Reviewerly/exHarmony huggingface dataset:
Multi-Stage Receptivity Model for Mobile Just-In-Time Health Intervention

A critical aspect of mobile just-in-time (JIT) health intervention is proper delivery timing, which correlates with successfully promoting target behaviors. Despite extensive prior studies on interruptibility, however, our understanding of the receptivity of mobile JIT health intervention is limited. This work extends prior interruptibility models to capture the JIT intervention process by including multiple stages of conscious and subconscious decisions. We built BeActive, a mobile intervention system for preventing prolonged sedentary behaviors, and we collected users' responses to a given JIT support and relevant contextual factors and cognitive/physical states for three weeks. Using a multi-stage model, we systematically analyzed the responses to deepen our understanding of receptivity using a mixed methodology. Herein, we identify the key factors relevant to each stage outcome and show that the receptivity of JIT intervention is nuanced and context-dependent. We propose several practical design implications for mobile JIT health intervention and context-aware computing.
Self-Paced Collaborative and Adversarial Network for Unsupervised Domain Adaptation

This paper proposes a new unsupervised domain adaptation approach called Collaborative and Adversarial Network (CAN), which uses the domain-collaborative and domain-adversarial learning strategies for training the neural network. The domain-collaborative learning strategy aims to learn domain specific feature representation to preserve the discriminability for the target domain, while the domain adversarial learning strategy aims to learn domain invariant feature representation to reduce the domain distribution mismatch between the source and target domains. We show that these two learning strategies can be uniformly formulated as domain classifier learning with positive or negative weights on the losses. We then design a collaborative and adversarial training scheme, which automatically learns domain specific representations from lower blocks in CNNs through collaborative learning and domain invariant representations from higher blocks through adversarial learning. Moreover, to further enhance the discriminability in the target domain, we propose Self-Paced CAN (SPCAN), which progressively selects pseudo-labeled target samples for re-training the classifiers. We employ a self-paced learning strategy such that we can select pseudo-labeled target samples in an easy-to-hard fashion. Additionally, we build upon the popular two-stream approach to extend our domain adaptation approach for more challenging video action recognition task, which additionally considers the cooperation between the RGB stream and the optical flow stream. We propose the Two-stream SPCAN (TS-SPCAN) method to select and reweight the pseudo labeled target samples of one stream (RGB/Flow) based on the information from the other stream (Flow/RGB) in a cooperative way. As a result, our TS-SPCAN model is able to exchange the information between the two streams. Comprehensive experiments on different benchmark datasets, Office-31, ImageCLEF-DA and VISDA-2017 for the object recognition task, and UCF101-10 and HMDB51-10 for the video action recognition task, show our newly proposed approaches achieve the state-of-the-art performance, which clearly demonstrates the effectiveness of our proposed approaches for unsupervised domain adaptation.
MC-BERT4HATE: Hate Speech Detection using Multi-channel BERT for Different Languages and Translations

The growth of social networking services (SNS) has altered the way and scale of communication in cyberspace. However, the amount of online hate speech is increasing because of the anonymity and mobility such services provide. As manual hate speech detection by human annotators is both costly and time consuming, there are needs to develop an algorithm for automatic recognition. Transferring knowledge by fine-tuning a pre-trained language model has been shown to be effective for improving many downstream tasks in the field of natural language processing. The Bidirectional Encoder Representations from Transformers (BERT) is a language model that is pre-trained to learn deep bidirectional representations from a large corpus. In this paper, we propose a multi-channel model with three versions of BERT (MC-BERT), the English, Chinese, and multilingual BERTs for hate speech detection. We also explored the usage of translations as additional input by translating training and test sentences to the corresponding languages required for different BERT models. We used three datasets in non-English languages to compare our model with previous approaches including the 2019 SemEval HatEval Spanish dataset, 2018 GermEval shared task on the identification of Offensive Language dataset, and 2018 EvalIta HaSpeeDe Italian dataset. Finally, we were able to achieve the state-of-the-art or comparable performance on these datasets by conducting thorough experiments.
A Study of Oracle Approximations in Testing Deep Learning Libraries

