Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital storytelling as a cultural-historical activity: effects on information text comprehension

New literacies in reading research demand for the study of comprehension skills using multiple modalities, through a more complex, multi-platform view of reading. Taking into account the robust roll of technology in our daily lives, research suggests that educators need activities to connect students' lack of reading skills with their growing multimodal literacy. During the post-reading phase of a directed reading activity (DRA), students were engaged in digital storytelling, where they created digital videos and slideshows based on information text read during DRA. Previous studies highlighting the use of digital storytelling have been limited to narrative formats and the influence that participation in this activity has on self-esteem and identity. This activity has been widely recommended for improving writing among teachers and in teaching journals, but it has not been empirically studied in the classroom as a comprehension activity. The theoretical framework that supported this study was Vygotsky's Cultural Historical Activity Theory (CHAT). The researcher used the CHAT framework and Burke's Pentad of Analysis (1969) to study the nature of student interactions. Research questions for this study were answered through both quantitative and qualitative methods. The questions being asked by the researcher were: (1) What were the effects of participation in directed reading activity (DRA) modified to include digital storytelling in the post-reading phase of DRA on 6th graders' comprehension of information text? (2) Did the interactions observed during participation in directed reading activity modified to include digital storytelling reflect the principles promoted by Cultural-Historical Activity Theory? Eighty sixth-grade students were randomly assigned to their digital storytelling groups. The subjects participated in whole-class DRA on two information texts, with the treatment group creating digital stories based on the texts. Cloze scores indicated that there was no significant difference in comprehension due to the treatment. However, there was ample evidence to support the claim that participation in digital storytelling instantiates the principles of CHAT. Overall, digital storytelling does show promise as a multimodal instructional activity, and the discussion expands to several implications and recommendations of future research on this instructional activity.
Manga High: Literacy, Identity, and Coming of Age in an Urban High School

Based on a four-year study, "Manga High" explores the convergence of literacy, creativity, social development, and personal identity in one of New York City s largest high schools. Since 2004, students at Martin Luther King, Jr., High School in Manhattan have been creating manga Japanese comic books. They write the stories, design the characters, and publish their works in print and on the Internet. These students African-American and Latino teenagers are more than interested in the art and medium of manga. They have become completely engrossed in Japanese language, culture, and society. "Manga High" is highlighted by reproductions and content analysis of students original art and writing. An appendix includes guidelines for educators on starting a comic book club. "
Elegy by W.S.: A Study in Attribution

This study investigates the authorship of A Funerall Elegye, composed by an unidentified "W. S. " in memory of William Peter, an Oxford scholar murdered on 25 January 1612. Is it a lost poem of William Shakespeare? Evidence both for and against is presented, along with the text of the 1612 quarto in facsimile, Foster's edited text, and a discussion of attributional theory and methodology. University of Delaware Press Award.
Coded Caching in Fog-RAN: $b$ -Matching Approach

Fog radio access network (Fog-RAN), which pushes caching and computing capabilities to the network edge, is capable of efficiently delivering content to users by using carefully designed caching placement and content replacement algorithms. In this paper, the transmission scheme design and coding parameter optimization will be considered for coded caching in Fog-RAN, where the reliability of content delivery, i.e., content outage probability, is used as the performance metric. The problem will be formulated as a complicated multi-objective probabilistic combinatorial optimization. A novel maximum <inline-formula> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula>-matching approach will then be proposed to obtain the Pareto optimal solution with fairness constraint. Based on the fast message passing approach, a distributed algorithm with a low memory usage of <inline-formula> <tex-math notation="LaTeX">$O(M+N)$ </tex-math></inline-formula> is also proposed, where <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> is the number of users and <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> is the number of fog access points (Fog-APs). Although it is usually very difficult to derive the closed-form formulas for the optimal solution, the approximation formulas of the content outage probability will also be obtained as a function of coding parameters. The asymptotic optimal coding parameters can then be obtained by defining and deriving the outage exponent region and diversity-multiplexing region. Simulation results will illustrate the accuracy of the theoretical derivations, and verify the outage performance of the proposed approach. Therefore, this paper not only proposes a practical distributed Fog-AP selection algorithm for coded caching but also provides a systematic way to evaluate and optimize the performance of Fog-RANs.
UAV Communications for 5G and Beyond: Recent Advances and Future Trends

