Reviewerly/exHarmony huggingface dataset:
Lightweight dynamic conditional GAN with pyramid attention for text-to-image synthesis

The text-to-image synthesis task aims to generate photographic images conditioned on semantic text descriptions. To ensure the sharpness and fidelity of generated images, this task tends to generate high-resolution images (e.g., 1282 or 2562). However, as the resolution increases, the network parameters and complexity increases dramatically. Recent works introduce network structures with extensive parameters and heavy computations to guarantee the production of high-resolution images. As a result, these models come across problems of the unstable training process and high training cost. To tackle these issues, in this paper, we propose an effective information compensation based approach, namely Lightweight Dynamic Conditional GAN (LD-CGAN). LD-CGAN is a compact and structured single-stream network, and it consists of one generator and two independent discriminators to regularize and generate 642 and 1282 images in one feed-forward process. Specifically, the generator of LD-CGAN is composed of three major components: (1) Conditional Embedding (CE), which is an automatically unsupervised learning process aiming at disentangling integrated semantic attributes in the text space; (2) Conditional Manipulating Modular (CM-M) in Conditional Manipulating Block (CM-B), which is designed to continuously provide the image features with the compensation information (i.e., the disentangled attribute); and (3) Pyramid Attention Refine Block (PAR-B), which is used to enrich multi-scale features by capturing spatial importance between multi-scale context. Consequently, experiments conducted under two benchmark datasets, CUB and Oxford-102, indicate that our generated 1282 images can achieve comparable performance with 2562 images generated by the state-of-the-arts on two evaluation metrics: Inception Score (IS) and Visual-semantic Similarity (VS). Compared with the current state-of-the-art HDGAN, our LD-CGAN significantly decreases the number of parameters and computation time by 86.8% and 94.9%, respectively.
QoS-aware Automatic Web Service Composition with Multiple Objectives

Automatic web service composition has received a significant research attention in service-oriented computing over decades of research. With increasing number of web services, providing an end-to-end Quality of Service (QoS) guarantee in responding to user queries is becoming an important concern. Multiple QoS parameters (e.g., response time, latency, throughput, reliability, availability, success rate) are associated with a service, thereby, service composition with a large number of candidate services is a challenging multi-objective optimization problem. In this article, we study the multi-constrained multi-objective QoS-aware web service composition problem and propose three different approaches to solve the same, one optimal, based on Pareto front construction, and two others based on heuristically traversing the solution space. We compare the performance of the heuristics against the optimal and show the effectiveness of our proposals over other classical approaches for the same problem setting, with experiments on WSC-2009 and ICEBE-2005 datasets.
State of the Art and Open Challenges in Natural Language Interfaces to Data

Recent advances in natural language understanding and processing resulted in renewed interest in natural language based interfaces to data, which provide an easy mechanism for non-technical users to access and query the data. While early systems only allowed simple selection queries over a single table, some recent work supports complex BI queries, with many joins and aggregation, and even nested queries. There are various approaches in the literature for interpreting user's natural language query. Rule-based systems try to identify the entities in the query, and understand the intended relationships between those entities. Recent years have seen the emergence and popularity of neural network based approaches which try to interpret the query holistically, by learning the patterns. In this tutorial, we will review these natural language interface solutions in terms of their interpretation approach, as well as the complexity of the queries they can generate. We will also discuss open research challenges.
Rhino: Efficient Management of Very Large Distributed State for Stream Processing Engines

Scale-out stream processing engines (SPEs) are powering large big data applications on high velocity data streams. Industrial setups require SPEs to sustain outages, varying data rates, and low-latency processing. SPEs need to transparently reconfigure stateful queries during runtime. However, state-of-the-art SPEs are not ready yet to handle on-the-fly reconfigurations of queries with terabytes of state due to three problems. These are network overhead for state migration, consistency, and overhead on data processing. In this paper, we propose Rhino, a library for efficient reconfigurations of running queries in the presence of very large distributed state. Rhino provides a handover protocol and a state migration protocol to consistently and efficiently migrate stream processing among servers. Overall, our evaluation shows that Rhino scales with state sizes of up to TBs, reconfigures a running query 15 times faster than the state-of-the-art, and reduces latency by three orders of magnitude upon a reconfiguration.
G-thinker: A Distributed Framework for Mining Subgraphs in a Big Graph

