Reviewerly/exHarmony huggingface dataset:
Grounding of Uncertain Force Parameters in Spoken Robot Commands

Speech-based robot instruction is a promising field in private households and in small and medium-sized enterprises. It facilitates the use of robot systems for non-experts as well as experts, even while the user executes other tasks. Considering force-based robot motions, the common approach is to map verbs to robot motions depending on the tool and the work piece. While this method works well for a wide variety of applications, it limits the user to a fixed force for a given manipulation task and does not allow extensions like “hard” or “soft”. To overcome this drawback, we contribute an approach for a defuzzification of uncertain force parameters to numerical robot motions. To proof the reliability of our approach, we apply it on a motion with varying material parameters.
Research on Consistency between Diploma Policies and Nomenclature of Major Disciplines

In order to showcase a possible real-world example of AI deployment, this paper compares comprehension abilities of humans and machines in a wide range of academic statements based on surveys of 330 people, and machines that have gone through deep learning. This study found that both parties demonstrate similar rates of success but different patterns of comprehension and correlation.
Towards a Method for Creating Personas with Knowledge and Cognitive Process for User Centered Design of a Learning Application

Persona, an archetypical user, has been widely used in the IT industry and has been studied by numerous researchers in academia, with the benefits flowing back to industry in the form of trained graduates and better tools. User Centered Design methodology places the end users at the center of design of an application. In this paper, we present a methodology towards the design of a questionnaire for gathering data about knowledge and cognitive process which is used for creating personas for design of an online learning application for professionals and for teaching User Centered Design to students. We tested our personas and found that they represent the target end users for the design purposes. We also present data from two studies about users' learning approaches and requirements and compare these. In the first study, we present results of our survey with 20 professionals in an educational institution in Australia. In the second study, we present the results of a survey with 272 undergraduate Computing students at an Australian University. The knowledge presented in this paper is useful for the design of learning applications.
Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems.

Machine learning techniques have a huge potential to support humans, some impressive results are still achieved, such as AlphaGo. Until now the results are on a sub-symbolic level which is hard to interpret for humans, because we think symbolically. Today, the mapping is typically static which does not satisfy the needs for fast changing CPSs which prohibit the usage of the full machine learning potential. To tackle this challenge, this paper introduces a knowledge based approach of an automatic mapping between the sub-symbolic results of algorithms and their symbolic representation. Clustering is used to detect groups of similar data points which are interpreted as concepts. The information of the clusters are extracted and further classied with the help of an ontology which infers the current operational state. Data from wind turbines is used to evaluate the approach. The achieved results are promising, the system can identify the operational state without an explicit mapping.
Arabic Logic Textual Entailment with Feature Extraction and Combination

Determining the textual entailment between texts is important in many NLP tasks, such as, question-answering, summarization, and information retrieval, etc. In question-answering, this technique is frequently used to validate an answer retrieved by a question-answering system. In this paper, we address the problem of textual entailment in Arabic. We employ some features to determine the textual entailment between pairs of logical representations of a question and a passage of text. We have implemented our approach in an Arabic recognizing textual entailment system called Ar-SLoTE (Arabic Semantic Logic based Textual Entailment). The experiments results of the entailments classification achieved a precision and a recall successively equal to 73% and 68% on average.
P-EAARM: A Generic Framework Based on Spark for EAs-based Association Rule Mining

Many researchers have proposed serialized evolutionary algorithms (EAs) which are utilized to discover association rules from datasets. However, serialized algorithms are ineffective to tackle with large datasets because of the hardware limitations. Aiming at this issue, we propose a generic framework based on Spark for EAs-based association rule mining (P-EAARM). Based on the mechanism of Resilient Distributed Dataset in Spark, large datasets can be scanned in parallel. The basic idea of P-EAARM is to reduce the computational cost when each idea of EAs scans the dataset to calculate measures. Instead of the reduceBy operator of RDD used in the parallel PSO algorithm for association rules mining (PPQAR), P-EAARM uses the mechanism of Accumulator provided by Spark to reduce computational cost. In addition, the final non-dominated solutions are stored as a RDD in order to improve its scalable ability. As a case study, we use a hybrid NSGA-II-MOPSO algorithm and an extended dataset taken from IBM SPSS modeler to validate P-EAARM in terms of computational cost and speedup. The results show that, compared with the scanning method mentioned in PPQAR, P-EAARM reduces the computational cost markedly. Compared with serialized algorithm, P-EAARM can acquire a 430% speedup approximately. The results prove the validity of our framework.
Calibrated Parsing Items Evaluation: a step towards objectifying the translation assessment

