A Comprehensive Review of Data-Driven Co-Speech Gesture Generation

Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co-speech gestures is a long-standing problem in computer animation and is considered an enabling technology for creating believable characters in film, games, and virtual social spaces, as well as for interaction with social robots. The problem is made challenging by the idiosyncratic and non-periodic nature of human co-speech gesture motion, and by the great diversity of communicative functions that gestures encompass. The field of gesture generation has seen surging interest in the last few years, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep-learning-based generative models that benefit from the growing availability of data. This review article summarizes co-speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule-based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text and non-linguistic input. Concurrent with the exposition of deep learning approaches, we chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method (e.g., optical motion capture or pose estimation from video). Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human-like motion; grounding the gesture in the co-occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.     

Evaluation of contextual information retrieval effectiveness: overview of issues and research

The increasing prominence of information arising from a wide range of sources delivered over electronic media has made traditional information retrieval systems less effective. Indeed, users are overwhelmed by the information delivered by such systems in response to their queries, particularly when the latter are ambiguous. In order to tackle this problem, the state-of-the-art reveals that there is a growing interest towards contextual information retrieval which relies on various sources of evidence issued from the user’s search background and environment like interests, preferences, time and location, in order to improve the retrieval accuracy. Contextual information retrieval systems are based on different definitions of the core concept of user’s context, various user’s context modeling approaches and several techniques of document relevance measurement, but all share the goal of providing the most useful information to the users in accordance with their context. However, the evaluation methodologies conceived in the past several years for traditional information retrieval and widely used in the evaluation campaigns have been challenged by the consideration of user’s context in the information retrieval process. Thus, we recognize that a critical review of existing evaluation methodologies in contextual information retrieval area is needed in order to design and develop standard evaluation frameworks. We present in this paper a comprehensive survey of contextual information retrieval evaluation methodologies and provide insights into how and why they are appropriate to measure the retrieval effectiveness. We also highlight some of the research challenges ahead that would constitute substantive research area for future research.     

Hardware transactional memory: A high performance parallel programming model

The transactional memory in multicore processors has been a major research area over past several years. Many transactional memory systems have been proposed to be used to solve the synchronization problem of multicore processors. Hardware transactional memory is one of the critical methods to speedup communications in multicore environment. In this paper, we give a review of the current hardware transactional memory systems for multicore processors. We take a top-down approach to characterizing and classifying various hardware transactional design issues and present a taxonomy of hardware transactional memory systems which is consist of the five fundamental design issues: version management, conflict detection, contention management, virtualization and nesting. Finally, we discussed the active research challenge: the relationship between transactional memory and Input/Output operations and system calls.     

An overview on optimal flocking

The decentralized aggregate motion of many individual robots is known as robotic flocking. The study of robotic flocking has received considerable attention in the past twenty years. As we begin to deploy flocking control algorithms on physical multi-agent and swarm systems, there is an increasing necessity for rigorous promises on safety and performance. In this paper, we present an overview the literature focusing on optimization approaches to achieve flocking behavior that provide strong safety guarantees. We separate the literature into cluster and line flocking, and categorize cluster flocking with respect to the system-level objective, which may be realized by a reactive or planning control algorithm. We also categorize the line flocking literature by the energy-saving mechanism that is exploited by the agents. We present several approaches aimed at minimizing the communication and computational requirements in real systems via neighbor filtering and event-driven planning, and conclude with our perspective on the outlook and future research direction of optimal flocking as a field.     

Approaches on crowd counting and density estimation: a review

In recent years, urgent needs for counting crowds and vehicles have greatly promoted research of crowd counting and density estimation. Benefiting from the rapid development of deep learning, the counting performance has been greatly improved, and the application scenarios have been further expanded. Aiming to deeply understand the development status of crowd counting and density estimation, we introduce and analyze the typical methods in this field and especially focus on elaborating deep learning-based counting methods. We summarize the existing approaches into four categories, i.e., detection-based, regression-based, convolutional neural network based and video-based. Each category is explicated in great detail. To provide more concrete reference, we compare the performance of typical methods on the popular benchmarks. We further elaborate on the datasets and metrics for the crowd counting community and discuss the work of solving the problem of small-sample-based counting, dataset annotation methods and so on. Finally, we summarize various challenges facing crowd counting and their corresponding solutions and propose a set of development trends in the future.

