OpenRaiser/Intern-Atlas huggingface dataset:
Interference-Aware Flexible TDD Design for Massive MIMO 5G Systems
Venue: arXiv cs.IT 2018
Year: 2018

Both the use of very large arrays of antennas and flexible time division duplexing (TDD) designs have become prominent features of next generation 5G cellular systems. However, both enabling technologies suffer from severe interference effects, respectively known as pilot contamination and base-station-to-base-station (B2B) interference. In this paper, a practical novel TDD design principle is proposed for massive multiple-input multiple-output (MIMO) heterogeneous networks (HetNets) that leverages the inherent features of a flexible TDD design to mitigate both the beamformed interference caused by the pilot contamination effect and B2B interference. The design is based on the key observation that the transmission path chosen for training by the non-massive MIMO base stations plays an important role in the interference behavior of the network, and the data slots need to be configured accordingly. We propose TDFLEX, a low-complexity heuristic solution that follows these design guidelines. Performance evaluation results show significant gains when our design is compared to the standard TD-LTE.
Malytics: A Malware Detection Scheme
Venue: arXiv cs.CR 2018
Year: 2018

An important problem of cyber-security is malware analysis. Besides good precision and recognition rate, a malware detection scheme needs to be able to generalize well for novel malware families (a.k.a zero-day attacks). It is important that the system does not require excessive computation particularly for deployment on the mobile devices. In this paper, we propose a novel scheme to detect malware which we call Malytics. It is not dependent on any particular tool or operating system. It extracts static features of any given binary file to distinguish malware from benign. Malytics consists of three stages: feature extraction, similarity measurement and classification. The three phases are implemented by a neural network with two hidden layers and an output layer. We show feature extraction, which is performed by tf -simhashing, is equivalent to the first layer of a particular neural network. We evaluate Malytics performance on both Android and Windows platforms. Malytics outperforms a wide range of learning-based techniques and also individual state-of-the-art models on both platforms. We also show Malytics is resilient and robust in addressing zero-day malware samples. The F1-score of Malytics is 97.21% and 99.45% on Android dex file and Windows PE files respectively, in the applied datasets. The speed and efficiency of Malytics are also evaluated.
Homomorphic Encryption for Speaker Recognition: Protection of Biometric Templates and Vendor Model Parameters
Venue: arXiv cs.CR 2018
Year: 2018

Data privacy is crucial when dealing with biometric data. Accounting for the latest European data privacy regulation and payment service directive, biometric template protection is essential for any commercial application. Ensuring unlinkability across biometric service operators, irreversibility of leaked encrypted templates, and renewability of e.g., voice models following the i-vector paradigm, biometric voice-based systems are prepared for the latest EU data privacy legislation. Employing Paillier cryptosystems, Euclidean and cosine comparators are known to ensure data privacy demands, without loss of discrimination nor calibration performance. Bridging gaps from template protection to speaker recognition, two architectures are proposed for the two-covariance comparator, serving as a generative model in this study. The first architecture preserves privacy of biometric data capture subjects. In the second architecture, model parameters of the comparator are encrypted as well, such that biometric service providers can supply the same comparison modules employing different key pairs to multiple biometric service operators. An experimental proof-of-concept and complexity analysis is carried out on the data from the 2013-2014 NIST i-vector machine learning challenge.
On Generation of Adversarial Examples using Convex Programming
Venue: arXiv cs.LG 2018
Year: 2018

It has been observed that deep learning architectures tend to make erroneous decisions with high reliability for particularly designed adversarial instances. In this work, we show that the perturbation analysis of these architectures provides a framework for generating adversarial instances by convex programming which, for classification tasks, is able to recover variants of existing non-adaptive adversarial methods. The proposed framework can be used for the design of adversarial noise under various desirable constraints and different types of networks. Moreover, this framework is capable of explaining various existing adversarial methods and can be used to derive new algorithms as well. We make use of these results to obtain novel algorithms. The experiments show the competitive performance of the obtained solutions, in terms of fooling ratio, when benchmarked with well-known adversarial methods.
The Trade-off between Privacy and Fidelity via Ehrhart Theory
Venue: arXiv cs.IT 2018
Year: 2018

