Reviewerly/exHarmony huggingface dataset:
Engaging Learners in Dialogue Interactivity Development for Mobile Robots

The use of robots in educational and STEM engagement activities is widespread. In this paper we describe a system developed for engaging learners with the design of dialogue-based interactivity for mobile robots. With an emphasis on a web-based solution that is grounded in both a real robot system and a real application domain – a museum guide robot – our intent is to enhance the benefits to both driving research through potential user-group engagement, and enhancing motivation by providing a real application context for the learners involved. The proposed system is designed to be highly scalable to both many simultaneous users and to users of different age groups, and specifically enables direct deployment of implemented systems onto both real and simulated robots. Our observations from preliminary events, involving both children and adults, support the view that the system is both usable and successful in supporting engagement with the dialogue interactivity problem presented to the participants, with indications that this engagement can persist over an extended period of time.
Universal Design Principles in Graphical User Interface: Understanding Visual Ergonomics for the Left-Handed Users in the Right-Handed World

The advent of graphical user interface in computers has helped users to easily access its features, thereby become familiar with the computer environment quickly and enhance the overall performance of the system. As the GUI evolved, the accessibility options of the user interfaces were expanded to cater benchmark users or extreme users. However, whether the accessibility options provided by operating system manufacturers are discoverable or readily available to the users is a questionable affair. One solution to this problem could be designing the interface in such a manner which could be universally acceptable and usable by all user categories. Although this option seems practically impossible since different user categories may have different needs, the user base of the particular GUI could be maximized. This paper intends to study the accessibility options provided in a computer interface by critically analyzing the problem faced/likely to occur to the users with the objective to come up with design solutions to expand the user base of the existing operating system.
Chapter 7 Assessing Compliance of Open Data in Politics with European Data Protection Regulation

This chapter analyzes the compliance of some category of Open Data in Politics with EU General Data Protection Regulation (GDPR) requirements. After clarifying the legal basis of this framework, with specific attention to the processing procedures that conform to the legitimate interests pursued by the data controller, including open data licenses or anonymization techniques, that can result in partial application of the GDPR, but there is no generic guarantee, and, as a consequence, an appropriate process of analysis and management of risks is required.
An Adaptive Mechanism for Recommendation Algorithm Ensemble

Popular recommendation algorithms such as k-nearest neighbors and Slope One are appropriate for different situations. In this paper, we propose an approach, which will be termed as adaptive mechanism for recommendation algorithm ensemble (AMRE). The AMRE consists of three parts, including a set of agents, a reward-function, and a roulette. First, each agent corresponds to a recommendation algorithm. It also contains a reward value to determine whether it should be retained or replaced. Second, a reward-function is designed to update the reward value according to recommendation results. Wrong recommendations bring punishments, while the right ones bring rewards. Finally, the roulette chooses another agent when the reward value is below a given threshold. The experimental results on MovieLens datasets show that the AMRE outperforms 11 well-known algorithms and two classical ensemble methods on Accuracy, Recall, and F1-measure.
Cross-Layer Adaptation in Multi-layer Autonomic Systems (Invited Talk)

This work presents a new reference architecture for multi-layer autonomic systems called context-controlled autonomic controllers (ConAC). Usually, the principle of multiple system layers contradicts the principle of a global adaptation strategy, because system layers are considered to be black boxes. The presented architecture relies on an explicit context model, so a simple change of contexts can consistently vary the adaptation strategies for all layers. This reveals that explicit context modeling enables consistent meta-adaptation in multi-layer autonomic systems. The paper presents two application areas for the ConAC architecture, robotic co-working and energy-adaptive servers, but many other multi-layered system designs should benefit from it.
Unsupervised Multi-task Learning Dialogue Management

Traditional dialog management systems used in task / goal-oriented scenario require a lot of domain-specific handcrafting, which hinders scaling up to new domains. End-to-end dialog management systems, in which all components are trained from the dialogs transcripts, overcome this limitation. This paper proposes a novel unsupervised dialogue-manager for goal-oriented dialogue applications. Set in the context of Corporate Information Organization (CIO) domain, our tasks require assessing user-initiated dialogues to understand the nature of the tickets, issue DB and API calls, and use the output of such calls to assist users. We show that an unsupervised dialog state-tracking and management system based on jointly trained LSTM-RNNs for utterance understanding and response generation can learn to perform non-trivial dialog management.
Anatomizing Lexicon With Natural Language Tokenizer Toolkit 3

