Reviewerly/exHarmony huggingface dataset:
A Novel Pedestrian Reidentification Method Based on a Multiview Generative Adversarial Network

Emerging deep learning (DL) techniques have greatly improved pedestrian reidentification (PRI) performance. However, the existing DL-based PRI methods cannot learn robust feature representations owing to the single view of query images and the limited number of extractable features. Inspired by generative adversarial networks (GANs), this paper proposes a novel PRI method based on a pedestrian multiview GAN (PmGAN) and a classification recognition network (CRN). The PmGAN consists of three generators and one multiclass discriminator. The three generators produce pedestrian images from the front, side and back, while the multiclass discriminator determines whether the input image is a real image or a generated image. In addition to expanding the existing pedestrian datasets, the PmGAN can generate pedestrian images from front, side and back views based on a given query image and thereby increase the feature semantic space of the query image. To verify the performance of our method, the PmGAN was compared with mainstream pedestrian image generation models, and then the proposed method was contrasted with mainstream PRI methods. The results show that the proposed PmGAN greatly improved the performance of mainstream PRI methods. For example, the combination of the PmGAN and Pyramidal increased the mean average precision (mAP) on three common datasets by 1.2% on average. The research findings provide new insights into the application of multiview generation in PRI tasks.
Fuzzy-based Cybersecurity Risk Analysis of the Human Factor from the Perspective of Classified Information Leakage

Nowadays, the digital transformation of organizations is not a challenge but a must-have. In the spring of 2020, practically the whole world worked from home offices. Now digital adaptation is the challenge for many people and orgaization. This situation poses challenges for the cybersecurity world. At the time of writing this article, there is no exact data yet on what cybersecurity incidents have occurred or how much damage they have caused. Nevertheless, it is certain that in the pandemic chaos, many corporates made mistakes during their digital adaptation processes. To a considerable extent, these mistakes are due to humans. Even though there are outstanding technological solutions or regulations at a company, if this riskfactor is not appropriately managed, then the other two are worthless. Despite the need, there is no widespread human risk anylisys method in cybersecurity, because it is difficult to measure, and covered in obscurity. In this paper, the authors propose a fuzzy model to organizations whereby they can measure this risk if they have sufficient information about the workforce. The model will be easier understood if presented through a specific threat, the digital leakage of classified information from a critical infrastructure.
Safe coordination of robots in cyclic paths

This paper presents a MILP (mixed integer linear programming) based formulation for the coordination of multiple robots. We consider robots that must follow closed intersecting paths persistently. We propose an off-line planning of velocity profiles preventing the need of online collision avoidance maneuvers or path replanning. Our robot model considers minimum and maximum speed constraints, which allows our strategy to be applied to fixed-wing aerial robots. We also deal with three important other issues which are usually disregarded in the related literature: minimum spatial separation; acceleration limits; and uncertainties on the speeds and positions. Simulations with up to 48 robots show the efficiency of our strategy. A real experiment with 3 actual e-puck robots is presented in order to demonstrate the robustness of our formulation in a real world scenario.
An Interactive Two-Pass Decoding Network for Joint Intent Detection and Slot Filling

Intent detection and slot filling are two closely related tasks for building a spoken language understanding (SLU) system. The joint methods for the two tasks focus on modeling the semantic correlations between the intent and slots and applying the information of one task to guide the other task, which helps them to promote each other. However, most existing joint approaches only unidirectionally utilize the intent information to guide slot filling while ignoring the fact that the slot information is beneficial to intent detection. To address this issue, in this paper, we propose an Interactive Two-pass Decoding Network (ITD-Net) for joint intent detection and slot filling, which explicitly establishes the token-level interactions between the intent and slots through performing an interactive two-pass decoding process. In ITD-Net, the task-specific information obtained by the first-pass decoder for one task is directly fed into the second-pass decoder for the other task, which can take full advantage of the explicit intent and slot information to achieve bidirectional guidance between the two tasks. Experiments on the ATIS and SNIPS datasets demonstrate the effectiveness and superiority of our ITD-Net.
Plumb: Efficient stream processing of multi‐user pipelines