Due to the increasing popularity of deep learning (DL) applications, testing DL libraries is becoming more and more important. Different from testing general software, for which output is often asserted definitely (e.g., an output is compared with an oracle for equality), testing deep learning libraries often requires to perform oracle approximations, i.e., the output is allowed to be within a restricted range of the oracle. However, oracle approximation practices have not been studied in prior empirical work that focuses on traditional testing practices. The prevalence, common practices, maintenance and evolution challenges of oracle approximations remain unknown in literature. In this work, we study oracle approximation assertions implemented to test four popular DL libraries. Our study shows that there exists a non-negligible portion of assertions that leverage oracle approximation in the test cases of DL libraries. Also, we identify the common sources of oracles on which oracle approximations are being performed through a comprehensive manual study. Moreover, we find that developers frequently modify code related to oracle approximations, i.e., using a different approximation API, modifying the oracle or the output from the code under test, and using a different approximation threshold. Last, we performed an in-depth study to understand the reasons behind the evolution of oracle approximation assertions. Our findings reveal important maintenance challenges that developers may face when maintaining oracle approximation practices as code evolves in DL libraries.
Modulation Pattern Recognition Based on Resnet50 Neural Network

In this paper, several typical convolutional neural networks, VGG16, VGG19, Inception, Xception and Resnet50, are used to identify the modulation pattern of the constellation. It is found through experiments that the Resnet50 network performs best in the recognition of constellations. Then, using the Resnet50 convolutional neural network, the two modes of the modulation mode and the signal-to-noise ratio of the constellation are simultaneously identified. When the accuracy of the signal-to-noise ratio is required to be 1 db, the identified quasi-group rate is 60 percent. When the accuracy of the signal to noise ratio is required to be 2db. The accuracy of recognition can reach 85 percent.
Standalone noise and anomaly detection in wireless sensor networks: A novel time‐series and adaptive Bayesian‐network‐based approach

Summary Wireless sensor networks (WSNs) consist of small sensors with limited computational and communication capabilities. Reading data in WSN is not always reliable due to open environmental factors such as noise, weakly received signal strength, and intrusion attacks. The process of detecting highly noisy data is called anomaly or outlier detection. The challenging aspect of noise detection in WSN is related to the limited computational and communication capabilities of sensors. The purpose of this research is to design a local time‐series‐based data noise and anomaly detection approach for WSN. The proposed local outlier detection algorithm (LODA) is a decentralized noise detection algorithm that runs on each sensor node individually with three important features: reduction mechanism that eliminates the noneffective features, determination of the memory size of data histogram to accomplish the effective available memory, and classification for predicting noisy data. An adaptive Bayesian network is used as the classification algorithm for prediction and identification of outliers in each sensor node locally. Results of our approach are compared with four well‐known algorithms using benchmark real‐life datasets, which demonstrate that LODA can achieve higher (up to 89%) accuracy in the prediction of outliers in real sensory data.
A novel batch image encryption algorithm using parallel computing

Chaos-based encryption provides a practical way to protect the confidentiality of digital images nowadays. The increasing convenience (e.g., larger bandwidth) of data sharing stimulates the need for encrypting amounts of images in a fast manner. Yet most existing works aim to encrypt an image for each time. Although some parallel encryptions have been proposed, the speed is still far from satisfactory to proceed with the huge increasing number of images. This inspires us to consider another promising way, encrypting a batch of images parallelly for each time. We use maximum available number of threads in parallel computation for full use of processor resources. Considering the batch images as a shared resource, every thread competes with others to encrypt images in the shared resource in a preemptive manner for encryption. A classical permutation-diffusion architecture for chaos-based encryption is utilized for each thread, where logistic map and Lorenz system are used for generating keystream for permutation and diffusion, respectively. We make cryptographical analyses and perform experiments to confirm that the security is guaranteed. The results of efficiency tests demonstrate that the encryption speed is greatly improved compared with the state-of-art image encryption algorithms in parallel as well as serial modes.
A Deep Neural Network With Multiplex Interactions for Cold-Start Service Recommendation

As service-oriented computing (SOC) technologies gradually mature, developing service-based systems (such as mashups) has become increasingly popular in recent years. Faced with the rapidly increasing number of Web services, recommending appropriate component services for developers on demand is a vital issue in the development of mashups. In particular, since a new mashup to develop contains no component services, it is a new “user” to a service recommender system. To address this new “user” cold-start problem, we propose a multiplex interaction-oriented service recommendation approach, named MISR, which incorporates three types of interactions between services and mashups into a deep neural network. In this article, we utilize the powerful representation learning abilities provided by deep learning to extract hidden structures and features from various types of interactions between mashups and services. Experiments conducted on a real-world dataset from ProgrammableWeb show that MISR outperforms several state-of-the-art approaches regarding commonly used evaluation metrics.
Errors in PV power modelling due to the lack of spectral and angular details of solar irradiance inputs

