Reviewerly/exHarmony huggingface dataset:
Hybridizing differential evolution and local search optimization for dimensional synthesis of linkages

This work deals with the optimal dimensional synthesis of planar linkages. The paper proposes a new search procedure to obtain the best linkage by means of hybridizing a Local Search (LS) approach and an Evolutionary Algorithm (EA), Hybrid Algorithms (HAs) combine the advantages of stochastic and deterministic optimization, enhancing their features in the search for the best solution. However, the way in which the integration of these two kinds of algorithms should be implemented is not a trivial issue and many options exist that have to be investigated. In this paper different schemes of hybrid algorithms are applied to the synthesis of planar linkages. The work discusses different approaches that use information and structures for achieving a balance between global and local searches. Differential Evolution (DE) is used together with the Generalized Reduced Gradient (GRG) approach for optimal search. Both Cluster and Elite Analyses are studied to select an effective hybrid optimization strategy. The different schemes of hybrid algorithms are presented and analyzed in different examples for the design of planar linkages and the best options are highlighted.
A Recursive General Regression Neural Network (R-GRNN) Oracle for classification problems

This research introduces the Recursive General Regression Neural Network Oracle (R-GRNN Oracle) and is demonstrated on several binary classification datasets. The traditional GRNN Oracle classifier (Masters et al., 1998) combines the predictive powers of several machine learning classifiers by weighing the amount of error each classifier has on the final predictions. Each classifier is assigned a weight based on the percentage of errors it contributes to the final predictions as the classifiers evaluate the dataset. The proposed R-GRNN Oracle is an enhancement to the GRNN Oracle in which the proposed algorithm consists of an oracle within an oracle – where the inner oracle acts as a classifier with its own predictions and error contribution. By combining the inner oracle with other classifiers, the R-GRNN Oracle produces superior results. The classifiers considered in this study are: Support Vector Machine (SVM), Multilayer Perceptron (MLP), Probabilistic Neural Network (PNN), Gaussian Naïve Bayes (GNB), K-Nearest Neighbor (KNN), and Random Forest (RF). To demonstrate the effectiveness of the proposed approach, several datasets were used, with the primary one being the publicly available Spambase dataset. The predictions of SVM, MLP, KNN, and RF were used to create the first GRNN Oracle, which was then enhanced with the high performances of SVM and RF to create the second oracle, the R-GRNN Oracle. The combined recursive model was 93.24% accurate using 10-fold cross validation, higher than the 91.94% of the inner GRNN Oracle and the 91.29% achieved by RF, the highest performance by a stand-alone classifier. The R-GRNN Oracle was not only the most accurate, but it also had the highest AUC, sensitivity, specificity, precision, and F1-score (97.99%, 91.86%, 94.40%, 93.28%, and 92.57%, respectively). The research contribution of this paper is introducing the concept of recursion (a concept not fully explored in machine learning models and applications) and testing this structure's ability on further enhancing the performance of the traditional oracle. The recursive model has also been applied to several other datasets: The Human Resources, Bank Marketing, and Monoclonal Gammopathy of Undetermined Significance (MGUS) datasets. The results of these implementations are summarized in this paper.
Release Early, Release Often, and Watch Your Users' Emotions: Lessons From Emotional Patterns

App stores are highly competitive markets, and unexpected app changes might incite even loyal users to explore alternative apps. In this article, we present five release lessons, from emotional patterns identified using sentiment analysis tools, to assist app vendors maintain positive emotions and gain competitive advantages.
Hierarchical Attention based Neural Network for Explainable Recommendation

In recent years, recommendation systems have attracted more and more attention due to the rapid development of e-commerce. Reviews information can offer help in modeling user's preference and item's performance. Some existing methods utilize reviews for the recommendation. However, few of those models consider the importance of reviews and words in corpus together. Therefore, we propose an approach for rating prediction using a hierarchical attention-based network named HANN, which can distinguish the importance of reviews at both word level and review level for explanations automatically. Experiments on four real-life datasets from Amazon demonstrate that our model achieves an improvement in prediction compared to several state-of-the-art approaches. The hierarchical attention weights in sampled test data verify the effect on selecting informative words and reviews.
ZAKI+: A Machine Learning Based Process Mapping Tool for SpMV Computations on Distributed Memory Architectures

