OpenRaiser/Intern-Atlas huggingface dataset:
TzK: Flow-Based Conditional Generative Model
Venue: arXiv cs.LG 2019
Year: 2019

We formulate a new class of conditional generative models based on probability flows. Trained with maximum likelihood, it provides efficient inference and sampling from class-conditionals or the joint distribution, and does not require a priori knowledge of the number of classes or the relationships between classes. This allows one to train generative models from multiple, heterogeneous datasets, while retaining strong prior models over subsets of the data (e.g., from a single dataset, class label, or attribute). In this paper, in addition to end-to-end learning, we show how one can learn a single model from multiple datasets with a relatively weak Glow architecture, and then extend it by conditioning on different knowledge types (e.g., a single dataset). This yields log likelihood comparable to state-of-the-art, compelling samples from conditional priors.
Fearless Concurrency? Understanding Concurrent Programming Safety in Real-World Rust Software
Venue: arXiv cs.PL 2019
Year: 2019

Rust is a popular programming language in building various low-level software in recent years. It aims to provide safe concurrency when implementing multi-threaded software through a suite of compiler checking rules. Unfortunately, there is limited understanding of how the checking rules influence the safety of concurrent programming in Rust applications. In this paper, we perform a preliminary study on Rust's concurrency safety from two aspects: concurrency usage and concurrency bugs. Our study can provide better understanding on Rust's concurrency and can guide future researchers and practitioners in writing better, more reliable Rust software and in developing debugging and bug detection tools for Rust.
Joint Channel Coding and Cooperative Network Coding on PSK Constellations in Wireless Networks
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we consider the application of Reed- Solomon (RS) channel coding for joint error correction and cooperative network coding on non-binary phase shift keying (PSK) modulated signals. The relay first decodes the RS channel coded messages received each in a time slot from all sources before applying network coding (NC) by the use of bit-level exclusive OR (XOR) operation. The network coded resulting message is then channel encoded before its transmission to the next relay or to the destination according to the network configuration. This scenario shows superior performance in comparison with the case where the relay does not perform channel coding/decoding. For different orders of PSK modulation and different wireless configurations, simulation results demonstrate the improvements resulting from the use of RS channel codes in terms of symbol error rate (SER) versus signal-to-noise ratio (SNR).
IoT Device Fingerprint using Deep Learning
Venue: arXiv cs.NI 2019
Year: 2019

Device Fingerprinting (DFP) is the identification of a device without using its network or other assigned identities including IP address, Medium Access Control (MAC) address, or International Mobile Equipment Identity (IMEI) number. DFP identifies a device using information from the packets which the device uses to communicate over the network. Packets are received at a router and processed to extract the information. In this paper, we worked on the DFP using Inter Arrival Time (IAT). IAT is the time interval between the two consecutive packets received. This has been observed that the IAT is unique for a device because of different hardware and the software used for the device. The existing work on the DFP uses the statistical techniques to analyze the IAT and to further generate the information using which a device can be identified uniquely. This work presents a novel idea of DFP by plotting graphs of IAT for packets with each graph plotting 100 IATs and subsequently processing the resulting graphs for the identification of the device. This approach improves the efficiency to identify a device DFP due to achieved benchmark of the deep learning libraries in the image processing. We configured Raspberry Pi to work as a router and installed our packet sniffer application on the Raspberry Pi . The packet sniffer application captured the packet information from the connected devices in a log file. We connected two Apple devices iPad4 and iPhone 7 Plus to the router and created IAT graphs for these two devices. We used Convolution Neural Network (CNN) to identify the devices and observed the accuracy of 86.7%.
A New Relation Between Energy Efficiency and Spectral Efficiency in Wireless Communications Systems
Venue: arXiv cs.NI 2019
Year: 2019

When designing wireless communication systems (WCS), spectral efficiency (SE) has been the main design performance metric. Recently, energy efficiency (EE) is attracting a huge interest due to the massive deployment of power limited WCS such as IoT devices, and stringent environmental concerns. For this reason, many works in the literature focused on optimizing the EE and highlighted the EE-SE relationship as a trade-off (meaning that increasing one decreases the other). In this article, after introducing the EE metric, we highlight a new perspective of the EE-SE relationship based on energy-efficient power control. In particular, we give insights about the EE-based performance of various transmission technologies and its impact on 5G future design. Via numerical results, we show that the corresponding power scheme allows an increase of both the SE as the EE with no trade-off. Finally, we present relevant open research problems.
A General Theory for Structured Prediction with Smooth Convex Surrogates
Venue: arXiv cs.LG 2019
Year: 2019

