Miracle-dz/UniFAR-FTUs huggingface dataset:
In-Store Augmented Reality-Enabled Product Comparison and Recommendation

We present an approach combining the AR-based presentation of product attributes in a physical retail store with recommendations for items only available online. The system supports users’ decision-making process by offering functions for comparing product features between items, both physical and online, and by providing recommendations based on selecting in-store products. The physical products may thus serve as anchors for forming the user’s preferences, also offering a richer and more engaging experience when exploring the products hands-on. Both objective product attributes as well as the visual appearance of a physical product are employed for generating recommendations from the online space. In this way, the advantages of online and in-store shopping can be combined, creating novel multi-channel opportunities for businesses. An empirical evaluation showed that the comparison and recommendation functions were appreciated by users, and hinted some possible benefits of a hybrid physical-online shopping support system. Despite the limitations of the study, there is sufficient evidence to consider this a viable approach worth to be further explored.
Using a Context-Aware Approach to Recommend Code Reviewers: Findings from an Industrial Case Study

Code review is a commonly used practice in software development. It refers to the process of reviewing new code changes before they are merged with the code base. However, to perform the review, developers are mostly assigned manually to code changes. This may lead to problems such as: a time-consuming selection process, limited pool of known candidates and risk of over-allocation of a few reviewers. To address the above problems, we developed Carrot, a machine learning-based tool to recommend code reviewers. We conducted an improvement case study at Ericsson. We evaluated Carrot using a mixed approach. we evaluated the prediction accuracy using historical data and the metrical Mean Reciprocal Rank (MRR). Furthermore, we deployed the tool in one Ericsson project and evaluated how adequate the recommendations were from the point of view of the tool users and the recommended reviewers. We also asked the opinion of senior developers about the usefulness of the tool. The results show that Carrot can help identify relevant non-obvious reviewers and be of great assistance to new developers. However, there were mixed opinions on Carrot’s ability to assist with workload balancing and the decrease code review lead time.
Ultrahapticons: “Haptifying” Drivers’ Mental Models to Transform Automotive Mid-Air Haptic Gesture Infotainment Interfaces

In-vehicle gesture interfaces show potential to reduce visual demand and improve task performance when supported with mid-air, ultrasound-haptic feedback. However, comparative studies have tended to select gestures and haptic sensations based either on experimental convenience or to conform with existing interfaces, and thus may have fallen short on realising their full potential. Aiming to design and validate an exemplar set of ultrasonic, mid-air haptic icons (“ultrahapticons”), a participatory design exercise was conducted, whereby seventeen participants were presented with seven in-vehicle infotainment tasks. Participants were asked to describe their mental models for each, and then sketch these visual, tactual and auditory associations. ‘Haptifiable’ elements were extracted, and these were analysed using semiotics principles, resulting in thirty ultrahapticon concepts. These were subsequently evaluated and further refined in a workshop involving user experience and haptics experts. The final seventeen concepts will be validated in a salience recognition and perspicuity study.
An algorithm for knowledge extraction

An algorithm with an embedded genetic algorithm is presented in this paper, for attribute reduction and knowledge extraction. Due to the high dimension of text data, the classification precision is applied as the threshold of an evaluation index for the iterative process of the algorithm, and is also utilized as the fitness function of the embedded genetic algorithm. Further, the rules from the reduced attribute set generated by the algorithm are given. Finally, the validity and accuracy of the algorithm are verified on the data set.
PURS: Personalized Unexpected Recommender System for Improving User Satisfaction

Classical recommender system methods typically face the filter bubble problem when users only receive recommendations of their familiar items, making them bored and dissatisfied. To address the filter bubble problem, unexpected recommendations have been proposed to recommend items significantly deviating from user’s prior expectations and thus surprising them by presenting ”fresh” and previously unexplored items to the users. In this paper, we describe a novel Personalized Unexpected Recommender System (PURS) model that incorporates unexpectedness into the recommendation process by providing multi-cluster modeling of user interests in the latent space and personalized unexpectedness via the self-attention mechanism and via selection of an appropriate unexpected activation function. Extensive offline experiments on three real-world datasets illustrate that the proposed PURS model significantly outperforms the state-of-the-art baseline approaches in terms of both accuracy and unexpectedness measures. In addition, we conduct an online A/B test at a major video platform Alibaba-Youku, where our model achieves over 3% increase in the average video view per user metric. The proposed model is in the process of being deployed by the company.
Heimdall: mobile GPU coordination platform for augmented reality applications

