OpenRaiser/Intern-Atlas huggingface dataset:
Determination of Critical Success Factors Affecting Mobile Learning: A Meta-Analysis Approach
Venue: arXiv cs.CY 2018
Year: 2018

With rapid technological advancements, mobile learning (m-Learning) offers incredible opportunities, especially in the area of higher education. However, while interest in this area has been significant and several pilot studies have been conducted within universities, relatively less is known about how higher educational institutions can make efficient use of the m-Learning platform to support teaching and learning. Although there are numerous studies in the area, the lack of this insight is mostly due to the fact that very little effort has been made to collate these studies and determine a common set of key success factors that affect the acceptance of m-Learning within universities. This study conducts a systematic analysis of several studies conducted in the area of m-Learning to assess the critical success factors, by making use of the meta-analysis technique. Our investigation has shown that the most important perceived advantages of m-Learning, from learner perspectives, are collaboration during studies, the prospect of ubiquitous learning in space and time, and user friendly application design.
A Workload Analysis of NSF's Innovative HPC Resources Using XDMoD
Venue: arXiv cs.DC 2018
Year: 2018

Workload characterization is an integral part of performance analysis of high performance computing (HPC) systems. An understanding of workload properties sheds light on resource utilization and can be used to inform performance optimization both at the software and system configuration levels. It can provide information on how computational science usage modalities are changing that could potentially aid holistic capacity planning for the wider HPC ecosystem. Here, we report on the results of a detailed workload analysis of the portfolio of supercomputers comprising the NSF Innovative HPC program in order to characterize its past and current workload and look for trends to understand the nature of how the broad portfolio of computational science research is being supported and how it is changing over time. The workload analysis also sought to illustrate a wide variety of usage patterns and performance requirements for jobs running on these systems. File system performance, memory utilization and the types of parallelism employed by users (MPI, threads, etc) were also studied for all systems for which job level performance data was available.
On the Capacity Region of the Deterministic Y-Channel with Common and Private Messages
Venue: arXiv cs.IT 2018
Year: 2018

In multi user Gaussian relay networks, it is desirable to transmit private information to each user as well as common information to all of them. However, the capacity region of such networks with both kinds of information is not easy to characterize. The prior art used simple linear deterministic models in order to approximate the capacities of these Gaussian networks. This paper discusses the capacity region of the deterministic Y-channel with private and common messages. In this channel, each user aims at delivering two private messages to the other two users in addition to a common message directed towards both of them. As there is no direct link between the users, all messages must pass through an intermediate relay. We present outer-bounds on the rate region using genie aided and cut-set bounds. Then, we develop a greedy scheme to define an achievable region and show that at a certain number of levels at the relay, our achievable region coincides with the upper bound. Finally, we argue that these bounds for this setup are not sufficient to characterize the capacity region.
Queue-aware Energy Efficient Control for Dense Wireless Networks
Venue: arXiv cs.IT 2018
Year: 2018

We consider the problem of long term power allocation in dense wireless networks. The framework considered in this paper is of interest for machine-type communications (MTC). In order to guarantee an optimal operation of the system while being as power efficient as possible, the allocation policy must take into account both the channel and queue states of the devices. This is a complex stochastic optimization problem, that can be cast as a Markov Decision Process (MDP) over a huge state space. In order to tackle this state space explosion, we perform a mean-field approximation on the MDP. Letting the number of devices grow to infinity the MDP converges to a deterministic control problem. By solving the Hamilton-Jacobi-Bellman Equation, we obtain a well-performing power allocation policy for the original stochastic problem, which turns out to be a threshold-based policy and can then be easily implemented in practice.
SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks
Venue: arXiv cs.DC 2018
Year: 2018

Going deeper and wider in neural architectures improves the accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) practitioners either need change to less desired network architectures, or nontrivially dissect a network across multiGPUs. These distract DL practitioners from concentrating on their original machine learning tasks. We present SuperNeurons: a dynamic GPU memory scheduling runtime to enable the network training far beyond the GPU DRAM capacity. SuperNeurons features 3 memory optimizations, \textit{Liveness Analysis}, \textit{Unified Tensor Pool}, and \textit{Cost-Aware Recomputation}, all together they effectively reduce the network-wide peak memory usage down to the maximal memory usage among layers. We also address the performance issues in those memory saving techniques. Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance. Evaluations against Caffe, Torch, MXNet and TensorFlow have demonstrated that SuperNeurons trains at least 3.2432 deeper network than current ones with the leading performance. Particularly, SuperNeurons can train ResNet2500 that has $10^4$ basic network layers on a 12GB K40c.
The Problem of Correlation and Substitution in SPARQL -- Extended Version
Venue: arXiv cs.LO 2018
Year: 2018