In this work we provide a theoretical framework for structured prediction that generalizes the existing theory of surrogate methods for binary and multiclass classification based on estimating conditional probabilities with smooth convex surrogates (e.g. logistic regression). The theory relies on a natural characterization of structural properties of the task loss and allows to derive statistical guarantees for many widely used methods in the context of multilabeling, ranking, ordinal regression and graph matching. In particular, we characterize the smooth convex surrogates compatible with a given task loss in terms of a suitable Bregman divergence composed with a link function. This allows to derive tight bounds for the calibration function and to obtain novel results on existing surrogate frameworks for structured prediction such as conditional random fields and quadratic surrogates.
Hawkes Process for Understanding the Influence of Pathogenic Social Media Accounts
Venue: arXiv cs.SI 2019
Year: 2019

Over the past years, political events and public opinion on the Web have been allegedly manipulated by accounts dedicated to spreading disinformation and performing malicious activities on social media. These accounts hereafter referred to as "Pathogenic Social Media (PSM)" accounts, are often controlled by terrorist supporters, water armies or fake news writers and hence can pose threats to social media and general public. Understanding and analyzing PSMs could help social media firms devise sophisticated and automated techniques that could be deployed to stop them from reaching their audience and consequently reduce their threat. In this paper, we leverage the well-known statistical technique "Hawkes Process" to quantify the influence of PSM accounts on the dissemination of malicious information on social media platforms. Our findings on a real-world ISIS-related dataset from Twitter indicate that PSMs are significantly different from regular users in making a message viral. Specifically, we observed that PSMs do not usually post URLs from mainstream news sources. Instead, their tweets usually receive large impact on audience, if contained URLs from Facebook and alternative news outlets. In contrary, tweets posted by regular users receive nearly equal impression regardless of the posted URLs and their sources. Our findings can further shed light on understanding and detecting PSM accounts.
An Automated Spectral Clustering for Multi-scale Data
Venue: arXiv cs.LG 2019
Year: 2019

Spectral clustering algorithms typically require a priori selection of input parameters such as the number of clusters, a scaling parameter for the affinity measure, or ranges of these values for parameter tuning. Despite efforts for automating the process of spectral clustering, the task of grouping data in multi-scale and higher dimensional spaces is yet to be explored. This study presents a spectral clustering heuristic algorithm that obviates the need for an input by estimating the parameters from the data itself. Specifically, it introduces the heuristic of iterative eigengap search with (1) global scaling and (2) local scaling. These approaches estimate the scaling parameter and implement iterative eigengap quantification along a search tree to reveal dissimilarities at different scales of a feature space and identify clusters. The performance of these approaches has been tested on various real-world datasets of power variation with multi-scale nature and gene expression. Our findings show that iterative eigengap search with a PCA-based global scaling scheme can discover different patterns with an accuracy of higher than 90% in most cases without asking for a priori input information.
The FA Quantifier Fuzzification Mechanism: analysis of convergence and efficient implementations
Venue: arXiv cs.AI 2019
Year: 2019

The fuzzy quantification model FA has been identified as one of the best behaved quantification models in several revisions of the field of fuzzy quantification. This model is, to our knowledge, the unique one fulfilling the strict Determiner Fuzzification Scheme axiomatic framework that does not induce the standard min and max operators. The main contribution of this paper is the proof of a convergence result that links this quantification model with the Zadeh's model when the size of the input sets tends to infinite. The convergence proof is, in any case, more general than the convergence to the Zadeh's model, being applicable to any quantitative quantifier. In addition, recent revisions papers have presented some doubts about the existence of suitable computational implementations to evaluate the FA model in practical applications. In order to prove that this model is not only a theoretical approach, we show exact algorithmic solutions for the most common linguistic quantifiers as well as an approximate implementation by means of Monte Carlo. Additionally, we will also give a general overview of the main properties fulfilled by the FA model, as a single compendium integrating the whole set of properties fulfilled by it has not been previously published.
Determinization of Büchi Automata: Unifying the Approaches of Safra and Muller-Schupp
Venue: arXiv cs.FL 2019
Year: 2019

Determinization of Büchi automata is a long-known difficult problem and after the seminal result of Safra, who developed the first asymptotically optimal construction from Büchi into Rabin automata, much work went into improving, simplifying or avoiding Safra's construction. A different, less known determinization construction was derived by Muller and Schupp and appears to be unrelated to Safra's construction on the first sight. In this paper we propose a new meta-construction from nondeterministic Büchi to deterministic parity automata which strictly subsumes both the construction of Safra and the construction of Muller and Schupp. It is based on a correspondence between structures that are encoded in the macrostates of the determinization procedures - Safra trees on one hand, and levels of the split-tree, which underlies the Muller and Schupp construction, on the other. Our construction allows for combining the mentioned constructions and opens up new directions for the development of heuristics.
Communication Interface Identifier Protocol (CIIP): An Energy Efficient Protocol for smaller IoT Sensor
Venue: arXiv cs.NI 2019
Year: 2019

