Miracle-dz/UniFAR-FTUs huggingface dataset:
The State of Empirical Evaluation in Static Feature Location

Feature location (FL) is the task of finding the source code that implements a specific, user-observable functionality in a software system. It plays a key role in many software maintenance tasks and a wide variety of Feature Location Techniques (FLTs), which rely on source code structure or textual analysis, have been proposed by researchers. As FLTs evolve and more novel FLTs are introduced, it is important to perform comparison studies to investigate “Which are the best FLTs? ” However, an initial reading of the literature suggests that performing such comparisons would be an arduous process, based on the large number of techniques to be compared, the heterogeneous nature of the empirical designs, and the lack of transparency in the literature. This article presents a systematic review of 170 FLT articles, published between the years 2000 and 2015. Results of the systematic review indicate that 95% of the articles studied are directed towards novelty, in that they propose a novel FLT. Sixty-nine percent of these novel FLTs are evaluated through standard empirical methods but, of those, only 9% use baseline technique(s) in their evaluations to allow cross comparison with other techniques. The heterogeneity of empirical evaluation is also clearly apparent: altogether, over 60 different FLT evaluation metrics are used across the 170 articles, 272 subject systems have been used, and 235 different benchmarks employed. The review also identifies numerous user input formats as contributing to the heterogeneity. Analysis of the existing research also suggests that only 27% of the FLTs presented might be reproduced from the published material. These findings suggest that comparison across the existing body of FLT evaluations is very difficult. We conclude by providing guidelines for empirical evaluation of FLTs that may ultimately help to standardise empirical research in the field, cognisant of FLTs with different goals, leveraging common practices in existing empirical evaluations and allied with rationalisations. This is seen as a step towards standardising evaluation in the field, thus facilitating comparison across FLTs.
A support to evaluate web accessibility and usability issues for older adults

The constant evolution of the Web has enabled that new online services in various segments be offered. Following this evolution, it is crucial that its content allows access to the different user's profiles. Older adults (age 60+) are users who typically have limited capacities due to ageing process and they have encountered difficulties in interacting with websites. Several accessibility and usability issues still have to be solved, despite the legislation and guidelines established for the development of accessible and usable web content. In particular, little attention has been devoted to the difficulties faced by older adults because most websites are not designed with these users in mind. This paper addresses the development of a checklist, a support to web accessibility and usability evaluations for the older adults profile and its validation. The checklist was elaborated from analysis of the state of the art, based on a literature review. During the checklist development we investigated the main barriers faced by older adults in web interaction. We conclude by highlighting the need for more studies on web interaction by older people and proposing new strategies for future research.
Towards a better video comparison: comparison as a way of browsing the video contents

Comparison between videos is a basic task used in a wide variety of activities and domains. With ever-increasing social media contents that allow the mass consumption of video medium, our needs and uses for video comparison will only increase in the coming years. However, the interaction to support the task of comparing between videos is not well understood today, and there is very little theoretical foundation to guide future design for supporting video comparison. In this paper, we take the comparison as the primary angle to look at video-related interaction such as watching, browsing, and editing. By reinterpreting those interaction and synthesising available knowledge from HCI, visualisation and multimedia fields, we construct a theoretical structure that embeds essential conceptual components for supporting video comparison. These concepts not only help explain the existing user-interfaces of video applications as a comparison interaction, but also generate discussion and implications for novel interactive features to enhance our everyday consumption of video contents.
Designing for reflection on sender effort in close personal communication

Research has identified that people in close relationships value effort that is invested into the creation of digital messages. This paper explores the potential for communication systems to encourage reflection by revealing evidence of effort to message recipients, allowing for it to be appreciated. Focusing on text-based communication, we report findings from an exploratory study of three interface prototypes that probe users' reactions to the notion of revealing sender effort. We find that information about effort can foster empathy and appreciation by encouraging reflection over meaningful actions. However, designers of communication tools must address the issues of authenticity, controlled disclosure and cost in access if reflection on effort is to be valued. We consider how designers might negotiate these issues in future effort-sensitive communication technologies.
Link Quality Optimization for Hybrid LEO-GSO Systems

In this paper, an optimised link design for LEO-GSO satellite systems is proposed so that a variable number of LEO satellites can maintain a required link quality when traversing GSO coverage. We identify the orbit, system and sata traffic parameters that are relevant, with which we obtain a parameterised optimal transmission energy function. Our results are useful for the design of energy-efficient hybrid satellite systems.
Integrated Test Development: An integrated and incremental approach to write software of high quality