As an increasing amount of data is gathered nowadays and stored in databases (DBs), the question arises of how to protect the privacy of individual records in a DB even while providing accurate answers to queries on the DB. Differential Privacy (DP) has gained acceptance as a framework to quantify vulnerability of algorithms to privacy breaches. We consider the problem of how to sanitize an entire DB via a DP mechanism, on which unlimited further querying is performed. While protecting privacy, it is important that the sanitized DB still provide accurate responses to queries. The central contribution of this work is to characterize the amount of information preserved in an optimal DP DB sanitizing mechanism (DSM). We precisely characterize the utility-privacy trade-off of mechanisms that sanitize DBs in the asymptotic regime of large DBs. We study this in an information-theoretic framework by modeling a generic distribution on the data, and a measure of fidelity between the histograms of the original and sanitized DBs. We consider the popular $\mathbb{L}_{1}-$distortion metric that leads to the formulation as a linear program (LP). This optimization problem is prohibitive in complexity with the number of constraints growing exponentially in the parameters of the problem. Leveraging tools from discrete geometry, analytic combinatorics, and duality theorems of optimization, we fully characterize the optimal solution in terms of a power series whose coefficients are the number of integer points on a multidimensional convex polytope studied by Ehrhart in 1967. Employing Ehrhart theory, we determine a simple closed form computable expression for the asymptotic growth of the optimal privacy-fidelity trade-off to infinite precision. At the heart of the findings is a deep connection between the minimum expected distortion and the Ehrhart series of an integral convex polytope.
Detecting Adversarial Examples - A Lesson from Multimedia Forensics
Venue: arXiv cs.CR 2018
Year: 2018

Adversarial classification is the task of performing robust classification in the presence of a strategic attacker. Originating from information hiding and multimedia forensics, adversarial classification recently received a lot of attention in a broader security context. In the domain of machine learning-based image classification, adversarial classification can be interpreted as detecting so-called adversarial examples, which are slightly altered versions of benign images. They are specifically crafted to be misclassified with a very high probability by the classifier under attack. Neural networks, which dominate among modern image classifiers, have been shown to be especially vulnerable to these adversarial examples. However, detecting subtle changes in digital images has always been the goal of multimedia forensics and steganalysis. In this paper, we highlight the parallels between these two fields and secure machine learning. Furthermore, we adapt a linear filter, similar to early steganalysis methods, to detect adversarial examples that are generated with the projected gradient descent (PGD) method, the state-of-the-art algorithm for this task. We test our method on the MNIST database and show for several parameter combinations of PGD that our method can reliably detect adversarial examples. Additionally, the combination of adversarial re-training and our detection method effectively reduces the attack surface of attacks against neural networks. Thus, we conclude that adversarial examples for image classification possibly do not withstand detection methods from steganalysis, and future work should explore the effectiveness of known techniques from multimedia forensics in other adversarial settings.
Virtual Network Embedding Approximations: Leveraging Randomized Rounding
Venue: arXiv cs.NI 2018
Year: 2018

The Virtual Network Embedding Problem (VNEP) captures the essence of many resource allocation problems of today's infrastructure providers, which offer their physical computation and networking resources to customers. Customers request resources in the form of Virtual Networks, i.e. as a directed graph which specifies computational requirements at the nodes and communication requirements on the edges. An embedding of a Virtual Network on the shared physical infrastructure is the joint mapping of (virtual) nodes to physical servers together with the mapping of (virtual) edges onto paths in the physical network connecting the respective servers. This work initiates the study of approximation algorithms for the VNEP. Concretely, we study the offline setting with admission control: given multiple request graphs the task is to embed the most profitable subset while not exceeding resource capacities. Our approximation is based on the randomized rounding of Linear Programming (LP) solutions. Interestingly, we uncover that the standard LP formulation for the VNEP exhibits an inherent structural deficit when considering general virtual network topologies: its solutions cannot be decomposed into valid embeddings. In turn, focusing on the class of cactus request graphs, we devise a novel LP formulation, whose solutions can be decomposed into convex combinations of valid embedding. Proving performance guarantees of our rounding scheme, we obtain the first approximation algorithm for the VNEP in the resource augmentation model. We propose two types of rounding heuristics and evaluate their performance in an extensive computational study. Our results indicate that randomized rounding can yield good solutions (even without augmentations). Specifically, heuristic rounding achieves 73.8% of the baseline's profit, while not exceeding capacities.
Directing Chemotaxis-Based Spatial Self-Organization via Biased, Random Initial Conditions
Venue: arXiv cs.MA 2018
Year: 2018

Inspired by the chemotaxis interaction of living cells, we have developed an agent-based approach for self-organizing shape formation. Since all our simulations begin with a different uniform random configuration and our agents move stochastically, it has been observed that the self-organization process may form two or more stable final configurations. These differing configurations may be characterized via statistical moments of the agents' locations. In order to direct the agents to robustly form one specific configuration, we generate biased initial conditions whose statistical moments are related to moments of the desired configuration. With this approach, we are able to successfully direct the aggregating swarms to produced a desired macroscopic shape, starting from randomized initial conditions with controlled statistical properties.
Network Traffic Driven Storage Repair
Venue: arXiv cs.IT 2018
Year: 2018

