Reviewerly/exHarmony huggingface dataset:
Voltage output comparison of self-sustaining and externally powered fixed angle, single axis and dual axis solar tracking systems

Dual axis solar tracking system generates the highest power against single axis and fixed panel. For self-sustaining small scale solar tracking system however, power consumption from its system may be too high, causing disruption to power generation. Self-sustaining system is when the system's solar panel provides its own power to its battery without additional external power. This paper provides evidence by comparing voltage outputs from 3 solar tracking systems that self-sustaining, small solar panel is not efficient enough to use dual axis solar tracking system. Power consumption in small solar panel system was found to increase by 175% and 100% while voltage generation may drop by 6V and 3V for battery charging when the system utilized dual axis and single axis tracking capability respectively when under self-sustained powering. Externally powered system generate 13% more voltage compared to self-sustaining system and this depends on how many times the battery in self-sustaining system needs to be charged.
Do speech registers differ in the predictability of words?

Abstract Previous research has demonstrated that language use can vary depending on the context of situation. The present paper extends this finding by comparing word predictability differences between 14 speech registers ranging from highly informal conversations to read-aloud books. We trained 14 statistical language models to compute register-specific word predictability and trained a register classifier on the perplexity score vector of the language models. The classifier distinguishes perfectly between samples from all speech registers and this result generalizes to unseen materials. We show that differences in vocabulary and sentence length cannot explain the speech register classifier’s performance. The combined results show that speech registers differ in word predictability.
A Human–Computer Fusion Framework for Aircraft Recognition in Remote Sensing Images

Existing aircraft recognition methods usually regard recognition as an isolated classification problem, supposing that aircraft detection has finished. These methods use image slices each containing single aircraft as input, which is often not the case in practice. In order to recognize aircraft in remote sensing images that contain multiple objects and background, we propose a human-computer fusion framework that combines the advantages of human and computer. First, we propose candidate aircraft using human eye tracking, making use of the efficient and accurate search ability of human. Then, we propose a two-step recognition method, which simulates the recognition process of image analysts, to identify the types of candidate aircraft. In the first step, we recognize aircraft using fully connected features of a fine-tuned convolutional neural network (CNN). If it does not work, we go to the second step. In the second step, we use convolutional features extracted from the fine-tuned CNN for recognition. To improve the representational ability of convolutional features, we introduce the Fisher Vector encoding. Thorough experiments demonstrate that the proposed framework is effective, surpassing state-of-the-art methods on recognition performance.
Modeling Decision-Making with Intelligent Agents to Aid Rural Commuters in Developing Nations

More than a billion rural merchants in the developing world depend on hiring on-demand transportation services to commute people or goods to markets. Selecting the optimal fare involves decision-making characterized by multiple alternatives and competing criteria. Decision support systems are used to solve this. However, those systems are based on object-based approaches which lack the high-level abstractions needed to effectively model and scale human-machine communication. This paper introduces AopifyJS, a novel agent-based decision-support tool. We developed a two-agent simulation. One agent makes a request, then another takes a dataset of a stratified sample of 104 Ethiopian commuter criteria preferences and a dataset of fare alternatives. The second agent computes HPA and TOPSIS algorithms to weight, score, rank those alternatives. Once we run the simulation, it returns an interpretable prescription to the first agent, storing all interactions in an architecture that allows developers to program further customization as interactions scale.
Secure and Compact Elliptic Curve Cryptosystems

Elliptic curve cryptosystems (ECCs) are widely used because of their short key size. They can ensure enough security with shorter keys, and use less memory space to reduce parameters. Hence, an elliptic curve is typically used in embedded systems. The dominant computation of an ECC is scalar multiplication $$Q = kP, P \in E({\mathbb F}_{q})$$ . Thus, the security and efficiency of scalar multiplication are paramount. To render secure ECCs, complete addition formulae can be employed for a secure scalar multiplication. However, this requires significant memory and is thus not suitable for compact devices. Several coordinates exist for elliptic curves such as affine, Jacobian, projective. The complete addition formulae are not based on affine coordinates and thus require considerable memory. In this study, we achieved a compact ECC by focusing on affine coordinates. In fact, affine coordinates are highly advantageous in terms of memory but require many if statements for scalar multiplication owing to exceptional points. We improve the scalar multiplication and reduce the limitations for input k. Furthermore, we extend the affine addition formulae to delete some exceptional inputs for scalar multiplication. Our compact ECC reduces memory complexity up to 26 % and is much more efficient compared to Joye's RL 2-ary algorithm with the complete addition of formulae when the ratio I / M of computational complexity of inversion (I) to multiplication (M) is less than 7.2.
Research Data Management and Scientific Evidence: A Strategic Imperative for SDGs