     

The computer and production-logistics. management

During the past two decades there has been a natural and continuing application of computers to problems in the functional area of Production and Logistics Management. These are important management problems, ranging from the design of operating systems to the development of control systems, which require the use of Computers for analysis. Therefore it is important that students in Production and Logistics Management courses become involved with the computer. The approaches to this problem run from a passing mention of the computer to an in-depth computer experience. We have experimented with several approaches, varying from the use of preprogrammed models to requiring student-developed programs. We have devised some compromise devices, including computer augmented cases and games, that help to focus the student's attention on the functional problem analysis and not on the computer technique alone. This paper presents examples of these devices and our experience in using them.     

A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.     

A systematic review of approaches for testing concurrent programs

Concurrent programs are replacing the sequential programs as they utilize the true capabilities of multicore architecture. The extensive use of multicore systems and multithreaded paradigms warrants more attention to the testing of the concurrent programs. The testing concurrent program is not a new field as it has been more than 40 years because the first problem related to the testing concurrent program was addressed by the researchers. The field covers various domains, which include concurrency problems, testing approaches, techniques, graphical representations, tools, and subject systems. This paper aims at providing an overview of research in the domain of testing concurrent programs by classifying it into eight categories: (a) reachability testing, (b) structural testing, (c) model‐based testing, (d) mutation‐based testing, (e) slicing‐based testing, (f) formal methods, (g) random testing, and (h) search‐based testing. The survey is focused on the techniques applied, methodologies followed, and tools used in these aforementioned approaches. Furthermore, the gaps are also identified in different approaches. The paper concludes with the consolidation of various testing parameters along with the future directions. Copyright © 2015 John Wiley & Sons, Ltd.     

A taxonomy of 3D occlusion management for visualization

While an important factor in depth perception, the occlusion effect in 3D environments also has a detrimental impact on tasks involving discovery, access, and spatial relation of objects in a 3D visualization. A number of interactive techniques have been developed in recent years to directly or indirectly deal with this problem using a wide range of different approaches. In this paper, we build on previous work on mapping out the problem space of 3D occlusion by defining a taxonomy of the design space of occlusion management techniques in an effort to formalize a common terminology and theoretical framework for this class of interactions. We classify a total of 50 different techniques for occlusion management using our taxonomy and then go on to analyze the results, deriving a set of five orthogonal design patterns for effective reduction of 3D occlusion. We also discuss the "gaps" in the design space, areas of the taxonomy not yet populated with existing techniques, and use these to suggest future research directions into occlusion management.     

Volume-aware positioning in the context of a marine port terminal

The rapid proliferation of mobile computing devices and local wireless networks over the past few years has promoted a continuously growing interest in location-aware systems and applications. The main problem with existing positioning techniques is that they are designed to position dimensionless objects. Such an assumption may lead to practical inconsistencies, as it results in neglecting the effects of the volume of an object, its physical characteristics, and its spatial arrangement on signal propagation. In this paper, we consider the problem of positioning cargo containers in a marine port terminal, where such characteristics can be finely estimated. Based on the signal propagation map of a container yard, we propose VAPS, a volume-aware positioning system that takes advantage of the waveguide effect generated by the containers. Although VAPS is specific to the marine port scenario, its design principles can be extended and adapted to other situations. VAPS maps discrete RSSI levels into hop-counts and relies on realistic propagation models to obtain near-perfect positioning at a very low control overhead. Our extensive evaluations show how to set the parameters required in the VAPS algorithm. The results demonstrate that, in scenarios where the assumptions made by traditional approaches fail, the considerations of VAPS make the difference.     