Today we can use technologies like switched Ethernet, TCP/IP, high-speed wide area networks, and high-performance low-cost computers very easily. However, protocols designed for those communication are inefficient or not energy efficient. Smart home, smart grid, blockchain, Internet of Things (IoT) all these technologies are coming very rapidly with higher communication facilities demands an energy efficient Ethernet. Due to controller and network equipment use a huge quantity of energy. Layer to layer communication making our communication method more complex and costly. In this work, we propose an architecture, which will make the communication of sensor devices to outside world easier. Our proposed system removes certain layer from TCP-IP communication. We used a communication interface identifier protocol (CIIP) which can be used for smaller IoT sensors.
Diseño de un espacio semántico sobre la base de la Wikipedia. Una propuesta de análisis de la semántica latente para el idioma español
Venue: arXiv cs.CL 2019
Year: 2019

Latent Semantic Analysis (LSA) was initially conceived by the cognitive psychology at the 90s decade. Since its emergence, the LSA has been used to model cognitive processes, pointing out academic texts, compare literature works and analyse political speeches, among other applications. Taking as starting point multivariate method for dimensionality reduction, this paper propose a semantic space for Spanish language. Out results include a document text matrix with dimensions 1.3 x10^6 and 5.9x10^6, which later is decomposed into singular values. Those singular values are used to semantically words or text.
Top Tree Compression of Tries
Venue: arXiv cs.DS 2019
Year: 2019

We present a compressed representation of tries based on top tree compression [ICALP 2013] that works on a standard, comparison-based, pointer machine model of computation and supports efficient prefix search queries. Namely, we show how to preprocess a set of strings of total length $n$ over an alphabet of size $σ$ into a compressed data structure of worst-case optimal size $O(n/\log_σn)$ that given a pattern string $P$ of length $m$ determines if $P$ is a prefix of one of the strings in time $O(\min(m\log σ,m + \log n))$. We show that this query time is in fact optimal regardless of the size of the data structure. Existing solutions either use $Ω(n)$ space or rely on word RAM techniques, such as tabulation, hashing, address arithmetic, or word-level parallelism, and hence do not work on a pointer machine. Our result is the first solution on a pointer machine that achieves worst-case $o(n)$ space. Along the way, we develop several interesting data structures that work on a pointer machine and are of independent interest. These include an optimal data structures for random access to a grammar-compressed string and an optimal data structure for a variant of the level ancestor problem.
Whittle Index Policy for Multichannel Scheduling in Queueing Systems
Venue: arXiv cs.NI 2019
Year: 2019

In this paper, we consider a queueing system with multiple channels (or servers) and multiple classes of users. We aim at allocating the available channels among the users in such a way to minimize the expected total average queue length of the system. This known scheduling problem falls in the framework of Restless Bandit Problems (RBP) for which an optimal solution is known to be out of reach for the general case. The contributions of this paper are as follows. We rely on the Lagrangian relaxation method to characterize the Whittle index values and to develop an index-based heuristic for the original scheduling problem. The main difficulty lies in the fact that, for some queue states, deriving the Whittle's index requires introducing a new approach which consists in introducing a new expected discounted cost function and deriving the Whittle's index values with respect to the discount parameter $β$. We then deduce the Whittle's indices for the original problem (i.e. with total average queue length minimization) by taking the limit $β\rightarrow 1$. The numerical results provided in this paper show that this policy performs very well and is very close to the optimal solution for high number of users.
Deep CSI Learning for Gait Biometric Sensing and Recognition
Venue: arXiv cs.LG 2019
Year: 2019

Gait is a person's natural walking style and a complex biological process that is unique to each person. Recently, the channel state information (CSI) of WiFi devices have been exploited to capture human gait biometrics for user identification. However, the performance of existing CSI-based gait identification systems is far from satisfactory. They can only achieve limited identification accuracy (maximum $93\%$) only for a very small group of people (i.e., between 2 to 10). To address such challenge, an end-to-end deep CSI learning system is developed, which exploits deep neural networks to automatically learn the salient gait features in CSI data that are discriminative enough to distinguish different people Firstly, the raw CSI data are sanitized through window-based denoising, mean centering and normalization. The sanitized data is then passed to a residual deep convolutional neural network (DCNN), which automatically extracts the hierarchical features of gait-signatures embedded in the CSI data. Finally, a softmax classifier utilizes the extracted features to make the final prediction about the identity of the user. In a typical indoor environment, a top-1 accuracy of $97.12 \pm 1.13\%$ is achieved for a dataset of 30 people.
Compression of Recurrent Neural Networks for Efficient Language Modeling
Venue: arXiv cs.CL 2019
Year: 2019

