OpenRaiser/Intern-Atlas huggingface dataset:
Identification of image source using serialnumber-based watermarking under Compressive Sensing conditions
Venue: arXiv cs.MM 2017
Year: 2017

Although the protection of ownership and the prevention of unauthorized manipulation of digital images becomes an important concern, there is also a big issue of image source origin authentication. This paper proposes a procedure for the identification of the image source and content by using the Public Key Cryptography Signature (PKCS). The procedure is based on the PKCS watermarking of the images captured with numerous automatic observing cameras in the Trap View cloud system. Watermark is created based on 32-bit PKCS serial number and embedded into the captured image. Watermark detection on the receiver side extracts the serial number and indicates the camera which captured the image by comparing the original and the extracted serial numbers. The watermarking procedure is designed to provide robustness to image optimization based on the Compressive Sensing approach. Also, the procedure is tested under various attacks and shows successful identification of ownership.
Combating the Cold Start User Problem in Model Based Collaborative Filtering
Venue: arXiv cs.IR 2017
Year: 2017

For tackling the well known cold-start user problem in model-based recommender systems, one approach is to recommend a few items to a cold-start user and use the feedback to learn a profile. The learned profile can then be used to make good recommendations to the cold user. In the absence of a good initial profile, the recommendations are like random probes, but if not chosen judiciously, both bad recommendations and too many recommendations may turn off a user. We formalize the cold-start user problem by asking what are the $b$ best items we should recommend to a cold-start user, in order to learn her profile most accurately, where $b$, a given budget, is typically a small number. We formalize the problem as an optimization problem and present multiple non-trivial results, including NP-hardness as well as hardness of approximation. We furthermore show that the objective function, i.e., the least square error of the learned profile w.r.t. the true user profile, is neither submodular nor supermodular, suggesting efficient approximations are unlikely to exist. Finally, we discuss several scalable heuristic approaches for identifying the $b$ best items to recommend to the user and experimentally evaluate their performance on 4 real datasets. Our experiments show that our proposed accelerated algorithms significantly outperform the prior art in runnning time, while achieving similar error in the learned user profile as well as in the rating predictions.
Learning to Optimize Neural Nets
Venue: arXiv cs.LG 2017
Year: 2017

Learning to Optimize is a recently proposed framework for learning optimization algorithms using reinforcement learning. In this paper, we explore learning an optimization algorithm for training shallow neural nets. Such high-dimensional stochastic optimization problems present interesting challenges for existing reinforcement learning algorithms. We develop an extension that is suited to learning optimization algorithms in this setting and demonstrate that the learned optimization algorithm consistently outperforms other known optimization algorithms even on unseen tasks and is robust to changes in stochasticity of gradients and the neural net architecture. More specifically, we show that an optimization algorithm trained with the proposed method on the problem of training a neural net on MNIST generalizes to the problems of training neural nets on the Toronto Faces Dataset, CIFAR-10 and CIFAR-100.
Design Automation for Obfuscated Circuits with Multiple Viable Functions
Venue: arXiv cs.CR 2017
Year: 2017

Gate camouflaging is a technique for obfuscating the function of a circuit against reverse engineering attacks. However, if an adversary has pre-existing knowledge about the set of functions that are viable for an application, random camouflaging of gates will not obfuscate the function well. In this case, the adversary can target their search, and only needs to decide whether each of the viable functions could be implemented by the circuit. In this work, we propose a method for using camouflaged cells to obfuscate a design that has a known set of viable functions. The circuit produced by this method ensures that an adversary will not be able to rule out any viable functions unless she is able to uncover the gate functions of the camouflaged cells. Our method comprises iterated synthesis within an overall optimization loop to combine the viable functions, followed by technology mapping to deploy camouflaged cells while maintaining the plausibility of all viable functions. We evaluate our technique on cryptographic S-box functions and show that, relative to a baseline approach, it achieves up to 38\% area reduction in PRESENT-style S-Boxes and 48\% in DES S-boxes.
Skin cancer reorganization and classification with deep neural network
Venue: arXiv cs.CV 2017
Year: 2017

