OpenRaiser/Intern-Atlas huggingface dataset:
Approximating Node-Weighted k-MST on Planar Graphs
Venue: arXiv cs.DS 2018
Year: 2018

We study the problem of finding a minimum weight connected subgraph spanning at least $k$ vertices on planar, node-weighted graphs. We give a $(4+\eps)$-approximation algorithm for this problem. We achieve this by utilizing the recent LMP primal-dual $3$-approximation for the node-weighted prize-collecting Steiner tree problem by Byrka et al (SWAT'16) and adopting an approach by Chudak et al. (Math.\ Prog.\ '04) regarding Lagrangian relaxation for the edge-weighted variant. In particular, we improve the procedure of picking additional vertices (tree merging procedure) given by Sadeghian (2013) by taking a constant number of recursive steps and utilizing the limited guessing procedure of Arora and Karakostas (Math.\ Prog.\ '06). More generally, our approach readily gives a $(\nicefrac{4}{3}\cdot r+\eps)$-approximation on any graph class where the algorithm of Byrka et al.\ for the prize-collecting version gives an $r$-approximation. We argue that this can be interpreted as a generalization of an analogous result by Könemann et al. (Algorithmica~'11) for partial cover problems. Together with a lower bound construction by Mestre (STACS'08) for partial cover this implies that our bound is essentially best possible among algorithms that utilize an LMP algorithm for the Lagrangian relaxation as a black box. In addition to that, we argue by a more involved lower bound construction that even using the LMP algorithm by Byrka et al.\ in a \emph{non-black-box} fashion could not beat the factor $\nicefrac{4}{3}\cdot r$ when the tree merging step relies only on the solutions output by the LMP algorithm.
Pull and Push&Pull in Random Evolving Graphs
Venue: arXiv cs.DS 2018
Year: 2018

The Push, the Pull and the Push&Pull algorithms are well-studied rumor spreading protocols. In all three, in the beginning one node of a graph is informed. In the Push setting, every round every informed node chooses a neighbor uniformly at random and, if it is not already informed anyway, informs it. In the Pull setting, each round each uninformed node chooses a neighbor uniformly at random and asks it for the rumor; if the asked neighbor is informed, now also the asking node is informed. Push&Pull is a combination of Push and Pull: In each round, each node picks a neighbor uniformly at random. If at least one of both knows the rumor, after this round, both know the rumor. Clementi et al. have considered Push in settings where the underlying graph changes each round. In one setting they investigated, in each round the underlying graph is a newly sampled Erdős-Rényi random graph $G(n,p)$. They show that if $p\geq 1/n$ then with probability $1-o(1)$ (as $n\rightarrow \infty$) the number of rounds needed until all nodes are informed is $\mathcal{O}(\ln(n))$. Doerr and Kostrygin introduced a general framework to analyze rumor spreading algorithms; using this framework, for $a>0$ and $p=a/n$ they improved the previous results in the described setting: The expected number of rounds needed by Push was determined to be $\log_{2-e^{-a}}(n)+1/(1-e^{-a})\ln(n)+\mathcal{O}(1)$; also large deviation bounds were obtained. Using their framework, we investigate Pull and Push&Pull in that setting: We prove that the expected number of rounds needed by Pull to inform all nodes is $\log_{2-e^{-a}}(n)+1/a \ln(n)+\mathcal{O}(1)$. Let $γ:= 2(1-e^{-a})-(1-e^{-a})^2/a$; we prove that the expected number of rounds needed by Push&Pull is $\log_{1+γ}(n)+1/a\ln(n)+\mathcal{O}(1)$; as a byproduct, we obtain large deviation bounds, too.
"Like Sheep Among Wolves": Characterizing Hateful Users on Twitter
Venue: arXiv cs.SI 2018
Year: 2018