Creating test cases is a difficult task for students. The number of existing recommendations on how to create test cases is overwhelming. There is a lack of guidelines on how to apply those recommendations one step after another. This problem even becomes more complicated when students are taught to refactor their code as a habit. We propose an approach to teach students how to develop and test their code systematically, with refactoring integrated. In our approach, we pay attention to both functionality and robustness.
Effect of Recommendations on Serving Content with Unknown Demand

We consider the task of content replication in distributed content delivery systems used by Video-on-Demand (VoD) services with large content catalogs. The prior work in this area focuses on the setting where each request is generated independent of all past requests. Motivated by the fact that most popular VoD services offer recommendations to users based on their viewing history, in a departure from existing studies, we study the setting with time-correlation in requests coming from each user. We use a Markovian process to model each user’s request process. In addition to introducing time-correlation in user requests, our model is consistent with empirically observed properties of the request process for VoD services with recommendation engines. In the setting where the underlying Markov Chain is unknown and has to be learned from the very requests the system is trying to serve, we show that separating the task of estimating content popularity and using the estimates to design a static content replication policy is strictly sub-optimal. To prove this, we show that an adaptive policy, which jointly performs the task of estimation and content replication, outperforms all policies that separate the task of estimation and content replication.
Spatiotemporal Representation Learning for Translation-Based POI Recommendation

The increasing proliferation of location-based social networks brings about a huge volume of user check-in data, which facilitates the recommendation of points of interest (POIs). Time and location are the two most important contextual factors in the user’s decision-making for choosing a POI to visit. In this article, we focus on the spatiotemporal context-aware POI recommendation, which considers the joint effect of time and location for POI recommendation. Inspired by the recent advances in knowledge graph embedding, we propose a spatiotemporal context-aware and translation-based recommender framework (STA) to model the third-order relationship among users, POIs, and spatiotemporal contexts for large-scale POI recommendation. Specifically, we embed both users and POIs into a “transition space” where spatiotemporal contexts (i.e., a pair) are modeled as translation vectors operating on users and POIs. We further develop a series of strategies to exploit various correlation information to address the data sparsity and cold-start issues for new spatiotemporal contexts, new users, and new POIs. We conduct extensive experiments on two real-world datasets. The experimental results demonstrate that our STA framework achieves the superior performance in terms of high recommendation accuracy, robustness to data sparsity, and effectiveness in handling the cold-start problem.
Multitask Learning for Pathomorphology Recognition of Squamous Intraepithelial Lesion in Thinprep Cytologic Test

This paper presents a multitask learning network for pathomorphology recognition of squamous intraepithelial lesion in Thinprep Cytologic Test. Detecting pathological cells is a quite challenging task due to large variations in cell appearance and indistinguishable changes in pathological cells. In addition, the high resolution of scanned cell images poses a further demand for efficient detection algorithm. Therefore, we propose a multi-task learning network aims at keeping a good balance between performance and computational efficiency. First we transfer knowledge from pre-trained VGG16 network to extract low level features, which alleviate the problem caused by small training data. Then, the potential regions of interest are generated by our proposed task oriented anchor network. Finally, a fully convolutional network is applied to accurately estimate the positions of the cells and classify its corresponding labels. To demonstrate the effectiveness of the proposed method, we conducted a dataset which is cross verified by two pathologists. In the test, we compare our method to the state-of-the-art detection algorithms, i.e. YOLO [1], and Faster-rcnn [2], which were both re-trained using our dataset. The results show that our method achieves the best detection accuracy with high computational efficiency, which only takes half time compared to Faster-rcnn.
Multi-dimensional Vectorization in LLVM

Loop vectorization is a classic technique to exploit SIMD instructions in a productive way. In multi-dimensional vectorization, multiple loops of a loop nest are vectorized at once. This exposes opportunities for data reuse, register tiling and more efficient memory accesses. In this work, we present TensorRV, a multi-dimensional vectorization framework for LLVM IR. TensorRV is a generalization of the Region Vectorizer, a general purpose outer-loop and whole-function vectorizer, to the multi-dimensional setting. We evaluate TensorRV on a set of stencil codes and matrix transpose. We find that stencil codes benefit from the reduction of load instructions with a speedup of x 1.45 on NEC SX-Aurora TSUBASA. Multi-loop vectorized matrix transpose leverages efficient SIMD shuffle instructions on AVX512, for which we report a speedup of x 3.27.
A Survey and Taxonomy of Core Concepts and Research Challenges in Cross-Platform Mobile Development