As one kind of skin cancer, melanoma is very dangerous. Dermoscopy based early detection and recarbonization strategy is critical for melanoma therapy. However, well-trained dermatologists dominant the diagnostic accuracy. In order to solve this problem, many effort focus on developing automatic image analysis systems. Here we report a novel strategy based on deep learning technique, and achieve very high skin lesion segmentation and melanoma diagnosis accuracy: 1) we build a segmentation neural network (skin_segnn), which achieved very high lesion boundary detection accuracy; 2) We build another very deep neural network based on Google inception v3 network (skin_recnn) and its well-trained weight. The novel designed transfer learning based deep neural network skin_inceptions_v3_nn helps to achieve a high prediction accuracy.
Signal-based Bayesian Seismic Monitoring
Venue: arXiv cs.LG 2017
Year: 2017

Detecting weak seismic events from noisy sensors is a difficult perceptual task. We formulate this task as Bayesian inference and propose a generative model of seismic events and signals across a network of spatially distributed stations. Our system, SIGVISA, is the first to directly model seismic waveforms, allowing it to incorporate a rich representation of the physics underlying the signal generation process. We use Gaussian processes over wavelet parameters to predict detailed waveform fluctuations based on historical events, while degrading smoothly to simple parametric envelopes in regions with no historical seismicity. Evaluating on data from the western US, we recover three times as many events as previous work, and reduce mean location errors by a factor of four while greatly increasing sensitivity to low-magnitude events.
Reflections on Cyberethics Education for Millennial Software Engineers
Venue: arXiv cs.SE 2017
Year: 2017

Software is a key component of solutions for 21st Century problems. These problems are often "wicked", complex, and unpredictable. To provide the best possible solution, millennial software engineers must be prepared to make ethical decisions, thinking critically, and acting systematically. This reality demands continuous changes in educational systems and curricula delivery, as misjudgment might have serious social impact. This study aims to investigate and reflect on Software Engineering (SE) Programs, proposing a conceptual framework for analyzing cyberethics education and a set of suggestions on how to integrate it into the SE undergraduate curriculum.
The RowHammer Problem and Other Issues We May Face as Memory Becomes Denser
Venue: arXiv cs.DC 2017
Year: 2017

As memory scales down to smaller technology nodes, new failure mechanisms emerge that threaten its correct operation. If such failure mechanisms are not anticipated and corrected, they can not only degrade system reliability and availability but also, perhaps even more importantly, open up security vulnerabilities: a malicious attacker can exploit the exposed failure mechanism to take over the entire system. As such, new failure mechanisms in memory can become practical and significant threats to system security. In this work, we discuss the RowHammer problem in DRAM, which is a prime (and perhaps the first) example of how a circuit-level failure mechanism in DRAM can cause a practical and widespread system security vulnerability. RowHammer, as it is popularly referred to, is the phenomenon that repeatedly accessing a row in a modern DRAM chip causes bit flips in physically-adjacent rows at consistently predictable bit locations. It is caused by a hardware failure mechanism called DRAM disturbance errors, which is a manifestation of circuit-level cell-to-cell interference in a scaled memory technology. We analyze the root causes of the RowHammer problem and examine various solutions. We also discuss what other vulnerabilities may be lurking in DRAM and other types of memories, e.g., NAND flash memory or Phase Change Memory, that can potentially threaten the foundations of secure systems, as the memory technologies scale to higher densities. We conclude by describing and advocating a principled approach to memory reliability and security research that can enable us to better anticipate and prevent such vulnerabilities.
Traffic-Aware Transmission Mode Selection in D2D-enabled Cellular Networks with Token System
Venue: arXiv cs.IT 2017
Year: 2017

