OpenRaiser/Intern-Atlas huggingface dataset:
Graph Autoencoder-Based Unsupervised Feature Selection with Broad and Local Data Structure Preservation
Venue: arXiv cs.CV 2018
Year: 2018

Feature selection is a dimensionality reduction technique that selects a subset of representative features from high dimensional data by eliminating irrelevant and redundant features. Recently, feature selection combined with sparse learning has attracted significant attention due to its outstanding performance compared with traditional feature selection methods that ignores correlation between features. These works first map data onto a low-dimensional subspace and then select features by posing a sparsity constraint on the transformation matrix. However, they are restricted by design to linear data transformation, a potential drawback given that the underlying correlation structures of data are often non-linear. To leverage a more sophisticated embedding, we propose an autoencoder-based unsupervised feature selection approach that leverages a single-layer autoencoder for a joint framework of feature selection and manifold learning. More specifically, we enforce column sparsity on the weight matrix connecting the input layer and the hidden layer, as in previous work. Additionally, we include spectral graph analysis on the projected data into the learning process to achieve local data geometry preservation from the original data space to the low-dimensional feature space. Extensive experiments are conducted on image, audio, text, and biological data. The promising experimental results validate the superiority of the proposed method.
Seasonal Goods and Spoiled Milk: Pricing for a Limited Shelf-Life
Venue: arXiv cs.GT 2018
Year: 2018

We examine the case of items with a limited shelf-life where storing an item (before consumption) may carry a cost to a buyer (or distributor). For example, eggs, milk, or Groupon coupons have a fixed expiry date, and seasonal goods can suffer a decrease in value. We show how this setting contrasts with recent results by Berbeglia et al (arXiv:1509.07330(v5)) for items with infinite shelf-life. We prove tight bounds on the seller's profits showing how they relate to the items' shelf-life. We show, counterintuitively, that in our limited shelf-life setting, increasing storage costs can sometimes lead to less profit for the seller which cannot happen when items have unlimited shelf-life. We also provide an algorithm that calculates optimal prices. Finally, we examine empirically the relationship between profits and buyer utility as the storage cost and shelf-life duration change, and observe properties, some of which are unique to the limited shelf-life setting.
Winograd Schema - Knowledge Extraction Using Narrative Chains
Venue: arXiv cs.AI 2018
Year: 2018

The Winograd Schema Challenge (WSC) is a test of machine intelligence, designed to be an improvement on the Turing test. A Winograd Schema consists of a sentence and a corresponding question. To successfully answer these questions, one requires the use of commonsense knowledge and reasoning. This work focuses on extracting common sense knowledge which can be used to generate answers for the Winograd schema challenge. Common sense knowledge is extracted based on events (or actions) and their participants; called Event-Based Conditional Commonsense (ECC). I propose an approach using Narrative Event Chains [Chambers et al., 2008] to extract ECC knowledge. These are stored in templates, to be later used for answering the WSC questions. This approach works well with respect to a subset of WSC tasks.
A Novel Framework for DDoS Detectionin Huge Scale Networks, Thanksto QoS Features
Venue: arXiv cs.NI 2018
Year: 2018

It is not been a long time since the advent of cloud-based technology. However, in this short period of timeseveral advantages and disadvantages have been emerged. This is a problem solving technology with some threats as well. These threats and potential damages are not only limited to the cloud-based technologies, but they have always been against computer network infrastructures. One of these examples is Distributed Denial-of-Service (DDoS) intrusion which is of course one of the most complex and the most dangerous types of attacks. The impact of this type of attack, due to its powerful nature, is much higher on cloud systems since in case of occurrence, the service providers lose their services completely as well as their reputationand loyal customers. This, apparently,can even lead to the collapse of the stock and other destructive consequences. On the other hand, due to the properties of cloud service providers including large-scale infrastructures, DDoS intrusion detection algorithms need high sensitivity, innovation, and general improvements. Traditional structures of DDoS attack detection algorithms are designed for small-scale networks or at most for application camps. Lack of efficient algorithm is seemingly apparentfor the large-scale networks. Therefore, in this context we utilize standardmethods as well as a proposed hybrid protocol which is more appropriate in connection with cloud structures in order to detect DDoS attacks
HeNet: A Deep Learning Approach on Intel$^\circledR$ Processor Trace for Effective Exploit Detection
Venue: arXiv cs.CR 2018
Year: 2018

