Reviewerly/exHarmony huggingface dataset:
Intelligent Language Tutoring System

Intelligent computer-assisted language learning (ICALL) is a multidisciplinary area of research that combines natural language processing (NLP), intelligent tutoring system (ITS), second language acquisition (SLA), and foreign language teaching and learning (FLTL). Intelligent tutoring systems (ITS) are able to provide a personalized approach to learning by assuming the role of a real teacher/expert who adapts and steers the learning process according to the specific needs of each learner. This article reviews and discusses the issues surrounding the development and use of ITSs for language learning and teaching. First, the authors look at ICALL history: its evolution from CALL. Second, issues in ICALL research and integration will be discussed. Third, they will explain how artificial intelligence (AI) techniques are being implemented in language education as ITS and intelligent language tutoring systems (ITLS). Finally, the successful integration and development of ITLS will be explained in detail.
The Application of Artificial Intelligence Technologies as a Substitute for Reading and to Support and Enhance the Authoring of Scientific Review Articles

To gain a comprehensive overview of new scientific findings with the enormous, ever-increasing amount of published information, we apply a new combinatorial approach that complements the process of reading scientific articles by supplementing artificial intelligence technologies. We present a combinatorial approach, which we illustrate in the form of a “double funnel of artificial intelligence.” Our approach suggests to largely increase the amount of data at the beginning of the data collection process and to subsequently clean and enrich the data set in order to gain much more knowledge at the end of the procedure compared to a “classical” literature review. We use natural language processing and text visualization techniques to uncover findings that are generally unbeknown to the human reader due to the inability to process very large amounts of text. By illustrating the individual steps using practical examples taken from use cases, we demonstrate the merits of our approach. With our methodology, we are able to reproduce findings from “regular” review papers; however, we discover additional and new findings in different fields, such as data science or medicine. We also point out the limitations of our approach. Finally, we make suggestions as to how the methodology could be further developed.
FIXER: Flow Integrity Extensions for Embedded RISC-V

With the recent proliferation of Internet of Things (IoT) and embedded devices, there is a growing need to develop a security framework to protect such devices. RISC-V is a promising open source architecture that targets low-power embedded devices and SoCs. However, there is a dearth of practical and low-overhead security solutions in the RISC-V architecture. Programs compiled using RISC-V toolchains are still vulnerable to code injection and code reuse attacks such as buffer overflow and return-oriented programming (ROP). In this paper, we propose FIXER, a hardware implemented security extension to RISC-V that provides a defense mechanism against such attacks. FIXER enforces fine-grained control-flow integrity (CFI) of running programs on backward edges (returns) and forward edges (calls) without requiring any architectural modifications to the RISC-V processor core. We implement FIXER on RocketChip, a RISC-V SoC platform, by leveraging the integrated Rocket Custom Coprocessor (RoCC) to detect and prevent attacks. Compared to existing software based solutions, FIXER reduces energy overhead by 60% at minimal execution time (1.5%) and area (2.9%) overheads.
Willingness to communicate in digital and non-digital EFL contexts: scale development and psychometric testing

This study introduces a new scale of willingness to communicate in a second language (L2 WTC) in digital and non-digital EFL contexts. The psychometric testing of the revised L2 WTC (L2 WTC-R) was undertaken with 114 Indonesian EFL students by means of exploratory factor analysis (EFA). The results yielded an 11-item scale composed of three factors (L2 WTC inside the classroom, L2 WTC outside the classroom, and L2 WTC in the context of informal digital learning of English [IDLE]), with strong reliability and validity evidences. Confirmatory Factor Analysis was subsequently administered for a new sample (215 Indonesian EFL students), confirming the three-factor model that emerged from EFA. These results not only validate the existing L2 WTC scale but also expand its scale into digital settings, which will enable future research to consider another domain of L2 WTC (the IDLE context). From a real-world perspective, this can serve as a useful instrument for practitioners to gauge and foster L2 WTC levels in digital and non-digital contexts.
A distributed group recommendation system based on extreme gradient boosting and big data technologies

Personalized recommendation systems have emerged as useful tools for recommending the appropriate items to individual users. However, in such situations, some items tend to be consumed by groups of users, such as tourist attractions or television programs. With this purpose in mind, Group Recommender Systems (GRSs) are tailored to help groups of users to find suitable items according to their preferences and needs. In general, these systems often confront the sparsity problem, which negatively affects their efficiency. With the increase in the number of users, items, groups, and ratings in the system. Data becomes too big to be processed efficiently by traditional systems. Thus, there is an increasing need for distributed recommendation approaches able to manage the issues related to Big Data and sparsity problem. In this paper, we propose a distributed group recommendation system, which is designed based on Apache Spark to handle large-scale data. It integrates a novel proposed recommendation method, a dimension reduction technique, with supervised and unsupervised learning for dealing efficiently with the curse of dimensionality problem, detecting the groups of users, and improving the prediction quality. Experimental results on three real-world data sets show that our proposal is significantly better than other competitors.
Person re-identification via adaptive verification loss