We consider a D2D-enabled cellular network where user equipments (UEs) owned by rational users are incentivized to form D2D pairs using tokens. They exchange tokens electronically to "buy" and "sell" D2D services. Meanwhile the devices have the ability to choose the transmission mode, i.e. receiving data via cellular links or D2D links. Thus taking the different benefits brought by diverse traffic types as a prior, the UEs can utilize their tokens more efficiently via transmission mode selection. In this paper, the optimal transmission mode selection strategy as well as token collection policy are investigated to maximize the long-term utility in the dynamic network environment. The optimal policy is proved to be a threshold strategy, and the thresholds have a monotonicity property. Numerical simulations verify our observations and the gain from transmission mode selection is observed.
On a User-Centric Base Station Cooperation Scheme for Reliable Communications
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we describe CoMP2flex, a user-centric base station (BS) cooperation scheme that provides improvements in reliability of both uplink (UL) and downlink (DL) communications of wireless cellular networks. CoMP2flex supports not only cooperation of two BSs with same direction of traffic but also cooperation of two BSs serving bidirectional traffic. The reliability performance of CoMP2flex is shown with numerical simulations and analytical expressions. We quantify and numerically validate the performance of the greedy BS pairing algorithm by comparing maximum weight matching methods, implemented as the Edmonds matching algorithm for weighted graphs.
Parity Games, Imperfect Information and Structural Complexity
Venue: arXiv cs.GT 2017
Year: 2017

We address the problem of solving parity games with imperfect information on finite graphs of bounded structural complexity. It is a major open problem whether parity games with perfect information can be solved in PTIME. Restricting the structural complexity of the game arenas, however, often leads to efficient algorithms for parity games. Such results are known for graph classes of bounded tree-width, DAG-width, directed path-width, and entanglement, which we describe in terms of cops and robber games. Conversely, the introduction of imperfect information makes the problem more difficult, it becomes EXPTIME-hard. We analyse the interaction of both approaches. We use a simple method to measure the amount of "unawareness"' of a player, the amount of imperfect information. It turns out that if it is unbounded, low structural complexity does not make the problem simpler. It remains EXPTIME-hard or PSPACE-hard even on very simple graphs. For games with bounded imperfect information we analyse the powerset construction, which is commonly used to convert a game of imperfect information into an equivalent game with perfect information. This construction preserves boundedness of directed path-width and DAG-width, but not of entanglement or of tree-width. Hence, if directed path-width or DAG-width are bounded, parity games with bounded imperfect information can be solved in PTIME. For DAG-width we follow two approaches. One leads to a generalization of the known fact that perfect information parity games are in PTIME if DAG-width is bounded. We prove this theorem for non-monotone DAG-width. The other approach introduces a cops and robbers game (with multiple robbers) on directed graphs, considered Richerby and Thilikos forundirected graphs. We show a tight linear bound for the number of additional cops needed to capture an additional robber.
Even faster sorting of (not only) integers
Venue: arXiv cs.DS 2017
Year: 2017

In this paper we introduce RADULS2, the fastest parallel sorter based on radix algorithm. It is optimized to process huge amounts of data making use of modern multicore CPUs. The main novelties include: extremely optimized algorithm for handling tiny arrays (up to about a hundred of records) that could appear even billions times as subproblems to handle and improved processing of larger subarrays with better use of non-temporal memory stores.
Unveiling Bias Compensation in Turbo-Based Algorithms for (Discrete) Compressed Sensing
Venue: arXiv cs.IT 2017
Year: 2017

In Compressed Sensing, a real-valued sparse vector has to be recovered from an underdetermined system of linear equations. In many applications, however, the elements of the sparse vector are drawn from a finite set. Adapted algorithms incorporating this additional knowledge are required for the discrete-valued setup. In this paper, turbo-based algorithms for both cases are elucidated and analyzed from a communications engineering perspective, leading to a deeper understanding of the algorithm. In particular, we gain the intriguing insight that the calculation of extrinsic values is equal to the unbiasing of a biased estimate and present an improved algorithm.
Local Voting: Optimal Distributed Node Scheduling Algorithm for Multihop Wireless Networks
Venue: arXiv cs.NI 2017
Year: 2017

