Seeing Remote Team Members as Leaders: A Study of US-Scandinavian Teams

Virtual teams enable knowledge workers from around the globe to participate in a variety of projects through computer-mediated interactions. Given that many of these virtual teams, including those involved in open source software development and other systems development, may not have preassigned leaders, it is important to understand the factors that explain why an individual may be considered a leader by team members in other locations. Specifically, the factors information systems development (ISD) ability, contribution, knowledge transfer, and team cohesion are hypothesized to influence remote leader emergence in distributed ISD teams. The proposed model is tested in a study using ISD teams composed of student members from both the US and Scandinavia. ISD ability, contribution, and knowledge transfer in cohesive teams were found to be significant predictors of remote leadership emergence. The study also suggests that US and Scandinavian members do not use the same criteria for identifying remote team members as leaders and offers a theoretical explanation of the results.      

The Process of Decision Modification in Small Discussion Groups

This study qualitatively examined the interaction patterns associated with group decision-making. Specifically, a category system identified the functions which verbal behavior performs upon tentatively introduced decision proposals and yielded a theory of decision modification within group discussions. Groups do not apparently make decisions gradually by direct amendments but in sudden jumps to different formulations of the same root proposal. That is, proposals were introduced, discussed, dropped, and reintroduced in modified form. Two discernible patterns of decision modification emerged. One pattern involved systematically reformulating the proposal in language at successively lower levels of abstraction until the proposal achieved consensus. The second pattern involved substitute decision proposals at essentially equivalent levels of abstraction until the revised proposal achieved consensus. Unlike previous theories of flight behavior resulting from interpersonal conflict, the present study indicates that periods of task avoidance are inherent within the group process of cumulative decision modification. “I don't think they play at all fairly,” Alice began, in a rather complaining tone, “and they all quarrel so dreadfully one can't hear oneself speak–and they don't seem to have any rules in particular; at least, if there are, nobody attends to them–and you've no idea how confusing it is all the things being alive….”     

Improved action proposals using fine-grained proposal features with recurrent attention models

Recent models for the temporal action proposal task show that local properties can be an alternative to the region proposal network (RPN) for generating good proposal candidates on untrimmed videos. In this study, we devise an RPN model with a new two-stage pipeline and a new joint scoring function for temporal proposals. The evaluation of local properties is integrated into our RPN model to search for the best proposal candidates that can be distinguished mainly in fine details of proposal regions. Our network models proposals in multiple scales using two recurrent neural network layers with attention mechanisms. We observe that joint training of the RPN with local clues and multi-scale modeling of proposals with recurrent attention mechanisms improve the performance of the proposal generation task. Our model yields state-of-the-art results on the THUMOS-14 and comparable results on the ActivityNet-1.3 datasets.     

The Case for Caring Colearners: The Effects of a Computer-Mediated Colearner Agent on Trust and Learning

This research examines the effects of a computer-mediated colearner agent that manifests caring orientations toward human learners. Just as caring orientations have positive effects on trust and learning in human–human interaction, caring orientations manifested by a computer-mediated colearner agent resulted in positive consequences. We conducted an experiment using a computer application designed to test the effects of colearner agents that manifest caring orientations in the English idiom–learning context. The findings demonstrate that a caring colearner agent led to greater trust and learning measured by recall memory when compared with a noncaring colearner agent; recognition memory, which was another measure of learning, did not show a significant difference. Two intervening variables, namely, feelings of social support and perceived intelligence of the colearner agent, were tested for mediation; the results showed that the effects of caring orientations of the colearner agent on trust were mediated both by feelings of social support and by perceived intelligence of the agent, whereas the effects of caring orientations on recall memory were mediated by feelings of social support only. The authors discuss implications for incorporating positive social virtues, such as caring orientations, into interactive media to enhance communication and learning.     

