Reviewerly/exHarmony huggingface dataset:
A systems thinking approach for eliciting mental models from visual boundary objects in hydropolitical contexts: a case study from the Pilcomayo River Basin

Transboundary collaborations related to international freshwater are critical for ensuring equitable, efficient, and sustainable shared access to our planet's most fundamental resources.Visual artifacts, such as knowledge maps, functioning as boundary objects, are used in hydropolitical contexts to convey understandings and facilitate discussion across scales about challenges and opportunities from multiple perspectives.Such focal points for discussion are valuable in creating shared, socially negotiated priorities and integrating diverse and often disparate cultural perspectives that naturally exist in the context of international transboundary water resources.Visual boundary objects can also represent the collective mental models of the actor countries and transboundary institutions and encompass their perspectives on the complex hydro-social cycles within specific "problem-shed" regions of the shared resources.To investigate and synthesize these multiple concepts, we developed a novel method of eliciting mental models from visual boundary objects in social-ecological contexts by combining content analysis with theoretical frameworks for boundary objects and systems thinking.Using this method, we analyzed visual boundary objects represented in publicly available documents formally related to decision making in the Pilcomayo River Basin in South America.The Pilcomayo River Basin is a unique case for investigating decision making in international collaboration among represented states, given the unique social and biophysical challenges that have plagued the region for over a century.Using our framework, we were able to develop insight into the collective mental models of stakeholders, organizations, and decision-making institutions, related to priorities, vulnerabilities, and adaptation strategies among the various socioeconomic, cultural, political, and biophysical drivers for different regions and scales of the basin.
Exploiting Background Knowledge for Argumentative Relation Classification

Argumentative relation classification is the task of determining the type of relation (e.g., support or attack) that holds between two argument units. Current state-of-the-art models primarily exploit surface-linguistic features including discourse markers, modals or adverbials to classify argumentative relations. However, a system that performs argument analysis using mainly rhetorical features can be easily fooled by the stylistic presentation of the argument as opposed to its content, in cases where a weak argument is concealed by strong rhetorical means. This paper explores the difficulties and the potential effectiveness of knowledge-enhanced argument analysis, with the aim of advancing the state-of-the-art in argument analysis towards a deeper, knowledge-based understanding and representation of arguments. We propose an argumentative relation classification system that employs linguistic as well as knowledge-based features, and investigate the effects of injecting background knowledge into a neural baseline model for argumentative relation classification. Starting from a Siamese neural network that classifies pairs of argument units into support vs. attack relations, we extend this system with a set of features that encode a variety of features extracted from two complementary background knowledge resources: ConceptNet and DBpedia. We evaluate our systems on three different datasets and show that the inclusion of background knowledge can improve the classification performance by considerable margins. Thus, our work offers a first step towards effective, knowledge-rich argument analysis.
Bio-Inspired Learning and Adaptation for Optimization and Control of Complex Systems

Learning and adaptation play an important role in solving numerous science and engineering problems, including artificial intelligence, control engineering, and many multidisciplinary topics.In this respect, a series of bioinspired methods, such as reinforcement learning, coevolution learning, and approximate dynamic programming as well as swarm evolutions, provide essential theoretical tools for solving various optimization and control problems.This has stimulated great research interests and developments on learning and adaptation.This special issue aims at providing a specific opportunity to review the state-of-the-art of this recently emerging and cross-disciplinary field of bio-inspired learning and adaptation.In this special issue, we bring together researchers to present the latest progress, novel research methodologies, and a broad spectrum of potential research topics.This special issue has been very successful.We have attracted more than 100 submissions, and after a thorough peer review process, more than 50 papers were accepted for publication.These papers cover important research topics in learning and adaptation for optimization and control, including differential evolution, neural network, adaptive control, system modeling, and identification, with particular emphasis on applications for robotics, UAV, autonomous
Online Collaborative Filtering with Implicit Feedback

