OpenRaiser/Intern-Atlas huggingface dataset:
Revisiting Network Support for RDMA
Venue: arXiv cs.NI 2018
Year: 2018

The advent of RoCE (RDMA over Converged Ethernet) has led to a significant increase in the use of RDMA in datacenter networks. To achieve good performance, RoCE requires a lossless network which is in turn achieved by enabling Priority Flow Control (PFC) within the network. However, PFC brings with it a host of problems such as head-of-the-line blocking, congestion spreading, and occasional deadlocks. Rather than seek to fix these issues, we instead ask: is PFC fundamentally required to support RDMA over Ethernet? We show that the need for PFC is an artifact of current RoCE NIC designs rather than a fundamental requirement. We propose an improved RoCE NIC (IRN) design that makes a few simple changes to the RoCE NIC for better handling of packet losses. We show that IRN (without PFC) outperforms RoCE (with PFC) by 6-83% for typical network scenarios. Thus not only does IRN eliminate the need for PFC, it improves performance in the process! We further show that the changes that IRN introduces can be implemented with modest overheads of about 3-10% to NIC resources. Based on our results, we argue that research and industry should rethink the current trajectory of network support for RDMA.
Metadata Enrichment of Multi-Disciplinary Digital Library: A Semantic-based Approach
Venue: arXiv cs.DL 2018
Year: 2018

In the scientific digital libraries, some papers from different research communities can be described by community-dependent keywords even if they share a semantically similar topic. Articles that are not tagged with enough keyword variations are poorly indexed in any information retrieval system which limits potentially fruitful exchanges between scientific disciplines. In this paper, we introduce a novel experimentally designed pipeline for multi-label semantic-based tagging developed for open-access metadata digital libraries. The approach starts by learning from a standard scientific categorization and a sample of topic tagged articles to find semantically relevant articles and enrich its metadata accordingly. Our proposed pipeline aims to enable researchers reaching articles from various disciplines that tend to use different terminologies. It allows retrieving semantically relevant articles given a limited known variation of search terms. In addition to achieving an accuracy that is higher than an expanded query based method using a topic synonym set extracted from a semantic network, our experiments also show a higher computational scalability versus other comparable techniques. We created a new benchmark extracted from the open-access metadata of a scientific digital library and published it along with the experiment code to allow further research in the topic.
Throughput Maximization in Cloud-Radio Access Networks using Rate-Aware Network Coding
Venue: arXiv cs.IT 2018
Year: 2018

One of the most promising techniques for network-wide interference management necessitates a redesign of the network architecture known as cloud radio access network (CRAN). The cloud is responsible for coordinating multiple Remote Radio Heads (RRHs) and scheduling users to their radio resources blocks (RRBs). The transmit frame of each RRH consists of several orthogonal RRBs each maintained at a certain power level (PL). While previous works considered a vanilla version in which each RRB can serve a single user, this paper proposes mixing the flows of multiple users using instantly decodable network coding (IDNC). As such, the total throughput is maximized. The joint user scheduling and power adaptation problem is solved by designing, for each RRB, a subgraph in which each vertex represents potential user-RRH associations, encoded files, transmission rates, and PLs for one specific RRB. It is shown that the original problem is equivalent to a maximum-weight clique problem over the union of all subgraphs, called herein the CRAN-IDNCgraph. Extensive simulation results are provided to attest the effectiveness of the proposed solution against state of the art algorithms. In particular, the presented simulation results reveal that the method achieves substantial performance gains for all system configurations which collaborates the theoretical findings.
Cycles to the Rescue! Novel Constraints to Compute Maximum Planar Subgraphs Fast
Venue: arXiv cs.DS 2018
Year: 2018

The NP-hard Maximum Planar Subgraph problem asks for a planar subgraph $H$ of a given graph $G$ such that $H$ has maximum edge cardinality. For more than two decades, the only known non-trivial exact algorithm was based on integer linear programming and Kuratowski's famous planarity criterion. We build upon this approach and present new constraint classes, together with a lifting of the polyhedron, to obtain provably stronger LP-relaxations, and in turn faster algorithms in practice. The new constraints take Euler's polyhedron formula as a starting point and combine it with considering cycles in $G$. This paper discusses both the theoretical as well as the practical sides of this strengthening.
Whom Are You Going to Call?: Determinants of @-Mentions in GitHub Discussions
Venue: arXiv cs.SE 2018
Year: 2018

