assessment of breast cancer is used for illustration.
Ontological model for student's knowledge assessment

This article considers the ontological model for knowledge representation on the example of the discipline "Database Theory".
Experimental Study on Multiple Face Detection with Depth and Skin Color

Human face considers as an important biometric trait for person identification or video surveillance due to the digital camera technology that available on our daily life gadget. Since the digital signage easily found in the public and uncontrolled environment, the common situation could be single or multiple audiences that viewing at the digital signage display. The digital camera acts as a non-invasive detector for non-obtrusive digital advertising to collect the surrounding people's face. The accuracy rate of face detection becomes the priority to detect the audience's face. Besides that, the processing time also become a concern for time-constrained applications. This paper develops a framework for non-obtrusive digital advertising that applied the depth camera to detect multiple audiences for the audience location simulation and gather the depth information restrict the region of interests (ROI). Viola-Jones algorithm detects the audience frontal face who is facing towards the digital signage in the ROI. Subsequently, skin color analysis verifies the skin face and exclude the non-skin face to improve the face detection true detection rate. The depth information is combined with the face XY-position to map the audience actual location in the real-world environment on the aerial map. The experiment result shown that post-processing approach for Viola-Jones algorithm with skin color analysis increases the face true detection rate with the short processing time. Meanwhile, the simulation of multiple audience locations in the ROI can be shown on the aerial map which corresponds to the actual location in the real-world environment.
A Survey on Data Mining Techniques in Research Paper Recommender Systems

In this chapter, the authors give an overview of the main data mining techniques that are utilized in the context of research paper recommender systems. These techniques refer to mathematical models and tools that are utilized in discovering patterns in data. Data mining is a term used to describe a collection of techniques that infer recommendation rules and build models from research paper datasets. The authors briefly describe how research paper recommender systems' data is processed, analyzed, and then, finally, interpreted using these techniques. They review different distance measures, sampling techniques, and dimensionality reduction methods employed in computing research paper recommendations. They also review the various clustering, classification, and association rule-mining methods employed to mine for hidden information. Finally, they highlight the major data mining issues that are affecting research paper recommender systems.
Concept and Implementation of Blockchain Demonstration Unit Within the Smart Grid

Modern energy technologies can provide a new way to energy supply. There is no need to be dependent on central power plants. Everybody can install his/her own renewable energy system and start producing energy. Consumers become producers and send excess energy to the electrical grid. A whole electrical grid becomes more reliable and efficient. However, the energy exchange process requires the third party that can regulate the process. Currently, they are utilities. In addition, they are not providing the best conditions for regulating the energy process. Moreover, customers stay dependent from them and cannot use all the potential of renewable energy sources. In this paper, we have proposed a blockchain based decentralized system for energy regulations and exchange. This system aims at avoiding the issues with dependency on central authorities, improving the efficiency of renewable sources. In addition, important questions are privacy and security. Because this system is classified as a Cyber-Physical system, other types of issues arise.
Optimal Transport of Deep Feature for Image Style Transfer

Image style transfer is a classic image editing task which aims to transfer arbitrary visual styles to content images. In recent years, it has been revealed that a well-trained convolutional neural network with sufficient labeled data is powerful to deal with the style transfer problem. Thanks to the recent advances in the analysis of neural style transfer, the image style transfer can be cast as a problem of distribution alignment. In this paper, we propose to solve this issue by incorporating the theory of optimal transport in a simple and intuitive way. The main component of our style transfer method is an optimal transportation map, which is derived from the Monge-Kantorovicth theory of mass transportation, to perform the alignment process from the content image to the style image. We compare the generated stylized images with a number of representative algorithms to demonstrate the effectiveness of our approach. We also show that our results are visually more consistent and well-stylized simultaneously.
Actionable Pattern Discovery for Tweet Emotions

