Reviewerly/exHarmony huggingface dataset:
Instance-Based Representation Using Multiple Kernel Learning for Predicting Conversion to Alzheimer Disease

The early detection of Alzheimer's disease and quantification of its progression poses multiple difficulties for machine learning algorithms. Two of the most relevant issues are related to missing data and results interpretability. To deal with both issues, we introduce a methodology to predict conversion of mild cognitive impairment patients to Alzheimer's from structural brain MRI volumes. First, we use morphological measures of each brain structure to build an instance-based feature mapping that copes with missed follow-up visits. Then, the extracted multiple feature mappings are combined into a single representation through the convex combination of reproducing kernels. The weighting parameters per structure are tuned based on the maximization of the centered-kernel alignment criterion. We evaluate the proposed methodology on a couple of well-known classification machines employing the ADNI database devoted to assessing the combined prognostic value of several AD biomarkers. The obtained experimental results show that our proposed method of Instance-based representation using multiple kernel learning enables detecting mild cognitive impairment as well as predicting conversion to Alzheimers disease within three years from the initial screening. Besides, the brain structures with larger combination weights are directly related to memory and cognitive functions.
A General Online Trajectory Planning Framework in the Case of Desired Function Unknown in Advance

Trajectory planning approaches including off-line and on-line algorithms are developed to deal with physical constraints in practical systems. However, by now the existing trajectory planning algorithms have to assume that the desired trajectory function to be planned is fully or at least partly known in advance, and it may not be true in some applications. To overcome this limitation, a general framework of on-line planning a desired trajectory under physical constraints whose function is unknown in advance is proposed. The desired trajectory is first on-line interpolated to achieve a mathematical expression, and then planned under the physical constraints by the bound estimator and nonlinear filter. A heuristic critical test curve algorithm is proposed to solve the potential stability issue. A telerobotic system, where the function of slave-desired trajectory is unknown in advance, is selected as a typical case. The experimental results validate the effectiveness of the proposed planning algorithm.
Evaluating different i*-based approaches for selecting functional requirements while balancing and optimizing non-functional requirements: A controlled experiment

A relevant question in requirements engineering is which set of functional requirements (FR) to prioritize and implement, while keeping non-functional requirements (NFR) balanced and optimized. We aim to provide empirical evidence that requirement engineers may perform better at the task of selecting FRs while optimizing and balancing NFRs using an alternative (automated) i* post-processed model, compared to the original i* model. We performed a controlled experiment, designed to compare the original i* graphical notation, with our post-processed i* visualizations based on Pareto efficiency (a tabular and a radar chart visualization). Our experiment consisted of solving different exercises of various complexity for selecting FRs while balancing NFR. We considered the efficiency (time spent to correctly answer exercises), and the effectiveness (regarding time: time spent to solve exercises, independent of correctness; and regarding correctness of the answer, independent of time). The efficiency analysis shows it is 3.51 times more likely to solve exercises correctly with our tabular and radar chart visualizations than with i*. Actually, i* was the most time-consuming (effectiveness regarding time), had a lower number of correct answers (effectiveness regarding correctness), and was affected by complexity. Visual or textual preference of the subjects had no effect on the score. Beginners took more time to solve exercises than experts if i* is used (no distinction if our Pareto-based visualizations are used). For complex model instances, the Pareto front based tabular visualization results in more correct answers, compared to radar chart visualization. When we consider effectiveness regarding time, the i* graphical notation is the most time consuming visualization, independent of the complexity of the exercise. Finally, regarding efficiency, subjects consume less time when using radar chart visualization than tabular visualization, and even more so compared to the original i* graphical notation.
Measuring public acceptance of climate-friendly technologies based on creativity and cognitive approaches: Practical guidelines for reforming risky energy policies in Iran

In order to mitigate climate change and reduce fossil fuel dependency in Iran, the government of the country has announced its renewable energy policies to increase the use of renewable energies, but the share of the energy generation mix of Iran is very low due to the lack of public acceptance. These challenges continue and can be hard to overcome at all levels. This paper investigates this problem at regional level by measuring public acceptance towards development of climate-friendly technologies in order to provide required feedbacks to policy-makers of the government for ushering changes in their policies already made or in their executing mechanism, possibly deducting future risks such as public opposition from their end and improving the level of acceptance. This study uses a mixed-methods analytical approach for quantitatively taking that level, including creativity techniques and fuzzy cognitive maps (FCMs). FCMs could directly map out policy-makers' cognitions on the problem. This study provides the mathematical formulation of the problem with its conceptual interpretation to import policy-makers' knowledge into FCMs when the knowledge is gained explicitly with creativity techniques. The authors focused on a case of Solar Water Heater adoption in the city of Behbahan in Khuzestan Province.
A convex formulation for multiple ordinal output classification