Person re-identification (re-ID) is the task of associating the image patches of the identical person across disjoint cameras, which is a very challenging topic in computer vision. Recently, with the advance of deep learning techniques and the public of huge datasets, person re-ID has achieved a great success. However, an obvious but ignored issue is that the sample pairs are biased, where the negative sample pairs are far more than the positive ones. In this paper, we develop an adaptive verification loss, termed as ADV-Loss to handle the imbalance of sample pairs. Our ADV-Loss empowers the popular verification loss with the flexibility of adaptively addressed the hard triple units. Specifically, we learn a re-weighted strategy from the triplet loss to balance the sample pairs. According to the objective values of triple loss, the hard triple units will be endowed with larger weights, while the less important triple units are de-emphasized or simply dropped. Thus, ADV-Loss, on the basis of the cross-entropy loss, handles the biased issue by only holding the equal number but informative sample pairs. Moreover, theoretical analysis of our designed ADV-Loss is provided to guarantee the important sample pairs not to be despised during the backpropagation. Ablation analysis verifies the potential efficacy of the proposed loss. Moreover, experiments of person re-ID on three large-scale benchmark datasets show that two popular deep networks with the proposed loss achieve satisfactory performance against several well-established person re-ID methods.
Internal capabilities, national governance and performance in African firms

We explore the relations between firms' internal capabilities, national governance quality (NGQ) and performance in the African context using a dataset comprised of 11,183 firm-year observations (1490 unique firms from 15 African countries over a 17-year period). Our study offers new insights into how interlinkages between firms' internal and external environment, shape corporate success. Specifically, we find that (1) firms' internal capabilities (captured by financial resource-availability and growth prospects) are critical enablers of performance in both weak and strong institutional environments, (2) individual firms perform well in environments where their peers performs well, (3) NGQ directly enhances aggregate firm performance, and in tandem, the performance of individual firms, and (4) NGQ moderates the capability-performance nexus, by enhancing the translation of growth opportunities into profitability. The results highlight the critical role of firm-level financial resource availability and growth prospects in shaping corporate success in this challenging institutional environment.
Aerial Grasping of an Object in the Strong Wind: Robust Control of an Aerial Manipulator

An aerial manipulator is a new kind of flying robot system composed of a rotorcraft unmanned aerial vehicle (UAV) and a multi-link robotic arm. It gives the flying robot the capacity to complete manipulation tasks. Steady flight is essential for an aerial manipulator to complete manipulation tasks. This paper focuses on the steady flight control performance of the aerial manipulator. A separate control strategy is used in the aerial manipulator system, in which the UAV and the manipulator are controlled separately. In order to complete tasks in environments with strong wind disturbance, an acceleration feedback enhanced robust H∞ controller was designed for the UAV in the aerial manipulator. The controller is based on the hierarchical inner-outer loop control structure of the UAV and composed of a robust H∞ controller and acceleration feedback enhanced term, which is used to compensate for the wind disturbance. Experimental results of aerial grasping of a target object show that the controller can suppress the wind disturbance effectively, and make the aerial manipulator hover steadily with sufficient accuracy to complete aerial manipulation tasks in strong wind.
A Study on E-commerce Recommender System Based on Big Data

Recommender system algorithms are widely used in e-commerce to provide personalized and more accurate recommendations to online users and enhance the sales and user stickiness of e-commerce. This paper discusses several recommendation algorithms and the challenge of tradition recommender system in big data situation, and then proposes a framework of distributed and scalable recommender system based on Hadoop. The recommender system based on Hadoop, combining the advantage of computational ability and scalability of MapReduce and hybrid recommendation algorithms, brings a solution to information overload problem in big e-commerce.
Cluster analysis for groundwater classification in multi-aquifer systems based on a novel correlation index