We present Heimdall, a mobile GPU coordination platform for emerging Augmented Reality (AR) applications. Future AR apps impose an explored challenging workload: i) concurrent execution of multiple Deep Neural Networks (DNNs) for physical world and user behavior analysis, and ii) seamless rendering in presence of the DNN execution for immersive user experience. Existing mobile deep learning frameworks, however, fail to support such workload: multi-DNN GPU contention slows down inference latency (e.g., from 59.93 to 1181 ms), and rendering-DNN GPU contention degrades frame rate (e.g., from 30 to ≈12 fps). Multi-tasking for desktop GPUs (e.g., parallelization, preemption) cannot be applied to mobile GPUs as well due to limited architectural support and memory bandwidth. To tackle the challenge, we design a Pseudo-Preemption mechanism which i) breaks down the bulky DNN into smaller units, and ii) prioritizes and flexibly schedules concurrent GPU tasks. We prototyped Heimdall over various mobile GPUs (i.e., recent Adreno series) and multiple AR app scenarios that involve combinations of 8 state-of-the-art DNNs. Our extensive evaluation shows that Heimdall enhances the frame rate from ≈12 to ≈30 fps while reducing the worst-case DNN inference latency by up to ≈15 times compared to the baseline multi-threading approach.
Property-based Testing for LG Home Appliances using Accelerated Software-in-the-Loop Simulation

As LG home appliances promise more convenience features to end-users, the complexity of their control software is also increasing, creating a higher pressure for software verification. However, since the embedded software is tightly coupled with its hardware counterpart, the development and verification schedules are dependent upon hardware development and this hinders integration testing to be performed as thoroughly as it deserves. Furthermore, the manually-crafted test cases have had limitations, both in terms of the thoroughness of state-space exploration and the power of test oracles.To overcome these problems and facilitate a more efficient software verification, we introduce a property-based testing framework using software-in-the-loop simulation (SILS). SILS allows the software to be integrated virtually and tested before the hardware is fully developed, and, further, it enables an acceleration in test executions of up to a few tens of thousand times. Property-based testing is achieved by translating the formalized properties to synchronous observers which can concurrently check the violation of the verification property during test executions. In the field application, we discovered two fault cases in real products under development using our framework. According to our analysis, these cases could not have been found using manual testing, but made possible by our testing framework. These cases could have cost the company tens of million dollars each, if they were not discovered until after sale.CCS CONCEPTS• Computer systems organization → Embedded software; • Software and its engineering → Software testing and debugging; Extra-functional properties.
Contextual and Sequential User Embeddings for Large-Scale Music Recommendation

Recommender systems play an important role in providing an engaging experience on online music streaming services. However, the musical domain presents distinctive challenges to recommender systems: tracks are short, listened to multiple times, typically consumed in sessions with other tracks, and relevance is highly context-dependent. In this paper, we argue that modeling users’ preferences at the beginning of a session is a practical and effective way to address these challenges. Using a dataset from Spotify, a popular music streaming service, we observe that a) consumption from the recent past and b) session-level contextual variables (such as the time of the day or the type of device used) are indeed predictive of the tracks a user will stream—much more so than static, average preferences. Driven by these findings, we propose CoSeRNN, a neural network architecture that models users’ preferences as a sequence of embeddings, one for each session. CoSeRNN predicts, at the beginning of a session, a preference vector, based on past consumption history and current context. This preference vector can then be used in downstream tasks to generate contextually relevant just-in-time recommendations efficiently, by using approximate nearest-neighbour search algorithms. We evaluate CoSeRNN on session and track ranking tasks, and find that it outperforms the current state of the art by upwards of 10% on different ranking metrics. Dissecting the performance of our approach, we find that sequential and contextual information are both crucial.
Internet-of-microchips: direct radio-to-bus communication with SPI backscatter

Energy consumption of Internet-of-Things end devices is a major constraint that limits their long-term and large-scale deployment. Conventionally, the radios and processors used in these end devices are major power consumption that drains at the level of milliwatts (mWs). However, in recent decades, backscatter communication has dramatically reduced the power consumed by the radios in end devices to microwatts (μWs), and thus the processor remains the major bottleneck for energy optimization. In this paper, we propose a processor-free architecture as a novel design that allows the radio to interface directly with peripheral sensor chips for control and data collection, thereby separating the processors from the end device design to significantly reduce the energy consumed by end devices. The main problem is that the peripheral chips are designed to be accessed by the processor via a standard digital bus and they cannot communicate directly with the radio. In order to support such processor-free design, we propose radio-to-bus (R2B) as a novel communication paradigm that allows direct data exchange between a backscatter radio and the serial peripheral interface (SPI) bus. We implement the processor-free architecture in proof-of-concept prototypes and demonstrate that the power consumption decreases by 4.5 times compared with the conventional end device design.
A Method to Anonymize Business Metrics to Publishing Implicit Feedback Datasets