NLTK toolkit is an API platform built with Python language to interact with humans through natural language. The very first version of NLTK was released in 2005 (1.4.3), which was compatible with Python 2.4. The latest version was in September 2017 NLTK (3.2.5), which incorporated features like Arabic stemmers, NIST evaluation, MOSES tokenizer, Stanford segmenter, treebank detokenizer, verbnet, and vader, etc. NLTK was created in 2001 as a part of Computational Linguistic Department at the University of Pennsylvania. Since then it has been tested and developed. The important packages of this system are 1) corpus builder, 2) tokenizer, 3) collocation, 4) tagging, 5) parsing, 6) metrics, and 7) probability distribution system. Toolbox NLTK was built to meet four primary requirements: 1) Simplicity: An substantive framework for building blocks; 2) Consistency: Consistent interface; 3) Extensibility: Which can be easily scaled; and 4) Modularity: All modules are independent of each other.
Virtual Organization for Fintech Management

A review of the state of the art on Fintech and the most important innovations in the financial technology is presented in this article. It is proposed a social computing platform based on VOs which allow to improve user experience in all that is associated with the process of investment recommendation. Moreover, a case study is shown in which the VOs modules have been described graphically, the agent functionalities have been explain and the algorithms responsible for making recommendation have been proposed.
Cost-based temporal reasoning

Reasoning about the behavior of real-world systems and processes faces problems such as repeating events or subprocesses, evolving component behaviors, and indefinite time horizons. To date, existing representations of time and uncertainty have been unable to fully address such requirements. They often tradeoff assumptions of available knowledge against richness of representing time and semantics for uncertainty. This has made representations either overly simplistic or cumbersome to model with, or both. In this paper, we present a new theoretical representation for reasoning about time and uncertainty extending our ability to better address real-world systems. Called cost-based temporal reasoning, we believe it is simple to understand, is rigorously defined, and allows for strong semantics of time and uncertainty. This paper formally details the definitions and proofs about the capabilities of cost-based temporal reasoning.
Semantic Harmonisation of Numeric Data from Open Government Data

Open tabular data published as part of the open government initiatives typically contain a spatial dimension, a temporal dimension and the actual numeric data capturing information such as health indicators, pollution readings, sanitation status etc. "Semantic Harmonisation" of numeric data entails linking numeric data columns with web-accessible semantic entities from an ontology - a machine readable knowledge representation. These semantic entities are embedded in a knowledge graph, allowing integration of information from disparate sources under common semantic definitions across spatial and temporal dimensions. Multiple research efforts have contributed to recovering semantics of numeric columns in tables, however they are either restricted to a single domain or rely on the existence of numeric data as linked data tuples in known ontologies. We present a novel yet simple approach using a supervised machine learning classifier (Random Forests) and semantic web techniques to generate semantics for numeric columns in tabular data. This approach has been tested with encouraging results for over 100 tabular datasets from data.gov.in (Indian Open Government Data Portal) downloaded from multiple domains such as "Health and Family Welfare", "Agriculture", "Environment" etc. We also present a use case for this work, being implemented in collaboration with the ministries of the Government of Karnataka for knowledge aggregation and dissemination of sustainable development data.
Christoph A. Hafner and Lindsay Miller: English in the Disciplines: A Multidimensional Model for ESP Course Design

In a globalized and multilingual world, English for Specific Purposes (ESP) is no longer confined to the development of learners’ linguistic competence in specific domains. Multimodal communicative competence is now necessary, demanding more efforts to design and implement multimodal ESP courses for diverse audiences. Recognizing this developmental trend, Christoph Hafner and Lindsay Miller showcase a multimodal English for science course aimed at both specialists and non-specialists. Based on their insights from this course, they formalize a multidimensional ESP course design model with valuable implications. The authors invite readers to firstly read Part 1, then familiarize themselves with the model in Part 3 before filling any gaps in understanding the five dimensions of the model in Part 2. This review will follow this reading suggestion by sequentially reviewing Part 1, Part 3, and Part 2. The first part, comprising four chapters, clarifies the principles and practices of ESP course design. Chapter 1 provides an overview, sketching the case study that informs the entire book and describing the book’s proposed readers, including academics, research students with particular interests in ESP, and frontline practitioners. Chapter 2 highlights the ongoing debate on ESP, reviewing the historical perspectives, teasing out recent trends in ESP textbook design, and explaining the need to develop non-textbook ESP courses. Drawing on previous work and learners’ current needs, the authors, in this chapter, outline five dimensions of contemporary ESP course design, which are fully discussed in Part 2. In Chapter 3, the authors introduce a case study, beginning with an explanation of the context and rationale of the course design, and from a practical perspective, mainly discuss the pedagogy and assessment decisions involved in designing a multimodal project-based course. Taking up methodology in Chapter 4, the authors focus on research methods, proceeding from a general introduction in applied linguistics to a more specific discussion of ESP studies. Because the case study in this book was conducted following mixed-methods, a detailed explanation of the mixed-method data collection and analysis process is given.
A Heuristic Approach for Solving the Longest Common Square Subsequence Problem

