OpenRaiser/Intern-Atlas huggingface dataset:
Mobile Social Services with Network Externality: From Separate Pricing to Bundled Pricing
Venue: arXiv cs.GT 2018
Year: 2018

Today, many wireless device providers choose to sell devices bundled with complementary mobile social services, which exhibit strong positive network externality. This paper aims to quantify the benefits of selling devices and complementary services under the following three strategies: separate pricing, bundled pricing, and hybrid pricing (both the separate and bundled options are offered). A comprehensive comparison of the above three strategies is carried out for two popular service models, namely physical connectivity sharing and virtual content sharing, respectively. We first study the physical service model where the provider (e.g., FON) offers users customized WiFi devices for indoor Internet access, and allows service subscribers to physically access all device owners' WiFi when traveling. Observing that all device-owners contribute to the connectivity sharing, we show, via a Stackelberg game theoretic approach, that bundled pricing outperforms separate pricing as long as the total cost of device and service is reasonably low to stimulate network externality. Further, hybrid pricing strictly dominates bundled pricing thanks to the pricing flexibility to keep high marginal profit of device-selling. Next, we investigate the virtual sharing service model where the provider (e.g., Apple) sells devices and device-supported applications. Different from the connectivity service model, in this model service subscribers directly contribute to the virtual content sharing, and the network externality can be fairly strong. We prove that hybrid pricing degenerates to bundled pricing if the network externality degree is larger than the average device valuation, which is in stark contrast with the connectivity service model in which hybrid pricing always outperforms bundled pricing.
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges
Venue: arXiv cs.AI 2018
Year: 2018

Issues regarding explainable AI involve four components: users, laws & regulations, explanations and algorithms. Together these components provide a context in which explanation methods can be evaluated regarding their adequacy. The goal of this chapter is to bridge the gap between expert users and lay users. Different kinds of users are identified and their concerns revealed, relevant statements from the General Data Protection Regulation are analyzed in the context of Deep Neural Networks (DNNs), a taxonomy for the classification of existing explanation methods is introduced, and finally, the various classes of explanation methods are analyzed to verify if user concerns are justified. Overall, it is clear that (visual) explanations can be given about various aspects of the influence of the input on the output. However, it is noted that explanation methods or interfaces for lay users are missing and we speculate which criteria these methods / interfaces should satisfy. Finally it is noted that two important concerns are difficult to address with explanation methods: the concern about bias in datasets that leads to biased DNNs, as well as the suspicion about unfair outcomes.
Thermal to Visible Synthesis of Face Images using Multiple Regions
Venue: arXiv cs.CV 2018
Year: 2018

Synthesis of visible spectrum faces from thermal facial imagery is a promising approach for heterogeneous face recognition; enabling existing face recognition software trained on visible imagery to be leveraged, and allowing human analysts to verify cross-spectrum matches more effectively. We propose a new synthesis method to enhance the discriminative quality of synthesized visible face imagery by leveraging both global (e.g., entire face) and local regions (e.g., eyes, nose, and mouth). Here, each region provides (1) an independent representation for the corresponding area, and (2) additional regularization terms, which impact the overall quality of synthesized images. We analyze the effects of using multiple regions to synthesize a visible face image from a thermal face. We demonstrate that our approach improves cross-spectrum verification rates over recently published synthesis approaches. Moreover, using our synthesized imagery, we report the results on facial landmark detection-commonly used for image registration-which is a critical part of the face recognition process.
The $\ell^\infty$-Cophenetic Metric for Phylogenetic Trees as an Interleaving Distance
Venue: arXiv cs.CG 2018
Year: 2018

There are many metrics available to compare phylogenetic trees since this is a fundamental task in computational biology. In this paper, we focus on one such metric, the $\ell^\infty$-cophenetic metric introduced by Cardona et al. This metric works by representing a phylogenetic tree with $n$ labeled leaves as a point in $\mathbb{R}^{n(n+1)/2}$ known as the cophenetic vector, then comparing the two resulting Euclidean points using the $\ell^\infty$ distance. Meanwhile, the interleaving distance is a formal categorical construction generalized from the definition of Chazal et al., originally introduced to compare persistence modules arising from the field of topological data analysis. We show that the $\ell^\infty$-cophenetic metric is an example of an interleaving distance. To do this, we define phylogenetic trees as a category of merge trees with some additional structure; namely labelings on the leaves plus a requirement that morphisms respect these labels. Then we can use the definition of a flow on this category to give an interleaving distance. Finally, we show that, because of the additional structure given by the categories defined, the map sending a labeled merge tree to the cophenetic vector is, in fact, an isometric embedding, thus proving that the $\ell^\infty$-cophenetic metric is, in fact, an interleaving distance.
Long term availability of raw experimental data in experimental fracture mechanics
Venue: arXiv cs.DL 2018
Year: 2018

