OpenRaiser/Intern-Atlas huggingface dataset:
Bounding Queue Delay in Cellular Networks to Support Ultra-Low Latency Applications
Venue: arXiv cs.NI 2019
Year: 2019

Most of the current active queue management (AQM) designs have major issues including severe hardship of being tuned for highly fluctuated cellular access link bandwidths. Consequently, most of the cellular network providers either give up using AQMs or use conservative offline configurations for them. However, these choices will significantly impact the performance of the emerging interactive and highly delay sensitive applications such as virtual reality and vehicle-to-vehicle communications. Therefore, in this paper, we investigate the problems of existing AQM schemes and show that they are not suitable options to support ultra-low latency applications in a highly dynamic network such as current and future cellular networks. Moreover, we believe that achieving good performance does not necessarily come from complex drop rate calculation algorithms or complicated AQM techniques. Consequently, we propose BoDe an extremely simple and deployment friendly AQM scheme to bound the queuing delay of served packets and support ultra-low latency applications. We have evaluated BoDe in extensive trace-based evaluations using cellular traces from 3 different service providers in the US and compared its performance with state-of-the-art AQM designs including CoDel and PIE under a variety of streaming applications, video conferencing applications, and various recently proposed TCP protocols. Results show that despite BoDe's simple design, it outperforms other schemes and achieves significantly lower queuing delay in all tested scenarios.
A Notion of Entropy for Stochastic Processes on Marked Rooted Graphs
Venue: arXiv cs.IT 2019
Year: 2019

In this document, we introduce a notion of entropy for stochastic processes on marked rooted graphs. For this, we employ the framework of local weak limit theory for sparse marked graphs, also known as the objective method, due to Benjamini, Schramm, Aldous, Steele and Lyons. Our contribution is a generalization of the notion of entropy introduced by Bordenave and Caputo to graphs which carry marks on their vertices and edges. The theory of time series is the engine driving an enormous range of applications in areas such as control theory, communications, information theory and signal processing. It is to be expected that a theory of stationary stochastic processes indexed by combinatorial structures, in particular graphs, would eventually have a similarly wide-ranging impact.
Research Opportunities in Sociotechnical Interventions for Health Disparity Reduction
Venue: arXiv cs.CY 2019
Year: 2019

The implicit and explicit biases built into our computing systems are becoming increasingly clear -- they impact everything from targeting of advertisements to how we are identified as people. These biases disproportionately affect marginalized groups -- people who are excluded from mainstream social, economic, cultural, or political life -- more acutely. While these biases can affect all aspects of our lives, from leisure to criminal justice to personal finances, they are all the more critical in the context of health and healthcare due to their significant personal and societal implications. In this interdisciplinary workshop, we explored how to design and build health systems for diverse populations through the following disciplinary lenses. The Computing Community Consortium (CCC) sponsored a two-day workshop titled Sociotechnical Interventions for Health Disparity Reduction in collaboration with the leadership of the Society for Behavioral Medicine's (SBM) 39th Annual Meeting on Monday, April 9 and Tuesday, April 10, 2018 in New Orleans, Louisiana. The workshop's goal was to bring together leading researchers in computing, health informatics, behavioral medicine, and health disparities to develop an integrative research agenda focused on sociotechnical interventions to reduce health disparities and improve the health of marginalized populations.
Adaptive Stress Testing with Reward Augmentation for Autonomous Vehicle Validation
Venue: arXiv cs.RO 2019
Year: 2019

Determining possible failure scenarios is a critical step in the evaluation of autonomous vehicle systems. Real-world vehicle testing is commonly employed for autonomous vehicle validation, but the costs and time requirements are high. Consequently, simulation-driven methods such as Adaptive Stress Testing (AST) have been proposed to aid in validation. AST formulates the problem of finding the most likely failure scenarios as a Markov decision process, which can be solved using reinforcement learning. In practice, AST tends to find scenarios where failure is unavoidable and tends to repeatedly discover the same types of failures of a system. This work addresses these issues by encoding domain relevant information into the search procedure. With this modification, the AST method discovers a larger and more expressive subset of the failure space when compared to the original AST formulation. We show that our approach is able to identify useful failure scenarios of an autonomous vehicle policy.
Spams meet Cryptocurrencies: Sextortion in the Bitcoin Ecosystem
Venue: arXiv cs.CR 2019
Year: 2019