The present research paper introduces a translation evaluation method called Calibrated Parsing Items Evaluation (CPIE hereafter). This evaluation method maximizes translators’ performance through identifying the parsing items with an optimal p-docimology and d-index (item discrimination). This method checks all the possible parses (annotations) in a source text by means of the Brat Visualization Stanford CoreNLP software. CPIE takes a step towards the objectification of translation assessment by allowing evaluators to assess values (impacts) of the items in source texts via docimologically justified parsing items. For this paper, 16 evaluators were recruited to score translation drafts by means of the holistic, analytic, Preselected Items Evaluation (PIE) methods and CPIE method. For the present research paper, “F-Statistics,” “Probability Plot,” “Spearman rho,” and “Regression Variable Plot” were applied to the evaluators’ translation assessments to ensure the degree of validity and reliability of CPIE compared to the holistic, analytic, and PIE methods, respectively. The results indicated that the CPIE method was more consistent and valid in terms of docimologically justified parsing items. The limitations and the possibilities of the CPIE method in web-based platforms were also discussed.
Disruptive Technologies in Intelligent Transportation Systems

Since the 90s, the Intelligent Transportation Systems (ITS) have always been looking for improving the technological solutions that has been applied into the transportation. In fact, today a concept known as “disruptive innovation” has began to re-order the way that ITS services can be achieved it. Disruptive Technology means that an established technology can be displaced by innovative solutions that require less resources and can grow exponentially, very often, shaking up the economy and structure of the related businesses. This paper presents some conclusions of the panel discussion regarding disruptive technologies held in the ITS LATAM 2019. Here, we present some aspects that practitioners should take into account in order to design and develop new ITS Services
Research of Flexible Load Analysis of Distribution Network Based on Big Data

Aiming at the problem of insufficient computing power in the analysis of flexible load under the condition of massive user data in distribution network, a flexible load analysis method based on big data is proposed. Firstly, the data fusion and feature extraction of the massive user data in distribution network are carried out to realize the preprocessing of the original data. Then, on the basis of MapReduce parallel computing model, the k-means algorithm is used to cluster the load characteristic data and calculate the industry flexibility coefficient and adjustable load. According to the results of load flexibility analysis based on the historical data of a power supply company in a certain city, this method can make statistics on load flexibility (load adjustable space) from industry dimension and time dimension, judge the current load flexibility level, and combine external data and temperature data to carry out load flexibility analysis. According to historical data, the adjustable load level of a certain area, a certain industry and a certain period in the future is predicted.
Robotic Knitting in String Art as a Tool for Creative Design Processes

The application of industrial robots in creative industries has shown great potential in the last decade. The interpretation of images as tangible and artistic works of art is depicted with various elements and techniques. The usage of a continuous piece of string allows for the design process to explore different methods of achieving comprehensible images in the form of string art. With different computational tools and design techniques, it becomes possible to explore design thinking coupled with automated fabrication. In this paper, the emphasis is placed on generating a large scale string art pieces out of smaller tiles, that can be used as wall art. By using an integrated workflow it is possible to generate the program for robotic knitting of the string art piece, by adjusting the parameters that govern the entire process.
Analyzing gaze transition behavior using bayesian mixed effects Markov models

The complex stochastic nature of eye tracking data calls for exploring sophisticated statistical models to ensure reliable inference in multi-trial eye-tracking experiments. We employ a Bayesian semi-parametric mixed-effects Markov model to compare gaze transition matrices between different experimental factors accommodating individual random effects. The model not only allows us to assess global influences of the external factors on the gaze transition dynamics but also provides comprehension of these effects at a deeper local level. We experimented to explore the impact of recognizing distorted images of artwork and landmarks on the gaze transition patterns. Our dataset comprises sequences representing areas of interest visited when applying a content independent grid to the resulting scan paths in a multi-trial setting. Results suggest that image recognition to some extent affects the dynamics of the transitions while image type played an essential role in the viewing behavior.
A public tangible interface for engaging and informing young citizens about climate change

This paper is focused on the design of a tangible interface to be installed in a public space for improving children's awareness of climate change and permitting them to understand the relation between CO2 and other environmental variables. The interface was developed in the context of an EU project targeted at re-populating the flora of selected sea shores of Northeast Italy and measuring the results of this work - in terms of CO2 concentration - using a network of sensors gathering a set of environmental parameters. A prototype of the tangible interface, which allows young citizens to access the collected data and understand the relations between them, was implemented and experimented with a class of children of the Primary School, in order to understand its benefits in terms of engagement and learning.
Spatio-Temporal Fusion Networks for Action Recognition

