Reviewerly/exHarmony huggingface dataset:
Impact of ImageNet Model Selection on Domain Adaptation

Deep neural networks are widely used in image classification problems. However, little work addresses how features from different deep neural networks affect the domain adaptation problem. Existing methods often extract deep features from one ImageNet model, without exploring other neural networks. In this paper, we investigate how different ImageNet models affect transfer accuracy on domain adaptation problems. We extract features from sixteen distinct pre-trained ImageNet models and examine the performance of twelve benchmarking methods when using the features. Extensive experimental results show that a higher accuracy ImageNet model produces better features, and leads to higher accuracy on domain adaptation problems (with a correlation coefficient of up to 0.95). We also examine the architecture of each neural network to find the best layer for feature extraction. Together, performance from our features exceeds that of the state-of-the-art in three benchmark datasets.
Refactoring effect on internal quality attributes: What haven’t they told you yet?

Code refactoring was conceived for enhancing code structures, often in terms of internal quality attributes such as cohesion and coupling. Developers may have to apply multiple refactoring operations to achieve the expected enhancement. Re-refactoring occurs whenever one or more refactoring operations are performed on a previously refactored code element. The literature often assumes each single refactoring improves rather than worsens internal quality attributes, while re-refactoring implies further improvements. Unfortunately, quantitative evidence on this matter is scarce if not nonexistent. This paper extends a large quantitative study about the refactoring effect on internal quality attributes with new insights, plus an unprecedented re-refactoring effect analysis. We particularly investigate if re-refactoring operations are more effective in improving attributes when compared to single operations. We analyzed 23 open software projects with 29,303 refactoring operations, from which nearly 50% constitute re-refactorings. We assessed five attributes: cohesion, complexity, coupling, inheritance, and size. We combined descriptive analysis and statistical tests to deeply understand the effect of both refactoring and re-refactoring on each attribute. Contrary to current knowledge, our study revealed that 90% of refactoring operations, and 100% of re-refactoring operations, were applied to code elements with at least one critical attribute. Critical attribute is an attribute whose metrics used for computing it have anomalous values, e.g. high coupling. Most operations (65%) improve attributes presumably associated with the refactoring type applied; the other operations (35%) keep those attributes unaffected. Whenever refactoring and re-refactoring operations are applied without additional changes, i.e., root-canal refactoring, attributes tend to improve or at least not worsen. Surprisingly, if these operations occur with additional changes such as feature additions, i.e., floss refactoring, they mostly improve rather than worsen attributes. Besides revealing the effect of refactoring and re-refactoring on each attribute, we derived insights on leveraging the current refactoring practices.
Machine Learning Based Computer Aided Diagnosis of Breast Cancer Utilizing Anthropometric and Clinical Features

Breast cancer is one of the most prevalent types of cancers in females, which has become rampant all over the world in recent years. The survival rate of breast cancer patients degrades considerably for patients diagnosed at an advanced stage compared to those diagnosed at an early stage. The objective of this study is two folds. The first one is to find the most relevant biomarkers of breast cancer, which can be attained from regular blood analysis and anthropometric measurements. The other one is to improve the performance of current computer-aided diagnosis (CAD) system of early breast cancer detection. This study utilized a recent data set containing nine anthropometric and clinical attributes. In our methodology, first, we performed multicollinearity analysis and ranked the features based on the weighted average score obtained from four filter-based feature evaluation methods such as F-score, information gain, chi-square statistic, and Minimum Redundancy Maximum Relevance. Next, to improve the separability of the target classes, we scaled and weighted the dataset using min-max normalization and similarity-based attribute weighting by the k-means clustering algorithm, respectively. Finally, we trained standard machine learning (ML) models and evaluated the performance metrics by 10-fold cross-validation method. Our support vector machine (SVM) model with radial basis function (RBF) kernel appeared to be the most successful classifier by utilizing six features, namely, Body Mass Index (BMI), Age, Glucose, MCP-1, Resistin, and Insulin. The obtained classification accuracy, sensitivity, and specificity are 93.9% (95% CI: 93.2–94.6%), 95.1% (95% CI: 94.4–95.8%), and 94.0% (95% CI: 93.3–94.7%), respectively; these performance metrics outperformed state-of-the-art methods reported in the literature. The developed model could potentially assist the medical experts for the early diagnosis of breast cancer by employing a set of attributes that can be easily obtained from regular blood analysis and anthropometric measurements.
Adaptive ROI generation for video object segmentation using reinforcement learning

