OpenRaiser/Intern-Atlas huggingface dataset:
ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting
Venue: arXiv cs.LG 2018
Year: 2018

Accurate demand forecasts can help on-line retail organizations better plan their supply-chain processes. The challenge, however, is the large number of associative factors that result in large, non-stationary shifts in demand, which traditional time series and regression approaches fail to model. In this paper, we propose a Neural Network architecture called AR-MDN, that simultaneously models associative factors, time-series trends and the variance in the demand. We first identify several causal features and use a combination of feature embeddings, MLP and LSTM to represent them. We then model the output density as a learned mixture of Gaussian distributions. The AR-MDN can be trained end-to-end without the need for additional supervision. We experiment on a dataset of an year's worth of data over tens-of-thousands of products from Flipkart. The proposed architecture yields a significant improvement in forecasting accuracy when compared with existing alternatives.
Learning Quick Fixes from Code Repositories
Venue: arXiv cs.SE 2018
Year: 2018

Code analyzers such as Error Prone and FindBugs detect code patterns symptomatic of bugs, performance issues, or bad style. These tools express patterns as quick fixes that detect and rewrite unwanted code. However, it is difficult to come up with new quick fixes and decide which ones are useful and frequently appear in real code. We propose to rely on the collective wisdom of programmers and learn quick fixes from revision histories in software repositories. We present REVISAR, a tool for discovering common Java edit patterns in code repositories. Given code repositories and their revision histories, REVISAR (i) identifies code edits from revisions and (ii) clusters edits into sets that can be described using an edit pattern. The designers of code analyzers can then inspect the patterns and add the corresponding quick fixes to their tools. We ran REVISAR on nine popular GitHub projects, and it discovered 89 useful edit patterns that appeared in 3 or more projects. Moreover, 64% of the discovered patterns did not appear in existing tools. We then conducted a survey with 164 programmers from 124 projects and found that programmers significantly preferred eight out of the nine of the discovered patterns. Finally, we submitted 16 pull requests applying our patterns to 9 projects and, at the time of the writing, programmers accepted 6 (60%) of them. The results of this work aid toolsmiths in discovering quick fixes and making informed decisions about which quick fixes to prioritize based on patterns programmers actually apply in practice.
ShuffleSeg: Real-time Semantic Segmentation Network
Venue: arXiv cs.CV 2018
Year: 2018

Real-time semantic segmentation is of significant importance for mobile and robotics related applications. We propose a computationally efficient segmentation network which we term as ShuffleSeg. The proposed architecture is based on grouped convolution and channel shuffling in its encoder for improving the performance. An ablation study of different decoding methods is compared including Skip architecture, UNet, and Dilation Frontend. Interesting insights on the speed and accuracy tradeoff is discussed. It is shown that skip architecture in the decoding method provides the best compromise for the goal of real-time performance, while it provides adequate accuracy by utilizing higher resolution feature maps for a more accurate segmentation. ShuffleSeg is evaluated on CityScapes and compared against the state of the art real-time segmentation networks. It achieves 2x GFLOPs reduction, while it provides on par mean intersection over union of 58.3% on CityScapes test set. ShuffleSeg runs at 15.7 frames per second on NVIDIA Jetson TX2, which makes it of great potential for real-time applications.
Graph-based Clustering under Differential Privacy
Venue: arXiv cs.DS 2018
Year: 2018

In this paper, we present the first differentially private clustering method for arbitrary-shaped node clusters in a graph. This algorithm takes as input only an approximate Minimum Spanning Tree (MST) $\mathcal{T}$ released under weight differential privacy constraints from the graph. Then, the underlying nonconvex clustering partition is successfully recovered from cutting optimal cuts on $\mathcal{T}$. As opposed to existing methods, our algorithm is theoretically well-motivated. Experiments support our theoretical findings.
Smartphone apps usage patterns as a predictor of perceived stress levels at workplace
Venue: arXiv cs.CY 2018
Year: 2018

Explosion of number of smartphone apps and their diversity has created a fertile ground to study behaviour of smartphone users. Patterns of app usage, specifically types of apps and their duration are influenced by the state of the user and this information can be correlated with the self-reported state of the users. The work in this paper is along the line of understanding patterns of app usage and investigating relationship of these patterns with the perceived stress level within the workplace context. Our results show that using a subject-centric behaviour model we can predict stress levels based on smartphone app usage. The results we have achieved, of average accuracy of 75% and precision of 85.7%, can be used as an indicator of overall stress levels in work environments and in turn inform stress reduction organisational policies, especially when considering interrelation between stress and productivity of workers.
The Secure Machine: Efficient Secure Execution On Untrusted Platforms
Venue: arXiv cs.CR 2018
Year: 2018

