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Most code-based quality measurement approaches are based, at least partially, on values of multiple source code metrics. A class will often be classified as being of poor quality if the values of its metrics are above given thresholds, which are different from one metric to another. The metrics thresholds are calculated using various techniques. In this paper, we investigated two specific techniques:...
With the immense growth of online social applications, trust plays a more and more important role in connecting users to each other, sharing their personal information and attracting him to receive recommendations. Therefore, how to obtain trust relationships through mining online social networks became a critical issue. To calculate the level of trust between two users, many computational trust models...
Person re-identification, aiming to match a specific person among non-overlapping cameras, has attracted plenty of attention in recent years. It can be regarded as a visual retrieval task, namely given a query person image, ranking all gallery images according to their similarities to the query. Conventionally, this similarity function is learnt by forcing intra-distances to be small while inter-distances...
This paper addresses the task of automatically detecting plagiarized responses in the context of a test of spoken English proficiency for non-native speakers. Text-to-text content similarity features are used jointly with speaking proficiency features extracted using an automated speech scoring system to train classifiers to distinguish between plagiarized and non-plagiarized spoken responses. A large...
In this paper, we motivate the utility of framing very common data analysis and business intelligence problems as a problem in understanding the differences between two datasets. We call this framework the Difference-of-Datasets (DoD) framework. We propose a simple and effective method to help find the root causes of changes, i.e. “Why did the observed change happen?” or “What drove the observed change?”...
In order to generate effective results, it is essential for a recommender system to model the information about the user interests (user profiles). A profile usually contains preferences that reflect the recommendation technique, so collaborative systems represent a user with the ratings given to items, while content-based approaches assign a score to semantic/text-based features of the evaluated...
Virtualisation encourages users to procure, relin-quish and scale resources frequently. Such fluid deployments require continuous performance assessment to inform resource allocation. In current systems Tick Accounting, Load Average, and Memory Usage are used to judge system performance and to trigger resource scaling. We argue that the readily available Context Switch (CS) counter is also an effective...
The synthesis of controllers enforcing safety properties is a well understood problem for which we have practical algorithms as well as a deep theoretical understanding. This problem is typically formulated as game between the controller seeking to enforce the safety property and the environment seeking to violate it. The solution of these games is given by a winning set: inside the winning set the...
The assessment of travel time reliability for segments and routes is a rapidly advancing frontier. The increasing availability of probe data is making it possible to monitor reliability in real-time based on individual vehicle data as opposed to ex-post-facto based on averages. This paper examines metrics that can be used to monitor reliability based on probe data. The merits of traditional metrics...
The interest for wireless sensor networks (WSNs) is continuously growing especially with the emergence of applications such as smart grids, smart cities, e-health, etc. where billions of objets (sensors) will be permanently connected to the Internet. In this context, the IPv6 Routing Protocol for Low power and Lossy Networks (RPL) is placed as the routing standard for the next generation multi-hop...
Traditional affective lexicons are mainly based on discrete classes, such as positive, happiness, sadness, which may limit its expressive power compared to the dimensional representation in which affective meanings are expressed through continuous numerical values on multiple dimensions, such as valence-arousal. Traditional methods for acquiring dimensional lexicons are mainly based on time-consuming...
Social media creates a novel marketing mechanism to boost opinion formation and information diffusion. As a crucial idea, social influence sheds light on individuals' features in the process of communicating brand stories, and changes other consumers' opinions and behavior. Social media marketing has drawn great interests from scholars in a past decade, but the extant work neglects to establish an...
The development of database systems and applications requires the use of metrics to evaluate the quality and the efficiency of each phase, especially as regards the physical phase, where logical, physical and hardware optimizations are mainly used. Since the 1980s, a large range of cost models has been proposed. Each cost model is dedicated to the calculation of specific metrics and mainly pertains...
This paper proposes a new semi-supervised clustering framework to represent and integrate quantitative preferences on attributes. A new metric learning algorithm is derived that achieves a compromise clustering between a data-driven and a user-driven solution and converges with a good complexity. We observe experimentally that the addition of preferences may be essential to achieve a better clustering...
Performance assessment of human teaming in complex, real-world contexts is a fundamental challenge for research and training communities alike. We highlight a unique partnership between the cybersecurity training and research communities with the common goal of capturing human team performance. Whether in the context of a training assessment or a research endeavor; both are two sides of the same coin...
Healthcare practitioners are increasingly using search functionality embedded in Electronic Medical Record (EMR) software to search for relevant evidence summaries at point of care. We introduce a learning to rank approach that exploits information carried in EMR data and UpToDate user accounts to (significantly) improve ranking results, compared to a comparable model that does not exploit such features.
Reputation management in social media has become crucial for firms that want to improve their brand's image and master the impact of harmful events. This is why, many companies are making important invests in acquiring powerful social media monitoring tools (SMM). Although SMM tools are displaying relevant insights useful for marketers, few calculate a global reputation score that can help companies...
Proper feedback can leverage students to better understand their difficulties and shorten the characteristic program-submit-refactor cycle of programming exercises. The ideal feedback is the result of a human inspection and analysis considering both functional and qualitative aspects of programs produced by students. On the other hand, automated assessment systems can provide rapid, cheap and standardized...
Robots are being increasingly used in safety-critical contexts, such as transportation and health. The need for flexible behavior in these contexts, due to human interaction factors or unstructured operating environments, led to a transition from hardware- to software-based safety mechanisms in robotic systems, whose reliability and quality is imperative to guarantee. Source code static analysis is...
Teaching agile software development by pairing lectures with hands-on projects has become the norm. This approach poses the problem of grading and evaluating practical project work as well as process conformance during development. Yet, few best practices exist for measuring the success of students in implementing agile practices. Most university courses rely on observations during the course or final...
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