Reviewerly/exHarmony huggingface dataset:
Design considerations for virtual laboratories: A comparative study of two virtual laboratories for learning about gas solubility and colour appearance

Building a virtual laboratory for teaching and learning is a highly complex process, incorporating diverse areas such as interaction design, visualisation, and pedagogy. This article focuses on the production and implementation issues that were found in the comparison of two different virtual laboratory projects, and discuss which design considerations can be drawn from these observations. Two web-based virtual laboratories - the Gas Laboratory and the Virtual Colour Laboratory - were developed independently of each other within two different content areas. The laboratories share considerable overlaps in goals and production circumstances. Through a comparison of production and outcome, similar problems related to design, development and implementation were observed. The research uses a mixed method approach combining quantitative pre- and post-tests for assessments, qualitative surveys, and qualitative, ethnographic observations and interviews. By comparing the background material, five design challenges for developing virtual laboratories are identified: 1) how to balance ambitions with available resources; 2) how to balance intended levels of user interaction with exploratory freedom; 3) how to find appropriate levels of realism depending on target group; 4) how to choose between mimicking real world appearance and enhanced features; and 5) how to find the best learning situation for the virtual laboratory. To meet these challenges, the following design considerations are proposed: Guide the design work with a clear understanding of purpose and context; select appropriate technology to ensure efficient design and media usage; select level of realism considering purpose and end users; and provide learning guides before and after the virtual lab session.
Low-frame-rate single camera system for 3D full-field high-frequency vibration measurements

A single-camera stereo-digital image correlation (stereo-DIC) system to obtain 3D full-field vibration measurements is proposed. The optical setup is composed of two planar mirrors and a single low frame rate camera, thus resulting in a compact and low-cost equipment. The two mirrors are used to create pseudo-stereo images of a target surface on the camera sensor, which are then correlated by using stereo-DIC. The image acquisition process is carried out at low frame rates and the Nyquist-Shannon frequency limitation is overcome by adopting a down-sampling approach under the hypothesis that the vibration signal is characterized by a single known frequency component. The developed pseudo-stereo DIC system allows to obtain 3D full-field vibration measurements in a frequency range up to 4 kHz even with an available frame rate (at full resolution) of 178 fps. The effectiveness of the described approach has been verified by performing vibration measurements on a cantilever plate and a turbine blade.
Toward a Fuzzy Logic System Based on General Forms of Interval Type-2 Fuzzy Sets

Recent years have witnessed a widespread in the use of interval type-2 fuzzy logic systems (IT2 FLSs) in real-world applications. It has been shown recently that interval type-2 fuzzy sets (IT2 FSs) are more general than interval-valued fuzzy sets (IV FSs) [1]. Hence, there is a need to explore the capabilities of the more general forms of IT2 FSs (beyond IV FSs) and the applications areas they will be more suitable for. In addition, there is a need to develop the theory of the general forms of IT2 FLSs (gfIT2 FLSs), which employ IT2 FSs that are not equivalent to IV FSs and can have nonconvex secondary membership functions (MFs). Although these systems could be considered within the scope of general type-2 FLSs (GT2 FLSs), the practical implementation of GT2 FLSs has traditionally required the secondary MFs to be convex and normal type-1 fuzzy sets (T1 FSs). In addition, the type-reduction operation still presents a challenge for GT2 FLSs because of its computational complexity. In this paper, we present a complete framework for a type-2 FLS that uses the most recent perception of IT2 FSs (the so called general forms of interval type-2 fuzzy sets, gfIT2 FSs), whose secondary grades can be nonconvex T1 FSs. This framework includes new equations for the meet and join operations of gfIT2 FSs, as well as a new type reduction procedure for the type-2 FLS involving gfIT2 FSs. In addition, we present the type-2 FLS operation for singleton and nonsingleton fuzzification. We will introduce the various operations employed within a gfIT2 FLSs, from fuzzification (including singleton and nonsingleton) to inference, type-reduction, and defuzzification. We will also present two examples in which these gfIT2 FSs arise naturally when modeling sonar sensors input noise and the antecedents/consequents from a survey including different users.
A hybrid machine learning algorithm for designing quantum experiments