In this work we present the Secure Machine, SeM for short, a CPU architecture extension for secure computing. SeM uses a small amount of in-chip additional hardware that monitors key communication channels inside the CPU chip, and only acts when required. SeM provides confidentiality and integrity for a secure program without trusting the platform software or any off-chip hardware. SeM supports existing binaries of single- and multi-threaded applications running on single- or multi-core, multi-CPU. The performance reduction caused by it is only few percent, most of which is due to the memory encryption layer that is commonly used in many secure architectures. We also developed SeM-Prepare, a software tool that automatically instruments existing applications (binaries) with additional instructions so they can be securely executed on our architecture without requiring any programming efforts or the availability of the desired program`s source code. To enable secure data sharing in shared memory environments, we developed Secure Distributed Shared Memory (SDSM), an efficient (time and memory) algorithm for allowing thousands of compute nodes to share data securely while running on an untrusted computing environment. SDSM shows a negligible reduction in performance, and it requires negligible and hardware resources. We developed Distributed Memory Integrity Trees, a method for enhancing single node integrity trees for preserving the integrity of a distributed application running on an untrusted computing environment. We show that our method is applicable to existing single node integrity trees such as Merkle Tree, Bonsai Merkle Tree, and Intel`s SGX memory integrity engine. All these building blocks may be used together to form a practical secure system, and some can be used in conjunction with other secure systems.
Preparing Bengali-English Code-Mixed Corpus for Sentiment Analysis of Indian Languages
Venue: arXiv cs.CL 2018
Year: 2018

Analysis of informative contents and sentiments of social users has been attempted quite intensively in the recent past. Most of the systems are usable only for monolingual data and fails or gives poor results when used on data with code-mixing property. To gather attention and encourage researchers to work on this crisis, we prepared gold standard Bengali-English code-mixed data with language and polarity tag for sentiment analysis purposes. In this paper, we discuss the systems we prepared to collect and filter raw Twitter data. In order to reduce manual work while annotation, hybrid systems combining rule based and supervised models were developed for both language and sentiment tagging. The final corpus was annotated by a group of annotators following a few guidelines. The gold standard corpus thus obtained has impressive inter-annotator agreement obtained in terms of Kappa values. Various metrics like Code-Mixed Index (CMI), Code-Mixed Factor (CF) along with various aspects (language and emotion) also qualitatively polled the code-mixed and sentiment properties of the corpus.
NVIDIA Tensor Core Programmability, Performance & Precision
Venue: arXiv cs.DC 2018
Year: 2018

The NVIDIA Volta GPU microarchitecture introduces a specialized unit, called "Tensor Core" that performs one matrix-multiply-and-accumulate on 4x4 matrices per clock cycle. The NVIDIA Tesla V100 accelerator, featuring the Volta microarchitecture, provides 640 Tensor Cores with a theoretical peak performance of 125 Tflops/s in mixed precision. In this paper, we investigate current approaches to program NVIDIA Tensor Cores, their performances and the precision loss due to computation in mixed precision. Currently, NVIDIA provides three different ways of programming matrix-multiply-and-accumulate on Tensor Cores: the CUDA Warp Matrix Multiply Accumulate (WMMA) API, CUTLASS, a templated library based on WMMA, and cuBLAS GEMM. After experimenting with different approaches, we found that NVIDIA Tensor Cores can deliver up to 83 Tflops/s in mixed precision on a Tesla V100 GPU, seven and three times the performance in single and half precision respectively. A WMMA implementation of batched GEMM reaches a performance of 4 Tflops/s. While precision loss due to matrix multiplication with half precision input might be critical in many HPC applications, it can be considerably reduced at the cost of increased computation. Our results indicate that HPC applications using matrix multiplications can strongly benefit from using of NVIDIA Tensor Cores.
Maximum Weight Spectrum Codes
Venue: arXiv cs.IT 2018
Year: 2018

In the recent work \cite{shi18}, a combinatorial problem concerning linear codes over a finite field $\F_q$ was introduced. In that work the authors studied the weight set of an $[n,k]_q$ linear code, that is the set of non-zero distinct Hamming weights, showing that its cardinality is upper bounded by $\frac{q^k-1}{q-1}$. They showed that this bound was sharp in the case $ q=2 $, and in the case $ k=2 $. They conjectured that the bound is sharp for every prime power $ q $ and every positive integer $ k $. In this work quickly establish the truth of this conjecture. We provide two proofs, each employing different construction techniques. The first relies on the geometric view of linear codes as systems of projective points. The second approach is purely algebraic. We establish some lower bounds on the length of codes that satisfy the conjecture, and the length of the new codes constructed here are discussed.
Reproducibility and Pseudo-Determinism in Log-Space
Venue: arXiv cs.CC 2018
Year: 2018

