Reviewerly/exHarmony huggingface dataset:
Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark

Abstract The choice of the most appropriate unsupervised machine-learning method for “heterogeneous” or “mixed” data, i.e. with both continuous and categorical variables, can be challenging. Our aim was to examine the performance of various clustering strategies for mixed data using both simulated and real-life data. We conducted a benchmark analysis of “ready-to-use” tools in R comparing 4 model-based (Kamila algorithm, Latent Class Analysis, Latent Class Model [LCM] and Clustering by Mixture Modeling) and 5 distance/dissimilarity-based (Gower distance or Unsupervised Extra Trees dissimilarity followed by hierarchical clustering or Partitioning Around Medoids, K-prototypes) clustering methods. Clustering performances were assessed by Adjusted Rand Index (ARI) on 1000 generated virtual populations consisting of mixed variables using 7 scenarios with varying population sizes, number of clusters, number of continuous and categorical variables, proportions of relevant (non-noisy) variables and degree of variable relevance (low, mild, high). Clustering methods were then applied on the EPHESUS randomized clinical trial data (a heart failure trial evaluating the effect of eplerenone) allowing to illustrate the differences between different clustering techniques. The simulations revealed the dominance of K-prototypes, Kamila and LCM models over all other methods. Overall, methods using dissimilarity matrices in classical algorithms such as Partitioning Around Medoids and Hierarchical Clustering had a lower ARI compared to model-based methods in all scenarios. When applying clustering methods to a real-life clinical dataset, LCM showed promising results with regard to differences in (1) clinical profiles across clusters, (2) prognostic performance (highest C-index) and (3) identification of patient subgroups with substantial treatment benefit. The present findings suggest key differences in clustering performance between the tested algorithms (limited to tools readily available in R). In most of the tested scenarios, model-based methods (in particular the Kamila and LCM packages) and K-prototypes typically performed best in the setting of heterogeneous data.
Multiview Subspace Clustering via Co-Training Robust Data Representation

Taking the assumption that data samples are able to be reconstructed with the dictionary formed by themselves, recent multiview subspace clustering (MSC) algorithms aim to find a consensus reconstruction matrix via exploring complementary information across multiple views. Most of them directly operate on the original data observations without preprocessing, while others operate on the corresponding kernel matrices. However, they both ignore that the collected features may be designed arbitrarily and hard guaranteed to be independent and nonoverlapping. As a result, original data observations and kernel matrices would contain a large number of redundant details. To address this issue, we propose an MSC algorithm that groups samples and removes data redundancy concurrently. In specific, eigendecomposition is employed to obtain the robust data representation of low redundancy for later clustering. By utilizing the two processes into a unified model, clustering results will guide eigendecomposition to generate more discriminative data representation, which, as feedback, helps obtain better clustering results. In addition, an alternate and convergent algorithm is designed to solve the optimization problem. Extensive experiments are conducted on eight benchmarks, and the proposed algorithm outperforms comparative ones in recent literature by a large margin, verifying its superiority. At the same time, its effectiveness, computational efficiency, and robustness to noise are validated experimentally.
Hybrid-attention guided network with multiple resolution features for person re-identification

Extracting effective and discriminative features is highly important for addressing the challenges of person re-identification (re-ID). At present, deep convolutional neural networks typically use high-level features to identify pedestrians. However, some essential spatial information contained in low-level features will be lost during the learning of high-level feature. Most existing person re-ID methods rely primarily on hand-crafted bounding boxes, in which person images are precisely aligned. However, these approaches are unrealistic in practical applications due to the inaccuracy of automatic detection algorithms. To address these problems, we propose a hybrid-attention guided network with multiple resolution features for person re-ID. First, we construct a multi-resolution fusion strategy to ensure that multi-resolution features can be spatially aligned during feature fusion, while at the same time ensuring the discriminability of features after fusion. We then introduce an attention mechanism and multi-granularity operation to reduce the impact of irregular bounding boxes by gently expanding the size of feature maps. In addition, a new multi-pool feature extractor is designed to obtain different types of information by using two different pools, enabling the feature representation capability to be further improved. Extensive experiments demonstrate the superiority of our approach. Our code is available at https://github.com/libraflower/MutipleFeature-for-PRID.
A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction

The adoption of deep neural networks (DNNs) in safety-critical domains has engendered serious reliability concerns. A prominent example is hardware transient faults that are growing in frequency due to the progressive technology scaling, and can lead to failures in DNNs. This work proposes Ranger, a low-cost fault corrector, which directly rectifies the faulty output due to transient faults without re-computation. DNNs are inherently resilient to benign faults (which will not cause output corruption), but not to critical faults (which can result in erroneous output). Ranger is an automated transformation to selectively restrict the value ranges in DNNs, which reduces the large deviations caused by critical faults and transforms them to benign faults that can be tolerated by the inherent resilience of the DNNs. Our evaluation on 8 DNNs demonstrates Ranger significantly increases the error resilience of the DNNs (by 3x to 50x), with no loss in accuracy, and with negligible overheads.
Hierarchical Interpretable Imitation Learning for End-to-End Autonomous Driving

End-to-end autonomous driving provides a simple and efficient framework for autonomous driving systems, which can directly obtain control commands from raw perception data. However, it fails to address stability and interpretability problems in complex urban scenarios. In this paper, we construct a two-stage end-to-end autonomous driving model for complex urban scenarios, named HIIL (Hierarchical Interpretable Imitation Learning), which integrates interpretable BEV mask and steering angle to solve the problems shown above. In Stage One, we propose a pretrained Bird's Eye View (BEV) model which leverages a BEV mask to present an interpretation of the surrounding environment. In Stage Two, we construct an Interpretable Imitation Learning (IIL) model that fuses BEV latent feature from Stage One with an additional steering angle from Pure-Pursuit (PP) algorithm. In the HIIL model, visual information is converted to semantic images by the semantic segmentation network, and the semantic images are encoded to extract the BEV latent feature, which are decoded to predict BEV masks and fed to the IIL as perception data. In this way, the BEV latent feature bridges the BEV and IIL models. Visual information can be supplemented by the calculated steering angle for PP algorithm, speed vector, and location information, thus it could have better performance in complex and terrible scenarios. Our HIIL model meets an urgent requirement for interpretability and robustness of autonomous driving. We validate the proposed model in the CARLA simulator with extensive experiments which show remarkable interpretability, generalization, and robustness capability in unknown scenarios for navigation tasks.
The Poisson Inverse Gaussian Regression Model in the Analysis of Clustered Counts Data

We explore the possibility of modeling clustered count data using the Poisson Inverse Gaussian distribution.We develop a regression model, which relates the number of mastitis cases in a sample of dairy farms in Ontario, Canada, to various farm level covariates, to illustrate the methodology.Residual plots are constructed to explore the quality of the fit.We compare the results with a negative binomial regression model using maximum likelihood estimation, and to the generalized linear mixed regression model fitted in SAS.
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents

User interaction with voice-powered agents generates large amounts of unlabeled utterances. In this paper, we explore techniques to efficiently transfer the knowledge from these unlabeled utterances to improve model performance on Spoken Language Understanding (SLU) tasks. We use Embeddings from Language Model (ELMo) to take advantage of unlabeled data by learning contextualized word representations. Additionally, we propose ELMo-Light (ELMoL), a faster and simpler unsupervised pre-training method for SLU. Our findings suggest unsupervised pre-training on a large corpora of unlabeled utterances leads to significantly better SLU performance compared to training from scratch and it can even outperform conventional supervised transfer. Additionally, we show that the gains from unsupervised transfer techniques can be further improved by supervised transfer. The improvements are more pronounced in low resource settings and when using only 1000 labeled in-domain samples, our techniques match the performance of training from scratch on 10-15x more labeled in-domain data.
An Empirical Study of Cross-Platform Mobile Development in Industry

The purpose of this study is to report on the industry’s perspectives and opinions on cross-platform mobile development, with an emphasis on the popularity, adoption, and arising issues related to the use of technical development frameworks and tools. We designed and conducted an online survey questionnaire, for which 101 participants were recruited from various developer-oriented online forums and websites. A total of five questions are reported in this study, of which two employed a Likert scale instrument, while three were based on multiple choice. In terms of technical frameworks, we find that PhoneGap, the Ionic Framework, and React Native were the most popular in use, both in hobby projects and in professional settings. The participants report an awareness of trade-offs when embracing cross-platform technologies and consider penalties in performance and user experience to be expected. This is also in line with what is reported in academic research. We find patterns in the reported perceived issues which match both older and newer research, thus rendering the findings a point of departure for further endevours.
DAGsim

