Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Stories in Robot-Assisted Therapy for Children with ASD

Mobile technology devices are commonly used as assistive technology to support children with Autism Spectrum Disorder (ASD) in gaining skills in interpersonal communication. While considered generally safe and effective, there are concerns that a child that is taught to communicate through interactive technologies may become dependent on the virtual world and its rewards, while interpersonal skills are sacrificed or not generalized to real world settings (Bauminger-Zviely, Eden, Zancanaro, Weiss, & Gal, 2013). This chapter theorizes that the anthropomorphic embodiment of humanoid robots may provide a compromise between the real and the virtual worlds. The authors suggest that a humanoid robot can use social stories within an Applied Behavioral Analysis (ABA) framework to support the acquisition of social interaction skills of children with ASD. The objective of this chapter is to contribute to the current literature by providing a description of this intervention and make suggestions for its implementation using a case study approach.
Chapter 18 Accessing Big Data in the Cloud Using Mobile Devices

The amount of data acquired, stored, and processed annually over the Internet has exceeded the processing capabilities of modern computer systems, including supercomputers with multiple-Petaflop processing power, giving rise to the term Big Data. Continuous research efforts to implement systems to cope with this insurmountable amount of data are underway. The authors introduce the ongoing research in three different facets: 1) in the Acquisition front, they introduce a concept that has come to the forefront in the past few years: Internet-of-Things (IoT), which will be one of the major sources for Big Data generation in the following decades. The authors provide a brief survey of IoT to understand the concept and the ongoing research in this field. 2) In the Cloud Storage and Processing front, they provide a survey of techniques to efficiently store the acquired Big Data in the cloud, index it, and get it ready for processing. While IoT relates primarily to sensor nodes and thin devices, the authors study this storage and processing aspect of Big Data within the framework of Cloud Computing. 3) In the Mobile Access front, they perform a survey of existing infrastructures to access the Big Data efficiently via mobile devices. This survey also includes intermediate devices, such as a Cloudlet, to accelerate the Big Data collection from IoT and access to Big Data for applications that require response times that are close to real-time. Haoliang Wang George Mason University, USA Wei Liu University of Rochester, USA Tolga Soyata University of Rochester, USA DOI: 10.4018/978-1-4666-5864-6.ch018
Complementary roles of two excitatory pathways in retinal directional selectivity

The two major excitatory synapses onto ON–OFF directionally selective (DS) ganglion cells of the rabbit retina appear to be nicotinic cholinergic and NMDA glutamatergic. Blockade of either of these synapses with antagonists does not eliminate directional selectivity. This suggests that these synapses may have complementary roles in the computation of the direction of motion. To test this hypothesis, quantitative features of the DS cell excitatory pathways were determined by collecting responses, under nicotinic and/or NMDA blockade, to a sweeping bar, hyperacute apparent motions, or a drifting sinusoidal grating. Sweeping bar responses were reduced, but directional selectivity not eliminated, by blockade of either excitatory path, as previously shown (Cohen & Miller, 1995; Kittila & Massey, 1997). However, residual responses under combined blockades were not statistically significantly DS. NMDA blockade reduced responses more than nicotinic blockade for each protocol, and shifted hyperacute motion thresholds to higher values. This supported the notion that glutamate provides the main excitatory drive to DS cells, that is, the one responsible for contrast sensitivity. In turn, nicotinic, but not NMDA blockade eliminated directional selectivity to a drifting low spatial-frequency sinusoidal grating in these cells. This suggested that acetylcholine (ACh) is the main excitatory input with regards to directional selectivity for some textured stimuli, that is, those with multiple peaks in their spatial luminance profile. Moreover, nicotinic blockade raised the low temporal-frequency cutoff of the grating responses, consistent with the proposal that preferred-direction facilitation, which is temporally sustained, is dependent on the cholinergic input. These different properties of the NMDA and nicotinic pathways are consistent with a recently proposed two-asymmetric-pathways model of directional selectivity.
Dopamine D1 and D3 Receptor Polypharmacology in Cocaine Reward and Cocaine Seeking