Hateful speech in Online Social Networks (OSNs) is a key challenge for companies and governments, as it impacts users and advertisers, and as several countries have strict legislation against the practice. This has motivated work on detecting and characterizing the phenomenon in tweets, social media posts and comments. However, these approaches face several shortcomings due to the noisiness of OSN data, the sparsity of the phenomenon, and the subjectivity of the definition of hate speech. This works presents a user-centric view of hate speech, paving the way for better detection methods and understanding. We collect a Twitter dataset of $100,386$ users along with up to $200$ tweets from their timelines with a random-walk-based crawler on the retweet graph, and select a subsample of $4,972$ to be manually annotated as hateful or not through crowdsourcing. We examine the difference between user activity patterns, the content disseminated between hateful and normal users, and network centrality measurements in the sampled graph. Our results show that hateful users have more recent account creation dates, and more statuses, and followees per day. Additionally, they favorite more tweets, tweet in shorter intervals and are more central in the retweet network, contradicting the "lone wolf" stereotype often associated with such behavior. Hateful users are more negative, more profane, and use less words associated with topics such as hate, terrorism, violence and anger. We also identify similarities between hateful/normal users and their 1-neighborhood, suggesting strong homophily.
Error-Robust Multi-View Clustering
Venue: arXiv cs.LG 2018
Year: 2018

In the era of big data, data may come from multiple sources, known as multi-view data. Multi-view clustering aims at generating better clusters by exploiting complementary and consistent information from multiple views rather than relying on the individual view. Due to inevitable system errors caused by data-captured sensors or others, the data in each view may be erroneous. Various types of errors behave differently and inconsistently in each view. More precisely, error could exhibit as noise and corruptions in reality. Unfortunately, none of the existing multi-view clustering approaches handle all of these error types. Consequently, their clustering performance is dramatically degraded. In this paper, we propose a novel Markov chain method for Error-Robust Multi-View Clustering (EMVC). By decomposing each view into a shared transition probability matrix and error matrix and imposing structured sparsity-inducing norms on error matrices, we characterize and handle typical types of errors explicitly. To solve the challenging optimization problem, we propose a new efficient algorithm based on Augmented Lagrangian Multipliers and prove its convergence rigorously. Experimental results on various synthetic and real-world datasets show the superiority of the proposed EMVC method over the baseline methods and its robustness against different types of errors.
Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge Bases
Venue: arXiv cs.CL 2018
Year: 2018

Text representations using neural word embeddings have proven effective in many NLP applications. Recent researches adapt the traditional word embedding models to learn vectors of multiword expressions (concepts/entities). However, these methods are limited to textual knowledge bases (e.g., Wikipedia). In this paper, we propose a novel and simple technique for integrating the knowledge about concepts from two large scale knowledge bases of different structure (Wikipedia and Probase) in order to learn concept representations. We adapt the efficient skip-gram model to seamlessly learn from the knowledge in Wikipedia text and Probase concept graph. We evaluate our concept embedding models on two tasks: (1) analogical reasoning, where we achieve a state-of-the-art performance of 91% on semantic analogies, (2) concept categorization, where we achieve a state-of-the-art performance on two benchmark datasets achieving categorization accuracy of 100% on one and 98% on the other. Additionally, we present a case study to evaluate our model on unsupervised argument type identification for neural semantic parsing. We demonstrate the competitive accuracy of our unsupervised method and its ability to better generalize to out of vocabulary entity mentions compared to the tedious and error prone methods which depend on gazetteers and regular expressions.
A Fast Algorithm for Line Clipping by Convex Polyhedron in E3
Venue: arXiv cs.GR 2018
Year: 2018

A new algorithm for line clipping against convex polyhedron is given. The suggested algorithm is faster for higher number of facets of the given polyhedron than the traditional Cyrus-Beck's and others algorithms with complexity O(N) . The suggested algorithm has O(N) complexity in the worst N case and expected O(sqrt(N))) complexity. The speed up is achieved because of 'known order' of triangles. Some principal results of comparisons of selected algorithms are presented and give some imagination how the proposed algorithm could be used effectively.
Auto-Generation of Pipelined Hardware Designs for Polar Encoder
Venue: arXiv cs.AR 2018
Year: 2018

