Recent works have investigated bio-inspired strategies to transport objects using decentralized robot networks that only use local measurements without the need for communication between robots. Typically, there is an inverse relationship between the object deformation and the transport time, i.e., reducing the object deformation requires an increase in the transport time needed for transitioning from the initial configuration to the final configuration of the object. In contrast, the main contribution in this article is the development of a delayed self-reinforcement (DSR) approach that seeks to decentralize the ideal centralized transport that has no object deformation, and thereby, reduces the object deformation without increasing the transport time for the same specified transport trajectory. In the DSR method, each robot only uses already available prior-information to reinforce each robot's actions, and thus, the DSR implementation does not require additional information or changes in the network structure. Another contribution in this article is to analyze the potentially time-varying dynamics to establish conditions on the control parameters for stability and quantify the performance of the proposed DSR-based transport. Furthermore, experimental results are presented to show that the proposed cohesive transport method can reduce the deformation by at least 72% for the same transport time, when compared with the case without DSR.

Recent years have witnessed a sharp increase of malicious apps that steal users' personal information. To address users' concerns about privacy risks and to comply with data protection laws, more and more apps are supplied with privacy policies written in natural language to help users understand an app's privacy practices. However, little is known whether these privacy policies are trustworthy or not. Questionable privacy policies may be prepared by careless app developers or someone with malicious intention. In this paper, we carry out a systematic study on privacy policy by proposing a novel approach to automatically identify five kinds of problems in privacy policy. After tackling several challenging issues, we implement the approach in a system, named PPChecker, and evaluate it with real apps and their privacy policies. The experimental results show that PPChecker can effectively identify questionable privacy policies with high precision. Applying PPChecker to 2,500 popular apps, we find that 1,850 apps (i.e., 74.0 percent) have at least one kind of problems. This study sheds light on the research of improving and regulating apps' privacy policies.

A privacy policy is a statement informing users how their information will be collected, used, and disclosed. Failing to provide a correct privacy policy may result in a fine. However, writing privacy policy is tedious and error-prone, because the author may not understand the source code well as it could have been written by others (e.g., outsourcing), or the author does not know the internal working of third-party libraries used. In this paper, we propose and develop a novel system named AutoPPG to automatically construct correct and readable descriptions to facilitate the generation of privacy policy for Android applications (i.e., apps). Given an app, AutoPPG first conducts static code analysis to characterize its behaviors related to users' personal information, and then applies natural language processing techniques to generating correct and accessible sentences for describing these behaviors. The experimental results using real apps and crowdsourcing indicate that: 1) AutoPPG creates correct and easy-to-understand descriptions for privacy policies; 2) the privacy policies constructed by AutoPPG usually reveal more operations related to users' personal information than existing privacy policies; and 3) most developers, who reply us, would like to use AutoPPG to facilitate them.

Procurement is a set of activities and processes related to acquisition of goods and services through purchase orders placed by organization employees,from external contractors. Thisarticle describes practical experiments with procurement dataset of a major governmental organization in Singapore. In particular, we highlight the problems that emerge when trying to implement analytics for prediction of future purchases. The goal of such analytics is to deliver beneficial information to procurement office that plans and manages relationships with external sellers. In the article we describe the characteristics of the procurement dataset specifics and its implications on the future purchase problem that we attempt to solve using Markov chains model. Our analysis showshigh diversity of purchase descriptionsresulting in low ability to detect sequential patterns of purchasing officers. The solution presented in the article is additional dataset preprocessing involving use of hierarchical clustering. Our experiments with various similarity measures show an improvement allowing a practical deployment within our procurement analytics system prepared for the case study governmental organization.

With the number of people considered to be obese and having chronic disease rising across the world, the role of IT solution in nutrition management and planning has been receiving increased attention by medical professionals in recent years. A key factor toward a successful personalized nutrition planning is an individual's food preference instead of dogmatic nutrition pattern since it is unlikely that an individual would accept the meal plan merely based on the nutrition supplements. However, personal preferences about foods are obviously relatively harder to acquire comparing to common nutrition requirements that can be easily obtained from guidelines. In this paper, we proposed a personalized nutrition planning method based on random walks theory to maximize the planning satisfaction. The personal guidelines generated by the proposed method are expected to potentially improve the healthy diet compliance and thus reduce the risk of chronic diseases of individuals.

