Degree Of Interest Research Articles

Qrator: An Interest-Aware Approach to ABR Streaming Based on User Engagement

Conventional approaches to adaptive bitrate (ABR) streaming fail to further improve the quality of experience (QoE) as they are indifferent to video semantics and user behaviors by focusing solely on the network conditions. These ABR schemes cannot enhance the perceptual video quality as they neglect the users’ varying degree of interest (DoI) over video sections and may experience QoE degradation due to frequent rebuffers caused by user behaviors such as selectively watching the interesting sections. To this end, this article proposes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Qrator</i> , which is the very first attempt to utilize timestamps and likes as user engagement data to infer users’ genuine interests and further elevate the user-perceived QoE. Based on timestamps and likes in video comments, Qrator improves the overall QoE by considering DoI variations and user behaviors in performing interest-aware rate adaptation and prefetching. Qrator can be widely applied to conventional ABR approaches without significantly modifying their implementations. Evaluation results show that Qrator can heighten the bitrates of user interest sections without degrading the average bitrate. Furthermore, applying Qrator under user behavior patterns can reduce the rebuffering ratio and the number of rebuffers by 32% and 31%, respectively, while maintaining other QoE metrics to a similar extent.

Automated evolution of feature logging statement levels using Git histories and degree of interest

Logging -- used for system events and security breaches to describe more informational yet essential aspects of software features -- is pervasive. Given the high transactionality of today's software, logging effectiveness can be reduced by information overload. Log levels help alleviate this problem by correlating a priority to logs that can be later filtered. As software evolves, however, levels of logs documenting surrounding feature implementations may also require modification as features once deemed important may have decreased in urgency and vice-versa. We present an automated approach that assists developers in evolving levels of such (feature) logs. The approach, based on mining Git histories and manipulating a degree of interest (DOI) model, transforms source code to revitalize feature log levels based on the "interestingness" of the surrounding code. Built upon JGit and Mylyn, the approach is implemented as an Eclipse IDE plug-in and evaluated on 18 Java projects with $\sim$3 million lines of code and $\sim$4K log statements. Our tool successfully analyzes 99.22% of logging statements, increases log level distributions by $\sim$20%, and increases the focus of logs in bug fix contexts $\sim$83% of the time. Moreover, pull (patch) requests were integrated into large and popular open-source projects. The results indicate that the approach is promising in assisting developers in evolving feature log levels.

Topology-aware space distortion for structured visualization spaces

We propose topology-aware space distortion (TASD), a family of interactive layout techniques for non-linearly distorting geometric space based on user attention and on the structure of the visual representation. TASD seamlessly adapts the visual substrate of any visualization to give more screen real estate to important regions of the representation at the expense of less important regions. In this paper, we present a concrete TASD technique that we call ZoomHalo for interactively distorting a two-dimensional space based on a degree-of-interest (DOI) function defined for the space. Using this DOI function, ZoomHalo derives several areas of interest, computes the available space around each area in relation to other areas and the current viewport extents, and then dynamically expands (or shrinks) each area given user input. We use our prototype to evaluate the technique in two user studies, as well as showcase examples of TASD for node-link diagrams, word clouds, and geographical maps.

Topical affinity in short text microblogs

Knowledge-based applications like recommender systems in social networks are powered by complex network of social discussions and user connections. Short text microblog platforms like Twitter are powerful in this aspect due to their real-time content dissemination as well as having a complex mesh of user connections. For example, users on Twitter tend to consume certain content to a greater or less extent depending on their interests over time. Quantifying this degree of content consumption in certain topics is an arduous task. This is further compounded by the amount of digital information that such platforms generate at any given time. Formulation of personalized user profiles based on user interests over time and friendship network is thus a problem. Therefore, user profiling based on their interests is important for personalized third-party content recommendations on the platform. In this paper we address this problem by presenting our solution in a two-step process:- (i) Firstly, we compute users’ Degree of Interest (DoI) towards a certain topic based on the overall users’ affinity towards that topic. (ii) Secondly, we affirm this DoI by correlating it to their friendship network. Furthermore, we describe our model for DoI computation and follow-back recommendation system by learning a low-dimensional vector representation of users and their disseminated content. This representation is used to train models for prediction of correct cluster classifications. In our experiments, we use a Twitter dataset to validate our approach by computing degrees of interest for certain test users in three diverse and generic topics. Experimental results show the effectiveness of our approach in the extraction of intra-user interests and better accuracy in follow-back recommendations with diversities in the topics.