The task of the proposed method is semi-supervised video object segmentation where only the ground-truth segmentation of the first frame is provided. The existing approaches rely on selecting the region of interest for model update; however it is rough and inflexible, leading to performance degradation. To overcome this limitation, a novel approach is proposed which utilizes reinforcement learning to select optimal adaptation areas for each frame, based on the historical segmentation information. The RL model learns to take optimal actions to adjust the region of interest inferred from the previous frame for online model updating. To speed up the model adaption, a novel multi-branch tree based exploration method is designed to quickly select the best state action pairs. The proposed method is evaluated on three common video object segmentation datasets including DAVIS 2016, SegTrack V2 and Youtube-Object. The results show that the proposed work improves the state-of-the-art of the mean region similarity to 87.1% on the DAVIS 2016 dataset, and to 79.5% on the Youtube-Object dataset. Meanwhile, competitive performance is obtained on the SegTrack V2 dataset. Code is at https://github.com/insomnia94/ARG.
The impacts of foreign direct investment and globalisation on economic growth in West Africa: examining the role of political governance

Purpose This paper measures the impacts of foreign direct investment (FDI), globalisation and political governance on economic growth in West Africa. The empirical analysis also includes the interaction effect of political governance and FDI on the growth of the sub-region, over the period of 1996–2016. Design/methodology/approach The study employs the autoregressive distributed lag technique on data obtained from the World Bank and the KOF institute. Findings The study findings suggest a positive relationship between globalisation and political governance on economic growth. Even though there have been inconclusive results on the FDI–growth nexus, the authors found that FDI stimulates the growth of the sub-region, while political governance enhances the positive impact of FDI on economic growth. The other factors of growth included are labour, capital and government size, whose effects on growth are, respectively, negative, negative and positive. Practical implications The governments of the West African countries promote policies that attract FDI into the sub-region, so that economic performances may be enhanced. In addition, the governments of the West African sub-region should work to reap the benefits of globalisation, by promoting the competitiveness of their local economies in order to keep pace with the global markets. Finally, the political-governance infrastructures should be overhauled; the culture of accountability and transparency should be promoted, while all efforts should be made to improve stability in the political environment in order to increase investors' confidence in the West African economy. Originality/value This study is the first to single out the impacts of political governance, as categorised by the World Bank, through both direct and interactive measures. This is necessary in view of the assertion that political governance largely accounts for improved economic performance in an economy. The use of the Pesaran (2007) technique of unit root is also a deviation from existing studies. This is in view of the fact that it tests variable unit root in the presence of cross-sectional dependence; thus, controlling for contemporaneous correlation which was not considered in the first-generation tests.
Introducing Peripheral Awareness as a Neurological State for Human-computer Integration

In this work we introduce peripheral awareness as a neurological state for real-time human-computer integration, where the human is assisted by a computer to interact with the world. Changes to the field of view in peripheral awareness have been linked with quality of human performance. This instinctive narrowing of vision that occurs as a threat is perceived has implications in activities that benefit from the user having a wide field of view, such as cycling to navigate the environment. We present "Ena", a novel EEG-eBike system that draws from the user's neural activity to determine when the user is in a state of peripheral awareness to regulate engine support. A study with 20 participants revealed various themes and tactics suggesting that peripheral awareness as a neurological state is viable to align human-machine integration with internal bodily processes. Ena suggests that our work facilitates a safe and enjoyable human-computer integration experience.
A cloud resource management framework for multiple online scientific workflows using cooperative reinforcement learning agents

Cloud is a common distributed environment to share strong and available resources to increase the efficiency of complex and heavy calculations. In return for the cost paid by cloud users, a variety of services have been provided for them, the quality of which has been guaranteed and the reliability of their corresponding resources have been supplied by cloud service providers. Due to the heterogeneity of resources and their several shared applications, efficient scheduling can increase the productivity of cloud resources. This will reduce users’ costs and energy consumption, considering the quality of service provided for them. Cloud resource management can be conducted to obtain several objectives. Reducing user costs, reducing energy consumption, load balancing of resources, enhancing utilization of resources, and improving availability and security are some of the key objectives in this area. Several methods have been proposed for cloud resource management, most of which are focused on one or more aspects of these objectives of cloud computing. This paper introduces a new framework consisting of multiple cooperative agents, in which, all phases of the task scheduling and resource provisioning is considered and the quality of service provided to the user is controlled. The proposed integrated model provides all task scheduling and resource provisioning processes, and its various parts serve the management of user applications and more efficient use of cloud resources. This framework works well on dependent simultaneous tasks, which have a complicated process of scheduling because of the dependence of its sub-tasks. The results of the experiments show the better performance of the proposed model in comparison with other cloud resource management methods.
Experiential Qualities of Whispering with Voice Assistants

