OpenRaiser/Intern-Atlas huggingface dataset:
Deep Learning-Based Constellation Optimization for Physical Network Coding in Two-Way Relay Networks
Venue: arXiv cs.IT 2019
Year: 2019

This paper studies a new application of deep learning (DL) for optimizing constellations in two-way relaying with physical-layer network coding (PNC), where deep neural network (DNN)-based modulation and demodulation are employed at each terminal and relay node. We train DNNs such that the cross entropy loss is directly minimized, and thus it maximizes the likelihood, rather than considering the Euclidean distance of the constellations. The proposed scheme can be extended to higher level constellations with slight modification of the DNN structure. Simulation results demonstrate a significant performance gain in terms of the achievable sum rate over conventional relaying schemes. Furthermore, since our DNN demodulator directly outputs bit-wise probabilities, it is straightforward to concatenate with soft-decision channel decoding.
A Lightweight McEliece Cryptosystem Co-processor Design
Venue: arXiv cs.CR 2019
Year: 2019

Due to the rapid advances in the development of quantum computers and their susceptibility to errors, there is a renewed interest in error correction algorithms. In particular, error correcting code-based cryptosystems have reemerged as a highly desirable coding technique. This is due to the fact that most classical asymmetric cryptosystems will fail in the quantum computing era. Quantum computers can solve many of the integer factorization and discrete logarithm problems efficiently. However, code-based cryptosystems are still secure against quantum computers, since the decoding of linear codes remains as NP-hard even on these computing systems. One such cryptosystem is the McEliece code-based cryptosystem. The original McEliece code-based cryptosystem uses binary Goppa code, which is known for its good code rate and error correction capability. However, its key generation and decoding procedures have a high computation complexity. In this work we propose a design and hardware implementation of an public-key encryption and decryption co-processor based on a new variant of McEliece system. This co-processor takes the advantage of the non-binary Orthogonal Latin Square Codes to achieve much smaller computation complexity, hardware cost, and the key size.
SAFECHAIN: Securing Trigger-Action Programming from Attack Chains (Extended Technical Report)
Venue: arXiv cs.CR 2019
Year: 2019

The proliferation of Internet of Things (IoT) is reshaping our lifestyle. With IoT sensors and devices communicating with each other via the Internet, people can customize automation rules to meet their needs. Unless carefully defined, however, such rules can easily become points of security failure as the number of devices and complexity of rules increase. Device owners may end up unintentionally providing access or revealing private information to unauthorized entities due to complex chain reactions among devices. Prior work on trigger-action programming either focuses on conflict resolution or usability issues, or fails to accurately and efficiently detect such attack chains. This paper explores security vulnerabilities when users have the freedom to customize automation rules using trigger-action programming. We define two broad classes of attack--privilege escalation and privacy leakage--and present a practical model-checking-based system called SAFECHAIN that detects hidden attack chains exploiting the combination of rules. Built upon existing model-checking techniques, SAFECHAIN identifies attack chains by modeling the IoT ecosystem as a Finite State Machine. To improve practicability, SAFECHAIN avoids the need to accurately model an environment by frequently re-checking the automation rules given the current states, and employs rule-aware optimizations to further reduce overhead. Our comparative analysis shows that SAFECHAIN can efficiently and accurately identify attack chains, and our prototype implementation of SAFECHAIN can verify 100 rules in less than one second with no false positives.
Mutual Clustering on Comparative Texts via Heterogeneous Information Networks
Venue: arXiv cs.IR 2019
Year: 2019

Currently, many intelligence systems contain the texts from multi-sources, e.g., bulletin board system (BBS) posts, tweets and news. These texts can be ``comparative'' since they may be semantically correlated and thus provide us with different perspectives toward the same topics or events. To better organize the multi-sourced texts and obtain more comprehensive knowledge, we propose to study the novel problem of Mutual Clustering on Comparative Texts (MCCT), which aims to cluster the comparative texts simultaneously and collaboratively. The MCCT problem is difficult to address because 1) comparative texts usually present different data formats and structures and thus they are hard to organize, and 2) there lacks an effective method to connect the semantically correlated comparative texts to facilitate clustering them in an unified way. To this aim, in this paper we propose a Heterogeneous Information Network-based Text clustering framework HINT. HINT first models multi-sourced texts (e.g. news and tweets) as heterogeneous information networks by introducing the shared ``anchor texts'' to connect the comparative texts. Next, two similarity matrices based on HINT as well as a transition matrix for cross-text-source knowledge transfer are constructed. Comparative texts clustering are then conducted by utilizing the constructed matrices. Finally, a mutual clustering algorithm is also proposed to further unify the separate clustering results of the comparative texts by introducing a clustering consistency constraint. We conduct extensive experimental on three tweets-news datasets, and the results demonstrate the effectiveness and robustness of the proposed method in addressing the MCCT problem.
Performance evaluation of a production line operated under an echelon buffer policy
Venue: arXiv cs.SY 2019
Year: 2019