Scientific evidenceScientific evidence comprisesResearch Data management Data, Information and Knowledge (DIK) often presented in a pyramidal structure. Data are the foundation base of the pyramid, followed by the information layer and the knowledge layer at the top. Data are rudimentary and expand into information and knowledge—the DIK pyramid—and also constitute scientific evidenceScientific evidence. Such evidence is critical for demonstrating prospects, best practices and successful developmentDevelopment models. The Internet and the evolution of the Web have resulted in easily discernible data that serve as scientific evidenceScientific evidence in the form of big data. Transformation of the African continent through the 17 Sustainable Developmentdevelopment Goals (SDGs) rests on the availability of scientific data. Data are not a panacea for societal problems but data science can nevertheless open up possibilities for innovations that could help fight hunger, povertyPoverty, inequalitiesInequality and underdevelopment. There is also a huge potential for big data to serve as evidence for successes and failures of the SDGs. However, without its proper creation, planning, verification, storage, securitySecurity and organisation; big data cannot be used appropriately. This is where ResearchResearch Data ManagementData management (RDM) adds value, mainly because RDM is concerned with planning and organisation of data in the entire researchResearch cycle, including the dissemination and archiving of results. This chapter draws on examples from KenyaKenya, MalawiMalawi and South AfricaSouth Africa to analyse RDM as a strategic imperative for scientific evidenceScientific evidence in the transformation of AfricaAfrica through the SDGs, with a specific reference to SDG 4 on the quality of educationEducation.
Design of Online Application for Agricultural Machinery Service based on Android Operating System

Utilization of agricultural machinery recently becoming big issue to increase agriculture production in Indonesia. In these couple years Indonesian Ministry of Agriculture massively granted agricultural machineries to the farmers for increasing rice production. Considering that farmer's paddy field are scattered in villages area, it can be happening that agricultural machinery in one village may overutilize where the other still underutilize. The objective of this research is to design an online application for agricultural machinery service for optimizing the utilization of agricultural machinery. The application was developed under Android operating system which is commonly used for mobile phone. The method of this research was following System Development Life Cycle which include investigation, system analysis, design, implementation, installation and testing steps. There are three sub systems in this application i.e. system for operator, machinery provider and machinery user. The application contains several functions such as machinery data management, order management and machinery services scheduling. In the user application, detail data such as field location, type of machinery, order time, and supporting map of the location can be input and read by the machinery provider easily. The application was tested under several Android operating systems and the results showed that the application can run satisfactorily.
TwitSenti: A Real-Time Twitter Sentiment Analysis and Visualization Framework

Twitter is considered as one of the world’s largest social networking sites which allow users to customize their public profile, connect with others and interact with connected users. The proposed work introduces a distributed real-time twitter sentiment analysis and visualization framework by implementing novel algorithms for twitter sentiment analysis called Emotion-Polarity-SentiWordNet. The framework is applied to build an interactive web application called “TwitSenti” which can benefit companies and other organizations in knowing the people’s sentiment towards the aspects such as brands, current events, etc., which in turn helps in quick decision-making and planning marketing strategies. The algorithm is validated against three existing classifiers and hence proved that Emotion-Polarity-SentiWordNet provides highest accuracy value of 85%. Also, the framework showed best scalability results when evaluated through web app as four node clusters, proves to be fast and can scale well with massive data.
Self-Adaptation Applied to MQTT via a Generic Autonomic Management Framework

Manufacturing enterprises are constantly exploring new ways to improve their own production processes to address the increasing demand of customized production. However, such enterprises show a low degree of flexibility, which mainly results from the need to configure new production equipment at design and run time. In this paper we propose self-adaptation as an approach to improve data transmission flexibility in Industry 4.0 environments. We implement an autonomic manager using a generic autonomic management framework, which applies the most appropriate data transmission configuration based on security and business process related requirements, such as performance. The experimental evaluation is carried out in a MQTT infrastructure and the results show that using self-adaptation can significantly improve the trade-off between security and performance. We then propose to integrate anomaly detection methods as a solution to support self-adaptation by monitoring and learning the normal behavior of an industrial system and show how this can be used by the generic autonomic management framework.
Aspect-Based Sentiment Analysis Using Deep Learning Convolutional Neural Network