We present a Research through Design project that explores how whispering influences the ways people experience and interact with voice assistants. The research project includes a co-speculation workshop and the use of a design probe, which culminated in the production of a design fiction short film. Our design-led inquiry contributes with experiential qualities of whispering with voice assistants: creepiness, trust, and intimacy. Furthermore, we present how whispering opens up new dimensions of how and when voice interaction could be used. We propose that designers of whispering voice assistants should reflect on how they facilitate the experiential qualities of creepiness, trust, and intimacy, and reflect on the potential challenges whispering brings to the relation between a user and a voice assistant.
Cheat Sheets for Data Visualization Techniques

This paper introduces the concept of 'cheat sheets' for data visualization techniques, a set of concise graphical explanations and textual annotations inspired by infographics, data comics, and cheat sheets in other domains. Cheat sheets aim to address the increasing need for accessible material that supports a wide audience in understanding data visualization techniques, their use, their fallacies and so forth. We have carried out an iterative design process with practitioners, teachers and students of data science and visualization, resulting six types of cheat sheet (anatomy, construction, visual patterns, pitfalls, false-friends and well-known relatives) for six types of visualization, and formats for presentation. We assess these with a qualitative user study using 11 participants that demonstrates the readability and usefulness of our cheat sheets.
Sprayable User Interfaces: Prototyping Large-Scale Interactive Surfaces with Sensors and Displays

We present Sprayable User Interfaces: room-sized interactive surfaces that contain sensor and display elements created by airbrushing functional inks. Since airbrushing is inherently mobile, designers can create large-scale user interfaces on complex 3D geometries where existing stationary fabrication methods fail. To enable Sprayable User Interfaces, we developed a novel design and fabrication pipeline that takes a desired user interface layout as input and automatically generates stencils for airbrushing the layout onto a physical surface. After fabricating stencils from cardboard or projecting stencils digitally, designers spray each layer with an airbrush, attach a microcontroller to the user interface, and the interface is ready to be used. Our technical evaluation shows that Sprayable User Interfaces work on various geometries and surface materials, such as porous stone and rough wood. We demonstrate our system with several application examples including interactive smart home applications on a wall and a soft leather sofa, an interactive smart city application, and interactive architecture in public office spaces.
GPU-Accelerated Subgraph Enumeration on Partitioned Graphs

Subgraph enumeration is important for many applications such as network motif discovery and community detection. Recent works utilize graphics processing units (GPUs) to parallelize subgraph enumeration, but they can only handle graphs that fit into the GPU memory. In this paper, we propose a new approach for GPU-accelerated subgraph enumeration that can efficiently scale to large graphs beyond the GPU memory. Our approach divides the graph into partitions, each of which fits into the GPU memory. The GPU processes one partition at a time and searches the matched subgraphs of a given pattern (i.e., instances) within the partition as in the small graph. The key challenge is on enumerating the instances across different partitions, because this search would enumerate considerably redundant subgraphs and cause the expensive data transfer cost via the PCI-e bus. Therefore, we propose a novel shared execution approach to eliminate the redundant subgraph searches and correctly generate all the instances across different partitions. The experimental evaluation shows that our approach can scale to large graphs and achieve significantly better performance than the existing single-machine solutions.
Storywell: Designing for Family Fitness App Motivation by Using Social Rewards and Reflection

Physical activity (PA) is critical for reducing the risk of obesity, a prevalent health concern that burdens low-socioeconomic status (SES) households. While self-tracking apps can increase PA, encouraging app engagement remains a challenge, thus limiting the app's efficacy. To understand how to better support caregiver's motivation to use family health apps, we designed and evaluated Storywell?a mobile app for promoting family PA. Guided by Self-Determination Theory, Storywell provides social rewards (e.g., storybooks with interactive reflective questions) aimed at supporting relatedness and motivation. Our 3-month qualitative study with 18 families revealed satisfying moments that can affect caregiver's motivation. We contribute new knowledge on designing satisfying moments that heighten the motivation to use health apps, especially for low-SES families who face many barriers to using such systems.
Group Recommendation with Latent Voting Mechanism