The most popular form of communication over the internet is text. There are wide range of services that allow users to communicate in the natural language using text messages. Twitter is one such popular Micro-blogging platform where users post their thoughts, feeling or opinion on a day-to-day basis. These text messages not only contain information about events, products and others but also the writer’s attitude. This kind of text data is useful to develop systems, which detect user emotions. Emotion detection has wide variety of applications including customer service, public policy making, education, future technology, and psychotherapy. In this work, we use Support Vector Machine classifier model to automatically classify user emotions. We achieve accuracy in the range of 88%. The Emotional information mined from such data is huge and these findings can be more useful if the system is able to provide some actionable recommendations to the user, which help them, achieve their goal and gain benefits. The recommendations or patterns are Actionable if user can perform action using the patterns to their advantage. Action Rules help discover ways to reclassify objects with respect to a specific target, which the user intends to change for their benefits. In this work, we focus on extracting Action Rules with respect to the Emotion class from user tweets. We discover actionable recommendations, which suggests ways to alter the user’s emotion to a better or more positive state.
Know your analysis: how instrumentation aids understanding static analysis

The development of a high-quality data-flow analysis---one that is precise and scalable---is a challenging task. A concrete client analysis not only requires data-flow but, in addition, type-hierarchy, points-to, and call-graph information, all of which need to be obtained by wisely chosen and correctly parameterized algorithms. Therefore, many static analysis frameworks have been developed that provide analysis writers with generic data-flow solvers as well as those additional pieces of information. Such frameworks ease the development of an analysis by requiring only a description of the data-flow problem to be solved and a set of framework parameters. Yet, analysis writers often struggle when an analysis does not behave as expected on real-world code. It is usually not apparent what causes a failure due to the complex interplay of the several algorithms and the client analysis code within such frameworks. In this work, we present some of the insights we gained by instrumenting the LLVM-based static analysis framework PhASAR for C/C++ code and show the broad area of applications at which flexible instrumentation supports analysis and framework developers. We present five cases in which instrumentation gave us valuable insights to debug and improve both, the concrete analyses and the underlying PhASAR framework.
Field Extension in Secret-Shared Form and Its Applications to Efficient Secure Computation

Secure computation enables participating parties to jointly compute a function over their inputs while keeping them private. Secret sharing plays an important role for maintaining privacy during the computation. In most schemes, secret sharing over the same finite field is normally utilized throughout all the steps in the secure computation. A major drawback of this "uniform" approach is that one has to set the size of the field to be as large as the maximum of all the lower bounds derived from all the steps in the protocol. This easily leads to a requirement for using a large field which, in turn, makes the protocol inefficient. In this paper, we propose a "non-uniform" approach: dynamically changing the fields so that they are suitable for each step of computation. At the core of our approach is a surprisingly simple method to extend the underlying field of a secret sharing scheme, in a non-interactive manner, while maintaining the secret being shared. Using our approach, default computations can hence be done in a small field, which allows better efficiency, while one would extend to a larger field only at the necessary steps. As the main application of our technique, we show an improvement upon the recent actively secure protocol proposed by Chida et al. (Crypto'18). The improved protocol can handle a binary field, which enables XOR-free computation of a boolean circuit. Other applications include efficient (batch) equality check and consistency check protocols, which are useful for, e.g., password-based threshold authentication.
MineAuth: Mining Behavioural Habits for Continuous Authentication on a Smartphone

The increasing use of smartphones raises many concerns related to data security, as the loss of a smartphone could compromise sensitive data. Authentication on smartphones plays an important role in protecting users' data from attacks. However, traditional authentication methods cannot provide continuous protection for a user's data after the user has passed the initial authentication. In this paper, we present a novel continuous authentication approach called MineAuth based on the user's daily interactive behaviours with his/her smartphone. We construct interactive behaviours from data captured by the smartphone. We then propose a weighting-based time period frequent pattern mining algorithm called WeMine to mine user's frequent patterns to characterize the habits of mobile users. We build an authenticator using a one-class classification technique that only relies on the legitimate user data. We also develop a decision procedure to perform the task continuously. The entire process occurs on the smartphone, which provides better privacy guarantees to users and eliminates dependency on cloud connectivity. We also integrate our approach into the Android system and evaluate the performance of our approach. Our approach can achieve good performance. Additionally, it can achieve a high authentication accuracy of up to 98.3%. Regarding resource consumption, our approach consumes less than 0.4% of power while running for an entire day.
Double-Pushout Rewriting in Context

Recently, we introduced double-pushout rewriting in context (DPO-C) as a conservative extension of the classical double-pushout approach (DPO) at monic matches. DPO-C allows non-monic rules such that the split and merge of items can be specified together with deterministic context distribution and joining. First results showed that DPO-C is practically applicable, for example in the area of model refactoring, and that the theory of the DPO-approach is very likely to carry over to DPO-C. In this paper, we extend the DPO-C-theory. We investigate rule composition and characterise parallel independence.
Join the Park!