We consider a production line consisting of several machines in series separated by intermediate finite-capacity buffers. The line operates under an "echelon buffer" (EB) policy according to which each machine can store the parts that it produces in any of its downstream buffers if the next machine is occupied. If the capacities of all but the last buffer are zero, the EB policy is equivalent to CONWIP. To evaluate the performance of the line under the EB policy, we model it as a queueing network, and we develop a method that decomposes this network into as many nested segments as there are buffers and approximates each segment with a two-machine subsystem that can be analyzed in isolation. For the case where the machines have geometrically distributed processing times, we model each subsystem as a two-dimensional Markov chain that can be solved numerically. The parameters of the subsystems are determined by relationships among the flows of parts through the echelon buffers in the original system. An iterative algorithm is developed to solve these relationships. We use this method to evaluate the performance of several instances of 5- and 10-machine lines including cases where the EB policy is equivalent to CONWIP. Our numerical results show that this method is highly accurate and computationally efficient. We also compare the performance of the EB policy against the performance of the traditional "installation buffer" policy according to which each machine can store the parts that it produces only in its immediate downstream buffer if the next machine is occupied.
Automated Segmentation of Knee MRI Using Hierarchical Classifiers and Just Enough Interaction Based Learning: Data from Osteoarthritis Initiative
Venue: arXiv cs.CV 2019
Year: 2019

We present a fully automated learning-based approach for segmenting knee cartilage in the presence of osteoarthritis (OA). The algorithm employs a hierarchical set of two random forest classifiers. The first is a neighborhood approximation forest, the output probability map of which is utilized as a feature set for the second random forest (RF) classifier. The output probabilities of the hierarchical approach are used as cost functions in a Layered Optimal Graph Segmentation of Multiple Objects and Surfaces (LOGISMOS). In this work, we highlight a novel post-processing interaction called just-enough interaction (JEI) which enables quick and accurate generation of a large set of training examples. Disjoint sets of 15 and 13 subjects were used for training and tested on another disjoint set of 53 knee datasets. All images were acquired using a double echo steady state (DESS) MRI sequence and are from the osteoarthritis initiative (OAI) database. Segmentation performance using the learning-based cost function showed significant reduction in segmentation errors ($p< 0.05$) in comparison with conventional gradient-based cost functions.
Improving Humanness of Virtual Agents and Users' Cooperation through Emotions
Venue: arXiv cs.AI 2019
Year: 2019

In this paper, we analyze the performance of an agent developed according to a well-accepted appraisal theory of human emotion with respect to how it modulates play in the context of a social dilemma. We ask if the agent will be capable of generating interactions that are considered to be more human than machine-like. We conduct an experiment with 117 participants and show how participants rate our agent on dimensions of human-uniqueness (which separates humans from animals) and human-nature (which separates humans from machines). We show that our appraisal theoretic agent is perceived to be more human-like than baseline models, by significantly improving both human-nature and human-uniqueness aspects of the intelligent agent. We also show that perception of humanness positively affects enjoyment and cooperation in the social dilemma.
Orbital stabilization of nonlinear systems via Mexican sombrero energy shaping and pumping-and-damping injection
Venue: arXiv cs.SY 2019
Year: 2019

In this paper we show that a slight modification to the widely popular interconnection and damping assignment passivity-based control method---originally proposed for stabilization of equilibria of nonlinear systems---allows us to provide a solution to the more challenging orbital stabilization problem. Two different, though related, ways how this procedure can be applied are proposed. First, the assignment of an energy function that has a minimum in a closed curve, i.e., with the shape of a Mexican sombrero. Second, the use of a damping matrix that changes "sign" according to the position of the state trajectory relative to the desired orbit, that is, pumping or dissipating energy. The proposed methodologies are illustrated with the example of the induction motor and prove that it yields the industry standard field oriented control.
Exploring OpenStreetMap Availability for Driving Environment Understanding
Venue: arXiv cs.HC 2019
Year: 2019

