One of the most important communicative skills children have to learn is to engage in meaningful conversations with people around them. At the heart of this learning lies the mastery of contingency, i.e., the ability to contribute to an ongoing exchange in a relevant fashion (e.g., by staying on topic). Current research on this question relies on the manual annotation of a small sample of children, which limits our ability to draw general conclusions about development. Here, we propose to mitigate the limitations of manual labor by relying on automatic tools for contingency judgment in children’s early natural interactions with caregivers. Drawing inspiration from the field of dialogue systems evaluation, we built and compared several automatic classifiers. We found that a Transformer-based pre-trained language model – when fine-tuned on a relatively small set of data we annotated manually (around 3,500 turns) – provided the best predictions. We used this model to automatically annotate, new and large-scale data, almost two orders of magnitude larger than our fine-tuning set. It was able to replicate existing results and generate new data-driven hypotheses. The broad impact of the work is to provide resources that can help the language development community study communicative development at scale, leading to more robust theories.

Existing studies of naturally occurring language-in-interaction have largely focused on the two ends of the developmental spectrum, i.e., early childhood and adulthood, leaving a gap in our knowledge about how development unfolds, especially across middle childhood. The current work contributes to filling this gap by introducing CHICA (for Child Interpersonal Communication Analysis), a developmental corpus of child-caregiver conversations at home, involving groups of French-speaking children aged 7, 9, and 11 years old. Each dyad was recorded twice: once in a face-to-face setting and once using computer-mediated video calls. For the face-to-face settings, we capitalized on recent advances in mobile, lightweight eye-tracking and head motion detection technology to optimize the naturalness of the recordings, allowing us to obtain both precise and ecologically valid data. Further, we mitigated the challenges of manual annotation by relying – to the extent possible – on automatic tools in speech processing and computer vision. Finally, to demonstrate the richness of this corpus for the study of child communicative development, we provide preliminary analyses comparing several measures of child-caregiver conversational dynamics across developmental age, modality, and communicative medium. We hope the current corpus will allow new discoveries into the properties and mechanisms of multimodal communicative development across middle childhood.

The acquisition of grammar has been a central question to adjudicate between theories of language acquisition. In order to conduct faster, more reproducible, and larger-scale corpus studies on grammaticality in child-caregiver conversations, tools for automatic annotation can offer an effective alternative to tedious manual annotation. We propose a coding scheme for context-dependent grammaticality in child-caregiver conversations and annotate more than 4,000 utterances from a large corpus of transcribed conversations. Based on these annotations, we train and evaluate a range of NLP models. Our results show that fine-tuned Transformer-based models perform best, achieving human inter-annotation agreement levels. As a first application and sanity check of this tool, we use the trained models to annotate a corpus almost two orders of magnitude larger than the manually annotated data and verify that children’s grammaticality shows a steady increase with age. This work contributes to the growing literature on applying state-of-the-art NLP methods to help study child language acquisition at scale.

The advent of the digital age has paved the way for a new form of bullying which often leads to social stigma. With an increase in the use of social media platforms by adolescents, cyber bullying has become quite rampant and in some extreme cases it has also resulted in suicides by the victims. Very little efforts have been taken to curb this social menace and hence, this paper tries to address this issue by reviewing the steps that can be undertaken to detect cyber bullying on online social networks. This paper aims to review the different methods and algorithms used for detection in cyber bullying and provide a comparative study amongst them so as to decide which method is the most effective approach and provides the best accuracy.

The battery life of mobile devices is one of their most important resources. Much of the literature focuses on accurately profiling the power consumption of device components or enabling application developers to develop energy-efficient applications through fine-grained power profiling. However, there is a lack of tools to enable users to extend battery life on demand. What can users do if they need their device to last for a specific duration in order to perform a specific task? To this extent, we developed BatteryExtender, a user-guided power management tool that enables the reconfiguration of the device's resources based on the workload requirement, similar to the principle of creating virtual machines in the cloud. It predicts the battery life savings based on the new configuration, in addition to predicting the impact of running applications on the battery life. Through our experimental analysis, BatteryExtender decreased the energy consumption between 10.03% and 20.21%, and in rare cases by up to 72.83%. The accuracy rate ranged between 92.37% and 99.72%.