Open Source Software (OSS) project success relies on crowd contributions. When an issue arises in pull-request based systems, @-mentions are used to call on people to task; previous studies have shown that @-mentions in discussions are associated with faster issue resolution. In most projects there may be many developers who could technically handle a variety of tasks. But OSS supports dynamic teams distributed across a wide variety of social and geographic backgrounds, as well as levels of involvement. It is, then, important to know whom to call on, i.e., who can be relied or trusted with important task-related duties, and why. In this paper, we sought to understand which observable socio-technical attributes of developers can be used to build good models of them being future @-mentioned in GitHub issues and pull request discussions. We built overall and project-specific predictive models of future @-mentions, in order to capture the determinants of @-mentions in each of two hundred GitHub projects, and to understand if and how those determinants differ between projects. We found that visibility, expertise, and productivity are associated with an increase in @-mentions, while responsiveness is not, in the presence of a number of control variables. Also, we find that though project-specific differences exist, the overall model can be used for cross-project prediction, indicating its GitHub-wide utility.
A Novel ECOC Algorithm with Centroid Distance Based Soft Coding Scheme
Venue: arXiv cs.AI 2018
Year: 2018

In ECOC framework, the ternary coding strategy is widely deployed in coding process. It relabels classes with {"-1,0,1" }, where -1/1 means to assign the corresponding classes to the negative/positive group, and label 0 leads to ignore the corresponding classes in the training process. However, the application of hard labels may lose some information about the tendency of class distributions. Instead, we propose a Centroid distance-based Soft coding scheme to indicate such tendency, named as CSECOC. In our algorithm, Sequential Forward Floating Selection (SFFS) is applied to search an optimal class assignment by minimizing the ratio of intra-group and inter-group distance. In this way, a hard coding matrix is generated initially. Then we propose a measure, named as coverage, to describe the probability of a sample in a class falling to a correct group. The coverage of a class a group replace the corresponding hard element, so as to form a soft coding matrix. Compared with the hard ones, such soft elements can reflect the tendency of a class belonging to positive or negative group. Instead of classifiers, regressors are used as base learners in this algorithm. To the best of our knowledge, it is the first time that soft coding scheme has been proposed. The results on five UCI datasets show that compared with some state-of-art ECOC algorithms, our algorithm can produce comparable or better classification accuracy with small scale ensembles.
Ad-Net: Audio-Visual Convolutional Neural Network for Advertisement Detection In Videos
Venue: arXiv cs.CV 2018
Year: 2018

Personalized advertisement is a crucial task for many of the online businesses and video broadcasters. Many of today's broadcasters use the same commercial for all customers, but as one can imagine different viewers have different interests and it seems reasonable to have customized commercial for different group of people, chosen based on their demographic features, and history. In this project, we propose a framework, which gets the broadcast videos, analyzes them, detects the commercial and replaces it with a more suitable commercial. We propose a two-stream audio-visual convolutional neural network, that one branch analyzes the visual information and the other one analyzes the audio information, and then the audio and visual embedding are fused together, and are used for commercial detection, and content categorization. We show that using both the visual and audio content of the videos significantly improves the model performance for video analysis. This network is trained on a dataset of more than 50k regular video and commercial shots, and achieved much better performance compared to the models based on hand-crafted features.
Weakly Supervised Training of Speaker Identification Models
Venue: arXiv cs.SD 2018
Year: 2018

We propose an approach for training speaker identification models in a weakly supervised manner. We concentrate on the setting where the training data consists of a set of audio recordings and the speaker annotation is provided only at the recording level. The method uses speaker diarization to find unique speakers in each recording, and i-vectors to project the speech of each speaker to a fixed-dimensional vector. A neural network is then trained to map i-vectors to speakers, using a special objective function that allows to optimize the model using recording-level speaker labels. We report experiments on two different real-world datasets. On the VoxCeleb dataset, the method provides 94.6% accuracy on a closed set speaker identification task, surpassing the baseline performance by a large margin. On an Estonian broadcast news dataset, the method provides 66% time-weighted speaker identification recall at 93% precision.
Is the 1-norm the best convex sparse regularization?
Venue: arXiv cs.IT 2018
Year: 2018

The 1-norm is a good convex regularization for the recovery of sparse vectors from under-determined linear measurements. No other convex regularization seems to surpass its sparse recovery performance. How can this be explained? To answer this question, we define several notions of "best" (convex) regulariza-tion in the context of general low-dimensional recovery and show that indeed the 1-norm is an optimal convex sparse regularization within this framework.
Learning Qualitatively Diverse and Interpretable Rules for Classification
Venue: arXiv cs.LG 2018
Year: 2018