Abstract Operational services run 24 × 7 and require analytics pipelines to evaluate performance. In mature services such as domain name system (DNS), these pipelines often grow to many stages developed by multiple, loosely coupled teams. Such pipelines pose two problems: first, computation and data storage may be duplicated across components developed by different groups, wasting resources. Second, processing can be skewed , with structural skew occurring when different pipeline stages need different amounts of resources, and computational skew occurring when a block of input data requires increased resources. Duplication and structural skew both decrease efficiency, increasing cost, latency, or both. Computational skew can cause pipeline failure or deadlock when resource consumption balloons; we have seen cases where pessimal traffic increases CPU requirements 6‐fold. Detecting duplication is challenging when components from multiple teams evolve independently and require fault isolation. Skew management is hard due to dynamic workloads coupled with the conflicting goals of both minimizing latency and maximizing utilization. We propose Plumb , a framework to abstract stream processing as large‐block streaming (LBS) for a multi‐stage, multi‐user workflow. Plumb users express analytics as a DAG of processing modules, allowing Plumb to integrate and optimize workflows from multiple users. Many real‐world applications map to the LBS abstraction. Plumb detects and eliminates duplicate computation and storage, and it detects and addresses both structural and computational skew by tracking computation across the pipeline. We exercise Plumb using the analytics pipeline for B‐Root DNS. We compare Plumb to a hand‐tuned system, cutting latency to one‐third the original, and requiring 39 % fewer container hours, while supporting more flexible, multi‐user analytics and providing greater robustness to DDoS‐driven demands.
A Suggestive Interface for Untangling Mathematical Knots

In this paper we present a user-friendly sketching-based suggestive interface for untangling mathematical knots with complicated structures. Rather than treating mathematical knots as if they were 3D ropes, our interface is designed to assist the user to interact with knots with the right sequence of mathematically legal moves. Our knot interface allows one to sketch and untangle knots by proposing the Reidemeister moves, and can guide the user to untangle mathematical knots to the fewest possible number of crossings by suggesting the moves needed. The system highlights parts of the knot where the Reidemeister moves are applicable, suggests the possible moves, and constrains the user's drawing to legal moves only. This ongoing suggestion is based on a Reidemeister move analyzer, that reads the evolving knot in its Gauss code and predicts the needed Reidemeister moves towards the fewest possible number of crossings. For our principal test case of mathematical knot diagrams, this for the first time permits us to visualize, analyze, and deform them in a mathematical visual interface. In addition, understanding of a fairly long mathematical deformation sequence in our interface can be aided by visual analysis and comparison over the identified “key moments” where only critical changes occur in the sequence. Our knot interface allows users to track and trace mathematical knot deformation with a significantly reduced number of visual frames containing only the Reidemeister moves being applied. All these combine to allow a much cleaner exploratory interface for us to analyze and study mathematical knots and their dynamics in topological space.
Computer-Aided Tumor Diagnosis in Automated Breast Ultrasound Using 3D Detection Network

Automated breast ultrasound (ABUS) is a new and promising imaging modality for breast cancer detection and diagnosis, which could provide intuitive 3D information and coronal plane information with great diagnostic value. However, manually screening and diagnosing tumors from ABUS images is very time-consuming and overlooks of abnormalities may happen. In this study, we propose a novel two-stage 3D detection network for locating suspected lesion areas and further classifying lesions as benign or malignant tumors. Specifically, we propose a 3D detection network rather than frequently-used segmentation network to locate lesions in ABUS images, thus our network can make full use of the spatial context information in ABUS images. A novel similarity loss is designed to effectively distinguish lesions from background. Then a classification network is employed to identify the located lesions as benign or malignant. An IoU-balanced classification loss is adopted to improve the correlation between classification and localization task. The efficacy of our network is verified from a collected dataset of 418 patients with 145 benign tumors and 273 malignant tumors. Experiments show our network attains a sensitivity of 97.66% with 1.23 false positives (FPs), and has an area under the curve(AUC) value of 0.8720.
A Soft Graph Attention Reinforcement Learning for Multi-Agent Cooperation

The multi-agent reinforcement learning (MARL) suffers from several issues when it is applied to large-scale environments. Specifically, the communication among the agents is limited by the communication distance or bandwidth. Besides, the interactions among the agents are complex in large-scale environments, which makes each agent hard to take different influences of the other agents into consideration and to learn a stable policy. To address these issues, a soft graph attention reinforcement learning (SGA-RL) is proposed. By taking the advantage of the chain propagation characteristics of graph neural networks, stacked graph convolution layers can overcome the limitation of the communication and enlarge the agents' receptive field to promote the cooperation behavior among the agents. Moreover, unlike traditional multi-head attention mechanism which takes all the heads into consideration equally, a soft attention mechanism is designed to learn each attention head's importance, which means that each agent can learn how to treat the other agents' influence more effectively during large-scale environments. The results of the simulations indicate that the agents can learn stable and complicated cooperative strategies with SGA-RL in large-scale environments.
MARS model for prediction of short- and long-term global solar radiation