MylynSDP \u2014 Process - aware artifact filtering based on interest

A software development process is used by software engineers to guide their activities during all phases of the software product development. When executing a software development process, software engineers may lose time and effort while searching for artifacts or changing contexts. This happens, for example, when they need to search for a specific code file in a list of hundreds of files or when they interrupt an activity to execute another but forget specific details and need to re-execute searches related to the previous activity. This impacts their productivity negatively, because extra time and effort are spent into non-productive work. Therefore, automated assistance is required to mitigate or avoid these issues. The Degree of Interest (DOI) function infers an element’s importance in a context, helping software engineers to handle many artifacts. Mylyn, an Eclipse IDE plugin, uses a DOI function on Java documents to assist programmers when looking for code documents during development. However, Mylyn’s DOI function is limited to the implementation phase of software processes and relies on manual task creation. This paper presents MylynSDP, a software Process-aware extension to Mylyn’s DOI function. MylynSDP’s DOI function infers an artifact’s importance during an activity and filters uninteresting artifacts, reducing the time taken to search items and improving productivity. Mylyn code was augmented, and an evaluation study was performed. Seven subjects executed a software process with many artifacts. Exercise times were recorded for productivity analysis. Subjects answered a Technology Acceptance Model (TAM) questionnaire. New task and artifact creation wizards link tasks and artifacts to specification activities and artifacts, respectively. A new interaction event handles context creation, and the DOI function was extended to other software process phases. Exercise time reduction shows a productivity increase. TAM questionnaire answers show a positive overall willingness to adopt MylynSDP and provide evidence that using a DOI function in different software process phases increases productivity. This work advances the state of the art in software engineering by providing additional methods to support artifact search and discovery, context change management, and artifact relevance mechanisms.

IMPLEMENTASI PENDEKATAN INTEGRATED SYSTEM DALAM MENINGKATKAN MINAT DAN PRESTASI BELAJAR BAHASA ARAB SISWA KELAS XI DI MA NU MIFTAHUL ULUM MARGASARI-TEGAL

This study aims to determine the concept of the IntegratedSystem approach, and the extent of the contribution of the IntegratedSystem Approach to learning interest and student learning achievementin learning Arabic.The approach used in the study is classroom actionresearch (PTK).Subjects in this study were students of class XI MA NUMiftahul Ulum Margasari.The results of this study indicate that in the preactioninterest of students was less interested as much as 40%, then afterthe implementation of the action in the first cycle increased to be quiteinterested as much as 53.30% (up 13.3%), in the second cycle increased to75% Interest (up by 21.7%), and in the third cycle experienced a degree ofinterest in the Interest category but the percentage increased to 78.30% (up3.3%). Based on Student Interest Questionnaire, from the average score ofpositive and negative criteria 33 items (25 positive items and 8 negativeitems) that have been obtained, the combined average score is (positivecriteria + negative criteria) = (22.82 + 2 , 79 = 25.61). 25.61 is a combinedaverage of positive and negative criteria, if it is reported to be 77.6%, theinterest in learning Arabic in class IX students is strong (interested) as muchas 77.6%. Based on the results of the tests, from the first, second and thirdcycles the value of Arabic subjects in class XI Science and Social Scienceswas above the KKM average, indicating that student learning achievementincreased by an average of 75.7 above the KKM score of 67.

Visualization and analysis of schema and instances of ontologies for improving user tasks and knowledge discovery

Abstract Ontologies are an important resource for knowledge representation. Their structure can be complex due to role relations between several concepts, distinct attributes, and different instances. In this paper, we discuss a Visual Analytics solution, relying on the use of multiple coordinated views for exploring different ontology aspects and a novel use of the degree of interest (DoI) suppression technique to reduce the complexity of the ontology visual representation. Visual Analytics facilitates the understanding of the domain and tasks represented by ontologies, thus allowing to carry out exploratory analysis to optimize the comprehension of data semantics including non-explicit relationships between data. Through the DoI technique, we place the main concept in focus, distinguishing it from the unnecessary information and facilitating the analysis and understanding of correlated data. We evaluated all the devised solutions, and the results reinforce the importance of providing visualization and analysis techniques dedicated to the schema and instances levels of ontologies for the discovery of non-explicit information.

Detecting broken pointcuts using structural commonality and degree of interest

Abstract Pointcut fragility is a well-documented problem in Aspect-Oriented Programming; changes to the base-code can lead to join points incorrectly falling in or out of the scope of pointcuts. Deciding which pointcuts have broken due to base-code changes is a daunting venture, especially in large and complex systems. We present an automated approach that recommends pointcuts that are likely to require modification due to a particular base-code change, as well as ones that do not. Our hypothesis is that join points selected by a pointcut exhibit common structural characteristics. Patterns describing such commonality are used to recommend pointcuts that have potentially broken with a degree of confidence as the developer is typing. The approach is implemented as an extension to the popular Mylyn Eclipse IDE plug-in, which maintains focused contexts of entities relevant to the task at hand using a Degree of Interest (DOI) model. We show that it is accurate in revealing broken pointcuts by applying it to multiple versions of several open source projects and evaluating the quality of the recommendations produced against actual modifications. We found that our tool made broken pointcuts 2.14 times more interesting in the DOI model than unbroken ones, with a p-value under 0.1, indicating a significant difference in final DOI value between the two kinds of pointcuts (i.e., broken and unbroken).