Sentiment analysis (known as opinion mining) is the computational study of unstructured textual information regarding a person’s perspective, attitudes, feeling, and emotions toward an event or an entity in the form of a piece of text. Sentiment classifiers automatically classify text in a positive and negative sense. In this process, two important aspects are presented. First is the feature extraction; it provides effective information about domain; and in our case, we use supervised learning with labels. The second aspect is training and validation in which tenfold cross-validation is done by using training and testing module with deep learning convolutional neural network. In this thesis, semantic feature is extracted by ontology and these features are trained in the convolution neural network, after that, tenfold cross-validation is done. In our experiments, CNN shows a significant difference in 72.17%, 71.66, and 71.66 in accuracy, precision, and recall, respectively.
A Group Recommendation Approach Based on Neural Network Collaborative Filtering

At present, the most popular recommendation algorithms belong to the class of latent factor models(LFM). Compared with traditional user-based and item-based collaborative filtering methods, the latent factor model effectively improves recommendation accuracy. In recent years, deep neural networks have succeeded in many research fields, such as computer vision, speech recognition, and natural language processing. However, there are few studies combining recommendation systems and deep neural networks, especially for group recommendation. Some academic studies have adopted deep learning methods, but they mainly use it to process auxiliary information, such as acoustic features of sounds, and semantic analysis of texts, the inner product is still used to deal with latent features of users and items. In this paper, we first obtain the nonlinear interaction of latent feature vectors between users and projects through multi-layer perceptron(MLP), and use the combination of LFM and MLP to achieve collaborative filtering recommendation between users and items. Secondly, based on the individual's recommendation score, a fusion strategy based on Nash equilibrium is designed to ensure the average satisfaction of the group users. Our experiments are conducted on the Track 1 of KDD CUP 2012 public data set, taking the square root mean square error(RMSE) as the evaluation index. The experiment compares the traditional LFM optimization model, the MLP model and the LFM-MLP hybrid model in individual recommendation, and compares the strategy proposed in this paper with the traditional three single group strategies, the most pleasure, the average strategy and the least misery. The experimental results show that the proposed method can effectively improve the accuracy of group recommendation.
Political socialization on Xbox Live: a sociocultural linguistic approach to adolescent identity

This article examines a group of adolescent boys discussing the 2012 presidential elections while chatting on Xbox Live. The article uses a sociocultural linguistic framework to analyze the local and global influences that shape how their identities are expressed and evaluated in the peer group. The analysis uses naturally occurring conversation that unfolded in the weeks leading up to Election Day, and focuses on how one particular adolescent molded his political identity in response to his peers’ evaluations of his stances. Through the analysis, I show how an adolescent’s political identity is not just a reflection of his political values, but the result of ongoing negotiations with his peers in an attempt to fit in with the group’s accepted political views.
Role-Based Development of Dynamically Evolving Esembles

An ensemble is a set of computing entities that collaborate to perform a certain task. Typically an ensemble changes dynamically its constitution such that new members can join and other members can leave an ensemble during its execution. The members of an ensemble interact through message exchange. They are modelled as instances of certain role types which can be adopted by components of an underlying component system. We propose a dynamic logic to describe the evolution of ensembles from a global perspective. Using the power of dynamic logic with diamond and box modalities over regular expressions of actions (involving role instance creation, message exchange and component access) we can specify safety and liveness properties as well as desired and forbidden interaction scenarios. Thus our approach is suitable to write formal requirements specifications for ensemble behaviours. For ensemble design and implementation we propose ensemble realisations. An ensemble realisation takes a local view by giving a constructive specification for each single role type in terms of a process algebraic expression. Correctness of an ensemble realisation is defined semantically: its generated ensemble transition system must be a model of the requirements specification. We consider bisimulation of ensemble transition systems and show that our approach enjoys the Hennessy-Milner property.
Automatic Detection of Latent Software Component Relationships from Online Q&amp;A Sites

