To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure no-reply@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
In order to understand what an algorithm is, let’s begin by taking a trip back a few millennia in the past to imagine one of our distant ancestors who had seen his late grandmother bake bread and then tries it himself. He doesn’t really know what to do. He hesitates, starts by boiling grains of wheat in water, then realizes it might be a bad idea. He does what we all do when confronted with a problem that we don’t know how to resolve: we think of solutions, we try them out, we feel our way, counting on a touch of serendipity until we succeed, or not.
In the age of algorithms, inventions follow each other in rapid succession. With each invention, there are many reasons to be amazed and, also, to be worried. These inventions make possible the better world we aspire to, as well as the nightmarish world we fear.
This work introduces robust multi-dialectal part of speech tagging trained on an annotated data set of Arabic tweets in four major dialect groups: Egyptian, Levantine, Gulf, and Maghrebi. We implement two different sequence tagging approaches. The first uses conditional random fields (CRFs), while the second combines word- and character-based representations in a deep neural network with stacked layers of convolutional and recurrent networks with a CRF output layer. We successfully exploit a variety of features that help generalize our models, such as Brown clusters and stem templates. Also, we develop robust joint models that tag multi-dialectal tweets and outperform uni-dialectal taggers. We achieve a combined accuracy of 92.4% across all dialects, with per dialect results ranging between 90.2% and 95.4%. We obtained the results using a train/dev/test split of 70/10/20 for a data set of 350 tweets per dialect.
Students’ personal learning networks can be a valuable resource of success in higher education: they offer opportunities for academic and personal support and provide sources of information related to exams or homework. We study the determinants of learning networks using a panel study among university students in their first and second year of study. A long-standing question in social network analysis has been whether the tendency of individuals with similar characteristics to form ties is a result of preferences “choice homophily” or rather selective opportunities “induced homophily”. We expect a latent preference for homophilic learning partnerships with regard to attributes, such as gender, ability, and social origin. We estimate recently developed temporal exponential random graph models to control for previous network structure and study changes in learning ties among students. The results show that especially for males, same-gender partnerships are preferred over heterogeneous ties, while chances for tie formation decrease with the difference in academic ability among students. Social origin is a significant factor in the crosssectional exploration but does appear to be less important in the formation of new (strong) partnerships during the course of studies.
This article considers the current position of computer-assisted language learning (CALL) research by producing an integrative synthetic overview of all the articles published in three leading international CALL journals: ReCALL (in its 31st year of publication), the CALICO Journal (its 36th) and Computer Assisted Language Learning journal (its 32nd) over a sustained recent period: 2006–2016. They are judged sufficiently representative to enable broad trends to be detected and the sector’s strengths and weaknesses to be identified. The focus is on CALL research’s international reach, the range of topics researched and the nature of the studies themselves. The findings suggest that CALL research is growing internationally in the number of countries and researchers involved. A wide range of topics is researched, but there is a concentration of papers published on a cluster of popular areas. Consequently, fewer articles are published on a large number of CALL topics or, in some cases, rarely studied. The research methods employed are rigorous: in writing, structure, theory, literature awareness, and discussion and presentation of results, yet there are still weaknesses. Most empirical studies are small scale: based on one institution, a small cohort of students, over a short period of time and seldom followed up. Based on these findings, suggestions are made with a view to broadening and strengthening CALL research through targeting neglected strategic areas with special journal issues and conferences, and improving the quality of research projects. Key areas for future research are proposed.
Network providers either attempt to handle massive distributed denial-of-service attacks themselves or redirect traffic to third-party scrubbing centers. If providers adopt the first option, it is sensible to counter such attacks in their infancy via provider collaborations deploying distributed security mechanisms across multiple domains in an attack path. This motivated our work presented in this paper. Specifically, we investigate the establishment of trusted federations among adjacent and disjoint network domains, that is, autonomous systems (ASes) that collectively mitigate malicious traffic. Our approach is based on Distributed Ledger Technologies for signaling, coordination, and orchestration of a collaborative mitigation schema via appropriate blockchain-based smart contracts. Reputation scores are used to rank ASes based on their mitigation track record. The allocation of defense resources across multiple collaborators is modeled as a combinatorial optimization problem considering reputation scores and network flow weights. Malicious flows are mitigated using programmable network data paths within the eXpress Data Path (XDP) framework; this enables operators with enhanced packet processing throughput and advanced filtering flexibility. Our schema was implemented in a proof-of-concept prototype and tested under realistic network conditions.
A multitude of design guidelines that are intended to support design engineers with knowledge and information during the embodiment design of physical products have been developed back in the 1980s and 1990s. However, since then, the setting in which products are developed and designed has changed and associated tasks and activities are carried out globally rather than locally. Yet, knowledge on the benefit of such design guidelines and their impact on the performance of multinational design engineers from different regions and with different levels of experience is still lacking. To address this, a mobile eye tracking study has been developed and carried out with 47 differently experienced practitioners from Germany, Eastern Europe and Asia. The results show differences in how design engineers from different regions with different levels of experience may benefit from design guidelines and how design guidelines may impact experts’ and novices’ performance, indicate beneficial ways of using them and point out the kind of information and the way of representation that attracts the most attention within a design guideline. The paper concludes that the improvement and development of design guidelines that are intended to support the embodiment design of physical products is needed and proposes to rethink current engineering design guidelines both content-wise and representation-wise.
Dielectric breakdown in a thin oxide is presented in terms of an interacting particle system on a two-dimensional lattice. All edges in the system are initially assumed to be closed. An edge between two adjacent vertices will open according to an exponentially distributed random variable. Breakdown occurs at the time an open path connects the top layer of the lattice to the bottom layer. Using the extreme value theory, we show that the time until breakdown is asymptotically Weibull distributed.