Understanding the spatial variations in groundwater chemistry is fundamental to assess the groundwater pathways and identify the most advantageous strategies for a sustainable use of groundwater resources. In fact, such variations can be the result of the complex structure of flow systems or local pressures of natural or anthropic origin. Thus, a detailed analysis of spatial variations in hydrochemical data provides an insight into natural and anthropogenic effects on groundwater quality and into scale-dependent heterogeneity. Multivariate statistics and, in particular, clustering methods can effectively support those analyses, as remarked by a large number of studies in the literature. However, open issues still affect the reliability and general applicability of multivariate statistics and cluster analysis in hydrogeology, especially if a limited number of data and information about well-casing and screen characteristics are available. Such questions are related to the appropriate selection of end-members on one hand, and to the subjectivity of clustering results on the other hand. Starting from hydrogeological data of the Solofrana River basin in Southern Italy, the present paper illustrates an original approach to end-member selection in a pyroclastic-alluvial aquifer, and to the analysis of the geochemical evolution of groundwater in a given basin based on k-means clustering. The proposed approach has been applied to a real dataset collected in July 2010. Comparing the results against classical hydrogeological models and graphical methods typically used to classify water samples, a robust validation of the methodology has been achieved.
A fuzzy rule based multimodal framework for face sketch-to-photo retrieval

Facial sketches are widely used by law enforcement agencies to assist in the identification and apprehension of suspects involved in criminal activities. Sketches used in forensic investigations are drawn by forensic artists following the verbal description provided by an eyewitness or the victim. In a forensic sketch, the facial description depends on the memory of the eyewitness, therefore, there is uncertainty in facial attributes. Most of the existing sketch-photo retrieval methods have ignored the uncertainty of facial attributes. In this paper, we have proposed a novel multimodal framework for sketch-photo retrieval. We have fused the facial attributes and the semantic color information using a fuzzy rule based layered classifier. We have also performed a survey to rank the importance of different facial attributes in the identification of a person. We have performed experiments using viewed, semi-forensic and forensic sketches dataset and results are compared with state-of-the-art methods. Experimental results show that the proposed fuzzy rule based method achieves promising performance as compared to state-of-art methods specifically in case of forensic sketches.
Online Heterogeneous Transfer Learning by Knowledge Transition

In this article, we study the problem of online heterogeneous transfer learning, where the objective is to make predictions for a target data sequence arriving in an online fashion, and some offline labeled instances from a heterogeneous source domain are provided as auxiliary data. The feature spaces of the source and target domains are completely different, thus the source data cannot be used directly to assist the learning task in the target domain. To address this issue, we take advantage of unlabeled co-occurrence instances as intermediate supplementary data to connect the source and target domains, and perform knowledge transition from the source domain into the target domain. We propose a novel online heterogeneous transfer learning algorithm called O nline H eterogeneous K nowledge T ransition (OHKT) for this purpose. In OHKT, we first seek to generate pseudo labels for the co-occurrence data based on the labeled source data, and then develop an online learning algorithm to classify the target sequence by leveraging the co-occurrence data with pseudo labels. Experimental results on real-world data sets demonstrate the effectiveness and efficiency of the proposed algorithm.
Bayesian filter for nonlinear systems with randomly delayed and lost measurements

In this note, a modified-likelihood Bayesian filter is proposed for a class of discrete-time nonlinear dynamical systems whose outputs are transmitted to the estimator with random delay and dropout. The likelihood function of the filter is computed by marginalizing out the delay variable to extract accurate information from the delayed measurements. Moreover, the measurement update stage of the filtering algorithm is omitted when no new measurement is received. Simulation results are presented to verify the superior performance of the introduced filter compared to some existing ones in the literature.
Efficient 3D reconstruction of random heterogeneous media via random process theory and stochastic reconstruction procedure

This paper introduces an algorithm for reconstructing three-dimensional (3D) heterogeneous media, in which the randomly distributed individual entities, such as aggregates, pores or other material phases with different sizes and shapes can be explicitly represented. The reconstruction procedure achieves high efficiency and reliability by integrating random process theory and stochastic reconstruction procedure. Specifically, the random process theory is applied to generate an initial model based on the statistics obtained from the digital image of heterogeneous material sample and then this coarse model is evolved by the simulated annealing algorithm to match the same statistical descriptors of sample structure. Two 3D models of different sizes are reconstructed. The autocorrelation function is applied to validate the accuracy of the reconstructed models. The comparison between the reconstructed models and the real model shows good agreement. The proposed method is suitable for reconstructing large size 3D random media for its high computational efficiency.
Smart University: A Validation of “Smartness Features—Main Components” Matrix by Real-World Examples and Best Practices from Universities Worldwide