This chapter applies a machine learning approach based on Multivariate Adaptive Regression Splines (MARS) algorithm for developing solar radiation estimation and forecasting models for regional Queensland. First, a short-term (daily) global solar radiation model is constructed using the MARS algorithm considering the nonlinear behavior of surface-level solar radiation with its plausible list of predictor variables. Second, a long-term (monthly) global solar radiation model is built using the MARS algorithm mainly to test the tool that can later be used for solar energy assessment over a long-term period and considering seasonal climatic cycles. The accuracy of the MARS model with respect to an alternative data-driven framework is evaluated using Autoregressive Integrated Moving Average at both time horizons. The results indicate that the MARS-based solar radiation forecast can be applied in Central Queensland in both short-term and long-term forecasting scenarios and is a valuable tool for future solar energy projects.
Enhancing Multi-turn Dialogue Modeling with Intent Information for E-Commerce Customer Service

Nowadays, it is a heated topic for many industries to build intelligent conversational bots for customer service. A critical solution to these dialogue systems is to understand the diverse and changing intents of customers accurately. However, few studies have focused on the intent information due to the lack of large-scale dialogue corpus with intent labelled. In this paper, we propose to leverage intent information to enhance multi-turn dialogue modeling. First, we construct a large-scale Chinese multi-turn E-commerce conversation corpus with intent labelled, namely E-IntentConv, which covers 289 fine-grained intents in after-sales domain. Specifically, we utilize the attention mechanism to extract Intent Description Words (IDW) for representing each intent explicitly. Then, based on E-IntentConv, we propose to integrate intent information for both retrieval-based model and generation-based model to verify its effectiveness for multi-turn dialogue modeling. Experimental results show that extra intent information is useful for improving both response selection and generation tasks.
Light field endoscope calibration based on virtual objective lens and virtual feature points

Three-dimensional light field imaging in laparoscopic surgery is an emerging technology, which has the potential to enable three-dimensional imaging. Calibration of the three-dimensional light field endoscope (3D LFE) is essential but challenging as the disparity is much smaller than that of the conventional light field camera. The geometrical model for 3D LFE is established, and a calibration method based on virtual objective lens and virtual feature points is proposed. First, the virtual objective lens is introduced and the parameters about it are calibrated using corner features in center subaperture images. Second, two types of virtual feature points are proposed to calibrate the parameters about the microlens array, one is on the black-and-white board line and the other is selectively determined but can be anywhere on the checkerboard. Moreover, the relationship between the virtual feature points mapping in the microlens image and the virtual feature points mapping in the central subaperture image is deduced to overcome tiny light field disparity. Experimental results verify the performance of our calibration method.
VISU: A 3D Printed Functional Robot for Crowd Surveillance

This article presents a novel three-dimensional printed robot for crowd surveillance called VISU. VISU captures surroundings using inbuilt camera and transmits them to the cloud. VISU's cloud architecture is connected to the criminal and suspect database provided by police authority. The captured crowd data is compared with criminal or suspect images and the authorities are alerted in case of any match. The algorithm detects the number of people captured in each frame and the authorities are prompted with the number of people in the crowd. With the help of instance segmentation, authorities can track every persons movement in the surroundings.
Blockchain Technology-Enabled Smart Product-Service System Lifecycle Management: A Conceptual Framework

Smart product-service system (SPSS) is a new business model that integrates smart products and e-services to satisfy customer needs better and improve enterprise competitiveness. In case to reasonably and efficiently manage the products, services, information, and data generated throughout the SPSS lifecycle, it is necessary to conquer the problem of data insecurity and the low-trust between stakeholders. Blockchain can provide solutions because of its characteristics such as decentralized, irreversibility of records and smart contracts, etc. To solve the problem, this paper develops a conceptual framework for smart PSS lifecycle management which consists of four layers: perception layer, business resource layer, blockchain layer, and application layer. Furthermore, this paper introduces the typical improved services at each stage and an illustrative case of smart coffee machine service system. Finally, the paper puts forward the future work combined with limitations. This framework will help stakeholders achieve more secure and efficient SPSS lifecycle management and further enhance enterprise competitiveness.
Attention distribution guided information transfer networks for recommendation in practice