Experimental data availability is a cornerstone for reproducibility in experimental fracture mechanics, which is crucial to the scientific method. This short communication focuses on the accessibility and long term availability of raw experimental data. The corresponding authors of the eleven most cited papers, related to experimental fracture mechanics, for every year from 2000 up to 2016, were kindly asked about the availability of the raw experimental data associated with each publication. For the 187 e-mails sent: 22.46% resulted in outdated contact information, 57.75% of the authors did received our request and did not reply, and 19.79 replied to our request. The availability of data is generally low with only $11$ available data sets (5.9%). The authors identified two main issues for the lacking availability of raw experimental data. First, the ability to retrieve data is strongly attached to the the possibility to contact the corresponding author. This study suggests that institutional e-mail addresses are insufficient means for obtaining experimental data sets. Second, lack of experimental data is also due that submission and publication does not require to make the raw experimental data available. The following solutions are proposed: (1) Requirement of unique identifiers, like ORCID or ResearcherID, to detach the author(s) from their institutional e-mail address, (2) Provide DOIs, like Zenodo or Dataverse, to make raw experimental data citable, and (3) grant providing organizations should ensure that experimental data by public funded projects is available to the public.
AllenNLP: A Deep Semantic Natural Language Processing Platform
Venue: arXiv cs.CL 2018
Year: 2018

This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding. AllenNLP is designed to support researchers who want to build novel language understanding models quickly and easily. It is built on top of PyTorch, allowing for dynamic computation graphs, and provides (1) a flexible data API that handles intelligent batching and padding, (2) high-level abstractions for common operations in working with text, and (3) a modular and extensible experiment framework that makes doing good science easy. It also includes reference implementations of high quality approaches for both core semantic problems (e.g. semantic role labeling (Palmer et al., 2005)) and language understanding applications (e.g. machine comprehension (Rajpurkar et al., 2016)). AllenNLP is an ongoing open-source effort maintained by engineers and researchers at the Allen Institute for Artificial Intelligence.
PaaS Cloud: The Business Perspective
Venue: arXiv cs.CY 2018
Year: 2018

The next generation of PaaS technology accomplishes the true promise of object-oriented and 4GLs development with less effort. Now PaaS is becoming one of the core technical services for application development organizations. PaaS offers a resourceful and agile approach to develop, operate and deploy applications in a cost-effective manner. It is now turning out to be one of the preferred choices throughout the world, especially for globally distributed development environment. However it still lacks the scale of popularity and acceptance which Software-as-a-Service (SaaS) and Infrastructure-as-a-Service (IaaS) have attained. PaaS offers a promising future with novel technology architecture and evolutionary development approach. In this article, we identify the strengths, weaknesses, opportunities and threats for the PaaS industry. We then identify the various issues that will affect the different stakeholders of PaaS industry. This research will outline a set of recommendations for the PaaS practitioners to better manage this technology. For PaaS technology researchers, we also outline the number of research areas that need attention in coming future. Finally, we also included an online survey to outline PaaS technology market leaders. This will facilitate PaaS technology practitioners to have a more deep insight into market trends and technologies.
A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation
Venue: arXiv cs.CV 2018
Year: 2018

A reliable Ultrasound (US)-to-US registration method to compensate for brain shift would substantially improve Image-Guided Neurological Surgery. Developing such a registration method is very challenging, due to factors such as missing correspondence in images, the complexity of brain pathology and the demand for fast computation. We propose a novel feature-driven active framework. Here, landmarks and their displacement are first estimated from a pair of US images using corresponding local image features. Subsequently, a Gaussian Process (GP) model is used to interpolate a dense deformation field from the sparse landmarks. Kernels of the GP are estimated by using variograms and a discrete grid search method. If necessary, the user can actively add new landmarks based on the image context and visualization of the uncertainty measure provided by the GP to further improve the result. We retrospectively demonstrate our registration framework as a robust and accurate brain shift compensation solution on clinical data acquired during neurosurgery.
Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions
Venue: arXiv cs.CV 2018
Year: 2018