The longest common square subsequence (LCSqS) problem, a variant of the longest common subsequence (LCS) problem, aims at finding a subsequence common to all input strings that is, at the same time, a square subsequence. So far the LCSqS was solved only for two input strings. We present a heuristic approach, based on randomized local search and a hybrid of variable neighborhood search and beam search, to solve the LCSqS for an arbitrary set of input strings. The beam search makes use of a novel heuristic estimation of the approximated expected length of a LCS to guide the search.
Complexity of checkers and draughts on different board sizes

Estimates for the complexity of checkers and draughts on different board sizes are calculated and experimentally determined. The estimates found for international draughts are a correction of previous estimates in the literature. The estimates found for the other board sizes are first estimates, with the exception of 8 × 8 checkers, which is used as a reference. The small games of 6 × 6 checkers and 6 × 6 draughts are also weakly solved. Both games are a draw with perfect play by both sides.
The Right Social Media Platform for Your Library

This chapter provides advice on how to evaluate social media platforms in order to determine which one is best for your academic library. The author explains his methodology for decision-making, which includes considering one’s institutional context, identifying intended outcomes or goals for social media, taking stock of current social media commitments, and understanding the benefits and drawbacks of each social media application under consideration. This chapter explains why Instagram was the best fit for the author’s institution after briefly evaluating that platform along with Blogger, Facebook, Twitter, and Tumblr. The guidelines provided will help librarians work through the decision-making process with regard to social media regardless of specific social media platform or institutional identity.
A Survey of Code Reuse Attack and Defense

Code reuse attack is a devastating way of attack. It has great threat and can bypass many kinds of existing security measures and become the mainstream attack mode of attackers. For this reason, research in the field of code reuse attacks is also increasing. This paper briefly describes the origin of code reuse attacks, the way to attack the implementation, systematically summarizes the existing defense mechanisms and evaluates these defense mechanisms. The basic reasons for the code reuse attack are briefly analyzed.
Combining Neural Networks and Pattern Matching for Ontology Mining - a Meta Learning Inspired Approach

Several applications dealing with natural language text involve automated validation of the membership in a given category (e.g. France is a country, Gladiator is a movie, but not a country). Meta-learning is a recent and powerful machine learning approach, which goal is to train a model (or a family of models) on a variety of learning tasks, such that it can solve new learning tasks in a more efficient way, e.g. using smaller number of training samples or in less time. We present an original approach inspired by meta-learning and consisting of two tiers of models: for any arbitrary category, our general model supplies high confidence training instances (seeds) for our category-specific models. Our general model is based on pattern matching and optimized for the precision at top N, while its recall is not important. Our category-specific models are based on recurrent neural networks (RNN -s), which recently showed themselves extremely effective in several natural language applications, such as machine translation, sentiment analysis, parsing, and chatbots. By following the meta-learning principles, we are training our highest level (general) model in such a way that our second - tier category -specific models (which are dependent on it) are optimized for the best possible performance in a specific application. This work is important because our approach is capable of verifying membership in an arbitrary category defined by a sequence of words including longer and more complex categories such as Ridley Scott movie or City in southern Germany that are currently not supported by existing manually created ontologies (such as Freebase, Wordnet or Wikidata). Also, our approach uses only raw text, and thus can be useful when there are no such ontologies available, which is a common situation with languages other than English. Even the largest English ontologies are known to have low coverage, insufficient for many practical applications such as automated question answering, which we use here to illustrate the advantages of our approach. We rigorously test it on a number of questions larger than the previous studies and demonstrate that when coupled with a simple answer-scoring mechanism, our meta-learning-inspired approach 1) provides up to 50% improvement over prior approaches that do not use any manually curated knowledge bases and 2) achieves the state of-the-art performance among all the current approaches including those taking advantage of such knowledge bases.
Deep Learning of Intelligent Speech Recognition in Power Dispatching