Providing ubiquitous connectivity to diverse device types is the key challenge for 5G and beyond 5G (B5G). Unmanned aerial vehicles (UAVs) are expected to be an important component of the upcoming wireless networks that can potentially facilitate wireless broadcast and support high rate transmissions. Compared to the communications with fixed infrastructure, UAV has salient attributes, such as flexible deployment, strong line-of-sight connection links, and additional design degrees of freedom with the controlled mobility. In this paper, a comprehensive survey on UAV communication toward 5G/B5G wireless networks is presented. We first briefly introduce essential background and the space–air–ground integrated networks, as well as discuss related research challenges faced by the emerging integrated network architecture. We then provide an exhaustive review of various 5G techniques based on UAV platforms, which we categorize by different domains, including physical layer, network layer, and joint communication, computing, and caching. In addition, a great number of open research problems are outlined and identified as possible future research directions.
RDCSim: a GPU-Accelerated, Interactive Simulator for Reaction-Diffusion Chemistry

This paper presents RDCSim, an interactive simulator for reaction--diffusion chemistry (RDC) research, being developed as part of an ongoing project studying how humans interact with unconventional computing systems. While much research into the computational properties of RDC makes use of simulations, the development of multiple RDC simulations across different research groups can lead to results that are harder to reproduce. By automating the storage of parameter values alongside simulation results, RDCSim aims to make reproducing RDC results painless. This paper presents the functionality of RDCSim, and verifies the behaviour of the underlying chemical simulation using two seminal examples from the RDC literature: logical AND gates and chemical diodes.
Elastic Multi-resource Network Slicing: Can Protection Lead to Improved Performance?

In order to meet the performance/privacy requirements of future data-intensive mobile applications, e.g., self-driving cars, mobile data analytics, and AR/VR, service providers are expected to draw on shared storage/computation/connectivity resources at the network “edge”. To be cost-effective, a key functional requirement for such infrastructure is enabling the sharing of heterogeneous resources amongst tenants/service providers supporting spatially varying and dynamic user demands. This paper proposes a resource allocation criterion, namely, Share Constrained Slicing (SCS), for slices allocated predefined shares of the network's resources, which extends traditional a-fairness criterion, by striking a balance among inter- and intra-slice fairness vs. overall efficiency. We show that SCS has several desirable properties including slice-level protection, envyfreeness, and load-driven elasticity. In practice, mobile users' dynamics could make the cost of implementing SCS high, so we discuss the feasibility of using a simpler (dynamically) weighted max-min as a surrogate resource allocation scheme. For a setting with stochastic loads and elastic user requirements, we establish a sufficient condition for the stability of the associated coupled network system. Finally, and perhaps surprisingly, we show via extensive simulations that while SCS (and/or the surrogate weighted max-min allocation) provides inter-slice protection, they can achieve improved job delay and/or perceived throughput, as compared to other weighted maxmin based allocation schemes whose intra-slice weight allocation is not share-constrained, e.g., traditional max-min or discriminatory processor sharing.
On Unified Vehicular Communications and Radar Sensing in Millimeter-Wave and Low Terahertz Bands

Future smart vehicles will incorporate highdata- rate communications and high-resolution radar sensing capabilities operating in the millimeter- wave and higher frequencies. These two systems are preparing to share and reuse many common functionalities, such as steerable millimeter- wave antenna arrays. Motivated by this growing overlap, which is advanced further by space and cost constraints, the vehicular community is pursuing a vision of unified vehicular communications and radar sensing that represents a major paradigm shift for next-generation connected and self-driving cars. This article outlines a path to materialize this decisive transformation. We begin by reviewing the latest developments in hybrid vehicular communications and radar systems, and then propose a concept of unified channel access over millimeter-wave and higher frequencies. Our supporting system-level performance characterization relies upon real-life measurements and extensive ray-based modeling to confirm the significant improvements brought by our proposal to mitigating the interference and deafness effects. Since our results aim to open the door to unified vehicular communications and radar sensing, we conclude by outlining the potential research directions in this rapidly developing field.
MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild

Face tracking serves as the crucial initial step in mobile applications trying to analyse target faces over time in mobile settings. However, this problem has received little attention, mainly due to the scarcity of dedicated face tracking benchmarks. In this work, we introduce MobiFace, the first dataset for single face tracking in mobile situations. It consists of 80 unedited live-streaming mobile videos captured by 70 different smartphone users in fully unconstrained environments. Over 95K bounding boxes are manually labelled. The videos are carefully selected to cover typical smartphone usage. The videos are also annotated with 14 attributes, including 6 newly proposed attributes and 8 commonly seen in object tracking. 36 state-of-the-art trackers, including facial landmark trackers, generic object trackers and trackers that we have fine-tuned or improved, are evaluated. The results suggest that mobile face tracking cannot be solved through existing approaches. In addition, we show that fine-tuning on the MobiFace training data significantly boosts the performance of deep learning-based trackers, suggesting that MobiFace captures the unique characteristics of mobile face tracking. Our goal is to offer the community a diverse dataset to enable the design and evaluation of mobile face trackers. The dataset, annotations and the evaluation server will be on https://mobiface.github. io/.
Android Malware Detection Using Autoencoder

