Anomaly Detection Techniques in the Gaia Space Mission Data

In this paper we deal with classification of anomalous data detected by the data reduction system of the Gaia space mission, in operation since 2013. Given the size and complexity of intermediate data and plots for diagnostics, beyond practical possibility of full human evaluation, the need for automated signal processing tools is becoming more and more relevant. Our classification task consists in discriminating among “normal” data and data affected by anomalies, which at present are grouped into four different classes. We investigate the use of some clever pre-processing approaches that allow the application of a tailored technique based on the Hough transform, and of some machine learning tools, evidencing that the task can be exactly solved in the former case. In the latter case, random forests and support vector machine provide less than satisfactory performance, while convolutional neural networks achieve very good classification accuracy, up to 91.22%. Statistics show satisfactory results also in terms of precision and recall of each class.

     

一种车载服务的快速深度Q学习网络边云迁移策略

智能网联交通系统中车载用户的高速移动，不可避免地造成了数据在边缘服务器之间频繁迁移，产生了额外的通信回传时延，对边缘服务器的实时计算服务带来了巨大的挑战。为此，该文提出一种基于车辆运动轨迹的快速深度Q学习网络(DQN-TP)边云迁移策略，实现数据迁移的离线评估和在线决策。车载决策神经网络实时获取接入的边缘服务器网络状态和通信回传时延，根据车辆的运动轨迹进行虚拟机或任务迁移的决策，同时将实时的决策信息和获取的边缘服务器网络状态信息发送到云端的经验回放池中；评估神经网络在云端读取经验回放池中的相关信息进行网络参数的优化训练，定时更新车载决策神经网络的权值，实现在线决策的优化。最后仿真验证了所提算法与虚拟机迁移算法和任务迁移算法相比能有效地降低时延。     

A fast method for identifying worldwide scientific collaborations using the Scopus database

Science is essential for human prosperity because social and technological advances often depend on scientific advances. Science is living a golden era characterized by a rapidly growing number of researchers worldwide exploring different disciplines and research fields. Keeping in mind that funding is limited, many researchers are encouraged to establish new collaborations with individuals or groups of researchers. Furthermore, the funding bodies use increasingly complex criteria to determine the researchers and projects to be supported. In this regard, the analysis of scientific collaboration networks can help to determine the main areas of specialization of universities and research centres, as well as the type of internal and external collaborations of their researchers. This paper presents an advanced method for analysing scientific collaboration networks at universities and research institutions. This method is based on automatically obtaining bibliographic data from scientific publications through the use of the Scopus Database API Interface, which are then analysed using graph visualization software and statistical tools. This model has been validated through the analysis of a real university, and the results show that it is possible to determine in a fast way and with high reliability the main research lines of an institution as well as the structure of the collaboration network. The method opens new perspectives for the study of scientific collaboration networks because it can be applied at different levels of detail, from small research groups to large academic and research centres, and over different time frames.     

Information-driven dynamic sensor collaboration

This article overviews the information-driven approach to sensor collaboration in ad hoc sensor networks. The main idea is for a network to determine participants in a "sensor collaboration" by dynamically optimizing the information utility of data for a given cost of communication and computation. A definition of information utility is introduced, and several approximate measures of the information utility are developed for reasons of computational tractability. We illustrate the use of this approach using examples drawn from tracking applications     

无线传感器网络协同任务分配研究

无线传感器网络的任务协同主要是任务的描述、分解、分配、调度和执行。任务分配是任务协同的主要内容．任务分配的方案直接决定着网络能耗,从而影响网络的生命周期。着重分析了无线传感器网络协同技术以及启发式算法解决任务分配的问题,并给出了无线传感器网络任务分配需要进一步研究的内容和方向。     

Leadership of information systems development projects

How best to lead an information systems project team continues to be an open issue. For three decades, the research literature has contrasted between the leadership of the "chief programmer" led team to that of the "egoless" software team with little clarity as to what is appropriate leadership and under what circumstances. Software project teams, like other knowledge teams, are characterized by distributed expertise, the reliance on methodologies, high levels of collaboration, as well as the need to meet the expectations of a diverse set of stakeholders. We propose a theoretical model of information systems project team leadership that focuses on empowering and directive leadership practices and investigate whether this leadership is more effective than the use of traditional coordination mechanisms; we also test whether these relationships are moderated by factors such as task uncertainty or professional experience. We test this model using data from 69 software development teams. Our results indicate that empowering leadership has an important impact on team performance but only under conditions of high task uncertainty or team expertise.     