Recently we constructed an explicit family of locally repairable and locally regenerating codes. Their existence was proven by Kamath et al. but no explicit construction was given. Our design is based on HashTag codes that can have different sub-packetization levels. In this work we emphasize the importance of having two ways to repair a node: repair only with local parity nodes or repair with both local and global parity nodes. We say that the repair strategy is network traffic driven since it is in connection with the concrete system and code parameters: the repair bandwidth of the code, the number of I/O operations, the access time for the contacted parts and the size of the stored file. We show the benefits of having repair duality in one practical example implemented in Hadoop. We also give algorithms for efficient repair of the global parity nodes.
Computational Complexity of Generalized Push Fight
Venue: arXiv cs.CC 2018
Year: 2018

We analyze the computational complexity of optimally playing the two-player board game Push Fight, generalized to an arbitrary board and number of pieces. We prove that the game is PSPACE-hard to decide who will win from a given position, even for simple (almost rectangular) hole-free boards. We also analyze the mate-in-1 problem: can the player win in a single turn? One turn in Push Fight consists of up to two "moves" followed by a mandatory "push". With these rules, or generalizing the number of allowed moves to any constant, we show mate-in-1 can be solved in polynomial time. If, however, the number of moves per turn is part of the input, the problem becomes NP-complete. On the other hand, without any limit on the number of moves per turn, the problem becomes polynomially solvable again.
Thermal Management in Fine-Grained 3-D Integrated Circuits
Venue: arXiv cs.ET 2018
Year: 2018

For beyond 2-D CMOS logic, various 3-D integration approaches specially transistor based 3-D integrations such as monolithic 3-D [1], Skybridge [2], SN3D [3] holds most promise. However, such 3D architectures within small form factor increase hotspots and demand careful consideration of thermal management at all levels of integration [4] as stacked transistors are detached from the substrate (i.e., heat sink). Traditional system level approaches such as liquid cooling [5], heat spreader [6], etc. are inadequate for transistor level 3-D integration and have huge cost overhead [7]. In this paper, we investigate the thermal profile for transistor level 3-D integration approaches through finite element based modeling. Additionally, we propose generic physical level heat management features for such transistor level 3-D integration and show their application through detailed thermal modeling and simulations. These features include a thermal junction and heat conducting nano pillar. The heat junction is a specialized junction to extract heat from a selected region in 3-D; it allows heat conduction without interference with the electrical activities of the circuit. In conjunction with the junction, our proposed thermal pillars enable heat dissipation through the substrate; these pillars are analogous to TSVs/Vias, but carry only heat. Such structures are generic and is applicable to any transistor level 3-D integration approaches. We perform 3-D finite element based analysis to capture both static and transient thermal behaviors of 3-D circuits, and show the effectiveness of heat management features. Our simulation results show that without any heat extraction feature, temperature for 3-D integrated circuits increased by almost 100K-200K. However, proposed heat extraction feature is very effective in heat management, reducing temperature from heated area by up to 53%.
Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

As the demand for enabling high-level autonomous driving has increased in recent years and visual perception is one of the critical features to enable fully autonomous driving, in this paper, we introduce an efficient approach for simultaneous object detection, depth estimation and pixel-level semantic segmentation using a shared convolutional architecture. The proposed network model, which we named Driving Scene Perception Network (DSPNet), uses multi-level feature maps and multi-task learning to improve the accuracy and efficiency of object detection, depth estimation and image segmentation tasks from a single input image. Hence, the resulting network model uses less than 850 MiB of GPU memory and achieves 14.0 fps on NVIDIA GeForce GTX 1080 with a 1024x512 input image, and both precision and efficiency have been improved over combination of single tasks.
CIoTA: Collaborative IoT Anomaly Detection via Blockchain
Venue: arXiv cs.CY 2018
Year: 2018

Due to their rapid growth and deployment, Internet of things (IoT) devices have become a central aspect of our daily lives. However, they tend to have many vulnerabilities which can be exploited by an attacker. Unsupervised techniques, such as anomaly detection, can help us secure the IoT devices. However, an anomaly detection model must be trained for a long time in order to capture all benign behaviors. This approach is vulnerable to adversarial attacks since all observations are assumed to be benign while training the anomaly detection model. In this paper, we propose CIoTA, a lightweight framework that utilizes the blockchain concept to perform distributed and collaborative anomaly detection for devices with limited resources. CIoTA uses blockchain to incrementally update a trusted anomaly detection model via self-attestation and consensus among IoT devices. We evaluate CIoTA on our own distributed IoT simulation platform, which consists of 48 Raspberry Pis, to demonstrate CIoTA's ability to enhance the security of each device and the security of the network as a whole.
5G NR Jamming, Spoofing, and Sniffing: Threat Assessment and Mitigation
Venue: arXiv cs.NI 2018
Year: 2018