Advisor: Professor Robert Ranaldi Background: In the search for efficacious pharmacotherapies to treat cocaine addiction much attention has been given to agents targeting D1 or D3 receptors because of the involvement of these receptors in cocaine-related behaviors. D1 and D3 receptor partial agonists and antagonists have been shown to reduce cocaine reward, reinstatement of cocaine seeking and conditioned place preference (CPP) in rodents and non-human primates. However, translation of these encouraging results with selective D1 or D3 receptor agents has been limited due to a number of factors including toxicity, poor pharmacokinetic properties and extrapyramidal and sedative side effects. Purpose: Given the role of D1 and D3 receptors in cocaine-related behaviors and the urgent need for effective anti-cocaine treatments, it is important that we continue to pursue new pharmacological approaches such as the combinations of compounds that have differential functions at multiple dopamine receptors. The aims of this dissertation were to evaluate the effects of the novel strategy of simultaneous treatments with a D3 receptor antagonist (NGB 2904) and D1 receptor partial agonist (SKF 77434) in animal models of drug addiction, including cue-induced reinstatement of cocaine seeking, cocaine conditioned place preference (CPP) and cocaine self-administration in rats.
Betrayal as Market Barrier: Identity-Based Limits to Diversification among High-Status Corporate Law Firms1

Why are some diversified market identities problematic but others are not? We examine this question in the context of high-status corporate law firms, which often diversify into one low-status area of work—family law (FL)—but face a barrier (strong disapproval from existing clients) that prevents diversification into another such area—plaintiffs’ personal injury law (PIL). Drawing on a qualitative study of the Boston legal market, we argue that this barrier reflects a situation where loyalty norms have been violated, and it surfaces because service to individual plaintiffs is tantamount to betraying the interests of corporate clients. Our analysis clarifies identity-based limits to diversification, indicating that they are rooted in concerns about the firm’s commitments as well as its capabilities, and suggests a more general refinement of theory on status and conformity.
Reliability analysis techniques explored through a communication network example

This paper reviews general methods used to perform dependability analysis on a given system. A communication network example is used in relation to a client/server type of application to illustrate the reliability and availability modeling techniques. We review both non-state space as well as state space based methods and discuss the bene ts and limitations of each. The paper assumes a general understanding of probability theory.
Non-stationary analysis of the convergence of the Non-Negative Least-Mean-Square algorithm

Non-negativity is a widely used constraint in parameter estimation procedures due to physical characteristics of systems under investigation. In this paper, we consider an LMS-type algorithm for system identification subject to non-negativity constraints, called Non-Negative Least-Mean-Square algorithm, and its normalized variant. An important contribution of this paper is that we study the stochastic behavior of these algorithms in a non-stationary environment, where the unconstrained solution is characterized by a time-variant mean and is affected by random perturbations. Convergence analysis of these algorithms in a stationary environment can be viewed as a particular case of the convergence model derived in this paper. Simulation results are presented to illustrate the performance of the algorithm and the accuracy of the derived models.
Delivery packets reliably and efficiently over error prone channel of WSNs

In wireless sensor networks, it has been proved that the reliable transmission protocols sending redundant packets to the upstream neighbour hop-by-hop have advantage on energy efficiency compared with those using end-to-end error recovery and control scheme. It provides an opportunity for applications to find a trade off point regarding transmission probability and energy consumption. The problem is, though, this kind of protocol loses a bit of its charm when considering burst and deep channel errors. According to this, the burst and deep Fading Avoid Transmission Protocol (FATP) is proposed. The novelty of this protocol is that, by using channel model technology and hidden Markov model (HMM) based algorithm, it can evade the burst and deep fading period of the wireless channel artfully with high probability. A detailed theoretic performance analysis process is presented in this paper. Compare with previous hop-by-hop reliable transmission protocols, the numerical results reveal that FATP can improve the energy efficiency significantly. Furthermore, if the channel bit error rate filter threshold is tuned in appropriate range, the delivery delay could be controlled in an acceptable value or even lower than other hop-by-hop reliable transmission protocols, especially in high BER conditions.
2.5-dimensional solution of the advective accretion disk: a self-similar approach

We provide a 2.5-dimensional solution to a complete set of viscous hydrodynamical equations describing accretion-induced outflow and then plausible jet around black holes/compact objects. We prescribe a self-consistent advective disk-outflow coupling model, which explicitly includes the information of vertical flux. Inter-connecting dynamics of inflow-outflow system essentially upholds the conservation laws. We provide a set of analytical family of solutions through the self-similar approach. The flow parameters of the disk-outflow system depend strongly on viscosity parameter $\alpha$ and cooling factor $f$.
Ontology Modelling Methodology for Temporal and Interdependent Applications