The current work explores the participation divide that is oftentimes at play within local citizen communities. The studied case illustrates a common situation where the majority of local citizens does not participate in public space improvement and maintenance activities organised by local community activists. The presented research involved semi-structured interviews supported by interactive service design probes. It has led to two strategies for stimulating community participation, namely 1) increasing transparency around community activities, and 2) embedding community participation in citizens' daily social practices.
Constraining the distribution of elements and their controlling factors in the Zhaojikou Pb–Zn ore deposit, SE China, via fractal and compositional data analysis

The distribution of elements in soil is affected by various natural processes, and therefore, it is essential that exploration geochemists are able to recognize whether an elemental concentration is related to mineralization or other processes. This research aimed to recognize the elemental distribution and its controlling factors around the Zhaojikou Pb–Zn ore deposit, SE China, via fractal and compositional data analysis. Firstly, histograms over the raw data and log-ratio transformations were used to check whether the concentration data followed a normal distribution or not. The concentration distribution maps of elements were then generated via spretrum–area fractal plot to show the spatial relationships. Afterward, the inter-relationships among different elements were studied through robust principal component analysis based on compositional data theory. The factors controlling the distribution of elements were discussed and geochemical indices were proposed for future exploration. Finally, a comprehensive anomaly map was generated to show important prospecting areas. Results showed how the interpretation of distributions of elements was enhanced due to the application of data log-ratio transformation in univariate and multivariate analysis compared to the use of raw data. The histograms based on isometric log-ratio performed best among the three log-ratio transformations. The concentration distribution maps showed three anomalous areas at the surface. It clearly allowed identifying the relationships between the elements and their possible source patterns. Area I with anomalous assemblage of Pb–Zn–Ag–Cu–Cd corresponded to the current known ore deposits, while Area II with anomalous assemblage of Pb–Zn–Ag–Cu–Cd may indicate the occurrence of new potential base metal (Pb–Zn) ore bodies, and Area III with anomalous assemblage of W–As–Sb may indicate the occurrence of new potential Au mineralization/ore occurences. The robust biplot of PCA shows that the distributions of elements of interest were generally controlled by three geological processes: Pb–Zn ore-forming process, Au mineralization and weathering. Lead, Zn, Ag, Cu, and Cd, were affected mainly by Pb–Zn ore-forming processes; W, Sb, and As were affected mainly by Au mineralization, whereas Al2O3, K2O, Rb, Ba, Sr, Na2O, CaO, MgO, and SiO2 were affected to a greater extent by weathering processes. Sulphur, a mineralizing agent, was controlled by the ore-forming processes, being affected greatly by weathering. Finally, we proposed Pb > 160 ppm, Zn > 120 ppm, Ag > 193 ppb, Cu > 50 ppm, Cd > 210 ppb, W > 5 ppm, As > 25 ppm, Sb > 3.5 ppm, and S > 120 ppm as geochemical indices based on concentration-area fractal model analysis for future surficial geochemical prospecting in the Zhaojikou District.
ACE with Compact Ciphertext Size and Decentralized Sanitizers

We present an access control encryption (ACE) scheme which enjoys advantages over previous works in several aspects. Our scheme ensures not only compact ciphertext size but also small size of keys installed in each user in the ACE system. Besides, to the best of our knowledge, our scheme is the first implementation of ACE with decentralized sanitizers. Comparing to ACE constructions with only one sanitizer, our scheme is more secure and reliable since it does not suffer from the single point failure. Considering the security of our scheme, we present two models catering to the no-read rule and no-write rule security requirements respectively. Additionally, our extended no-write rule model allows the corruption of some sanitizers in the ACE system and thus is stronger than the one for schemes with only one sanitizer. We prove the security of our scheme under the two models.
Third Age Education and the Senior University Movement in South Korea

