Reviewerly/exHarmony huggingface dataset:
Harvesting Drug Effectiveness from Social Media

Drug effectiveness describes the capacity of a drug to cure a disease, which is of great importance for drug safety. To get this information, a number of real-world patient-oriented outcomes are required. However, current surveillance systems can only capture a small portion of them, and there is a time lag in processing the reported data. Since social media provides quantities of patient-oriented user posts in real-time, it is of great value to automatically extract drug effectiveness from these data. To this end, we build a dataset containing 25K tweets describing drug use, and further harvest drug effectiveness by performing Relation Extraction (RE) between chemicals and diseases. Most prior works about RE deal with mention pairs independently, which is not suitable for our task since interactions across mention pairs are widespread. In this paper, we propose a model regarding mention pairs as nodes connected by multiple types of edges. With the help of graph-based information transfers over time, it deals with all mention pairs simultaneously to capture their interactions. Besides, a novel idea is used to perform multiple instance learning, a big challenge in general RE tasks. Extensive experimental results show that our model outperforms previous work by a substantial margin.
Depth From Texture Integration

We present a new approach for active ranging, which can be compounded with traditional methods such as active depth from defocus or off-axis structured illumination. The object is illuminated by an active textured pattern having high spatial-frequency content. The illumination texture varies in time while the object undergoes a focal sweep. Consequently, in a single exposure, the illumination textures are encoded as a function of the object depth. Per-object depth, a particular illumination texture, with its high spatial frequency content, is focused; the other textures, projected when the system is defocused, are blurred. Analysis of the time-integrated image decodes the depth map. The plurality of projected and sensed color channels enhances the performance of the process, as we demonstrate experimentally. Using a wide aperture and only one or two readout frames, the method is particularly useful for imaging that requires high sensitivity to weak signals and high spatial resolution. Using a focal sweep during an exposure, the imaging has a wide dynamic depth range while being fast.
Towards an Annotation System for Collaborative Peer Review

Peers providing feedback on their peers’ work is called peer review which has been shown to have beneficial effects on students’ learning. This article presents a novel approach to peer review where reviewers, reviewees, and lecturers alike have access to the same documents, and reviews are created in form of annotations which are automatically shared among all users, can be up- or downvoted, and can themselves be commented on. Working on a same document and seeing annotations immediately after they have been created enables various forms of collaboration among learners: Between reviewers who can agree or disagree with a review by up- or downvoting or comment on the review thus providing further insight, between reviewers and reviewees by allowing the reviewees to inquire about reviews and reviewers to provide clarifications and justifications, and between lecturers and reviewers, when lecturers take the role of reviewers and up- or downvote and comment on reviews. The contributions of this article are twofold: First, a report on the conception and implementation of a collaborative annotation environment that supports the collaborative peer review described in this article, and second, an analysis of the communication that happened during collaborative peer review in three courses pointing to the approach’s effectiveness but uncovering problems of the collaborative annotation system as well.
Analysis of Industrial Control System Software to Detect Semantic Clones

The detection of software clones is gaining more attention due to the advantages it can bring to software maintenance. Clone detection helps in code optimization (code present in multiple locations can be updated and optimized once), bug detection (discovering bugs that are copied to various locations in the code), and analysis of re-used code in software systems. There are several approaches to detect clones at the code level, but existing methods do not address the issue of clone detection in the PLC-based IEC 61131-3 languages. In this paper, we present a novel approach to detect clones in PLC-based IEC 61131-3 software using semantic-based analysis. For the semantic analysis, we use I/O based dependency analysis to detect PLC program clones. Our approach is a semantic-based technique to identify clones, making it feasible even for large code bases. Further, experiments indicate that the proposed method is successful in identifying software clones.
Executives' Commitment to Information Security

Two aspects of decision-making on information security spending, executives' varying preferences for how proposals should be presented and the framing of the proposals, are developed. The proposed model of executives' commitment to information security is an interaction model (in addition to the cost of a security solution, and the risk and the potential loss of a security threat) consisting of the interaction between an executive's preferred subordinate influence approach (PSIA), rational or inspirational, and the framing, positive or negative, of a security proposal. The interaction of these two constructs affects the executive's commitment to an information security proposal. The model is tested using a scenario-based experiment that elicited responses from business executives across 100+ organizations. Results show that the interaction of the negative framing of a proposal and the inspirational PSIA of an executive affects his or her commitment to information security. Further, negative framing of a proposal and the cost of the security solution interact to decrease the executive's commitment to information security. This study underscores that prescriptions for business executives from normative models in information security spending must be complemented with appropriately framed messages to account for the differences in executives' PSIA (rational and inspirational) and cognitive biases.
Social Book Recommendation Algorithm Based on Improved Collaborative Filtering