Multiple ordinal output classification (MOOC) which specifically refers to learning an association between individual inputs (e.g. face images) and a set of discrete ordinal response/output variables (e.g. facial action units), is a special case of multi-output classification and also a relatively-understudied topic in machine learning. It is very challenging in how to jointly model the relationship among multiple output variables and their discrete ordinal values. In this paper, we propose an effective formulation to address the above challenging problems. Under this formulation, the objective function is convex and thus leads to a convex multiple ordinal output classification (ConMOOC). Specifically, we use a regularization formulation to model the relationships among multiple output variables and an effective threshold-based loss function to fit their ordinal values. To enhance ability of the model, we also apply the kernel trick to provide a nonlinear extension. For efficiency, we use an alternating iteration method to learn the optimal model parameters for each variable as well as the relationships between different variables. Experiments conducted on synthetic and real datasets demonstrate that ConMOOC not only achieves effective classification performance but also reveals the structures among output variables. To the best of our knowledge, MOOC as a general machine learning task is the first time to be studied.
Exploiting Parts-of-Speech for effective automated requirements traceability

Requirement traceability (RT) is defined as the ability to describe and follow the life of a requirement. RT helps developers ensure that relevant requirements are implemented and that the source code is consistent with its requirement with respect to a set of traceability links called trace links. Previous work leverages Parts Of Speech (POS) tagging of software artifacts to recover trace links among them. These studies work on the premise that discarding one or more POS tags results in an improved accuracy of Information Retrieval (IR) techniques. First, we show empirically that excluding one or more POS tags could negatively impact the accuracy of existing IR-based traceability approaches, namely the Vector Space Model (VSM) and the Jensen Shannon Model (JSM). Second, we propose a method that improves the accuracy of IR-based traceability approaches. We developed an approach, called ConPOS, to recover trace links using constraint-based pruning. ConPOS uses major POS categories and applies constraints to the recovered trace links for pruning as a filtering process to significantly improve the effectiveness of IR-based techniques. We conducted an experiment to provide evidence that removing POSs does not improve the accuracy of IR techniques. Furthermore, we conducted two empirical studies to evaluate the effectiveness of ConPOS in recovering trace links compared to existing peer RT approaches. The results of the first empirical study show that removing one or more POS negatively impacts the accuracy of VSM and JSM. Furthermore, the results from the other empirical studies show that ConPOS provides 11%-107%, 8%-64%, and 15%-170% higher precision, recall, and mean average precision (MAP) than VSM and JSM. We showed that ConPos outperforms existing IR-based RT approaches that discard some POS tags from the input documents.
A Proposed Complete Methodology to Predict Gravity Flow Obstruction of Pharmaceutical Powders in Drug Product Manufacturing

We present herein a comprehensive methodology to evaluate the risks involved in gravity-driven flow of pharmaceutical powders, including mass flow/funnel flow pattern, arch formation under active stress state (initial discharging) and passive stress state (following initial discharging), and rathole formation. Built on original theories underpinning the hopper design procedure, the methodology was modified to accommodate practices of pharmaceutical powder handling. All data required are generated from conventional ring shear tester. We applied the methodology to evaluate the powder flow risks during drug product manufacturing campaigns, where two powder blends with distinct flow behavior were discharged from a 200-L bin. The predicted results are in agreement with experiments where visual observations were possible, including the flow pattern, arch formation under active stress state, and rathole formation. One notable discovery is that pharmaceutical powders exhibit high risk of arch formation under active stress state, because of the exceeding major principal stress than the passive state. This phenomenon has been so far overlooked and the existing flow function-based classification cannot capture this risk. We propose, through this methodology, that reliable powder flow assessment should consider factors preventing flow (i.e., flow function), as well as factors facilitating flow (i.e., external stress).
Multi–scale Recursive and Perception–Distortion Controllable Image Super–Resolution