The video based CNN works have focused on effective ways to fuse appearance and motion networks, but they typically lack utilizing temporal information over video frames. In this work, we present a novel spatio-temporal fusion network (STFN) that integrates temporal dynamics of appearance and motion information from entire videos. The captured temporal dynamic information is then aggregated for a better video level representation and learned via end-to-end training. The spatio-temporal fusion network consists of two set of Residual Inception blocks that extract temporal dynamics and a fusion connection for appearance and motion features. The benefits of STFN are: (a) it captures local and global temporal dynamics of complementary data to learn video-wide information; and (b) it is applicable to any network for video classification to boost performance. We explore a variety of design choices for STFN and verify how the network performance is varied with the ablation studies. We perform experiments on two challenging human activity datasets, UCF101 and HMDB51, and achieve the state-of-the-art results with the best network.
Cracking the DES Cipher with Cost-Optimized FPGA Devices

This paper examines efficiency of hardware realizations of DES cracking engines implemented in contemporary low-cost Spartan-7 devices from Xilinx, Inc. The engines are designed for the known plaintext attack scheme and find the secret cipher key through brute-force exhaustive search of the entire key space. In order to comprehensively evaluate potential of the selected FPGA family in this task three architectures of DES decoders were tested: the standard iterative organization, the fully unrolled i.e. purely combinational one and the fully unrolled pipelined version. Various sizes of individual decipher units based on these three architectures led to evaluation of optimal ratios of unit speeds vs. their number which fit in one chip. The results are compared with other known hardware platforms and illustrate progress in the cipher cracking systems which was made possible by improvements in the new FPGA technologies.
Results on the homotopy type of the spaces of locally convex curves on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>𝕊</mml:mi> <mml:mn>3</mml:mn> </mml:msup></mml:math>

A curve γ : [0, 1] → S n of class C k (k n) is locally convex if the vectors γ(t), γ (t), γ (t), . . ., γ (n) (t) are a positive basis to R n+1 for all t ∈ [0, 1].Given an integer n 2 and Q ∈ SO n+1 , let LS n (Q) be the set of all locally convex curves γ : [0, 1] → S n with fixed initial and final Frenet frame Fγ (0) = I and Fγ (1) = Q.Saldanha and Shapiro proved that there are just finitely many nonhomeomorphic spaces among LS n (Q) when Q varies in SO n+1 (in particular, at most 3 for n = 3).For any n 2, one of these spaces is proved to be homeomorphic to the (well understood) space of generic curves (see below), but very little is known in general about the others.For n = 2, Saldanha determined the homotopy type of the spaces LS 2 (Q).The purpose of this work is to study the case n = 3.We will obtain information on the homotopy type of one of these two other spaces, allowing us to conclude that none of the connected components of LS 3 (-I) is homeomorphic to a connected component of the space of generic curves.Résumé.-La courbe γ : [0, 1] → S n de classe C k (k n) est localement convexe si les vecteurs γ(t), γ (t), γ (t), . . ., γ (n) (t) forment une base positive de R n+1 pour chaque t ∈ [0, 1].Pour un entier n 2 et Q ∈ SO n+1 , soit LS n (Q) l'ensemble de toutes les courbes localement convexes γ : [0, 1] → S n avec repères de Frenet initial et final fixes Fγ (0) = I et Fγ (1) = Q.Saldanha et Shapiro ont demontré qu'il n'y a qu'un nombre fini d'espaces non-homéomorphes parmi les LS n (Q) avec Q ∈ SO n+1 (en particulier, au plus 3 pour n = 3).Pour n 2, ils demontrent qu'un de ces espaces est homéomorphe à l'espace (bien compris) des courbes génériques (défini ci-dessous) mais on connaît très peu les autres espaces.Pour n = 2, Saldanha a déterminé le type d'homotopie des espaces LS 2 (Q).Le but de ce travail est d'étudier le cas n = 3.On obtient des informations sur le type d'homotopie d'un de ces autres deux espaces, ce qui nous permet de déduire qu'aucune des composantes connexes de LS 3 (-I) n'est homéomorphe à une composante connexe de l'espace des courbes génériques.
Validation-Driven Design in the Early Phase of Product Development

