Reviewerly/exHarmony huggingface dataset:
Towards Early Prototyping of Services based on Open Transport Data: A Feasibility Study

Data under open licenses and in reusable formats, often referred to as ”open data”, is increasingly being made accessible by both public and private actors. Government institutions, municipalities, private companies and entrepreneurs are among the stakeholders either having visions of new open data-based services, or just looking for new ideas on potential innovations based on open data. It is, however, in both cases, often unclear to the service developers how the open data actually can be utilized. A main reason is that the data needs to be retrieved from multiple sources, understood, quality checked and processed. While gaining insights on possible services that can be created on the top of open data, a service developer has to undergo an iterative ”trying and failing” exercise of service prototyping. In order to be practically feasible, such a process needs to be agile and efficient. Open data from the transport sector is used as a case. The open transport data are characterized by many challenges common for open data in general, but also a few specific ones. One of those challenges is the need for combining (often real-time) data from rather many sources in order to create a new service. In this paper we propose an agile approach to early service prototyping and we try out the approach on an open transport data service. Finally, we propose the priorities for future work towards a comprehensive approach for agile prototyping of open transport data-based services.
Search, Find and Resolve: Towards a Taxonomy for Searchable Encryption Schemes

Searchable Encryption (SE) schemes are a promising solution to the problem of outsourcing one’s data to a cloud provider in a secure way, while still retaining the ability to search for and easily retrieve specific documents. A multitude of different schemes have been proposed and designed, yet in general they still lack usability/applicability for a specific use case or proper security analysis in order to be widely implemented and used. To address this issue we started a project to determine which SE schemes fit certain use cases - mainly focusing on usability. We examined nearly 400 papers on SE schemes from the last 13 years and extracted categorization domains for SE schemes. Furthermore we took a time-based look at these domains and tried to identify future trends in SE technologies. In this position paper we introduce our methodology and give a short overview of our current work-in-progress.
Automatic discrete differentiation and its applications.

Physical phenomena in the real world are often described by energy-based modeling theories, such as Hamiltonian mechanics or the Landau theory, which yield various physical laws. Recent developments in neural networks have enabled the mimicking of the energy conservation law by learning the underlying continuous-time differential equations. However, this may not be possible in discrete time, which is often the case in practical learning and computation. Moreover, other physical laws have been overlooked in the previous neural network models. In this study, we propose a deep energy-based physical model that admits a specific differential geometric structure. From this structure, the conservation or dissipation law of energy and the mass conservation law follow naturally. To ensure the energetic behavior in discrete time, we also propose an automatic discrete differential algorithm that enables neural networks to employ the discrete gradient method.
Color Image Enhancement with Different Image Segmentation Techniques

Image can be improved using different image enhancement techniques.It includes different types of operations like image segmentation, clustering, smoothing, etc. Basically in the process of Image segmentation features having homogenous characteristics are identified.The Input for the system is color image.The image gets converted into horizontal and vertical shape histogram.Then cluster formation is done using hill climbing technique and k means clustering.K Means clustering consider colour intensity as criteria.Sequential probability ratio test checks similar characteristics between different regions.Merging of these regions follows dynamic region merging algorithm.Depending on similar properties partitions are merged.The output is enhanced segmented image.Nearest neighbour graph technique is helpful to speed up the above process.This improved image is useful in the field of medical as well as security purpose.
Lightning Talk–Towards Robust Detection of Cyberbullying in Social Media

The potentially detrimental effects of cyberbullying have led to the development of numerous automated, data–driven approaches, with emphasis on classification accuracy. Cyberbullying, as a form of abusive online behavior, although not well–defined, is a repetitive process, i.e., a sequence of aggressive messages sent from a bully to a victim over a period of time with the intent to harm the victim.
Generalized nets with additional intuitionistic fuzzy conditions for tokens transfer

A new extension of the concept of generalized nets (GNs), namely GNs with additional intuitionistic fuzzy conditions, is presented in the present paper.The tokens, which are the dynamic components of the net, are assigned with intuitioninstic fuzzy pairs, which are interpreted as degrees of membership and non-membership, and here indicate characteristic of tokens validly passing through a certain route.The new extension is proved to be a conservative extension of the class of the standard GNs, as its functioning and the results of its works can be described by standard GNs.
De-Agentializing Data Practices: The Shifting Power of Metaphor in 1990s Discourses on Data Mining