This paper presents HeNet, a hierarchical ensemble neural network, applied to classify hardware-generated control flow traces for malware detection. Deep learning-based malware detection has so far focused on analyzing executable files and runtime API calls. Static code analysis approaches face challenges due to obfuscated code and adversarial perturbations. Behavioral data collected during execution is more difficult to obfuscate but recent research has shown successful attacks against API call based malware classifiers. We investigate control flow based characterization of a program execution to build robust deep learning malware classifiers. HeNet consists of a low-level behavior model and a top-level ensemble model. The low-level model is a per-application behavior model, trained via transfer learning on a time-series of images generated from control flow trace of an execution. We use Intel$^\circledR$ Processor Trace enabled processor for low overhead execution tracing and design a lightweight image conversion and segmentation of the control flow trace. The top-level ensemble model aggregates the behavior classification of all the trace segments and detects an attack. The use of hardware trace adds portability to our system and the use of deep learning eliminates the manual effort of feature engineering. We evaluate HeNet against real-world exploitations of PDF readers. HeNet achieves 100\% accuracy and 0\% false positive on test set, and higher classification accuracy compared to classical machine learning algorithms.
Long-term Multi-granularity Deep Framework for Driver Drowsiness Detection
Venue: arXiv cs.CV 2018
Year: 2018

For real-world driver drowsiness detection from videos, the variation of head pose is so large that the existing methods on global face is not capable of extracting effective features, such as looking aside and lowering head. Temporal dependencies with variable length are also rarely considered by the previous approaches, e.g., yawning and speaking. In this paper, we propose a Long-term Multi-granularity Deep Framework to detect driver drowsiness in driving videos containing the frontal faces. The framework includes two key components: (1) Multi-granularity Convolutional Neural Network (MCNN), a novel network utilizes a group of parallel CNN extractors on well-aligned facial patches of different granularities, and extracts facial representations effectively for large variation of head pose, furthermore, it can flexibly fuse both detailed appearance clues of the main parts and local to global spatial constraints; (2) a deep Long Short Term Memory network is applied on facial representations to explore long-term relationships with variable length over sequential frames, which is capable to distinguish the states with temporal dependencies, such as blinking and closing eyes. Our approach achieves 90.05% accuracy and about 37 fps speed on the evaluation set of the public NTHU-DDD dataset, which is the state-of-the-art method on driver drowsiness detection. Moreover, we build a new dataset named FI-DDD, which is of higher precision of drowsy locations in temporal dimension.
A Novel Hybrid Biometric Electronic Voting System: Integrating Finger Print and Face Recognition
Venue: arXiv cs.CR 2018
Year: 2018

A novel hybrid design based electronic voting system is proposed, implemented and analyzed. The proposed system uses two voter verification techniques to give better results in comparison to single identification based systems. Finger print and facial recognition based methods are used for voter identification. Cross verification of a voter during an election process provides better accuracy than single parameter identification method. The facial recognition system uses Viola-Jones algorithm along with rectangular Haar feature selection method for detection and extraction of features to develop a biometric template and for feature extraction during the voting process. Cascaded machine learning based classifiers are used for comparing the features for identity verification using GPCA (Generalized Principle Component Analysis) and K-NN (K-Nearest Neighbor). It is accomplished through comparing the Eigen-vectors of the extracted features with the biometric template pre-stored in the election regulatory body database. The results of the proposed system show that the proposed cascaded design based system performs better than the systems using other classifiers or separate schemes i.e. facial or finger print based schemes. The proposed system will be highly useful for real time applications due to the reason that it has 91% accuracy under nominal light in terms of facial recognition. with bags of paper votes. The central station compiles and publishes the names of winners and losers through television and radio stations. This method is useful only if the whole process is completed in a transparent way. However, there are some drawbacks to this system. These include higher expenses, longer time to complete the voting process, fraudulent practices by the authorities administering elections as well as malpractices by the voters [1]. These challenges result in manipulated election results.
Asymptotic Miss Ratio of LRU Caching with Consistent Hashing
Venue: arXiv cs.PF 2018
Year: 2018

To efficiently scale data caching infrastructure to support emerging big data applications, many caching systems rely on consistent hashing to group a large number of servers to form a cooperative cluster. These servers are organized together according to a random hash function. They jointly provide a unified but distributed hash table to serve swift and voluminous data item requests. Different from the single least-recently-used (LRU) server that has already been extensively studied, theoretically characterizing a cluster that consists of multiple LRU servers remains yet to be explored. These servers are not simply added together; the random hashing complicates the behavior. To this end, we derive the asymptotic miss ratio of data item requests on a LRU cluster with consistent hashing. We show that these individual cache spaces on different servers can be effectively viewed as if they could be pooled together to form a single virtual LRU cache space parametrized by an appropriate cache size. This equivalence can be established rigorously under the condition that the cache sizes of the individual servers are large. For typical data caching systems this condition is common. Our theoretical framework provides a convenient abstraction that can directly apply the results from the simpler single LRU cache to the more complex LRU cluster with consistent hashing.
How to find a GSMem malicious activity via an AI approach
Venue: arXiv cs.CR 2018
Year: 2018