Given its leverage effect on downstream processes, the early phase of product development has special significance for the success of an innovation project. The scope for decision-making in the early phase is high and the time and effort required for changes relatively low. The late correction of wrong decisions made in early phases, on the other hand, is often associated with a disproportionately high effort. Validation activities in the early phase of product development are thus key to development success. At the same time, however, early validation activities come with special challenges, partly resulting from the high degree of uncertainty and a lack of resources in this phase. Early validation starts from product models. The product profile is a product model established for the use in the early development phase and beyond. It contains both solution-open and solution-specific elements. A particular challenge that developers face in practice when preparing early validation activities is the specification of solution-open elements of the product profile. Contributing to a targeted validation in early development phases, this paper presents a method that supports the developer in prioritizing, selecting and concretizing solution-open product attributes for early creation-validation cycles starting from the initial system of objectives. The method enables product developers to reduce uncertainties systematically regarding relevant customer and user requirements by means of targeted validation already in early development phases. We will explain the findings of this paper by using an example from the automotive industry. We will also discuss results from the evaluation of the method which we generated in a development project for mining equipment.
How to Plan Roadworks in Urban Regions? A Principled Approach Based on AI Planning

Roadworks are required to keep roads in acceptable condition, and to perform maintenance of essential infrastructure. Road agencies are facing the problem of how to effectively plan frequent roadworks. In this paper, we exploit Automated Planning for roadworks planning. We introduce a planning domain model that allows us to plan a set of required roadworks, over a period of time, in a large urban region, by specifying constraints to be satisfied and suitable quality metrics. Our empirical analysis shows the suitability of the proposed approach.
Development of an Approach to Measuring Learnability Based on NGOMSL from Perspectives of Extended Learnability

Sangwon Leea & Young June Saha* a Department of Interaction Science, Sungkyunkwan University, Seoul, South KoreaSangwon Lee is an associate professor in the department of Interaction Science, Sungkyunkwan University. He has obtained his PhD degree in Industrial Engineering at the Pennsylvania State University in 2010. His research interests include human-computer interaction, user experience, affective computing, and user modeling.Young June Sah (PhD, Michigan State University) is an adjunct professor in the department of Interaction Science, Sungkyunkwan University. His research interests include psychological and behavioral effects of media technologies and their cognitive mechanisms.
2018 Edith Ackermann Award

In 2017, Paulo Blikstein, Dor Abrahamson, and the Interaction Design and Children Community established an annual award in the memory of Edith Ackermann to recognize the work of two scholars, one emergent and one eminent. These individuals would be acknowledged for their accomplishments but also tasked with a mission to complete for the following year's conference. Through a series of meetings they would produce an artifact that would be shared with IDC. In 2018, Michael Horn was recognized as the emergent scholar and Heidi Schelhowe was recognized as the eminent scholar. Through a series of conversations culminating in an in-person visit, Mike and Heidi have written this essay in which they share resonating themes from their conversations.
Autocompletion for Prefix-Abbreviated Input

Query autocompletion (QAC) is an important interactive feature that assists users in formulating queries and saving keystrokes. Due to the convenience it brings to users, QAC has been adopted in many applications, including Web search engines, integrated development environments (IDEs), and mobile devices. For existing QAC methods, users have to manually type delimiters to separate keywords in their inputs. In this paper, we propose a novel QAC paradigm through which users may abbreviate keywords by prefixes and do not have to explicitly separate them. Such paradigm is useful for applications where it is inconvenient to specify delimiters, such as desktop search, text editors, and input method editors. E.g., in an IDE, users may input getnev and we suggest GetNextValue. We show that the query processing method for traditional QAC, which utilizes a trie index, is inefficient under the new problem setting. A novel indexing and query processing scheme is hence proposed to efficiently complete queries. To suggest meaningful results, we devise a ranking method based on a Gaussian mixture model, taking into consideration the way in which users abbreviate keywords, as opposed to the traditional ranking method that merely considers popularity. Efficient top-k query processing techniques are developed on top of the new index structure. Experiments demonstrate the effectiveness of the new QAC paradigm and the efficiency of the proposed query processing method.
Incremental Sequential Three-Way Decision Using a Deep Stacked Autoencoder

Most traditional face recognition classifiers attempt to minimize recognition error rate rather than misclassification costs, which is unreasonable in many real world applications. On the other hand, many facial images are usually unlabeled, and the label process may result in high costs. Considering imbalanced misclassification costs and the hardship of gathering sufficient labeled images, an incremental sequential three-way decisions (3WD) model for cost-sensitive face recognition is proposed, in which a deep stacked autoencoder (DSAE) is used to extract an efficient deep feature set. The model takes full account of the costs of obtaining labeled data in real life. In addition, the model incorporates the boundary decision into the process of making decision, leading to a delayed decision with insufficient labeled images, which simulates the decision-making process from a small amount to a large amount of data. In summary, the model aims to select an optimal decision step so as to gain the desirable recognition results with the least amount of data. This strategy is applied to two facial image databases, which validate the effectiveness of the proposed methods.