Predictors of support for doctoral programs in media information technologies

This study assesses interest in curricular development in new technologies among communication faculty, with a focus on doctoral-level programs. Diffusion of innovations theory explains both individual- and organizational-level factors that may account for adoption of innovations, including those stemming from new technologies. In the field of communication, media information technology programs represent an organizational-level innovation of clear importance but with uncertain faculty support, due to individual factors (e.g., interest area, age) and departmental constraints (e.g., lack of resources). In light of this, several research questions are advanced to address: (1) the current level of support for media information technology programs among communication faculty members and (2) predictors of faculty interest in having more media information technology programs. Data were collected through a random sample of faculty members who filled out a survey on the World Wide Web. Results indicate that media information technology programs are viewed by faculty as the most needed type of program in the field of communication. Findings from a logistic regression analysis also reveal several predictors of support for new technology programs, including male gender, support for organizational communication programs, support for programs focusing on mass communication, and the belief that multimedia teaching facilities are important. The implications of these findings are discussed, with an emphasis on their relevance to faculty working in communication and related fields.     

R&D project selection using the analytic network process

The analytic network process (ANP) is presented as a potentially valuable method to support the selection of projects in a research and development (R&D) environment. This paper first discusses the requirements of the R&D project selection problem, which requires the allocation of resources to a set of competing and often disparate project proposals. Among the factors complicating this task is the need to make the decision within the framework of an enterprise's strategic objectives and organizational structure while considering and integrating financial and strategic benefits of each project. The paper discusses the use of the ANP, a general form of Saaty's analytic hierarchy process, as a model to evaluate the value of competing R&D project proposals. A generic ANP model developed by the authors, which includes in its decision levels the actors involved in the decision, the stages of research, categories of metrics, and individual metrics, is presented. The paper concludes with a case study describing the implementation of this model at a small high-tech company, including data based on the actual use of the decision making model     

ETS-3D: An Efficient Two-Stage Framework for Stereo 3D Object Detection

We propose an efficient two-stage framework for stereo 3D object detection, called ETS-3D. Contrary to many recent approaches that rely on depth maps predicted using time-consuming stereo matching models, our approach utilizes the well-designed features to generate high-quality 3D proposals in stage-1, without explicitly exploiting predicted depth map. Specifically, we leverage pixel-wise correlation to produce normalized cost volumes to weight the left image features, and fuse multi-scale weighted features to obtain the weighted and fused features for 3D proposal generation. To maintain fast computation, only the filtered positive 3D proposals are fed into the stage-2 sub-network for further proposal refinement and quality prediction. Furthermore, we reconstruct the 3D proposal features in stage-2 to make use of different feature representations, achieving more accurate detection results. The experimental results on the KITTI 3D object detection benchmark demonstrate that our method achieves state-of-the-art performance, and can run at more than 10 fps.     

Computer-mediated social support, older adults, and coping

One important new context of communication for older adults is computer-mediated communication (CMC). Although the Internet has become an important resource for information, little is known about the ways in which individuals use this technology for social support. Older adults (mean age = 62) using SeniorNet and other related websites completed an on-line questionnaire (N = 136) investigating social support in the computer-mediated environment. Satisfaction with Internet providers of social support was significantly higher for high Internet users than for low Internet users whereas low Internet users were more satisfied with their non-Internet support networks than high Internet users. Internet companionship network size was significantly larger than Internet social support network size. Greater involvement with the on-line community was predictive of lower perceived life stress. Social support and companionship satisfaction were not related to the types of coping strategies used; however, the results indicate that direct action was the most common coping strategy for the sample.     

基于动作注意策略的树形DDQN目标候选区域提取方法

针对机器人在家庭环境下的目标检测问题,该文提出一种基于动作注意策略的树形双深度Q网络(TDDQN)目标候选区域提取的方法,该方法将双深度Q网络(DDQN)的方法与树结构的方法相结合,通过执行改变检测框的动作以使目标逐渐集中在检测框内。首先采用DDQN方法在执行较少的动作后选择出当前状态的最佳动作,获取符合条件的候选区域。然后根据执行所选择动作之后所得到的状态重复执行上述过程,以此构成树结构的多条“最佳”路径。最后采用非极大值抑制的方法从多个符合条件的候选区域选择出最佳候选区域。在Pascal VOC2007以及Pascal VOC2012上的实验结果表明,在不同数量的候选区域、不同阈值的IoU和不同大小以及不同种类对象的实验条件下,所提方法较其他方法都有着更好的检测性能,可以较好地实现目标检测。