This paper shows a method for building and publishing datasets in commercial services. Datasets contribute to the development of research in machine learning and recommender systems. In particular, because recommender systems play a central role in many commercial services, publishing datasets from the services are in great demand from the recommender system community. However, the publication of datasets by commercial services may have some business risks to those companies. To publish a dataset, this must be approved by a business manager of the service. Because many business managers are not specialists in machine learning or recommender systems, the researchers are responsible for explaining to them the risks and benefits. We first summarize three challenges in building datasets from commercial services: (1) anonymize the business metrics, (2) maintain fairness, and (3) reduce the popularity bias. Then, we formulate the problem of building and publishing datasets as an optimization problem that seeks the sampling weight of users, where the challenges are encoded as appropriate loss functions. We applied our method to build datasets from the raw data of our real-world mobile news delivery service. The raw data has more than 1,000,000 users with 100,000,000 interactions. Each dataset was built in less than 10 minutes. We discussed the properties of our method by checking the statistics of the datasets and the performances of typical recommender system algorithms.
Efficient reordering and replay of execution traces of distributed reactive systems in the context of model-driven development

Ordering and replaying of execution traces of distributed systems is a challenging problem. State-of-the-art approaches annotate the traces with logical or physical timestamps. However, both kinds of timestamps have their drawbacks, including increased trace size. We examine the problem of determining consistent orderings of execution traces in the context of model-driven development of reactive distributed systems, that is, systems whose code has been generated from communicating state machine models. By leveraging key concepts of state machines and existing model analysis and transformation techniques, we propose an approach to collecting and reordering execution traces that does not rely on timestamps. We describe a prototype implementation of our approach and an evaluation. The experimental results show that compared to reordering based on logical timestamps using vector time (clocks), our approach reduces the size of the trace information collected by more than half while incurring similar runtime overhead.
The Forgotten Case of the Dependency Bugs : On the Example of the Robot Operating System

A dependency bug is a software fault that manifests itself when accessing an unavailable asset. Dependency bugs are pervasive and we all hate them. This paper presents a case study of dependency bugs in the Robot Operating System (ROS), applying mixed methods: a qualitative investigation of 78 dependency bug reports, a quantitative analysis of 1354 ROS bug reports against 19553 reports in the top 30 GitHub projects, and a design of three dependency linters evaluated on 406 ROS packages.The paper presents a definition and a taxonomy of dependency bugs extracted from data. It describes multiple facets of these bugs and estimates that as many as 15% (! ) of all reported bugs are dependency bugs. We show that lightweight tools can find dependency bugs efficiently, although it is challenging to decide which tools to build and difficult to build general tools. We present the research problem to the community, and posit that it should be feasible to eradicate it from software development practice.
Applying Deep Learning to Reduce Large Adaptation Spaces of Self-Adaptive Systems with Multiple Types of Goals

When a self-adaptive system needs to adapt, it has to analyze the possible options for adaptation, i.e., the adaptation space. For systems with large adaptation spaces, this analysis process can be resource- and time-consuming. One approach to tackle this problem is using machine learning techniques to reduce the adaptation space to only the relevant adaptation options. However, existing approaches only handle threshold goals, while practical systems often need to address also optimization goals. To tackle this limitation, we propose a two-stage learning approach called Deep Learning for Adaptation Space Reduction (DLASeR). DLASeR applies a deep learner first to reduce the adaptation space for the threshold goals and then ranks these options for the optimization goal. A benefit of deep learning is that it does not require feature engineering. Results on two instances of the DeltaIoT artifact (with different sizes of adaptation space) show that DLASeR outperforms a state-of-the-art approach for settings with only threshold goals. The results for settings with both threshold goals and an optimization goal show that DLASeR is effective with a negligible effect on the realization of the adaptation goals. Finally, we observe no noteworthy effect on the effectiveness of DLASeR for larger sizes of adaptation spaces.
A model-based approach for developing event-driven architectures with AsyncAPI