This paper presents a general framework for auto-generation of pipelined polar encoder architectures. The proposed framework could be well represented by a general formula. Given arbitrary code length $N$ and the level of parallelism $M$, the formula could specify the corresponding hardware architecture. We have written a compiler which could read the formula and then automatically generate its register-transfer level (RTL) description suitable for FPGA or ASIC implementation. With this hardware generation system, one could explore the design space and make a trade-off between cost and performance. Our experimental results have demonstrated the efficiency of this auto-generator for polar encoder architectures.
Depth-Adaptive Computational Policies for Efficient Visual Tracking
Venue: arXiv cs.CV 2018
Year: 2018

Current convolutional neural networks algorithms for video object tracking spend the same amount of computation for each object and video frame. However, it is harder to track an object in some frames than others, due to the varying amount of clutter, scene complexity, amount of motion, and object's distinctiveness against its background. We propose a depth-adaptive convolutional Siamese network that performs video tracking adaptively at multiple neural network depths. Parametric gating functions are trained to control the depth of the convolutional feature extractor by minimizing a joint loss of computational cost and tracking error. Our network achieves accuracy comparable to the state-of-the-art on the VOT2016 benchmark. Furthermore, our adaptive depth computation achieves higher accuracy for a given computational cost than traditional fixed-structure neural networks. The presented framework extends to other tasks that use convolutional neural networks and enables trading speed for accuracy at runtime.
Bayesian Tabulation Audits: Explained and Extended
Venue: arXiv cs.CR 2018
Year: 2018

Tabulation audits for an election provide statistical evidence that a reported contest outcome is "correct" (meaning that the tabulation of votes was properly performed), or else the tabulation audit determines the correct outcome. Stark proposed risk-limiting tabulation audits for this purpose; such audits are effective and are beginning to be used in practice. We expand the study of election audits based on Bayesian methods, first introduced by Rivest and Shen in 2012. (The risk-limiting audits proposed by Stark are "frequentist" rather than Bayesian in character.) We first provide a simplified presentation of Bayesian tabulation audits. A Bayesian tabulation audit begins by drawing a random sample of the votes in that contest, and tallying those votes. It then considers what effect statistical variations of this tally have on the contest outcome. If such variations almost always yield the previously-reported outcome, the audit terminates, accepting the reported outcome. Otherwise the audit is repeated with an enlarged sample. Bayesian audits are attractive because they work with any method for determining the winner (such as ranked-choice voting). We then show how Bayesian audits may be extended to handle more complex situations, such as auditing contests that \emph{span multiple jurisdictions}, or are otherwise "stratified." We highlight the auditing of such multiple-jurisdiction contests where some of the jurisdictions have an electronic cast vote record (CVR) for each cast paper vote, while the others do not. Complex situations such as this may arise naturally when some counties in a state have upgraded to new equipment, while others have not. Bayesian audits are able to handle such situations in a straightforward manner. We also discuss the benefits and relevant considerations for using Bayesian audits in practice.
Did you hear that? Adversarial Examples Against Automatic Speech Recognition
Venue: arXiv cs.CL 2018
Year: 2018

Speech is a common and effective way of communication between humans, and modern consumer devices such as smartphones and home hubs are equipped with deep learning based accurate automatic speech recognition to enable natural interaction between humans and machines. Recently, researchers have demonstrated powerful attacks against machine learning models that can fool them to produceincorrect results. However, nearly all previous research in adversarial attacks has focused on image recognition and object detection models. In this short paper, we present a first of its kind demonstration of adversarial attacks against speech classification model. Our algorithm performs targeted attacks with 87% success by adding small background noise without having to know the underlying model parameter and architecture. Our attack only changes the least significant bits of a subset of audio clip samples, and the noise does not change 89% the human listener's perception of the audio clip as evaluated in our human study.
Block Diagonalization Type Precoding Algorithms for IEEE 802.11ac Systems
Venue: arXiv cs.IT 2018
Year: 2018