With the great achievement of artificial intelligence, vehicle technologies have advanced significantly from human centric driving towards fully automated driving. An intelligent vehicle should be able to understand the driver's perception of the environment as well as controlling behavior of the vehicle. Since high digital map information has been available to provide rich environmental context about static roads, buildings and traffic infrastructures, it would be worthwhile to explore map data capability for driving task understanding. Alternative to commercial used maps, the OpenStreetMap (OSM) data is a free open dataset, which makes it unique for the exploration research. This study is focused on two tasks that leverage OSM for driving environment understanding. First, driving scenario attributes are retrieved from OSM elements, which are combined with vehicle dynamic signals for the driving event recognition. Utilizing steering angle changes and based on a Bi-directional Recurrent Neural Network (Bi-RNN), a driving sequence is segmented and classified as lane-keeping, lane-change-left, lane-change-right, turn-left, and turn-right events. Second, for autonomous driving perception, OSM data can be used to render virtual street views, represented as prior knowledge to fuse with vision/laser systems for road semantic segmentation. Five different types of road masks are generated from OSM, images, and Lidar points, and fused to characterize the drivable space at the driver's perspective. An alternative data-driven approach is based on a Fully Convolutional Network (FCN), OSM availability for deep learning methods are discussed to reveal potential usage on compensating street view images and automatic road semantic annotation.
Estimating Individual Advertising Effect in E-Commerce
Venue: arXiv cs.GT 2019
Year: 2019

Online advertising has been the major monetization approach for Internet companies. Advertisers invest budgets to bid for real-time impressions to gain direct and indirect returns. Existing works have been concentrating on optimizing direct returns brought by advertising traffic. However, indirect returns induced by advertising traffic such as influencing the online organic traffic and offline mouth-to-mouth marketing provide extra significant motivation to advertisers. Modeling and quantization of causal effects between the overall advertising return and budget enable the advertisers to spend their money more judiciously. In this paper, we model the overall return as individual advertising effect in causal inference with multiple treatments and bound the expected estimation error with learnable factual loss and distance of treatment-specific context distributions. Accordingly, a representation and hypothesis network is used to minimize the loss bound. We apply the learned causal effect in the online bidding engine of an industry-level sponsored search system. Online experiments show that the causal inference based bidding outperforms the existing online bidding algorithm.
Gathering Insights from Teenagers' Hacking Experience with Authentic Cybersecurity Tools
Venue: arXiv cs.CY 2019
Year: 2019

This Work-In-Progress Paper for the Innovative Practice Category presents a novel experiment in active learning of cybersecurity. We introduced a new workshop on hacking for an existing science-popularizing program at our university. The workshop participants, 28 teenagers, played a cybersecurity game designed for training undergraduates and professionals in penetration testing. Unlike in learning environments that are simplified for young learners, the game features a realistic virtual network infrastructure. This allows exploring security tools in an authentic scenario, which is complemented by a background story. Our research aim is to examine how young players approach using cybersecurity tools by interacting with the professional game. A preliminary analysis of the game session showed several challenges that the workshop participants faced. Nevertheless, they reported learning about security tools and exploits, and 61% of them reported wanting to learn more about cybersecurity after the workshop. Our results support the notion that young learners should be allowed more hands-on experience with security topics, both in formal education and informal extracurricular events.
Multiobject fusion with minimum information loss
Venue: arXiv cs.SY 2019
Year: 2019

Generalized covariance intersection (GCI) has been effective in fusing multiobject densities from multiple agents for multitarget tracking and mapping purposes. From an information-theoretic viewpoint, it has been shown that GCI fusion essentially minimizes the weighted information gain (WIG) from local densities to the fused one. In this paper, the interest is in the fusion rule that dually minimizes the weighted information loss (WIL) and it turns out that such a fusion rule is consistent with the so-called linear opinion pool (LOP). However, the LOP cannot be directly applied to multiobject fusion since the resulting fused multiobject density (FMD), in general, no longer belongs to the same family of the local ones, thus it cannot be utilized as prior information for the next recursion in the context of Bayesian multiobject filtering. In order to overcome such a difficulty, the principle of minimizing WIL is further exploited in that the optimal FMD in the same family of the local ones is looked for. Implementation issues relative to the proposed minimum WIL (MWIL) fusion rule are discussed. Finally, the performance of the MWIL rule is assessed via simulation experiments concerning distributed multitarget tracking over a wireless sensor network.
Auto-Vectorizing TensorFlow Graphs: Jacobians, Auto-Batching And Beyond
Venue: arXiv cs.DC 2019
Year: 2019