Recurrent neural networks have proved to be an effective method for statistical language modeling. However, in practice their memory and run-time complexity are usually too large to be implemented in real-time offline mobile applications. In this paper we consider several compression techniques for recurrent neural networks including Long-Short Term Memory models. We make particular attention to the high-dimensional output problem caused by the very large vocabulary size. We focus on effective compression methods in the context of their exploitation on devices: pruning, quantization, and matrix decomposition approaches (low-rank factorization and tensor train decomposition, in particular). For each model we investigate the trade-off between its size, suitability for fast inference and perplexity. We propose a general pipeline for applying the most suitable methods to compress recurrent neural networks for language modeling. It has been shown in the experimental study with the Penn Treebank (PTB) dataset that the most efficient results in terms of speed and compression-perplexity balance are obtained by matrix decomposition techniques.
Safeguarding Wireless Network with UAVs: A Physical Layer Security Perspective
Venue: arXiv cs.IT 2019
Year: 2019

Integrating unmanned aerial vehicles (UAVs) into future wireless systems such as the fifth-generation (5G) cellular network is anticipated to bring significant benefits for both UAV and telecommunication industries. Generally speaking, UAVs can be used as new aerial platforms in the cellular network to provide communication services for terrestrial users, or become new aerial users of the cellular network served by the terrestrial base stations. Due to their high altitude, UAVs usually have dominant line-of-sight (LoS) channels with the ground nodes, which, however, pose new security challenges to future wireless networks with widely deployed UAVs. On one hand, UAV-ground communications are more prone than terrestrial communications to eavesdropping and jamming attacks by malicious nodes on the ground. On the other hand, compared to malicious ground nodes, malicious UAVs can launch more effective eavesdropping and jamming attacks to terrestrial communications. Motivated by the above, in this article, we aim to identify such new issues from a physical-layer security viewpoint and propose novel solutions to tackle them efficiently. Numerical results are provided to validate their effectiveness and promising directions for future research are also discussed.
A fast algorithm for constructing balanced binary search trees
Venue: arXiv cs.DS 2019
Year: 2019

We suggest a new non-recursive algorithm for constructing a binary search tree given an array of numbers. The algorithm has $O(N)$ time and $O(1)$ memory complexity if the given array of $N$ numbers is sorted. The resulting tree is of minimal height and can be transformed to a complete binary search tree (retaining minimal height) with $O(\log N)$ time and $O(1)$ memory. The algorithm allows simple and effective parallelization.
Towards Autoencoding Variational Inference for Aspect-based Opinion Summary
Venue: arXiv cs.CL 2019
Year: 2019

Aspect-based Opinion Summary (AOS), consisting of aspect discovery and sentiment classification steps, has recently been emerging as one of the most crucial data mining tasks in e-commerce systems. Along this direction, the LDA-based model is considered as a notably suitable approach, since this model offers both topic modeling and sentiment classification. However, unlike traditional topic modeling, in the context of aspect discovery it is often required some initial seed words, whose prior knowledge is not easy to be incorporated into LDA models. Moreover, LDA approaches rely on sampling methods, which need to load the whole corpus into memory, making them hardly scalable. In this research, we study an alternative approach for AOS problem, based on Autoencoding Variational Inference (AVI). Firstly, we introduce the Autoencoding Variational Inference for Aspect Discovery (AVIAD) model, which extends the previous work of Autoencoding Variational Inference for Topic Models (AVITM) to embed prior knowledge of seed words. This work includes enhancement of the previous AVI architecture and also modification of the loss function. Ultimately, we present the Autoencoding Variational Inference for Joint Sentiment/Topic (AVIJST) model. In this model, we substantially extend the AVI model to support the JST model, which performs topic modeling for corresponding sentiment. The experimental results show that our proposed models enjoy higher topic coherent, faster convergence time and better accuracy on sentiment classification, as compared to their LDA-based counterparts.
Enhanced Performance for the encrypted Web through TLS Resumption across Hostnames
Venue: arXiv cs.CR 2019
Year: 2019

TLS can resume previous connections via abbreviated resumption handshakes that significantly decrease the delay and save expensive cryptographic operations. For that, cryptographic TLS state from previous connections is reused. TLS version 1.3 recommends to avoid resumption handshakes, and thus the reuse of cryptographic state, when connecting to a different hostname. In this work, we reassess this recommendation, as we find that sharing cryptographic TLS state across hostnames is a common practice on the web. We propose a TLS extension that allows the server to inform the client about TLS state sharing with other hostnames. This information enables the client to efficiently resume TLS sessions across hostnames. Our evaluation indicates that our TLS extension provides huge performance gains for the web. For example, about 58.7% of the 20.24 full TLS handshakes that are required to retrieve an average website on the web can be converted to resumed connection establishments. This yields to a reduction of 44% of the CPU time consumed for TLS connection establishments. Furthermore, our TLS extension accelerates the connection establishment with an average website by up to 30.6% for TLS 1.3. Thus, our proposal significantly reduces the (energy) costs and the delay overhead in the encrypted web.