More applicable environmental scanning systems leveraging “modern” information systems

With Ansoff’s article about weak signals as a flagship example, a substantial body of knowledge about environmental scanning systems exists. However, these concepts often go unused in practice. The 2008/2009 economic crisis provided a strong, ongoing impulse for redesigning such information systems (IS). This article develops six guidelines for the conceptual design of environmental scanning systems that are more applicable than those specified by previous research. We start with literature research, which reveals three gaps in existing approaches. Then we develop design guidelines to fill these gaps with the help of “modern” IS. To address the lack of sound requirements analysis, our first design principle proposes 360-degree environmental scanning systems for executives and suggests how to select the most important scanning areas. Three further findings cover weaknesses in the IS model perspective, focusing on more effective implications of weak signals. In terms of method, we propose incorporating scanning results more closely into executives’ decision-making processes. Applying the design guidelines at a raw materials and engineering company, we arrive at a prototype we call the “corporate radar.” It includes an IS-based tree with economic value added at risk on top. The resulting lessons learned help to evaluate our findings and the research method presented here, as well provide concrete starting points for future research.     

On decidability of LTL model checking for process rewrite systems

We establish a decidability boundary of the model checking problem for infinite-state systems defined by Process Rewrite Systems (PRS) or weakly extended Process Rewrite Systems (wPRS), and properties described by basic fragments of action-based Linear Temporal Logic (LTL) with both future and past operators. It is known that the problem for general LTL properties is decidable for Petri nets and for pushdown processes, while it is undecidable for PA processes.We show that the problem is decidable for wPRS if we consider properties defined by LTL formulae with only modalities strict eventually, strict always, and their past counterparts. Moreover, we show that the problem remains undecidable for PA processes even with respect to the LTL fragment with the only modality until or the fragment with modalities next and infinitely often.     

Multimodal research in vision and language: A review of current and emerging trends

Deep Learning and its applications have cascaded impactful research and development with a diverse range of modalities present in the real-world data. More recently, this has enhanced research interests in the intersection of the Vision and Language arena with its numerous applications and fast-paced growth. In this paper, we present a detailed overview of the latest trends in research pertaining to visual and language modalities. We look at its applications in their task formulations and how to solve various problems related to semantic perception and content generation. We also address task-specific trends, along with their evaluation strategies and upcoming challenges. Moreover, we shed some light on multi-disciplinary patterns and insights that have emerged in the recent past, directing this field toward more modular and transparent intelligent systems. This survey identifies key trends gravitating recent literature in VisLang research and attempts to unearth directions that the field is heading toward.     

CBPOP: A Domain-Independent Multi-Case Reuse Planner

The reuse of multiple cases to solve a single planning problem presents a promise of better utilization of past experience over single-reuse planning, which can lead to better planning performance. In this paper, we present the theory and implementation of CBPOP, and show how it addresses the multi-reuse planning problems. In particular, we present novel approaches to retrieval and refitting. We also explore the difficult issue of when to retrieve in multi-reuse scenarios, and we empirically compare the results of several solutions we propose. Results from our experiments show that the best ranking function for pure generative planning is not necessarily the best ranking function for multi-reuse planning. The surprising result in the reuse scenarios is that the single-goal case library performed better than larger case libraries consisting of solutions to multi-goal problems.     

Data dependent loop scheduling based on genetic algorithms for distributed and shared memory systems

Many approaches have been described for the parallel loop scheduling problem for shared-memory systems, but little work has been done on the data-dependent loop scheduling problem (nested loops with loop carried dependencies). In this paper, we propose a general model for the data-dependent loop scheduling problem on distributed as well as shared memory systems. In order to achieve load balancing and low runtime scheduling and communication overhead, our model is based on a loop task graph and the notion of critical path. In addition, we develop a heuristic algorithm based on our model and on genetic algorithms to test the reliability of the model. We test our approach on different scenarios and benchmarks. The results are very encouraging and suggest a future parallel compiler implementation based on our model.     