An efficient and fair node scheduling is a big challenge in multihop wireless networks. In this work, we propose a distributed node scheduling algorithm, called Local Voting. The idea comes from the finding that the shortest delivery time or delay is obtained when the load is equalized throughout the network. Simulation results demonstrate that Local Voting achieves better performance in terms of average delay, maximum delay, and fairness compared to several representative scheduling algorithms from the literature. Despite being distributed, Local Voting has a very close performance to a centralized algorithm that is considered to have the optimal performance.
Attentive Recurrent Comparators
Venue: arXiv cs.CV 2017
Year: 2017

Rapid learning requires flexible representations to quickly adopt to new evidence. We develop a novel class of models called Attentive Recurrent Comparators (ARCs) that form representations of objects by cycling through them and making observations. Using the representations extracted by ARCs, we develop a way of approximating a \textit{dynamic representation space} and use it for one-shot learning. In the task of one-shot classification on the Omniglot dataset, we achieve the state of the art performance with an error rate of 1.5\%. This represents the first super-human result achieved for this task with a generic model that uses only pixel information.
Exact Topology Reconstruction of Radial Dynamical Systems with Applications to Distribution System of the Power Grid
Venue: arXiv cs.LG 2017
Year: 2017

In this article we present a method to reconstruct the interconnectedness of dynamically related stochastic processes, where the interactions are bi-directional and the underlying topology is a tree. Our approach is based on multivariate Wiener filtering which recovers spurious edges apart from the true edges in the topology reconstruction. The main contribution of this work is to show that all spurious links obtained using Wiener filtering can be eliminated if the underlying topology is a tree based on which we present a three stage network reconstruction procedure for trees. We illustrate the effectiveness of the method developed by applying it on a typical distribution system of the electric grid.
Wireless Node Cooperation with Resource Availability Constraints
Venue: arXiv cs.NI 2017
Year: 2017

Base station cooperation is a promising scheme to improve network performance for next generation cellular networks. Up to this point research has focused on station grouping criteria based solely on geographic proximity. However, for the cooperation to be meaningful, each station participating in a group should have sufficient available resources to share with others. In this work we consider an alternative grouping criterion based on a distance that considers both geographic proximity and available resources of the stations. When the network is modelled by a Poisson Point Process, we derive analytical formulas on the proportion of cooperative pairs or single stations, and the expected sum interference from each of the groups. The results illustrate that cooperation gains strongly depend on the distribution of available resources over the network.
Araguaia Medical Vision Lab at ISIC 2017 Skin Lesion Classification Challenge
Venue: arXiv cs.CV 2017
Year: 2017

This paper describes the participation of Araguaia Medical Vision Lab at the International Skin Imaging Collaboration 2017 Skin Lesion Challenge. We describe the use of deep convolutional neural networks in attempt to classify images of Melanoma and Seborrheic Keratosis lesions. With use of finetuned GoogleNet and AlexNet we attained results of 0.950 and 0.846 AUC on Seborrheic Keratosis and Melanoma respectively.
Friendship Maintenance and Prediction in Multiple Social Networks
Venue: arXiv cs.SI 2017
Year: 2017

Due to the proliferation of online social networks (OSNs), users find themselves participating in multiple OSNs. These users leave their activity traces as they maintain friendships and interact with other users in these OSNs. In this work, we analyze how users maintain friendship in multiple OSNs by studying users who have accounts in both Twitter and Instagram. Specifically, we study the similarity of a user's friendship and the evenness of friendship distribution in multiple OSNs. Our study shows that most users in Twitter and Instagram prefer to maintain different friendships in the two OSNs, keeping only a small clique of common friends in across the OSNs. Based upon our empirical study, we conduct link prediction experiments to predict missing friendship links in multiple OSNs using the neighborhood features, neighborhood friendship maintenance features and cross-link features. Our link prediction experiments shows that un- supervised methods can yield good accuracy in predicting links in one OSN using another OSN data and the link prediction accuracy can be further improved using supervised method with friendship maintenance and others measures as features.
Adapting the DMTCP Plugin Model for Checkpointing of Hardware Emulation
Venue: arXiv cs.OS 2017
Year: 2017