In the past year, a new spamming scheme has emerged: sexual extortion messages requiring payments in the cryptocurrency Bitcoin, also known as sextortion. This scheme represents a first integration of the use of cryptocurrencies by members of the spamming industry. Using a dataset of 4,340,736 sextortion spams, this research aims at understanding such new amalgamation by uncovering spammers' operations. To do so, a simple, yet effective method for projecting Bitcoin addresses mentioned in sextortion spams onto transaction graph abstractions is computed over the entire Bitcoin blockchain. This allows us to track and investigate monetary flows between involved actors and gain insights into the financial structure of sextortion campaigns. We find that sextortion spammers are somewhat sophisticated, following pricing strategies and benefiting from cost reductions as their operations cut the upper-tail of the spamming supply chain. We discover that one single entity is likely controlling the financial backbone of the majority of the sextortion campaigns and that the 11-month operation studied yielded a lower-bound revenue between \$1,300,620 and \$1,352,266. We conclude that sextortion spamming is a lucrative business and spammers will likely continue to send bulk emails that try to extort money through cryptocurrencies.
Weight Friction: A Simple Method to Overcome Catastrophic Forgetting and Enable Continual Learning
Venue: arXiv cs.LG 2019
Year: 2019

In recent years, deep neural networks have found success in replicating human-level cognitive skills, yet they suffer from several major obstacles. One significant limitation is the inability to learn new tasks without forgetting previously learned tasks, a shortcoming known as catastrophic forgetting. In this research, we propose a simple method to overcome catastrophic forgetting and enable continual learning in neural networks. We draw inspiration from principles in neurology and physics to develop the concept of weight friction. Weight friction operates by a modification to the update rule in the gradient descent optimization method. It converges at a rate comparable to that of the stochastic gradient descent algorithm and can operate over multiple task domains. It performs comparably to current methods while offering improvements in computation and memory efficiency.
Quantale semantics of Lambek calculus with subexponential modalities
Venue: arXiv cs.LO 2019
Year: 2019

In this paper, we consider the polymodal version of Lambek calculus with subexponential modalities initially introduced by Kanovich, Kuznetsov, Nigam, and Scedrov and its quantale semantics. In our approach, subexponential modalities have an interpretation in terms of quantic conuclei. We show that this extension of Lambek calculus is complete w.r.t quantales with quantic conuclei. Also, we prove a representation theorem for quantales with quantic conuclei and show that Lambek calculus with subexponentials is relationally complete. Finally, we extend this representation theorem to the category of quantales with quantic conuclei.
Adaloss: Adaptive Loss Function for Landmark Localization
Venue: arXiv cs.CV 2019
Year: 2019

Landmark localization is a challenging problem in computer vision with a multitude of applications. Recent deep learning based methods have shown improved results by regressing likelihood maps instead of regressing the coordinates directly. However, setting the precision of these regression targets during the training is a cumbersome process since it creates a trade-off between trainability vs localization accuracy. Using precise targets introduces a significant sampling bias and hence makes the training more difficult, whereas using imprecise targets results in inaccurate landmark detectors. In this paper, we introduce "Adaloss", an objective function that adapts itself during the training by updating the target precision based on the training statistics. This approach does not require setting problem-specific parameters and shows improved stability in training and better localization accuracy during inference. We demonstrate the effectiveness of our proposed method in three different applications of landmark localization: 1) the challenging task of precisely detecting catheter tips in medical X-ray images, 2) localizing surgical instruments in endoscopic images, and 3) localizing facial features on in-the-wild images where we show state-of-the-art results on the 300-W benchmark dataset.
Faster algorithms for cograph edge modification problems
Venue: arXiv cs.DS 2019
Year: 2019

In the Cograph Deletion (resp., Cograph Editing) problem the input is a graph $G$ and an integer $k$, and the goal is to decide whether there is a set of edges of size at most $k$ whose removal from $G$ (resp., removal and addition to $G$) results in a graph that does not contain an induced path with four vertices. In this paper we give algorithms for Cograph Deletion and Cograph Editing whose running times are $O^*(2.303^k)$ and $O^*(4.329^k)$, respectively.
Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models
Venue: arXiv cs.CV 2019
Year: 2019

Gaining insight into how deep convolutional neural network models perform image classification and how to explain their outputs have been a concern to computer vision researchers and decision makers. These deep models are often referred to as black box due to low comprehension of their internal workings. As an effort to developing explainable deep learning models, several methods have been proposed such as finding gradients of class output with respect to input image (sensitivity maps), class activation map (CAM), and Gradient based Class Activation Maps (Grad-CAM). These methods under perform when localizing multiple occurrences of the same class and do not work for all CNNs. In addition, Grad-CAM does not capture the entire object in completeness when used on single object images, this affect performance on recognition tasks. With the intention to create an enhanced visual explanation in terms of visual sharpness, object localization and explaining multiple occurrences of objects in a single image, we present Smooth Grad-CAM++ \footnote{Simple demo: http://35.238.22.135:5000/}, a technique that combines methods from two other recent techniques---SMOOTHGRAD and Grad-CAM++. Our Smooth Grad-CAM++ technique provides the capability of either visualizing a layer, subset of feature maps, or subset of neurons within a feature map at each instance at the inference level (model prediction process). After experimenting with few images, Smooth Grad-CAM++ produced more visually sharp maps with better localization of objects in the given input images when compared with other methods.
Matching reads to many genomes with the $r$-index
Venue: arXiv cs.DS 2019
Year: 2019