Implementations of a standard language are expected to give same outputs to identical queries. In this paper we study why different implementations of SPARQL (Fuseki, Virtuoso, Blazegraph and rdf4j) behave differently when evaluating queries with correlated variables. We show that at the core of this problem lies the historically troubling notion of logical substitution. We present a formal framework to study this issue based on Datalog that besides clarifying the problem, gives a solid base to define and implement nesting.
Cost-Sensitive Convolution based Neural Networks for Imbalanced Time-Series Classification
Venue: arXiv cs.LG 2018
Year: 2018

Some deep convolutional neural networks were proposed for time-series classification and class imbalanced problems. However, those models performed degraded and even failed to recognize the minority class of an imbalanced temporal sequences dataset. Minority samples would bring troubles for temporal deep learning classifiers due to the equal treatments of majority and minority class. Until recently, there were few works applying deep learning on imbalanced time-series classification (ITSC) tasks. Here, this paper aimed at tackling ITSC problems with deep learning. An adaptive cost-sensitive learning strategy was proposed to modify temporal deep learning models. Through the proposed strategy, classifiers could automatically assign misclassification penalties to each class. In the experimental section, the proposed method was utilized to modify five neural networks. They were evaluated on a large volume, real-life and imbalanced time-series dataset with six metrics. Each single network was also tested alone and combined with several mainstream data samplers. Experimental results illustrated that the proposed cost-sensitive modified networks worked well on ITSC tasks. Compared to other methods, the cost-sensitive convolution neural network and residual network won out in the terms of all metrics. Consequently, the proposed cost-sensitive learning strategy can be used to modify deep learning classifiers from cost-insensitive to cost-sensitive. Those cost-sensitive convolutional networks can be effectively applied to address ITSC issues.
Towards a more efficient representation of imputation operators in TPOT
Venue: arXiv cs.LG 2018
Year: 2018

Automated Machine Learning encompasses a set of meta-algorithms intended to design and apply machine learning techniques (e.g., model selection, hyperparameter tuning, model assessment, etc.). TPOT, a software for optimizing machine learning pipelines based on genetic programming (GP), is a novel example of this kind of applications. Recently we have proposed a way to introduce imputation methods as part of TPOT. While our approach was able to deal with problems with missing data, it can produce a high number of unfeasible pipelines. In this paper we propose a strongly-typed-GP based approach that enforces constraint satisfaction by GP solutions. The enhancement we introduce is based on the redefinition of the operators and implicit enforcement of constraints in the generation of the GP trees. We evaluate the method to introduce imputation methods as part of TPOT. We show that the method can notably increase the efficiency of the GP search for optimal pipelines.
Not-All-Equal and 1-in-Degree Decompositions: Algorithmic Complexity and Applications
Venue: arXiv cs.DM 2018
Year: 2018

A Not-All-Equal (NAE) decomposition of a graph $G$ is a decomposition of the vertices of $G$ into two parts such that each vertex in $G$ has at least one neighbor in each part. Also, a 1-in-Degree decomposition of a graph $G$ is a decomposition of the vertices of $G$ into two parts $A$ and $B$ such that each vertex in the graph $G$ has exactly one neighbor in part $A$. Among our results, we show that for a given graph $G$, if $G$ does not have any cycle of length congruent to 2 mod 4, then there is a polynomial time algorithm to decide whether $G$ has a 1-in-Degree decomposition. In sharp contrast, we prove that for every $r$, $r\geq 3$, for a given $r$-regular bipartite graph $G$ determining whether $G$ has a 1-in-Degree decomposition is $ \mathbf{NP} $-complete. These complexity results have been especially useful in proving $ \mathbf{NP} $-completeness of various graph related problems for restricted classes of graphs. In consequence of these results we show that for a given bipartite 3-regular graph $G$ determining whether there is a vector in the null-space of the 0,1-adjacency matrix of $G$ such that its entries belong to $\{\pm 1,\pm 2\}$ is $\mathbf{NP} $-complete. Among other results, we introduce a new version of {Planar 1-in-3 SAT} and we prove that this version is also $ \mathbf{NP} $-complete. In consequence of this result, we show that for a given planar $(3,4)$-semiregular graph $G$ determining whether there is a vector in the null-space of the 0,1-incidence matrix of $G$ such that its entries belong to $\{\pm 1,\pm 2\}$ is $\mathbf{NP} $-complete.
A Stochastic Singular Vector Based MIMO Channel Model for MAC Layer Tracking
Venue: arXiv cs.IT 2018
Year: 2018