The establishment of speech acoustic model system based on Long Short-Term Memory (LSTM) makes further improvements for the speech recognition. However, the connectionist temporal classification (CTC) training method performances more better in directly corresponding to the phoneme sequence or bound sequence of the speech. This paper combines CTC and LSTM to establish a power dispatching speech recognition model and compares the LSTM-CTC methods with traditional GMM-HMM methods, RNN-based speech recognition methods, and unidirectional LSTM networks through experiments. The results show that the speech recognition framework of LSTM-CTC has higher precision than other methods, and also has strong generalization ability. The LSTM-CTC methods can provide higher speech recognition accuracy and are more suitable for speech recognition in power dispatching as well.
User and Item Preference Learning for Hybrid Recommendation Systems

With the abundance of items, features, services and products available on the web, it becomes quite tedious to sort through and find what is desired. A recommendation system, therefore, provides a solution for this by only retrieving desired information from relevant data sets. A similarity metric is applied on the retrieved data and recommendations are made based on the predicted similarity. Typically, there are three major recommendation methods—collaborative, content-based and hybrid. Collaborative (user-based) works on the premise that similar users are bound to like the same items. Whereas, content-based (item-based) filtering tries to recommend items that are similar to the ones the user has liked previously. While functioning independently, the above two methods have certain flaws. Content-based systems cannot make predictions outside of the user’s content profile and collaborative systems cannot make recommendations for cold start problems. To alleviate these issues, we seek to combine these two methods into a hybrid recommender system. Hybrid recommender, as the name suggests, is a segmented approach to recommendation where each segment consists of either the collaborative or content-based model. In this paper, we propose meta-level cum switching hybrid model that incorporates the dual hybridization techniques into the above mentioned hybrid model. The type of hybridization method applied depends on the ability of each individual model to generate results. I.e. In situations where both the individual segments can generate results, the meta-level hybridization is used. Whereas, in a situation where any of the individual methods are unable to provide a result, the switching hybridization is enabled to shift to the method that is able to function in that scenario.
Data Deposit in a CKAN Repository: A Dublin Core-Based Simplified Workflow

Researchers are currently encouraged by their institutions and the funding agencies to deposit data resulting from projects. Activities related to research data management, namely organization, description, and deposit, are not obvious for researchers due to the lack of knowledge on metadata and the limited data publication experience. Institutions are looking for solutions to help researchers organize their data and make them ready for publication. We consider here the deposit process for a CKAN-powered data repository managed as part of the IT services of a large research institute. A simplified data deposit process is illustrated here by means of a set of examples where researchers describe their data and complete the publication in the repository. The process is organised around a Dublin Core-based dataset deposit form, filled by the researchers as preparation for data deposit. The contacts with researchers provided the opportunity to gather feedback about the Dublin Core metadata and the overall experience. Reflections on the ongoing process highlight a few difficulties in data description, but also show that researchers are motivated to get involved in data publication activities.
Design and Implementation of Multi-level CIC Filter Based on FPGA

The integral comb (CIC) filter is an efficient filter which is widely used in the digital down-conversion and up-conversion of wireless communication technology. However, the level width of each register of the traditional structure is fixed, and the low frequency signal can cause high frequency operation bandwidth and waste the shortage of computer hardware resources. This paper, by using Hogenauer “cut off” theory on each level of output bits wide truncated, improve the performance of CIC filter, through a cascade of multiple single stage CIC filter to optimize its structure, build the multi-stage CIC filter; At the same time using FPGA technology is strong, good extensibility and occupies less hardware resources, the characteristics of low cost, high reliability, using Verilog HDL language design the various modules, the final model of multistage CIC filter based on FPGA design, not only save the hardware resources, also make the CIC filter each register bits wide variable. The model was simulated by Modelsim and downloaded to the EP2C35F672C6 of Altera DE2 as the target chip verification, which met the design requirements.
Case Illustrations of 3D Printing in Brazilian Schools and Community

This chapter discusses 3D printing technologies in Brazilian schools through analysis of selected cases including one case implemented in the community with implications to schools. A description of the methodology and review of the cases are presented. Analysis is supported by international literature to enrich the review and compare how the field is evolving in the country. Results indicate that students and teachers are predominantly experiencing 3D printing via extra-curricular activities supported by research projects implemented outside the schools. On the other hand, the Lite Maker project made it possible to bring a 3D printer to schools. The community-based project illustrates how 3D printed objects can support visually impaired students. 3D printing technology is moving slowly into the mainstream of Brazilian educational landscape. However, schools will increase access to this technology as printers and its accessories become more affordable. Implications for practice and future research are discussed.