Modern software system stacks are composed of large numbers of software components. These components may include a broad range of entities such as services, libraries, and frameworks, all intended to address specific requirements. It is not only necessary that these components satisfy respective functional and non-functional concerns, but also that the combinations of selected components work well together. The space of component combinations to explore is huge. Together with the almost universal lack of formal documentation suggesting desirable combinations and cautioning against undesirable ones, this renders the proper selection of combinations very challenging. For this reason, software engineers often solicit advice and document their experience on online forums such as community Q&A sites. In this paper, we show that these Q&A sites contain valuable knowledge about inter-component relations. We develop an approach using information extraction techniques to automatically identify three different types of compatibility relations from unstructured text on Q&A site postings. Our work demonstrates that identifying such relations is valuable for the design of component-based systems and that automatic relation extraction is a promising technique to systematically harness such community knowledge.
Interaction and collaboration in computer music using computer networks: An UbiMus perspective

Networked musical performance, telematic concerts and laptop orchestras are a common sense about how music can be done over computer networks. This type of musical performances using computer networks are valuable and interesting but still strongly attached with western classical music tradition. This article presents another perspective of interaction and collaboration in computer music using networks: the ubimus perspective. Three installations are presented, examined and discussed in order to illustrate how it is possible to reconsider network music installations using relevant emerging notions from ubiquitous music such as metaphors, audience participation and sustainability.
Stability in Software Engineering: Survey of the State-of-the-Art and Research Directions

With the increasing dependence on software systems, their longevity is becoming a pressing need. Stability is envisioned as a primary property to achieve longevity. Stability has been defined and treated in many different ways in the literature. We conduct a systematic literature review to analyse the state-of-the-art related to stability as a software property. We formulate a taxonomy for characterising the notion, analyse the definitions found in the literature, and present research studies dealing with stability. Also, as architecture is one of the software artefacts with profound effects throughout the software lifecycle, we focus on software engineering practices for realising architectural stability. The analysis results show a wide variation in dimensions when dealing with stability. The state-of-the-art indicates the need for a shift towards a multi-dimensional concept that could cope with runtime dynamics and emerging software paradigms. More research efforts should be directed toward the identified gaps. The presented taxonomy and analysis of the literature aim to help the research community in consolidating the existing research efforts and deriving future developments.
Labelling issue reports in mobile apps

Millions of mobile apps have been released to the market. Developers need to maintain these apps so that they can continue to benefit end users, who usually submit issue reports to describe the bugs, the feature requests, and other changes appearing in apps. The labels (e.g. bug, feature request) are important resources to indicate which issue reports should be resolved first or next. According to the investigation, 35.6% of issue reports in top-17 popular mobile apps are not labelled. Developers have to spend additional time to manually verify each unlabelled issue report so that they can decide to resolve the most important issues. In order to help developers to reduce the workload, in this study, the authors propose a novel approach to automatically tag the unlabelled issue reports. This approach not only computes the similarity between each unlabelled issue report and user reviews related to bugs and features but also calculates the textual similarity scores between each unlabelled issue report and labelled ones. As a result, among all textual similarity measures, this approach using cosine similarity with MCG shows the best performance. Moreover, this approach performs better than the method proposed in the authors' previous study.
A Crowdsourcing Approach to Evaluate the Quality of Query-based Extractive Text Summaries

High cost and time consumption are concurrent barriers for research and application of automated summarization. In order to explore options to overcome this barrier, we analyze the feasibility and appropriateness of micro-task crowdsourcing for evaluation of different summary quality characteristics and report an ongoing work on the crowdsourced evaluation of query-based extractive text summaries. To do so, we assess and evaluate a number of linguistic quality factors such as grammaticality, non-redundancy, referential clarity, focus and structure & coherence. Our first results imply that referential clarity, focus and structure & coherence are the main factors effecting the perceived summary quality by crowdworkers. Further, we compare these results using an initial set of expert annotations that is currently being collected, as well as an initial set of automatic quality score ROUGE for summary evaluation. Preliminary results show that ROUGE does not correlate with linguistic quality factors, regardless if assessed by crowd or experts. Further, crowd and expert ratings show highest degree of correlation when assessing low quality summaries. Assessments increasingly divert when attributing high quality judgments.