We propose a static loop vectorization optimization on top of high level dataflow IR used by frameworks like TensorFlow. A new statically vectorized parallel-for abstraction is provided on top of TensorFlow, and used for applications ranging from auto-batching and per-example gradients, to jacobian computation, optimized map functions and input pipeline optimization. We report huge speedups compared to both loop based implementations, as well as run-time batching adopted by the DyNet framework.
Large Scale Product Categorization using Structured and Unstructured Attributes
Venue: arXiv cs.IR 2019
Year: 2019

Product categorization using text data for eCommerce is a very challenging extreme classification problem with several thousands of classes and several millions of products to classify. Even though multi-class text classification is a well studied problem both in academia and industry, most approaches either deal with treating product content as a single pile of text, or only consider a few product attributes for modelling purposes. Given the variety of products sold on popular eCommerce platforms, it is hard to consider all available product attributes as part of the modeling exercise, considering that products possess their own unique set of attributes based on category. In this paper, we compare hierarchical models to flat models and show that in specific cases, flat models perform better. We explore two Deep Learning based models that extract features from individual pieces of unstructured data from each product and then combine them to create a product signature. We also propose a novel idea of using structured attributes and their values together in an unstructured fashion along with convolutional filters such that the ordering of the attributes and the differing attributes by product categories no longer becomes a modelling challenge. This approach is also more robust to the presence of faulty product attribute names and values and can elegantly generalize to use both closed list and open list attributes.
Bullwhip effect attenuation in supply chain management via control-theoretic tools and short-term forecasts: A preliminary study with an application to perishable inventories
Venue: arXiv cs.SY 2019
Year: 2019

Supply chain management and inventory control provide most exciting examples of control systems with delays. Here, Smith predictors, model-free control and new time series forecasting techniques are mixed in order to derive an efficient control synthesis. Perishable inventories are also taken into account. The most intriguing "bullwhip effect" is explained and attenuated, at least in some important situations. Numerous convincing computer simulations are presented and discussed.
SECCS: SECure Context Saving for IoT Devices
Venue: arXiv cs.CR 2019
Year: 2019

Energy consumption of IoT devices is a very important issue. For this reason, many techniques have been developed to allow IoT nodes to be aware of the amount of available energy. When energy is missing, the device halts and saves its state. One of those techniques is context saving, relying on the use of Non-Volatile Memories (NVM) to store and restore the state of the device. However, this information, as far as IoT devices deal with security, might be the target of attacks, including tampering and theft of confidential data. In this paper, we propose a SECure Context Saving (SECCS) approach that provides a context saving procedure and a hardware module easy to implement inside a System on Chip (SoC). This approach provides both confidentiality and integrity to all the CPU content saved into the target NVM.
Joint inference on structural and diffusion MRI for sequence-adaptive Bayesian segmentation of thalamic nuclei with probabilistic atlases
Venue: arXiv cs.CV 2019
Year: 2019

Segmentation of structural and diffusion MRI (sMRI/dMRI) is usually performed independently in neuroimaging pipelines. However, some brain structures (e.g., globus pallidus, thalamus and its nuclei) can be extracted more accurately by fusing the two modalities. Following the framework of Bayesian segmentation with probabilistic atlases and unsupervised appearance modeling, we present here a novel algorithm to jointly segment multi-modal sMRI/dMRI data. We propose a hierarchical likelihood term for the dMRI defined on the unit ball, which combines the Beta and Dimroth-Scheidegger-Watson distributions to model the data at each voxel. This term is integrated with a mixture of Gaussians for the sMRI data, such that the resulting joint unsupervised likelihood enables the analysis of multi-modal scans acquired with any type of MRI contrast, b-values, or number of directions, which enables wide applicability. We also propose an inference algorithm to estimate the maximum-a-posteriori model parameters from input images, and to compute the most likely segmentation. Using a recently published atlas derived from histology, we apply our method to thalamic nuclei segmentation on two datasets: HCP (state of the art) and ADNI (legacy) - producing lower sample sizes than Bayesian segmentation with sMRI alone.
Automatic Ontology Learning from Domain-Specific Short Unstructured Text Data
Venue: arXiv cs.IR 2019
Year: 2019

Ontology learning is a critical task in industry, dealing with identifying and extracting concepts captured in text data such that these concepts can be used in different tasks, e.g. information retrieval. Ontology learning is non-trivial due to several reasons with limited amount of prior research work that automatically learns a domain specific ontology from data. In our work, we propose a two-stage classification system to automatically learn an ontology from unstructured text data. We first collect candidate concepts, which are classified into concepts and irrelevant collocates by our first classifier. The concepts from the first classifier are further classified by the second classifier into different concept types. The proposed system is deployed as a prototype at a company and its performance is validated by using complaint and repair verbatim data collected in automotive industry from different data sources.