Using contexts similarity to predict relationships between tasks

We can predict tasks relationships by comparing the contexts of the tasks.The more related the tasks, the better context similarity predicts the relationship.Comparing contexts is roughly as accurate as mining text descriptions of tasks.Depending on data availability context similarity complements content similarity. Developers tasks are often interrelated. A task might succeed, precede, block, or depend on another task. Or, two tasks might simply have a similar aim or require similar expertise. When working on tasks, developers interact with artifacts and tools, which constitute the contexts of the tasks. This work investigates the extent to which the similarity of the contexts predicts whether and how the respective tasks are related. The underlying assumption is simple: if during two tasks the same artifacts are touched or similar interactions are observed, the tasks might be interrelated.We define a task context as the set of all developers interactions with the artifacts during the task. We then apply Jaccard index, a popular similarity measure to compare two contexts. Instead of only counting the artifacts in the intersection and union of the contexts as Jaccard does, we scale the artifacts with their relevance to the task. For this, we suggest a simple heuristic based on the Frequency, Duration, and Age of the interactions with the artifacts (FDA). Alternatively, artifact relevance can be estimated by the Degree-of-Interest (DOI) used in task-focused programming.To compare the accuracy of the context similarity models for predicting task relationships, we conducted a field study with professionals, analyzed data from the open source task repository Bugzilla, and ran an experiment with students. We studied two types of relationships useful for work coordination (dependsOn and blocks) and two types useful for personal work management (isNextTo and isSimilarTo). We found that context similarity models clearly outperform a random prediction for all studied task relationships. We also found evidence that, the more interrelated the tasks are, the more accurate the context similarity predictions are.Our results show that context similarity is roughly as accurate to predict task relationships as comparing the textual content of the task descriptions. Context and content similarity models might thus be complementary in practice, depending on the availability of text descriptions or context data. We discuss several use cases for this research, e.g. to assist developers choose the next task or to recommend other tasks they should be aware of.

AVOCADO: Visualization of Workflow-Derived Data Provenance for Reproducible Biomedical Research.

A major challenge in data-driven biomedical research lies in the collection and representation of data provenance information to ensure that findings are reproducibile. In order to communicate and reproduce multi-step analysis workflows executed on datasets that contain data for dozens or hundreds of samples, it is crucial to be able to visualize the provenance graph at different levels of aggregation. Most existing approaches are based on node-link diagrams, which do not scale to the complexity of typical data provenance graphs. In our proposed approach, we reduce the complexity of the graph using hierarchical and motif-based aggregation. Based on user action and graph attributes, a modular degree-of-interest (DoI) function is applied to expand parts of the graph that are relevant to the user. This interest-driven adaptive approach to provenance visualization allows users to review and communicate complex multi-step analyses, which can be based on hundreds of files that are processed by numerous workflows. We have integrated our approach into an analysis platform that captures extensive data provenance information, and demonstrate its effectiveness by means of a biomedical usage scenario.

ThermalPlot: Visualizing Multi-Attribute Time-Series Data Using a Thermal Metaphor.

Multi-attribute time-series data plays a vital role in many different domains, such as economics, sensor networks, and biology. An important task when making sense of such data is to provide users with an overview to identify items that show an interesting development over time, including both absolute and relative changes in multiple attributes simultaneously. However, this is not well supported by existing visualization techniques. To address this issue, we present ThermalPlot, a visualization technique that summarizes combinations of multiple attributes over time using an items position, the most salient visual variable. More precisely, the x-position in the ThermalPlot is based on a user-defined degree-of-interest (DoI) function that combines multiple attributes over time. The y-position is determined by the relative change in the DoI value ( ∆DoI) within a user-specified time window. Animating this mapping via a moving time window gives rise to circular movements of items over time-as in thermal systems. To help the user to identify important items that match user-defined temporal patterns and to increase the technique's scalability, we adapt the level of detail of the items' representation based on the DoI value. Furthermore, we present an interactive exploration environment for multi-attribute time-series data that ties together a carefully chosen set of visualizations, designed to support analysts in interacting with the ThermalPlot technique. We demonstrate the effectiveness of our technique by means of two usage scenarios that address the visual analysis of economic development data and of stock market data.