This paper investigates the following problem: how to find a GSMem malicious activity effectively. To this end, this paper puts forward a new method based on Artificial Intelligence (AI). At first, we use a large quantity of data in terms of frequencies and amplitudes of some electromagnetic waves to train our models. And then, we input a given frequency and amplitude into the obtained models, predicting that whether a GSMem malicious activity occurs or not. The simulated experiments show that the new method is potential to detect a GSMem one, with low False Positive Rates (FPR) and low False Negative Rates (FNR).
Precoding via Approximate Message Passing with Instantaneous Signal Constraints
Venue: arXiv cs.IT 2018
Year: 2018

This paper proposes a low complexity precoding algorithm based on the recently proposed Generalized Least Square Error (GLSE) scheme with generic penalty and support. The algorithm iteratively constructs the transmit vector via Approximate Message Passing (AMP). Using the asymptotic decoupling property of GLSE precoders, we derive closed form fixed point equations to tune the parameters in the proposed algorithm for a general set of instantaneous signal constraints. The tuning strategy is then utilized to construct transmit vectors with restricted peak-to-average power ratios and to efficiently select a subset of transmit antennas. The numerical investigations show that the proposed algorithm tracks the large-system performance of GLSE precoders even for a moderate number of antennas.
Weighted Contrastive Divergence
Venue: arXiv cs.LG 2018
Year: 2018

Learning algorithms for energy based Boltzmann architectures that rely on gradient descent are in general computationally prohibitive, typically due to the exponential number of terms involved in computing the partition function. In this way one has to resort to approximation schemes for the evaluation of the gradient. This is the case of Restricted Boltzmann Machines (RBM) and its learning algorithm Contrastive Divergence (CD). It is well-known that CD has a number of shortcomings, and its approximation to the gradient has several drawbacks. Overcoming these defects has been the basis of much research and new algorithms have been devised, such as persistent CD. In this manuscript we propose a new algorithm that we call Weighted CD (WCD), built from small modifications of the negative phase in standard CD. However small these modifications may be, experimental work reported in this paper suggest that WCD provides a significant improvement over standard CD and persistent CD at a small additional computational cost.
Secure Beamforming in Full-Duplex SWIPT Systems With Loopback Self-Interference Cancellation
Venue: arXiv cs.NI 2018
Year: 2018

Security is a critical issue in full duplex (FD) communication systems due to the broadcast nature of wireless channels. In this paper, joint design of information and artificial noise beamforming vectors is proposed for the FD simultaneous wireless information and power transferring (FD-SWIPT) systems with loopback self-interference cancellation. To guarantee high security and energy harvesting performance of the FD-SWIPT system, the proposed design is formulated as a secrecy rate maximization problem under energy transfer rate constraints. Although the secrecy rate maximization problem is non-convex, we solve it via semidefinite relaxation and a two-dimensional search. We prove the optimality of our proposed algorithm and demonstrate its performance via simulations.
A Machine Learning Framework for Register Placement Optimization in Digital Circuit Design
Venue: arXiv cs.LG 2018
Year: 2018

In modern digital circuit back-end design, designers heavily rely on electronic-design-automoation (EDA) tool to close timing. However, the heuristic algorithms used in the place and route tool usually does not result in optimal solution. Thus, significant design effort is used to tune parameters or provide user constraints or guidelines to improve the tool performance. In this paper, we targeted at those optimization space left behind by the EDA tools and propose a machine learning framework that helps to define what are the guidelines and constraints for registers placement, which can yield better performance and quality for back-end design. In other words, the framework is trying to learn what are the flaws of the existing EDA tools and tries to optimize it by providing additional information. We discuss what is the proper input feature vector to be extracted, and what is metric to be used for reference output. We also develop a scheme to generate perturbed training samples using existing design based on Gaussian randomization. By applying our methodology, we are able to improve the design runtime by up to 36% and timing quality by up to 23%.
Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition
Venue: arXiv cs.CV 2018
Year: 2018

This paper introduces a deep learning enabled generative sensing framework which integrates low-end sensors with computational intelligence to attain a high recognition accuracy on par with that attained with high-end sensors. The proposed generative sensing framework aims at transforming low-end, low-quality sensor data into higher quality sensor data in terms of achieved classification accuracy. The low-end data can be transformed into higher quality data of the same modality or into data of another modality. Different from existing methods for image generation, the proposed framework is based on discriminative models and targets to maximize the recognition accuracy rather than a similarity measure. This is achieved through the introduction of selective feature regeneration in a deep neural network (DNN). The proposed generative sensing will essentially transform low-quality sensor data into high-quality information for robust perception. Results are presented to illustrate the performance of the proposed framework.
Term Relevance Feedback for Contextual Named Entity Retrieval
Venue: arXiv cs.IR 2018
Year: 2018