Mining from a big graph those subgraphs that satisfy certain conditions is useful in many applications such as community detection and subgraph matching. These problems have a high time complexity, but existing systems to scale them are all IO-bound in execution. We propose the first truly CPU-bound distributed framework called G-thinker that adopts a user-friendly subgraph-centric vertex-pulling API for writing distributed subgraph mining algorithms. To utilize all CPU cores of a cluster, G-thinker features (1) a highly-concurrent vertex cache for parallel task access and (2) a lightweight task scheduling approach that ensures high task throughput. These designs well overlap communication with computation to minimize the CPU idle time. Extensive experiments demonstrate that G-thinker achieves orders of magnitude speedup compared even with the fastest existing subgraph-centric system, and it scales well to much larger and denser real network data. G-thinker is open-sourced at http://bit.ly/gthinker with detailed documentation.
Federated Object Detection

Object detection with deep learning model has achieved good results in many fields, but in some fields that think highly of data privacy, such as medical care, its applications is greatly limited by data. And Federated Learning allows clients to train a model together, while leaving their data in the local, without sharing with the server or other clients. Using the methods of Federated Learning, such as Federated Averaging(FedAvg), to train models can provide privacy, security benefits. Nonetheless, there is little experiment applying Federated Learning algorithms to train the model with a large number of parameters, such as deep learning object detection model. With non-IID data, the accuracy of object detection model trained by FedAvg reduces significantly, and need more rounds to coverage. In this work, we use Kullback-Leibler divergence(KLD) measure the weights divergence between different model trained with non-IID data. And we propose a useful scheme to improve FedAvg based Abnormal Weights Supression, reducing the influence of the weights divergence caused by non-IID and unbalanced data. As a representative of object detection, we choose Single Shot MultiBox Detector(SSD) as the base model. The results of the experiments show that the Mean Average Precision(mAP) get obvious improvement in Pascal VOC 2007 test dataset.
The Low/High Index of Pupillary Activity

A novel eye-tracked measure of pupil diameter oscillation is derived as an indicator of cognitive load. The new metric, termed the Low/High Index of Pupillary Activity (LHIPA), is able to discriminate cognitive load (vis-a-vis task difficulty) in several experiments where the Index of Pupillary Activity fails to do so. Rationale for the LHIPA is tied to the functioning of the human autonomic nervous system yielding a hybrid measure based on the ratio of Low/High frequencies of pupil oscillation. The paper's contribution is twofold. First, full documentation is provided for the calculation of the LHIPA. As with the IPA, it is possible for researchers to apply this metric to their own experiments where a measure of cognitive load is of interest. Second, robustness of the LHIPA is shown in analysis of three experiments, a restrictive fixed-gaze number counting task, a less restrictive fixed-gaze n-back task, and an applied eye-typing task.
Mixed Reality Light Fields for Interactive Remote Assistance

Remote assistance represents an important use case for mixed reality. With the rise of handheld and wearable devices, remote assistance has become practical in the wild. However, spontaneous provisioning of remote assistance requires an easy, fast and robust approach for capturing and sharing of unprepared environments. In this work, we make a case for utilizing interactive light fields for remote assistance. We demonstrate the advantages of object representation using light fields over conventional geometric reconstruction. Moreover, we introduce an interaction method for quickly annotating light fields in 3D space without requiring surface geometry to anchor annotations. We present results from a user study demonstrating the effectiveness of our interaction techniques, and we provide feedback on the usability of our overall system.
"In VR, everything is possible!": Sketching and Simulating Spatially-Aware Interactive Spaces in Virtual Reality

We propose using virtual reality (VR) as a design tool for sketching and simulating spatially-aware interactive spaces. Using VR, designers can quickly experience their envisioned spaces and interactions by simulating technologies such as motion tracking, multiple networked devices, or unusual form factors such as spherical touchscreens or bezel-less display tiles. Design ideas can be rapidly iterated without restrictions by the number, size, or shape and availability of devices or sensors in the lab. To understand the potentials and challenges of designing in VR, we conducted a user study with 12 interaction designers. As their tool, they used a custom-built virtual design environment with finger tracking and physics simulations for natural interactions with virtual devices and objects. Our study identified the designers' experience of space in relation to their own bodies and playful design explorations as key opportunities. Key challenges were the complexities of building a usable yet versatile VR-based "World Editor".
Improved Most Likely Heteroscedastic Gaussian Process Regression via Bayesian Residual Moment Estimator