A multi-agent approach to Intelligent Transportation Systems modeling with combinatorial auctions

Challenges of urbanization require new, more flexible approaches to design of public transportation systems. Demand Responsive Transport systems (DRT) that provide a share transportation services with flexible routes and focus on optimizing of economic and environmental value are becoming an important part of public transportation. In this paper we propose a new approach to design of DRT models which considers DRT as a multi-agent system (MAS) where various autonomous agents represent interests of system’s stakeholders. The distributed nature of the MAS facilitates design of scalable implementations in modern cloud environments. We also propose a planning algorithm based on combinatorial auctions (CA) that allows to express commodity of multiple transportation scenarios by evident means of the bids. Using the mechanism of CA we may fully take into account the presence of complementariness and substitutability among the items that differ across bidders. Further, we describe design principles of our proposed software with a prototype implementation. We believe that our approach to multi-agent modeling is general enough to provide the flexibility necessary for adoption of DRT-services modeling into real-world scenarios. The results of modeling have been compared against several cases of a local bus provider and validated in a set of computational experiments.     

Managing Scientific Uncertainty in Environmental Health: The Example of Electric and Magnetic Fields

Over the past thirty years, the Electric and Magnetic Fields (EMF) health issue has raised public concern, received media attention, created electric utilities and government agencies awareness, and led to an important effort of scientific investigation. It has been an exemplary experience in managing different aspects of a complex environmental health problem through research, information, and communication. In this article, the historical context and the various approaches to the problem will be described. We will identify the effective rules that were followed and helped to manage the risks and to prevent a public health crisis.     

Takagi-Sugeno-Kang fuzzy system fusion: A survey at hierarchical,wide and stacked levels

With excellent global approximation performance and interpretability, Takagi-Sugeno-Kang (TSK) fuzzy systems have enjoyed a wide range of applications in various fields, such as smart control, medical, and finance. However, in handling high-dimensional complex data, the performance and interpretability of a single TSK fuzzy system are easily degraded by rule explosion due to the curse of dimensionality. Ensemble learning comes into play to deal with the problem by the fusion of multiple TSK fuzzy systems using appropriate ensemble learning strategies, which has shown to be effective in eliminating the issue of the curse of dimensionality curse problem and reducing the number of fuzzy rules, thereby maintaining the interpretability of fuzzy systems. To this end, this paper gives a comprehensive survey of TSK fuzzy system fusion to provide insights into further research development. First, we briefly review the fundamental concepts related to TSK fuzzy systems, including fuzzy rule structures, training methods, and interpretability, and discuss the three different development directions of TSK fuzzy systems. Next, along the direction of TSK fuzzy system fusion, we investigate in detail the current ensemble strategies for fusion at hierarchical, wide and stacked levels, and discuss their differences, merits and weaknesses from the aspects of time complexity, interpretability (model complexity) and classification performance. We then present some applications of TSK fuzzy systems in real-world scenarios. Finally, the challenges and future directions of TSK fuzzy system fusion are discussed to foster prospective research.     

Distributed Partial Order Reduction of State Spaces

State space explosion is a fundamental obstacle in formal verification of concurrent systems. Several techniques for combating this problem have emerged in the past few years, among which the two we are interested in are: partial order reduction and distributed memory state exploration. While the first one tries to reduce the problem to a smaller one, the other one tries to extend the computational power to solve the same problem. In this paper, we consider a combination of these two approaches and propose a distributed memory algorithm for partial order reduction.     

Organisations- und Technologieoptionen des Geschäftsprozessmanagements aus der Perspektive des Web 2.0

Corporate operational systems are often highly dynamic, a fact which is only insufficiently taken into account by recent process management approaches. In contrast, the perspective of Web 2.0 opens up new options for action in process management. In this contribution, we will identify new organizational and technological options of process management using a design-oriented research approach. The analysis especially considers the aspects of self-organization and collective intelligence in process management. We conceptually develop options for action and illustrate them based on a prototype platform for process management. The results are completed by a presentation of real-world application scenarios in the construction industry and results of an evaluation of the design-oriented research approach.