Diagnostic imaging often requires the simultaneous identification of a multitude of findings of varied size and appearance. Beyond global indication of said findings, the prediction and display of localization information improves trust in and understanding of results when augmenting clinical workflow. Medical training data rarely includes more than global image-level labels as segmentations are time-consuming and expensive to collect. We introduce an approach to managing these practical constraints by applying a novel architecture which learns at multiple resolutions while generating saliency maps with weak supervision. Further, we parameterize the Log-Sum-Exp pooling function with a learnable lower-bounded adaptation (LSE-LBA) to build in a sharpness prior and better handle localizing abnormalities of different sizes using only image-level labels. Applying this approach to interpreting chest x-rays, we set the state of the art on 9 abnormalities in the NIH's CXR14 dataset while generating saliency maps with the highest resolution to date.
Fast Semantic Segmentation on Video Using Block Motion-Based Feature Interpolation
Venue: arXiv cs.CV 2018
Year: 2018

Convolutional networks optimized for accuracy on challenging, dense prediction tasks are prohibitively slow to run on each frame in a video. The spatial similarity of nearby video frames, however, suggests opportunity to reuse computation. Existing work has explored basic feature reuse and feature warping based on optical flow, but has encountered limits to the speedup attainable with these techniques. In this paper, we present a new, two part approach to accelerating inference on video. First, we propose a fast feature propagation technique that utilizes the block motion vectors present in compressed video (e.g. H.264 codecs) to cheaply propagate features from frame to frame. Second, we develop a novel feature estimation scheme, termed feature interpolation, that fuses features propagated from enclosing keyframes to render accurate feature estimates, even at sparse keyframe frequencies. We evaluate our system on the Cityscapes and CamVid datasets, comparing to both a frame-by-frame baseline and related work. We find that we are able to substantially accelerate segmentation on video, achieving near real-time frame rates (20.1 frames per second) on large images (960 x 720 pixels), while maintaining competitive accuracy. This represents an improvement of almost 6x over the single-frame baseline and 2.5x over the fastest prior work.
A Secure Proxy-based Access Control Scheme for Implantable Medical Devices
Venue: arXiv cs.CR 2018
Year: 2018

With the rapid development of health equipments, increasingly more patients have installed the implantable medical devices (IMD) in their bodies for diagnostic, monitoring, and therapeutic purposes. IMDs are extremely limited in computation power and battery capacity. Meanwhile, IMDs have to communicate with an external programmer device (i.e., IMD programmer) through the wireless channel, which put them under the risk of unauthorized access and malicious wireless attacks. In this paper, we propose a proxy-based fine-grained access control scheme for IMDs, which can prolong the IMD's lifetime by delegating the access control computations to the proxy device (e.g., smartphone). In our scheme, the proxy communicates with the IMD programmer through an audio cable, which is resistant to a number of wireless attacks. Additionally, we use the ciphertext-policy attribute-based encryption (CP-ABE) to enforce fine-grained access control. The proposed scheme is implemented on real emulator devices and evaluated through experimental tests. The experiments show that the proposed scheme is lightweight and effective.
Estimating defectiveness of source code: A predictive model using GitHub content
Venue: arXiv cs.SE 2018
Year: 2018

Two key contributions presented in this paper are: i) A method for building a dataset containing source code features extracted from source files taken from Open Source Software (OSS) and associated bug reports, ii) A predictive model for estimating defectiveness of a given source code. These artifacts can be useful for building tools and techniques pertaining to several automated software engineering areas such as bug localization, code review, and recommendation and program repair. In order to achieve our goal, we first extract coding style information (e.g. related to programming language constructs used in the source code) for source code files present on GitHub. Then the information available in bug reports (if any) associated with these source code files are extracted. Thus fetched un(/ semi)-structured information is then transformed into a structured knowledge base. We considered more than 30400 source code files from 20 different GitHub repositories with about 14950 associated bug reports across 4 bug tracking portals. The source code files considered are written in four programming languages (viz., C, C++, Java, and Python) and belong to different types of applications. A machine learning (ML) model for estimating the defectiveness of a given input source code is then trained using the knowledge base. In order to pick the best ML model, we evaluated 8 different ML algorithms such as Random Forest, K Nearest Neighbour and SVM with around 50 parameter configurations to compare their performance on our tasks. One of our findings shows that best K-fold (with k=5) cross-validation results are obtained with the NuSVM technique that gives a mean F1 score of 0.914.
Looking for Bird Nests: Identifying Stay Points with Bounded Gaps
Venue: arXiv cs.CG 2018
Year: 2018