Checkpoint-restart is now a mature technology. It allows a user to save and later restore the state of a running process. The new plugin model for the upcoming version 3.0 of DMTCP (Distributed MultiThreaded Checkpointing) is described here. This plugin model allows a target application to disconnect from the hardware emulator at checkpoint time and then re-connect to a possibly different hardware emulator at the time of restart. The DMTCP plugin model is important in allowing three distinct parties to seamlessly inter-operate. The three parties are: the EDA designer, who is concerned with formal verification of a circuit design; the DMTCP developers, who are concerned with providing transparent checkpointing during the circuit emulation; and the hardware emulator vendor, who provides a plugin library that responds to checkpoint, restart, and other events. The new plugin model is an example of process-level virtualization: virtualization of external abstractions from within a process. This capability is motivated by scenarios for testing circuit models with the help of a hardware emulator. The plugin model enables a three-way collaboration: allowing a circuit designer and emulator vendor to each contribute separate proprietary plugins while sharing an open source software framework from the DMTCP developers. This provides a more flexible platform, where different fault injection models based on plugins can be designed within the DMTCP checkpointing framework. After initialization, one restarts from a checkpointed state under the control of the desired plugin. This restart saves the time spent in simulating the initialization phase, while enabling fault injection exactly at the region of interest. Upon restart, one can inject faults or otherwise modify the remainder of the simulation. The work concludes with a brief survey of checkpointing and process-level virtualization.
Atomic Norm Minimization for Modal Analysis from Random and Compressed Samples
Venue: arXiv cs.IT 2017
Year: 2017

Modal analysis is the process of estimating a system's modal parameters such as its natural frequencies and mode shapes. One application of modal analysis is in structural health monitoring (SHM), where a network of sensors may be used to collect vibration data from a physical structure such as a building or bridge. There is a growing interest in developing automated techniques for SHM based on data collected in a wireless sensor network. In order to conserve power and extend battery life, however, it is desirable to minimize the amount of data that must be collected and transmitted in such a sensor network. In this paper, we highlight the fact that modal analysis can be formulated as an atomic norm minimization (ANM) problem, which can be solved efficiently and in some cases recover perfectly a structure's mode shapes and frequencies. We survey a broad class of sampling and compression strategies that one might consider in a physical sensor network, and we provide bounds on the sample complexity of these compressive schemes in order to recover a structure's mode shapes and frequencies via ANM. A main contribution of our paper is to establish a bound on the sample complexity of modal analysis with random temporal compression, and in this scenario we prove that the samples per sensor can actually decrease as the number of sensors increases. We also extend an atomic norm denoising problem to the multiple measurement vector (MMV) setting in the case of uniform sampling.
Hedging Strategies for Load-Serving Entities in Wholesale Electricity Markets
Venue: arXiv cs.SY 2017
Year: 2017

Load-serving entities which procure electricity from the wholesale electricity market to service end-users face significant quantity and price risks due to the volatile nature of electricity demand and quasi-fixed residential tariffs at which electricity is sold. This paper investigates strategies for load serving entities to hedge against such price risks. Specifically, we compute profit-maximizing portfolios of forward contract and call options as a function of the uncertain aggregate user demand. We compare the profit to the case of Demand Response, where users are offered monetary incentives to temporarily reduce their consumption during periods of supply shortages. Using smart meter data of residential customers in California, we simulate optimal portfolios and derive conditions under which Demand Response outperforms call options and forward contracts.