We describe our solution for the PIRM Super–Resolution Challenge 2018 where we achieved the $$\varvec{2^{nd}}$$ best perceptual quality for average $$RMSE\leqslant 16$$ , $$5^{th}$$ best for $$RMSE\leqslant 12.5$$ , and $$7^{th}$$ best for $$RMSE\leqslant 11.5$$ . We modify a recently proposed Multi–Grid Back–Projection (MGBP) architecture to work as a generative system with an input parameter that can control the amount of artificial details in the output. We propose a discriminator for adversarial training with the following novel properties: it is multi–scale that resembles a progressive–GAN; it is recursive that balances the architecture of the generator; and it includes a new layer to capture significant statistics of natural images. Finally, we propose a training strategy that avoids conflicts between reconstruction and perceptual losses. Our configuration uses only 281 k parameters and upscales each image of the competition in 0.2 s in average.
An indexed set representation based multi-objective evolutionary approach for mining diversified top-k high utility patterns

How to discover top-k patterns with the largest utility values, namely, mining top-k high utility patterns, is a hot topic in data mining. However, most of the existing works for mining top-k high utility patterns consider each pattern separately during the mining process, thus many mined patterns are highly similar and lack diversity. In this paper, we propose to mine top-k high utility patterns with high diversity for enhancing users'satisfaction in recommendation. Specifically, we first introduce a simple measure of coverage to quantify the diversity of the whole set, that is, the top-k patterns as a complete entity. Then we propose an i ndexed s et r epresentation based m ulti-o bjective e volutionary a pproach named ISR-MOEA to mine diversified top-k high utility patterns, due to the fact that the two measures utility and coverage are conflicting. In ISR-MOEA, an indexed set individual representation scheme is suggested for fast encoding and decoding the top-k pattern set. Experimental results on six real-world and two synthetic datasets demonstrate the effectiveness of the proposed approach. The proposed approach can obtain several groups of top-k pattern set with different trade-offs between utility and diversity in only one run, which would further enhance the satisfaction of users.
Evaluation of virtualization and traffic filtering methods for container networks

Future distributed scientific applications will rely on containerization for data handling and processing. Likewise, emerging digital marketplaces will be built using these technologies. In particular, support for multi-tenancy will be a major requirement. The question is whether container networking is already mature enough to support the level of usability required in these environments. With the work we present here we set out to experiment and evaluate three novel technologies that support addressing and filtering in container overlays: EVPN, ILA and Cilium/eBPF. We show that it is possible to integrate ILA and eBPF to support dynamic connections between containers. We describe performance testing of filtering operations with Cilium/eBPF and the initial demonstration of ILA showcased during the 2017 SuperComputing conference. All the experiments presented in this article identify the basic building blocks for the much needed container networking multi-tenancy.
On the expressiveness of modal transition systems with variability constraints

We demonstrate that modal transition systems with variability constraints are equally expressive as featured transition systems, by defining a transformation of the latter into the former, a transformation of the former into the latter, and proving the soundness and completeness of both transformations. Modal transition systems and featured transition systems are widely recognised as fundamental behavioural models for software product lines and our results thus contribute to the expressiveness hierarchy of such basic models studied in other papers published in this journal.
Video Streaming Adaptation Strategy for Multiview Navigation Over DASH

Video content delivery over Internet is receiving increasing attention from both industry and academia, especially for the multiview video contents, as it is the basis to support various applications, such as 3-D video, virtual reality, free view video, and so on. To cope with the dynamic nature of Internet throughput, dynamic adaptive streaming over HTTP (DASH) has been introduced to control the video streaming based on the network conditions. In this paper, we design a streaming framework to improve the user experience of the multiview video streaming over DASH, considering the user behavior of the viewpoint navigation during the streaming process. To eliminate the view switching delay, a multiple view navigation rule is introduced to pre-fetch the possible switching viewpoints. An optimal bitrate allocation scheme is proposed for the introduced rule, allowing the clients to maximize the video quality. Moreover, we found the video quality and the playback starvation probability are conflicting factors, while both are essential for the user's quality of experience (QoE). To tackle this issue, a QoE optimization solution is designed to maximize the overall performance in the proposed framework. Several experiments verify the effectiveness of the proposed framework, and the results demonstrate that the proposed framework outperforms two typical DASH methods.
Heuristics for improving the rigour and relevance of grey literature searches for software engineering research