Group Recommendation (GR) is the task of suggesting relevant items/events for a group of users in online systems, whose major challenge is to aggregate the preferences of group members to infer the decision of a group. Prior group recommendation methods applied predefined static strategies for preference aggregation. However, these static strategies are insufficient to model the complicated decision making process of a group, especially for occasional groups which are formed adhoc. Compared to conventional individual recommendation task, GR is rather dynamic and each group member may contribute differently to the final group decision. Recent works argue that group members should have non-uniform weights in forming the decision of a group, and try to utilize a standard attention mechanism to aggregate the preferences of group members, but they do not model the interaction behavior among group members, and the decision making process is largely unexplored.In this work, we study GR in a more general scenario, that is Occasional Group Recommendation (OGR), and focus on solving the preference aggregation problem and the data sparsity issue of group-item interactions. Instead of exploring new heuristic or vanilla attention-based mechanism, we propose a new social self-attention based aggregation strategy by directly modeling the interactions among group members, namely Group Self-Attention (GroupSA). In GroupSA, we treat the group decision making process as multiple voting processes, and develop a stacked social self-attention network to simulate how a group consensus is reached. To overcome the data sparsity issue, we resort to the relatively abundant user-item and user-user interaction data, and enhance the representation of users by two types of aggregation methods. In the training process, we further propose a joint training method to learn the user/item embeddings in the group-item recommendation task and the user-item recommendation task simultaneously. Finally, we conduct extensive experiments on two real-world datasets. The experimental results demonstrate the superiority of our proposed GroupSA method compared to several state-of-the-art methods in terms of HR and NDCG.
Adapting Deep Visuomotor Representations with Weak Pairwise Constraints

Real-world robotics problems often occur in domains that differ significantly from the robot's prior training environment. For many robotic control tasks, real world experience is expensive to obtain, but data is easy to collect in either an instrumented environment or in simulation. We propose a novel domain adaptation approach for robot perception that adapts visual representations learned on a large easy-to-obtain source dataset (e.g. synthetic images) to a target real-world domain, without requiring expensive manual data annotation of real world data before policy search. Supervised domain adaptation methods minimize cross-domain differences using pairs of aligned images that contain the same object or scene in both the source and target domains, thus learning a domain-invariant representation. However, they require manual alignment of such image pairs. Fully unsupervised adaptation methods rely on minimizing the discrepancy between the feature distributions across domains. We propose a novel, more powerful combination of both distribution and pairwise image alignment, and remove the requirement for expensive annotation by using weakly aligned pairs of images in the source and target domains. Focusing on adapting from simulation to real world data using a PR2 robot, we evaluate our approach on a manipulation task and show that by using weakly paired images, our method compensates for domain shift more effectively than previous techniques, enabling better robot performance in the real world.
Private Aggregation from Fewer Anonymous Messages

Consider the setup where n parties are each given an element $$x_i$$ in the finite field $$\mathbb {F}_q$$ and the goal is to compute the sum $$\sum _i x_i$$ in a secure fashion and with as little communication as possible. We study this problem in the anonymized model of Ishai et al. (FOCS 2006) where each party may broadcast anonymous messages on an insecure channel. We present a new analysis of the one-round "split and mix" protocol of Ishai et al. In order to achieve the same security parameter, our analysis reduces the required number of messages by a $$\varTheta (\log n)$$ multiplicative factor. We also prove lower bounds showing that the dependence of the number of messages on the domain size, the number of parties, and the security parameter is essentially tight. Using a reduction of Balle et al. (2019), our improved analysis of the protocol of Ishai et al. yields, in the same model, an $$\left( \varepsilon , \delta \right) $$ -differentially private protocol for aggregation that, for any constant $$\varepsilon > 0$$ and any $$\delta = \frac{1}{\text {poly}(n)}$$ , incurs only a constant error and requires only a constant number of messages per party. Previously, such a protocol was known only for $$\varOmega (\log n)$$ messages per party.
Providing Input-Discriminative Protection for Local Differential Privacy

Local Differential Privacy (LDP) provides provable privacy protection for data collection without the assumption of the trusted data server. In the real-world scenario, different data have different privacy requirements due to the distinct sensitivity levels. However, LDP provides the same protection for all data. In this paper, we tackle the challenge of providing input-discriminative protection to reflect the distinct privacy requirements of different inputs. We first present the Input- Discriminative LDP (ID-LDP) privacy notion and focus on a specific version termed MinID-LDP, which is shown to be a fine-grained version of LDP. Then, we focus on the application of frequency estimation and develop the IDUE mechanism based on Unary Encoding for single-item input and the extended mechanism IDUE-PS (with Padding-and-Sampling protocol) for item-set input. The results on both synthetic and real-world datasets validate the correctness of our theoretical analysis and show that the proposed mechanisms satisfying MinID-LDP have better utility than the state-of-the-art mechanisms satisfying LDP due to the input-discriminative protection.