A stay point of a moving entity is a region in which it spends a significant amount of time. In this paper, we identify all stay points of an entity in a certain time interval, where the entity is allowed to leave the region but it should return within a given time limit. This definition of stay points seems more natural in many applications of trajectory analysis than those that do not limit the time of entity's absence from the region. We present an $O(n \log n)$ algorithm for trajectories in $R^1$ with $n$ vertices and a $(1 + ε)$-approximation algorithm for trajectories in $R^2$ to identify all such stay points. Our algorithm runs in $O(kn^2)$, where $k$ depends on $ε$ and the ratio of the duration of the trajectory to the allowed gap time.
Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Automatic human action recognition is indispensable for almost artificial intelligent systems such as video surveillance, human-computer interfaces, video retrieval, etc. Despite a lot of progress, recognizing actions in an unknown video is still a challenging task in computer vision. Recently, deep learning algorithms have proved its great potential in many vision-related recognition tasks. In this paper, we propose the use of Deep Residual Neural Networks (ResNets) to learn and recognize human action from skeleton data provided by Kinect sensor. Firstly, the body joint coordinates are transformed into 3D-arrays and saved in RGB images space. Five different deep learning models based on ResNet have been designed to extract image features and classify them into classes. Experiments are conducted on two public video datasets for human action recognition containing various challenges. The results show that our method achieves the state-of-the-art performance comparing with existing approaches.
Gaze-Assisted User Authentication to Counter Shoulder-surfing Attacks
Venue: arXiv cs.HC 2018
Year: 2018

A highly secure, foolproof, user authentication system is still a primary focus of research in the field of User Privacy and Security. Shoulder-surfing is an act of spying when an authorized user is logging into a system, and is promoted by a malicious intent of gaining unauthorized access. We present a gaze-assisted user authentication system as a potential solution to counter shoulder-surfing attacks. The system comprises of an eye tracker and an authentication interface with 12 pre-defined shapes (e.g., triangle, circle, etc.) that move simultaneously on the screen. A user chooses a set of three shapes as a password. To authenticate, the user follows the paths of three shapes as they move, one on each frame, over three consecutive frames. The system uses either the template matching or decision tree algorithms to match the scan-path of the user's gaze with the path traversed by the shape. The system was evaluated with seven users to test the accuracy of both the algorithms. We found that with the template matching algorithm the system achieves an accuracy of 95%, and with the decision tree algorithm an accuracy of 90.2%. We also present the advantages and disadvantages of using both the algorithms. Our study suggests that gaze-based authentication is a highly secure method against shoulder-surfing attacks as the unique pattern of eye movements for each individual makes the system hard to break into.
QoE-Oriented Resource Allocation for 360-degree Video Transmission over Heterogeneous Networks
Venue: arXiv cs.MM 2018
Year: 2018

Immersive media streaming, especially virtual reality (VR)/360-degree video streaming which is very bandwidth demanding, has become more and more popular due to the rapid growth of the multimedia and networking deployments. To better explore the usage of resource and achieve better quality of experience (QoE) perceived by users, this paper develops an application-layer scheme to jointly exploit the available bandwidth from the LTE and Wi-Fi networks in 360-degree video streaming. This newly proposed scheme and the corresponding solution algorithms utilize the saliency of video, prediction of users' view and the status information of users to obtain an optimal association of the users with different Wi-Fi access points (APs) for maximizing the system's utility. Besides, a novel buffer strategy is proposed to mitigate the influence of short-time prediction problem for transmitting 360-degree videos in time-varying networks. The promising performance and low complexity of the proposed scheme and algorithms are validated in simulations with various 360-degree videos.
Covert Wireless Communications with Channel Inversion Power Control in Rayleigh Fading
Venue: arXiv cs.IT 2018
Year: 2018

In this work, we adopt channel inversion power control (CIPC) to achieve covert communications aided by a full-duplex receiver. Specifically, the transmitter varies the power and phase of transmitted signals as per the channel to the receiver, such that the receiver can decode these signals without knowing the channel state information. This eliminates the required feedback from the transmitter to the receiver, which aids hiding the transmitter from a warden. The truncated CIPC and conventional CIPC schemes are proposed and examined, where for truncated CIPC covert transmission ceases when the channel quality from the transmitter to the receiver is low, while for conventional CIPC covert transmission always occurs regardless of this channel quality. We examine their performance in terms of the achieved effective covert throughput (ECT), which quantifies the amount of information that the transmitter can reliably convey to the receiver, subject to the constraint that the warden's detection error probability is no less than some specific value. Our examination shows that the truncated CIPC scheme can outperform the conventional CIPC scheme due to this constraint.