Developing applications targeting mobile devices is a complex task involving numerous options, technologies, and trade-offs, mostly due to the proliferation and fragmentation of devices and platforms. As a result of this, cross-platform app development has enjoyed the attention of practitioners and academia for the previous decade. Throughout this review, we assess the academic body of knowledge and report on the state of research on the field. We do so with a particular emphasis on core concepts, including those of user experience, device features, performance, and security. Our findings illustrate that the state of research demand for empirical verification of an array of unbacked claims, and that a particular focus on qualitative user-oriented research is essential. Through our outlined taxonomy and state of research overview, we identify research gaps and challenges, and provide numerous suggestions for further research.
A Variational Inference-Based Heteroscedastic Gaussian Process Approach for Simulation Metamodeling

In this article, we propose a variational Bayesian inference-based Gaussian process metamodeling approach (VBGP) that is suitable for the design and analysis of stochastic simulation experiments. This approach enables statistically and computationally efficient approximations to the mean and variance response surfaces implied by a stochastic simulation, while taking into full account the uncertainty in the heteroscedastic variance; furthermore, it can accommodate the situation where either one or multiple simulation replications are available at every design point. We demonstrate the superior performance of VBGP compared with existing simulation metamodeling methods through two numerical examples.
Adaptive Sampling for GPU-based 3-D Volume Rendering

3-D interactive volume rendering can be rather complicated through conventional ray casting with simple sampling and texture mapping. Owing to the limitation of hardware resources, volume rendering algorithms are considerably time-consuming. Therefore, adaptive sampling technique is proposed to tackle the problem of excessive computational cost. In this paper, considering an optimization in parallelized ray-casting algorithms of volume rendering, we propose an adaptive sampling method, which mainly reduces the number of sampling points through non-linear sampling function. This new method can be rather effective while making a trade-off between performance and rendering quality. Our experimental results demonstrate that the proposed adaptive sampling method can result in high efficiency in computation, and produce high-quality image based on the MSE and SSIM metrics.
An Autotuning Protocol to Rapidly Build Autotuners

Automatic performance tuning (Autotuning) is an increasingly critical tuning technique for the high portable performance of Exascale applications. However, constructing an autotuner from scratch remains a challenge, even for domain experts. In this work, we propose a performance tuning and knowledge management suite (PAK) to help rapidly build autotuners. In order to accommodate existing autotuning techniques, we present an autotuning protocol that is composed of an extractor, producer, optimizer, evaluator, and learner. To achieve modularity and reusability, we also define programming interfaces for each protocol component as the fundamental infrastructure, which provides a customizable mechanism to deploy knowledge mining in the performance database. PAK’s usability is demonstrated by studying two important computational kernels: stencil computation and sparse matrix-vector multiplication (SpMV). Our proposed autotuner based on PAK shows comparable performance and higher productivity than traditional autotuners by writing just a few tens of code using our autotuning protocol.
Investigating differences between native english and non-native english speakers in interacting with a voice user interface: a case of google home

This paper reports on findings from a pilot usability study of the Google Home Smart Speaker undertaken with native English speakers and non-native English speakers to understand the differences in usability and user experiences of the two distinct groups. The study shows that, while both user groups felt satisfied in their use of the device, native English speakers had a better user experience overall than their counterparts. Importantly, preliminary findings from the study demonstrate that cultural distinctions in English expression and system engagement may be as significant to usability as English language proficiency. The findings provide a baseline for the next stages of this research and insights for developers and researchers in the design and use of Voice User Interfaces.
Linked Vocabulary Recommendation Tools for Internet of Things

The Semantic Web emerged with the vision of eased integration of heterogeneous, distributed data on the Web. The approach fundamentally relies on the linkage between and reuse of previously published vocabularies to facilitate semantic interoperability. In recent years, the Semantic Web has been perceived as a potential enabling technology to overcome interoperability issues in the Internet of Things (IoT), especially for service discovery and composition. Despite the importance of making vocabulary terms discoverable and selecting the most suitable ones in forthcoming IoT applications, no state-of-the-art survey of tools achieving such recommendation tasks exists to date. This survey covers this gap by specifying an extensive evaluation framework and assessing linked vocabulary recommendation tools. Furthermore, we discuss challenges and opportunities of vocabulary recommendation and related tools in the context of emerging IoT ecosystems. Overall, 40 recommendation tools for linked vocabularies were evaluated, both empirically and experimentally. Some of the key findings include that (i) many tools neglect to thoroughly address both the curation of a vocabulary collection and effective selection mechanisms, (ii) modern information retrieval techniques are underrepresented, and (iii) the reviewed tools that emerged from Semantic Web use cases are not yet sufficiently extended to fit today’s IoT projects.
A Survey of On-Chip Optical Interconnects