Block diagonalization (BD) based precoding schemes are well-known linear transmit strategies employed in the downlink of multi-user multiple-input multipleoutput (MU-MIMO) systems. BD type precoding algorithms employed at the transmit side effect the suppression of multi-user interference (MUI) by the decomposition of MU-MIMO broadcast channel into multiple single-user MIMO (SU-MIMO) channels followed by parallelization of the SU-MIMO channels to obtain independent streams of each user. Given that the design of linear precoding algorithms has made significant progress, the implementation of these techniques in standards for wireless local area networks (WLAN) remains an open question. In this work, schemes for implementation of BD based precoding techniques in the framework of IEEE 802.11ac standard are proposed followed by performance evaluation of these techniques in the proposed framework. I analyze the sum-rate and the bit-error-rate (BER) performance of the techniques in my framework and obtain computational complexity-wise as well as performance-wise optimal algorithm for my system.
Secrecy Capacity-Memory Tradeoff of Erasure Broadcast Channels
Venue: arXiv cs.IT 2018
Year: 2018

This paper derives upper and lower bounds on the secrecy capacity-memory tradeoff of a wiretap erasure broadcast channel (BC) with Kw weak receivers and Ks strong receivers, where weak receivers, respectively strong receivers, have same erasure probabilities and cache sizes. The lower bounds are achieved by schemes that meticulously combine joint cache-channel coding with wiretap coding and key-aided one-time pads. The presented upper bound holds more generally for arbitrary degraded BCs and arbitrary cache sizes. When only weak receivers have cache memories, upper and lower bounds coincide for small and large cache memories, thus providing the exact secrecy capacity-memory tradeoff for this setup. The derived bounds allow to further conclude that the secrecy capacity is positive even when the eavesdropper is stronger than all the legitimate receivers with cache memories. Moreover, they show that the secrecy capacity-memory tradeoff can be significantly smaller than its non-secure counterpart, but it grows much faster when cache memories are small. The paper also presents a lower bound on the global secrecy capacity-memory tradeoff where one is allowed to optimize the cache assignment subject to a total cache budget. It is close to the best known lower bound without secrecy constraint. For small total cache budget, the global secrecy capacity-memory tradeoff is achieved by assigning all the available cache memory uniformly over all receivers if the eavesdropper is stronger than all legitimate receivers, and it is achieved by assigning the cache memory uniformly only over the weak receivers if the eavesdropper is weaker than the strong receivers.
Deep Learning: A Critical Appraisal
Venue: arXiv cs.AI 2018
Year: 2018

Although deep learning has historical roots going back decades, neither the term "deep learning" nor the approach was popular just over five years ago, when the field was reignited by papers such as Krizhevsky, Sutskever and Hinton's now classic (2012) deep network model of Imagenet. What has the field discovered in the five subsequent years? Against a background of considerable progress in areas such as speech recognition, image recognition, and game playing, and considerable enthusiasm in the popular press, I present ten concerns for deep learning, and suggest that deep learning must be supplemented by other techniques if we are to reach artificial general intelligence.
Character-level Recurrent Neural Networks in Practice: Comparing Training and Sampling Schemes
Venue: arXiv cs.LG 2018
Year: 2018

Recurrent neural networks are nowadays successfully used in an abundance of applications, going from text, speech and image processing to recommender systems. Backpropagation through time is the algorithm that is commonly used to train these networks on specific tasks. Many deep learning frameworks have their own implementation of training and sampling procedures for recurrent neural networks, while there are in fact multiple other possibilities to choose from and other parameters to tune. In existing literature this is very often overlooked or ignored. In this paper we therefore give an overview of possible training and sampling schemes for character-level recurrent neural networks to solve the task of predicting the next token in a given sequence. We test these different schemes on a variety of datasets, neural network architectures and parameter settings, and formulate a number of take-home recommendations. The choice of training and sampling scheme turns out to be subject to a number of trade-offs, such as training stability, sampling time, model performance and implementation effort, but is largely independent of the data. Perhaps the most surprising result is that transferring hidden states for correctly initializing the model on subsequences often leads to unstable training behavior depending on the dataset.
Validation of Hardware Security and Trust: A Survey
Venue: arXiv cs.CR 2018
Year: 2018