This paper proposes an improved most likely heteroscedastic Gaussian process (MLHGP) algorithm to handle a kind of nonlinear regression problems involving input-dependent noise. The improved MLHGP follows the same learning scheme as the current algorithm by use of two Gaussian processes (GPs), with the first GP for recovering the unknown function and the second GP for modeling the input-dependent noise. Unlike the current MLHGP pursuing an empirical estimate of the noise level which is provably biased in most of local noise cases, the improved algorithm gives rise to an approximately unbiased estimate of the input-dependent noise. The approximately unbiased noise estimate is elicited from Bayesian residuals by the method of moments. As a by-product of this improvement, the expectation maximization (EM)-like procedure in the current MLHGP is avoided such that the improved algorithm requires only standard GP learnings to be performed twice. Four benchmark experiments, consisting of two synthetic cases and two real-world datasets, demonstrate that the improved MLHGP algorithm outperforms the current version not only in accuracy and stability, but also in computational efficiency.
Automated arrival-time picking using a pixel-level network

Arrival-time picking is an essential step in seismic processing and imaging. The explosion of seismic data volume requires automated arrival-time picking in a faster and more reliable way than existing methods. We have treated arrival-time picking as a binary image segmentation problem and used an improved pixel-wise convolutional network to pick arrival times automatically. Incorporating continuous spatial information in training enables us to preserve the arrival-time correlation between nearby traces, thus helping to reduce the risk of picking outliers that are common in a traditional trace-by-trace picking method. To train the network, we first convert seismic traces into gray-scale images. Image pixels before manually picked arrival times are labeled with zeros, and those after are tagged with ones. After training and validation, the network automatically learns representative features and generates a probability map to predict the arrival time. We apply the network to a field microseismic data set that was not used for training or validation to test the performance of the method. Then, we analyze the effects of training data volume and signal-to-noise ratio on our autopicking method. We also find the difference between 1D and 2D training data with borehole seismic data. Microseismic and borehole seismic data indicate the proposed network can improve efficiency and accuracy over traditional automated picking methods.
Interactive Lifelog Retrieval with vitrivr

The variety and amount of data being collected in our everyday life poses unique challenges for multimedia retrieval. In the Lifelog Search Challenge (LSC), multimedia retrieval systems compete in finding events based on descriptions containing hints about structured, semi-structured an unstructured data. In this paper, we present the multimedia retrieval system vitrivr with a focus on the changes and additions made based on the new dataset, and our successful participation at LSC 2019. Specifically, we show how the new dataset can be used for retrieval in different modalities without sacrificing efficiency, describe two recent additions, temporal scoring and staged querying, and discuss the deep learning methods used to enrich the dataset.
Online Reinforcement Learning for Self-adaptive Information Systems

A self-adaptive information system is capable of maintaining its quality requirements in the presence of dynamic environment changes. To develop a self-adaptive information system, information system engineers have to create self-adaptation logic that encodes when and how the system should adapt itself. However, developing self-adaptation logic may be difficult due to design time uncertainty; e.g., anticipating all potential environment changes at design time is in most cases infeasible. Online reinforcement learning (RL) addresses design time uncertainty by learning the effectiveness of adaptation actions through interactions with the system’s environment at run time, thereby automating the development of self-adaptation logic. Existing online RL approaches for self-adaptive information systems exhibit two shortcomings that limit the degree of automation: they require manually fine-tuning the exploration rate and may require manually quantizing environment states to foster scalability. We introduce an approach to automate the aforementioned manual activities by employing policy-based RL as a fundamentally different type of RL. We demonstrate the feasibility and applicability of our approach using two self-adaptive information system exemplars.
Thresholds

We examine the experience of Thresholds , a virtual reality (VR) recreation of the world's first photographic exhibition, which has toured to multiple museums. Following the method of performance-led research in the wild, we provide an account of the artist's design rationale and the experiences of visitors as the work toured. We reveal how the overlaying and juxtaposing of virtual and physical spaces established a VR architecture that underpinned the extended user experience. Overlaying was used to layer a virtual model onto a corresponding physical set to deliver physical sensations of touch and movement alongside visual and audio stimuli. Juxtaposition was used to embed the VR installation within the surrounding gallery space at each host museum, dealing with the challenges of entering, exiting, spectating, and invigilating the experience. We propose that museum designers can use these techniques to deliver VR installations that are compelling but also scalable and tourable.
MPM: Joint Representation of Motion and Position Map for Cell Tracking

Conventional cell tracking methods detect multiple cells in each frame (detection) and then associate the detection results in successive time-frames (association). Most cell tracking methods perform the association task independently from the detection task. However, there is no guarantee of preserving coherence between these tasks, and lack of coherence may adversely affect tracking performance. In this paper, we propose the Motion and Position Map (MPM) that jointly represents both detection and association for not only migration but also cell division. It guarantees coherence such that if a cell is detected, the corresponding motion flow can always be obtained. It is a simple but powerful method for multi-object tracking in dense environments. We compared the proposed method with current tracking methods under various conditions in real biological images and found that it outperformed the state-of-the-art (+5.2% improvement compared to the second-best).
Low-Cost Hyperspectral Imaging System: Design and Testing for Laboratory-Based Environmental Applications