One current direction to enhance the search accuracy in visual object retrieval is to reformulate the original query through (pseudo-)relevance feedback, which augments a query with visual terms from the image documents most highly ranked by an initial search or identified by user. However, query and feedback images usually contain multiple objects or aspects, and as a consequence the original query's focus may drift because of the newly added terms and noises. The results of using an augmented query are thus often inferior to that of using only the original one. In this paper we propose the topic-sensitive image retrieval with noise-proof relevance feedback to address the query drift problem in visual object retrieval. The proposed method removes irrelevant noises and topics from both query and feedback images to prevent query drift. A discriminative learning strategy is then employed to re-rank and improve the initial search result. Experiments on a real world data set demonstrate the effectiveness of our approach and show that the proposed approach can better learn user intention.

Effective personal dietary guidelines are essential for health management and preventing chronic diseases. The objective of this research is to achieve nutrient-balanced food recommendations for each individual, while considering individual's requirements at the same time. To reach this goal, we developed a location-aware interactive diet consultant named SmartDiet based on the multi-objective optimization. The proposed personalized diet planning approach not only translates nutrient recommendations into realistic dish choices, but also accepts feedbacks from users to fine-tune their meal plans. The results showed that daily nutrition needs can be fulfilled by the designated meals, and the interactive diet planning scheme helps a user adjust the plan in an easier way. The guidelines generated by SmartDiet are expected to potentially improve the overall health and reduce the risk of chronic diseases of individuals.

Offering personalized services through dynamically formed ecosystems is essential to personal wellness management. In this paper, we present the design of a cloud-enabled platform to facilitate the collection and delivery of evidence for personalization in a multi-provider ecosystem environment. In

Just as digital design did 15 years ago; analog design has now reached a transition. The move to CMOS has made analog circuits more functionally complex, and that complexity leads naturally to functional errors in the designs, which in turn leads to respins and delays. And just as digital designers did 15 years ago, analog designers are beginning to realize that they need to employ a rigorous functional verification methodology.

The design of complex mixed-signal system-on-a-chip (SOC) designer poses challenging requirements on the simulation design environment. The simulation platform has to include simulations at the behavioral, gate and transistor-level which have traditionally been done in separate environments. As the scaling trend continues, the designer needs additional accuracy and capacity, new capabilities such as efficient statistical simulation that takes into account layout dependent effects. In this panel we have representatives from the CAD and design community discussing the challenges and current solutions available to the mixed-signal simulation challenge.

Meeting performance specifications in the design of analog and RF (A/RF) blocks and integrated circuits (IC) continues to require a high degree of skill, creativity, and expertise. However, today's A/RF designers are increasingly faced with a new challenge. Functional complexity in terms modes of operation, extensive digital calibration, and architectural algorithms is now overwhelming traditional A/RF design methodologies. Functionally verifying A/RF designs is a daunting task requiring a rigorous methodology. As occurred in digital design, analog verification is becoming a separate and critical task. This paper describes the verification issues faced by the A/RF designer and presents a verification methodology to address these challenges. It presents a systematic approach to A/RF verification, the concept of an analog verification engineer, how to establish the methodology, and concludes with an example

The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online business-to-business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different quality of service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming.

As the desire for business intelligence capabilities for e-business processes expands, existing workflow management systems and decision support systems are not able to provide continuous, real-time analytics for decision makers. Business intelligence requirements may appear to be different across the various industries, but the underlying requirements are similar nformation that is integrated, current, detailed, and immediately accessible. In this paper we introduce an agent-based architecture that supports a complete business intelligence process to sense, interpret, predict, automate and respond to business processes and aims to decrease the time it takes to make business decisions. In fact, there should be almost zero-latency between the cause and effect of a business decision. Our architecture enables analysis across corporate business processes notifies the business of auctionable recommendations or automatically triggers business operations, effectively closing the gap between business intelligence systems and business processes.