With ever advancing in digital system, security has been emerged as a major concern. Many researchers all around the world come up with solutions to address various challenges that are crucial for industry and market. The aim of this survey is a brief review of challenges of security validation as well as define and classify Hardware Trojans. Then, we provide more details about various validation techniques for hardware security and trust.
Accounting for hidden common causes when inferring cause and effect from observational data
Venue: arXiv cs.AI 2018
Year: 2018

Identifying causal relationships from observation data is difficult, in large part, due to the presence of hidden common causes. In some cases, where just the right patterns of conditional independence and dependence lie in the data---for example, Y-structures---it is possible to identify cause and effect. In other cases, the analyst deliberately makes an uncertain assumption that hidden common causes are absent, and infers putative causal relationships to be tested in a randomized trial. Here, we consider a third approach, where there are sufficient clues in the data such that hidden common causes can be inferred.
Large Flocks of Small Birds: On the Minimal Size of Population Protocols
Venue: arXiv cs.DC 2018
Year: 2018

Population protocols are a well established model of distributed computation by mobile finite-state agents with very limited storage. A classical result establishes that population protocols compute exactly predicates definable in Presburger arithmetic. We initiate the study of the minimal amount of memory required to compute a given predicate as a function of its size. We present results on the predicates $x \geq n$ for $n \in \mathbb{N}$, and more generally on the predicates corresponding to systems of linear inequalities. We show that they can be computed by protocols with $O(\log n)$ states (or, more generally, logarithmic in the coefficients of the predicate), and that, surprisingly, some families of predicates can be computed by protocols with $O(\log\log n)$ states. We give essentially matching lower bounds for the class of 1-aware protocols.
Fundamental Latency Limits for D2D-Aided Content Delivery in Fog Wireless Networks
Venue: arXiv cs.IT 2018
Year: 2018

Device-to-Device (D2D) communication can support the operation of cellular systems by reducing the traffic in the network infrastructure. In this paper, the benefits of D2D communication are investigated in the context of a Fog-Radio Access Network (F-RAN) that leverages edge caching and fronthaul connectivity for the purpose of content delivery. Assuming offline caching, out-of-band D2D communication, and an F-RAN with two edge nodes and two user equipments, an information-theoretically optimal caching and delivery strategy is presented that minimizes the delivery time in the high signal-to-noise ratio regime. The delivery time accounts for the latency caused by fronthaul, downlink, and D2D transmissions. The proposed optimal strategy is based on a novel scheme for an X-channel with receiver cooperation that leverages tools from real interference alignment. Insights are provided on the regimes in which D2D communication is beneficial.
A Semantic-Rich Similarity Measure in Heterogeneous Information Networks
Venue: arXiv cs.DB 2018
Year: 2018

Measuring the similarities between objects in information networks has fundamental importance in recommendation systems, clustering and web search. The existing metrics depend on the meta path or meta structure specified by users. In this paper, we propose a stratified meta structure based similarity $SMSS$ in heterogeneous information networks. The stratified meta structure can be constructed automatically and capture rich semantics. Then, we define the commuting matrix of the stratified meta structure by virtue of the commuting matrices of meta paths and meta structures. As a result, $SMSS$ is defined by virtue of these commuting matrices. Experimental evaluations show that the proposed $SMSS$ on the whole outperforms the state-of-the-art metrics in terms of ranking and clustering.
DeepJ: Style-Specific Music Generation
Venue: arXiv cs.SD 2018
Year: 2018

Recent advances in deep neural networks have enabled algorithms to compose music that is comparable to music composed by humans. However, few algorithms allow the user to generate music with tunable parameters. The ability to tune properties of generated music will yield more practical benefits for aiding artists, filmmakers, and composers in their creative tasks. In this paper, we introduce DeepJ - an end-to-end generative model that is capable of composing music conditioned on a specific mixture of composer styles. Our innovations include methods to learn musical style and music dynamics. We use our model to demonstrate a simple technique for controlling the style of generated music as a proof of concept. Evaluation of our model using human raters shows that we have improved over the Biaxial LSTM approach.