This chapter outlines the origins, characteristics and structures of the Senior University and the perspectives that have guided its development. The objective to promote active ageing in the population makes a strong case for government to continue to support Senior Universities and, in doing so, to prioritise the least advantaged group of South Korean citizens. It is argued that there is also a role for the encouragement of self-help and mutual aid among those who are able to organise with less support and less intervention from the government. The Senior University was created by the Korean Older People’s Association in 1981. As an increasing number of incoming older adults in the future will be characterised by higher levels of education, it is argued that Senior Universities should take action to develop new programmes and learning approaches, with a renewed focus on active ageing and wider social participation.
Sanskrit lemmatizer for improvisation of morphological analyzer

The process of stripping off affixes from a word to arrive at root word or lemma is known as Lemmatization. The usefulness of lemmatizer in natural language operations cannot be overlooked especially if the language is rich in its morphology. A lexicon cum rule based lemmatizer is built for Sanskrit Language. The Lemmatizer has profound applications in NLP main stream tasks like Information Retrieval, Morphological Analyzer ,POS taggers, Question-Answering Systems, Machine Translation Systems etc. Here the Sanskrit rule based morphological analyzer augmented with lemmatizer proved, analyzing Sanskrit word more precise and accurate to its morphological characteristics.
PERCEIVED VISUAL CGI FAMILIARITY TOWARDS UNCANNY VALLEY THEORY IN FILM

In the enhancement of the advanced technology, the uncanny valley is becoming a high-stakes concern of the entertainment industry to produce good films and animations (Chaminade et al., 2007). Therefore, this study aims to analyse participants’ familiarity towards the usage of digital characters as actors. Then, this article is to convey on how the uncanny valley factors affect audience’s attention in watching films with computer graphic imagery (CGI) elements in films. The researcher has selected visual stimuli that are divided into (4 x 4 factorial design) with 2 subjects of realistic and accurate human characters, meanwhile the second stimuli, researcher selected 2 subjects with minimum characteristic of human likeness. The surveys conducted are self-administered manner with combination of videos and images, distributed online via email and social network. This research concludes, the more familiarity and expectations of the audiences, the higher discomfort feeling when looking to a CGI made character. This illustrates that the longer a duration of CG actors in action, the higher significant weaknesses and substantial of superficial visuals. Therefore, this research is beneficial to assists artists and digital creative directors in digital actor’s creation, and guidance for developing more realistic actors in future projects.
Learning from demonstration for locally assistive mobility aids

Active assistive systems for mobility aids are largely restricted to environments mapped a-priori, while passive assistance primarily provides collision mitigation and other hand-crafted behaviors in the platform’s immediate space. This paper presents a framework providing active short-term assistance, combining the freedom of location independence with the intelligence of active assistance. Demonstration data consisting of on-board sensor data and driving inputs is gathered from an able-bodied expert maneuvring the mobility aid around a generic interior setting, and used in constructing a probabilistic intention model built with Radial Basis Function Networks. This allows for short-term intention prediction relying only upon immediately available user input and on-board sensor data, to be coupled with real-time path generation based upon the same expert demonstration data via Dynamic Policy Programming, a stochastic optimal control method. Together these two elements provide a combined assistive mobility system, capable of operating in restrictive environments without the need for additional obstacle avoidance protocols. Experimental results in both simulation and on the University of Technology Sydney semi-autonomous wheelchair in settings not seen in training data show promise in assisting users of power mobility aids.
Opportunities for Sustainable Mobility

In developed nations, a growing emphasis is being placed on the promotion of sustainable behaviours amongst individuals, or 'citizen-consumers'. In HCI, various eco-feedback tools have been designed as persuasive instruments, with a strong normative appeal geared to encouraging citizens to conduct a more sustainable mobility. However, many critiques have been formulated regarding this 'paternalistic' stance. In this paper, we switched the perspective from a designer's to a citizen's point of view and explored how people would use eco-feedback tools to support sustainable mobility in their city. In the study, we conducted 14 interviews with citizens who had used eco-feedback previously. The findings indicate new starting points that could inform future eco-feedback tools. These encompass: (1) better information regarding how sustainable mobility is measured and monitored; (2) respect for individual mobility situations and preferences; and (3) the scope for participation and the sharing of responsibility between citizens and municipal city services.