Recommendation is one of the new personalized services in social book search system that assists the user to find the appropriate books. This paper proposes an improved collaborative filtering recommendation algorithm based on the user interest objects, which try to settle the cold start problems of traditional collaborative filtering. Firstly, set up a book retrieval system. Then, use the traditional collaborative filtering algorithm to calculate the recommended collection of books (Bc). Next, get the collection of user interest objects, calculate the similarity, and get the recommended collection of books (Bi). Finally, reorder the books of the set Bc and the set Bi according to certain weights, and get the first ten books as the books that are finally recommended to users. Experiments on real-world datasets results show that the proposed algorithm improves the performance of the collaborative filtering algorithms.
Development of a Platform for RNN Driven Multimodal Interaction with Embodied Conversational Agents

This paper describes our ongoing project to build a platform that enables real-time multimodal interaction with embodied conversational agents. All of the components are in modular design and can be switched to other models easily. A prototype listener agent has been developed upon the platform. Its spontaneous reactive behaviors are trained from a multimodal data corpus collected in a human-human conversation experiment. Two Gated Recurrent Unit (GRU) based models are switched when the agent is speaking or is not speaking. These models generate the agent's facial expressions, head movements, and postures from the corresponding behaviors of the human user in real-time. Benefits from the flexible design, the utterance generation part can be an autonomous dialogue manager with hand crafted rules, an on-line chatbot engine, or a human operator.
Ring Signatures Based on Middle-Product Learning with Errors Problems

Lattice-based (linkable) ring signatures are an important notion to cryptography since it protects signer anonymity against quantum computers. In this paper, we proposed a new lattice-based linkable ring signature scheme using a variant of Learning with Errors problem called Middle-Product Learning with Errors ( $$\mathsf {MPLWE}$$ ). The proposed scheme follows a framework from [10, 12] with the following improvements. Firstly, this scheme relies on a much weaker assumption. Secondly, our approach relies on a decisional problem, thus, the security analysis does not require the Forking Lemma which has been a fundamental obstacle for provable security under the quantum random oracle model (QROM).
Using Trails to Support Users with Tasks of Varying Scope

A search trail is an interactive visualization of how a previous searcher approached a related task. Using search trails to assist users requires understanding aspects of the task, user, and trails. In this paper, we examine two questions. First, what are task characteristics that influence a user's ability to gain benefits from others' trails? Second, what is the impact of a "mismatch" between a current user's task and previous user's task which originated the trail? We report on a study that investigated the influence of two factors on participants' perceptions and behaviors while using search trails to complete tasks. Our first factor, task scope, focused on the scope of the task assigned to the participant (broad to narrow). Our manipulation of this factor involved varying the number of constraints associated with tasks. Our second factor, trail scope, focused on the scope of the task that originated the search trails given to participants. We investigated how task scope and trail scope affected participants' (RQ1) pre-task perceptions, (RQ2) post-task perceptions, and (RQ3) search behaviors. We discuss implications of our results for systems that use search trails to provide assistance.
Recent Trends in ICT-Enabled Renewable Energy Systems

Information and communication technology has wide applications; nowadays, the focus is on use of ICT services in multiple areas for easy and rapid communications. Renewable energy systems are in greater demand, and experimentation is going on in optimization, efficiency improvement, development of fault-tolerant architectures, etc. Recently, a trend is seen to use ICT-enabled services in renewable energy applications. In this manuscript, an overview of ICT-enabled services in renewable energy applications is presented. ICT-based facility makes easy towards monitoring the plant conditions, faults, abnormalities, end applications, etc. Advancements in IoT will be proved to be cheap, easy and user-friendly way for remote power plant monitoring.
Linear algebraic depth-first search

