Carnegie Mellon University Qatar

Market-based multirobot coordination approaches have received significant attention and are growing in popularity within the robotics research community. They have been successfully implemented in a variety of domains ranging from mapping and exploration to robot soccer. The research literature on market-based approaches to coordination has now reached a critical mass that warrants a survey and analysis. This paper addresses this need for a survey of the relevant literature by providing an introduction to market-based multirobot coordination, a review and analysis of the state of the art in the field, and a discussion of remaining research challenges

We present the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet. For each task, we created labeled data from English, Arabic, and Spanish tweets. The individual tasks are: 1. emotion intensity regression, 2. emotion intensity ordinal classification, 3. valence (sentiment) regression, 4. valence ordinal classification, and 5. emotion classification. Seventy-five teams (about 200 team members) participated in the shared task. We summarize the methods, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful. We also analyze systems for consistent bias towards a particular race or gender. The data is made freely available to further improve our understanding of how people convey emotions through language.

Rapid technological advances have revolutionized the industrial sector. These advances range from automation of industrial processes to autonomous industrial processes, where a human input is not required. Internet of Things (IoT), which has emerged a few years ago, has been embraced by industry, resulting in what is known as the Industrial Internet of Things (IIoT). IIoT refers to making industrial processes and entities part of the Internet. Restricting the definition of IIoT to manufacturing yields another subset of IoT, known as Industry 4.0. IIoT and Industry 4.0, will consist of sensor networks, actuators, robots, machines, appliances, business processes, and personnel. Hence, a lot of data of diverse nature would be generated. The industrial process requires most of the tasks to be performed locally because of delay and security requirements and structured data to be communicated over the Internet to web services and the cloud. To achieve this task, middleware support is required between the industrial environment and the cloud/web services. In this context, fog is a potential middleware that can be very useful for different industrial scenarios. Fog can provide local processing support with acceptable latency to actuators and robots in a manufacturing industry. Additionally, as industrial big data are often unstructured, it can be trimmed and refined by the fog locally, before sending it to the cloud. We present an architectural overview of IIoT and Industry 4.0. We discuss how fog can provide local computing support in the IIoT environment and the core elements and building blocks of IIoT. We also present a few interesting prospective use cases of IIoT. Finally, we discuss some emerging research challenges related to IIoT.

Are election outcomes driven by events beyond the control of politicians? Democratic accountability requires that voters make reasonable evaluations of incumbents. Although natural disasters are beyond human control, the response to these events is the responsibility of elected officials. In a county-level analysis of gubernatorial and presidential elections from 1970 to 2006, we examine the effects of weather events and governmental responses. We find that electorates punish presidents and governors for severe weather damage. However, we find that these effects are dwarfed by the response of attentive electorates to the actions of their officials. When the president rejects a request by the governor for federal assistance, the president is punished and the governor is rewarded at the polls. The electorate is able to separate random events from governmental responses and attribute actions based on the defined roles of these two politicians.

Unmanned aerial vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas. - 1998-2012 IEEE.

Blockchain technology (BCT) has been gaining popularity due to its benefits for almost every industry. However, despite its benefits, the organizational adoption of BCT is rather limited. This lack of uptake motivated us to identify the factors that influence the adoption of BCT from an organizational perspective. In doing this, we reviewed the BCT literature, interviewed BCT experts, and proposed a research model based on the TOE framework. Specifically, we theorized the role of technological (perceived benefits, compatibility, information transparency, and disintermediation), organizational (organization innovativeness, organizational learning capability, and top management support), and environmental (competition intensity, government support, trading partners readiness, and standards uncertainty) factors in the organizational adoption of BCT in Australia. We confirmed the model with a sample of adopters and potential adopter organizations in Australia. The results show a significant role of the proposed factors in the organizational adoption of BCT in Australia. Additionally, we found that the relationship between the influential factors and BCT adoption is moderated by “perceived risks”. The study extends the TOE framework by adding factors that were ignored in previous studies on BCT adoption, such as perceived information transparency, perceived disintermediation, organizational innovativeness, organizational learning capability, and standards uncertainty.

