Reviewerly/exHarmony huggingface dataset:
Improving the Generalization Ability of Deep Neural Networks for Cross-Domain Visual Recognition

Feature learning with deep neural networks (DNNs) has made remarkable progress in recent years. However, its data-driven nature makes the collection of labeled training data expensive or impossible when the testing domain changes. Here, we propose a method of transferable feature learning and instance-level adaptation to improve the generalization ability of DNNs so as to mitigate the domain shift challenge for cross-domain visual recognition. When less labeled information is available, our proposed method shows attractive results in the new target domain and outperforms the typical fine-tuning method. Two DNNs are chosen as the representatives working with our proposed method, to do a comprehensive study about the generalization ability on the tasks of image-to-image transfer, image-to-video transfer, multidomain image classification, and weakly supervised detection. The experimental results show that our proposed method is superior to other existing works in the literature. In addition, a large scale of cross-domain database is merged from three different domains, providing a quantitative platform to evaluate different approaches in the field of cross-domain object detection.
A Part-Aware Multi-Scale Fully Convolutional Network for Pedestrian Detection

Pedestrian detection is a crucial task in intelligent transportation systems, which can be applied in autonomous vehicles and traffic scene video surveillance systems. The past few years have witnessed much progress on the research of pedestrian detection methods, especially through the successful use of the deep learning based techniques. However, occlusion and large scale variation remain the challenging issues for pedestrian detection. In this work, we propose a Part-Aware Multi-Scale Fully Convolutional Network (PAMS-FCN) to tackle these difficulties. Specifically, we present a part-aware Region-of-Interest (RoI) pooling module to mine body parts with different responses, and select the part with the strongest response via voting. As such, a partially visible pedestrian instance can receive a high detection confidence score, making it less likely to become a missing detection. This module operates in parallel with an instance RoI pooling module to combine local parts and global context information. To handle vast scale variation, we construct a fully convolutional network in which multi-scale feature maps are generated efficiently, and small-scale and large-scale pedestrians are detected separately. By integrating these structures, the proposed detector achieves the state-of-the-art performance on the Caltech, KITTI, INRIA and ETH pedestrian detection datasets.
Blind watermarking for color images using EMMQ based on QDFT

In this study, we developed a novel scheme for the blind watermarking of color images. The proposed scheme incorporates extreme pixel adjustment (EPA), multi-bit partly sign-altered mean modulation (MPSAM), mixed modulation (MM), and particle swarm optimization (PSO) within a scheme based on crisscross inter-block quaternion discrete Fourier transform (QDFT). Accordingly, the proposed scheme employing EPA, MPSAM, MM, and QDFT is referred to as EMMQ. The image is separated into non-overlapping 8 × 8 pixel blocks, whereupon MPSAM is used to map multiple bits within a single block using multiple coefficients in one of the four transformed components of QDFT. The use of MM to embed the watermark allows for superior image quality and strong resistance to image processing attacks. Our use of PSO also makes it possible to optimize the EMMQ parameters, thereby enabling outstanding robustness without compromising imperceptibility. Experiment results demonstrate the efficacy of the proposed scheme in resisting a variety of image-processing attacks, with performance superior to that of existing watermarking schemes in terms of imperceptibility and robustness for a given payload capacity.
Global horizontal irradiance forecast for Finland based on geostationary weather satellite data

Global horizontal irradiance (GHI) estimates and forecasts are necessary for the efficient use of a naturally fluctuating energy source like solar energy. However, few forecasting methods exist for high latitudes. In this study we present the development and validation of a satellite-based GHI forecast for southern Finland, called Solis-Heliosat. The forecast is formed by combining information from the clear sky (CS) model Pvlib Solis with consecutive geostationary weather satellite imagery, using the Heliosat method. Forecasts are produced up to 4 h with a 15-min temporal resolution. The CS model, the satellite-based all-sky estimate, and the GHI forecast have been compared and validated against other methods and in situ GHI measurements. An additional comparison was made for two datasets representing a changing cloud environment. The CS model estimates had an average rMBE (relative Mean Bias Error) of −6% to 1% and a rRMSE (relative Root Mean Square Error) of 6–10%. For the all-sky estimates the rMBE was −4% to −2%, and rRMSE 2–33%. With increasing forecast time the Solis-Heliosat rMBE descends to −9% and rRMSE reaches 50% at 4 h. Solis-Heliosat performs better than the persistence forecasts in most cases, particularly in a changing cloud environment. Our study indicates the use of satellite-based forecasts as a viable tool for forecasting GHI for the solar energy industry also in the high latitudes. In high latitudes geostationary satellite-based methods are at their limit; however, the information they can provide will enable efficient solar energy production.
CONCEPTUAL FRAMEWORK FOR INTEGRATING BIM AND AUGMENTED REALITY IN CONSTRUCTION MANAGEMENT