Big Data represents a tremendous opportunity to drill down and tap into these critical insights. In fact, the powerful potential to mine and refine this vital, valuable resource points to a direct comparison to a similarly vital resource in the modern economy: crude oil.
Are the Tools up to the Task? an Evaluation of Commercial Dialog Tools in Developing Conversational Enterprise-grade Dialog Systems

Marie Meteer, Meghan Hickey, Carmi Rothberg, David Nahamoo, Ellen Eide Kislal. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers). 2019.
RPA Based Digital Marketing Robot

In the modern era of advertising, the role of marketing has increased exponentially.The marketing strategies of big brands nowadays highly rely on digital media.A new method of marketing known as digital marketing has evolved more than the rest of the marketing strategies.Digital marketing has now become the need of the hour for various brands and e-commerce platform.The proposed system will help such brands to reach out to the end user more efficiently and frequently.The proposed system is based on the implementation of Robotic Process Automation (RPA) which is a method used for deployment of software-based robots that mimic the functionalities of human operating a machine.
Congressbr: An R Package for Analyzing Data from Brazil’s Chamber of Deputies and Federal Senate

In this research note, we introduce congressbr, an R package for retrieving data from the Brazilian houses of legislature. The package contains easy-to-use functions that allow researchers to query the Application Programming Interfaces of Brazil’s Chamber of Deputies and the Federal Senate, perform cleaning data operations, and store information in a format convenient for future analyses, making a previously difficult task fast and convenient. Congressbr downloads data on legislators, submitted and ratified law proposals, Senate and Chamber commissions, and other information of interest to social scientists across various fields. We outline the main features of the package and demonstrate its use with practical examples.
Feature Engineering for Text Representation

In the previous chapters, we saw how to understand, process, and wrangle text data. However, all machine learning or deep learning models are limited because they cannot understand text data directly and they only understand numeric representations of features as inputs. In this chapter, we look at how to work with text data, which is definitely one of the most abundant sources of unstructured data. Text data usually consists of documents that can represent words, sentences, or even paragraphs of free-flowing text. The inherent lack of structure (no neatly formatted data columns!) and noisy nature of textual data makes it harder for machine learning methods to directly work on raw text data. Hence, in this chapter, we follow a hands-on approach to exploring some of the most popular and effective strategies for extracting meaningful features from text data. These features can then be used to represent text efficiently, which can be further leveraged in building machine learning or deep learning models easily to solve complex tasks.
Research on Binding Memory of Icon Features Based on Event-related Potential

Abstract The visual characteristics are important factors influencing the cognition and memory of the icon. In order to study the process of memory coding of feature binding in working memory of icon, the experiment requires 20 subjects to remember whether the icon features or feature binding are consistent and response quickly by pressing buttons, and the behaviour data and ERP data of the subjects are recorded. The experiment results show that compared with single feature memory of an icon, the accuracy under the icon-color feature binding memory condition is lower and the reaction time is longer while the P300 brain electric component with larger amplitude can be triggered. Studies have shown that the binding of visual features in icon memory requires additional cognitive resources. Compared with the other three memory conditions, the latency of the P300 under icon semantic-color feature binding is more significant, indicating that the task of semantic coding of icon is more difficult to complete and the cognitive load is greater.
A Secure Biometric Based User Authentication Scheme for Multi-Server Environment Using Chaotic Map

In recent time, multi-server environment is used to provide convenient and efficient online services that demand efficient authentication schemes for practical applications. However, single server authentication(SSA) scheme are not applicable to the multiple-server environment. Unlike, SSA scheme, in multiserver authentication scheme (MSA) user registers only once but get services at any time. A MSA scheme is the need of the modern age online services. Hence, we propose a MSA uses based on three factors and use Chebyshev chaotic map. We prove that the scheme is resistant to various active and passive attacks. As compared to other related schemes, it has less computational cost and communicational cost. The AVISPA tool is used for formal verification of the scheme. Low computational cost and communicational cost shows the efficiency and practical use of the proposed scheme.
Efficient Algorithms for Finding Edit-Distance Based Motifs

Motif mining is a classical data mining problem which aims to extract relevant information and discover knowledge from voluminous datasets in a variety of domains. Specifically, for the temporal data containing real numbers, it is formulated as time series motif mining (TSMM) problem. If the input is alphabetical and edit-distance is considered, this is called Edit-distance Motif Search (EMS). In EMS, the problem of interest is to find a pattern of length l which occurs with an edit-distance of at most d in each of the input sequences. There exists some algorithms proposed in the literature to solve EMS problem. However, in terms of challenging instances and large datasets, they are still not efficient. In this paper, EMS3, a motif mining algorithm, that advances the state-of-the-art EMS solvers by exploiting the idea of projection is proposed. Solid theoretical analyses and extensive experiments on commonly used benchmark datasets show that EMS3 is efficient and outperforms the existing state-of-the-art algorithm (EMS2).
Recognising and Explaining Bidding Strategies in Negotiation Support Systems