Smart cities and other cyber-physical systems (CPSs) rely on various scientific, engineering, business, and social applications that provide timely intelligence for their design, operations, and management. Many of these scientific and analytics applications require the solution of sparse linear equation systems, where sparse matrix-vector (SpMV) product is a key computing operation. Several factors determine the performance of parallel SpMV computations, including matrix characteristics, storage formats, and the rising complexity and heterogeneity of computer systems. There is a pressing need for new ways of exploiting parallelism, and mapping data and applications to the computing resources. We propose here ZAKI+, a data-driven machine-learning approach, allowing users to automatically, effortlessly, and speedily obtain the best configuration (the data distribution, the optimal number of processes, and mapping strategy) and performance for the execution of the parallel SpMV computations on distributed memory machines. We train and test the tool using three machine learning methods-decision trees, random forest, and Xtreme boosting-and nearly 2000 real-world matrices obtained from 45 application domains, including computer vision and robotics. ZAKI+ provides optimal process mapping and outperforms the MPI default mapping policy by a factor of 4.24. This is the first work where the sparsity structure of matrices has been exploited to predict the optimal mapping of processes and data in distributed-memory environments by using different base and ensemble machine learning methods. Various CPSs comprise compute-intensive machine learning applications, such as the SpMV, and hence, the process and data mapping contributions of this paper would be of paramount impact for the CPSs.
Smart Autonomous Wheelchair

Smart wheelchairs are an assistive wheeled mobility device. The world health organization reported 10 % of global population (650 million person) have disability and 10% among them need wheelchair. Wheelchair made it easier for many to pursue their life activities including education, work and social life. A smart autonomous wheelchair is developed in this paper to enhance the maneuvering tasks. The wheelchair requires no human intervention during navigation and perception in addition to processing which is based on computer vision techniques.
Statistical methods for the geochemical characterisation of surface waters: The case study of the Tiber River basin (Central Italy)

Studying the chemistry of surface waters and their resilience to compositional changes represents a global challenge to preserve them from human- and changing climate-induced modifications. River waters are sentinel parameters for all processes occurring in a river basin. This is particularly true for regional watersheds, which can include both natural- and anthropogenic-sourced solutes such as those present in the Tiber River basin, the biggest one in Central Italy. In this work, innovative applications of statistical methods are proposed, from the perspective of Compositional Data Analysis theory, in order to consider the geochemical riverine system as a whole and detect compositional changes throughout the catchment. Robust compositional biplots highlighted different sources of variability linked to geological (low variability) and anthropogenic origin (high variability) of the main compounds, thus identifying a hierarchy in the variance of the riverine geochemical processes. On a different scale, the innovative use of the robust Mahalanobis distance in an iterative way monitored spatial compositional shifts for single cases. The effectiveness of this method consists in minimising the influence of an individual anomalous point on the compositional centre and the covariance structure of the data highlighting when along the river a significant compositional shift occurs. The study provides powerful compositional tools for detecting potential contamination events or climate-induced modifications both at catchment and river scale.
Sentiment Classification of Drug Reviews Using Fuzzy-rough Feature Selection

Sentiment analysis mines people's opinions and attitudes regarding a certain issue from source materials.Recently, it has drawn significant attention in a number of application areas.The sentiment analysis of healthcare in general and that of users' drug experience in particular could shed significant light on how to improve public health and make the right decisions.However, one of the major challenges in sentiment classification lies in the very large number of extracted features.Fuzzy-rough feature selection provides a means by which discrete or real-valued noisy data can be effectively reduced without human intervention.This paper proposes an implementation for automatic sentiment classification of drug reviews employing fuzzy rough feature selection.Experimental results demonstrate that the employment of fuzzy-rough feature selection can indeed significantly reduce the complexity of feature space and the classification run-time overheads while maintaining classification accuracy.
Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning

This paper proposes a topological clustering algorithm by integrating topological structure and information theoretic learning, i.e., correntropy, into adaptive resonance theory (ART). Specifically, the proposed algorithm utilizes the correntropy induced metric (CIM) for defining a similarity measure, a node insertion criterion, and an edge creation criterion. Other types of the ART-based topological clustering algorithms have been developed, however, these algorithms have various drawbacks such as a large number of parameters, sensitivity to noisy data. Moreover, generated topological networks cannot represent the distribution of data. In contrast, the proposed algorithm realizes a stable computation and reduces the number of parameters compared to existing algorithms. Furthermore, improving the ability to express the data structure more appropriately by the topological network, a mechanism that adaptively controls the node insertion criterion is introduced to the proposed algorithm. The experimental results showed that the proposed algorithm has superior performance with respect to the self-organizing and the classification abilities compared with the state-of-the-art topological clustering algorithms.
$\mathsf{PrivateLink}$ : Privacy-Preserving Integration and Sharing of Datasets