A novel stochastic technique is presented to directly model singular vectors and singular values of a multiple input multiple output channel. Thus the component smodeled directly in the eigen domain can be adapted to exhibit realistic physical domain behavior when assembled. The model exploites natural paths of eigenmodes, such that a simple Doppler filter generator process can be used. Furthermore it is possible to directly manipulate the singular vector dynamics in a way that an unrealistic "stress channel" can be modeled in the eigen domain. This is particularly useful for testing the eigenmode channel tracking ability internal to a communication device such as a modem, where impairments in tracking will cause interference between eigenmodes. The model can also facilitate mode tracking testing as it directly produces tracked ungtangled eigenmodes, providing the narrowest possible singular vector Doppler spectra and consequently lowest required update rates of each eigenmode. The singular vector based model targets testing of the eigen domain functionality of MIMO modems/devices, an apparatus focus, without the need for including the decomposition stages.
Near-optimal approximation algorithm for simultaneous Max-Cut
Venue: arXiv cs.CC 2018
Year: 2018

In the simultaneous Max-Cut problem, we are given $k$ weighted graphs on the same set of $n$ vertices, and the goal is to find a cut of the vertex set so that the minimum, over the $k$ graphs, of the cut value is as large as possible. Previous work [BKS15] gave a polynomial time algorithm which achieved an approximation factor of $1/2 - o(1)$ for this problem (and an approximation factor of $1/2 + ε_k$ in the unweighted case, where $ε_k \rightarrow 0$ as $k \rightarrow \infty$). In this work, we give a polynomial time approximation algorithm for simultaneous Max-Cut with an approximation factor of $0.8780$ (for all constant $k$). The natural SDP formulation for simultaneous Max-Cut was shown to have an integrality gap of $1/2+ε_k$ in [BKS15]. In achieving the better approximation guarantee, we use a stronger Sum-of-Squares hierarchy SDP relaxation and a rounding algorithm based on Raghavendra-Tan [RT12], in addition to techniques from [BKS15].
PACER: Peripheral Activity Completion Estimation and Recognition
Venue: arXiv cs.DC 2018
Year: 2018

Embedded peripheral devices such as memories, sensors and communications interfaces are used to perform a function external to a host microcontroller. The device manufacturer typically specifies worst-case current consumption and latency estimates for each of these peripheral actions. Peripheral Activity Completion, Estimation and Recognition (PACER) is introduced as a suite of algorithms that can be applied to detect completed peripheral operations in real-time. By detecting activity completion, PACER enables the host to exploit slack between the worst-case estimate and the actual response time. These methods were tested independently and in conjunction with IODVS on multiple common peripheral devices. For the peripheral devices under test, the test fixture confirmed decreases in energy expenditures of up to 80% and latency reductions of up to 67%.
Tyche: Risk-Based Permissions for Smart Home Platforms
Venue: arXiv cs.CR 2018
Year: 2018

Emerging smart home platforms, which interface with a variety of physical devices and support third-party application development, currently use permission models inspired by smartphone operating systems-they group functionally similar device operations into separate units, and require users to grant apps access to devices at that granularity. Unfortunately, this leads to two issues: (1) apps that do not require access to all of the granted device operations have overprivileged access to them, (2) apps might pose a higher risk to users than needed because physical device operations are fundamentally risk-asymmetric-"door.unlock" provides access to burglars, and "door.lock" can potentially lead to getting locked out. Overprivileged apps with access to mixed-risk operations only increase the potential for damage. We present Tyche, a system that leverages the risk-asymmetry in physical device operations to limit the risk that apps pose to smart home users, without increasing the user's decision overhead. Tyche introduces the notion of risk-based permissions. When using risk-based permissions, device operations are grouped into units of similar risk, and users grant apps access to devices at that risk-based granularity. Starting from a set of permissions derived from the popular Samsung SmartThings platform, we conduct a user study involving domain-experts and Mechanical Turk users to compute a relative ranking of risks associated with device operations. We find that user assessment of risk closely matches that of domain experts. Using this ranking, we define risk-based groupings of device operations, and apply it to existing SmartThings apps, showing that risk-based permissions indeed limit risk if apps are malicious or exploitable.
Innovative Non-parametric Texture Synthesis via Patch Permutations
Venue: arXiv cs.GR 2018
Year: 2018

In this work, we present a non-parametric texture synthesis algorithm capable of producing plausible images without copying large tiles of the exemplar. We focus on a simple synthesis algorithm, where we explore two patch match heuristics; the well known Bidirectional Similarity (BS) measure and a heuristic that finds near permutations using the solution of an entropy regularized optimal transport (OT) problem. Innovative synthesis is achieved with a small patch size, where global plausibility relies on the qualities of the match. For OT, less entropic regularization also meant near permutations and more plausible images. We examine the tile maps of the synthesized images, showing that they are indeed novel superpositions of the input and contain few or no verbatim copies. Synthesis results are compared to a statistical method, namely a random convolutional network. We conclude by remarking simple algorithms using only the input image can synthesize textures decently well and call for more modest approaches in future algorithm design.
Mixing Time on the Kagome Lattice
Venue: arXiv cs.DM 2018
Year: 2018