To improve a negotiator's ability to recognise bidding strategies, we pro-actively provide explanations that are based on the opponent's bids and the negotiator's guesses about the opponent's strategy. We introduce an aberration detection mechanism for recognising strategies and the notion of an explanation matrix. The aberration detection mechanism identifies when a bid falls outside the range of expected behaviour for a specific strategy. The explanation matrix is used to decide when to provide what explanations. We evaluated our work experimentally in a task in which participants are asked to identify their opponent's strategy in the environment of a negotiation support system, namely the Pocket Negotiator (PN). We implemented our explanation mechanism in the PN and experimented with different explanation matrices. As the number of correct guesses increases with explanations, indirectly, these experiments show the effectiveness of our aberration detection mechanism. Our experiments with over 100 participants show that suggesting consistent strategies is more effective than explaining why observed behaviour is inconsistent.
Quality Management for Big 3D Data Analytics: A Case Study of Protein Data Bank

3D data have been widely used to represent complex data objects in different domains such as virtual reality, 3D printing or biological data analytics. Due to complexity of 3D data, it is usually featured as big 3D data. One of the typical big 3D data is the protein data, which can be used to visualize the protein structure in a 3D style. However, the 3D data also bring various data quality problems, which may cause the delay, inaccurate analysis results, even fatal errors for the critical decision making. Therefore, this paper proposes a novel big 3D data process model with specific consideration of 3D data quality. In order to validate this model, we conduct a case study for cleaning and analyzing the protein data. Our case study includes a comprehensive taxonomy of data quality problems for the 3D protein data and demonstrates the utility of our proposed model. Furthermore, this work can guide the researchers and domain experts such as biologists to manage the quality of their 3D protein data.
Integrating Agent Advice and Previous Task Solutions in Multiagent Reinforcement Learning

Reinforcement learning methods have successfully been applied to build autonomous agents that solve challenging sequential decision-making problems. However, agents need a long time to learn a task, especially when multiple autonomous agents are in the environment. This research aims to propose a Transfer Learning framework to accelerate learning by combining two knowledge sources: (i) previously learned tasks; and (ii) advice from a more experienced agent. The definition of such framework requires answering several challenging research questions, including: How to abstract and represent knowledge, in order to allow generalization and posterior reuse?, How and when to transfer and receive knowledge in an efficient manner?, and How to consistently combine knowledge from several sources?
Link Analysis to discover relevant documents using Information Retrieval

In recent year's growth of World Wide Web become faster to cross all expectations; World Wide Web is becoming most valuable resources to information retrieval and knowledge discovery from Web.It is a fertile area for web mining research; an emerging challenge for web mining is the problem of mining richly qualitative documents, where the objects are linked via multiple types of relations.These links provide additional context that can be helpful for web mining tasks.Traditional link analysis treats all hyperlinks equally and makes the assumption that links are endorsement, so there is need to only extract links which are valuable.Unfortunately, this assumption does not incorporate in present World-Wide Web.Hyperlinks are not identical; they may be created in different contexts and for different purposes.By using novel characteristics of web page and hyperlinks to help a search engine focus on relevant and high quality content.The important hyperlink features-topicality -is proposed.
STEREOTYPED LANGUAGE ABOUT WOMEN ON INTERNET MEMES OF MEME COMIC INDONESIA (A Multimodal Critical Discourse Analysis)

This study examined 21 (twenty one) Internet memes taken from instagram wall of Meme Comic Indonesia (MCI). This studies applied Multimodal Critical Discourse Analysis promoted by Van Leeuwen which particularly in this study was the merger between Three Dimensional of Fairclough and Visual Grammatical of Kress and Van Leeuwen. The results of this study revealed five kinds of stereotyped language about women which are women’s personality traits, women’s cooking domestics behaviour, women’s physical appearances, women’s teaching occupation and women’s driving motorcycle behaviour which considered as new kind of stereotype about women. It is also revealed that the process of realization of stereotyped language about women is by seeing the context of the features used by the creator of the internet memes. The reasons of using stereotyped language about women as humour can be seen as critics and warning attemptions by the creator of the internet memes.Key words: Stereotyped Language about Women, internet memes, Multimodal Critical Discourse Analysis
Strategi pengelolaan jurnal akses terbuka menggunakan open journal system (OJS)