Opportunistic fleets for road event detection in vehicular sensor networks

High mobility and variable road surface condition brings big challenges for road event detection by using vehicular sensor networks (VSNs). VSNs are data-centric networks, so that the efficient collaborations among opportunistic topology structure in the network can help to improve the performance. In this paper, we present the behavior-aware fleet construction schemes to form and maintain the opportunistic fleet topology, and then to make fleet based data collaboration. The fleet formation scheme is to group vehicles according to behavior similarities and stability. The fleet maintenance scheme includes the periodic update and deputy selection to adapt the fleet topology. The collaboration within the fleet is executed in the way of “follower-to-leader” with data aggregation among behavior-aware vehicles. Our analysis show the schemes are with low overhead and in an on-line and decentralized way, which is well suited for VSNs. We made simulations to testify the scheme performance for road event detection applications. The results show that our schemes achieve better accuracy for road event detection when compared with the other related work.     

Poisoning attacks and countermeasures in intelligent networks: Status quo and prospects

Over the past years, the emergence of intelligent networks empowered by machine learning techniques has brought great facilitates to different aspects of human life. However, using machine learning in intelligent networks also presents potential security and privacy threats. A common practice is the so-called poisoning attacks where malicious users inject fake training data with the aim of corrupting the learned model. In this survey, we comprehensively review existing poisoning attacks as well as the countermeasures in intelligent networks for the first time. We emphasize and compare the principles of the formal poisoning attacks employed in different categories of learning algorithms, and analyze the strengths and limitations of corresponding defense methods in a compact form. We also highlight some remaining challenges and future directions in the attack-defense confrontation to promote further research in this emerging yet promising area.     

Human motion prediction using optimized sliding window polynomial fitting and recursive least squares

Human motion prediction is a critical issue in human-robot collaboration (HRC) tasks. In order to reduce thelocal error caused by the limitation of the capture range and sampling frequency of the depth sensor, a hybrid human motion prediction algorithm, optimized sliding window polynomial fitting and recursive least squares (OSWPF-RLS) was proposed. The OSWPF-RLS algorithm uses the human body joint data obtained under the HRC task as input, and uses recursive least squares (RLS) to predict the human movement trajectories within the time window. Then, the optimized sliding window polynomial fitting (OSWPF) is used to calculate the multi-step prediction value, and the increment of multi-step prediction value was appropriately constrained. Experimental results show that compared with the existing benchmark algorithms, the OSWPF-RLS algorithm improved the multi-     

Automatic Recognition of Human Walking in Monocular Image Sequences

In numerous content-based video applications, it is important to extract from a video sequence a representation for humans in motion. This task is difficult, because humans are not rigid objects and they are capable of performing a wide variety of actions. However, often, human movements can be categorized into repetitive and rhythmic patterns of motion. Identifying the motion pattern of a human significantly alleviates the task of construction of its representation. We propose here a model-based recognition of the generic posture of human walking in dynamic scenes. We model the human body as an articulated object connected by joints and rigid parts, and model the human walking as a periodic motion. The recognition task is to fit the model walker sequence to the walker in the live video (data walker sequence). We achieve this by determining the period of the data walker sequence and finding its phase with respect to the model walker sequence. We present promising results of how our system performs with a live video sequence.     