Studying recommender systems with implicit feedback has become increasingly important. However, most existing works are designed in an offline setting while online recommendation is quite challenging due to the one-class nature of implicit feedback. In this paper, we propose an online collaborative filtering method for implicit feedback. We highlight three critical issues of existing works. First, when positive feedback arrives sequentially, if we treat all the other missing items for this given user as the negative samples, the mis-classified items will incur a large deviation since some items might appear as the positive feedback in the subsequent rounds. Second, the cost of missing a positive feedback should be much higher than that of having a false-positive. Third, the existing works usually assume that a fixed model is given prior to the learning task, which could result in poor performance if the chosen model is inappropriate. To address these issues, we propose a unified framework for Online Collaborative Filtering with Implicit Feedback (OCFIF). Motivated by the regret aversion, we propose a divestiture loss to heal the bias derived from the past mis-classified negative samples. Furthermore, we adopt cost-sensitive learning method to efficiently optimize the implicit MF model without imposing a heuristic weight restriction on missing data. By leveraging meta-learning, we dynamically explore a pool of multiple models to avoid the limitations of a single fixed model so as to remedy the drawback of manual/heuristic model selection. We also analyze the theoretical bounds of the proposed OCFIF method and conduct extensive experiments to evaluate its empirical performance on real-world datasets.
HeliCoach

Orientation and mobility (O&M) training is essential for improving the independence and wellbeing of the visually impaired. However, the shortage of qualified trainers and the unengaging training contents limit the number of O&M training recipients. In this paper, we propose a drone-based intelligent tutor system - HeliCoach - to provide cost-effective and personalized O&M training. We first elaborate on the system design and potential scenarios of HeliCoach use. We then demonstrate the effectiveness of this concept using a preliminary user study. Finally, we discuss the implication and challenges of this system.
Ontology Driven Social Big Data Analytics for Fog enabled Sentic-Social Governance

Conventional e-government has many practical infrastructure development and implementation challenges. The recent surge of SMAC (Social media, Mobile, Analytics, Cloud) technologies re-defines the e-governance ecosystem. Cloud-based e-governance has numerous operational challenges which range from development to implementation. Moreover, the contemplation and vocalization of public opinion about any government initiative is quintessential to be cognizant of how citizens perceives and get benefitted from cloud/fog enabled governance. This research puts forward a semantic knowledge model for investigating public opinion towards adaption of fog enabled services for governance and comprehending the significance of two s-components (sentic and social) in aforesaid structure that specifically visualize fog enabled Sentic-Social Governance. The results using conventional TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction are empirically compared with ontology driven TF-IDF feature extraction to find the best opinion mining model with optimal accuracy. The results depict that the implementation of ontology driven opinion mining for feature extraction in polarity classification outperforms the traditional TF-IDF method validated over baseline supervised learning algorithms. An average of 7.3% improvement in accuracy and approximately 38% reduction in features has been reported.
Transforming the Quality of Metadata in Institutional Repositories

Established in 2005, the University of Nebraska–Lincoln (UNL)’s Libraries’ institutional repository (IR) has approximately 99,500 unique items. It is one of the largest and most-used institutional repositories in the United States. This presentation explores how UNL is working to meet the challenge of consistently and accurately identifying individual researchers and their scholarly activities. It examines UNL’s metadata practices for its IR, giving a more detailed explanation of personal names and Open Researcher and Contributor ID identifiers. It reviews some typical metadata problems found in UNL’s IR. Finally, it explores priorities for metadata maintenance and what is sustainable and worthwhile.
STUDENTS’ DIFFICULTIES IN TRANSLATING ENGLISH TEXT

Nowdays, there are many translation problems although software application to assist translation are available. The objectives of this research were to identify types of error in translation, students’ difficulties in translating text, and factors which influence students’ error in translating in English department of State Institute for Islamic Studies Bengkulu. This research used descriptive quantitative method. The results of this research showed that students’ difficulties in translating English text, were elliptical errors (67.29%), idioms (87.5%), and textual meaning (73.54). The difficulties of students in translating were lack of vocabulary (87,50%), difficult translating Islamic texts (75,00%), literary works (66,66%), and grammatical issues (62,50%). Then, the factors affected students’ error in translation were ignorance of ellipsis; unable to identify ellipsis, idiom, and lexical meaning; lack of strategy in translating ellipsis, idiom, and lexical meaning; translating words per word; most students lack a strong background on the content of the text. It was concluded that the students got three types of error in transalation, four points difficulties in translation, and six factors which influence the students’ error in translation.Keywords: Translation, difficulties, and English text
Automatic Clustering of Different Solutions to Programming Assignments in Computing Education

A computer programming assignment may have various solutions, and extracting them is of great significance for both teaching and learning. However, it could be challenging for instructors and students to identify the differences between those solutions if they are on a large scale. Since code similarity is of vital importance in identifying the differences between solutions, we review previous researches on code similarity and design a neural network-based algorithm for detecting the similarity between codes in a pair as well as identifying the features that have a big impact on code similarity. Then we develop a clustering algorithm based on code similarity that can automatically generate clusters for all correct solutions to a given programming assignment. Our experiment demonstrates that the clustering algorithm can successfully obtain distinctive clusters in our dataset. Our analysis of typical solutions can provide inspirations for instructors and students.
Immersive Analytics