The $r$-index is a tool for compressed indexing of genomic databases for exact pattern matching, which can be used to completely align reads that perfectly match some part of a genome in the database or to find seeds for reads that do not. This paper shows how to download and install the programs ri-buildfasta and ri-align; how to call ri-buildfasta on a FASTA file to build an $r$-index for that file; and how to query that index with ri-align. Availability: The source code for these programs is released under GPLv3 and available at https://github.com/alshai/r-index .
Semi-supervised Thai Sentence Segmentation Using Local and Distant Word Representations
Venue: arXiv cs.CL 2019
Year: 2019

A sentence is typically treated as the minimal syntactic unit used for extracting valuable information from a longer piece of text. However, in written Thai, there are no explicit sentence markers. We proposed a deep learning model for the task of sentence segmentation that includes three main contributions. First, we integrate n-gram embedding as a local representation to capture word groups near sentence boundaries. Second, to focus on the keywords of dependent clauses, we combine the model with a distant representation obtained from self-attention modules. Finally, due to the scarcity of labeled data, for which annotation is difficult and time-consuming, we also investigate and adapt Cross-View Training (CVT) as a semi-supervised learning technique, allowing us to utilize unlabeled data to improve the model representations. In the Thai sentence segmentation experiments, our model reduced the relative error by 7.4% and 10.5% compared with the baseline models on the Orchid and UGWC datasets, respectively. We also applied our model to the task of pronunciation recovery on the IWSLT English dataset. Our model outperformed the prior sequence tagging models, achieving a relative error reduction of 2.5%. Ablation studies revealed that utilizing n-gram presentations was the main contributing factor for Thai, while the semi-supervised training helped the most for English.
Automatic Fact-Checking Using Context and Discourse Information
Venue: arXiv cs.CL 2019
Year: 2019

We study the problem of automatic fact-checking, paying special attention to the impact of contextual and discourse information. We address two related tasks: (i) detecting check-worthy claims, and (ii) fact-checking claims. We develop supervised systems based on neural networks, kernel-based support vector machines, and combinations thereof, which make use of rich input representations in terms of discourse cues and contextual features. For the check-worthiness estimation task, we focus on political debates, and we model the target claim in the context of the full intervention of a participant and the previous and the following turns in the debate, taking into account contextual meta information. For the fact-checking task, we focus on answer verification in a community forum, and we model the veracity of the answer with respect to the entire question--answer thread in which it occurs as well as with respect to other related posts from the entire forum. We develop annotated datasets for both tasks and we run extensive experimental evaluation, confirming that both types of information ---but especially contextual features--- play an important role.
Graph Drawing with Morphing Partial Edges
Venue: arXiv cs.DS 2019
Year: 2019

A partial edge drawing (PED) of a graph is a variation of a node-link diagram. PED draws a link, which is a partial visual representation of an edge, and reduces visual clutter of the node-link diagram. However, more time is required to read a PED to infer undrawn parts. The authors propose a morphing edge drawing (MED), which is a PED that changes with time. In MED, links morph between partial and complete drawings; thus, a reduced load for estimation of undrawn parts in a PED is expected. Herein, a formalization of MED is shown based on a formalization of PED. Then, requirements for the scheduling of morphing are specified. The requirements inhibit morphing from crossing and shorten the overall time for morphing the edges. Moreover, an algorithm for a scheduling method implemented by the authors is illustrated and the effectiveness of PED from a reading time viewpoint is shown through an experimental evaluation.
PERI: A Posit Enabled RISC-V Core
Venue: arXiv cs.AR 2019
Year: 2019

Owing to the failure of Dennard's scaling the last decade has seen a steep growth of prominent new paradigms leveraging opportunities in computer architecture. Two technologies of interest are Posit and RISC-V. Posit was introduced in mid-2017 as a viable alternative to IEEE 754-2008. Posit promises more accuracy, higher dynamic range, and fewer unused states along with simpler hardware designs as compared to IEEE 754-2008. RISC-V, on the other hand, provides a commercial-grade open-source ISA. It is not only elegant and simple but also highly extensible and customizable, thereby facilitating novel micro-architectural research and exploration. In this paper, we bring these two technologies together and propose the first Posit Enabled RISC-V core. The paper provides insights on how the current 'F' extension and the custom op-code space of RISC-V can be leveraged/modified to support Posit arithmetic. We also present implementation details of a parameterized and feature-complete Posit FPU which is integrated with the RISC-V compliant SHAKTI C-class core either as an execution unit or as an accelerator. To fully leverage the potential of Posit, we further enhance our Posit FPU, with minimal overheads, to support two different exponent sizes (with posit-size being 32-bits). This allows applications to switch from high-accuracy computation mode to a mode with higher dynamic-range at run-time. In the absence of viable software tool-chain to enable porting of applications in the Posit domain, we present a workaround on how certain applications can be modified minimally to exploit the existing RISC-V tool-chain. We also provide examples of applications which can perform better with Posit as compared to IEEE 754-2008. The proposed Posit FPU consumes 3507 slice LUTs and 1294 slice registers on an Artix-7-100T Xilinx FPGA while capable of operating at 100 MHz.
Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve
Venue: arXiv cs.CV 2019
Year: 2019