Scalability of distributed ledgers is a key adoption factor. As an alternative to blockchain-based protocols, directed acyclic graph (DAG) protocols are proposed with the intention to allow a higher volume of transactions to be processed. However, there is still limited understanding of the behaviour and security considerations of DAG-based systems. We present an asynchronous, continuous time, and multi-agent simulation framework for DAG-based cryptocurrencies. We model honest and semi-honest actors in the system to analyse the behaviour of one specific cryptocurrency, IOTA. Our simulations show that the agents that have low latency and a high connection degree have a higher probability of having their transactions accepted in the network with honest and semi-honest strategies. Last, the simulator is built with extensibility in mind. We are in the process of implementing SPECTRE as well as including malicious agents.
Purchase intention in an electronic commerce environment

Purpose The purpose of this paper is to understand the integrated impact of the application of protection measures against identity theft on consumers’ synergistic perception of trust, the cost of products/services and operational performance (OP) – all of which in turn is postulated to contribute to purchase intention (PI) when shopping online. Design/methodology/approach In order to accomplish the specified aim, this study first conducted an experiment by involving the students from a university in Bangladesh. Then a survey was conducted to capture their opinion based on the previous experiment. Findings The study identified that in e-commerce, OP and trust have potential impact on pursuing consumers’ PI. Traditionally, price is always an issue in marketing; however, for e-commerce, this issue does not have direct impact on PI. Research limitations/implications The main limitation of this study is that a less established e-commerce example was utilized to conduct the experiment and survey for validating the model. Also, the study was conducted only in the context of Bangladesh and a student sample was utilized. Future studies can test the model in different contexts (particularly to verify the impact of privacy) by utilizing data from consumers. Practical implications This study has resolved a controversial issue by generating clear guidelines that the overall conjoint effect of OP, trust, and price on PI is neither negative nor neutral. Synergistically, the application of these controlling tools of identity theft can substantially enhance consumers’ trust, which is the single most predictor to pursue consumer PI. Originality/value This study has provided in-depth insight into the impact of different controlling measures in e-commerce PI. Practitioners have potential learning from this study that if consumers find the application of different controlling mechanisms against cybercrimes, particularly identity theft, enhancing the reliability, authenticity and security of transactions in this virtual medium, they do not mind paying a higher price. Such insights have not been provided by existing studies on this topic. Developing trust on e-commerce purchase is the driving force, not the price.
MArBled Circuits: Mixing Arithmetic and Boolean Circuits with Active Security

Most modern actively-secure multiparty computation (MPC) protocols involve generating random data that is secret-shared and authenticated, and using it to evaluate arithmetic or Boolean circuits in different ways. In this work we present a generic method for converting authenticated secret-shared data between different fields, and show how to use it to evaluate so-called “mixed” circuits with active security and in the full-threshold setting. A mixed circuit is one in which parties switch between different subprotocols dynamically as computation proceeds, the idea being that some protocols are more efficient for evaluating arithmetic circuits, and others for Boolean circuits. One use case of our switching mechanism is for converting between secret-sharing-based MPC and garbled circuits (GCs). The former is more suited to the evaluation of arithmetic circuits and can easily be used to emulate arithmetic over the integers, whereas the latter is better for Boolean circuits and has constant round complexity. Much work already exists in the two-party semi-honest setting, but the n-party dishonest majority case was hitherto neglected. We call the actively-secure mixed arithmetic/Boolean circuit a marbled circuit. Our implementation showed that mixing protocols in this way allows us to evaluate a linear Support Vector Machine with 400 times fewer AND gates than a solution using GC alone albeit with twice the preprocessing required using only SPDZ (Damgård et al., CRYPTO’12), and thus our solution offers a tradeoff between online and preprocessing complexity. When evaluating over a WAN network, our online phase is 10 times faster than the plain SPDZ protocol.
Improving change prediction models with code smell-related information

Code smells are sub-optimal implementation choices applied by developers that have the effect of negatively impacting, among others, the change-proneness of the affected classes. Based on this consideration, in this paper we conjecture that code smell-related information can be effectively exploited to improve the performance of change prediction models, i.e., models having the goal of indicating which classes are more likely to change in the future. We exploit the so-called intensity index—a previously defined metric that captures the severity of a code smell—and evaluate its contribution when added as additional feature in the context of three state of the art change prediction models based on product, process, and developer-based features. We also compare the performance achieved by the proposed model with a model based on previously defined antipattern metrics, a set of indicators computed considering the history of code smells in files. Our results report that (i) the prediction performance of the intensity-including models is statistically better than the baselines and, (ii) the intensity is a better predictor than antipattern metrics. We observed some orthogonality between the set of change-prone and non-change-prone classes correctly classified by the models relying on intensity and antipattern metrics: for this reason, we also devise and evaluate a smell-aware combined change prediction model including product, process, developer-based, and smell-related features. We show that the F-Measure of this model is notably higher than other models.
Effects of virtual agent and object representation on experiencing exhibited artifacts