The need for systematic data collection and processing to generate real-time building site progress information is critical. Building Information Modelling (BIM) provides the benefit of aggregating information about the building site on a single platform. Augmented reality (AR) emerges to enhance BIM concerning visualization of the building site, through processing and automatic absorption of information. This work aims to analyse the potential of AR association to BIM, by adopting an approach based on literature review. Trends in contemporary research are checked by categorizing applied research methods, areas of expertise, and AR technologies. Publications produced between 2008 and 2018 from journals of architecture, engineering, and construction areas in databases Web of Science, SciSearch, SCOPUS, INSPEC, Google Scholar, Academic OneFile, EBSCO, OCLC, VINITI, SCImago, and ProQuest were investigated. As main results, it was found that the case study approach was adopted in 41% of the publications analysed. The building site inspection was the research object in 48% of papers. Fiducial markers, GIS/GPS, laser scanners, and photogrammetry emerged as main options for automatic data capture on the progress of the building site. Integration between AR and BIM has the potential to solve information processing problems and improving construction management.
Optimal Remote Entanglement Distribution

Distributing entanglement between distant nodes is an essential task in quantum networks. To achieve this task, quantum repeaters have been introduced to perform entanglement swapping. This paper offers a design of remote entanglement distribution (RED) protocols that maximize the entanglement distribution rate (EDR). We introduce the concept of enodes, representing the entangled quantum bit (qubit) pairs in the network. This concept enables us to design the optimal RED protocols based on the solutions of some linear programming problems. Moreover, we investigate RED in a homogeneous repeater chain, which is a building block for many quantum networks. In particular, we determine the maximum EDR for homogeneous repeater chains in a closed form. Our results enable the distribution of long-distance entanglement with noisy intermediate-scale quantum (NISQ) technologies and provide insights into the design and implementation of general quantum networks.
Automated Product Recommendations with Preference-Based Explanations

Many online retailers, such as Amazon, use automated product recommender systems to encourage customer loyalty and cross-sell products. Despite significant improvements to the predictive accuracy of contemporary recommender system algorithms, they remain prone to errors. Erroneous recommendations pose potential threats to online retailers in particular, because they diminish customers’ trust in, acceptance of, satisfaction with, and loyalty to a recommender system. Explanations of the reasoning that lead to recommendations might mitigate these negative effects. That is, a recommendation algorithm ideally would provide both accurate recommendations and explanations of the reasoning for those recommendations. This article proposes a novel method to balance these concurrent objectives. The application of this method, using a combination of content-based and collaborative filtering, to two real-world data sets with more than 100 million product ratings reveals that the proposed method outperforms established recommender approaches in terms of predictive accuracy (more than five percent better than the Netflix Prize winner algorithm according to normalized root mean squared error) and its ability to provide actionable explanations, which is also an ethical requirement of artificial intelligence systems.
Theoretical and Experimental Analysis of a 4 × 4 Reconfigurable MZI-Based Linear Optical Processor

A 4 × 4 reconfigurable Mach-Zehnder interferometer (MZI)-based linear optical processor is investigated through its theoretical analyses and characterized experimentally. The linear transformation matrix of the structure is theoretically determined using its building block, which is a 2 × 2 reconfigurable MZI. To program the device, the linear transformation matrix of a given application is decomposed into that of the constituent MZIs of the structure. Thus, the required phase shifts for implementing the transformation matrix of the application by means of the optical processor are determined theoretically. Due to random phase offsets in the MZIs resulting from fabrication process variations, they are initially configured through an experimental protocol. The presented calibration scheme allows to straightforwardly characterize the MZIs to mitigate the possible input phase errors and determine the bar and cross states of each MZI for tuning it at the required sate before programming the device. After the configuration process, the device can be programmed to construct the linear transformation matrix of the application. In this regard, using the required bias voltages, the phase shifts obtained from the decomposition process are applied to the phase shifters of the MZIs in the device.
Multi-Modality Latent Interaction Network for Visual Question Answering