Photovoltaic (PV) modules are sensitive to the spectral distribution of solar irradiance. Although numerical weather prediction (NWP) models compute irradiance in several spectral bands, only broadband quantities are provided in the standard outputs used for PV forecasts. This study investigates how this lack of information impacts PV power estimation. A physical PV model is first designed that accounts for the spectral distribution of irradiance and the spectral response of the panels. This model is evaluated using measurements performed at Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA), Palaiseau, France. The mean relative difference between simulated and measured PV power for a monocrystalline silicon module of 250 W nominal power is −0.9%, and the mean bias is −2.0 W. This model is then used to investigate the impact of solar zenith angle and clouds on the performance of PV modules. PV performance can increase in cloudy conditions by 5% through spectral filtering of near-infrared irradiance, and by 18% when only the useful irradiance is considered to compute performance. This spectral effects is not captured by the PV model when only broadband irradiances are used. In such case errors up to 15% are obtained in simulated PV power compared to using a state-of-the-art NWP model providing irradiance in 14 spectral bands. More generally, broadband global horizontal irradiance appears insufficient for accurate PV power modelling, highlighting the added value of spectrally-and-angularly-refined irradiances. This stresses that PV models should use more detailed irradiance inputs, which could be easily achieved by exploiting internal variables of NWP models.
An improved whale optimization algorithm with armed force program and strategic adjustment

In this paper, we propose a new whale army optimization algorithm with a view to solving multifarious optimization problems. The key novelty of our approach is to modify the original whale optimization algorithm to make it effective to solve the complicated, large-scale and constrained optimization problems. Our modifications mainly embody two aspects: the beneficial strategic adjustment to set key parameters and to help establish base principles in the original optimizer and the introduction of armed force program which classifies the search whales into different categories to achieve efficient cooperation. We evaluate the performance of the proposed algorithm, using three simple benchmark test functions over thirty CEC-2014 real-parameter numerical optimization problems and three constraint engineering design problems. The test results indicate that this algorithm can provide a faster local convergence rate, a higher convergence accuracy, and a lower computational complexity in comparison to traditional whale optimization algorithms and other sophisticated state of the art whale optimizers. Performance wise, it also surpasses many advanced methods for large-scaled complex functions. Furthermore, in this paper we propose a variant of whale army optimization algorithm to specifically address and solve optimizing constrained problems with a high degree of precision.
Context-Aware Human Activity Recognition (CAHAR) in-the-Wild Using Smartphone Accelerometer

Smartphones are a promising platform for continuous monitoring of human behavior. However, the ability to capture people's behavioral patterns in-the-wild is a challenge, as the user's behavior and physical activities can vary, given the variability of settings and environments. Modeling and understanding of human activity in-the-wild must not overlook a user's behavioral context, which is just as crucial as recognizing the range of physical activities. The work in this paper presents a novel framework for context-aware human activity recognition by incorporating human behavioral contexts with physical activities. The proposed framework utilizes a series of machine learning classifiers to validate the efficiency of the proposed method.
Older Adults' Perception and Use of Voice User Interfaces

Voice User Interfaces (VUIs) are quickly becoming ubiquitous. The natural language interface of VUIs may be more usable for some groups of users, such as those who may face challenges using physical input methods including older adults and people living with a disability. This study explores research on the use of VUIs by one such group, older adults. We conducted a systematic literature review of research published in the Association of Computing Machinery Digital Library that addresses perception and use of VUIs by older adults. We identified an emerging body of research examining older adults' use and perceptions of VUIs. This research revealed several potential benefits of voice interaction for older adults while also highlighting how the novelty of the technology may be a barrier to adoption. We conclude with a call for further HCI research in this area.
Deep and Ordinal Ensemble Learning for Human Age Estimation From Facial Images

Some recent work treats age estimation as an ordinal ranking task and decomposes it into multiple binary classifications. However, a theoretical defect lies in this type of methods: the ignorance of possible contradictions in individual ranking results. In this paper, we partially embrace the decomposition idea and propose the Deep and Ordinal Ensemble Learning with Two Groups Classification (DOEL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2groups</sub> ) for age prediction. An important advantage of our approach is that it theoretically allows the prediction even when the contradictory cases occur. The proposed method is characterized by a deep and ordinal ensemble and a two-stage aggregation strategy. Specifically, we first set up the ensemble based on Convolutional Neural Network (CNN) techniques, while the ordinal relationship is implicitly constructed among its base learners. Each base learner will classify the target face into one of two specific age groups. After achieving probability predictions of different age groups, then we make aggregation by transforming them into counting value distributions of whole age classes and getting the final age estimation from their votes. Moreover, to further improve the estimation performance, we suggest to regard the age class at the boundary of original two age groups as another age group and this modified version is named the Deep and Ordinal Ensemble Learning with Three Groups Classification (DOEL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3groups</sub> ). Effectiveness of this new grouping scheme is validated in theory and practice. Finally, we evaluate the proposed two ensemble methods on controlled and wild aging databases, and both of them produce competitive results. Note that the DOEL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3groups</sub> shows the state-of-the-art performance in most cases.
Efficient and Fast Real-World Noisy Image Denoising by Combining Pyramid Neural Network and Two-Pathway Unscented Kalman Filter