The increasing adoption of Semantic Web technology by several classes of applications in recent years, has made ontology engineering a crucial part of application development. Nowadays, the abundant accessibility of interdependent information from multiple resources and representing various fields such as health, transport, and banking etc., further evidence the growing need for utilising ontology for the development of Web applications. While there have been several advances in the adoption of the ontology for application development, less emphasis is being made on the modelling methodologies for representing modern-day application that are characterised by the temporal nature of the data they process, which is captured from multiple sources. Taking into account the benefits of a methodology in the system development, we propose a novel methodology for modelling ontologies representing Context-Aware Temporal and Interdependent Systems (CATIS). CATIS is an ontology development methodology for modelling temporal interdependent applications in order to achieve the desired results when modelling sophisticated applications with temporal and interdependent attributes to suit today's application requirements.
Performance tuning and analysis of future vector processors based on the roofline model

Because of a recent steep drop in the ratio of memory bandwidth to computational performance (B/F) of vector processors, their advantage against scalar ones regarding relatively high sustained performance is decaying. To cover the insufficient B/F rate, an on-chip vector cache mechanism is promising for the vector processors. Although the effectiveness of the vector cache has been evaluated, cache-conscious tuning of vector codes and the analysis of the obtained performance have not been discussed yet. Under this situation, the purpose of this paper is to establish a strategy for performance tuning of a vector processor with a cache to exploit its potential. To analyze its sustained performance, this paper uses the roofline model. Several optimization techniques are applied to real scientific and engineering applications, and their effects are assessed with the model. We confirm that the model can guide users to effective tuning so as to maximize its gain. We also discuss the energy efficiency of the on-chip vector cache.
Vector precoding with bounded complexity

We investigate nonlinear pre-equalization techniques for the downlink of multiuser systems from a transmitter equipped with multiple antennas to non-cooperative single antenna mobile receivers. In the focus of this paper is the strongly varying complexity of the tree-structured vector precoding problem. Since the maximum latency of signal processing algorithms is of major interest for practical implementations, suboptimum schemes with an upper bounded complexity are discussed. Simulation results reveal a close to optimum performance at the expense of moderate computational efforts for the considered scenario.
Scan-SLAM : Recursive Mapping and Localisation with Arbitrary-Shaped Landmarks

Scan-SLAM is a simultaneous localisation and mapping algorithm that combines scan-matching methods with recursive estimation of landmark locations (using an EKF or other Bayesian filter). The scan-matching capability allows landmarks with arbitrary shapes to be modelled directly by sensed data and tracked within a conventional filter framework. This paper presents the essential Scan-SLAM algorithm, and implementation details for application with a scanning range-laser: segmentation, alignment, covariance estimation, data association, and landmark model augmentation.
4D Mesh Reconstruction from Time-Varying Voxelized Geometry through ARAP Tracking

We present a method to derive a time-evolving triangle mesh representation from a sequence of binary volumetric data representing an arbitrary motion. Multi-view reconstruction studios use a multiple camera set to turn an actor’s performance into a time series of visual hulls. However, the reconstructed sequence lacks temporal coherence as each frame is generated independently, preventing easy post-production editing with off-the-shelf modeling tools. We propose an automated tracking approach to convert the raw input sequence into a single, animated mesh. An initial mesh is globally deformed via as-rigid-as-possible, detail-preserving transformations guided by a motion flow estimated from consecutive frames. Local optimization is added to better match the mesh surface to the current visual hull, leading to a robust 4D mesh reconstruction.
Pricing and Hedging Short Sterling Options Using Neural Networks

This paper compares the performance of artificial neural networks (ANNs) with that of the modified Black model in both pricing and hedging Short Sterling options. Using high frequency data, standard and hybrid ANNs are trained to generate option prices. The hybrid ANN is significantly superior to both the modified Black model and the standard ANN in pricing call and put options. Hedge ratios for hedging Short Sterling options positions using Short Sterling futures are produced using the standard and hybrid ANN pricing models, the modified Black model, and also standard and hybrid ANNs trained directly on the hedge ratios. The performance of hedge ratios from ANNs directly trained on actual hedge ratios is significantly superior to those based on a pricing model, and to the modified Black model.
Interactive Processes in Matching Identity and Expressions of Unfamiliar Faces: Evidence for Mutual Facilitation Effects

We investigated the interactions between matching identity and expressions of unfamiliar faces. In experiment 1, participants matched expressions in frontal and in oblique views, while we manipulated facial identity. In experiment 2, participants matched identity in frontal and in oblique views, while facial expressions were manipulated. Labeling of expressions was not required. Results showed mutual facilitation between matching facial identity and facial expressions, in accuracy as well as in reaction times. Thus, matching expressions was better and faster for same-identity images in oblique as well as in frontal views (experiment 1), and matching identity was better and faster for same-expression images in oblique as well as in frontal views (experiment 2). The discussion focuses on the implications of these results for the structural encoding of facial identity and facial expressions.
Future Water Availability from Hindukush-Karakoram-Himalaya upper Indus Basin under Conflicting Climate Change Scenarios