Integration versus segregation in functional brain networks

We propose a new methodology to evaluate the balance between segregation and integration in functional brain networks by using singular value decomposition techniques. By means of magnetoencephalography, we obtain the brain activity of a control group of 19 individuals during a memory task. Next, we project the node-to-node correlations into a complex network that is analyzed from the perspective of its modular structure encoded in the contribution matrix. In this way, we are able to study the role that nodes play I/O its community and to identify connector and local hubs. At the mesoscale level, the analysis of the contribution matrix allows us to measure the degree of overlapping between communities and quantify how far the functional networks are from the configuration that better balances the integrated and segregated activity.     

Using replicator dynamics for analyzing fMRI data of the human brain

The understanding of brain networks becomes increasingly the focus of current research. In the context of functional magnetic resonance imagery (fMRI) data of the human brain, networks have been mostly detected using standard clustering approaches. In this work, we present a new method of detecting functional networks using fMRI data. The novelty of this method is that these networks have the property that every network member is closely connected with every other member. This definition might to be better suited to model important aspects of brain activity than standard cluster definitions. The algorithm that we present here is based on a concept from theoretical biology called "replicator dynamics."     

Estimating volumetric motion in human thorax with parametric matching constraints

In radiotherapy (RT), organ motion caused by breathing prevents accurate patient positioning, radiation dose, and target volume determination. Most of the motion-compensated trial techniques require collaboration of the patient and expensive equipment. Estimating the motion between two computed tomography (CT) three-dimensional scans at the extremes of the breathing cycle and including this information in the RT planning has been shyly considered, mainly because that is a tedious manual task. This paper proposes a method to compute in a fully automatic fashion the spatial correspondence between those sets of volumetric CT data. Given the large ambiguity present in this problem, the method aims to reduce gradually this uncertainty through two main phases: a similarity-parametrization data analysis and a projection-regularization phase. Results on a real study show a high accuracy in establishing the spatial correspondence between both sets. Embedding this method in RT planning tools is foreseen, after making some suggested improvements and proving the validity of the two-scan approach.     

Automated analysis of repetitive joint motion

Automated measurement, analysis, and comparison of human motion during performance of workplace tasks or exercise therapy are core competencies required to realize many telemedicine applications. Ergonomic studies and telemonitoring of patients performing rehabilitation exercises are examples of applications that would benefit from a representation of complex human motion in a form amenable to comparison. We present a representation of joint motion suitable for the analysis of multidimensional angular joint motion time series data. Complex motion is reduced to a concatenation motion segments, where simple dynamic models approximate the observed motion on each segment. This compact representation still enables measurement of statistics familiar to ergonomics practitioners such as cycle length and task duration. An algorithm to obtain this representation from observed motion data (time series) is given. We introduce a metric, based on a kinetic energy-like measure, to compare motions. Experiments are presented to demonstrate the representation, its relationship to previous measures and the applicability of the kinetic energy metric for motion comparison.     

Video frame interpolation using deep cascaded network structure

Video frame interpolation is a technology that generates high frame rate videos from low frame rate videos by using the correlation between consecutive frames. Presently, convolutional neural networks (CNN) exhibit outstanding performance in image processing and computer vision. Many variant methods of CNN have been proposed for video frame interpolation by estimating either dense motion flows or kernels for moving objects. However, most methods focus on estimating accurate motion. In this study, we exhaustively analyze the advantages of both motion estimation schemes and propose a cascaded system to maximize the advantages of both the schemes. The proposed cascaded network consists of three autoencoder networks, that process the initial frame interpolation and its refinement. The quantitative and qualitative evaluations demonstrate that the proposed cascaded structure exhibits a promising performance compared to currently existing state-of-the-art-methods.     