In privacy-enhancing technology, it has been inevitably challenging to strike a reasonable balance between privacy, efficiency, and usability (utility). To this, we propose a highly practical solution for the privacy-preserving integration and sharing of datasets among a group of participants. At the heart of our solution is a new interactive protocol, PrivateLink. Through PrivateLink, each participant is able to randomize his/her dataset via an independent and untrusted third party, such that the resulting dataset can be merged with other randomized datasets contributed by other participants in a privacy-preserving manner. Our approach does not require key sharing among participants in order to integrate different datasets. This, in turn, leads to a user-friendly and scalable solution. Moreover, the correctness of a randomized dataset returned by the third party can be securely verified by the participant. We further demonstrate PrivateLink's general utilities: using it to construct a structure-preserving data integration protocol. This is particularly useful for private, fine-grained integration of network traffic data. We state the security of our protocols under the well-established real-ideal simulation paradigm and demonstrate practicality by a prototype implementation on: 1) healthcare datasets and 2) DNS and NetFlow datasets.
Children’s agency by design: Design parameters for personalization in story-making apps

The importance of children’s agency in the use of technologies is well-established but it continues to be challenged with applications that automatically personalize children’s content. This paper integrates educational theory of personalization with the design principles and empirical work of a story-making app called Our Story, which places a child’s agency at the center of its design. The impact of a series of studies with the OS app is summarized and implications for future design are offered. The Agentic Personalization Framework that consists of a design principle and a set of guiding questions is presented. The design principle is based on two continuous axes: agency/structure and individualism/collectivism, which give rise to four processes: personalization/customization and standardization/individualization. The guiding questions are intended to promote reflection among researchers and designers interested in supporting children’s agency with stories that children can make or interact with on tablets. The Agentic Personalization Framework is rooted in empirical studies, iterative design and theoretical developments and provides a fertile ground for research-design collaborations that place children’s agency at the heart of innovative work.
Performance Impact of Parameter Tuning on the CCSDS-123.0-B-2 Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression Standard

This article studies the performance impact related to different parameter choices for the new CCSDS-123.0-B-2 Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression standard. This standard supersedes CCSDS-123.0-B-1 and extends it by incorporating a new near-lossless compression capability, as well as other new features. This article studies the coding performance impact of different choices for the principal parameters of the new extensions, in addition to reviewing related parameter choices for existing features. Experimental results include data from 16 different instruments with varying detector types, image dimensions, number of spectral bands, bit depth, level of noise, level of calibration, and other image characteristics. Guidelines are provided on how to adjust the parameters in relation to their coding performance impact.
RETRACTED: Video associated cross-modal recommendation algorithm based on deep learning

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the authors due to an error within the experiment, therefore resulting in an incomplete data set and inaccurate results. The authors apologize for the inconvenience caused.
Eye-to-Hand Robotic Visual Tracking Based on Template Matching on FPGAs

This paper presents a delta robotic visual-servoing tracking method using zero-mean normalized cross-correlation (ZNCC)-based grayscale template matching hardware core on field-programmable gate arrays (FPGAs). The concurrent FPGA-based ZNCC hardware core with cascading multiplication-accumulate (MAC) circuits is designed, which can largely reduce FPGA hardware resource consumptions. A compact optical imaging system with a front 45°-slant mirror and an optical filter film are proposed, which can efficiently filter out the background cluttered artifacts. The trajectory visual-servoing tracking and dynamic tracking experiments based on our built-up delta robotic visual tracking platform are implemented. The experimental results indicate that the presented FPGA-based embedded robotic visual tracking method can efficiently improve an object trajectory tracking performance.
On the Meaning and Purpose of Attack Trees

Attack trees are a popular notation for describing threats to systems, both in academia and industry. Originally, attack trees lacked a formal semantics, but formal semantics for different variants of attack trees were proposed later. These semantics focus on the attacker's actions defined in the leaves and the logical structure defined by the inner nodes of an attack tree. Surprisingly, they do not clarify the connection to the goal defined at the root node in a satisfactory fashion. In this article, we aim at a better clarification of this connection between the attacks and the attacker goal specified by an attack tree. We argue that there are multiple sensible success criteria for attacks wrt. a given attacker goal and develop a framework for defining such criteria. We exploit our framework to identify similarities and differences between automatic attack-tree generation techniques. Finally, we propose a novel variant of attack trees that allows one to express exploits in an explicit fashion.
Dispute Resolution for Smart Contract-based Two-Party Protocols