There is a recent push by a segment of the graph community to implement graph algorithms in the language of linear algebra. However, graph algorithms that depend on depth-first search (DFS) techniques are often highlighted as limitations of the linear algebraic approach as linear algebraic formulation of DFS algorithms are few, if any. This paper provides a linear algebraic approach for developing DFS graph algorithms and demonstrates its use for defining three classical DFS-based computations: Binary tree traversal, topological sort, and biconnected components.
Measurement of freeforms and complex geometries by use of tactile profilometry and multi-wavelength interferometry

A comparison of two different measurement approaches, a tactile profilometer as well as a non-contact point-probe based metrology system, is conducted. The properties of each approach are highlighted, and measurement results examined.
MDC-Checker: A novel network risk assessment framework for multiple domain configurations

It is a challenging task to generate correct network configurations to minimize the attack surface while meeting practical requirements, especially when the interdependent relationships between configurations scattered across physical, network and information domain are taken into consideration. As configurations in different domains are commonly generated separately while their complex inter-relationships are ignored, extra vulnerabilities would be introduced. This paper proposes a novel risk assessment framework named MDC-Checker to help network administrators to assess the risk involved. Firstly, the framework extracts the semantics from multiple domain configurations and network documents. Then, the Multiple Domain Semantics Graph (MDSG) and the Privilege Dependency Graph (PDG) are established in sequence to reason user actual privileges from their initial privileges. Finally, the differences between user deserved privileges and user actual privileges are used to measure the impact of multiple domain configurations on network security. A simulated network scenario with different sets of configurations is employed to validate the effectiveness and scalability of our framework and approach. The experimental results show that the framework captures the influences of multiple domain configurations on network security successfully and the approach is scalable to different sizes of the network.
Trusted Data Transformation with Blockchain Technology in Open Data

Trusted open data can be used for auditing, accountability, business development, or as an anti-corruption mechanism. Metadata information can address provenance concerns, and trust issues can somehow be mitigated by digital signatures. Those approaches can trace the data origin, but usually lack information about the transformation process. Creating trust in an open data service through technology can reduce the need for third-party certifications, and creating a distributed consensus mechanism capable of validating all the transformations can guarantee that the datasets are reliable and easy to use. This work aims to leverage blockchain technologies to track open data transformations, allowing consumers to verify the data using a distributed ledger, and providing a mechanism capable of publishing trusted transformed data without relying on third-party certifications. To validate the proposed approach, use cases for data transformation will be used. The consensus protocol must be capable of validating the transformations according to a predefined algorithm, the provider must be capable of publishing verifiable transformed data, and the consumer should be able to check if a dataset originated by a transformation is legit.
Development of an Accessible Video Game to Improve the Understanding of the Test of Honey-Alonso

When evaluating the learning styles of several individuals using the Honey-Alonso test, some users did not understand the meaning of several of the questions. This may be due to problems of context, tiredness in front of the extension of the test, lack of understanding or disinterest. The Honey-Alonso test consists of four groups of twenty questions each. Each group of questions allows identifying the level that an individual possesses on each one of the four learning styles. These styles are: active, reflective, theoretical and pragmatic. Answering a questionnaire of eighty questions is not an easy task from an andragogical point of view. This article proposes the creation of an educational video game designed with a script based on the questions of the Honey-Alonso test. The answers selected by the player are taken as a condition to determine the order of the next questions presented to the player.
Text Classification for Italian Proficiency Evaluation

NLP technologies and components have an increasing diffusion in mass analysis of text based dialogues, such as classifiers for sentiment polarity, trends clustering of online messages and hate speech detection. In this work we present the design and the implementation an automatic classification tool for the evaluation of the complexity of Italian texts as understood by a speaker of Italian as a second language. The classification is done within the Common European Framework of Reference for Languages (CEFR) which aims at classifying speakers language proficiency. Results of preliminary experiments on a data set of real texts, annotated by experts and used in actual CEFR exam sessions, show a strong ability of the proposed system to label texts with the correct language proficiency class and a great potential for its integration in learning tools, such systems supporting examiners in tests design and automatic evaluation of writing abilities.
Parallel computing research in India : A scientometric assessment of Indian publications output during 1998-2017