With the emergence of speech-controlled virtual agents (VAs) in consumer devices such as Amazon’s Echo or Apple’s HomePod, we have seen a large public interest in related technologies. While most of the current interactive conversational VAs appear in the form of voice-only assistants, other representations showing, for example, a contextually related or generic humanoid body are possible. In our previous work, we analyzed the effectiveness of different forms of VAs in the context of a virtual reality (VR) exhibition space. We found positive evidence that agent embodiment induces a higher sense of spatial and social presence. The results also suggest that both embodied and thematically related audio-visual representations of VAs positively affect the overall user experience. We extend this work by further analyzing the effects of the physicality of the agent’s environment (i.e., virtual vs. real). The results of the follow-up study indicate some benefits of virtual environments, e.g., regarding user engagement and learning of visual facts. We also evaluate some interaction effects between the representations of the virtual agent and its surrounding and discuss implications on the design of exhibition spaces.
DroneSURF: Benchmark Dataset for Drone-based Face Recognition

Unmanned Aerial Vehicles (UAVs) or drones are often used to reach remote areas or regions which are inaccessible to humans. Equipped with a large field of view, compact size, and remote control abilities, drones are deemed suitable for monitoring crowded or disaster-hit areas, and performing aerial surveillance. While research has focused on area monitoring, object detection and tracking, limited attention has been given to person identification, especially face recognition, using drones. This research presents a novel large-scale drone dataset, DroneSURF: Drone Surveillance of Faces, in order to facilitate research for face recognition. The dataset contains 200 videos of 58 subjects, captured across 411K frames, having over 786K face annotations. The proposed dataset demonstrates variations across two surveillance use cases: (i) active and (ii) passive, two locations, and two acquisition times. DroneSURF encapsulates challenges due to the effect of motion, variations in pose, illumination, background, altitude, and resolution, especially due to the large and varying distance between the drone and the subjects. This research presents a detailed description of the proposed DroneSURF dataset, along with information regarding the data distribution, protocols for evaluation, and baseline results.
Automatically Composing Representation Transformations as a Means for Generalization

A generally intelligent learner should generalize to more complex tasks than it has previously encountered, but the two common paradigms in machine learning -- either training a separate learner per task or training a single learner for all tasks -- both have difficulty with such generalization because they do not leverage the compositional structure of the task distribution. This paper introduces the compositional problem graph as a broadly applicable formalism to relate tasks of different complexity in terms of problems with shared subproblems. We propose the compositional generalization problem for measuring how readily old knowledge can be reused and hence built upon. As a first step for tackling compositional generalization, we introduce the compositional recursive learner, a domain-general framework for learning algorithmic procedures for composing representation transformations, producing a learner that reasons about what computation to execute by making analogies to previously seen problems. We show on a symbolic and a high-dimensional domain that our compositional approach can generalize to more complex problems than the learner has previously encountered, whereas baselines that are not explicitly compositional do not.
Set of Usability Heuristics for Quality Assessment of Mobile Applications on Smartphones

The innovations proposed by the cell phone market have grown steadily in recent years, along with the increasing complexity of the hardware, operating systems, and applications available in this market.These changes bring new challenges related to usability that need to be considered during the development process of these applications since the new forms of user-application interactions increasingly require adapting the behavior of smartphone users.In this situation, usability is an important issue that depends on factors such as the Users, their characteristics and abilities, the Task which the users intend to achieve and also the application usage Context.This work presents a systematic literature review with the objective of identifying the heuristics and usability metrics used in the literature and/or industry.Based on the review results, this work presents another contribution with a proposal of a set of usability heuristics focused in mobile applications on smartphones, considering the User, Task and Context as usability factors and Cognitive Load as an important attribute of usability.The components of this set are detailed in a model intended to be used in empirical validations allowing to dynamically incorporate improvements to the proposal.
An Overview of Google Cloud Platform Services

Google Cloud Platform offers a wide range of services for securing, storing, serving, and analyzing data. These cloud services form a secure cloud perimeter for data, where different operations and transformations can be carried out on the data without it ever leaving the cloud ecosystem.