Abstract We introduce a hybrid machine learning algorithm for designing quantum optics experiments to produce specific quantum states. Our algorithm successfully found experimental schemes to produce all 5 states we asked it to, including Schrödinger cat states and cubic phase states, all to a fidelity of over 96%. Here, we specifically focus on designing realistic experiments, and hence all of the algorithm’s designs only contain experimental elements that are available with current technology. The core of our algorithm is a genetic algorithm that searches for optimal arrangements of the experimental elements, but to speed up the initial search, we incorporate a neural network that classifies quantum states. The latter is of independent interest, as it quickly learned to accurately classify quantum states given their photon number distributions.
Pragmatic Sensory Data Semantics With Service-Oriented Computing

The importance of an architectural semantic for service-oriented computing starts with the characteristics of software systems that has been recognized with sharing and the utilization of resources. However, an architectural characteristic for service-oriented based applications depends on interaction patterns that utilizes a data format in communication. These patterns also help in establishing communication between service components. The goal of service-oriented computing methodologies could be achieved by adopting a logical separation of services from the actual mechanism of resource assignment and allotment. This shows that how services are reliable as interaction patterns between service provider and requester. It generates the binding patterns between these two and establishes communication between them. This article discusses the mathematical-based semantic model that presents an architectural view for systems that follows a service-oriented computing methodology. It also briefs describes about different execution states and routes, which addresses the service specifications in service-oriented computing.
Convolutional neural network based on an extreme learning machine for image classification

Over the last decade, substantial advances have been made in various computer vision technologies and many of them are based on convolutional neural network (CNN) architecture. Typically, CNN is trained by a stochastic gradient descent algorithm using Back-Propagation (BP) but the training process is adversely affected by slow convergence and the need for extensive parameter tuning. In this paper, we propose a new CNN architecture and training algorithm based on an Extreme Learning Machine (ELM) to overcome these drawbacks. The proposed training algorithm is a layer-wise training method for CNN, and uses an alternating strategy of random convolutional filters and semi-supervised filters to combine the advantages of both approaches. On each semi-supervised layer, the CNN efficiently solves a convex optimization problem based on nonlinear random projection. It is faster and requires less human effort than BP-based training. We experimentally validated the proposed method using a well-known character and object recognition benchmark. In our experiment, the performance of our method is comparable to approaches based on deep features and has higher accuracy than other unsupervised feature-learning methods.
An efficient convolutional neural network for small traffic sign detection

Deep learning has become a ubiquitous method in object detection among multiple domains recently. However, in the era of edge computing, deploying deep neural networks on mobile edge platforms are challenging due to long latency and huge computational cost. As previous research efforts were usually focused on accuracy, achieving the balance between computational consumption and accuracy is a more significant problem to be tackled in mobile edge computing domain. To this end, we proposed an efficient convolutional neural network (CNN), which can remarkably minimize the redundancy, reduce the parameters and speed up the networks. The effectiveness of the network is further proved with experiments on a Tsinghua-Tencent 100K traffic sign dataset. Results show that under the same-level model size, our network outperforms the state-of-the-art Fast R-CNN and Faster R-CNN with 10% improvement in accuracy. Compared to similar work, the computational consumption on running time and memory of our network has been also reduced in the premise of little loss in accuracy.
Active semi-supervised learning based on self-expressive correlation with generative adversarial networks

Typically in practical applications, the learning performance of a model is inclined to be jeopardized by the inadequacy of labeled instances and the unbalance within various classes. This paper aims to address the issues and develop a novel framework for effective and efficient model learning, which fully explores both labeled and unlabeled instances for robust training and meanwhile leverages reliable synthetic instances for further augmentation. We firstly present a self-expressive correlation estimation method to reveal the underlying inter-instance correlation. After that, a novel active semi-supervised learning with GANs (ASSL-GANs) is presented, which simultaneously maintains three component modules, i.e., a generator, a discriminator, and a classifier. The learners work with each other in either adversarial or cooperative manner to obtain a comprehensive perception of the entire data distribution. The whole architecture is trained end-to-end by jointly optimizing loss functions w.r.t. the corresponding component networks in an alternating update fashion. Experimental results validate the superiority of the proposed algorithm over state-of-the-art models.
On Attribution of Recurrent Neural Network Predictions via Additive Decomposition