The present study has examined research output of India in parallel computing, as indexed in Scopus database during 1998-17 on a series of biblometric measures, such as publications growth rate, global publications share, citation impact, national-level share of international collaborative papers, distribution of publications by broad subjects. In addition, the study discusses the citation profile of top organizations and authors from India in parallel computing, and the preferred media for research communication, and characteristics of highly cited papers. The study finds that India registered 23.32% growth rate, contributed 3.70% global publications share, published 14.73% share of its output as international collaborative publications, and averaged citation impact of 5.15 citations per paper in the subject during the period. Hardware research has been found to be most popular sub-area of research in parallel computing. India contributed 26.03% share of output in parallel computing in top 15 most productive journals, and 14 of its papers have been rated as highly cited papers each with 82 to 663 citations per paper.
Computing Happiness from Textual Data

In this paper, we use a corpus of about 100,000 happy moments written by people of different genders, marital statuses, parenthood statuses, and ages to explore the following questions: Are there differences between men and women, married and unmarried individuals, parents and non-parents, and people of different age groups in terms of their causes of happiness and how they express happiness? Can gender, marital status, parenthood status and/or age be predicted from textual data expressing happiness? The first question is tackled in two steps: first, we transform the happy moments into a set of topics, lemmas, part of speech sequences, and dependency relations; then, we use each set as predictors in multi-variable binary and multinomial logistic regressions to rank these predictors in terms of their influence on each outcome variable (gender, marital status, parenthood status and age). For the prediction task, we use character, lexical, grammatical, semantic, and syntactic features in a machine learning document classification approach. The classification algorithms used include logistic regression, gradient boosting, and fastText. Our results show that textual data expressing moments of happiness can be quite beneficial in understanding the “causes of happiness” for different social groups, and that social characteristics like gender, marital status, parenthood status, and, to some extent age, can be successfully predicted form such textual data. This research aims to bring together elements from philosophy and psychology to be examined by computational corpus linguistics methods in a way that promotes the use of Natural Language Processing for the Humanities.
Multi-Level Chaotic Maps for 3D Textured Model Encryption

With the rapid progress of virtual reality and augmented reality technologies, 3D contents are the next widespread media in many applications. Thus, the protection of 3D models is primarily important. Encryption of 3D models is essential to maintain confidentiality. Previous work on encryption of 3D surface model often considers the point clouds, the meshes, and the textures individually. In this work, a multi-level chaotic maps model for 3D textured encryption was presented by observing the different contributions for recognizing cipher 3D models between vertices (point cloud), polygons, and textures. For vertices which make main contribution for recognizing, we use high-level 3D Lu chaotic map to encrypt them. For polygons and textures which make relatively smaller contributions for recognizing, we use 2D Arnold's cat map and 1D logistic map to encrypt them, respectively. The experimental results show that our method can get similar performance with the other method and use the same high-level chaotic map for point cloud, polygons, and textures, while we use less time. Besides, our method can resist more method of attacks such as statistic attack, brute-force attack, and correlation attack.
Automatic Alignment and Annotation Projection for Literary Texts

This paper presents a modular NLP pipeline for the creation of a parallel literature corpus, followed by annotation transfer from the source to the target language.The test case we use to evaluate our pipeline is the automatic transfer of quote and speaker mention annotations from English to German.We evaluate the different components of the pipeline and discuss challenges specific to literary texts.Our experiments show that after applying a reasonable amount of semi-automatic postprocessing we can obtain high-quality aligned and annotated resources for a new language.
Chapter 3 Data Visualizations and Human Resource Management: The State of Science and Practice

Data visualizations in some form or another have served as decision-support tools for many centuries. In conjunction with advancements in information technology, data visualizations have become more accessible and more efficient to generate. In fact, virtually all enterprise resource planning and human resource (HR) information system vendors offer off-the-shelf data visualizations as part of decision-support dashboards as well as stand-alone images and displays for reporting. Plus, advances in programing languages and software such as Tableau, Microsoft Power BI, R, and Python have expanded the possibilities of fully customized graphics. Despite the proliferation of data visualization, relatively little is known about how to design data visualizations for displaying different types of HR data to different user groups, for different purposes, and with the overarching goal of improving the ways in which users comprehend and interpret data visualizations for decision-making purposes. To understand the state of science and practice as they relate to HR data visualizations and data visualizations in general, we review the literature on data visualizations across disciplines and offer an organizing framework that emphasizes the roles data visualization characteristics (e.g., display type, features), user characteristics (e.g., experience, individual differences), tasks, and objectives (e.g., compare values) play in user comprehension, interpretation, and decision-making. Finally, we close by proposing future directions for science and practice.