In this paper, we present the Web services outsourcing manager framework via a mathematical model for dynamic business processes configuration using existing Web services to meet customers' requirements. An XML-based annotation document is proposed to capture the business requirements and used to dynamically generate search scripts for an advanced Web services discovery engine to find Web services from UDDI registries and Web service inspection language documents. A list of available Web services is returned for further composition and optimization to produce the final business process. This paper proposes a novel mechanism to map a service selection problem into a solution space {0,1} to utilize gold optimization algorithms such as genetic algorithms (GA). A working research prototype has been implemented to demonstrate the feasibility of the on-demand Web services flow composition.

In this paper, a manageable Web services hub framework and enabling technologies for e-sourcing are proposed for buyers, suppliers, service providers and trading partners to register business, user and service information as well as to provision and subscribe to services. The proposed framework enables a business to dynamically configure its business processes to integrate with existing Web services provided by other enterprises based on service requirements. Next, a multi-level security mechanism ensuring secure communications in a hub or among hubs is presented. An advanced Web services discovery mechanism is introduced after that for looking up multiple UDDI registries and aggregating results in an efficient manner. A working system of this framework, Managed e-Hub, which provides an infrastructure for enabling next generation e-utilities in a secure and controlled environment, is also presented in this paper. Some further research ideas are given at the end of the paper.

More and more large ASICs require analog subsystems to interface to the real world--to wireless and wired networks, to sensors and transducers in embedded applications, to electrically complex high-speed interconnect. This is a major problem, since these analog subsystems break almost every assumption we know and love about digital systems. Analog circuits interact tightly with the technology, exploit rather than hide the physics of the fab, and manipulate precise electrical quantities rather than friendly binary abstractions. With respect to today's logic-centric CAD flows, analog blocks fit poorly and abstract badly. The digital side of SoC designs is addressed via a mix of cell-based logical and physical synthesis, commercially available soft and hard IP, and company-specific reuse methodologies for migrating complex functional blocks. On the analog side, “reuse” usually means hoping you still employ the person who designed the legacy analog block you are desperately trying to update.

The electronics industry is increasingly focused on the consumer marketplace, which requires low-cost high-volume products to be developed very rapidly. This, combined with advances in deep submicrometer technology have resulted in the ability and the need to put entire systems on a single chip. As more of the system is included on a single chip, it is increasingly likely that the chip will contain both analog and digital sections. Developing these mixed-signal (MS) systems-on-chip presents enormous challenges both to the designers of the chips and to the developers of the computer-aided design (CAD) systems that are used during the design process. This paper presents many of the issues that act to complicate the development of large single-chip MS systems and how CAD systems are expected to develop to overcome these issues.

In a previous paper [l], we described ARTour Web Express, a software system that makes it possible to run World Wide Web applications over wide-area wireless nehvorks. Our earlier paper discussed how our system significantly reduces user cost and response time during online browsing over wireless communications links. Even with these savings, however, users may experience slow performance. This is a result of the inherent delay of wireless communication coupled with congestion in the Internet and Web servers, which cannot be masked Corn users under the synchronous request/response model of browsing. Furthermore, disconnection - both voluntary and involuntary - is common in the mobile environment, and the standard browsing model provides no support for disconnected operation. This paper describes how ARTour Web Express has been enhanced to support both disconnected and asynchronous operation.

To accelerate the design cycle for analog and mixed-signal systems, we have proposed a top-down, constraint-driven design methodology. The key idea of the proposed methodology is hierarchically propagating constraints from performance specifications to layout. Consequently, it is essential to provide the necessary tools and techniques enabling the efficient constraint propagation. To illustrate the applicability of the proposed methodology to the design of larger systems, we present in this paper the complete design flow for a video driver system. Critical advantages of the methodology illustrated with this design example include avoiding costly low level re-designs and getting working silicon parts from the first run. Following our approach, a jitter constraint is imposed at the system level and then is propagated hierarchically to the circuit blocks and layout, using behavioral modeling and simulation. Experimental results are presented from working fabricated parts.

Mobile computing adds a new wrinkle to the ageold problem of caching. Today's wireless links are both slow and expensive, and are not always available to a user. Therefore, when a mobile user is disconnected, a cache miss means (at best) a substantial cost in time and money, or (at worst) a complete halt to work if critical information has not been cached. Existing solutions to this problem rely on some combination of explicit hoard profiles and spying on a user's file accesses.