Immersive analytics is a recent research initiative growing out of the fields of data visualization, human–computer interaction, AR/VR, and computer graphics among others. This special issue presents recent work in the field including the introduction of novel immersive visualization concepts and techniques for different types of data and a critical reflection on the history and development of the field.
Counting Z2 Z4 Z8-additive Codes

In Algebraic Coding Theory, all linear codes are described by generator matrices. Any linear code has many generator matrices which are equivalent. It is important to find the number of the generator matrices for constructing of these codes. In this paper, we study Z_2 Z_4 Z_8-additive codes, which are the extension of recently introduced Z_2 Z_4-additive codes. We count the number of arbitrary Z_2 Z_4 Z_8-additive codes. Then we investigate connections to Z_2 Z_4 and Z_2 Z_8-additive codes with Z_2 Z_4 Z_8, and give some illustrative examples.
Efficient Network Resource Preallocation on Demand in Multitenant Cloud Systems

Fair bandwidth guarantee has always been a problem in cloud center networks. Normally, tenants are selfish and their selfishness is not well controlled by existing network resource allocation methods, which usually results in link congestion and unfair allocation. Thus, it is critical for cloud providers to balance and at the same time maximize the network resource utilization rate to fairly provide bandwidth for tenants on demand. Inspired by this, this paper proposes a network resource preallocation model, which could be applied in cloud centers using software-defined network technology. Based on network model and tenants' bandwidth demands, cloud providers could generate enormous resource preallocation strategies and choose the optimal one from them. As an issue of computational time consumption, we further use a genetic algorithm in the optimal strategy choosing procedure. The experiment results show that our method has maximally 15% improvement on the decrease of unsatisfied bandwidth demands of tenants comparing with common equal cost multipaths method.
Stern–Brocot-Based Non-Repudiation Dynamic Provable Data Possession

With the widespread popularity of cloud storage, cloud storage security issues have also received much attention. A provable data possession (PDP) scheme can effectively help users to verify the integrity of data stored remotely in the cloud. For this reason, the client's PDP scheme is constantly improving and developing. A non-repudiable dynamic PDP scheme based on the Stern-Brocot tree (Stern-Brocot-based non-repudiation dynamic provable data possession, abbreviated as SB-NR-DPDP) is proposed in view of the problem that the existing PDP scheme pays less attention to the clients deceiving the cloud server. We put forward a dynamic storage structure and dynamic operation algorithm based on the Stern-Brocot tree so that it can satisfy the client's dynamic data operations and realize the non-repudiation feature of the scheme. This scheme provides tag-unforgeability and proof-unforgeability against adaptive chosen-message attacks. The scheme can also resist hash value attacks, delete-insert attacks, and tamper with cloud return value attacks. The theoretical analysis shows that the proposed scheme has less computing overhead and storage overhead. As such, it has better safety and efficiency than other schemes.
Colorization for Anime Sketches with Cycle Consistent Adversarial Network

Coloring animation sketches has always been a complex and interesting task, but as the sketch is the first part of animation creation that neither presents gray value nor presents semantic information, the lack of real animation sketches is the biggest difficulty in current model training.It is also usually expensive to collect such data.In recent years, some methods based on generative adversarial networks (GANs) have achieved great success.They can generate colorized anime illustration on given sketches.Many existing sketch coloring tools are based on this supervised learning method, but the marking of data is particularly important for supervised learning, and much time is spent on the marking of data.To address these challenges, we propose a novel approach for unsupervised learning based on U-net and periodic consistent confrontation.Specifically, we combine the periodic consistent antagonism framework with the U-net structure and residual network, enabling us to robustly train a deep network to make the resulting images more natural and realistic.We also adopted some special data generation methods, so that our model can not only color anime sketches but also extract line drafts from colored pictures.By comparing the mainstream models of supervised learning, we show that the image processed by the proposed method can achieve a similar effect.
Can health promotion videos ‘go viral’? A non-randomised, controlled, before-and-after pilot study to measure the spread and impact of local language mobile videos in Burkina Faso