There has been growing interest in developing accurate models that can also be explained to humans. Unfortunately, if there exist multiple distinct but accurate models for some dataset, current machine learning methods are unlikely to find them: standard techniques will likely recover a complex model that combines them. In this work, we introduce a way to identify a maximal set of distinct but accurate models for a dataset. We demonstrate empirically that, in situations where the data supports multiple accurate classifiers, we tend to recover simpler, more interpretable classifiers rather than more complex ones.
New Sufficient Conditions for Lower Bounding the Optimal Policy of a POMDP using Lehmann Precision
Venue: arXiv cs.SY 2018
Year: 2018

This paper provides new sufficient conditions so that the optimal policy of a partially observed Markov decision process (POMDP) can be lower bounded by a myopic policy. The two new proposed conditions, namely, Lehmann precision and copositive dominance, completely fix the problems with two crucial assumptions in the well known papers of Lovejoy 1987 and Rieder 1991. For controlled sensing POMDPs, Lehmann precision exploits both convexity and monotonicity of the value function, whereas the classical Blackwell dominance only exploits convexity. Numerical examples are presented where Lehmann precision holds but Blackwell dominance does not hold, thereby illustrating the usefulness of the main result in controlled sensing applications.
Control Plane Compression
Venue: arXiv cs.NI 2018
Year: 2018

We develop an algorithm capable of compressing large networks into a smaller ones with similar control plane behavior: For every stable routing solution in the large, original network, there exists a corresponding solution in the compressed network, and vice versa. Our compression algorithm preserves a wide variety of network properties including reachability, loop freedom, and path length. Consequently, operators may speed up network analysis, based on simulation, emulation, or verification, by analyzing only the compressed network. Our approach is based on a new theory of control plane equivalence. We implement these ideas in a tool called Bonsai and apply it to real and synthetic networks. Bonsai can shrink real networks by over a factor of 5 and speed up analysis by several orders of magnitude.
Uniform 2D-Monotone Minimum Spanning Graphs
Venue: arXiv cs.CG 2018
Year: 2018

A geometric graph $G$ is $xy-$monotone if each pair of vertices of $G$ is connected by a $xy-$monotone path. We study the problem of producing the $xy-$monotone spanning geometric graph of a point set $P$ that (i) has the minimum cost, where the cost of a geometric graph is the sum of the Euclidean lengths of its edges, and (ii) has the least number of edges, in the cases that the Cartesian System $xy$ is specified or freely selected. Building upon previous results, we easily obtain that the two solutions coincide when the Cartesian System is specified and are both equal to the rectangle of influence graph of $P$. The rectangle of influence graph of $P$ is the geometric graph with vertex set $P$ such that two points $p,q \in P$ are adjacent if and only if the rectangle with corners $p$ and $q$ does not include any other point of $P$. When the Cartesian System can be freely chosen, we note that the two solutions do not necessarily coincide, however we show that they can both be obtained in $O(|P|^3)$ time. We also give a simple $2-$approximation algorithm for the problem of computing the spanning geometric graph of a $k-$rooted point set $P$, in which each root is connected to all the other points (including the other roots) of $P$ by $y-$monotone paths, that has the minimum cost.
The Foundations of Deep Learning with a Path Towards General Intelligence
Venue: arXiv cs.AI 2018
Year: 2018

Like any field of empirical science, AI may be approached axiomatically. We formulate requirements for a general-purpose, human-level AI system in terms of postulates. We review the methodology of deep learning, examining the explicit and tacit assumptions in deep learning research. Deep Learning methodology seeks to overcome limitations in traditional machine learning research as it combines facets of model richness, generality, and practical applicability. The methodology so far has produced outstanding results due to a productive synergy of function approximation, under plausible assumptions of irreducibility and the efficiency of back-propagation family of algorithms. We examine these winning traits of deep learning, and also observe the various known failure modes of deep learning. We conclude by giving recommendations on how to extend deep learning methodology to cover the postulates of general-purpose AI including modularity, and cognitive architecture. We also relate deep learning to advances in theoretical neuroscience research.
MRAttractor: Detecting Communities from Large-Scale Graphs
Venue: arXiv cs.DC 2018
Year: 2018

Detecting groups of users, who have similar opinions, interests, or social behavior, has become an important task for many applications. A recent study showed that dynamic distance based Attractor, a community detection algorithm, outperformed other community detection algorithms such as Spectral clustering, Louvain and Infomap, achieving higher Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI). However, Attractor often takes long time to detect communities, requiring many iterations. To overcome the drawback and handle large-scale graphs, in this paper we propose MRAttractor, an advanced version of Attractor to be runnable on a MapReduce framework. In particular, we (i) apply a sliding window technique to reduce the running time, keeping the same community detection quality; (ii) design and implement the Attractor algorithm for a MapReduce framework; and (iii) evaluate MRAttractor's performance on synthetic and real-world datasets. Experimental results show that our algorithm significantly reduced running time and was able to handle large-scale graphs.
Privacy-Protective-GAN for Face De-identification
Venue: arXiv cs.CV 2018
Year: 2018