Recently, an increasing number of deep learning-based methods have been applied in recommendation. Most such methods outperform traditional methods, especially when using the natural language processing (NLP) technique with review texts. Many deep learning-based recommender systems are used to learn latent representations of reviews written by target users and reviews written for target items. They are then combined to predict the rating of the target user for the target item. However, most previously proposed review-based deep learning methods do not conform to real-world application scenarios, in which we cannot obtain the reviews of the target user for the target item (called U2I review). In real-world recommendation settings, items are always recommended to users before they have experienced them. Therefore, the review of a target user for a target item would not be available during the testing and validation process. Many methods, such as DeepCoNN and D-ATT, do not exclude the U2I review in the process of validation and testing. Therefore, the process of testing is different from real-world application scenarios, and these methods obtain substantial valuable information from the U2I review that target users write for target items. We propose a model called ADGITN and a training strategy to solve this problem. When training, the auxiliary model learns two attention distributions that the U2I reviews over user reviews and item reviews by auxiliary tasks. These two distributions are used to guide the learning of attention distributions between user reviews and item reviews of the main model. Thus, the main model could learn how to extract attention distributions between user reviews and item reviews according to the valuable information extracted from U2I reviews. During validation, only the main model works, and it could extract better attention distributions even without the help of a U2I review. Extensive experiments show the effectiveness of our model. We validate our model on the Amazon and Yelp19 datasets, and the results show that our model outperforms existing excellent models, with up to 13.8% relative improvement compared to the performance of MPCN, which is one of the best review-based deep learning models for recommendation.
Detecting Engagement Bots on Social Influencer Marketing

Influencer fraud, which can significantly damage authentic influencers and companies, has become one of the most important problems that can adversely affect the influencer marketing industry. Fraudulent influencers obtain fake engagements on their posts by purchasing engagement bots that automatically generate likes and comments. To identify bots that make fake engagements to influencers, we perform an in-depth analysis on the social network of influencer engagements, which consists of 14,221 influencers, 9,290,895 users, and 65,848,717 engagements. We find that bots tend to have low local clustering coefficients and write short comments which are similar to each other. Based on the analysis results of the unique engagement behavior of bots, we propose a neural network-based model that learns text, behavior, and graph representations of social media users to detect the engagement bots from audiences of influencers. The experimental results show that the proposed model outperforms well-known baseline methods by achieving 80% accuracy.
Real-time person orientation estimation and tracking using colored point clouds

Robustly estimating the orientations of people is a crucial precondition for a wide range of applications. Especially for autonomous systems operating in populated environments, the orientation of a person can give valuable information to increase their acceptance. Given people’s orientations, mobile systems can apply navigation strategies which take people’s proxemics into account or approach them in a human like manner to perform human robot interaction (HRI) tasks. In this paper, we present an approach for person orientation estimation based on computationally efficient features extracted from colored point clouds, formerly used for a two-class person attribute classification. The classification approach has been extended to the continuous domain while treating the problem of orientation estimation in real time. Furthermore, we present an approach for tracking estimated orientations over time using a Bayesian filter. We will show that tracking can increase the accuracy of orientations by up to 3.69° on a dataset recorded with a mobile robot. Best results on this highly challenging dataset are achieved with a regression approach for orientation estimation in combination with tracking. The mean angular error of just 16.49° proofs the applicability in real-world scenarios.
Design of High Performance Image Acquisition and Processing Platform Based on DSP and FPGA

This paper presents a high-speed embedded platform for image acquisition and processing. We proposed a low-cost and high-performance overall structure based on the field programmable gate array (FPGA) and the digital signal processor (DSP). FPGA implement the configurations of complementary metal oxide semiconductor (CMOS) sensor, image data acquisition and storage. DSP is in charge of some high level complex algorithm. The combination of DSP and FPGA efficiently realizes various advanced and complex algorithms. The platform uses a Cyclone-III FPGA from Altera and a DSP TMS320C6713 from Texas Instruments (TI) and communicates with an ARM9 (Advanced RISC Machines) S3C2410 host through host port interface (HPI) interface. The platform can realize lossless storage and efficient transmission of image data. The flexibility of this hardware platform allows us to benefit from its high data throughput and processing ability. Experiments results show that this system is useful and powerful. Moreover, it can be easily migrated to other high speed data acquisition application.