Background: Software engineering research has a growing interest in grey literature (GL). Aim: To improve the identification of relevant and rigorous GL. Method: We develop and demonstrate heuristics to find more relevant and rigorous GL. The heuristics generate stratified samples of search and post–search datasets using a formally structured set of search keywords. Conclusion: The heuristics require further evaluation. We are developing a tool to implement the heuristics.
Dynamic Update of Discrete Event Controllers

Discrete event controllers are at the heart of many software systems that require continuous operation. Changing these controllers at runtime to cope with changes in its execution environment or system requirements change is a challenging open problem. In this paper we address the problem of dynamic update of controllers in reactive systems. We present a general approach to specifying correctness criteria for dynamic update and a technique for automatically computing a controller that handles the transition from the old to the new specification, assuring that the system will reach a state in which such a transition can correctly occur and in which the underlying system architecture can reconfigure. Our solution uses discrete event controller synthesis to automatically build a controller that guarantees both progress towards update and safe update.
An integrated data analytics process to optimize data governance of non-profit organization

Many nations have successfully implemented their own e-Government systems in response to changes in many subjective and objective environments. However, different government agencies now hold a tremendous quantity of client data and have encountered the challenge of how to effectively manage, analyze, and apply that data in order to improve service quality and customer satisfaction levels. In Taiwan, the National Health Insurance Administration (NHIA) is a state-run national health insurance agency and a nonprofit organization (NPO) responsible for managing health insurance affairs and improving healthcare quality for all people in Taiwan. In this study, we apply an integration of the Knowledge Discovery in Databases (KDD) process to analyze the call center data of the NHIA. We take the major processes of processing, selection, data mining, and, evaluation as our foundation for two types of data mining analyses: data sorting and data association. Furthermore, we generalize the analysis results and consult professionals in NHIA for their professional opinion about those results and existing health insurance policies. We also present, interpret, and draw conclusions from these results via data visualization. This visualized analysis can help NHIA decision makers quickly understand and reflect the public's needs and discover deeper client requirements to achieve the goal of upgraded public service quality and performance.
Forecasting compositional risk allocations

We analyse models for panel data that arise in risk allocation problems, when a given set of sources are the cause of an aggregate risk value. We focus on the modelling and forecasting of proportional contributions to risk over time. Compositional data methods are proposed and the time-series regression is flexible to incorporate external information from other variables. We guarantee that projected proportional contributions add up to 100%, and we introduce a method to generate confidence regions with the same restriction. An illustration is provided for risk capital allocations.
Synset expansion on translation graph for automatic wordnet construction

Research on clustering algorithms in synonymy graphs of a single language yields promising results, however, this idea is not yet explored in a multilingual setting. Nevertheless, moving the problem to a multilingual translation graph enables the use of more clues and techniques not possible in a monolingual synonymy graph. This article explores the potential of sense induction methods in a massively multilingual translation graph. For this purpose, the performance of graph clustering methods in synset detection are investigated. In the context of translation graphs, the use of existing Wordnets in different languages is an important clue for synset detection which cannot be utilized in a monolingual setting. Casting the problem into an unsupervised synset expansion task rather than a clustering or community detection task improves the results substantially. Furthermore, instead of a greedy unsupervised expansion algorithm guided by heuristics, we devise a supervised learning algorithm able to learn synset expansion patterns from the words in existing Wordnets to achieve superior results. As the training data is formed of already existing Wordnets, as opposed to previous work, manual labeling is not required. To evaluate our methods, Wordnets for Slovenian, Persian, German and Russian are built from scratch and compared to their manually built Wordnets or labeled test-sets. Results reveal a clear improvement over 2 state-of-the-art algorithms targeting massively multilingual Wordnets and competitive results with Wordnet construction methods targeting a single language. The system is able to produce Wordnets from scratch with a Wordnet base concept coverage ranging from 20% to 88% for 51 languages and expands existing Wordnets up to 30%.
Arguing with Behavior Influence: A Model for Web-Based Group Decision Support Systems

In this work, we propose an argumentation-based dialogue model designed for Web-based Group Decision Support Systems, that considers the decision-makers’ intentions. The intentions are modeled as behavior styles which allow agents to interact with each other as humans would in face-to-face meetings. In addition, we propose a set of arguments that can be used by the agents to perform and evaluate requests, while considering the agents’ behavior style. The inclusion of decision-makers’ intentions intends to create a more reliable and realistic process. Our model proved, in different contexts, that higher levels of consensus and satisfaction are achieved when using agents modeled with behavior styles compared to agents without any features to represent the decision-makers’ intentions.