Mobile phones present a new health communications opportunity but use of mobile videos warrants more exploration. Our study tested a new idea: to produce health promotion videos in languages for which films have never previously been produced to see if they were widely shared.To investigate whether the novelty of films in local languages focusing on health messages would be shared 'virally' among the target population.A non-randomised, controlled, before-and-after study was used to evaluate the reach and impact of the intervention. We gave short health promotion videos on memory cards to distributors in eight intervention villages. Ten control villages, where no video distribution took place were randomly selected. We conducted cluster-level difference-in-difference logistic regression to assess self-reported knowledge indicators. We calculated odds ratios for intervention relative to control at baseline and endline and p-values for the change in odds ratios.Seven hundred and eight mothers were interviewed across all villages at baseline and 728 different mothers and 726 men were interviewed in the same villages a year later in October 2015. At endline, 32% of women and 44% of men in the intervention arm had ever seen a film on a mobile phone in Lobiri, compared to 1% of women and 2% of men in the control arm. There was a significant increase in the odds of knowing about giving Orasel to a child with diarrhoea in the intervention area relative to the control area. Awareness of the need to take a child with fever or symptoms of pneumonia to a health centre increased in the intervention area, but not significantly.Viral sharing of films on mobile phones has the potential to be an effective health promotion tool for communities whose languages are not served by existing mass media channels.
The Influence of Differentiated Instruction on Academic Achievement of Students in Mixed Ability Classrooms

The present study aims at describing the influence of differentiated instruction on the academic achievement of English Language Learners low achievers and high achievers in a mixed ability classroom. It explores the strategies used by teacher to apply some principles of differentiated instruction in mixed ability classrooms and how pupils including low achievers and high achievers progress academically in English classrooms and how much they benefit taking into consideration teacher's time and effort. A total of 20 students from one intact English class were used as a sample of this experimental study that was conducted on 10 low achievers and 10 high achievers. In order to obtain the data, the achievement test pre-test and post-test was used as an instrument to gauge the low achiever's and high achiever's academic performance. In this experimental class, the researcher used differentiated instruction as an intervention. This intervention class was able to improve their academic score from pre-test to post-test. Therefore, the results revealed a marked improvement in the low achiever's academic scores following the implementation of differentiated instruction in a great way. But for high achievers, their scores were somehow stable between the pre-test and post-test following the implementation of this process. It is evident that differentiated instruction is a strategy that has a great influence on the academic achievement of low achievers in a great way.
The importance of context-dependent learning in negotiation agents

Automated negotiation between artificial agents is essential to deploy Cognitive Computing and Internet of Things. The behavior of a negotiation agent depends significantly on the influence of environmental conditions or contextual variables, since they affect not only a given agent preferences and strategies, but also those of other agents. Despite this, the existing literature on automated negotiation is scarce about how to properly account for the effect of context-relevant variables in learning and evolving strategies. In this paper, a novel context-driven representation for automated negotiation is introduced. Also, a simple negotiation agent that queries available information from its environment, internally models contextual variables, and learns how to take advantage of this knowledge by playing against himself using reinforcement learning is proposed. Through a set of episodes against other negotiation agents in the existing literature, it is shown using our context-aware agent that it makes no sense to negotiate without taking context-relevant variables into account. Our context-aware negotiation agent has been implemented in the GENIUS environment, and results obtained are significant and quite revealing.
Knowledge evidenced by prospective mathematics teachers when performing a task involving geometry, teaching and the use of technology

The types of knowledge that prospective mathematics teachers use when faced with tasks involving technology are interesting because this knowledge can be used as a basis for analyzing the relevance of these types of activities when teaching mathematics. This investigation is intended to identify the knowledge evidenced by these prospective teachers when they carry out an activity involving geometry, technology and pedagogy. To do so, a task was designed which was used with 65 trainee teachers, and the Technological Pedagogical Content Knowledge model was used to analyze the information gathered. The results show that prospective teachers make use of knowledge in all of the domains and almost all of the subdomains of the model, confirming that this type of activity triggers these types of knowledge.
Application of support vector machine algorithm based gesture recognition technology in human-computer interaction

A gesture recognition technology is an important part of the human-computer interaction.This study focuses on the application of the support vector machine (SVM) in gesture recognition.The gesture image is segmented by YCgCr color space based skin color segmentation method.Then four Hu invariant moments and the ratio of area to circumference of gesture are taken as eigenvalues to extract gesture features.Finally, SVM is used for recognition.It was discovered that the proposed method has a good performance in the gesture recognition and can segment the collected images accurately.The recognition rate of Hu invariant moments based SVM algorithm reaches 99.2% in the recognition of the six gestures designed in this study, which is 9.2% higher than that of the HMM algorithm.The proposed method is reliable and feasible and can contribute to the simple man-machine interaction.