Smartphones have become an intrinsic part of human's life. The smartphone unifies diverse advanced characteristics. It enables users to store various data such as photos, health data, credential bank data, and personal information. The Android operating system is the prevalent mobile operating system and, in the meantime, the most targeted operating system by malware developers. Recently the unparalleled development of Android malware put pressure on researchers to propose effective methods to suppress the spread of the malware. In this paper, we propose a deep learning approach for Android malware detection. The proposed approach investigates five different feature sets and applies Autoencoder to identify malware. The experimental results show that the proposed approach can identify malware with high accuracy.
Optical Flow augmented Semantic Segmentation networks for Automated Driving

Motion is a dominant cue in automated driving systems. Optical flow is typically computed to detect moving objects and to estimate depth using triangulation. In this paper, our motivation is to leverage the existing dense optical flow to improve the performance of semantic segmentation. To provide a systematic study, we construct four different architectures which use RGB only, flow only, RGBF concatenated and two-stream RGB + flow. We evaluate these networks on two automotive datasets namely Virtual KITTI and Cityscapes using the state-of-the-art flow estimator FlowNet v2. We also make use of the ground truth optical flow in Virtual KITTI to serve as an ideal estimator and a standard Farneback optical flow algorithm to study the effect of noise. Using the flow ground truth in Virtual KITTI, two-stream architecture achieves the best results with an improvement of 4% IoU. As expected, there is a large improvement for moving objects like trucks, vans and cars with 38%, 28% and 6% increase in IoU. FlowNet produces an improvement of 2.4% in average IoU with larger improvement in the moving objects corresponding to 26%, 11% and 5% in trucks, vans and cars. In Cityscapes, flow augmentation provided an improvement for moving objects like motorcycle and train with an increase of 17% and 7% in IoU.
A combined entropy and utility based generative model for large scale multiple discrete-continuous travel behaviour data

Generative models, either by simple clustering algorithms or deep neural network architecture, have been developed as a probabilistic estimation method for dimension reduction or to model the underlying properties of data structures. Although their apparent use has largely been limited to image recognition and classification, generative machine learning algorithms can be a powerful tool for travel behaviour research. In this paper, we examine the generative machine learning approach for analyzing multiple discrete-continuous (MDC) travel behaviour data to understand the underlying heterogeneity and correlation, increasing the representational power of such travel behaviour models. We show that generative models are conceptually similar to choice selection behaviour process through information entropy and variational Bayesian inference. Specifically, we consider a restricted Boltzmann machine (RBM) based algorithm with multiple discrete-continuous layer, formulated as a variational Bayesian inference optimization problem. We systematically describe the proposed machine learning algorithm and develop a process of analyzing travel behaviour data from a generative learning perspective. We show parameter stability from model analysis and simulation tests on an open dataset with multiple discrete-continuous dimensions and a size of 293,330 observations. For interpretability, we derive analytical methods for conditional probabilities as well as elasticities. Our results indicate that latent variables in generative models can accurately represent joint distribution consistently w.r. t multiple discrete-continuous variables. Lastly, we show that our model can generate statistically similar data distributions for travel forecasting and prediction.
Fitting ReLUs via SGD and Quantized SGD

In this paper we focus on the problem of finding the optimal weights of the shallowest of neural networks consisting of a single Rectified Linear Unit (ReLU). These functions are of the form x → max(0, 〈w, x〉) with w ∈ ℝd denoting the weight vector. We focus on a planted i model where the inputs are chosen i.i. d. from a Gaussian distribution and the labels are generated according to a planted weight vector. We first show that mini-batch stochastic gradient descent when suitably initialized, converges at a geometric rate to the planted model with a number of samples that is optimal up to numerical constants. Next we focus on a parallel implementation where in each iteration the mini-batch gradient is calculated in a distributed manner across multiple processors and then broadcast to a master or all other processors. To reduce the communication cost in this setting we utilize a Quanitzed Stochastic Gradient Scheme (QSGD) where the partial gradients are quantized. Perhaps unexpectedly, we show that QSGD maintains the fast convergence of SGD to a globally optimal model while significantly reducing the communication cost. We further corroborate our numerical findings via various experiments including distributed implementations over Amazon EC2.
Friend, Collaborator, Student, Manager: How Design of an AI-Driven Game Level Editor Affects Creators

