Reviewerly/exHarmony huggingface dataset:
Taming Invisible Realms: New Tools for Managing Learning Space Wireless Environments

Wireless communication technologies underlie a wide range of teaching and learning activities in higher education today. The scope and reach of wireless systems now extend to most activities that occur inside lecture halls, classrooms, computer labs, and informal study areas. Instructors and students rely upon continuous wireless connections in a traditional lecture, seminar, or even newer active learning formats while they coordinate their activities around digital presentations. Unfortunately, the very success of the wireless revolution regularly contributes to chronic problems as so many people and activities rely upon this convenient mode to connect. In particular, the very invisibility inherent in wireless technology tends to obscure the state, capacity, and coverage options for managing the many and varied connections across different providers, the multiple protocols in use, and the number of users operating in the same space. When it works, we take it for granted. When it fails, we experience a crisis. There's little in between in terms of visualizing, analyzing, and forecasting the performance of this vital but invisible realm. Front-line educational technology support staff are left with little information to analyze what's going on, let alone contribute towards sustainable solutions for making it more reliable. This paper surveys low-cost devices along with PC and smartphone software applications in the hands of academic technology support staff can help them identify and track down connectivity problems in the ever-more complicated wireless environments on campus.
The Evolution of User Knowledge during Search-as-Learning Sessions: A Benchmark and Baseline

In this paper, we present a new benchmark collection that shows how 404 users' knowledge changed over the course of a search-as-learning session. We estimate the knowledge a user gains from each visited document and monitor knowledge change on a document-by-document basis. We describe the specifics of how this collection was created and provide a use case that illustrates potential future applications.
Drag-and-Drop Query Refinement and Query History Visualization for Mobile Exploratory Search

Conducting exploratory searches within digital libraries requires that searchers revise, refine, and reformulate their queries multiple times. Challenges that searchers of digital public libraries face include choosing how to refine their queries and making spelling or typographical errors. These are compounded when using mobile devices, where typing is time-consuming and error-prone. Conducting searches in a mobile context adds yet another challenge: the possibility of being interrupted and losing track of what was being done. In this paper we demonstrate a novel digital public library search interface tuned for mobile device use, which was designed to address these challenges through two key features: drag-and-drop query refinement and query history visualization. This work represents an example of how thoughtful search interface design and the judicious use of visualization techniques can be used to enhance exploratory search processes within digital public libraries.
Representing Tasks with a Graph-Based Method for Supporting Users in Complex Search Tasks

Despite the considerable advancements in modern search systems for assisting users in search tasks of varying types, support for complex tasks that call for multi-round interactions remains challenging. Identifying users' tasks is essential to understanding their evolving information needs and search goals during search sessions to simulate and achieve real-time adaptive search retrievals; thus, it is a crucial research thrust in interactive information retrieval (IIR). While a series of descriptive and formal models have been proposed to characterize complex information search sessions, only a few focus on leveraging dynamic task features in search personalizations to support users in different task stages in an adaptive fashion. This preliminary study presents a heterogeneous graph neural network model for extracting and representing tasks to better understand users' interactive search processes by connecting tasks with search interactions. Our approach's novelty lies in our application of task representation learning, which enables systems to extract hidden task information from users' search behaviors. The results of our evaluative experiments on TREC Session track data highlight the value of our proposed task representation model and illustrate a promising research direction on task-oriented intelligent systems.
Towards Understanding and Supporting Exploratory Searches

Exploratory search is an intuitive concept in interactive information retrieval. It is known that searchers use exploratory search strategies when learning and investigating a new domain. Many definitions for Exploratory Search have been proposed. However, the main dimensions involve high uncertainty with respect to the problem context, the user expertise, and the search process. In this work, we study exploratory search in the literature, and we provide a conceptual model of exploratory search. We also conduct a user study to examine how literature searches are exploratory and what factors influence the exploratory dimensions and characteristics. Moreover, we review the exploratory support tasks, and we try to better design and evaluate exploratory user interfaces.
Collaboration Patterns and Impact of Sharing at CHIIR

We studied the collaboration patterns of CHIIR authors, and found that most papers are collaborative. A core of 33% of the CHIIR researchers are directly connected and frequently co-author, and several disconnected clusters also make frequent CHIIR contributions. We also studied citation impact of the CHIIR papers and show that in relation to research design type, theoretical and empirical papers tend to receive more citations than resource papers. With regards to sharing and re-use, papers that share at least one resource tend to have significantly higher citation impact—in particular when sharing data resources and design resources. Re-using resources does not significantly increase citation impact in itself.
Southern European Journalists’ Perceptions of Discursive Menaces in the Age of (Online) Delegitimization