Exploiting relationships between visual regions and question words have achieved great success in learning multi-modality features for Visual Question Answering (VQA). However, we argue that existing methods mostly model relations between individual visual regions and words, which are not enough to correctly answer the question. From humans' perspective, answering a visual question requires understanding the summarizations of visual and language information. In this paper, we proposed the Multi-modality Latent Interaction module (MLI) to tackle this problem. The proposed module learns the cross-modality relationships between latent visual and language summarizations, which summarize visual regions and question into a small number of latent representations to avoid modeling uninformative individual region-word relations. The cross-modality information between the latent summarizations are propagated to fuse valuable information from both modalities and are used to update the visual and word features. Such MLI modules can be stacked for several stages to model complex and latent relations between the two modalities and achieves highly competitive performance on public VQA benchmarks, VQA v2.0 and TDIUC . In addition, we show that the performance of our methods could be significantly improved by combining with pre-trained language model BERT.
Soil subgroup prediction via portable X-ray fluorescence and visible near-infrared spectroscopy

Recently, portable X-ray fluorescence (pXRF) spectrometer and visible near-infrared (Vis-NIR) spectroscopy are increasingly being applied for soil types and attributes prediction, but a few works have used them combined in tropical regions. Thus, this work aimed at analyzing models’ performance when predicting soil types at subgroup taxonomic level via pXRF and Vis-NIR separately and together. 315 soil samples were collected in both A and B horizons in three important Brazilian states. Samples undergone laboratorial analyses for soil classification and were submitted to pXRF and Vis-NIR (350–2500 nm) analyses. Vis-NIR spectral data preprocessing was evaluated utilizing Savitzky-Golay (WT) and Savitzky-Golay with Binning (WB) methods. Four classification algorithms were employed in modeling: Support Vector Machine with Linear (SVM-L) and Radial (SVM-R) kernel, C5.0, and Random Forest (RF). Predictions were made using only B horizon and using A + B horizon data. Overall accuracy and Cohen’s Kappa index evaluated model quality. Both sensors displayed efficacy in soil types prediction. A + B horizons data combined using pXRF + Vis-NIR via SVM-R (WT and WB) delivered accurate predictions (89.32% overall accuracy and 0.75 Kappa index), but the best predictions were achieved using only B horizon data via pXRF with RF, pXRF + Vis-NIR (WT) with RF, pXRF + Vis-NIR (WB) with C5.0, and pXRF + Vis-NIR (WB) with RF (89.23% overall accuracy and 0.80 Kappa index). For tropical soils, soil subgroup prediction using only B horizon data obtained by pXRF in tandem with RF algorithm may be a viable alternative to assist in soil classification, especially when the acquisition of Vis-NIR is not possible.
Application of Blockchain to Supply Chain: Flexible Blockchain Technology

It has been ten years since Satoshi Nakamoto created bitcoin and introduced the concept of a blockchain. The original goal was to propose a solution to the double-spending problem using a peer-to-peer network. Now, Blockchain proves to have the capacity to deliver a new kind of trust to a wide range of services. Applications are being explored in healthcare (patient records), government (land registries) and electronics (Internet of Things). The supply chain is one of the fields that Blockchain is expected to be applied. The paper aims to combine blockchain with distributed storage and propose blockchain for the supply chain. Blockchain is not fit to record a lot of information. It requires both on-chain storage of the core ledger data and off-chain storage of data required by smart contracts for verification and documentation. The Inter Planetary File System (IPFS) is a concrete solution. IPFS is a peer-to-peer distributed file system that seeks to connect all computing devices with the same system of files. Participants can address large amounts of data with IPFS and place the immutable, permanent IPFS links into a blockchain transaction. This timestamps and secures their content, without having to put the data itself on the chain. By combining blockchain with distributed storage, the supply chain system is fit to the industry of the next generation. The characteristics of Industry 4.0 meets the blockchain-based system and the model can aid these changes.
Cross-Dataset Activity Recognition via Adaptive Spatial-Temporal Transfer Learning

Human activity recognition (HAR) aims at recognizing activities by training models on the large quantity of sensor data. Since it is time-consuming and expensive to acquire abundant labeled data, transfer learning becomes necessary for HAR by transferring knowledge from existing domains. However, there are two challenges existing in cross-dataset activity recognition. The first challenge is source domain selection. Given a target task and several available source domains, it is difficult to determine how to select the most similar source domain to the target domain such that negative transfer can be avoided. The second one is accurately activity transfer. After source domain selection, how to achieve accurate knowledge transfer between the selected source and the target domain remains another challenge. In this paper, we propose an Adaptive Spatial-Temporal Transfer Learning (ASTTL) approach to tackle both of the above two challenges in cross-dataset HAR. ASTTL learns the spatial features in transfer learning by adaptively evaluating the relative importance between the marginal and conditional probability distributions. Besides, it captures the temporal features via incremental manifold learning. Therefore, ASTTL can learn the adaptive spatial-temporal features for cross-dataset HAR and can be used for both source domain selection and accurate activity transfer. We evaluate the performance of ASTTL through extensive experiments on 4 public HAR datasets, which demonstrates its effectiveness. Furthermore, based on ASTTL, we design and implement an adaptive cross-dataset HAR system called Client-Cloud Collaborative Adaptive Activity Recognition System (3C2ARS) to perform HAR in the real environment. By collecting activities in the smartphone and transferring knowledge in the cloud server, ASTTL can significantly improve the performance of source domain selection and accurate activity transfer.
Mochi: Composing Data Services for High-Performance Computing Environments