Numerous challenges present themselves when scaling traditional on-chip electrical networks to large manycore processors. Some of these challenges include high latency, limitations on bandwidth, and power consumption. Researchers have therefore been looking for alternatives. As a result, on-chip nanophotonics has emerged as a strong substitute for traditional electrical NoCs. As of 2017, on-chip optical networks have moved out of textbooks and found commercial applicability in short-haul networks such as links between servers on the same rack or between two components on the motherboard. It is widely acknowledged that in the near future, optical technologies will move beyond research prototypes and find their way into the chip. Optical networks already feature in the roadmaps of major processor manufacturers and most on-chip optical devices are beginning to show signs of maturity. This article is designed to provide a survey of on-chip optical technologies covering the basic physics underlying the operation of optical technologies, optical devices, popular architectures, power reduction techniques, and applications. The aim of this survey article is to start from the fundamental concepts and move on to the latest in the field of on-chip optical interconnects.
Engagement in HCI

Engaging users is a priority for designers of products and services of every kind. The need to understand users’ experiences has motivated a focus on user engagement across computer science. However, to date, there has been limited review of how Human--Computer Interaction and computer science research interprets and employs the concept. Questions persist concerning its conception, abstraction, and measurement. This article presents a systematic review of engagement spanning a corpus of 351 articles and 102 definitions. We map the current state of engagement research, including the diverse interpretation, theory, and measurement of the concept. We describe the ecology of engagement and strategies for the design of engaging experiences, discuss the value of the concept and its relationship to other terms, and present a set of guidelines and opportunities for future research.
Precision Medicine for Relapsed Multiple Myeloma on the Basis of an Integrative Multiomics Approach

Purpose Multiple myeloma (MM) is a malignancy of plasma cells, with a median survival of 6 years. Despite recent therapeutic advancements, relapse remains mostly inevitable, and the disease is fatal in the majority of patients. A major challenge in the treatment of patients with relapsed MM is the timely identification of treatment options in a personalized manner. Current approaches in precision oncology aim at matching specific DNA mutations to drugs, but incorporation of genome-wide RNA profiles has not yet been clinically assessed. Methods We have developed a novel computational platform for precision medicine of relapsed and/or refractory MM on the basis of DNA and RNA sequencing. Our approach expands on the traditional DNA-based approaches by integrating somatic mutations and copy number alterations with RNA-based drug repurposing and pathway analysis. We tested our approach in a pilot precision medicine clinical trial with 64 patients with relapsed and/or refractory MM. Results We generated treatment recommendations in 63 of 64 patients. Twenty-six patients had treatment implemented, and 21 were assessable. Of these, 11 received a drug that was based on RNA findings, eight received a drug that was based on DNA, and two received a drug that was based on both RNA and DNA. Sixteen of the 21 evaluable patients had a clinical response (ie, reduction of disease marker ≥ 25%), giving a clinical benefit rate of 76% and an overall response rate of 66%, with five patients having ongoing responses at the end of the trial. The median duration of response was 131 days. Conclusion Our results show that a comprehensive sequencing approach can identify viable options in patients with relapsed and/or refractory myeloma, and they represent proof of principle of how RNA sequencing can contribute beyond DNA mutation analysis to the development of a reliable drug recommendation tool.
Getting the right fit: Convergence between preferred and perceived involvement in treatment decision making among medical oncology outpatients

While cancer patients' preferences for their level of involvement in treatment decision making (TDM) vary, previous research indicates a large proportion of patients are not experiencing TDM that meets their preferences. Evidence is needed to identify the characteristics of cancer patients who are less likely to report experiencing their preferred level of involvement in TDM, so that appropriate decision‐making support can be provided to them. We examined in a sample of medical oncology outpatients (1) the level of agreement between preferred and perceived involvement in TDM and (2) demographic, psychological, disease, and treatment characteristics associated with having unmet preferences for involvement in TDM.