In December 2017, the Third Generation Partnership Project (3GPP) released the first set of specifications for 5G New Radio (NR), which is currently the most widely accepted 5G cellular standard. 5G NR is expected to replace LTE and previous generations of cellular technology over the next several years, providing higher throughput, lower latency, and a host of new features. Similar to LTE, the 5G NR physical layer consists of several physical channels and signals, most of which are vital to the operation of the network. Unfortunately, like for any wireless technology, disruption through radio jamming is possible. This paper investigates the extent to which 5G NR is vulnerable to jamming and spoofing, by analyzing the physical downlink and uplink control channels and signals. We identify the weakest links in the 5G NR frame, and propose mitigation strategies that should be taken into account during implementation of 5G NR chipsets and base stations.
Reality-check for Econophysics: Likelihood-based fitting of physics-inspired market models to empirical data
Venue: arXiv cs.CE 2018
Year: 2018

The statistical description and modeling of volatility plays a prominent role in econometrics, risk management and finance. GARCH and stochastic volatility models have been extensively studied and are routinely fitted to market data, albeit providing a phenomenological description only. In contrast, the field of econophysics starts from the premise that modern economies consist of a vast number of individual actors with heterogeneous expectations and incentives. In turn explaining observed market statistics as emerging from the collective dynamics of many actors following heterogeneous, yet simple, rather mechanistic rules. While such models generate volatility dynamics qualitatively matching several stylized facts and thus illustrate the possible role of different mechanisms, such as chartist trading, herding behavior etc., rigorous and quantitative statistical fits are still mostly lacking. Here, we show how Stan, a modern probabilistic programming language for Bayesian modeling, can be used to fit several models from econophysics. In contrast to the method of moment matching, which is currently popular, our fits are purely likelihood based with many advantages, including systematic model comparison and principled generation of model predictions conditional on the observed price history. In particular, we investigate models by Vikram & Sinha and Franke & Westerhoff, and provide a quantitative comparison with standard econometric models.
Path of Vowel Raising in Chengdu Dialect of Mandarin
Venue: arXiv cs.CL 2018
Year: 2018

He and Rao (2013) reported a raising phenomenon of /a/ in /Xan/ (X being a consonant or a vowel) in Chengdu dialect of Mandarin, i.e. /a/ is realized as [epsilon] for young speakers but [ae] for older speakers, but they offered no acoustic analysis. We designed an acoustic study that examined the realization of /Xan/ in speakers of different age (old vs. young) and gender (male vs. female) groups, where X represents three conditions: 1) unaspirated consonants: C ([p], [t], [k]), 2) aspirated consonants: Ch ([ph], [th], [kh]), and 3) high vowels: V ([i], [y], [u]). 17 native speakers were asked to read /Xan/ characters and the F1 values are extracted for comparison. Our results confirmed the raising effect in He and Rao (2013), i.e., young speakers realize /a/ as [epsilon] in /an/, whereas older speakers in the most part realize it as [ae]. Also, female speakers raise more than male speakers within the same age group. Interestingly, within the /Van/ condition, older speakers do raise /a/ in /ian/ and /yan/. We interpret this as /a/ first assimilates to its preceding front high vowels /i/ and /y/ for older speakers, which then becomes phonologized in younger speakers in all conditions, including /Chan/ and /Can/. This shows a possible trajectory of the ongoing sound change in the Chengdu dialect.
Graph Laplacian Spectrum and Primary Frequency Regulation
Venue: arXiv cs.SY 2018
Year: 2018

We present a framework based on spectral graph theory that captures the interplay among network topology, system inertia, and generator and load damping in determining the overall grid behavior and performance. Specifically, we show that the impact of network topology on a power system can be quantified through the network Laplacian eigenvalues, and such eigenvalues determine the grid robustness against low frequency disturbances. Moreover, we can explicitly decompose the frequency signal along scaled Laplacian eigenvectors when damping-inertia ratios are uniform across buses. The insight revealed by this framework partially explains why load-side participation in frequency regulation not only makes the system respond faster, but also helps lower the system nadir after a disturbance. Finally, by presenting a new controller specifically tailored to suppress high frequency disturbances, we demonstrate that our results can provide useful guidelines in the controller design for load-side primary frequency regulation. This improved controller is simulated on the IEEE 39-bus New England interconnection system to illustrate its robustness against high frequency oscillations compared to both the conventional droop control and a recent controller design.