In a new communication context, factors such as the rise of hate speech, disinformation, or a precarious financial and employment situation in the media have made discursive menaces gain increasing significance. Threats of this kind challenge the legitimacy of institutional news media and professional journalists. This article contributes to the existing literature on the legitimization of journalism and boundary work through a study that seeks to understand the perceptions of Southern European journalists of the threats that they encounter in their work and the factors that help explain them. To this end, a survey of 398 journalists in Spain, Italy, and Greece was conducted to learn what personal or professional factors influenced their views and experiences of discursive and non-discursive menaces. Results show that discursive threats, such as hateful or demeaning speech and public discrediting of one’s work, are the most frequent to the safety of journalists, while expressions of physical violence are less common. Younger and more educated journalists tended to perceive themselves as having been victims of discursive menaces more often, although not many significant differences were observed between different groups of journalists. Even though it could show a worrying trend, this finding can also indicate a growing awareness about menaces of this kind.
Incubation and Verification Processes in Information Seeking: A Case Study in the Context of Autonomous Learning

Autonomous learning regards students as the center and orientation rather than teachers' guidance. During autonomous learning, information seeking is not only a process of interactions with systems and stakeholders, but also a process of learning and cognitive transformation from low-level to high-level activities. This study investigated users' cognitive process and cognitive paths, as well as the creation strategies for independent topic selection during the information seeking process. We conducted a longitudinal study through tracking interviews with eight university students who planned to select a topic for their theses or independent study. The interviews were conducted weekly to collect data of their cognitive process and seeking behaviors. It is found that: (1) four lower levels of cognitive process (understanding, applying, analyzing and evaluating) often occur during the stages before formulation, while creating occurs in the formulation stage; (2) three cognitive paths for topic selection were identified: "understand - apply - create", "understand - analyze - create" and "understand - analyze - evaluate - create", and (3) two creation strategies for topic selection according to the duration of creation stages were identified: Incubation and Verification. These results shed light on the design of search systems that could better assist the autonomous learning process and for users to accomplish creative learning tasks.
Incoherent frames design and dictionary learning using a distance barrier

We present a unitary approach to the design of incoherent frames and to dictionary learning, by using a single function that promotes incoherence for both problems. This function has a context-dependent quadratic term and a distance barrier term that was never used in this context. We provide simple and efficient algorithms for both problems. Numerical results show that we can obtain large frames whose incoherence is better than of those designed by other methods. Also, in dictionary learning, we can improve both the representation error and the incoherence of the dictionary, compared with the standard approach.
Investigating the effect of network latency on users’ performance in Collaborative Virtual Environments using navigation aids

Serious games play a pivotal role in engaging users in activities which are considered less-engaging but healthy. Collaborative Virtual Environments (CVEs), one of the enablers of serious games are used in various fields including education, healthcare, tele-conferencing and assembly tasks. There are different factors that affect user performance in CVE. These factors include network latency, loose coordination, lack of task distributions strategy and lack of awareness. As compared to other factors,network latency has unfortunately got less attention of the researcher and therefore, it sometimes becomes a bottleneck for the user performance in CVE. In this paper, we analyzed the effect of network latency on user performance in CVE with textual, audio, 3D Map-Liner (3DML) and arrows-casting aids on different participants. Based on task distribution model, a simulated environment for collaborative assembly task was developed to conduct the experiments. The experiments were performed on users with textual, audio, 3DML and arrows-casting aids with five level of latency (i.e. 0, 50, 100, 150 and 200 ms) to evaluate 19 groups(two participants per group) to check their performance in terms of network latency. Overall, results showed that the user's performance was better in arrows-casting based navigation as compared to other navigation aids (i.e textual, audio and 3DML) using five level of latency (i.e 0, 50, 100, 150 and 200 ms). Whenever the value of latency was increased the task completion time increased significantly with slight decrease in errors rate.
Predicting the need for XAI from high-granularity interaction data

Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) brought light on the need for explainability in multiple domains (e.g., healthcare, finance, justice, and recruiting). Explainability or Explainable AI (XAI) can be defined as everything that makes AI more understandable to human beings. However, XAI features may vary according to the AI algorithm used. Beyond XAI features, different AI algorithms vary in terms of speed, performance, and costs associated with training/running models. Knowing when to choose the right algorithm for the task at hand, therefore, is fundamental in multiple AI systems, for instance, AutoML and AutoAI. In this paper, we propose a method to analyze patterns of high-granularity user interface (UI) events (i.e., mouse, keyboard, and additional custom events triggered on the millisecond scale) to predict when users will interact with UI elements that provide explainability for the AI in place. In this context, this paper presents: (1) a user study involving 37 participants (7 in the pilot phase and 30 in the main experiment phase) in which people performed a task of reporting a bug using a text form associated with an AI data quality meter and its XAI UI element and (2) an approach to model micro behavior using node2vec to predict when the interaction with XAI UI element will occur. The proposed approach uses a rich dataset (approximately 129k events) and combines node2vec and a Logistic Regression classifier. Results obtained show we have obtained an event-by-event prediction of the interaction with XAI with an average F-score of 0.90 (σ=0.06). From the presented results, one expects to support researchers in the realm of UI personalization to consider high-granularity interaction data when predicting the need for XAI while users are interacting with AI model outputs.
Gold, antimony and mercury ore formation and metallogenic link in the Sandu-Danzhai area, Jiangnan Orogen, SW China

The turbidite-hosted Au-Sb-Hg deposits in the Sandu-Danzhai area are distributed along the southwestern marginal fault of the Jiangnan Orogen, yet any genetic link among the Au, Sb and Hg mineralization and their respective ore-forming process remain unclear. Here, we investigate the Paiting Au(-Hg) and Miaolong Au-Sb deposits and present a new metallogenic model based on detailed petrographic observations, and in-situ trace element (EPMA and LA-ICP-MS) and sulfur isotope (fs-LA-MC-ICPMS) analyses on sulfide minerals. Our results show that the ore formation in the Sandu-Danzhai metallogenic belt (SDMB) can be divided into Au (pyrite + arsenopyrite + dolomite), Au-Sb (pyrite + arsenopyrite + stibnite + quartz), Sb (stibnite + native antimony + barite + dolomite + quartz) and Hg (cinnabar + calcite) stages, based on mineral paragenetic and vein crosscutting relationships. Trace element compositions suggest that progressive As enrichment in pyrite is accompanied by increasing Au, Sb and Cu contents, implying that these elements may have been leached from the same source via fluid-rock reactions. Sulfur isotope compositions of the sulfates and sulfides in the Au-Sb-Hg ores reveal that the sulfur was originated from thermal-chemical sulfate reduction (TSR) of marine sulfates in the Cambrian ore-bearing strata. Thus, we propose that deep metamorphic fluids (as evidenced from the high fluid CO2 and CH4 contents) may have promoted ore-material (e.g., Au, Sb, As, Hg) mobilization in the metamorphic basement and/or Cambrian strata. As for the metal transport and precipitation mechanism, fluid-rock interaction may have decreased the ore-fluid As content and caused auriferous pyrite precipitation. Meanwhile, changes in temperature and oxygen-sulfur fugacity may have significantly decreased Sb solubility, leading to extensive stibnite precipitation. Cinnabar associated with calcite was likely deposited via carbonate dissolution in the Cambrian strata in late hydrothermal (waning) stage. Overall, we propose that the Au-Sb-Hg deposits in the SDMB are best classified as orogenic type, and the metallogeny was controlled by boundary faults of the Jiangnan Orogen.
An Efficient Technique for Improving Trust and Privacy in Blockchain as a Service (BaaS)

Cloud computing is a developing technology used for providing various services such as PaaS, IaaS and SaaS for various applications based on the pay as per usage. The foundation of trust is the assurance and confidence that information, people, entities, data, or processes will deliver the desired outcome with a particular level of assurance. Many times, users even do not have knowledge about processing their personal information over the clouds. In cloud computing, centralized architecture is used for trust modeling, which causes various trust and privacy issues. The trust model is subject to a number of limitations, including single point failure, overload, vulnerability to malicious assaults, particularly harmful recommendations, and lack of accessibility and untraceability of trust proof. There are numerous initiatives underway to increase confidence between cloud users and CSPs. The privacy issues including data leakage and compromising data integrity in the cloud environment. The blockchain technology can provide the solution for the above issues. The Blockchain technology has various advantages as it is decentralized in nature, immutable ledgers, providing full transparency, Instant traceability, data encrypted end-to-end. In the proposed system hybrid consensus algorithms are created to overcome problems of the existing blockchain consensus algorithm. By using blockchain technology creates a Blockchain as a Service (BaaS) in the cloud environment to improve the trust and privacy of users.
Data Driven UX/UI design for reproductive health tracker