RNN models have achieved the state-of-the-art performance in a wide range of text mining tasks. However, these models are often regarded as black-boxes and are criticized due to the lack of interpretability. In this paper, we enhance the interpretability of RNNs by providing interpretable rationales for RNN predictions. Nevertheless, interpreting RNNs is a challenging problem. Firstly, unlike existing methods that rely on local approximation, we aim to provide rationales that are more faithful to the decision making process of RNN models. Secondly, a flexible interpretation method should be able to assign contribution scores to text segments of varying lengths, instead of only to individual words. To tackle these challenges, we propose a novel attribution method, called REAT, to provide interpretations to RNN predictions. REAT decomposes the final prediction of a RNN into additive contribution of each word in the input text. This additive decomposition enables REAT to further obtain phrase-level attribution scores. In addition, REAT is generally applicable to various RNN architectures, including GRU, LSTM and their bidirectional versions. Experimental results demonstrate the faithfulness and interpretability of the proposed attribution method. Comprehensive analysis shows that our attribution method could unveil the useful linguistic knowledge captured by RNNs. Some analysis further demonstrates our method could be utilized as a debugging tool to examine the vulnerability and failure reasons of RNNs, which may lead to several promising future directions to promote generalization ability of RNNs.
Where is my feature and what is it about? A case study on recovering feature facets

Developers commonly use features to define, manage, and communicate functionalities of a system. Unfortunately, the locations of features in code and other characteristics (feature facets), relevant for evolution and maintenance, are often poorly documented. Since developers change, and knowledge fades with time, such information often needs to be recovered. Modern projects boast a richness of information sources, such as pull requests, release logs, and otherwise specified domain knowledge. However, it is largely unknown from what sources the features, their locations, and their facets can be recovered. We present an exploratory study on identifying such information in two popular, variant-rich, and long-living systems: The 3D-printer firmware Marlin and the Android application Bitcoin-wallet. Besides the available information sources, we also investigated the projects’ communities, communications, and development cultures. Our results show that a multitude of information sources (e.g., commit messages and pull requests) is helpful to recover features, locations, and facets to different extents. Pull requests were the most valuable source to recover facets, followed by commit messages and the issue tracker. As many of the studied information sources are, so far, rarely exploited in techniques for recovering features and their facets, we hope to inspire researchers and tool builders with our results.
Modeling Heart Rate and Activity Data for Personalized Fitness Recommendation

Activity logs collected from wearable devices (e.g. Apple Watch, Fitbit, etc.) are a promising source of data to facilitate a wide range of applications such as personalized exercise scheduling, workout recommendation, and heart rate anomaly detection. However, such data are heterogeneous, noisy, diverse in scale and resolution, and have complex interdependencies, making them challenging to model. In this paper, we develop context-aware sequential models to capture the personalized and temporal patterns of fitness data. Specifically, we propose FitRec - an LSTM-based model that captures two levels of context information: context within a specific activity, and context across a user's activity history. We are specifically interested in (a) estimating a user's heart rate profile for a candidate activity; and (b) predicting and recommending suitable activities on this basis. We evaluate our model on a novel dataset containing over 250 thousand workout records coupled with hundreds of millions of parallel sensor measurements (e.g. heart rate, GPS) and metadata. We demonstrate that the model is able to learn contextual, personalized, and activity-specific dynamics of users' heart rate profiles during exercise. We evaluate the proposed model against baselines on several personalized recommendation tasks, showing the promise of using wearable data for activity modeling and recommendation.
Political institutions and finance-growth nexus in emerging markets and developing countries: A tale of one threshold

The empirical evidence predicts that political institutions are the core component of institutional matrix. They set stages for other institutions to be devised and influence the economic growth. Applying Kremer et al.'s (2013) dynamic panel threshold regression to a selected panel of 77 emerging market and developing countries over the period 1976–2010, we provide compelling evidence in favor of a split base on political institutions in the finance-growth nexus. The asymmetric impact of institutions on growth sets in around the optimum threshold level. Minimizing political risks through improvement in the quality of political institutions can improve the growth dividend due to financial development. Our empirical results appear to be robust across a wide range of alternative empirical strategies, several new disaggregate measures of political institutions, and also when addressing the endogeneity issues of the regressors.
Multi-criteria active deep learning for image classification

As a robust and heuristic technique in machine learning, active learning has been established as an effective method for addressing large volumes of unlabeled data; it interactively queries users (or certain information sources) to obtain desired outputs at new data points. With regard to deep learning techniques (e.g., CNN) and their applications (e.g., image classification), labeling work is of great significance as training processes for obtaining parameters in neural networks which requires abundant labeled samples. Although a few active learning algorithms have been proposed for devising certain straightforward sampling strategies (e.g., density, similarity, uncertainty, and label-based measure) for deep learning algorithms, these employ onefold sampling strategies and do not consider the relationship among multiple sampling strategies. To this end, we devised a novel solution “multi-criteria active leep learning”(MCADL) to learn an active learning strategy for deep neural networks in image classification. Our sample selection strategy selects informative samples by considering multiple criteria simultaneously (i.e., density, similarity, uncertainty, and label-based measure). Moreover, our approach is capable of adjusting weights adaptively to fuse the results from multiple criteria effectively by exploring the utilities of the criteria at different training stages. Through extensive experiments on two popular image datasets (i.e., MNIST and CIFAR-10), we demonstrate that our proposed method consistently outperforms highly competitive active learning approaches; thereby, it can be verified that our multi-criteria active learning proposal is rational and our solution is effective.
Face recognition with Bayesian convolutional networks for robust surveillance systems