Recently, image prior learning has emerged as an effective tool for image denoising, which exploits prior knowledge to obtain sparse coding models and utilize them to reconstruct the clean image from the noisy one. Albeit promising, these prior-learning based methods suffer from some limitations such as lack of adaptivity and failed attempts to improve performance and efficiency simultaneously. With the purpose of addressing these problems, in this paper, we propose a Pyramid Guided Filter Network (PGF-Net) integrated with pyramid-based neural network and Two-Pathway Unscented Kalman Filter (TP-UKF). The combination of pyramid network and TP-UKF is based on the consideration that the former enables our model to better exploit hierarchical and multi-scale features, while the latter can guide the network to produce an improved (a posteriori) estimation of the denoising results with fine-scale image details. Through synthesizing the respective advantages of pyramid network and TP-UKF, our proposed architecture, in stark contrast to prior learning methods, is able to decompose the image denoising task into a series of more manageable stages and adaptively eliminate the noise on real images in an efficient manner. We conduct extensive experiments and show that our PGF-Net achieves notable improvement on visual perceptual quality and higher computational efficiency compared to state-of-the-art methods.
Zero-Shot Depth Estimation From Light Field Using A Convolutional Neural Network

This article proposes a zero-shot learning-based framework for light field depth estimation, which learns an end-to-end mapping solely from an input light field to the corresponding disparity map with neither extra training data nor supervision of groundtruth depth. The proposed method overcomes two major difficulties posed in existing learning-based methods and is thus much more feasible in practice. First, it saves the huge burden of obtaining groundtruth depth of a variety of scenes to serve as labels during training. Second, it avoids the severe domain shift effect when applied to light fields with drastically different content or captured under different camera configurations from the training data. On the other hand, compared with conventional non-learning-based methods, the proposed method better exploits the correlations in the 4D light field and generates much superior depth results. Moreover, we extend this zero-shot learning framework to depth estimation from light field videos. For the first time, we demonstrate that more accurate and robust depth can be estimated from light field videos by jointly exploiting the correlations across spatial, angular, and temporal dimensions. We conduct comprehensive experiments on both synthetic and real-world light field image datasets, as well as a self collected light field video dataset. Quantitative and qualitative results validate the superior performance of our method over the state-of-the-arts, especially for the challenging real-world scenes.
Learning a Joint Search and Recommendation Model from User-Item Interactions

Existing learning to rank models for information retrieval are trained based on explicit or implicit query-document relevance information. In this paper, we study the task of learning a retrieval model based on user-item interactions. Our model has potential applications to the systems with rich user-item interaction data, such as browsing and recommendation, in which having an accurate search engine is desired. This includes media streaming services and e-commerce websites among others. Inspired by the neural approaches to collaborative filtering and the language modeling approaches to information retrieval, our model is jointly optimized to predict user-item interactions and reconstruct the item textual descriptions. In more details, our model learns user and item representations such that they can accurately predict future user-item interactions, while generating an effective unigram language model for each item. Our experiments on four diverse datasets in the context of movie and product search and recommendation demonstrate that our model substantially outperforms competitive retrieval baselines, in addition to providing comparable performance to state-of-the-art hybrid recommendation models.
TECHNOLOGY CHALLENGES AND OPPORTUNITIES FACING MARKETING EDUCATION

New technologies, including artificial intelligence (AI), enablers of big data analysis, blockchain data systems, robotics, and drones are transforming marketing. Marketing education has adapted over the last 120 years driven by changes in marketing technology that have helped shape the courses taught. Marketing educators are facing challenges in assessing theories and concepts that need to adapt to changes in technology. Marketing educators need to become more interdisciplinary in order to acquire knowledge associated with complex technology changing the marketing environment. Textbooks and other educational materials need to quickly adapt to prepare students for the careers of the future. While challenges exist, the opportunities to advance the marketing discipline are compelling.