Alternative splicing allows for the expression of multiple RNA and protein isoforms from one gene, making it a major contributor to transcriptome and proteome diversification in eukaryotes. Advances in next generation sequencing technologies and genome-wide analyses have recently underscored the fact that the vast majority of multi-exon genes under normal physiology engage in alternative splicing in tissue-specific and developmental-specific manner. On the other hand, cancer cells exhibit remarkable transcriptome alterations partly by adopting cancer-specific splicing isoforms. These isoforms and their encoded proteins are not insignificant byproducts of the abnormal physiology of cancer cells, but either drivers of cancer progression or small but significant contributors to specific cancer hallmarks. Thus, it is paramount that the pathways that regulate alternative splicing in cancer, including the splicing factors that bind to pre-mRNAs and modulate spliceosome recruitment. In this review, we present a few distinct cases of alternative splicing in cancer, with an emphasis on their regulation as well as their contribution to cancer cell phenotype. Several categories of splicing aberrations are highlighted, including alterations in cancer-related genes that directly affect their pre-mRNA splicing, mutations in genes encoding splicing factors or core spliceosomal subunits, and the seemingly mutation-free disruptions in the balance of the expression of RNA-binding proteins, including components of both the major (U2-dependent) and minor (U12-dependent) spliceosomes. Given that the latter two classes cause global alterations in splicing that affect a wide range of genes, it remains a challenge to identify the ones that contribute to cancer progression. These challenges necessitate a systematic approach to decipher these aberrations and their impact on cancer. Ultimately, a sufficient understanding of splicing deregulation in cancer is predicted to pave the way for novel and innovative RNA-based therapies.

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.

We present BranchScope - a new side-channel attack where the attacker infers the direction of an arbitrary conditional branch instruction in a victim program by manipulating the shared directional branch predictor. The directional component of the branch predictor stores the prediction on a given branch (taken or not-taken) and is a different component from the branch target buffer (BTB) attacked by previous work. BranchScope is the first fine-grained attack on the directional branch predictor, expanding our understanding of the side channel vulnerability of the branch prediction unit. Our attack targets complex hybrid branch predictors with unknown organization. We demonstrate how an attacker can force these predictors to switch to a simple 1-level mode to simplify the direction recovery. We carry out BranchScope on several recent Intel CPUs and also demonstrate the attack against an SGX enclave.

Abstract Drug use represents a significant burden to public health, through disease, disability and social problems, and policy makers are becoming increasingly interested in how to develop evidence-based drug policy. It is therefore crucial to strengthen the links between addiction science and drug policy. Drug Policy and the Public Good is collaboratively written by an international group of career scientists, to provide an analytical basis on which to build relevant global drug policies, and to inform policy makers who have direct responsibility for public health and social welfare. Drug Policy and the Public Good presents the accumulated scientific knowledge on illicit drugs that has direct relevance to the development of drug policy on local, national, and international levels. The authors describe the conceptual basis for a rational drug policy, and present new epidemiological data on the global dimensions of drug misuse. The core of the book is a critical review of the cumulative scientific evidence in five general areas of drug policy: primary prevention programs in schools and other settings; supply reduction approaches, including drug interdiction and legal enforcement; treatment interventions and harm reduction approaches; criminal sanctions and decriminalization; and control of the legal market through prescription drug regimes. The final chapters discuss the current state of drug policy in different parts of the world, and describe the need for a new approach to drug policy that is evidence-based, realistic, and coordinated.

The need for high performance computing applications for computational science and engineering projects is growing rapidly, yet there have been few detailed studies of the software engineering process used for these applications. The DARPA High Productivity Computing Systems Program has sponsored a series of case studies of representative computational science and engineering projects to identify the steps involved in developing such applications (i.e. the life cycle, the workflows, technical challenges, and organizational challenges). Secondary goals were to characterize tool usage and identify enhancements that would increase the programmers' productivity. Finally, these studies were designed to develop a set of lessons learned that can be transferred to the general computational science and engineering community to improve the software engineering process used for their applications. Nine lessons learned from five representative projects are presented, along with their software engineering implications, to provide insight into the software development environments in this domain.