We address the role of a user in Contextual Named Entity Retrieval (CNER), showing (1) that user identification of important context-bearing terms is superior to automated approaches, and (2) that further gains are possible if the user indicates the relative importance of those terms. CNER is similar in spirit to List Question answering and Entity disambiguation. However, the main focus of CNER is to obtain user feedback for constructing a profile for a class of entities on the fly and use that to retrieve entities from free text. Given a sentence, and an entity selected from that sentence, CNER aims to retrieve sentences that have entities similar to query entity. This paper explores obtaining term relevance feedback and importance weighting from humans in order to improve a CNER system. We report our findings based on the efforts of IR researchers as well as crowdsourced workers.
A Trip to the Moon: Personalized Animated Movies for Self-reflection
Venue: arXiv cs.HC 2018
Year: 2018

Self-tracking physiological and psychological data poses the challenge of presentation and interpretation. Insightful narratives for self-tracking data can motivate the user towards constructive self-reflection. One powerful form of narrative that engages audience across various culture and age groups is animated movies. We collected a week of self-reported mood and behavior data from each user and created in Unity a personalized animation based on their data. We evaluated the impact of their video in a randomized control trial with a non-personalized animated video as control. We found that personalized videos tend to be more emotionally engaging, encouraging greater and lengthier writing that indicated self-reflection about moods and behaviors, compared to non-personalized control videos.
Towards Stability Analysis of Data Transport Mechanisms: a Fluid Model and an Application
Venue: arXiv cs.NI 2018
Year: 2018

The Transmission Control Protocol (TCP) utilizes congestion avoidance and control mechanisms as a preventive measure against congestive collapse and as an adaptive measure in the presence of changing network conditions. The set of available congestion control algorithms is diverse, and while many have been studied from empirical and simulation perspectives, there is a notable lack of analytical work for some variants. To gain more insight into the dynamics of these algorithms, we: (1) propose a general modeling scheme consisting of a set of functional differential equations of retarded type (RFDEs) and of the congestion window as a function of time; (2) apply this scheme to TCP Reno and demonstrate its equivalence to a previous, well known model for TCP Reno; (3) show an application of the new framework to the widely-deployed congestion control algorithm TCP CUBIC, for which analytical models are few and limited; and (4) validate the model using simulations. Our modeling framework yields a fluid model for TCP CUBIC. From a theoretical analysis of this model, we discover that TCP CUBIC is locally uniformly asymptotically stable -- a property of the algorithm previously unknown.
A Large Scale Investigation of Obfuscation Use in Google Play
Venue: arXiv cs.CR 2018
Year: 2018

Android applications are frequently plagiarized or repackaged, and software obfuscation is a recommended protection against these practices. However, there is very little data on the overall rates of app obfuscation, the techniques used, or factors that lead to developers to choose to obfuscate their apps. In this paper, we present the first comprehensive analysis of the use of and challenges to software obfuscation in Android applications. We analyzed 1.7 million free Android apps from Google Play to detect various obfuscation techniques, finding that only 24.92% of apps are obfuscated by the developer. To better understand this rate of obfuscation, we surveyed 308 Google Play developers about their experiences and attitudes about obfuscation. We found that while developers feel that apps in general are at risk of plagiarism, they do not fear theft of their own apps. Developers also self-report difficulties applying obfuscation for their own apps. To better understand this, we conducted a follow-up study where the vast majority of 70 participants failed to obfuscate a realistic sample app even while many mistakenly believed they had been successful. Our findings show that more work is needed to make obfuscation tools more usable, to educate developers on the risk of their apps being reverse engineered, their intellectual property stolen, their apps being repackaged and redistributed as malware and to improve the health of the overall Android ecosystem.
An efficient and secure two-party key agreement protocol based on chaotic maps
Venue: arXiv cs.CR 2018
Year: 2018

Secure communication is a matter of genuine concern that includes means whereby entities can share information without a third party's interception. Key agreement protocols are one of the common approaches in which two or more parties can agree upon a key, which precludes undesired third parties from forcing a key choice on them. Over the past decade, chaos-based key agreement protocols have been studied and employed widely. Recently, Yoon and Jeon proposed a novel key agreement protocol based on chaotic maps and claimed security and practicality for their protocol. We find that Yoon-Jeon's protocol suffers certain issues: (1) It introduces a trusted third party whose very presence increases the implementation cost. (2) requires a multiplicity of encryption/decryption computations and (3) does not protect the user's anonymity. In order to overcome these problems, we present an enhanced key agreement protocol with user anonymity. Theoretical analysis demonstrates that the proposed protocol is efficient and resists current attacks.