A curious property of randomized log-space search algorithms is that their outputs are often longer than their workspace. This leads to the question: how can we reproduce the results of a randomized log space computation without storing the output or randomness verbatim? Running the algorithm again with new random bits may result in a new (and potentially different) output. We show that every problem in search-RL has a randomized log-space algorithm where the output can be reproduced. Specifically, we show that for every problem in search-RL, there are a pair of log-space randomized algorithms A and B where for every input x, A will output some string t_x of size O(log n), such that B when running on (x, t_x) will be pseudo-deterministic: that is, running B multiple times on the same input (x, t_x) will result in the same output on all executions with high probability. Thus, by storing only O(log n) bits in memory, it is possible to reproduce the output of a randomized log-space algorithm. An algorithm is reproducible without storing any bits in memory (i.e., |t_x|=0) if and only if it is pseudo-deterministic. We show pseudo-deterministic algorithms for finding paths in undirected graphs and Eulerian graphs using logarithmic space. Our algorithms are substantially faster than the best known deterministic algorithms for finding paths in such graphs in log-space. The algorithm for search-RL has the additional property that its output, when viewed as a random variable depending on the randomness used by the algorithm, has entropy O(log n).
Cascade context encoder for improved inpainting
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we analyze if cascade usage of the context encoder with increasing input can improve the results of the inpainting. For this purpose, we train context encoder for 64x64 pixels images in a standard way and use its resized output to fill in the missing input region of the 128x128 context encoder, both in training and evaluation phase. As the result, the inpainting is visibly more plausible. In order to thoroughly verify the results, we introduce normalized squared-distortion, a measure for quantitative inpainting evaluation, and we provide its mathematical explanation. This is the first attempt to formalize the inpainting measure, which is based on the properties of latent feature representation, instead of L2 reconstruction loss.
Learning Partially Structured Environmental Dynamics for Marine Robotic Navigation
Venue: arXiv cs.RO 2018
Year: 2018

We investigate the scenario that a robot needs to reach a designated goal after taking a sequence of appropriate actions in a non-static environment that is partially structured. One application example is to control a marine vehicle to move in the ocean. The ocean environment is dynamic and oftentimes the ocean waves result in strong disturbances that can disturb the vehicle's motion. Modeling such dynamic environment is non-trivial, and integrating such model in the robotic motion control is particularly difficult. Fortunately, the ocean currents usually form some local patterns (e.g. vortex) and thus the environment is partially structured. The historically observed data can be used to train the robot to learn to interact with the ocean tidal disturbances. In this paper we propose a method that applies the deep reinforcement learning framework to learn such partially structured complex disturbances. Our results show that, by training the robot under artificial and real ocean disturbances, the robot is able to successfully act in complex and spatiotemporal environments.
Social shaping of information infrastructure: on being specific about the technology
Venue: arXiv cs.CY 2018
Year: 2018

We are in this paper discussing conceptualisations of the relationship between IT and organisational issues. To move beyond an IT enables/ constrains position, we argue that it is necessary to take the specifics of an information system (IS) more serious. A theoretical framework called actor network theory from social studies of science and technology is presented as promising in this regard. With respect to new organisational forms, the class of ISs which need closer scrutiny is information infrastructures (INIs). They have characteristics which distinguish them from other ISs, namely the role and pattern of diffusion of standards. These standards are neither ready-made nor neutral: they inscribe organisational behaviour deeply within their technical details. Diffusion and adoption of standards depart from other kinds of ISs by requiring the coordination of the surrounding actors, institutional arrangements and work practices.
Causal Consistency and Latency Optimality: Friend or Foe?
Venue: arXiv cs.DC 2018
Year: 2018

Causal consistency is an attractive consistency model for replicated data stores. It is provably the strongest model that tolerates partitions, it avoids the long latencies associated with strong consistency, and, especially when using read-only transactions, it prevents many of the anomalies of weaker consistency models. Recent work has shown that causal consistency allows "latency-optimal" read-only transactions, that are nonblocking, single-version and single-round in terms of communication. On the surface, this latency optimality is very appealing, as the vast majority of applications are assumed to have read-dominated workloads. In this paper, we show that such "latency-optimal" read-only transactions induce an extra overhead on writes, the extra overhead is so high that performance is actually jeopardized, even in read-dominated workloads. We show this result from a practical and a theoretical angle. First, we present a protocol that implements "almost laten- cy-optimal" ROTs but does not impose on the writes any of the overhead of latency-optimal protocols. In this protocol, ROTs are nonblocking, one version and can be configured to use either two or one and a half rounds of client-server communication. We experimentally show that this protocol not only provides better throughput, as expected, but also surprisingly better latencies for all but the lowest loads and most read-heavy workloads. Then, we prove that the extra overhead imposed on writes by latency-optimal read-only transactions is inherent, i.e., it is not an artifact of the design we consider, and cannot be avoided by any implementation of latency-optimal read-only transactions. We show in particular that this overhead grows linearly with the number of clients.
Listen Before Receive for Coexistence in Unlicensed mmWave Bands
Venue: arXiv cs.NI 2018
Year: 2018