Recognition of facial images is one of the most challenging research issues in surveillance systems due to different problems including varying pose, expression, illumination, and resolution. The robustness of recognition method strongly relies on the strength of extracted features and the ability to deal with low-quality face images. The proficiency to learn robust features from raw face images makes deep convolutional neural networks (DCNNs) attractive for face recognition. The DCNNs use softmax for quantifying model confidence of a class for an input face image to make a prediction. However, the softmax probabilities are not a true representation of model confidence and often misleading in feature space that may not be represented with available training examples. The primary goal of this paper is to improve the efficacy of face recognition systems by dealing with false positives through employing model uncertainty. Results of experimentations on open-source datasets show that 3–4% of accuracy is improved with model uncertainty over the DCNNs and conventional machine learning techniques.
Supervised Deep Feature Embedding With Handcrafted Feature

Image representation methods based on deep convolutional neural networks (CNNs) have achieved the state-of-the-art performance in various computer vision tasks, such as image retrieval and person re-identification. We recognize that more discriminative feature embeddings can be learned with supervised deep metric learning and handcrafted features for image retrieval and similar applications. In this paper, we propose a new supervised deep feature embedding with a handcrafted feature model. To fuse handcrafted feature information into CNNs and realize feature embeddings, a general fusion unit is proposed (called Fusion-Net). We also define a network loss function with image label information to realize supervised deep metric learning. Our extensive experimental results on the Stanford online products' data set and the in-shop clothes retrieval data set demonstrate that our proposed methods outperform the existing state-of-the-art methods of image retrieval by a large margin. Moreover, we also explore the applications of the proposed methods in person re-identification and vehicle re-identification; the experimental results demonstrate both the effectiveness and efficiency of the proposed methods.
An interpretable machine learning model for diagnosis of Alzheimer's disease

We present an interpretable machine learning model for medical diagnosis called sparse high-order interaction model with rejection option (SHIMR). A decision tree explains to a patient the diagnosis with a long rule (i.e., conjunction of many intervals), while SHIMR employs a weighted sum of short rules. Using proteomics data of 151 subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, SHIMR is shown to be as accurate as other non-interpretable methods (Sensitivity, SN = 0.84 ± 0.1, Specificity, SP = 0.69 ± 0.15 and Area Under the Curve, AUC = 0.86 ± 0.09). For clinical usage, SHIMR has a function to abstain from making any diagnosis when it is not confident enough, so that a medical doctor can choose more accurate but invasive and/or more costly pathologies. The incorporation of a rejection option complements SHIMR in designing a multistage cost-effective diagnosis framework. Using a baseline concentration of cerebrospinal fluid (CSF) and plasma proteins from a common cohort of 141 subjects, SHIMR is shown to be effective in designing a patient-specific cost-effective Alzheimer's disease (AD) pathology. Thus, interpretability, reliability and having the potential to design a patient-specific multistage cost-effective diagnosis framework can make SHIMR serve as an indispensable tool in the era of precision medicine that can cater to the demand of both doctors and patients, and reduce the overwhelming financial burden of medical diagnosis.
A comprehensive survey on computer vision based approaches for automatic identification of products in retail store

The ability to recognize a product on the shelf of a retail store is an ordinary human skill. The same recognition problem presents an exceptional challenge for machine vision systems. Automatic detection of products on the shelf of a retail store provides enhanced value-added consumer experience and commercial benefits to retailers. Compared to machine vision based object recognition system, automatic detection of retail products in a store setting has lesser number of successful attempts. In this paper, we present a survey of machine vision based retail product recognition system and define a new taxonomy for this field. We also describe the intrinsic challenges associated with the problem. In this comprehensive survey of published papers, we analyze features used in state-of-the-art attempts. The performances of these approaches are compared. The details of publicly available datasets are presented. The paper concludes pointing to possible directions of research in related fields.