     

Compensatory adaptation to a lean medium: an action researchinvestigation of electronic communication in process improvement groups

Previous empirical findings from the computer-mediated communication research literature are consistent with media richness theory because they suggest that the use of electronic communication media is likely to have a negative impact on the success and outcome quality of process improvement groups. These findings lead to the expectation that electronic communication media will not be as appropriate as the face-to face medium to support the type of complex and knowledge-rich communication that takes place in process improvement groups. The paper analyzes 12 process improvement groups interacting through an electronic communication medium and finds this expectation unfounded. In fact, the use of an electronic communication medium can actually have the opposite effect, that is, a "positive" effect, on process improvement group success and outcome quality. Two other theoretical models, namely the compensatory adaptation and social influence models, are used to explain these counter intuitive findings     

A site entropy rate and degree centrality based algorithm for image co-segmentation

In this paper, we propose a graph based algorithm that efficiently segments common objects from multiple images. We first generate a number of object proposals from each image. Then, an undirected graph is constructed based on proposal similarities and co-saliency maps. Two different methods are followed to extract the proposals containing common objects. They are: (1) degree centrality of nodes obtained after graph thresholding and (2) site entropy rate of nodes calculated on the stationary distribution of Markov chain constructed on the graph. Finally, we obtain the co-segmented image region by selecting the more salient of the object proposals obtained by the two methods, for each image. Multiple instances of the common object are also segmented efficiently. The proposed method has been compared with many existing co-segmentation methods on three standard co-segmentation datasets. Experimental results show its effectiveness in co-segmentation, with larger IoU values as compared to other co-segmentation methods.     

Improving the Effectiveness of Virtual Teams: A Comparison of Video-Conferencing and Face-to-Face Communication in China

As virtual teams become more and more important in organizations, understanding how to improve virtual team relational development and meeting outcomes is vital to project success. The objective of this study was to investigate how the dialogue technique that facilitated building of shared understanding in virtual teams can be used to enhance virtual team relational development and decision outcomes in a Chinese cultural context. The results from an experiment demonstrate that the adopted dialogue technique can indeed help team members develop their team relations and enhance their perceived team meeting outcomes. Video-conferencing virtual teams with shared mental models may be engaged as effectively as traditional face-to-face teams. Moreover, this study reveals that the dialogue technique can enhance face-to-face team outcomes. Therefore, the findings of this study have both theoretical and practical implications for helping teams develop shared understanding of effective communication and enhance decision-making outcomes in the Chinese cultural context.      

Sustainable decision making: the role of decision support systems

Sustainable decision making stands for decision making which contributes to the transition to a sustainable society. It raises a number of challenging problems for which existing decision support systems (DSS) may not be equipped. The role of DSS in sustainable decision making is considered. The different models of decision making and their appropriateness in sustainable decision making are discussed. Examples from the areas of water resources and energy planning and management are presented to illustrate some of the issues in sustainable decision making and the role of DSS. Conclusions include a suggested research program for further development of models of decision making and the development of DSS for use in sustainable decision making     

Conditional Random Field based salient proposal set generation and its application in content aware seam carving

Most of the existing generic object localization algorithms usually give the plausible object locations without taking into consideration the saliency ordering of the proposal set. This paper presents a novel object proposal generation which ranks the key objects according to their saliency score in the proposal pool. First, we formulate a Bayesian framework for generating a probabilistic edgemap which is used to assign a saliency value to the edgelets. A conditional random field is then learnt for edge-labeling by effectively combining the edge features with the relative spatial layout of the edge segments. Lastly, we propose an objectness score for the generated proposal set by analyzing the salient object edge density completely lying within the candidate boxes. Extensive experiments on the benchmark PASCAL VOC 2007 and 2012 datasets demonstrate that the proposed method provides competitive performance against popular generic object detection techniques while using fewer number of proposals. Additionally, we demonstrate the applicability of the generated proposal set for content aware image retargeting.     