Human-computer interaction (HCI) design seeks to improve the naturalness of user-system interaction. User experience (UX) is important because it tries to meet the needs of users. It is designed to provide a positive experience that connects users to the product or brand. In addition, a meaningful user experience allows us to define the customer journey on the product that best ensures the success of the business. This project focuses on its UX/UI development for an application that is a useful platform for reproductive health monitoring. We used data analysis techniques to guide UX design decisions. A detailed survey was created to help gather data on application preferences and expectations from a large number of potential users. We analyzed collected data sets, using data analysis algorithms to make decisions at different stages of the design process. User personas are often qualitatively developed by UX researchers. Data-driven personas, or personas created using unsupervised methods, can reveal underlying trends and patterns within groups of users. As a result, we are better able to recognize the many user personas that this application is aiming for. The choice of color palette, typography, and other decisions in the design process was made by analyzing user data collected through extensive research. By combining these two technologies, we can create a data-driven user experience.
IOT Based Surveillance Camera with GPS Module

The main intention of this paper is to bring about a surveillance camera to execute the act of surveillance in a particular area. At the present time IoT plays a major role in science and technology. The Internet of Things is taking us to make secure homes, cities and businesses areas by facilitating both the personal and common organizations to remotely and securely monitor the circumstances. The proposed project is to develop a surveillance camera with GPS module. When the signal given to the Raspberry Pi, it sends the corresponding images taken before and after of the signal given to the Raspberry Pi along with the GPS location of the camera to the higher authority through Internet of Things.
A Survey on AR-Based Digitization for Smart Education System

Online Educational System requires higher efforts from both the teachers and the students, which would feel strenuous psychologically. The system proposed in this paper is an alternative way of handling hybrid (online and offline) educational classes. The traditional educational concepts and their diagrams, considered complex to teach in offline classrooms, could be modularized, eased out, and made simple for students to understand. This project aims to create an augmented reality environment where students can access specific 3-dimensional objects and audio related to any topic in their subject, interact with the environment, and evaluate their performance. It will make the hybrid educational system more exciting, interesting, and engaging. Because of this, learning occurs in subtle but essential phases, leading to acquiring information.
An Improved Indexing Technique for Tribal Art Retrieval System

Nowadays the handmade tribal art products attract the people due to their smooth finishing. Currently many platforms are available for them but, they are fetching result with irrelevant list of the product and supports only keyword search. So, there is a need of the tribal art product retrieval system which fetch the results quickly and accurately based on the indexing mechanism. So, the proposed retrieval system has been implemented for fetching the correct result within minimum time from large tribal product dataset. The indexing and retrieved mechanism for image has been improved using deep learning and distributed structure of indexing mechanism. The deep learning technique has been used to fetch the accurate results and distributed indexing mechanism reduces the time of searching the images through the very large tribal art dataset. The proposed system has been analyzed using different parameters. The proposed system gives 82% accuracy of the result based on the input query image.
Aspect Based Neural Recommender Using Adaptive Prediction

Recommendation systems are information processing systems that analyze user behavior and make suggestions relevant to the users' interests. These systems recommend movies, products etc., based on many different factors. These recommended items are the items which are most likely in the interest of the users. This work focuses on aspects of users' textual comments to make recommendations that are relevant to users. We propose a model that recommends the items based on extracted aspects, taking the reviews and information from the items. Based on the item's aspect level importance, a user may rate them high or low, and we have modelled our recommendation system considering the same. This calculation is based on the neural co-attention mechanism. The proposed model points to various shortcomings of the model that were introduced before. We have used the datasets from amazon, which are extracted using web scraping. The existing recommendation system predicts ratings alone, while the proposed model gives rankings and ratings. The model's efficiency is checked using precision, recall and F1 score.
A Survey of A-Star Algorithm Family for Motion Planning of Autonomous Vehicles

For autonomous vehicles, one of the prime tasks to be able to function without any human assistance is the task of motion planning. Motion planning involves planning a safe, collision-free and a computationally feasible path to avoid the obstacles in their way and precisely reach the desired location in as less time as possible. The continual efforts in developing new and improving existing path planning algorithms has led to significant advances in improving the autonomy of the autonomous vehicles. One of the most popular algorithms used in path planning is the A * search algorithm, whose several variations have been formulated over the years. This gives a rise to the need of a comprehensive survey of such variations of the algorithm. This paper presents a review of various variations of the A * algorithm along with a brief review of the classical A * algorithm.