A novel acoustic based moisture meter has been designed with the specific purpose of measuring moisture in raw cashew nuts. The meter employs pattern matching techniques to determine the moisture content. The meter applies non-invasive, non-destructive methods and the techniques are easily scalable to perform online measurements suitable in an industrial set up. For test trials, a set 15-20 cashew nuts of varying size and weight were considered for our system training and testing. The feature vectors for our training algorithm were based on the acoustic signature of shell to shell impact of cashew nuts. Algorithmic implementation and the electronic and mechanical aspects of the system prototype are discussed. By this method, we were able to distinguish between the dry and wet cashew nuts with high correlation accuracy.

This paper describes WOW, a distributed system that combines virtual machine, overlay networking and peer-to-peer techniques to create scalable wide-area networks of virtual workstations for high-throughput computing. The system is architected to: facilitate the addition of nodes to a pool of resources through the use of system virtual machines (VMs) and self-organizing virtual network links; to maintain IP connectivity even if VMs migrate across network domains; and to present to end-users and applications an environment that is functionally identical to a local-area network or cluster of workstations. We describe a novel, extensible user-level decentralized technique to discover, establish and maintain overlay links to tunnel IP packets over different transports (including UDP and TCP) and across firewalls. We also report on several experiments conducted on a testbed WOW deployment with 118 P2P router nodes over PlanetLab and 33 VMware-based VM nodes distributed across six firewalled domains. Experiments show that the latency in joining a WOW network is of the order of seconds: in a set of 300 trials, 90% of the nodes self-configured P2P routes within 10 seconds, and more than 99% established direct connections to other nodes within 200 seconds. Experiments also show that the testbed delivers good performance for two unmodified, representative benchmarks drawn from the life-sciences domain. The testbed WOW achieves an overall throughput of 53 jobs/minute for PBS-scheduled executions of the MEME application (with average single-job sequential running time of 24.1s) and a parallel speedup of 13.5 for the PVM-based fastDNAml application. Experiments also demonstrate that the system is capable of seamlessly maintaining connectivity at the virtual IP layer for typical client/server applications (NFS, SSH, PBS) when VMs migrate across a WAN

Peer-to-peer (P2P) networks have mostly focused on task oriented networking, where networks are constructed for single applications, i.e. file-sharing, DNS caching, etc. In this work, we introduce IPOP, a system for creating virtual IP networks on top of a P2P overlay. IPOP enables seamless access to grid resources spanning multiple domains by aggregating them into a virtual IP network that is completely isolated from the physical network. The virtual IP network provided by IPOP supports deployment of existing IP-based protocols over a robust, self-configuring P2P overlay. We present implementation details as well as experimental measurement results taken from LAN, WAN, and Planet-Lab tests

This poster describes current work on the application of network virtualization and peer-to-peer routing techniques that overlay IP traffic and provide seamless connectivity to virtual machines in grid computing. Such IP-over-P2P virtual network - IPOP - is an overlay network that uses a virtual IP address space and allows nodes that belong to a grid to be seamlessly pooled together. The overlay network is self-configured as nodes join/leave the virtualized grid, and IP-level bi-directional connectivity among peers is provided. The decoupling provided by the overlay enables grid applications to leverage a wealth of IP-based software typically available in local-area environments. Such virtual networks, when combined to complementary resource virtualization techniques provided by O/S virtual machines [Xen, Rarham et al. (2003), VMware, Sugerman et al., (2001), User Mode Linux, Dike, J. (2000)], provide a scalable framework for dealing with a fundamental goal of grid computing: sharing resources in a secure and flexible manner by Figueiredo, Dinda and Fortes (2003)

Gravity balancing is often used in industrial machines to decrease the actuator efforts during motion. Through the use of rehabilitation devices one can also partially balance the leg during motion with the goal to reduce net joint torque during motion. However, it is not clear as to what are the effects of such devices on legged locomotion. This paper aims to study the effect of such gravity balancing devices on the motion of bipeds during walking.

Being able to identify clusters of nearby hosts among Internet clients provides very useful information for a number of internet and p2p applications. Examples of such applications include web applications, request routing in peer-to-peer overlay network, and distributed computing applications. In this paper, we present and formulate the internet host clustering problem. Leveraging previous work on internet host distance measurement, we propose two hierarchical clustering techniques to solve this problem. The first technique is a marker based hierarchical partitioning approach. The second technique is based on the well known K-means clustering algorithm. We evaluated these two approaches in simulation using a representative Internet topology generated with the GT ITM generator for over 1,000 hosts. Our simulation results demonstrate that our algorithmic clustering approaches effectively identify clusters with arbitrary diameters. Our conclusion is that by leveraging previous work on internet host distance estimation, it is possible to cluster Internet hosts to benefit various applications with various requirements.