Machine learning advances have afforded an increase in algorithms capable of creating art, music, stories, games, and more. However, it is not yet well-understood how machine learning algorithms might best collaborate with people to support creative expression. To investigate how practicing designers perceive the role of AI in the creative process, we developed a game level design tool for Super Mario Bros. -style games with a built-in AI level designer. In this paper we discuss our design of the Morai Maker intelligent tool through two mixed-methods studies with a total of over one-hundred participants. Our findings are as follows: (1) level designers vary in their desired interactions with, and role of, the AI, (2) the AI prompted the level designers to alter their design practices, and (3) the level designers perceived the AI as having potential value in their design practice, varying based on their desired role for the AI.
On the enumeration of closures and environments with an application to random generation

Environments and closures are two of the main ingredients of evaluation in lambda-calculus. A closure is a pair consisting of a lambda-term and an environment, whereas an environment is a list of lambda-terms assigned to free variables. In this paper we investigate some dynamic aspects of evaluation in lambda-calculus considering the quantitative, combinatorial properties of environments and closures. Focusing on two classes of environments and closures, namely the so-called plain and closed ones, we consider the problem of their asymptotic counting and effective random generation. We provide an asymptotic approximation of the number of both plain environments and closures of size $n$. Using the associated generating functions, we construct effective samplers for both classes of combinatorial structures. Finally, we discuss the related problem of asymptotic counting and random generation of closed environemnts and closures.
An Optimal Vector Clock Algorithm for Multithreaded Systems

Tracking causality (or happened-before relation) between events is useful for many applications such as debugging and recovery from failures. Consider a concurrent system with n threads and m objects. For such systems, either a vector clock of size n is used with one component per thread or a vector clock of size m is used with one component per object. A natural question is whether one can use a vector clock of size strictly less than the minimum of m and n to timestamp events. We give an algorithm in this paper that uses a hybrid of thread and object components. Our algorithm is guaranteed to return the minimum number of components necessary for vector clocks. We first consider the case when the interaction between objects and threads is statically known. This interaction is modeled by a thread-object bipartite graph. Our algorithm is based on finding the maximum bipartite matching of such a graph and then applying König-Egerváry Theorem to compute the minimum vertex cover to determine the optimal number of components necessary for the vector clock. We also propose two mechanisms to compute such an vector clock when computation is revealed in an online fashion. Evaluation on different types of graphs indicates that our offline algorithm generates a size vector clock which is significantly less than the minimum of m and n. These mechanisms are more effective when the underlying bipartite graph is not dense.
Generating Textual Adversarial Examples for Deep Learning Models: A Survey

With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications. However, previous efforts have shown that DNNs were vulnerable to strategically modified samples, named adversarial examples. These samples are generated with some imperceptible perturbations but can fool the DNNs to give false predictions. Inspired by the popularity of generating adversarial examples for image DNNs, research efforts on attacking DNNs for textual applications emerges in recent years. However, existing perturbation methods for images cannotbe directly applied to texts as text data is discrete. In this article, we review research works that address this difference and generatetextual adversarial examples on DNNs. We collect, select, summarize, discuss and analyze these works in a comprehensive way andcover all the related information to make the article self-contained. Finally, drawing on the reviewed literature, we provide further discussions and suggestions on this topic.
IOTA-based Directed Acyclic Graphs without Orphans

Directed Acylic Graphs (DAGs) are emerging as an attractive alternative to traditional blockchain architectures for distributed ledger technology (DLT). In particular DAG ledgers with stochastic attachment mechanisms potentially offer many advantages over blockchain, including scalability and faster transaction speeds. However, the random nature of the attachment mechanism coupled with the requirement of protection against double-spend transactions leaves open the possibility that not all transactions will be eventually validated. Such transactions are said to be orphaned, and will never be validated. Our principal contribution is to propose a simple modification to the attachment mechanism for the Tangle (the IOTA DAG architecture). This modification ensures that all transactions are validated in finite time, and preserves essential features of the popular Monte-Carlo selection algorithm. In order to demonstrate these results we derive a fluid approximation for the Tangle (in the limit of infinite arrival rate) and prove that this fluid model exhibits the desired behavior. We also present simulations which validate the results for finite arrival rates.