Blockchain systems promise to mediate interactions of mutually distrusting parties without a trusted third party. However, protocols with full smart contract-based security are either limited in functionality or complex, with high costs for secured interactions. This observation leads to the development of protocol-specific schemes to avoid costly dispute resolution in case all participants remain honest. In this paper, we introduce SmartJudge, an extensible generalization of this trend for smart contract-based two-party protocols. SmartJudge relies on a protocol-independent mediator smart contract that moderates two-party interactions and only consults protocol-specific verifier smart contracts in case of a dispute. This way, SmartJudge avoids verification costs in absence of disputes and sustains interaction confidentiality among honest parties. We implement verifier smart contracts for cross-blockchain trades and exchanging digital goods and show that SmartJudge can reduce costs by 46-50% and 22% over current state of the art, respectively.
Fundamental Flaws with the Fundamental Niche

For more than 70 years, Hutchinson's concept of the fundamental niche has guided ecological research. Hutchinson envisioned the niche as a multidimensional hypervolume relating the fitness of an organism to relevant environmental factors. Here, we challenge the utility of the concept to modern ecologists, based on its inability to account for environmental variation and phenotypic plasticity. We have ample evidence that the frequency, duration, and sequence of abiotic stress influence the survivorship and performance of organisms. Recent work shows that organisms also respond to the spatial configuration of abiotic conditions. Spatiotemporal variation of the environment interacts with the genotype to generate a unique phenotype at each life stage. These dynamics cannot be captured adequately by a multidimensional hypervolume. Therefore, we recommend that ecologists abandon the niche as a tool for predicting the persistence of species and embrace mechanistic models of population growth that incorporate spatiotemporal dynamics.
SemCluster: clustering of imperative programming assignments based on quantitative semantic features

A fundamental challenge in automated reasoning about programming assignments at scale is clustering student submissions based on their underlying algorithms. State-of-the-art clustering techniques are sensitive to control structure variations, cannot cluster buggy solutions with similar correct solutions, and either require expensive pair-wise program analyses or training efforts. We propose a novel technique that can cluster small imperative programs based on their algorithmic essence: (A) how the input space is partitioned into equivalence classes and (B) how the problem is uniquely addressed within individual equivalence classes. We capture these algorithmic aspects as two quantitative semantic program features that are merged into a program's vector representation. Programs are then clustered using their vector representations. The computation of our first semantic feature leverages model counting to identify the number of inputs belonging to an input equivalence class. The computation of our second semantic feature abstracts the program's data flow by tracking the number of occurrences of a unique pair of consecutive values of a variable during its lifetime. The comprehensive evaluation of our tool SemCluster on benchmarks drawn from solutions to small programming assignments shows that SemCluster (1) generates far fewer clusters than other clustering techniques, (2) precisely identifies distinct solution strategies, and (3) boosts the performance of clustering-based program repair, all within a reasonable amount of time.
Ontology Summarization: Graph-Based Methods and Beyond

Ontologies have been widely used in numerous and varied applications, e.g. to support data modeling, information integration, and knowledge management. With the increasing size of ontologies, ontology understanding, which is playing an important role in different tasks, is becoming more difficult. Consequently, ontology summarization, as a way to distill key information from an ontology and generate an abridged version to facilitate a better understanding, is getting growing attention. In this survey paper we review existing ontology summarization techniques and focus mainly on graph-based methods, which represent an ontology as a graph and apply centrality-based and other measures to identify the most important elements of an ontology as its summary. After analyzing their strengths and weaknesses, we highlight a few potential directions for future research.
Context-aware instance matching through graph embedding in lexical semantic space

In recent years, the growing availability of open-accessed data (e.g., Wikipedia) combined with the advances in algorithmic techniques for information extraction have facilitated the design and structuring of information giving rise to knowledge bases. A major challenge relies in the integration of these independently designed knowledge bases. Instance matching is presented as one of the solutions to facilitate this process. It aims to link co-referent instances with an owl:sameAs connection to allow knowledge bases to complement each other. In this work, we present an approach for automatic alignment of instances in knowledge bases in the form of Resource Description Framework (RDF) graphs. Our approach generates for each instance a virtual document from its local description (i.e., data-type properties) and instances related to it through object-type properties (i.e., neighbors). We transform the instance matching problem into a document matching problem and solve it by a vector space embedding technique. We consider the pre-trained word embeddings to assess words similarities at both the lexical and semantic levels. We evaluate our approach on multiple knowledge bases from the instance track of the Ontology Alignment Evaluation Initiative (OAEI). The experiments show that our approach gets prominent results compared to several state-of-the-art existing approaches.