Integrated Scheduling of Grid Applications in WDM Optical Light-Trail Networks

A novel task graph model, flexible task model (FTM), is proposed for modeling the grid computing tasks and the relationships among the tasks. In this model, a task may generate output before the task completes whereas previous work assumes that no output is available until the task is completed. In addition, a task in FTM can start to execute when it has collected a minimum amount of required input from its predecessors. FTM is more general and flexible than the conventional task graph model considered in previous work. Based on FTM, we investigate the problem of scheduling grid applications that integrates the resource allocation for task execution and service provisioning for subwavelength data communication between the tasks. Data communication between grid tasks under the FTM model is better supported using light-trails in wavelength division multiplexing (WDM) networks, than lightpaths. The objective is to minimize the total amount of time for task completion or makespan. Simulation results show that our proposed scheduling algorithm under FTM significantly reduces the total task completion time compared with that under the conventional task graph model. Moreover, the communication service provisioning using light-trails is very resource efficient.      

Programming by touch: the different way of human-robot interaction

Mobile service robots will share their workspaces, e.g., offices, hospitals, or households, with humans. Thus, a direct contact between man and machine is inevitable. Robots equipped with appropriate sensors can sense the touch. In this paper, we present how an unskilled user can intuitively teach the lightweight robot at the German Aerospace Center (DLR), We/spl szlig/ling, Germany, just by touching the arm. Programming by "touch" is very intuitive as you take the robot by the hand and demonstrate the movements. This feature can also be used to interact with the service robot while executing a task. Therefore, if our seven-degrees-of-freedom robot arm senses a touch, it will react by an evasive motion of the touched links while keeping the orientation of the tool center point.     

Unmanned aerial vehicles in collaboration with fog computing network for improving quality of service

In this paper, we study a UAV-based fog or edge computing network in which UAVs and fog/edge nodes work together intelligently to provide numerous benefits in reduced latency, data offloading, storage, coverage, high throughput, fast computation, and rapid responses. In an existing UAV-based computing network, the users send continuous requests to offload their data from the ground users to UAV–fog nodes and vice versa, which causes high congestion in the whole network. However, the UAV-based networks for real-time applications require low-latency networks during the offloading of large volumes of data. Thus, the QoS is compromised in such networks when communicating in real-time emergencies. To handle this problem, we aim to minimize the latency during offloading large amounts of data, take less computing time, and provide better throughput. First, this paper proposed the four-tier architecture of the UAVs–fog collaborative network in which local UAVs and UAV–fog nodes do smart task offloading with low latency. In this network, the UAVs act as a fog server to compute data with the collaboration of local UAVs and offload their data efficiently to the ground devices. Next, we considered the Q-learning Markov decision process (QLMDP) based on the optimal path to handle the massive data requests from ground devices and optimize the overall delay in the UAV-based fog computing network. The simulation results show that this proposed collaborative network achieves high throughput, reduces average latency up to 0.2, and takes less computing time compared with UAV-based networks and UAV-based MEC networks; thus, it can achieve high QoS.     

The physical body as a computing interface: Theoretical conceptualization of embodied affordances and empirical validation

Despite the growing interest in immersive virtual reality (IVR) for collaboration and the rising prevalence of commercial applications, the extant body of knowledge on IVR is still in a nascent stage. A paucity of empirical studies have investigated the affordances engendered for collaboration, resulting in a lack of theoretical underpinnings for comprehending the impact of IVR on collaboration outcomes. In an effort to address these lacunae in research, this study explores how IVR affordances for collaboration influence task performance and collaboration satisfaction. Drawing on the metaverse framework for collaboration against the backdrop of the corporeal embodiment concept, this study develops a research model that investigates the interplay between IVR affordances for collaboration, collaborative behavior enactment, and collaboration outcomes. The model was tested using data collected from 168 subjects who participated in a virtual collaboration using IVR in a laboratory setting. The results of the study showed that avatar customizability was a key antecedent to embodied affordances, among which embodied communication and embodied team processing jointly influenced collaborative behavior enactment, which, in turn, influenced collaboration outcomes (task performance and collaboration satisfaction). This study contributes to the IVR literature by conceptualizing novel affordances for collaboration facilitated by IVR and empirically scrutinizing the manner in which perceived affordances precipitate their actualization, subsequently affecting collaboration outcomes. With respect to practice, the findings of this study provide useful insights for organizational managers and IVR developers who seek to harness the benefits of IVR for effective collaboration.