Networks of Names: Visual Exploration and Semi‐Automatic Tagging of Social Networks from Newspaper Articles

AbstractUnderstanding relationships between people and organizations by reading newspaper articles is difficult to manage for humans due to the large amount of data. To address this problem, we present and evaluate a new visual analytics system, which offers interactive exploration and tagging of social networks extracted from newspapers. For the visual exploration of the network, we extract “interesting” neighbourhoods of nodes, using a new degree of interest (DOI) measure based on edges instead of nodes. It improves the seminal definition of DOI, which we find to produce the same “globally interesting” neighbourhoods in our use case, regardless of the query. Our approach allows answering different user queries appropriately, avoiding uniform search results.We propose a user‐driven pattern‐based classifier for discovery and tagging of non‐taxonomic semantic relations. Our approach does not require any a‐priori user knowledge, such as expertise in syntax or pattern creation. An evaluation shows that our classifier is capable of identifying known lexico‐syntactic patterns as well as various domain‐specific patters. Our classifier yields good results already with a small amount of training, and continuously improves through user feedback.We conduct a user study to evaluate whether our visual interactive system has an impact on how users tag relationships, as compared to traditional text‐based interfaces. Study results suggest that users of the visual system tend to tag more concisely, avoiding too abstract or overly specific relationship labels.

The structure of interests in different artistic disciplines of secondary school students

The paper deals with the degree of development of interest in arts in young people, and the justifiability of the current tendency of revision and abbreviation of the art curricula in schools. The research is focused on the structure of interests in various artistic disciplines in secondary school students, and the aim is to determine whether the degree of students? interest depends on the school type, gender and previous experience. The research was conducted on the sample of 555 students from 111 schools, who filled out the questionnaire during one school period. The data were processed by multifactor analysis of variance. There was a higher degree of interest in video and performing arts, which are not present enough in students? surroundings. The girls showed a higher degree of interest in all disciplines, as well as grammar school students compared to their peers from vocational schools. It was shown that the higher the number of programmes attended, the higher the interest in all forms of art. Young people who were completely inactive showed interest in video arts, which serves as an important guideline in the process of moving and socialising them. The results confirm that youth?s interests and needs are discrepant with what is offered to them. It is necessary to introduce various artistic contents as parts of the curriculum, which would make school an integral part of their life, the part that directly influences the development of creativity and increases responsible participation in the society.

A trade-off between energy and delay in data dissemination for wireless sensor networks using transmission range slicing

Data dissemination is an essential function in wireless sensor networks (WSNs). A WSN consists of a large number of unattended sensors with limited storage, battery power, computation, and communication capabilities, where battery power (or energy) is the most crucial resource for sensor nodes. Because delay time is also a critical metric for certain applications, data dissemination between source sensors (or simply sources) and a sink (or central gathering point) should be done in an energy-efficient and timely manner. In this paper, we present an approach that characterizes a trade-off between energy and source-to-sink delay (or simply delay). Specifically, we decompose the transmission range of sensors into concentric circular bands ( CCBs) based on a minimum transmission distance between any pair of sensors. Our decomposition strategy provides a classification of these CCBs that helps a sensor express its degree of interest ( DoI) in minimizing two conflicting metrics, namely energy consumption and delay. We also propose a data dissemination protocol that exploits the above-mentioned decomposition to meet the specific requirements of a sensing application in terms of energy and delay. We prove that the use of sensors nodes, which lie on or closely to the shortest path between a source and a sink, as proxy forwarders in data dissemination from sources to a sink, helps simultaneously minimize energy consumption and delay. Also, we compute theoretical lower and upper bounds on these two metrics. Our simulation results are found to be consistent with our theoretical results, and show that the first CCB minimizes energy consumption; the last CCB minimizes delay; and the middle CCBs trade-off energy consumption with delay in data dissemination in WSNs.

Scalable Hybrid Unstructured and Structured Grid Raycasting

This paper presents a scalable framework for real-time raycasting of large unstructured volumes that employs a hybrid bricking approach. It adaptively combines original unstructured bricks in important (focus) regions, with structured bricks that are resampled on demand in less important (context) regions. The basis of this focus+context approach is interactive specification of a scalar degree of interest (DOI) function. Thus, rendering always considers two volumes simultaneously: a scalar data volume, and the current DOI volume. The crucial problem of visibility sorting is solved by raycasting individual bricks and compositing in visibility order from front to back. In order to minimize visual errors at the grid boundary, it is always rendered accurately, even for resampled bricks. A variety of different rendering modes can be combined, including contour enhancement. A very important property of our approach is that it supports a variety of cell types natively, i.e., it is not constrained to tetrahedral grids, even when interpolation within cells is used. Moreover, our framework can handle multi-variate data, e.g., multiple scalar channels such as temperature or pressure, as well as time-dependent data. The combination of unstructured and structured bricks with different quality characteristics such as the type of interpolation or resampling resolution in conjunction with custom texture memory management yields a very scalable system.