Handing over objects to humans is an essential capability for assistive robots. While there are infinite ways to hand an object, robots should be able to choose the one that is best for the human. In this paper we focus on choosing the robot and object configuration at which the transfer of the object occurs, i.e. the hand-over configuration. We advocate the incorporation of user preferences in choosing hand-over configurations. We present a user study in which we collect data on human preferences and a human-robot interaction experiment in which we compare hand-over configurations learned from human examples against configurations planned using a kinematic model of the human. We find that the learned configurations are preferred in terms of several criteria, however planned configurations provide better reachability. Additionally, we find that humans prefer hand-overs with default orientations of objects and we identify several latent variables about the robot's arm that capture significant human preferences. These findings point towards planners that can generate not only optimal but also preferable hand-over configurations for novel objects.

MapReduce offers a promising programming model for big data processing. Inspired by functional languages, MapReduce allows programmers to write functional-style code which gets automatically divided into multiple map and/or reduce tasks and scheduled over distributed data across multiple machines. Hadoop, an open source implementation of MapReduce, schedules map tasks in the vicinity of their inputs in order to diminish network traffic and improve performance. However, Hadoop schedules reduce tasks at requesting nodes without considering data locality leading to performance degradation. This paper describes Locality-Aware Reduce Task Scheduler (LARTS), a practical strategy for improving MapReduce performance. LARTS attempts to collocate reduce tasks with the maximum required data computed after recognizing input data network locations and sizes. LARTS adopts a cooperative paradigm seeking a good data locality while circumventing scheduling delay, scheduling skew, poor system utilization, and low degree of parallelism. We implemented LARTS in Hadoop-0.20.2. Evaluation results show that LARTS outperforms the native Hadoop reduce task scheduler by an average of 7%, and up to 11.6%.

Anoxygenic phototrophic bacteria (APB) are a phylogenetically diverse group of organisms that can harness solar energy for their growth and metabolism. These bacteria vary broadly in terms of their metabolism as well as the composition of their photosynthetic apparatus. Unlike oxygenic phototrophic bacteria such as algae and cyanobacteria, APB can use both organic and inorganic electron donors for light-dependent fixation of carbon dioxide without generating oxygen. Their versatile metabolism, ability to adapt in extreme conditions, low maintenance cost and high biomass yield make APB ideal for wastewater treatment, resource recovery and in the production of high value substances. This review highlights the advantages of APB over algae and cyanobacteria, and their applications in photo-bioelectrochemical systems, production of poly-β-hydroxyalkanoates, single-cell protein, biofertilizers and pigments. The ecology of ABP, their distinguishing factors, various physiochemical parameters governing the production of high-value substances and future directions of APB utilization are also discussed.

Fifth generation mobile communication networks are currently being deployed, thus making Tactile Internet possible. Tactile Internet is the future advancement of the current Internet of Things (IoT) vision wherein haptics, or touch and senses, can be communicated from one geographical place to another, enabling near real-time control and navigation of remote objects. Tactile Internet will have its use cases in several application domains, with the industrial sector being among the most prominent ones. With the Industrial Internet of Things (IIoT), Tactile Internet will be used in healthcare, manufacturing, mining, education, autonomous driving, etc. The acceptable delay in most of these tactile applications will be under one millisecond. Since Tactile Internet communicates haptics and gives visual feedback, quality of service (QoS) becomes an important issue. Similarly, user's satisfaction on the service quality [often measured as quality of experience (QoE)] becomes equally important. To reap the true potential of Tactile Internet, sophisticated and intelligent mechanisms are required between the end-nodes. A middleware such as fog computing can be vital in this context, since it can allocate resources based on the QoS/QoE requirements of each service. In this context, we present a QoE-aware model for dynamic resource allocation for tactile applications in IIoT. We implement the model using Java and discuss the empirical results to elaborate more on the impact of such a model for QoE-aware resource allocation that can be very important in the context of Tactile Internet, especially IIoT. We also discuss some of the most prominent use cases of Tactile IIoT.

This study reports the descriptive and inferential statistical findings of a survey of academic reading format preferences and behaviors of 10,293 tertiary students worldwide. The study hypothesized that country-based differences in schooling systems, socioeconomic development, culture or other factors might have an influence on preferred formats, print or electronic, for academic reading, as well as the learning engagement behaviors of students. The main findings are that country of origin has little to no relationship with or effect on reading format preferences of university students, and that the broad majority of students worldwide prefer to read academic course materials in print. The majority of participants report better focus and retention of information presented in print formats, and more frequently prefer print for longer texts. Additional demographic and post-hoc analysis suggests that format preference has a small relationship with academic rank. The relationship between task demands, format preferences and reading comprehension are discussed. Additional outcomes and implications for the fields of education, psychology, computer science, information science and human-computer interaction are considered.