In this Internet of Things (IoT) era, our everyday objects have evolved into the so-called cyber-physical systems (CPS). The use and deployment of CPS has especially penetrated the industry, giving rise to the Industry 4.0 or Industrial IoT (IIoT). Typically, architectures in IIoT environments are distributed and asynchronous, communication being guided by events such as the publication of (and corresponding subscription to) messages. While these architectures have some clear advantages (such as scalability and flexibility), they also raise interoperability challenges among the agents in the network. Indeed, the knowledge about the message content and its categorization (topics) gets diluted, leading to consistency problems, potential losses of information and complex processing requirements on the subscriber side to try to understand the received messages. In this paper, we present our proposal relying on AsyncAPI to automate the design and implementation of these architectures using model-based techniques for the generation of (part of) event-driven infrastructures. We have implemented our proposal as an open-source tool freely available online.
Exploiting Performance Estimates for Augmenting Recommendation Ensembles

Ensembling multiple recommender systems via stacking has shown to be effective at improving collaborative recommendation. Recent work extends stacking to use additional user performance predictors (e.g., the total number of ratings made by the user) to help determine how much each base recommender should contribute to the ensemble. Nonetheless, despite the cost of handcrafting discriminative predictors, which typically requires deep knowledge of the strengths and weaknesses of each recommender in the ensemble, only minor improvements have been observed. To overcome this limitation, instead of engineering complex features to predict the performance of different recommenders for a given user, we propose to directly estimate these performances by leveraging the user’s own historical ratings. Experiments on real-world datasets from multiple domains demonstrate that using performance estimates as additional features can significantly improve the accuracy of state-of-the-art ensemblers, achieving nDCG@20 improvements by an average of 23% over not using them.
Predicting responses in multiple environments : Issues in relation to genotype × Environment interactions

Prediction of the phenotypes for a set of genotypes across multiple environments is a fundamental task in any plant breeding program. Genomic prediction (GP) can assist selection decisions by combining incomplete phenotypic information over multiple environments (MEs) with dense sets of markers. We compared a range of ME-GP models differing in the way environment-specific genetic effects were modeled. Information among environments was shared either implicitly via the response variable, or by the introduction of explicit environmental covariables. We discuss the models not only in the light of their accuracy, but also in their ability to predict the different parts of the incomplete genotype × environment interaction (G × E) table: (G t ; E t ), (G u ; E t ), (G t ; E u ), and (G u ; E u ), where G is genotype, E is environment, both tested (t; in one or more instances) and untested (u). Using the ‘Steptoe’ × ‘Morex’ barley (Hordeum vulgare L.) population as an example, we show the advantage of ME-GP models that account for G × E. In addition, for our example data set, we show that for prediction in the most challenging scenario of untested environments (E u ), the use of explicit environmental information is preferable over the simpler approach of predicting from a main effects model. Besides producing the most general ME-GP model, the use of environmental covariables naturally links with ecophysiological and crop-growth models (CGMs) for G × E. We conclude with a list of future research topics in ME-GP, where we see CGMs playing a central role.
Towards an Open Repository for Teaching Software Modeling applying Active Learning Strategies

Modeling is a core topic in Software Engineering Education. Nevertheless, students face difficulties while learning software modeling. To teach software modeling effectively in computing courses, instructors who usually employed traditional methods could use active learning strategies. However, instructors are reluctant to change their teaching approaches due to several barriers that hinder the application of active learning strategies. Besides, relatively little research addresses how to mitigate them. The objective of this research is to help instructors implementing active learning strategies when teaching software modeling with UML diagrams. To achieve this objective, we conducted a Design Science Research (DSR). We proposed an artifact called OpenSMALS, an Open Repository for teaching Software Modeling applying Active Learning Strategies. OpenSMALS provides specific guidelines on how instructors can apply these strategies and helping instructors to identify the active learning strategies best suit their teaching context. We performed four DSR Design Cycles-in four different universities-to evaluate and evolve OpenSMALS. Our results show that OpenSMALS satisfactorily reduced the barriers faced by instructors, and it achieved an appropriate maturity level to be adopted by other instructors.
Debiasing Item-to-Item Recommendations With Small Annotated Datasets

Item-to-item recommendation (e.g., “People who like this also like.”) is a ubiquitous and important type of recommendation in real-world systems. Observational data from historical interaction logs abound in these settings. However, since virtually all observational data exhibit biases, such as time-in-inventory or interface biases, it is crucial that recommender algorithms account for these biases. In this paper, we develop a principled approach for item-to-item recommendation based on causal inference and present a practical and highly effective method for estimating the causal parameters from a small annotated dataset. Empirically, we find that our approach substantially improves upon existing methods while requiring only small amounts of annotated data.