The recent surge in the development of low-cost, miniaturised technologies provides a significant opportunity to develop miniaturised hyperspectral imagers at a fraction of the cost of currently available commercial set-ups. This article introduces a low-cost laboratory-based hyperspectral imager developed using commercially available components. The imager is capable of quantitative and qualitative hyperspectral measurements, and it was tested in a variety of laboratory-based environmental applications where it demonstrated its ability to collect data that correlates well with existing datasets. In its current format, the imager is an accurate laboratory measurement tool, with significant potential for ongoing future developments. It represents an initial development in accessible hyperspectral technologies, providing a robust basis for future improvements.
Medical Image Analysis using Deep Convolutional Neural Networks: CNN Architectures and Transfer Learning

Over the past two decades, computer-aided detection or diagnosis (CAD) has been a fruitful area of research. Medical imaging technology can provide the radiologists and physicians with a more efficient diagnosis and treatment process through the medical image analysis. However, data analysis has slowly become a challenging task with the manual advancement of science and technology in the modern CAD systems. This problem can be successfully solved with the assistance of Deep Learning Methods, particularly convolutional neural networks (CNNs) for procedures such as breast cancer treatment, lung nodule detection and prostate cancer localization.Important progress has been made in image recognition, mainly due to the availability of large-scale annotated datasets and the revival of deeply convolutional neural networks (CNNs). CNNs may help to enable highly representative, data-driven, layered hierarchical image features to be learned from sufficient training data. CNNs can help enable highly representative, data-driven, layered hierarchical image features to be learned with sufficient training data. The three main strategies that successfully employ CNNs for medical image classification are currently training the CNN from scratch, using pre-trained CNN features off - theshelf and undertaking unattended pre-training with supervised fine tuning.An effective way to do this is by transfer learning, i.e. fine-tuning (supervised) pre-trained CNN models from the natural image dataset to medical image tasks (although the domain between two medical image datasets can also be transferred). Training is difficult if we are to train from scratch a profoundly convolutional neural network (CNN), because it requires a lot of labeled training data and a lot of experience to ensure proper convergence. To do this better, we can fine-tune a pre-trained CNN using, for example, a large set of natural images marked out.
A Vehicle Counts by Class Framework using Distinguished Regions Tracking at Multiple Intersections

Turning movement counting plays an important step for traffic analysis at complex areas (e.g. intersections). Specifically, accurate and detailed traffic flow information enables the traffic control system to be more efficient and valuable. Recently, with the successful development of Deep Learning for vehicle detection and tracking, the current research focuses on video-based traffic analysis which is regarded as an emergent approach to monitoring vehicle movements. In this study, we present a comprehensive vehicle counting framework by integrating state-of-the-art techniques of object detection and tracking such as Yolo and DeepSort. Furthermore, in order to improve the vehicle counting problem, we propose a distinguished region tracking approach for the vehicle trajectory monitoring, which is able to work well with various scenarios, especially in complex areas with complicated movements. Regarding the experiment, the proposed framework is evaluated on the CVPR AI City Challenge 2020 dataset. Accordingly, our method is able to achieve around 85% of the accuracy which places to the top 10 of the leaderboard in Track 1 of the Challenge.
Information and cyber security maturity models: a systematic literature review

Purpose This paper aims to clarify the uncertainty reflected in the current state of information security maturity evaluation where it has not enough matured and converged so that a generic approach or many specfics approaches become the go-to choice. In fact, in the past decade, many secruity maturity models are still being produced and remain unproven regardless of the existence of ISO 21827. Design/methodology/approach The authors have used the systematic literature review to summarize existing research, help identify gaps in the existing literature and provide background for positioning new research studies. Findings The authors highlighted the prevalent influence of the ISO/IEC 27001/27002 standard but raised the necessity for an in-depth investigation of ISO 21827. The authors also made the implementation facet a central topic of our review. The authors found out that, compared to the number of proposed models, implementation experiments are lacking. This could be due to the arduous task of validation and it could also be the reason why specific models are dominant. Originality/value While the research literature contains many experience reports and a few case studies on information security maturity evaluation, a systematic review and synthesis of this growing field of research is unavailable as far as the authors know. In fact, the authors only picked-up one bodywork [Maturity models in cyber security A systematic review (2017)] carrying out a literature review on security maturity models between 2012 and 2017, written in Spanish.