Introduction. This study tries to map a conceptual framework in the application of open journal system as a platform for open access journals. Research on scientific journals used as sources of references has been widely conducted, however, more research is needed to understand scientific journals as the object of study. Method and analysis data. This study uses descriptive analysis by using multi-discipline perspective (prosumption -production and consumption- theory) in understanding the management and publication for academic e-journal in digital era, also to review the benefits and the obstacles.Results and Discussions. The results provide better understanding of how a scientific journal should work. This study offers a framework for understanding that scientific journals should also build good relationships with authors and readers who have the potential to become potential authors, and reviewers.Conclusion. This study tries to offer a strategy for managing open access journals so that conceptually can be used in understanding the development of continuous scientific journals, maintaining the quality of publications, and forming good communication between journal managers, writers, readers and reviewers.
Reach to Teach: Preparing Cybersecurity Experts as Adjunct Community College Faculty

While the nation should maintain and expand the educational capabilities in cybersecurity given the current high demand for this sector, currently there exists a capacity issue: students cannot readily be added to the education system, especially at the Community Colleges level, because trained faculty to accommodate expanded academic demand are scarce. Cybersecurity experts in the workforce have the potential to fill the need for part-time cybersecurity faculty at the Community College level. The challenge is to prepare these technology-savvy individuals with classroom pedagogical insights and skills which not usually part of a cybersecurity experts skillset. A research question for this development project is “Can we use an online environment to provide pedagogy training for potential adjunct community college faculty.” Currently, the Reach To Teach project is exploring this possibility through a research effort engaging current faculty, as well as education experts, and a pilot Reach To Teach online course that is being made available to these cybersecurity experts. The Reach to Teach pilot is a set of six sessions, each of which includes the following: introduction to Community Colleges, ethics, and ideas for classroom pedagogy (e.g. the general structure of a course, crafting goals and objectives, techniques for moving explanations from the concreate to the abstract, using group work using case studies, and using discussions in classes). The team hosted a content review with community college educators and the pilot has been evaluated by 12 members of the target population. Their suggestions for improvement included. In addition to addressing these concerns, the revised pilot includes a modified interactive experience, Viewers are now able leave comments that can be read and replied to by course leaders or other individuals viewing the material.
Analisis Nilai-nilai Karakter Hasil Karya Menulis Kreatif Siswa

&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; The purpose of this study was to describe the character values in the creativity of students writing fable stories. This study used a descriptive qualitative design with a type of text analysis. The tex analyzed was created by students writing fable stories. The results show of the analysis show five character values in the work of students writing stories. These five values consist of values that are socially caring, friendly/communicative, hard work, creative, and peaceful. Creative writing is telling events that have been experienced by using imagination then poured in the writing. Writing activities require reading skills. The reading used is the story of fables. Fable stories contain moral values in life that can add student knowledge.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt;&lt;em&gt; &lt;/em&gt;Tujuan penelitian ini adalah mendeskripsikan nilai-nilai karakter dalam hasil cipta siswa menulis cerita fabel. Penelitian ini menggunakan rancangan deskriptif kualitatif dengan jenis analisis teks. Teks yang dianalisis adalah hasil karya siswa menulis cerita fabel. Hasil analisis menunjukkan lima nilai karakter dalam karya siswa menulis cerita. Lima nilai tersebut, meliputi nilai peduli sosial, bersahabat/komunikatif, kerja keras, kreatif, dan cinta damai. Menulis kreatif adalah menceritakan peristiwa yang pernah dialami dengan memanfaatkan imajinasi kemudian dituangkan dalam sebuah tulisan. Kegiatan menulis membutuhkan keterampilan membaca. Bacaan yang digunakan adalah cerita fabel. Cerita fabel memuat nilai-nilai moral dalam kehidupan yang dapat menambahkan pengetahuan siswa.&lt;/p&gt;
Explainable Agency in Intelligent Agents: Doctoral Consortium

Explainability of intelligent agents has gained attention in recent years with their widespread utilization in society. Most work in Explainable AI (XAI) pay little attention to the humans that interact with these models, which risks resulting in unsatisfactory explanations. Theories of explainability and the nature of explanation has been widely explored in cognitive psychology and philosophy. This thesis aims to incorporate these insights to build explainable models and interfaces that can provide better and sufficient explanations to the interacting human.