The suitability of different parsing methods for different languages is an important topic in syntactic parsing. Especially lesser-studied languages, typologically different from the languages for which methods have originally been developed, pose interesting challenges in this respect. This article presents an investigation of data-driven dependency parsing of Turkish, an agglutinative, free constituent order language that can be seen as the representative of a wider class of languages of similar type. Our investigations show that morphological structure plays an essential role in finding syntactic relations in such a language. In particular, we show that employing sublexical units called <i>inflectional groups</i>, rather than word forms, as the basic parsing units improves parsing accuracy. We test our claim on two different parsing methods, one based on a probabilistic model with beam search and the other based on discriminative classifiers and a deterministic parsing strategy, and show that the usefulness of sublexical units holds regardless of the parsing method. We examine the impact of morphological and lexical information in detail and show that, properly used, this kind of information can improve parsing accuracy substantially. Applying the techniques presented in this article, we achieve the highest reported accuracy for parsing the Turkish Treebank.<br>

The Resource Description Framework (RDF) and SPARQL query language are gaining wide popularity and acceptance. In this paper, we present DREAM, a distributed and adaptive RDF system. As opposed to existing RDF systems, DREAM avoids partitioning RDF datasets and partitions only SPARQL queries. By not partitioning datasets, DREAM offers a general paradigm for different types of pattern matching queries, and entirely averts intermediate data shuffling (only auxiliary data are shuffled). Besides, by partitioning queries, DREAM presents an adaptive scheme, which automatically runs queries on various numbers of machines depending on their complexities. Hence, in essence DREAM combines the advantages of the state-of-the-art centralized and distributed RDF systems, whereby data communication is avoided and cluster resources are aggregated. Likewise, it precludes their disadvantages, wherein system resources are limited and communication overhead is typically hindering. DREAM achieves all its goals via employing a novel graph-based, rule-oriented query planner and a new cost model. We implemented DREAM and conducted comprehensive experiments on a private cluster and on the Amazon EC2 platform. Results show that DREAM can significantly outperform three related popular RDF systems.

Despite the increased capabilities of mobile devices, mobile application resource requirements can often transcend what can be accomplished on a single device. This has been addressed through several proposals for efficient computation offloading from mobile devices to remote cloud resources or closely located computing resources known as cloudlets. In this paper we consider an environment in which computational offloading is performed among a set of mobile devices. We call this environment a Mobile Device Cloud (MDC). We are interested in MDCs where nodes are {\em highly collaborative}. We develop computational offloading schemes that {\em maximize the lifetime} of the ensemble of mobile devices where we consider the network to be alive as long as no device has depleted its battery. As a secondary contribution in this work, we develop and use an experimentation platform that allows us to evaluate a range of computational models and profiles derived from a realistic testbed. We use this platform as a first step in an evaluation exercise that demonstrates the effectiveness of our computation offloading algorithms in extending the lifetime of an MDC.

We investigate the relationship between colour and structure within galaxies using a large, volume-limited sample of bright, low-redshift galaxies with optical-near-infrared imaging from the Galaxy And Mass Assembly survey. We fit single-component, wavelength-dependent, elliptical Srsic models to all passbands simultaneously, using software developed by the MegaMorph project. Dividing our sample by n and colour, the recovered wavelength variations in effective radius (R e ) and Srsic index (n) reveal the internal structure, and hence formation history, of different types of galaxies. All these trends depend on n; some have an additional dependence on galaxy colour. Late-type galaxies (n r < 2.5) show a dramatic increase in Srsic index with wavelength. This might be a result of their two-component (bulge-disc) nature, though stellar population gradients within each component and dust attenuation are likely to play a role. All galaxies show a substantial decrease in R e with wavelength. This is strongest for early types (n r > 2.5), even though they maintain constant n with wavelength, revealing that ellipticals are a superimposition of different stellar populations associated with multiple collapse and merging events. Processes leading to structures with larger R e must be associated with lower metallicity or younger stellar populations. This appears to rule out the formation of young cores through dissipative gas accretion as an important mechanism in the recent lives of luminous elliptical galaxies.