Markov processes play an important role in reliability analysis and particularly in modeling the stochastic evolution of survival/failure behavior of systems. The probability law of Markov processes is described by its generator or the transition rate matrix. In this paper, we suppose that the process is doubly stochastic in the sense that the generator is also stochastic. In our model, we suppose that the entries in the generator change with respect to the changing states of yet another Markov process. This process represents the random environment that the stochastic model operates in. In fact, we have a Markov modulated Markov process which can be modeled as a bivariate Markov process that can be analyzed probabilistically using Markovian analysis. In this setting, however, we are interested in Bayesian inference on model parameters. We present a computationally tractable approach using Gibbs sampling and demonstrate it by numerical illustrations. We also discuss cases that involve complete and partial data sets on both processes.
The architecture of a system is decided at the initial stage of the design. However, the robustness of the system is not usually assessed in detail during the initial stages, and the exploration of alternative system architectures is limited due to the influence of previous designs and opinions. This article presents a novel network generator that enables the analysis of the robustness of alternative system architectures in the initial stages of design. The generator is proposed as a network tool for system architectures dictated by their configuration of source and sink components structured in a way to deliver a particular functionality. Its parameters allow exploration with theoretical patterns to define the main structure and hub structure, vary the number, size, and connectivity of hub components, define source and sink components and directionality at the hub level and adapt a redundancy threshold criterion. The methodology in this article assesses the system architecture patterns through robustness and modularity network based metrics and methods. Two naval distributed engineering system architectures are examined as the basis of reference for the simulated networks. The generator provides the capacity to create alternative complex system architecture options with identifiable patterns and key features, aiding in a broader explorative and analytical, in-depth, time and cost-efficient initial design process.
Subwords have become very popular, but the BERTa and ERNIEbtokenizers often produce surprising results. Byte pair encoding (BPE) trains a dictionary with a simple information theoretic criterion that sidesteps the need for special treatment of unknown words. BPE is more about training (populating a dictionary of word pieces) than inference (parsing an unknown word into word pieces). The parse at inference time can be ambiguous. Which parse should we use? For example, “electroneutral” can be parsed as electron-eu-tral or electro-neutral, and “bidirectional” can be parsed as bid-ire-ction-al and bi-directional. BERT and ERNIE tend to favor the parse with more word pieces. We propose minimizing the number of word pieces. To justify our proposal, a number of criteria will be considered: sound, meaning, etc. The prefix, bi-, has the desired vowel (unlike bid) and the desired meaning (bi is Latin for two, unlike bid, which is Germanic for offer).
The reachability semantics for Petri nets can be studied using open Petri nets. For us, an “open” Petri net is one with certain places designated as inputs and outputs via a cospan of sets. We can compose open Petri nets by gluing the outputs of one to the inputs of another. Open Petri nets can be treated as morphisms of a category Open(Petri), which becomes symmetric monoidal under disjoint union. However, since the composite of open Petri nets is defined only up to isomorphism, it is better to treat them as morphisms of a symmetric monoidal double category ${\mathbb O}$pen(Petri). We describe two forms of semantics for open Petri nets using symmetric monoidal double functors out of ${\mathbb O}$pen(Petri). The first, an operational semantics, gives for each open Petri net a category whose morphisms are the processes that this net can carry out. This is done in a compositional way, so that these categories can be computed on smaller subnets and then glued together. The second, a reachability semantics, simply says which markings of the outputs can be reached from a given marking of the inputs.
Creating collaborative working and learning experiences has long been at the forefront of computer-assisted language learning research. It is in this context that, in recent years, the integration of social networking sites and Web 2.0 in learning settings has surged, generating new opportunities to establish and explore virtual communities of practice (VCoPs). However, despite the number of studies on the concept, research remains inconclusive on how learners develop a sense of community in a VCoP, and what effect this may have on interaction and learning. This research project proposes to use social network analysis, part of graph theory, to explore the configuration of a set of VCoPs, and presents an empirical approach to determine how interaction in such communities takes shape. The present paper studies the concept of “community” in two VCoPs on Facebook. Participants (Group 1: N = 123, Group 2: N = 34) in both VCoPs are enrolled in English as a foreign language courses at two Belgian institutions of higher education. Social network analysis is used to show how both learner groups establish and develop a network of peers, and how different participants in those groups adopt different roles. Participation matrices reveal that interaction mainly revolves around a number of active key figures and that certain factors such as the incorporation of online and offline assignments and the inclusion of a teacher online result in varying levels of success when establishing collaborative dialogue within the VCoPs. Recommendations are formulated to inform and improve future practice.
The use of robots in performance arts is increasing. But, it is hard for robots to cope with unexpected circumstances during a performance, and it is almost impossible for robots to act fully autonomously in such situations. IROS-HAC is a new challenge in robotics research and a new opportunity for cross-disciplinary collaborative research. In this paper, we describe a practical method for generating different personalities of a robot entertainer. The personalities are created by selecting speech or gestures from a set of options. The selection uses roulette wheel selection to select answers that are more closely aligned with the desired personality. In particular, we focus on a robot magician, as a good magic show includes good interaction with the audience and it may also include other robots and performers. The magician with a variety of personalities increased the audience immersion and appreciation and maintained the audience’s interest. The magic show was awarded first prize in the competition for a comprehensive evaluation of technology, story, and performance. This paper contains both the research methodology and a critical evaluation of our research.