We consider tilings of a closed region of the Kagome lattice (partition of the plane into regular hexagons and equilateral triangles such that each edge is shared by one triangle and one hexagon). We are interested in the rate of convergence to the stationarity of a natural Markov chain defined on the set of Kagome tilings. The rate of convergence can be represented by the mixing time which mesures the amount of time it takes the chain to be close to its stationary distribution. We obtain a $\mathcal{O}(N^4)$ upper bound on the mixing time of a weighted version of the natural Markov chain. We also consider Kagome tilings restrained to two prototiles, prove flip-connectivity and draw a $\mathcal{O}(N^4)$ upper bound as well on the mixing time of the natural Markov chain in a general (non weighted) case. Finally, we present simulations that suggest existence of a long range phenomenon.
Modified SI Epidemic Model for Combating Virus Spread in Spatially Correlated Wireless Sensor Networks
Venue: arXiv cs.NI 2018
Year: 2018

In wireless sensor networks (WSNs), main task of each sensor node is to sense the physical activity (i.e., targets or disaster conditions) and then to report it to the control center for further process. For this, sensor nodes are attached with many sensors having ability to measure the environmental information. Spatial correlation between nodes exists in such wireless sensor network based on common sensory coverage and then the redundant data communication is observed. To study virus spreading dynamics in such scenario, a modified SI epidemic model is derived mathematically by incorporating WSN parameters such as spatial correlation, node density, sensing range, transmission range, total sensor nodes etc. The solution for proposed SI model is also determined to study the dynamics with time. Initially, a small number of nodes are attacked by viruses and then virus infection propagates through its neighboring nodes over normal data communication. Since redundant nodes exists in correlated sensor field, virus spread process could be different with different sensory coverage. The proposed SI model captures spatial and temporal dynamics than existing ones which are global. The infection process leads to network failure. By exploiting spatial correlation between nodes, spread control scheme is developed to limit the further infection in the network. Numerical result analysis is provided with comparison for validation.
Reversible Embedding to Covers Full of Boundaries
Venue: arXiv cs.MM 2018
Year: 2018

In reversible data embedding, to avoid overflow and underflow problem, before data embedding, boundary pixels are recorded as side information, which may be losslessly compressed. The existing algorithms often assume that a natural image has little boundary pixels so that the size of side information is small. Accordingly, a relatively high pure payload could be achieved. However, there actually may exist a lot of boundary pixels in a natural image, implying that, the size of side information could be very large. Therefore, when to directly use the existing algorithms, the pure embedding capacity may be not sufficient. In order to address this problem, in this paper, we present a new and efficient framework to reversible data embedding in images that have lots of boundary pixels. The core idea is to losslessly preprocess boundary pixels so that it can significantly reduce the side information. Experimental results have shown the superiority and applicability of our work.
Two High-performance Schemes of Transmit Antenna Selection for Secure Spatial Modulation
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, a secure spatial modulation (SM) system with artificial noise (AN)-aided is investigated. To achieve higher secrecy rate (SR) in such a system, two high-performance schemes of transmit antenna selection (TAS), leakage-based and maximum secrecy rate (Max-SR), are proposed and a generalized Euclidean distance-optimized antenna selection (EDAS) method is designed. From simulation results and analysis, the four TAS schemes have an decreasing order: Max-SR, leakage-based, generalized EDAS, and random (conventional), in terms of SR performance. However, the proposed Max-SR method requires the exhaustive search to achieve the optimal SR performance, thus its complexity is extremely high as the number of antennas tends to medium and large scale. The proposed leakage-based method approaches the Max-SR method with much lower complexity. Thus, it achieves a good balance between complexity and SR performance. In terms of bit error rate (BER), their performances are in an increasing order: random, leakage-based, Max-SR, and generalized EDAS.
The Design Space of Social Robots
Venue: arXiv cs.RO 2018
Year: 2018

We consider the design space available for social robots in terms of a hierarchy of functional definitions: the essential properties in terms of a locus of interaction, autonomy, intelligence, awareness of humans as possessors of mental state, and awareness of humans as social interactors. We also suggest that the emphasis on physical embodiment in some segments of the social robotics community has obscured commonalities with a class of agents that are identical in all other respects. These definitions naturally suggest research issues, directions, and possibilities which we explore. Social robotics also lacks compelling 'killer apps' which we suggest would help focus the community on a research agenda.
Leapfrogging for parallelism in deep neural networks
Venue: arXiv cs.LG 2018
Year: 2018

We present a technique, which we term leapfrogging, to parallelize back- propagation in deep neural networks. We show that this technique yields a savings of $1-1/k$ of a dominant term in backpropagation, where k is the number of threads (or gpus).