Developing an optimal match within online communities: an exploration of CMC support communities and traditional support

This study investigates the complementary nature of face-to-face and computer-mediated social support and the development of a context through which hyperpersonal communication can develop within online communities. Optimal matching theory is used as a framework for explaining how hyperpersonal communication develops within online cancer support communities. We compared online participants' perceptions of illness support from the list with the support they received from a nonmediated relationship. Respondents participated more within the online community only when they perceived that the depth and support that they received from the online community was high, and when the depth and support they received from the specific person in their life was low.     

Enthusiasm in technical proposals: verifying a method of lexicalanalysis

Analyzing proposals for evidence of enthusiasm verified a method of lexical analysis and substantiated the presence of enthusiasm in social science/humanities proposals but not in science/engineering proposals. Proposal evaluators, both experts and nonexperts, react to technical accuracy as well as subjective elements in the proposal document itself. A study of word usage identified a lexicon that reflected enthusiasm in proposals, then analyzed 1000-word samples of text for the presence of this vocabulary. Testing this method on government requests for proposals (RFPs) and business salesmanship texts determined a range of values for an enthusiasm index (EI). Subsequent analysis of fifteen technical proposals as a group revealed no significant difference between the RFPs and the proposals themselves. However, a breakdown by subject yielded a significant difference between those from social science/humanities and those from science/engineering. The successful proposals contained occurrences of enthusiastic lexicon, but the method only examined this one indicator of enthusiasm     

电信运营支撑系统的软件体系结构模式系统

基于对通用软件体系结构模式系统的研究,提出了利用模式系统构建电信运营支撑系统(OSS/BSS)软件体系结构,旨在解决当今支撑系统软件体系结构中存在的片面性问题,同时也为了提高今后支撑系统软件体系结构的构建效率与质量.文中通过对现有相关研究成果的分析,提出了相对全面、完善的支撑系统软件体系结构,基于该体系结构,给出了支撑系统的软件体系结构模式系统,不仅为模式系统开拓了新的应用领域,同时也为支撑系统软件体系结构提出了一种新的构建方法,此外,还为今后进一步的研究工作提供了理论基础和依据.     

Low-cost group rekeying for unattended wireless sensor networks

Wireless sensor networks (WSNs) are made up of large groups of nodes that perform distributed monitoring services. Since sensor measurements are often sensitive data acquired in hostile environments, securing WSN becomes mandatory. However, WSNs consists of low-end devices and frequently preclude the presence of a centralized security manager. Therefore, achieving security is even more challenging. State-of-the-art proposals rely on: (1) attended and centralized security systems; or (2) establishing initial keys without taking into account how to efficiently manage rekeying. In this paper we present a scalable group key management proposal for unattended WSNs that is designed to reduce the rekeying cost when the group membership changes.     

Imbalanced SVM‐Based Anomaly Detection Algorithm for Imbalanced Training Datasets

Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)‐based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned classification hyperplane skews toward the positive samples, resulting in a high false‐negative rate. This article proposes a new imbalanced SVM (termed ImSVM)‐based anomaly detection algorithm, which assigns a different weight for each positive support vector in the decision function. ImSVM adjusts the learned classification hyperplane to make the decision function achieve a maximum GMean measure value on the dataset. The above problem is converted into an unconstrained optimization problem to search the optimal weight vector. Experiments are carried out on both Cloud datasets and Knowledge Discovery and Data Mining datasets to evaluate ImSVM. Highly imbalanced training sample sets are constructed. The experimental results show that ImSVM outperforms over‐sampling techniques and several existing imbalanced SVM‐based techniques.     

WeBox:locating small objects from weak edges

In the object detection task, how to better deal with small objects is a great challenge. The detection accuracy of small objects greatly affects the final detection performance. Our propose a detection framework WeBox based on weak edges for small object detection in dense scenes, and proposes to train the richer convolutional features (RCF) edges detection network in a weakly supervised way to generate multi-instance proposals. Then through the region proposal network (RPN) network to locate each object in the multi-instance proposals, in order to ensure the effectiveness of the multi-instance proposals, we correspondingly proposed a multi-instance proposals evaluation criterion. Finally, we use faster region-based convolutional neural network (R-CNN) to process WeBox single-instance proposals and fine-tune the final results at the pixel level. The experiments have been carried out on BDCI and TT100K proves that our method maintains high computational efficiency while effectively improving the accuracy of small objects detection.