Listen-Before-Talk (LBT) has been adopted as the spectrum sharing technique that guarantees a fair LTE/Wi-Fi coexistence in the unlicensed spectrum at the 5 GHz band. Differently, at mmWave bands, where beamforming is a must to overcome propagation limits, LBT scope becomes limited because the interference layout changes due to the directionality of transmissions. In this regard, this paper proposes a Listen-Before-Receive (LBR) technique for shared spectrum access and analyzes its potentials to promote a fair coexistence of multiple Radio Access Technologies (RATs) in unlicensed mmWave bands, as, e.g., 5G New Radio (NR) access technology and Wireless Gigabit (WiGig) devices using IEEE 802.11ad/ay standard. Since the less likely but still harmful interference situations with directional transmissions can no longer be detected easily at the transmitter, we believe that the receiver has useful information to be used. The main idea of LBR is that we provide to the receiver a say when it comes to allowing/preventing the access to the channel. In this line, we propose potential implementations of LBR, in conjunction with LBT and the self-contained slot, for NR-based access to unlicensed mmWave bands.
FDRC: Flow-Driven Rule Caching Optimization in Software Defined Networking
Venue: arXiv cs.DC 2018
Year: 2018

With the sharp growth of cloud services and their possible combinations, the scale of data center network traffic has an inevitable explosive increasing in recent years. Software defined network (SDN) provides a scalable and flexible structure to simplify network traffic management. It has been shown that Ternary Content Addressable Memory (TCAM) management plays an important role on the performance of SDN. However, previous literatures, in point of view on rule placement strategies, are still insufficient to provide high scalability for processing large flow sets with a limited TCAM size. So caching is a brand new method for TCAM management which can provide better performance than rule placement. In this paper, we propose FDRC, an efficient flow-driven rule caching algorithm to optimize the cache replacement in SDN-based networks. Different from the previous packet-driven caching algorithm, FDRC is characterized by trying to deal with the challenges of limited cache size constraint and unpredictable flows. In particular, we design a caching algorithm with low-complexity to achieve high cache hit ratio by prefetching and special replacement strategy for predictable and unpredictable flows, respectively. By conducting extensive simulations, we demonstrate that our proposed caching algorithm significantly outperforms FIFO and least recently used (LRU) algorithms under various network settings.
In-depth Assessment of an Interactive Graph-based Approach for the Segmentation for Pancreatic Metastasis in Ultrasound Acquisitions of the Liver with two Specialists in Internal Medicine
Venue: arXiv cs.CV 2018
Year: 2018

The manual outlining of hepatic metastasis in (US) ultrasound acquisitions from patients suffering from pancreatic cancer is common practice. However, such pure manual measurements are often very time consuming, and the results repeatedly differ between the raters. In this contribution, we study the in-depth assessment of an interactive graph-based approach for the segmentation for pancreatic metastasis in US images of the liver with two specialists in Internal Medicine. Thereby, evaluating the approach with over one hundred different acquisitions of metastases. The two physicians or the algorithm had never assessed the acquisitions before the evaluation. In summary, the physicians first performed a pure manual outlining followed by an algorithmic segmentation over one month later. As a result, the experts satisfied in up to ninety percent of algorithmic segmentation results. Furthermore, the algorithmic segmentation was much faster than manual outlining and achieved a median Dice Similarity Coefficient (DSC) of over eighty percent. Ultimately, the algorithm enables a fast and accurate segmentation of liver metastasis in clinical US images, which can support the manual outlining in daily practice.
Power-Efficient Deployment of UAVs as Relays
Venue: arXiv cs.IT 2018
Year: 2018

Optimal deployment of unmanned aerial vehicles (UAVs) as communication relays is studied for fixed-rate variable-power systems. The considered setup is a set of ground transmitters (GTs) wishing to communicate with a set of ground receivers (GRs) through the UAVs. Each GT-GR pair communicates through only one selected UAV and have no direct link. Two different UAV selection scenarios are studied: In centralized selection, a decision center assigns an optimal UAV depending on the locations of all terminals. In distributed selection, a GT selects its relaying UAV using only the local knowledge of its distances to the UAVs. For both selection scenarios, the optimal tradeoff between the UAV and GT power consumptions are determined using tools from quantization theory. Specifically, the two extremal regimes of one UAV and very large number of UAVs are analyzed for a path loss exponent of $2$. Numerical optimization of UAV locations are also discussed. Simulations are provided to confirm the analytical findings.