Future of the crucial Himalayan water supplies has generally been assessed under the anthropogenic warming, typically consistent amid observations and climate model projections. However, conflicting mid-to-late melt-season cooling within the upper Indus basin (UIB) suggests that the future of its melt-dominated hydrological regime and the subsequent water availability under changing climate has yet been understood only indistinctly. Here, the future water availability from the UIB is presented under both observed and projected—though likely but contrasting—climate change scenarios. Continuation of prevailing climatic changes suggests decreased and delayed glacier melt but increased and early snowmelt, leading to reduction in the overall water availability and profound changes in the overall seasonality of the hydrological regime. Hence, initial increases in the water availability due to enhanced glacier melt under typically projected warmer climates, and then abrupt decrease upon vanishing of the glaciers, as reported earlier, is only true given the UIB starts following uniformly the global warming signal. Such discordant future water availability findings caution the impact assessment communities to consider the relevance of likely (near-future) climate change scenarios—consistent to prevalent climatic change patterns—in order to adequately support the water resource planning in Pakistan.
Classification of blood vessels as arteries and veins for diagnosis of hypertensive retinopathy

Vasculature abnormalities are the indicator of different diseases in the human body. Retinal blood vessels are very sensitive to blood pressure changes. Diameter abnormalities of retinal blood vessels are the first clinical finding in many retinal diseases such as glaucoma, Diabetic retinopathy, hypertensive retinopathy and macular degeneration. Automated and accurate classification of blood vessels into arteries and veins may help the ophthalmologist to find the retinal disorders. In this paper, we present a novel method for automated detection of hypertensive retinopathy. The proposed system classifies the vessel into arteries and veins using different machine learning techniques and then detects hypertensive retinopathy by computing arteriolar to Venular ratio. The proposed system is tested on one publicly available database and one locally gathered database. The quantitative results show the validity of proposed system.
Inferring protocol state machine for binary communication protocol

Communication protocol reverse engineering has played an important role in the field of network security. Inferring protocol state machine for unknown protocol is a part of protocol specifications mining. This paper proposed a novel approach in the mining of unknown binary protocol state machine. It allows to automatically generating the state models for binary protocol by listening to network traces. We present a new methodology to align the corresponding fields and extract the state relevant fields from binary protocol communication traces, and then based on the state relevant fields to construct the protocol state model. The experimental results of ARP and TCP show that our approach is effective.
Lift-based search for significant dependencies in dense data sets

Dependency analysis is an important but computationally demanding problem in all empirical science. It is especially problematic in bioinformatics, where data sets are often high dimensional, dense and/or strongly correlated. As a solution, we introduce a new algorithm which searches the most significant association rules expressing positive dependencies. The algorithm uses several effective pruning principles, which enable search without any minimum frequency thresholds. According to our initial experiments, the algorithm suits especially well for typical biological and medical data sets.
State-feedback control of LPV sampled-data systems

We address analysis and state-feedback synthesis problems for linear parameter-varying (LPV) sampled-data control systems. We assume that the state-space matrices of the plant and the sampling interval depend on parameters that are measurable in real-time and vary in a compact set with bounded variation rates. We explore criteria such as the stability, the energy-to-energy gain (induced L/sup 2/ norm) and the energy-to-peak gain (induced L/sup 2/-to-L/sup /spl infin// norm) of such sampled-data LPV systems using parameter-dependent Lyapunov functions. Based on these analysis results, the sampled-data state-feedback control synthesis problems are examined. Both analysis and synthesis conditions are formulated in terms of linear matrix inequalities (LMIs) that can be solved via efficient interior-point algorithms.
An efficient greedy-based autonomous resource block assignment scheme for 5G cellular networks with self-organizing relaying terminals

In future cellular networks, self-organizing relaying terminals (RTs) are expected to play a crucial role in assisting the communication between base stations and wireless terminals (WTs), which include, not only active user terminals, but also machine-type communication devices. In the absence of channel quality indicators, the effective utilization of RTs requires a mechanism by which these RTs can assign available resource blocks (RBs) to a potentially large number of WTs with minimal conflicts. This requires optimizing RB assignments over a large set of lengthy sequences, which is computationally prohibitive for networks with large numbers of RTs. To alleviate the difficulty in designing such sequences, we develop a greedy algorithm, whereby pairs of RB assignment sequences are selected in an efficient sequential manner. The performance of the sequences generated by this algorithm is comparable to that of the sequences generated by exhaustive search, but with a significantly less computational cost.