Technology enhancements and the growing breadth of application workflows running on high-performance computing (HPC) platforms drive the development of new data services that provide high performance on these new platforms, provide capable and productive interfaces and abstractions for a variety of applications, and are readily adapted when new technologies are deployed. The Mochi framework enables composition of specialized distributed data services from a collection of connectable modules and subservices. Rather than forcing all applications to use a one-size-fits-all data staging and I/O software configuration, Mochi allows each application to use a data service specialized to its needs and access patterns. This paper introduces the Mochi framework and methodology. The Mochi core components and microservices are described. Examples of the application of the Mochi methodology to the development of four specialized services are detailed. Finally, a performance evaluation of a Mochi core component, a Mochi microservice, and a composed service providing an object model is performed. The paper concludes by positioning Mochi relative to related work in the HPC space and indicating directions for future work.
A novel hybrid deep recommendation system to differentiate user’s preference and item’s attractiveness

With the fast development of online E-commerce Websites and mobile applications, users' auxiliary information as well as products' textual information can be easily collected to form a vast amount of training data. Therefore, research efforts are urgently needed to make customized recommendations using such large but sparse data. Deep recommendation model is a natural choice for this research issue. However, most existing approaches try to investigate either user's auxiliary information such as age and zipcode, or item's textual information such as product descriptions, reviews or comments. Therefore, it is desired to see whether user's auxiliary information and item's textual information could be modeled simultaneously. This paper proposes a novel approach which is essentially a hybrid probabilistic matrix factorization model. Particularly, it has two sub components. One component tries to predict user's rating scores by capturing user's personal preferences extracted from auxiliary information. Another component tries to model item's textual attractiveness to different users via a proposed attention based convolutional neural network. We then propose a global objective function and optimize these two sub components under a unified framework. Extensive experiments are performed on five real-world datasets, i.e., ML-100K, ML-1M, ML-10M, AIV and Amazon sub dataset. The promising experimental results have demonstrated the superiority of our proposed approach when compared with both baseline models and state-of-the-art deep recommendation approaches, i.e., PMF, CDL, CTR, ConvMF, ConvMF+ and D-Attn with respect to RMSE criterion.
Image Classification Algorithm Based on Deep Learning-Kernel Function

Although the existing traditional image classification methods have been widely applied in practical problems, there are some problems in the application process, such as unsatisfactory effects, low classification accuracy, and weak adaptive ability. This method separates image feature extraction and classification into two steps for classification operation. The deep learning model has a powerful learning ability, which integrates the feature extraction and classification process into a whole to complete the image classification test, which can effectively improve the image classification accuracy. However, this method has the following problems in the application process: first, it is impossible to effectively approximate the complex functions in the deep learning model. Second, the deep learning model comes with a low classifier with low accuracy. So, this paper introduces the idea of sparse representation into the architecture of the deep learning network and comprehensively utilizes the sparse representation of well multidimensional data linear decomposition ability and the deep structural advantages of multilayer nonlinear mapping to complete the complex function approximation in the deep learning model. And a sparse representation classification method based on the optimized kernel function is proposed to replace the classifier in the deep learning model, thereby improving the image classification effect. Therefore, this paper proposes an image classification algorithm based on the stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation. The experimental results show that the proposed method not only has a higher average accuracy than other mainstream methods but also can be good adapted to various image databases. Compared with other deep learning methods, it can better solve the problems of complex function approximation and poor classifier effect, thus further improving image classification accuracy.
Passive continuous-variable quantum secret sharing using a thermal source

We propose a continuous-variable quantum secret sharing (CV-QSS) protocol in which the quantum states are prepared passively using a thermal source. In our protocol, one player splits the output of a thermal source into two spatial modes with the help of a beam splitter, then he (or she) measures one of the correlated thermal states and sends the others to the secure station of the adjacent player in sequence after using appropriate optical attenuation, which waives the necessity of employment of high extinction ratio modulators with good stability. Therefore, the proposed protocol provides convenient implementation in QSS framework. Moreover, our scheme is immune to Trojan horse attacks. We derive the security bounds for our protocol against both eavesdroppers and dishonest players. Furthermore, we can achieve tighter bound of QSS distance by considering the finite-size effect, which is more practical than that obtained in the asymptotic limit.