Deep neural networks are being applied in many tasks with encouraging results, and have often reached human-level performance. However, deep neural networks are vulnerable to well-designed input samples called adversarial examples. In particular, neural networks tend to misclassify adversarial examples that are imperceptible to humans. This paper introduces a new detection system that automatically detects adversarial examples on deep neural networks. Our proposed system can mostly distinguish adversarial samples and benign images in an end-to-end manner without human intervention. We exploit the important role of the frequency domain in adversarial samples and propose a method that detects malicious samples in observations. When evaluated on two standard benchmark datasets (MNIST and ImageNet), our method achieved an out-detection rate of 99.7 - 100% in many settings.
Walking with MIND: Mental Imagery eNhanceD Embodied QA
Venue: arXiv cs.CV 2019
Year: 2019

The EmbodiedQA is a task of training an embodied agent by intelligently navigating in a simulated environment and gathering visual information to answer questions. Existing approaches fail to explicitly model the mental imagery function of the agent, while the mental imagery is crucial to embodied cognition, and has a close relation to many high-level meta-skills such as generalization and interpretation. In this paper, we propose a novel Mental Imagery eNhanceD (MIND) module for the embodied agent, as well as a relevant deep reinforcement framework for training. The MIND module can not only model the dynamics of the environment (e.g. 'what might happen if the agent passes through a door') but also help the agent to create a better understanding of the environment (e.g. 'The refrigerator is usually in the kitchen'). Such knowledge makes the agent a faster and better learner in locating a feasible policy with only a few trails. Furthermore, the MIND module can generate mental images that are treated as short-term subgoals by our proposed deep reinforcement framework. These mental images facilitate policy learning since short-term subgoals are easy to achieve and reusable. This yields better planning efficiency than other algorithms that learn a policy directly from primitive actions. Finally, the mental images visualize the agent's intentions in a way that human can understand, and this endows our agent's actions with more interpretability. The experimental results and further analysis prove that the agent with the MIND module is superior to its counterparts not only in EQA performance but in many other aspects such as route planning, behavioral interpretation, and the ability to generalize from a few examples.
An Empirical Study on Technical Debt in a Finnish SME
Venue: arXiv cs.SE 2019
Year: 2019

Objective. In this work, we report the experience of a Finnish SME in managing Technical Debt (TD), investigating the most common types of TD they faced in the past, their causes, and their effects. Method. We set up a focus group in the case-company, involving different roles. Results. The results showed that the most significant TD in the company stems from disagreements with the supplier and lack of test automation. Specification and test TD are the most significant types of TD. Budget and time constraints were identified as the most important root causes of TD. Conclusion. TD occurs when time or budget is limited or the amount of work are not understood properly. However, not all postponed activities generated "debt". Sometimes the accumulation of TD helped meet deadlines without a major impact, while in other cases the cost for repaying the TD was much higher than the benefits. From this study, we learned that learning, careful estimations, and continuous improvement could be good strategies to mitigate TD. These strategies include iterative validation with customers, efficient communication with stakeholders, meta-cognition in estimations, and value orientation in budgeting and scheduling.
Optimal Scheduling for Discounted Age Penalty Minimization in Multi-Loop Networked Control
Venue: arXiv cs.IT 2019
Year: 2019

Age-of-information (AoI) is a metric quantifying information freshness at the receiver. Since AoI combines packet generation frequency, packet loss, and delay into a single metric, it has received a lot of research attention as an interface between communication network and application. In this work, we apply AoI to the problem of wireless scheduling for multi-loop networked control systems (NCS), i.e., feedback control loops closed over a shared wireless network. We model the scheduling problem as a Markov decision process (MDP) with AoI as its observable states and derive a relation of control system error and AoI. We further derive a stationary scheduling policy to minimize control error over an infinite horizon. We show that our scheduler outperforms the state-of-the-art scheduling policies for NCS. To the best of our knowledge, this is the first work proposing an AoI-based wireless scheduling policy that minimizes the control error over an infinite horizon for multi-loop NCS.