Face de-identification has become increasingly important as the image sources are explosively growing and easily accessible. The advance of new face recognition techniques also arises people's concern regarding the privacy leakage. The mainstream pipelines of face de-identification are mostly based on the k-same framework, which bears critiques of low effectiveness and poor visual quality. In this paper, we propose a new framework called Privacy-Protective-GAN (PP-GAN) that adapts GAN with novel verificator and regulator modules specially designed for the face de-identification problem to ensure generating de-identified output with retained structure similarity according to a single input. We evaluate the proposed approach in terms of privacy protection, utility preservation, and structure similarity. Our approach not only outperforms existing face de-identification techniques but also provides a practical framework of adapting GAN with priors of domain knowledge.
A Note on Digitization of Real-Time Models and Logics
Venue: arXiv cs.LO 2018
Year: 2018

Digitization provides a sound and complete method to reduce the problem of verifying whether a real-time system satisfies a property under dense-time semantics to whether the same real-time system satisfies the property over discrete-time. This is a brief overview of digitization of real-time models and logics covering known results, value, limitations, and alternatives.
Probabilistic Software Modeling
Venue: arXiv cs.SE 2018
Year: 2018

Software Engineering and the implementation of software has become a challenging task as many tools, frameworks and languages must be orchestrated into one functioning piece. This complexity increases the need for testing and analysis methodologies that aid the developers and engineers as the software grows and evolves. The amount of resources that companies budget for testing and analysis is limited, highlighting the importance of automation for economic software development. We propose Probabilistic Software Modeling, a new paradigm for software modeling that builds on the fact that software is an easy-to-monitor environment from which statistical models can be built. Probabilistic Software Modeling provides increased comprehension for engineers without changing the level of abstraction. The approach relies on the recursive decomposition principle of object-oriented programming to build hierarchies of probabilistic models that are fitted via observations collected at runtime of a software system. This leads to a network of models that mirror the static structure of the software system while modeling its dynamic runtime behavior. The resulting models can be used in applications such as test-case generation, anomaly and outlier detection, probabilistic program simulation, or state predictions. Ideally, probabilistic software modeling allows the use of the entire spectrum of statistical modeling and inference for software, enabling in-depth analysis and generative procedures for software.
Challenges of Internet of Things and Big Data Integration
Venue: arXiv cs.CY 2018
Year: 2018

The Internet of Things anticipates the conjunction of physical gadgets to the In-ternet and their access to wireless sensor data which makes it expedient to restrain the physical world. Big Data convergence has put multifarious new opportunities ahead of business ventures to get into a new market or enhance their operations in the current market. considering the existing techniques and technologies, it is probably safe to say that the best solution is to use big data tools to provide an analytical solution to the Internet of Things. Based on the current technology deployment and adoption trends, it is envisioned that the Internet of Things is the technology of the future, while to-day's real-world devices can provide real and valuable analytics, and people in the real world use many IoT devices. Despite all the advertisements that companies offer in connection with the Internet of Things, you as a liable consumer, have the right to be suspicious about IoT advertise-ments. The primary question is: What is the promise of the Internet of things con-cerning reality and what are the prospects for the future.
An Improved Generic Bet-and-Run Strategy for Speeding Up Stochastic Local Search
Venue: arXiv cs.NE 2018
Year: 2018

A commonly used strategy for improving optimization algorithms is to restart the algorithm when it is believed to be trapped in an inferior part of the search space. Building on the recent success of Bet-and-Run approaches for restarted local search solvers, we introduce an improved generic Bet-and-Run strategy. The goal is to obtain the best possible results within a given time budget t using a given black-box optimization algorithm. If no prior knowledge about problem features and algorithm behavior is available, the question about how to use the time budget most efficiently arises. We propose to first start k>=1 independent runs of the algorithm during an initialization budget t1<t, pausing these runs, then apply a decision maker D to choose 1<=m<=k runs from them (consuming t2>=0 time units in doing so), and then continuing these runs for the remaining t3=t-t1-t2 time units. In previous Bet-and-Run strategies, the decision maker D=currentBest would simply select the run with the best- so-far results at negligible time. We propose using more advanced methods to discriminate between "good" and "bad" sample runs, with the goal of increasing the correlation of the chosen run with the a-posteriori best one. We test several different approaches, including neural networks trained or polynomials fitted on the current trace of the algorithm to predict which run may yield the best results if granted the remaining budget. We show with extensive experiments that this approach can yield better results than the previous methods, but also find that the currentBest method is a very reliable and robust baseline approach.