A smart university may have numerous components of a traditional university; however, it must have multiple additional “smart” components to implement, maintain and actively use distinctive “smartness” features such as adaptation, sensing, inferring, self-learning, anticipation, and self-optimization. This paper presents the outcomes of an ongoing research project at the InterLabs Research Institute, Bradley University (Peoria, IL, U.S.A.) aimed to validate the proposed “Smartness Features—Main Components” matrix for a smart university by finding relevant real-world examples and best practices from universities worldwide. This matrix contains relations between smart university’s smart features and main components—smart software and hardware systems, smart technologies, smart pedagogy, and smart classrooms—those that go well beyond the ones in a traditional university. More than 300 various pertinent examples have been identified and analyzed by our research team to support the proposed matrix; 36 selected examples are briefly presented in this paper. Research outcomes unambiguously prove the correctness of the proposed “Smartness Features—Main Components” matrix for a smart university.
The investigation of mechanical properties of a structural adhesive via digital image correlation (DIC) technic

The most important point to be taken into consideration in the use of bonding joints is the reliability of the joints created with the adhesive. Therefore, many analytical, experimental and numerical studies have been done to determine the mechanical properties of adhesives and to ensure the reliable use of adhesively bonding joints. This study presents the first stage of the two-stage study, which aims to determine the mechanical properties of the DP410 structural adhesive in the first stage and the mechanical behavior of the adhesively bonded single lap joints formed using the same adhesive in the second stage. In the study, mechanical properties of the DP410 structural adhesive were investigated experimentally via two dimensional Digital Image Correlation (DIC) technique. Bulk (tensile, Iosipescu and Arcan) and joint form (TAST) specimens were produced from structural bicomponent liquid adhesive (DP410) and subjected to uniaxial tensile and shear loads in the study. Mechanical properties of the DP410 bulk specimens were determined by two different measurement methods (DIC and video extensometer). It was observed upon comparing the results obtained that the DIC technique yielded reproducible results in the determination of the mechanical properties of adhesives and this method of optical measurement could be an alternative to other strain measurement methods. Shear mechanical properties of the DP410 adhesive were obtained via two different strain measurement methods, (DIC and contact extensometer) in the TAST specimens. When the results obtained from TAST specimens were examined, it was observed that the results obtained from DIC method were compatible with contact extensometer and it could be an alternative to contact extensometer. In addition, since local strain distributions can be obtained in the DIC method, it has been observed that this method allows to performe damage analysis of the specimens in bulk and joint form and to obtain the strain values at the desired positions. In this study, the accuracy of the results obtained from the 2D DIC analysis can be increased by using the software which allows to perform the 3D DIC analysis via high resolution cameras and by preparing the sample with the right methods.
Relaxing Unanswerable Geographic Questions Using A Spatially Explicit Knowledge Graph Embedding Model

Recent years have witnessed a rapid increase in Question Answering (QA) research and products in both academic and industry. However, geographic question answering remained nearly untouched although geographic questions account for a substantial part of daily communication. Compared to general QA systems, geographic QA has its own uniqueness, one of which can be seen during the process of handling unanswerable questions. Since users typically focus on the geographic constraints when they ask questions, if the question is unanswerable based on the knowledge base used by a QA system, users should be provided with a relaxed query which takes distance decay into account during the query relaxation and rewriting process. In this work, we present a spatially explicit translational knowledge graph embedding model called TransGeo which utilizes an edge-weighted PageRank and sampling strategy to encode the distance decay into the embedding model training process. This embedding model is further applied to relax and rewrite unanswerable geographic questions. We carry out two evaluation tasks: link prediction as well as query relaxation/rewriting for an approximate answer prediction task. A geographic knowledge graph training/testing dataset, DB18, as well as an unanswerable geographic query dataset, GeoUQ, are constructed. Compared to four other baseline models, our TransGeo model shows substantial advantages in both tasks.
Voice Agents Supporting High-Quality Social Play

While the design of Voice User Interfaces (VUIs) has mostly focused on applications for adults, VUIs also provide potential advantages for young children in enabling concurrent interactions with the physical and social world. Current applications for young children focus mostly on media playing, answering questions, and highly-structured activities. There is an opportunity to go beyond these applications by using VUIs to support less structured, developmentally appropriate activities. In this paper, we describe our first step in pursuing this opportunity through an exploration of voice agents to facilitate high-quality social play guided by a partnership with eight 3-4 year old children. During 24 design sessions, we explored making voice agents tangible and enabling children to control what voice agents say. After analyzing the sessions, we learned voice agents could help keep children socially engaged in play and children liked incorporating the agents with the physical aspects of their play. On the other hand, enabling children to control the voice agents caused distractions from play.
A Scenario Approach to the Simonshaven Case

In this paper, we present a scenario approach and apply it to the Simonshaven case. We offer an outline in which we spell out the core notions of the scenario approach. Next, we give a summing up of criteria to assess and compare scenarios. We use examples of the Simonshaven case to illustrate how the scenario-approach works. The last section contains a discussion of the main strengths and weaknesses of the scenario approach and a brief comparison with argumentation-based and probabilistic approaches.